diff --git a/.circleci/config.yml b/.circleci/config.yml deleted file mode 100644 index 39aa8939..00000000 --- a/.circleci/config.yml +++ /dev/null @@ -1,109 +0,0 @@ -version: 2 -jobs: - build: - docker: - - image: circleci/python:3.8.5 - - working_directory: ~/repo - - steps: - - checkout - - - restore_cache: - keys: - - v2-dependencies-{{ checksum "requirements.txt" }} - - v2-dependencies- - - - run: - name: Install pandoc - command: | - sudo apt-get update - wget https://github.com/jgm/pandoc/releases/download/2.2.1/pandoc-2.2.1-1-amd64.deb - sudo dpkg -i pandoc-2.2.1-1-amd64.deb - - - run: - name: Install tex - command: | - sudo apt-get install -y texlive - sudo apt-get install -y texlive-latex-extra - sudo apt-get install -y texlive-lang-french - sudo apt-get install -y dvipng - - - run: - name: Install 7z - command: | - sudo apt-get install -y p7zip-full - - - run: - name: Install InkScape - command: | - sudo apt-get install -y inkscape - - - run: - name: Install graphviz - command: | - sudo apt-get install -y graphviz - - # statsmodels setup.py requires it - - run: - name: install numpy - command: | - python3 -m venv venv - . venv/bin/activate - pip install numpy - - - run: - name: install dependencies 1 - command: | - python3 -m venv venv - . venv/bin/activate - conda install -c conda-forge xgboost - pip install -r requirements_conda.txt - - - run: - name: install dependencies 2 - command: | - python3 -m venv venv - . venv/bin/activate - pip install -r requirements.txt - - - save_cache: - paths: - - ./venv - key: v2-dependencies-{{ checksum "requirements.txt" }} - - - run: - name: run tests - command: | - . venv/bin/activate - export PYTHONPATH=src - python setup.py unittests - - - run: - name: wheel - command: | - . venv/bin/activate - export PYTHONPATH=src - python setup.py bdist_wheel - mkdir -p test-reports/dist - cp dist/*.whl test-reports/dist - - - run: - name: documentation - command: | - . venv/bin/activate - export PYTHONPATH=src - python setup.py build_sphinx - - - run: - name: copy documentation - command: | - mkdir -p test-reports/doc - zip -r -9 test-reports/doc/documentation_html.zip _doc/sphinxdoc/build/html - mkdir -p test-reports/pdf - cp _doc/sphinxdoc/build/elatex/*.pdf test-reports/pdf - cp _doc/sphinxdoc/build/elatex/ml*.tex* test-reports/pdf - - - store_artifacts: - path: test-reports - destination: test-reports \ No newline at end of file diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 412eeda7..00000000 --- a/.gitattributes +++ /dev/null @@ -1,22 +0,0 @@ -# Auto detect text files and perform LF normalization -* text=auto - -# Custom for Visual Studio -*.cs diff=csharp -*.sln merge=union -*.csproj merge=union -*.vbproj merge=union -*.fsproj merge=union -*.dbproj merge=union - -# Standard to msysgit -*.doc diff=astextplain -*.DOC diff=astextplain -*.docx diff=astextplain -*.DOCX diff=astextplain -*.dot diff=astextplain -*.DOT diff=astextplain -*.pdf diff=astextplain -*.PDF diff=astextplain -*.rtf diff=astextplain -*.RTF diff=astextplain diff --git a/.github/workflows/black-ruff.yml b/.github/workflows/black-ruff.yml new file mode 100644 index 00000000..9a047430 --- /dev/null +++ b/.github/workflows/black-ruff.yml @@ -0,0 +1,16 @@ +name: Black + Ruff Format Checker +on: [push, pull_request] +jobs: + black-format-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: psf/black@stable + with: + options: "--diff --check" + src: "." + ruff-format-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: chartboost/ruff-action@v1 diff --git a/.github/workflows/check-urls.yml b/.github/workflows/check-urls.yml new file mode 100644 index 00000000..3e0fbaa6 --- /dev/null +++ b/.github/workflows/check-urls.yml @@ -0,0 +1,47 @@ +name: Check URLs + +on: + pull_request: + branches: [main] + schedule: + # ┌───────────── minute (0 - 59) + # │ ┌───────────── hour (0 - 23) + # │ │ ┌───────────── day of the month (1 - 31) + # │ │ │ ┌───────────── month (1 - 12 or JAN-DEC) + # │ │ │ │ ┌───────────── day of the week (0 - 6 or SUN-SAT) + # │ │ │ │ │ + # │ │ │ │ │ + # │ │ │ │ │ + # * * * * * + - cron: '30 1 * * 0' + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + + - name: urls-checker-code + uses: urlstechie/urlchecker-action@master + with: + subfolder: mlstatpy + file_types: .md,.py,.rst,.ipynb + print_all: false + timeout: 5 + retry_count# : 3 + # exclude_urls: https://dumps.wikimedia.org/other/pageviews/%Y/%Y-%m/pageviews-%Y%m%d-%H0000.gz,https://dumps.wikimedia.org/frwiki/latest/latest-all-titles-in-ns0.gz + exclude_patterns: https://dumps.wikimedia.org/ + # force_pass : true + + - name: urls-checker-docs + uses: urlstechie/urlchecker-action@master + with: + subfolder: _doc + file_types: .md,.py,.rst,.ipynb + print_all: false + timeout: 5 + retry_count# : 3 + exclude_urls: https://hal.archives-ouvertes.fr/hal-00990252/document,https://github.com/onnx/models/raw/main/vision/classification/mobilenet/model/mobilenetv2-12.onnx,https://arxiv.org/ftp/arxiv/papers/1510/1510.04863.pdf,https://hal.science/hal-01125940 + exclude_patterns: https://www.data.gouv.fr/fr/datasets/r/e3d83ab3-dc52-4c99-abaf-8a38050cc68c,https://github.com/onnx/models/raw/main/vision/classification/mobilenet/model/mobilenetv2-12.onnx + # force_pass : true diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 00000000..bea1259d --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,61 @@ +name: "Code Scanning - Action" + +on: + push: + branches: [main] + pull_request: + branches: [main] + schedule: + # ┌───────────── minute (0 - 59) + # │ ┌───────────── hour (0 - 23) + # │ │ ┌───────────── day of the month (1 - 31) + # │ │ │ ┌───────────── month (1 - 12 or JAN-DEC) + # │ │ │ │ ┌───────────── day of the week (0 - 6 or SUN-SAT) + # │ │ │ │ │ + # │ │ │ │ │ + # │ │ │ │ │ + # * * * * * + - cron: '30 1 * * 0' + +jobs: + CodeQL-Build: + # CodeQL runs on ubuntu-latest, windows-latest, and macos-latest + runs-on: ubuntu-latest + + permissions: + # required for all workflows + security-events: write + + # only required for workflows in private repositories + actions: read + contents: read + + steps: + - name: Checkout repository + uses: actions/checkout@v3 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v2 + # Override language selection by uncommenting this and choosing your languages + # with: + # languages: go, javascript, csharp, python, cpp, java, ruby + + # Autobuild attempts to build any compiled languages (C/C++, C#, Go, or Java). + # If this step fails, then you should remove it and run the build manually (see below). + - name: Autobuild + uses: github/codeql-action/autobuild@v2 + + # ℹ️ Command-line programs to run using the OS shell. + # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun + + # ✏️ If the Autobuild fails above, remove it and uncomment the following + # three lines and modify them (or add more) to build your code if your + # project uses a compiled language + + #- run: | + # make bootstrap + # make release + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v2 diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml new file mode 100644 index 00000000..0df27e1c --- /dev/null +++ b/.github/workflows/documentation.yml @@ -0,0 +1,91 @@ +name: Test, Documentation and Code Coverage + +on: + push: + pull_request: + types: + - closed + branches: + - main + +jobs: + run: + name: Build documentation on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + + steps: + - uses: actions/checkout@v3 + + - uses: actions/setup-python@v4 + with: + python-version: '3.12' + + - uses: tlylt/install-graphviz@v1 + + - name: Install pandoc + run: sudo apt-get install -y pandoc + + - name: Install requirements + run: python -m pip install -r requirements.txt + + - name: Install requirements dev + run: python -m pip install -r requirements-dev.txt + + - name: Cache pip + uses: actions/cache@v4 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ hashFiles('requirements-dev.txt') }} + restore-keys: | + ${{ runner.os }}-pip- + ${{ runner.os }}- + + - name: Generate coverage report + run: | + pip install pytest + pip install pytest-cov + export PYTHONPATH=. + pytest --cov=./mlstatpy/ --cov-report=xml --durations=10 --ignore-glob=**LONG*.py --ignore-glob=**notebook*.py + export PYTHONPATH= + + - name: test notebooks + run: SCIPY_ARRAY_API=1 python -m pytest _unittests/ut_xrun_doc/test_documentation_notebook.py --durations=10 + + - name: Upload coverage reports to Codecov + uses: codecov/codecov-action@v3 + env: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + + - name: Install + run: python setup.py install + + - name: Copy license + run: cp LICENSE* ./_doc + - name: Copy changelogs + run: cp CHANGELOGS* ./_doc + + - name: Documentation + run: SCIPY_ARRAY_API=1 python -m sphinx ./_doc ./dist/html -n -w doc.txt + + - name: Summary + run: cat doc.txt + + - name: Check for errors and warnings + run: | + if [[ $(grep ERROR doc.txt) ]]; then + echo "Documentation produces errors." + grep ERROR doc.txt + exit 1 + fi + if [[ $(grep WARNING doc.txt) ]]; then + echo "Documentation produces warnings." + grep WARNING doc.txt + exit 1 + fi + + - uses: actions/upload-artifact@v4 + with: + path: ./dist/html/** diff --git a/.github/workflows/rstcheck.yml b/.github/workflows/rstcheck.yml new file mode 100644 index 00000000..b79f42dd --- /dev/null +++ b/.github/workflows/rstcheck.yml @@ -0,0 +1,27 @@ +name: RST Check + +on: [push, pull_request] + +jobs: + build_wheels: + name: rstcheck ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + + steps: + - uses: actions/checkout@v3 + + - uses: actions/setup-python@v4 + with: + python-version: '3.11' + + - name: Install requirements + run: python -m pip install -r requirements.txt + + - name: Install rstcheck + run: python -m pip install sphinx tomli rstcheck[toml,sphinx] + + - name: rstcheck + run: rstcheck -r _doc mlstatpy diff --git a/.github/workflows/wheels-any.yml b/.github/workflows/wheels-any.yml new file mode 100644 index 00000000..7088b13d --- /dev/null +++ b/.github/workflows/wheels-any.yml @@ -0,0 +1,29 @@ +name: Build Any Wheel + +on: + push: + branches: + - main + - 'releases/**' + +jobs: + build_wheels: + name: Build wheels on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest] + + steps: + - uses: actions/checkout@v3 + + - uses: actions/setup-python@v4 + with: + python-version: '3.11' + + - name: build wheel + run: python -m pip wheel . + + - uses: actions/upload-artifact@v4 + with: + path: ./mlstatpy*.whl diff --git a/.gitignore b/.gitignore index d9bd1fa9..9011cb17 100644 --- a/.gitignore +++ b/.gitignore @@ -1,279 +1,42 @@ -################# -## Eclipse -################# - -*.pydevproject -.project -.metadata -bin/ -tmp/ -_virtualenv/ -*.tmp -*.bak -*.swp -*~.nib -local.properties -.classpath -.settings/ -.loadpath -*.pyproj -dummy.py - -# External tool builders -.externalToolBuilders/ - -# Locally stored "Eclipse launch configurations" -*.launch - -# CDT-specific -.cproject - -# PDT-specific -.buildpath - - -################# -## Visual Studio -################# - -## Ignore Visual Studio temporary files, build results, and -## files generated by popular Visual Studio add-ons. - -# User-specific files -*.suo -*.user -*.sln.docstates - -# Build results - -[Dd]ebug/ -[Rr]elease/ -x64/ -build/ -[Bb]in/ -[Oo]bj/ - -# MSTest test Results -[Tt]est[Rr]esult*/ -[Bb]uild[Ll]og.* - -*_i.c -*_p.c -*.ilk -*.meta -*.obj -*.pch -*.pdb -*.pgc -*.pgd -*.rsp -*.sbr -*.tlb -*.tli -*.tlh -*.tmp -*.tmp_proj -*.log -*.vspscc -*.vssscc -.builds -*.pidb -*.log -*.scc - -# Visual C++ cache files -ipch/ -*.aps -*.ncb -*.opensdf -*.sdf -*.cachefile - -# Visual Studio profiler -*.psess -*.vsp -*.vspx - -# Guidance Automation Toolkit -*.gpState - -# ReSharper is a .NET coding add-in -_ReSharper*/ -*.[Rr]e[Ss]harper - -# TeamCity is a build add-in -_TeamCity* - -# DotCover is a Code Coverage Tool -*.dotCover - -# NCrunch -*.ncrunch* -.*crunch*.local.xml - -# Installshield output folder -[Ee]xpress/ - -# DocProject is a documentation generator add-in -DocProject/buildhelp/ -DocProject/Help/*.HxT -DocProject/Help/*.HxC -DocProject/Help/*.hhc -DocProject/Help/*.hhk -DocProject/Help/*.hhp -DocProject/Help/Html2 -DocProject/Help/html - -# Click-Once directory -publish/ - -# Publish Web Output -*.Publish.xml -*.pubxml - -# NuGet Packages Directory -## TODO: If you have NuGet Package Restore enabled, uncomment the next line -#packages/ - -# Windows Azure Build Output -csx -*.build.csdef - -# Windows Store app package directory -AppPackages/ - -# Others -sql/ -*.Cache -ClientBin/ -[Ss]tyle[Cc]op.* -~$* -*~ -*.dbmdl -*.[Pp]ublish.xml -*.pfx -*.publishsettings - -# RIA/Silverlight projects -Generated_Code/ - -# Backup & report files from converting an old project file to a newer -# Visual Studio version. Backup files are not needed, because we have git ;-) -_UpgradeReport_Files/ -Backup*/ -UpgradeLog*.XML -UpgradeLog*.htm - -# SQL Server files -App_Data/*.mdf -App_Data/*.ldf - -############# -## Windows detritus -############# - -# Windows image file caches -Thumbs.db -ehthumbs.db - -# Folder config file -Desktop.ini - -# Recycle Bin used on file shares -$RECYCLE.BIN/ - -# Mac crap -.DS_Store - - -############# -## Python -############# - -*.py[co] - -# Packages -*.egg -*.egg-info -dist/ -build/ -eggs/ -parts/ -var/ -sdist/ -develop-eggs/ -__pycache__/ -.installed.cfg - -# Installer logs -pip-log.txt - -# Unit test / coverage reports +*.dot +*.dylib +*.prof +*.pyc +*.pyd +*.so +*.gv +*.gv.* +*.jpg .coverage -.tox - -#Translations -*.mo - -#Mr Developer -.mr.developer.cfg - -# py* packages +.eggs/* +_cache/* +build/* +dist/* +*egg-info/* +onnxruntime_profile* +prof temp_* -out_* -*/sphinxdoc/source/index_* -*/sphinxdoc/source/readme.* -*/sphinxdoc/source/LICENSE.txt -*/sphinxdoc/source/filechanges.* -version.txt -_doc/sphinxdoc/source/python_template/*box.html -_doc/sphinxdoc/source/python_template/*toc.html -_doc/sphinxdoc/source/mlstatpy/ -_doc/sphinxdoc/source/coverage/* -*/sphinxdoc/source/all*.rst -_doc/sphinxdoc/source/notebooks/* -*/sphinxdoc/source/gynotebooks/* -_doc/sphinxdoc/source/gyexamples/* -_doc/sphinxdoc/source/examples/* -_doc/sphinxdoc/source/gallery/* -_doc/sphinxdoc/source/gallerynb/* -build_help.bat -_doc/sphinxdoc/source/blog/*.rst -_doc/sphinxdoc/source/blog/rss.xml -_doc/sphinxdoc/source/phdoc_templates/*toc.html -_doc/sphinxdoc/source/phdoc_templates/*box.html -_doc/sphinxdoc/source/blog/feed-icon*.png -_doc/sphinxdoc/source/phdoc_static/reveal.js/* -_doc/notebooks/.ipynb_checkpoints/* -dist_module27/* -auto_*.bat -auto_*.sh -auto_*.py -auto_*.xml -auto_*.db3 -_doc/sphinxdoc/source/phdoc_static/require.js -_doc/sphinxdoc/require.js -ex.* -m.temp -_doc/notebooks/*/.ipynb_checkpoints -_doc/notebooks/nlp/frwiki-latest-all-titles-in-ns0 +.ipynb_checkpoints +_doc/CHANGELOGS.rst +_doc/sg_execution_times.rst +_doc/LICENSE.txt +_doc/auto_examples/* +_doc/examples/_cache/* +_doc/examples/onnxruntime_profile* +_doc/examples/plot_*.png +_doc/examples/plot_*.xlsx +_doc/examples/data/*.optimized.onnx +_doc/examples/*.html +_doc/_static/require.js +_doc/_static/viz.js +_unittests/ut__main/*.png +_unittests/ut__main/_cache/* +_unittests/ut__main/*.html +_unittests/.hypothesis/* +_doc/notebooks/ml/*.onnx +_doc/notebooks/dsgarden/*.onnx +_doc/notebooks/nlp/frwiki-* _doc/notebooks/nlp/sample*.txt -_doc/notebooks/nlp/completion.prof -_doc/notebooks/nlp/profile.png -_doc/notebooks/nlp/completion.dot -_doc/notebooks/nlp/completion.png -_doc/notebooks/nlp/completion.pstat -_doc/sphinxdoc/source/c_dist/edit_bibliographie.rst -_doc/notebooks/Untitled.ipynb -_doc/notebooks/ml/*.clean_cache -_doc/notebooks/ml/img-*.png -_doc/notebooks/ml/*.pickle - -_doc/sphinxdoc/source/nbcov.png -_doc/notebooks/dsgarden/arbre.png -_doc/notebooks/dsgarden/arbre.dot -_doc/notebooks/nlp/output_30_0.jpeg -_doc/notebooks/nlp/output_38_0.jpeg -_doc/notebooks/nlp/output_41_0.jpeg -_doc/sphinxdoc/source/c_ml/math/*.png -_doc/sphinxdoc/source/c_ml/piecewise_.html +frwiki-* +mobilenetv2-12.onnx +sample1000.txt \ No newline at end of file diff --git a/.landscape.yml b/.landscape.yml deleted file mode 100644 index 1cc7ed43..00000000 --- a/.landscape.yml +++ /dev/null @@ -1,16 +0,0 @@ -doc-warnings: yes -test-warnings: no -strictness: veryhigh -max-line-length: 120 -autodetect: yes -requirements: - - requirement.txt -ignore-paths: - - _unittests - - _doc - - src - - dist - - build -ignore-patterns: - - .*Parser\.py$ - - .*Lexer\.py$ diff --git a/.local.jenkins.lin.yml b/.local.jenkins.lin.yml deleted file mode 100644 index 42f5e0bc..00000000 --- a/.local.jenkins.lin.yml +++ /dev/null @@ -1,32 +0,0 @@ - -language: python - -python: - - { PATH: "{{Python37}}", VERSION: 3.7, DIST: std, PYINT: python3.7, PYTHONPATH: src } - - { PATH: "{{Python38}}", VERSION: 3.8, DIST: std, PYINT: python3.8, PYTHONPATH: src } - -virtualenv: - - path: {{ospathjoin(root_path, pickname("$NAME_JENKINS", project_name + "_$VERSION_$DIST_$NAME"), "_venv")}} - -install: - - $PYINT -m pip install --upgrade pip - - $PYINT -m pip install --upgrade --no-cache-dir --no-deps --index http://localhost:8067/simple/ scikit-learn>=0.21 --extra-index-url=https://pypi.python.org/simple/ - - $PYINT -m pip install --upgrade --no-cache-dir --no-deps --index http://localhost:8067/simple/ jyquickhelper pyquickhelper pyensae pymmails pymyinstall pyrsslocal --extra-index-url=https://pypi.python.org/simple/ - - $PYINT -m pip install --upgrade --no-cache-dir --no-deps --index http://localhost:8067/simple/ mlinsights>=0.2.312 --extra-index-url=https://pypi.python.org/simple/ - - $PYINT -m pip install -r requirements_conda.txt - - $PYINT -m pip install -r requirements.txt - - $PYINT --version - - $PYINT -m pip freeze - -script: - - { CMD: "$PYINT -u setup.py unittests", NAME: "UT" } - - { CMD: "$PYINT -u setup.py unittests_LONG", NAME: "UT_LONG", TIMEOUT: 7200 } - -after_script: - - $PYINT -u setup.py bdist_wheel - - if [ ${VERSION} == "3.7" and ${DIST} != "conda" and ${NAME} == "UT" ] then cp dist/*.whl {{root_path}}/../local_pypi/local_pypi_server fi - -documentation: - - if [ ${NAME} == "UT" ] then $PYINT -u setup.py build_sphinx fi - - if [ ${NAME} == "UT" ] then cp -R -f _doc/sphinxdoc/build/html dist/html fi - - if [ ${NAME} == "UT" ] then cp -R -f _doc/sphinxdoc/build/elatex/*.pdf dist/html fi diff --git a/.local.jenkins.win.yml b/.local.jenkins.win.yml deleted file mode 100644 index fee771a9..00000000 --- a/.local.jenkins.win.yml +++ /dev/null @@ -1,21 +0,0 @@ -language: python -python: - - { PATH: "{{replace(Python37, '\\', '\\\\')}}", VERSION: 3.7, DIST: std, PYTHONPATH: src } -virtualenv: - - path: {{ospathjoin(root_path, pickname("%NAME_JENKINS%", project_name + "_%VERSION%_%DIST%_%NAME%"), "_venv")}} -install: - - pip install --upgrade pip - - pip install --no-cache-dir --no-deps --index http://localhost:8067/simple/ pyquickhelper pyensae pymmails pymyinstall pyrsslocal mlinsights - - pip install -r requirements.txt - - pip freeze - - pip freeze > pip_freeze.txt -script: - - { CMD: "python -X faulthandler -X showrefcount -u setup.py unittests", NAME: "UT" } - - { CMD: "python -X faulthandler -X showrefcount -u setup.py unittests_LONG", NAME: "UT_LONG", TIMEOUT: 7200 } -after_script: - - python -u setup.py bdist_wheel - - if [ ${DIST} != "conda" and ${NAME} == "UT" ] then copy dist\*.whl {{root_path}}\..\..\local_pypi\local_pypi_server fi -documentation: - - if [ ${NAME} == "UT" ] then python -u setup.py build_sphinx fi - - if [ ${NAME} == "UT" ] then xcopy /E /C /I /Y _doc\sphinxdoc\build\html dist\html fi - - if [ ${NAME} == "UT" ] then xcopy /E /C /I /Y _doc\sphinxdoc\build\elatex\*.pdf dist\html fi diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 877ae9c7..00000000 --- a/.travis.yml +++ /dev/null @@ -1,34 +0,0 @@ -dist: bionic -sudo: true -language: python -python: - - "3.8" -env: - - SDL_VIDEODRIVER=dummy SDL_AUDIODRIVER=disk -install: - # We do this conditionally because it saves us some downloading if the - # version is the same. - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - - bash miniconda.sh -b -p $HOME/miniconda - - export PATH="$HOME/miniconda/bin:$PATH" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - - conda install conda-build - # Useful for debugging any issues with conda - - conda info -a - - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION numpy mkl scipy nose cython scikit-learn - - source activate test-environment - # - make all - #- conda build build_tools/conda-recipe --quiet - - conda install -c conda-forge xgboost - - conda install -q --file=requirements_conda.txt - - pip install pyquickhelper - - pip install cpyquickhelper - - pip install scikit-learn>=0.21 - - pip install -r requirements.txt - - pip install -U git+https://github.com/quantopian/qgrid --no-deps - # - pip install hg+http://bitbucket.org/pygame/pygame - - export PYTHONPATH=src -script: - - python setup.py unittests diff --git a/CHANGELOGS.rst b/CHANGELOGS.rst new file mode 100644 index 00000000..fd5dee6e --- /dev/null +++ b/CHANGELOGS.rst @@ -0,0 +1,30 @@ +Change Logs +=========== + +0.5.0 ++++++ + +0.4.0 ++++++ + +* :pr:`42`: quantization +* :pr:`39`: refactoring, use black, better documentation +* :pr:`32`: Improves usability of distance_matching_graphs_paths (2021-08-10) +* :pr:`31`: Links to notebooks are broken, notebooks slides are not working. (2021-03-31) +* :pr:`30`: Fixes #26, implements a compact architecture (2021-01-23) +* :pr:`26`: Aborder les régressions logistiques sous forme d'arbres (2021-01-23) +* :pr:`27`: Convertir un arbre de décision en réseaux de neurones et apprendre (2020-08-31) +* :pr:`25`: k-means norme L1 (2020-01-13) +* :pr:`23`: uses function dtrtri to invert an upper triangular matrix in function linear_regression (2019-07-21) +* :pr:`21`: implements streaming linear regression (2019-05-05) +* :pr:`19`: removes dependency on line_profiler, not maintained anymore (2019-04-09) +* :pr:`17`: move to CI to python 3.7 (2019-04-09) +* :pr:`15`: add page on quantile regression + notebook (2019-02-02) +* :pr:`2`: [won't fix] réseaux de neurones, utiliser des notations matricielles (2018-06-17) +* :pr:`13`: fix bug: ValueError: label should be list-like and same length as y in ROC.plot (2018-05-17) +* :pr:`12`: implements voronoi inference from a logistic regression solved with a linear regression (2018-05-08) +* :pr:`11`: logistic regression and voronoi (2018-05-01) +* :pr:`10`: add code on segment detection written a while ago (2018-04-18) +* :pr:`9`: fix unittest on wikipedia_dump after a change on wikipedia website (2018-04-01) +* :pr:`4`: implémentation la complétion en C++ (2016-09-25) +* :pr:`1`: ajouter les petits exposés finance... (2016-06-29) diff --git a/HISTORY.rst b/HISTORY.rst deleted file mode 100644 index c0729e92..00000000 --- a/HISTORY.rst +++ /dev/null @@ -1,22 +0,0 @@ - -.. _l-HISTORY: - -======= -History -======= - -current - 2018-06-18 - 0.00Mb -============================= - -* `2`: [won't fix] réseaux de neurones, utiliser des notations matricielles (2018-06-17) -* `13`: fix bug: ValueError: label should be list-like and same length as y in ROC.plot (2018-05-17) -* `12`: implements voronoi inference from a logistic regression solved with a linear regression (2018-05-08) -* `11`: logistic regression and voronoi (2018-05-01) -* `10`: add code on segment detection written a while ago (2018-04-18) -* `9`: fix unittest on wikipedia_dump after a change on wikipedia website (2018-04-01) - -0.1.335 - 2018-02-24 - 0.04Mb -============================= - -* `4`: implémentation la complétion en C++ (2016-09-25) -* `1`: ajouter les petits exposés finance... (2016-06-29) diff --git a/LICENSE.txt b/LICENSE.txt index df822f15..a1b7c791 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,4 +1,4 @@ -Copyright (c) 2016-2020, Xavier Dupré +Copyright (c) 2016-2024, Xavier Dupré Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/MANIFEST.in b/MANIFEST.in index d4aa6161..0c421b23 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,3 +1,6 @@ +include pyproject.toml +include MANIFEST.in +include setup.cfg prune _doc prune _todo prune _unittests diff --git a/README.rst b/README.rst index 4706931f..9ecc2485 100644 --- a/README.rst +++ b/README.rst @@ -1,58 +1,44 @@ -.. image:: https://travis-ci.org/sdpython/mlstatpy.svg?branch=master - :target: https://travis-ci.org/sdpython/mlstatpy - :alt: Build status +.. image:: https://github.com/sdpython/sphinx-runpython/raw/main/_doc/_static/logo.png + :width: 120 + +mlstatpy: détours mathématiques autour du machine learning +========================================================== .. image:: https://ci.appveyor.com/api/projects/status/5env33qptorgshaq?svg=true :target: https://ci.appveyor.com/project/sdpython/mlstatpy :alt: Build Status Windows -.. image:: https://circleci.com/gh/sdpython/mlstatpy/tree/master.svg?style=svg - :target: https://circleci.com/gh/sdpython/mlstatpy/tree/master +.. image:: https://circleci.com/gh/sdpython/mlstatpy/tree/main.svg?style=svg + :target: https://circleci.com/gh/sdpython/mlstatpy/tree/main .. image:: https://badge.fury.io/py/mlstatpy.svg :target: https://pypi.org/project/mlstatpy/ .. image:: https://img.shields.io/badge/license-MIT-blue.svg :alt: MIT License - :target: http://opensource.org/licenses/MIT - -.. image:: https://requires.io/github/sdpython/mlstatpy/requirements.svg?branch=master - :target: https://requires.io/github/sdpython/mlstatpy/requirements/?branch=master - :alt: Requirements Status + :target: https://opensource.org/license/MIT/ -.. image:: https://codecov.io/github/sdpython/mlstatpy/coverage.svg?branch=master - :target: https://codecov.io/github/sdpython/mlstatpy?branch=master +.. image:: https://codecov.io/github/sdpython/mlstatpy/coverage.svg + :target: https://codecov.io/github/sdpython/mlstatpy/ .. image:: http://img.shields.io/github/issues/sdpython/mlstatpy.png :alt: GitHub Issues :target: https://github.com/sdpython/mlstatpy/issues -.. image:: https://api.codacy.com/project/badge/Grade/677db5dda93b40d4ba1ec2f870cfd934 - :target: https://www.codacy.com/app/sdpython/mlstatpy?utm_source=github.com&utm_medium=referral&utm_content=sdpython/mlstatpy&utm_campaign=Badge_Grade - :alt: Codacy - -.. image:: http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/_images/nbcov.png - :target: http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/all_notebooks_coverage.html - :alt: Notebook Coverage - -.. _l-README: - -mlstatpy -======== - Le module contient essentiellement des digressions mathématiques autour du machine learning. Parmi les choses intéressantes, une courbe *ROC* avec intervalle de confiance, détection automatique de segment dans une image, un algorithme d'autocomplétion, une distance d'édition entre graphes, -des petites choses pour les données de Wikipedia. +des petites choses pour les données de Wikipedia, +un algorithme de conversion d'un arbre de décision en +réseaux de neurones. The package mostly contains documentation. It also implements some code rarely needed such as ROC curve with bandwidth, automated segment detection in a image, some simple autocomplete -algorithm, a graph edit distance, some helpers on Wikipedia data. +algorithm, a graph edit distance, some helpers on Wikipedia data, +an algorithm to convert decision trees into neural network. -* `GitHub/mlstatpy `_ -* `documentation `_ -* `Blog `_ +* `documentation `_ diff --git a/_doc/sphinxdoc/source/phdoc_static/git_logo.png b/_doc/_static/git_logo.png similarity index 100% rename from _doc/sphinxdoc/source/phdoc_static/git_logo.png rename to _doc/_static/git_logo.png diff --git a/_doc/_static/project_ico.png b/_doc/_static/project_ico.png new file mode 100644 index 00000000..fb931aa1 Binary files /dev/null and b/_doc/_static/project_ico.png differ diff --git a/_doc/api/data.rst b/_doc/api/data.rst new file mode 100644 index 00000000..cc771fa3 --- /dev/null +++ b/_doc/api/data.rst @@ -0,0 +1,14 @@ + +Source de données +================= + +Wikipédia ++++++++++ + +.. autofunction:: mlstatpy.data.wikipedia.download_dump + +.. autofunction:: mlstatpy.data.wikipedia.download_pageviews + +.. autofunction:: mlstatpy.data.wikipedia.download_titles + +.. autofunction:: mlstatpy.data.wikipedia.enumerate_titles diff --git a/_doc/api/graph.rst b/_doc/api/graph.rst new file mode 100644 index 00000000..517a3324 --- /dev/null +++ b/_doc/api/graph.rst @@ -0,0 +1,10 @@ + +Graphes +======= + +Distance +++++++++ + +.. autoclass:: mlstatpy.graph.graph_distance.GraphDistance + :members: distance_matching_graphs_paths + :noindex: diff --git a/_doc/api/image.rst b/_doc/api/image.rst new file mode 100644 index 00000000..214df9af --- /dev/null +++ b/_doc/api/image.rst @@ -0,0 +1,24 @@ + +Image +===== + +Conversion +++++++++++ + +.. autofunction:: mlstatpy.image.detection_segment.detection_segment.convert_array2PIL + +.. autofunction:: mlstatpy.image.detection_segment.detection_segment.convert_PIL2array + +Images aléatoires ++++++++++++++++++ + +.. autofunction:: mlstatpy.image.detection_segment.random_image.random_noise_image + +.. autofunction:: mlstatpy.image.detection_segment.random_image.random_segment_image + +Segments +++++++++ + +.. autofunction:: mlstatpy.image.detection_segment.detection_segment.detect_segments + +.. autofunction:: mlstatpy.image.detection_segment.detection_segment.plot_segments diff --git a/_doc/sphinxdoc/source/api/index.rst b/_doc/api/index.rst similarity index 84% rename from _doc/sphinxdoc/source/api/index.rst rename to _doc/api/index.rst index 33a5ae06..b2057ab5 100644 --- a/_doc/sphinxdoc/source/api/index.rst +++ b/_doc/api/index.rst @@ -11,3 +11,4 @@ API data graph image + modules/index diff --git a/_doc/api/ml.rst b/_doc/api/ml.rst new file mode 100644 index 00000000..5413544a --- /dev/null +++ b/_doc/api/ml.rst @@ -0,0 +1,57 @@ + +Machine Learning +================ + +Matrices +++++++++ + +.. autofunction:: mlstatpy.ml.matrices.gram_schmidt + +.. autofunction:: mlstatpy.ml.matrices.linear_regression + +.. autofunction:: mlstatpy.ml.matrices.streaming_gram_schmidt_update + +.. autofunction:: mlstatpy.ml.matrices.streaming_gram_schmidt + +.. autofunction:: mlstatpy.ml.matrices.streaming_linear_regression_update + +.. autofunction:: mlstatpy.ml.matrices.streaming_linear_regression + +.. autofunction:: mlstatpy.ml.matrices.streaming_linear_regression_gram_schmidt_update + +.. autofunction:: mlstatpy.ml.matrices.streaming_linear_regression_gram_schmidt + +Métriques ++++++++++ + +.. autoclass:: mlstatpy.ml.roc.ROC + :noindex: + +.. autofunction:: mlstatpy.ml.voronoi.voronoi_estimation_from_lr + +Plus proches voisins +++++++++++++++++++++ + +.. autoclass:: mlstatpy.ml.kppv.NuagePoints + :noindex: + +.. autoclass:: mlstatpy.ml.kppv_laesa.NuagePointsLaesa + :noindex: + +Tree and neural networks +++++++++++++++++++++++++ + +.. autoclass:: mlstatpy.ml._neural_tree_node.NeuralTreeNode + :noindex: + +.. autoclass:: mlstatpy.ml.neural_tree.NeuralTreeNet + :noindex: + +.. autoclass:: mlstatpy.ml.neural_tree.BaseNeuralTreeNet + :noindex: + +.. autoclass:: mlstatpy.ml.neural_tree.NeuralTreeNetClassifier + :noindex: + +.. autoclass:: mlstatpy.ml.neural_tree.NeuralTreeNetRegressor + :noindex: diff --git a/_doc/api/modules/completion.rst b/_doc/api/modules/completion.rst new file mode 100644 index 00000000..e5a08cde --- /dev/null +++ b/_doc/api/modules/completion.rst @@ -0,0 +1,5 @@ +mlstatpy.nlp.completion +======================= + +.. automodule:: mlstatpy.nlp.completion + :members: diff --git a/_doc/api/modules/completion_simple.rst b/_doc/api/modules/completion_simple.rst new file mode 100644 index 00000000..caae0892 --- /dev/null +++ b/_doc/api/modules/completion_simple.rst @@ -0,0 +1,5 @@ +mlstatpy.nlp.completion_simple +============================== + +.. automodule:: mlstatpy.nlp.completion_simple + :members: diff --git a/_doc/api/modules/graph_distance.rst b/_doc/api/modules/graph_distance.rst new file mode 100644 index 00000000..6d1bd877 --- /dev/null +++ b/_doc/api/modules/graph_distance.rst @@ -0,0 +1,5 @@ +mlstatpy.graph.graph_distance +============================= + +.. automodule:: mlstatpy.graph.graph_distance + :members: diff --git a/_doc/api/modules/index.rst b/_doc/api/modules/index.rst new file mode 100644 index 00000000..c3a8f9ee --- /dev/null +++ b/_doc/api/modules/index.rst @@ -0,0 +1,17 @@ +======= +Modules +======= + +.. toctree:: + :maxdepth: 1 + + poulet + graph_distance + kppv + kppv_laesa + logreg + neural_tree + roc + completion + completion_simple + sgd diff --git a/_doc/api/modules/kppv.rst b/_doc/api/modules/kppv.rst new file mode 100644 index 00000000..4d4be4aa --- /dev/null +++ b/_doc/api/modules/kppv.rst @@ -0,0 +1,5 @@ +mlstatpy.ml.kppv +================ + +.. automodule:: mlstatpy.ml.kppv + :members: diff --git a/_doc/api/modules/kppv_laesa.rst b/_doc/api/modules/kppv_laesa.rst new file mode 100644 index 00000000..b331595f --- /dev/null +++ b/_doc/api/modules/kppv_laesa.rst @@ -0,0 +1,5 @@ +mlstatpy.ml.kppv_laesa +====================== + +.. automodule:: mlstatpy.ml.kppv_laesa + :members: diff --git a/_doc/api/modules/logreg.rst b/_doc/api/modules/logreg.rst new file mode 100644 index 00000000..76f0e982 --- /dev/null +++ b/_doc/api/modules/logreg.rst @@ -0,0 +1,5 @@ +mlstatpy.ml.logreg +================== + +.. automodule:: mlstatpy.ml.logreg + :members: diff --git a/_doc/api/modules/neural_tree.rst b/_doc/api/modules/neural_tree.rst new file mode 100644 index 00000000..e820a461 --- /dev/null +++ b/_doc/api/modules/neural_tree.rst @@ -0,0 +1,11 @@ +mlstatpy.ml.neural_tree +======================= + +.. automodule:: mlstatpy.ml.neural_tree + :members: + +.. automodule:: mlstatpy.ml._neural_tree_node + :members: NeuralTreeNode + +.. automodule:: mlstatpy.ml._neural_tree_api + :members: _TrainingAPI diff --git a/_doc/api/modules/poulet.rst b/_doc/api/modules/poulet.rst new file mode 100644 index 00000000..3b411d2a --- /dev/null +++ b/_doc/api/modules/poulet.rst @@ -0,0 +1,5 @@ +mlstatpy.garden.poulet +====================== + +.. automodule:: mlstatpy.garden.poulet + :members: diff --git a/_doc/api/modules/roc.rst b/_doc/api/modules/roc.rst new file mode 100644 index 00000000..93f638c7 --- /dev/null +++ b/_doc/api/modules/roc.rst @@ -0,0 +1,5 @@ +mlstatpy.ml.roc +=============== + +.. automodule:: mlstatpy.ml.roc + :members: diff --git a/_doc/api/modules/sgd.rst b/_doc/api/modules/sgd.rst new file mode 100644 index 00000000..892b45ca --- /dev/null +++ b/_doc/api/modules/sgd.rst @@ -0,0 +1,5 @@ +mlstatpy.optim.sgd +================== + +.. automodule:: mlstatpy.optim.sgd + :members: diff --git a/_doc/api/optim.rst b/_doc/api/optim.rst new file mode 100644 index 00000000..5ccb55ba --- /dev/null +++ b/_doc/api/optim.rst @@ -0,0 +1,9 @@ + +Optimisation +================ + +Gradient +++++++++ + +.. autoclass:: mlstatpy.optim.sgd.SGDOptimizer + :noindex: diff --git a/_doc/api/text.rst b/_doc/api/text.rst new file mode 100644 index 00000000..8f2952aa --- /dev/null +++ b/_doc/api/text.rst @@ -0,0 +1,20 @@ +Traitement du langage naturel +============================= + +Complétion +++++++++++ + +.. autoclass:: mlstatpy.nlp.completion_simple.CompletionElement + :members: + :noindex: + +.. autoclass:: mlstatpy.nlp.completion_simple.CompletionSystem + :members: + :noindex: + +Normalisation ++++++++++++++ + +.. autofunction:: mlstatpy.data.wikipedia.normalize_wiki_text + +.. autofunction:: mlstatpy.nlp.normalize.remove_diacritics diff --git a/_doc/sphinxdoc/source/c_algo/bruit.png b/_doc/c_algo/bruit.png similarity index 100% rename from _doc/sphinxdoc/source/c_algo/bruit.png rename to _doc/c_algo/bruit.png diff --git a/_doc/sphinxdoc/source/c_dist/edit_distance.rst b/_doc/c_algo/edit_distance.rst similarity index 93% rename from _doc/sphinxdoc/source/c_dist/edit_distance.rst rename to _doc/c_algo/edit_distance.rst index ba9fbbbd..9e6af3b3 100644 --- a/_doc/sphinxdoc/source/c_dist/edit_distance.rst +++ b/_doc/c_algo/edit_distance.rst @@ -117,7 +117,7 @@ On peut définir la distance d'édition : .. math:: \begin{array}{crcl} - d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \R^+\\ + d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \mathbb{R}^+\\ & \pa{m_1,m_2} & \longrightarrow & \underset{ \begin{subarray} OO \text{ séquence} \\ \text{d'opérations} \end{subarray}}{ \min} \, d\pa{m_1,m_2,O} \end{array} @@ -140,7 +140,7 @@ Ce paragraphe a pour objectif de démontrer que la Soit :math:`\mathcal{C}' = \mathcal{C} \bigcup \acc{.}` l'ensemble des caractères ajouté au caractère vide ``.``. - On note :math:`c : \pa{\mathcal{C}'}^2 \longrightarrow \R^+` + On note :math:`c : \pa{\mathcal{C}'}^2 \longrightarrow \mathbb{R}^+` la fonction coût définie comme suit : .. math:: @@ -151,9 +151,9 @@ Ce paragraphe a pour objectif de démontrer que la \forall \pa{x,y} \in \pa{\mathcal{C}'}^2, \; c\pa{x,y} \text{ est le coût } \left\{ \begin{array}{ll} \text { d'une comparaison} & \text{si } \pa{x,y} \in \pa{\mathcal{C}}^2\\ - \text { d'une insertion} & \text{si } \pa{x,y} \in \pa{\mathcal{C}} \times \acc{.}\\ - \text { d'une suppression} & \text{si } \pa{x,y} \in \acc {.} \times \pa{\mathcal{C}} \\ - 0 & \text{si } \pa{x,y} = \pa{\acc{.},\acc{.}} + \text { d'une insertion} & \text{si } \pa{x,y} \in \pa{\mathcal{C}} \times \acc{.}\\ + \text { d'une suppression} & \text{si } \pa{x,y} \in \acc {.} \times \pa{\mathcal{C}} \\ + 0 & \text{si } \pa{x,y} = \pa{\acc{.},\acc{.}} \end{array} \right. \end{eqnarray*} @@ -197,7 +197,7 @@ en utilisant les mots acceptables : \begin{eqnarray} \begin{array}{crcl} - d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \R^+\\ + d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \mathbb{R}^+\\ & \pa{m_1,m_2} & \longrightarrow & \min \acc{ \sum_{i=1}^{+\infty} c\pa{M_1^i, M_2^i} | \pa{M_1,M_2} \in acc\pa{m_1} \times acc\pa{m_2}} @@ -264,7 +264,7 @@ tels que :math:`d\pa{m_1,m_2} = d\pa{N_2,N_1}` alors : Il reste à démontrer l'inégalité triangulaire. Soient trois mots :math:`\pa{m_1,m_2,m_3}`, on veut démontrer que -:math:`d\pa{m_1,m_3} \infegal d\pa{m_1,m_2} + d \pa{m_2,m_3}`. +:math:`d\pa{m_1,m_3} \leqslant d\pa{m_1,m_2} + d \pa{m_2,m_3}`. On définit : .. math:: @@ -292,14 +292,18 @@ tels que : \end{eqnarray*} Or comme la fonction :math:`c` est une distance sur :math:`\mathcal{C}'`, on peut affirmer que : -:math:`d\pa{M_1,M_3} \infegal d\pa{M_1,M_2} + d \pa{M_2,M_3}`. +:math:`d\pa{M_1,M_3} \leqslant d\pa{M_1,M_2} + d \pa{M_2,M_3}`. D'où : - \begin{eqnarray} - d\pa{m_1,m_3} \infegal d\pa{m_1,m_2} + d \pa{m_2,m_3} \label{edit_demo_eq_3} - \end{eqnarray} +.. math:: + :nowrap: + :label: edit_demo_eq_3 + + \begin{eqnarray*} + d\pa{m_1,m_3} \leqslant d\pa{m_1,m_2} + d \pa{m_2,m_3} + \end{eqnarray*} -Les assertions :ref:`1 `, :ref:`2 `, :ref:`3 ` +Les assertions :eq:`1 `, :eq:`2 `, :eq:`3 ` montrent que :math:`d` est bien une distance. Le tableau suivant illustre la démonstration pour les suites :math:`M_1,M_2,M_3` pour les mots et les mots ``idtzance``, ``tonce``, ``distances``. @@ -330,7 +334,7 @@ serait tenté de définir une nouvelle distance d'édition inspirée de la préc \begin{eqnarray*} \begin{array}{crcl} - d' : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \R^+\\ + d' : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \mathbb{R}^+\\ & \pa{m_1,m_2} & \longrightarrow & d'\pa{m_1,m_2} = \dfrac{d^*\pa{m_1,m_2}}{ \max \acc {l\pa{m_1}, l\pa{m_2}}} \\ \\ & & & \text{où } l\pa{m} \text{ est la longueur du mot } m \end{array} @@ -362,33 +366,33 @@ d'exprimer cette distance d'une autre manière afin de résoudre ce problème :tag: Définition :title: distance d'édition tronquée :label: definition_edit_dist_tronc - + Soient deux mots :math:`\pa{m_1,m_2}`, on définit la suite : .. math:: - + \left( d_{i,j}^{m_{1},m_{2}}\right) _{\substack{0\leqslant i\leqslant n_{1}\\0\leqslant j\leqslant n_{2}}}\left( =\left(d_{i,j}\right) _{\substack{0\leqslant i\leqslant n_{1}\\0\leqslant j\leqslant n_{2}}}\text{ pour ne pas alourdir les notations}\right) - + Par : .. math:: - + \left\{ \begin{array}[c]{l}% d_{0,0}=0\\ d_{i,j}=\min\left\{ \begin{array}{lll} - d_{i-1,j-1} & + & \text{comparaison} \left( m_1^i,m_2^j\right), \\ - d_{i,j-1} & + & \text{insertion} \left( m_2^j\right), \\ - d_{i-1,j} & + & \text{suppression} \left( m_1^i\right) + d_{i-1,j-1} & + & \text{comparaison} \left( m_1^i,m_2^j\right), \\ + d_{i,j-1} & + & \text{insertion} \left( m_2^j\right), \\ + d_{i-1,j} & + & \text{suppression} \left( m_1^i\right) \end{array} \right\}% \end{array} \right. - + Cette suite tronquée permet d'obtenir le résultat de la propriété suivante : @@ -402,13 +406,11 @@ Cette suite tronquée permet d'obtenir le résultat de la propriété suivante : où :math:`d` est la distance d'édition définie en :ref:`1 ` ou :ref:`2 `. -Cette factorisation des calculs est illustrée par les tableaux de -cette :ref:`figure `. La démonstration s'effectue par récurrence, la définition :ref:`3 ` est bien sûr équivalente :ref:`1 ` pour des mots de longueur un. On suppose donc que ce résultat est vrai pour un couple de mots :math:`\pa{m_1,m_2}` de longueur :math:`\pa{l_1,l_2}` -vérifiant :math:`l_1 \infegal i` et `l_2 \infegal j` avec au plus une égalité. +vérifiant :math:`l_1 \leqslant i` et `l_2 \leqslant j` avec au plus une égalité. Soit :math:`m` un mot, on note :math:`n` le nombre de lettres qu'il contient. On note :math:`m\left( l\right)` le mot formé des :math:`l` premières lettres de :math:`m`. Alors : @@ -422,11 +424,11 @@ Alors : \min\left\{ \begin{array}{lll}% d\left( m_{1}\left( i-1\right) ,m_{2}\left( j-1\right) \right) - & + & \text{comparaison}\left( m_{1,i},m_{2,j}\right), \\ + & + & \text{comparaison}\left( m_{1,i},m_{2,j}\right), \\ d\left( m_{1}\left( i\right) ,m_{2}\left( j-1\right) \right) - & + & \text{insertion}\left( m_{2,j}\right), \\ + & + & \text{insertion}\left( m_{2,j}\right), \\ d\left( m_{1}\left( i-1\right) ,m_{2}\left( j\right) \right) - & + & \text{suppression}\left( m_{1,i}\right) + & + & \text{suppression}\left( m_{1,i}\right) \end{array} \right\} \end{eqnarray*} @@ -510,7 +512,7 @@ aux permutations de lettres : :tag: Définition :title: distance d'édition tronquée étendue :label: definition_edit_dist_tronc_2 - + Soit deux mots :math:`\pa{m_1,m_2}`, on définit la suite : .. math:: @@ -523,7 +525,7 @@ aux permutations de lettres : par : .. math:: - + \left\{ \begin{array}[c]{l}% d_{0,0}=0\\ @@ -562,7 +564,7 @@ possible de calculer une erreur s'exprimant sous la forme : \begin{eqnarray*} E = \sum_{i=1}^{N} \; \pa{d\pa{X_i,Y_i} - c_i}^2 =\sum_{i=1}^{N} \; \pa{ \sum_{k=1}^{n} \alpha_{ik}\pa{\Theta} \, \theta_k - c_i}^2 \\ - \end{eqnarray*} + \end{eqnarray*} Les coefficients :math:`\alpha_{ik}\pa{\Theta}` dépendent des paramètres :math:`\Theta` car la distance d'édition correspond au coût de la transformation de moindre coût @@ -580,7 +582,7 @@ contrainte, ces coûts sont modélisés de la façon suivante : \begin{eqnarray*} E = \sum_{i=1}^{N} \; \pa{ \sum_{k=1}^{n} \, \alpha_{ik}\pa{\Omega} \, \frac{1}{1 + e^{-\omega_k}} - c_i}^2 - \end{eqnarray*} + \end{eqnarray*} Les fonctions :math:`\alpha_{ik}\pa{\Omega}` ne sont pas dérivable par rapport :math:`\Omega` mais il est possible d'effectuer une optimisation sans contrainte @@ -602,7 +604,7 @@ par descente de gradient. Les coûts sont donc appris en deux étapes : Dans cette étape, les coefficients :math:`\alpha_{ik}\pa{\Omega}` restent constants. Il suffit alors de minimiser la fonction - dérivable :math:`E\pa{\Omega}` sur :math:`\R^n`, ceci peut être + dérivable :math:`E\pa{\Omega}` sur :math:`\mathbb{R}^n`, ceci peut être effectué au moyen d'un algorithme de descente de gradient similaire à ceux utilisés pour les réseaux de neurones. diff --git a/_doc/sphinxdoc/source/c_algo/gest.rst b/_doc/c_algo/gest.rst similarity index 93% rename from _doc/sphinxdoc/source/c_algo/gest.rst rename to _doc/c_algo/gest.rst index 5505dec7..b4d35c89 100644 --- a/_doc/sphinxdoc/source/c_algo/gest.rst +++ b/_doc/c_algo/gest.rst @@ -5,9 +5,6 @@ Détection de segments ===================== -.. contents:: - :local: - L'idée ====== @@ -43,7 +40,7 @@ Illustration .. toctree:: :maxdepth: 1 - ../notebooks/segment_detection + ../notebooks/image/segment_detection La fonction :func:`detect_segments ` @@ -53,7 +50,7 @@ Explications ============ La présentation -`Détection des images dans les images digitales `_ +`Détection des images dans les images digitales `_ détaille le principe de l'algorithme. L'idée de l'algorithme est assez proche de la `transformée de Hough `_. Celle-ci est implémentée dans le module diff --git a/_doc/sphinxdoc/source/c_algo/gradient--1.png b/_doc/c_algo/gradient--1.png similarity index 100% rename from _doc/sphinxdoc/source/c_algo/gradient--1.png rename to _doc/c_algo/gradient--1.png diff --git a/_doc/sphinxdoc/source/c_algo/gradient-0.png b/_doc/c_algo/gradient-0.png similarity index 100% rename from _doc/sphinxdoc/source/c_algo/gradient-0.png rename to _doc/c_algo/gradient-0.png diff --git a/_doc/sphinxdoc/source/c_graph/graph_distance.rst b/_doc/c_algo/graph_distance.rst similarity index 99% rename from _doc/sphinxdoc/source/c_graph/graph_distance.rst rename to _doc/c_algo/graph_distance.rst index 998d744b..b07dacd5 100644 --- a/_doc/sphinxdoc/source/c_graph/graph_distance.rst +++ b/_doc/c_algo/graph_distance.rst @@ -13,13 +13,11 @@ One of the solution is the a better solution is described in [Blondel2004]_. You can also read `Graph similarity `_. -.. contents:: - :local: - Definitions =========== -The first approach is implemented in module :mod:`graph_distance `. +The first approach is implemented in module +:mod:`graph_distance `. Example of use: :: diff --git a/_doc/sphinxdoc/source/c_graph/images/graphmerge1.png b/_doc/c_algo/images/graphmerge1.png similarity index 100% rename from _doc/sphinxdoc/source/c_graph/images/graphmerge1.png rename to _doc/c_algo/images/graphmerge1.png diff --git a/_doc/sphinxdoc/source/c_graph/images/graphmergeall.png b/_doc/c_algo/images/graphmergeall.png similarity index 100% rename from _doc/sphinxdoc/source/c_graph/images/graphmergeall.png rename to _doc/c_algo/images/graphmergeall.png diff --git a/_doc/sphinxdoc/source/c_algo/index.rst b/_doc/c_algo/index.rst similarity index 87% rename from _doc/sphinxdoc/source/c_algo/index.rst rename to _doc/c_algo/index.rst index 4f270bb4..2fc63b7b 100644 --- a/_doc/sphinxdoc/source/c_algo/index.rst +++ b/_doc/c_algo/index.rst @@ -13,4 +13,6 @@ c'est-à-dire la grande majorité des cas. .. toctree:: :maxdepth: 1 + edit_distance + graph_distance gest diff --git a/_doc/sphinxdoc/source/c_algo/seg.png b/_doc/c_algo/seg.png similarity index 100% rename from _doc/sphinxdoc/source/c_algo/seg.png rename to _doc/c_algo/seg.png diff --git a/_doc/sphinxdoc/source/c_clus/gauss_mixture.rst b/_doc/c_clus/gauss_mixture.rst similarity index 94% rename from _doc/sphinxdoc/source/c_clus/gauss_mixture.rst rename to _doc/c_clus/gauss_mixture.rst index 113cd26f..c96dcd84 100644 --- a/_doc/sphinxdoc/source/c_clus/gauss_mixture.rst +++ b/_doc/c_clus/gauss_mixture.rst @@ -5,9 +5,6 @@ Mélange de lois normales ======================== -.. contents:: - :local: - Algorithme EM ============= @@ -17,7 +14,7 @@ Algorithme EM Soit :math:`X` une variable aléatoire d'un espace vectoriel de dimension :math:`d`, :math:`X` suit un la loi d'un mélange de :math:`N` lois gaussiennes de paramètres - :math:`\pa{\mu_i, \Sigma_i}_ {1 \infegal i \infegal N}`, + :math:`\pa{\mu_i, \Sigma_i}_ {1 \leqslant i \leqslant N}`, alors la densité :math:`f` de :math:`X` est de la forme : .. math:: @@ -56,8 +53,8 @@ L'estimation d'une telle densité s'effectue par l'intermédiaire d'un algorithme de type `Expectation Maximization (EM) `_ (voir [Dempster1977]_) ou de ses variantes `SEM `_, -`SAEM `_, ... -(voir [Celeux1995]_, [Celeux1985b]_). +`SAEM `_, ... +(voir [Celeux1985]_, [Celeux1985b]_). La sélection du nombre de lois dans le mélange reste un problème ouvert abordé par l'article [Biernacki2001]_. @@ -92,7 +89,7 @@ on suppose que :math:`X` suit la loi du mélange suivant : f\pa{X \sac \theta} = \sum_{i=1}^{k} \alpha_i \, f\pa{X \sac \theta_i} -Avec : :math:`\theta = \pa{\alpha_i,\theta_i}_{1 \infegal i \infegal k}, \; \forall i, \; \alpha_i \supegal 0` +Avec : :math:`\theta = \pa{\alpha_i,\theta_i}_{1 \leqslant i \leqslant k}, \; \forall i, \; \alpha_i \supegal 0` et :math:`\sum_{i=1}^{k} \alpha_i = 1`. On définit pour une classe :math:`m` la probabilité @@ -169,7 +166,7 @@ est dérivé de l'algorithme EM : Dans le cas contraire, on estime les probabilités :math:`P_{split}(m, \theta)` et :math:`P_{merge}(m,l, \theta)` - définie par les expressions :ref:`eq_split_merge`. On choisit aléatoirement + définie par les expressions :eq:`eq_split_merge`. On choisit aléatoirement une division ou un regroupement (les choix les plus probables ayant le plus de chance d'être sélectionnés). Ceci mène au paramètre :math:`\theta'_t` dont la partie modifiée par rapport à :math:`\hat{\theta}_t` est déterminée de manière aléatoire. L'algorithme EM est alors appliqué aux @@ -184,7 +181,7 @@ est dérivé de l'algorithme EM : P_a = \min \acc{ \exp\cro{ \frac{ L\pa{ \theta''_t, X} - L\pa{ \theta_t, X} }{\gamma} }, 1} On génére aléatoirement une variable :math:`u \sim U\cro{0,1}`, - si :math:`u \infegal P_a`, alors les paramètres :math:`\theta''_t` + si :math:`u \leqslant P_a`, alors les paramètres :math:`\theta''_t` sont validés. :math:`\hat{\theta}_t \longleftarrow \theta''_t` et retour à l'étape d'expectation. Dans le cas contraire, les paramètres :math:`\theta''_t` sont refusés et retour à l'étape précédente. diff --git a/_doc/sphinxdoc/source/c_clus/images/class6.png b/_doc/c_clus/images/class6.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/class6.png rename to _doc/c_clus/images/class6.png diff --git a/_doc/sphinxdoc/source/c_clus/images/class_4.png b/_doc/c_clus/images/class_4.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/class_4.png rename to _doc/c_clus/images/class_4.png diff --git a/_doc/sphinxdoc/source/c_clus/images/class_4_db.png b/_doc/c_clus/images/class_4_db.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/class_4_db.png rename to _doc/c_clus/images/class_4_db.png diff --git a/_doc/sphinxdoc/source/c_clus/images/cm.png b/_doc/c_clus/images/cm.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/cm.png rename to _doc/c_clus/images/cm.png diff --git a/_doc/sphinxdoc/source/c_clus/images/herbin1.png b/_doc/c_clus/images/herbin1.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/herbin1.png rename to _doc/c_clus/images/herbin1.png diff --git a/_doc/sphinxdoc/source/c_clus/images/herbin2.png b/_doc/c_clus/images/herbin2.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/herbin2.png rename to _doc/c_clus/images/herbin2.png diff --git a/_doc/sphinxdoc/source/c_clus/images/kohov.png b/_doc/c_clus/images/kohov.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/kohov.png rename to _doc/c_clus/images/kohov.png diff --git a/_doc/sphinxdoc/source/c_clus/images/koth1.png b/_doc/c_clus/images/koth1.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/koth1.png rename to _doc/c_clus/images/koth1.png diff --git a/_doc/sphinxdoc/source/c_clus/images/koth2.png b/_doc/c_clus/images/koth2.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/koth2.png rename to _doc/c_clus/images/koth2.png diff --git a/_doc/sphinxdoc/source/c_clus/images/liu3.png b/_doc/c_clus/images/liu3.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/liu3.png rename to _doc/c_clus/images/liu3.png diff --git a/_doc/sphinxdoc/source/c_clus/images/zhang1.png b/_doc/c_clus/images/zhang1.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/zhang1.png rename to _doc/c_clus/images/zhang1.png diff --git a/_doc/sphinxdoc/source/c_clus/images/zhangc1.png b/_doc/c_clus/images/zhangc1.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/zhangc1.png rename to _doc/c_clus/images/zhangc1.png diff --git a/_doc/sphinxdoc/source/c_clus/images/zhangc2.png b/_doc/c_clus/images/zhangc2.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/zhangc2.png rename to _doc/c_clus/images/zhangc2.png diff --git a/_doc/sphinxdoc/source/c_clus/images/zhangc3.png b/_doc/c_clus/images/zhangc3.png similarity index 100% rename from _doc/sphinxdoc/source/c_clus/images/zhangc3.png rename to _doc/c_clus/images/zhangc3.png diff --git a/_doc/sphinxdoc/source/c_clus/index.rst b/_doc/c_clus/index.rst similarity index 100% rename from _doc/sphinxdoc/source/c_clus/index.rst rename to _doc/c_clus/index.rst diff --git a/_doc/sphinxdoc/source/c_clus/kmeans.rst b/_doc/c_clus/kmeans.rst similarity index 94% rename from _doc/sphinxdoc/source/c_clus/kmeans.rst rename to _doc/c_clus/kmeans.rst index ddb38685..fe1b4b9a 100644 --- a/_doc/sphinxdoc/source/c_clus/kmeans.rst +++ b/_doc/c_clus/kmeans.rst @@ -5,9 +5,6 @@ k-means ======= -.. contents:: - :local: - *Dénomination française : algorithme des centres mobiles.* .. index:: centres mobiles, k-means, variance intra-classe, inertie @@ -29,12 +26,12 @@ critère appelé *inertie* ou variance *intra-classe*. .. math:: - \left(X_i\right)_{1\leqslant i\leqslant P}\in\left(\R^N\right)^P + \left(X_i\right)_{1\leqslant i\leqslant P}\in\left(\mathbb{R}^N\right)^P A chaque point est associée une classe : :math:`\left(c_i\right)_{1\leqslant i\leqslant P}\in\left\{1,...,C\right\}^P`. On définit les barycentres des classes : - :math:`\left( G_i\right)_{1\leqslant i\leqslant C}\in\left(\R^N\right)^C`. + :math:`\left( G_i\right)_{1\leqslant i\leqslant C}\in\left(\mathbb{R}^N\right)^C`. *Initialisation* @@ -86,9 +83,9 @@ La démonstration du théorème nécessite le lemme suivant. :tag: Lemme :lid: lemme_inertie_minimum - Soit :math:`\vecteur{X_1}{X_P} \in \pa{\R^N}^P`, - :math:`P` points de :math:`\R^N`, le minimum de la quantité - :math:`Q\pa{Y \in \R^N}` : + Soit :math:`\vecteur{X_1}{X_P} \in \pa{\mathbb{R}^N}^P`, + :math:`P` points de :math:`\mathbb{R}^N`, le minimum de la quantité + :math:`Q\pa{Y \in \mathbb{R}^N}` : .. math:: :nowrap: @@ -100,8 +97,8 @@ La démonstration du théorème nécessite le lemme suivant. est atteint pour :math:`Y=G=\dfrac{1}{P} \sum_{i=1}^{P} X_i` le barycentre des points :math:`\vecteur{X_1}{X_P}`. -Soit :math:`\vecteur{X_1}{X_P} \in \pa{\R^N}^P`, -:math:`P` points de :math:`\R^N`. +Soit :math:`\vecteur{X_1}{X_P} \in \pa{\mathbb{R}^N}^P`, +:math:`P` points de :math:`\mathbb{R}^N`. .. math:: :nowrap: @@ -109,7 +106,7 @@ Soit :math:`\vecteur{X_1}{X_P} \in \pa{\R^N}^P`, \begin{eqnarray*} \sum_{i=1}^{P} \overrightarrow{GX_{i}} = \overrightarrow{0} &\Longrightarrow& \sum_{i=1}^{P} d^2\pa{X_i,Y} = \sum_{i=1}^{P} d^2\pa{X_i,G}+ P \, d^2\pa{G,Y} \\ - &\Longrightarrow& \underset{Y\in\R^N}{\arg\min} \; \sum_{i=1}^{P} d^2\pa{X_i,Y} = \acc{G} + &\Longrightarrow& \underset{Y\in\mathbb{R}^N}{\arg\min} \; \sum_{i=1}^{P} d^2\pa{X_i,Y} = \acc{G} \end{eqnarray*} On peut maintenant démontrer le théorème. @@ -130,12 +127,12 @@ On en déduit que : \begin{eqnarray} J^{t+1} &=& \sum_{i, c_i^t \neq c_i^{t+1}} \; d^2\pa{ X_i, G_{c_i^{t+1}}^t} + J^{t+1} \sum_{i, c_i^t = c_i^{t+1}} \; d^2\pa{ X_i, G_{c_i^{t+1}}^t} \\ - J^{t+1} &\infegal& \sum_{i, c_i^t \neq c_i^{t+1}} \; d^2\pa{ X_i, G_{c_i^{t}}^t} + \sum_{i, c_i^t = c_i^{t+1}} \; d^2\pa{ X_i, G_{c_i^{t}}^t} \\ - J^{t+1} &\infegal& I^t + J^{t+1} &\leqslant& \sum_{i, c_i^t \neq c_i^{t+1}} \; d^2\pa{ X_i, G_{c_i^{t}}^t} + \sum_{i, c_i^t = c_i^{t+1}} \; d^2\pa{ X_i, G_{c_i^{t}}^t} \\ + J^{t+1} &\leqslant& I^t \end{eqnarray} Le lemme précédent appliqué à chacune des classes :math:`\ensemble{1}{C}`, -permet d'affirmer que :math:`I^{t+1} \infegal J^{t+1}`. +permet d'affirmer que :math:`I^{t+1} \leqslant J^{t+1}`. Par conséquent, la suite :math:`\pa{I_t}_{t\supegal 0}` est décroissante et minorée par 0, elle est donc convergente. @@ -166,7 +163,7 @@ Homogénéité des dimensions ++++++++++++++++++++++++++ Les coordonnées des points -:math:`\left(X_i\right) \in \R^N` sont généralement non homogènes : +:math:`\left(X_i\right) \in \mathbb{R}^N` sont généralement non homogènes : les ordres de grandeurs de chaque dimension sont différents. C'est pourquoi il est conseillé de centrer et normaliser chaque dimension. On note : :math:`\forall i \in \intervalle{1}{P}, \; X_i = \vecteur{X_{i,1}}{X_{i,N}}` : @@ -225,7 +222,7 @@ par la suivante : .. math:: - X=\left(X_i\right)_{1\leqslant i\leqslant P}\in\left(\R^N\right)^P + X=\left(X_i\right)_{1\leqslant i\leqslant P}\in\left(\mathbb{R}^N\right)^P A chaque point est associée une classe : :math:`\left(c_i\right)_{1\leqslant i\leqslant P}\in\left\{1,...,C\right\}^P`. @@ -242,7 +239,7 @@ par la suivante : La fonction :math:`D_k` est définie par la distance du point :math:`x` au centre :math:`G_l` choisi parmi les :math:`k` premiers centres. - :math:`D_k(x) = \min_{1 \infegal l \infegal k} d(x - G_l)`. + :math:`D_k(x) = \min_{1 \leqslant l \leqslant k} d(x - G_l)`. La suite de l'algorithme *k-means++* reprend les mêmes étapes que :ref:`k-means `. @@ -257,7 +254,7 @@ centres déjà choisis. L'article montre que : On définit l'inertie par :math:`J_(X) = \sum_{i=1}^{P} \; \min_G d^2(X_i, G)`. Si :math:`J_{OPT}` définit l'inertie optimale alors - :math:`\esp{J(X)} \infegal 8 (\ln C + 2) J_{OPT}(X)`. + :math:`\esp{J(X)} \leqslant 8 (\ln C + 2) J_{OPT}(X)`. La démonstration est disponible dans l'article [Arthur2007]_. @@ -279,7 +276,7 @@ que :ref:`l-kmeanspp` mais plus rapide et parallélisable. .. math:: - X=\left(X_i\right)_{1\leqslant i\leqslant P}\in\left(\R^N\right)^P + X=\left(X_i\right)_{1\leqslant i\leqslant P}\in\left(\mathbb{R}^N\right)^P A chaque point est associée une classe : :math:`\left(c_i\right)_{1\leqslant i\leqslant P}\in\left\{1,...,C\right\}^P`. @@ -314,7 +311,7 @@ Estimation de probabilités ========================== A partir de cette classification en :math:`C` classes, on construit un -vecteur de probabilités pour chaque point :math:`\pa{X_{i}}_{1 \infegal i \infegal P}` +vecteur de probabilités pour chaque point :math:`\pa{X_{i}}_{1 \leqslant i \leqslant P}` en supposant que la loi de :math:`X` sachant sa classe :math:`c_X` est une loi normale multidimensionnelle. La classe de :math:`X_i` est notée :math:`c_i`. On peut alors écrire : @@ -429,7 +426,7 @@ Maxima de la fonction densité L'article [Herbin2001]_ propose une méthode différente pour estimer le nombre de classes, il s'agit tout d'abord d'estimer la fonction densité du nuage de points qui est une fonction de -:math:`\R^n \longrightarrow \R`. Cette estimation est effectuée au moyen +:math:`\mathbb{R}^n \longrightarrow \mathbb{R}`. Cette estimation est effectuée au moyen d'une méthode non paramètrique telle que les estimateurs à noyau (voir [Silverman1986]_) Soit :math:`\vecteur{X_1}{X_N}` un nuage de points inclus dans une image, @@ -451,7 +448,7 @@ d'image qui ne peut pas être résolu par la méthode des nuées dynamiques puisque la forme des classes n'est pas convexe, ainsi que le montre la figure suivante. La fonction de densité :math:`f` est seuillée de manière à obtenir une fonction -:math:`g : \R^n \longrightarrow \acc{0,1}` définie par : +:math:`g : \mathbb{R}^n \longrightarrow \acc{0,1}` définie par : .. math:: @@ -459,7 +456,7 @@ ainsi que le montre la figure suivante. La fonction de densité .. index:: composante connexe -L'ensemble :math:`g^{-1}\pa{\acc{1}} \subset \R^n` +L'ensemble :math:`g^{-1}\pa{\acc{1}} \subset \mathbb{R}^n` est composée de :math:`N` composantes connexes notées :math:`\vecteur{C_1}{C_N}`, la classe d'un point :math:`x` est alors l'indice de la composante connexe à la @@ -499,8 +496,8 @@ L'inertie de ce nuage de points est définie par : I = \sum_{x \in X} \; \norme{ x - y_{C\pa{x} }}^2 On définit tout d'abord une distance -:math:`\alpha \in \R^+`, puis l'ensemble -:math:`V\pa{y,\alpha} = \acc{ z \in Y \sac d\pa{y,z} \infegal \alpha }`, +:math:`\alpha \in \mathbb{R}^+`, puis l'ensemble +:math:`V\pa{y,\alpha} = \acc{ z \in Y \sac d\pa{y,z} \leqslant \alpha }`, :math:`V\pa{y,\alpha}` est donc l'ensemble des voisins des centres dont la distance avec :math:`y` est inférieur à :math:`\alpha`. L'article [Kothari1999]_ propose de minimiser le coût :math:`J\pa{\alpha}` @@ -596,7 +593,7 @@ Il s'appuie sur la méthode des multiplicateurs de Lagrange. | for i in :math:`1..N` | Mise à jour d'après le premier terme de la fonction de coût :math:`J\pa{\alpha}`. - | :math:`w \longleftarrow \underset{1 \infegal l \infegal K}{\arg \min} \; \norme{x_i - y_l}^2` + | :math:`w \longleftarrow \underset{1 \leqslant l \leqslant K}{\arg \min} \; \norme{x_i - y_l}^2` | :math:`z^1_w \longleftarrow z^1_w + \eta \pa{ x_i - y_w}` | :math:`c^1_w \longleftarrow c^1_w + 1` | @@ -617,7 +614,7 @@ Il s'appuie sur la méthode des multiplicateurs de Lagrange. :math:`y_k`, retour à l'étape précédente. Sinon, tous les couples de classes :math:`\pa{i,j}` vérifiant :math:`\norme{y_i - y_j} > \alpha` sont fusionnés : :math:`\alpha \longleftarrow \alpha + \alpha_t`. - Si :math:`\alpha \infegal \alpha2`, retour à l'étape de préparation. + Si :math:`\alpha \leqslant \alpha2`, retour à l'étape de préparation. *terminaison* @@ -652,7 +649,7 @@ L'algorithme qui suit a pour objectif de minimiser la quantité pour un échanti .. math:: - I = \sum_{i=1}^{N}\sum_{k=1}^{K} \indicatrice{ i = \underset{1 \infegal j \infegal N}{\arg \max} + I = \sum_{i=1}^{N}\sum_{k=1}^{K} \indicatrice{ i = \underset{1 \leqslant j \leqslant N}{\arg \max} G\pa{X_k, \mu_j,\Sigma_j} } \; \ln \cro{ p_i G\pa{ X_k, \mu_i, \Sigma_i } } .. mathdef:: @@ -666,7 +663,7 @@ L'algorithme qui suit a pour objectif de minimiser la quantité pour un échanti *initialisation* :math:`t \longleftarrow 0`. - Les paramètres :math:`\acc{p_i^0, \mu_i^0, \Sigma_i^0 \sac 1 \infegal i \infegal N}` sont initialisés + Les paramètres :math:`\acc{p_i^0, \mu_i^0, \Sigma_i^0 \sac 1 \leqslant i \leqslant N}` sont initialisés grâce à un algorithme des :ref:`k-means ` ou :ref:`FSCL `. :math:`\forall i, \; p_i^0 = \frac{1}{N}` et :math:`\beta_i^0 = 0`. @@ -676,7 +673,7 @@ L'algorithme qui suit a pour objectif de minimiser la quantité pour un échanti .. math:: - i = \underset{1 \infegal i \infegal N}{\arg \min} \; G\pa{X_k, \mu_i^t, \Sigma_i^t} + i = \underset{1 \leqslant i \leqslant N}{\arg \min} \; G\pa{X_k, \mu_i^t, \Sigma_i^t} | for i in :math:`1..N` | :math:`\mu_i^{t+1} = \mu_i^t + \eta \, \pa{\Sigma_i^t}^{-1} \, \pa{ X_k - \mu_i^t}` @@ -690,7 +687,7 @@ L'algorithme qui suit a pour objectif de minimiser la quantité pour un échanti *terminaison* - Tant que :math:`\underset{1 \infegal i \infegal N}{\arg \min} \; G\pa{X_k, \mu_i^t, \Sigma_i^t}` + Tant que :math:`\underset{1 \leqslant i \leqslant N}{\arg \min} \; G\pa{X_k, \mu_i^t, \Sigma_i^t}` change pour au moins un des points :math:`X_k`. Lors de la mise à jour de :math:`\Sigma^{-1}`, @@ -877,7 +874,7 @@ lors de l'estimation des centres des classes, l'algorithme évite la formation d Soit un nuage de points :math:`\vecteur{X_1}{X_N}`, soit :math:`C` vecteurs :math:`\vecteur{\omega_1}{\omega_C}` initialisés de manière aléatoires. - Soit :math:`F : \pa{u,t} \in \R^2 \longrightarrow \R^+` + Soit :math:`F : \pa{u,t} \in \mathbb{R}^2 \longrightarrow \mathbb{R}^+` croissante par rapport à :math:`u`. Soit une suite de réels :math:`\vecteur{u_1}{u_C}`, soit une suite :math:`\epsilon\pa{t} \in \cro{0,1}` décroissante où :math:`t` @@ -977,7 +974,7 @@ Bibliographie *Arthur, D.; Vassilvitskii, S.*, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics Philadelphia, PA, USA. pp. 1027–1035. - `PDF `_. + `2006-13.pdf `_. .. [Balakrishnan1996] Comparative performance of the FSCL neural net and K-means algorithm for market segmentation (1996), P. V. Sundar Balakrishnan, Martha Cooper, Varghese S. Jacob, Phillip A. Lewis, @@ -986,8 +983,8 @@ Bibliographie .. [Bahmani2012] Scalable K-Means++ (2012), *Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii*, Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 7, pp. 622-633 (2012) - `PDF `_, - `arXiv `_ + `vldb12-kmpar.pdf `_, + `arXiv.1203.6402 `_ .. [Cheung2003] :math:`k^*`-Means: A new generalized k-means clustering algorithm (2003), Yiu-Ming Cheung, diff --git a/_doc/sphinxdoc/source/c_clus/kohonen.rst b/_doc/c_clus/kohonen.rst similarity index 91% rename from _doc/sphinxdoc/source/c_clus/kohonen.rst rename to _doc/c_clus/kohonen.rst index 6543584e..909562e8 100644 --- a/_doc/sphinxdoc/source/c_clus/kohonen.rst +++ b/_doc/c_clus/kohonen.rst @@ -5,9 +5,6 @@ Carte de Kohonen ================ -.. contents:: - :local: - Principe ======== @@ -35,12 +32,12 @@ linéaire, rectangulaire, triangulaire. :tag: Algorithme :lid: classification_som_algo - Soient :math:`\vecteur{\mu_1^t}{\mu_N^t} \in \pa{\R^n}^N` - des neurones de l'espace vectoriel :math:`\R^n`. On + Soient :math:`\vecteur{\mu_1^t}{\mu_N^t} \in \pa{\mathbb{R}^n}^N` + des neurones de l'espace vectoriel :math:`\mathbb{R}^n`. On désigne par :math:`V\pa{\mu_j}` l'ensemble des neurones voisins de :math:`\mu_j` pour cette carte de Kohonen. Par définition, on a :math:`\mu_j \in V\pa{\mu_j}`. - Soit :math:`\vecteur{X_1}{X_K} \in \pa{\R^n}^K` un nuage de points. + Soit :math:`\vecteur{X_1}{X_K} \in \pa{\mathbb{R}^n}^K` un nuage de points. On utilise une suite de réels positifs :math:`\pa{\alpha_t}` vérifiant :math:`\sum_{t \supegal 0} \alpha_t^2 < \infty` et @@ -49,7 +46,7 @@ linéaire, rectangulaire, triangulaire. *initialisation* Les neurones :math:`\vecteur{\mu_1^0}{\mu_N^0}` - sont répartis dans l'espace :math:`\R^n` + sont répartis dans l'espace :math:`\mathbb{R}^n` de manière régulière selon la forme de leur voisinage. :math:`t \longleftarrow 0`. @@ -58,7 +55,7 @@ linéaire, rectangulaire, triangulaire. On choisi aléatoirement un points du nuage :math:`X_i` puis on définit le neurone :math:`\mu_{k^*}^t` de telle sorte que : - :math:`\norme{ \mu_{k^*}^t - X_i} = \underset{1 \infegal j \infegal N}{\min } \; \norme{ \mu_j^t - X_i }`. + :math:`\norme{ \mu_{k^*}^t - X_i} = \underset{1 \leqslant j \leqslant N}{\min } \; \norme{ \mu_j^t - X_i }`. *mise à jour* @@ -153,7 +150,7 @@ L'article définit ensuite la densité interne pour :math:`C` classes : \begin{eqnarray*} D_{int} (C) &=& \frac{1}{C} \; \sum_{k=1}^{C} \; \sum_{i=1}^{N} \; \sum_{j=1}^{N} \; - a_{ik} a_{jk} \indicatrice{ \norme{ X_i - X_j} \infegal \sigma } + a_{ik} a_{jk} \indicatrice{ \norme{ X_i - X_j} \leqslant \sigma } \end{eqnarray*} On définit la distance :math:`d^*_{kl}` pour :math:`\pa{k,l} \in \ensemble{1}{C}^2`, @@ -175,7 +172,7 @@ La densité externe est alors définie en fonction du nombre de classes :math:`C \begin{eqnarray*} D_{ext} (C) = \sum_{k=1}^{C} \; \sum_{l=1}^{C} \; \cro{ \frac{ d_{kl} } { \sigma\pa{k} \sigma\pa{l} } \; \sum_{i=1}^{N} \; \indicatrice{ a_{ik} + a_{il} > 0 } \indicatrice{ \norme{ X_i - \frac{X_{i^*}^{kl} + X_{j^*}^{kl}}{2} } - \infegal \frac{\sigma\pa{k} +\sigma\pa{l}}{2} } } + \leqslant \frac{\sigma\pa{k} +\sigma\pa{l}}{2} } } \end{eqnarray*} L'article définit ensuite la séparabilité en fonction du nombre de classes :math:`C` : @@ -202,7 +199,8 @@ Autres utilisation des cartes de Kohenen On peut les utiliser pour déterminer le plus court chemin passant par tous les noeuds d'un graphe, c'est à dire appliquer -`Kohonen au problème du voyageur de commerce `_. +`Kohonen au problème du voyageur de commerce +`_. Bibliographie ============= @@ -219,9 +217,6 @@ Bibliographie Z. Lo, B. Bavarian, *Biological Cybernetics*, volume 63, pages 55-63 -.. [Rougier] `Dynamic Self-Organising Map `_, - Nicolas P. Rougier and Yann Boniface - .. [Wu2004] Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density (2004), Sitao Wu, Tommy W. S. Chow, *Pattern Recognition*, volume (37), pages 175-188 diff --git a/_doc/sphinxdoc/source/c_garden/file_dattente.rst b/_doc/c_garden/file_dattente.rst similarity index 96% rename from _doc/sphinxdoc/source/c_garden/file_dattente.rst rename to _doc/c_garden/file_dattente.rst index 5d74edac..832b43bb 100644 --- a/_doc/sphinxdoc/source/c_garden/file_dattente.rst +++ b/_doc/c_garden/file_dattente.rst @@ -5,9 +5,6 @@ File d'attente, un petit exemple *Psychokinèse, les ampoules grillent à distance* -.. contents:: - :local: - Petite histoire =============== @@ -83,7 +80,7 @@ pas du temps. :nowrap: \begin{eqnarray} - f(t) &=& \mu \; e^{- \mu t} \text{ et } \pr {X \infegal t} = + f(t) &=& \mu \; e^{- \mu t} \text{ et } \pr {X \leqslant t} = \int_0^t \mu \; e^{- \mu x} dx = 1 - e^{-\mu t} \end{eqnarray} @@ -117,7 +114,7 @@ suivant une loi exponentielle, alors : :nowrap: \begin{eqnarray*} - \pr{B(x,t,dt)} &=& \pr{ D \infegal t+dt-x \sac D > t-x } \\ + \pr{B(x,t,dt)} &=& \pr{ D \leqslant t+dt-x \sac D > t-x } \\ &=& \frac{ \pr{ t+dt-x \supegal D > t-x } } { \pr{ D > t-x }} \\ &=& \frac{ \int_{t-x}^{t+dt-x} \mu e^{-\mu u} du } { \int_{t-x}^{\infty} \mu e^{-\mu u} du } = \frac{ e^{- \mu (t-x) } - e^{- \mu (t-x+dt) } } { e^{-\mu (t-x) }} \\ @@ -250,16 +247,16 @@ la probabilité qu'une personne parmi :math:`k` quitte un guichet est : :nowrap: \begin{eqnarray*} - \pr{ \min \ensemble{D_1}{D_k} \infegal dt } &=& 1 - \pr {\min \ensemble{D_1}{D_k} > dt} \\ + \pr{ \min \ensemble{D_1}{D_k} \leqslant dt } &=& 1 - \pr {\min \ensemble{D_1}{D_k} > dt} \\ &=& 1 - \cro{\prod_{n=1}^{k} \pr {D_n > dt}} \\ - &=& 1 - \cro{\prod_{n=1}^{k} 1 - \pr {D_n \infegal dt}} \\ + &=& 1 - \cro{\prod_{n=1}^{k} 1 - \pr {D_n \leqslant dt}} \\ &=& 1 - \cro{\prod_{n=1}^{k} e^{-\mu dt}} \\ &=& 1 - e^{- k\mu dt} \sim k \mu dt + o(dt) \end{eqnarray*} Pour déterminer les probabilités :math:`\pa{p_n}_n`, on applique le même raisonnement que pour un système :math:`M/M/1` en distinguant -les cas :math:`n \infegal S` et :math:`n > S`. On adapte la récurrence +les cas :math:`n \leqslant S` et :math:`n > S`. On adapte la récurrence donnée par le système d'équations :eq:`systeme_mm1` au cas :math:`M/M/S` : .. math:: @@ -269,7 +266,7 @@ donnée par le système d'équations :eq:`systeme_mm1` au cas :math:`M/M/S` : \begin{eqnarray*} && \left \{ \begin{array}{lll} \mu p_1 - \lambda p_0 &=& 0 \\ - \lambda p_{n-1} + \pa{n+1} \mu p_{n+1} - \pa {n \mu + \lambda } p_n &=& 0 \text{ si } 1 \infegal n < S \\ + \lambda p_{n-1} + \pa{n+1} \mu p_{n+1} - \pa {n \mu + \lambda } p_n &=& 0 \text{ si } 1 \leqslant n < S \\ \lambda p_{n-1} + S \mu p_{n+1} - \pa { S \mu + \lambda } p_n &=& 0 \text{ si } n \supegal S \end{array}\right. \end{eqnarray*} @@ -453,7 +450,7 @@ Cette fonction vérifie :math:`F_{\mu}^{-1}\pa{F_{\mu}(x)} = 1`. Or si :math:`U` est une variable aléatoire uniforme sur :math:`\cro{0,1}`, alors la variable :math:`V = F_{\mu}^{-1}(U)` suit la loi exponentielle avec :math:`\mu` pour paramètre. -Effectivement, :math:`\pr{ V \infegal t} = \pr{ F_{\mu}^{-1}(U) \infegal t} = \pr{U \infegal F_{\mu}(t)} = F_{\mu}(x)`. +Effectivement, :math:`\pr{ V \leqslant t} = \pr{ F_{\mu}^{-1}(U) \leqslant t} = \pr{U \leqslant F_{\mu}(t)} = F_{\mu}(x)`. La fonction de répartition de la variable :math:`V` est :math:`F_{\mu}`, :math:`V` est donc une loi exponentielle de paramètre :math:`\mu`. La première fonction simule une variable exponentielle de paramètre :math:`\mu` : @@ -472,7 +469,7 @@ La première fonction simule une variable exponentielle de paramètre :math:`\mu print(generate_expo(2)) -Le module :epkg:`*py:random` propose aussi une fonction +Le module :mod:`random` propose aussi une fonction qui simule automatiquement une variable exponentielle. .. runpython:: @@ -508,7 +505,7 @@ La valeur obtenue est proche de :math:`S \mu = 100`. .. toctree:: - ../notebooks/file_dattente + ../notebooks/dsgarden/file_dattente_ex Bibliographie ============= diff --git a/_doc/sphinxdoc/source/c_garden/images/poishis.png b/_doc/c_garden/images/poishis.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poishis.png rename to _doc/c_garden/images/poishis.png diff --git a/_doc/sphinxdoc/source/c_garden/images/poishist2.png b/_doc/c_garden/images/poishist2.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poishist2.png rename to _doc/c_garden/images/poishist2.png diff --git a/_doc/sphinxdoc/source/c_garden/images/poishist3.png b/_doc/c_garden/images/poishist3.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poishist3.png rename to _doc/c_garden/images/poishist3.png diff --git a/_doc/sphinxdoc/source/c_garden/images/poisson.png b/_doc/c_garden/images/poisson.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poisson.png rename to _doc/c_garden/images/poisson.png diff --git a/_doc/sphinxdoc/source/c_garden/images/poissonb.png b/_doc/c_garden/images/poissonb.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poissonb.png rename to _doc/c_garden/images/poissonb.png diff --git a/_doc/sphinxdoc/source/c_garden/images/poissonb2.png b/_doc/c_garden/images/poissonb2.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poissonb2.png rename to _doc/c_garden/images/poissonb2.png diff --git a/_doc/sphinxdoc/source/c_garden/images/poissond.png b/_doc/c_garden/images/poissond.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poissond.png rename to _doc/c_garden/images/poissond.png diff --git a/_doc/sphinxdoc/source/c_garden/images/poulet10.png b/_doc/c_garden/images/poulet10.png similarity index 100% rename from _doc/sphinxdoc/source/c_garden/images/poulet10.png rename to _doc/c_garden/images/poulet10.png diff --git a/_doc/sphinxdoc/source/c_garden/index.rst b/_doc/c_garden/index.rst similarity index 53% rename from _doc/sphinxdoc/source/c_garden/index.rst rename to _doc/c_garden/index.rst index 7a9eb0ba..1bd2bf0c 100644 --- a/_doc/sphinxdoc/source/c_garden/index.rst +++ b/_doc/c_garden/index.rst @@ -1,7 +1,7 @@ -################################## -Pérégrinations d'un data scientist -################################## +############## +Pérégrinations +############## Ce sont quelques notebooks sur des points particuliers qui surgissent au quotidien quand on traite des données. @@ -11,8 +11,11 @@ découvrir quelques poussières sous le tapis. .. toctree:: :maxdepth: 1 - ../notebooks/split_train_test - ../notebooks/correlation_non_lineaire + ../notebooks/dsgarden/split_train_test + ../notebooks/dsgarden/correlation_non_lineaire file_dattente strategie_avec_alea - ../notebooks/discret_gradient + ../notebooks/dsgarden/discret_gradient + quantization + ../notebooks/dsgarden/classification_multiple + diff --git a/_doc/c_garden/quantization.rst b/_doc/c_garden/quantization.rst new file mode 100644 index 00000000..89893bbf --- /dev/null +++ b/_doc/c_garden/quantization.rst @@ -0,0 +1,203 @@ + +.. _l-quantization: + +============ +Quantization +============ + +Un problème simple +================== + +Les réseaux de neurones (deep learning) sont de plus en plus gros +et nécessitent de plus en plus de puissance de calcul. La +**quantization** est une façon de contourner en réduisant +la mémoire nécessaire pour stocker les coefficients et le +temps de calcul avec les dernières cartes graphiques. +La quantization est équivalent à une discrétisation. +Voir aussi [Gholami2021]_ et quelques bout de codes +`quantization.py +`_. + +Les produits matriciels sont fréquents dans les réseaux de neurones où +on multiple les entrées *X* d'une couche de neurones avec les coefficients +*A* : :math:`X B`. Lors de l'apprentissage, la matrice B est apprise soit en float 32 bit +soit en float 16 bit puis elle est discrétisée sur 8 bit, soit 256 valeurs. +Si on note *q(B)* la forme *discrétisée* de *B*, le plus simple de minimiser +:math:`\norm{B - q(B)}^2`. On pourrait également se pencher sur la minimisation +de :math:`X (B - q(B))^2` mais la matrice *X* vient des données. +Il faudrait soit prendre des hypothèses de distribution sur X +ou optimiser sur un jeu de données :math:`(X_i)_i`. + +On considère le cas simple qui consiste à minimiser +:math:`\norm{B - q(B)}^2`. + +Discrétisation sur un octet +=========================== + +On discrétise sur 256 valeurs espacées de façon régulière sur un intervalle. +Mais les coefficients ne sont pas tous situés dans un même intervalle. +On doit alors trouver les meilleurs paramètres :math:`z` et :math:`\lambda` +qui définissent la quantization au travers de deux fonctions. On note +:math:`c_{0}^{255}(x)=\max(0, \min(255, x))` la fonction qui *x* +dans l'intervalle *[0, 255]*, 0 à gauche, 255 à droite. + +.. math:: + + \begin{array}{rcl} + q_1(z, \lambda, x) &=& c_{0}^{255}\pa{\intf{\frac{x}{\lambda}}_{i8} + z} \text{ quantization}\\ + q_2(z, \lambda, i) &=& \lambda(i - z) \text{ déquantization} \\ + q(z, \lambda, x) &=& q_2(z, \lambda, q_1(z, \lambda, x)) \\ + &=& \lambda\pa{c_{0}^{255}\pa{\intf{\frac{x}{\lambda}}_{i8} + z} - z} \\ + &=& \lambda\intf{\frac{x}{\lambda}}_{i8,z} + \end{array} + +La fonction :math:`\intf{x}_{i8,z}` est la partie entière asociée à la fonction +:math:`c_{0}^{255}(i)`. + +.. math:: + + \norm{B - q(z,\lambda,B)}^2 = \sum_{ij} \pa{b_{ij} - \lambda\intf{\frac{x}{\lambda}}_{i8,z}}^2 + +Le problème est la fonction :math:`\intf{.}_{i8,z}` qui n'est pas dérivable. +C'est un problème d'optimisation discrète. Le paramètre :math:`\lambda` +est appelé *scale* ou *échelle*. Il peut y en avoir un ou plusieurs +mais dans ce cas, on considère les différentes parties de *B* +qui sont quantizées avec les mêmes paramètres :math:`\lambda` et *z* + +Cette quantization est appelée *quantization linéaire*. Elle est privilégiée +car elle est très rapide et la transformation inverse (ou déquantization) +l'est tout autant. + +Discrétisation sur une float 8 +============================== + +Un float 8 est un réel codé sur 8 bits. Il y a plusieurs variantes. +Nous allons considérer la version *E4M3FN*, :math:`S.1111.111_2` : + +* 1 bit pour le signe +* 4 bits pour l'exposant +* 3 bits pour la mantisse + +Et la valeur réelle est : + +* si l'exposant est nul, + :math:`(-1)^S 2^{\sum_{i=3}^6 b_i 2^{i-3}- 7}\left(1+\sum_{i=0}^2 b_i 2^{i-3}\right)` +* sinon :math:`(-1)^S 2^{-6} \sum_{i=0}^2 b_i 2^{i-3}` +* le réel vaut NaN s'il suit le format : :math:`S.1111.111_2` (255 ou 127), +* le réel vaut zéro s'il suit le format : :math:`S.0000.000_2` (0 ou 128) + +Les valeurs ne sont plus uniformément espacées mais il y en a toujours entre 252 et 255 +selon les formats de float 8 et on cherche toujours à trouver les meilleurs +paramètres :math:`\lambda` et *z*. La formule est quasiment la même. On n'arrondit +plus à l'entier inférieur (ou le plus proche) mais au float 8 +inférieur (ou le plus proche). + +.. math:: + + \norm{B - q(z,\lambda,B)}^2 = \sum_{ij} \pa{b_{ij} - \lambda\intf{\frac{x}{\lambda}}_{f8,z} }^2 + +Optimisation +============ + +L'idée est de traiter la discrétisation sur un ensemble fini de valeurs, +quel qu'il soit, des entiers ou des réels codés sur 8 bits. On note cet +ensemble :math:`(d_1, ..., d_n)`. On réécrit le problème d'optimisation : + +.. math:: + + \begin{array}{rcl} + \norm{B - q(z,\lambda,B)}^2 &=& \sum_{ij} \pa{b_{ij} - \lambda\intf{\frac{x}{\lambda}}_{f8,z} }^2 \\ + &=& \sum_{k=1}^{n} \sum_{ij} \pa{b_{ij} - \lambda\intf{\frac{x}{\lambda}}_{f8} }^2 + \indicatrice{\intf{\frac{x}{\lambda}}_{f8} = d_k} \\ + &=& \sum_{k=1}^{n} \sum_{ij} \pa{b_{ij} - \lambda d_k }^2 + \indicatrice{\intf{\frac{x}{\lambda}}_{f8} = d_k} \\ + \end{array} + +On note :math:`K(u)=\frac{1}{\sqrt{2\pi}}e^{-\frac{1}{2}u^2}` le noyau gaussien. + +.. math:: + + \begin{array}{rcl} + \norm{B - q(z,\lambda,B)}^2 &=& \lim_{h\to 0} \sum_{k=1}^{n} \sum_{ij} \pa{b_{ij} - \lambda d_k }^2 + \frac{1}{h} K\pa{\frac{b_{ij} - \lambda d_k}{h}}\indicatrice{\intf{\frac{x}{\lambda}}_{f8} = d_k} + \end{array} + +Cette notation ne tient pas compte du décalage *z* qu'on peut ajouter comme suit : + +.. math:: + + \begin{array}{rcl} + \norm{B - q(z,\lambda,B)}^2 &=& \lim_{h\to 0} \sum_{k=1}^{n} \sum_{ij} \pa{b_{ij} - \lambda d_k - z }^2 + \frac{1}{h} K\pa{\frac{b_{ij} - \lambda d_k - z}{h}}\indicatrice{\intf{\frac{x}{\lambda}}_{?,z} = d_k} + \end{array} + +Le problème est beaucoup plus simple à résoudre si on enlève l'indicatrice +et la fonction devient dérivable. L'idée est de regarder l'évolution des valeurs trouvées +pour :math:`\lambda` et *z* en faisant tendre *h* vers 0. +On commence par le plus simple, le cas float 8 pour lequel on impose :math:`z=0`. + +.. math:: + :label: eq-qua-1 + + f(B,\lambda,h) = \frac{1}{h} \sum_{k=1}^{n} \sum_{ij} \pa{b_{ij} - \lambda d_k - z }^2 + K\pa{\frac{b_{ij} - \lambda d_k - z}{h}} + +Si on suppose que les coefficients de *B* suivent une certaine loi de probabilité, +ce calcul devient une somme d'espérence. + +.. math:: + + f(X,\lambda,h) = \frac{1}{h} \sum_{k=1}^{n} \esp\pa{X - \lambda d_k - z }^2 + K\pa{\frac{X - \lambda d_k - z}{h}} + +Résolution +========== + +If :math:`K(u)=\frac{1}{\sqrt{2\pi}}e^{-\frac{1}{2}u^2}` then +:math:`K'(u) = -u \frac{1}{\sqrt{2\pi}}e^{-\frac{1}{2}u^2} = -u K(x)`. +Let's denote :math:`g(b,x) = (b-xd)^2 K\pa{\frac{b-xd}{h}}`. Then: + +.. math:: + + \begin{array}{rcl} + g(b,x) &=& \frac{1}{h} (b-xd)^2 K\pa{\frac{b-xd}{h}} \\ + \frac{\partial g}{\partial x}(b,x) &=& + \frac{1}{h}\cro{ -2d(b-xd)K\pa{\frac{b-xd}{h}} -\frac{d}{h} (b-xd)^2 K'\pa{\frac{b-xd}{h}} } \\ + &=& -\frac{d(b-xd)}{h}\cro{2 K\pa{\frac{b-xd}{h}} + \frac{b-xd}{h} K'\pa{\frac{b-xd}{h}} } + \end{array} + +Applied to :eq:`eq-qua-1`: + +.. math:: + + \begin{array}{rcl} + f(B,\lambda,h) &=& \frac{1}{h} \sum_{k=1}^{n} \sum_{ij} \pa{b_{ij} - \lambda d_k}^2 + K\pa{\frac{b_{ij} - \lambda d_k}{h}} \\ + &=& \sum_{k=1}^{n} \sum_{ij} g(b_{ij}, \lambda) + \end{array} + +Then: + +.. math:: + + \begin{array}{rcl} + \frac{\partial f}{\partial \lambda} &=& \sum_{k=1}^{n} \sum_{ij} + \frac{\partial g}{\partial \lambda}(b_{ij}, \lambda) + \end{array} + +Notebooks +========= + +.. toctree:: + :maxdepth: 1 + + ../notebooks/dsgarden/quantization_f8 + +Bibliographie +============= + +.. [Gholami2021] Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer, + University of California, Berkeley + `A Survey of Quantization Methods for Efficient + Neural Network Inference `_ + diff --git a/_doc/sphinxdoc/source/c_garden/strategie_avec_alea.rst b/_doc/c_garden/strategie_avec_alea.rst similarity index 82% rename from _doc/sphinxdoc/source/c_garden/strategie_avec_alea.rst rename to _doc/c_garden/strategie_avec_alea.rst index 1f01b33a..4b48687b 100644 --- a/_doc/sphinxdoc/source/c_garden/strategie_avec_alea.rst +++ b/_doc/c_garden/strategie_avec_alea.rst @@ -5,9 +5,6 @@ Optimisation avec données aléatoires ==================================== -.. contents:: - :local: - Un problème simple ================== @@ -54,12 +51,12 @@ La figure suivante montre l'allure de cette distribution. \pr{X=i} = e^{-\lambda} \frac{ \lambda^i}{i!} -.. figure:: images/poisson.png +.. image:: images/poisson.png - Ce graphe répresente la fonction de densité d'une loi de Poisson de paramètre 80. - On observe que le pic est obtenu pour une valeur - proche de 80, c'est la valeur la plus probable. - Ceci signifie que le nombre de poulets achetés le plus probable est 80. +Ce graphe répresente la fonction de densité d'une loi de Poisson de paramètre 80. +On observe que le pic est obtenu pour une valeur +proche de 80, c'est la valeur la plus probable. +Ceci signifie que le nombre de poulets achetés le plus probable est 80. Comme le nombre de poulets achetés varie d'une semaine à l'autre, le bénéfice du supermarché varie aussi d'une semaine à l'autre. @@ -115,26 +112,24 @@ les dernières lignes servent à tracer la courbe présentée par la figure qui # res est la courbe affichée plus bas print(res[:4]) -.. figure:: - - .. list-table:: - :widths: auto - :header-rows: 0 - - * - .. image:: images/poissonb.png - - .. image:: images/poissonb2.png - - Cette courbe est celle de l'évolution des profits en fonction du - nombre de poulets commandés. On suppose que - le nombre de poulets achetés suit une loi de Poisson de paramètre 80, - que les poulets sont achetés 2 euros, revendu 5 euros et soldés 1 euros. - Le maximum de 228 euros est obtenu pour 86 poulets. - La seconde courbe montre le résultat dans le cas où les poulets - soldés sont vendus 2 euros - égal au prix des poulets achetés. Le modèle montre ses limites dans ce - cas car il suppose que tous les poulets - soldés seront achetés et que les contraintes de stockage - sont négligeables. +.. list-table:: + :widths: auto + :header-rows: 0 + + * - .. image:: images/poissonb.png + - .. image:: images/poissonb2.png + +Cette courbe est celle de l'évolution des profits en fonction du +nombre de poulets commandés. On suppose que +le nombre de poulets achetés suit une loi de Poisson de paramètre 80, +que les poulets sont achetés 2 euros, revendu 5 euros et soldés 1 euros. +Le maximum de 228 euros est obtenu pour 86 poulets. +La seconde courbe montre le résultat dans le cas où les poulets +soldés sont vendus 2 euros +égal au prix des poulets achetés. Le modèle montre ses limites dans ce +cas car il suppose que tous les poulets +soldés seront achetés et que les contraintes de stockage +sont négligeables. Modélisation de la demande ========================== @@ -150,7 +145,7 @@ des lois de Poisson de paramètres différents dont il faudra estimer les paramètres. Cette modification implique l'écriture d'une fonction -:func:`proba_poisson_melange ` +:func:`proba_poisson_melange ` au lieu de :func:`proba_poisson `. La demande n'est plus une loi connue mais un mélange de lois connues dont la densité n'a pas d'expression connue : il faut la tabuler. @@ -168,17 +163,17 @@ Pour cela, on utilise deux propriétés sur les lois exponentielles. La démonstration est courte. Soit :math:`X` une variable aléatoire de densité :math:`f`, -par définition, :math:`\pr{X \infegal x} = F(x)`. Soit :math:`U` une +par définition, :math:`\pr{X \leqslant x} = F(x)`. Soit :math:`U` une variable aléatoire uniformément distribué sur :math:`\cro{0,1}`, alors : .. math:: :nowrap: \begin{eqnarray*} - \forall u \in \cro{0,1}, \; \pr{U \infegal u} &=& u \\ - \Longleftrightarrow \pr{F^{-1}(U)\infegal F^{-1}(u)} &=& u \\ - \Longleftrightarrow \pr{F^{-1}(U)\infegal F^{-1}(F(t))} &=& F(t) \\ - \Longleftrightarrow \pr{F^{-1}(U)\infegal t} &=& F(t) + \forall u \in \cro{0,1}, \; \pr{U \leqslant u} &=& u \\ + \Longleftrightarrow \pr{F^{-1}(U)\leqslant F^{-1}(u)} &=& u \\ + \Longleftrightarrow \pr{F^{-1}(U)\leqslant F^{-1}(F(t))} &=& F(t) \\ + \Longleftrightarrow \pr{F^{-1}(U)\leqslant t} &=& F(t) \end{eqnarray*} Si la fonction :math:`F` n'est pas strictement croissante, @@ -248,12 +243,12 @@ de la somme est celle d'une loi Gamma. On suppose que Ces lignes démontrent le théorème. On démontre maintenant :ref:`simulation d'une loi de Poisson `. La démonstration repose sur le fait que -:math:`\pr{N(t) \supegal n} \Longleftrightarrow \pr{S_n \infegal t}`. +:math:`\pr{N(t) \supegal n} \Longleftrightarrow \pr{S_n \leqslant t}`. On en déduit que : .. math:: - \pr{N(t) = n} = \pr{N(t) \supegal n} - \pr{N(t) \supegal n+1} = \pr{S_n \infegal t} - \pr{S_{n+1} \infegal t} + \pr{N(t) = n} = \pr{N(t) \supegal n} - \pr{N(t) \supegal n+1} = \pr{S_n \leqslant t} - \pr{S_{n+1} \leqslant t} Or d'après le théorème :ref:`somme de loi exponentielle iid `, :math:`S_n` suit une loi :math:`Gamma(n,\lambda)`. @@ -270,7 +265,7 @@ Or d'après le théorème :ref:`somme de loi exponentielle iid ` suivante : +:func:`poisson ` suivante : .. runpython:: :showcode: @@ -298,12 +293,12 @@ variable suivant une loi de Poisson avec :math:`\lambda=10` puis on compte le nombre de fois qu'on obtient chaque entier compris entre 0 et 40. La figure qui suit permet de comparer les résultats obtenus. -.. figure:: images/poishis.png +.. image:: images/poishis.png - Comparaison entre une fonction de densité estimée - empiriquement pour la loi de Poisson de paramètre - :math:`\lambda=10` et sa densité théorique - :math:`f(i) = e^{-\lambda} \frac{ \lambda^i}{i!}`. +Comparaison entre une fonction de densité estimée +empiriquement pour la loi de Poisson de paramètre +:math:`\lambda=10` et sa densité théorique +:math:`f(i) = e^{-\lambda} \frac{ \lambda^i}{i!}`. On cherche maintenant à calculer les probabilités :math:`\pr{N = i}` sachant que :math:`N = N_1 + 2 N_2 + 3 N_3` @@ -320,34 +315,32 @@ De la même manière, on estime l'histogramme du mélange avec cette fois-ci un plus grand nombre de tirages (10000) pour aboutir à la figure suivante. -.. figure:: - - .. list-table:: - :widths: auto - :header-rows: 0 +.. list-table:: + :widths: auto + :header-rows: 0 - * - .. image:: images/poishist2.png - - .. image:: images/poishist3.png + * - .. image:: images/poishist2.png + - .. image:: images/poishist3.png - Comparaison entre une fonction de densité estimée empiriquement - pour un mélange de loi Poisson :math:`N = N_1 + 2 N_2 + 3 N_3` - vérifiant :math:`N_1 \sim \mathcal{P}(48)`, - :math:`N_2 \sim \mathcal{P}(10)`, :math:`N_3 \sim \mathcal{P}(4)` - avec la densité de la loi de Poisson de paramètre :math:`\lambda=80=48+2*10+3*4`. - Il apparaît que ce sont deux densités différentes, celle du mélange - étant plus applatie. La seconde image montre ce qu'on obtient lorsque - le nombre de tirages n'est pas assez important. +Comparaison entre une fonction de densité estimée empiriquement +pour un mélange de loi Poisson :math:`N = N_1 + 2 N_2 + 3 N_3` +vérifiant :math:`N_1 \sim \mathcal{P}(48)`, +:math:`N_2 \sim \mathcal{P}(10)`, :math:`N_3 \sim \mathcal{P}(4)` +avec la densité de la loi de Poisson de paramètre :math:`\lambda=80=48+2*10+3*4`. +Il apparaît que ce sont deux densités différentes, celle du mélange +étant plus applatie. La seconde image montre ce qu'on obtient lorsque +le nombre de tirages n'est pas assez important. On utilise ces éléments pour modéliser la demande de poulets selon ce mélange de lois Poisson. Le premier programme est modifié pour aboutir au suivant. -.. figure:: images/poulet10.png +.. image:: images/poulet10.png - Dans le cas du mélange de lois Poisson, - le maximum est cette-fois ci obtenu pour 87 poulets et est - de 225 euros. Ces résultats sont légèrement différents - de ceux obtenus par une simple loi Poisson (80). +Dans le cas du mélange de lois Poisson, +le maximum est cette-fois ci obtenu pour 87 poulets et est +de 225 euros. Ces résultats sont légèrement différents +de ceux obtenus par une simple loi Poisson (80). Variations saisonnières et prolongations ======================================== diff --git a/_doc/sphinxdoc/source/c_metric/images/pvaluescor.png b/_doc/c_metric/images/pvaluescor.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/images/pvaluescor.png rename to _doc/c_metric/images/pvaluescor.png diff --git a/_doc/sphinxdoc/source/c_metric/images/pvaluescor2.png b/_doc/c_metric/images/pvaluescor2.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/images/pvaluescor2.png rename to _doc/c_metric/images/pvaluescor2.png diff --git a/_doc/sphinxdoc/source/c_metric/index.rst b/_doc/c_metric/index.rst similarity index 100% rename from _doc/sphinxdoc/source/c_metric/index.rst rename to _doc/c_metric/index.rst diff --git a/_doc/sphinxdoc/source/c_metric/pvalues.rst b/_doc/c_metric/pvalues.rst similarity index 85% rename from _doc/sphinxdoc/source/c_metric/pvalues.rst rename to _doc/c_metric/pvalues.rst index 523518ff..6ac99c9f 100644 --- a/_doc/sphinxdoc/source/c_metric/pvalues.rst +++ b/_doc/c_metric/pvalues.rst @@ -1,13 +1,10 @@ +.. index:: p-value, intervalle de confiance + =============================== Confidence Interval and p-Value =============================== -.. contents:: - :local: - -.. index:: p-value, intervalle de confiance - This document explains the relationship between p-value and confidence intervals. It goes on with the specific case of a binamial law. Assuming we want to determine whether or not two binomial laws are significantly different, how many observations @@ -26,12 +23,13 @@ Howerver p-Values and confidence interval are similar: they tell you whether or not a metric difference is significant. Usually, it starts from a set of identically distributed random variables -:math:`(X_i)_{1 \infegal i \infegal N}`. We then estimate the average +:math:`(X_i)_{1 \leqslant i \leqslant N}`. We then estimate the average :math:`\widehat{\theta}_N = \frac{1}{N} \sum_{i=1}^{N} X_i` and we ask the question is :math:`\widehat{\theta}_N` null? In others terms, we want to know if the average is significantly different from zero. If the random variable :math:`X` follows a random law which has a standard -deviation, we can use the `central limit theorem `_ +deviation, we can use the `central limit theorem +`_ which tells us: .. math:: @@ -47,13 +45,14 @@ Not all of them have a standard deviation. For example, if :math:`X` follows a This remark also concerns every distribution known as heavy tail distribution. If :math:`Y \sim \loinormale{0}{\sigma}`, then we have -:math:`\pr{\abs{Y} \infegal 1.96} = 0.95`. That is why we can say: +:math:`\pr{\abs{Y} \leqslant 1.96} = 0.95`. That is why we can say: .. math:: :nowrap: \begin{eqnarray*} - \widehat{\theta}_N \text{ is not null with 95\% confidence if } \sqrt{N} \frac{|\widehat{\theta}_N|}{\sigma} > 1.96 + \widehat{\theta}_N \text{ is not null with 95\% confidence if } + \sqrt{N} \frac{|\widehat{\theta}_N|}{\sigma} > 1.96 \end{eqnarray*} And the confidence intervalle at 95% would be: @@ -74,10 +73,13 @@ When :math:`\esp{ \widehat{\theta}_N } = \theta_0 \neq 0`, it becomes: :nowrap: \begin{eqnarray*} - \sqrt{N} \cro{ \widehat{\theta}_N - \theta_0} \underset{N \rightarrow \infty}{\longrightarrow} \loinormale{0}{\sigma} + \sqrt{N} \cro{ \widehat{\theta}_N - \theta_0} + \underset{N \rightarrow \infty}{\longrightarrow} + \loinormale{0}{\sigma} \end{eqnarray*} -We usually want to check if the mean is equal to a specific value using a statistical test: +We usually want to check if the mean is equal to a specific value +using a statistical test: .. math:: :nowrap: @@ -93,11 +95,12 @@ We validate :math:`H0` if: :nowrap: \begin{eqnarray*} - \widehat{\theta}_N \in \cro{ \theta_0 - \frac{1.96 \sigma}{\sqrt{N}}, \theta_0 + \frac{1.96 \sigma}{\sqrt{N}}} + \widehat{\theta}_N \in \cro{ \theta_0 - \frac{1.96 \sigma}{\sqrt{N}}, + \theta_0 + \frac{1.96 \sigma}{\sqrt{N}}} \end{eqnarray*} -p-value -======= +Why p-value? +============ With confidence intervals, you first choose a confidence level and then you get an interval. You then check if your value is inside or outside your interval. @@ -113,7 +116,7 @@ We are looking for: :nowrap: \begin{eqnarray*} - \pr{ \abs{Y} > \sqrt{N} \frac{|\widehat{\theta}_N|}{\sigma} } = \alpha + \pr{ \abs{Y} > \sqrt{N} \frac{|\widehat{\theta}_N|}{\sigma} } = \alpha \end{eqnarray*} :math:`\alpha` is the p-value. @@ -123,8 +126,9 @@ We are looking for: :label: p_value_expression \begin{eqnarray*} - \alpha &=& 1-\int_{-\beta_N}^{\beta_N} \frac{1}{\sqrt{2\pi}} e^{\frac{-x^2}{2}} dx = - 2 \int_{\beta_N}^{\infty} \frac{1}{\sqrt{2\pi}} e^{\frac{-x^2}{2}} dx \\ + \alpha &=& 1-\int_{-\beta_N}^{\beta_N} \frac{1}{\sqrt{2\pi}} + e^{\frac{-x^2}{2}} dx = + 2 \int_{\beta_N}^{\infty} \frac{1}{\sqrt{2\pi}} e^{\frac{-x^2}{2}} dx \\ \text{where } \beta_N &=& \sqrt{N} \frac{|\widehat{\theta}_N|}{\sigma} \end{eqnarray*} @@ -148,12 +152,13 @@ difference to be null. :label: pvalues_exp2 \begin{eqnarray*} - \widehat{\eta}_N &=& \frac{1}{N} \sum_{i=1}^{N} X_i - \frac{1}{N} \sum_{i=1}^{N} Y_i - = \frac{1}{N} \cro{ \sum_{i=1}^{N} X_i - Y_i } + \widehat{\eta}_N &=& \frac{1}{N} \sum_{i=1}^{N} X_i - + \frac{1}{N} \sum_{i=1}^{N} Y_i + = \frac{1}{N} \cro{ \sum_{i=1}^{N} X_i - Y_i } \end{eqnarray*} -Considering expression :eq:`pvalues_exp2`, we can applying the central limit theorem -on variable :math:`Z=X-Y`, we get (:math:`\eta_0=0`): +Considering expression :eq:`pvalues_exp2`, we can applying the central +limit theorem on variable :math:`Z=X-Y`, we get (:math:`\eta_0=0`): .. math:: :nowrap: @@ -163,20 +168,20 @@ on variable :math:`Z=X-Y`, we get (:math:`\eta_0=0`): \loinormale{\eta_0}{\sqrt{ \frac{\var{Z} }{N } }} \end{eqnarray*} -If both samples do not have the same number of observations, this expression becomes: +If both samples do not have the same number of observations, +this expression becomes: .. math:: :nowrap: \begin{eqnarray*} - \sqrt{N} \widehat{\eta}_N \underset{ \begin{subarray}{c} N_1 \rightarrow \infty \\ - N_2 \rightarrow \infty \\ - \frac{N_1}{N_2} \rightarrow x \end{subarray} - }{\longrightarrow} \loinormale{\eta_0}{\sqrt{ \frac{\var{X}}{N_1}+\frac{\var{Y}}{N_2} }} + \sqrt{N} \widehat{\eta}_N \underset{ \begin{subarray}{c} N_1 + \rightarrow \infty \\ + N_2 \rightarrow \infty \\ + \frac{N_1}{N_2} \rightarrow x \end{subarray}}{\longrightarrow} + \loinormale{\eta_0}{\sqrt{ \frac{\var{X}}{N_1}+\frac{\var{Y}}{N_2} }} \end{eqnarray*} -.. _l-section_pvalues_table: - Application on binomial variables ================================= @@ -262,7 +267,7 @@ density function of :math:`X`. We also consider an interval :nowrap: \begin{eqnarray*} - \pr{X \in I} = \pr{ \abs{X} \infegal a } = \pr{ f(X) \supegal f(a)} + \pr{X \in I} = \pr{ \abs{X} \leqslant a } = \pr{ f(X) \supegal f(a)} \end{eqnarray*} This is true because :math:`f` is decreasing for :math:`x>0`. @@ -359,11 +364,9 @@ That's allways the case when two version of the sam websire are compared in a test `A/B `_. The metrics are correlated but it is unlikely that all metrics differences will be significant or not. -The `Holm–Bonferroni method `_ +The :epkg:`Holm-Bonferroni method` proposes a way to define an hypthesis on the top of the existing ones. -.. _l-section_pvalues_table_em: - Algorithm Expectation-Maximization ================================== @@ -384,9 +387,9 @@ the class we do not observe are :math:`(C_1,...C_n)`: .. math:: :nowrap: - \begin{eqnarray} + \begin{eqnarray*} L(\theta) = \prod_i \cro{ p^{X_i}(1-p)^{(1-X_i)} \pi }^{1-C_i} \cro{q^{X_i}(1-q)^{(1-X_i)} (1-\pi) }^{C_i} - \end{eqnarray} + \end{eqnarray*} The parameters are :math:`\theta=(\pi,p,q)`. We use an algorithm `Expectation-Maximization (EM) `_ @@ -408,12 +411,13 @@ We then update the parameters: :nowrap: \begin{eqnarray*} - \widehat{\pi} &=& \frac{1}{n} \sum_{i = 1}^n w_i \\ - \widehat{p} &=& \frac{ \sum_{i = 1}^n w_i X_i }{ \sum_{i = 1}^n w_i} \\ - \widehat{q} &=& \frac{ \sum_{i = 1}^n (1-w_i) X_i }{ \sum_{i = 1}^n (1-w_i)} + \widehat{\pi} &=& \frac{1}{n} \sum_{i = 1}^n w_i \\ + \widehat{p} &=& \frac{ \sum_{i = 1}^n w_i X_i }{ \sum_{i = 1}^n w_i} \\ + \widehat{q} &=& \frac{ \sum_{i = 1}^n (1-w_i) X_i }{ \sum_{i = 1}^n (1-w_i)} \end{eqnarray*} -See also `Applying the EM Algorithm: Binomial Mixtures `_. +See also `Applying the EM Algorithm: Binomial Mixtures +`_. Notebooks ========= @@ -422,11 +426,13 @@ The following notebook produces the figures displayed in this document. .. toctree:: - ../notebooks/pvalues_examples + ../notebooks/metric/pvalues_examples Bibliographie ============= -* `p-Value and Statistical Practice `_ -* `An investigation of the false discovery rate and the misinterpretation of p-values `_ -* `Holm–Bonferroni method `_ +* `p-Value and Statistical Practice + `_ +* `An investigation of the false discovery rate and the misinterpretation of p-values + `_ +* :epkg:`Holm-Bonferroni method` diff --git a/_doc/sphinxdoc/source/c_metric/roc.rst b/_doc/c_metric/roc.rst similarity index 86% rename from _doc/sphinxdoc/source/c_metric/roc.rst rename to _doc/c_metric/roc.rst index f7eea40f..7a17a18e 100644 --- a/_doc/sphinxdoc/source/c_metric/roc.rst +++ b/_doc/c_metric/roc.rst @@ -5,18 +5,20 @@ Courbe ROC ========== -.. contents:: - :local: - .. index:: ROC -Ce document introduit la `courbe ROC `_ +Ce document introduit la `courbe ROC +`_ (Receiving Operator Characteristic) qui est communément utilisée pour mesurer la performance d'un classifieur. Il introduit aussi des termes comme précision, -rappel, `AUC `_, +rappel, :epkg:`AUC`, qui sont présents dans la plupart des articles qui traitent de machine learning. Le module :mod:`roc ` implémente les calculs ci-dessous -qu'on peut tester avec le notebook :ref:`rocexamplerst`. +qu'on peut tester avec le notebook suivant : + +.. toctree:: + + ../notebooks/metric/roc_example Définitions =========== @@ -63,8 +65,10 @@ réponse attendue 0 1 A partir de ces définitions, on définit : -* la `précision `_ : :math:`\frac{ TP }{ TP + FP }` -* le `rappel ou recall `_ : :math:`\frac{ TP }{ TP + TN }` +* la `précision `_ : + :math:`\frac{ TP }{ TP + FP }` +* le `rappel ou recall `_ : + :math:`\frac{ TP }{ TP + TN }` En choisissant un seuil relatif au score de pertinence :math:`x`, au-dessus, on valide la réponse du classifieur, en-dessous, @@ -77,16 +81,18 @@ La courbe ROC s'obtient en faisant varier :math:`s`. :lid: def_roc_2 :tag: Définition - On suppose que :math:`Y` est la variable aléatoire des scores des expériences qui ont réussi. + On suppose que :math:`Y` est la variable aléatoire des scores + des expériences qui ont réussi. :math:`X` est celle des scores des expériences qui ont échoué. On suppose également que tous les scores sont indépendants. - On note :math:`F_X` et :math:`F_Y` les fonctions de répartition de ces variables. - On définit en fonction d'un seuil :math:`s \in \R` : + On note :math:`F_Y` et :math:`F_X` les fonctions de répartition de ces variables. + :math:`F_Y(s)=\pr{Y \leqslant s}` et :math:`F_X(s)=\pr{X \leqslant s}`. + On définit en fonction d'un seuil :math:`s \in \mathbb{R}` : - * :math:`R(s) = 1 - F_Y(s)` - * :math:`E(s) = 1 - F_X(s)` + * :math:`R(s) = 1 - F_Y(s) = \pr{Y > s}` + * :math:`E(s) = 1 - F_X(s) = \pr{X > s}` - La courbe ROC est le graphe :math:`\pa{E(s),R(s)}` lorsque :math:`s` varie dans :math:`\R`. + La courbe ROC est le graphe :math:`\pa{E(s),R(s)}` lorsque :math:`s` varie dans :math:`\mathbb{R}`. :math:`TP(s)` désigne les true positifs au-dessus du seuil :math:`s`, avec les notations *TP*, *FP*, *FN*, *TN*, cela revient à : @@ -105,7 +111,8 @@ De même pour :math:`FP(s) + FN(s)`. .. image:: rocimg/rocwi.png :width: 500 -On remarque que les fonctions :math:`s \longrightarrow E(s)` et :math:`s \longrightarrow R(s)` +On remarque que les fonctions :math:`s \longrightarrow E(s)` +et :math:`s \longrightarrow R(s)` sont décroissantes toutes deux. Elles sont donc inversibles. Dans le cas où la variable aléatoire :math:`\theta` est indépendante de la variable :math:`X`, la courbe ROC est une droite reliant les points @@ -120,6 +127,24 @@ classe prédite est égale à la classe attendue, il est négatif dans le cas contraire. La courbe peut être adaptée pour d'autres problèmes tels que le ranking (voir [Agarwal2005]_). +Une autre façon de l'exprimer car je ne retiens jamais la définition +des FP, TP, FN, TN... Pour quelqu'un qui doit réfléchir trois secondes +à chaque fois qu'on me demande où est la gauche, ce n'est jamais +évident. + +.. math:: + + \begin{array}{rcl} + N_+ &=& \sum_{i=1}^n \indicatrice{y_i == 1}\\ + TPR(s) &=& \frac{1}{N_+}\sum_{i=1}^n \indicatrice{score(X_i) \geqslant s}\indicatrice{y_i == 1}\\ + FPR(s) &=& \frac{1}{1 - N_+}\sum_{i=1}^n \indicatrice{score(X_i) \geqslant s}\indicatrice{y_i \neq 1} + \end{array} + +*x = FPR(s), y = TPR(s)*. (FPR = False Positive Rate, TPR = True Positive Rate) + +.. image:: rocimg/rocwi2.png + :width: 300 + .. index:: AUC Aire sous la courbe @@ -145,14 +170,15 @@ de fonction de répartition :math:`F`. Si :math:`U = F(X)`, alors : .. math:: - \pr{ U \infegal t} = \pr{ F(X) \infegal t} = \pr{ X \infegal F^{-1}(t)} = F \pa{ F^{-1}(t) } = t + \pr{ U \leqslant t} = \pr{ F(X) \leqslant t} = + \pr{ X \leqslant F^{-1}(t)} = F \pa{ F^{-1}(t) } = t La variable :math:`U` est de loi uniforme sur :math:`\cro{0,1}`. De plus, soit :math:`g` une fonction intégrable quelconque, on pose :math:`u = F(x)` et : .. math:: - \int_{\R} g(x) \, f(x) \,dx = \int_{\cro{0,1}} g(F^{-1}(u)) \, du + \int_{\mathbb{R}} g(x) \, f(x) \,dx = \int_{\cro{0,1}} g(F^{-1}(u)) \, du **Démonstration** @@ -166,7 +192,7 @@ celle de la variable :math:`Y`. On peut alors définir la probabilité \begin{eqnarray*} P \pa{Y>X} &=& \int_x \int_y f_X(x) \; f_Y(y) \; \indicatrice{y > x} dx dy \end{eqnarray*} - + On note :math:`F_X` la fonction de répartition de :math:`X` soit :math:`F_X(x) = \int_{-\infty}^x f_X(u)du`. On pose comme changement de variable : :math:`u = F_X(x)`. @@ -178,23 +204,24 @@ est uniforme et comprise dans :math:`\cro{0,1}`. \begin{eqnarray*} P \pa{Y>X} &=& \int_x f_X(x) dx \int_y \; f_Y(y) \; \indicatrice{y > x} dy \\ - &=& \int_u du \int_y \; f_Y(y) \; \indicatrice{y > F_X^{-1}(u)} dy \\ - &=& \int_u du \; \pr{Y > F_X^{-1}(u)} \nonumber + &=& \int_u du \int_y \; f_Y(y) \; \indicatrice{y > F_X^{-1}(u)} dy \\ + &=& \int_u du \; \pr{Y > F_X^{-1}(u)} \nonumber \end{eqnarray*} Or si :math:`u = F_X(s) = E(s)`, alors :math:`F_X^{-1}(u) = s` et :math:`\pr{Y > F_X^{-1}(u)} = R'(s)`. Par conséquent : - + .. math:: P \pa{Y>X} = \int_u du \; \pr{Y > F_X^{-1}(u)} = \int_u du \; R'(F_X^{-1}(u)) - + .. index:: U-statistique, Mann-Whitney Cette dernière expression est l'aire recherchée. Ce théorème nous permet de définir un estimateur pour l'aire sous la courbe ROC à l'aide des `U-statistiques `_ -de `Mann-Whitney `_ (voir [Saporta1990]_). +de `Mann-Whitney `_ +(voir [Saporta1990]_). .. mathdef:: :tag: Corollaire @@ -211,7 +238,8 @@ de `Mann-Whitney `_ .. math:: :label: estimateur_roc - \hat{A} = \frac{1}{nm} \; \sum_{i=1}^{m}\sum_{j=1}^{n} \pa{\indicatrice{ Y_j > X_i} + \frac{1}{2} \indicatrice{ Y_j = X_i}} + \hat{A} = \frac{1}{nm} \; \sum_{i=1}^{m}\sum_{j=1}^{n} + \pa{\indicatrice{ Y_j > X_i} + \frac{1}{2} \indicatrice{ Y_j = X_i}} **Démonstration** @@ -220,7 +248,8 @@ La démonstration est évidente : .. math:: \esp\pa{\hat{A}} = \frac{1}{nm} \; \sum_{i=1}^{m}\sum_{j=1}^{n} - \pa{\pr{ Y_j > X_i} + \frac{1}{2} \pr{X_i=Y_j}} = \pr{ Y > X} + \frac{1}{2}\pr{ Y = X} + \pa{\pr{ Y_j > X_i} + \frac{1}{2} \pr{X_i=Y_j}} = + \pr{ Y > X} + \frac{1}{2}\pr{ Y = X} Dans le cas où :math:`X` ou :math:`Y` sont continues, :math:`\pr{X=Y} = 0`. @@ -242,9 +271,8 @@ une loi normale lorsque :math:`n` et :math:`m` tendent vers l'infini. .. math:: \var{\hat{A}} = \frac{ \hat{A} (1-\hat{A})}{nm} \; \cro{ - 1 + (n-1) \frac { P_Y - \hat{A}^2 } { \hat{A} (1-\hat{A}) } + - (m-1) \frac { P_X - \hat{A}^2 } { \hat{A} (1-\hat{A}) } - } + 1 + (n-1) \frac { P_Y - \hat{A}^2 } { \hat{A} (1-\hat{A}) } + + (m-1) \frac { P_X - \hat{A}^2 } { \hat{A} (1-\hat{A}) } } **Démonstration** @@ -266,22 +294,22 @@ et on utilise le fait que :math:`\var{\hat{A}} = \esp\pa{\hat{A}^2} - \hat{A}^2` && + \frac{1}{n^2 m^2} \sum_{i=1}^{m}\sum_{j=1}^{n}\sum_{l \neq j} \indicatrice{ X_i < Y_j} \indicatrice{ X_i < Y_l} \\ && +\frac{1}{n^2 m^2} \sum_{i=1}^{m}\sum_{j=1}^{n}\sum_{k \neq i}\sum_{l \neq j} \indicatrice{ X_i < Y_j} \indicatrice{ X_k < Y_l} \end{array} - + On en déduit que : .. math:: :nowrap: \begin{eqnarray*} - \esp{\hat{A}^2} &=& \frac{\hat{A}}{nm} + \frac{n-1 }{nm} \; \pr{ \max\acc{X_i,X_k} < Y_j} + \nonumber \\ && - \frac{m-1 }{nm} \; \pr{ X_i < \min\acc{Y_j,Y_l}} + \frac{nm-n-m-1 }{n m} \; \hat{A}^2 \\ - \var{\hat{A}^2} &=& \frac{1}{nm} \cro{ \hat{A} + (n-1) P_Y + (m-1) P_X - (n+m+1) \hat{A}^2 } \nonumber \\ - &=& \frac{1}{nm} \cro{ \hat{A} + (n-1) \pa{P_Y - \hat{A}^2}+ (m-1) \pa{P_X - \hat{A}^2} + \hat{A}^2 } + \esp{\hat{A}^2} &=& \frac{\hat{A}}{nm} + \frac{n-1 }{nm} \; \pr{ \max\acc{X_i,X_k} < Y_j} + \nonumber \\ && + \frac{m-1 }{nm} \; \pr{ X_i < \min\acc{Y_j,Y_l}} + \frac{nm-n-m-1 }{n m} \; \hat{A}^2 \\ + \var{\hat{A}^2} &=& \frac{1}{nm} \cro{ \hat{A} + (n-1) P_Y + (m-1) P_X - (n+m+1) \hat{A}^2 } \nonumber \\ + &=& \frac{1}{nm} \cro{ \hat{A} + (n-1) \pa{P_Y - \hat{A}^2}+ (m-1) \pa{P_X - \hat{A}^2} + \hat{A}^2 } \end{eqnarray*} -On retrouve l'expression cherchée. - - +On retrouve l'expression cherchée. + + .. _roc_confiance_inter: @@ -301,16 +329,16 @@ Ce premier paragraphe détaille la manière dont est construite une courbe ROC (voir :ref:`Courbe ROC `). .. mathdef:: - :title: Courbe ROC + :title: Calcul de la courbe ROC :tag: Algorithme :lid: algo_courb_ROC On suppose qu'on dispose d'un ensemble de points :math:`\pa{X_i,\theta_i} - \in \R \times \acc{0,1}` pour :math:`i \in \ensemble{1}{n}`. + \in \mathbb{R} \times \acc{0,1}` pour :math:`i \in \ensemble{1}{n}`. `X_i` est le score obtenu pour l'expérience :math:`i`, `\theta_i` vaut 1 si elle a réussi et 0 si elle a échoué. On suppose également que cette liste est triée par ordre croissant : - `\forall i, \; X_i \infegal X_{i+1}`. + `\forall i, \; X_i \leqslant X_{i+1}`. On souhaite également tracer :math:`k` points sur la courbe, on détermine pour cela :math:`k` seuils `\ensemble{s_1}{s_k}` définis par : :math:`\forall j, s_k = X_{\frac{j \, k}{n}}`. @@ -324,8 +352,8 @@ est construite une courbe ROC (voir :ref:`Courbe ROC `). E_j = \frac{1}{n} \, \sum_{i=1}^{n} \pa{1-\theta_i} \; \indicatrice{X_i \supegal s_j} \end{eqnarray*} - La courbe ROC est composée de l'ensemble :math:`R_{OC} = \acc{ \pa{E_j,R_j} | 1 \infegal j \infegal k}`. - + La courbe ROC est composée de l'ensemble :math:`R_{OC} = \acc{ \pa{E_j,R_j} | 1 \leqslant j \leqslant k}`. + Les deux suites :math:`(R_j)_j` et :math:`(E_j)_j` sont toutes les deux décroissantes d'après leur définition. La courbe peut être rendue continue par interpolation. @@ -337,7 +365,7 @@ d'après leur définition. La courbe peut être rendue continue par interpolatio On cherche un taux de reconnaissance pour un taux d'erreur donné. On dispose pour cela d'une courbe ROC obtenue par l'algorithme de la :ref:`courbe ROC ` et définie par les points - :math:`R_{OC} = \acc{ \pa{e_j,r_j} | 1 \infegal j \infegal k}`. + :math:`R_{OC} = \acc{ \pa{e_j,r_j} | 1 \leqslant j \leqslant k}`. On suppose ici que :math:`\pa{e_1,r_1} = \pa{1,1}` et :math:`\pa{e_k,r_k} = \pa{0,}`. Si ce n'est pas le cas, on ajoute ces valeurs à l'ensemble :math:`R_{OC}`. @@ -346,14 +374,14 @@ d'après leur définition. La courbe peut être rendue continue par interpolatio .. math:: - e_{j^*+1} \infegal e^* \infegal e_{j^*} + e_{j^*+1} \leqslant e^* \leqslant e_{j^*} Le taux de reconnaissance :math:`\rho` cherché est donné par : .. math:: \rho = \frac{e^* - x_{j^*}} { x_{j^*+1} - x_{j^*} } \; \cro{ r_{j^*+1} - r_{j^*} } + r_{j^*} - + Il ne reste plus qu'à détailler la méthode *bootstrap*. @@ -374,11 +402,11 @@ On s'inspire pour cela des méthodes de `bootstrap ` permet de constuire la courbe :math:`R_{OC}^k`. * L'algorithme de :ref:`taux de classification à erreur fixe ` permet ensuite de déterminer @@ -417,8 +445,8 @@ Cette expérience a été reproduite plusieurs fois et ces bornes sont assez stables contrairement (`\pm 0,05 \%`) aux extremas (`\pm 1\%`). -Aire sous la courbe -+++++++++++++++++++ +Aire sous la courbe et intervalles de confiance ++++++++++++++++++++++++++++++++++++++++++++++++ La méthode bootstrap peut elle aussi être appliquée pour calculer un intervalle de confiance pour l'aire sous la courbe (AUC). @@ -483,7 +511,7 @@ Les courbes ne sont pas monotones et montre qu'il existe parfois plusieurs taux lecture pour un même taux de substitution. Comme le calcul des intervalles de confiance fait intervenir une interpolation linéaire, lorsque les courbes sont trop cahotiques, le calcul retourne des valeurs fausses. - + On peut démontrer que la courbe taux de lecture / taux de substitution n'est pas une courbe ni monotone ni inversible. Pour cela on dispose d'une suite de couple :math:`\pa{X_i, \theta_i}` croissante selon les @@ -499,7 +527,7 @@ Pour un seuil donné :math:`s`, on note :math:`E'(s)` le taux de substitution et E'(s) &=& \frac{1}{n \, R'(s)} \sum_{i=1}^{n} \pa{1 - \theta_i} \, \indicatrice{X_i \supegal s} \end{eqnarray*} -On écrit différemment ces expressions en supposant que :math:`X_{i(s_1)-1} < s_1 \infegal X_{i(s_1)} :math:` : +On écrit différemment ces expressions en supposant que :math:`X_{i(s_1)-1} < s_1 \leqslant X_{i(s_1)} :math:` : .. math:: :nowrap: @@ -508,10 +536,10 @@ On écrit différemment ces expressions en supposant que :math:`X_{i(s_1)-1} < s R'(s_1) &=& \frac{n-i(s_1)}{n} \\ E'(s_1) &=& \frac{1}{n - i(s_1)} \sum_{i=i(s_1)}^{n} \pa{1 - \theta_i} \end{eqnarray*} - -On suppose maintenant que :math:`X_{i(s_2)-1} < s_2 \infegal X_{i(s_2)} :math:` + +On suppose maintenant que :math:`X_{i(s_2)-1} < s_2 \leqslant X_{i(s_2)} :math:` et :math:`i(s_1) +1 = i(s_2)` : - + .. math:: :nowrap: @@ -534,7 +562,7 @@ autrement dit, l'expérience :math:`s_1` a réussi, on en déduit que : \begin{eqnarray*} E'(s_2) &=& E'(s_1) \frac{ n - i(s_1) } {n - i(s_2) } = E'(s_1) \frac{ n - i(s_2) + 1 } {n - i(s_2) } > E'(s_1) \end{eqnarray*} - + En revanche si :math:`\theta_i = 0` : .. math:: @@ -583,14 +611,11 @@ Le premier cas correspond par exemple à des problèmes de `détection de fraude `_. Le second cas correspond à taux de classification global. La courbe ROC pour ce cas est en règle général moins bonne que la plupart des -courbes ROC obtenues pour chacune des classes prise séparément -(voir `Régression logistique `_). +courbes ROC obtenues pour chacune des classes prise séparément. Exemple ======= -Voir `ROC `_. - .. [Agarwal2005] Generalization Bounds for the Area Under the ROC Curve (2005), Shivani Agarwal, Thore Graepel, Ralf Herbich, Sariel Har-Peled, Dan Roth *Journal of Machine Learning Research, volume 6, pages 393-425* diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/Roccurves.png b/_doc/c_metric/rocimg/Roccurves.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/Roccurves.png rename to _doc/c_metric/rocimg/Roccurves.png diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/lecture_5_curve.png b/_doc/c_metric/rocimg/lecture_5_curve.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/lecture_5_curve.png rename to _doc/c_metric/rocimg/lecture_5_curve.png diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/lecture_intervalle.png b/_doc/c_metric/rocimg/lecture_intervalle.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/lecture_intervalle.png rename to _doc/c_metric/rocimg/lecture_intervalle.png diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/roc_1.png b/_doc/c_metric/rocimg/roc_1.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/roc_1.png rename to _doc/c_metric/rocimg/roc_1.png diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/roc_100.png b/_doc/c_metric/rocimg/roc_100.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/roc_100.png rename to _doc/c_metric/rocimg/roc_100.png diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/roc_3.png b/_doc/c_metric/rocimg/roc_3.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/roc_3.png rename to _doc/c_metric/rocimg/roc_3.png diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/roc_p100.png b/_doc/c_metric/rocimg/roc_p100.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/roc_p100.png rename to _doc/c_metric/rocimg/roc_p100.png diff --git a/_doc/c_metric/rocimg/rocwi.png b/_doc/c_metric/rocimg/rocwi.png new file mode 100644 index 00000000..5eb52b1d Binary files /dev/null and b/_doc/c_metric/rocimg/rocwi.png differ diff --git a/_doc/c_metric/rocimg/rocwi2.png b/_doc/c_metric/rocimg/rocwi2.png new file mode 100644 index 00000000..82a01aea Binary files /dev/null and b/_doc/c_metric/rocimg/rocwi2.png differ diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/score_dist_1.png b/_doc/c_metric/rocimg/score_dist_1.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/score_dist_1.png rename to _doc/c_metric/rocimg/score_dist_1.png diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/score_dist_2.png b/_doc/c_metric/rocimg/score_dist_2.png similarity index 100% rename from _doc/sphinxdoc/source/c_metric/rocimg/score_dist_2.png rename to _doc/c_metric/rocimg/score_dist_2.png diff --git a/_doc/sphinxdoc/source/c_ml/index.rst b/_doc/c_ml/index.rst similarity index 56% rename from _doc/sphinxdoc/source/c_ml/index.rst rename to _doc/c_ml/index.rst index 91da04e1..05245959 100644 --- a/_doc/sphinxdoc/source/c_ml/index.rst +++ b/_doc/c_ml/index.rst @@ -1,9 +1,9 @@ .. _l-model-ml: -########################### -Modèles de Machine Learning -########################### +############ +Non linéaire +############ Les paragraphes suivant abordent de façon plutôt théorique des modèles de machine learning. @@ -14,3 +14,6 @@ des modèles de machine learning. rn/rn kppv missing_values_mf + ../notebooks/ml/neural_tree + ../notebooks/ml/neural_tree_onnx + ../notebooks/ml/neural_tree_cost diff --git a/_doc/sphinxdoc/source/c_ml/index_reglin.rst b/_doc/c_ml/index_reg_lin.rst similarity index 84% rename from _doc/sphinxdoc/source/c_ml/index_reglin.rst rename to _doc/c_ml/index_reg_lin.rst index 20667676..6d5c2992 100644 --- a/_doc/sphinxdoc/source/c_ml/index_reglin.rst +++ b/_doc/c_ml/index_reg_lin.rst @@ -1,17 +1,17 @@ .. _l-reglin-variations: -########################################## -Régressions linéaires et autres variations -########################################## +################### +Régression linéaire +################### La `régression linéaire `_ est le modèle prédictif le plus simple et celui qu'on préfère quand il marche car il est facilement interprétable à l'inverse des modèles non linéaires qui gardent leurs secrets si on s'en tient seulement à leurs coefficients. Concrètement, on dispose d'un nuage -de point :math:`(X_i, y_i)` où :math:`X_i \in \R^d` est un vecteur -de dimension *d* et :math:`y_i \in \R` un réel. La régression +de point :math:`(X_i, y_i)` où :math:`X_i \in \mathbb{R}^d` est un vecteur +de dimension *d* et :math:`y_i \in \mathbb{R}` un réel. La régression linéaire consiste à construire une fonction prédictive :math:`\hat{y_i} = f(X_i) = = X_i \beta` où :math:`\beta` est un vecteur de dimension *d*. Dans le cas le plus @@ -43,7 +43,7 @@ ni de valeurs propres. .. toctree:: :maxdepth: 1 - ../notebooks/regression_lineaire + ../notebooks/dsgarden/regression_lineaire regression_quantile piecewise l1l2 diff --git a/_doc/sphinxdoc/source/c_ml/index_reglog.rst b/_doc/c_ml/index_reg_log.rst similarity index 75% rename from _doc/sphinxdoc/source/c_ml/index_reglog.rst rename to _doc/c_ml/index_reg_log.rst index 78c00077..ec511d92 100644 --- a/_doc/sphinxdoc/source/c_ml/index_reglog.rst +++ b/_doc/c_ml/index_reg_log.rst @@ -1,23 +1,23 @@ .. _l-reglog-variations: -############################################ -Régressions logistiques et autres variations -############################################ +##################### +Régression logistique +##################### La `régression logistique `_ est le modèle prédictif le plus simple et celui qu'on préfère quand il marche car il est facilement interprétable à l'inverse des modèles non linéaires qui gardent leurs secrets si on s'en tient seulement à leurs coefficients. Concrètement, on dispose d'un nuage -de point :math:`(X_i, y_i)` où :math:`X_i \in \R^d` est un vecteur +de point :math:`(X_i, y_i)` où :math:`X_i \in \mathbb{R}^d` est un vecteur de dimension *d* et :math:`y_i \in \acc{0, 1}` un entier binaire. Le problème de la régression linéaire consiste à construire une fonction prédictive :math:`\hat{y_i} = f(X_i) = = X_i \beta` où :math:`\beta` est un vecteur de dimension *d* (voir `classification -`_). +`_). Le signe de la fonction :math:`f(X_i)` indique la classe de l'observation :math:`X_i` et la valeur :math:`\frac{1}{1 + e^{f(X)}}` la probabilité d'être dans la classe 1. @@ -27,3 +27,5 @@ indique la classe de l'observation :math:`X_i` et la valeur lr_voronoi lr_trees + ../notebooks/ml/reseau_neurones + survival_analysis diff --git a/_doc/sphinxdoc/source/c_ml/knnimg/btree.png b/_doc/c_ml/knnimg/btree.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/knnimg/btree.png rename to _doc/c_ml/knnimg/btree.png diff --git a/_doc/sphinxdoc/source/c_ml/knnimg/classif.png b/_doc/c_ml/knnimg/classif.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/knnimg/classif.png rename to _doc/c_ml/knnimg/classif.png diff --git a/_doc/sphinxdoc/source/c_ml/knnimg/rtree1.png b/_doc/c_ml/knnimg/rtree1.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/knnimg/rtree1.png rename to _doc/c_ml/knnimg/rtree1.png diff --git a/_doc/sphinxdoc/source/c_ml/knnimg/rtree2.png b/_doc/c_ml/knnimg/rtree2.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/knnimg/rtree2.png rename to _doc/c_ml/knnimg/rtree2.png diff --git a/_doc/sphinxdoc/source/c_ml/kppv.rst b/_doc/c_ml/kppv.rst similarity index 92% rename from _doc/sphinxdoc/source/c_ml/kppv.rst rename to _doc/c_ml/kppv.rst index 0d84c989..f062b5e8 100644 --- a/_doc/sphinxdoc/source/c_ml/kppv.rst +++ b/_doc/c_ml/kppv.rst @@ -85,7 +85,7 @@ la plus représentée parmi ses :math:`k` plus proches voisins. .. math:: card{S^*_k} = 0 \text{ et } \underset{y \in S^*_k}{\max} \; d\pa{y,x} - \infegal \underset{y \in X - S^*_k}{\min} \; d\pa{y,x} + \leqslant \underset{y \in X - S^*_k}{\min} \; d\pa{y,x} On calcule les occurrences :math:`f(i)` de chaque classe :math:`i` dans l'ensemble :math:`S^*_k` : @@ -95,7 +95,7 @@ la plus représentée parmi ses :math:`k` plus proches voisins. f(i) = \sum_{y \in S^*_k} \, \omega\pa{x,y} \, \indicatrice{c(y) = i} - On assigne alors à :math:`x` la classe :math:`c(x)$ choisie dans l'ensemble : + On assigne alors à :math:`x` la classe :math:`c(x)` choisie dans l'ensemble : .. math:: @@ -109,13 +109,13 @@ La table suivante donne quelques exemples de contributions possibles. .. list-table:: :header-rows: 0 - :widths: auto + :widths: 5 10 - * - fonction constante + * - fonction constante - :math:`\omega\pa{x,y} = 1` * - distance inverse - :math:`\omega\pa{x,y} = \frac{1}{1 + d\pa{x,y}}` - * - noyau + * - noyau - :math:`\omega\pa{x,y} = \exp\pa{ - d^2 \pa{x,y}}` Exemple de contribution :math:`w\pa{x,y}` pour @@ -150,7 +150,7 @@ père et pas plus de :math:`n` fils. Soit :math:`B_n` un :epkg:`B+ tree`, soit :math:`N` un noeud de :math:`B_n`, il contient un vecteur :math:`V\pa{N} = \vecteur{x_1}{x_t}` - avec :math:`0 \infegal t \infegal n` et :math:`x_1 < ... < x_t`. + avec :math:`0 \leqslant t \leqslant n` et :math:`x_1 < ... < x_t`. Ce noeud contient aussi exactement :math:`t-1` noeuds fils notés :math:`\vecteur{N_1}{N_{t-1}}`. On désigne par :math:`D\pa{N_t}` l'ensemble des descendants du noeud :math:`N_t` et @@ -161,12 +161,12 @@ père et pas plus de :math:`n` fils. :nowrap: \begin{eqnarray*} - && \forall x \in G\pa{N_t}, \; x_{t} \infegal x < x_{t+1} \\ + && \forall x \in G\pa{N_t}, \; x_{t} \leqslant x < x_{t+1} \\ && \text{avec par convention } x_0 = -\infty \text{ et } x_{t+1} = + \infty \end{eqnarray*} - + .. index:: quicksort - + Cet arbre permet de trier une liste de nombres, c'est une généralisation du tri `quicksort `_ pour lequel :math:`n=2`. Comme pour le tri *quicksort*, l'arbre est construit @@ -185,20 +185,18 @@ de manière globale - construction de l'arbre sachant l'ensemble de points à cl ou de manière progressive - insertion des points dans l'arbre les uns à la suite des autres -. Toutefois, ces méthodes sont resteintes à des espaces vectoriels. -.. figure:: - - .. list-table:: - :header-rows: 0 - :widths: auto +.. list-table:: + :header-rows: 0 + :widths: auto - * - .. image:: knnimg/rtree1.png - - .. image:: knnimg/rtree2.png + * - .. image:: knnimg/rtree1.png + - .. image:: knnimg/rtree2.png - Illustration d'un :epkg:`R-tree` en deux dimensions, - figure extraite de [Sellis1987]_, la première image montre des rectangles - pointillés englobant d'autres rectangles en trait plein. Chaque style de trait correspond - à un niveau dans le graphe de la seconde image. - +Illustration d'un :epkg:`R-tree` en deux dimensions, +figure extraite de [Sellis1987]_, la première image montre des rectangles +pointillés englobant d'autres rectangles en trait plein. Chaque style de trait correspond +à un niveau dans le graphe de la seconde image. + Il n'existe pas une seule manière de construire un :epkg:`R-tree`, les noeuds de ces arbres suivent toujours la contrainte des :epkg:`B+ tree` qui est d'avoir un père et au plus :math:`n` fils. @@ -237,22 +235,22 @@ Cet ensemble est construit grâce à l'algorithme suivant : On désigne par :math:`r` le noeud racine d'un :epkg:`R-tree`. Soit :math:`n` un noeud, on désigne par :math:`F\pa{n}` l'ensemble des fils de ce noeud. - - *initialisation* + + *initialisation* | :math:`L \longleftarrow 0` - | :math:`N \longleftarrow \acc{r}` + | :math:`N \longleftarrow \acc{r}` - *itération* - - | while :math:`N \neq \emptyset` + *itération* + + | while :math:`N \neq \emptyset` | for n in :math:`1..N` | if :math:`W \cap B\pa{n} \neq \emptyset` | :math:`N \longleftarrow N \cup F\pa{n}` | if :math:`B\pa{n}` est un objet | :math:`L \longleftarrow B\pa{n}` - - :math:`L` est l'ensemble cherché. + + :math:`L` est l'ensemble cherché. Il reste à construire le :epkg:`R-tree`, opération effectuée par la répétition successive de l'algorithme suivant @@ -301,7 +299,7 @@ permettant d'insérer un objet dans un :epkg:`R-tree`. | tant que :math:`G \neq \emptyset` | On choisit un noeud :math:`n \in G`, on détermine :math:`i^*` - | tel que :math:`v\pa{\acc{n} \cup G_i} - v\pa{G_i}$ soit minimal. + | tel que :math:`v\pa{\acc{n} \cup G_i} - v\pa{G_i}` soit minimal. | :math:`G \longleftarrow G - \acc{n}` | :math:`G_{i^*} \longleftarrow G_{i^*} \cup \acc{n}` @@ -387,10 +385,10 @@ ne peut être l'élément le plus proche. .. math:: - \forall y \in V\pa{x}, \; d\pa{x,y} \infegal \rho + \forall y \in V\pa{x}, \; d\pa{x,y} \leqslant \rho On suppose que la matrice - :math:`M = \pa{m_{ij}}_{ \begin{subarray} 1 \infegal i \infegal P \\ 1 \infegal j \infegal N \end{subarray} }` + :math:`M = \pa{m_{ij}}_{ \begin{subarray} 1 \leqslant i \leqslant P \\ 1 \leqslant j \leqslant N \end{subarray} }` a été calculée préalablement comme suit : .. math:: @@ -401,7 +399,7 @@ ne peut être l'élément le plus proche. | for i in :math:`1..P` | :math:`d_i \longleftarrow d\pa{x, p_i}` - | :math:`d^* \longleftarrow \min \acc{ d_i \sac 1 \infegal i \infegal P }` + | :math:`d^* \longleftarrow \min \acc{ d_i \sac 1 \leqslant i \leqslant P }` | :math:`d^*` est la distance du point :math:`x` au pivot le plus proche. *recherche du plus proche élément* @@ -411,7 +409,7 @@ ne peut être l'élément le plus proche. | :math:`d' \longleftarrow \max \acc{ \abs{ d_j - m_{ji} } }` | if :math:`d' < d^*` | :math:`d \longleftarrow d\pa{x,y_i}` - | if :math:`d' \infegal d^*` + | if :math:`d' \leqslant d^*` | :math:`d^* \longleftarrow d'` | :math:`S \longleftarrow \acc{y_i}` @@ -428,7 +426,7 @@ et l'élément :math:`x` soit connue et que l'ensemble :nowrap: \begin{eqnarray*} - \exists \pa{\alpha,\beta} \in \R^+_* \text{ tels que } && \nonumber\\ + \exists \pa{\alpha,\beta} \in \mathbb{R}^+_* \text{ tels que } && \nonumber\\ \forall \pa{x,y} \in E^2, \; \forall i\, && \alpha \, d\pa{x,y} \supegal \abs{d\pa{x,p_i} - d\pa{p_i,y}} \label{space_metric_cond_1} \\ \forall \pa{x,y} \in E^2, && \underset{i}{\max} \; \abs{d\pa{x,p_i} - d\pa{p_i,y}} \supegal @@ -437,7 +435,7 @@ et l'élément :math:`x` soit connue et que l'ensemble L'algorithme développé dans [Farago1993]_ permet de trouver le point de plus proche d'un élément :math:`x` dans un -ensemble :math:`E = \ensemble{x_1}{x_N}`selon l'algorithme suivant : +ensemble :math:`E = \ensemble{x_1}{x_N}` selon l'algorithme suivant : .. mathdef:: :title: plus proche voisin d'après [Farago1993]_ @@ -464,7 +462,7 @@ ensemble :math:`E = \ensemble{x_1}{x_N}`selon l'algorithme suivant : On définit :math:`t_0 \longleftarrow \underset{i} {\min} \; \gamma\pa{x_i}`. Puis on construit l'ensemble - :math:`F\pa{x} = \acc{ x_i \in E \sac \gamma\pa{x_i} }\infegal \frac{\alpha}{\beta} \, t_0`. + :math:`F\pa{x} = \acc{ x_i \in E \sac \gamma\pa{x_i} }\leqslant \frac{\alpha}{\beta} \, t_0`. *plus proche voisin* @@ -498,7 +496,7 @@ Et un petit théorème. p\pa{x,r} = P_X \pa{B\pa{x,r}} = \pr{ Z \in B\pa{x,r}} - On suppose qu'il existe :math:`d > 0` et une fonction :math:`f : X \longrightarrow \R` + On suppose qu'il existe :math:`d > 0` et une fonction :math:`f : X \longrightarrow \mathbb{R}` tels que : .. math:: @@ -513,15 +511,15 @@ Et un petit théorème. .. math:: - \underset{ n \rightarrow \infty } { \lim \sup } \; \esp{F_N} \infegal k + \pa{\frac{\alpha}{\beta}}^{2d} + \underset{ n \rightarrow \infty } { \lim \sup } \; \esp{F_N} \leqslant k + \pa{\frac{\alpha}{\beta}}^{2d} Implémentation ============== La classe :class:`NuagePoints ` implémente les nuages de points sans optimisation. Il utilise la même interface que -:epkg:`sklearn:neighbors:NearestNeighbors`. La second classe -:class:`NuagePointsLeasa `. +:class:`sklearn.neighbors.NearestNeighbors`. La second classe +:class:`NuagePointsLaesa `. .. runpython:: :showcode: diff --git a/_doc/sphinxdoc/source/c_ml/l1l2.rst b/_doc/c_ml/l1l2.rst similarity index 97% rename from _doc/sphinxdoc/source/c_ml/l1l2.rst rename to _doc/c_ml/l1l2.rst index 82fb68f2..cc392395 100644 --- a/_doc/sphinxdoc/source/c_ml/l1l2.rst +++ b/_doc/c_ml/l1l2.rst @@ -24,9 +24,6 @@ transposée dans l'espace initial. Une autre astuce consiste à imposer une contrainte supplémentaire sur le poids des coefficients de la régression, le plus souvent en les pénalisant. -.. contents:: - :local: - Réduction de dimension ====================== @@ -109,7 +106,7 @@ Plus on ajoute de variables, plus l'erreur diminue. Pour aller plus loin, voir [Char]_ et voir son application à la séelection d'arbres dans une forêt aléatoire `Réduction d’une forêt aléatoire -`_. +`_. Bibliographie ============= diff --git a/_doc/sphinxdoc/source/c_ml/lr_trees.rst b/_doc/c_ml/lr_trees.rst similarity index 90% rename from _doc/sphinxdoc/source/c_ml/lr_trees.rst rename to _doc/c_ml/lr_trees.rst index 99a6bb29..7bb11362 100644 --- a/_doc/sphinxdoc/source/c_ml/lr_trees.rst +++ b/_doc/c_ml/lr_trees.rst @@ -12,16 +12,13 @@ logistiques à mi-chemin entre les arbres de décisions et les réseaux de neurones. Dans un premier temps, on s'intéresse uniquement à une classification binaire. -.. contents:: - :local: - Parallèle entre un neurone et une régression logistique ======================================================= Les paragraphes :ref:`rn-classification` et :ref:`nn-classification` présente le problème de la classification qui consiste à trouver une fonction *f* qui maximise la vraisemblance -du nuage de points :math:`(X_i, y_i)_i` où :math:`X_i \in \R^d` +du nuage de points :math:`(X_i, y_i)_i` où :math:`X_i \in \mathbb{R}^d` et :math:`y_i \in \acc{0, 1}`. .. math:: @@ -47,7 +44,7 @@ Un arbre de décision se construit peu à peu en répétant toujours la même optimisation sur des sous-ensemble de plus en plus petit. Il faut d'abord un critère qui permette d'évaluer la pertinence de la division effectuée par un noeud de l'arbre. -Pour un ensemble :math:`(X_i, y_i)_{1 \infegal i \infegal n}`, on +Pour un ensemble :math:`(X_i, y_i)_{1 \leqslant i \leqslant n}`, on peut estimer la probabilité :math:`p(y_1, ..., y_n) = p(Y) = \frac{1}{n}\sum{i=1}^n y_i`. Le critère de Gini *G* qui évalue la pertinence d'une classification est @@ -65,7 +62,7 @@ du critère choisi : .. math:: \begin{array}{rcl} - S_{ik} &=& \acc{ m | x_{mk} \infegal x_{ik}} \\ + S_{ik} &=& \acc{ m | x_{mk} \leqslant x_{ik}} \\ \Delta_{ik} &=& H(Y) - ( H(Y_{S_{ik}}) + H(Y_{S_{ik}^C} ) \end{array} @@ -216,9 +213,9 @@ différents :math:`\theta`. .. math:: LL(x_0, \theta) = \max \left\{ \begin{array}{ll} - \frac{1}{1 + \exp((x-x_0) / \theta)} \\ - \frac{1}{1+\exp(- (x-x_0) / \theta)} - \end{array}\right + \frac{1}{1 + \exp{\left(\frac{x-x_0}{\theta}\right)}} \\ + \frac{1}{1 + \exp{\left(-\frac{x-x_0}{\theta}\right)}} + \end{array}\right. Aparté mathématique =================== @@ -241,14 +238,14 @@ On remarque que : \begin{array}{rcl} f(x) &=& \frac{1}{1 + e^{-x}} \\ - \Rightarrow f(-x) &=& \frac{1}{1 + e^{x}} = \frac{e^{-x}}{1 + e^{-x}} \\ - \Rightarrow f(x) + f(-x) &=& \frac{1}{1 + e^{-x}} + \frac{e^{-x}}{1 + e^{-x}} = 1 + \mathbb{R}ightarrow f(-x) &=& \frac{1}{1 + e^{x}} = \frac{e^{-x}}{1 + e^{-x}} \\ + \mathbb{R}ightarrow f(x) + f(-x) &=& \frac{1}{1 + e^{-x}} + \frac{e^{-x}}{1 + e^{-x}} = 1 \end{array} Cela explique pour on utilise souvent cette fonction pour transformer une distance en probabilité pour un classifieur binaire. L'apprentissage d'un arbre de décision -:epkg:`sklearn:tree:DecisionTreeClassifier` propose le +:class:`sklearn.tree.DecisionTreeClassifier` propose le paramètre ``min_samples_leaf``. On se propose dans le cadre de la régression logistique de chercher le paramètre :math:`\beta_0` qui permet de vérifier la contrainte @@ -270,16 +267,6 @@ serait meilleure qu'une seule régression logistique sur la réunion des deux parties. Cet algorithme devrait trouver à la fois les modèles et la séparation entre les deux parties. -.. todoext:: - :title: Arbre de régressions logistiques et EM - :tag: idée - :issue: 28 - - Chaque noeud du graphe serait modélisé comme étant la réunion - de trois régressions logistiques, une pour diviser - l'espace en deux, deux autres apprenant à classifier sur chacune - des parties. - .. _l-decnntrees: Lien vers les réseaux de neurones @@ -358,8 +345,8 @@ sans doute pas porteuse d'une innovation majeure. Et ce n'est pas la première fois que quelqu'un se lance dans la conversion d'un arbre en réseaux de neurones. -J'ai quand même essayé avec le notebook :ref:`neuraltreerst` -et les classes :class:`NeuralTreeNode `, +J'ai quand même essayé avec le notebook :ref:`/notebooks/ml/neural_tree.ipynb` +et les classes :class:`NeuralTreeNode `, :class:`NeuralTreeNet `. Si l'idée de départ est séduisante, elle requiert une contrainte supplémentaire qui est de créer un réseau de neurones qui ne soit @@ -416,18 +403,14 @@ vecteurs :math:`\Theta_1, \Theta_2` pour maximiser la vraisemblance sur chacune des parties. Il ne reste plus qu'à montrer que la vraisemblance globale sera supérieur à celle obtenue par la première régression logistique. -.. todoext:: - :title: Arbre de régressions logistiques en cascade orthogonale - :tag: idée - :issue: 29 +On pourrait implémenter l'algorithme suivant +(Arbre de régressions logistiques en cascade orthogonale) : - Implémenter la l'algorithme suivant : - - * Apprendre une régression logistique - * Choisir un hyperplan perpendiculaire en optimisation - un critère :ref:`l-criteria-reg-log` - * Apprendre une régression logistique sur chacune des parties. - * Continuer jusqu'à ce l'amélioration soit négligeable +* Apprendre une régression logistique +* Choisir un hyperplan perpendiculaire en optimisation + un critère :ref:`l-criteria-reg-log` +* Apprendre une régression logistique sur chacune des parties. +* Continuer jusqu'à ce l'amélioration soit négligeable Interprétabilité ================ @@ -435,10 +418,10 @@ Interprétabilité Bibliographie ============= -[Scott2013] `Expectation-maximization for logistic regression `_, +.. [Scott2013] `Expectation-maximization for logistic regression `_, James G. Scott, Liang Sun - -[Nakandalam2020] A Tensor-based Approach for One-size-fits-all ML Prediction Serving. + +.. [Nakandalam2020] A Tensor-based Approach for One-size-fits-all ML Prediction Serving. Supun Nakandalam, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi. To appear at `OSDI 2020 `_. diff --git a/_doc/sphinxdoc/source/c_ml/lr_voronoi.rst b/_doc/c_ml/lr_voronoi.rst similarity index 99% rename from _doc/sphinxdoc/source/c_ml/lr_voronoi.rst rename to _doc/c_ml/lr_voronoi.rst index 213d5a72..dae50061 100644 --- a/_doc/sphinxdoc/source/c_ml/lr_voronoi.rst +++ b/_doc/c_ml/lr_voronoi.rst @@ -13,9 +13,6 @@ qui allie la régression logistique et les clustering type :ref:`l-k-means`. Le point de départ est une conjecture : les régions créées par une régression logistique sont convexes. -.. contents:: - :local: - Diagramme de Voronoï ==================== @@ -54,8 +51,8 @@ initiaux sont les frontières du diagramme de Voronoï formé par ces *n* points ? Les paragraphes qui suivent expliquent explorent cette hypothèse. -Régression logistique -===================== +Régression logistique et classification +======================================= :epkg:`scikit-learn` a rendu populaire le jeu de données `Iris `_ @@ -370,4 +367,4 @@ unité de l'espace des features. .. toctree:: :maxdepth: 1 - ../notebooks/logreg_voronoi + ../notebooks/ml/logreg_voronoi diff --git a/_doc/sphinxdoc/source/c_ml/lrtreesimg/bayes.png b/_doc/c_ml/lrtreesimg/bayes.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrtreesimg/bayes.png rename to _doc/c_ml/lrtreesimg/bayes.png diff --git a/_doc/sphinxdoc/source/c_ml/lrtreesimg/hb.png b/_doc/c_ml/lrtreesimg/hb.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrtreesimg/hb.png rename to _doc/c_ml/lrtreesimg/hb.png diff --git a/_doc/sphinxdoc/source/c_ml/lrtreesimg/mloc.png b/_doc/c_ml/lrtreesimg/mloc.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrtreesimg/mloc.png rename to _doc/c_ml/lrtreesimg/mloc.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/Coloured_Voronoi_2D.png b/_doc/c_ml/lrvor/Coloured_Voronoi_2D.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/Coloured_Voronoi_2D.png rename to _doc/c_ml/lrvor/Coloured_Voronoi_2D.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/biss.png b/_doc/c_ml/lrvor/biss.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/biss.png rename to _doc/c_ml/lrvor/biss.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/hexa.png b/_doc/c_ml/lrvor/hexa.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/hexa.png rename to _doc/c_ml/lrvor/hexa.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/hexa2.png b/_doc/c_ml/lrvor/hexa2.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/hexa2.png rename to _doc/c_ml/lrvor/hexa2.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/iris.png b/_doc/c_ml/lrvor/iris.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/iris.png rename to _doc/c_ml/lrvor/iris.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/partabc.png b/_doc/c_ml/lrvor/partabc.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/partabc.png rename to _doc/c_ml/lrvor/partabc.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/vor2.png b/_doc/c_ml/lrvor/vor2.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/vor2.png rename to _doc/c_ml/lrvor/vor2.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/vor4.png b/_doc/c_ml/lrvor/vor4.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/vor4.png rename to _doc/c_ml/lrvor/vor4.png diff --git a/_doc/sphinxdoc/source/c_ml/lrvor/zoneangle.png b/_doc/c_ml/lrvor/zoneangle.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/lrvor/zoneangle.png rename to _doc/c_ml/lrvor/zoneangle.png diff --git a/_doc/sphinxdoc/source/c_ml/mfimg/plan.jpg b/_doc/c_ml/mfimg/plan.jpg similarity index 100% rename from _doc/sphinxdoc/source/c_ml/mfimg/plan.jpg rename to _doc/c_ml/mfimg/plan.jpg diff --git a/_doc/sphinxdoc/source/c_ml/missing_values_mf.rst b/_doc/c_ml/missing_values_mf.rst similarity index 92% rename from _doc/sphinxdoc/source/c_ml/missing_values_mf.rst rename to _doc/c_ml/missing_values_mf.rst index 99fe8b4e..97ebe5a4 100644 --- a/_doc/sphinxdoc/source/c_ml/missing_values_mf.rst +++ b/_doc/c_ml/missing_values_mf.rst @@ -10,9 +10,6 @@ Cette méthode est utilisée dans le cadre de la recommandation de produits à des utilisateurs. Lire également [Acara2011]_, [Gupta2010]_. -.. contents:: - :local: - Factorisation de matrices et rang ================================= @@ -45,7 +42,7 @@ Dans ce cas, on cherchera les matrices qui minimise : Quelques cas simples ==================== -Le notebook :ref:`valeursmanquantesmfrst` montre la décroissante de l'erreur +Le notebook :ref:`/notebooks/ml/valeurs_manquantes_mf.ipynb` montre la décroissante de l'erreur en fonction du rang et l'impact de la corrélation sur cette même erreur. Le dernier paragraphe montre qu'il n'existe pas de solution unique à un problème donné. L'exemple suivant s'intéresse à une matrice 3x3. @@ -95,8 +92,8 @@ Nous allons le montrer grâce à quelques lemmes et théorèmes. On note :math:`M=(m_{ij})`, :math:`W^k=(w^k_{il})`, :math:`H^k=(h^k_{lj})` avec - :math:`1 \infegal i \infegal p`, :math:`1 \infegal j \infegal q`, - et :math:`1 \infegal l \infegal k` avec :math:`k < \min(p,q)`. + :math:`1 \leqslant i \leqslant p`, :math:`1 \leqslant j \leqslant q`, + et :math:`1 \leqslant l \leqslant k` avec :math:`k < \min(p,q)`. On suppose que les matrices sont solution du problème d'optimisation :math:`\min_{W,H} \norm{ M - WH }^2`. @@ -147,8 +144,8 @@ Cela signifie qu'on peut écrire la matrice :math:`W_k` dans une base On note :math:`M=(m_{ij})`, :math:`W^k=(w^k_{il})`, :math:`H^k=(h^k_{lj})` avec - :math:`1 \infegal i \infegal p`, :math:`1 \infegal j \infegal q`, - et :math:`1 \infegal l \infegal k` avec :math:`k < \min(p,q)`. + :math:`1 \leqslant i \leqslant p`, :math:`1 \leqslant j \leqslant q`, + et :math:`1 \leqslant l \leqslant k` avec :math:`k < \min(p,q)`. On suppose que les matrices sont solution du problème d'optimisation :math:`\min_{W,H} \norm{ M - WH }^2`. @@ -181,8 +178,8 @@ sur ce plan. On note :math:`M=(m_{ij})`, :math:`W^k=(w^k_{il})`, :math:`H^k=(h^k_{lj})` avec - :math:`1 \infegal i \infegal p`, :math:`1 \infegal j \infegal q`, - et :math:`1 \infegal l \infegal k` avec :math:`k < \min(p,q)`. + :math:`1 \leqslant i \leqslant p`, :math:`1 \leqslant j \leqslant q`, + et :math:`1 \leqslant l \leqslant k` avec :math:`k < \min(p,q)`. On suppose que les matrices sont solution du problème d'optimisation :math:`\min_{W,H} \norm{ M - WH }^2`. @@ -206,16 +203,15 @@ a montré que : \begin{eqnarray*} S = - \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \arg \max } \; + \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \arg \max } \; \cro { \sum_{i=1}^{N} \norm{W'X_i}^2 } &=& - \underset{ W \in M_{p,d}\pa{\R} } { \arg \min } \; \cro { \sum_{i=1}^{N} \norm{WW'X_i - X_i}^2 } + \underset{ W \in M_{p,d}\pa{\mathbb{R}} } { \arg \min } \; \cro { \sum_{i=1}^{N} \norm{WW'X_i - X_i}^2 } \end{eqnarray*} Dans notre cas, chaque ligne de la matrice :math:`M` est un vecteur :math:`X_i`. La matrice :math:`W_k` est identique à celle cherchée lors du problème de factorisation de matrices. Les colonnes de la matrice :math:`H_k` sont égales à :math:`W'X_i`. Il reste à montrer que le minimum trouvé dans les deux problèmes est le même. -Le notebook :ref:`mfacprst` montre que cela fonctionne sur un exemple. La démonstration du théorème montre également que :math:`W'W = I_d` et dans ce cas précis, :math:`WW'X_i` représente les coordonnées de la projection du point :math:`X_i` sur le plan défini par les vecteurs :math:`W`. @@ -242,11 +238,19 @@ On peut alors essayer de forcer la factorisation de matrice vers une matrice :math:`H` avec pas de un 1 sur chaque colonne et des zéros partout ailleurs. Le résultat sera assez proche d'un clustering. +.. _l-mf-acp-notebook: + Quelques résultats ================== -Le notebook :ref:`mfacprst` illustre le lien entre ACP et -factorisation de matrice en deux dimensions. +Le notebook suivant illustre le lien entre ACP et +factorisation de matrices en deux dimensions. + +.. toctree:: + + ../notebooks/ml/mf_acp + ../notebooks/ml/valeurs_manquantes_mf + Prolongements ============= @@ -284,9 +288,8 @@ revient à déterminer les coordonnées de la projection d'un nouveau point :mat dans le plan défini par les vecteurs de la matrice :math:`H`. Pour de nouvelles observations :math:`M_2=X_{q+1}`, la fonction `transform -`_ -de la classe :epkg:`sklearn:decomposition:NMF` réestime une matrice +`_ +de la classe :class:`sklearn.decomposition.NMF` réestime une matrice :math:`W_2` qui projette les vecteurs lignes de :math:`M_2` sur les vecteurs de *H* en conservant des coefficients de projection positifs. @@ -342,7 +345,8 @@ avec une factorisation de matrices. On peut également se server de la méthode pour calculer une ACP avec des valeurs manquantes. * `Imputation de données manquantes `_ -* `Principal component analysis with missing values: a comparative survey of methods `_ +* `Principal component analysis with missing values: a comparative survey of methods + `_ Interprétation ++++++++++++++ diff --git a/_doc/sphinxdoc/source/c_ml/piecewise.rst b/_doc/c_ml/piecewise.rst similarity index 95% rename from _doc/sphinxdoc/source/c_ml/piecewise.rst rename to _doc/c_ml/piecewise.rst index bfa4162e..1dd12a6e 100644 --- a/_doc/sphinxdoc/source/c_ml/piecewise.rst +++ b/_doc/c_ml/piecewise.rst @@ -5,7 +5,7 @@ Régression linéaire par morceaux ================================ -Le paragraphe :ref:`regressionlineairerst` +Le paragraphe :ref:`/notebooks/dsgarden/regression_lineaire.ipynb` étudie le lien entre le coefficient :math:`R^2` et la corrélation pour finalement illustrer une façon de réaliser une régression linéaire par @@ -22,16 +22,13 @@ n'est pas de le faire mais de le faire efficacement. Et pour comprendre là où je veux vous emmener, il faudra un peu de mathématiques. -.. contents:: - :local: - Une implémentation de ce type de méthode est proposée dans la pull request `Model trees (M5P and co) `_ qui répond à au problème posée dans `Model trees (M5P) `_ et originellement implémentée dans -`Building Model Trees `_. +`Building Model Trees `_. Cette dernière implémentation réestime les modèles comme l'implémentation décrite au paragraphe :ref:`l-decisiontree-reglin-piecewise-naive` mais étendue à tout type de modèle. @@ -44,13 +41,13 @@ Problème et regréssion linéaire dans un espace à une dimension Tout d'abord, une petite illustration du problème avec la classe `PiecewiseRegression -`_ +`_ implémentée selon l'API de :epkg:`scikit-learn`. .. toctree:: :maxdepth: 1 - ../notebooks/piecewise_linear_regression + ../notebooks/ml/piecewise_linear_regression .. image:: piecewise/piecenaive.png :width: 250 @@ -91,7 +88,7 @@ oranges. Il suffirait donc de remplacer l'erreur *E* par celle obtenue par une régression linéaire. Mais si c'était aussi simple, -l'implémentation de :epkg:`sklearn:tree:DecisionTreeRegressor` +l'implémentation de :class:`sklearn.tree.DecisionTreeRegressor` la proposerait. Alors pourquoi ? La raison principale est que cela coûte trop cher en temps de calcul. Pour trouver l'indice *k*, il faut calculer @@ -204,11 +201,11 @@ calculer rapidement : A_{k-1} - A_k = (X_{1..k-1}'X_{1..k-1})^{-1} X'_{1..k-1} Y_{1..k-1} - (X_{1..k}'X_{1..k})^{-1} X'_{1..k} Y_{1..k} -La documentation de :epkg:`sklearn:tree:DecisionTreeRegressor` +La documentation de :class:`sklearn.tree.DecisionTreeRegressor` ne mentionne que deux critères pour apprendre un arbre de décision de régression, *MSE* pour -:epkg:`sklearn:metrics:mean_squared_error` et *MAE* pour -:epkg:`sklearn:metrics:mean_absolute_error`. Les autres critères n'ont +:func:`sklearn.metrics.mean_squared_error` et *MAE* pour +:func:`sklearn.metrics.mean_absolute_error`. Les autres critères n'ont probablement pas été envisagés. L'article [Acharya2016]_ étudie la possibilité de ne pas calculer la matrice :math:`A_k` pour tous les *k*. Le paragraphe :ref:`l-piecewise-linear-regression` utilise @@ -232,7 +229,7 @@ on peut utiliser la librairie :epkg:`LAPACK`. Je ne vais pas plus loin ici car cela serait un peu hors sujet mais ce n'était pas une partie de plaisir. Cela donne : `piecewise_tree_regression_criterion_linear.pyx -`_ +`_ C'est illustré toujours par le notebook :epkg:`DecisionTreeRegressor optimized for Linear Regression`. @@ -375,7 +372,7 @@ On en déduit que : :lid: algo_decision_tree_mselin On dipose qu'un nuage de points :math:`(X_i, y_i)` avec - :math:`X_i \in \R^d` et :math:`y_i \in \R`. Les points sont + :math:`X_i \in \mathbb{R}^d` et :math:`y_i \in \mathbb{R}`. Les points sont triés selon une dimension. On note *X* la matrice composée des lignes :math:`X_1, ..., X_n` et le vecteur colonne :math:`y=(y_1, ..., y_n)`. @@ -483,7 +480,7 @@ de Gram-Schmidt qui est implémentée dans la fonction L'avantage est que cette formulation s'exprime uniquement à partir de produits scalaires. -Voir le notebook :ref:`regressionnoinversionrst`. +Voir le notebook svuiant :ref:`/notebooks/ml/regression_no_inversion.ipynb`. .. _l-reglin-acp-svd: @@ -520,7 +517,7 @@ Synthèse mathématique :lid: algo_gram_schmidt_reglin Soit une matrice :math:`X \in \mathcal{M}_{nd}` avec - :math:`n \supegal d`. Et un vecteur :math:`y \in \R^n`. + :math:`n \supegal d`. Et un vecteur :math:`y \in \mathbb{R}^n`. D'après l':ref:`algorithme de Gram-Schmidt `, il existe deux matrices telles que :math:`X P = T` ou :math:`P' X' = T'`. @@ -643,10 +640,10 @@ est que le coût de la mise à jour pour l'itération *k+1* ne dépend pas de *k*. On suppose donc que :math:`(T_k, P_k)` sont les deux matrices -retournées par l'algorithme de :ref:`algo_gram_schmidt `. +retournées par l'algorithme de :ref:`Gram-Schmidt `. On construit la matrice :math:`V_{k+1} = [ T_k, X_{k+1} P_k ]` : on ajoute une ligne à la matrice :math:`T_k`. On applique -une itération de algorithme de :ref:`algo_gram_schmidt ` +une itération de algorithme de :ref:`Gram-Schmidt ` pour obtenir :math:`(T_{k+1}, P)`. On en déduit que :math:`(T_{k+1}, P_{k+1}) = (T_{k+1}, P_k P)`. L'expression de la régression ne change pas mais il reste à l'expression @@ -849,7 +846,9 @@ Notebooks .. toctree:: - ../notebooks/regression_no_inversion + ../notebooks/ml/regression_no_inversion + +Voir aussi [Cai2020]_, [Nie2016]_, [Preda2010]_. Implémentations =============== @@ -862,8 +861,16 @@ Bilbiographie .. [Acharya2016] `Fast Algorithms for Segmented Regression `_, Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt, :epkg:`ICML 2016` -.. [Cai2020] `Online Sufficient Dimension Reduction Through Sliced Inverse Regression `_, +.. [Cai2020] `Online Sufficient Dimension Reduction Through Sliced Inverse Regression + `_, Zhanrui Cai, Runze Li, Liping Zhu .. [Nie2016] `Online PCA with Optimal Regret `_, Jiazhong Nie, Wojciech Kotlowski, Manfred K. Warmuth + +.. [Preda2010] `The NIPALS Algorithm for Missing Functional Data `_, + Cristian Preda, Gilbert Saporta, Mohamed Hadj Mbarek, + Revue roumaine de mathématiques pures et appliquées 2010, 55 (4), pp.315-326. + +Voir aussi `The NIPALS algorithm +`_. diff --git a/_doc/sphinxdoc/source/c_ml/piecewise/piecenaive.png b/_doc/c_ml/piecewise/piecenaive.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/piecewise/piecenaive.png rename to _doc/c_ml/piecewise/piecenaive.png diff --git a/_doc/sphinxdoc/source/c_ml/piecewise/piecenaive2.png b/_doc/c_ml/piecewise/piecenaive2.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/piecewise/piecenaive2.png rename to _doc/c_ml/piecewise/piecenaive2.png diff --git a/_doc/sphinxdoc/source/c_ml/piecewise/voisin.png b/_doc/c_ml/piecewise/voisin.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/piecewise/voisin.png rename to _doc/c_ml/piecewise/voisin.png diff --git a/_doc/sphinxdoc/source/c_ml/qureg/mediane1.png b/_doc/c_ml/qureg/mediane1.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/qureg/mediane1.png rename to _doc/c_ml/qureg/mediane1.png diff --git a/_doc/sphinxdoc/source/c_ml/qureg/mediane2.png b/_doc/c_ml/qureg/mediane2.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/qureg/mediane2.png rename to _doc/c_ml/qureg/mediane2.png diff --git a/_doc/sphinxdoc/source/c_ml/qureg/q02.png b/_doc/c_ml/qureg/q02.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/qureg/q02.png rename to _doc/c_ml/qureg/q02.png diff --git a/_doc/sphinxdoc/source/c_ml/regression_quantile.rst b/_doc/c_ml/regression_quantile.rst similarity index 90% rename from _doc/sphinxdoc/source/c_ml/regression_quantile.rst rename to _doc/c_ml/regression_quantile.rst index c1ee072f..21fb5177 100644 --- a/_doc/sphinxdoc/source/c_ml/regression_quantile.rst +++ b/_doc/c_ml/regression_quantile.rst @@ -1,17 +1,14 @@ .. _l-reg-quantile: -=================== -Régression quantile -=================== +==================================== +Régression quantile ou régression L1 +==================================== La régression quantile est moins sensible aux points aberrants. Elle peut être définie comme une régression avec une norme *L1* (une valeur absolue). -.. contents:: - :local: - .. _l-reg-quantile-demo: Médiane et valeur absolue @@ -66,8 +63,8 @@ que n'importe quel point dans cet intervalle minimise :math:`\abs{X_1 - M} + \abs{X_n - M} + \abs{X_2 - M} + \abs{X_{n-1} - M} + ... = E`. La propriété est démontrée. -Régression quantile -=================== +Régression et quantile +====================== Maintenant que la médiane est définie par un problème de minimisation, il est possible de l'appliquer à un @@ -78,8 +75,8 @@ problème de régression. :tag: Définition On dispose d'un ensemble de *n* couples - :math:`(X_i, Y_i)` avec :math:`X_i \in \R^d` - et :math:`Y_i \in \R`. La régression quantile + :math:`(X_i, Y_i)` avec :math:`X_i \in \mathbb{R}^d` + et :math:`Y_i \in \mathbb{R}`. La régression quantile consiste à trouver :math:`\alpha, \beta` tels que la somme :math:`\sum_i \abs{\alpha + \beta X_i - Y_i}` est minimale. @@ -93,8 +90,7 @@ de descente de gradient puisque la fonction partout dérivable. Une autre option consiste à utiliser l'algorithme `Iteratively reweighted least squares `_. -L'implémentation est faite par la classe -`QuantileLinearRegression `_. +L'implémentation est faite par la classe :epkg:`QuantileLinearRegression`. L'algorithme est tiré de [Chen2014]_. .. mathdef:: @@ -192,7 +188,7 @@ la médiane avec :math:`p=\frac{1}{2}`. Il faut démontrer que la solution de ce programme d'optimisation atterrit dans l'intervalle souhaité. -.. images:: qureg/q02.png +.. image:: qureg/q02.png :height: 80 On choisit un réel *P* à l'intérieur d'un intervale et on calcule : @@ -231,8 +227,8 @@ pour un quantile autre que la médiane. :tag: Définition On dispose d'un ensemble de *n* couples - :math:`(X_i, Y_i)` avec :math:`X_i \in \R^d` - et :math:`Y_i \in \R`. La régression quantile + :math:`(X_i, Y_i)` avec :math:`X_i \in \mathbb{R}^d` + et :math:`Y_i \in \mathbb{R}`. La régression quantile consiste à trouver :math:`\alpha, \beta` tels que la somme :math:`\sum_i p \abs{\alpha + \beta X_i - Y_i}^+ + (1-p) \abs{\alpha + \beta X_i - Y_i}^-` est minimale. @@ -245,9 +241,7 @@ de descente de gradient puisque la fonction à minimiser est presque partout dérivable. On peut aussi adapter l'algorithme :ref:`Iteratively reweighted least squares `. -L'implémentation est faite par la classe -`QuantileLinearRegression -`_ +L'implémentation est faite par la classe :epkg:`QuantileLinearRegression` (voir [Koenker2017]_). .. mathdef:: @@ -294,14 +288,14 @@ Notebook .. toctree:: - ../notebooks/quantile_regression_example + ../notebooks/dsgarden/quantile_regression_example Bilbiographie ============= Des références sont disponibles sur la page de :epkg:`statsmodels` : `QuantReg `_ ou -là : `Régression quantile `_. +là : `Régression quantile `_. .. [Koenker2017] `Quantile Regression, 40 years on `_, Roger Koenker (2017) diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn.rst b/_doc/c_ml/rn/rn.rst similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rn.rst rename to _doc/c_ml/rn/rn.rst diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_1_def.rst b/_doc/c_ml/rn/rn_1_def.rst similarity index 86% rename from _doc/sphinxdoc/source/c_ml/rn/rn_1_def.rst rename to _doc/c_ml/rn/rn_1_def.rst index c372b017..b68ab285 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_1_def.rst +++ b/_doc/c_ml/rn/rn_1_def.rst @@ -21,12 +21,12 @@ Un neurone :lid: def-neurone Un neurone à :math:`p` entrées est une fonction - :math:`f : \R^{p+1} \times \R^p \longrightarrow \R` + :math:`f : \mathbb{R}^{p+1} \times \mathbb{R}^p \longrightarrow \mathbb{R}` définie par : - * :math:`g : \R \longrightarrow \R` - * :math:`W \in \R^{p+1}`, :math:`W=\pa{w_1,\dots,w_{p+1}}` - * :math:`\forall x \in \R^p, \; f\pa{W,x} = g \pa { \sum_{i=1}^{p} w_i x_i + w_{p+1}}` + * :math:`g : \mathbb{R} \longrightarrow \mathbb{R}` + * :math:`W \in \mathbb{R}^{p+1}`, :math:`W=\pa{w_1,\dots,w_{p+1}}` + * :math:`\forall x \in \mathbb{R}^p, \; f\pa{W,x} = g \pa { \sum_{i=1}^{p} w_i x_i + w_{p+1}}` avec :math:`x = \pa{x_1,\dots,x_p}` Cette définition est inspirée du neurone biologique, les poids jouant le rôle @@ -72,7 +72,7 @@ La fonction *g* est appelée *fonction de transfert* ou *fonction de seuil*. \end{picture} - Le vecteur :math:`\left( x_1,...,x_p\right) \in \R^p` + Le vecteur :math:`\left( x_1,...,x_p\right) \in \mathbb{R}^p` joue le rôle des *entrées*. :math:`y` est appelé parfois le *potentiel*. :math:`y=\sum_{i=1}^{p} w_ix_i+b`. @@ -98,6 +98,7 @@ sigmoïde entre :math:`\cro{0,1}` :math:`\dfrac{1}{1+e^{-x}}` sigmoïde entre :math:`\cro{-1,1}` :math:`1-\dfrac{2}{1+e^{x}}` normale :math:`e^{-\frac{x^{2}}{2}}` exponentielle :math:`e^{x}` +relu :math:`x \indicatrice{x \supegal 0}` ============================================= ====================================== La plupart des fonctions utilisées sont dérivables et cette propriété @@ -106,6 +107,9 @@ l'algorithme de rétropropagation découvert par [Rumelhart1986]_. Ce dernier permet le calcul de la dérivée ouvre ainsi les portes des méthodes d'optimisation basées sur cette propriété. +La fonction :epkg:`relu` a progressivement remplacé la fonction *sigmoïde* +sur les couches cachées car elle est non linéaire et +beaucoup plus rapide à calculer. Une couche de neurones ++++++++++++++++++++++ @@ -116,18 +120,18 @@ Une couche de neurones :lid: rn_definition_couche_neurone_1 Soit :math:`p` et :math:`n` deux entiers naturels, - on note :math:`W \in \R^{n\pa{p+1}} = \pa{W_1,\dots,W_n}` - avec :math:`\forall i \in \intervalle{1}{n}, \; W_i \in \R^{p+1}`. + on note :math:`W \in \mathbb{R}^{n\pa{p+1}} = \pa{W_1,\dots,W_n}` + avec :math:`\forall i \in \intervalle{1}{n}, \; W_i \in \mathbb{R}^{p+1}`. Une couche de :math:`n` neurones et :math:`p` entrées est une fonction : .. math:: - F : \R^{n\pa{p+1}} \times \R^p \longrightarrow \R^n + F : \mathbb{R}^{n\pa{p+1}} \times \mathbb{R}^p \longrightarrow \mathbb{R}^n vérfifiant : * :math:`\forall i \in \intervalle {1}{n}, \; f_i` est un neurone. - * :math:`\forall W \in \R^{n\pa{p+1}} \times \R^p, \; F\pa{W,x} = \pa {f_1\pa{W_1,x}, \dots, f_n\pa{W_n,x}}` + * :math:`\forall W \in \mathbb{R}^{n\pa{p+1}} \times \mathbb{R}^p, \; F\pa{W,x} = \pa {f_1\pa{W_1,x}, \dots, f_n\pa{W_n,x}}` Une couche de neurones représente la juxtaposition de plusieurs neurones partageant les mêmes entrées mais ayant chacun leur propre vecteur de @@ -153,12 +157,12 @@ Un réseau de neurones : le perceptron Les coefficients de la couche :math:`C_i` sont notés :math:`\pa {W_1^i,\dots,W_{n_i}^i}`, cette couche définit une fonction :math:`F_i`. - Soit la suite :math:`\pa{Z_i}_{0\infegal i \infegal C}` définie par : + Soit la suite :math:`\pa{Z_i}_{0\leqslant i \leqslant C}` définie par : .. math:: \begin{array}{l} - Z_0 \in \R^p \\ + Z_0 \in \mathbb{R}^p \\ \forall i \in \intervalle{1}{C}, \; Z_i = F_i \pa {W_1^i,\dots,W_{n_i}^i,Z_{i-1}}\end{array} On pose :math:`M = M = \sum_{i=1}^{C}n_i\pa{p_i+1}`, @@ -167,7 +171,7 @@ Un réseau de neurones : le perceptron .. math:: \begin{array}{lrll} - F : & \R ^ M \times \R^p & \longrightarrow & \R^n \\ + F : & \mathbb{R} ^ M \times \mathbb{R}^p & \longrightarrow & \mathbb{R}^n \\ & \pa{W,Z_0} & \longrightarrow & Z_C \end{array} @@ -212,7 +216,7 @@ sachant ses poids est appelé *propagation*. | :math:`Z_c \longleftarrow X` Vient ensuite le calcul itératif de la suite - :math:`\pa{Z_c}_{1 \infegal c \infegal C}` : + :math:`\pa{Z_c}_{1 \leqslant c \leqslant C}` : | for c in :math:`1..C` : | :math:`Y_c \longleftarrow W_c Z_{c-1} + B_c` diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_2_reg.rst b/_doc/c_ml/rn/rn_2_reg.rst similarity index 92% rename from _doc/sphinxdoc/source/c_ml/rn/rn_2_reg.rst rename to _doc/c_ml/rn/rn_2_reg.rst index 00b4fe8a..857d58b8 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_2_reg.rst +++ b/_doc/c_ml/rn/rn_2_reg.rst @@ -15,7 +15,7 @@ On suppose parfois que ce bruit suive une loi normale. :lid: def-bruit-blanc Une suite de variables aléatoires réelles - :math:`\pa{\epsilon_i}_{1 \infegal i \infegal N}` + :math:`\pa{\epsilon_i}_{1 \leqslant i \leqslant N}` est un bruit blanc : * :math:`\exists \sigma > 0`, :math:`\forall i \in \intervalle{1}{N}, \; \epsilon_i \sim \loinormale{0}{\sigma}` @@ -32,7 +32,7 @@ Une régression consiste à résoudre le problème suivant : l'objectif est d'approximer la fonction :math:`\esp\pa{Y | X} = f\pa{X}`. Les données du problème sont - un échantillon de points :math:`\acc{ \pa{ X_{i},Y_{i} } | 1 \infegal i \infegal N }` + un échantillon de points :math:`\acc{ \pa{ X_{i},Y_{i} } | 1 \leqslant i \leqslant N }` et un modèle paramétré avec :math:\theta` : .. math:: @@ -40,9 +40,9 @@ Une régression consiste à résoudre le problème suivant : \forall i \in \intervalle{1}{N}, \; Y_{i} = f \pa{\theta,X_{i}} + \epsilon_{i} avec :math:`n \in \N`, - :math:`\pa{\epsilon_{i}}_{1 \infegal i \infegal N}` :ref:`bruit blanc `, + :math:`\pa{\epsilon_{i}}_{1 \leqslant i \leqslant N}` :ref:`bruit blanc `, :math:`f` est une fonction de paramètre :math:`\theta`. - + La fonction :math:`f` peut être une fonction linéaire, un polynôme, un réseau de neurones... @@ -53,8 +53,8 @@ minimisant l'erreur de prédiction est : .. math:: - \hat{\theta} = \underset {\theta \in \R^p}{\arg \min} \; \esp \pa {\theta} - = \underset {\theta \in \R^p}{\arg \min} + \hat{\theta} = \underset {\theta \in \mathbb{R}^p}{\arg \min} \; \esp \pa {\theta} + = \underset {\theta \in \mathbb{R}^p}{\arg \min} \cro{ \sum_{i=1}^{N} \cro{Y_{i}-f \pa{\theta,X_{i}}}^{2}} Le lien entre les variables :math:`X` et :math:`Y` dépend des hypothèses faites diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_3_clas.rst b/_doc/c_ml/rn/rn_3_clas.rst similarity index 94% rename from _doc/sphinxdoc/source/c_ml/rn/rn_3_clas.rst rename to _doc/c_ml/rn/rn_3_clas.rst index 7f6aedd9..d37b609a 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_3_clas.rst +++ b/_doc/c_ml/rn/rn_3_clas.rst @@ -14,10 +14,10 @@ suivant une `loi multinomiale `_ :lid: probleme_classification Soit une variable aléatoire :math:`X` - et une variable aléatoire discrète :math:`Y`, + et une variable aléatoire discrète :math:`Y \in \N`, l'objectif est d'approximer la fonction :math:`\esp\pa{Y | X} = f\pa{X}`. Les données du problème sont - un échantillon de points : :math:`\acc { \pa{ X_{i},Y_{i} } | 1 \infegal i \infegal N }` + un échantillon de points : :math:`\acc { \pa{ X_{i},Y_{i} } | 1 \leqslant i \leqslant N }` avec :math:`\forall i \in \ensemble{1}{N}, \; Y_i \in \ensemble{1}{C}` et un modèle paramétré avec :math:`\theta` : @@ -29,7 +29,7 @@ suivant une `loi multinomiale `_ avec :math:`n \in \N`, :math:`h` est une fonction de paramètre :math:`\theta` à valeur dans :math:`\cro{0,1}` et vérifiant la contrainte : :math:`\sum_{c=1}^C h(\theta,X,c) = 1`. - + Le premier exemple est une classification en deux classes, elle consiste à découvrir le lien qui @@ -38,7 +38,7 @@ discrète et :math:`Y \in \acc{0,1}`, on dispose pour cela d'une liste : .. math:: - \acc{ \pa{ X_i,Y_i } \in \R \times \acc{0,1} | 1 \infegal i \infegal N } + \acc{ \pa{ X_i,Y_i } \in \mathbb{R} \times \acc{0,1} | 1 \leqslant i \leqslant N } .. image:: rnimg/classificationnd.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_4_densite.rst b/_doc/c_ml/rn/rn_4_densite.rst similarity index 84% rename from _doc/sphinxdoc/source/c_ml/rn/rn_4_densite.rst rename to _doc/c_ml/rn/rn_4_densite.rst index ca315138..79e7fe49 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_4_densite.rst +++ b/_doc/c_ml/rn/rn_4_densite.rst @@ -2,24 +2,21 @@ Démonstration du théorème de la densité des réseaux de neurones =============================================================== -.. contents:: - :local: - .. _rn_enonce_probleme_regression: Formulation du problème de la régression ++++++++++++++++++++++++++++++++++++++++ Soient deux variables aléatoires continues -:math:`\pa{X,Y} \in \R^p \times \R^q \sim \loi` quelconque, +:math:`\pa{X,Y} \in \mathbb{R}^p \times \mathbb{R}^q \sim \loi` quelconque, la résolution du problème de :ref:`régression ` est l'estimation de la fonction :math:`\esp(Y|X) = F\pa{X}`. Pour cela, on dispose d'un ensemble de points -:math:`A = \acc{ \pa{X_{i},Y_{i}} \sim \loi | 1 \infegal i \infegal N }`. +:math:`A = \acc{ \pa{X_{i},Y_{i}} \sim \loi | 1 \leqslant i \leqslant N }`. -Soit :math:`f : \R^M \times \R^p \longrightarrow \R^q` une fonction, on définit +Soit :math:`f : \mathbb{R}^M \times \mathbb{R}^p \longrightarrow \mathbb{R}^q` une fonction, on définit :math:`\forall i \in \intervalle{1}{N}, \; \widehat{Y_{i}^{W}} = f \pa{W,X_{i}}`. -:math:`\widehat{Y_{i}^{W}}` est appelée la valeur prédite pour `X_{i}`. +On appelle aussi :math:`\widehat{Y_{i}^{W}}` la valeur prédite pour :math:`X_{i}`. On pose alors :math:`\epsilon_{i}^{W} = Y_{i} - \widehat{Y_{i}^{W}} = Y_{i} - f \pa{W,X_{i}}`. @@ -28,7 +25,7 @@ Les résidus sont supposés et suivant une loi normale :math:`\forall i \in \intervalle{1}{N}, \; \epsilon_{i}^{W} \sim \loinormale{\mu_{W}}{\sigma_{W}}` La vraisemblance d'un échantillon -:math:`\pa{Z_i}_{1\infegal i \infegal N}`, +:math:`\pa{Z_i}_{1\leqslant i \leqslant N}`, où les :math:`Z_i` sont indépendantes entre elles et suivent la loi de densité :math:`f \pa{z | \theta}` est la densité du vecteur :math:`\vecteur{Z_1}{Z_N}` qu'on exprime @@ -65,9 +62,9 @@ qui maximisent la vraisemblance :math:`L_W` sont : :label: rn_eqn_regression_1 \begin{array}{rcl} - \overset{*}{W} &=& \underset{W \in \R^M}{\arg \min} \sum_{i=1}^{N} + \overset{*}{W} &=& \underset{W \in \mathbb{R}^M}{\arg \min} \sum_{i=1}^{N} \pa {Y_{i} - \widehat{Y_{i}^W}}^2 \\ - &=& \underset{W \in \R^M}{\arg \min} \sum_{i=1}^{N} + &=& \underset{W \in \mathbb{R}^M}{\arg \min} \sum_{i=1}^{N} \pa {Y_{i} - f \pa{W,X_{i}}}^2 \end{array} @@ -76,7 +73,7 @@ l'équation :eq:`rn_eqn_regression_1` alors l'estimateur défini par :math:`f` est sans biais Il suffit pour s'en convaincre de poser :math:`g = f + \alpha` avec -:math:`\alpha \in \R` et de vérifier que la valeur optimale pour +:math:`\alpha \in \mathbb{R}` et de vérifier que la valeur optimale pour :math:`\alpha` est :math:`\alpha = - \frac{1}{N}\, \sum_{i=1}^{N} \, \left. Y_i - f\pa{W,X_i} \right.`. L'estimateur minimise la vraisemblance :math:`L_W`. @@ -89,13 +86,13 @@ peut généralisée par :eq:`rn_eqn_regression_2`. :label: rn_eqn_regression_2 \begin{array}{rcl} - \overset{*}{W} &=& \underset{W \in \R^M}{\arg \min} \sum_{i=1}^{N} + \overset{*}{W} &=& \underset{W \in \mathbb{R}^M}{\arg \min} \sum_{i=1}^{N} e\pa {Y_{i} - \widehat{Y_{i}^W}} \\ - &=& \underset{W \in \R^M}{\arg \min} \sum_{i=1}^{N} + &=& \underset{W \in \mathbb{R}^M}{\arg \min} \sum_{i=1}^{N} e\pa{Y_{i} - f \pa{W,X_{i}}} \end{array} -Où la fonction :math:`e : \R^q \in \R` est appelée fonction d'erreur. +Où la fonction :math:`e : \mathbb{R}^q \in \mathbb{R}` est appelée fonction d'erreur. Densité des réseaux de neurones +++++++++++++++++++++++++++++++ @@ -141,30 +138,30 @@ il faut inclure des polynômes de degré plus élevé que ceux déjà employés :math:`\left( x\rightarrow 1-\frac{2}{1+e^{x}}\right)`, une couche de sortie dont la fonction de seuil est linéaire Soit :math:`F_{p}^{q}` l'ensemble des fonctions continues de - :math:`C\subset\R^{p}\longrightarrow\R^{q}` avec :math:`C` + :math:`C\subset\mathbb{R}^{p}\longrightarrow\mathbb{R}^{q}` avec :math:`C` compact muni de la norme :math:`\left\| f\right\| =\underset{x\in C}{\sup}\left\| f\left( x\right) \right\|` Alors :math:`E_{p}^{q}` est dense dans :math:`F_{p}^{q}`. - + La démonstration de ce théorème nécessite deux lemmes. Ceux-ci utilisent la définition usuelle du produit scalaire -sur :math:`\R^p` défini par -:math:`\pa{x,y} = \pa{\vecteurno{x_1}{x_p},\vecteurno{y_1}{y_p}} \in \R^{2p} \longrightarrow +sur :math:`\mathbb{R}^p` défini par +:math:`\pa{x,y} = \pa{\vecteurno{x_1}{x_p},\vecteurno{y_1}{y_p}} \in \mathbb{R}^{2p} \longrightarrow \left\langle x,y \right\rangle = \sum_{i=1}^{p} x_i y_i`. et la norme infinie : -:math:`x = \vecteur{x_1}{x_p} \in \R^p \longrightarrow \norm{x} = +:math:`x = \vecteur{x_1}{x_p} \in \mathbb{R}^p \longrightarrow \norm{x} = \underset{i \in \intervalle{1}{p}}{\max} x_i`. Toutes les normes sont `équivalentes `_ -sur :math:`\R^p`. +sur :math:`\mathbb{R}^p`. .. mathdef:: :title: approximation d'une fonction créneau :lid: theoreme_densite_lemme_a :tag: Corollaire - Soit :math:`C \subset \R^p, \; C= \acc { \vecteur{y_1}{y_p} \in \R^p \, | \forall i\in \intervalle{1}{p},\, 0 \leqslant y_{i}\leqslant 1 }`, + Soit :math:`C \subset \mathbb{R}^p, \; C= \acc { \vecteur{y_1}{y_p} \in \mathbb{R}^p \, | \forall i\in \intervalle{1}{p},\, 0 \leqslant y_{i}\leqslant 1 }`, alors : .. math:: @@ -172,20 +169,20 @@ sur :math:`\R^p`. \begin{array}{l} \forall \varepsilon > 0, \; \forall \alpha>0, \; \exists n \in \N^*, \; \exists \vecteur{x_1}{x_n} - \in\left( \R^p\right) ^{n}, \; \exists - \vecteur{\gamma_1}{\gamma_n} \in \R^n \text{ tels que } \forall x\in \R^p, \\ \\ + \in\left( \mathbb{R}^p\right) ^{n}, \; \exists + \vecteur{\gamma_1}{\gamma_n} \in \mathbb{R}^n \text{ tels que } \forall x\in \mathbb{R}^p, \\ \\ \begin{array}{ll} & \left| \underset{i=1}{\overset{n}{\sum}}\dfrac{\gamma_i} {1+e^{\left\langle x_{i},x\right\rangle +b_{i}}}-\indicatrice{x\in C }\right| \leqslant1 \\ \\ \text{ et } & \underset{y\in Fr\left( C\right) }{\inf }\left\| x-y\right\| > - \alpha\Rightarrow\left| \underset{i=1}{\overset + \alpha\mathbb{R}ightarrow\left| \underset{i=1}{\overset {n}{\sum}}\dfrac{\gamma_i}{1+e^{\left\langle x_{i},x\right\rangle +b_{i}}} -\indicatrice{x\in C}\right| \leqslant\varepsilon \end{array} \end{array} - - + + **Démonstration du corollaire** *Partie 1* @@ -213,7 +210,7 @@ Soit :math:`\alpha>0` et :math:`1\geqslant\varepsilon>0, \, k>0`, On pose :math:`f\left( y_{1},...,y_{p}\right) =\underset{i=1}{\overset{p}{\prod}} \dfrac{1}{1+e^{-ky_{i}}}\underset{i=1}{\overset{p}{\prod}}\dfrac {1}{1+e^{-k\left( 1-y_{i}\right)}}` -d'après sa définition, :math:`0 \infegal f\left( y_{1},...,y_{p}\right) \infegal 1`. +d'après sa définition, :math:`0 \leqslant f\left( y_{1},...,y_{p}\right) \leqslant 1`. Pour :math:`k \supegal k_0 \pa{\epsilon,\alpha,2p}` obtenu dans la partie précédente : @@ -222,7 +219,7 @@ obtenu dans la partie précédente : \underset{_{i\in\left\{ 1,...,p\right\}}}{\inf} \cro { \min\left\{ \left| y_{i}\right| ,\left| 1-y_{i}\right| \right\} } >\alpha - \Longrightarrow\left\| f\left( y_{1},...,y_{p}\right) - \indicatrice{x\in C}\right\| \infegal\varepsilon + \Longrightarrow\left\| f\left( y_{1},...,y_{p}\right) - \indicatrice{x\in C}\right\| \leqslant\varepsilon *Partie 3* @@ -268,20 +265,20 @@ Il existe :math:`n \in \N` tel qu'il soit possible d'écrire :math:`f` sous la f :lid: theoreme_densite_lemme_b :tag: Corollaire - Soit :math:`C\subset\R^p` compact, alors : + Soit :math:`C\subset\mathbb{R}^p` compact, alors : .. math:: \begin{array}{c} \forall\varepsilon>0, \; \forall\alpha>0, \; \exists\left( x_{1},...,x_{n}\right) - \in\left( \R^{p}\right)^{n}, \; \exists\left( - b_{1},...,b_{n}\right) \in\R^n \text{ tels que } \forall x\in\R^{p},\\ \\ + \in\left( \mathbb{R}^{p}\right)^{n}, \; \exists\left( + b_{1},...,b_{n}\right) \in\mathbb{R}^n \text{ tels que } \forall x\in\mathbb{R}^{p},\\ \\ \begin{array}{ll} & \left| \sum_{i=1}^n \dfrac{\gamma_i} {1+e^{\left\langle x_{i},x\right\rangle +b_{i}}}-\indicatrice{x\in C }\right| \leqslant1+2\varepsilon^2\\ \\ \text{ et } & \underset{y\in Fr\left( C\right) }{\inf}\left\| x-y\right\| - >\alpha\Rightarrow\left| \sum_{i=1}^n + >\alpha\mathbb{R}ightarrow\left| \sum_{i=1}^n \dfrac{\gamma_i}{1+e^{\left\langle x_{i} ,x\right\rangle +b_{i}}}- \indicatrice{x\in C}\right| \leqslant \varepsilon \end{array} @@ -291,10 +288,10 @@ Il existe :math:`n \in \N` tel qu'il soit possible d'écrire :math:`f` sous la f *Partie 1* -Soit :math:`C_1=\left\{ y=\left( y_{1},...,y_{p}\right) \in\R^p +Soit :math:`C_1=\left\{ y=\left( y_{1},...,y_{p}\right) \in\mathbb{R}^p \,\left| \, \forall i\in\left\{ 1,...,n\right\} ,\,0\leqslant y_{i}\leqslant1\right. \right\}` et :math:`C_{2}^{j}=\left\{ y=\left( -y_{1},...,y_{p}\right) \in\R^p\,\left| \, +y_{1},...,y_{p}\right) \in\mathbb{R}^p\,\left| \, \forall i\neq j,\,0\leqslant y_{i}\leqslant1 \text{ et }1\leqslant y_{j}\leqslant2\right. \right\}` @@ -367,7 +364,7 @@ compacts connexes par arcs et disjoints On démontre le théorème dans le cas où :math:`q=1`. Soit :math:`f` une fonction continue du compact -:math:`C\subset\R^p\rightarrow \R` et soit :math:`\varepsilon>0`. +:math:`C\subset\mathbb{R}^p\rightarrow \mathbb{R}` et soit :math:`\varepsilon>0`. On suppose également que :math:`f` est positive, dans le cas contraire, on pose :math:`f=\underset{\text{fonction positive}}{\underbrace{f-\inf f}}+\inf f`. @@ -437,7 +434,7 @@ telles que : \left( \left\| h_{k}\left( x\right) -\indicatrice{x\in C_{k}}\right\| \leqslant1 \right) \text{ et } \left( \underset{y\in Fr\left( C\right) }{\inf}\left\| x-y\right\| >\dfrac{\alpha}{2}% - \Rightarrow\left\| h_{k}\left( x\right) -\indicatrice{x\in C_{k}}\right\| \leqslant\varepsilon^{2}\right) + \mathbb{R}ightarrow\left\| h_{k}\left( x\right) -\indicatrice{x\in C_{k}}\right\| \leqslant\varepsilon^{2}\right) On en déduit que : @@ -459,7 +456,7 @@ h_{k}\left( x\right)` est de la forme désirée, le théorème est démontré d *Partie 2* Dans le cas :math:`q>1`, on utilise la méthode précédente pour chacune des projections de :math:`f` -dans un repère orthonormé de :math:`\R^{q}`. Il suffit de +dans un repère orthonormé de :math:`\mathbb{R}^{q}`. Il suffit de sommer sur chacune des dimensions. Ce théorème montre qu'il est judicieux de modéliser la fonction @@ -475,7 +472,7 @@ Ce théorème permet de déduire le corollaire suivant : :lid: corollaire_famille_libre Soit :math:`F_{p}` l'ensemble des fonctions continues de - :math:`C\subset\R^{p}\longrightarrow\R` avec :math:`C` + :math:`C\subset\mathbb{R}^{p}\longrightarrow\mathbb{R}` avec :math:`C` compact muni de la norme : :math:`\left\| f\right\| =\underset{x\in C}{\sup}\left\| f\left( x\right) \right\|` Alors l'ensemble :math:`E_{p}` des fonctions sigmoïdes : @@ -483,7 +480,7 @@ Ce théorème permet de déduire le corollaire suivant : .. math:: E_{p} = \acc{ x \longrightarrow 1 - \dfrac{2}{1 + e^{+b}} | y - \in \R^p \text{ et } b \in \R} + \in \mathbb{R}^p \text{ et } b \in \mathbb{R}} est une base de :math:`F_{p}`. @@ -491,17 +488,17 @@ Ce théorème permet de déduire le corollaire suivant : Le théorème de :ref:`densité ` montre que la famille :math:`E_{p}` est une famille génératrice. Il reste à montrer que c'est une -famille libre. Soient :math:`\pa{y_i}_{1 \infegal i \infegal N} \in \pa{\R^p}^N` et -:math:`\pa{b_i}_{1 \infegal i \infegal N} \in \R^N` vérifiant : +famille libre. Soient :math:`\pa{y_i}_{1 \leqslant i \leqslant N} \in \pa{\mathbb{R}^p}^N` et +:math:`\pa{b_i}_{1 \leqslant i \leqslant N} \in \mathbb{R}^N` vérifiant : :math:`i \neq j \Longrightarrow y_i \neq y_j \text{ ou } b_i \neq b_j`. -Soit :math:`\pa{\lambda_i}_{1 \infegal i \infegal N} \in \R^N`, il faut montrer que : +Soit :math:`\pa{\lambda_i}_{1 \leqslant i \leqslant N} \in \mathbb{R}^N`, il faut montrer que : .. math:: :nowrap: :label: corollaire_demo_recurrence_base \begin{eqnarray} - \forall x \in \R^p, \; \sum_{i=1}^{N} \lambda_i \pa{ 1 - \dfrac{2}{1 + e^{+b_i} }} = 0 + \forall x \in \mathbb{R}^p, \; \sum_{i=1}^{N} \lambda_i \pa{ 1 - \dfrac{2}{1 + e^{+b_i} }} = 0 \Longrightarrow \forall i \, \lambda_i = 0 \end{eqnarray} @@ -536,11 +533,11 @@ On cherche à résoude le système de :math:`N` équations à :math:`N` inconnue \end{eqnarray} On note le vecteur -:math:`\Lambda = \pa{\lambda_i}_{ 1 \infegal i \infegal N}` et :math:`M` la matrice : +:math:`\Lambda = \pa{\lambda_i}_{ 1 \leqslant i \leqslant N}` et :math:`M` la matrice : .. math:: - M= \pa{m_{ij}}_{ 1 \infegal i,j \infegal N} = \pa{ 1 - \dfrac{2}{1 + e^{}} }_{ 1 \infegal i,j \infegal N} + M= \pa{m_{ij}}_{ 1 \leqslant i,j \leqslant N} = \pa{ 1 - \dfrac{2}{1 + e^{}} }_{ 1 \leqslant i,j \leqslant N} L'équation :eq:`rn_coro_eq_1` est équivalente à l'équation matricielle : :math:`M\Lambda = 0`. On effectue une itération du pivot de Gauss. @@ -552,23 +549,23 @@ L'équation :eq:`rn_coro_eq_1` est équivalente à l'équation matricielle : &\Longleftrightarrow& \left\{ \begin{array}{ccllllllll} \lambda_1 m_{11} &+& \lambda_2 & m_{12} &+& \ldots &+& \lambda_N & m_{1N} & = 0 \\ 0 &+& \lambda_2 & \pa{ m_{22} m_{11} - m_{12} m_{21} } - &+& \ldots &+& \lambda_N & \pa{ m_{2N} m_{11} - m_{1N} m_{21} } - & = 0 \\ + &+& \ldots &+& \lambda_N & \pa{ m_{2N} m_{11} - m_{1N} m_{21} } + & = 0 \\ \ldots \\ 0 &+& \lambda_2 & \pa{ m_{N2} m_{11} - m_{12} m_{N1} } &+& \ldots - &+& \lambda_N & \pa{ m_{NN} m_{11} - m_{1N} m_{N1} } & = 0 + &+& \lambda_N & \pa{ m_{NN} m_{11} - m_{1N} m_{N1} } & = 0 \end{array} \right. \end{array} -On note :math:`\Lambda_* = \pa{\lambda_i}_{ 2 \infegal i \infegal N}` et +On note :math:`\Lambda_* = \pa{\lambda_i}_{ 2 \leqslant i \leqslant N}` et :math:`\Delta_*`, :math:`M_*` les matrices : .. math:: \begin{array}{rcl} - M_* &=& \pa{m_{ij}}_{ 2 \infegal i,j \infegal N} \\ - \Delta_* &=& \pa{ m_{1j} \, m_{i1} }_{ 2 \infegal i,j \infegal N} + M_* &=& \pa{m_{ij}}_{ 2 \leqslant i,j \leqslant N} \\ + \Delta_* &=& \pa{ m_{1j} \, m_{i1} }_{ 2 \leqslant i,j \leqslant N} \end{array} Donc :eq:`rn_coro_eq_1` est équivalent à : @@ -588,7 +585,7 @@ Donc :eq:`rn_coro_eq_1` est équivalent à : \end{eqnarray} Il est possible de choisir :math:`X_1\pa{\alpha} = \pa{\alpha x_1, 1}` -de telle sorte qu'il existe une suite :math:`\pa{s_l}_{ 1 \infegal l \infegal N } \in \acc{-1,1}^{N}` +de telle sorte qu'il existe une suite :math:`\pa{s_l}_{ 1 \leqslant l \leqslant N } \in \acc{-1,1}^{N}` avec :math:`s_1=1` et vérifiant : .. math:: @@ -604,14 +601,14 @@ On définit : \begin{array}{rll} U_* &=& \vecteur{m_{21}}{m_{N1}}' \\ V_* &=& \vecteur{s_2 \, m_{21}}{s_N \, m_{N1}}' \\ - \text{ et la matrice } L_* &=& \pa{V_*}_ { 2 \infegal i \infegal N } \text{ dont les $N-1$ colonnes sont identiques } + \text{ et la matrice } L_* &=& \pa{V_*}_ { 2 \leqslant i \leqslant N } \text{ dont les $N-1$ colonnes sont identiques } \end{array} On vérifie que : .. math:: - \underset{\alpha \longrightarrow +\infty} {\lim } \Delta\pa{\alpha} = V_* + \underset{\alpha \longrightarrow +\infty} {\lim } \Delta\pa{\alpha} = V_* On obtient, toujours pour :eq:`rn_coro_eq_1` : @@ -621,16 +618,16 @@ On obtient, toujours pour :eq:`rn_coro_eq_1` : \begin{eqnarray} &\Longleftrightarrow& \left\{ \begin{array}{cclc} - \lambda_1 m_{11}\pa{\alpha} &+& - \lambda_2 m_{12}\pa{\alpha} + \ldots + \lambda_N m_{1N}\pa{\alpha} &= 0 \\ + \lambda_1 m_{11}\pa{\alpha} &+& + \lambda_2 m_{12}\pa{\alpha} + \ldots + \lambda_N m_{1N}\pa{\alpha} &= 0 \\ 0 &+& \cro{m_{11}\pa{\alpha} M_* - - \pa{ L_* + \pa{ \Delta_*\pa{\alpha} - L_* } } } - \Lambda_* & = 0 + \pa{ L_* + \pa{ \Delta_*\pa{\alpha} - L_* } } } + \Lambda_* & = 0 \end{array} \right. \\ \nonumber\\ &\Longleftrightarrow& \left\{ \begin{array}{cclc} - \lambda_1 m_{11}\pa{\alpha} &+& - \lambda_2 m_{12}\pa{\alpha} + \ldots + \lambda_N m_{1N}\pa{\alpha} &= 0 \\ + \lambda_1 m_{11}\pa{\alpha} &+& + \lambda_2 m_{12}\pa{\alpha} + \ldots + \lambda_N m_{1N}\pa{\alpha} &= 0 \\ 0 &+& \pa{m_{11}\pa{\alpha} M_* - L_* } \Lambda_* + \pa{ \Delta_*\pa{\alpha} - L_* } \Lambda_* & = 0 \end{array} @@ -643,7 +640,7 @@ On étudie la limite lorsque :math:`\alpha \longrightarrow +\infty` : \begin{array}{crcl} & \pa{ \Delta_*\pa{\alpha} - L_* } & - \underset{ \alpha \rightarrow +\infty}{ \longrightarrow} & 0 \\ + \underset{ \alpha \rightarrow +\infty}{ \longrightarrow} & 0 \\ \Longrightarrow & \pa{m_{11}\pa{\alpha} M_* - L_* } \Lambda_* & \underset{ \alpha \rightarrow +\infty}{ \longrightarrow} & 0\\ \Longrightarrow & \pa{M_* - L_* } \Lambda_* & = & 0\\ @@ -675,7 +672,7 @@ C'est une des raisons pour lesquelles les réseaux de neurones ont du succès. Le théorème :ref:`densité ` et le corollaire :ref:`famille libre ` sont aussi vraies pour des fonctions du type exponentielle : -:math:`\pa{y,b} \in \R^p \times \R \longrightarrow e^{-\pa{+b}^2}`. +:math:`\pa{y,b} \in \mathbb{R}^p \times \mathbb{R} \longrightarrow e^{-\pa{+b}^2}`. Maintenant qu'il est prouvé que les réseaux de neurones conviennent pour modéliser :math:`f` dans l'équation :eq:`rn_eqn_regression_2`, il reste à étudier les méthodes qui permettent de trouver diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_5_newton.rst b/_doc/c_ml/rn/rn_5_newton.rst similarity index 90% rename from _doc/sphinxdoc/source/c_ml/rn/rn_5_newton.rst rename to _doc/c_ml/rn/rn_5_newton.rst index 414f8f45..0c06ff04 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_5_newton.rst +++ b/_doc/c_ml/rn/rn_5_newton.rst @@ -2,9 +2,6 @@ Descente de gradient ==================== -.. contents:: - :local: - Lorsqu'un problème d'optimisation n'est pas soluble de manière déterministe, il existe des algorithmes permettant de trouver une solution approchée à condition toutefois que la fonction à maximiser ou minimiser soit dérivable, @@ -12,7 +9,7 @@ ce qui est le cas des réseaux de neurones. Plusieurs variantes seront proposée regroupées sous le terme de descente de gradient. Quelques lectures : -* `An overview of gradient descent optimization algorithms `_ +* `An overview of gradient descent optimization algorithms `_ * `Implementing a Neural Network from Scratch in Python – An Introduction `_ .. _optimisation_newton: @@ -20,8 +17,8 @@ Quelques lectures : Algorithme et convergence +++++++++++++++++++++++++ -Soit :math:`g : \R \dans \R` une fonction dérivable dont il faut trouver -:math:`\overset{*}{x} = \underset{x \in \R}{\arg \min} \; g\pa{x}`, +Soit :math:`g : \mathbb{R} \dans \mathbb{R}` une fonction dérivable dont il faut trouver +:math:`\overset{*}{x} = \underset{x \in \mathbb{R}}{\arg \min} \; g\pa{x}`, le schéma suivant illustre la méthode de descente de gradient dans le cas où :math:`g \pa{x} = x^2`. @@ -35,7 +32,7 @@ L'abscisse à l'itération :math:`t+1` sera :math:`\varepsilon_{t}` est le pas de gradient à l'itération :math:`t`. On suppose maintenant que :math:`g` est une fonction dérivable -:math:`g : \R^q \dans \R` dont il faut trouver le minimum, le théorème suivant démontre +:math:`g : \mathbb{R}^q \dans \mathbb{R}` dont il faut trouver le minimum, le théorème suivant démontre la convergence de l'algorithme de descente de gradient à condition que certaines hypothèses soient vérifiées. Une généralisation de ce théorème est présentée dans [Driancourt1996]_. @@ -47,25 +44,25 @@ que certaines hypothèses soient vérifiées. Une généralisation de ce théor [Bottou1991]_ - Soit une fonction continue :math:`g : W \in \R^M \dans \R` + Soit une fonction continue :math:`g : W \in \mathbb{R}^M \dans \mathbb{R}` de classe :math:`C^{1}`. On suppose les hypothèses suivantes vérifiées : - * **H1** : :math:`\underset{W\in \R^q}{\arg\min} \; + * **H1** : :math:`\underset{W\in \mathbb{R}^q}{\arg\min} \; g\left( W\right) =\left\{ W^{\ast}\right\}` est un singleton * **H2** : :math:`\forall\varepsilon>0, \; \underset{\left| W-W^{\ast}\right| >\varepsilon}{\inf}\left[ \left( W-W^{\ast}\right) ^{\prime}.\nabla g\left( W\right) \right] >0` - * **H3** : :math:`\exists\left( A,B\right) \in \R^2` tels que :math:`\forall W\in\R^p,\; \left\| + * **H3** : :math:`\exists\left( A,B\right) \in \mathbb{R}^2` tels que :math:`\forall W\in\mathbb{R}^p,\; \left\| \nabla g\left( W\right) \right\| ^{2}\leqslant A^{2}+B^{2}\left\| W-W^{\ast}\right\| ^{2}` * **H4** : la suite :math:`\left( \varepsilon_{t}\right)_{t\geqslant0}` vérifie, - :math:`\forall t>0, \; \varepsilon_{t}\in \R_{+}^{\ast}` + :math:`\forall t>0, \; \varepsilon_{t}\in \mathbb{R}_{+}^{\ast}` et :math:`\sum_{t\geqslant 0}\varepsilon_{t}=+\infty`, :math:`\sum_{t\geqslant 0}\varepsilon_{t}^{2}<+\infty` Alors la suite :math:`\left( W_{t}\right) _{t\geqslant 0}` construite de la manière suivante - :math:`W_{0} \in \R^M`, :math:`\forall t\geqslant0` : + :math:`W_{0} \in \mathbb{R}^M`, :math:`\forall t\geqslant0` : :math:`W_{t+1}=W_{t}-\varepsilon_{t}\,\nabla g\left( W_{t}\right)` vérifie :math:`\lim_{ t \dans+\infty}W_{t}=W^{\ast}`. @@ -80,10 +77,10 @@ de fonctions sigmoïdes que sont les réseaux de neurones à une couche cachée. *Partie 1* Soit la suite :math:`u_{t}=\ln\left( 1+\varepsilon_{t}^{2}x^{2}\right)` -avec :math:`x\in\R`, comme :math:`\sum_{t\geqslant 0} \varepsilon_{t}^{2} < +\infty, \; +avec :math:`x\in\mathbb{R}`, comme :math:`\sum_{t\geqslant 0} \varepsilon_{t}^{2} < +\infty, \; u_{t}\thicksim\varepsilon_{t}^{2}x^{2}`, on a :math:`\sum_{t\geqslant 0} u_{t} < +\infty`. -Par conséquent, si :math:`v_{t}=e^{u_{t}}` alors :math:`\prod_{t=1}^T v_{t}\overset{T \rightarrow \infty}{\longrightarrow}D \in \R`. +Par conséquent, si :math:`v_{t}=e^{u_{t}}` alors :math:`\prod_{t=1}^T v_{t}\overset{T \rightarrow \infty}{\longrightarrow}D \in \mathbb{R}`. *Partie 2* @@ -96,7 +93,7 @@ Donc : \begin{eqnarray} h_{t+1} -h_{t} &=&\left\| W_{t}-\varepsilon_{t}\,\nabla g\left( W_{t}\right) -W^{\ast }\right\| - ^{2}-\left\|W_{t}-W^{\ast}\right\| ^{2} + ^{2}-\left\|W_{t}-W^{\ast}\right\| ^{2} \end{eqnarray} Par conséquent : @@ -125,7 +122,7 @@ alors en multipliant des deux côtés par :math:`\pi_{t+1}`, on obtient : \text{d'où }\pi_{q+1}h_{q+1}-\pi_{p}h_{p} &\leqslant& \sum_{t=p}^q \varepsilon_{t}^{2}\,A^{2}\pi_{t+1} \leqslant \sum_{t=p}^{q} \varepsilon_{t}^{2} \, A^{2}\Pi \leqslant \sum_{t=p}^{q} \varepsilon_{t}^{2}\,A^{2}\Pi - \underset{t \longrightarrow + \underset{t \longrightarrow \infty}{\longrightarrow} 0 \end{array} @@ -224,7 +221,7 @@ puisque le gradient s'en déduit facilement. La dernière couche du réseau de n &=& \partialfrac{e}{z_{C,i}} \pa{W,X} f'_{c,i}\pa{y_{C,i}} \nonumber \end{eqnarray} -Pour les autres couches :math:`c` telles que :math:`1 \infegal c \infegal C-1`, on a : +Pour les autres couches :math:`c` telles que :math:`1 \leqslant c \leqslant C-1`, on a : .. math:: :nowrap: @@ -302,3 +299,10 @@ du perceptron classique pour faire des choses hors des clous. Je la laisse comme ça sans trop d'explications. .. image:: rnimg/neurone2.jpg + +L'idée de la rétropropagation : en supposant connu le gradient de l'erreur +par rapport à la sortie, comment en déduir le gradient par rapport +aux coefficients du réseau puis comment le propager à chaque entrée +de sorte qu'il puisse être transmis aux neurones de la couche inférieure. + +.. image:: rnimg/backp.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_6_apprentissage.rst b/_doc/c_ml/rn/rn_6_apprentissage.rst similarity index 97% rename from _doc/sphinxdoc/source/c_ml/rn/rn_6_apprentissage.rst rename to _doc/c_ml/rn/rn_6_apprentissage.rst index af7b8bb9..4cefff1a 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_6_apprentissage.rst +++ b/_doc/c_ml/rn/rn_6_apprentissage.rst @@ -2,9 +2,6 @@ Apprentissage d'un réseau de neurones ===================================== -.. contents:: - :local: - Le terme apprentissage est encore inspiré de la biologie et se traduit par la minimisation de la fonction :eq:`equation_fonction_erreur_g` où :math:`f` est un réseau de neurone défini par un :ref:`perceptron `. @@ -23,7 +20,7 @@ et proposent une convergence vers un minimum local. .. math:: :label: rn_suite_epsilon_train - \forall t>0,\quad\varepsilon_{t}\in \R_{+}^{\ast} \text{ et } + \forall t>0,\quad\varepsilon_{t}\in \mathbb{R}_{+}^{\ast} \text{ et } \sum_{t\geqslant0}\varepsilon_{t}=+\infty,\quad \sum_{t\geqslant0}\varepsilon_{t}^{2}<+\infty @@ -67,7 +64,7 @@ la section :ref:`optimisation_newton` : :tag: Algorithme *Initialiation* - + Le premier jeu de coefficients :math:`W_0` du réseau de neurones est choisi aléatoirement. @@ -117,7 +114,7 @@ cette direction est appelée gradient conjugué (voir [Moré1977]_). Ces techniques sont basées sur une approximation du second degré de la fonction à minimiser. On note :math:`M` le nombre de coefficients du réseau de neurones (biais compris). -Soit :math:`h: \R^{M} \dans \R` la fonction d'erreur associée au réseau de neurones : +Soit :math:`h: \mathbb{R}^{M} \dans \mathbb{R}` la fonction d'erreur associée au réseau de neurones : :math:`h \pa {W} = \sum_{i} e \pa{Y_i,f \pa{ W,X_i} }`. Au voisinage de :math:`W_{0}`, un développement limité donne : @@ -132,7 +129,7 @@ admet un minimum local si :math:`\frac{\partial^{2}h\left( W_{0}\right) }{\parti est définie positive strictement. *Rappel :* :math:`\dfrac{\partial^{2}h\left( W_{0}\right) }{\partial W^{2}}` -est définie positive strictement :math:`\Longleftrightarrow\forall Z\in\R^{N},\; Z\neq0\Longrightarrow +est définie positive strictement :math:`\Longleftrightarrow\forall Z\in\mathbb{R}^{N},\; Z\neq0\Longrightarrow Z^{\prime}\dfrac{\partial ^{2}h\left( W_{0}\right) }{\partial W^{2}}Z>0`. Une matrice symétrique définie strictement positive est inversible, @@ -144,7 +141,7 @@ et le minimum est atteint pour la valeur : \begin{eqnarray} W_{\min}= W_0 + \frac{1}{2}\left[ \dfrac{\partial^{2}h\left( W_{0}\right) } - {\partial W^{2}}\right] ^{-1}\left[ \frac{\partial h\left( W_{0}\right) + {\partial W^{2}}\right] ^{-1}\left[ \frac{\partial h\left( W_{0}\right) }{\partial W}\right] \nonumber \end{eqnarray} @@ -237,7 +234,7 @@ itération en tenant de l'information apportée par chaque déplacement. *Mise à jour de la marice :math:`B_t`* - | si :math:`t - i \supegal M` ou :math:`g'_{t-1} B_{t-1} g_{t-1} \infegal 0` ou :math:`g'_{t-1} B_{t-1} \pa {g_t - g_{t-1}} \infegal 0` + | si :math:`t - i \supegal M` ou :math:`g'_{t-1} B_{t-1} g_{t-1} \leqslant 0` ou :math:`g'_{t-1} B_{t-1} \pa {g_t - g_{t-1}} \leqslant 0` | :math:`B_{t} \longleftarrow I_M` | :math:`i \longleftarrow t` | sinon @@ -308,17 +305,17 @@ par celle-ci : Voir :ref:`BFGS `. - + L'algorithme DFP est aussi un algorithme de gradient conjugué qui propose une approximation différente de l'inverse de la dérivée seconde. - + .. mathdef:: :title: DFP :lid: rn_algo_dfp :tag: Algorithme Le nombre de paramètre de la fonction :math:`f` est :math:`M`. - + *Initialisation* Le premier jeu de coefficients :math:`W_0` @@ -356,7 +353,7 @@ qui propose une approximation différente de l'inverse de la dérivée seconde. *Mise à jour de la matrice :math:`B_t`* - | si :math:`t - i \supegal M` ou :math:`g'_{t-1} B_{t-1} g_{t-1} \infegal 0` ou :math:`g'_{t-1} B_{t-1} \pa {g_t - g_{t-1}} \infegal 0` + | si :math:`t - i \supegal M` ou :math:`g'_{t-1} B_{t-1} g_{t-1} \leqslant 0` ou :math:`g'_{t-1} B_{t-1} \pa {g_t - g_{t-1}} \leqslant 0` | :math:`B_{t} \longleftarrow I_M` | :math:`i \longleftarrow t` | sinon @@ -429,11 +426,11 @@ l'utilisation de quasi-martingales et est une convergence presque sûre [Bottou1 | :math:`t \longleftarrow t+1` *Terminaison* - + Si :math:`\frac{E_t}{E_{t-1}} \approx 1` alors l'apprentissage a convergé sinon retour au calcul du gradient. - + En pratique, il est utile de converser le meilleur jeu de coefficients : :math:`W^* = \underset{u \supegal 0}{\arg \min} \; E_{u}` diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_7_clas2.rst b/_doc/c_ml/rn/rn_7_clas2.rst similarity index 83% rename from _doc/sphinxdoc/source/c_ml/rn/rn_7_clas2.rst rename to _doc/c_ml/rn/rn_7_clas2.rst index 8639d0ab..449014cb 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_7_clas2.rst +++ b/_doc/c_ml/rn/rn_7_clas2.rst @@ -4,13 +4,10 @@ Classification ============== -.. contents:: - :local: - Vraisemblance d'un échantillon de variable suivant une loi multinomiale +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -Soit :math:`\pa{Y_i}_{1 \infegal i \infegal N}` +Soit :math:`\pa{Y_i}_{1 \leqslant i \leqslant N}` un échantillon de variables aléatoires i.i.d. suivant la loi multinomiale :math:`\loimultinomiale { \vecteurno{p_1}{p_C}}`. On définit :math:`\forall k \in \intervalle{1}{C}, \; d_k = \frac{1}{N} @@ -58,7 +55,7 @@ est la solution du problème suivant : .. math:: \begin{array}{l} - \vecteur{p_1^*}{p_N^*} = \underset{ \vecteur{p_1}{p_C} \in \R^C }{\arg \max} + \vecteur{p_1^*}{p_N^*} = \underset{ \vecteur{p_1}{p_C} \in \mathbb{R}^C }{\arg \max} \sum_{k=1}^{C} d_k \ln p_k \medskip \\ \quad \text{avec } \left \{ \begin{array}{l} @@ -101,8 +98,8 @@ La fonction :math:`x \longrightarrow \ln x` est concave, d'où : \begin{eqnarray*} \Delta &=& \sum_{k=1}^{C} d_k \ln p_k - \sum_{k=1}^{C} d_k \ln d_k \\ &=& \sum_{k=1}^{C} d_k \pa{ \ln p_k - \ln d_k } = \sum_{k=1}^{C} d_k \ln \frac{p_k}{d_k} \\ - &\infegal& \ln \pa{ \sum_{k=1}^{C} d_k \frac{p_k}{d_k} } = \ln \pa { \sum_{k=1}^{C} p_k } = \ln 1 = 0 \\ - &\infegal& 0 + &\leqslant& \ln \pa{ \sum_{k=1}^{C} d_k \frac{p_k}{d_k} } = \ln \pa { \sum_{k=1}^{C} p_k } = \ln 1 = 0 \\ + &\leqslant& 0 \end{eqnarray*} La distance de KullBack-Leiber compare deux distributions de @@ -121,14 +118,14 @@ n'est pas nécessaire de connaître la classe d'appartenance de chaque exemple mais seulement les probabilités d'appartenance de cet exemple à chacune des classes. -Soient une variable aléatoire continue :math:`X \in \R^p` +Soient une variable aléatoire continue :math:`X \in \mathbb{R}^p` et une variable aléatoire discrète multinomiale :math:`Y \in \intervalle{1}{C}`, on veut estimer la loi de : .. math:: Y|X \sim \loimultinomiale {p_1\pa{W,X},\dots , p_C\pa{W,X}} - \text { avec } W \in \R^M + \text { avec } W \in \mathbb{R}^M Le vecteur :math:`\vecteur{p_1\pa{W,X}}{p_C\pa{W,X}}` est une fonction :math:`f` de :math:`\pa{W,X}` où @@ -139,11 +136,11 @@ poids :math:`W` qui correspondent le mieux à l'échantillon : .. math:: - A = \acc{\left. \pa {X_i,y_i=\pa{\eta_i^k}_{1 \infegal k \infegal C}} \in \R^p \times \cro{0,1}^C - \text{ tel que } \sum_{k=1}^{c}y_i^k=1 \right| 1 \infegal i \infegal N } + A = \acc{\left. \pa {X_i,y_i=\pa{\eta_i^k}_{1 \leqslant k \leqslant C}} \in \mathbb{R}^p \times \cro{0,1}^C + \text{ tel que } \sum_{k=1}^{c}y_i^k=1 \right| 1 \leqslant i \leqslant N } -On suppose que les variables :math:`\pa{Y_i|X_i}_{1 \infegal i \infegal N}` -suivent les lois respectives :math:`\pa{\loimultinomiale{y_i}}_{1 \infegal i \infegal N}` +On suppose que les variables :math:`\pa{Y_i|X_i}_{1 \leqslant i \leqslant N}` +suivent les lois respectives :math:`\pa{\loimultinomiale{y_i}}_{1 \leqslant i \leqslant N}` et sont indépendantes entre elles, la vraisemblance du modèle vérifie d'après l'équation :eq:`rn_equation_vraisemblance_kullbck_leiber` : @@ -155,18 +152,18 @@ vérifie d'après l'équation :eq:`rn_equation_vraisemblance_kullbck_leiber` : \ln L_W & \propto & \sum_{i=1}^{N}\sum_{k=1}^{C} \eta_i^k \ln\cro { p_k\pa{W,X_i}} \end{eqnarray*} -La solution du problème :math:`\overset{*}{W} = \underset{W \in \R^l}{\arg \max} \; L_W` +La solution du problème :math:`\overset{*}{W} = \underset{W \in \mathbb{R}^l}{\arg \max} \; L_W` est celle d'un problème d'optimisation sous contrainte. Afin de contourner ce problème, on définit la fonction :math:`f` : .. math:: \begin{array}{l} - f : \R^M \times \R^p \longrightarrow \R^C \\ - \forall \pa{W,x} \in \R^M \times \R^p, \; f\pa{W,x} = \pa{f_1\pa{W,x}}, \dots , + f : \mathbb{R}^M \times \mathbb{R}^p \longrightarrow \mathbb{R}^C \\ + \forall \pa{W,x} \in \mathbb{R}^M \times \mathbb{R}^p, \; f\pa{W,x} = \pa{f_1\pa{W,x}}, \dots , f_C\pa{W,x} \vspace{0.5ex}\\ \text{et }\forall i \in \intervalle{1}{N}, \; \forall k \in \intervalle{1}{C}, \; - p^k \pa{W,X_i} = \dfrac{e^{f_k\pa{W,X_i}}} + p^k \pa{W,X_i} = \dfrac{e^{f_k\pa{W,X_i}}} {\sum_{l=1}^{C}e^{f_l\pa{W,X_i}}} \end{array} @@ -184,13 +181,13 @@ On en déduit que : .. math:: :nowrap: - \begin{eqnarray*} - \ln L_W & \propto & \sum_{i=1}^{N}\sum_{k=1}^{C} \; \eta_i^k \cro{ f_k\pa{W,X_i} - \ln - \cro{\sum_{l=1}^{C}e^{f_l\pa{W,X_i}}}} \\ - \ln L_W & \propto & \sum_{i=1}^{N}\sum_{k=1}^{C} \; \eta_i^k f_k\pa{W,X_i} - - \sum_{i=1}^{N} \ln \cro{\sum_{l=1}^{C}e^{f_l\pa{W,X_i}}} - \underset{=1}{\underbrace{\sum_{k=1}^{C} \eta_i^k}} - \end{eqnarray*} + \begin{eqnarray*} + \ln L_W & \propto & \sum_{i=1}^{N}\sum_{k=1}^{C} \; \eta_i^k \cro{ f_k\pa{W,X_i} - \ln + \cro{\sum_{l=1}^{C}e^{f_l\pa{W,X_i}}}} \\ + \ln L_W & \propto & \sum_{i=1}^{N}\sum_{k=1}^{C} \; \eta_i^k f_k\pa{W,X_i} - + \sum_{i=1}^{N} \ln \cro{\sum_{l=1}^{C}e^{f_l\pa{W,X_i}}} + \underset{=1}{\underbrace{\sum_{k=1}^{C} \eta_i^k}} + \end{eqnarray*} D'où : @@ -216,9 +213,9 @@ Ceci mène à la définition du problème de classification suivant : .. math:: - A = \acc {\left. \pa {X_i,y_i=\pa{\eta_i^k}_{1 \infegal k \infegal C}} \in - \R^p \times \R^C - \text{ tel que } \sum_{k=1}^{c}\eta_i^k=1 \right| 1 \infegal i \infegal N } + A = \acc {\left. \pa {X_i,y_i=\pa{\eta_i^k}_{1 \leqslant k \leqslant C}} \in + \mathbb{R}^p \times \mathbb{R}^C + \text{ tel que } \sum_{k=1}^{c}\eta_i^k=1 \right| 1 \leqslant i \leqslant N } :math:`y_i^k` représente la probabilité que l'élément :math:`X_i` appartiennent à la classe :math:`k` : @@ -229,7 +226,7 @@ Ceci mène à la définition du problème de classification suivant : .. math:: \begin{array}{rcl} - f : \R^M \times \R^p &\longrightarrow& \R^C \\ + f : \mathbb{R}^M \times \mathbb{R}^p &\longrightarrow& \mathbb{R}^C \\ \pa{W,X} &\longrightarrow& \vecteur{f_1\pa{W,X}}{f_p\pa{W,X}} \\ \end{array} @@ -245,21 +242,21 @@ Réseau de neurones adéquat ++++++++++++++++++++++++++ Dans le problème précédent, la maximisation de -:math:`\overset{*}{W} = \underset{W \in \R^M}{\arg \max} \, L_W` +:math:`\overset{*}{W} = \underset{W \in \mathbb{R}^M}{\arg \max} \, L_W` aboutit au choix d'une fonction : .. math:: - X \in \R^p \longrightarrow f(\overset{*}{W},X) \in \R^C + X \in \mathbb{R}^p \longrightarrow f(\overset{*}{W},X) \in \mathbb{R}^C Le réseau de neurones :ref:`suivant ` -:math:`g : \pa{W,X} \in \R^M \times \R^p \longrightarrow \R^C` +:math:`g : \pa{W,X} \in \mathbb{R}^M \times \mathbb{R}^p \longrightarrow \mathbb{R}^C` choisi pour modéliser :math:`f` aura pour sorties : .. math:: \begin{array}{l} - X \in \R^p \longrightarrow g(\overset{*}{W},X) \in \R^C\\ + X \in \mathbb{R}^p \longrightarrow g(\overset{*}{W},X) \in \mathbb{R}^C\\ \forall k \in \intervalle{1}{C}, \; g_k \pa{W,X} = e^{f_k\pa{W,X}} \end{array} @@ -272,7 +269,7 @@ choisi pour modéliser :math:`f` aura pour sorties : On en déduit que la fonction de transert des neurones de la couche de sortie est : :math:`x \longrightarrow e^x`. -La probabilité pour le vecteur :math:`x\in\R^p` +La probabilité pour le vecteur :math:`x\in\mathbb{R}^p` d'appartenir à la classe :math:`k\in\intervalle{1}{C}` est :math:`p_k(\overset{*}{W},x) = \pr{Y=k|x} = \dfrac { g_k(\overset{*}{W},x)} {\sum_{l=1}^{C} g_l(\overset{*}{W},x) }`. @@ -282,10 +279,10 @@ La fonction d'erreur à minimiser est l'opposé de la log-vraisemblance du modè :nowrap: \begin{eqnarray*} - \overset{*}{W} &=& \underset{W \in \R^M}{\arg \min} + \overset{*}{W} &=& \underset{W \in \mathbb{R}^M}{\arg \min} \cro {\sum_{i=1}^{N} \pa { - \sum_{k=1}^{C} \eta_i^k \ln \pa{g_k\pa{W,X_i}} + \ln \cro{ \sum_{l=1}^{C} g_l\pa{W,X_i} }}} \\ - &=& \underset{W \in \R^M}{\arg \min} \cro {\sum_{i=1}^{N} h\pa{W,X_i,\eta_i^k}} + &=& \underset{W \in \mathbb{R}^M}{\arg \min} \cro {\sum_{i=1}^{N} h\pa{W,X_i,\eta_i^k}} \end{eqnarray*} On note :math:`C_{rn}` le nombre de couches du réseau de neurones, @@ -306,7 +303,7 @@ On calcule : Cette équation permet d'adapter l'algorithme de la :ref:`rétropropagation ` décrivant rétropropagation pour le problème de la classification et pour -un exemple :math:`\pa {X,y=\pa{\eta^k}_{1 \infegal k \infegal C}}`. +un exemple :math:`\pa {X,y=\pa{\eta^k}_{1 \leqslant k \leqslant C}}`. Seule la couche de sortie change. .. mathdef:: diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_8_prol.rst b/_doc/c_ml/rn/rn_8_prol.rst similarity index 86% rename from _doc/sphinxdoc/source/c_ml/rn/rn_8_prol.rst rename to _doc/c_ml/rn/rn_8_prol.rst index 545c7c27..0cde1c21 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_8_prol.rst +++ b/_doc/c_ml/rn/rn_8_prol.rst @@ -2,9 +2,6 @@ Prolongements ============= -.. contents:: - :local: - Base d'apprentissage et base de test ++++++++++++++++++++++++++++++++++++ @@ -19,7 +16,7 @@ le modèle appris est de vérifier si l'erreur obtenue sur une base ayant servi à l'apprentissage (ou *base d'apprentissage*) est conservée sur une autre base (ou *base de test*) que le modèle découvre pour la première fois. -Soit :math:`B=\acc{\pa{X_i,Y_i} | 1 \infegal i \infegal N}` +Soit :math:`B=\acc{\pa{X_i,Y_i} | 1 \leqslant i \leqslant N}` l'ensemble des observations disponibles. Cet ensemble est aléatoirement scindé en deux sous-ensembles :math:`B_a` et :math:`B_t` de telle sorte que : @@ -30,7 +27,7 @@ de telle sorte que : B_a \neq \emptyset \text{ et } B_t \neq \emptyset \\ B_a \cup B_t = B \text{ et } B_a \cap B_t = \emptyset \\ \frac{\#{B_a}}{\#{B_a \cup B_t}} = p \in ]0,1[ - \text{, en règle générale, } p \in \cro{\frac{1}{2},\frac{3}{4}} + \text{, en règle générale, } p \in \cro{\frac{1}{2},\frac{3}{4}} \end{array} Ce découpage est valide si tous les exemples de la base :math:`B` @@ -81,11 +78,11 @@ vecteur des poids et celui des entrées mais :tag: Définition Un neurone distance à :math:`p` entrées est une fonction - :math:`f : \R^{p+1} \times \R^p \longrightarrow \R` définie par : + :math:`f : \mathbb{R}^{p+1} \times \mathbb{R}^p \longrightarrow \mathbb{R}` définie par : - * :math:`g : \R \dans \R` - * :math:`W \in \R^{p+1}`, :math:`W=\pa{w_1,\dots,w_{p+1}} = \pa{W',w_{p+1}}` - * :math:`\forall x \in \R^p, \; f\pa{W,x} = e^{-\norm{W'-x}^2 + w_{p+1}}` + * :math:`g : \mathbb{R} \dans \mathbb{R}` + * :math:`W \in \mathbb{R}^{p+1}`, :math:`W=\pa{w_1,\dots,w_{p+1}} = \pa{W',w_{p+1}}` + * :math:`\forall x \in \mathbb{R}^p, \; f\pa{W,x} = e^{-\norm{W'-x}^2 + w_{p+1}}` avec :math:`x = \pa{x_1,\dots,x_p}` Ce neurone est un cas particulier du suivant qui pondère chaque @@ -97,14 +94,14 @@ coefficients où :math:`p` est le nombre d'entrée. :tag: Définition :lid: rn_definition_neurone_dist_pond - Pour un vecteur donné :math:`W \in \R^p = \pa{w_1,\dots,w_p}`, + Pour un vecteur donné :math:`W \in \mathbb{R}^p = \pa{w_1,\dots,w_p}`, on note :math:`W_i^j = \pa{w_i,\dots,w_j}`. Un neurone distance pondérée à :math:`p` entrées est une fonction - :math:`f : \R^{2p+1} \times \R^p \longrightarrow \R` définie par : + :math:`f : \mathbb{R}^{2p+1} \times \mathbb{R}^p \longrightarrow \mathbb{R}` définie par : - * :math:`g : \R \dans \R` - * :math:`W \in \R^{2p+1}`, :math:`W=\pa{w_1,\dots,w_{2p+1}} = \pa{w_1,w_{2p+1}}` - * :math:`\forall x \in \R^p, \; f\pa{W,x} = + * :math:`g : \mathbb{R} \dans \mathbb{R}` + * :math:`W \in \mathbb{R}^{2p+1}`, :math:`W=\pa{w_1,\dots,w_{2p+1}} = \pa{w_1,w_{2p+1}}` + * :math:`\forall x \in \mathbb{R}^p, \; f\pa{W,x} = \exp \cro {-\cro{\sum_{i=1}^{p} w_{p+i}\pa{w_i - x_i}^2 } + w_{p+1}}` avec :math:`x = \pa{x_1,\dots,x_p}` @@ -121,13 +118,13 @@ Le plus simple tout d'abord : :label: eq_no_distance_nn \begin{eqnarray*} - 1 \infegal i \infegal p, & \dfrac{\partial y}{\partial w_{i}} = & - 2 w_{p+i}\pa{w_i - x_i} \\ - p+1 \infegal i \infegal 2p, & \dfrac{\partial y}{\partial w_{i}} = & - \pa{w_i - x_i}^2 \\ + 1 \leqslant i \leqslant p, & \dfrac{\partial y}{\partial w_{i}} = & - 2 w_{p+i}\pa{w_i - x_i} \\ + p+1 \leqslant i \leqslant 2p, & \dfrac{\partial y}{\partial w_{i}} = & - \pa{w_i - x_i}^2 \\ i = 2p+1, & \dfrac{\partial y}{\partial w_{i}} = & -1 \end{eqnarray*} Pour le neurone distance simple, la ligne :eq:`eq_no_distance_nn` -est superflue, tous les coefficients :math:`(w_i)_{p+1 \infegal i \infegal 2p}` +est superflue, tous les coefficients :math:`(w_i)_{p+1 \leqslant i \leqslant 2p}` sont égaux à 1. La relation :eq:`retro_eq_nn_3` reste vraie mais n'aboutit plus à:eq:`algo_retro_5`, celle-ci devient en supposant que la couche d'indice :math:`c+1` ne contient que des neurones définie par la définition précédente. @@ -141,7 +138,7 @@ ne contient que des neurones définie par la définition précédente. \partialfrac{y_{c+1,l}}{z_{c,i}} \partialfrac{z_{c,i}}{y_{c,i}} \\ &=& \cro{ \sum_{l=1}^{C_{c+1}} - \partialfrac{e}{y_{c+1,l}} + \partialfrac{e}{y_{c+1,l}} \pa{ 2 w_{c+1,l,p+i} \pa{ w_{c+1,l,i} - z_{c,i} } } } \partialfrac{z_{c,i}}{y_{c,i}} \end{eqnarray*} @@ -187,11 +184,11 @@ sont les potentiels des neurones de la première couche, on en déduit que, dans .. math:: - \dfrac{\partial e\left( W,X_{k},Y_{k}\right) }{\partial z_{0,i}} = - \underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( W,X_{k} - ,Y_{k}\right) }{\partial y_{1,j}}\dfrac{\partial y_{1,j}}{\partial z_{0,i} - }=\underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( W,X_{k} - ,Y_{k}\right) }{\partial y_{1,j}}w_{1,j,i} + \dfrac{\partial e\left( W,X_{k},Y_{k}\right) }{\partial z_{0,i}} = + \underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( W,X_{k} + ,Y_{k}\right) }{\partial y_{1,j}}\dfrac{\partial y_{1,j}}{\partial z_{0,i} + }=\underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( W,X_{k} + ,Y_{k}\right) }{\partial y_{1,j}}w_{1,j,i} Comme le potentiel d'un neurone distance n'est pas linéaire par rapport aux entrées :math:`\left( y=\overset{N} {\underset{i=1}{\sum}}\left( w_{i}-z_{0,i}\right) ^{2}+b\right)`, @@ -199,11 +196,11 @@ la formule devient dans ce cas : .. math:: - \dfrac{\partial e\left( W,X_{k},Y_{k}\right) }{\partial z_{0,i}} = - \underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( W,X_{k} - ,Y_{k}\right) }{\partial y_{1,j}}\dfrac{\partial y_{1,j}}{\partial z_{0,i} - }=-2\underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( - W,X_{k},Y_{k}\right) }{\partial y_{1,j}}\left( w_{1,j,i}-z_{0,i}\right) + \dfrac{\partial e\left( W,X_{k},Y_{k}\right) }{\partial z_{0,i}} = + \underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( W,X_{k} + ,Y_{k}\right) }{\partial y_{1,j}}\dfrac{\partial y_{1,j}}{\partial z_{0,i} + }=-2\underset{j=1}{\overset{C_{1}}{\sum}}\dfrac{\partial e\left( + W,X_{k},Y_{k}\right) }{\partial y_{1,j}}\left( w_{1,j,i}-z_{0,i}\right) .. _rn_decay: @@ -299,7 +296,7 @@ On note :math:`\widehat{W}` les poids trouvés par apprentissage et \begin{eqnarray*} \text{la suite } \widehat{\varepsilon_{k}} &=& f\left( \widehat{W} ,X_{k}\right) -Y_{k}, \; - \widehat{\sigma}_{N}^{2}=\dfrac{1}{N}\underset + \widehat{\sigma}_{N}^{2}=\dfrac{1}{N}\underset {k=1}{\overset{N}{\sum}}\widehat{\varepsilon_{k}}^{2} \\ \text{la matrice } \widehat{\Sigma_{N}} &=& \dfrac{1}{N}\left[ \nabla_{\widehat{W}% @@ -354,7 +351,7 @@ Ce théorème mène au corollaire suivant : .. mathdef:: :title: nullité d'un coefficient :tag: Corollaire - + Les notations utilisées sont celles du théorème sur :ref:`loi asymptotique des coefficients `. Soit :math:`w_k` un poids du réseau de neurones d'indice quelconque :math:`k`. Sa valeur estimée est :math:`\widehat{w_k}`, @@ -426,15 +423,15 @@ régression grâce à l'algorithme suivant. *Choix aléatoire d'un modèle* Un réseau de neurones est choisi aléatoirement, - soit :math:`f : \R^p \dans \R` la fonction qu'il représente. + soit :math:`f : \mathbb{R}^p \dans \mathbb{R}` la fonction qu'il représente. Une base d'apprentissage :math:`A` (ou échantillon) de :math:`N` observations est générée aléatoirement à partir de ce modèle : .. math:: \begin{array}{l} - \text{soit } \pa{\epsilon_i}_{1 \infegal i \infegal N} \text{ un bruit blanc} \\ - A = \acc{ \left. \pa{X_i,Y_i}_{1 \infegal i \infegal N} \right| + \text{soit } \pa{\epsilon_i}_{1 \leqslant i \leqslant N} \text{ un bruit blanc} \\ + A = \acc{ \left. \pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \right| \forall i \in \intervalle{1}{N}, \; Y_i = f\pa{X_i} + \epsilon_i } \end{array} @@ -444,7 +441,14 @@ régression grâce à l'algorithme suivant. à un réseau de neurones plus riche que le modèle choisi dans l'étape d'initilisation. Le modèle sélectionné est noté :math:`g`. - *Validation* + *Validation* + + Si :math:`\norm{f-g} \approx 0`, + l'algorithme de + :ref:`sélection ` + est validé. - Si :math:`\norm{f-g} \approx 0`, - l'algorithme de :ref:`sélection ` est validé. +La réduction des réseaux de neurones ne se posent plus en ce sens. +Les réseaux de neurones sont aujourd'hui des réseaux de neurones +de neurones profonds qui ne suivent plus cette architecture à une +couche. diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_9_auto.rst b/_doc/c_ml/rn/rn_9_auto.rst similarity index 85% rename from _doc/sphinxdoc/source/c_ml/rn/rn_9_auto.rst rename to _doc/c_ml/rn/rn_9_auto.rst index d6340aea..17be24c2 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_9_auto.rst +++ b/_doc/c_ml/rn/rn_9_auto.rst @@ -4,9 +4,6 @@ Analyse en composantes principales (ACP) et Auto Encoders ========================================================= -.. contents:: - :local: - .. index:: ACP Cet algorithme est proposé dans [Song1997]_. @@ -26,7 +23,7 @@ L'algorithme implémentant l'analyse en composantes principales est basé sur un réseau linéaire dit *"diabolo"*, ce réseau possède une couche d'entrées à :math:`N` entrées, une couche cachée et une couche de sortie à :math:`N` sorties. L'objectif est -d'apprendre la fonction identité sur l'espace :math:`\R^N`. +d'apprendre la fonction identité sur l'espace :math:`\mathbb{R}^N`. Ce ne sont plus les sorties qui nous intéressent mais la couche cachée intermédiaire qui effectue une compression ou projection des vecteurs d'entrées puisque les entrées et les @@ -42,8 +39,8 @@ sorties du réseau auront pour but d'être identiques. \begin{picture}(241,100)(0,-10) - \put(1,1) {\framebox(40,22){\footnotesize \begin{tabular}{c}vecteur \\ $X \in \R^N$ \end{tabular}}} - \put(85,-9) {\framebox(45,32){\footnotesize \begin{tabular}{c}vecteur \\ $Y \in \R^M$ \\ et $M < N$ \end{tabular}}} + \put(1,1) {\framebox(40,22){\footnotesize \begin{tabular}{c}vecteur \\ $X \in \mathbb{R}^N$ \end{tabular}}} + \put(85,-9) {\framebox(45,32){\footnotesize \begin{tabular}{c}vecteur \\ $Y \in \mathbb{R}^M$ \\ et $M < N$ \end{tabular}}} \put(200,1) {\framebox(40,22){\footnotesize \begin{tabular}{c}vecteur \\ $Z \approx X$ \end{tabular}}} \put(20,40) {\framebox(90,45){\footnotesize @@ -63,8 +60,8 @@ sorties du réseau auront pour but d'être identiques. \end{picture} -La figure suivante illustre un exemple de compression de vecteur de :math:`\R^3` -dans :math:`\R^2`. +La figure suivante illustre un exemple de compression de vecteur de :math:`\mathbb{R}^3` +dans :math:`\mathbb{R}^2`. .. mathdef:: :title: Réseau diabolo : réduction d'une dimension @@ -142,9 +139,9 @@ L'analyse en composantes principales ou ACP est définie de la manière suivante :lid: problem_acp :tag: Problème - Soit :math:`\pa{X_i}_{1 \infegal i \infegal N}` avec :math:`\forall i \in \ensemble{1}{N}, - \; X_i \in \R^p`. - Soit :math:`W \in M_{p,d}\pa{\R}`, :math:`W = \vecteur{C_1}{C_d}` + Soit :math:`\pa{X_i}_{1 \leqslant i \leqslant N}` avec :math:`\forall i \in \ensemble{1}{N}, + \; X_i \in \mathbb{R}^p`. + Soit :math:`W \in M_{p,d}\pa{\mathbb{R}}`, :math:`W = \vecteur{C_1}{C_d}` où les vecteurs :math:`\pa{C_i}` sont les colonnes de :math:`W` et :math:`d < p`. On suppose également que les :math:`\pa{C_i}` forment une base othonormée. @@ -154,7 +151,7 @@ L'analyse en composantes principales ou ACP est définie de la manière suivante W'W = I_d - :math:`\pa{W'X_i}_{1 \infegal i \infegal N}` est l'ensemble des + :math:`\pa{W'X_i}_{1 \leqslant i \leqslant N}` est l'ensemble des vecteurs :math:`\pa{X_i}` projetés sur le sous-espace vectoriel engendré par les vecteurs :math:`\pa{C_i}`. Réaliser une analyse en composantes principales, c'est trouver le @@ -167,9 +164,9 @@ L'analyse en composantes principales ou ACP est définie de la manière suivante :label: rn_equation_acp_error \begin{eqnarray*} - W^* &=& \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } + W^* &=& \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \arg \max } \; E\pa{W} - = \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \arg \max } \; + = \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \arg \max } \; \cro { \sum_{i=1}^{N} \norm{W'X_i}^2 } \end{eqnarray*} @@ -177,7 +174,7 @@ L'analyse en composantes principales ou ACP est définie de la manière suivante projeté sur le sous-espace vectoriel défini par les vecteurs colonnes de la matrice :math:`W`. - + Résolution d'une ACP avec un réseau de neurones diabolo +++++++++++++++++++++++++++++++++++++++++++++++++++++++ @@ -197,14 +194,14 @@ afin de passer d'une optimisation sous contrainte à une optimisation sans contr .. math:: :nowrap: :label: rn_acp_contrainte - + \begin{eqnarray*} S = - \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \arg \max } \; + \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \arg \max } \; \cro { \sum_{i=1}^{N} \norm{W'X_i}^2 } &=& - \underset{ W \in M_{p,d}\pa{\R} } { \arg \min } \; \cro { \sum_{i=1}^{N} \norm{WW'X_i - X_i}^2 } + \underset{ W \in M_{p,d}\pa{\mathbb{R}} } { \arg \min } \; \cro { \sum_{i=1}^{N} \norm{WW'X_i - X_i}^2 } \end{eqnarray*} - + De plus :math:`S` est l'espace vectoriel engendré par les :math:`d` vecteurs propres de la matrice :math:`XX' = \sum_{i=1}^{N} X_i X_i'` associées aux @@ -218,16 +215,16 @@ L'objectif de cette partie est de chercher la valeur de : .. math:: - \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \max }\; E\pa{W} + \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \max }\; E\pa{W} -Soit :math:`X=\vecteur{X_1}{X_N} \in \pa{\R^p}^N`, alors : +Soit :math:`X=\vecteur{X_1}{X_N} \in \pa{\mathbb{R}^p}^N`, alors : .. math:: E\pa{W} = \sum_{i=1}^{N} \norm{W'X_i}^2 = \trace{X'WW'X} = \trace{XX'WW'} La matrice :math:`XX'` est symétrique, elle est donc diagonalisable -et il existe une matrice :math:`P \in M_p\pa{\R}:math:` telle qu : +et il existe une matrice :math:`P \in M_p\pa{\mathbb{R}}:math:` telle qu : .. math:: :label: acp_equation_memo_1 @@ -269,18 +266,18 @@ Donc : :label: acp_demo_partie_a \begin{eqnarray*} - \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \max }\; E\pa{W} = - \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \max }\; - \trace{ D_X P'WW'P } - = \underset{ \begin{subarray}{c} Y \in M_{p,d}\pa{\R} \\ Y'Y = I_d \end{subarray} } { \max }\; \trace{ D_X YY' + \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \max }\; E\pa{W} = + \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \max }\; + \trace{ D_X P'WW'P } + = \underset{ \begin{subarray}{c} Y \in M_{p,d}\pa{\mathbb{R}} \\ Y'Y = I_d \end{subarray} } { \max }\; \trace{ D_X YY' } = \sum_{i=1}{d} \lambda_i \end{eqnarray*} *Partie 2* -Soit :math:`Y \in \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \max }\; \trace{X'WW'X}`, -:math:`Y = \vecteur{Y_1}{Y_d} = \pa{y_i^k}_{ \begin{subarray}{c} 1 \infegal i \infegal d \\ 1 \infegal k \infegal p \end{subarray} }`. +Soit :math:`Y \in \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \max }\; \trace{X'WW'X}`, +:math:`Y = \vecteur{Y_1}{Y_d} = \pa{y_i^k}_{ \begin{subarray}{c} 1 \leqslant i \leqslant d \\ 1 \leqslant k \leqslant p \end{subarray} }`. Chaque vecteur :math:`Y_i` est écrit dans la base :math:`\vecteur{P_1}{P_p}` définie en :eq:`acp_equation_memo_1` : @@ -332,7 +329,7 @@ Et : \begin{eqnarray*} \trace{ XX' YY'} &=& \sum_{i=1}^{d} \sum_{k=1}^{p} \lambda_k \pa{y_i^k}^2 \\ \trace{ XX' YY'} &=& \sum_{k=1}^{p} \lambda_k \pa {\sum_{i=1}^{d} \pa{y_i^k}^2} = - \sum_{k=1}^{p} \; \lambda_k + \sum_{k=1}^{p} \; \lambda_k \end{eqnarray*} Ceci permet d'affirmer que : @@ -342,7 +339,7 @@ Ceci permet d'affirmer que : :label: acp_demo_partie_b \begin{eqnarray*} - Y \in \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \max }\; + Y \in \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \max }\; \trace{X'WW'X} \Longrightarrow vect \vecteur{Y_1}{Y_d} = vect \vecteur{P_1}{P_d} \end{eqnarray*} @@ -373,10 +370,10 @@ D'où : :label: acp_demo_partie_c \begin{eqnarray*} - \underset{ \begin{subarray} \, W \in M_{p,d} \pa{\R} \\ - W'W=I_d \end{subarray}} { \; \max \; } \; \pa { \sum_{i=1}^{N} \norm{ W'X_i}^2 } = - \underset{ \begin{subarray} \, W \in M_{p,d} \pa{\R} \\ - W'W=I_d \end{subarray}} { \; \min \; } \; \pa { \sum_{i=1}^{N} \norm{ WW'X_i - X_i}^2 } + \underset{ \begin{subarray} \, W \in M_{p,d} \pa{\mathbb{R}} \\ + W'W=I_d \end{subarray}} { \; \max \; } \; \pa { \sum_{i=1}^{N} \norm{ W'X_i}^2 } = + \underset{ \begin{subarray} \, W \in M_{p,d} \pa{\mathbb{R}} \\ + W'W=I_d \end{subarray}} { \; \min \; } \; \pa { \sum_{i=1}^{N} \norm{ WW'X_i - X_i}^2 } \end{eqnarray*} *Partie 4* @@ -385,7 +382,7 @@ D'où : .. math:: - \exists P\in GL_N \pa{\R} \text{ telle que } P'XX'P=D_p \text{ où } D_p \text{ est diagonale} + \exists P\in GL_N \pa{\mathbb{R}} \text{ telle que } P'XX'P=D_p \text{ où } D_p \text{ est diagonale} On en déduit que : @@ -409,7 +406,7 @@ D'où : \begin{eqnarray*} \underset{Y}{\arg\min}\acc{ tr\left( D_{p}\left( YY^{\prime}-I_{p}\right) ^{2}\right)} = \left\{ Y\in - M_{Nd}\left( \R\right) \left| + M_{Nd}\left( \mathbb{R}\right) \left| YY^{\prime}=I_{d}\right. \right\} \end{eqnarray*} @@ -421,9 +418,9 @@ première partie du théorème, à savoir :eq:`rn_acp_contrainte` : \begin{eqnarray*} S = - \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\R} \\ W'W = I_d \end{subarray} } { \arg \max } \; + \underset{ \begin{subarray}{c} W \in M_{p,d}\pa{\mathbb{R}} \\ W'W = I_d \end{subarray} } { \arg \max } \; \cro { \sum_{i=1}^{N} \norm{W'X_i}^2 } &=& - \underset{ W \in M_{p,d}\pa{\R} } { \arg \min } \; \cro { \sum_{i=1}^{N} \norm{WW'X_i - X_i}^2 } + \underset{ W \in M_{p,d}\pa{\mathbb{R}} } { \arg \min } \; \cro { \sum_{i=1}^{N} \norm{WW'X_i - X_i}^2 } \end{eqnarray*} .. _par_ACP_deux: @@ -443,8 +440,8 @@ neurones sur la couche cachée, et :math:`p` le nombre d'entrées. \forall i\in\left\{ 1,...,d\right\} ,\,y_{1,i}=\sum_{j=1}^p w_{ji}x_{j} -Soit :math:`X\in\R^{p}` les entrées, -:math:`Y=\left( y_{1,1},...,y_{1,d}\right) \in\R^{d}`, +Soit :math:`X\in\mathbb{R}^{p}` les entrées, +:math:`Y=\left( y_{1,1},...,y_{1,d}\right) \in\mathbb{R}^{d}`, on obtient que : :math:`Y=W'X`. Les poids de la seconde couche sont définis comme suit : @@ -453,7 +450,7 @@ Les poids de la seconde couche sont définis comme suit : \forall\left( i,j\right) \in\left\{ 1,...,p\right\} \times\left\{ 1,...,d\right\} \,w_{2,j,i}=w_{1,i,j} -Par conséquent, le vecteur des sorties :math:`Z\in\R^{p}` +Par conséquent, le vecteur des sorties :math:`Z\in\mathbb{R}^{p}` du réseau ainsi construit est :math:`Z=WW'X`. On veut minimiser l'erreur pour :math:`\left( X_{i}\right) _{1\leqslant i\leqslant N}` : @@ -465,7 +462,7 @@ Il suffit d'apprendre le réseau de neurones pour obtenir : .. math:: - W_{d}^{\ast}=\underset{W\in M_{pd}\left( \R\right) } + W_{d}^{\ast}=\underset{W\in M_{pd}\left( \mathbb{R}\right) } {\arg\max }\,\sum_{i=1}^N\left\| WW'X_{i}-X_{i}\right\| ^{2} @@ -491,7 +488,7 @@ on trouve l'ensemble des vecteurs propres de la matrice :math:`XX^{\prime}`. L'orthonormalisation de Shmidt : Soit :math:`\left( e_{i}\right) _{1\leqslant i\leqslant N}` - une base de :math:`\R^{p}` + une base de :math:`\mathbb{R}^{p}` On définit la famille :math:`\left( \varepsilon_{i}\right) _{1\leqslant i\leqslant p}` par : @@ -516,7 +513,7 @@ car :math:`\forall k\in\left\{ 1,...,N\right\} ,\; vect\left( e_{1},...,e_{k}\r :tag: Propriété La famille :math:`\left( \varepsilon_{i}\right) _{1\leqslant i\leqslant p}` - est une base orthonormée de :math:`\R^{p}`. + est une base orthonormée de :math:`\mathbb{R}^{p}`. L'algorithme qui permet de déterminer les vecteurs propres de la matrice :math:`XX'` définie par le théorème de l':ref:`ACP ` est le suivant : @@ -532,7 +529,7 @@ définie par le théorème de l':ref:`ACP ` est le suiva :math:`d` valeurs propres de plus grands module. | for :math:`d, p` - | Un réseau diabolo est construit avec les poids :math:`W_d \in M_{p,d}\pa{\R}` puis appris. + | Un réseau diabolo est construit avec les poids :math:`W_d \in M_{p,d}\pa{\mathbb{R}}` puis appris. | Le résultat de cet apprentissage sont les poids :math:`W^*_d`. | if :math:`d > 1` | L'orthonormalisation de Schmit permet de déduire :math:`V^*_d` de :math:`V^*_{d-1}` et :math:`W^*_d`. @@ -554,7 +551,7 @@ Soit :math:`\left( X_{1},...,X_{N}\right)` un ensemble de .. math:: - \forall i\in\left\{ 1,...,N\right\},\;X_{i}\in\R^{p} + \forall i\in\left\{ 1,...,N\right\},\;X_{i}\in\mathbb{R}^{p} L'ACP consiste à projeter ce nuage de point sur un plan qui conserve le maximum d'information. Par conséquent, il @@ -562,7 +559,7 @@ s'agit de résoudre le problème : .. math:: - W^{\ast}=\underset{ \begin{subarray} \, W\in M_{p,d}\left( \R\right) \\ + W^{\ast}=\underset{ \begin{subarray} \, W\in M_{p,d}\left( \mathbb{R}\right) \\ W^{\prime }W=I_{d} \end{subarray}}{\arg\min}% \left(\underset{i=1}{\overset{N}{\sum}}\left\| W'X_{i}\right\| ^{2}\right) \text{ avec }d`. Soit :math:`\left( X_{i}\right) _{1\leqslant i\leqslant N}` avec -:math:`\forall i\in\left\{ 1,...,N\right\} ,\,X_{i}\in\R^{p}`. +:math:`\forall i\in\left\{ 1,...,N\right\} ,\,X_{i}\in\mathbb{R}^{p}`. Soit :math:`\pa{P_1,\dots,P_p}` l'ensemble des vecteurs propres normés de la matrice :math:`XX'` associés aux valeurs propres :math:`\pa{\lambda_1,\dots,\lambda_p}` classées par ordre décroissant de modules. @@ -582,10 +579,10 @@ On suppose que le nuage de points est centré, alors : .. math:: - \forall d \in \intervalle{1}{p}, \; I_d = \sum_{k=1}^{N} - \pa{P_d' X_k}^2 = tr \pa{X' P_d P_d' X} = tr \pa{XX' P_d P_d'} = \lambda_d + \forall d \in \intervalle{1}{p}, \; I_d = \sum_{k=1}^{N} + \pa{P_d' X_k}^2 = tr \pa{X' P_d P_d' X} = tr \pa{XX' P_d P_d'} = \lambda_d -Comme :math:`\pa{P_1,\dots,P_p}` est une base orthonormée de :math:`\R^p`, +Comme :math:`\pa{P_1,\dots,P_p}` est une base orthonormée de :math:`\mathbb{R}^p`, on en déduit que : .. math:: @@ -593,7 +590,7 @@ on en déduit que : I = \sum_{k=1}^{P} X_k'X_k = \sum_{d=1}^{N} I_d = \sum_{d=1}^{p} \lambda_d De manière empirique, on observe fréquemment que la courbe -:math:`\pa{d,I_d}_{1 \infegal d \infegal p}` montre un point +:math:`\pa{d,I_d}_{1 \leqslant d \leqslant p}` montre un point d'inflexion (voir figure ci-dessous). Dans cet exemple, le point d'inflexion correspond à :math:`d=4`. En analyse des données, on considère empiriquement que seuls les diff --git a/_doc/sphinxdoc/source/c_ml/rn/rn_biblio.rst b/_doc/c_ml/rn/rn_biblio.rst similarity index 93% rename from _doc/sphinxdoc/source/c_ml/rn/rn_biblio.rst rename to _doc/c_ml/rn/rn_biblio.rst index 44a22b7a..fde17429 100644 --- a/_doc/sphinxdoc/source/c_ml/rn/rn_biblio.rst +++ b/_doc/c_ml/rn/rn_biblio.rst @@ -3,7 +3,7 @@ Bibliographie ============= .. [Bottou1991] Une approche théorique de l'apprentissage connexionniste, - Application à la reconnaissance de la parole, Léon Bottou, + Application à la reconnaissance de la parole, Léon Bottou, *Thèse de l'Université de Paris Sud, Centre d'Orsay*. .. [Broyden1967] Quasi-Newton methods and their application to function minimization (1967), @@ -47,9 +47,6 @@ Bibliographie D. E. Rumelhart, G. E. Hinton, R. J. Williams in *Parallel distributed processing: explorations in the microstructures of cohniyionn MIT Press, Cambridge* -.. [Saporta1990] Probabilités, analyse des données et statistique (1990), - Gilbert Saporta, *Editions Technip* - .. [Song1997] Self-organizing algorithm of robust PCA based on single layer NN (1997) Song Wang, Shaowei Xia, *Proceedings of the 4th International Conference Document Analysis and Recognition* diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/Conjugate_gradient_illustration.png b/_doc/c_ml/rn/rnimg/Conjugate_gradient_illustration.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/Conjugate_gradient_illustration.png rename to _doc/c_ml/rn/rnimg/Conjugate_gradient_illustration.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/Roccurves.bmp b/_doc/c_ml/rn/rnimg/Roccurves.bmp similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/Roccurves.bmp rename to _doc/c_ml/rn/rnimg/Roccurves.bmp diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/acp_inertie.png b/_doc/c_ml/rn/rnimg/acp_inertie.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/acp_inertie.png rename to _doc/c_ml/rn/rnimg/acp_inertie.png diff --git a/_doc/c_ml/rn/rnimg/backp.png b/_doc/c_ml/rn/rnimg/backp.png new file mode 100644 index 00000000..953d34ef Binary files /dev/null and b/_doc/c_ml/rn/rnimg/backp.png differ diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/classificationnd.png b/_doc/c_ml/rn/rnimg/classificationnd.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/classificationnd.png rename to _doc/c_ml/rn/rnimg/classificationnd.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/classificationnt.png b/_doc/c_ml/rn/rnimg/classificationnt.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/classificationnt.png rename to _doc/c_ml/rn/rnimg/classificationnt.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/errapptest.png b/_doc/c_ml/rn/rnimg/errapptest.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/errapptest.png rename to _doc/c_ml/rn/rnimg/errapptest.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/errminloc.png b/_doc/c_ml/rn/rnimg/errminloc.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/errminloc.png rename to _doc/c_ml/rn/rnimg/errminloc.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/neurone2.jpg b/_doc/c_ml/rn/rnimg/neurone2.jpg similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/neurone2.jpg rename to _doc/c_ml/rn/rnimg/neurone2.jpg diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/regressionl.png b/_doc/c_ml/rn/rnimg/regressionl.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/regressionl.png rename to _doc/c_ml/rn/rnimg/regressionl.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/regressionnc.png b/_doc/c_ml/rn/rnimg/regressionnc.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/regressionnc.png rename to _doc/c_ml/rn/rnimg/regressionnc.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/regressionnd.png b/_doc/c_ml/rn/rnimg/regressionnd.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/regressionnd.png rename to _doc/c_ml/rn/rnimg/regressionnd.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/regressionnu.png b/_doc/c_ml/rn/rnimg/regressionnu.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/regressionnu.png rename to _doc/c_ml/rn/rnimg/regressionnu.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_clad.png b/_doc/c_ml/rn/rnimg/rn_clad.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_clad.png rename to _doc/c_ml/rn/rnimg/rn_clad.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_courbe.png b/_doc/c_ml/rn/rnimg/rn_courbe.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_courbe.png rename to _doc/c_ml/rn/rnimg/rn_courbe.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_densite_idee.png b/_doc/c_ml/rn/rnimg/rn_densite_idee.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_densite_idee.png rename to _doc/c_ml/rn/rnimg/rn_densite_idee.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_diabolo.png b/_doc/c_ml/rn/rnimg/rn_diabolo.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_diabolo.png rename to _doc/c_ml/rn/rnimg/rn_diabolo.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_gradient.png b/_doc/c_ml/rn/rnimg/rn_gradient.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_gradient.png rename to _doc/c_ml/rn/rnimg/rn_gradient.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_graphe_trans_1.png b/_doc/c_ml/rn/rnimg/rn_graphe_trans_1.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_graphe_trans_1.png rename to _doc/c_ml/rn/rnimg/rn_graphe_trans_1.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_graphe_trans_2.png b/_doc/c_ml/rn/rnimg/rn_graphe_trans_2.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_graphe_trans_2.png rename to _doc/c_ml/rn/rnimg/rn_graphe_trans_2.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_graphe_trans_3.png b/_doc/c_ml/rn/rnimg/rn_graphe_trans_3.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_graphe_trans_3.png rename to _doc/c_ml/rn/rnimg/rn_graphe_trans_3.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/rn_neurone.png b/_doc/c_ml/rn/rnimg/rn_neurone.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/rn_neurone.png rename to _doc/c_ml/rn/rnimg/rn_neurone.png diff --git a/_doc/sphinxdoc/source/c_ml/rn/rnimg/selection_connexion.png b/_doc/c_ml/rn/rnimg/selection_connexion.png similarity index 100% rename from _doc/sphinxdoc/source/c_ml/rn/rnimg/selection_connexion.png rename to _doc/c_ml/rn/rnimg/selection_connexion.png diff --git a/_doc/c_ml/survival_analysis.rst b/_doc/c_ml/survival_analysis.rst new file mode 100644 index 00000000..9e5907cc --- /dev/null +++ b/_doc/c_ml/survival_analysis.rst @@ -0,0 +1,204 @@ + +.. _l-survival-analysis: + +================= +Analyse de survie +================= + +.. index:: analyse de survie + +L'`analyse de survie +`_ +est un sujet qu'on commence à voir +poindre en assurance et plus généralement en assurance. +C'est domaine développé +pour mesurer les effets d'une substance, d'un médicament +sur un corps vivant, une personne. + +Lien avec le machine learning +============================= + +En assurance, on cherche souvent à prédire si une personne aura +un accident ou pas. Pour cela, il faut avoir des données, +une base de données dans laquelle sont enregistrés des accidents. +L'accident en question peut avoir lieu au début du contrat, quelques +années plus tard ou jamais. Lorsqu'aucun accident n'est associé +à une personne, il se peut qu'il ne se produise aucun accident +ou que celui-ci ne s'est pas encore produit. Modéliser ce problème +de prédiction permet d'introduire le temps et prendre en compte +le fait que les données sont tronquées : on ne sait pour une personne +que si un accident s'est produit ou pas entre le début du contrat +et aujourd'hui. + +Courbe de Kaplan-Meier +====================== + +.. index:: Kaplan-Meier, espérance de vie + +On reprend la même terminologie. A une date :math:`t_0`, on administre +un traitement à une personne, un animal, une plante. Cet être vivant +meurt à un temps *t + d*. Le traitement a-t-il amélioré sa survie ? +On considère deux temps :math:`t_1` et :math:`t_2`, la probabilité +de décès entre ces deux temps peut être estimé par +:math:`\frac{n_{t_2} - n_{t_1}}{n_{t_1}}` où :math:`n_{t_i}` est la +population vivante au temps :math:`t_i` (depuis le début du traitement). + +On en déduit la probabilité de rester vivant jusqu'au temps :math:`t_i` +qui est l'estimateur de `Kaplan-Meier +`_ +:math:`\hat{S}(t_i)` : + +.. math:: + + \begin{array}{rcl} + \hat{S}(t_i) &=& \prod_{i=1}^i \left( 1 - \frac{n_{t_{i-1}} - n_{t_{i}}}{n_{t_{i-1}}} \right) \\ + &=& \prod_{i=1}^i \frac{n_{t_i}}{n_{t_{i-1}}} = \prod_{i=1}^i \frac{n_i}{n_{i-1}} + \end{array} + +Par simplification, on note :math:`n_i = n_{t_i}`. On suppose les :math:`t_i` +des dates à intervalles plutôt réguliers et croissants. La suite :math:`(n_i)` +est décroissantes (on ne rescuscite pas). +Ces calculs rappellent les calculs liés à l'espérance de vie +(voir `Evolution d’une population - énoncé +`_, +`Evolution d'une population (correction) +`_). +L'espérance de vie est définie par : + +.. math:: + + \esp(D) = \sum_{i=1}^{\infty} t_i \pr{ \text{mort au temps } t_i} = + \sum_{i=1}^{\infty} t_i \frac{n_i - n_{i+1}}{n_{i}} \prod_{j=0}^i\frac{n_j}{n_{j-1}} = + \sum_{i=1}^{\infty} t_i \frac{n_i - n_{i+1}}{n_{i}} \frac{n_i}{n_0} = + \sum_{i=1}^{\infty} t_i \frac{n_i - n_{i+1}}{n_0} + +.. index:: fonction de survie, taux de défaillance + +La courbe :math:`S(t)` est aussi appelée la fonction de survie. Si *T* +est la durée de vie d'une personne, :math:`S(t) = \pr{T > t}`. +On appelle :math:`\lambda(t)` le taux de défaillance, c'est la probabilité +que le décès survienne au temps *t* : + +.. math:: + + \lambda(t)dt = \pr{t \leqslant T < t + dt | T \supegal T} = - \frac{S'(t)}{S(t)} dt + +Régression de Cox +================= + +.. index:: Cox, régression de Cox, risque de base + +Le `modèle de Cox `_ +modélise le risque de décès instantané au temps *t* selon le modèle qui suit. +Une personne est décrite par les variables :math:`X_1, ..., X_k`. + +.. math:: + + \lambda(t, X_1, ..., X_k) = \lambda_0(t) \exp\left(\sum_{i=1}^k \beta_i X_i\right) = + \lambda_0(t) \exp (\beta X) + +La partie :math:`\lambda_0(t)` correspond à ce qu'on observe sans +autre informations que les décès. On l'appelle aussi le *risque de base*. +C'est la probabilité moyenne +de décès instantanée. La seconde partie permet de faire varier +cette quantité selon ce qu'on sait de chaque personne. + +On dit que c'est un modèle à risque proportionnel car si deux personnes sont quasiment +identiques excepté sur une variable :math:`X_i` (comme la quantité d'un poison ingérée), alors le ratio +de probabilité est : + +.. math:: + + \frac{\lambda(t, X_1, ..., X_i^a, ..., X_k)}{\lambda(t, X_1, ..., X_i^b, ..., X_k)} = + \frac{\exp(\beta_i X_i^a)} {\exp(\beta_i X_i^b)} = + \exp\left(\beta_i (X_i^a - X_i^b)\right) + +L'hypothèse des risques proportionnel est en quelque sorte intuitive. +Plus on ingère un poison, plus on a de chances d'en subir les conséquences. +Mais ce n'est pas toujours le cas, le documentaire +`La fabrique de l'ignorance +`_ +revient sur les effets du `bisphénol A `_ +qui serait déjà pertubateur à très petite dose. Il ne prend pas en compte +les effets croisés non plus (voir `Les perturbateurs endocriniens Comprendre où en est la recherche +`_). + +La fonction :math:`\lambda_0(t)` est en quelque sorte le taux de défaillance +moyen. On peut le calculer à partir des formules introduites au +paragraphe précédent en lissant la courbe de Kaplan-Meier avec des +splines. On peut aussi le calculer avec l'estimateur +de Breslow (voir `Analyse de survie : Méthodes non paramétriques +`_, +`Introduction à l'analyse des durées de survie +`_). +qui repose aussi la courbe de Kaplan-Meier. + +On sait que si :math:`g(t) = \log S'(t)` alors +:math:`g'(t) = \frac{S'(t)}{S(t)}`. On en déduit que : + +.. math:: + + \hat{\lambda_0}(t) = - \frac{d (\log(\hat{S}(t)))}{dt} + +Pour la suite, on pose :math:`h(X_i, \beta) = \exp(\beta X_i)`, +et l'individu meurt au temps :math:`t_i` de l'expérience. +Une expérience est définie par la liste des couples +:math:`(X_i, t_i)`. On souhaite trouver les paramètres +:math:`\beta` qui représentent au mieux les données +de l'expérience. On définit donc : + +* :math:`R_t` : l'ensemble des personnes en vie au temps *t* +* :math:`D_t` : l'ensemble qui décèdent au *t* + +Par définition :math:`i \in R_{t_i}` et :math:`i \in D_{t_i}`. +On calcule le ratio : + +.. math:: + + Pr(\beta, t, X_i) = \frac{h(X_i, \beta) \lambda_0(t)}{\sum_{j \in R_t} h(X_j, \beta) \lambda_0(t)} = + \frac{h(X_i, \beta) }{\sum_{j \in R_t} h(X_j, \beta) } + +Pour une personne qui décède au temps *t*, ce ratio devrait être proche de 1 +car on souhaite que :math:`h(X_i, \beta)` soit grand et tous les autres nuls. +On définit la vraisemblance partielle du modèle par : + +.. math:: + + L(\beta) = \prod_i Pr(\beta, t_i, X_i) = + \prod_i \frac{h(X_i, \beta) }{\sum_{j \in R_{t_i}} h(X_j, \beta) } + +.. index:: Breslow + +Une fois qu'on a calculé les coefficients :math:`\beta` optimaux, +on peut affiner la partie :math:`\lambda_0(t)`. L'estimateur +de Breslow est : + +.. math:: + + \hat{B}(t) = \sum_{i | t_i \leqslant t} \frac{1}{ \sum_{j \in R_{t_i}} h(\beta, X_j)} + +C'est un estimateur de la fonction de survie : + +.. math:: + + \hat{S}(t) = \exp(-\hat{B}(t)) + +Notebooks +========= + +.. toctree:: + :maxdepth: 1 + + ../notebooks/ml/survival + +Liens, articles +=============== + +* `Notes de lectures `_ +* `On the Breslow estimator `_ + +Modules +======= + +* `lifelines `_ +* `scikit-survival `_ diff --git a/_doc/sphinxdoc/source/c_nlp/completion.rst b/_doc/c_nlp/completion.rst similarity index 78% rename from _doc/sphinxdoc/source/c_nlp/completion.rst rename to _doc/c_nlp/completion.rst index 4734ded7..8ee9f7b9 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion.rst +++ b/_doc/c_nlp/completion.rst @@ -27,10 +27,11 @@ La plus connue en Python est `whoosh ` Quelques éléments de codes sont disponibles dans le module :mod:`completion ` et le notebook -:ref:`completiontrierst`. Vous pouvez également lire +:ref:`/notebooks/nlp/completion_trie.ipynb`. Vous pouvez également lire `How to Write a Spelling Corrector `_ de `Peter Norvig `_ et découvrir le sujet -avec `On User Interactions with Query Auto-Completion `_ +avec `On User Interactions with Query Auto-Completion +`_ de Bhaskar Mitra, Milad Shokouhi, Filip Radlinski, Katja Hofmann. .. toctree:: @@ -46,7 +47,9 @@ de Bhaskar Mitra, Milad Shokouhi, Filip Radlinski, Katja Hofmann. Notebooks associés : -* :ref:`completiontrierst` -* :ref:`completionprofilingrst` -* :ref:`completiontrielongrst` -* :ref:`completionsimplerst` +.. toctree:: + + ../notebooks/nlp/completion_trie + ../notebooks/nlp/completion_profiling + ../notebooks/nlp/completion_trie_long + ../notebooks/nlp/completion_simple diff --git a/_doc/sphinxdoc/source/c_nlp/completion_digression.rst b/_doc/c_nlp/completion_digression.rst similarity index 93% rename from _doc/sphinxdoc/source/c_nlp/completion_digression.rst rename to _doc/c_nlp/completion_digression.rst index e8464f8a..c9ed1209 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion_digression.rst +++ b/_doc/c_nlp/completion_digression.rst @@ -2,9 +2,6 @@ Digressions =========== -.. contents:: - :local: - Synonymes, Contexte +++++++++++++++++++ @@ -14,7 +11,8 @@ Avec les préfixes, un noeud a au plus 27 (26 lettres + espaces) caractères suivant possibles. Si le préfixe a des synonymes, rien n'empêche de relier ce noeud avec les successeurs de ses synonymes. -A ce sujet, voir `Context-Sensitive Query Auto-Completion `_, +A ce sujet, voir `Context-Sensitive Query Auto-Completion +`_, de Ziv Bar-Yossef et Naama Kraus. Source @@ -80,7 +78,7 @@ On rappelle la métrique :eq:`completion-metric2` (voir aussi :eq:`nlp-comp-k`). :nowrap: \begin{eqnarray*} - M'(q, S) &=& \min_{0 \infegal k \infegal l(q)} \acc{ M'(q[1..k], S) + K(q, k, S) } + M'(q, S) &=& \min_{0 \leqslant k \leqslant l(q)} \acc{ M'(q[1..k], S) + K(q, k, S) } \end{eqnarray*} Si on note :math:`L(p, S)` l'ensemble des complétions @@ -91,7 +89,7 @@ Que dire de la définition suivante ? :nowrap: \begin{eqnarray*} - M'_p(q, S) &=& \min_{0 \infegal k \infegal l(q)} \acc{ \begin{array}{l} + M'_p(q, S) &=& \min_{0 \leqslant k \leqslant l(q)} \acc{ \begin{array}{l} \indicatrice{ L(q[1..k], S) \neq \emptyset} \cro{M'_p(q[1..k], S) + K(q, k, S)} + \\ \;\;\;\;\indicatrice{L(q[1..k], S) = \emptyset} \cro { \min_j M'_p(q[1..j], S) + M'_p(q[j+1..], S) } \end{array} } @@ -141,12 +139,12 @@ On note la métrique :math:`M'_b`. :math:`S` comme étant le minimum obtenu : .. math:: - :label: completion-metric2 + :label: completion-metric2-back :nowrap: \begin{eqnarray*} M'_b(q, S) &=& \min\acc{\begin{array}{l} - \min_{0 \infegal k < l(q)} \acc{ M'_b(q[1..k], S) + + \min_{0 \leqslant k < l(q)} \acc{ M'_b(q[1..k], S) + \min( K(q, k, S), l(q) - k) } \\ \min_{s \succ q} \acc{ M'_b(s, S) + l(s) - l(q) } \end{array} } diff --git a/_doc/sphinxdoc/source/c_nlp/completion_fausse.rst b/_doc/c_nlp/completion_fausse.rst similarity index 98% rename from _doc/sphinxdoc/source/c_nlp/completion_fausse.rst rename to _doc/c_nlp/completion_fausse.rst index 072b5ba9..d8d71562 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion_fausse.rst +++ b/_doc/c_nlp/completion_fausse.rst @@ -2,9 +2,6 @@ Fausses idées reçues ==================== -.. contents:: - :local: - Il faut trier les complétions par fréquence décroissante ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ @@ -107,7 +104,7 @@ Et si le poids de chaque complétion est uniforme On suppose que les complétions ont toutes le même poids :math:`w_i=1`. Dans quel ordre faut-il ranger les complétions pour économiser le plus de caractères. On aurait tendance à dire la plus longue d'abord -ce qu'on peut vérifier dans le notebook :ref:`completiontrierst`. +ce qu'on peut vérifier dans le notebook :ref:`/notebooks/nlp/completion_trie.ipynb`. ====== ========= ============== ================ q fréquence ordre :math:`M(q, S)` diff --git a/_doc/sphinxdoc/source/c_nlp/completion_formalisation.rst b/_doc/c_nlp/completion_formalisation.rst similarity index 96% rename from _doc/sphinxdoc/source/c_nlp/completion_formalisation.rst rename to _doc/c_nlp/completion_formalisation.rst index 23dadd8b..76123669 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion_formalisation.rst +++ b/_doc/c_nlp/completion_formalisation.rst @@ -2,9 +2,6 @@ Formalisation ============= -.. contents:: - :local: - .. _l-completion-optim: Problème d'optimisation @@ -50,13 +47,13 @@ dans le premier cas ou 8+1=9 touches dans le second cas. .. math:: :label: completion-metric1 - M(q,S) = \min_{0 \infegal k \infegal l(q)} k + K(q, k, S) + M(q,S) = \min_{0 \leqslant k \leqslant l(q)} k + K(q, k, S) La quantité :math:`K(q, k, S)` représente le nombre de touche vers le bas qu'il faut taper pour obtenir la chaîne :math:`q` avec le système de complétion :math:`S` et les :math:`k` premières lettres de :math:`q`. -De façon évidente, :math:`K(q, l(q), S)=0` et :math:`M(q,S) \infegal l(q)` +De façon évidente, :math:`K(q, l(q), S)=0` et :math:`M(q,S) \leqslant l(q)` et :math:`K(q, k, S) > 0` si :math:`k < l(q)`. On prend également comme convention :math:`\forall q \notin S, \; K(q, k, S) = \infty` et :math:`\forall q \notin S, \; M(q, S) = l(q)`. diff --git a/_doc/sphinxdoc/source/c_nlp/completion_img/algocomp.png b/_doc/c_nlp/completion_img/algocomp.png similarity index 100% rename from _doc/sphinxdoc/source/c_nlp/completion_img/algocomp.png rename to _doc/c_nlp/completion_img/algocomp.png diff --git a/_doc/sphinxdoc/source/c_nlp/completion_img/comp.png b/_doc/c_nlp/completion_img/comp.png similarity index 100% rename from _doc/sphinxdoc/source/c_nlp/completion_img/comp.png rename to _doc/c_nlp/completion_img/comp.png diff --git a/_doc/sphinxdoc/source/c_nlp/completion_img/trieex.png b/_doc/c_nlp/completion_img/trieex.png similarity index 100% rename from _doc/sphinxdoc/source/c_nlp/completion_img/trieex.png rename to _doc/c_nlp/completion_img/trieex.png diff --git a/_doc/sphinxdoc/source/c_nlp/completion_img/wiki.png b/_doc/c_nlp/completion_img/wiki.png similarity index 100% rename from _doc/sphinxdoc/source/c_nlp/completion_img/wiki.png rename to _doc/c_nlp/completion_img/wiki.png diff --git a/_doc/sphinxdoc/source/c_nlp/completion_implementation.rst b/_doc/c_nlp/completion_implementation.rst similarity index 91% rename from _doc/sphinxdoc/source/c_nlp/completion_implementation.rst rename to _doc/c_nlp/completion_implementation.rst index 2fecb078..0c7bf913 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion_implementation.rst +++ b/_doc/c_nlp/completion_implementation.rst @@ -2,9 +2,6 @@ Implémentation ============== -.. contents:: - :local: - .. _trie: https://fr.wikipedia.org/wiki/Trie_(informatique) J'allais vous raconter en détail ce qu'est un trie_ et le paragraphe suivant @@ -18,11 +15,11 @@ les listes des mots et des complétions sont connues à l'avance. Notion de trie ++++++++++++++ -Une implémentation des tries est décrites dans deux notebooks : -`Arbre et Trie `_. +Une implémentation des tries est décrite dans ce notebook : +`Arbre et Trie `_. Les résultats de ce chapitre ont été produits avec le module :mod:`completion ` -et le notebook :ref:`completiontrierst`. Le notebook -:ref:`completionprofilingrst` montre les résultats du profiling. +et le notebook :ref:`/notebooks/nlp/completion_trie.ipynb`. Le notebook +:ref:`/notebooks/nlp/completion_profiling.ipynb` montre les résultats du profiling. L'implémentation Python est très gourmande en mémoire et elle serait plus efficace en C++. diff --git a/_doc/sphinxdoc/source/c_nlp/completion_metrique.rst b/_doc/c_nlp/completion_metrique.rst similarity index 88% rename from _doc/sphinxdoc/source/c_nlp/completion_metrique.rst rename to _doc/c_nlp/completion_metrique.rst index 8ea4e49e..cea98489 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion_metrique.rst +++ b/_doc/c_nlp/completion_metrique.rst @@ -2,9 +2,6 @@ Nouvelle métrique ================= -.. contents:: - :local: - Intuitions ++++++++++ @@ -21,7 +18,7 @@ On considère l'ensemble des complétions :math:`S` composé de deux mots *actuellement*, *actualité*. Le gain moyen par mots est de 9 caractères économisés. Si on ajoute le grand préfixe commun à la liste *actu*, -ce gain moyen tombe à 6.33 (voir :ref:`completiontrierst`) quelque +ce gain moyen tombe à 6.33 (voir :ref:`/notebooks/nlp/completion_trie.ipynb`) quelque soit l'ordre choisi pour les complétions. Toutefois, si on ne prend pas en compte le gain sur le mot *actu* car ce n'est pas un mot correct mais plus un mot qui aide la lecture de la liste, ce gain @@ -71,7 +68,7 @@ On reprend la première métrique :eq:`completion-metric1` : :nowrap: \begin{eqnarray*} - M(q, S) &=& \min_{0 \infegal k \infegal l(q)} k + K(q, k, S) + M(q, S) &=& \min_{0 \leqslant k \leqslant l(q)} k + K(q, k, S) \end{eqnarray*} La fonction :math:`K(q, k, S)` est définie par :eq:`nlp-comp-k`. @@ -90,7 +87,7 @@ La fonction :math:`K(q, k, S)` est définie par :eq:`nlp-comp-k`. :nowrap: \begin{eqnarray*} - M'(q, S) &=& \min_{0 \infegal k < l(q)} \acc{ M'(q[1..k], S) + + M'(q, S) &=& \min_{0 \leqslant k < l(q)} \acc{ M'(q[1..k], S) + \min( K(q, k, S), l(q) - k) } \end{eqnarray*} @@ -104,11 +101,11 @@ tous les préfixes d'une complétion. :title: métriques :tag: propriété - :math:`\forall q, \; M'(q, S) \infegal M(q, S)` + :math:`\forall q, \; M'(q, S) \leqslant M(q, S)` -Si :math:`q \notin S`, c'est évident puisque :math:`M'(q, S) \infegal M'(\emptyset, S) + l(q)`. +Si :math:`q \notin S`, c'est évident puisque :math:`M'(q, S) \leqslant M'(\emptyset, S) + l(q)`. Si :math:`q \in S`, cela découle de la constation précédente puisque : -:math:`M'(q, S) \infegal M'(q[[1..k]], S) + K(q, k, S) \infegal k + K(q, k, S)`. +:math:`M'(q, S) \leqslant M'(q[[1..k]], S) + K(q, k, S) \leqslant k + K(q, k, S)`. Quelques résultats ++++++++++++++++++ @@ -133,7 +130,7 @@ mot *actuellement*, plus long sans que cela souffre d'ambiguïté. Définition avancée ++++++++++++++++++ -Dans les faits, le :ref:`Dynamic Minimum Keystroke ` sous-estime +Dans les faits, le Dynamic Minimum Keystroke :eq:`completion-metric2` sous-estime le nombre de caractères nécessaires. Lorsqu'on utilise un mot comme tremplin, on peut aisément le compléter mais il faut presser une touche ou attendre un peu pour voir les nouvelles complétions associées à la première complétion choisie et maintenant @@ -154,8 +151,8 @@ considéré comme préfixe. C'est ce que prend en compte la définition suivante \begin{eqnarray*} M"(q, S) &=& \min \left\{ \begin{array}{l} - \min_{1 \infegal k \infegal l(q)} \acc{ M"(q[1..k-1], S) + 1 +\min( K(q, k, S), l(q) - k) } \\ - \min_{0 \infegal k \infegal l(q)} \acc{ M"(q[1..k], S) + \delta + \min( K(q, k, S), l(q) - k) } + \min_{1 \leqslant k \leqslant l(q)} \acc{ M"(q[1..k-1], S) + 1 +\min( K(q, k, S), l(q) - k) } \\ + \min_{0 \leqslant k \leqslant l(q)} \acc{ M"(q[1..k], S) + \delta + \min( K(q, k, S), l(q) - k) } \end{array} \right . \end{eqnarray*} @@ -171,12 +168,12 @@ Et le second cas à la séquence : * pression de la touche droite pour afficher les nouvelles complétions * sélection de la complétion *machine learning* -Le coût de la pression de la touche droite est noté :math:`\delta \infegal 1` qu'on prendra inférieur à 1. +Le coût de la pression de la touche droite est noté :math:`\delta \leqslant 1` qu'on prendra inférieur à 1. On remarque également qu'avec cette nouvelle métrique, il est possible de diminuer le nombre minimum de touches à presser pour des requêtes en dehors de l'ensemble :math:`S` à partir du moment où elles prolongent une complétion existante. C'est là un point très intéressant de cette métrique. -De manière évidente, :math:`\forall q, \; M'(q, S) \infegal M"(q, S)`. +De manière évidente, :math:`\forall q, \; M'(q, S) \leqslant M"(q, S)`. Questions +++++++++ diff --git a/_doc/sphinxdoc/source/c_nlp/completion_optimisation.rst b/_doc/c_nlp/completion_optimisation.rst similarity index 96% rename from _doc/sphinxdoc/source/c_nlp/completion_optimisation.rst rename to _doc/c_nlp/completion_optimisation.rst index b2481a40..14864b51 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion_optimisation.rst +++ b/_doc/c_nlp/completion_optimisation.rst @@ -2,9 +2,6 @@ Problème d'optimisation ======================= -.. contents:: - :local: - Enoncé 1 ++++++++ @@ -18,7 +15,7 @@ Enoncé 1 des complétions :math:`S=(s_i)` qu'on considère comme une permutation :math:`\sigma` de l'ensemble de départ : :math:`S(\sigma) = (s_i) = (c_{\sigma(j)})`. Ce système de complétion est destiné à un des utilisateurs qui forment des recherches ou requêtes - :math:`Q=(q_i, w_i)_{1 \infegal i \infegal N_Q}`. + :math:`Q=(q_i, w_i)_{1 \leqslant i \leqslant N_Q}`. :math:`q_i` est la requête, :math:`w_i` est la fréquence associée à cette requête. On définit l'effort demandé aux utilisateurs par ce système de complétion : @@ -82,7 +79,7 @@ Enoncé 2 à l'utilisateur, elle ne change pas l'ordre mais peut cacher certaines suggestions si elles ne sont pas pertinentes. Ce système de complétion est destiné à un des utilisateurs qui forment des recherches ou requêtes - :math:`Q=(q_i, w_i)_{1 \infegal i \infegal N_Q}`. + :math:`Q=(q_i, w_i)_{1 \leqslant i \leqslant N_Q}`. :math:`q_i` est la requête, :math:`w_i` est la fréquence associée à cette requête. On définit l'effort demandé aux utilisateurs par ce système de complétion : @@ -128,7 +125,7 @@ partageant le même préfixe. M'(q', S) = M'(q'-q, S') + M'(q, S) On sait déjà, par construction que -:math:`M'(q', S) \infegal M'(q'-q, S') + M'(q, S)`. +:math:`M'(q', S) \leqslant M'(q'-q, S') + M'(q, S)`. Par l'absurde, on suppose que :math:`M'(q', S) < M'(q'-q, S') + M'(q, S)`, comme la requête :math:`q-q'` est toujours affichée avant la requête :math:`q'`, cela voudrait dire qu'on aurait trouvé une façon plus optimale @@ -153,7 +150,7 @@ permettre de procéder par sous-ensemble pour trouver l'ordre optimal. .. math:: :label: best-order-lemme-completion - \forall k, \; \sigma(q[1..k]) \infegal \sigma(q[1..k+1]) + \forall k, \; \sigma(q[1..k]) \leqslant \sigma(q[1..k+1]) On note l'ensemble :math:`S'(q[1..k]) = \acc{ q[k+1..len(q)] \in S }` : diff --git a/_doc/sphinxdoc/source/c_nlp/completion_propriete.rst b/_doc/c_nlp/completion_propriete.rst similarity index 93% rename from _doc/sphinxdoc/source/c_nlp/completion_propriete.rst rename to _doc/c_nlp/completion_propriete.rst index e634b4be..2420558e 100644 --- a/_doc/sphinxdoc/source/c_nlp/completion_propriete.rst +++ b/_doc/c_nlp/completion_propriete.rst @@ -3,12 +3,9 @@ Propriétés mathématiques ======================== On s'intéresse principalement à la métrique :math:`M'` définie par -:ref:`Dynamic Minimum Keystroke ` mais les résultats +Dynamic Minimum Keystroke :eq:`completion-metric2` mais les résultats seront étendues aux autres quand cela est possible. -.. contents:: - :local: - Calcul pour une complétion ++++++++++++++++++++++++++ @@ -31,7 +28,7 @@ pour l'ensemble des complétions. :nowrap: \begin{eqnarray*} - M'(q, S) &=& \min_{d(q, S) \infegal k < l(q)} \acc{ M'(q[1..k], S) + \min( K(q, k, S), l(q) - k) } + M'(q, S) &=& \min_{d(q, S) \leqslant k < l(q)} \acc{ M'(q[1..k], S) + \min( K(q, k, S), l(q) - k) } \end{eqnarray*} Il n'est pas nécessaire de regarder tous les préfixes mais seulement ceux entre le plus long préfixe @@ -52,7 +49,7 @@ Calcul pour une requête en dehors +++++++++++++++++++++++++++++++++ Mais il est faux de dire que pour deux requêtes en dehors de l'ensemble -des complétions, :math:`q_1 \preceq q_2 \Longrightarrow M'(q_1, S) \infegal M'(q_2, S)`. +des complétions, :math:`q_1 \preceq q_2 \Longrightarrow M'(q_1, S) \leqslant M'(q_2, S)`. Le lemme suivant précise pourquoi .. mathdef:: @@ -110,10 +107,7 @@ Il y a un brin de poésie dans ce +1. L'application de l'implémentation du calc de la métrique montre que :math:`M'` et :math:`M"` sont très souvent égales. Je vais laisser ce :math:`\delta` sous forme de poésie pour le moment. -.. todoext:: - :title: terminer la démonstration pour *M* - - La côte anglaise. +Il faudrait terminer la démonstration pour *M*... Complétions emboîtées +++++++++++++++++++++ @@ -154,7 +148,7 @@ utilise la fonction :math:`K(q, k, S)` définie en :eq:`nlp-comp-k`. :nowrap: \begin{eqnarray*} - M(q, S) &=& \min_{0 \infegal k \infegal l(q)} k + K(q, k, S) + M(q, S) &=& \min_{0 \leqslant k \leqslant l(q)} k + K(q, k, S) \end{eqnarray*} Etant donné que le nombre minimum de caractères pour obtenir une complétion dans le trie diff --git a/_doc/c_nlp/index.rst b/_doc/c_nlp/index.rst new file mode 100644 index 00000000..62feded0 --- /dev/null +++ b/_doc/c_nlp/index.rst @@ -0,0 +1,14 @@ + +### +NLP +### + +NLP ou Natural Language Processing +u `traitement du langage naturel +`_. + +.. toctree:: + :maxdepth: 1 + + completion diff --git a/_doc/conf.py b/_doc/conf.py new file mode 100644 index 00000000..61c0ab4d --- /dev/null +++ b/_doc/conf.py @@ -0,0 +1,232 @@ +import sys +import os +from sphinx_runpython.github_link import make_linkcode_resolve +from sphinx_runpython.conf_helper import has_dvipng, has_dvisvgm +from mlstatpy import __version__ + + +extensions = [ + "nbsphinx", + "sphinx.ext.autodoc", + "sphinx.ext.coverage", + "sphinx.ext.githubpages", + "sphinx.ext.ifconfig", + "sphinx.ext.intersphinx", + "sphinx.ext.linkcode", + "sphinx.ext.viewcode", + "sphinx.ext.napoleon", + "sphinx.ext.todo", + "sphinx_gallery.gen_gallery", + "sphinx_issues", + "sphinx_runpython.blocdefs.sphinx_exref_extension", + "sphinx_runpython.blocdefs.sphinx_faqref_extension", + "sphinx_runpython.blocdefs.sphinx_mathdef_extension", + "sphinx_runpython.epkg", + "sphinx_runpython.gdot", + "sphinx_runpython.runpython", + "matplotlib.sphinxext.plot_directive", +] + +if has_dvisvgm(): + extensions.append("sphinx.ext.imgmath") + imgmath_image_format = "svg" +elif has_dvipng(): + extensions.append("sphinx.ext.pngmath") + imgmath_image_format = "png" +else: + extensions.append("sphinx.ext.mathjax") + +templates_path = ["_templates"] +html_logo = "_static/project_ico.png" +source_suffix = ".rst" +master_doc = "index" +project = "mlstatpy" +copyright = "2016-2025, Xavier Dupré" +author = "Xavier Dupré" +version = __version__ +release = __version__ +language = "fr" +exclude_patterns = ["auto_examples/*.ipynb"] +pygments_style = "sphinx" +todo_include_todos = True +nbsphinx_execute = "never" + +html_theme = "furo" +html_theme_path = ["_static"] +html_theme_options = {} +html_sourcelink_suffix = "" +html_static_path = ["_static"] + +issues_github_path = "sdpython/mlstatpy" + +nbsphinx_prolog = """ + +.. _nbl-{{ env.doc2path(env.docname, base=None).replace("/", "-").split(".")[0] }}: + +""" + +nbsphinx_epilog = """ +---- + +`Notebook on github `_ +""" + +# The following is used by sphinx.ext.linkcode to provide links to github +_linkcode_resolve = make_linkcode_resolve( + "mlstatpy", + ( + "https://github.com/sdpython/mlstatpy/" + "blob/{revision}/{package}/" + "{path}#L{lineno}" + ), +) + + +def linkcode_resolve(domain, info): + return _linkcode_resolve(domain, info) + + +latex_elements = { + "papersize": "a4", + "pointsize": "10pt", + "title": project, +} + +mathjax3_config = {"chtml": {"displayAlign": "left"}} + +intersphinx_mapping = { + "onnx": ("https://onnx.ai/onnx/", None), + "matplotlib": ("https://matplotlib.org/", None), + "numpy": ("https://numpy.org/doc/stable", None), + "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), + "python": (f"https://docs.python.org/{sys.version_info.major}", None), + "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), + "sklearn": ("https://scikit-learn.org/stable/", None), + "sklearn-onnx": ("https://onnx.ai/sklearn-onnx/", None), + "torch": ("https://pytorch.org/docs/stable/", None), +} + +# Check intersphinx reference targets exist +nitpicky = True +# See also scikit-learn/scikit-learn#26761 +nitpick_ignore = [ + ("py:class", "False"), + ("py:class", "True"), + ("py:class", "pipeline.Pipeline"), + ("py:class", "default=sklearn.utils.metadata_routing.UNCHANGED"), +] + +sphinx_gallery_conf = { + # path to your examples scripts + "examples_dirs": os.path.join(os.path.dirname(__file__), "examples"), + # path where to save gallery generated examples + "gallery_dirs": "auto_examples", +} + +# next + +preamble = """ +\\usepackage{etex} +\\usepackage{fixltx2e} % LaTeX patches, \\textsubscript +\\usepackage{cmap} % fix search and cut-and-paste in Acrobat +\\usepackage[raccourcis]{fast-diagram} +\\usepackage{titlesec} +\\usepackage{amsmath} +\\usepackage{amssymb} +\\usepackage{amsfonts} +\\usepackage{graphics} +\\usepackage{epic} +\\usepackage{eepic} +%\\usepackage{pict2e} +%%% Redefined titleformat +\\setlength{\\parindent}{0cm} +\\setlength{\\parskip}{1ex plus 0.5ex minus 0.2ex} +\\newcommand{\\hsp}{\\hspace{20pt}} +\\newcommand{\\acc}[1]{\\left\\{#1\\right\\}} +\\newcommand{\\cro}[1]{\\left[#1\\right]} +\\newcommand{\\pa}[1]{\\left(#1\\right)} +\\newcommand{\\R}{\\mathbb{R}} +\\newcommand{\\HRule}{\\rule{\\linewidth}{0.5mm}} +%\\titleformat{\\chapter}[hang]{\\Huge\\bfseries\\sffamily}{\\thechapter\\hsp}{0pt}{\\Huge\\bfseries\\sffamily} + +\\usepackage[all]{xy} +\\newcommand{\\vecteur}[2]{\\pa{#1,\\dots,#2}} +\\newcommand{\\N}[0]{\\mathbb{N}} +\\newcommand{\\indicatrice}[1]{ {1\\!\\!1}_{\\acc{#1}} } +\\newcommand{\\infegal}[0]{\\leqslant} +\\newcommand{\\supegal}[0]{\\geqslant} +\\newcommand{\\ensemble}[2]{\\acc{#1,\\dots,#2}} +\\newcommand{\\fleche}[1]{\\overrightarrow{ #1 }} +\\newcommand{\\intervalle}[2]{\\left\\{#1,\\cdots,#2\\right\\}} +\\newcommand{\\independant}[0]{\\perp \\!\\!\\! \\perp} +\\newcommand{\\esp}{\\mathbb{E}} +\\newcommand{\\espf}[2]{\\mathbb{E}_{#1}\\pa{#2}} +\\newcommand{\\var}{\\mathbb{V}} +\\newcommand{\\pr}[1]{\\mathbb{P}\\pa{#1}} +\\newcommand{\\loi}[0]{{\\cal L}} +\\newcommand{\\vecteurno}[2]{#1,\\dots,#2} +\\newcommand{\\norm}[1]{\\left\\Vert#1\\right\\Vert} +\\newcommand{\\norme}[1]{\\left\\Vert#1\\right\\Vert} +\\newcommand{\\scal}[2]{\\left<#1,#2\\right>} +\\newcommand{\\dans}[0]{\\rightarrow} +\\newcommand{\\partialfrac}[2]{\\frac{\\partial #1}{\\partial #2}} +\\newcommand{\\partialdfrac}[2]{\\dfrac{\\partial #1}{\\partial #2}} +\\newcommand{\\trace}[1]{tr\\pa{#1}} +\\newcommand{\\sac}[0]{|} +\\newcommand{\\abs}[1]{\\left|#1\\right|} +\\newcommand{\\loinormale}[2]{{\\cal N} \\pa{#1,#2}} +\\newcommand{\\loibinomialea}[1]{{\\cal B} \\pa{#1}} +\\newcommand{\\loibinomiale}[2]{{\\cal B} \\pa{#1,#2}} +\\newcommand{\\loimultinomiale}[1]{{\\cal M} \\pa{#1}} +\\newcommand{\\variance}[1]{\\mathbb{V}\\pa{#1}} +\\newcommand{\\intf}[1]{\\left\\lfloor #1 \\right\\rfloor} +""" + +epkg_dictionary = { + "ACP": "https://fr.wikipedia.org/wiki/Analyse_en_composantes_principales", + "AESA": "https://tavianator.com/aesa/", + "ApproximateNMFPredictor": "https://sdpython.github.io/doc/mlinsights/dev/api/mlmodel.html", + "AUC": "https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve", + "B+ tree": "https://en.wikipedia.org/wiki/B%2B_tree", + "BLAS": "https://www.netlib.org/blas/", + "Branch and Bound": "https://en.wikipedia.org/wiki/Branch_and_bound", + "C++": "https://fr.wikipedia.org/wiki/C%2B%2B", + "Custom Criterion for DecisionTreeRegressor": "https://sdpython.github.io/doc/mlinsights/dev/auto_examples/plot_piecewise_linear_regression_criterion.html", + "cython": "https://cython.org/", + "DecisionTreeClassifier": "https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html", + "DecisionTreeRegressor optimized for Linear Regression": "https://sdpython.github.io/doc/mlinsights/dev/auto_examples/plot_piecewise_linear_regression_criterion.html", + "dot": "https://fr.wikipedia.org/wiki/DOT_(langage)", + "Holm-Bonferroni method": "https://en.wikipedia.org/wiki/Holm%E2%80%93Bonferroni_method", + "ICML 2016": "https://icml.cc/2016/index.html", + "KMeans": "https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html", + "LAESA": "https://tavianator.com/aesa/", + "LAPACK": "http://www.netlib.org/lapack/", + "mlinsights": "https://sdpython.github.io/doc/mlinsights/dev/index.html", + "mlstatpy": "https://sdpython.github.io/doc/mlstatpy/dev/", + "numpy": ( + "https://www.numpy.org/", + ("https://docs.scipy.org/doc/numpy/reference/generated/numpy.{0}.html", 1), + ("https://docs.scipy.org/doc/numpy/reference/generated/numpy.{0}.{1}.html", 2), + ), + "PiecewiseTreeRegressor": "https://sdpython.github.io/doc/mlinsights/dev/api/mlmodel_tree.html#piecewisetreeregressor", + "Pillow": "https://pillow.readthedocs.io/en/stable/", + "Predictable t-SNE": "https://sdpython.github.io/doc/mlinsights/dev/auto_examples/plot_predictable_tsne.html", + "QuantileLinearRegression": "https://sdpython.github.io/doc/mlinsights/dev/api/mlmodel.html#quantilelinearregression", + "R-tree": "https://en.wikipedia.org/wiki/R-tree", + "R* tree": "https://en.wikipedia.org/wiki/R*_tree", + "Regression with confidence interval": "https://sdpython.github.io/doc/mlinsights/dev/auto_examples/plot_regression_confidence_interval.html", + "relu": "https://en.wikipedia.org/wiki/Rectifier_(neural_networks)", + "ROC": "https://fr.wikipedia.org/wiki/Courbe_ROC", + "scikit-learn": "https://scikit-learn.org/stable/index.html", + "sklearn": "https://scikit-learn.org/stable/index.html", + "sklearn-onnx": "https://onnx.ai/sklearn-onnx/", + "statsmodels": "http://www.statsmodels.org/stable/index.html", + "SVD": "https://fr.wikipedia.org/wiki/D%C3%A9composition_en_valeurs_singuli%C3%A8res", + "tqdm": "https://tqdm.github.io/", + "Visualize a scikit-learn pipeline": "https://sdpython.github.io/doc/mlinsights/dev/auto_examples/plot_visualize_pipeline.html", + "X-tree": "https://en.wikipedia.org/wiki/X-tree", + "wikipedia dumps": "https://dumps.wikimedia.org/frwiki/latest/", +} + +imgmath_latex_preamble = preamble +latex_elements["preamble"] = imgmath_latex_preamble diff --git a/_doc/sphinxdoc/source/defthe_index.rst b/_doc/defthe_index.rst similarity index 94% rename from _doc/sphinxdoc/source/defthe_index.rst rename to _doc/defthe_index.rst index 4fa626d9..6c37a5ef 100644 --- a/_doc/sphinxdoc/source/defthe_index.rst +++ b/_doc/defthe_index.rst @@ -2,9 +2,6 @@ Listes des définitions et théorèmes =================================== -.. contents:: . - :depth: 2 - Corollaires +++++++++++ diff --git a/_doc/examples/plot_logistic_decision.py b/_doc/examples/plot_logistic_decision.py index 6e9a3a5a..d41b4daf 100644 --- a/_doc/examples/plot_logistic_decision.py +++ b/_doc/examples/plot_logistic_decision.py @@ -11,16 +11,13 @@ une classe est majoritaire. Mais certains cas, c'est une chose compliquée. - -.. contents:: - :local: - Un cas simple et un cas compliqué +++++++++++++++++++++++++++++++++ Il faut choisir un seuil sur l'axe des abscisses qui permette de classer le jeu de données. """ + import numpy import matplotlib.pyplot as plt from pandas import DataFrame @@ -44,7 +41,7 @@ def random_set_1d(n, kind): def plot_ds(X, y, ax=None, title=None): if ax is None: ax = plt.gca() - ax.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor='k', lw=0.5) + ax.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k", lw=0.5) if title is not None: ax.set_title(title) return ax @@ -79,12 +76,12 @@ def plog2(p): def logistic(x): - return 1. / (1. + numpy.exp(-x)) + return 1.0 / (1.0 + numpy.exp(-x)) -def likelihood(x, y, theta=1., th=0.): +def likelihood(x, y, theta=1.0, th=0.0): lr = logistic((x - th) * theta) - return y * lr + (1. - y) * (1 - lr) + return y * lr + (1.0 - y) * (1 - lr) def criteria(X, y): @@ -102,12 +99,14 @@ def criteria(X, y): p2 = numpy.sum(y[i:]) / (y.shape[0] - i) res[i, 2] = p1 res[i, 3] = p2 - res[i, 4] = 1 - p1**2 - (1 - p1)**2 + 1 - p2**2 - (1 - p2)**2 - res[i, 5] = - plog2(p1) - plog2(1 - p1) - plog2(p2) - plog2(1 - p2) + res[i, 4] = 1 - p1**2 - (1 - p1) ** 2 + 1 - p2**2 - (1 - p2) ** 2 + res[i, 5] = -plog2(p1) - plog2(1 - p1) - plog2(p2) - plog2(1 - p2) th = x[i] - res[i, 6] = logistic(th * 10.) - res[i, 7] = numpy.sum(likelihood(x, y, 10., th)) / res.shape[0] - return DataFrame(res[1:-1], columns=['X', 'y', 'p1', 'p2', 'Gini', 'Gain', 'lr', 'LL-10']) + res[i, 6] = logistic(th * 10.0) + res[i, 7] = numpy.sum(likelihood(x, y, 10.0, th)) / res.shape[0] + return DataFrame( + res[1:-1], columns=["X", "y", "p1", "p2", "Gini", "Gain", "lr", "LL-10"] + ) X1, y1 = random_set_1d(1000, False) @@ -123,7 +122,7 @@ def criteria(X, y): def plot_ds(X, y, ax=None, title=None): if ax is None: ax = plt.gca() - ax.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor='k', lw=0.5) + ax.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k", lw=0.5) if title is not None: ax.set_title(title) return ax @@ -135,8 +134,8 @@ def plot_ds(X, y, ax=None, title=None): fig, ax = plt.subplots(1, 2, figsize=(12, 6), sharey=True) plot_ds(X1, y1, ax=ax[0], title="easy") plot_ds(X2, y2, ax=ax[1], title="difficult") -df1.plot(x='X', y=['Gini', 'Gain', 'LL-10', 'p1', 'p2'], ax=ax[0], lw=5.) -df2.plot(x='X', y=['Gini', 'Gain', 'LL-10', 'p1', 'p2'], ax=ax[1], lw=5.) +df1.plot(x="X", y=["Gini", "Gain", "LL-10", "p1", "p2"], ax=ax[0], lw=5.0) +df2.plot(x="X", y=["Gini", "Gain", "LL-10", "p1", "p2"], ax=ax[1], lw=5.0) ######################################## # Le premier exemple est le cas simple et tous les diff --git a/_doc/sphinxdoc/source/glossary.rst b/_doc/glossary.rst similarity index 100% rename from _doc/sphinxdoc/source/glossary.rst rename to _doc/glossary.rst diff --git a/_doc/sphinxdoc/source/i_ex.rst b/_doc/i_ex.rst similarity index 69% rename from _doc/sphinxdoc/source/i_ex.rst rename to _doc/i_ex.rst index 1d5e6c76..6c6676df 100644 --- a/_doc/sphinxdoc/source/i_ex.rst +++ b/_doc/i_ex.rst @@ -4,8 +4,5 @@ Examples ======== -.. contents:: - :local: - .. exreflist:: :contents: diff --git a/_doc/index.rst b/_doc/index.rst new file mode 100644 index 00000000..86cdd522 --- /dev/null +++ b/_doc/index.rst @@ -0,0 +1,82 @@ + +*en construction permanente* + +.. |gitlogo| image:: _static/git_logo.png + :height: 20 + +Les maths d'abord, la programmation ensuite +=========================================== + +Le livre `The Elements of Statistical Learning `_ +est considéré comme la bible en matière de machine learning. Ce site aborde des sujets connexes. +Le site est aussi disponible (format brut de fonderie) sur +`github/mlstatpy `_ |gitlogo|. + +.. toctree:: + :maxdepth: 1 + :caption: Mathematics + + c_clus/index + c_ml/index + c_ml/index_reg_lin + c_ml/index_reg_log + c_nlp/index + c_metric/index + c_algo/index + c_garden/index + +.. toctree:: + :maxdepth: 1 + :caption: Examples + + api/index + i_ex + defthe_index + auto_examples/index + notebooks/index + +.. toctree:: + :maxdepth: 1 + :caption: More + + glossary + CHANGELOGS + license + genindex + modindex + search + +On fait beaucoup de choses avec l'informatique mais en pratique +on doit maintenir, on doit réécrire sans cesse. +Faire un peu de théorie ça repose. + +Xavier Dupré + +.. only:: html + + .. image:: https://ci.appveyor.com/api/projects/status/5env33qptorgshaq?svg=true + :target: https://ci.appveyor.com/project/sdpython/mlstatpy + :alt: Build Status Windows + + .. image:: https://circleci.com/gh/sdpython/mlstatpy/tree/main.svg?style=svg + :target: https://circleci.com/gh/sdpython/mlstatpy/tree/main + + .. image:: https://badge.fury.io/py/mlstatpy.svg + :target: https://pypi.org/project/mlstatpy/ + + .. image:: https://img.shields.io/badge/license-MIT-blue.svg + :alt: MIT License + :target: https://opensource.org/license/MIT/ + + .. image:: https://codecov.io/github/sdpython/mlstatpy/coverage.svg + :target: https://codecov.io/github/sdpython/mlstatpy + + .. image:: http://img.shields.io/github/issues/sdpython/mlstatpy.png + :alt: GitHub Issues + :target: https://github.com/sdpython/mlstatpy/issues + +Older versions +++++++++++++++ + +* `0.5.0 <../v0.5.0/index.html>`_ +* `0.4.0 <../v0.4.0/index.html>`_ diff --git a/_doc/license.rst b/_doc/license.rst new file mode 100644 index 00000000..f4b00c96 --- /dev/null +++ b/_doc/license.rst @@ -0,0 +1,7 @@ +.. _l-license: + +License +======= + +.. literalinclude:: LICENSE.txt + :language: none diff --git a/_doc/notebooks/dsgarden/classification_multiple.ipynb b/_doc/notebooks/dsgarden/classification_multiple.ipynb index 890f9d4a..233fc711 100644 --- a/_doc/notebooks/dsgarden/classification_multiple.ipynb +++ b/_doc/notebooks/dsgarden/classification_multiple.ipynb @@ -1,256 +1,114 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Classification multiple\n", - "\n", - "Explorations autour d'un probl\u00e8me de classification multiple." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## D\u00e9but de l'histoire" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$\\mathbf{1\\!\\!1}_{y_i}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Confusions\n", - "\n", - "Un des premiers r\u00e9flexes apr\u00e8s avoir appris une classification multi-classe est de regarder la [matrice de confusion](https://fr.wikipedia.org/wiki/Matrice_de_confusion). Certaines classes sont difficiles \u00e0 classer, d'autres non. Je me demandais s'il existait un moyen de d\u00e9terminer cela sans apprendre un classifieur. On souhaite apprendre la classification des points $(X_i, y_i)$, $X_i$ est un vecteur, $y_i$ la classe attendue. Si $\\hat{y_i}$ est la classe pr\u00e9dite, l'erreur de classification est :\n", - "\n", - "$$E=\\sum_i \\mathbb{1}_{y_i \\neq \\hat{y_i}}$$\n", - "\n", - "On note $c_{ij} = \\mathbb{1}_{y_i = j}$ et $\\hat{c_{ij}} = \\mathbb{1}_{\\hat{y_i} = j}$. On note le vecteur $C_j=(c_{ij})_i$ et $\\hat{C_j}=(\\hat{c_{ij}})_i$. On peut r\u00e9\u00e9crire l'erreur comme :\n", - "\n", - "$$E=\\sum_{ij} \\mathbb{1}_{y_i = j} \\mathbb{1}_{\\hat{y_i} \\neq j} =\\sum_{ij} \\mathbb{1}_{y_i = j} (1-\\mathbb{1}_{\\hat{y_i} = j}) =\\sum_{ij} c_{ij} (1-\\hat{c_{ij}})= \\sum_j < C_j , 1-\\hat{C_j}>$$\n", - "\n", - "C'est aussi \u00e9gal \u00e0 :\n", - "\n", - "$$E = \\sum_{k \\neq j} $$\n", - "\n", - "Et $$ correspond au nombre d'erreurs de confusion : le nombre d'\u00e9l\u00e9ments de la classe $j$ class\u00e9s dans la classe $k$. $$ est le nombre d'\u00e9l\u00e9ments correctement class\u00e9s dans la classe $j$. On peut montrer que $$\\sum_{k, j} = N$$ o\u00f9 $N$ est le nombre d'observations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Clustering\n", - "\n", - "Et si nous introduisions un clustering interm\u00e9diaire. On construit $Q$ cluster, $q_i$ est le cluster du point $X_i$ et on note $d_{il} = \\mathbb{1}_{q_i = l}$ et le vecteur $D_l=(d_{il})_i$.\n", - "\n", - "$$E = \\sum_{k \\neq j} $$\n", - "\n", - "On note $X.Y$ le produit terme \u00e0 terme de deux vecteurs.\n", - "\n", - "$$E = \\sum_{k \\neq j, l } = \\sum_{k \\neq j, l } $$\n", - "\n", - "Le nombre d'erreurs est la somme des erreurs faites sur chaque cluster. Supposons maintenant qu'un classifieur retourne une r\u00e9ponse constante sur chacun des clusters, on choisit la classe plus repr\u00e9sent\u00e9e. Ca ressemble beaucoup \u00e0 un [classifieur bay\u00e9sien](http://scikit-learn.org/stable/modules/naive_bayes.html). On note $f(l)$ cette classe la plus repr\u00e9sent\u00e9e. Elle v\u00e9rifie :\n", - "\n", - "$$f(l) = \\arg \\max_j $$\n", - "\n", - "Cela signifie que $\\hat{c_{ij}} = \\sum_l \\mathbb{1}_{j = f(l)} d_{il}$. Si on note $l(i)$ le cluster associ\u00e9 \u00e0 $i$. On continue : $\\hat{c_{ij}} = \\mathbb{1}_{j = f(l(i))}$. On d\u00e9finit l'erreur $e(l)$ l'erreur de classification faite sur chaque cluster $l$ :\n", - "\n", - "$$e(l) = \\sum_i d_{il}\\sum_j c_{ij} (1-\\mathbb{1}_{j = f(l)}) = \\sum_i d_{il}\\left(\\sum_j c_{ij} -\\sum_j c_{ij}\\mathbb{1}_{j = f(l)}\\right) = \\sum_i d_{il}\\left(1 -c_{i,f(l)}\\right)= \\sum_i d_{il} -\\sum_i d_{il}c_{i,f(l)}$$\n", - "\n", - "Pour r\u00e9sumer, l'erreur est le nombre d'\u00e9l\u00e9ments moins le nombre d'\u00e9l\u00e9ments dans la classe majoritaire du cluster. Si le nombre de clusters $Q$ devient sup\u00e9rieur ou \u00e9gal au nombre d'observations, cette erreur devient nulle." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Mise en pratique\n", - "\n", - "L'id\u00e9e est de voir comment \u00e9volue cette erreur de classification na\u00efve en fonction du nombre de clusters. La diff\u00e9rence par rapport \u00e0 un classifieur est qu'on sait comment sont fabriqu\u00e9s les clusters et qu'on peut imaginer les classes comme un assemblage de clusters d'une forme connue." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classification multiple\n", + "\n", + "Explorations autour d'un problème de classification multiple." + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Début de l'histoire" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\mathbf{1\\!\\!1}_{y_i}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Confusions\n", + "\n", + "Un des premiers réflexes après avoir appris une classification multi-classe est de regarder la [matrice de confusion](https://fr.wikipedia.org/wiki/Matrice_de_confusion). Certaines classes sont difficiles à classer, d'autres non. Je me demandais s'il existait un moyen de déterminer cela sans apprendre un classifieur. On souhaite apprendre la classification des points $(X_i, y_i)$, $X_i$ est un vecteur, $y_i$ la classe attendue. Si $\\hat{y_i}$ est la classe prédite, l'erreur de classification est :\n", + "\n", + "$$E=\\sum_i \\mathbb{1}_{y_i \\neq \\hat{y_i}}$$\n", + "\n", + "On note $c_{ij} = \\mathbb{1}_{y_i = j}$ et $\\hat{c_{ij}} = \\mathbb{1}_{\\hat{y_i} = j}$. On note le vecteur $C_j=(c_{ij})_i$ et $\\hat{C_j}=(\\hat{c_{ij}})_i$. On peut réécrire l'erreur comme :\n", + "\n", + "$$E=\\sum_{ij} \\mathbb{1}_{y_i = j} \\mathbb{1}_{\\hat{y_i} \\neq j} =\\sum_{ij} \\mathbb{1}_{y_i = j} (1-\\mathbb{1}_{\\hat{y_i} = j}) =\\sum_{ij} c_{ij} (1-\\hat{c_{ij}})= \\sum_j < C_j , 1-\\hat{C_j}>$$\n", + "\n", + "C'est aussi égal à :\n", + "\n", + "$$E = \\sum_{k \\neq j} $$\n", + "\n", + "Et $$ correspond au nombre d'erreurs de confusion : le nombre d'éléments de la classe $j$ classés dans la classe $k$. $$ est le nombre d'éléments correctement classés dans la classe $j$. On peut montrer que $$\\sum_{k, j} = N$$ où $N$ est le nombre d'observations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clustering\n", + "\n", + "Et si nous introduisions un clustering intermédiaire. On construit $Q$ cluster, $q_i$ est le cluster du point $X_i$ et on note $d_{il} = \\mathbb{1}_{q_i = l}$ et le vecteur $D_l=(d_{il})_i$.\n", + "\n", + "$$E = \\sum_{k \\neq j} $$\n", + "\n", + "On note $X.Y$ le produit terme à terme de deux vecteurs.\n", + "\n", + "$$E = \\sum_{k \\neq j, l } = \\sum_{k \\neq j, l } $$\n", + "\n", + "Le nombre d'erreurs est la somme des erreurs faites sur chaque cluster. Supposons maintenant qu'un classifieur retourne une réponse constante sur chacun des clusters, on choisit la classe plus représentée. Ca ressemble beaucoup à un [classifieur bayésien](http://scikit-learn.org/stable/modules/naive_bayes.html). On note $f(l)$ cette classe la plus représentée. Elle vérifie :\n", + "\n", + "$$f(l) = \\arg \\max_j $$\n", + "\n", + "Cela signifie que $\\hat{c_{ij}} = \\sum_l \\mathbb{1}_{j = f(l)} d_{il}$. Si on note $l(i)$ le cluster associé à $i$. On continue : $\\hat{c_{ij}} = \\mathbb{1}_{j = f(l(i))}$. On définit l'erreur $e(l)$ l'erreur de classification faite sur chaque cluster $l$ :\n", + "\n", + "$$e(l) = \\sum_i d_{il}\\sum_j c_{ij} (1-\\mathbb{1}_{j = f(l)}) = \\sum_i d_{il}\\left(\\sum_j c_{ij} -\\sum_j c_{ij}\\mathbb{1}_{j = f(l)}\\right) = \\sum_i d_{il}\\left(1 -c_{i,f(l)}\\right)= \\sum_i d_{il} -\\sum_i d_{il}c_{i,f(l)}$$\n", + "\n", + "Pour résumer, l'erreur est le nombre d'éléments moins le nombre d'éléments dans la classe majoritaire du cluster. Si le nombre de clusters $Q$ devient supérieur ou égal au nombre d'observations, cette erreur devient nulle." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Mise en pratique\n", + "\n", + "L'idée est de voir comment évolue cette erreur de classification naïve en fonction du nombre de clusters. La différence par rapport à un classifieur est qu'on sait comment sont fabriqués les clusters et qu'on peut imaginer les classes comme un assemblage de clusters d'une forme connue." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/dsgarden/correlation_non_lineaire.ipynb b/_doc/notebooks/dsgarden/correlation_non_lineaire.ipynb index 56196f0e..009ddf49 100644 --- a/_doc/notebooks/dsgarden/correlation_non_lineaire.ipynb +++ b/_doc/notebooks/dsgarden/correlation_non_lineaire.ipynb @@ -1,3379 +1,3043 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Corr\u00e9lations non lin\u00e9aires\n", - "\n", - "Les corr\u00e9lations indiquent si deux variables sont lin\u00e9airement \u00e9quivalentes. Comment \u00e9tendre cette notion \u00e0 des variables li\u00e9es mais pas de fa\u00e7on lin\u00e9aire." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Un exemple" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "0 5.1 3.5 1.4 0.2\n", - "1 4.9 3.0 1.4 0.2\n", - "2 4.7 3.2 1.3 0.2\n", - "3 4.6 3.1 1.5 0.2\n", - "4 5.0 3.6 1.4 0.2" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import datasets\n", - "\n", - "iris = datasets.load_iris()\n", - "X = iris.data\n", - "Y = iris.target\n", - "import pandas\n", - "df = pandas.DataFrame(X)\n", - "df.columns = [\"X1\", \"X2\", \"X3\", \"X4\"]\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAALECAYAAAACS1bEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsvXucFNWZ//+pqr7MjDQ4wAy6uWBMvAUh33HDgDBeUTaCBNQkkJjIqMDqJAFZk8iAEcwKgybGlTW4UVSiScQYg2j0tUvErCuoDCb8ViFIjBo0q+Fmgw1z6e6q+v3RU01X16mZ6mtVdX/erxcvnZrq6ueces5TZ+o85/NIuq7rIIQQQgghpMqQ3TaAEEIIIYQQN+BEmBBCCCGEVCWcCBNCCCGEkKqEE2FCCCGEEFKVcCJMCCGEEEKqEk6ECSGEEEJIVcKJMCGEEEIIqUo4ESaEEEIIIVUJJ8KEEEIIIaQq4USYEEIIIYRUJZwIE0IIIYSQqoQTYUIIIYQQUpUE3DagEA4ePAJN0902w3Xq6+sQjXa5bYZnsOuPhoZI2W1x4qOVcv8qpR2Ae23xqo8WGz/4ih9sBMpvp9s+6pf74gS2pTTk6qN8I1wBBAKK2yZ4Cr/1h9/staNS2gFUVlu8iB/61w82Av6xs1hUUnvZFm/g+kR4w4YNmDp1KqZOnYrbb7/dbXMIIYQQQkiV4OpEuLu7G8uXL8cjjzyCDRs24NVXX8VLL73kpkmEEEIIIaRKcHUirKoqNE1Dd3c3kskkkskkwuGwmyYRQgghhJAqwdXNcoMGDcKCBQtwySWXoLa2FmPHjsVZZ53lpkmEEEIIIaRKkHRdd0124Y033sCiRYvwwAMPIBKJ4Dvf+Q7GjBmDOXPmuGUSIaQC0TQdh4/2IpHUEAzIGHJcGLIsuW0WISWDPk/KQSX4matvhDdv3oyzzz4bw4YNAwBcfvnl+OUvf+l4IuxH+bTI4FrUhJ13e09vErGPuvs9p6Ehgv37Y4WaVjHY9Yfbsj92VMr982o7FEVGtCuBFWs7sS/ajcb6WixubUZ9XRCqqgk/41ZbvOqjxcarvpKJH2wExHbm4/O5fF+5yfRRv9wXJ/i9LaX0s0LI1UddnQiffvrp+OEPf4iuri7U1tbi+eefx+jRo900qeTUhAOYduMGx+c/fed0+HeYEOI+SSAdqAFgX7QbK9Z2oqNtIvz13oIQZ9DnSTmoFD9zdSLc0tKCP/3pT7j88ssRDAYxevRozJs3z02TCCEVhqrp6UBtsC/aDVXT/V1RiBAb6POkHFSKn7lu67x58zj5JYSUDEWW0FhfawrYjfW1UGQJUP2VWkWIE+jzpBxUip+5XlCDEEJKSQDA4tZmNNbXAkA6j831twCElAj6PCkHleJnfrOXDECum/EAZxvyCPErqqqhvi6IjraJUDUdiiwh0HeckEqEPk/KQaafQZIAXfeln3EiXGHkuhkP4Ia8akNRZCSBAR+QuZ63L9oFKLIvAyEhfkXTdOiKbBmnqqpBQt9DXtWh9p0fDCmIq4CqaVBkGeGghN6ExgkzKSmi54msSCZfDClAIq4OdKmiw4kwIVWEU7mbYp/nJn6wkZB8UBQZe/7+EW57cKsj3w6GFByIxdGRMRbaW5uxbuMb2LpzL8cGyYlCnhO3XT8BR2MJiy8Oj4TKPhlmjjAhVYSd3E2yxOe5iR9sJCQfkkB6EgwM7NtxFemJh3F+x9pOTBo70tHnCcmkkOdEMqkLfdGFF8KcCBNSTfQnd1PK89zEDzYSkg+5+raqacLzI3VBR58nJJNCnhOyBJvPln8lghNhQqoIQ+4mk7TcTQnPcxM/2EhIPuTi24oiQ5Fl4fmxrsSAnyckGzv/C8gydEVGUpKgKzICAr/TdNj4bvmnpZwIE1JFOJW7cXpeSJHQnnVee2szQop3HqSVIvFDSDYBADdfM25A3zZyNH+7+S0smj3WMl43bdvT7+cJESGKrUtam9GTSKJ99RbM69iE9tVb0JNIYknWeYGA3bOj/O2QdF337RpIZv1xv9DQEMm5xPJAtcgz65Xnen2n3+En7Oq351p/vBg48dFy15svpmqErsi478nXMWnsSETqgoh1JbBp2x7MmzEakoc22zhts0G570nm95YbN+KoW/2bC36wEQCGDRuEA4e6Bhyn7au3YF+0GxeN/QQuO/8UKIqEoJK7aoTbPuqX++KESmiLEVsN+TRFkfC9f99sKbJxx7dboKp6WVQjcvVR/uFHSJVhJ6uUz3mqpmPrzr3YunOv6fi1XzzTU8HFaZsJ8RuyLEFStQHHqTExeW7be3hu23sAgPvaJyHeo3NskLwxYqsxqY9rkjD3N57QEOjTGTb8TFWR4XsqEi45H1MjCCF5w/xbQrwPxykpF370NU6ECSF5w/xbQrwPxykpF370NS/bRgjxOJVSYpOQSoYll0m58KOv8Y0wIUSIopglcBRFHC5UVYOkamisr4PUV9qVEFIejBLLTsdpQNc5Tkle5PpM8Iuv8Y0wIcQCyxIT4n1yLbFMSL5U8jOBb4QJIRZYlpgQ75NriWVC8qWSnwl8I5xFZHAtasLOu6WnN4nYR90Dn0iIj7ArnalpgKzIvsn9IqSSsS9xC0CSOEZJ3mRrr2v9lFP2+0TS7/YXnZpwIOeCF/6WwybEiiGBky2KrkNH++qXKm5pjBA/YjdO/29/DLeu2coxSvJClAZx23UThL6myBKg+quwWTZMjSCEWLCTwHngqR0VuTRGiB8RlVheMKsJ6zb+GQDHKMkPURrEA0/t8J0smlMqoQ2EkCIjksCBBEsFuUpZGiPEj6iqhpEnDE6PU1mW8MNH/oDd70bT53CMklwRpdxs3bkX8y4b7StZNKdwbBBChGSXJdYVWbg0FgrKUFUd+6JdgCLbBsfsnLNiBNFSXJMQL5Lt6yFFQlzVceBQNwAJigJAl3B8JGT6XKUsX5Pi4CRmKrKEcaNGYNLYkYjUBRHrSmDTtj2AjgHLebtlcyFwIkwIcYSRLpGZN7Zs7ngcjsWxfABJnVJI71SynA8hmYh8vb21Ges2voGtO/eisb4W82c24ekX38KsyacDQPq4sXxdrkkL8S5OY2ZIkTBr8unoyPK3kCIhUWZHKkecZ44wIcQRmekS97VPQkfbRNQEA+lJMGCfk1gK6Z1KlvMhJBORr3es7cSksSPTP696bDsmjR2JjrWdmDdjTHqM8g9DYuA0ZsZVPT0JNs7rWNuJuAurCuWI85wIE0Ick10xKKlptpI6ps/1I72Tty0luCYhXsTO1yN1QcvPqTHgn6pepHw4jZmFxlanFeiKaXMhcCJMCMmbgCyndxEbNNbXIiCbQ4sh85R9niJLeX93Ka5JiBex8/VYV8LyM8cAscNpzCwkthqpDO2rt2Bexya0r96CaFci78lwOeI8J8KEkLyRFWDBrCaLfJOsmM+zk2MrZJNCKa5JiBcR+fqi2WNTG5j6fp4/swmbtu3hGCC2OI2ZhcTWYqcylCPOuzpeHn/8cfz85z9P//y3v/0N06dPxy233OKiVYQQp8QTGh5+ZhfmTB+d3l388DO7cOOVZ5mCi0iOrdCdv6W4JiFexPD1FW0TsT/ajVhXAs9ueQeTxo7ElyadiqGRGkDSMW/GaI4BYovTmFlIbO0vlSGfCWc54ryrE+Evf/nL+PKXvwwAePPNN/HNb34T3/rWt9w0iRCSA4osIRrrwYq1nelj5ZRrypZ448544iallHlSVQ2SIuOuR/+Ynmg8t+09NNbXoqNtIqRk6ns4BqqPXPzOaczMN7baVTss5JlQ6jjvmdSIZcuWYeHChRg6dKjbphBCHOJ02arYeWOEeI1y+LhovN18zTimQlQxXoutfkxZ84RtL730Enp6enDJJZe4bQohJAcyl60gSYCuC99G2OWNdbRNBLf1kEqgHD4uWiYefnwdDh48UqRvIH7Da7HVjylrnpgIr1u3DldffXXOnxs2bFAJrMmdhoaI69cv1IZSt6HceKU9Tn3UK/YWi0RCRfRIL1RVg6LICAdl1EdqTLnETzz/JiBJnm27V+0qNm7FUT/0by427ot2WXIj6yM1gCRBDygIBmREakOIdceRSGoIBmQMOS4MWZagaToOH+11fLwQO/1Ito9WUnsLbYvI7/ZFu6EoMuSgMqDvFBOjLYbfYoDvTiY1RGM9SKo6AoqE+kgNAoHyv8l2fSIcj8exbds2rFy5MufPHjx4BFqRNUPzccr9+2OuXr+hIZI+J99BlUsbvE5mf2QfLzdOfNTOXr9htCNUE8D+w72WqkTzZ/0/3Hr/K+ljC2Y1QZG96Xtu3ROv+mix8YPP52xjVgny0z5Zj6umnoH2n2zGvmg3xo0aYanWtbi1GcMiIRyMxS2Vs+yOZxfIKHdfuu2jfvAdpxSlLVl+BwDjRo3AoVhvWatuGm1xWgkuGFJwIBa3PCeGR0JIxAvLAs7VR11P0Nu9ezdOOukk1NXVuW0KIaQI9CbEVYkkSKZjd6/bDo07e0iFkJ0bOWvyqbh73fa0zxtV37KXsOOqeGnb7jgrJ5JMRDm5137xTNd8x3n1OthUryuDkVm4/kb4vffewwknnOC2GYSQIqHaVJvLXhnbF+1GUtPcD0KEFIHs3EgApnFgVH3LxKgCl9vx/GSoSGUiysnViixhlpM9Dr+7P78vt3+7Pp6mTJmCKVOmuG0GISQLp5I8xnn7ol2AIkORxPI5siJjcWtzOkd407Y9ZZNZI6QcGDJPYUVGEhJu/1YLDh+J44nn30xXfbPKSlmXtvs7HgrKUFXdNOkhJJNg0M6nSh9vncqnKbKMcaNGYNLYkVnPBBlQy/ta2PWJMCHEezjN8xKdd9v1E9De2mzK/Vo6dzy6uhNYs+F1Uz5YSJGQYHoEqSBEY2L+zCb8YdffLeNicWszQkpqaTv7/N9ufsty/rK543E4FsfyjGM3XzMOQ2oCnt6VT0qHyN+WtDZj2dzxWJaxJ8OQMCt1uA0pksVvRbE+HJQsOfPtrc0IB6Wyp0dwIkwIseBUkkd03s33voQ7F5yLjrYWqJoGRZYhS8BN92+25INRPo1UGqIxseqx7ehoa0FIgUVWKhFX+5a2W3DgcDcOH4njkWd3Yfe7Ubx/4GjGOJIgQcKyrHF024NbOY6qGJG/LV/biZVtLa5ImMVVHes2vmFSCFq38Q3MmzHa5KO9CU2YI+yGL3MiTEgFk2+lK+d5XrpQFi2RrSPcz/VqQwriKtKT5pCCgncNE1JK7MaVoshIAFj41bPSY2H3u1HUR2qgQ0d3InV+bVBGXNXRq+npwgeqpuGmezYDSClOLG5txgnDMzeRS4AMR+OSVA92sVqXdMBhFoTInwHk/ew4FIubjh2KxR0/OzQNkBW5KLY4hWOHkArFaXqD8LMO87zCQQVXTT0jvTu+sb4W//K1f0RPb9K0LHfbdROE1wsHFRyIWaXWiiGhQ0gpsBtXIrmz+TOb8N9/eA8XfP4TWLx6S78yasdHQmisr0V9pAbfmHIGnn7xLUw759O47YGtpvPGjRqBrTv3pu0pZ0lz4j0CglzycaNGIHbUWewX+fOyueORSGimFBynz45QULY8ExbMakIoKEPLiOl258kK0P6TLUWxxSmuy6cRQkqDUxkbEU7LZKqabpKI2hftxo9/+Qfs/bDLdOyBp3YIr6dqYqk1zoGJV7EbVyK5s1WPbcflF5ziSEZNU1NjbtbkU7Hqse2YNHYkVj223XLetV88kyWWSRpZARbMajL5xNWXOpdPE/nz3g+70hPPgT6fjabC8kwQSWXanbf3YHfRbHEKxw8hFYrT9AbhZx2WyUyqYgmcmpD5G7bu3It5l422XK/X5vNuSOgQ4gT7cSX25YAimY7byagltdSYq6sJYF+02/Y8XUd6ObmhvhYNLLFc1cQTGh5+ZpcpxSDWHXcc+0X+XBMK5P3sSNqMg2ypTLvzpKwE4UJscQrfCBNSoRjpDZmkl1EdoKoaJFVDQNchqVo6B1JXZCQlCboipyWesr+jJ560HIMuWa9n83lFZmgi3kSRJYwbNQKLW5vR0TYxna7Qny9nHjdk1KznSX1jIjVu7c5LqjpWrO3EXY/+ERJQ8rK5xFtkx+CALOP4SMh0TiKhOY79oudETzyZ97PD6XPH7rzsZ0chtjiFTxuCeEJFQ0PE8b/I4NqBL0pcx2l6g1OMXLL21Vswr2MT2ldvAWQd7YLvaBxaZzpmyOJkE1Jg+Xx7n6QUIV4kpKRkn9ZseB3tq7dgzYbXMWvy6Xhlx/uYP7PJMhYMeTTj+KZte4RjxhiXxrjdtG2P5XqLZo/F+v9+s+CxTPyJKAYndc3ij0MiISy52lnsFz0nRgytw5I8nx1Onzui85a0NmNE1rOjEFucIum67tsM+8z648WioSGCaTducHz+03dOz6lWeCmun1mvPNfrG99RyjaXG7v67bnWHy8GTny0KPXmbchXNUKErsho79vwY7B0zji89uZ+XNR8EhQZUDXguc6/4vILTkE8oaWVIMJBCfEecVZX0IOqEaW8JwN9b7kpRRwdCLf6NxfsbBSNg8b6WsyZPhpPPP8mrrjwFAwZFMLwIbUIQE+vpGSOw5AiIZ5RFCN7XBrnS5IEXQdUXYciSQgGJfT0qqbPlLsv3fZRP/iOU3Jti10MvveJ1yz+eMe3W0yFV/qL/cVQjchsS67FmAb63nxsyQX+QUlIBWNUugoAgKrnJKaeHaREZTtrQgHU1QQRCsrQdR2KIqGuJoju3iQCel8Ol6oirtoHx0RczbBRZYEN4mnscoQjdUHsfjeKFWs7AQD3tU+C1PeeyVxxDohrOgAJUpa+lTFGevvGiKKnJtKBvt8lEr59b0WKQC75vImEDqcZZqLnhCHrNxDGi4wPDhyFoijpFxmi64niv+j55PRYseBEmBBiQVgxTiCBNiQSxOc/ewKW3HtM7qa9tRl1NQHEuxP9Xq/YEjiElAM7acFYV8L0cyCrVKwxBh7d+AamnfPptCJEf/JrxhgBYDt+SPUg8j0jhzbbH3XoaF/9Ul7x1mm8DoYUHIjFB5S/9Hr8Z44wIcSCSFJHJIEWDgSF8me9cW3A6xVbAoeQciDKbVw0eyw2bduT/tnQQ83EGAN2smgi+TVjjHD8ECC3fN4HntqRt7849be4Ckfyl173X74RJoRYEC3BiSTQ7CSjsuXPCpFyI8RLZEsLyrKEdRt3Y9LYkZhx3mcQ60rg4Wd24cYrzxKOATtZNPuxpKf/3+53pDqwk7VEUDEdgwRT0RUgt3jrvLJoZcR/L9hACHEZ0Waey8472bIJLiXxdOzhqwiqGjXW1yKgpHKGM683btQITBo7Mq11uWnbHlbEIr4iO4c3VSoZeO0vB/DctvfS54mqvWXLomWPGUWW02Okob4Wg2qDkKRUJnEw2E+lR1L1ZOfa6oo4LjuNt4ZEYHa8DsgydGRswJPEfuk0/oeCsuMNfaWEE2FCqhxhPvD1E3DuWZ/ArWteNuV+xZMabr73WN5Zx7cmor212VIutieRxLL7jpVYXnJ1M66a+lncmlF2OSWpJiPugRwxQgZCNE7aW5vxP398D/NnNllyfgOAaVOPsaz96MY3LOfPn9mEV3a8bym9PH9mE55+8S3Mmnw6brt+gmnsUT6t+nCaaxtSJEtcTslSSo42IxsSgdmfT+rm+L9s7ngsbm222NMTT2JZRqxfNnc8vjr5dMt5XT3m89zKG+Y4IqTKEeVvJZPi0sfL5p5tOtZ+zxb8+IZz0dHWcuyvekXCd//9RdN5yx/qxPVXjLFcr6OtBXynRfyAaJx0rO3EnOmj8cizqcpe2bJpmRjL2vNmjAYkoKOtBQcOd+PwkTgeeXYXrrjwFMuYW/XYdsyZPjo9Vgaq9EgqG7tc2462iaY4Gld1rNv4hqna3LqNb2DejNGO4m1cFcf/7Bi+7P5X8MNvn5OK/30Sf6L4v/fDLpPEm2F39vVEbSkHnAgT4kOc6jQ60ehVNR31kRpT0JRlyXLsieffhCxLWNzabDrW1ZOSSjuxT0eyVzOXlAXEZZeNXLJagY2aqhdN/5iQYpCZ53jaJ+txxYWnIFIXRP3gGgCpCcppn6zHd7/xj6nUCUVGOCijN6EDkg7oEuK6DlmSAOjQoePBp3Zi97tRAPallz/eOAj1kZpU3mVaklAH+iqM7Yt2AYrMMVIFiGL1E8+/Kcjd1RGpC+FjDYOgyMDg48KI1IWganpawq+/2GqX03v8oLAl/vcm1AHjv53Em+iZACmll1yIjnCucCLscYyqbwPhhsg5cYdiS9uEgjKumnoG7l53bKn29m+3WI4tmNWEcEjGmg2vm46FgjK0TKkcG3kpUdnlgCJbbFw6dzySSQ3LH/Km1A6pTgy/ro/U4BtTzrCkNvz3H97DBZ//BBb3FTy47LyTce5Zn8A6gVyakfJw1dQz8PAzu7D73aht7vDeD4/iqqlnIBRUoPWNIa/LUZHSIIrVohhcE1YwZeKnTKlti2aPRV1NAAcO9QzoNwFBjvG4USOgQ8or/ttJvGU/E8aNGoHYUbNfL2ltRjAolzSFgvJpHicUVDDtxg2O/5HKp9jSNpqKdGA1zjt4qMdy7O5123HwUI/lmJZ1PWHpzKvFZZdlWbLYuO/DrvQkuL/2EVJODL+eNflUi/zZqse24/ILTjGNmYuaT0KHjVzaqse2Y9LYkbh73XbMmnwqAHHp5fkzm7Bu459T4yxDIcLrclSkNIhitSgGJxI6Vv5sm+m8lT/bht645shvZFnCglnm8t5XX3omVv6sM6/4L5J4E5VTvvaLZ1rsW762E3s/7Cqpr/ONMCE+o9jSNknBeYmk+LOJpGY5lsyWyrGR+JEVqS+X+FgKRHdCdbyM5hWpHVKdGH5dVyP2z4BiXhJW5GMV50TnG8c/1hDBfe2T0qXIV7RNxP5oN2JdCTzy7K506kRSPTbOvC5HRUqDKFYLY3A/sd+J38QTKh5+ZpcpBSPWHXf23Q4l3ozPZB4TVS+1T6srnq9zzBDicaz5wP1IKTmQNlOyK14VsJRlJ8kjKp2pqrCUUlYURfjdlFojXkWRZdz+rRYcPhLHE8+/id3vRk3yZ1t37sVpn6yHoqTOG1Qbsq1El/ocEICEJHR09ab+SHzyhb+YdGCzx61d+hHHSGVjJ2uWS+x3+uw4PhIyfXcioTn2Oaelk7PLOAeDYvucPnfyhakRhHgYIxewffUWzOvYhPbVW9CTSAorCWX/VRtSYFlqTUnomM8TLWV9rHGQ8LMfaxwkuF7+e3xFNn6scRBmTT4daza8jvbVW7Bmw+uYNfn0gr6HkEI5NhY346Z7NmPNhtfxjSlnYNyoEZg/swn3PfkaZk0+HZeddzK+MeUMLLl3C266ZzMefnYnFs0ea0l52LRtDxb3jR/zGN+MWZNPx7hRI9LnZ49b0ZilnFrlY8iaDRQbw0FJGL9rQrIjvwkHrd8zJBJKpbgVyedEz7bDsTiWzR0/YApFsX1d0nXdt38+Hjx4xJQ3VQwaGiI55do+fed07N8fK+n1S3l+vt+RS5vLTUPf7lXR8XLjxEft7AVSu2fb+zbfGDTW1+KOb7c4EiJ3ohoBWN86S5Dw0ydfs7x5mH7uZ3CkO2E6Nm/GaEiq1m87+iPbRkkCFv1ks6XNHW0TIZVpI1C+bSnG95abUsTRgXCrf3Mh20a7sbh0ztlY9dj29JvhjrYWtK82+++4USMwb8YYaIZqhKQDempCmwSE181OI8oet8aYhSQBfWoS5dgo57aP+sF3nJJrW+x8MDs26oqM325+21IQ6dKWk9M+19+zI5/nTrHasrKtxVy0o+93uahG5OqjBU2qH3rooX5/f/XVVxdyeUKqHrtcwHjCLKVkp5GeiKuCdASxfE7mslVSkrB1515Lmc4Z530GK9Z2mo7982VjoCsKPjhwFIqi2E627ci2MSmJ5deY/0jKgTE+smXJ7MbiR0d7AaTe0NYPDtvKrOnQEZIANSMtSQWg9uPvKXTLhiTg2Jg1JiDORxzxC9mx2i6HVpIl6FDSfzgFFGD9C29j/Qtvm869ZMKnIOm6MG0hEzuZNqfPHSfYjaekJv6OgWwuhIKeK7t378Z//dd/4Qtf+EKx7CGEZFDsXECnsksi+Rw7uZuPjsYt1bayJdpyspH5j8Ql+hsfOiD0y6SqmeTUls4ZZyuzJhprdv7+f/tjuHXNVkqjVSnCip/XTRDKmn10xBqDLzvvZNNEOJcY6lSmraD2eSjOF5QjvHLlSowZMwYtLS3o6Oiw/COEFEaxcwGdyi6J5HP+5Wv/6EjuRiTRlgvMfyRu0d/4EPlle2szQkHZJI+2buOfsWBWk1BmTTTWRNddMCslm9bf50hlI/LFB57aYck3t4vBU1tOzjuGOpVpKwQvxfmCv/OWW27BL3/5S0ydOjWvzz///PO455570N3djYkTJ+Lmm28u1CRCKgY7KZp83ww5lV0Syec89PROfPcb/2g6pulwJNGWk41FbjMhTulvfEi6bvHLkCKhO2GWU9v9bhQPP7MLN155ljOZwyx/l2UJP3zkD2nZNLvPkcpG5Itbd+7FrItPcxSDNQ15x1CnMm2F4KU4X1CbXnjhBZx33nn4/ve/bzqu6zp+/OMf48Ybb+z38++99x6WLl2Kxx9/HMOGDcPs2bPT1/QLTiu/EZIvsiKlkgmhA5AgKyb1s5ywW44KyLJpg0JIkRGN9ZjygRvra2HZU6XDRs5Hzt9IQlxioOXa7Fx6KIpQkur4SAiSJNnKrOkZm+VUVTNdV4eEaKzHZBdTgyoLuzx00zk2vnjoSK8pLq/+3oXimK5ISEkhmJ8bTjZQO5VpKxQ7mbVyU9BE+LbbbsPu3bsxb9689LGDBw/ihhtuwOHDhwecCP/ud7/DlClTcMIJJwAA7rrrLoTD4UJMKjtG5TenPH3n9BJaQyoNp2WSnWIsR2WXsOxJJE0lLJdc3Yylc8fj1oxjS+eOR1d3wlRis+NbEzFr8ukW+8JBKe/0CJaPJW4RUlKyU9n+HFIkJLL82Rib6za+gfkzm9JpEONGjcDMi0/Dknu3pK9hlFSeds6ncd+Tr2HaOZ/G0y++ha9OPt2anw/rGDWWjPmnpf9xvE9eqEG0AAAgAElEQVQD4lidqbXbWF+LQMDqs4tbm9ETN8f09tZmNB5fg32HegZ8nhgybU7GQSVQ0ET4sccew8KFC7Fjxw7cfvvt2LFjBxYuXIhzzz0X999//4Cf37NnD4LBIK677jp88MEHOP/883HDDTcUYhIhFYVdmeSOthbko6orWo5SFAnf+/fNpu9Y/lAnVn6zxSThJEnArfebz3v3gxjufeI1gX0T87IPsM/TLOSahDghrupYt/EN09Lzuo1vpCQCLeceG5uHYnHMmT4aQwaFMHxIrUk+bV80VVI5U2btnfc/wpzpo4V+7aUlY1J8nMY32wptsKY81A0Jm2J1QJHw3X9/UfjccPI8iau6zXmVGYMLmggPHToUDz74IH70ox/h0ksvRSwWQ3t7Oy677DJHn1dVFa+++ioeeeQR1NXV4frrr8f69etx+eWXO/r8sGGDCjGfFIDX00G8Yp9TH7Wz94MDR8V5hrqOE4vUxn3RLuF36AAah9b1e15NKCCU2YEkWdqkaToOH+1FIqkhGJAx5LgwZNkaVu3sEV2zlHjFh0qNW3HUi/27L9ollA2cd9kYBGqCJt/d++ExP939bjS9XH1f+6QBZdYidUHUD65BfaSmKH7txb4sJtk+6uf2liO+2T437EosZz1P9kW7HMf1TPx6XwrOe+7q6sJ7772HQCCAUCiUU2rD8OHDcfbZZ2Po0KEAgIsuugivvfaa44lwqQpqkIHxsqB5JRXUEJUgbqyvhSJJxbsHNlJp0HXzdwjOk2UIZXYCstm+nNIdnNpTQlhQo7R4tiiCje+pqob2viIvhu9G6sSlk+3K2Gq6bpFTWzCrCYpcWDwtd1+67aOe9R2nlCG+2T437EosZz1PAqGAo7ieiZfuS64+WpB82s6dOzFjxgwcd9xxePLJJ/HTn/4UP/rRj7B8+XIkkwOLvVxwwQXYvHkzPvroI6iqihdffBGjRo0qxCRCKgqnZZILwamMjei8oYNrhTI7atbEyqlsWy72EFJs7Hzvgad2WHw3aFPGNhySLdKDi2aPhQTJIqdWbEkq4n3KEd+CQckis7Zo9ljUhGVHzxNV0x3F9UqhoL6fPXs2vve97+ErX/kKAGDUqFH49a9/jX/5l3/B17/+daxbt67fz3/uc5/DnDlz8LWvfQ2JRAITJ07EFVdcUYhJhFQUibiK4ZHQgOVWC8FpTqLoPNvqQKpZZsepbFsu9hBSbDJ9zyhdDAmWVIl90W709IrHZldP0iI9+OyWd/CVi04tuSQV8T4iHyt2fOvpVfHslnewdM7Z6RLL6//7TXzlolMdPU+Sqo18mlqZvlpQmx5++GF89rOfNR0z8obvuOMOR9f40pe+hC996UuFmEFIRSMqk1wIohLLgZCCZFyDIbUTCMlAXFzfPVPupr8luEz5tFyrCLklq+NE1qiasSvPXQkYbUtKAHQpNRQgAdCxdM44rNv457S2r+G7diXMRdKDX518umcqaVUyleSjTqTORCiyhNf+cgDPbXsvfSzlg6c5ep7YpVAEFBm6nrs9ueDG/StoIpw9CTaQZRmLFi0q5NKEkBIgLNvZNgEfZknqLG5tRigkY9l9r5iOZef0GqkbVpkdmAKsHyShKNvWP5XcP0bbHt34Bqad82lTHu/8mU34z5f/iqumnoGHn9mFaKynX9+18/XavmXp7LFSE1LQ2+3v/vMKfvBRpzYWIp1ZaLwVxfXFrc3ojidNkpqFSHmKcOv+VeJbbkKIDaJc3WTCKpWzYm0nrr9izIASP6bUDV2HIknCtwR+SHegbFv/VHL/GG2bM320JY931WPbMWf6aNy9bjtW9LW1P9+18/XuXs1Gmm2M7/vPK/jBR53aWIh0ZqHxVpSSl5LZFEuyFatv3bp/nAgTUsFkLzNBspbjlAXH9kW7URMKWI5pGiArsim4an3Luoqcynez2/zjZhUhJ8ttueQxF/N7/UJ//RPOamdIkRBXdU+1u797YbQtUhdMt/G0T9bjigtPQaQuiOHH1+K26yZC03Qospyu0mV3TWsFOhmqpgml2a79YmXmXbpBLj5q9Hm5x6eq6UJpMkvpbU0TnqcoElQo6QlqOCgh3mPddlxovM1OoYhrkq30mqLIRUknK0UMdgLHHyEVit0y07hRI0wPY02HMB+sJ24Oro31tYAEtK8+VjHLqHSUWcHIr0uRueYxF+t7/YJd/4SCMqKxeLqd40aNsFSlcrvdA90Lo22xrgQa62tRH6kRSp0ZqRHtrc1oGBLGgcO9A/tV33eHgs7y6Un+OPVRN2NXKCgLpclCQRlaZnW3oGI57/tzxiEa67WkSzQMCQsnw8XELm8Yuo72e18qSh8WOwY7pSD5NEKId7FbZrr60jNN8jk6dIukzuLWZhwfqTEdWzCrCR9+1G263vK1nabCAv3JormFU+m2Yssa5SIZ5wfs+kdTze2cNHakMNXGzXYPdC+Mtm3atgfzZzZh1uRThVJnV1x4SnpJuDehO7q/xnf/5vdvWiStjHLkpDg49VE3Y5emQihNlr2SpgkkzBRJFqZL9CZKv9lSJOVpJy2Ybx+6JZ3JN8KEVCh2y0yx7rhpuW3Vuv8PN1/TbC7RGZCw4qFtpvMefmYXWi/9rOV6ohSKUi9l5YLT5bZiyxq5tcxXKuzyDnuzqlVlphcYuN3uge6F0bZ5M0YDUuoFrej8SF0w47M2VbosS9yp7zZ28BuSVsGAYrusTfLDqY8C7sWupI0t2TJ6IgkzuzQ2tQwSfKK8YUnShdKC+fahW3tJ/BiPCSEOsFtmOtIVt0g7qZohnQYAOlQVOD4SMl3v+EhImC4hOuYlSahcltuMvDqjSlIhC9ZuLfOVElHeoZJVKctIL/BSu+3vhQwFuulBq6q67TJwrCth+uy4USMwaezI9B+Lm7btMbVTUWTokHD7t1pw+EgcTzz/Jr75w+fRWF+LjraJiPcwJaLYOPFRwL3YZeeLAVmGjsy8eut5dmlsovSaUuxPyM4b1pWBx0CuuLGXhKkRhFQoIcVa+WpxazNGDK0zHVs2dzwOx+JoX70F8zo2oX31FiQ1DbMmn441G15H++otWLPhdcyafDo+1jjIfL2rm9GYdb3Ucq93Qouby23VUCEvu52btu0R+p2b7Rbdi/kzm3Dfk68h2pVAMKQg2pVIj4Hfbn7L0oYFs5rwxPNvpn28NiwLx0hISaU6GLnB7as346Z7NmPNhtfxjSlnYNyoEa73R7Uhuv9LBLHQrbiwpLUZPYmkKQb3JJJYknVewKaaYV3YHG+P+d6x60W7ElCU4sblkCL1Owb8gqTruj9fTcBcf7xYNDREMO3GDY7Pf/rO6b4+P9/v8EpNcRF2Nc9zrT9eDJz4aKlqtOuKjPuefN3y1/r1V4yBmrGjX4KERas3m94yLJ0zDvc+8ZrlzcMNs87Cke5E+non/cNgPLDB+h3zZoyB5KENQLm+HSnWPcnne8tNMeJodjsHUo0olc8PbKOEA4e7029nd78b7Xs724L2jDFg5AtPGjsSH28chK6eBAYfF4amaVA14LnOv+LSlk+bPgMg/aZXUjXoipzeWGr+fQsCWW+hC6Hcfem2j+bbXtFYBMqvGpFpi5GClZIms/rSHd9uMcVqRZGx4YW/4KLmk9IV4wxfzIy39r6X8s1iUa7vyZVcfZR/lBLiQ0RBXVYkUxUiRQEOxeKmzx2KxS314kW5jjWhgDAXTZJgSqv4j5suLJskVCFLfcVebnNqi5uScaXEaL+m6ZBlGUlJB3QgLEtQVQ0JFZ5rt6pqUCUJN92zGUBKHm1xazPqB4cBAO2zx2L/oR507vwAnzwhghnnfQaxrgSO9iTw3VUvWq53yYSTsfCrZ6VlrXa/GzXnHdvmJWuQ/Pv+yfPYVWOzG4te8NP+JNUyiSdUrH/hbax/4W3T8UsmfCrvkvaF2u21/QD54CdbCSGwqQ53/QQcjSVMsjpL547HtdNH4Uc//0P62E2zP48jXQksfyjjs9dNsOSd9cSTjnLqcslZK3ab3ZLk8pItbiBq//yZTXj6xbfw1cmne7ofjPxMkTza/JlN2PXOAUyZ+Cnc8tNjclC3zrOOj8b6Wvzf/hhuXbM1/dlHnk1Jqxn5kZWYI+51CqnGVi5E42fJ1c2Yd/mZuP1nr+YcqxvrU6WPkcy/pH3ebakQH/dOIh8hxBHC6nBJa3W4fR92pSfBxrGPjsTTgdU49sBTOyw5ayOG1lny00Q5dYGAOGctpJS+zW5JcnnJFjcQtX/VY9sxaexIz/eDkZ8pkkdb9dh2XNR8Elb+bJvp+Nrf7rDIni2Y1YR1G/9s+uysyaeackyrJUfcS9hVY/PIHBiAePwsf6gTHx2JO4rVIl9UZHNObrl8r1J83G/2ElL1iJajRLI6ovQG0bGtO/di3mWjLZI1CCrWY4DlWK2DEsulaLNbS3BessUN7NpvyKZ5uR8Meaa6GnHqjyJbx1FqfIzByrYWJDUNsizhh4/8AbvfjZo++7GGCMJyRrU6H5QVrzTsJe28U73PbvxkS7nZxepZF59mkbW88cqzbKUgS+l7xZacdAu+ESbEZxjLUZkYKQqZGOkNAx1rrK8FdEBSNQR0HVJfiVhV1RwdS5VY7ivhDD1dcrkQgiEFuqIgKUnQFQWBPvmjbLuz34QAfZJVitz3Wdl2p3T2dwQdvsYW9b+dLZWIXfsN2TRFlhGuDebVt+Ugs5JcJikZQes4MsYHVDXl9wCisR7LOaL7LxovpHQYsneZpFO1BIRqAiY/DdWUfrps53vZaWd2sfrQkVQ1w/bVW7BibWdfOo57UznDxxvr63zr45wIE+IzRMtRohSFxqF1WHL1wCkPhSxlZcr0XHvb74oi02Pk+bWv3twn/bMZ3fGkpS0iu53KBom+40As7mjCVinLgfkikuWbP7MpXZXtvidfw75DPbjvyddy7ttyYSen9lznXwdM9RlIiq3YElXEOTUhWXj/akLWexKqCWD/4V5TDNh/uLfkk2GnUm7C9DQbucrsoVUu+bRKgfJpWVA+zdn5lE9zRqnk05yoRoQUQFP1kkoGlUI+R1cUoSzVHd8+J/VWuh+7ndpj9x0dbS2QVHXAe1IKsXrAuz6aSaYsX/3gMI4fFMbRngT2fdhtkiSbM310WmEks28Bd+TTsjmmfAHIsgQYyhdBGb0Jvd9Un/6l2MorHUX5tGPoioLfbn5rQHkx49z+YkApyZZPs4vLomOiOJ/tn27ImnlhTBtQPo2QKkAkBaSaJKtUJPpiYyklg0qRL2uX5xdPpJam+7O7Pxkis7xQYbmElSqL5gRV002SeR1tE7H2t3/CFReegtZLP5vuc6McMeC9PE3g2D1UAGTewHjf8RP7HuwJwc3NlmIz8HqOdKWjahr+9E4UZ3xqeHr8/+mdKC6ZYPU9N/OJ7SpYOonVdnHedP0q38eQK+wTQkjelEI+x668rRNJtlBQxlVTz8Dd647JYi2Y1YRQUIYWz5QXyv87qp3se67purDPtYzFxkrs20qRjqokQkHFZvwr0LJycCs5BtA3c6PiE0Yig2vR0BBx/I8Q4pxS5MuGFOQtyaapSD8EgdRbkLvXbYeW9Vwr5Duqnex7LkES9rmE1OaxSu3bas8V9yKapovHvyD1J2xTrjgc9P+mV/pmblR8v9SEAznnvxJCnFEK+ZxEXMVwQ5Ktnzw4EUmb5c5k1nJnId9R7WRLMwFWybF90W4MP74W97VPqti+pTya90iqNuNftaY7xHuSaBgSNsWAcFBCvMfLStjOoG/mRsW/ESaEDIxTyTERpZDPMSTZUqQk2ZzYmIu0WSKuQjIksVTVdqJWSN9UKsY9D8tSP5JVGLBvvUT2fXaygZDyaO6Sfc8Ctr4ofssb70maYoBfJ8GiGEXfdE7FvxEmhPSP10oGC0uQtjYjGJSx7P5X+rXRWBLMbksAyGtDm9f6xksYffPCH9/Dotlj0xXZji0xy4j7pI9E9/nma8ZhSE2g6u+zVxHds2XzxlvGv998MVcYowqHrzYIqXK8VjJYWIJ0bSf2ftg1oI2ZS4L3tU9CR9vEgh4IXusbL2H0zRmfGo7Hfrcbc6anqhPOmT4a6za+gd6EfzbliO7zbQ9u5X32MKJ7tuy+V9CbSPraF3OFMapw+EaYkArGid6t21I72TZqNhJo2SVIc7ExVBNIacPmmAvodt94GaNvInVBHIrFAQA1IQWDjwvjS5NOTSW2BGRATxXhiKu66R4Xi2JoOvM++w+7ezaoNohBtWEoMjD4uDAidSGomobakGLR3wXgSHu9HG9W8/Vj+m7hsJ8IqVCcLpm5KbUjsrHjmxOFEkjZVUQb62sRUGQgqfZ7vduun4Cjh3vRkbVc2jAkPOBkmDJE9hhlrw35tKf+5y1MO+fT6MhIkZg/swl/2PV3nHvWJ0z9X6y0g2ItC/M++w/RPbvsvJMRT+roWPty2h8WzR6LmnAABw73mHzwtusn4Gh30nRscWszQg5SsIrelgL8mL5bOEyNIKRCcbpk5qbUjsjGvQe7hRJI9ZEak42pybE04PWSST39sDOOdaztdLRcShkie2RZwoJZTWn5tEljR2LVY+b7tuqx7bio+SRL/xcr7aBYy8Ki+3zzNeN4nz2M6J5NbTnZ4msrf7YNiaRmOS6KCyscpmAVm0L8mDGqcFzvq2984xv48MMPEQikTPnBD36Az33ucy5bRYj/cbpk5qbUjshGSRLLcR0+GjelSzz8zC7ceOVZWRXjrNeTba7npIIUZYjsiSfU9D0wUiRE/azIdv1f+NJtsZaFRfd5+PF1OHjwSIEWklIhume2/iCQVbOLC4WkYOVLIX7MGFU4rk6EdV3HX//6V/z+979PT4QJqRaMnLB90S5AkXMKXk7yyXJZMiukZLDTdohs1gGMGzUCk8aOTE9wZRlCu4MBCR9rGJTO/fvsp+oRUGToup5xTWubNV18PacVpKq5nHI2xj2EBCi6hG995XNQZBl3LjgXNaEAxo0akS69DKT6WdXs+r/wpdv+fFyBZPK37DzlbD/Nvs/Zqw3Ee2TfM0VRLPFk07Y9UGTZclySxL7Tk1WBrrG+FqGgDLUf38mVYF++8gcHjkJRFASkwsYIY1RhuDr7fPvttwEA11xzDQ4dOoSvfOUr+PrXv+6mSYSUhYJywhx+tthSYoXYYnfe8CFhzJp8uiVPb+nc8bg1I0/PyOe7dc2x3L/21mboko72n2w5Jp80dzyWtDZjecb1AoFUBansHOFwUIIP5G09g3EPH934Bqad82k8/WIqJ/hfH9hq6lcA2Lpzb/rnA4eOWvrfSDsotPvtfDykSDgYi1tktNZtfCNtGyWmKo/jwrIlnrS3NguPL5srllo7rjaQnpQa5x2OxU0xpRDfCYYUHIjFTbYsnTseS65uxvKHSheriT2SruuuZVNv374djz76KL7//e8jkUjgqquuQnt7OyZOnOjo8wcPHhlw93FDQyTnynLVdH6+37F/fyyn7ygnDQ0RoX1ulNC281FdkdG+eovlDUBH20RIAwTXXD5bjB31xbDF/rwWtK/ebDl+w6yzcKQ7kX5788kREdxy30uW85bNPRttdzxvOnbHt1ssb2+UoJyTaoSdD5UaL/loNsY9nDN9NNZseD393+x7snTO2fjoaG/6bdy8GWMQUmB6G1vMtAORjycBob/NmT4aK9Z2pn/ub7y55QO5Um473fbR/tqrK4owntjFmRuvPAuHjyRMb4//ecYY6DjmqxIkLBJec+BYnYuNd3z7HKiq5tv0Bi+Nl1x91NU3wk1NTWhqakr//KUvfQkvvPCC44nwsGGDSmUaGQA3gmEueMU+Ox/dF+0yBcLUsW5Akga0vb/PBmqCSCQ1BAMyhhwXBgAcPtoLZBwr5pKvnS2KIkOTJCRVHQFFAnT7PFHRcUlCesICAP9x04XC82RZwuLWZpPMmqoBjUOPG9j4AVzEKz5UapzGUeNeG7nAdjnBHx3tRfvqLelj135Rx/GC+1HK/rXzy0hd0PTzQONtIBs1Tcfho72mMedGSkWl+2q2jxrtTSY1RGM96TgjygU29gOIjmuaOc4AwLzLgMb64zLOyz9Wi/jgwFHh9eJJDScOcxC3SkCx/NivfujqRPjVV19FIpHA2WefDSCVM5xLrrDTN8Kk+HjlLz8RfngjjD7pqey3AtD1gfvW5rOqqqH9J5tNS3qJhFa0JT0RkqJYbBk3agQOxXrNEmbXTbDNgXOSp2eX5xtQpPRbSUNJQpEL90++ERbQ53exroTpv9n3JNaVMP2syJKlL0vdvyK/FNnW33gbyEavVPSq1jfCohQD2zhjEzNF+cAWnygkVgtQbHxTkazjpBwUy4/9/EbYVfm0WCyGO+64A729vThy5AjWr1+Piy++2E2TCCkLhUje2H32gad2mOR39n7YlZ4EG8eKLQUkK8CCWU0mW6794pkWKaAHntohtDmkWNuy5OpmNA6tMx0z8nwzj7W3NuOZzW+bvufudduhMamuJBh+t2nbHsyf2ZT+b+Y9WTR7LDZt25P+2cjFLjciv8y2rVCJKVb0cpe4Cov8mSjOLJo9FuGQbI0zrc0YkRVnRD5RbHmyYFDCotljLTYGXRgnAP0YcPmN8AUXXID//d//xYwZM6BpGr72ta+ZUiWIN4kn1Jz+4urpTSL2UffAJ/YRGVyLmrBz18z1+l4gU/IGkgTouvNKQgK5HEgw7dYHgJpQoGSyVQbxhIaHn9llkjXTBGkQW3fuxbzLRlskfhJxVSj9IysSOtpaTBWfaiMh0zFFAda/8LalfUkHsmgkdwy/mzdjNCABc2eMRuxoHEvnnI2u3gSiH/Xi2S3v4OppZ2LGeZ9BQ30dahxW8Cs2Ir98dss7mDtjNK794plFycFkRS93EaU7bN25F1decgaWzjkbigyoGrD+v9/EVy46VRhnAAwoO1ZsebKeXhXPbnlHaKMbfkM/9oCO8A033IAbbrjBbTNIDoSCSs6b63JZMKkJB0p6fa9gSN4YS0q5vMjMlsvRBct3PfFkyWSrDBRZwmc/VW+SNQsq4nQH6BIkVbVI/Iikf2RFsXxXIq5mnKdChXjJkhWVSodxrwAAioJ1v9udlqQCgFhXHO/+PYY1G15HR1sL9L7lbFEZ7VKiyBKisR5T/mdjfS2+Ovk0BHS9KBJTrOjlLoosHv8HD3Xj1jVbTce+Ovl0pMQazTiVHSumPJkiS3jtLwfw3Lb3smw8zRW/oR+zshwhFYFo+W7E0DosKXHFoXBQxrlnfQK3rnkZ193+PG5d8zJiXQksnTveksbgdIncyP1rX70Z8zo2oX31ZhyIxREMmSfHrKjkLjWhlCTVmg2vo331FqzZ8DpmXnwadr1zAPNnNuG+J1/DgVgcoZoAol0JtK/e0nc/t2DP3z+CopTu8VMO36D/uUs4aE2XWixIq2pvbUZdWLb4YLQrUVIftMNrfuM1e9ygmtpKSMVit3yHoFLSikO9CXGZ0htmnWVall638Q3MmzEaTqbCoty/jrWd6GhrMX2eFZXcpSduLVu78mfbsHTO2Vj12HbsfjeKd97/CB1tLZYcxNse3JqSnyqRbeXwDfqfu/QmNKzb+IYpztTVBPFAn7SfOfaMEebBltIH7SgkLa7U9lSrH3MiTEiFYLd8V8qKQ3ayRNnyZwAwZ/poyIpiyvtNCCpa2F1TVBKZFZXcQVFk9Nrcp4+O9mL3u9H0z/b3s8Rla8vgG/Q/91A1HVt37jXtjfiPmy60HAOAa7/ojg/aUUhaXCntqVY/5kSYlJxcN9cR/2CXpyeSJdJ1He33HqsC197ajOGRkGUybHdNpyWRSWkx5JZCQWcSZQEb+alqykEkxUeU22pXNtlOPo0+SABOhEkZyGdzHfEHIQWW8rmLW5sRCh578NjJu4nSHeyu2d4ntZbgPNh1DLmlMZ8ZjkWzx2Llz7al79Oi2WPx2O92A0CGrrNkKWVbrBLLpHoRldcOhmQsmNWEu9dtN2mLhwJyycvNE//CiTAhJG8ScRXDDVkzXYciSQgpgKbqA8q72aU7mK45QBoFKT+G3JKx692QgQoEZKzbmFKRmHHeZxDrSuDhZ3bhxivPsuQgFrPEMqlORLmtSYFsnp0PVlseLLGHE2FCSEEYsmYn9uW7JVRYdmNLkDBu1Ii01FasK4FN2/bYpjtopuVK3bZIRrYsFx9uxcOub0VL0pAkSJAQ64pbJMsUWbLkILpRhphUHtl+pSgKjo+ETOccHwml4oxAPo0QgBNhQkiREZXsvO36CZg1+XRLukM4KCH7Ra/Tkp9eKXFbifTXt0BqSfqFP76Hc5o+jlvXvGy6p0Dq7T+Xn0m5qQ3LwjhTF5axN9rDWEGEUEeYEFJURCU7/2/fEaEkWm/C+hByWvKTpUFLR399ayxJX9ry6XR+sHFOx9pOzJk+Grd/qwUdbS2caJCy0t1rlfTrWNuJrl6NsYLYwjfChPgQL6cEiEp25lLu2WnJT5YGLR1O+tZWFk3VcNM9m3Ff+yRIOpejSfnoT3qxPlJjyh1+4vk3GSsIAE6ECfEdXk8JEOWQ5lLu2WnJT5YGLR399a0CCdGuBKKxHuE5qsb7QNzBTnoxoMi4auoZVjWJoAyNm3CrHqZGEOIzvJ4SUGi5Z6clP1katHT017eG/63b+GfMn9lkOmfR7LF4rvOvvA/EFURll9tbm6HIUnoSDKRi5t3rtttuwiXVBWMVIT7D6ykBhZZ7dlryk6VBS0d/fatKEvZFu7Ev2o1Hnj0mVdVQX4eAAlzacjLvA3GFeE8SDUPCJunFcFBCV68qjJlJgXwjqT7oA4T4DDdTAkS5yUDqLeG+aBegyOlJUHbJzmxJtf5wWvKz2kuDlhK7vlVkqxTeky/8BfNmjIGk6pBUjfeBeAqmUZH+4ESYEJ8hqqhUDpkqUW7ysrnjkUhoWE6ps6ohHBRLVL2y432MOnk47ylxjVBNACncviAAACAASURBVPsP91p8s/H4GlaWI7YwR5gQn5G5bH1f+yR0tE0sy+RDlJu898Ou9CTYOEaps8qmN6ELJar+36kjeE+Jq9j5Zk/cnZhJ/AHfCBPiQ9xICShEFs3rec2kfzJTYmAjUaXIvKekvGSnavUnnybpOtOoiBC+ESaEOMLIs8tE12E51lhfi4BsDi2iz6Zz9IinMdJa2ldvwbyOTUiquvBemmTTCCkx2X7ZvnoLZNs4w6kOsYfeQQhxhFAWbVhKjzPz2IJZTZCVgT9LiS1/kJ3W8pvfv4lFs8dSNo24iijd6pnNbwvl08JB/nFG7GHMIoQ4QiSppak6Hn5ml6li08PP7MKNV55lTo2g1JlvyU5reW7bewDQJ1GVupcKZdNImRGlW61/4W3MOO8zFvm0eA8z14k9nAgTQhyTnZssKzKisR6sWNuZPsdOlohSZ/5EJD312l8O4KuTdQR0DVABTQUkcAc+KR92kmhJVYOk9ukDqypYOI4MBFMjCCF5w5SHyof3mHgR+iUpFvQZQkjeZKY8QJIAXefyeIXBtBbiReiXpFjwjTAhpCDUvqXIxvq6VFUxPogqDuMeB3Sd95h4BvolKQacCBNCCCGEkKqEE2FCCCGEEFKVcCJMCCGEEEKqEk6ECSGEEEJIVeJr1QiZpTxJH9m+4BXfcGqHV+wtlEppB1BZbekPt9rph/71g42Af+zMF6/G92LAtriPpOu6PvBphBBCCCGEVBZMjSCEEEIIIVUJJ8KEEEIIIaQq4USYEEIIIYRUJZwIE0IIIYSQqoQTYUIIIYQQUpVwIkwIIYQQQqoSToQJIYQQQkhVwokwIYQQQgipSjgRJoQQQgghVQknwoQQQgghpCrhRJgQQgghhFQlnAgTQgghhJCqhBNhQgghhBBSlXAiTAghhBBCqhJOhAkhhBBCSFXCiTAhhBBCCKlKOBEmhBBCCCFVCSfChBBCCCGkKuFEmBBCCCGEVCWcCBNCCCGEkKqEE2FCCCGEEFKVcCJMCCGEEEKqEk6ECSGEEEJIVcKJMCGEEEIIqUoC5fiS22+/HdFoFCtXrjQdX79+Pe68804MGzYMAHD++edj4cKFjq978OARaJpeVFv9SH19HaLRLrfN8Ax2/dHQECm7LU58tFLuX6W0A3CvLV710WLjB1/xg41A+e1020f9cl+cwLaUhlx9tOQT4Zdffhnr16/H+eefb/ndjh07sGjRIlx66aWlNqOiCQQUt03wFH7rD7/Za0eltAOorLZ4ET/0rx9sBPxjZ7GopPayLd6gpKkRhw4dwl133YXrrrtO+PvXX38d69evx7Rp0/Cd73wHhw8fLqU5hBBCCCGEpCnpRPiWW27BwoULMXjwYOHvGxoa0NbWhqeeegonnngifvCDH5TSHEIIIYQQQtJIuq6XJDns8ccfx1/+8he0t7fjN7/5DTo7Oy05wpkcPnwYF198MTo7O0thDiGEEEIIISZKliP87LPPYv/+/Zg+fToOHz6Mrq4urFixAosXLwYAxGIxPPHEE2htbQUA6LoORcktx4Sb5VI0NESwf3/MbTM8g11/uL3Jw45KuX+V0g7AvbZ41UeLjR98xQ82AuW3020f9ct9cQLbUho8s1nuoYceSv+/8UbYmAQDQF1dHdasWYOmpiZ87nOfw89//nNcfPHFpTLHVyiKjCQAVdOhyBICAFRVc9ssQggRwphF/Aj9lgBlkk/LZMmSJbjwwgsxadIk/Nu//RuWLVuGnp4enHTSSbjjjjvKbY7nUBQZ0a4EVqztxL5oNxrra7G4tRn1dUEOUOJJIoNrURNOhRInf4n39CYR+6i71GaRMsGYRfwI/ZYYlCxHuBxUYmqErshoX70F+6LHJgqN9bXoaJsIyWZwemlJwgswNaK8NDREMO3GDY7Pf/rO6Z5vL1MjnFOpMcsPNgJMjciXfPy22PjFx5zgpbbk6qOsLOcxVE03DUwA2BfthlphE35CSGXAmEX8CP2WGHAi7DEUWUJjfa3pWGN9LRRZcskiQgixhzGL+BH6LTHgRNhjBAAsbm1OD1Ajb6nsydyEEOIAxiziR+i3xID33GOoqob6uiA62iZyJyshxPMwZhE/Qr8lBpwIexBV1SCh7+aoOlSX7SGEkP5gzCJ+hH5LAKZGEEIIIYSQKoUTYUIIIYQQUpVwIkwIIYQQQqoSToRdQlFk6IqMpCRBV2QoCm8FIcRfMI4Rr0GfJLnCzXIuwNKOhBC/wzhGvAZ9kuQD/1RygSSQHqhAqprNirWdSLprFiGEOIZxjHgN+iTJB06EXYClHQkhfodxjHgN+iTJB06EXYClHQkhfodxjHgN+iTJB06Ei4yTRH2WdiSE+B27OKYoEjcokbKQ/bwNKRKfrSRn6B9FxGmiPks7EkL8jhHHVra1IKFqeP/AEdz7xGuIxnq4QYmUHLvn7bBIiM9WkhP8s72I5JKor6oaJFVDQNchqRoHKiHEd6iqBh06vv/Tl3Drmq3Y/W6UG5RIWbB73sZVnc9WkhOcCBcRJuoTQqoNxj3iBvQ7Uiw4ES4iTNQnhFQbjHvEDeh3pFiUfCJ8++23Y9GiRZbj77//Pq688kp84QtfwPXXX4+jR4+W2pSSU4pNcKySQwjxCqJ4xM2/xA3s/C6kSHxmkpwoaax6+eWXsX79epx//vmW391666342te+hqlTp+InP/kJVq9eje9+97ulNKfkFHsTHKvkEEK8Qn/xiJt/SbkRPW9DioSDsTifmSQnSvan0qFDh3DXXXfhuuuus/wukUhg27Zt+Kd/+icAwOWXX47//M//LJUpZaWYm+BYJYdUIvGEioaGSE7/IoNrB74wKSn9xSNu/iVukO13cVXnM5PkTMneCN9yyy1YuHAhPvjgA8vvotEoBg0ahEAg9fUNDQ3Yu3dvzt8xbNiggu30MvuiXcLNAJAkNDRETMezf652vNIfTn3UK/aWg1BQwbQbN+T0mafvnI6aMvdRtdwTpz6aSzxygh/61w82Av6xM1+yfdSuvcX20XLgVbvywa9tKclE+PHHH8eJJ56Is88+G7/5zW8sv9d1HZJkTmjP/tkJBw8egVbJO0QVGY31taaB3VhfC+g69u+PpY81NERMP1c7dv3hxiB14qN+v3/l6tdy9pFb98SrPgrAcTxygh983g82AuW3020f7be9RfTRcuAXH3OCl9qSq4+WJDXi2WefxZYtWzB9+nSsWrUKzz//PFasWJH+/dChQxGLxaCqKgBg//79aGxsLIUpniIYUqArSl8Sv4JgSOn3fG5CIYR4hcx4dNon67F0zjj86z9PgARrJTlu8iVukOszM9dnMqlMSjKneuihh9L//5vf/AadnZ1YvHhx+lgwGMTnP/95PPvss5g2bRqefPJJnHvuuaUwxTMEQwoOxOLoyEjib29txvBICIm4KvwMK9ARQryCEY/u+HYLDvWzIYmbfIlb5PLMzOeZTCqTsv6ZvmTJEmzatAkAsHTpUvzqV7/ClClT8Oqrr+KGG24opyllJ64iPeCAVN5Sx9pODDTeuAmFEOIVVFWDOsCGJG7yJW7i9JmZ7zOZVB4lX2W//PLLcfnllwMAli9fnj7+sY99DI888kipv94zqJpmUwVHY6oDIcQ39FfRK+Dg94R4AT6TiQETt8qEIss2VXB4Cwgh/mGgil6s+EX8AJ/JxIB3vEyEFKA9K4m/vbUZzM0nhPiJgTYkcZMv8QN8JhMDxqYiEKoJoDehQ9U0KLKMcFBCvMecEZeIqxgeCaGjrSV9XkiBMClfUeSUSD03yAGw7w/2EyHlx66iV1zVgYCMpC7huNogOtpaAEkHdAnhoITehAZVkqDIUmXLXnqEao2bdu0LhhTEVaSfvwDQMCRseiaLnt2kf4z+3hftAvpKrov8yct+x4lwgYRqAth/uNey87RhSFg4GZbQ1+mqioQgKZ87rs3Y9cewSMi2lCYhpLSoqpaOZQpSZW0f3fgGpp3zaax6bHt6TM6f2YQ/7Po7zj3rE6YYefM14zCkJlCVMa0c5BM3K+Fe2LV7+JCw5Tm9dO54JJMalj9UmX1RDpzOV7w+r2FqRIH0JnThztPeRH5vPLjj2oxdf8RV9hMhXsAYo5PGjkxPgoHUmFz12HZc1HySJUbe9uBWjtUSUq1x067douf0vg+70pPgzHMrpS/KgdP5itfnNZwIF0h/O0/zu579jutqxL4/7Pq9OvuJELcwxmikLigck4oMjtUyU61xM5d214QCFd0X5cDpfMXr8xpOhAuk2DtPuePajH1/2PV7dfYTIW5hjNFYV0I4JlUNHKtlplrjZi7t7oknK7ovyoHT+YrX5zWcCBdIOCgJd56Gg5KlzGiqnGP/ZUe549qMXX+EFPYTIV4gAOC26ydg5AkR/OCfJ2DpnHE47ZP16Rzh5zr/aomRN18zjmO1hFRr3AwAWDZ3PJbOGYeOtolYOmccls0dL3xONw6tw5KrzceWVFBflAOn8xWvz2skXde98W46Dw4ePOL67mNFkdGr60gmdcgSoOlAICChNiBbNiW0tzZj3cY3sHXn3n6TxXPdXdnQEMH+/bHSNtRFct39bNcfDQ2RMlvuzEf9fv8aGiKYduMGx+c/fef0nM43PlPOPnLrnnjVR/tDtGF4cWsz6iNhJDUN0JFWlTDG6vDj63Dw4JEitqL4+GVc2tlZKtUIt320v/sSDCnCzYDDIiEAMKlGhIMSPupKYO+HXagJBdATT2LE0DocF1TKtoHLLz7WH4Y/QZIAXfeEakSuPuqVCblvSQK4+d6XTPkvjfW16GhrsSSHd6ztxJzpo7F15950snhH20RkLw5k7siGqqPaKz7a9Qf7iRD3EW1ESsW2Fkh9D7qECtNYlT2yJFrJVGPctNsMmPJFs2pTL2Qsu/8VwbPb+kwm9hj+ZEzq7fzJy37H1IgCyXVTQiRD3stLyeKEEJIPxd4wTEi+5OKLXt/ARcoHJ8IFkuumhFhXIus8/u1JCPEvLFVLvEIuvuj1DVykfFR9pMre0CbawJZ9HhQFckhBUpKgKBKWONyU0N7ajE3b9pjOq/TcFKf9SwjxFk7GbjCkIKBIwlgXDnJCUQ40Ta/aGJvto+Gg1ReN53E2Xt/ARY5R6nlEVd/zQqqiLJjVhIef2YVorAfL5o7HyrYWJDUtnQSeiKvCMqTzZozGtV8803MlBkuB16vJEELEOBm7wZCCA7E4OtZ2oj5Sg+uvGIN/GD4IwYCMUIClasuBosjY8/ePcNuDW6suxop8dMnVzairDeD6K8akN8CFguJJk6hUeKU/k/1IOeYR1fOno4BCqqLcvW47rrjwFOyLdmPZ/a9Ah46ArkNStfTNUVUNkqqljyfiqunnSh9wXq8mQwgR42TsxlWkN8ntfjeKW9dsxfd/+hJ0HZwEl4kkkJ4EA9UVY0U+uvyhTvzfviO4dc1WtK/eglvXbMWy+19B3GZnVvYzutKfyX6kHPOIqp4IF1oVxdj4xgR7MdyMQIg/cTJ2uUnOfao5xtq1vSYUsByjT/qXcvh4VU+EC62KYmx8Y4K9GG5GIMSfOBm73CTnPtUcY+3a3hNPWo7RJ/1LOXy8pN5x9913Y8qUKZg6dSoeeughy+/vueceXHDBBZg+fTqmT5+OX/ziF6U0x0K+VVEuO+9krGibiE+eEMHq712Ijm9NhCRJSEoS5FAACCg5bb6r1A0O3IxAiD8ZaOwGQwokCfjXrEpyy+aOhyQBagXHNS8RAHDzNeOqMsaKfHTJ1c345Imp5/J/3HQhVn/vQtx2/QSEFPRVdjWezQqCoh10NlTD89qrlGMeUbLx0tnZiVdeeQVPPfUUkskkpkyZgvPOOw8nn3xy+pwdO3bgxz/+MZqamkplRr84TZbPPE9RJERjcSxevcVUMe5//vge/vROFFdNPQN3r9ue8+a7StzgwM0IhPiT/sZu5ia5zPgVCEg4fCSOZY++UtFxzUuoqoaRJwyuyhgr8tGakIJ9h3pMvtne2oxB9TXYG7UeHx4JIWGXQNxHtTyvvUo55hEl+7OmubkZDz/8MAKBAA4ePAhVVVFXV2c6Z8eOHfjpT3+KadOm4Qc/+AF6e3tLZY4tTpPljfPUjA0iwLGKcRc1n4QrLjwlPQk2fud0812lbnDgZgT/ERlci4aGiON/pDKxG7txQQxcsbYT4WAAdz36x6qIa15ClqWqjbHZPtoT14TP5+5e8fEB5sAAqut57VVKPY8o6QpKMBjEqlWr8OCDD+ILX/gCRowYkf7d0aNHccYZZ+C73/0uRo4ciUWLFmH16tVYuHCh4+sPGzaoFGb3ywcHjgoTtxUZiNQFhb+DJJkmDPuiXY7OywVOSMx4pT+c+qhX7DWYduMGx+c+fef0ElpyjHL3kdfuSanINY7axUBdF29qsYtrfuhfP9gI+MfOfMn2Ubv2fnDgSE6VXlVdx4kD9F0pnteZVNK982tbSp5KNH/+fMydOxfXXXcdfvWrX2HmzJkAgOOOOw73339/+rxrrrkGixcvzmkifPDgEWhl3h2rKAoa62st9clVDYh1JYS/g65j//5YxkVkZ+c5xKjxTVLY9Ycbg9SJj3rt/nk1mJWzj9y6J1710UzsYqAkSY7jmtd8XoQfbATKb6fbPtpfe+1809jYaTkuSQP3XZGf15n4xcec4KW25OqjJUuNeOutt7Br1y4AQG1tLSZPnozdu3enf//+++/j17/+dfpnXdcRCHg/xT+kAO2CKkrPdf4VTzz/JhbMasp58101bXAghPgbuxjYufMDzJ85cPwjpFSEg5LQN2vDsvC4k/1yfF5XPiW7l3/729+watUqPProowCATZs24Yorrkj/vqamBj/84Q8xbtw4fPzjH8cvfvELXHzxxaUyxxZFkZEEhEnYwZCCuJrSy1RkGSElVTGu8fgadLS1pI8fF5ZxacuncckEDQFFxh3fPgfxhGr6TOb1ejUNx0fCWPnNFiRVLe/kb8P2fdEuQJFN18hsV0CWIStAPJH/dxFCCJCKZ8Mjob4YmIqb4bCM8Wf+AyDp6GhrgabrkCUJAQVIqjpCIQVxVU+fX+6VvFLR3/OjkOuEFMnUX4zZzoj3JHFivfX53NWVQMOQsOl4OChB13ToimJ5xmdSaZu+i+WzpbIFQNntK9lE+LzzzsP/z977h0dRnf3/7/mxs5uECAECiq3aWiuKQUFZgqBUUewjKlTbL2CfSrAJSrxE/fpUSaBNsECQ2sdL24IKraifPmJbqtS2fotC20egEEQ/DwUVqU9FKpjwI4T82uzuzHz/2MxmfpzZnc3+mt29X9flJZmdOXPmzJn7nDlz3/d73759mDVrFgRBwPTp0zFjxgzU1NRg0aJFqKiowGOPPYaFCxciFAph/PjxmD9/frqqwyRWNCgvcJbI6LoqP0YM8RmiUieOGYk500cb9tPLL8crT4tadeCz77juAGJKQlPEK0EQyRAKypAkASe6Q/jvd4/gmnFfwKoX9kTtzeJ5E/D2e//ClZecjb0ffI5rx3/RYPuW3j0Rg31iTtugVGUTYJVTV+XHxi0fYveBFspSkABFxR4cY2SHOKfMh8/bAoY2Xr7wanT1hB1lkpBlBRz6JkyymvB47RbclAGDKZFd5YfHw6NxXWYzz6Q1Gd7999+PP/7xj3j99ddx//33AwDWrVuHiooKAMBNN92E3//+9/jTn/6EpqYmSJKUzupYiBUNyoqMbtrQbIlKnTbhfMt+evnleOU5iVpNtO7xJKEp4pUgiGTRbNoN/guik2AgYm9WvbAHN/gvwNOvvIcb/BdYbN/yX+zOeRuUqmwCrHKaNjRj2oTzkyq3EOm2yQ7R1atY2jgcVlM6JucCbsqAwZTI3tCMllPdGa9fQbu5xJbus/vNGH1qlynCKr9sX95AbkI82UEndSrom19AlJ5VBJ+X7jaRWjRbKPBse6Ntt/s9121QLBucyHXZlaPZ64GWW4jEkv02b+c5u345sDE5F0hVn01nXdgS2emtX77eb0do0n3WCFMOgN1vxghSu0wRVvll+/IgJ/4KGrvucFYnOT/89IjY+LyiK9OhEbmNZgtlhW1vtO12v+e6DYppgxO4LrtyNHs90HILEdvsEIztimrXLwc2JucCqeqz6awLWyI7vfUraJ3AWNGgdpHRPolHQ00lGqonoql2MgYVedBQU2nY74E547Bp2yFH5SWg8ui47qzf9HVaMt9ZxGsykpQEQeQ3kgA01FSipzeEx0xSy4vnTcBbzZ9g8Tw/QnLYYo+W3j0x51dhmBK/VX4IApeQFC+rnLoqP7buOYyLzytDQ/VE/PCeq8FxnMEGa7K/rW3dJPvbR7HXOD43VE9EQ00lSry8pY1FkZ1hwuvh8lZOWQSwhNFnU/0sOpGktnt+Rg4ttsxrvB4+rXMRTlXVnH3FTEUe4USzRiiyitM9Iax43uhs3hsKQ1EAVQVGDiuCIgM8D0dZKJKtOzgOUFVm1giteU6d6YGiAIFgGCOGFuMsnxjz3CwZVaeSlNmG8ghby090RdhN+2vHUB7h9DBQOyoIPNMWDin1AlDxz6NnsHHLR2jrCKCxphI+j4iwEslcM3xIMU6e7Ez9xaQQJ33AnJ0nEAoPKNCHlTVCAYe2jl5LEN3wUgmKrGYt6CnbfTR2HmFrn1wy348hRRE3E/NYzwucYUz2ejicaO/NWLtm2s4IAo+ukIyWU93wSSICwTBGDi1GiUdI+vq0a0kkIM9J1givh8fx9t6E5iKuySOcK8SS7gsFZXCy3PebjFBQRhiIPmRAvzN3e2cIdWt2oH7tDtT9bAd4Ho7KS0XdR5QVW86l/cZzHJas3YFHf7oDdWt2YNn63Vi2blfcgIBUB/cRBJFf2NlCWVbxyE+2Y9n63Tj4aRta23rQuG4XVKhRO8v3uXDlOvrxQ4UanQQDiQX6mMehUFBGWFaZQXRB2V1BT26C1SdXPB9pF9ZYbx6Te0PWoLp8atcwgMZ1u7Bs/e7ofKBx3a6UXl8ifZN1T8zbekPpD2qMOxEOh63Vb29vT10NcoxEAhvcQKzggXQcRxBEYWAfeGNnO9xhE9NFvADmxMuzt8GpPle+kGy75Hu7ZuL6MvkcpArbifD+/ftx3XXXYdy4cXjwwQfR2dn/GauqqiplFcg1NAdvPbaBDS5ACxLQEw0ISMNxBEEUBna20N52uMMmpgv79hjYdceywak+V76QbLvke7tm4voy+RykCtuSVqxYgcbGRvzlL3+BKIqorq5GMBgEEJFDziViOW7rf4MogJfEmA7ZLGfz+r7ABv3f6QoEceKErocVpNdQUwmOQ/Saheg195fHOq6+yg+OU6EKfF8gXV+7CQJ4SYjbvvkWeEAQhYb2PEOMPMv1803BLvP9EAUOPzQFz+WrJK3evgkChyV97WEIcgMXtXtme6i3o2b7aBdg7fVw4MDhh/dejTWPXI8nH7wWDdUT0VhTmZdtnAgiEL0HgK5PJnB8Lsops8ZZ1rZErs9psLw5aFMSOMfnkHyi4RySz7pXqhMNsLC9v4FAAFOnTgUAPPHEE1i0aBHq6urw4x//OHVnzwDJKLDZOWR7PDwW3jE26mwu9f393dvSK2Fsdy1Dhti/mBjlUBVIHgGnO3qxbN125jVrbWM4TlUBVcXPf7c/qnRkVj5ilcFqX1JIIojcRLM/L2/5ELdecyFef/tjfOuGrxpsoSjyWLPpfwyKaENKJXCymnfPPMseN9RUYvG8CQA4rHrBaPeGlUo42RGM7s9SJdXbR0VWUVIkorFmEnguEvjs9fI42d6LFbpjFs0eh//vb5/gzumjAU9hZ/bxeAWIIdnSJz1eAXJ3/P6Xi3LKrH7YWFOJUEgx9BOtbzm5PqfB8nZzkmGlUtxzSD6RGQRXPtiLYKDfJdc8h0lFogEztstziqLg5MmT0b8ff/xx/OMf/8DPfvYzcFzufCZIRoGN5ZBt52wuyyoz4C4T19Le1RvzOH1AgMwIwLBTndOOEzhg6TM7sftAS/QYs/IRqwwK6CCI/EF7nqdNOB9Pv/Iepk04H6tffMdgC5et22VRRJPzcBIMsO3bsnW74PWI0Umwtn1l31ii35+lSqq3j2EAS9fuRO3qbbj38W2oXb0Nnx7riE5utGO0e7GCbCt6ehUsM43Py9btQk+v8/4XK4DejbD6Ycupbks/0fqWk+tzGixvN8YHZTXuOeyC4HpD1oW9VCcaMGM7Eb777rsxa9Ys/PWvfwUAFBUVYe3atfjtb3+Ljz76KKWVSCexHLfjBb6xHLKz6Uxvd+5QOIGH3Mbx3KqEF/+85gBBcxn5HnhAEIWE9jxraprxVDW1v/P1ebezb/ZKes5USbX2YpXvk0TbNs/ntnZKIQZ5J9JPnAcNOmvHZMZ4N90r24nwkCFD8OKLL+IrX/lKdNuoUaOwefNmCELufH6J5bgdL/CN5ZCdTWd6u3N7ROd+t3aO51YlvPjnNQcImsvI98ADgigktOdZU9PU/q/HzYHDqcbOvmlKeubtZttr135ae7HKDwTDtm2ez23tlEIM8k6knzgPGnTWjsmM8W66V7aCGjfeeCO+9a1vYcGCBdFtJ0+exIMPPoj29nb87ne/y1gl7XCSCD6ej/Dp7pDBj0bv69pQU4kiSURYVuD1CJCVSK7Ijq7s+L3aXcuXRg12nJzeIwk4EwijVZdQ+6xBEp777X6Df68sK5B8YuQzBce+5uIiDxRZgaICKlQ8vfH/usJHmAQ1rOW7SSCDBDVinzfTOBXUYPkI33rNhXj6lfcsdqGrO4jTnb22yfqz1b6JEE+4QQaHkKzg6InOqHBIQ00legJhlBR58PnJruj2+io/hg72orM7FBUzUFVgcKmEZet2oazUhznTv4pR5YMg8jwEIaLyG1YUHD3eaRAmMft+Lpo9Dq+//THmTh9dcIIaZkGGYq+Az9sCFr/TkWU+BHQvaG4hFc8BSyTj3BGDAADhsBr1skaXhAAAIABJREFULxdFDl6Oc9Q/WPMElhBXLB/hoKzG9RFmiZeUD/EhEJST8tFOtI/aToRPnTqFhx56CIMHD8bjjz+O/fv346GHHsK1116LH/zgB/D5fAmdKB0kYsBZ6nHmDqSpwskKIIk82jt7seL5ZpSV+nDXjEvw1Mb3okEO373tMnAcB55DRp3pWdcydGiJ44fJTnlnULEHUPuvxezIrr9mkefQHQxjmU5Bqa7Kj2GDvQiFFKbCXSYDD2gibC3fTRNbmgjHPm+mSURZTnueOY6DqiLyTVEFwrJxwqZNzu6cPhpDGJOzXJ4I2w3+Qwd70Xaml6k6+ovfvY8hpRLmTh9tOG7JfD/KzvLilG5CMHHMSMy+8WKsemGPMSi6L+gQiPhmKgrA85FsFbKsZGwcynYfjaVgtnzh1RAEDoocyW7FcRx4AfAKvCEAyy2kaiJsXtBrXFCJYFAxtE0iyrCxFPrs1OE0hVtJ4AxBoXYLYILAoysso+WkTuVuWDGgYkDqjHpSpiw3dOhQ/OIXv8C5556LW265Bffddx8efvhhrFy50hWT4ESwcw43B75pqnACFzlG6wR3XH9RdBIMALsPtGDpMzvBc1zGnemTdeS3U96BalTCMzuy669ZURGdBGtlNG1oRihkrVOuBR4QBGGP9jwjHAle4UIyoALff2anQUkunwO47AKEQiHVVnX04KdtmDbhfMtxK56PHGcOotMmwfpytKBD7R4IqgJOljFscFFB2lbWfQiHVdT9dAdqVr6FBU1bUbPyLdT9dAczACtfCAOWwLiWk922qoSOy7RR6DNjVrgNMgLyWUHyYQCNz5kSDzy3Cy2nujMeYB/TGaO7uxtHjhyBKIqQJAlerzfN1cksTgPp7IMacs/wOHVuj61q5B4nd4IgskusANx8DOBKVFFPCx6MNY4kEkRHRGDdB56zD1TMVxILlnPWDskFwTmdY7D380miZVu6+77tRPjAgQOYNWsWSkpK8Nprr+HZZ5/FE088gRUrVjBll3MRp4F09kENueeA79S5PbaqkXuc3AmCyC6xAnDzMYArUUU9LXgw1jiSSBAdEYF1HxTVPlAxX0ksWM5ZOyQXBOd0jsHeLxAMW7alu+/btsq8efNwzz334PHHH0dRURHGjBmD3/zmNzh06BD+/d//3VHhTz31FG6++WbMmDEDzz//vOX3Dz74ALfffjtuuukmLFmyJOMT7FgqK/rfNm07hAfmjEurskmmcKos4/VwtqpGsX4jCKKwYNmDRbPHYeuewzmhypUodjZUEqzb63Sqo1v3HGaqdZqP27rnMBbPmxDXRhc6rPsgioU3NrHaYeTQYmZfdDpnSUZhz+mxrP2WVPkxcmhxxvu+bbDc+++/j0svvdSyXVEUrF69GosXL45ZcHNzM5588km89NJLCIfDuPnmm7F+/Xp8+ctfju5zyy23YPny5bjiiitQX1+Pyy67DHfeeafjyscK8tAHaUkeHkpfBK45YCtWMJeWNUFWFIhCRN+9NyQb/p1IAFisc3kkAUEZUfU3RVERlq311e+n1SOsqOAQCZwIhuS4yiv6emjZMCLn4uGVeHQHwtEI3O7eiCuEIPCQRB49vZHffJKAQFCJKr14PRyCgbDlGiWBixs9Gq+dEi2DguWs5bsp+I2C5WKfN9M4zb7DeiY5noOqAIqqRmImeEBVAHAAVNjaoVwIlhs2bBBOnO5m2kmfxCMQVABOBVQu+n+tHbSsD9rf2u+Sh0MwpFq2ez0cekORDDzW4yOTBl7gorZfiGaWUDH0rCKcOtMT1z6mKnA5231U33f0Y7Q2Dgk81z9u8TyKvTx6ukNZCdyOR7zngFVnAJZtgoe3tAPPc+jRtUORl0fAph1YZXq8AvN4uzpqwXIiANE0P/BJPMJB2VG95ZCS9H1KtI/aTrRZk2AA4Hk+7iQYAPx+P1588UWIooiWlhbIsozi4uLo75999hkCgQCuuOIKAMDtt9+Op59+OqGJsB36aFJzxgdzFKIsK+DQ1xCyCs1keyTBIv+3eN4E7P2gBZd+eZhteU7qZD6OF7ionGGs+ur3Y6V7cyIPDSB6zcU2EofvvP85ugMhXDv+i7bn0urEqSogywjK1muMJyHqpJ0GUgZBEKnD7pnc2JdGTZ8+zSy9nqvPqiDwOPz5GSz/xW6mTa6r8uO/3z2CKy8525JGjmWzFs0eh70ffI5rxn3BkA1Cy64xZ/pojBjiQ+tpa+qv4aUSAFhs/+J5E/DBP0/i0i8Pj2sfY40/uXZvNFhj9PKFV6OrJ2xpwxFDfDh+OpBT18+6Z0uq/PB4eENWhcaaSgQZ91Yy7WfXDqwyVy+aghZGGroRQ3zo7QnFruN8P8SQbMkqVVIkYunancZ7xZh/DC+VwAVly5wsnaTVccbj8eDpp5/GjBkzMGnSJIwcOTL6W2trK8rLy6N/l5eXo6WlJSXn1UeTmjM+OI1CZEkMrnphD7525RcHVF4suWH9uWLVl1UnvbRxPHloM3YSh1+78ou4wX9BzHPZRYEmIiHqpJ0GUgZBEKnD7pnUpJbN9sMssZyLz2oYwPJf7La1yU0bmnGD/4Jodgx9O7Bs1tOvvIcb/BdYskFoxzdtaEYgqNjK2tqNR/4x5ziyj/kod89qk3CYPaYFdKnEtO1uv37WPVuxodmSVaHllDVDxErGfnbtwCpTDrMllgNB40sDs47PN6OVce5wWHV0r1KsnuyItLsdLVq0CDU1Nbj33nvxq1/9CrNnzwYQcbHguH6/HS3nXyIMGzaIub21rf8m2EXgguNiLp8fO9HFPE5V2ZGO8crT18l8nCzHz1Bh3k//m17a2CBzrKo4J+Y1dtpeo51MqL588zWbr3EgbZ+KMoDsfL5jYddHzbilvm4m021UKPckXh+1eyadSizbPatubl8nY4hmI82/x9vfvF2fXYP1u6yqgMo+1ul4FGv8cfN90DD30fLyUuYYHStrhFuv3+lYGNlmzapglyGCnX2B3Q7mfe0zQxnnFE7r2NrWA3O8m+29ijNvSQdpmwh//PHHCAaDuOSSS1BUVITp06fj4MGD0d/PPvtsHD9+PPr3iRMnMGLEiITOYevbJkSicFvbeqIRuPoGH1FWBKhqHN8cgXkcx3EDKk9fJ/NxWvRkvPrq99P/ppc2Nsgcc9yAr1FW1Ljnslyz6RoH1PYpKIN8hK3l5wPkI5we4vZRm2fS7tk0SyyznlXX+wg7GEM0KWXz7/H2Z7WXFhnP+l3gOIBjH+t4PIox/iR6H7LdR/sFNazjl5Y1wtKGfOquP5XEfA5s7pk5q4KWISLefrHawbyv3X4CzznqV6xzm02M7b2KM29xQsoENZLlX//6F5YuXYpgMIhgMIitW7fiyiuvjP5+7rnnwuv1Yu/evQCAzZs349prr03JueNlfFjiIApREmCJPl08bwL+sveIpTwnUY2xIin159q07RAenXcVGqonoql2MhqqJ6KxptKyn1bGA3PGYdO2Q5Z/11f5IQocwhwHXhIAQUCY46AKPDySEPm/TfaHv+w9greaP4l5LidRoHZR0uAAVeAhCNbu57QMiqAmiMxg90xu3XMYi2Zbs+loWRLc+qwKAg9V4KP20M4OLb17osUmP/ngtVjzyPX44b1Xo6c3hLp5EyztwLJZi2aPw1vNn1iyQWjZNeqq/PBJPNMee2wy9SyeNwHNB45Zti+p8kPos/3a9SWTBcCtsMZDu6wRPonPueu3y6pw3qhSrHnkejzz6PVY88j1OG9UKfPaWNkXWO3AytQgiNa21doxbh3n+zHCVF5dlR+iyDm6V9nIxmWbNSIV/OQnP8Ebb7wBQRAwffp03H///aipqcGiRYtQUVGBDz/8EEuXLkVnZyfGjBmDpqYmSJLkuHwnWSMUBeAFoOVkDzgu8vY0cmgxSjxCXCd5fYYGgY9MHAO9cswsFLGIJfXcq6oIhyMZLjoZDvKaTGm0TmokU8SpMz1QFMAj8hg2xIfeYCQQTlYUNPZp2LMCPbSAlpqZY1B52ahonXrDIbR3hBAIhnHeOaWAwlmyZiQSmaxFlysKoELFz3+3P24gDWWNSC2UNSJxaEXYCOuZDMmAykWebaVPhMjr5cEpXFzbmK32TSRoTMsaAQ7o7A7hv/5kDQ7UJJQlUYBXEiEIHE6eDmDbO59i8uWjMKp8EESe1wLq7bNJ9GXX0I85YVnFb/98CPv+cQL1VX4MH+zti7CP3ANW1giR5xEIhZkStYA1M0CuZ40wj9FeD4fuoIxwOJJtQ1EBUeTg7XO7zPWsEcVeAZ8zgtjKy3w40xmKXrPXy6M3aJUvLhEjs8x4WSO8Hh5hVYUc1mUpEQFBtWaBYWWNcJoNwpwJJVa2q0TbNRHSOhFON04MuCrwqFuzw7L83lQ7OSIV6gL0dayv8mP95r/Hra/ddVXPrACAaBl25VXPrMDKDc268qegbs32tLVTJu8DTYSt5btpYksT4djnzTRO+iiLZJ7pbLVvInXW6qgdUz2zwpEttdrWxGxcou2qb8tM2Nls99FYfScXxns9iT4HqiDYjNNT8N0Vb0a3NVRPxNpN+wbcDrHOw8nsiaqb3J1c4xrhFpKRCswUzuScnckTlhZ7DGU4DWiJ5RyfCnLhPhAE4ZxcfKYHUmftGKe21GpbE2uPTMjb5iv5fv3247RxcmsvseysHZyeJ1/I+4lwMlKBmcKZnLMzecKO7pChDLvyzAEt9rLJqWmnXLgPBEE4Jxef6YHUWTvGqS212tbE2iMT8rb5Sr5fv/04bZzK2UssO2sHp+fJF/LeNSIXEokLAo+ukIyWU90YVCxhcImEto5AxL+ZB4aeFYlu5U1+xazrGlTsgaIC4XB/eWWlXrSe6veRPu+cUoSCKgA16kMliTxOdwRt2ylZVR5WUv7v3nZZVBEvVnmJnptcI6zlu8nVgVwjYp830wzUNSIZ2+pmH2HN3ggCD1mOqMCpqoo3dv4TV15ydkwBkboqP4KhMAYVeSB5xL5UlDw4ToWqAl6T7yTLJ1IfM2L2c2W1q14BL5aPsBM1VSdku4/G6ju5MN7rGYiP8KnOIFpP9fv+jhhajNISDz4+0h7ddu6IQQjLitFH2GFsFBDxvT4TCFvOM7jYg96QYuPnq8UPAIqsOlKvS5cCYsqU5fIFWVZQVuxBU+1kVznJmwmFlKhPj5ah4c/vHMF1V30RS9buYD7U2nUBkYA2LRBt4piRmDt9NNZu2hc3WC4SfezHH3f8Lzq6g1h+79XgwIHnEXfSnYhxMdSXAzq6Qlj6zM645eWaYSOIQiFXbKueeHXW7M3LDNW8+io/ykq9aKqdAllVI2nNeBVfn3QBvjntqzirWIKsKPjvd4/hmnFfwLL1/XZbU5Yzq3WyFEB5gUNXR8iyX1GpBLN7pl4BTx9cvfr+KQiGrAGL+W5Pc7FP2sG6V40LKhEOG+cKS+b70dkdMmxrXFCJYNC4X32VH6U+0dKHWCiyajlP44JKnGzvxQqzipzIG1TkGmoidVzxvK7eNZUIhRTDsW5SQMzPdW4TsqyAkxWIqgquT1bZTYSBaAcB+lXcvvG12Kp42nVxHLD0mZ3YfSCizDdtwvkxlfXM6k+rXmjGN752EXYfaMHSZ3aC52Fop1SpEmn1heq8vHxURCKIfMHttpVFrDpr9oalmrdyQzPCsgJOliEqCgAVdT/dgWXrd+N7T7+Nw593oHHdLlsFOZZaJ0tJi6WYZqe4pVfA0/ZdsaEZsqzGvL58tqe52CdZsO5Vy8nu6ART27bi+Wa0nOy27Me6z04TMoQBy3laTnZb5iksFbnWU9Y6tpyyHusmBcSCmAi7HTsHfzslIrPDe9ikODeQYDktlWYigXkDDUBIpLx8D34gCMI9xAuM09sds23SjrGz2/b23DhRSyRQKVH7SPY0d2DdK6cqcvbBcg6/4Kb43E6D97LVP2ki7ALsHPw1JSLzdrPDuygYjx9IsJz20pxIYN5AAxASKS/fgx8IgnAP8QLj9HbHbJu0Y+zstr09Nw7DiQQqJWofyZ7mDqx7ZRcEZ6c2Z97PabBbqs/tNHgvW/1TaGxsbEzrGdJIT08Qbgv1EwQeCs9BBsAJPESeQ7x4RJHn4B9zDt492IquQDjqI/zGzn/i7tsuQ+VlZ+PrlRdgyuWjcMs1X4KP5yNBGH3nAjhcd+UX0NrWjc+OdyEYCmPhHZfjvYOt+OfRM7jn9grs//hEtOy6Kj9ef/tjfHa8K+oj/PrbH6OzJ4T6Kj9KPIKhziLPYYKpfqz9nJJIeQM5d0mJF93dQeb2TOOkj9rVN1WUlHjx8paD8Xfs486bRrtqf+2YdLaRmXTfk1jnzTTZsKPZat9YeCQBCjhMHf8FACoqLzsHf//HCYPd8UoCwHEQeQ48YLBNwVAY994+Fn/Z+ym+/fVL8J7OZi2e54cKBdMmnIerLhkZteczpnwJRT4BQVmNjheiwOHKS882HF9X5UepT4AiW+3jxIpR2PthiyP7mApbnu0+6sa+kyja2N0VCEf7E2vsm3zFuYb+cu7IQahk3L/BpRL2vN/fB2655ku4umKUYT+tD3Hg4s5RWHOSW6Z8CV+76guG+txyzZdQUuTBO7pzz5jyJUy5fBTe/dB47OSxo+L2u1TNNRLto3mfNSKTDNTRW581wieJUFVg5LAiiAKPtjO9lvLKh/gQDsrMc5WWeAA1IsnZG1KYynqsrBECzwEq0hrJOdDyKGtEclDWiMShrBHpxU3J94HIJPhER9AQoNZYUwmB59HdG0aRJOL531tVMQH0K5hG1d4iX+lkRUUorODoiU5s3PIRhpRKmDt9tMVmv6wLXNbKTURxS581wol9zOesEbmA03mCIPA43R0yBJg11lQCHCyKcaLA47PWTkOGiLOKPZYMJYqsOj63fk4SCIZx7shB6O4OW44dUeZDT68SDSClrBEFjp2jd1PtZMRa2A8D0XQ3GiPKIiou7PKmALHOJcsIyoikJhN41P2MrUDHUj+y86WX5Yh0swgAsmq7n1MSKS/V5yYIgtDDClBrXLfLotSp/dZvayO2SQAAGVD67K4sR1TOvv/szuhx9VV+ps2unlmB3QdajOUGZZ3NkxGKYfR4nosEhwGO7CPZ0+zidJ7ACqJvOdXNVIxbeMdYLFu/27BN65/6PqQKvONzm+ckZrU6/XyEk2Wc0/eCovVVVh9z0u+y0T/JRziFDNTR2/44+6AJp+eKpUCXSB0JgiDyFTtba1bq1P+WqF13rvJJtjifSWbsdhqwlmzweSLnzge1OZoIp5CBOnrbH2cfNOH0XLEU6BKpI0EQRL5iZ2vNSp363xK1685VPskW5zPJjN1OA9aSDT5P5Nz5oDaX+1fgIkSgz2cm0lk0H5p4/id2xxV5edSZttdV+eGTeMfnYu1XV+XH1j2HE6ojQRBEviIJYNrarXsOY9O2Q3hgzrik7frWPYdtz5FIuURuk8zYPXJoMZaYti2p8mPk0GJH/TOpcw8rZs4lvJ7cf3GjYLkU49TR27yfJHAIympUJpMXgGBIQUmRiEAwkhRc4HkUeXkE+lYQtDLAcRD4iH9aWGGrCdmdKxeUdyhYLjkoWC5xKFguvWSjfePZEY8kRAQHOBVQOaiI+DSCi+yvt69Obaj5nD5JiNjzvgAmn8QjEJQt5SRi85Jty4HY10yTT8FyQH9f08sSs4IhJZ9oCHjzejjIIcVyvxIJrhzoHEUEIJgkwr0eDsFAZDV6IPcl1UH4GhQsl2WcOHrHihoVeQ5t3UGs3NDMlEfWR3hq5xo2bBD+ebTdNhLUXKeQ7Mxp3Q3kuyQoQRDpx4kdCQXlSKaeLvZ+6As8EsDhZEfQkU0y297eHsUQBNfbI1tscSZtHtnXzCMIvKP+45EEHG/vZUpyc0E52mfgsDwNp8ForP2M22THSnV27eCWvkeuEVkgloyg/jeWPDJLbrC9q9e2vFynECRBidRTelYRystLHf9XelZR/EKJnMWpHXGyX7ptUiZtHtnXzOO0zZ1KbefqPXRTvWlFOAvEi9zUfosVray/caGwXXYJNedvcKy2yvVrI9KHzysm7K6R2x9biVg4tSNO9ku3TcqkzSP7mnmc90X7rFGJ9lk34qZ6p3VF+Kc//SlmzJiBGTNmYPXq1czfr7vuOsycORMzZ87EL3/5y3RWxzXEitzU/+Y0Wtkj2mWXyH0ndpIEJQgiWZLNshNLWtmurHTXNdfORURw3hedSW3n6j10U73TNhHeuXMntm/fjldffRWvvfYaDhw4gDfffNOwz/79+/Gf//mf2Lx5MzZv3oxvf/vb6apO0ggCD1XgEeY4qAIPQeAd/cZCBKyRn/P9EAQO4IDGmko0VE9E2VlePLbgakwcMzK6HyvCc3CJN2YkqEcSoApCX/0EeCQh6WvOFAPNxEEQRGHgxE45sSOCwEMQONTPt0blizZlXXxeGRqqJ+KH91wNDpzl3Frd+ARssF1dJYFLuT0m+5p57Nrc6+ENfcQnsbNGeT3GfiAJXNL9hfUMJbqtta07oX7ppr6XtnOWl5dj8eLFkCQJAHDhhRfi6NGjhn3279+PZ599Fp999hkmTJiARx99FF5v5nXM4xHLqRvAgBy+PR4eC+8YG5UvFEUeazftw+mOIObfOiaq4KKVt+AbFbYSyDzPoazYg6bayZboS5Z0qOZwbxdVGu+aM+nILsuK7bURhUMwJGclWp1wN07tVDw7opXz8pYP8a0bvmqwzR6PcWDXylp9/xScjhGkpJX513eP4JpxX8CqF/Y4ssGsukqCfYBeMpB9zTz6NgfHAaoKr4dnBsaNGOJDU+0UQ5aGE+29ln4wrFRy3F/M95b1DDXWVCIUUizyzuZtS6r88Hj4qApdIvMEN/W9tC3xXXTRRbjiiisAAJ988gneeOMNTJ06Nfp7V1cXLrnkEnzve9/Dq6++ijNnzmDNmjXpqk5SOA1uM/8Wq7zGdbuwbP1u1K3ZgWXrd2PZul2YNuF83HH9RfjP/9prKQ8qIhLINp1ElpWInKKqGvZz6nCfyDVnGrtrIwoHySPg1oc3O/6PKAwSsVOx7IhWzrQJ52P1i+8YbHPjul2W8iLZeNSY59bKvMF/QXQSrO0Xzwab6xqMc65kIPuaebQ2H1FWDE5W0BtSmeN0IKiAk+W+eyOjN6Qw+0FQVgfcX1jPUMupbqa8s3nbig3NaDnVPeB+6Za+l/ZV6EOHDuGee+7BI488ggsuuCC6vaSkBOvWrYv+fffdd6O+vh4PPfSQ47KHDRuUyqra0trWf6P7t/VE3uYA29/sVrDsytOkNhMtD7DPm3fsRBfbIV1VcU6M8mJdcy6szLmljk77qFvqW8iY70Gh3JNM2VEzqWjfVNkprRy7AGVWefHOrf0u8GybHs8GOz0XkP991dxH8+l6y8tLHY/TTvt7Is8Fa1+WnHIi8s65Mk/QSOtEeO/evVi0aBHq6+sxY8YMw29Hjx7Fzp078c1vfhMAoKoqRDGx6mQsEbwQcVrXd4IRZUVAnxaJ3W+2yaVtytOkNhMtL1Yia0EQmOUJHBc7+XWMa3Z7MnMS1LCWT8RHfw9IUCO9pKx9U2Wn+srRApQdlRfv3H2/ywrbpse1wQ7PBSDjYjOZJt8ENTS0a3E8Tjvt74k8F4x9NTnleNtGlLHlnbM9T0i0j6bNNeLYsWO477778MQTT1gmwQDg8/nwox/9CEeOHIGqqvjlL3+JG2+8MV3VSQpmcFufU/dAHL5FAEvmW49JRs7TDjvp0Hjxcm5yZCcIgmARy07FC6LTfpc5DgCHptrJGDzIg4fmjndk9+LZSO33t5o/weJ5ExK2wYmci8gttAD2Yye6ooFx7IA343HJSCQn0o9ZUs7Jyju7mbRJLC9fvhybNm3CeeedF902Z84cbNu2DYsWLUJFRQX+9Kc/4Sc/+QlCoRDGjx+PZcuWRYPrnJCplQxB4NEVktFyqjsaQDFyaDFKPELCcphA5CE4EwijVVfeiKHFKC32INArQ/LwtnLJLOK9IffLOcaXXzRfdzrkD9MNrQhby3eTZPJAJZbTXSdaEc4cqWxflp0CYgcxswKEFs0eh9ff/hjzZlwKnyRCBQBVjWn34tlI7XdB4CA7kNRN9DplWcl4X812H831FWFWAHt9lR/FxSI+a+lkzjH0JCORnEg/BpDQNi3wzw3zBNdILC9duhRLly61bJ87d2703zfddBNuuummdFUhZWjBbeZPAk21k8HBuWShRlAGljHLmwJRVaH0GchUSSCHgrJBFjHksMBEr4sgCCLTsOyUKvDMYCHNZrMChJ5+5T1Uz6xA47pdaKqdjBFDS3D8eEdMuxfPRmq/KwZZezi2wYmci8gNWAHsKzc0Y+EdY7Fs/e7ofvo5hp5kJJLtsNs3kW3aC0ou9kuSWHZAPCW4xMuzV4whCIIgkiOezbb7XQuWG6htJ4h42I3/rKAz6oeZgSbCDki1AopTxRiCIAgiceLZbLvftWA5t6tyEbmL3fjPCjqjfpgZcs2nOWkG4veqOZNrn9ImjhmJ7952GRRFBS/wCfvEaAFs5uTZkjCwT2bxyFVfX4Ig3InbbYqIiEKnOa5DBCDDatP1PsJLqiIqn61t3YCNfXf79RNW3HLPJAFoqKk0xAiNHFoMoD+7iD7ozDwlGGjMD2FPQU2EB6qWZlSCATq6Qlj6zM6ElVQ0FFlFSZGIxppJ4DlAUQFR5KDIqf8M4haFOIIg8oNcsSmhkGJQ6FxS5Qc8kTB8vU1XlIg6JzgV98wai0AojEd+st322nLl+ol+3HTPFFlFOGzqm/P9GDpIiquyNlClWCI2BfUtPhm1NE0BBWryimthAEvX7kTt6m249/FtqF29DUvX7kyLapubFOKI1FB6VhHKy0sd/0cQqSQXbEoYYKpg6euo2XRBjah3cWEFKlRDYDTr2nLh+gkjbrpnYQArnjf1zeet6nCsCfpAlWKJ2BQuKVm2AAAgAElEQVTUinCsAAqnDeGWMpySyXMRmcHnFRNODUYQqSIXbMpA6+jkuFy4fsKIm+5ZMnWJFWhPfW/gFNSKcCqC3txShhvPRRBE/pMLNmWgdXRyXC5cP2HETfcsmbpQoH16KKjWS4U6j1vKAKzqNB6dDI2mmgSOFIkIgkgduaByNtA6akF2DdUT0VQ7GQ3VE9FYU2k4zq5sQeAs6nUa8VTuiPTipj4bS6k2HokoxWrzg0ifM84PCCNusl1pRx8gMdDIUbeUEctpXpFVQ2DAxDEjsfzeq8GBA8+DIpwJghgwqbBf6SaZOsYKstOXvap2CkKygqMnOrF20z60dQSYAVhuCtQqVNzWZz0eHgvvGBvNGuHxOHsxCgVlDC+V0FQ7JWbWCAqqS4yCey3VAiRiOaTnQhmxnObNgQG7D7Rg6TM7wfMYcH0JgiA0UmED081A6ugkyE4rW4WK7z+7E8vW78bBT9tsA7DcFKhVyLilz2pKtcvW70bdmh1Ytn43GtftctwfQkEZnCz3XYfMnNhSUF1iFNxEOF+I5TSfaiU8giCIQiAR2+l0X7LHhJ5M9AdSr00MmgjnKLGc5t0UGEAQBJErJGI7ne5L9pjQk4n+QEF1iUGtkqPEcpp3U2AAQRBErpCI7XS6L9ljQk8m+kMiQXVEgQXL5RMGp3lVhcBxBqd5NwUGEARB5AJGFVEOUFVb2+k0AMttgVpEdkmkjw0Up0F1RARaEc5hNKf5c4aVWJzm3RIYQBAEkUtotnNEWXFc2+nUzpI9JvQk0scGipOgOiICTYQJgiAIgiCIgoRcIwgiywRDMsrLS7NdDYIgCIIoONI6Ef7pT3+KN954AwAwdepUPPLII4bfP/jgAyxZsgRdXV246qqrsGzZMogizc2JwkLyCLj14c2O93/9xzPTWBuCIAiCKBzS5hqxc+dObN++Ha+++ipee+01HDhwAG+++aZhn+9973v4wQ9+gD/96U9QVRW/+tWv0lWdlEJymQRBELkF2W3CLWh9sbWtm/qiC0jb8mt5eTkWL14MSZIAABdeeCGOHj0a/f2zzz5DIBDAFVdcAQC4/fbb8fTTT+POO+9MV5VSAsllEgRB5BZktwm3QH3RfaTtNeSiiy6KTnI/+eQTvPHGG5g6dWr099bWVpSXl0f/Li8vR0tLS7qqkzJILpMgCCK3ILtNuAXqi+4j7Q65hw4dwj333INHHnkEF1xwQXS7oijguH4lFVVVDX87YdiwQamqpmNa27qZ0oXguKwGPFGwlRG3tEc2+igxMMx9xi19KN1kq49msn0HardzpQ/kSj0HirmP5vL1unUOkQpytf5pnQjv3bsXixYtQn19PWbMmGH47eyzz8bx48ejf584cQIjRoxIqPyTJzuhZFqvXYhIF+o78oiyIkBVcfx4R2br0kd5eWnWzu1G7NojGw+pkz6aq8Yj39D3mWw9U27to6km4+07ALudK3Y10/XMdh/NlftiiwvnEKnATfcl0T6atonwsWPHcN999+HJJ5/EpEmTLL+fe+658Hq92Lt3L6688kps3rwZ1157bbqqkzI0eUSzf48IgNJVE0RuwkphF8uY9gZleBPQKw30htFxpif+jkRaILtNuAXqi+4jbRPhn//85+jt7cWqVaui2+bMmYNt27Zh0aJFqKiowBNPPIGlS5eis7MTY8aMwV133ZWu6qQMksskiPxjICnsEt3fHWslhQnZbcItZEJimUiMtE2Ely5diqVLl1q2z507N/rv0aNH4ze/+U26qpA2ZFkBh77Gk1V6iyMIgnA5ZLcJt6D1Rc2dgPpidqHkdQRBEARBEERBQhNhgiAIgiAIoiChiTBBEARBEARRkNBEmCAIgiAIgihI0i6okU54PjEBjnyG2sKIW9rDLfUgso9b+0K26uXW9tCTC3UEcqeeA8V8ffl0vXQt2YdTVTXDihQEQRAEQRAEkX3INYIgCIIgCIIoSGgiTBAEQRAEQRQkNBEmCIIgCIIgChKaCBMEQRAEQRAFCU2ECYIgCIIgiIKEJsIEQRAEQRBEQUITYYIgCIIgCKIgoYkwQRAEQRAEUZDQRJggCIIgCIIoSGgiTBAEQRAEQRQkNBEmCIIgCIIgChKaCBMEQRAEQRAFCU2ECYIgCIIgiIKEJsIEQRAEQRBEQUITYYIgCIIgCKIgoYkwQRAEQRAEUZDQRJggCIIgCIIoSGgiTBAEQRAEQRQkNBEmCIIgCIIgChKaCBMEQRAEQRAFCU2ECYIgCIIgiIKEJsIEQRAEQRBEQUITYYIgCIIgCKIgEbNdgWQ4ebITiqJmuxpZp6ysGG1t3dmuhmuwa4/y8tKM18VJH82X+5cv1wFk71rc2kdTTS70lVyoI5D5ema7j+bKfXECXUt6SLSP0opwHiCKQrar4CpyrT1yrb525Mt1APl1LW4kF9o3F+oI5E49U0U+XS9dizugiTBBEARBEARRkNBEmCAIgiAIgihIaCJMEARBEARBFCQ0ESYIgiAIgiAKEpoIEwRBEARBEAVJTqdPIwoDQeARBiArKgSegwhAlpVsVytnofYkEqH0rCL4vIkNFYHeMDrO9KSpRgRB5ANuGYtoIky4GkHg0dYdwsoNzWht68GIsiLUV/lRVuyhydsAoPYkEsXnFXHrw5sTOub1H89ER5rqQxBE7uOmsYhcIwhXEwaiDwoAtLb1YOWGZoSzW62chdqTIAiCyDZuGotoIky4GllRow+KRmtbD2RSFBwQ1J4EQRBEtnHTWESuEYSrEXgOE8eMxLQJ56O02IOO7hC27jkMgecAmSZviSLwHEaUFRkM0IiyImZ7usV/KxXk07UQBEG4Bc22trZ1AwJva1utNtj5WJRuaCJMuBqvh8ec6aPRpPMjqqvyw+vhEaSJTMKIAOqr/Ba/LBGArNvPTf5byZJP10IQBOEWnNpW1n6NNZVYUuXHijhjUSYg1wjCdQgCD1XgEeY49IbU6CQYiHw6adrQjN4QrQYPBFlWUFbsQVPtZDxXNw1NtZOZE8JE/Le0+9Xa1g1V4CEI7jIrbvJFIwiCyBfsbKsMLjqGqwIPGZxlv8Z1uzC4VIo7FmUCWhEmXIX5zfGZR6+38SNSqPMOEFlWwKHv4ZdV5tt3LP8tfbvnwmqr02shCIIgnGNnW0Oygu8/uzM6JvzwnquZ+wVDCkRVjTkWZQJ3Ld0QBY/5DVNWIn5DeiJ+RNR104nmS6wn6r+lIxdWW51eC0EQBOEcO9t69ESnYUw4eqLT1TaYZhOEqzC/Yb76l0NYPG9C9CHSfIQlIVs1LAw0X2J9u2v+W3rcFPlrh9NrIQiCIJxjZ1s3bvnIsN/GLR+52ga7pR4EAcCa1eCtPUdQWuxBU+0UyIoCgechCUAomK2PKIWB3pc4VqaFRLJQZAun10IQBEE4R29bwXGAqkIQOLR1BAz7tXUEMKTPH9iNNphWhAlXwXrDnDr+ixChQlRVcLIcnQTrg+rcGKSV68iyAk5W+tpdYRotWm0lCIIgNASwxwROVuOOJ9mCxivCVTheiYwRpEVkDtaKgJve9IHcCOgjCILINexs6zAXr/6yoCU0ImM4XcF1shKZC0Fa+QjrHmr3a0RZseve9AHqKwRBEIniZLy2s61BF6/+sqAVYSIjpHpVLheCtPKNXF1ZpfRpBEEQznFq6/PFttKKMJERUr0qRymxMk+urqxSXyEIgnCOU1ufL7aVJsJERkj1Ci4FaWWeXF2Fp75CEAThHKe2Pl9sa67Vl8gxBIGPvEUqKhqqJ2Ljlo9w8NM2XHxeGeZM/yoAQBX4mM70Whlmx3tKiZVezO0uCexUaZKHhyyraG3rBuLcy3TXkXVu6isEQRDOsUuLqdl6zY4CSCpY2on9TmS/gUITYSJtsPyMHpgzDn9+5wiuu+qLeGrje3F9TeP5KsWTCiYGBqvdl1T50VhTicZ1u6LbGmsq0d4RxIos+A0n4rNMfYUgCMIZ2kqv3rbGsvWcrKC8vBTHj3c4tq1O7XcmYlOy7hqxbds23H777fi3f/s3LF++PNvVyUuylW83DODlLR+iemYFmmono3pmBX733x9j7vTR0UkwENvXNFf9UnMdVruv2NAMn0dEU+1kPFc3DU21k+HziFHDqO1nd3/s+uFA+yf1DYIgiNQjy0pfCrQpfbZ+CnySc1vvBKf2OxN2PqsrwkeOHEFDQwN+/etfY9iwYZg3bx7++te/YurUqdmsVl6R1Uh/Drj1mgvx9Cv9K7+LZo+DrCiOI03zJSo117Br97ASSYmjrayGOc7R/YmVb/JkR3BA/ZP6BkEQROoRBN5il394z9UptbdO7bdis5+iRMQ7UkFWV4TffPNN3HzzzTj77LPh8Xjw5JNP4vLLL89mlfKOVL1NOV210+8HlYtOgrVzP/3KexB43nGkab5EpeYaTtvd6X6srwMvb/kQQXng/ZP6BkEQROphzRuOnuh0Pm47mC8IPIeJY0aivsqPptrJqK/yY+KYkZbyeJv5Ap9CO5/VhZPDhw/D4/Hg3nvvxbFjx/C1r30NDz74oOPjhw0blMba5Rbl5aXM7a1t3cy3KXCc7TFmFEXF4c/PYPkvdkffDpfePRHnn32WoTOa9/vxA9cyzy0IHJbePdFS3vAhxZbOrSiq432dtEemcdpH3VJfDaftHg4rqKvyo0m3clBX5cfQs4ogiv3G72R7D/PrAHgMuH8OtG84xW33JF2ky47Ga79caN9cqCOQO/UcKOY+mk/X68ZrYc0bNm75CPXz/Vj5fLOtvS0vL3U8XwiHFcyZPtrR2LFo9jjL2CEIHIYNTU3bZXUiLMsy3nnnHbz00ksoLi7GwoUL8eqrr+L22293dPzJk51QXJ66KRNoTupMBJ4Z/QlVtT/GhCrw0U4NRCYqy3+xG021k8HpPl+b9zvZHmCeW5YVDPaJlij+kyc7medPZN9Y7ZENg+Okj8a8f1nESburAo+NfSu9pcUedHSHsHHLh1gwq8LUNwTm14Gm2ilJ9c9E+4ZTsnVP3NhHB1qnWO3n1j6vJxfqCGS+ntnuo7lyX5zg2mthzBvaOgIYMsgqnazZW+1aEpkvNJlWnZs2NDP3e/3tjw1jzOtvf4wFsyps2y7RPprVifDw4cMxadIkDB06FABwww03YN++fY4nwkR8WNGfWp4/GbHTkuh/c+LLY95v07ZDeOSuq9DRFYRPEhEIhjFyaHH0HE6j+Cnif+Akk3bGSbvLiorSYgnnlg+CwANnlXhRWiwx/LzYfuGKqsbsn6moI0EQBBHByZggAlhS5TdkiFhS5QenSScDMccE9le+yKRWO6+d76957BABzJ0+esBjhBOyOhG+7rrr8Oijj+LMmTMoKSnB22+/jWnTpmWzSnlHrByqsQLpAER/q55ZwVy1E3gOkPtXkli5B8NhFWs37TM8TPCkysWdiEUmAiV9XgE3T/4Slq3/W/Qci+dNgM8rIBzo9/TlbfJS8hwoxy9BEEQGSGRM8Hh4LLxjbHQRy+NxFlLGmgdMHDMSHV3G8y6/92pH84pM5IHParDc5Zdfjurqatx55524+eabMWrUKNxxxx3ZrFJeIstK5C1OjbzNaR0oViBdGMBf3z2ChupJOO/sUiy/92pMHDMSAKITWkHgDM7wZpWZOdO/iv/8r72WFFyU3iozZCLtTCikYtULewznWPXCHoRCVgWiJSYFoiV9b/W8wAHQfMe4vr8JgiCIVJJIyrLGdbuwbP1u1K3ZgWXrd6Nx3S5HY4cIoLGmEg3VE9FUOxkN1ROx4BsVlvP+/Hf7sWQ+e0zINFnPMvTNb34T3/zmN7NdjYIklsuD5OFxzbgvGFb66qv8WHD7WHAKEAiF8chPtlveKvVvblp5rPKz3vEKgEykF7NPhadYzsFaYRA8PI6391oCJoaXSggFydGBIAgiVQzUzdFuPztCIcXwJbi+yo+yUp+hzNMdQXjE+KvOmfiySfORPCGW349HEhCUI5MWgefhk3gEggqgKFjzyPX47Z8P4a09RwD0f5qQZVhW+lZuaEZT7RSoUKPqYsbfIk7ums+mahOoZ/70QaQHpzKZdp+ZzP1GEgBFVg39TIDdOQTIshLdTxU4NK7ZZdmvqXYKM2Bi1X1TDP5k5C5BEATBxrFUsc2Y4MTN0enYHQbwX6YA6pe3fIi7b7sU7Z2h6LbBgzyGeYR2jiceuAYquOi4I3PsVeym2slI1bdDmgjnAYqi2r4x8QKHEx1By4rbxi0fYveBlqhPJwDs+8eJqBN6r91Kn6oCqnMn92QCoYjkSFQmU284PZLA7DclRSKWrt3Z7+e18GpL+rSGmkq0d/ZixfPNMVcEtJVjVl8KhRV8/9mdMetIEARR6CSyYup0TJYEjpkWUxI4hOIN3gwhrUfuugoiz2P95ndjjgljvzIcJ01fCB9LsZAHC5oI5wHtXb22b0yQYVlx27jlQ9x18xjMmvoVdHSH8MqbB7Fg1ljMnQ5IAhAKyhAEgf1GyHEA5+xtMRNO7oQ9rPbnwKFx3fa4b9dBRr9p2tCMxppJhm1L1+7Ekw9di6baKdE3eJ4HHmWcY+EdY7Fs/e7oOSJ9hv3V4OiJzrSuABBEqik9qwg+r/MhNdAbRseZnvg7EkQM7Px+WfbS6ZgclFW8/78nsGLhZKiqCo7j0HzgGCovOye+DWYIaXV0BaOuEvo6mseE26+7CI3r/mbY71ifkEc6vyzTRDgPCIXtJYsB4+rtxeeV4dZrLjT4/i6aPQ4qVNSv2dH/JsmpzCTWXb0h/PKNDyxvi3YrvZTeKruY29+pJLLdSq1Zp2LsV4ajtS1g6As/vJf9Bj+qfFDUoGl9xuuxrjzUV/mxdtO+uHUkCDfh84q49eHNjvd//ccz4cIMskSOkag/r5MxWRA4XPKlYViydochG5AgcFDiDOIK44uxTxLZY8Jw45ggCtbxaeOWj9L+ZZnGlTzAI1pX1SJShREfy4bqidi45SMc/LQNd1x/EVPYYMXCycaVZBXMJNbTJpyP3QdaMGp4iWEVUFtJJtyNcx8x9kqtWXeB9QYvyyrzWJHnLSsRwUAYw0slQ18SBQ5DSiXUV/mjfW/rnsPkW04QBGEiGX9eO1gxQqte2IOm2ilxV4R5LiKdPG3C+VH7zfNg1tEnCQbbLwjWa2nrCGBIqTd/06cRqWFwideQtmzimJGYM3006tZsx4Kmt7B20z7cNeMSXHxeGQYPkphvZp09oei/tTfJudNHY/3mv6NuzQ6s3/x33HrNhdi07RAuPq8MV15ydl/5W1G3ZjtOdgSZeuKEuzCnuNO/XeuRBKDOtF9dlR+iyBm2sd7gVVXB4nkTDPtFVhPATOMXCsrgZLlvuwweKuaY+t6c6aMhUVo1giAIA05teiLYZwOKP7EWBGD2jRcb7LfkEdG4oNJQx8aaSpzu6DXMI9o7e9FQU2kZdwSozLEjVdCKcB7A85zJ74dH3Rqjj+ZTG9/DytrJ4Gyi/I/3/d2fNULvSwR8drwDL/3xAxz8tA31VX7LqjL5cOYGdj5igEn1R1YtK7Va1gj9sWD0J47j8cqbHxi+Jmh+6HCQCSIoq/bSm+lvIoIgiJwhHbE4dl8EBZ6Ditg2nLWarGUCihevsuJ5bb/Mfm2miXCeoPf7CdvJ2Soqir28xSfzgTnj8OIfPrD43mhlegUeZaU+tHUEAMB2VZl8OHMDs48YYkQd98tpytFoYf2xHkmw9CdB4LD7QAt2H2gxnPfu2xR8/5n4mSAykf+YIAgiX0h1LI72RdCcNUJWFUPWIJYNV+xWk2UVgqqTZ7aLVzHsJ8fPUpECaFzJQ2L5DPWGVGzU5fgTRR5nFUt4+NvjLW9fWm7CXkXFWcUSVt8/BcGQEvNtkXw4c49Eoo7NhIIyRgzxGd/gPTaZII47ywSRDp83giAIwhmhoGz5IujxcPiPp9622PDV90+BCh6tbd2AwMPD8PMdUVYEnochuo23mUfwPJfx/Krk1JmHxPIZkhUFuw+0YOWGZmz4/fvoDcr4/rM7Lb6+Wm7CujU7sKBpKxav2Y7THUF4eQ4i1JT7JBHZI9YKbDw8koDW0wGDn9eJ9gCWL7za0j82bvnI0TnS4fNGEARBOMccuxHolS3jRFmpD6c7gqhbswPfXf4m6tbsQHtHEI0mP1+W/RYE2MaSZBoaW1yO5BPRG1JNqnBy5DMxz4MXEH0T0/x1zD5D2n69fau5E8eMxO4DLcwMEtGsEYixSigrGMbwH6WsEbmJwFujfLfuOQyfV0BI1/dY9zgow/CFoaM7hI1bPsSCb4y1rCZorjUadqu8lH+aIAgiOezU5hyr0Jn2kwTrODGoyGOZJ6zY0IxVtVPi2m9ZBl558+CAY0lSCU2EXYzkE3Fcp7KiZYNg+fe2dQQM/jp6/9627qDB/7Ouyg8AKC322PgSAyrsVwm9Ao+THcG0an8TmcMnCZZ+VV/lR0d3CMv6JDC1fjO8VDJOhjnVoiK0aPY4ACrq1vTnoFwy34+GmkpLeXZKRZR/miAIYmDYqc0NK5Ucjd2s45fM9+OuGZcabHgs1TeDPzCjjoqqMmNJvnubgqUOYklSCblGuJjekDF6ftqE8y3R9E9tfA93XH9RdMU2bCqD5f/ZtKEZC2aNRXlZcfSzhIbmo6P5aZp/E3jO1qfUfG4iNwgEFUu/WrmhGa2nui39xrLoz1ARevqV96AosEQDt3cEUT2zAk21k1E9swIbt3yIIPn8EgRBpBS7MTooOxu7WceveN46Jmiqb3qifr5x4Dn2HCMsqxmfW9CKsAvp/yRhjL60W8E9b+Qg1Ff5sWnbIYZCmIqyUp/h80NkPwWCwDHV48CpEFVgSZUfK/RvhH1+Pr0U1Z9RnH7KYuGRBARlxHRvsMsZ6ZNEyzZZUQz3mKUipM9Lrd/GcRHjque7t11GfYZwHaVnRQbo8vLSLNeEIBLHPu7DLqMUwOvTZ9ocP6jYKHS043+Oom6eH00v9M8THpo7HuAcLHDYqNf2Bo3T3kzMLWgMchn6TxIN1ZMMUZUd3SFmlOWnLZ1Yv/nveGDOOEgeHopuoiN5eNw14xI8tbG/s2n7ybLKVI9bMKsCAODx8Fh4x1j4JBGBYBgeT+QDAkX1Zw67T1xOPhV5JAEnOoKWFDhm9wa7LCABk0GK3GM+4tylHcux+8Jphj8wuzzqM4T7SFQuGYhIJhOEGxBtbLoosLerUFG3pt8dYfm9VzP3Ky2S8KOX3jHMJYacJRnmCZLERybT8Sppo1779UkXGHbLxDhBrhEuQRB4qAKPXkVFW0cAZaU+vPqXQ4aoyq17DlvUvhbNHodN2w6hrNSHYEhGMKRC7cv6AACKjOgkGOh3p1BkMNXj5k4fHclFDKBx3S4sW78bdWt2YNn63WhctwthRI5bYqrHEorqTwvJuKEEZTCFKczuDV4PZ83SMN+PkUOLjduq/JBMEb28APy/d15p2O8//v1Ky7FLbMqjPkMQBJFaeAF4YM44g719YM44CDzD1lf58fPf7TeMEz//3X4smW8dE57//X7LXOLk6QBC4cgkNRRW8as3P4LiIKiDNf+4c/rorIwTNA65ANaq36LZ4/DSHz/AH3f8Ew3Vk+ARuWjWCC0a85NjEbU3APjOzZcYPjFoq4ZBm0/XsqoCin10vm2ya0WFyHO2q8VEaklGXCKWTKbBfSakQJKM91Py8BAF6zbFnOFBUSGKnGE/nudQ7BON/UPkUeoTKRMEQRBEmgmFVbz4B6O654t/+AD/8e9XWsZ8cLAErJUWSxBFo/0fVOSx7Nfa1oOSIg9W61aJF80eB5VT4+agj6VymulxgibCLoC16vf0K++hemYFVm5oxr5/nOhLWyYjHIxMRHiew9Y9h3HH9RfhCyMGoeVUF8pKfWht69GlOpsCjgM7aTUX6aZ20fl27g88zyEMDi/84X3DQzGirIgkcNNAMm4osYVP+vcLA2h8bpdlv4e/PR6hsAqfFDGsL/zhfSyYVWG8xyqHX7/1EaZNOD+636/f+gjzZozBsvW7DeWtvn/KQJuBIAiCsMEcRyJwkXSV+pgMbdyXZdkw5qsMd4nbr7sIL/zhgMGut57qYY4nn5/sssxdmmqd2Xq7+UemMwbRMp4LsFv1Ky32GD4N6EUuNm45iNk3Xoz1m/+O2tXbsHbTPnzn5ktw8Xll0eNPtPfgRy+9Y/lEogXExYIlavDAnHH40Ut7UbdmO2695sLoubTzORFgIBIjGXEJpwnLWf2vrNQHjygaPlvdes2F4Djjqw7Xlz7NvF9YUSzlaYnXI8IbO9DWHYq68BAEQRCJYxa/qluzA4FgGA/NHe9o3GeNMV6Jt9h1SeJRb3aXsBFKUnJsLkArwi7AbtWvvG+VVfs0oAp8dOXYP+YcrHphj+0q8oiyIrR3BnHw0za8+IcPsPCOsRg5tAT/au3sC4gbG7NOskk0Iyyr+O2fD+Hgp20AYDiXVl+e56CCPnmnkmTEJWRZxdvv/QsN1ZMg8ICsAG81f4JbpnzZsKrL6n9zpn8Vq16wfqVoqp1iSHZulz7tsQVXG6KLBw+yJl7XBFrEJLJiEARB5CtOMgaxvig3rtuF+vkTGLb/QkuZAKzuErDa9dUvvoPV919j2E8Q2EJJZjllt0MTYRegvZGZMwN40Oev27effuXOLpWatoqs+RgDwMFP27Bs/W401U6OvOFV+eH1cNacsDoEhmjGotnjcKSlEwc/bUNrWw8GD5IAGFeLzcIeRPIMVFxCBDB1/BexbP3fDP1KhNFGsfrfqOGDbH3Ll67tF8r44b3shOocD6zf/Hejz3qf645+P0UF2noGlhWDIAgiX3GaMUhRYbHBZaU+QOUMtr+uyg+fxOP46QCzTE7uF8AI28QIBcMKRKV/Pw48c+5iHmPcDk2EXYDTVT/9yp1dKrXysiKsWBiZ8Gqrt/rfGqonGVYF9W+HkWAoIKwoUMHF9FseUVaE4YOL8FzdDfjseNwi+WQAACAASURBVAde/MMH0fNFpZjT3XBETJz2K9Z+nE1aNA5GoYyjxzuZ+x093mlZ/V14x1iL3zDPWfsZ9R+CIAodu4xBq2qnQNV9leM5q/Qx6wtcU1/ckBN7axtfYnKN048d4DhAVXPyix456LkEWVYib2SqCq5PItmM3pdn07ZDzPQoP3ppL9Zv/jtm33ixxT/oRy/txX0/2oZX//q/kYdI51v041++i8+Od2Hxmu1Y0LQVJ9p7HPgtqwBULFu/2zDpJn9h9+CkX7H2E3i2f3EwZEzctuN/jlpS+tVV+bHjf44a9mtt68HZw0qYPmt2mS0IgiAKFbvYoZCsGPyBBR7ReCHNn9frESOrwqZjY2US0uP1cEy77vVYlye0sWNEWXHMMcbN0IqwQ5JR93JShv43kefBC0AwpBhTmplW7op9Yp8Pr2pZlQVg69+rCSOEoEbfDqtnVhjyDbd3Bh35LQs2CbpJKCF1JNP3BnpsWAbTv7iy4hyD7++gIg82bvnQkKZn45YP8fVJF+CtPUei5Y0oK0JXT4jps8bqP5rgC/kNEwRRiNjFDh09Yfza1h2QLfFCdl/gBJ63rB5v3XPYYm/lkILywd7oHELg+Yg7ZSDdYsfZgSbCDkhG3ctJGQAsvz0wZxxe/MMHFp9bzV+0SBJw/HQATRua8dDc8YYOD0TyAv4/N/Ti4af+O1rekZZOtHUEUFflx3Ov7cOsqV+x9TnetO2QRf6Q5bds59+caz5CbiWZvpfcscA1475g8DFrrKlEb0jB+s3vGnyEdx9oseSXvPvWy6JGXBPUEEUey9YZfdY0MQ99HRtrKtHeETTIe5PfMEEQhYTd2Lp20z7Dfl2BEHOVd9TwQQYbXFflR7GXx5zpow1qow01lWjvDGLF87H8huWYMUW5Dk2EHWDnq5OIH6O5jLJSH9o6Aij2iRB4Hi9v+dBQ/lMb+/1xWefSq4bZ+QufbA9EzxUMyXj42+Mh8Dx+v/1j7D7QgmkTzjf4HJvfFA8faze8EUoCDNK8QHJZDYj42PW91fdPgQo+4Whilo8Z228YeOXNg4aVXoHn0bRhl6E8WVaZfc8j8oa+4/Fw+I+n3mb4rE22+ieDQ+O67eQ3TBBEwWLO3BSxozyGlEqGr3KhkGI7/pu/1C2YNdaiNtp6qhtrN+0raHtLE2EHJKPuxSrj4vPKLEpwi2aPw+mOYNR9QfPHtTuX3teHtXqrZY2wO9f7/2wzHNd84Bhm33hx9BOL9gb53Gv7sPtAS8xVuYFmNSDiY5fj97Qpowfr3sTyMfv+sztjHqsoimWl90eLrrGU1xsMM/ueCqBuzfa4WSNkRQWnqob+YxexnMjzRhAEkcuwMjctme/Hd26+BI+t321Y0bVbOda7SgLAd2+z+gj7JLHg7W2hXGdSJKPuxSrjjusvYuZeNefl7egO2Z5LH9V58NM2vPTHSK7gc8tLIfAcfr/9Y1vVuadfeQ8N1ZNwpqsXYVnBw98ej2GDi1G/xrgK17ShGdUzK7D7QEtBviW6Abscv84if535mLGO5RnHdnYHLV8NgmEZr7/9sWHlQfvbmc+a9RlKxfNGEASRy7C+6K14vhkPf3u8STr5fdx+3VcM24qLPMz8vqxsEIFguODtLWWNcEAy6l6sMuxyAJvz8m7adsj2XJIAQ1RnW0cAQ0p9KPJwkATg2vFfjKk619kTUfn6ya/+LySPCFW1V7fT/03R/JmF1fdsc/ya7o1dv2UpATk59rxzSjFn+mhLdPIcU8TyrddciJ5g2HIOzWdNXxfWM5SK540gCCKXSUTxUxIFrNzQjLo1O7ByQzN+9eZBZtaHYi9vsa0jhxZjSYHb20K61gGTCj9YfRkqwHwDi+TlnRbNGhHx6WWfKxSUMVzzH1JVCBwX9eFVBd7iB8RSndN+0/ILsuqkrUprfxfSW6IbYOb4hbMVU9axdkpATo6Fwln61coNzXhwznjLivDXJ11gOYdH4B09Q+R3ThBEoZOI4mdjzSTDsfv+cQLzZlxq8C8u8vLo6Q4xbSs8QkHbW1oRdojTfKxOyvDAuuLVWFMZ/TQtejjIUUdbDrzQ/9FaEHioAo8wxyEoqxCh4pxhJeBkORrIZucbqled27TtkOE3cKqlTnVVfmzdczj6d6G9JboFS45fWO+V3b0xH8vJ7GMlgYv2K1XgIQi8tc/b5KAcNsQHjxjpox6Rw7xbLsXIocWWcwhQHT9DqXjeCIIgchURQGNNJRqqJ6KpdjIaqidiVDn7a6AgcJaxm+vL8x9BRai3b37AsK2Fbm9pXpMFzCtekodHe0cQjeu2Y+xXhuPmyV+yBK0NL5WgyCozHdaQIc58LCM5gKfgudf2WVTnoFr1xiWBw4JZFfjubZcV5FuiW0lmxZR1rCRwlqAMVgCdaJMzmue5aNSxduzwwd6CXmEgCIJIllBIMdjWFbWT2XnXTVl6vB4OJ9p7SbreIbQinCX0b2CyrEZzpn7jaxdF01Y11U5G9cwKbNzyIYIyEAZnSbO2ckMz2rt6DWXb+Vh6AIhQMXf6aOZqovmtMBSUC/ot0c0k8wZvPjYoq8zgO9W0SuwReaaa4SmdCqF2bG/I+eovQRBEIaF92W1t645+gTMTBqLzAiBiW0+19+DReVcZVokfnXcVOI4DJ8t99lZGb0hh2vT8lMNInpSvCLe3t2Pw4MEJH/f444+jra0Nq1atSnWVXI+ic2XwSjxuveZCRjoqFfVrdjDTrIXCiiHiP96KIflfEnqUBFK0/fmdI6aI5Q9QdculhmNb23ogq4WTeocgCMIpToWOWC6OkiggEJQNq8QPzR0fSUOp2y8VKV8LiaRWhE+dOoWGhgY8+eST+PTTT3HDDTdg0qRJmDVrFj777DPH5fztb3/Dq6++mkxVcg6PJEAVBIQ5DnxfShMAUFUwU6tx4FA9swKvvx1JiwZE8hE3VE+EoqjgJQHoK0/te7u0W5ErdH+gXEPvF66tHrC2JXOs1v807FK0Tb58lCE6ua0jgIApQ8SIsiIIHCXZIwiCMGMndGRerdVcHPV4JRFPvvyu4dgnX3633xU4xrHRoGjCQlIT4e9///soLi7Gv/71L3znO9/BPffcg/3796OqqgqPPfaYozJOnz6NJ598Evfee28yVckpPJKAEx1B1K3ZjgVNW/Hca/uweN4EjCgrQmcPWy7xTHcwmiplxNAiXHxeGe6acQnWbtqHJ/7PXnx2vAuL+8qrW7MDbd0h28kRkTtoqwd1a3ZE7+3p7hC6QrJhG+t+s47tCsk4bdoWCIbx0NzxBpeHc4aVMPuhJQXafD9KSyTDtkWzx4HmwQRBEFZYX+Ba23qgmNajJIGzpEATBLbYkKI6S51Jq8FskmqXTz/9FD/72c/Q29uLqVOn4lvf+hYAYNasWXjhhRcclfGDH/wADz30EI4dO5ZMVVyDIPAIAzHdDvTyyACi6l0N1ZOi0Z9mZ/jjOjGMptop+N53rkT9mh1obetB9cwKPLXxPcsbJolf5D7MpOp94hTx7jfr2BaGnGbjul1YPG9CpP/xgKywBTX6pZON6djWbtpnSZ+2YFYF9T2CIAgTPEPUYkRZETweHrLcP3cIycDGLR8abOvJ0wHbgGWV4/tTXYJcIBMhqYmwqqro6upCSUkJli9fHt1++vRphMPx3bJ//etf45xzzsGkSZPw29/+NuHzDxs2KOFj0omiqDj8+Rks/0W//OH/z97bh0lRnXn/36pT/TIDAwwwg4kmuDEmGgI/IWFAQX3iC+5GCYrZiJpHBhckjAku8RKZgWcBAwwSTZ4QfxgDKvq7XDGPBFk3a5Yom0RFGEK4YiCaJcmDGjUzvIwwMC/dXVW/P3qqp+utu/pturr7+7kuLqarTlWdqnPfp0+fOt/7XnHnFIw9ZxjkpFcSHx4/a/tVt+9wO2Z/6UI88W+Hcc+ciYmBbXK6ZACJ46SkNLRuCTogSairqynkLfsWv9y3Vxt1q29HZ7dj24aDim2btb07TtqPdUunOSQcMKVdXrVgKpbcOinxGs6wQ1mWMGp4deJYTdNx+99fbLP50SOqTTZfivjFhgpNofrRdM+vUp5vKvL1DMr9WVpttJTv98SpHlta+qV3fBGnzvRh7ZMD64a/843LbGnuP/vJWiyb25CIJWz0ywDQ3D8x5jbuGAxKtV1yGgjPmTMHX/nKV7Br1y5cc801AIADBw7g29/+NhYtWpT2+P/4j//AsWPHMGvWLJw6dQrd3d1Yt24dWlpaPF3/xIkz0HyU6UwXcmJAAMQHGGue2BefqUv6JSaEcPxVN7QqiMYbPgdN1/HPcyZh9IgqvH+sC//ff7yVEMcl1v3oeuIcXd1Rx/NB13HsWNcg3Lm/qKurcbzvYjipFxt1qy8AwCVkmeO6XBnoOHl2IPGGZJ/VdUunaU27vGrzXlsqT2Om11rX4WEFrU3TAEkC+kVyJ06c8fJ4fEvKNinwdQebdDaabZ1SPb9iPV+vDFY75OMZDPazLLaN+t120qEL2ZaWPhLR8H9+/d+mbbpmT7zV2dWLaCxm65fnXj8u7bij0PipXTK10ZwGwp/4xCewceNGCCGSKlCHhx56CL/+9a/THv/kk08m/v7pT3+KtrY2z4NgP+JVqRkKxNf+tCapRpfNnYwdvzyCl/e/lyj3+IprEFBEIhNY4tefpEPR42uA1m1tw/bdR2yzyImQaINw36RwGGu9khXGyxsbEAgMDJCNGdxTXZFEuB23Wd2aIUG0zGvAuifNiuVHt79pum5HZw9GDA3j4Wd+m9amVDUetcToCGlzhBDijALg1hkXmfr01qZptmhRa5um2WaOl9w6CTXVQVO/3NzYgJ/+1xHTNRghIjNyek5r1qzBP/7jP2LcuHGJbUOGDMHGjRtx6tQp3HvvvTlXsJRwS2RhTV/bF9Xw69++Z1qT+XLbUTSM+1hiIByf0ZVsvxyNWbnkEGmQJAgZWN80HTFN43qgMsItFB4AW9rlp35mXqv71M/+gNlf+rRp2/95+b+x6OYJlnW+smPaZet6YNoUIYTkhqpqGFUT7E+AMaC1sEaL+mt7F37+xlFT//3Cr/6Eppv/H1PyjGBAxpt/Om66htO4g7iT00D4ueeew5IlS3Do0CE8+OCDOHToEJYsWYIrrrgCmzdvzuhcs2fPxuzZs3OpTtFxmr1znEWTgC9cfA5Wb3nDtM5naHUAQHLaW/svx+TzWWfijDpA1TkrV0YY7Wxt2+RtuuIcf3r08CrbrK6k9ie76D9WCPsbiubGBgRkIBrRaFOEEJInhJCdM3nWhE2TaNt2xSctrOVghD8FAFWFBt3buIO4ktNAeOTIkXjiiSfw0EMP4YYbbkBXVxeam5tx00035at+JYXn1Le6/defEQ3ix81Xx3/lCSAaUan8JN5IYVPp7CcaUTE6MUOhmezPipeoKIQQQpxxiyO86OYJWL1lX6JcZ1cvRtQEHd8G6oIRIvJJzktIuru78d5770FRFASDQYRCoXzUq2Rxm71LRtM0x7XEH53pw70/+LUt00y68xHiZlOapkPo6Wd1oxE1yc5URB0Kes2IRAhJTySqZiTq6e2Loet0T/qCxNe4aYmMGO2p3t4hRR+cXI7jhMzIaSB8+PBhLF68GF/84hfxwgsv4C9/+Qu+9a1v4eDBg7j//vuhKFyq7YRbjNYTp+LrNBkHmGSKm03JMvL2fsxtJoN2SkjmBAMCM+/d6bn8iw/Pgj80+SQX3LREAZFek8E+uDDklHps7ty5WLhwIR588EFUVVVh3LhxeP7553HkyBF8/etfz1cdfYfX9LZuOGV9WXzLRGzfHVd+fvaTtZg/azxUTc/q/KQ0MOyoo7M753bOJJNQtvabKioKIYSQ9CgAls8z99XL5zVAoH/2V4//7/SWjX1wYchpyvbpp5/G5z73OdM2Y93whg0bcqqYX8nH62H7WmIZP37hTfzx3U589pO1+J9fvtgkeuLr5/Ij38sMvK5Pz+W6XqOiEEIIcUYWEhRFxqKbJyAcVNAbiUFRZMhCgprm7R374MKQ01SjdRCcOKksY9myZbmc2re4vZpIn0fPjGooP3UdCnTcOuMi1NdW4earLrSJnrI5P/E3+bKjZJJtym1GIZfrMn89IYTkRkQFVm/ei9Vb9qF50+tYvWUfVm/eCwdtsg32wYWBzy9DvCbNyOicltm8fJ+f+I9itXMu1/UcFYUQQogjqouwWdU09sFFgmMrjxhho2QAU8aNwdWTxyaCXL+y/52cX00Y0SGES0pdvvooL3J9xeUUxgxA2tBmuV6XUUwIISR7hCy7jCFkpF0bAfbBhYADYQ8kr6uc8OnRuOXaz2L9U/tNyQeCQnIMOZUpnpNykJIml3Z2WudrpF1etXlvyrW/tC9CCCke4aCMOTMusiUwCgdl9PWwFy4GHAh7IHldZcO4jyUGwUD8lUZrHsOX8NVHZWBNkQ1d99zOTut81/YHZE8XVof2RQghxaM3oiUGwUDyGGI6Q6AVCcbl8kDyusqa6kDBw5d4ET2R0sdo5/ra6oza2W2dbzio2LY52SXtixBCikOqNcKkOHAg7AFjXSUAdHVHE38bJNZYEjIIJNujQX1tFXojMds22iUhhPgHIcsuYwgOx4oFn7wHkkOWbN99BPfMmcjwJaRoOIXQWd7YgDEjq2mXhBDiY4ICaLb033GdUZErVsHwe9ID1nWVwYCM9U3TEdM0CFlCUEiIqDpUSeKaS1Jw3Nb5AnDcpguZ64EJIcQHRCMqRtcE0do0HaquQ0gSggLQVJ19dZHgQNgjySFLtP7I1woAAQknuiJ5yxBGiBfcQugkb0Oes9cRQgjJnWhEhQTgY3U1OHasCxrYVxcTLo3IkUJkCCMkH9A2CSHE/7CvLi4cCOdIqkxdhBQT2iYhhPgf9tXFhQPhHHFT8FOtT4oNbZMQQvwP++riwoFwjjgp+KnWJ36AtkkIIf6HfXVx4XPOEWbqIn6FtkkIIf6HfXVx4UA4D7gp+AkpNrRNUgwiURV1dTUpyyTv7+2Loet0T4rShJQ37KuLBwfChBBC8kowIDDz3p2ey7/48Cx0FbA+hBDiBtcIE0IIIYSQioQDYUIIIYQQUpFwIEwIIYQQQioSDoQJIYQQQkhFwoEwIYQQQgipSBg1ghBCCPE5qULSOW1nSDpCvMGBMCGEEOJzGJKOkMLApRGEEEIIIaQi4UCYEEIIIYRUJBwIE0IIIYSQioQDYUIIIYQQUpFwIEwIIYQQQiqSokeNeOSRR/DSSy8BAK688kosXbq0yDUihBBCCCGVQFFnhPfs2YPXXnsNO3bswAsvvIDDhw/jF7/4RTGrRAghhBBCKoSizgjX1dVh2bJlCAaDAIALLrgAH3zwQTGrRAghhBBCKoSiDoQvvPDCxN9Hjx7FSy+9hGeffbaINTIjhIwYAFXTIWQJCgBV1bIuRwjxRr59ij5KSGWQia+zXyCAD9YIA8CRI0ewcOFCLF26FOeff77n40aNGlqwOmmajnf+dhprntiHjs4e1NdWYcWdUzD2nGGQZSnjcoXGLfVmpeKX5+HVRv1S31zJx33k26eyPV+5tEk6CtmPZkKlPO/BpFyeqdVG3e4rE1/nd3f+KdV7KfpA+MCBA1i8eDFaWlpw/fXXZ3TsiRNnoGl6QeqlCznhIADQ0dmDNU/sQ2vTNEhJvxi9liskdXU1OHaMyTQN3J5HMZzUi42WS/vl6z7y7VPZnK9YbeJHGx2sOhXyedcMq0I4VPSvu0GnEM+02Daayjcz8XV+d+cXP91LpjZa1J7hww8/xN13343vf//7uPTSS4tZFRuqpiccxKCjsweqppsemtdyhBBv5Nun6KMkHFIw896dnsu/+PCsAtaGFIpMfJ39AjEoatSIxx9/HH19fVi/fj1mzZqFWbNm+WaNsJAl1NdWmbbV11ZBWF6ZeC0HAIGggC4EYpIEXQgEgsL52kKGLuT+cjKEYLhnUr5Y7V2RZc8+5en8Lj6qyPQzQsqJVN/HXvsZ9guVR1F/+KxYsQIrVqwoZhVcUQC0NDZg3da2xPqhlsaG+GL6pHJBIaG5sQGtSeWaGxsQFBKiSQUDQYHjXRFbudE1QUQjAwWFkNHZHbVdt7Y6wEX8pOxwsvdVC6bafM/Jp7zi5qMxXcOKR/fQzwgpE9x8PRSQcfxUX9p+pqWxAb3RGFZt3st+oYLgG4AkkhWkOoC6EWG0Nk2HqmkQsoxQQEKkN4ZgWEFfVIeqadBV4PSZXqycfymEDKga8HLbUdww/VNInr+KqEg4JxB/BdO6tQ2tTdNN5WJAwjGNcuu2tsXXLQ3ScyAkEwy/6ejsBoSc6FSsamynbU72vmrzXrTMm2zzqVlXXgBdyBkrvCOqjl//9j3b+SZcWEc/I6SMiKg6Tp/pNX1v/7XjNIZVBz31M1F1YBBslFu3tQ0bvjUdOjLve0hpwIFwP9aZqSnjxmDOjItsvyzH1IbR3tmb2G6UW73ljUS5xbdMhPXbVNU0l/VIGtcck5LFbUY3GtWwNmnb8sYGBAKybaalZkjAZu+1NWFomoTVTw741NI7voiPzkSw7sks3pRIwBcuPsfmo0PCZo+inxFS2oSCAsOGhtG86TXzjHBQ9tTPLJvbgNqasKlsbU0YH3VF+Ja2jKn4Pt+YzYrCPDN19eSxphncuDP0oqY6aNpuLdfR2YONzx3EuqZpEEEFmqYjpsZ/md505adw8d+NRk11AF3dUbyy/x0IWQbUpKUR/Wuckh0xsT5SLUyEDEKyxWlGt/1kN37+xlHMnzU+Yev/uutt/P2l5zvMwE632fucGZ/BqwfNM7g9fdHEINh8vH0GN/mNjZBlKLKEjc8dtPnoqgVmga6bn+USa5RxSgkpHIGgQERFwtcjUQ3bdr1t6nu27Xobd904AVPGjcHVk8cmtg+tCuC5X5jLPveLtzFnxmewesu+xDXmzPgM39KWORU9EE6ezVpy6yTTl3FN9cBM1Wc/WYv/+eWLsfG5gynLGXR09qC7L4ausxH8YNtB0y/Tbbvexr7D7YnP4aCMvp6BgbDXNceE+AGnNxgjhoYw8/ILEoPPVDOwmqbb1un93ceHYfjQkGkG94GFlzn6maYByZLTYFjBsVN9Jv9pabTP8nR09kARAz863TQAuazZ53p/QgqHk+6m9e5pjn2PJMP2htet7MdGVZv6hY+PHsq3tGVORbSj26xMDMCv+tcOBhQJK+dPwbZd/40/vtuJru5o4hfkefVD8VFXH+6ZMxG1w8KmX5bDhoQwZdwY7DvcnrhefW0VqoIK1jxujlHYurUN82eNx77D7YkZ5iHhAET/ukpV1RBRdZdftOP565MUHbsvSbaZliHVQbQ+td82A7tm0bT4coikNyKyDIyqCZrW9AES1luO//D4Gec3JUKCrouBGaGYbntDs25rGxbdPME0yxOf/ZXR2jQt5Wyt25p9L2sGud6fkMIRUWH7roQu4cVX/2za9uKrf8ZdN06wlZUk5zdFrXdPN/ULEviWttwp+4FwqlkZXUi4fOJ5ppmne+ZMxNM/ewtv/d/jtl+Q98yZiFcP/hW3XPvZxBe1MWMLIDHTu/iWia5rfWuqA6YZZmud+jQd+w63mwbWAPBPX/l8+TcW8TVOvtT6zWk2P3Gaga2tCaO7J4otO3+fVs3tdPy2Xf9tmzlecusk9EbMCu/vuMwcf2z0UNMsT3wdvw4p1r9GX9Xh9MLFyY+9rhnken9CCoik22Z0W7853XVG2Lrd7U2RqulQtIF+QQjZUwQpUrqUfX+cPOsbDwcoobsvij5Nh3CYefrBtoNY1x/JwVhwn7xv5fxLEwNnY3vr1jasnH8pbrzy04lfoHd8eZzjr8iu7ihuvupC26/WZ/tnfblGmHihGGtPnWY4+/o0TzOwTuvsjKgpTrOm1uM7u3pROzyMtYumQdd1SJKEvqhd4f2By8yxBNhmiRbeOCFtFAonf/S6ZpC+TEgB0e0zutDgPMvbNN223fVNkWR+X6OqGmqrA2nfHpHSpewHwsJl1vexn/4ed35lnOOMTUzVAN15NkcIyXF7bySG5k2vJ86/45dHcM+ciY5rhOdc+1nHX62QAEX3Fr+YVC7FWnvqNMMpy87+8PE68wzsx+vc1tk5R1OxzuCuXDAVnad6s5o5bm5sgCTDNBu93GO8UKd44l7XDHqNRU5IIYhE1YxSzfb2xdB1uid9QZ/g1HfE3KIzuXyfn2vpp5x0O0B80CsBKd8ekdKl7AfCqgrHWd/5s8bj1JmI44yN2j9b4zab47R9xNAQftx8DY5/1AMdOq6e/Elouo5/njMJo0fEj/v31/4cX0s5xHkNZWvTdKiqyl+fJCXFWnvqZPtu/qAI2bzuV3LzJ9lx+5nuiClqREBIWO0Q39Nx5rgmZIv/rUY1k08JIWHpD19L+wydZoO8rhnkTBIpJsGAyDitdFcB65NvnPoOxaU/EZKbz8ppcwCQ8qfscwe6zTjVVAewffcRLL5lYiLNYjyO4GTs+OUR7PjlESybO9m07545ExGJxmzHLL5lIrp6Inj4mQPQoeMH2w6iedPr+MG2g6gOKwjJ8Xrs+NVfsG5rGzq7+hzrpGnxL1FV1SCpGhRdTyQdYLpHYpBq7WkhMWY4k21fluHoD5IESKoKRdchqSoA3bGcEPZzLp/XgKqwgtVb3sA3HtyN1VveSNyj9Z4/3j9zbBzb3NgAGbrp2pHemMmnJFVDJOrcL2gaoAsZHZ3dCX+zHiug2+pszPRasR7LQTAh+SEogGaLHwoB2/f2srmTEQhIjv2PDh13f3c3vvHgbtz93d3Y8au/FLwfJf6j7GeE3Wacurqj+OO7nXjx1T/3zx7F4/3+9L+O4OX97wGIh0YzZpYUIUPIEqKqjhdffcu+3vCmCbj39kkIBgTW3z29P3bwwAyQEAP16Dzd51gnWYbpnSnDLxEnirX21HF21FWlbY5yIsG53MKb3z8J8wAAIABJREFUJjjOmspC8jSjHFDMM89BAVPKcjfcnqEOHc2bUqdd5kwvIcUnGlEx2hJxRtWAVw/+1T7Le/kFjv3P3OvHmc7JNfyVSdlOLwoh98/o2H813jNnIrbvPoL62ircOuMiKNARkuNrqt780/FEuSsnfQIKdCi6DsRUqJEYFAF85YoLsGXn79G86XVs2fl7fOWKCyDLgKLr0CIxIKbaZoCSZ9O2746vH043o+T2Cjw2GA+Q+BanmVm3Gcl84zQ7etuMi0z+cNuMi2x1EbLk6DdClhxnTaMR1TSrG5Ttftzc2ICAbJ559jIIBtyf4eP/dsiTv3Gml5Dio6k6AGPQqiMUkHHFpE+Y3iZdMekTqArKmGPpp+bMuAiKIhWlHyX+oizb3DqTetOVn8K6pmnQNEAREoQs4d7bJ9lmcrzM8kSiGp7+mXlG+OmfvYV7b5+U8mFaZ5GCARnrm6Yjpmmu12L4JeKE32YkAwEZi26egHBQQW8khkDA/vu6L6pm5TcGTrM/Xmd/nXB6hpBgC1tIfyPEnzi9MV3zjcvwmz/8zRRd5pcH3sOVk85z7D80VfdNP0qKR1n279aZ1B2/+gtef/NDtDZNi8/sYkD9if6ZY1XTofdvl3TdVRkqZAmdXXH1uoHX1ynJylOt/ws8lQqV4ZeIG35RMccAUwgzIG6jTqHEsvUbg2hETbpnFVE1tzBy1meoCxehDf2NEN/h9MY0pur4Rdu7eOY//5goV19bhau++EnH/gOAL/pRUlzKcmmEVzGR8YuyedPruKv1FTRveh2d3dGUgrTBfC1dzFfghHjBq68Vwpaz8d9U0N9Kh5phVairq/H8j5QfTn3P679733EJVSjAOBDEnbLs473OpGYThmowX0v77RU4IVa8+lohbDnfYeSS6whJAvqjttDf/Ec4pGQcGoyUF059z6c/McKWSnlbf7IqDoWJG2U5I+x1ZifbMFSDKZShKIf4mUxmUfNty4UII2fUsb62mv5GiI9x6ns+Pnoo9h1ux7qtbWje9DrWbW3DvsPtDIlGUlKWM8JeZ5+4BpeQ3CjmLCr9l5DKJZdkN4QkU5YzwoC32SeuCSQkd4o1i0r/JaSyySXZDSEGFW0fXINLSOlC/yWEJMM+gWRDRQ+EAf+EoSKEZA79lxCSDPsEkilluzSCEEIIIYSQVHAgTAghhBBCKhIOhAkhhBBCSEVS0muEZZkhsg34LMz45Xl4rYdf6psr5XIfQHndSyr8cp9+qUcl49c2sNbLr/XMBt5L8ZF0XWdwPUIIIYQQUnFwaQQhhBBCCKlIOBAmhBBCCCEVCQfChBBCCCGkIuFAmBBCCCGEVCQcCBNCCCGEkIqEA2FCCCGEEFKRcCBMCCGEEEIqEg6ECSGEEEJIRcKBMCGEEEIIqUg4ECaEEEIIIRUJB8KEEEIIIaQi4UCYEEIIIYRUJBwIE0IIIYSQioQDYUIIIYQQUpFwIEwIIYQQQioSDoQJIYQQQkhFwoEwIYQQQgipSDgQJoQQQgghFQkHwoQQQgghpCLhQJgQQgghhFQkHAgTQgghhJCKhANhQgghhBBSkXAgTAghhBBCKhIOhAkhhBBCSEWiFLsCuXDixBloml7sahSd2tpqdHZ2F7savsHtedTV1Qx6XbzYaLm0X7ncB1C8e/GrjeabUrCVUqgjMPj1LLaNlkq7eIH3UhgytVHOCJcBiiKKXQVfUWrPo9Tq60a53AdQXvfiR0rh+ZZCHYHSqWe+KKf75b34Aw6ECSGEEEJIRcKBMCGEEEIIqUg4ECaEEEIIIRUJB8KEEEIIIaQiKWjUiEceeQQvvfQSAODKK6/E0qVLbfu3b9+OYcOGAQC+9rWv4fbbby9klQghhBBCCAFQwIHwnj178Nprr2HHjh2QJAnz58/HL37xC1x77bWJMocOHcL3vvc9TJw4sVDVIACEkBEDoGo6hCxBAaCqWsGOy/c5SHkTCApEVEDVNAhZRlAA0Yjq6VjaFylnUtm3se/EqR7oQkDTNMgV6gM1w6oQDmU2nOnti6HrdE+BakRKiYINhOvq6rBs2TIEg0EAwAUXXIAPPvjAVObQoUN47LHH8P7772Py5Mm4//77EQqFClWlikQIGZ3dUazb2oaOzh7U11ahpbEBtdWBlJ1ltsfl+xykvAkEBY53RdCaZCPNjQ0YXRNMOximfZFyJpV9A0BndxTP7nobMy+/ABufO1jRPhAOKZh5786Mjnnx4VnoKlB9SGlRsDXCF154IS655BIAwNGjR/HSSy/hyiuvTOw/e/YsLr74Ytx3333YsWMHTp8+jU2bNhWqOhVLDEh0pADQ0dmDdVvbECvQcfk+BylvIioSg2AgbiOtW9vgZUKY9kXKmVT2bey7evLYxCDYWoYQ4o2CZ5Y7cuQIFi5ciKVLl+L8889PbB8yZAg2b96c+HznnXeipaUFS5Ys8XzuUaOG5rOqJY1bJpWOzu5EJzmwrQeQpJTZV7I9Lt/nyJZiZD9ywquN+qW+uZLpfXx4/Kyjjai6jo+lOVeh7atc2iQdxepHS+H5FrOOqezb+LumOlC0PnYwsdpovu7ND8/ID3XIF6V6LwUdCB84cACLFy9GS0sLrr/+etO+Dz74AHv27MFXv/pVAICu61CUzKrDFMtx6upqcOyYy0seIaO+tsrUWdbXVgG67n5MLsfl+xxZ4PY8ip0a1I2U7VdCZHMfQghHGxGSlP5cBbSvYrWJX20035SCzRe9jins2/i7qzs66H1ssW3UqV2yrVOxbbDoNpZH/HQvvkmx/OGHH+Luu+/GQw89ZBsEA0A4HMZ3v/tdvPfee9B1Hc8884xJSEfiCCFDFzJikgRdyBAisyZTAKxaMBUr509Ba9M0rJw/BasWTE37C0gBsLyxId6pIt65Lm9syOiXkwKgxXKOlgzPQYpHLrbn9digAJotNtLc2IBQQEp7PO2LlBpe/SIQFBBCdrVvw/Zf2f8OFt8ykT5ASA4UzF8ef/xx9PX1Yf369Yltc+bMwe7du7F48WKMHz8eDzzwABYtWoRoNIpJkyZh3rx5hapOSZIvMVA0quHR7W8mzrG8sQEIpM8LHgjIWHTzBISDCnojMQQCmQ3CVVVDbXUArU3TqOovMXKxvUyOjUZUjK4JorVpeiJqRCgg4fipvrTH075IKeHVL5IFpLU1YSy6eQI+PnooAooMoeuJsrXVAdx143gIIaO1aTo0TYcsgz5ASIZIuq6X7NqCcl8aoQsZzZtet732am2aBimpo0v1SsLrOfJ1nB/g0ojcyab9jfvI1Xb8YHtcGlFY/GjzVvJdR692rQuB5k2vOZSbDkm1q0gH+1kW20bdlkZkEzWi2DZYCn7gFT/di2+WRpDcUTXdWUiUwZdWtufIx7VJ6ZJL++dqO7Q9Uo54tWtV01zK+XsCgpBShQNhHyNkKbH2y6C+tgpClgp+jnxcm5QuubR/rrZD2yPliFe7FrLsUo5f14QUAq6p9zGGIMK6pkwBkC7MqpF1SNN0rPnGZXj83w5h3+H2xBphISRENMl1XaUhsms/2Y1wUIGuA2NGVUFTdchCRiggoy+qZ5UNLFNyyTxG7HjJxpaJ7Rnt8+HxsxBCIBSQTLbTG4lhzMhqhIMCvRHJtBY40muPeGq1PeN4L3ZPiF9JtusRQ0MIBRUoQgIgQQ4KqCqgaRqEBKxcMBWrN+81JZkJBCTo/XNXyf4bi2nQhZz1OnlmZySVDgfCPiZbMZCbKOOum8ZD0iX0RmNY+sPX0guZLCK7e+ZMxNM/ewudXb1obmzAtl1vJwbXXrOBZUoumceIHa+CHa+259Q+LY0NCAZlk+2sumsqjn3Ua7puc2MD6oaHHAfD2Qo8CfEz0aiGn79xFDMvvwCtT+1HR2cPpowbg1uu/SzW93+ur63C8nkN2PCtyxGJqoipOn76X0fw5p+OY9WCqYhGNay1+FFyX5yJoJrZGQnh0gjfo6oaJFWDouuQVM1T5+SWkQg6oEPHqv6ZhuR91qFIDEh0tka5H2w7iJuvujCR/evqyWMT+7xmA8uUXDKPETuZZGPzYntO7bNuaxvaT3SbtrWf6LZdt3VrG/qi9nW/Tra3ltmySIlj2LU1G9zVk8cmBsFAv70/2QZV1bHiR3vQtGE3Xt7/XtyPTnbbfMPaF2eSWY7ZGQnhQLgsSSXK8C7YcC5X05/nPvnvgXPkfwaBwpH8km8hmlv7hIPml03hoOK5HSmWI+WIYdfWbHBu2eGcfMvNj+x9MYWphHiFA+EyJJUow7tgw7lcV3fU9vfAOfJvThSO5Jd8C9Hc2qc3Yp5T6o3EPLcjxXKkHDHs2sgGZ2D9DAz4hnW7mx/Z+2IKUwnxCkcTZUiqjFtes3E5lbtnzkRs330ksS7tlf3vJPY1NzYgWIAlnG6ZxwpxrUog39nYnNqnpbEBY0ZWm7aNGVVtu66RQa7QdSTED7hlg3tl/ztYNneyzd6Dwu4HY0ZW2zJ+WvviTHyFvkYIE2qUBU6BrFMpga1RGAIBCb19qq1c8jkUWYYsgEhUg5ClQY0aEQwrpmu5RRtI9TyM7YONHxNqeFWJey030D56v21IUKOa7VhhsZlU7ZhvJXum52NCjcLip+D7bhSijoYdQgKgS9B0HbIkQQj0R42IZ4cLCgkRVe//LAOSDkmXIIu4DUOXEmVHDqvCydM9nm3b6gvGtTLxjcGGCTX8j5/uJVMb5Q+/MkVVNUjob2BVT4SdEkLGia6ISSWcHA0iWTFsPocKTR04X8SyL1og8ZoQsqd0u8Q7braRjFc1ear2kVTNdA2rPaX63eSljl6hMp74BcOuDYwXW5oaHxsLAAL2Pnp5YwMCARmrNu212bCiyDZfcyOVL0i6nrOvEVKKcGlEheGkEk6OBuE3xTBVzcXB63MvhfYphToSYuBkr2u3tqH9ZHfONkxfIMQOB8IVhpdoEH5SDFPVXBxyjS7ip/YphToSYuBmr9ZILNnYMH2BEDscCFcYXqJB+EkxTFVzccg1uoif2qcU6kiIgZu9WiOxZGPD9AVC7HAgXGEogE11/O3bvpCIBpGsGBZChi5kxCQJEAJyUCAmSdCFjEBQJPbpQoYQsu2Y5O2ZkHwOISRbfalqLjxOdrLcJbrImkWXYdPSq/Cj+6/CpqVXYc2iyxAUkmc7yIfNpLsXKuOJnzF8QJUkSJKEVXdNNdvrvAZ88pwaTBk3ZmBbYwOEkDwLHYWQIYREXyDEAu2/AgkEZCy6eQLCQQW9kRiGDQlg6R1fAHQkFMNOogpDVDeiJog5My6ypdUdVRO0iTwyFSU5XXfVgqlY3zQdMU3LSwQB4g2rnQQCDjF/AzLOnuoz2cLKBVPR0xvD2ifT28FgCNmyTVVOyGDg5APNjQ249/ZJ0LR47OCYquHHO36PW2dchLtuHI9327vw6PY30dnVixV3TsHwsJI2UoRxjdqaMBbdPAEfHz0UASFDQKcvkIqG4dPKgEzCluhCRvOm103rxOprq9DaNA1SUmfoVm7+rPEAgC07f+9wjulo3vRa2nPno36pYPi03PFuJ8LW5ivnT8Gj29/01Ib5aO9CwPBphcWPNm9lsOqYqq9dt7XN9HnLzt9j0c0TsHrLPlPZdP6STz8rto0yfJo/8dO9ZGqjXBpRYeQjxXImKUEzFWJQzOEPvNuJ9zSwTm3I9iaVTjoBc/LnbEVz9DNC3OFAuMLIR4rlTFKCZirEoJjDH3i3E+9pYJ3akO1NKp10Aubkz9mK5uhnhLhT0IHwI488guuvvx7XX389NmzYYNv/1ltvYfbs2bjuuuuwfPlyxGKVG80wlTAtn+Ihq3BoyrgxWPONy6BqOnQhEOjPXZwqxfIr+99xTKvrlBI0UyGG03VXLZgKCVJBhHnlTrbPSAGwfJ5FLDfP3pahgISVC6Zi5fwpaG2ahpXzp+Dc+qG2Y+P2YRfQDZaQjbZCikGyCE4XAlBk6P39uy5EXBwHCasWmMVxy+ZONqVNXnzLRLyy/x0sd0hfvuLOKY7+kmzzydf47CdrsXL+FHxn4WWQINEXSMVTsDXCe/bswcaNG/H0009DkiTMnz8fX//613Httdcmytxwww1Ys2YNLrnkErS0tODzn/88brvtNs/XKJc1wqmEadZsb05kujbHSLEpSRJOn43YRBqja4KIRtSUKZbd0nLmIzVu8jmCARmnuiJYm4Ewb+TIIVwjjNyEaIGgwOneGDpOdifEcvUjqzEsrJhSaQsh46OeqEkYt3xeA0YODZrsIygk1/YCkNd0yvl4DlwjXFj8tJ7QjVzr6GR3i2+ZiANv/Q2XTzwP65/ab8ocN7wmiGhUhywnpVzuT8EMSU+ImQGzv4weUY0TJ86kvfbyxgbUDg/hZI6ZOotto1wjHKdmWBXCIe9TBr19MXSd7klfMEv85NO+WSNcV1eHZcuWIRgMIhAI4IILLsAHH3yQ2P/++++jt7cXl1xyCQBg9uzZ+PnPf16o6viawc72pqoaJFWDrtuv27q1LZH61iin6Ho8xXJEhaLrkFQN0Yia2Cf1p2O2HpO8PZv6KboOVdUTg2Cjjuv668gMSanJJYtURAVWb96L1Vv2oXnT61i9ZR9Wb95rS4scAxKDYOMaa59sQ0TVTXYQUXXXuuTDZlLBbFqkGDjZ3cbnDuKahvMTg2Bj+9qtbVBVHULXIPX3tZKqQmjxz1JMS/iG1V9kh+UNbtnpolF3PySlRTikYOa9Oz3/y2TQXGkU7MlceOGFib+PHj2Kl156Cc8++2xiW0dHB+rq6hKf6+rq0N7entE1Ro0amntFfUBH50DqzIFt5mxvkKSUv3Ky+ZX+4fGzzgIKXcfHivCr3wm3Z+Mm/oAU/1IoxqyFE15ttBD1dXt26WwJ8G4bXq+RS11yJdtr+8WGCk2x+tFSeL651NHN7oSMvPuC9TjXflN37zf93B5WG81XXf1wz4NZh0Jfyw/PMxsK/hPhyJEjWLhwIZYuXYrzzz8/sV3TNEjSwC9ZXddNn71QLksjIOKCI2tom+Rsb9B119cO2b6SEEI4XldIkm9ecbg9G0P8Yd2O/pU+XBoB12eXypYSh3q1Da/XyKEuOZPFtbk0orD46TWqGznX0cXuVA159QXHerr1m5J7v+n12sW2UbelEdlQbBvMxcayuedC3q+ffNo3SyMA4MCBA2hsbMS9996Lm266ybTvnHPOwbFjxxKfjx8/jvr6+kJWx7ekEqa5idkywSoUMrLCSZJuu25zv+it0HgVL7mJqfIhzCt3FACrLEK2VQumOj6jYFiBLgyBpkBVSLaJ4FYumGqzDa9it2Jmd2NmOVIMku3OEKg9sPAyhIKyoz0KkZ1wTdN0zyJU9puE2CmY/X/44Ye4++678f3vfx+XXnqpbf+5556LUCiEAwcO4Atf+AJ27tyJK664olDV8TXWzFeGMO3+O76I02cjWPGjPY5iNi+4ZS3atutt7DvcnhhoQ5IgJAlBAc/nzpZMxEtuWcGiEZXZwjwQjWqJ5BaGYAYB82g2GFZwzJIdrqWxAcGgbD52XgM01RJHOKl9IEmArju2QzGzuzGzHCkGht1t+NZ0fGQRii6944v45zmTMGp4GCdO9SayxGWTifOdv53Gmif22fpS9puEeKNgM8KPP/44+vr6sH79esyaNQuzZs3Cs88+iwULFuD3v/89AOChhx5Ca2sr/v7v/x7d3d244447ClUd3+MkTEsnZvOCk2iidWsbrp48FgCw73A7VvxoD4QESKpa8EGwW51SCTbcxFSFFlmVOjHAJjRc6/Cc+6J6YhBslFu3tQ3tJ7rNxz7p3EZGO9TXVqdsh2K2F22FFIO4uM0uUNvw9G9wpieK//XYHpzpieKP73ZmJVyLAYlBsHHudCJU+gIhZgo2I7xixQqsWLHCtv3WW29N/H3RRRfh+eefL1QVSh73TG2a54bzmrVI1fRBez2WKssRX9HlD6/P2c3O3DJYsY0I8U6qPjjXvph9KSG5w0jaPsY9U5v3ZvOatWgwMwwxy9HgkEt2uGwzWBFCzKTqg3Pti9mXEpI7HAj7mKCALYNbpmI2J9FEc2ODKWvRYIslKF4aHLw+51BAcswUOGZUNduIkBxx8kMjU1yufbECYMWdU+inhORAwTLLDQZlEz4tBYGgQESNv74WsoxwUEZvRIsvjxAyhCwhpumQdLhmfgsFBFRNR0yNn6MqJKOnz3yOvqhqE07kO0tccna6YECGpgIxTcu7YMMtjEuxw/64UciwM1b7cRNDhqoCCbsy7ExTNduxAGzbZCHbjo1FVJvtyELyVBc/wPBphcVPoZbcyKWOwbCCvqiesHUjU5wkA7pm7c/VhJ+EArLDcQPLHJJ9yujjhZChqjo0TYcswzXrZ74oto0ys9zAsZncc6Hv108+namN8oejz4lGVEiIN1RAAB0f9ZrU/cmpmJP/To4MkVxuRE0Qc2Zc5HqO5LS32abnNcgkdbQ/h0OljRCya1rj5DYUQsaxj3pN5VYtmIpoVDOltl4+rwGKImP15r2JbRsWT8dxi002NzZgSLWCFZv2pDw20wgohJQCTlFYjNTKV0z6hC06S211AJKuIyBk23HL5k7Gqwf/imunjDX545RxY2z9eLrU8xTFEeIMl0aUEBEVNnV/cirm5L+TI0Mk77t68tiU5zAUx/lISzvYqaOJGa9t6FSu/WS3PeLEk23oOGmOJKHG7DbZurUNsaie9thMI6AQUgo4RWExUis7RWcx/NGpf1//1H5c03C+zR+d+nGmnickOzgjXEK4qfuTUzE7/e20L9U51P5XULmqkdNFrKC6ubB4jxphLxcOKp4iSbjZpFWr4x6FwnsEFEJKATefcEutbPhjquOs/mhEnLCfyy3SEPtZQtzgjHAJ4abuT07F7PR38mdDqZzqHEKW8qJGThexgurmwuI9aoS9XG8k5imShJtNWpecukehYBdEygs3nzBSK1u3G/6Y6jirP7r14+6RhtjPEuIGv4U84jUlcCFxiiJhpGK2/m1VIxv7DKWy2zkMxbECYLml3PIsFM1uqaOpbi48XtvQqZ3GjKy2HzuvAfUjzZEkhOIc2UQJSGmPHax03oQUCqfvBacoLItvmYiX2446Rmcx/NGpf182dzJebjtq80enfpwplAnJDkaN8EAmKYELTXIUAK9RIxQhQ5YlRKIqhBzvqPuimi2SQ7LCWAgZZ6Mq2k92IxxU0BuJYczIagwJiIzu2S1qRCFTezJqRJxM2tApQgiAtJEfQgEJMV2HGhvYJhRA6LAp1xk1wtt1BxtGjXAmXR1TfS8II/qDridmY/siKl7/3fv49CdG4OOjhyIgZAjoJl8c6N/jx3mJGmFNbZ6PaD/pnstgw6gRzscyaoQzjBpRANxER61N0zDYL5ySo0ggpkIFUN9vgJrav13VEVVhKqcZf6sqIsn7VNV0nDEsiQFY1a/wN6ivrcr4nlVVS3stUhgyaUNzOw20jXWbarGdPsho3vS68zVULeWxURoAKWFSfS+ovbGEreuSu49YB6im/l0FtH6fcfJHo483BiBGGTdfJoQ4w6URHkglOipXKvGey43BaEPaCalUvNo+fYQQf8OBsAcqMY1lJd5zuTEYbUg7IZVKLmJU+ggh/oFLIzxgiImsa8EUoKivnYz1ZB8ePwshhOuay2zWjOXrngu9Xo24k0kbZttOCoBVC6ba1iFnYide1yfTboifMMSo1qQzQkiIaJLJlg0/rK0JY86Mz+Djo4dCgtSfFc57giL6BCH5hwNhD6iqhtrqQHxNl086oUBQ4HhXxJbRy5qpK1uhXz7u2U8iw0rEaxvm2k7RqIZHt785MBhobIinQfSA07WdstrRbogfCQRkLLp5QuJHoKLIeHT7m4mMnobd1lYHsOFb0/FRllnfUvkoISQ3uDTCI6qqxcU/ug5J1Yr+heyUhcgpU1cuGeJyved8ZKcjueGlDXNppxhgz0CXQRt7zWpHuyF+wxCjrt6yD82bXsfqLfuwevNeU0ZPw25VVYOq6jn5GftSQgoDB8IlinsGIc1SrnhCDYpESoNc2inXNs4kqx3thviJdJkzjc+G3RbTzwgh7nAgXKK4ZxCSLeWKJ9SgSKQ0yKWdcm3jTLLa0W6In0iXOdP4PJA5rnh+Rghxp+BrhM+cOYM5c+bgRz/6Ec477zzTvkceeQTbt2/HsGHDAABf+9rXcPvttxe6SkUjldjBqxDCKCcEsHLBVHQkiZTqR1bHBXPqQDlN07HhW9PR26cB0KHpgKJICUFHMCBDU4GYZk9ykWt9/SoyLDWSk6hkmogil3YKBWT0QTJdV1N1W1D/5fMasPZJs2DIy7FWIZFxvJFFy7pGmHZDioWboNNq+6vumoqPTkfQ2jQNvZEYzq0fCkmSoCsyJEhobZqGvqiGs71RnOmOeBaWpupLCSG5UVA/+t3vfocVK1bg6NGjjvsPHTqE733ve5g4cWIhq+EL0okdvIiVks8x4dOjccP0T5lESi2NDUDYXu7L0/4O65/abxLVvfTb9/CH/9uJO66/GD/YdjCjOnmtrx9FhqWGV1GkE15FcE7tFArIOHaqz3TdlsYGBANyIkmH8cUfUMyCoaqwguOn+7DuSXOdh1QpWPHoHltdnGwEAUG7Ib7AzY/G1IahRNWE7csyEIlo+N/bfmuy+1//9j184eJz8OKrf8bMyy/AxucG+luvwlL2pYQUjoIujfjJT36ClStXor6+3nH/oUOH8Nhjj2HmzJl44IEH0NfXV8jqFJVUYgevQojkcjf9jwsdj4mo9nLGINgo17q1Ddc0nI+br7owMQjOpE6ZCDf8JjIsNbyKIp3IpZ36orrtuuu2tqH9ZLdZ2Hai2yYYer/jTGIQnFznWEx3rIuTjdBuiF9w86PuPg2rk2z/1JmorZzR12587iCunjw2MQg29mciLKVPEFIYCjojvHbtWtd9Z8+excUXX4z77rsPY8eOxbKtKOdmAAAgAElEQVRly7Bp0yYsWbLE8/lHjRqaj2oOCh2dAwOIgW098Tzxxt8O+5JzZiefQ8jOxxi57dOVEzJQUx3Iqk5e61tM/FIPrzbqVt8Pj591beePpbnHVDaX7vm4XTccNHcZTsI2N7GbdTmj32zGil/rlW+K1Y+WwvOtq6tx9SOrYNmtPzX64FT9ba7PohSeZS5YbTRf9+uH5zaYdSj0tfzwPLOhaEuMhgwZgs2bNyc+33nnnWhpacloIHzixBlopaKaFXFxW3JHWF9bBeh64m+nfceOdTmeQ9WcjxGSBOh62nKqBnR1R7Oqk+f6Fom6uhrHehTDSb3YqFt9AUAI4drOaZ91CptLd6zbdXsj5vkrQ9iWXM5pW31tFayPwU82YyVVmxT6uoNNMfrRYj3fTEjU0cWPDMGysd2tPzX64FT9bS7PYrCfZbFt1Ol+s61TsW0wl7bL5p4Leb9+8ulMn03RokZ88MEHeP755xOfdV2HopTv0n9D7GAof5PFDqn2uZ1jxy+PYNncyaZjmhsbEBTeyr3cdhTbdx/BPXMmZlwnr/UluRMUQLPlWRvtnI5c2snpui2NDRgzstq0bcyoats16kdWo2Wevc6KItFmSMnh5kfVIdnkI6/sf8fRV19uO4rFt0zEK/vfweJbnPtbQkjxkHRdL/hUwFVXXYWnn37aFDXi5MmT+Id/+Ac8//zzOO+887B8+XKMHTsWCxcu9HxeP84IZxtpIdOoEaqmIxwSiEZ1qLqOkCJD1XTE1LhKPxyU0RtRzeX6FfyhgIS+qAZV001RI6wRJEIBGX39xylChpAl9EXVRLSAiKpD1XQosgxZAJGoPfJEMSinGWEg/1EjAHv6YqdtIqn9DbvRNd1WF6HI6OnTEtuqQjI0Tfd0rNf7GGw4I1xY/DR7ZGD1ldEjqnHixBnHfUbfqCgSYjEdkADogKbrkCUpPsWkAZIM6Fo8yo+qDnzWNB2yDE/9fLo+lTPC8W0z792Z0TlffHhW0W0w1xnhTO650PfrJ5/O1EYH/cfoggULsHjxYowfPx4PPPAAFi1ahGg0ikmTJmHevHmDXZ28kk6lr6oaJPQ/dFU3hcxJtQ8u5WK9MUgARo+oxjvtXbbIAnXDQ4j0xhLl4udWEVGR+Kz1D0RCQkanJf1nc2MDtu16Gx91RVyjSyiyhM7u7NKGEm9EI6qp/aIZjB2tdgUHG13e2ICANRrEgqmIdkdNIcycyq1ZdBnOOkS1sEaIcDqWdkL8glPfveLOKRgeVmx9d0CYI6pMGTcGc2ZcZPKBZXMn47lf/NGWalmNxs8jAEB1DpvG1PSEDC6DsjRi9+7didngzZs3Y/z48QCA6667Dv/+7/+O//zP/0RrayuCweBgVKdgFCsNZueZPsfIAn1R77M8TnVv3dqGqyePzTq6BPEfTu211ikahEOaY6dysZg9uoRThAinY2knxC84+cWaJ/Y52qc1ksvVk8fafGD9U/sdUy1nWxf6CiGFI6MZ4WPHjqGurq5QdSl5UqXBLOTUu6q6p1v2et106ULd7ivVPq598x9u7ewlGoRTOVlybn+nCBHWY2knxC9k0nd7jRbhlGrZi60X63uEECs1w6oQDnm3upphVeg63ZO+oM/IyK/uuusu7Nixo1B1KXmMNJh2dbEUfy1dqOumUDVD9biW1KXuRrpQ1/tKta+A90yyw62dvUSDcCqn6c7t7xQhwnos7YT4hUz6bq/RIhxTLXuw9WJ9jxBiJRxSMl6H7I9VwpnhOhCeNGmSbVtvby8mTpwISZLw29/+tqAVK0UySSlsFUBVhQYER+GQgmgsvi7NEBpFet1fjNUODaG5scGWBUwREvo0ySZmSxa6mdKFWtLaJq8RvmfORNsaYbcUuUyHW3i8CuisopugkGzttbyxAVVVClbOn5LIDndu/VCsumsq2k8MpPAeM6oaQ6sDeHz5tQMiuJDs3P7KwJd58hrh5G20E1IM3ASkhh1P+PRozP7ShQgFZQASYrIMRZYgy/GU4QCwZtFliMV09EViiMRUmw8Ya4QBZGzrTE1PyODiGjXi5ZdfxoYNG9Dc3IzPfOYz0HUdCxcuxI9//GMAwLnnnjuoFXWi1KJGGFjT5iaLLdxSIhvCNydGjRqK9s6ziMV0yBIgSfEOO1mYdM+ciXj6Z2+hs6s3McBNFnKMqgmiqzeG9pPdpsGQIsmOESWyiXgxWJRb1AgrXtMuu4luRtUETT+EgkLCya6I6UfQqrumIhLRzAPmeQ1QFBmrk+yqubEBw2oCePeDroEB88hq1IQVxx9bfrKTVDBqRGEp1vNNl+peDggcP9WLbbvetqVD/vZtX4CiSNjw9G8S2xbfMhEvvvpn3HH953CqK4JAQMaQcADVYQHViA4huUeHSFVPRo0YgFEjnI8tdNQIv0Wm8Ere4ghfc801+PGPf4xHH30UbW1tOO+88xAMBnHuuef6YhDsV7ykwUwltnBLiZxK+HbqbB9WPLoHTRt24xsP7sY7f+tKDIKNc/xg20HcfNWFJhGcsc9IzWxNlbvi0T3QoUPR9Xh0CVV1vC+m/hxcvKZddhPdRFTd1F4RVbcJ49pPdNtFdU+2ocMieGvd2oa+Xs1kN6s277Vdg2mTiR9IJURTVQ2RqJboH63pkL/3rwfQdTZi2makTl69eS/O9ERx38ZX0bRhN5b+8DVA1yE0LStbp68QMnikjBpx/vnn46mnnsIbb7yBlpYWxGLUreaDVGIL19TJmntHGI1lJt5wFnK4Ce78NeNO7PYDONtIKtFNunJexXJuwjjaDfEj6XzC8C23PtTJ/o2yTuI4Qoj/cR0I/+pXvwIAVFVVYcOGDRg3bhzC4TB0XcfDDz88aBUsRwyxhYEhtgCQSMeZTEL45kJAcT9f8jmShW92IYfscl3LKIcUHfe2ki3lJE9t6lTOEMtZj3USvDkJ42g3xI+k8wnDt9z6UCf7N8o6iuMIIb7HdXS1Zs2axHpgALj99tvx6KOP4o477kgMkkl2BAWwcsFUrJw/Ba1N0zB8aMCSEtmeplMIQBcCAYfcusOHhLDK5XzGOe6ZMxHbdx9JnO+V/e8k9rX0p+xl2uTSwGvaZbfUsKGADF0IxCQJuhAIBWQst5RzSp28fF4D6i0plpsbGxAKywnbWzl/ClYtmEq7Ib4kVdpxIWQoQsKqBVMxfGjA1g9/+7YvoGZI0LRt2dwGDB8awL/Mn4JX9r+Dz36yFivnT8F3Fl4GCRKEGJRQ/YSQHHAVy508eRJLlizB8OHD8eCDD+LQoUP49re/jcsvvxz/8i//gnA4PNh1teFHsZwXhJDxUU80vuYySZwUDijQERcYGcI3TQdUXcMjz/0uIXSziqJGjRqKox+cMoudFsTPF9M0T1EjVFXznegtW8pdLAdkHzUiFDBnxUoI3oYG8O6HZsGbEpDxfvsZU9SI2iFBdCelU64Oyejo7LVloBtR4lmwKJYrLMVMx+oWNaKzO4pnd72NG6/8NL7/7G9RWxPGnBmfwcfrhkKWJZw81YOgIhAKKhBCwomPevHUz/6Azq5eLJ/XgNphYZw81TvoGeEolqNYzgsUy7njOnEzcuRIPPHEE3jooYdwww03oKurC83NzbjppptyrmSlEwMSg2Agvp5s1Y/3orVpGgAkUtMa1NdWYf6s8Vi3tQ2tW9vQ2jQdyS/dTp3ts4mdVm2On0/RdUBVoakDaXajSSmWk9M5e03zTIqP17TL1jbtg+QotFu14FKs3rIvcVx9bRUW3TzBtq21aVpcxNN/3e4+u9Bu7da2eLn83zYhOePUz+lCxrqtbZg/azy+/+xv0dHZg47OHqzesg8r50/Bo9vfdOyT//huJ4B4f97aNN1RiEdfIMTfpHxv093djffeew+KoiAYDCIUCg1WvcqaVIINLxnerKIoq1gu+XyEJOMmtMskE5z5fN4EeYT4GcOOnURybsJRCo4JKQ9cB8KHDx/GjTfeiCFDhuCFF17AY489hoceeghr165l9IgcSSXYcNuXLHSziqKsYrnk8xGSjJvQLqNMcKbzeRPkEeJnDDt2Esm5CUcpOCakPHAdCM+dOxcLFy7Egw8+iKqqKowbNw7PP/88jhw5gq9//euDWUffI4QMXcj94iM5rUBCAWzipOXzGiCEBEh2MYdV6GYVRQ0fEqLQjTjaYSAoTMK4cFB2FNoZmeCMbcsbGzDGIoxzsqlU4iNCBoNM+1+n40V/xsVX9r+DxbdMNAtHR1bb+msKjgkpH1zFcn/4wx/wuc99zrZd0zRs2LABy5YtK3jl0uEHsVyqTEVuAgkhZJyNqqYsbvUjq/H0z/6AfYfbMWXcGPzTVz7frzqOp/aMRFVXUVRdXQ1OnjxbFkK3fFAJYjkrTna4asFURKLm7HDNjQ2oHxFGb0QzCe00VXcUEHmxKUN8BEkCdL0sbI9iucKSr+ebTf/rdrwhjjtvTA3UmA5ZBhRZhkD82VhTlftFcEyxHMVyXqBYzh3Xn85Og2AAkGXZF4Ngv5AqU1GqY6xZ3FZv3pvI9rbvcDtW/GgPZBlATIUWifVnGFIdIwMAzERU6TjZYftJe3a41q1t6I1okBJZAuM25WQ/Xm3KKFdfW03bI4NKNv2v2/F/fLcTq7fsw/JNr+Pd9i6s+FE8s6aTL0QjqqNvsB8mpPRgkMMcyUYslE4Q5+UchCSTSXa4VFkKCSklchVrpuqL2QcTUhlwIJwj2YiF0gnivJyDkGQyyQ6XKkshIaVErmLNVH0x+2BCKoOCfyOeOXMGN9xwA/7617/a9r311luYPXs2rrvuOixfvrwko1FkIxZyOsZJfEGRBfGKk02NGWnPDucktiSkVMlVrOl0/OJbJuKV/e9gxZ1T2AcTUgEU1M9/97vfYcWKFTh69Kjj/vvuuw9r1qzBJZdcgpaWFvzkJz/BbbfdVsgqZU2yCCIcEohGdaiaBh0SRg8PobVpmmeBhKpqGFUTRGvT9IRgKRSQcNeN4/FPX/k8ggEZmgr0aRoUIUxZ4ZLPbdSpo7MbEHJZiJQqCa/t55YJK3kbgH47NNuUrummbYYwThdyVsI4QvyEqmqorQ449r/W7IvhoGwSiYYCEvqiGoaElXiSorjWE5IE3HXjBAQCMqJRDZoUFyzTJwgpTwo6EP7JT36ClStXYunSpbZ977//Pnp7e3HJJZcAAGbPno2NGzf6ciCcrCye8OnR+PK0v8P6p/ablPija4KIqpqnjGxCyDjRFXFUOiuyhE7LvnvmTMTTP3sLnV29iXIAclJLk+LiVe3uFg0iGtXMaY3nNUCJyli9ea/NLiVVTWSC02A/3/LGBgQCMlYlHUtbIqWCU6a4QFDgeFfElkp82663E5F55sy4yLS/pbEBo2qCONEVwbO73sbMyy/AxucO0icIKXMKujRi7dq1+OIXv+i4r6OjA3V1dYnPdXV1aG9vL2R1siZZWXzT/7gwMQgGBpT4LsEc0p7POIehdHba94NtB3HzVRemLZeJWpoUF6/t5xYNwpbW+Mk2dJzsTmuXTudbu7UN7ZZjaUuklImocEwlbkTmuXryWNv+df3+sq6/nDEITt5PnyCk/CjaEihN0yBJA0IEXddNn70watTQfFfLkY7OgUGCkOGsUtZ1fMxj7Lrk8yWfA/33ny7FcqpykKSixHn0G355Bm42msoGkuvuVM4tGoRjSmSLXbpd1+nYbGzJL889H5TTvaRisPpRK4V8vh8eP5uyH3VKpWz4i1uqZT/3r36sUz6x2mi+7tcPz20w6zAY1/LDM82Uog2EzznnHBw7dizx+fjx46ivr8/oHIMWCF7EU2fGw+kg8bdBfW0VhCR5DySddL7kc6A/t4nTvuQUy6nKQdd9EdC6mJREQo0UNmCqu0M5IxqE9VjHlMhWu3S5rtOxmdpSsZJQFAIm1CgshX6+QoiU/agRFcKpH0+OGlEK/SsTamRfp2K3Za4JNTIlm4Qahb5GIchbQo1Cc+655yIUCuHAgQMAgJ07d+KKK64oVnVSkqws3vHLI1g2d3JOSvxUSmenfckpllOVY6SJ0sFr+7lFg3BK0V1vSYnsZJdO5/OaTpmQUiEo4JhK3IjM88r+d2z7k9MkO6Vapk8QUp64pljOJ1dddRWefvppnHfeeViwYAEWL16M8ePH4+2338aKFStw5swZjBs3Dq2trQgGg57PO5gzGW5RI9zSHmdyPrdoEKqmQ5HltFEjyim1bT4oiRlheG8/L1EjFACykEwqeTe79Hq+TG2JM8L5ue5gU44zwgA8R41wS5MshAxV1aFp/emW4c/+lTPCTLHsBaZYdmdQfuDu3r078ffmzZsTf1900UV4/vnnB6MKOZOsTI71xpJUyiqimY2BbeezRpow71OhqanLGc6URTVIEfHafm62Yt2mqvBkl17PR0gpE42oJn/o6zF/jqjONm/4x6iRQ3DsWBcEAKigTxBSpjDFFCGEEEIIqUg4ECaEEEIIIRUJB8KEEEIIIaQi4UCYEEIIIYRUJIwGQwghhBBCBpWaYVUIh7wPQ3v7Yug63ZO+YIZwIEwIIYQQQgaVcEjJODxbIYKzcWkEIYQQQgipSDgQJoQQQgghFQkHwoQQQgghpCLhQJgQQgghhFQkHAgTQgghhJCKhANhQgghhBBSkXAgTAghhBBCKhIOhLNACBm6kBGTJOhChhB8jIQUGvqd/2EbEUJKDSbUyBAhZHR2R7Fuaxs6OntQX1uFlsYG1FYHoKpasatHSFlCv/M/bCNCSCnCn+sZEgMSHT0AdHT2YN3WNsSKWy1Cyhr6nf9hGxFCShEOhDNE1fRER2/Q0dkDVdOLVCNCyh/6nf9hGxFCSpGCDoRffPFFfPnLX8aMGTPwzDPP2PY/8sgj+NKXvoRZs2Zh1qxZjmX8hpAl1NdWmbbV11ZByFKRakRI+UO/8z9sI0JIKVKwgXB7ezu+//3v41//9V/xwgsv4LnnnsOf/vQnU5lDhw7he9/7Hnbu3ImdO3fi9ttvL1R18oYCoKWxIdHh19dWYXljA4SQKBAhJAu8CKyc/K6lsYEiBx/h1kZCSOwTCSG+pWDfI3v27MHUqVMxYsQIAMB1112Hn//85/jmN7+ZKHPo0CE89thjeP/99zF58mTcf//9CIVChapSXlBVDbXVAbQ2TYOq6VBkGb3RGJb+8DUKRAjJEK8CK6vfCVmC0r+d+AOjjdY3TUdU1fDB8TN4dPub6OzqZZ9ICPEtBfuZ3tHRgbq6usTn+vp6tLe3Jz6fPXsWF198Me677z7s2LEDp0+fxqZNmwpVnbyiqhokVYOi69ChY9XmvRSIEJIFmQiskv1OUjUOqnyIqmrQoeN/PbYHq7fswx/f7WSfSAjxNQWbEdY0DZI0sDZM13XT5yFDhmDz5s2Jz3feeSdaWlqwZMkSz9cYNWpofiqbAx2d3Y4CEUgS6upqBq0eg3mtUsAvz8OrjfqlvrmS6X34xX+cKPb1B4t896Ne27QUnm8p1BEonXpmi9VG83W/fnhu5TROiETVgl+jEOcv2ED4nHPOwW9+85vE52PHjqG+vj7x+YMPPsCePXvw1a9+FUB8oKwomVXnxIkz0IqtSBYy6murTB1/fW0VoOs4dqxrUKpQV1czaNcqBdyeRzE6PS82Wi7tl9V9+MB/nChWm/jVRjPCQ5uWgs2XQh2Bwa9nsW3U6X6zrVOx2zeXtsvmnjO9VqbXCAYEZt6703P5Fx+eldH5AW/3kGm9CzYQvuyyy/DDH/4QJ0+eRFVVFXbt2oXvfOc7if3hcBjf/e53MWXKFJx33nl45plncO211xaqOnlFCBkxxMMFKZCwZtFleL/jDMJBBb2RGMaMrI6vX7QcFwwr6IvqUDUNQpYRCkiI9NpfGCafn2shSTljCKysa4RDARl9kBK+EhRANGL1KGec/AeAbZssJERUZHUNr5SrL1vvKxwU6I1o8WcpSWhtmoZ327uwbdd/o7OrF6sWTIWEuKBYyFLxJzAIIaSfgg2Ex4wZgyVLluCOO+5ANBrFV7/6VUyYMAELFizA4sWLMX78eDzwwANYtGgRotEoJk2ahHnz5hWqOnnDKu6ZMm4M5sy4CI9uf9P0RV4TVqAmfacGwwqOnepDa9IXfnNjA+qGh0yDYWZnIpWEkwguFJAdfWV0TTDtQNXJf1YtmIpoVMPapG3L5zVAUWSs7l/fn8k1vFKuvmy9r5uu/BSumPQJU3stvmUifv7GUSycPQGyrCMS1bBq84CgeMWdUzA8rJT0cyClTaav8Xv7Yug63ZO+YJbUDKtCOMQ4OMWgoE995syZmDlzpmlb8rrg6667Dtddd10hq5B3rOKeqyePTXwBAANin9am6UiOntkX1W3lWh3KuYmHWpumgdE4STmiqhok9HdGqo4+SJ58xQkn/2k/2Z34oWpsW/tkGxbdPCGra3ilXH3Zel/XNJyP1VveMN3nxucOYv6s8Vj/VBtWzr/Utn/NE/tK/jmQ0iab1/iFXEgRDikFX1ZAnGFwxwyxZk+qqQ64ZFPSLMdpHssxOxOpbLz6ivOxdv8JBxXH84WDim2bl2t4pVx92XpfQobjfRp9o9v+Un8OhJDygAPhDLFmT+rqjrpkU5Itx8keyzE7E6lsvPqK87F2/+mNxBzP1xuJ2bZ5uYZXytWXrfelanC8T6NvdNtf6s+BEFIeVNyCFKvIIygkRFTdUcziJrpZtWAq2k92IxxUIMt2sU9zY0NceJO01DAUkNDc2GBb9xgKxAU7Borl/KnEd6Ry8ZMIy6hLR2c3IOSc6xIUcPSVcFBGbyS1uM1JfDdmZDWWz2vA2ifta4SN6AZufpsLbkLAUvdlBcCaRZchFtMRDMiQIGHNNy7D+8fOJMRxi2+ZiBdf/TOWzW1AVI3ZnsOKO6eU/HMgJBVe1/z6IYRbpVNRA2En8UpzYwO27Xob+w63m8QsAByFLqNqgoh2ayZx3KoFU/GdhZdB13VoOqAo9pmOSG8MdcNDaG2anjZqRDRqPv/yxgYgIAr+fEhp4CcRViHqoqk6hlQpWLXgUsgSoOlAdZVAx0e9aQV0buK7U91RLLp5QuLHpaLIGDEkaPLHfEeNKNdseLKQcLYrim273sbMyy/AxucOmtp+2NAgdF3HrCs+jcd++mYiasT6pumIaRqELGH0iGqcOHGm2LdCSMHgmt/SoaKWRjiJV1q3tuHqyWMTn40MSG5Cl4iKhPrc2L5q816887cufOPB3WjasBsrHt0Dp+/TSG8Mkqr2Z8ZSHQfBMdjPv5ZZmUgSmWRjK8W6xACseHQPmjbsTvhUd4/qKKBz8jNrBrq+qI7Vm/di9ZZ9aN70OlZv2YfVm/eiN6KZ/DHfodOc6lLqg2AAiKhI9JvGIBgYaPs/vXcKzf/v6zjTE01kllu1eS906InnIHNZBCHEJ1TUjLCbeKWmfwbY+GyIONwEO97OoWX1cFMJbCqqsYgrfrKRQtTF6Zyy5O6P6a6TSnxHn8oc43m6CYWN7U79Kp83IcVhMLK+lSoV1S8ZIg9rxqOu7qjpsyHicCprCHnSn0OGKZBwjnUUsgSoVFkTf9lIIeridE5Nd/fHdH7m5rPZ+milYzxPQwzn1Be69qvswwgpCpmGiwMqZ7lGRQyEkzO6rfnGZXj83w4l1gSvWjAVQpbxo/uvgiRJCARlxKIaggFhE9i0zGuAEMCqu6ai/cSAmO3cMUOhq4ifQ5YQDgn09akIBhVomo6YqiXWKhr1CIcURGPxV6XJ6xPdBDaQAD0PQiRS+hRThOUkNnUSosUzjUmm9fCyLKGnT0tsqwrJ6O2OOp7ze0uuQJ+lrNM9OwnoAJgyxoWDMlYumIqOJAFq/chqR2GcV+Gfn8SKhcLpHmUhQZfjz/NMdwTf+cZlUFUdshzPGBcPuiFh/TenQ9eAx1dcg3f/1oVz64cysxwhxJeU/UDYKaNbS2MDFs6eACFJ6Ozqw7qtA9ml7pkzEU//7C2MqAnijus/h0U3T8DQ6iBqqoJ48t8PoaY6iOunf8okZrMK7pbNnYwDb7Xjc58ahR9sO2grV1MdxJen/R3WP7XfUfhjCGw0DdChmwbu5ZCZiuRGsURYTsK4NYsug6LIJiFaQJHx0dmIKWtb6zen4fSpqE3sdk5tGO2dvaZzOmV9a2lsQHWVYr5OQMbZvhhWPLrHJFyNRDXbgDkYlM0C1HkN0Cyzk16Ff34SKxYKtwx9kW4Nh/9yHOM+NRp7f/8hLp/4Cax/ypxR7sVX/4yZl1+AF1/9M+bMuAiyBJztiZnanpnlCCF+oezFck4Z3db9/+3de2xU5ZsH8O/MdKa0yP64tYXosk1IjRsExApSJNyWFGnBQmmyQGLhJ2rKT2gpUimXwEYBBYkNxcvGFVARtBhCh6JyrRCglQoSawoaQEAuTaktgjBtZ+bM2T/oDHM70+v0nTPn+0lIO3OmZ55z5nlO3w7zPu+nFZAdgF2SfSb6bPrqLGZMSMB/Df8P1wSb23ebsOrjMpyqqsH0cQl+J+24T7h757MfMS7x312DYO/HTR+X4BoEu29zztVxTrDR64GV//vged1j58Q5EjEJy9/EOLvddyLa//zfD7hVb/F4nKN5gpV3zluaHD77XLutwufn131agRu37vlMeLPbZY/H1dRb/E7eq6mz+DyHdx21duJfKE1WDBalFfrWfVqBEYP6Y92nFZg4It41CHY+prDorGsSnXPVzcdi/83ntV+z9VRYnS8iUq+wf0e4pVWqAk18c25znxQSaBUl99uyrDwxT3mlJc/JO6E0KYqoLZPYvFdtU6oHpfr0t+qbv/u8mw+0bRU5uV31poW6DLRCn/O1bGlFOedX5Wtw+JwvIlKvsH9HONAqVUrb/rbYPFaMc/8+0CpK7rd1Ov+rSv1tsQVYaYmrzFHo8pePzkls7vyt2qZUD0o16G/VN3/3eX/UtG2ryHnWUWvrTQt1GWiFPudr2dKKcs6vysYrQnwAAA6jSURBVNfg8DlfRKReYT8Qdq7o5rwQu6/o5m9bzsxh2F16AUd+vOratrv0AnJmDkNsryjsOXoB+XOG++zvyI9XXbfz5wzH0TPXXD/j/TilfZi81sxwTopyf5xzUhRRV/OXjxERvjW0fO4IxPaO9rhP37xanHfORzdPgnO/f8U/fX9++dwRiPO6b9ncEYiI0HncF9c72m/NeP+svzpqbb1poS79HaPz3FZUVWPZ3BE4XHHF5zqW/d/DcOTHq66vy+aOwPVbd31ee+fKckREoulkWVbt9N26unuKs4/dZzxHRUbA6tahwX1FN/eOEhEGPQx6HZpsEgx6PaIj9bA0z1x37/IQaTRAcnWD0KN7pB73nTPcDXoYI/RobLLDZDRAdsiwtaFrRKDjUJoUFRPTA7W1f3fy2VUvpfMhoodioBx1UtPr5y8fjZEGn24Qsgw0Wh0enRt0OrSqa0QEAFOkwVV7zlqUHLKrfpx1DMDnPtkhe3SNMBkerFbXmi4Pzlig0wHyw/+697fUejC6RoRSjrq/LhF6PfTNf6hLEmA06mGzOWAw6CBJMiRZhkGng04PyA64vkaa9GiySj5L2athZTm11GVXxyk6R/0db0xMj3a1Bmvrym9tOc9tjak98QTz8aEYU2tfg7bmaFj+UR54VrfksRqVtdEOHZpPhF2C1Py9AbJrNnuvHt2QmfqfPh0gnF0eLBYJOgCRBj1u37cqzia3Sg7Xc9kbbQ+fV5J82jg5SW4/A0kOenssokC881FvMqDmtu/Sx92jIjy6OTjrQCc5XDnfaJH87lMC0GBxeNRHQ/Nj3e+ToFzn7s/jrK3W1JEzFtcv2wDXkofPEZ516TwXkQY9blt8r2t9/xHp05HH2TViZvIT6NvDBGvDg+ucTfI8/1xZjohCRVh+NKIzZnW772PGhAS/HSC838DVwmxyIndWhW4Q3t0cglEHXVFvrGnlc+CvI497t4ggrFhNRNTpwnIgHGhWd3v2obSUqLPzRGc+L5GaKHUE8H7DLxh10BX1xpoOdA6Ul5v3d30kIgpFQR0Il5SUICUlBcnJydixY4fP9vPnzyM9PR2TJk3CihUrYLd3zvssnTGr230f7l0jPPfHLg+kbUodAbzHicGog66oN9Z0oHOg3HXH3/WRiCgUBe1KVVNTg4KCAuzcuRPFxcUoKirCxYsXPR6Tl5eHVatW4cCBA5BlGbt27eqU5+6MWd3u+3DvGuHcH7s8EAEmhW4Q3t0cglEHXVFvrGnlc+Cv6457twjv6yMRUSgK2vW8rKwMI0eORM+ePQEAkyZNwv79+7FgwQIAwI0bN9DY2IinnnoKAJCeno7CwkLMnj27w8/dGUvQeu8j0mjAO6+NdnWK8NflQdTSt0Si2KwS+vYw4e1/jXZ1DnB2aQh2HXRFvbGmlc+BtdHu89pDD7w6bYhiFxwiolATtIHwrVu3EBMT47odGxuLyspKxe0xMTGoqanptOfvjG4L7vuQmhvys8sDkSeb9UHXlP7NnRba0qWho7qi3ljTyufA+dq7fpE0b1C6PhIRhZqgDYQdDgd0uoefo5Nl2eN2S9tbo0+fRzoeaJgQ0dsxlIXK+WhtjoZKvB0VLscBhNexBCLqOqqG86uGGAH1xNle3jkq6njD/TyrQTBeg6ANhPv164fTp0+7btfW1iI2NtZje21trev2n3/+6bG9NVqzWIEWqKXxe1fhghpihMtxAOKOJVRztLOpIVfUECPABTW6KiarTYLJyA++i6aqBTVGjRqFzZs3o76+HlFRUTh48CDeeust1/ZHH30UkZGROHPmDBITE2E2mzFmzJhghUNERETULiajoc2roJE6BG0gHBcXh9zcXGRmZsJmsyEjIwNDhgzBK6+8guzsbAwePBgbN27EypUrce/ePQwaNAiZmZlteg6uTvQQz4WnUDkfrY0jVOLtqHA5DiC8jiUQUcephvOrhhgB9cTZXt7HF+7HS8qC8drrZFnmZwuIiIiISHPY8ZyIiIiINIkDYSIiIiLSJA6EiYiIiEiTOBAmIiIiIk3iQJiIiIiINIkDYSIiIiLSJA6EiYiIiEiTOBAmIiIiIk3iQJiIiIiINIkDYRUrLS1Feno6Jk+ejDVr1ogORziz2YzU1FSkpqZi/fr1osNp0fvvv++Kd8OGDaLD6ZBNmzYhJSUFqamp2LZtm+hwOmz9+vXIz88XHUbYUVPOh3oOaPH6X1JSgpSUFCQnJ2PHjh2iw+mQe/fuYcqUKbh+/broUDpETTWthANhlbp27RpWr16NDz/8EHv37sW5c+dw7Ngx0WEJ09DQgLVr12L79u0wm804ffo0ysrKRIelqKysDCdOnMCePXtQXFyMqqoqHDp0SHRY7VJRUYEffvgBe/fuxe7du7F9+3b8/vvvosNqt/LycuzZs0d0GGFHTTkf6jmgxet/TU0NCgoKsHPnThQXF6OoqAgXL14UHVa7/Pzzz5g1axauXLkiOpQOUVNNB8KBsEodOnQIKSkp6NevH4xGIwoKCjB06FDRYQkjSRIcDgcaGhpgt9tht9sRGRkpOixFMTExyM/Ph8lkgtFoxMCBA3Hz5k3RYbXLiBEj8PnnnyMiIgJ1dXWQJAnR0dGiw2qXv/76CwUFBcjKyhIdSthRS86rIQe0eP0vKyvDyJEj0bNnT0RHR2PSpEnYv3+/6LDaZdeuXVi9ejViY2NFh9IhaqnplkSIDoDa5+rVqzAajcjKykJ1dTXGjRuHRYsWiQ5LmEceeQQ5OTmYPHkyoqKiMHz4cDz99NOiw1KUkJDg+v7KlSv47rvv8OWXXwqMqGOMRiMKCwuxdetWPP/884iLixMdUrusWrUKubm5qK6uFh1K2FFLzqshB7R4/b916xZiYmJct2NjY1FZWSkwovZbu3at6BA6hVpquiV8R1ilJElCeXk51q1bh6KiIlRWVob0f+UF26+//ordu3fj+++/x/Hjx6HX67FlyxbRYbXowoULeOmll/DGG28gPj5edDgdkp2djfLyclRXV2PXrl2iw2mzr7/+Gv3790dSUpLoUMJaKOe8WnJAi9d/h8MBnU7nui3LssdtEieUa7o1OBBWqb59+yIpKQm9e/dGt27dMHHiRNX+ddwZTpw4gaSkJPTp0wcmkwnp6emoqKgQHVZAZ86cwdy5c/H6669j+vTposNpt0uXLuH8+fMAgKioKCQnJ+O3334THFXbffvttzh58iTS0tJQWFiI0tJSrFu3TnRYYSXUc14tOaDF63+/fv1QW1vrul1bW6v6jxaEg1Cv6dbgQFilxo8fjxMnTuDu3buQJAnHjx/HoEGDRIclzBNPPIGysjJYLBbIsozS0lIMHjxYdFiKqqur8dprr2Hjxo1ITU0VHU6HXL9+HStXroTVaoXVasWRI0eQmJgoOqw227ZtG/bt2wez2Yzs7GxMmDABy5cvFx1W2FBDzqslB7R4/R81ahTKy8tRX1+PhoYGHDx4EGPGjBEdlqapoaZbg58RVqmhQ4fi5ZdfxuzZs2Gz2fDcc89hxowZosMSZvTo0Th37hzS09NhNBoxePBgvPrqq6LDUrRlyxY0NTXhnXfecd03c+ZMzJo1S2BU7TN27FhUVlZi2rRpMBgMSE5OVvVFkYIjnHJeNC1e/+Pi4pCbm4vMzEzYbDZkZGRgyJAhosPStHCpaZ0sy7LoIIiIiIiIuho/GkFEREREmsSBMBERERFpEgfCRERERKRJHAgTERERkSZxIExEREREmsSBsApVVVUhMTERv/zyi+u++vp6TJw4EUePHgXwYNWdpUuXqmJ1NQo/LeWo2WzGCy+8gLS0NMycOdPjcURdoaUc/eKLL5CamoopU6Zg/vz5qKurExgtaVFrftcDwOHDhzFs2DABEYYHDoRVaNCgQcjLy0NOTg7u3LkDu92ORYsWYcaMGRg3bhwuXbqEOXPm4MCBA6JDJY0KlKMDBgzAu+++i08++QRmsxnz58/HwoULRYdMGhMoR/v27YutW7fiq6++wr59+xAfH49NmzaJDpk0pqXf9QBw5coVrF+/XmygKsc+wiqWl5cHi8WCAQMG4Nq1a9i8eTN0Oh3efPNNDBs2DCdPnkRCQgLmzZsnOlTSKH85euPGDVy8eNF1Ia+rq8PYsWPx008/wWQyiQ2YNEfpOmqz2WA0GtHU1IRly5bhsccew+LFi0WHSxqklKMNDQ3IzMxEVlYWlixZgrNnz4oOVZU4EFYxi8WCadOmwW63o6SkBN27d/fYnp+fz4EwCdVSjsqyjLy8PFitVhQWFgqKkrQsUI4ePnwYK1asgMlkwvbt2xEfHy8uUNIspRzNy8vDs88+i5EjR2Lq1KkcCLcTPxqhYpcvX8b9+/dx9+5dVFVViQ6HyEegHLVYLMjJycEff/yBNWvWCIqQtC5Qjk6cOBGnTp3CwoULMW/ePDgcDkFRkpb5y9EdO3YgIiICGRkZgqNTP74jrFL19fXIyMjA4sWL0dTUhIKCAuzZswcxMTGux/AdYRIpUI7evHkTWVlZGDhwIN5++21069ZNdLikQUo5arFYUFtbi2eeeQYAIEkSnnzySZSVlaFXr16CoyYtUcrR+fPno7GxEQaDATabDZcvX8bjjz+Ojz/+GHFxcaLDVpUI0QFQ20mShNzcXIwfPx5TpkwBAJw+fRq5ubn47LPPYDAYBEdIWhcoRz/66CO8+OKLmD59OhYsWCA4UtKqQDmanZ2NJUuWoLi4GL1790ZJSQkSEhI4CKYuFShHi4qKXL/rr1+/jqlTp8JsNosMV7X40QgV2rBhAxoaGrB06VLXfatWrcKdO3fw3nvvCYyM6IFAOZqUlISbN2/i0KFDSEtLc/27ffu2wIhJawLl6LFjx5CVlYXMzEykpaXhm2++wQcffCAwWtIi/q7vGvxoBBERERFpEt8RJiIiIiJN4kCYiIiIiDSJA2EiIiIi0iQOhImIiIhIkzgQJiIiIiJN4kCYiIiIiDSJA2EiIiIi0iQOhImIiIhIk/4fs0MtXg/68e8AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "sns.set()\n", - "sns.pairplot(df);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et les corr\u00e9lations :" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X11.000000-0.1175700.8717540.817941
X2-0.1175701.000000-0.428440-0.366126
X30.871754-0.4284401.0000000.962865
X40.817941-0.3661260.9628651.000000
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 1.000000 -0.117570 0.871754 0.817941\n", - "X2 -0.117570 1.000000 -0.428440 -0.366126\n", - "X3 0.871754 -0.428440 1.000000 0.962865\n", - "X4 0.817941 -0.366126 0.962865 1.000000" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.corr()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Un peu de th\u00e9orie\n", - "\n", - "Le coefficient de [corr\u00e9lation](https://fr.wikipedia.org/wiki/Corr%C3%A9lation_(statistiques)) de Pearson est calcul\u00e9 comme suit :\n", - "\n", - "$$cor(X_i, X_j) = \\frac{cov(X_i, Y_i)}{\\sigma(X_i)\\sigma(X_j)}$$\n", - "\n", - "Lorsque les variables sont centr\u00e9es $\\mathbb{E}X_i=\\mathbb{E}X_j=0$, cette formule devient :\n", - "\n", - "$$cor(X_i, X_j) = \\frac{\\mathbb{E}(X_i X_j)}{\\sqrt{\\mathbb{E}X_i^2 \\mathbb{E}X_j^2}}$$\n", - "\n", - "Lorsque les variables sont r\u00e9duites $\\mathbb{E}X_i^2=\\mathbb{E}X_j^2=1$, cette formule devient $cor(X_i, X_j) = \\mathbb{E}(X_i X_j)$. Admettons maintenant que l'on cherche \u00e0 trouver le coefficient $\\alpha_{ij}$ qui minimise la variance du bruit $\\epsilon_{ij}$ :\n", - "\n", - "$$X_j = \\alpha_{ij}X_i + \\epsilon_{ij}$$\n", - "\n", - "Le coefficient $\\alpha_{ij}$ est le r\u00e9sultat d'une r\u00e9gression lin\u00e9aire qui minimise $\\mathbb{E}(X_j - \\alpha_{ij}X_i)^2$. Si les variables $X_i$, $X_j$ sont centr\u00e9es et r\u00e9duites : $\\alpha_{ij}^* = \\mathbb{E}(X_i X_j) = cor(X_i, X_j)$. On \u00e9tend cette d\u00e9finition dans le cas d'une fonction param\u00e9trable $f$ : $f(\\omega, X) \\rightarrow \\mathbb{R}$ et d'une r\u00e9gression non lin\u00e9aire. On suppose que les param\u00e8tres $\\omega^*$ minimisent la quantit\u00e9 $\\min_\\omega (X_j - f(\\omega, X_i))^2$. On \u00e9crit alors $X_j = \\alpha_{ij} \\frac{f(\\omega^*, X_i)}{\\alpha_{ij}} + \\epsilon_{ij}$ et on choisit $\\alpha_{ij}$ de telle sorte que $\\mathbb{E}\\left(\\frac{f(\\omega^*, X_i)^2}{\\alpha_{ij}^2}\\right) = 1$. On d\u00e9finit la corr\u00e9lation non lin\u00e9aire au sens de $f$ : \n", - "\n", - "$$cor^f(X_i, X_j) = \\sqrt{ \\mathbb{E} (f(\\omega, X_i)^2 )}$$\n", - "\n", - "On v\u00e9rifie que ce coefficient est compris entre [0, 1]. Il est positif de mani\u00e8re \u00e9vidente. Il est \u00e9galement inf\u00e9rieur \u00e0 1, si cela n'\u00e9tait pas le cas, nous pourrions construire une fonction $f(\\omega^*, X)+c$ qui est une meilleur solution pour le programme de minimisation. Ce nombre ressemble \u00e0 une corr\u00e9lation \u00e0 ceci pr\u00e8s qu'elle ne peut \u00eatre n\u00e9gative." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## V\u00e9rifications\n", - "\n", - "Tout d'abord le cas lin\u00e9aire :" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X11.0000000.1175700.8717540.817941
X20.1175701.0000000.4284400.366126
X30.8717540.4284401.0000000.962865
X40.8179410.3661260.9628651.000000
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 1.000000 0.117570 0.871754 0.817941\n", - "X2 0.117570 1.000000 0.428440 0.366126\n", - "X3 0.871754 0.428440 1.000000 0.962865\n", - "X4 0.817941 0.366126 0.962865 1.000000" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.preprocessing import scale\n", - "import numpy\n", - "\n", - "def correlation_etendue(df, model, **params):\n", - " cor = df.corr()\n", - " df = scale(df) \n", - " for i in range(cor.shape[0]):\n", - " xi = df[:, i:i+1]\n", - " for j in range(cor.shape[1]):\n", - " mod = model(**params)\n", - " xj = df[:, j]\n", - " mod.fit(xi, xj)\n", - " v = mod.predict(xi)\n", - " c = numpy.std(v)\n", - " cor.iloc[i,j] = c\n", - " return cor\n", - "\n", - "from sklearn.linear_model import LinearRegression\n", - "cor = correlation_etendue(df, LinearRegression, fit_intercept=False)\n", - "cor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On affiche \u00e0 nouveau les corr\u00e9lations qui sont identiques au signe pr\u00e8s." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X11.000000-0.1175700.8717540.817941
X2-0.1175701.000000-0.428440-0.366126
X30.871754-0.4284401.0000000.962865
X40.817941-0.3661260.9628651.000000
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 1.000000 -0.117570 0.871754 0.817941\n", - "X2 -0.117570 1.000000 -0.428440 -0.366126\n", - "X3 0.871754 -0.428440 1.000000 0.962865\n", - "X4 0.817941 -0.366126 0.962865 1.000000" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.corr()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et le cas non lin\u00e9aire :" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X11.0000000.5444410.9158470.879583
X20.4182741.0000000.5918390.539524
X30.9370560.7897271.0000000.978332
X40.8461610.7616520.9800051.000000
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 1.000000 0.544441 0.915847 0.879583\n", - "X2 0.418274 1.000000 0.591839 0.539524\n", - "X3 0.937056 0.789727 1.000000 0.978332\n", - "X4 0.846161 0.761652 0.980005 1.000000" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.tree import DecisionTreeRegressor\n", - "cor = correlation_etendue(df, DecisionTreeRegressor)\n", - "cor" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X10.9980970.5176130.9280510.866090
X20.4362190.9900190.5998290.548466
X30.9182260.7915500.9972410.979319
X40.8256090.7392010.9834660.999659
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 0.998097 0.517613 0.928051 0.866090\n", - "X2 0.436219 0.990019 0.599829 0.548466\n", - "X3 0.918226 0.791550 0.997241 0.979319\n", - "X4 0.825609 0.739201 0.983466 0.999659" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Corrélations non linéaires\n", + "\n", + "Les corrélations indiquent si deux variables sont linéairement équivalentes. Comment étendre cette notion à des variables liées mais pas de façon linéaire." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Un exemple" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
\n", + "
" ], - "source": [ - "from sklearn.ensemble import RandomForestRegressor\n", - "cor = correlation_etendue(df, RandomForestRegressor, n_estimators=10)\n", - "cor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overfitting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ces chiffres sont beaucoup trop optimistes. Les mod\u00e8les de machine learning peuvent tout \u00e0 fait faire de l'overfitting. Il faut am\u00e9liorer la fonction en divisant en apprentissage et test plusieurs fois. Il faut \u00e9galement tenir compte de l'erreur de pr\u00e9diction. On rappelle que : \n", - "\n", - "$$X_j = \\alpha_{ij} \\frac{f(\\omega^*, X_i)}{\\alpha_{ij}} + \\epsilon_{ij} = cor^f(X_i, X_j) \\frac{f(\\omega^*, X_i)}{\\sqrt{ \\mathbb{E} (f(\\omega, X_i)^2 )}} + \\epsilon_{ij}$$\n", - "\n", - "Or $\\mathbb{E}(X_j^2)=1$ et on suppose que les bruits ne sont pas corr\u00e9l\u00e9es lin\u00e9airement aux $f(\\omega^*, X_i)$. On en d\u00e9duit que $cor^f(X_i, X_j) = \\sqrt{ 1 - \\mathbb{E}\\epsilon_{ij}^2}$." + "text/plain": [ + " X1 X2 X3 X4\n", + "0 5.1 3.5 1.4 0.2\n", + "1 4.9 3.0 1.4 0.2\n", + "2 4.7 3.2 1.3 0.2\n", + "3 4.6 3.1 1.5 0.2\n", + "4 5.0 3.6 1.4 0.2" ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X11.0000000.1492440.8733460.820386
X20.1009141.0000000.3726240.357596
X30.8714910.4256781.0000000.962125
X40.8244800.3835220.9616601.000000
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 1.000000 0.149244 0.873346 0.820386\n", - "X2 0.100914 1.000000 0.372624 0.357596\n", - "X3 0.871491 0.425678 1.000000 0.962125\n", - "X4 0.824480 0.383522 0.961660 1.000000" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "import numpy\n", - "\n", - "def correlation_cross_val(df, model, draws=5, **params):\n", - " cor = df.corr()\n", - " df = scale(df) \n", - " for i in range(cor.shape[0]):\n", - " xi = df[:, i:i+1]\n", - " for j in range(cor.shape[1]):\n", - " xj = df[:, j]\n", - " mem = []\n", - " for k in range(0, draws):\n", - " xi_train, xi_test, xj_train, xj_test = train_test_split(xi, xj, test_size=0.5)\n", - " mod = model(**params) \n", - " mod.fit(xi_train, xj_train)\n", - " v = mod.predict(xi_test)\n", - " c = (1 - numpy.var(v - xj_test))\n", - " mem.append(max(c, 0) **0.5)\n", - " cor.iloc[i,j] = sum(mem) / len(mem)\n", - " return cor\n", - "\n", - "cor = correlation_cross_val(df, LinearRegression, fit_intercept=False, draws=20)\n", - "cor" + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import datasets\n", + "\n", + "iris = datasets.load_iris()\n", + "X = iris.data\n", + "Y = iris.target\n", + "import pandas\n", + "\n", + "df = pandas.DataFrame(X)\n", + "df.columns = [\"X1\", \"X2\", \"X3\", \"X4\"]\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X10.9983150.0978900.8670010.779327
X20.0000000.9951220.3566060.064479
X30.8697690.4207630.9986500.949499
X40.7656920.5149830.9668990.999886
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 0.998315 0.097890 0.867001 0.779327\n", - "X2 0.000000 0.995122 0.356606 0.064479\n", - "X3 0.869769 0.420763 0.998650 0.949499\n", - "X4 0.765692 0.514983 0.966899 0.999886" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "sns.set()\n", + "sns.pairplot(df);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et les corrélations :" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X11.000000-0.1175700.8717540.817941
X2-0.1175701.000000-0.428440-0.366126
X30.871754-0.4284401.0000000.962865
X40.817941-0.3661260.9628651.000000
\n", + "
" ], - "source": [ - "cor = correlation_cross_val(df, DecisionTreeRegressor)\n", - "cor" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 1.000000 -0.117570 0.871754 0.817941\n", + "X2 -0.117570 1.000000 -0.428440 -0.366126\n", + "X3 0.871754 -0.428440 1.000000 0.962865\n", + "X4 0.817941 -0.366126 0.962865 1.000000" ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X3X4
X10.9986880.1194850.8751770.809729
X20.0233340.9951350.3302580.082126
X30.8767010.5243840.9988170.960836
X40.7618970.6714740.9704920.999014
\n", - "
" - ], - "text/plain": [ - " X1 X2 X3 X4\n", - "X1 0.998688 0.119485 0.875177 0.809729\n", - "X2 0.023334 0.995135 0.330258 0.082126\n", - "X3 0.876701 0.524384 0.998817 0.960836\n", - "X4 0.761897 0.671474 0.970492 0.999014" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Un peu de théorie\n", + "\n", + "Le coefficient de [corrélation](https://fr.wikipedia.org/wiki/Corr%C3%A9lation_(statistiques)) de Pearson est calculé comme suit :\n", + "\n", + "$$cor(X_i, X_j) = \\frac{cov(X_i, Y_i)}{\\sigma(X_i)\\sigma(X_j)}$$\n", + "\n", + "Lorsque les variables sont centrées $\\mathbb{E}X_i=\\mathbb{E}X_j=0$, cette formule devient :\n", + "\n", + "$$cor(X_i, X_j) = \\frac{\\mathbb{E}(X_i X_j)}{\\sqrt{\\mathbb{E}X_i^2 \\mathbb{E}X_j^2}}$$\n", + "\n", + "Lorsque les variables sont réduites $\\mathbb{E}X_i^2=\\mathbb{E}X_j^2=1$, cette formule devient $cor(X_i, X_j) = \\mathbb{E}(X_i X_j)$. Admettons maintenant que l'on cherche à trouver le coefficient $\\alpha_{ij}$ qui minimise la variance du bruit $\\epsilon_{ij}$ :\n", + "\n", + "$$X_j = \\alpha_{ij}X_i + \\epsilon_{ij}$$\n", + "\n", + "Le coefficient $\\alpha_{ij}$ est le résultat d'une régression linéaire qui minimise $\\mathbb{E}(X_j - \\alpha_{ij}X_i)^2$. Si les variables $X_i$, $X_j$ sont centrées et réduites : $\\alpha_{ij}^* = \\mathbb{E}(X_i X_j) = cor(X_i, X_j)$. On étend cette définition dans le cas d'une fonction paramétrable $f$ : $f(\\omega, X) \\rightarrow \\mathbb{R}$ et d'une régression non linéaire. On suppose que les paramètres $\\omega^*$ minimisent la quantité $\\min_\\omega (X_j - f(\\omega, X_i))^2$. On écrit alors $X_j = \\alpha_{ij} \\frac{f(\\omega^*, X_i)}{\\alpha_{ij}} + \\epsilon_{ij}$ et on choisit $\\alpha_{ij}$ de telle sorte que $\\mathbb{E}\\left(\\frac{f(\\omega^*, X_i)^2}{\\alpha_{ij}^2}\\right) = 1$. On définit la corrélation non linéaire au sens de $f$ : \n", + "\n", + "$$cor^f(X_i, X_j) = \\sqrt{ \\mathbb{E} (f(\\omega, X_i)^2 )}$$\n", + "\n", + "On vérifie que ce coefficient est compris entre [0, 1]. Il est positif de manière évidente. Il est également inférieur à 1, si cela n'était pas le cas, nous pourrions construire une fonction $f(\\omega^*, X)+c$ qui est une meilleur solution pour le programme de minimisation. Ce nombre ressemble à une corrélation à ceci près qu'elle ne peut être négative." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vérifications\n", + "\n", + "Tout d'abord le cas linéaire :" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X11.0000000.1175700.8717540.817941
X20.1175701.0000000.4284400.366126
X30.8717540.4284401.0000000.962865
X40.8179410.3661260.9628651.000000
\n", + "
" ], - "source": [ - "cor = correlation_cross_val(df, RandomForestRegressor, n_estimators=10)\n", - "cor" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 1.000000 0.117570 0.871754 0.817941\n", + "X2 0.117570 1.000000 0.428440 0.366126\n", + "X3 0.871754 0.428440 1.000000 0.962865\n", + "X4 0.817941 0.366126 0.962865 1.000000" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les r\u00e9sultats sont assez fluctuants lorsque les donn\u00e9es sont mal corr\u00e9l\u00e9es. On remarque \u00e9galement que la matrice n'est plus n\u00e9cessairement symm\u00e9trique." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import scale\n", + "import numpy\n", + "\n", + "\n", + "def correlation_etendue(df, model, **params):\n", + " cor = df.corr()\n", + " df = scale(df)\n", + " for i in range(cor.shape[0]):\n", + " xi = df[:, i : i + 1]\n", + " for j in range(cor.shape[1]):\n", + " mod = model(**params)\n", + " xj = df[:, j]\n", + " mod.fit(xi, xj)\n", + " v = mod.predict(xi)\n", + " c = numpy.std(v)\n", + " cor.iloc[i, j] = c\n", + " return cor\n", + "\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "cor = correlation_etendue(df, LinearRegression, fit_intercept=False)\n", + "cor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On affiche à nouveau les corrélations qui sont identiques au signe près." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X11.000000-0.1175700.8717540.817941
X2-0.1175701.000000-0.428440-0.366126
X30.871754-0.4284401.0000000.962865
X40.817941-0.3661260.9628651.000000
\n", + "
" ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def pairplot_cross_val(data, model=None, ax=None, **params):\n", - " if ax is None:\n", - " fig, ax = plt.subplots(data.shape[1], data.shape[1], figsize=params.get('figsize', (10,10)))\n", - " if 'figsize' in params:\n", - " del params[\"figsize\"]\n", - " if model is None:\n", - " from sklearn.linear_model import LinearRegression\n", - " model = LinearRegression\n", - " \n", - " df = scale(data)\n", - " cor = numpy.corrcoef(df.T)\n", - " for i in range(cor.shape[0]):\n", - " xi = df[:, i:i+1]\n", - " for j in range(cor.shape[1]):\n", - " xj = df[:, j]\n", - " mem = []\n", - " xi_train, xi_test, xj_train, xj_test = train_test_split(xi, xj, test_size=0.5)\n", - " mod = model(**params) \n", - " mod.fit(xi_train, xj_train)\n", - " v = mod.predict(xi_test)\n", - " mod = model(**params) \n", - " mod.fit(xi_test, xj_test)\n", - " v2 = mod.predict(xi_train)\n", - " ax[i,j].plot(xj_test, v, \".\")\n", - " ax[i,j].plot(xj_train, v2, \".\")\n", - " if j == 0:\n", - " ax[i,j].set_ylabel(data.columns[i])\n", - " if i == data.shape[1]-1:\n", - " ax[i,j].set_xlabel(data.columns[j])\n", - " mi = min(min(xj_test), min(v), min(xj_train), min(v2))\n", - " ma = max(max(xj_test), max(v), max(xj_train), max(v2))\n", - " ax[i,j].plot([mi, ma], [mi, ma], \"--\")\n", - " return ax\n", - " \n", - "ax = pairplot_cross_val(df)\n", - "ax;" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 1.000000 -0.117570 0.871754 0.817941\n", + "X2 -0.117570 1.000000 -0.428440 -0.366126\n", + "X3 0.871754 -0.428440 1.000000 0.962865\n", + "X4 0.817941 -0.366126 0.962865 1.000000" ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et le cas non linéaire :" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X11.0000000.5444410.9158470.879583
X20.4182741.0000000.5918390.539524
X30.9370560.7897271.0000000.978332
X40.8461610.7616520.9800051.000000
\n", + "
" ], - "source": [ - "ax = pairplot_cross_val(df, model=DecisionTreeRegressor)\n", - "ax;" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 1.000000 0.544441 0.915847 0.879583\n", + "X2 0.418274 1.000000 0.591839 0.539524\n", + "X3 0.937056 0.789727 1.000000 0.978332\n", + "X4 0.846161 0.761652 0.980005 1.000000" ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "cor = correlation_etendue(df, DecisionTreeRegressor)\n", + "cor" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X10.9975520.5567040.9206430.886171
X20.4266750.9896910.5749840.584174
X30.9434190.7962930.9992730.961729
X40.8483000.7640060.9732000.999983
\n", + "
" ], - "source": [ - "ax = pairplot_cross_val(df, model=RandomForestRegressor, n_estimators=10)\n", - "ax;" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 0.997552 0.556704 0.920643 0.886171\n", + "X2 0.426675 0.989691 0.574984 0.584174\n", + "X3 0.943419 0.796293 0.999273 0.961729\n", + "X4 0.848300 0.764006 0.973200 0.999983" ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "cor = correlation_etendue(df, RandomForestRegressor, n_estimators=10)\n", + "cor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overfitting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ces chiffres sont beaucoup trop optimistes. Les modèles de machine learning peuvent tout à fait faire de l'overfitting. Il faut améliorer la fonction en divisant en apprentissage et test plusieurs fois. Il faut également tenir compte de l'erreur de prédiction. On rappelle que : \n", + "\n", + "$$X_j = \\alpha_{ij} \\frac{f(\\omega^*, X_i)}{\\alpha_{ij}} + \\epsilon_{ij} = cor^f(X_i, X_j) \\frac{f(\\omega^*, X_i)}{\\sqrt{ \\mathbb{E} (f(\\omega, X_i)^2 )}} + \\epsilon_{ij}$$\n", + "\n", + "Or $\\mathbb{E}(X_j^2)=1$ et on suppose que les bruits ne sont pas corrélées linéairement aux $f(\\omega^*, X_i)$. On en déduit que $cor^f(X_i, X_j) = \\sqrt{ 1 - \\mathbb{E}\\epsilon_{ij}^2}$." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X11.0000000.1539270.8747860.814982
X20.1619701.0000000.3799410.323331
X30.8667260.4455841.0000000.964216
X40.8168490.4052120.9622881.000000
\n", + "
" ], - "source": [ - "from sklearn.neighbors import KNeighborsRegressor\n", - "ax = pairplot_cross_val(df, model=KNeighborsRegressor)\n", - "ax;" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 1.000000 0.153927 0.874786 0.814982\n", + "X2 0.161970 1.000000 0.379941 0.323331\n", + "X3 0.866726 0.445584 1.000000 0.964216\n", + "X4 0.816849 0.405212 0.962288 1.000000" ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Corr\u00e9lations de variables cat\u00e9gorielles\n", - "\n", - "C'est le probl\u00e8me \u00e9pineux si on se restreint au lin\u00e9aire. Cela n'a pas trop de sens d'affecter une valeur \u00e0 chaque cat\u00e9gorie et la corr\u00e9lation de deux variables binaires (des modalit\u00e9s) est toujours \u00e9trange car il n'y a que deux valeurs possibles.\n", - "\n", - "$$cov(X,Y) = \\mathbb{E}\\left[(X - \\mathbb{E}X)(Y - \\mathbb{E}Y)\\right] = \\mathbb{E}(XY) - \\mathbb{E}X\\mathbb{E}Y = \\mathbb{P}(X=1 \\, et \\, Y=1) - \\mathbb{E}X\\mathbb{E}Y$$\n", - "\n", - "Dans le cas de variables binaires g\u00e9n\u00e9r\u00e9es de modalit\u00e9s de la m\u00eame variables cat\u00e9gorielles, le premier terme est toujours nul puisque les modalit\u00e9s sont exclusives et la corr\u00e9lation est toujours n\u00e9gative." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0., 1.],\n", - " [1., 0.],\n", - " [1., 0.],\n", - " [1., 0.],\n", - " [1., 0.]])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "def correlation_cross_val(df, model, draws=5, **params):\n", + " cor = df.corr()\n", + " df = scale(df)\n", + " for i in range(cor.shape[0]):\n", + " xi = df[:, i : i + 1]\n", + " for j in range(cor.shape[1]):\n", + " xj = df[:, j]\n", + " mem = []\n", + " for k in range(draws):\n", + " xi_train, xi_test, xj_train, xj_test = train_test_split(\n", + " xi, xj, test_size=0.5\n", + " )\n", + " mod = model(**params)\n", + " mod.fit(xi_train, xj_train)\n", + " v = mod.predict(xi_test)\n", + " c = 1 - numpy.var(v - xj_test)\n", + " mem.append(max(c, 0) ** 0.5)\n", + " cor.iloc[i, j] = sum(mem) / len(mem)\n", + " return cor\n", + "\n", + "\n", + "cor = correlation_cross_val(df, LinearRegression, fit_intercept=False, draws=20)\n", + "cor" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X10.9988620.0000000.8579590.790670
X20.0891740.9961040.3179620.064916
X30.8655240.4949740.9992270.952608
X40.7160080.6119720.9623780.999387
\n", + "
" ], - "source": [ - "import random\n", - "ex = numpy.zeros((100, 2))\n", - "for i in range(0, ex.shape[0]):\n", - " h = random.randint(0, ex.shape[1]-1)\n", - " ex[i, h] = 1\n", - "ex[:5]" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 0.998862 0.000000 0.857959 0.790670\n", + "X2 0.089174 0.996104 0.317962 0.064916\n", + "X3 0.865524 0.494974 0.999227 0.952608\n", + "X4 0.716008 0.611972 0.962378 0.999387" ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., -1.],\n", - " [-1., 1.]])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cor = correlation_cross_val(df, DecisionTreeRegressor)\n", + "cor" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X3X4
X10.9975630.0368070.8682020.809539
X20.0079060.9968460.3536070.150800
X30.8804750.5479800.9991670.956861
X40.7388630.5911240.9665000.999798
\n", + "
" ], - "source": [ - "numpy.corrcoef(ex.T)" + "text/plain": [ + " X1 X2 X3 X4\n", + "X1 0.997563 0.036807 0.868202 0.809539\n", + "X2 0.007906 0.996846 0.353607 0.150800\n", + "X3 0.880475 0.547980 0.999167 0.956861\n", + "X4 0.738863 0.591124 0.966500 0.999798" ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -0.55708601, -0.40824829],\n", - " [-0.55708601, 1. , -0.53066863],\n", - " [-0.40824829, -0.53066863, 1. ]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import random\n", - "ex = numpy.zeros((100, 3))\n", - "for i in range(0, ex.shape[0]):\n", - " h = random.randint(0, ex.shape[1]-1)\n", - " ex[i, h] = 1\n", - "ex[:5]\n", - "numpy.corrcoef(ex.T)" + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cor = correlation_cross_val(df, RandomForestRegressor, n_estimators=10)\n", + "cor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les résultats sont assez fluctuants lorsque les données sont mal corrélées. On remarque également que la matrice n'est plus nécessairement symmétrique." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Supposons maintenant que nous avons deux variables cat\u00e9gorielles tr\u00e8s proches :\n", - "\n", - "* $X_1$ est une couleur rouge, bleu, gris.\n", - "* $X_2$ est une nuance rose, orange, cyan, magenta, blanc noir." + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def pairplot_cross_val(data, model=None, ax=None, **params):\n", + " if ax is None:\n", + " _fig, ax = plt.subplots(\n", + " data.shape[1], data.shape[1], figsize=params.get(\"figsize\", (10, 10))\n", + " )\n", + " if \"figsize\" in params:\n", + " del params[\"figsize\"]\n", + " if model is None:\n", + " from sklearn.linear_model import LinearRegression\n", + "\n", + " model = LinearRegression\n", + "\n", + " df = scale(data)\n", + " cor = numpy.corrcoef(df.T)\n", + " for i in range(cor.shape[0]):\n", + " xi = df[:, i : i + 1]\n", + " for j in range(cor.shape[1]):\n", + " xj = df[:, j]\n", + " xi_train, xi_test, xj_train, xj_test = train_test_split(\n", + " xi, xj, test_size=0.5\n", + " )\n", + " mod = model(**params)\n", + " mod.fit(xi_train, xj_train)\n", + " v = mod.predict(xi_test)\n", + " mod = model(**params)\n", + " mod.fit(xi_test, xj_test)\n", + " v2 = mod.predict(xi_train)\n", + " ax[i, j].plot(xj_test, v, \".\")\n", + " ax[i, j].plot(xj_train, v2, \".\")\n", + " if j == 0:\n", + " ax[i, j].set_ylabel(data.columns[i])\n", + " if i == data.shape[1] - 1:\n", + " ax[i, j].set_xlabel(data.columns[j])\n", + " mi = min(min(xj_test), min(v), min(xj_train), min(v2))\n", + " ma = max(max(xj_test), max(v), max(xj_train), max(v2))\n", + " ax[i, j].plot([mi, ma], [mi, ma], \"--\")\n", + " return ax\n", + "\n", + "\n", + "ax = pairplot_cross_val(df)\n", + "ax;" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2
0rougerose
1bleumagenta
2bleumagenta
3bleucyan
4bleumagenta
\n", - "
" - ], - "text/plain": [ - " X1 X2\n", - "0 rouge rose\n", - "1 bleu magenta\n", - "2 bleu magenta\n", - "3 bleu cyan\n", - "4 bleu magenta" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c1 = [\"rouge\", \"bleu\", \"gris\"]\n", - "c2 = [\"rose\" ,\"orange\" ,\"cyan\" ,\"magenta\", \"blanc\", \"noir\"]\n", - "ind = [random.randint(0, 2) for i in range(0, 100)]\n", - "x1 = [c1[i] for i in ind]\n", - "x2 = [c2[i*2 + random.randint(0,1)] for i in ind]\n", - "df = pandas.DataFrame(dict(X1=x1, X2=x2))\n", - "df.head()" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = pairplot_cross_val(df, model=DecisionTreeRegressor)\n", + "ax;" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On peut \u00e9videmment transformer en entier." + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = pairplot_cross_val(df, model=RandomForestRegressor, n_estimators=10)\n", + "ax;" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1_bleuX1_grisX1_rougeX2_blancX2_cyanX2_magentaX2_noirX2_orangeX2_rose
0001000001
1100001000
2100001000
3100010000
4100001000
\n", - "
" - ], - "text/plain": [ - " X1_bleu X1_gris X1_rouge X2_blanc X2_cyan X2_magenta X2_noir \\\n", - "0 0 0 1 0 0 0 0 \n", - "1 1 0 0 0 0 1 0 \n", - "2 1 0 0 0 0 1 0 \n", - "3 1 0 0 0 1 0 0 \n", - "4 1 0 0 0 0 1 0 \n", - "\n", - " X2_orange X2_rose \n", - "0 0 1 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dummies = pandas.get_dummies(df)\n", - "dummies.head()" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsRegressor\n", + "\n", + "ax = pairplot_cross_val(df, model=KNeighborsRegressor)\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Corrélations de variables catégorielles\n", + "\n", + "C'est le problème épineux si on se restreint au linéaire. Cela n'a pas trop de sens d'affecter une valeur à chaque catégorie et la corrélation de deux variables binaires (des modalités) est toujours étrange car il n'y a que deux valeurs possibles.\n", + "\n", + "$$cov(X,Y) = \\mathbb{E}\\left[(X - \\mathbb{E}X)(Y - \\mathbb{E}Y)\\right] = \\mathbb{E}(XY) - \\mathbb{E}X\\mathbb{E}Y = \\mathbb{P}(X=1 \\, et \\, Y=1) - \\mathbb{E}X\\mathbb{E}Y$$\n", + "\n", + "Dans le cas de variables binaires générées de modalités de la même variables catégorielles, le premier terme est toujours nul puisque les modalités sont exclusives et la corrélation est toujours négative." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 1.],\n", + " [0., 1.],\n", + " [0., 1.],\n", + " [1., 0.],\n", + " [0., 1.]])" ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1_bleuX1_grisX1_rougeX2_blancX2_cyanX2_magentaX2_noirX2_orangeX2_rose
X1_bleu1.000000-0.480384-0.538462-0.2836540.5947620.660020-0.332095-0.343801-0.332095
X1_gris-0.4803841.000000-0.4803840.590474-0.285714-0.3170630.691312-0.306719-0.296276
X1_rouge-0.538462-0.4803841.000000-0.283654-0.320256-0.355395-0.3320950.6384870.616748
X2_blanc-0.2836540.590474-0.2836541.000000-0.168707-0.187217-0.174943-0.181110-0.174943
X2_cyan0.594762-0.285714-0.320256-0.1687071.000000-0.211375-0.197518-0.204479-0.197518
X2_magenta0.660020-0.317063-0.355395-0.187217-0.2113751.000000-0.219189-0.226915-0.219189
X2_noir-0.3320950.691312-0.332095-0.174943-0.197518-0.2191891.000000-0.212039-0.204819
X2_orange-0.343801-0.3067190.638487-0.181110-0.204479-0.226915-0.2120391.000000-0.212039
X2_rose-0.332095-0.2962760.616748-0.174943-0.197518-0.219189-0.204819-0.2120391.000000
\n", - "
" - ], - "text/plain": [ - " X1_bleu X1_gris X1_rouge X2_blanc X2_cyan X2_magenta \\\n", - "X1_bleu 1.000000 -0.480384 -0.538462 -0.283654 0.594762 0.660020 \n", - "X1_gris -0.480384 1.000000 -0.480384 0.590474 -0.285714 -0.317063 \n", - "X1_rouge -0.538462 -0.480384 1.000000 -0.283654 -0.320256 -0.355395 \n", - "X2_blanc -0.283654 0.590474 -0.283654 1.000000 -0.168707 -0.187217 \n", - "X2_cyan 0.594762 -0.285714 -0.320256 -0.168707 1.000000 -0.211375 \n", - "X2_magenta 0.660020 -0.317063 -0.355395 -0.187217 -0.211375 1.000000 \n", - "X2_noir -0.332095 0.691312 -0.332095 -0.174943 -0.197518 -0.219189 \n", - "X2_orange -0.343801 -0.306719 0.638487 -0.181110 -0.204479 -0.226915 \n", - "X2_rose -0.332095 -0.296276 0.616748 -0.174943 -0.197518 -0.219189 \n", - "\n", - " X2_noir X2_orange X2_rose \n", - "X1_bleu -0.332095 -0.343801 -0.332095 \n", - "X1_gris 0.691312 -0.306719 -0.296276 \n", - "X1_rouge -0.332095 0.638487 0.616748 \n", - "X2_blanc -0.174943 -0.181110 -0.174943 \n", - "X2_cyan -0.197518 -0.204479 -0.197518 \n", - "X2_magenta -0.219189 -0.226915 -0.219189 \n", - "X2_noir 1.000000 -0.212039 -0.204819 \n", - "X2_orange -0.212039 1.000000 -0.212039 \n", - "X2_rose -0.204819 -0.212039 1.000000 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dummies.corr()" + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "ex = numpy.zeros((100, 2))\n", + "for i in range(ex.shape[0]):\n", + " h = random.randint(0, ex.shape[1] - 1)\n", + " ex[i, h] = 1\n", + "ex[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., -1.],\n", + " [-1., 1.]])" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ca ne dit pas grand-chose." + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy.corrcoef(ex.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1. , -0.59969254, -0.46164354],\n", + " [-0.59969254, 1. , -0.4330127 ],\n", + " [-0.46164354, -0.4330127 , 1. ]])" ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X1eX2e
0rougerose25
1bleumagenta02
2bleumagenta02
3bleucyan01
4bleumagenta02
\n", - "
" - ], - "text/plain": [ - " X1 X2 X1e X2e\n", - "0 rouge rose 2 5\n", - "1 bleu magenta 0 2\n", - "2 bleu magenta 0 2\n", - "3 bleu cyan 0 1\n", - "4 bleu magenta 0 2" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "ex = numpy.zeros((100, 3))\n", + "for i in range(ex.shape[0]):\n", + " h = random.randint(0, ex.shape[1] - 1)\n", + " ex[i, h] = 1\n", + "ex[:5]\n", + "numpy.corrcoef(ex.T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Supposons maintenant que nous avons deux variables catégorielles très proches :\n", + "\n", + "* $X_1$ est une couleur rouge, bleu, gris.\n", + "* $X_2$ est une nuance rose, orange, cyan, magenta, blanc noir." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2
0rougerose
1grisblanc
2grisblanc
3grisnoir
4bleumagenta
\n", + "
" ], - "source": [ - "from sklearn.preprocessing import LabelEncoder\n", - "enc = LabelEncoder()\n", - "df[\"X1e\"] = enc.fit_transform(df[\"X1\"])\n", - "df[\"X2e\"] = enc.fit_transform(df[\"X2\"])\n", - "df.head()" + "text/plain": [ + " X1 X2\n", + "0 rouge rose\n", + "1 gris blanc\n", + "2 gris blanc\n", + "3 gris noir\n", + "4 bleu magenta" ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1eX2e
X1e1.000000.74706
X2e0.747061.00000
\n", - "
" - ], - "text/plain": [ - " X1e X2e\n", - "X1e 1.00000 0.74706\n", - "X2e 0.74706 1.00000" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c1 = [\"rouge\", \"bleu\", \"gris\"]\n", + "c2 = [\"rose\", \"orange\", \"cyan\", \"magenta\", \"blanc\", \"noir\"]\n", + "ind = [random.randint(0, 2) for i in range(100)]\n", + "x1 = [c1[i] for i in ind]\n", + "x2 = [c2[i * 2 + random.randint(0, 1)] for i in ind]\n", + "df = pandas.DataFrame(dict(X1=x1, X2=x2))\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut évidemment transformer en entier." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1_bleuX1_grisX1_rougeX2_blancX2_cyanX2_magentaX2_noirX2_orangeX2_rose
0FalseFalseTrueFalseFalseFalseFalseFalseTrue
1FalseTrueFalseTrueFalseFalseFalseFalseFalse
2FalseTrueFalseTrueFalseFalseFalseFalseFalse
3FalseTrueFalseFalseFalseFalseTrueFalseFalse
4TrueFalseFalseFalseFalseTrueFalseFalseFalse
\n", + "
" ], - "source": [ - "df.corr()" + "text/plain": [ + " X1_bleu X1_gris X1_rouge X2_blanc X2_cyan X2_magenta X2_noir \\\n", + "0 False False True False False False False \n", + "1 False True False True False False False \n", + "2 False True False True False False False \n", + "3 False True False False False False True \n", + "4 True False False False False True False \n", + "\n", + " X2_orange X2_rose \n", + "0 False True \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False " ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ca ne veut toujours pas dire grand-chose. Et si on change la premi\u00e8re colonne en permutant les lables :" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2X1eX2e
0rougerose05
1bleumagenta12
2bleumagenta12
3bleucyan11
4bleumagenta12
\n", - "
" - ], - "text/plain": [ - " X1 X2 X1e X2e\n", - "0 rouge rose 0 5\n", - "1 bleu magenta 1 2\n", - "2 bleu magenta 1 2\n", - "3 bleu cyan 1 1\n", - "4 bleu magenta 1 2" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dummies = pandas.get_dummies(df)\n", + "dummies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1_bleuX1_grisX1_rougeX2_blancX2_cyanX2_magentaX2_noirX2_orangeX2_rose
X1_bleu1.000000-0.488085-0.394383-0.3330960.5247500.776643-0.254322-0.236067-0.263286
X1_gris-0.4880851.000000-0.6095600.682455-0.256123-0.3790680.521061-0.364866-0.406936
X1_rouge-0.394383-0.6095601.000000-0.415997-0.206952-0.306295-0.3176180.5985720.667590
X2_blanc-0.3330960.682455-0.4159971.000000-0.174792-0.258697-0.268260-0.249004-0.277716
X2_cyan0.524750-0.256123-0.206952-0.1747921.000000-0.128698-0.133456-0.123876-0.138159
X2_magenta0.776643-0.379068-0.306295-0.258697-0.1286981.000000-0.197518-0.183340-0.204479
X2_noir-0.2543220.521061-0.317618-0.268260-0.133456-0.1975181.000000-0.190117-0.212039
X2_orange-0.236067-0.3648660.598572-0.249004-0.123876-0.183340-0.1901171.000000-0.196818
X2_rose-0.263286-0.4069360.667590-0.277716-0.138159-0.204479-0.212039-0.1968181.000000
\n", + "
" ], - "source": [ - "df[\"X1e\"] = df[\"X1e\"].apply(lambda i: (i+1)%3)\n", - "df.head()" + "text/plain": [ + " X1_bleu X1_gris X1_rouge X2_blanc X2_cyan X2_magenta \\\n", + "X1_bleu 1.000000 -0.488085 -0.394383 -0.333096 0.524750 0.776643 \n", + "X1_gris -0.488085 1.000000 -0.609560 0.682455 -0.256123 -0.379068 \n", + "X1_rouge -0.394383 -0.609560 1.000000 -0.415997 -0.206952 -0.306295 \n", + "X2_blanc -0.333096 0.682455 -0.415997 1.000000 -0.174792 -0.258697 \n", + "X2_cyan 0.524750 -0.256123 -0.206952 -0.174792 1.000000 -0.128698 \n", + "X2_magenta 0.776643 -0.379068 -0.306295 -0.258697 -0.128698 1.000000 \n", + "X2_noir -0.254322 0.521061 -0.317618 -0.268260 -0.133456 -0.197518 \n", + "X2_orange -0.236067 -0.364866 0.598572 -0.249004 -0.123876 -0.183340 \n", + "X2_rose -0.263286 -0.406936 0.667590 -0.277716 -0.138159 -0.204479 \n", + "\n", + " X2_noir X2_orange X2_rose \n", + "X1_bleu -0.254322 -0.236067 -0.263286 \n", + "X1_gris 0.521061 -0.364866 -0.406936 \n", + "X1_rouge -0.317618 0.598572 0.667590 \n", + "X2_blanc -0.268260 -0.249004 -0.277716 \n", + "X2_cyan -0.133456 -0.123876 -0.138159 \n", + "X2_magenta -0.197518 -0.183340 -0.204479 \n", + "X2_noir 1.000000 -0.190117 -0.212039 \n", + "X2_orange -0.190117 1.000000 -0.196818 \n", + "X2_rose -0.212039 -0.196818 1.000000 " ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1eX2e
X1e1.000000-0.700588
X2e-0.7005881.000000
\n", - "
" - ], - "text/plain": [ - " X1e X2e\n", - "X1e 1.000000 -0.700588\n", - "X2e -0.700588 1.000000" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dummies.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ca ne dit pas grand-chose." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X1eX2e
0rougerose25
1grisblanc10
2grisblanc10
3grisnoir13
4bleumagenta02
\n", + "
" ], - "source": [ - "df.corr()" + "text/plain": [ + " X1 X2 X1e X2e\n", + "0 rouge rose 2 5\n", + "1 gris blanc 1 0\n", + "2 gris blanc 1 0\n", + "3 gris noir 1 3\n", + "4 bleu magenta 0 2" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La corr\u00e9lation lin\u00e9aire sur des variables cat\u00e9gorielles n'a pas de sens. Essayons avec un arbre de d\u00e9cision. C'est le mod\u00e8le ad\u00e9quat pour ce type de valeur discr\u00e8tes :" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python370_x64\\lib\\site-packages\\ipykernel_launcher.py:6: DataConversionWarning: Data with input dtype int32, int64 were all converted to float64 by the scale function.\n", - " \n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1eX2e
X1e1.00.822127
X2e1.01.000000
\n", - "
" - ], - "text/plain": [ - " X1e X2e\n", - "X1e 1.0 0.822127\n", - "X2e 1.0 1.000000" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "enc = LabelEncoder()\n", + "df[\"X1e\"] = enc.fit_transform(df[\"X1\"])\n", + "df[\"X2e\"] = enc.fit_transform(df[\"X2\"])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1eX2e
X1e1.0000000.644442
X2e0.6444421.000000
\n", + "
" ], - "source": [ - "cor = correlation_cross_val(df[[\"X1e\", \"X2e\"]], DecisionTreeRegressor)\n", - "cor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et si on permute le premier label :" + "text/plain": [ + " X1e X2e\n", + "X1e 1.000000 0.644442\n", + "X2e 0.644442 1.000000" ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python370_x64\\lib\\site-packages\\ipykernel_launcher.py:6: DataConversionWarning: Data with input dtype int32, int64 were all converted to float64 by the scale function.\n", - " \n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1eX2e
X1e1.00.807566
X2e1.01.000000
\n", - "
" - ], - "text/plain": [ - " X1e X2e\n", - "X1e 1.0 0.807566\n", - "X2e 1.0 1.000000" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.select_dtypes(exclude=[\"object\"]).corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ca ne veut toujours pas dire grand-chose. Et si on change la première colonne en permutant les lables :" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2X1eX2e
0rougerose05
1grisblanc20
2grisblanc20
3grisnoir23
4bleumagenta12
\n", + "
" ], - "source": [ - "df[\"X1e\"] = df[\"X1e\"].apply(lambda i: (i+1)%3)\n", - "correlation_cross_val(df[[\"X1e\", \"X2e\"]], DecisionTreeRegressor)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "M\u00eame r\u00e9sultat qui s'interpr\u00e8te de la sorte :\n", - "\n", - "* La variable *X1e* se d\u00e9duit de *X2e* (car *cor(X2e, X1e) = 1*).\n", - "* La variable *X2e* et fortement li\u00e9 \u00e0 *X2e*.\n", - "\n", - "La valeur num\u00e9rique choisie pour repr\u00e9sente la variable cat\u00e9gorielle n'a pas d'impact sur les r\u00e9sultats." + "text/plain": [ + " X1 X2 X1e X2e\n", + "0 rouge rose 0 5\n", + "1 gris blanc 2 0\n", + "2 gris blanc 2 0\n", + "3 gris noir 2 3\n", + "4 bleu magenta 1 2" ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python370_x64\\lib\\site-packages\\ipykernel_launcher.py:12: DataConversionWarning: Data with input dtype int32, int64 were all converted to float64 by the scale function.\n", - " if sys.path[0] == '':\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAJVCAYAAACI1xJDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzs3XtcVHXiP/7XzADDXRAGUBTvVxTBNM1Kc7dEFK/ZxSyvecvS9bMf9mHa6m/9rN8+leU1U/G2ueQnspRq1Wozy9I2LyCgeL8hCAwXgeE+c96/P1zZSJEZnJlzDrye/yyHM8x5dRbevuZc3kcjhBAgIiIiIlXQyh2AiIiIiKzH8kZERESkIixvRERERCrC8kZERESkIixvRERERCrC8kZERESkIixvRERERCrC8kZERESkIixvRERERCrC8kZERESkIixvRERERCrC8kZERESkIixvRERERCriIncARygqKoMkCblj3FNAgDcKCkxyx2gUtWZnbudzZnatVgN/fy+nbMtZ7DGGKfX3h7lsw1y2UVuuxo5fTbK8SZJQbHkDoOhsDVFrduZ2PjVnl5u9xjCl/n/AXLZhLts0h1w8bUpEZCOTyYTY2FjcuHHjrnXr16/H0KFDMWbMGIwZMwYJCQkyJCSipqxJHnkjInKUU6dO4Y033sDVq1fvuT49PR3vvfceoqKinBuMiJoNHnkjIrJBYmIili1bhqCgoHuuT09Px6ZNmzBq1CgsX74cVVVVTk5IRE0dyxsRkQ1WrFiBfv363XNdWVkZevTogbi4OOzZswclJSXYsGGDkxMSUVPH06ZERHbi5eWF+Pj42uXp06dj8eLFWLhwoU3vExDgbZc8BoOPXd7H3pjLNsxlm+aQi+WNiMhOsrOzceTIEUyYMAEAIISAi4vtw2xBgemB70wzGHxgNJY+0Hs4AnPZhrlso7ZcWq2mUR/WWN6ISHGKNkyFVgtIEuD/yg6541jN3d0d77zzDgYMGIA2bdogISEBTz31lNyxiKiJ4TVvRKQoZ7fMwE1PF2g0gE53u8gp3cyZM5GWloaWLVti+fLlmDt3LoYPHw4hBKZNmyZ3PCJykhrJjOO5KQ7fDo+8EZFiZJZmY2s7f/iYJSzILIRGAFqFfsQ8ePBg7de/vs4tOjoa0dHRckQiIhlVW2oQn/4hzhScQ5BHIMJ82zhsWyxvRKQI10oysT5lC1wlgRdvFkPz70u+JEneXEREDam2VGNT6t9wrugiXuj2tEOLG8DTpkSkEFmmm/Bw8cAfBy+Bf6UFQgAWi7queSOi5qm02oTcciNe7PEMHg0d4PDt8cgbEcmqylINvc4Ng1o/jH7BkXDTuQEsbESkAtWWarhqXRHg0RJLB/737fHLCXjkjYhkc67wIpYeeROXi68CgNMGPiKiB1VeU47VyZuw5+I/ADh3/GJ5IyJZnCk4hw9St8HHzRuBHgFyxyEispqppgxrU+JxozQbnfw6OH37PG1KRE6Xln8GW9J2ItgrCK9FzoSPm32eKEBE5Gil1SasS4lHbrkRsyOmIDygu9MzsLwRkVNdK8lEfNpOhHq3wquRL8PL1VPuSEREVpGEhPdTtiCvPB9zI6ahe8susuRgeSMip2rrE4qY9k/iibaD4OHiIXccIiKraTVajOjwFNxd3NHVv5N8OWTbMhE1K8l5aSiqvAWtRouYDr9ncSMi1SioKEKKMR0AEGEIl7W4ASxvROQER7KPYWv637HvyjdyRyEiskl+RQFWnfwAu85+igpzpdxxAPC0KRE52OGsn/F/5z5Dj5Zd8UzXMXLHISKyWl65EWuSN6PGUoNXo16Gh4u73JEAsLwRkQMdyvwJn1xIQq+A7ni510tw1bnKHYmIyCo5ZblYk7wZkpCwoO9shHq3kjtSLdlOm5pMJsTGxuLGjRt3rcvIyMD48eMRHR2NJUuWwGw2y5CQiB5EjWTG0ZvH0MfQCzN7T2ZxIyJVSTWeAQD8oe8cRRU3QKbydurUKUycOBFXr1695/q4uDgsXboUX331FYQQSExMdG5AInogFskCV60LFkTNwozwSXDR8iA/EamDRbIAAJ5q9wQWP7wQrbyCZU50N1nKW2JiIpYtW4agoKC71mVlZaGyshKRkZEAgPHjx+PAgQPOjkhEjSCEwD8uf413ftwIs2SGp6sndFqd3LGISIVKt81B6eapKN02x2nbvFaSib/+611km3Kg0WgaNYG46bPlKI2fAdNnyx2Q8DZZPg6vWLGi3nV5eXkwGAy1ywaDAbm5uTa9f0CAsmdrNxh85I7QaGrNztyOJ4TArrQk7Lv6TzzR4REEG1pAq+UN7URku9Jtc4A7d3aaK1G6bQ58pm906DYvF1/F+ynb4OXqCb1O36j3MH22HCL/MgBA5F+G6bPl8B6/1J4xASjwhgVJkqDRaGqXhRB1lq1RUGCCJAl7R7MLg8EHRmOp3DEaRa3ZmdvxhBD47OKXOJh5GI+FDsSc/i+iIL/MKdvWajWK/8BGRDb67ZQcDp6i40LRZWxI3QY/N1/Mj5oFf3e/Rr2PKLh232V7UdzH4pCQEBiNxtrl/Pz8e55eJSLl+PzyARzMPIwn2jyK57uOg1ajuKGFiNTkt1NyOHCKjmslmXj/1Fa01PvhD33nNLq4AYAmoN19l+1FcSNsaGgo9Ho9Tpw4AQBISkrC4MGDZU5FRPcTFdQbMe2fxIQuo20+Uk5E9Fs+0zf+p7C5uDv0lGkrrxA80qof/tB3DlrofR/ovbzHL4UmsCOg0UET2NEhp0wBBZ02nTlzJubPn4/evXtj5cqVeOONN2AymRAeHo7JkyfLHY+IfkMSEtLyM9DHEI4wnzYI82kjdyQiakIcfY3bucKLaOsTCk9XDzzXbZzd3tdRhe3XZC1vBw8erP06Pj6+9uvu3btj9+7dckQiIitYJAs+zPgYx3NT8F99X0Env/ZyRyIislpKXhq2nk7AoFb9MbH703LHsZlijrwRkTpYJAu2n/4IycY0jOkYw+JGRKpyPDcFfzvzf2jn0xZjO4+QO06jsLwRkdVqJDO2pScgNf80nu4ci9+F8XpUIlKPf908gZ0Ziejk1x5zI6bBXSHPKrUVyxsRWe1i0WWk5Z/Bs13HYkibQXLHISKyWrWlBv+48jW6+HfCnIip0Ovc5I7UaCxvRNSgO/Mt9gjoij8P+COCvTh9DxGphxACbjpX/KHvHHi7esNN5c9aVtxUIUSkLJXmKrx/aisyCs4DAIsbEanKwczD2HXuM0hCQkt3f9UXN4BH3ojoPirMldhwahuullzHwFb95I5DRM3Iks0/I6eoHCH+nlgxa2Cj3uOba4ew99I+RBp6QwgBOGEayrnvHkJVjQS9qxYf/PEJh2yDR96I6J7Ka8qxLiUeV0uuY3r4JPQLjpQ7EhE1E0s2/4ybheUQArhZWI4lm3+2+T32X/kn9l7ah4eC+mB6+AvQaXUOSFrXneIGAFU1Eua+e8gh22F5I6K7VJgrsTYlHjdKszGz10uICuotdyRFMZlMiI2NxY0bN+5al5GRgfHjxyM6OhpLliyB2WyWISGRuuUUld93uSH7rnyDL698jYdD+mJq+ESnFDcAtcWtvmV7YXkjorvodW7o4BuGWb0nI8IQLnccRTl16hQmTpyIq1ev3nN9XFwcli5diq+++gpCCCQmJjo3IFETEOLved/lhrTzDcPjoY/gpR7POvVZy3pX7X2X7YXljYhqFVeVIL+iEFqNFs91G4degT3kjqQ4iYmJWLZsGYKC7r5xIysrC5WVlYiMvH2Kefz48Thw4ICzIxKp3opZA9GqpSc0GqBVS+uueRNC4Hz+ZQBAeEA3PN9tnFOLGwB88McnagubI6954w0LRAQAKKq8hbXJm+GidcHrD//B6YOeWqxYsaLedXl5eTAYDLXLBoMBubm5zohF1OTYcpOCJCQknk/Cj1k/I67fq2jn29aBye7PUYXt11jeiAgFFUVYm7wJppoyvNJnBotbI0mSBI3mP7ez3Zkfz1YBAd52yWMw+NjlfeyNuWzDXPcnCQmbjyXgcNZRjO4+DA917NGovztHs+f+YnkjaubyKwqwJnkzKswVeDVyJjq0CJM7kmqFhITAaDTWLufn59/z9GpDCgpMkCTxQFkMBh8YjaUP9B6OwFy2Ya77k4SEv2d8gn/lnEBM+99jUsRY5Oeb5I51l/r2l1aradSHNX68Jmrm9lz8B6rMVZgfNYvF7QGFhoZCr9fjxIkTAICkpCQMHsznvxI5Slr+Gfwr5wRiOwxDbMdoRR5xcwQeeSNq5iZ1n4BbVSVo7R0idxTVmjlzJubPn4/evXtj5cqVeOONN2AymRAeHo7JkyfLHY+oyepj6IWFfeeis18HuaM4FcsbUTOUZbqJr699hxe7PwNPV094utp2Gz4BBw8erP06Pj6+9uvu3btj9+7dckQiahZqJDM+Orsbv2v7ONr6hDa74gbwtClRs5NZmoU1yZtwoegyiqvlv2aFiMha1ZYabE79G37JOYnM0iy548iGR96ImpFrJZlYl7IF7jo9FkTNRqBHS7kjERFZpcpSjU2pO3C+6BImdZ+AQa0fljuSbFjeiJqJy8XX8H7KVni5emJB1GwEePjLHYmIyCqV5ip8kLoNl25dxUs9nsWAVg/JHUlWLG9EzYRe54ZWXsGY0WsS/N395I5DRGQ1nVYHDxcPTA2fiH7BkXLHkR3LG1ETl1eeD4NHAEK9W+GPD73SbG6lJyL1K68phwQBb1cvzO49hePXv/GGBaIm7EzBOfy/X97DD1lHAYADHxGphqm6DGuTN2Pjqe2QhMTx61dY3oiaqLT8M9iUugPBnkF4KKiP3HGIiKxWWm3CmuRNyCnPQ0yHp/jIvt/gaVOiJiglLw1bTyegrXcoXo2cwXnciEg1iqtKsDZ5MwoqizAnYhq6t+widyTFYXkjamJuVRVj+5ldaOfTFvMip8PDxUPuSEREVvv72U9QWHUL8/rMQBf/jnLHUSSWN6Imxk/fArN6T0anFu3h7uIudxwiIptM7DYexVUl6NCindxRFIsnkYmaiCPZx5CWfwYAEB7QncWNiFTDWF6APRf/AUlIaOnuz+LWAJY3oibghxtHkXD2ExzJPgYhhNxxiIislluWh9XJG3H05jEUVt6SO44q8LQpkcp9l/kjdl/4HL0De2B6r0m8nZ6IVONmWS7WJG8CBPjIPhuwvBGp2DfXDmHvpX2INPTGtPCJcNHyT5qI1CHLdBNrkzdDp9Fift9ZCPEKljuSanCkJ1IpIQSKqm7hoaA+mNLzeei0OrkjERFZraymHJ6uHpgbMQ1Bnga546gKyxuRygghUFZTDm83L0zoMhoAOIElEamGqboM3m5e6OrfCW88/Ed+8GwEjvhEKiKEQNKl/Xjz2GoUV5VCq9GyuBGRaly6dRXLjr6FYznJAMDi1kgc9YlUQgiBTy98gW+uH0LvwJ7wcfOSOxIRkdXOF13C+lNb4Kv35uS7D4inTYlUQBISEs8n4XDWUQxt8xie7jKKd5USkWqcLbyAjak7EODREvMjZ6GF3kfuSKrG8kakAgczD+Nw1lE8FfYExnSKYXEjItUoqCjCxtTtMHgEYn7ULPi4ecsdSfVkKW9ffPEFPvjgA5jNZkyZMgWTJk2qs379+vX49NNP4evrCwB49tln73oNUXPyaOsBcNfp8WjrASxuRKQqAR7+eK7rOPQ29IS3Ky/3sAenl7fc3FysWrUKn332Gdzc3PD8889jwIAB6Ny5c+1r0tPT8d577yEqKsrZ8YgUwyxZcODqtxja9nF4uLjjsdCBckciIrJaSl4aWuhboEOLMDzSur/ccZoUp9+wcOTIEQwcOBB+fn7w9PREdHQ0Dhw4UOc16enp2LRpE0aNGoXly5ejqqrK2TGJZGWWzFh9dAu+uPwV0v/9vFIiIrU4lpOMracTcODqP+WO0iQ5/chbXl4eDIb/TMYXFBSE1NTU2uWysjL06NEDcXFxaNeuHRYtWoQNGzZg4cKFVm8jIEDZ59MNBvVeqKnW7GrKXWOpwXtH4nEiOw1To57B8K6Pyx2pUdS0z4nIfn6+eRx/z/gEnf06YFo4L3lyBKeXN0mS6lyzI4Sos+zl5YX4+Pja5enTp2Px4sU2lbeCAhMkSZkP5zYYfGA0lsodo1HUml1NuastNYhP+xBnCs/h5YeeR1SLvqrJ/mvO3OdarUbxH9iImoufsv+FXWc/Qzf/zpgdMQVuOje5IzVJTj9tGhISAqPRWLtsNBoRFBRUu5ydnY3du3fXLgsh4OLCm2KpeSipLkGWKRuTuk/AsM5D5I5DRGQ1IQTOFJxHj4CumBMxlcXNgZxe3gYNGoSjR4+isLAQFRUV+PrrrzF48ODa9e7u7njnnXeQmZkJIQQSEhLw1FNPOTsmkVNVW2oghECgRwCWDozDoNYPyx2JiMhq1ZYaaDQaTAufiFm9p8BV5yp3pCbN6eUtODgYCxcuxOTJkzF27FjExsYiIiICM2fORFpaGlq2bInly5dj7ty5GD58OIQQmDZtmrNjEjlNhbkCa5M34/PLt2/ccXdxlzkREZH1vr72Hd4+vhZlNeVw0brAVcuzZY4myx4eNWoURo0aVed7v77OLTo6GtHR0c6OReR05TXlWJ+yFZmmLPw+bHDDP0CKwLkqiW7bf+Wf+PLK13goqA/cdXq54zQbrMdEMjFVl2FdSjxyynIxq/dk9A7sKXcksgLnqiS6fX3bF5cO4MC1gxgQ8hBe7PEMtBo+Lt1ZuKeJZCAJCetPbUFOeR5mRUxlcVMRzlVJBPzj/Lc4cO0gBrV6mMVNBtzbRDLQarQY1m4o5kZMQ3hAN7njkA3uNVdlbm5u7fKv56rcs2cPSkpKsGHDBjmiEjnMo2H9EdthGCZ2H8/iJgOeNiVyoqLKW7hhykbvwJ7oGxQhdxxqBGfMVWmveeuUOlEyc9lGKbkkIeHQlaMY0n4gdFodJj88Tu5I96SU/fVb9szF8kbkJAUVRVibvAkVlkr85ZFF8OBdpaoUEhKC48eP1y7fa67KI0eOYMKECQAaN1elPSYaV+rk1MxlG6XkkoSEj85+iqM3j6G6XMLwXo8rItdvKWV//VZ9uRo7yTiPdRI5gbG8AKtOfoAycwVe6TOdxU3FOFclNTcWyYIPzyTi6M1jiGn/JPoG9ZE7UrPHI29EDpZbloe1KfGosdRgftRMhPm0kTsSPYBfz1VZU1ODCRMm1M5VOX/+fPTu3bt2rsqamhr07duXc1WSalkkC3ac2YWTeakY1TEaw9v/Xu5IBJY3Ioc7mZcGs2TGgr6zEerdSu44ZAecq5Kai9xyI04XnMW4ziPxZBgf2acULG9EDiIJCVqNFsPb/w6DWvdHC72v3JGIiKxyZ/xq7R2CZQP/xPFLYXjNG5EDXC+9gRW/rEJOWR40Gg0HPiJSjWpLDTac2oZDmT8BAMcvBWJ5I7KzqyXXsTY5HtWWarhodXLHISKyWpWlGh+c2oazhReg17nJHYfqwdOmRHZ06dZVbDi1Fd6uXpgfNRsBHv5yRyIiskqluRIbTm3H5eKrmNzzOTwc0lfuSFQPljciO7lecgPrT22Bn94XC6Jmw0/fQu5IRERWsUgWrE/ZimulmZgW/gIeCuZ0IErG8kZkJ8FeQegXFInYjtFooVfmDN9ERPei0+rQLzgST7YbgkhDL7njUANY3oge0IWiy2jj0xoeLu6Y1GOC3HGIiKxmqi6DsaIAHVqE4Ym2j8odh6zEGxaIHkCq8TTWp8Rj76V9ckchIrJJSXUpVidvxMbU7aiyVMsdh2zA8kbUSCfzUhGfvhOhPq0xpmOM3HGIiKx2q6oYq09uQkFFIaaHT+KdpSrD06ZEjXAsJxkfZnyM9r5hfFYpEalKUeUtrEnehJLqUsyLfBmd/TrIHYlsxPJGZKNqSw2SLu1HpxbtMSdiGtxd9HJHIiKy2sHMwzDVlOG1yJno0KKd3HGoEVjeiGzkpnPFH/rOga+bN9x4qoGIVGZspxF4rPUABHsFyR2FGonXvBFZ6fsbR5B4PglCCAR6tGRxIyLVyCnLw5qTm1BcVQKdVsfipnIsb0RWOHj9BySe34vCyiJIQpI7DhGR1bJNOVidvBE3y3JRbq6QOw7ZAU+bEjXg62vfIenSfkQaemNa+ETo+LxSIlKJG6XZWJcSD51Gi/l9ZyOER9yaBJY3ovv46upBfH75APoFR2Jyj+dY3IhINW6UZmNN8ia46dywIGoWgjwNckciO2F5I7qP1t4hGNSqPyZ2fxpaDa8yICL1aKH3RYcW7fBs17EI9GgpdxyyI5Y3ot8QQiCzNAthvm3QO7Anegf2lDsSEZHVskw3EexpgI+bN17pM13uOOQAPJRA9CtCCHx64Qu8fXwdrpfckDsOEZFNzhddwsoT7yPp0n65o5AD8cgb0b9JQsLH5/fix6yf8bu2j6OtT6jckYiIrJZReB6bUv+GAI+WeDLsCbnjkAOxvBHhdnH76OynOHrzGIa1G4rRHYdDo9HIHYuIyCrp+RmIT9+JYE8DXoucCR83b7kjkQOxvBEBSDWextGbxxDT/kmM7PAUixsRqUaluQo7MxLRyisYr0a+DG9XL7kjkYOxvBEB6GPohQVRs9DVv7PcUYiIbOLuose8yBkIdA+Ap6uH3HHICXjDAjVbZsmMhIxPkGW6CY1Gw+JGRKpyLCcZ31w7BAAI82nD4taMsLxRs1RjqUF82k4cuXkMl4uvyR2HiMgmR28ex9/O/B9OF5yFRbLIHYecjKdNqdmpttRgc9rfkFF4Hs93G4fHQwfKHYmIyGo/Zv2MXec+Q3f/LpgdMYVPfmmGWN6oWamyVGNj6g5cKLqESd2fwaDW/eWORERktUM3fsIn55MQHtAdM3u9BFedq9yRSAYsb9SsaKGBi0aHyT2fw8MhfeWOQ0RkExeNDn0MvTAt/AW4avlPeHMlyzVvX3zxBUaMGIFhw4YhISHhrvUZGRkYP348oqOjsWTJEpjNZhlSUlNSYa5AeU05XHWueKXPdBY3ajSOXySHgooiAMBjoQNvH3FjcWvWGixvZWVl+Mtf/oIpU6bg1q1bWLp0KcrKyhq9wdzcXKxatQofffQR9u7di48//hgXL16s85q4uDgsXboUX331FYQQSExMbPT2iExVZVibHI8PUndACME53KjROH6Rswkh8I8r3+B//rUS2aYcAOAYRg2Xt7/+9a/w9fVFQUEB9Ho9TCYTli5d2ugNHjlyBAMHDoSfnx88PT0RHR2NAwcO1K7PyspCZWUlIiMjAQDjx4+vs57IFqbqMiw/tBrZppsY1u4JDnr0QDh+kTMJIbArLQn7rnyDh4L6IMQrSO5IpBANlreMjAwsXLgQLi4u8PDwwMqVK5GRkdHoDebl5cFgMNQuBwUFITc3t971BoOhznoia5VUl2J18kZkleZidsRU9A7sKXckUjmOX+QsQgh8dvFL7M34Co+2HoBJPSZAq+HsXnRbgyfNtdq6vywWi+Wu79lCkqQ6Rz9+exqrofXWCAhQ9jPdDAYfuSM0mpqyx3+/A4WVRXj98VfQK7i73HEaRU37+7fUnL0+zhi/APuNYUr9/4C5GvbjtV9wMPMwhnd+AtP6PqvIswZK2l+/1hxyNVje+vfvj3feeQeVlZU4fPgwEhISMGDAgEZvMCQkBMePH69dNhqNCAoKqrPeaDTWLufn59dZb42CAhMkSTQ6oyMZDD4wGkvljtEoass+rsNoDG1djF7B3VWV+w617e9fc2Z2rVbjtA9szhi/APuMYUr9/WEu63Tx6IZpPSdieK/HkZ9vkjvOXZS2v+5QW67Gjl8NHkL77//+b3h6esLHxwerVq1Ct27d8Kc//cnmDd0xaNAgHD16FIWFhaioqMDXX3+NwYMH164PDQ2FXq/HiRMnAABJSUl11hPdT0FFIb64dACSkBDo0RKd/TrIHYmaEI5f5EiSkJB0aT+KKm9Bq9GiX0iUIo+4kfwaPPLm6uqKefPmYd68eXbZYHBwMBYuXIjJkyejpqYGEyZMQEREBGbOnIn58+ejd+/eWLlyJd544w2YTCaEh4dj8uTJdtk2NV2FG6bill6HLW38UOPhjUdaP4xAj5Zyx6ImhuMXOYpFsuDDjI9xPDcFLfS+eKLNo3JHIgXTCCGUeX7xAfC0qWMoNXvhhqkodNdhaxs/WKDBlMxbCJ+xpXa9UnM3RK25gaZ72tRZeNrU+eTMZZEs2H5mF5LzUjGmYwyGtR+qiFz3w1y2sfdp03qPvG3fvv2+Pzht2jSbN0bkCEZ3Hba18QMAvJxVhOAaPqSZiNShRjJje3oCTuWfxtOdY/G7MJ5mp4bVW97OnTuHr776CsOHD3dmHiKblWi1cBXA1KxbCKqxwCzJnYiIyDo1lhoUVhbh2a5jMaTNILnjkErUW97+93//Fzdv3sRjjz2GkSNHOjMTkVXKa8rh6eqJ/lPj0W7DVOi1gFkCWr6yQ+5oRET3VW2phgYaeLp64L/7vQoXPu6KbHDfu02XLl2KkydPOisLkdWuFF/DsqNv4URuCgAg6JUdaDFnB4sbESlepbkKG05tw9bTCRBCsLiRzeotb99//z06deqEP//5z3W+L4TAu+++6/BgRPW5eOsK1qXEw9PVEx1atJM7DhGR1SrMlXj/1FZcvHUFDwX14VQg1Cj1lre//vWv2Lx5c53vFRQUYPLkyfj+++8dHozoXs4XXcL7KVvgp2+BhX3noKW7v9yRiIisUl5TgfUpW3C15Dqm95qE/iFRckcilaq3vH388cf46aefMH/+fFRUVODYsWMYM2YM2rZti8TERGdmJAIAFFQUYcOpbWjp0RILoubAT99C7khERFbbdjoBmaVZeLnXi+gbFCF3HFKxek+0t2zZEtu2bcPKlSsRGxuL0tJSvP766xg3bpwz8xHVCvDwx9NdRiHS0As+bk1rXi8iavpGdxqOkqpS9ArsIXcUUrn73rBQXl6OzMxMuLi4wM3NDXq93lm5iGqlGk/jeskNAMDjoQNZ3IhINYqrSvHDjSMAgDCfNixuZBf1lrfTp09j7Nix8PLywt69e7Fp0yasXLkSK1asgNlsdmZGasZO5qUiPn3msr0CAAAgAElEQVQnvrjyldxRiIhscquqGGuSN2LPxX+goKJI7jjUhNRb3qZMmYLZs2fjrbfegoeHB8LDw7F7925cuHABL774ojMzUjN1LCcZ29IT0N43DNPDJ8kdh4jIaoWVRVh1ciNuVRVjXuTLCPDgzVVkP/WWtw8//BDPPvtsne/duQ4uMjLS4cGoefv55nH87cz/obNfB8zrMwMeLu5yRyIiskp+RSFWn9yIspoyvBY5E539OsgdiZqYem9Y6Nmz5z2/r9VqsWjRIocFIhJCIMWYjm7+nTE7YgrcdG5yRyIistq1kuuotFRhfuQshPm2kTsONUGc1pkUpUYyw1Xrghn/Pk3qqnOVORERkXXujF8PBUeiR8tu8HT1kDsSNVH3vduUyJm+vf4DVh5fj/KaCrjqXFnciEg1sk05+MvRt5FReB4AWNzIoVjeSBG+unoQn138EgbPQOh5mpSIVCSzNBurkzdCEhJa6v3kjkPNAE+bkqyEENh39Z/Yd+Ub9AuOxOQez0Gn1ckdi4jIKtdKMrE+ZQv0Oj3mR81CkGeg3JGoGWB5I1l9l3kY+658g4Eh/TCpxwRoNTwYTETqYCwvwNrkeHi5emBB1GwEeLSUOxI1EyxvJKuooAiUmyswosNTLG5EpCoBHv4Y2vZRPNp6APzdebqUnIf/WpLTCSHwr5snIAkJ/u5+iO0YzeJGRKpxoegS8isKodVoEdsxmsWNnI7/YpJTSULCrnOf4cOMj5Gclyp3HCIim5wpOIf3T23FJ+f3yh2FmjGeNiWnkYSEhIzd+DnnOIa1G4q+QX3kjkREZLW0/DPYkrYTwV5BeLHHsw3/AJGDsLyRU1gkCz7M+BjHc1MwosNTGNH+SWg0GrljERFZJcWYjm3pCQj1boVXI1+Gl6un3JGoGWN5I6fIKc/DKeNpjO44HNHtfyd3HCIiq0lCwrfXf0CYTyjmRc6Ahwsn4CV5sbyRQwkhoNFoEOrdCssGxvHCXiJSFSEEtBot5kZMhVajhbuLu9yRiHjDAjlOjaUGG1O343DWzwDA4kZEqnIk+xjeP7UVNZYaeLp6sriRYrC8kUNUW6qxMXUH0gvOgle2EZHaHM76GQlnPwEACAiZ0xDVxdOmZHeV5ipsTN2Oi7eu4MXuz+CR1v3ljkREZLVDmT/hkwtJ6BXQHS/3egmuOle5IxHVwfJGdmWRLNhwaisuF1/D5J7P4eGQvnJHIrKb7OxsxMXFoaCgAB06dMDKlSvh5eVV5zVZWVmIjY1FWFgYACAwMBBbt26VIy41wg83juCTC0noY+iF6eEvwEXLfyZJeXjalOxKp9UhwhCO6b0msbhRk/OXv/wFL7zwAg4cOIBevXphw4YNd70mPT0do0aNQlJSEpKSkljcVKazX0c8FjoQM8InsbiRYrG8kV2U1ZTjeskNAMCTYUPQNyhC5kRE9lVTU4Njx44hOjoaADB+/HgcOHDgrtelpaXh/PnzGDNmDCZPnoxz5845OyrZSAiB1JwMCCHQ2jsEE7uNh06rkzsWUb1Y3uiBlVabsCZ5Ezac2oYqS7XccYgcoqioCN7e3nBxuX00xmAwIDc3967X6fV6jB49Gnv27MGMGTMwb948VFfz70KphBBIurQff/1+LVLzT8sdh8gqPCZMD6S4qhRrUzajoKIAs3tPhV7nJnckoge2f/9+vPnmm3W+165du7ueCnKvp4S89tprtV8PGTIE7777Li5fvozu3btbvf2AAG8bE9+bweBjl/exN6XkEkLgw5RP8c31Q3iq0+P4XY8B0GqUd0xDKfvrt5jLNvbMxfJGjXarqhhrkzejqPIWXukzHV39O8sdicguYmJiEBMTU+d7NTU1GDBgACwWC3Q6HYxGI4KCgu762Z07dyI2Nhb+/v4AbheEO0frrFVQYIIkPdj0FAaDD4zG0gd6D0dQSi5JSPjk/Of4IesInmjzKF5+aCLy801yx7qLUvbXbzGXberLpdVqGvVhjeWNGu3b6z+guKoE8yJfRme/DnLHIXIoV1dX9OvXD/v27cOoUaOwd+9eDB48+K7XHTt2DJWVlZg5cyZ++eUXSJKEjh072j1P6ceLgeKbQItW8Hnu/9n9/Zu6zNIsHM46iifDhmBspxFN+lnLpR8uACqLAfcW8Jm8Ru44ZAcaIUSTm33QHp9aHUWpnwqs8dvsFsmC3HIjWnuHyJiqYWrd52rNDTg3e2M/uTZGVlYWFi1ahIKCArRq1QrvvfceWrRogV27diEvLw8LFixAbm4uFi1aBKPRCL1ejxUrVth0yhRoeAwr/XgxRHE2IABoAE2L1ncVOKX+/igp143SbIR6t4JGo1FUrl970FylHy6AqCiuXdZ42KfANdX95SiqP/LGeZLULa88H4nn92JKz+fh4+at+OJGZE+hoaHYuXPnXd+fOHFi7dfBwcHYvn27Q3NIxdnQCECjAYS4vUwNs0gWJJzdjUhDL0QYwtHGp7XckRxOKi+GRvOr35Xy4oZ/iBTP6eXtzjxJI0eOxPvvv48NGzYgLi6uzmvuzJO0fPlyZ8ej+8gqycHqkx/AIiSUVJfCx805RzuIqK4csy9a6Upw57xJjtkXLeSNZLUX/vwPlJab4anXYf3CIU7brkWyYPvpj5BsTEMrr+BGvUfc+z+hoLQKAT56vDPvUTsndIxiSQ8/XVXt70qxpFfN7wrVz6m31XCeJPXKNuXg/zv4HiQILIiajVDvVnJHImq2Ej1fwk2LLyQB3LT4ItHzJbkjWeXVVd+jtNwMACivsuDVVd87Zbs1khnx6TuRbEzD051j8VS7J2x+jzvFDQAKSqsQ9/5Pdk7pGOvEZNyy6CEEcMuixzoxWe5IZAdOPfJm6zxJzz//PA4fPox58+Zh3759cHPjNBRyyDLdxJrkTXDTuWJ+5EyEeN19hx0ROc+fp/bH/+wAruWZ0C7IG3+eqo7nB5dXWe677AhmyYzNaX/DmYJzeLbrWAxpM6hR73OnuNW3rFTvzHsUce8DBbfUdcSQ7s9h5U3OeZKcdfFyYyl1Dpr6uHpL6NgyDDMfmogQH3UWN7Xt8zvUmhtQd3Y1UEth+zVPva5OYfPUO/4pBjqNDkEegYjs1guPhg5o9PsE+OjrFLYAH7094jkFC1vT47DyJuc8Sbzb1D5uluUiyCMQOq0Oc8Knw+Cjnuy/pqZ9/mtqzQ003btN6cGsXzgEC9b+4JRr3irNVSitNsHgGYBnuo554Pe7fQRLfde8UdPk1NOmSpsniep38dYVbDi1FY+FDsT4zrFyxyGiJuKj/xnp8GJfYa7EhlPbUFR5C8sGxsFV52qX92VhI6Vw+nNAli1bhsTERIwYMQLHjx/HH/7wBwDArl27sGbN7blnlixZgiNHjiA2NhZvvfUW3n33XWi1yntkSVN1rvAi3k/ZAj+9H37X9nG54xARWa28phzrUuJxteQ6nu4yym7FjUhJnD5ViFLmSaJ7yyg4j01pOxDoEYD5UbPg68brlohIHUw1ZVifsgXZphzM7PUSIgzhckcicgg+HotqVZqrsOPMLgR5GvBa5EzO40ZEqvL5pQO4WZaL2RFTEB5g21MtiNSE5Y1qubvoMbfPNBg8AuHl6il3HCJqgm5sW4TqnMvQBLSD9/ildn3v8Z1HYkDIQ+jk196u73tH6bY5gLkScHGHz/SNDtkGkTV4IRnhRO4pfJf5IwCgvW8YixsROYTps+WovnkBEBaI/MswffbgT9G5VVWMnWcSUWWphruLu+OLGwCYK28vE8mE5a2Z+yXnJLaf/ggpxjRYJMdPmElEzZcouHbfZVsVVBRh1YkPkGJMQ1658YHeq0F3ilt9y0ROxPLWjB3JPoYPz3yMLv6d8EqfGdBpHT9hJhE1X5qAdvddtkV+RQFWnfwAZeYKvBY1E219Qh803v25uN9/mciJWN6aqcNZPyPh7Cfo3rIL5kZMg17HR48RkWN5j18Kt1ZdAI0OmsCOjb7mLa/ciFUnN6LaUo35UTPR3jfMzknv5jN9438KG695I5nxhoVmyiIs6BXQAy/3epHzIBGR07SZ/r8PPEmvEAKeLh6Y2mc6Qr1b2SlZw1jYSClY3pqZW1XF8NO3wBNtHsXg0Eeg1fDgKxGpw62qYrRw80WwVxBef/gPHL+o2eJvfjNy4OpB/OXnd5BtygEADnxEpBqZpVn4f7+swlfXDgLg+EXNG3/7mwEhBP5x+Wt8cfkA+gT2QrCnQe5IRERWu1aSiTXJm+GmdcNDQZFyxyGSHU+bNnFCCHx++QC+vvYdBrbqh0ndJ/ATKxGpxuXiq3g/ZRu8XD2xIGo2Ajz85Y5EJDuWtybuRG4Kvr72HR4LHYjnuo5lcSMi1agwV+CDU9vh6+aN+VGz4O/uJ3ckIkVgeWviooIiYBYWDAh5CBqNRu44RERW83DxwJSez6ONT2v46VvIHYdIMXgYpgmShIQvL3+N4qoS6LQ6DGzVj8WNiFTjTME5nMxLBQD0CuzB4kb0GyxvTYwkJPw94xPsv/rP2sGPiEgt0vLPYFPqDvzz+veQhCR3HCJF4mnTJsQiWfBhxsc4npuC2A7DMLTtY3JHIiKyWkpeGraeTkBb71C82mcGr9ElqgfLWxNhkSzYfvojJBvTMKZTDIa1Gyp3JCIiqx3PTcHfzvwf2vu2xSt9psPDxUPuSESKxfLWRFRaqpBbbsTTnWPxu7DBcschIrJJlukmOrZoh7kR0+DOh74T3RfLm8rVWGqg0Wjg5eqJP/V7jc8pJSJVqTBXwsPFHaM7DodZWOCq5T9LRA3hBQUqVm2pxsbUHdh++iMIIVjciEhVfrhxFMt/fgf5FQXQaDQsbkRWYnlTqUpzFTac2oZzRRfRO7AnpwIhcqLVq1dj3bp191xXXV2NuLg4xMTEYNy4cbh06ZKT06nDd5k/4uPze9DOtw1acCoQIpuwvKlQhbkS75/aikvFVzG15/MY2Kqf3JGImoXS0lIsXrwY27dvr/c1O3fuhIeHB/bv34/Fixfj9ddfd2JCdfjm2iHsvvA5Ig298XKvl3jEjchGLG8qtDX977hach3TwyehX0iU3HGImo1vv/0W7du3x7Rp0+p9zaFDhzB69GgAQP/+/VFYWIjs7GxnRVS8w1d/wd5L+/BQUB9MD38BLixuRDbjX40KxXYchsFVjyDCEC53FKJmZezYsQBQ7ylTAMjLy4PBYKhdNhgMyMnJQevWrR2eTw0ebhOJsfkj8PuwwZzHjaiRWN5UorTahLT8MxjU+mG09w2TOw5Rk7Z//368+eabdb7XsWNH7Nixo8GfFULUuQZVCAGt1raSEhDgbdPr62Mw+NjlfR6UEAJfXfweg9sPgN7FDS/0GyV3pHtSyv76LeayTXPIxfKmAsVVJVibEo+CikJ0b9kFLd395Y5E1KTFxMQgJiamUT8bHByMvLw8hIXd/pCVn5+PoKAgm96joMAESRKN2v4dBoMPjMbSB3oPexBC4NMLX+C7Gz+iuLQcz/UdoYhcv6WU/fVbzGUbteXSajWN+rDGY9YKd6uqGKuTN6Kwsgjz+kxncSNSuCFDhiApKQkAcPz4cej1+mZ7ylQSEj4+vxff3fgRQ9s8hqFt+Mg+IntgeVOwgooirDrxAUqqSvFqn5fRxb+T3JGI6B527dqFNWvWAABeeuklVFdXY+TIkVixYgXefvttmdPJQxISdp39FIezjuKpsCfwdJdRnNKIyE542lTBLhVfQbm5Aq9GzkSHFrzOjUgpXnvttTrLEydOrP1ar9fjrbfecnYkxSmtNuFM4XnEtP89RnYYxuJGZEcsbwpkkSzQaXV4OKQvegZ0g7erl9yRiIisYpEs0Gg0aKH3xesP/4HjF5ED8LSpwuSU5WL5z+/gfNFFAODAR0SqYZbM2HY6AZ+cT4IQguMXkYOwvClIlukmVp3ciGqpBj5uyrzVmYjoXmokM7ak70SKMR1BngaeJiVyIJ42VYjM0iysS4mHq9YV86NmIdjT0PAPEREpQLWlBpvT/oaMwvN4rus4DG7ziNyRiJo0ljcFMJYXYE3yZrjr9FgQNRsGzwC5IxERWUUIgS3pO3G28AImdZ+AQa0fljsSUZPH8qYAAR7+eKz1ADwe+ggCPDiPGxGph0ajweOhA/FQUB8MaPWQ3HGImgWWNxldvHUF/no/BHj4Y2znEXLHISKyWoW5ApduXUWvwB7oHdhT7jhEzQpvWJDJ2cILWJ+yBYnn98odhYjIJuU15ViXvAVb0neiuKpE7jhEzY5s5W316tVYt27dPddVV1cjLi4OMTExGDduHC5duuTkdI51uuAcNqZuh8EjAC/2eEbuOEREVjNVl2FN8mZkmbIxo9eLaKH3lTsSUbPj9PJWWlqKxYsXY/v27fW+ZufOnfDw8MD+/fuxePFivP76605M6FjHs1KxOXUHgj2DsCBqNnzcbH8gLRGRHEqrTViTvAm55XmYFTGVp0uJZOL08vbtt9+iffv2mDZtWr2vOXToEEaPHg0A6N+/PwoLC5Gdne2siA4jCQl7Mg4g1Ls1FkTNgrcbJ7AkIvU4mZcKY0UB5kRMQ3hAN7njEDVbTr9hYezYsQBQ7ylTAMjLy4PB8J95zgwGA3JyctC6dWurthEQoLyjWUIIaDQaLHr8Feg0Oni6ecgdqVEMBnVOHszczqfm7FTXnfFrcOgjCA/ohkAPTmdEJCeHlbf9+/fjzTffrPO9jh07YseOHQ3+7J2B4tfLWq31BwkLCkyQJGH16x3tXzdPINmYihm9XkLrYH8YjaUoQ6ncsWxmMPjAaGRuZ1FrbsC52bVajSI/sDUVBRVF2Jr+d0zqMQGh3q1Y3IgUwGHlLSYmBjExMY362eDgYOTl5SEsLAwAkJ+fj6CgIHvGc5oj2cfw0dnd6OLfCZKQ5I5DRGS12xOIb0KlpQo1Uo3ccYjo3xQ5VciQIUOQlJQEADh+/Dj0er3Vp0yV5IcbR5Fw9hP0aNkVcyOmQa9zkzsSEZFVcsvysDp5I6ot1ZgfNRPtfcPkjkRE/6aY8rZr1y6sWbMGAPDSSy+huroaI0eOxIoVK/D222/LnM52h7N+xsfn96B3YA/MipgCN52r3JGIiKxiLC/A6uRNMEtmLOg7G2E+beSORES/ItsTFl577bU6yxMnTqz9Wq/X46233nJ2JLtq7xuGR1r1x/PdxsFFywdZEJF6+Ol90b1lFwxrNxStvILljkNEv6GYI29NxYWi2xMKt/VpjRd7PMPiRkSqkWW6ifKacrjqXDGl5/MsbkQKxfJmJ0IIfHn5K6xO3oRTxtNyxyEissnVkutYdXIjEs5+KncUImoADwvZgRACSZf245vrh/BIq/7oHdhD7khERFa7dOsqNpzaCm9XL4zvHCt3HCJqAMvbAxJC4NMLX+C7Gz/i8dBH8GzXMdBqeECTiNThfNElfJC6HX56X8yPnAV/dz+5IxFRA1jeHtC10kwcuvEThrZ5DE93GVVncmEiIiWThITE83vR0t0f8yNn8iHzRCrB8vaA2vuGIa7fqwjzacPiRkSqotVoMTdiGtx0bvBx41MqiNSC5/caQRISEjJ243TBOQBAO9+2LG5EpBqpxtPYde4zSEJCgEdLFjcilWF5s5FFsmDH6V04cvMXZJVmyx2HiMgmJ/NSEZ++E5mlWai2VMsdh4gagadNbWCWzNh++iOkGNMxttMIPNXuCbkjERFZ7VhOMj7M+BjtfcPwSp/pcHdxlzsSETUCy5uVzJIZW9J3Ii0/AxO6jMbQto/JHYmIyGr/unkCOzMS0dmvA+ZETIO7i17uSETUSCxvVtJpdGihb4Hnuo7D4DaPyB2HiMgmvnof9Arsjunhk+Cmc5M7DhE9AJa3BlRZqmGqLkOAhz8mdhsvdxwiUoDVq1dDp9Pd9YxmAMjKykJsbCzCwsIAAIGBgdi6dauzI9bKKctFiFcwerTsih4tu8qWg4jshzcs3EeluRIbTm3FmuRNqLHUyB2HiGRWWlqKxYsXY/v27fW+Jj09HaNGjUJSUhKSkpJkLW4Hr/+Av/7rPWQUnpctAxHZH8tbPSrMFVifshWXi69hdKfhcNW5yh2JiGT27bffon379pg2bVq9r0lLS8P58+cxZswYTJ48GefOnXNiwv/Ym/EVPr34JSINvdDVr5MsGYjIMVje7qG8phzrkrfgWmkmpodPQr/gSLkjEZECjB07FrNmzYJOp6v3NXq9HqNHj8aePXswY8YMzJs3D9XVzp2SY/+Vf+Kj1L3oFxyJaeEvQKetPy8RqQ+vebuHPRf3IcuUjVm9J6N3YE+54xCRk+3fvx9vvvlmne917NgRO3bsaPBnf30d3JAhQ/Duu+/i8uXL6N69u9XbDwho/KS5GcYL+PLK1xjSfiDm9n8JWq3yPqMbDD5yR7gn5rINc9nGnrlY3u5hXOeReDgkCl38eaqBqDmKiYlBTExMo352586diI2Nhb+/PwBACAEXF9uG2oICEyRJNGr7gQjBnIipeKJ7fxTklzXqPRzJYPCB0Vgqd4y7MJdtmMs29eXSajWN+rCmvI9kMimuKsFHZz9FtaUGnq4eLG5E1CjHjh3D7t27AQC//PILJElCx44dHbpNIQSSLu3H9ZIbAIDegT2h1XB4J2qqeOQNQFHlLaxN3ozi6hI8FjoAYT5t5I5ERCqya9cu5OXlYcGCBViyZAkWLVqEpKQk6PV6vPvuuw49dSkJCR+f34sfs36GTqNFmC/HL6KmrtmXt4KKIqxN3gRTTTlejXyZxY2IGvTb+d0mTpxY+3VwcPB9pxKxJ0lI+Ojspzh68xiGtRuKkR2GOWW7RCSvZl3eLsZPx/Ywf1RqNXhtwHy09w2TOxIRkVUskgU7Mz7BsdyTiGn/JEZ2eAoajUbuWETkBM32oojCDVMhXDRwFQIvZ92C79+Xyh2JiMhqAgKVlgqM6hiN2I7DWNyImpFme+RNpwWCqy1YeL0QGgGIZltjiUiNXLQumNV7Cm9MIGqGmu1fvUW6/b8aUXeZiEgtWNyImqdm+5ff8pUdMFsAIQCz5fYyERERkdI129OmAAsbERERqU+zPfJGREREpEYsb0REREQqwvJGREREpCIsb0REREQqwvJGREREpCIsb0REREQq0iSnCtFqlf2YGKXnux+1Zmdu53NWdjXvo/rY679JqfuGuWzDXLZRU67GZtUIIcSDBiIiIiIi5+BpUyIiIiIVYXkjIiIiUhGWNyIiIiIVYXkjIiIiUhGWNyIiIiIVYXkjIiIiUhGWNyIiIiIVYXkjIiIiUhGWNyIiIiIVYXlzktWrV2PdunX3XFddXY24uDjExMRg3LhxuHTpkpPT3S07OxuTJk3C8OHDMXfuXJSVld31mqysLERFRWHMmDEYM2YMZsyYIUPS//jiiy8wYsQIDBs2DAkJCXetz8jIwPjx4xEdHY0lS5bAbDbLkPJuDeVev349hg4dWruf7/UauZhMJsTGxuLGjRt3rVPq/m7q7jfWyPk3q7QxUGljnFLHLyWPT0odf+6Xy277S5BDlZSUiNdff11ERESItWvX3vM1W7ZsEX/+85+FEEL88ssv4plnnnFmxHuaNWuW+PLLL4UQQqxfv168/fbbd73mwIEDtbnllpOTI4YOHSqKiopEWVmZGDVqlLhw4UKd14wcOVIkJycLIYR4/fXXRUJCghxR67Am9+zZs8XJkydlSli/lJQUERsbK8LDw0VmZuZd65W4v5sya8YaOf5mlToGKmmMU+r4peTxSanjT0O57LW/eOTNwb799lu0b98e06ZNq/c1hw4dwujRowEA/fv3R2FhIbKzs50V8S41NTU4duwYoqOjAQDjx4/HgQMH7npdWloazp8/jzFjxmDy5Mk4d+6cs6PWOnLkCAYOHAg/Pz94enoiOjq6TuasrCxUVlYiMjISQP3/Tc7WUG4ASE9Px6ZNmzBq1CgsX74cVVVVMqWtKzExEcuWLUNQUNBd65S6v5sya8YaOf5mlTgGKm2MU+r4peTxSanjz/1yAfbbXyxvDjZ27FjMmjULOp2u3tfk5eXBYDDULhsMBuTk5Dgj3j0VFRXB29sbLi4utXlyc3Pvep1er8fo0aOxZ88ezJgxA/PmzUN1dbWz4wK4ex8GBQXVyXyvfXyv/yZnayh3WVkZevTogbi4OOzZswclJSXYsGGDHFHvsmLFCvTr1++e65S6v5sya8YaOf5mlTgGKm2MU+r4peTxSanjz/1y2XN/uTxISPqP/fv3480336zzvY4dO2LHjh0N/qwQAhqNps6yVuucXn2v3O3atauTB8BdywDw2muv1X49ZMgQvPvuu7h8+TK6d+/umLD3IUnSXfvw18sNrZdLQ7m8vLwQHx9fuzx9+nQsXrwYCxcudGpOWyl1fzcFDzLWOPJvVqljoBrGOKWOX2odn5Q6/thzf7G82UlMTAxiYmIa9bPBwcHIy8tDWFgYACA/P7/eQ672dq/cNTU1GDBgACwWC3Q6HYxG4z3z7Ny5E7GxsfD39wdw+w/kzidZZwsJCcHx48drl3+bOSQkBEajsXbZmfv4fhrKnZ2djSNHjmDChAkA5N3HtlDq/m4KHmSsceTfrFLHQDWMcUodv9Q6Pil1/LHn/uJpUwUYMmQIkpKSAADHjx+HXq9H69atZcvj6uqKfv36Yd++fQCAvXv3YvDgwXe97tixY9i9ezcA4JdffoEkSejYsaNTs94xaNAgHD16FIWFhaioqMDXX39dJ3NoaCj0ej1OnDgBAEhKSrrnf5OzNZTb3d0d77zzDjIzMyGEQEJCAp566ikZE1tHqfu7uVPS3+yvOXsMVNoYp9TxS63jk1LHH7vurwe+5YGssnbt2jp3Wn300Udi9erVQgghKisrxZ/+9CcxYsQIMXbsWJGeni5XzFo3btwQL774ooiJiRHTp08Xt27dEkLUzZ2TkyOmTp0qRo4cKcaPHy8yMjLkjCw+//xzMXLkSLu6Lw4AACAASURBVDFs2DCxefNmIYQQL7/8skhNTRVCCJGRkSGefvppER0dLf7rv/5LVFVVyRm3VkO5Dxw4ULt+0aJFisl9x9ChQ2vvqlLD/m7q7jfWyPk3q7QxUGljnFLHL6WPT0odf+rLZa/9pRFCCPt2SyIiIiJyFJ42JSIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRljciIiIiFWF5IyIiIlIRF7kDOEJRURkkScgd454CArxRUGCSO0ajqDU7czufM7NrtRr4+3s5ZVvOYo8xTKm/P8xlG+ayjdpyNXb8apLlTZKEYssbAEVna4haszO386k5u9zsNYYp9f8D5rINc9mmOeTiaVMiIiIiFWF5IyKykclkQmxsLG7cuHHXuvXr12Po0KEYM2YMxowZg4SEBBkSElFT1iRPmxIROcqpU6fwxhtv4OrVq/dcn56ejvfeew9RUVHODUZEzQaPvBER2SAxMRHLli1DUFDQPdenp6dj06ZNGDVqFJYvX46qqionJySipo5H3oiIbLBixYp615WVlaFHjx6Ii4tDu3btsGjRImzYsAELFy60aRsBAd4PGhMAYDD42OV97I25bMNctmkOuVjeiIjsxMvLC/Hx8bXL06dPx+LFi20ubwUFpge+M81g8IHRWPpA7+EIzGUb5rKN2nJptZpGfVhjeSMixSnaMBVaLSBJgP8rO+SOY7Xs7GwcOXIEEyZMAAAIIeDiwmGWqDnJ2TwLHlI1KrRuCJm12SHb4DVvRKQohRumQqMDNBpAp7td5NTC3d0d77zzzv/f3p3HVVUnbAB/7r3AZZX1sogi4r4hqKlZo9kioqioZVlquWumZu/QuDQ54+THt8nKLUvUtEwts1GyEm2zmdFyBQHFJXdB4LIoO3c55/3DkVdG0QvC/Z0Dz/cvzj0H78Mpfjz3LL+DK1euQJZlbN68GU899ZToWERkJ1nxU+Asm6DRAK6yCVnxU+rlfVjeiEgxJFnCt4Hu2OHvAVlz8zWtCkapyZMnIzU1FT4+Pli0aBGmT5+OgQMHQpZljB8/XnQ8IrKTDL2MFSE+uOF4c+BykUz18j48nk9EiiDJErae+gd+83JF3/wS4D+XfEmS2FzV+emnnyq/vv06t6ioKERFRYmIREQCncg7jU+DveBntkL3n2tWy7RO8KyH91LBZ1oiagx2X/gBB64dwsDQJ/BUzs3yZrWq65o3ImqcrpVkIz5lI4KaBOOFjBK4WWSUaurvmjceeSMiRejbrA88nDzQt9nDwMs8ckVE6hHo6o/Y1oPRK7AbXHu6AkC9HHG7hUfeiEgYq2TFD5d/gVWywsPJ/WZxIyJSiWM5KcgqyYZGo0H/5o/C1dHVLu/L8kZEQpglC9ambcKO37/FyfzTouMQEdXIwWtH8XHaZnx74Xu7vzdPmxKR3ZmsZqxN+xQn807j2bax6OLXUXQkIiKbHcg8hC2nvkJb71YY02GU3d+f5Y2I7MpkNWFNyic4XfA7nm8/Eo807SU6EhGRzf559Vd8cWYHOvq0w+Qu4+Ckc7R7BpY3IrIrY1keLhddxdgOo9ArqLvoOERENpNkCUnGVHTx64CJncfCUSumRrG8EZFdWCQLHLQOCHYPwl8fngtXRxfRkYiIbHZrDJsW/hJ0Gi0cBBU3gDcsEJEdlJpL8d6xD/HD5V8AgMWNiFRl94UfsDxpDSqsJuh1TkKLG8DyRkT1rNhcghXJa3G1KBP+Ln6i4xAR2UyWZew6vwffXNgLPxdfYadJ/5syUhBRg1RkKsbK5LXILjViaviL6OTbXnQkIiKbyLKMhHO78f3lfegT9BBGtx8JrUYZx7yEpSguLkZMTAyuXr16x7r09HSMGDECUVFRWLBgASwWi4CERPQgzJIFK5LikVOai+nh41nciEhVEi/+hO8v78Mfgh9WVHEDBJW348ePY/To0bh48eJd18fFxeHNN9/Enj17IMsytm3bZt+ARPTAHLUOeKzZI3i56wS092kjOg4RUY10D+iKQaFP4tm2sYoqboCg8rZt2zYsXLgQ/v7+d6zLyMhAeXk5IiIiAAAjRoxAYmKivSMSUS3llRXgZM4ZAMAjwb3Q1ruV4ERERLaRZAmHso5BlmX4u/phcNgAaDQa0bHuIOSat8WLF1e7LicnBwaDoXLZYDAgOzvbHrGI6AHlluVheVI8ZI2EhT1fh6OAySuJiGpDkiVsSt+GQ1nH4Orggs5+HURHqpbibliQJKlKy5Vlucat19fXva5j1SmDwUN0hFpTa3bmrn/XinKw4td4mCQT3ug3C019fERHIiKyiVWy4pOTn+NoznHEtIxSdHEDFFjeAgMDYTQaK5dzc3Pvenr1XvLyiiFJcl1HqxMGgweMxiLRMWpFrdmZu/5llWRjeVI8JFnCrMgpCPMJsVt2rVaj+A9sRKRcFsmCDSe2INmYhthWg/BUi8dER7ovZV2BByA4OBh6vR5Hjx4FACQkJKBv376CUxHRvezPPAQZMmZHTkWwe5DoOERENrtSlIm03HSMbDNEFcUNUFB5mzx5MlJTUwEAS5cuxZIlSzBw4ECUlpZi3LhxgtMR0d3I8s0j3MNbD8afesxCU/dAwYmIiGxza/xq6RmCN3u/jseb/0FwItsJPW36008/VX69du3ayq/bt2+P7du3i4hERDa6WHgZX5zegSldXoS3sxe8nb1ERyIiskmF1YT4lE/wcFAP9AiMhK+Lt+hINaKYI29EpB7nb1zEyqS1KDGXQZIl0XGIiGxWbinHB8nrcbrgd1hVOn4p7oYFIlK2swXnsTrlY3g5NcGsyCk84kZEqlFmKcMHyR/jUtEVvNRpNHoERIiOVCssb0Rks/M3LuKD4+vh4+yN2ZFT4KlvIjoSEZFNTFYzViatw5XiDEzo9AIi/buIjlRrLG9EZLMAV390NXTCyDZD0MRJPXPQERE5ah3Q0bcdols+gS5+HUXHeSAsb0R0X+euX0RIk2Zwc3TF+E7Pi45DRGSzIlMxikzFaOoeiJiwAaLj1AnesEBE95Sck4plSR/h2/N7RUchIqqRGxWFWHbsI3yYsgEWySI6Tp1heSOiah3JTsb6E5vRwqM5okL7i45DRGSzgvLrWHbsI+RXXMe4DqPgoG04Jxsbzk9CRHXq4LWj2JS+Da28QjE9fDycHZxFRyIiskleWT6WJ8WjxFyKmRGTEOYZKjpSneKRNyK6Q5mlDP/4/Ru08W6Fl7tOZHH7L8XFxYiJicHVq1fvWJeeno4RI0YgKioKCxYsgMXScE7VEKnF7os/otRShlmRkxtccQNY3ojoLlwcXPBqt2mYHj4eep2T6DiKcvz4cYwePRoXL1686/q4uDi8+eab2LNnD2RZxrZt2+wbkIgwqm0s/qf7y2jRpLnoKPWC5Y2IKv185d/YfeEHAECQWwCcdI6CEynPtm3bsHDhQvj7+9+xLiMjA+Xl5YiIuDnx54gRI5CYmGjviESN0rWSbPzvv1ajzFIGJ50jgtwCREeqN7zmjYgAAN9f2oed575DhKEzJFmCVsPPdnezePHiatfl5OTAYDBULhsMBmRnZ9f4PXx93WuV7b8ZDMqci4+5aoa57u/S9atYkbwGWo0WDu4yDB7KyXZLXe4vljciwu4LP+KbC3vQ3b8rXuz4HItbLUmSBI1GU7ksy3KVZVvl5RVDkuQHymIweMBoLHqgf6M+MFfNMNf9XS66ilVJ6+Coc8RfHp8Dx3JXGMuVke2W6vaXVqup1Yc1jtBEjdy35/fimwt70CuwO17qNBo6rU50JNUKDAyE0WisXM7Nzb3r6VUiqhuXCq9gRdJa6B30mNNtGpp6NNxTpbdjeSNq5HxdfPBI014Y0+EZHnF7QMHBwdDr9Th69CgAICEhAX379hWciqjhcnd0R3OPYLwaOQ1+Lr6i49gNR2qiRkiWZWQWZwEAegf1wPPtR7K4PYDJkycjNTUVALB06VIsWbIEAwcORGlpKcaNGyc4HVHDc60kG5IswdfFG7Mjp8DXxVt0JLviNW9EjYwkS9h2JgG/XjuMeQ+9ikA3ntarjZ9++qny67Vr11Z+3b59e2zfvl1EJKJG4VT+WXyUshFRLR5HdMsnRMcRguWNqBGRZAlbT32FA9cO46mQxxDgarj/NxERKcSJvFOIT/0UAa4GPBrcS3QcYVjeiBoJSZawKX0bDmUdQ3ToExjcckCt7oQkIhIhxXgC69M+Q5B7IF6JmAR3RzfRkYRheSNqJA5mHcOhrGOIaRnVaE81EJE6lZhL8cnJzxHs0RSvdJ0EV0cX0ZGEYnkjaiR6BXZDEycPdPJtJzoKEVGNuDm6YnrXCQh2D4ILn7XM8kbUkJklC748k4ABLfrDz8WHxY2IVCNr9RSke2lRITtg4Aur0NqrpehIisG5AYgaKJPVjPiUT7A/8yDOXb8gOg4Rkc2yVk/BCW8tvgrwwKkmOlxbPUV0JEXhkTeiBqjCasKalI04U3AOL7R/Gr2CuouORERks2RvHXYFeKBdSQVeuHYDDjzUVAXLG1EDU24px4cpG3Du+kWM7TCKxY2IVOWny//ErgAPdCiuwPNZN+AgA6WSEzxFB1MQdlmiBkaGDKsk4aVOo1nciEh1TJIZEYYuGH61DDoJKLU6IfDleNGxFIVH3ogaiFJzKRy0DnBxcMFr3afzcVdEpBqyLOOGqRBeek8MDH0CkixB22UsAPCI211wdCdqAIpNJVieFI/1aZshyzKLGxGphizL+Ob8Hrx18D3kluUBAMew++DeIVK5IlMxlietQXZpDvo268OnJhCRasiyjB3nvkXipZ/Qzb8LfJwb1wPma4unTYlU7EZFIVYkxSOvvADTwsejvU8b0ZGIiGwiyzK2n/0a+67uR9/gPnim7VAecbMRyxuRSsmyjPVpnyG/4jpmdJ2INt5hoiMREdlsf+ZB7Lu6H483/wNGtI7hWYMaYHkjUimNRoNn2w1HhbUCYZ6houMQEdVIr6Ae0Gkd0DuwO4tbDfH4JJHKGEvz8P2lfZBlGcHuQSxuRKQaVsmKb87vQbG5BI5aBzwc1IPFrRZ45I1IRbJLcrA8KR4WyYKegd3gqW8iOhIRkU2skhUbT27FsZwU+Dh7o0/TnqIjqRbLG5FKXCvJxvKkNZBlGbO7TWVxIyLVsEgWfHxiC44b0zC89WAWtwfE8kakAhnF17AiKR5ajRazu01FkFuA6EhERDYxW81Yl7YJaXmn8HSboejf/FHRkVRPyDVvu3btwqBBgzBgwABs3rz5jvWrVq1C//79MWzYMAwbNuyu2xA1JtmlRjjpnPBqt2ksbkSkKqWWcmSXGvFcuxEsbnXE7kfesrOz8f777+Mf//gHnJyc8Nxzz6FXr15o3bp15TZpaWl47733EBkZae94RIpSbimHs4MzuvmHo7NvBzjpHEVHIiKyiclqgoPWAZ56D8zv+RrHrzpk9yNvBw4cQO/eveHl5QVXV1dERUUhMTGxyjZpaWlYs2YNhgwZgkWLFqGiosLeMYmEO2U8hzd//V+czDsNABz4iEg1yi3lWJW8HltOfQWA41dds3t5y8nJgcFgqFz29/dHdnZ25XJJSQk6dOiAuLg47NixA4WFhVi9erW9YxIJdbbgHBb/cyXcHF15mpSIVKXMUoZVyetwofASOvCpL/XC7qdNJUmqMqeLLMtVlt3c3LB27drK5QkTJmD+/PmYM2eOze/h6+teN2HricHgITpCrak1u5pyp2SlY3XKx/B388OfH5sNbxdP0ZFqRU37nIjqRom5FKuS1yGj+Bomdh6DCENn0ZEaJLuXt8DAQBw5cqRy2Wg0wt/fv3I5MzMTBw4cwNNPPw3gZrlzcKhZzLy8YkiSXDeB65jB4AGjsUh0jFpRa3Y15b5Wko3/Pbwa/i5+WNj/VZiKNDAWqyP77ey5z7VajeI/sBE1BrIsY03KRmQWX8PkLmPRxa+j6EgNlt1Pm/bp0we//vor8vPzUVZWhr1796Jv376V652dnfHOO+/gypUrkGUZmzdvxlNPPWXvmERCBLr6Y0hY1M153Jw5jxsRqYdGo0FM2ABMDX+Jxa2e2b28BQQEYM6cORg3bhxiY2MRExOD8PBwTJ48GampqfDx8cGiRYswffp0DBw4ELIsY/z48faOSWRXx40nkFNqhEajwZMh/eDu6CY6EhGRTa5X3MChrGMAgLberdHRt53gRA2fkEl6hwwZgiFDhlR57fbr3KKiohAVFWXvWERCHM5KwqfpXyDC0BkTO48RHYeIyGYF5dexPGkNikzF6ODTFh5OvITBHvhgeiKBfrt2BJ+c/BytPEPxQvtnRMchG3GicSIgrywf7x/7CEWmEsyImMTiZkd8PBaRIPszD2LrqX+gnXdrTA1/EU46J9GRyAacaJwIyCrKwfvHPkKFtQKzIiejRZPmoiM1KjzyRiSAJEs4eO0YOvi2xbTwl1jcVIQTjRMBKdmnYJbMmBU5lcVNAB55I7Izq2SFTqvDy13HQ6d1gKOWv4ZqcreJxlNSUiqXb59ovEWLFpg7dy5Wr14tZK5Kpc61x1w1o6Rct8avAYa+6N28G5rolXeqVEn763Z1mYt/NYjsaO+ln3Ei7xRmdJ0IZwdn0XGoFuwx0XhdzFWp1PkNmatmlJTralEm1qZtwvhOo/FQq06oKJRhhDKy3aKk/XW76nLVdp5KnjYlspPdF35Awrnd8NJ7QqfRiY5DtRQYGAij0Vi5fLeJxrdv3165XJuJxomU5nLhVSxPWgOLZIGrg4voOI0eyxtRPZNlGbvOJeKbC3vRK7A7Xuz4HHRalje14kTj1NhcuHEJK5Lj4eLgjDndpsPf1XD/b6J6xfJGVM/2XPoZiZd+wiNNe2JMh2eg1fDXTs040Tg1JpnFWViZvBZujm54tds0+Ln4iI5E4DVvRPUuwtAZZqsJg8MGsLg1EJxonBqLAFcDHmnaC0+E9IWX3lN0HPoP/iUhqgeSLOFYTgpkWUagmz+GtBrI4kZEqnGm4BwKTUXQaXUY2WYIi5vC8K8JUR2TZAlbTn2F9Wmf4WT+adFxiIhqJC03HR8cX4+vzu4SHYWqwdOmRHXIKlmxKf1LHM4+hkGhT6KjDx/QTETqcdyYhvVpmxHsHohRbWNFx6FqsLwR1RGrZMXGk1txLCcFQ8KiMDD0CdGRiIhsdiwnBRtObEGIRzPM6DoRro6cEkSpWN6I6siloqtINqZheOvBeDKkn+g4REQ2s0pW7L7wA1o2CcH0rhPgwknEFY3ljegB3ZphP8yzBf7c64/wd/UTHYmIyGayLEOn1eGViMnQ65zg7KAXHYnugzcsED0Ak9WMD1M24FjOzWdbsrgRkZr8O+M3bDixBVbJCk+9B4ubSrC8EdVShdWED49/jJN5p1FhqRAdh4ioRvZd3Y+tp/+BCmsFJFkSHYdqgKdNiWqh3FKO1cc34PyNixjX8Vn0DOwmOhIRkc1+uPwLdvz+Lbr6dcKEzi/AQcs6oCb8r0VUQyarGauS1+FS0VWM7/Q8ugd0FR2JiMhmt4pbpH84xncczWctqxDLG1ENOWod0NorDE+2eAwRhs6i4xAR1UjLJi3wSNOeeLbtcBY3lWJ5I7JRkakYJeZSBLr5I7b1INFxiIhsJssyzt24iNZeLdHKKxStvEJFR6IHwPJGZINCUxFWJMXDZDXjzd5/5PUhRKQaKav/iDT/cuz3ccWsiClo59NadCR6QLzblOg+rlfcwLJja5BXlo8X2j/N4kZEqpGy+o9ICbxZ3B6+XoqKzz8SHYnqAP8KEd1DQfl1LE9ag0JTEWZETEJrr5aiIxER2USSJRwNrMARL1f8oaAU0bnFkLQlomNRHeCRN6J7+ObCXhSZSvBKxGQWNyJSlbMF53HEywWP5ZcgOrcYGgBXJV/RsagOsLwR3cOotrF4rft0hHm2EB2FiKhG2vm0xh+7z0C7HBdIsgaXrH7o8vJS0bGoDvC0KdF/yS7JwdfnEzG2wyg4Ozgj2D1IdCQiIptYJSu2nPoKDzd9CK29WqKlZwvgP4XNW3A2qjssb0S3ySzOworkeEAGblQUwtnBWXQkIiKbmCULNqRtxvHcE2jm0ZSXejRgLG9E/3G1KBMrk9dCp9FiVrepCHDzFx2JiMgmZqsZa9M24UTeKTzTZhgea/6I6EhUj1jeiABcLrqKlUlr4aRzwuzIKfB3NYiORERkE5PVjDUpG3Gq4CxGtxuBR4N7i45E9YzljQiAi84FQW4BGNfxOfi5+IiOQ0RkMwetDh5OHhjT/hk83PQh0XHIDljeqFHLLjXC38UPBldfzOk2HRqNRnQkIiKblFnKUWGtgJfeEy92fJbjVyPCqUKo0TpTcA7/e3g59lz6GQA48BGRapSay7AqeR1WJa+DVbJy/GpkWN6oUUrPP4PVxz+Gj7M3Hg7iaQYiUo8ScylWJsfjSlEGhoQNhE6rEx2J7IynTanRSctNx9q0TQhwNWBmxGR4OLmLjkREZJMiUzFWJq9FdqkRU7qMQ2e/DqIjkQAsb9SoFJtL8PGJzWjqFoBXIibDzdFVdCQiIpt9eSYBOaW5mBb+Ejr4tBUdhwRheaNGxd3RDdPCX0Iz92C4OrqIjkNEVCPPtB2Gfs0eQSuvUNFRSCAh17zt2rULgwYNwoABA7B58+Y71qenp2PEiBGIiorCggULYLFYBKSkhuRwVhIOZR0DALT1bs3iRrXG8YvsLb+8ANvO7IRFssDDyZ3Fje5f3oxGI6ZMmYKoqCjk5uZi4sSJyMnJqfUbZmdn4/3338eWLVuwc+dOfPHFF/j999+rbBMXF4c333wTe/bsgSzL2LZtW63fj2jfhV/xycnPcfDaUciyLDoOqRjHL7K3nOJcLDv2EQ5lHUNuWZ7oOKQQ9y1vf/3rX/Hkk09Cr9fD09MT7du3xxtvvFHrNzxw4AB69+4NLy8vuLq6IioqComJiZXrMzIyUF5ejoiICADAiBEjqqwnqol/Z/yG1Yc+RTvv1pga/iJvp6cHwvGL7Cmn1IiFP72HMks5ZkVMQaBbgOhIpBD3veYtIyMDo0aNwpYtW+Do6Ii4uDgMGTKk1m+Yk5MDg+H/Hz3k7++PlJSUatcbDAZkZ2fX6D18fZV996DB4CE6Qq2pKXvi2X3YevofiAzqjP95ZAqcdI6iI9WYmvb3f1Nz9urYY/wC6m4MU+p/A+a6v6uF17DiQDwsshV/efw1hHo3Ex3pDkraX7drDLnuW940Gg0kSapcLi4urrJcU5IkVTn6IctyleX7rbdFXl4xJEmZp8cMBg8YjUWiY9SK2rJfy89FuF8n/PGRKbieXw6gXHSkGlHb/r6dPbNrtRq7fWCzx/gF1M0YptT/f5jLNlmFBXDUOuKNvrPgbFJWNkB5++sWteWq7fh139OmAwYMwB//+EcUFRXh888/x4svvojo6Ogav9EtgYGBMBqNlctGoxH+/v7Vrs/Nza2ynuh+ikzFAIDBLQdgcpexcFThETdSJo5fVN9ujV8tmjTHGz3/B809mwpOREp03/I2bdo09O3bF126dMGBAwfw7LPPYsaMGbV+wz59+uDXX39Ffn4+ysrKsHfvXvTt27dyfXBwMPR6PY4ePQoASEhIqLKeqDqyLOPbC9/jbweXIrcsHxqNBloNHyJCdYfjF9WnS4VXsOi3d/DPq78CAJ+cQNW672lTi8WC2NhYxMbGVr5248YNeHp61uoNAwICMGfOHIwbNw5msxlPP/00wsPDMXnyZMyaNQtdunTB0qVL8cYbb6C4uBidOnXCuHHjavVe1HjkrX4JPxrc8IuvG3oH9oCPs5foSNQAcfyi+nL+xiV8kLwebo6u6OTbTnQcUjiNXM3cCWlpaZg5cyZyc3PxxBNP4K233oK7+83zssOHD8eOHTvsGrQmeM1b/VBq9rzVL2FvgDv+7e2KnjfKMPhaEfxe3li5Xqm570etuYGGe82bvfCaN/sTmev36xew+vh6NHHywOzIqfC+7cMn91fNqC1XnV/ztnjxYvzlL3/Bvn374ODggEmTJsFkMgEA58oiRTnq7Yx/e7vi4euliM0pgiPPlBKRShSZirH6+Hp46b3wardpVYobUXWq/TNXXl6Ofv36wdfXF0uXLoW/vz/mzZtnz2xENul6vRzDswsxxFgMDQBr7W+GJiKyKw8ndzzf/mm82m0qvPS1uxyJGp9qy5skScjL+//ZnN9++238/vvv+OCDDzjRKQknyRJ2X/gBpeZSBEzfiG4F5YAMWKyAz22nTImIlCg19yRO5Z8FAPQIiEATJ2XOTUbKVO0NCxMmTEBsbCzeeust9OvXDy4uLvjwww8xduxYZGVl2TMjURVWyYpP07/AkexkNNF74JGmvVjYiEg1ko1p+DhtM8I8W6Cdd2seEKEaq/bIm5eXFz799FO0bt268rWmTZsiISEBOh1vXyYxrJIVG05uxZHsZAwNG4hHmvYSHYmIyGZHs5OxPu0zhHg04yP7qNaqLW9vvfUWvv/+ewQHB1e+lpeXh+nTpyM0NNQe2YiqMEsWrE/7DEk5KRjROgZRoY+LjkREZLNDWcew4cRWtGzSAq9ETISLg4voSKRS1Za3L774Avv378esWbNQVlaGw4cPY9iwYWjevDm2bdtmz4xEAIAScwmuFl/DM22H4YkQTnxKROpytuAc2niFYUbERDg7OIuOQypW7TVvPj4++Pjjj7F06VLExMSgqKgI8+bNe7QJLAAAIABJREFUw/Dhw+2Zjwhmqxk6rQ5eek8s6PUa9Don0ZGIiGxmsprgpHPC6PYjYZGscOIj++gB3XNGrNLSUly5cgUODg5wcnKCXq+3Vy4iAECF1YTVxz/G56dvTgrN4kZEarLvyn4sPvgeblQUQqvRsrhRnai2vJ04cQKxsbFwc3PDzp07sWbNGixduhSLFy+GxWKxZ0ZqpMos5fggeR3OXj+P1l4tRcchIqqRHy7/gi/PJiDYPQhujq6i41ADUm15e/HFFzF16lS8/fbbcHFxQadOnbB9+3acPXsWY8aMsWdGaoRKzWVYlbwOFwovY3yn59EzsJvoSERENku8+CN2/P4tuvmHY2LnMXDQ3vdR4kQ2q7a8ffrppxg1alSV125dBxcREVHvwajxkmUZH6VsxJWiDEzqPAbdA7qKjkREZLN/Z/yGXef34KGAbnip42jotJxei+pWtR8FOnbseNfXtVot5s6dW2+BiDQaDaJDn4BVtqKzXwfRcYiIaiTSPxxFpmJEhT4OrYYPW6a6x/+rSDEKTUU4lpMCAOjg25bFjYhUQ5Zl7M84CLPVDDdHV0S3fJLFjeoNT8KTIlyvuIEVSfG4UVGINl5h8HByFx2JiMgmkizhyzNf458ZBwAN+OQXqncsbyRcfnkBlifFo8hUhJe7TmRxIyLVkGQJW0/9AweuHcITIX3RJ6in6EjUCLC8kVC5ZflYkbQGpZYyzIyYjJaeLURHIiKyiSRL+Cz9SxzMOoqBLR5HTFgUn1VKdsHyRkKl5aWjzFKOWRFTENKkmeg4REQ2yy8vQFpuOmJaDkB0yydFx6FGhOWNhJBkCVqNFo81ewTd/MPRxMlDdCQiIptIsgQNNPBz8cUbvf+H4xfZHW+FIbvLLM7CWwffxeWiqwDAgY+IVMMsWRCf+il2X/wBAMcvEoNH3siurhRlYmVyPBw0DtBr+ZxSIlKPMx/OwO5mDjjjpkdHn7ai41AjxiNvZDeXCq9gRdIaOGmd8Gq3aQhw8xcdiYjIJmc+nIFvmzvirKsTRmQXovnXm0VHokaMR97ILjKLs7AiaS3cHF0xO3IKfF18REciIrKJJEvY1dwRF1wc8XROEboVlkPmoQ8SiOWN7MLf1Q89A7thQIvH4O3sJToOEZHNtBot2l6X8dCNQkQUVwAA8iU3eArORY0XPztQvfr9+gUUm0rgoHXAs+1iWdyISDVKzaU4W3AOADBkzHI0v+EAWQbyrG5o+fIHgtNRY8Yjb1RvTuadRnzqJ+hq6IzxnZ4XHYeIyGbF5hKsSloLY1k+/tZnLlwdXSsLG4+4kWgsb1QvUnNPYl3qJgS6BeCZNsNExyEislmRqRgrkuKRU5aLKV3GwdXRVXQkoipY3qjOJRvT8HHaZgS7B+GViElw48BHRCpxo6IQK5LikVdegOnh49Hep43oSER3YHmjOmWVrNh1LhEhHsGYETERLg4uoiMR1ZnMzEzExcUhLy8PLVu2xNKlS+Hm5lZlm4yMDMTExCAkJAQA4Ofnh/Xr14uIS7VwIPMQ8iuuY0bXCWjj3Up0HKK7YnmjOiPLMnRaHWZGToazTg9nB2fRkYjq1F//+lc8//zzGDx4MD744AOsXr0acXFxVbZJS0vDkCFDsGjRIkEpqTZkWQYARIU+jm7+4ZyHkhSNd5tSnTiQeRib0rdBkiV46T1Z3KjBMZvNOHz4MKKiogAAI0aMQGJi4h3bpaam4syZMxg2bBjGjRuH06dP2zsq1VBuWR4W/vQucsvyodVoWdxI8XjkjR7YvzJ+w+en/4EOPm1hlazQ6viZgBqegoICuLu7w8Hh5rBpMBiQnZ19x3Z6vR5Dhw7Fc889h3/961+YMWMGvvvuOzg52f44OF9f9zrJbDAo87mbSsp1rSgHy39dA5PVDGcPLQzeysl2i5L21+2Yq2bqMhfLGz2QfVf248uzCejs2wGTOo+Bo85RdCSiB7Z7924sWbKkymstWrSARqOp8tp/LwPAzJkzK7/u168f3n33XZw/fx7t27e3+f3z8oohSXINU1dlMHjAaCx6oH+jPigpV1ZJNpYnxUOSJSx8/FW4mj0Vk+0WJe2v2zFXzVSXS6vV1OrDGssb1drPV/6N7We/RldDZ0zo9DwctPzfiRqG6OhoREdHV3nNbDajV69esFqt0Ol0MBqN8Pe/8/Tapk2bEBMTA29vbwA3r6W6dbSOlCOrJAfvH/sIGo0Gr3abhhZezRT5R5/obnh+i2qtmXsQegV2x8ROL7C4UYPn6OiIHj164LvvvgMA7Ny5E3379r1ju8OHD2P79u0AgEOHDkGSJISFhdk1K92fp94DrTxDMSdyGoLcAkTHIaoR/sWlGpFlGRcLr6ClZwjaeLfirfTUqCxcuBBz587Fhx9+iKCgILz33nsAgK1btyInJwezZ8/GggULMHfuXCQkJECv1+Pdd9+FVsvPyUqRUXwNBhdfuDi4YEr4i6LjENWK3csb50lSL1mW8fX5ROy99DNmR05BW+/WoiMR2VVwcDA2bdp0x+ujR4+u/DogIAAbNmywZyyy0fkbF/FB8npE+odjTIdnRMchqjW7fxy8NU9SYmIiOnfujNWrV9+xza15khISEpCQkMDipgCyLOPT5K+w99LPeDS4N1p78TQQEanH2YLzWJm8Dk2cPDC45VOi4xA9ELuWN86TpE6SLGHbmQR8e+ZHPNbsETzXdji0Gp4GIiJ1OJV/Fh8cXw8fvRde7TYN3s5eoiMRPRC7nja11zxJdTVHUn1R6hw01UnJSsc/Mw5gSLsnMabriLtOj6B0atvnt6g1N6Du7NRwmCULNp/aDoOLL2ZFToGHk7L/PhDZot7Km8h5kupijqT6otQ5aO4lSNcMc7pNR+/WXZCbWyw6To2pcZ8D6s0N2Dd7bedJosbBUeuAl7tOgIejO9yd3O7/DUQqUG/ljfMkqZtVsuKLMzvQO+ghhHm2QGuvlqo84kZEjVNyTiquFGUgJiyKU4FQg2PXC5c4T5I6WCUrNpzYgv2Zh3DxxiXRcYiIauRIdjLWn9iM0wXnYJEsouMQ1Tm7X3W+cOFCbNu2DYMGDcKRI0fw6quvArg5T9Ly5csBAAsWLMCBAwcQExODt99+m/Mk2ZFZsmBt2iYkGVMxsnUMHg+5s1wTESnVwWtHsfHEVoR5tsArERP5yD5qkOx+LpLzJCmX2WpGfNqnOJl3GqPaxqJfsz6iIxER2exA5iFsOfUV2ni3wrTwl6DX2XaTG5Ha8EIyqqTVaKHX6fF8u5F4JLiX6DhERDWi1zmhk287TOw8Fk484kYNGMsbodxSAZNkQhMnD0zs9AJvTCAiVckty4Ofiy+6B0Sgm39XjmHU4PFCskauzFKOD46vx6rkdbBKVg56RKQq31/ah0W/LcWFG5cB3H36KaKGhuWtESs1l2Jl8lpcLLyMgaFPQKfViY5ERGSz3Rd+wM5z3yHC0BkhHsGi4xDZDU+bNlLF5hKsSlqLzJJsTOo8Fl0NnURHIiKyiSzL+ObCXiRe/BE9A7thbIdRfGQfNSosb43UF6d34FppDqaGv4hOvrY9uYKISAnS8tKRePFH9Al6CKPbj2Rxo0aH5a2RerrNUPQNfhhtvFuJjkJEVCOdfTtgYucxiDB0ZnGjRon/1zci1ytu4Kuzu2CVrPDUN2FxIyLVkGQJX59LRHapERqNBt38w1ncqNHi//mNRF5ZAd4/+iEOZB5CTlmu6DhERDaTZAlbT32FPZd+QnJOqug4RMLxtGkjkFuWh+VJ8SizlGNm5GQ+pJmIVEOSJWxK34ZDWccQHfoEBrToLzoSkXAsbw1cTqkRy5PiYbaaMStyMkI8momORERkE6tkxScnP8fRnOOIaRmF6JZPiI5EpAgsbw1cibkUDloHvNx1AoLdg0THISKymUW24npFIWJbDcJTLR4THYdIMVjeGqhicwncHd3Q0rMF3uz1R07AS0SqYZYssEpWODvoMTtyCscvov/CGxYaoCtFGVj02zvYn3EQADjwEZFqmKxmxKd8go9SNkCSJY5fRHfB8tbAXCy8jOVJ8XDSOqGtd2vRcYiIbFZhNeGjlA1Izz+DnoHdORUIUTV42rQBOX/jIj5IXg83RzfMjpwKXxdv0ZGIiGxSbinHhykbcO76RYztMAq9grqLjkSkWCxvDUShqQgfJK9HEycPzIqcAm9nL9GRiIhs9ln6lzh/4xJe6jQaPQIiRMchUjSWtwaiiZMHRrWNRTuf1vDSe4qOQ0RUI0PCovBQYCS6GjqLjkKkeLygQOVO5p3G2YJzAIBeQd1Z3IhINYpNJfjh8i+QZRkBbv4sbkQ2YnlTsdTck1iTshG7zu+FLMui4xAR2azIVIzlSWvwzfk9yC7NER2HSFV42lSlknNSsf7EZjR3D8a08Beh0WhERyJqNJYtWwadToeZM2fesc5kMmHBggVIS0uDs7Mzli5dilatWglIqUypi5+HWWPGuubeuOHsjGnh4xHIR/YR1QiPvKnQ0exkrD+xGS08mmNm5CS4OrqKjkTUKBQVFWH+/PnYsGFDtdts2rQJLi4u2L17N+bPn4958+bZMaGyZcVPhVlnxtrmXih00GD05QK092kjOhaR6rC8qdCJvNMI82yBVyImwsXBRXQcokbjxx9/RGhoKMaPH1/tNvv27cPQoUMBAA899BDy8/ORmZlpr4iK5iJVIMvJAWU6LSZkXEf70lLRkYhUiadNVcRsNcNR54gX2j8Nq2yFk85JdCSiRiU2NhYAsHLlymq3ycnJgcFgqFw2GAzIyspC06ZNbX4fX1/32oe8jcHgUSf/Tl0wWc3I0urRrrQCcRfz4CzJKNXq0VpBGZW0v27HXDXTGHKxvKnEP6/+ip+v/gtzuk1HEycP6MBHxhDVl927d2PJkiVVXgsLC8PGjRvv+72yLFe5BlWWZWi1NTvJkZdXDEl6sJuQDAYPGI1FD/Rv1JXskhysTF6Hp2NfQeDOVXCxVqBUq0fglDWKyaik/XU75qoZteXSajW1+rDG8qYCP1/5N7af/Rpd/DrwNCmRHURHRyM6OrpW3xsQEICcnByEhIQAAHJzc+Hv71+X8VTlWkk2lietAWTA4OqHLgu2wGgsAic1Iqo9XvOmcN9f2oftZ79GhKELJnUeC0ct+zaRkvXr1w8JCQkAgCNHjkCv19folGlDklF8DcuOfQQtNHi121QEuweJjkTUILC8Kdj+jIPYee47dPfvigmdnocDixuRIm3duhXLly8HAIwdOxYmkwmDBw/G4sWL8fe//11wOjGuV9zA8mNr4KB1wKvdpnE6EKI6xDagYF0NnVFQcR3RoU9Cp+U1bkRK8d/zu40ePbrya71ej7ffftvekRTH06kJngzph24B4fBz8RUdh6hB4ZE3hZFlGb9dOwKLZIG7kxtiwqJY3IhINc7fuIjM4ixoNBoMCO3P4kZUD1jeFESWZXx1dhc2pW/DwayjouMQEdXImYJzWJm8Dl+c2cFH9hHVI542VQhJlrDtTAL+lfEr+jd7FH2CeoqORERks1P5Z/FRykb4uvhgQqcX+Mg+onrE8qYAkixh66mvcODaYTwV8hiGtYrmwEdEqpGWm461aZvg7+KHWZFT4OFUN5MME9HdsbwpQF5ZAZKMqYgOfQKDWw5gcSMi1ZBlGb9kHECQWwBeiZgEd0c30ZGIGjyWN4EkWYJWo4XB1Rdv9PofeOk5bSURqcetMWxS57GwSla4OnIScSJ74A0LglgkC9anfYa9F38GABY3IlKVw1lJWHbsI5RZyqHXObG4EdmRsPK2bNmyah/ubDKZEBcXh+joaAwfPhznzp2zc7r6ZZYsWJe2CcnGNDjqHEXHISKqkd+uHcEnJz+HVqOFBrzMg8je7F7eioqKMH/+fGzYsKHabTZt2gQXFxfs3r0b8+fPx7x58+yYsH6ZLCbEp3yC1Nx0PNt2OPo3f1R0JCIim+3PPIjP0r9EO+/WeLnrBDg76EVHImp07F7efvzxR4SGhmL8+PHVbrNv3z4MHToUAPDQQw8hPz8fmZmZ9opYbyRZwt///RHS88/ghfZPo2+zh0VHIiKy2W/XjmDLqa/QwbctpoW/BCedk+hIRI2S3W9YiI2NBYBqT5kCQE5ODgwGQ+WywWBAVlaW6h/urNVo8XDzbojwCUevoO6i4xAR1Uhrr5Z4tGkvPN12GBz5rGUiYertt2/37t1YsmRJldfCwsKwcePG+36vLMtVpsuQZRlare0HCX19lTXHUKmpDFcKM9HOrxWeMKj7NKnB4CE6Qq0wt/2pOTtVdSLvNDr4tIGfiy9Gtx8pOg5Ro1dv5S06OhrR0dG1+t6AgADk5OQgJCQEAJCbmwt/f3+bvz8vrxiSpIxHs5SaS7EqeT1yyoxY9PBctGgaAKOxSHSsWjEYPFSZnbntz57ZtVqN4j6wNSS7L/yAby7sxdgOo9A7qIfoOEQEhU4V0q9fPyQkJAAAjhw5Ar1er8pTpsWmEixPikdGcSZe7PgcXB1dRUciIrKJLMvYdS4R31zYi16B3dEzsJvoSET0H4opb1u3bsXy5csBAGPHjoXJZMLgwYOxePFi/P3vfxecruaKTMVYnrQG2aU5mBL+Err4dRQdiYjIJrIsY8e5b5F46Sc80rQnxnR4BlqNYv5cEDV6wq44nTlzZpXl0aNHV36t1+vx9ttv2ztSnfpnxq8wluVhWvh4tPdpIzoOEZHNskpz8MuV/egb3AfPtB3K4kakMLxdqJ5Ehz6Bbv7hCHILEB2FiKhGgtwC8KeHZiPILYDPWiZSIH6cqkN5ZQVYnhSP/PICaDVaFjciUg1JlrA5fTsOXjsKAGjqHsjiRqRQPPJWR4yleVietAbl1goUm0rg4+wtOhIRkU2skhWb0r/E4exj8HLmc5aJlI7lrQ5kl+RgRfJamK1mzI6cguYewaIjERHZxCpZsfHkVhzLScGQsCgMDH1CdCQiug+WtweUXZKDZUlrIMkSZnebimD3INGRiIhsIskS1p/YjOPGNAxvPRhPhvQTHYmIbMDy9oA8nNzR3CMYw1sP5jVuRKQqWo0WTd0C0cYrDP2bq/vpL0SNCctbLV0ryYafsw9cHV3xctcJouMQEdnMZDUjvzwfgW4BiAkbIDoOEdUQ7zathYuFl/Hu0dX48myC6ChERDVSYTXhw+Mf4/1jH6HMUi46DhHVAstbDZ27fhErk9bCzcEFUS14YS8RqUe5pRwfJK/H2evnMbLNELg4OIuORES1wNOmNXCm4Bw+TNkAL30TzI6cCi89b6knInUoNZdh9fH1uFR0FeM7PY/uAV1FRyKiWmJ5s5FZsuDTk1/Ax9kbsyImw1PfRHQkIiKbJV76EZeLMjCx8xhEGDqLjkNED4DlzUaOWgdM7zoeTZw84OHkLjoOEVGNDGkZhXC/Tmjt1VJ0FCJ6QCxv95FiPIGrxZkY1PIpzuFGRACAZcuWQafTYebMmXesy8jIQExMDEJCQgAAfn5+WL9+vb0jAgAKTUX46uwuPNs2Fq6OrixuRA0Eb1i4h2M5KVibtglpeadgtppFxyEiwYqKijB//nxs2LCh2m3S0tIwZMgQJCQkICEhQVhxyy+7jmXH1iDFeAJZpUYhGYiofrC8VeNwVhI2nNiC0CYhmBkxGY46R9GRiEiwH3/8EaGhoRg/fny126SmpuLMmTMYNmwYxo0bh9OnT9sx4U0F5dfxl5/ew/WK65gRMQlhni3snoGI6g9Pm97Fb9eO4LP0L9HaqyWmhY+Hs4NedCQiUoDY2FgAwMqVK6vdRq/XY+jQoXjuuefwr3/9CzNmzMB3330HJycnm9/H17f219XmlORhxcE1KDKV4M+PzUZbv7Ba/1v1xWDwEB3hrpirZpirZuoyF8vbXWigQXufNpjSZRycdLYPuETUMOzevRtLliyp8lpYWBg2btx43++9/Tq4fv364d1338X58+fRvn17m98/L68YkiTbvP3tCspLoNc6Y85jk9HE6gOjsahW/059MRg8FJcJYK6aYq6aqS6XVqup1Yc1lrfb5JcXwMfZG72CuqNnYDdoNBrRkYhIgOjoaERHR9fqezdt2oSYmBh4e3sDAGRZhoND/Q+1BeXX4alvAh9nb/ypxyz4+zRR5B8xInpwvObtP366/E/89bd3cKnwCgCwuBFRrRw+fBjbt28HABw6dAiSJCEsrH5PXWYWZ+HtIyuw89x3ADh+ETV0PPIGYO+ln5FwbjciDF04HQgR1djWrVuRk5OD2bNnY8GCBZg7dy4SEhKg1+vx7rvvQqutv8/JV4sysTJ5LXQaLfoE9ay39yEi5WjU5S1/9Uv4xc8VP/i5o0dABMZ1eBY6rU50LCJSuP+e32306NGVXwcEBNxzKpG6kh4fB6vjdXzczAsurl6YHTkF/q6Gen9fIhKv0Z42zV/9En73cMIPfu6ILCzDkF/2srgRkSqkx8fBH0Z8FtwELpKEoaevs7gRNSKN9sibTgu0LTXh2awbCC+qgKbR1lgiUptAKRc6DfBcdiF8TFZ4mGt3ZyoRqVOjrSxW6eYPH1FUAe1/lomI1CBL6wcACCs1w8siVS4TUePQaMubz8sbYbECsgxYrDeXiYjUoMOUd5ChMcAqa5ChMaDDlHdERyIiO2q0p00BFjYiUq9bhc1bcA4isr9Ge+SNiIiISI1Y3oiIiIhUhOWNiIiISEVY3oiIiIhUhOWNiIiISEVY3oiIiIhUpEFOFaLVakRHuCel57sXtWZnbvuzV3Y176Pq1NXPpNR9w1w1w1w1o6Zctc2qkWWZz1UhIiIiUgmeNiUiIiJSEZY3IiIiIhVheSMiIiJSEZY3IiIiIhVheSMiIiJSEZY3IiIiIhVheSMiIiJSEZY3IiIiIhVheSMiIiJSEZY3O1m2bBlWrlx513UmkwlxcXGIjo7G8OHDce7cOTunu1NmZiZeeOEFDBw4ENOnT0dJSckd22RkZCAyMhLDhg3DsGHDMHHiRAFJ/9+uXbswaNAgDBgwAJs3b75jfXp6OkaMGIGoqCgsWLAAFotFQMo73S/3qlWr0L9//8r9fLdtRCkuLkZMTAyuXr16xzql7u+G7l5jjcjfWaWNgUob45Q6fil5fFLq+HOvXHW2v2SqV4WFhfK8efPk8PBwecWKFXfdZt26dfKf//xnWZZl+dChQ/Izzzxjz4h3NWXKFPmbb76RZVmWV61aJf/973+/Y5vExMTK3KJlZWXJ/fv3lwsKCuSSkhJ5yJAh8tmzZ6tsM3jwYDkpKUmWZVmeN2+evHnzZhFRq7Al99SpU+Vjx44JSli95ORkOSYmRu7UqZN85cqVO9YrcX83ZLaMNSJ+Z5U6BippjFPq+KXk8Ump48/9ctXV/uKRt3r2448/IjQ0FOPHj692m3379mHo0KEAgIceegj5+fnIzMy0V8Q7mM1mHD58GFFRUQCAESNGIDEx8Y7tUlNTcebMGQwbNgzjxo3D6dOn7R210oEDB9C7d294eXnB1dUVUVFRVTJnZGSgvLwcERERAKr/meztfrkBIC0tDWvWrMGQIUOwaNEiVFRUCEpb1bZt27Bw4UL4+/vfsU6p+7shs2WsEfE7q8QxUGljnFLHLyWPT0odf+6VC6i7/cXyVs9iY2MxZcoU6HS6arfJycmBwWCoXDYYDMjKyrJHvLsqKCiAu7s7HBwcKvNkZ2ffsZ1er8fQoUOxY8cOTJw4ETNmzIDJZLJ3XAB37kN/f/8qme+2j+/2M9nb/XKXlJSgQ4cOiIuLw44dO1BYWIjVq1eLiHqHxYsXo0ePHnddp9T93ZDZMtaI+J1V4hiotDFOqeOXkscnpY4/98pVl/vL4UFC0v/bvXs3lixZUuW1sLAwbNy48b7fK8syNBpNlWWt1j69+m65W7RoUSUPgDuWAWDmzJmVX/fr1w/vvvsuzp8/j/bt29dP2HuQJOmOfXj78v3Wi3K/XG5ubli7dm3l8oQJEzB//nzMmTPHrjlrSqn7uyF4kLGmPn9nlToGqmGMU+r4pdbxSanjT13uL5a3OhIdHY3o6OhafW9AQABycnIQEhICAMjNza32kGtdu1tus9mMXr16wWq1QqfTwWg03jXPpk2bEBMTA29vbwA3f0FufZK1t8DAQBw5cqRy+b8zBwYGwmg0Vi7bcx/fy/1yZ2Zm4sCBA3j66acBiN3HNaHU/d0QPMhYU5+/s0odA9Uwxil1/FLr+KTU8acu9xdPmypAv379kJCQAAA4cuQI9Ho9mjZtKiyPo6MjevToge+++w4AsHPnTvTt2/eO7Q4fPozt27cDAA4dOgRJkhAWFmbXrLf06dMHv/76K/Lz81FWVoa9e/dWyRwcHAy9Xo+jR48CABISEu76M9nb/XI7OzvjnXfewZUrVyDLMjZv3oynnnpKYGLbKHV/N3ZK+p29nb3HQKWNcUodv9Q6Pil1/KnT/fXAtzyQTVasWFHlTqstW7bIy5Ytk2VZlsvLy+XXX39dHjRokBwbGyunpaWJilnp6tWr8pgxY+To6Gh5woQJ8vXr12VZrpo7KytLfumll+TBgwfLI0aMkNPT00VGlr/++mt58ODB8oABA+T4+HhZlmV50qRJckpKiizLspyeni6PHDlSjoqKkl977TW5oqJCZNxK98udmJhYuX7u3LmKyX1L//79K++qUsP+bujuNdaI/J1V2hiotDFOqeOX0scnpY4/1eWqq/2lkWVZrttuSURERET1hadNiYiIiFSE5Y2IiIhIRVjeiIiIiFSE5Y2IiIhIRVjeiIiIiFSE5Y0U78SJE+jevTtSU1MrX8vPz8eTTz6Jffv2Abg52eGf/vQnrF+/XlBKIqI73W/8SkhIwNChQzFs2DA899xzVbYjqg7LGylep06dEBcXh9mzZ+PGjRuwWCx49dVXMXLkSDz22GM4d+4cXnzxRezZs0d0VCKiKu41foWEhOCdd97BunXrkJCQgOnTp1faaW1gAAAC0UlEQVR5JBdRdTjPG6lGXFwcSktLERISgitXrmDlypXQaDRYtGgRIiMjsX//frRp0wYTJ04EAJhMJixduhSHDx+G1WpFx44d8cYbb8Dd3V3wT0JEjc3dxq+MjAz8/vvveOyxxwAAeXl56NevH44dOwYnJyd8+OGH2Lt3LyRJQnBwMBYuXIiAgACxPwgpAssbqUZpaSliY2NhsViwa9cuuLm5VVk/d+7cKuVt1apVKCkpweuvvw6NRoP33nsPhYWF+Mtf/iIgPRE1Zvcbv2RZRlxcHEwmE1asWIGdO3fil19+wTvvvAMHBwd88cUX+OGHH6o82JwaL/FPkCWy0YULF1BSUoKKigqcOHECPXv2vOf2+/btQ1FREQ4cOADg5sOofX197RGViKiKe41fpaWlmDt3LrKysrBu3ToAwM8//4zU1FSMHDkSACBJEsrKyoRkJ+VheSNVyM/Px8yZMzFv3jxUVFTgtddew44dO2AwGKr9HkmSMH/+fPTr1w8AKgdOIiJ7utf4lZmZiWnTpqFVq1b49NNP4ezsDODm+DVp0iQ8//zzAG5eBnLjxg2RPwYpCG9YIMWzWq2YM2cO+vfvj5iYGIwcORJ/+MMfMGfOHFit1mq/79FHH8XmzZthMpkgSRL+/Oc/47333rNjciJq7O41fhUVFWHs2LEYMGAA3n///criBtwcv7Zv347i4mIAwPLly/H666+L+jFIYXjNGynekiVLkJSUhM8++wxOTk4AgLKyMowaNQp9+/ZFXFwcgDuveSsvL8fbb7+NQ4cOwWq1okOHDvjb3/7GGxaIyG7uNX5duHABVqsVbdu2rfI9GzduhKenJ1atWoXExERoNBoEBQVh8eLFvGGBALC8EREREakKT5sSERERqQjLGxEREZGKsLwRERERqQjLGxEREZGKsLwRERERqQjLGxEREZGKsLwRERERqQjLGxEREZGK/B8ofs49c/SO+QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"X1e\"] = df[\"X1e\"].apply(lambda i: (i + 1) % 3)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1eX2e
X1e1.000000-0.777554
X2e-0.7775541.000000
\n", + "
" ], - "source": [ - "ax = pairplot_cross_val(df[[\"X1e\", \"X2e\"]], model=DecisionTreeRegressor)\n", - "ax;" + "text/plain": [ + " X1e X2e\n", + "X1e 1.000000 -0.777554\n", + "X2e -0.777554 1.000000" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et sur un jeu de donn\u00e9es plus complet." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.98
10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.14
20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.03
30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.94
40.069050.02.180.00.4587.14754.26.06223.0222.018.7396.905.33
\n", - "
" - ], - "text/plain": [ - " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", - "0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \n", - "1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \n", - "2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \n", - "3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \n", - "4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \n", - "\n", - " PTRATIO B LSTAT \n", - "0 15.3 396.90 4.98 \n", - "1 17.8 396.90 9.14 \n", - "2 17.8 392.83 4.03 \n", - "3 18.7 394.63 2.94 \n", - "4 18.7 396.90 5.33 " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.select_dtypes(exclude=[\"object\"]).corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La corrélation linéaire sur des variables catégorielles n'a pas de sens. Essayons avec un arbre de décision. C'est le modèle adéquat pour ce type de valeur discrètes :" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1eX2e
X1e1.00.786412
X2e1.01.000000
\n", + "
" ], - "source": [ - "from sklearn.datasets import load_boston\n", - "df = load_boston()\n", - "df = pandas.DataFrame(df.data, columns=df.feature_names)\n", - "df.head()" + "text/plain": [ + " X1e X2e\n", + "X1e 1.0 0.786412\n", + "X2e 1.0 1.000000" ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
CRIM1.000000-0.2004690.406583-0.0558920.420972-0.2192470.352734-0.3796700.6255050.5827640.289946-0.3850640.455621
ZN-0.2004691.000000-0.533828-0.042697-0.5166040.311991-0.5695370.664408-0.311948-0.314563-0.3916790.175520-0.412995
INDUS0.406583-0.5338281.0000000.0629380.763651-0.3916760.644779-0.7080270.5951290.7207600.383248-0.3569770.603800
CHAS-0.055892-0.0426970.0629381.0000000.0912030.0912510.086518-0.099176-0.007368-0.035587-0.1215150.048788-0.053929
NOX0.420972-0.5166040.7636510.0912031.000000-0.3021880.731470-0.7692300.6114410.6680230.188933-0.3800510.590879
RM-0.2192470.311991-0.3916760.091251-0.3021881.000000-0.2402650.205246-0.209847-0.292048-0.3555010.128069-0.613808
AGE0.352734-0.5695370.6447790.0865180.731470-0.2402651.000000-0.7478810.4560220.5064560.261515-0.2735340.602339
DIS-0.3796700.664408-0.708027-0.099176-0.7692300.205246-0.7478811.000000-0.494588-0.534432-0.2324710.291512-0.496996
RAD0.625505-0.3119480.595129-0.0073680.611441-0.2098470.456022-0.4945881.0000000.9102280.464741-0.4444130.488676
TAX0.582764-0.3145630.720760-0.0355870.668023-0.2920480.506456-0.5344320.9102281.0000000.460853-0.4418080.543993
PTRATIO0.289946-0.3916790.383248-0.1215150.188933-0.3555010.261515-0.2324710.4647410.4608531.000000-0.1773830.374044
B-0.3850640.175520-0.3569770.048788-0.3800510.128069-0.2735340.291512-0.444413-0.441808-0.1773831.000000-0.366087
LSTAT0.455621-0.4129950.603800-0.0539290.590879-0.6138080.602339-0.4969960.4886760.5439930.374044-0.3660871.000000
\n", - "
" - ], - "text/plain": [ - " CRIM ZN INDUS CHAS NOX RM AGE \\\n", - "CRIM 1.000000 -0.200469 0.406583 -0.055892 0.420972 -0.219247 0.352734 \n", - "ZN -0.200469 1.000000 -0.533828 -0.042697 -0.516604 0.311991 -0.569537 \n", - "INDUS 0.406583 -0.533828 1.000000 0.062938 0.763651 -0.391676 0.644779 \n", - "CHAS -0.055892 -0.042697 0.062938 1.000000 0.091203 0.091251 0.086518 \n", - "NOX 0.420972 -0.516604 0.763651 0.091203 1.000000 -0.302188 0.731470 \n", - "RM -0.219247 0.311991 -0.391676 0.091251 -0.302188 1.000000 -0.240265 \n", - "AGE 0.352734 -0.569537 0.644779 0.086518 0.731470 -0.240265 1.000000 \n", - "DIS -0.379670 0.664408 -0.708027 -0.099176 -0.769230 0.205246 -0.747881 \n", - "RAD 0.625505 -0.311948 0.595129 -0.007368 0.611441 -0.209847 0.456022 \n", - "TAX 0.582764 -0.314563 0.720760 -0.035587 0.668023 -0.292048 0.506456 \n", - "PTRATIO 0.289946 -0.391679 0.383248 -0.121515 0.188933 -0.355501 0.261515 \n", - "B -0.385064 0.175520 -0.356977 0.048788 -0.380051 0.128069 -0.273534 \n", - "LSTAT 0.455621 -0.412995 0.603800 -0.053929 0.590879 -0.613808 0.602339 \n", - "\n", - " DIS RAD TAX PTRATIO B LSTAT \n", - "CRIM -0.379670 0.625505 0.582764 0.289946 -0.385064 0.455621 \n", - "ZN 0.664408 -0.311948 -0.314563 -0.391679 0.175520 -0.412995 \n", - "INDUS -0.708027 0.595129 0.720760 0.383248 -0.356977 0.603800 \n", - "CHAS -0.099176 -0.007368 -0.035587 -0.121515 0.048788 -0.053929 \n", - "NOX -0.769230 0.611441 0.668023 0.188933 -0.380051 0.590879 \n", - "RM 0.205246 -0.209847 -0.292048 -0.355501 0.128069 -0.613808 \n", - "AGE -0.747881 0.456022 0.506456 0.261515 -0.273534 0.602339 \n", - "DIS 1.000000 -0.494588 -0.534432 -0.232471 0.291512 -0.496996 \n", - "RAD -0.494588 1.000000 0.910228 0.464741 -0.444413 0.488676 \n", - "TAX -0.534432 0.910228 1.000000 0.460853 -0.441808 0.543993 \n", - "PTRATIO -0.232471 0.464741 0.460853 1.000000 -0.177383 0.374044 \n", - "B 0.291512 -0.444413 -0.441808 -0.177383 1.000000 -0.366087 \n", - "LSTAT -0.496996 0.488676 0.543993 0.374044 -0.366087 1.000000 " - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cor = correlation_cross_val(df[[\"X1e\", \"X2e\"]], DecisionTreeRegressor)\n", + "cor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et si on permute le premier label :" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1eX2e
X1e1.00.828978
X2e1.01.000000
\n", + "
" ], - "source": [ - "df.corr()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On dessine les 5 premi\u00e8res variables. On voit que la variable CHAS est binaire." + "text/plain": [ + " X1e X2e\n", + "X1e 1.0 0.828978\n", + "X2e 1.0 1.000000" ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.pairplot(df[df.columns[:6]]);" + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"X1e\"] = df[\"X1e\"].apply(lambda i: (i + 1) % 3)\n", + "correlation_cross_val(df[[\"X1e\", \"X2e\"]], DecisionTreeRegressor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Même résultat qui s'interprète de la sorte :\n", + "\n", + "* La variable *X1e* se déduit de *X2e* (car *cor(X2e, X1e) = 1*).\n", + "* La variable *X2e* et fortement lié à *X2e*.\n", + "\n", + "La valeur numérique choisie pour représente la variable catégorielle n'a pas d'impact sur les résultats." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
CRIM0.9789870.1192190.3684090.0000000.5753610.0000000.1326040.1817860.9342630.7916310.0000000.0000000.000000
ZN0.2412390.9998320.6010170.1746230.5665980.1879680.5753790.7514640.3696650.3885120.5288910.2481160.447769
INDUS0.6037410.8953850.9999840.1893580.8980070.4535060.7496200.8886080.9968180.9883770.9297040.3902210.665031
CHAS0.1056020.1688730.0916471.0000000.0904030.0424940.0838610.0768510.0405790.0407660.1586530.1254770.310272
NOX0.6162740.8964780.9655940.1842430.9999760.3638520.8018080.9220330.9677680.9722930.9206790.6282760.665110
RM0.0000000.0000000.0000000.0000000.0000000.9991850.0000000.0000000.0000000.0000000.0000000.0000000.057636
AGE0.0000000.1126780.2824170.0000000.4836650.0000000.9999430.5070020.0000000.0000000.0000000.0000000.000000
DIS0.0000000.5471270.4757430.0000000.6774920.0000000.6758200.9995860.0000000.0000000.0000000.0000000.045766
RAD0.6436230.4477460.6714600.0922600.6780970.2811230.4900360.5431281.0000000.9248860.6188220.3581090.497215
TAX0.5985080.7080490.9267940.1940890.8514160.3153290.7132470.8225650.9927880.9999310.8717350.4660750.582098
PTRATIO0.3815030.5609230.8354490.0000000.7897910.4327550.5908050.7481060.9610270.9050850.9996820.3791150.477494
B0.0464010.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.9995260.000000
LSTAT0.0000000.0000000.0000000.0000000.0000000.1184260.0000000.0000000.0000000.0000000.0000000.0000000.999819
\n", - "
" - ], - "text/plain": [ - " CRIM ZN INDUS CHAS NOX RM AGE \\\n", - "CRIM 0.978987 0.119219 0.368409 0.000000 0.575361 0.000000 0.132604 \n", - "ZN 0.241239 0.999832 0.601017 0.174623 0.566598 0.187968 0.575379 \n", - "INDUS 0.603741 0.895385 0.999984 0.189358 0.898007 0.453506 0.749620 \n", - "CHAS 0.105602 0.168873 0.091647 1.000000 0.090403 0.042494 0.083861 \n", - "NOX 0.616274 0.896478 0.965594 0.184243 0.999976 0.363852 0.801808 \n", - "RM 0.000000 0.000000 0.000000 0.000000 0.000000 0.999185 0.000000 \n", - "AGE 0.000000 0.112678 0.282417 0.000000 0.483665 0.000000 0.999943 \n", - "DIS 0.000000 0.547127 0.475743 0.000000 0.677492 0.000000 0.675820 \n", - "RAD 0.643623 0.447746 0.671460 0.092260 0.678097 0.281123 0.490036 \n", - "TAX 0.598508 0.708049 0.926794 0.194089 0.851416 0.315329 0.713247 \n", - "PTRATIO 0.381503 0.560923 0.835449 0.000000 0.789791 0.432755 0.590805 \n", - "B 0.046401 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "LSTAT 0.000000 0.000000 0.000000 0.000000 0.000000 0.118426 0.000000 \n", - "\n", - " DIS RAD TAX PTRATIO B LSTAT \n", - "CRIM 0.181786 0.934263 0.791631 0.000000 0.000000 0.000000 \n", - "ZN 0.751464 0.369665 0.388512 0.528891 0.248116 0.447769 \n", - "INDUS 0.888608 0.996818 0.988377 0.929704 0.390221 0.665031 \n", - "CHAS 0.076851 0.040579 0.040766 0.158653 0.125477 0.310272 \n", - "NOX 0.922033 0.967768 0.972293 0.920679 0.628276 0.665110 \n", - "RM 0.000000 0.000000 0.000000 0.000000 0.000000 0.057636 \n", - "AGE 0.507002 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "DIS 0.999586 0.000000 0.000000 0.000000 0.000000 0.045766 \n", - "RAD 0.543128 1.000000 0.924886 0.618822 0.358109 0.497215 \n", - "TAX 0.822565 0.992788 0.999931 0.871735 0.466075 0.582098 \n", - "PTRATIO 0.748106 0.961027 0.905085 0.999682 0.379115 0.477494 \n", - "B 0.000000 0.000000 0.000000 0.000000 0.999526 0.000000 \n", - "LSTAT 0.000000 0.000000 0.000000 0.000000 0.000000 0.999819 " - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = pairplot_cross_val(df[[\"X1e\", \"X2e\"]], model=DecisionTreeRegressor)\n", + "ax;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et sur un jeu de données plus complet." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agesexbmibps1s2s3s4s5s6
00.0380760.0506800.0616960.021872-0.044223-0.034821-0.043401-0.0025920.019907-0.017646
1-0.001882-0.044642-0.051474-0.026328-0.008449-0.0191630.074412-0.039493-0.068332-0.092204
20.0852990.0506800.044451-0.005670-0.045599-0.034194-0.032356-0.0025920.002861-0.025930
3-0.089063-0.044642-0.011595-0.0366560.0121910.024991-0.0360380.0343090.022688-0.009362
40.005383-0.044642-0.0363850.0218720.0039350.0155960.008142-0.002592-0.031988-0.046641
\n", + "
" ], - "source": [ - "correlation_cross_val(df, DecisionTreeRegressor)" + "text/plain": [ + " age sex bmi bp s1 s2 s3 \\\n", + "0 0.038076 0.050680 0.061696 0.021872 -0.044223 -0.034821 -0.043401 \n", + "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163 0.074412 \n", + "2 0.085299 0.050680 0.044451 -0.005670 -0.045599 -0.034194 -0.032356 \n", + "3 -0.089063 -0.044642 -0.011595 -0.036656 0.012191 0.024991 -0.036038 \n", + "4 0.005383 -0.044642 -0.036385 0.021872 0.003935 0.015596 0.008142 \n", + "\n", + " s4 s5 s6 \n", + "0 -0.002592 0.019907 -0.017646 \n", + "1 -0.039493 -0.068332 -0.092204 \n", + "2 -0.002592 0.002861 -0.025930 \n", + "3 0.034309 0.022688 -0.009362 \n", + "4 -0.002592 -0.031988 -0.046641 " ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.datasets import load_diabetes\n", + "\n", + "df = load_diabetes()\n", + "df = pandas.DataFrame(df.data, columns=df.feature_names)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agesexbmibps1s2s3s4s5s6
age1.0000000.1737370.1850850.3354280.2600610.219243-0.0751810.2038410.2707740.301731
sex0.1737371.0000000.0881610.2410100.0352770.142637-0.3790900.3321150.1499160.208133
bmi0.1850850.0881611.0000000.3954110.2497770.261170-0.3668110.4138070.4461570.388680
bp0.3354280.2410100.3954111.0000000.2424640.185548-0.1787620.2576500.3934800.390430
s10.2600610.0352770.2497770.2424641.0000000.8966630.0515190.5422070.5155030.325717
s20.2192430.1426370.2611700.1855480.8966631.000000-0.1964550.6598170.3183570.290600
s3-0.075181-0.379090-0.366811-0.1787620.051519-0.1964551.000000-0.738493-0.398577-0.273697
s40.2038410.3321150.4138070.2576500.5422070.659817-0.7384931.0000000.6178590.417212
s50.2707740.1499160.4461570.3934800.5155030.318357-0.3985770.6178591.0000000.464669
s60.3017310.2081330.3886800.3904300.3257170.290600-0.2736970.4172120.4646691.000000
\n", + "
" ], - "source": [ - "pairplot_cross_val(df[df.columns[:6]], model=DecisionTreeRegressor, figsize=(16,16));" + "text/plain": [ + " age sex bmi bp s1 s2 s3 \\\n", + "age 1.000000 0.173737 0.185085 0.335428 0.260061 0.219243 -0.075181 \n", + "sex 0.173737 1.000000 0.088161 0.241010 0.035277 0.142637 -0.379090 \n", + "bmi 0.185085 0.088161 1.000000 0.395411 0.249777 0.261170 -0.366811 \n", + "bp 0.335428 0.241010 0.395411 1.000000 0.242464 0.185548 -0.178762 \n", + "s1 0.260061 0.035277 0.249777 0.242464 1.000000 0.896663 0.051519 \n", + "s2 0.219243 0.142637 0.261170 0.185548 0.896663 1.000000 -0.196455 \n", + "s3 -0.075181 -0.379090 -0.366811 -0.178762 0.051519 -0.196455 1.000000 \n", + "s4 0.203841 0.332115 0.413807 0.257650 0.542207 0.659817 -0.738493 \n", + "s5 0.270774 0.149916 0.446157 0.393480 0.515503 0.318357 -0.398577 \n", + "s6 0.301731 0.208133 0.388680 0.390430 0.325717 0.290600 -0.273697 \n", + "\n", + " s4 s5 s6 \n", + "age 0.203841 0.270774 0.301731 \n", + "sex 0.332115 0.149916 0.208133 \n", + "bmi 0.413807 0.446157 0.388680 \n", + "bp 0.257650 0.393480 0.390430 \n", + "s1 0.542207 0.515503 0.325717 \n", + "s2 0.659817 0.318357 0.290600 \n", + "s3 -0.738493 -0.398577 -0.273697 \n", + "s4 1.000000 0.617859 0.417212 \n", + "s5 0.617859 1.000000 0.464669 \n", + "s6 0.417212 0.464669 1.000000 " ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On regarde en pariculier les variables TAX, RAD, PTRATIO." + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.corr()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On dessine les 5 premières variables. On voit que la variable CHAS est binaire." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(df[df.columns[:6]]);" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agesexbmibps1s2s3s4s5s6
age0.9997960.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
sex0.1649761.0000000.1646440.2504770.0457770.1113870.3694740.2527410.1820650.180895
bmi0.0000000.0000000.9989160.0000000.0000000.0000000.0000000.0000000.0000000.000000
bp0.0000000.0000000.0000000.9995040.0000000.0000000.0000000.0000000.0326320.000000
s10.0000000.0000000.0000000.0000000.9996360.8124380.0000000.0000000.0000000.000000
s20.0000000.0000000.0000000.0000000.7687080.9979990.0000000.0000000.0000000.000000
s30.0000000.0794950.0000000.0000000.0000000.0000000.9995420.7197990.1577110.000000
s40.0538030.1045780.2857130.0000000.3755670.6136880.7286830.9986550.5543120.285438
s50.0000000.0000000.0000000.0000000.0000000.0000000.0000000.1271210.9993320.000000
s60.0000000.0000000.0000000.0539450.0133720.0000000.0000000.0000000.0471380.999331
\n", + "
" ], - "source": [ - "sns.pairplot(df[[\"RAD\", \"TAX\", \"PTRATIO\"]]);" + "text/plain": [ + " age sex bmi bp s1 s2 s3 \\\n", + "age 0.999796 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "sex 0.164976 1.000000 0.164644 0.250477 0.045777 0.111387 0.369474 \n", + "bmi 0.000000 0.000000 0.998916 0.000000 0.000000 0.000000 0.000000 \n", + "bp 0.000000 0.000000 0.000000 0.999504 0.000000 0.000000 0.000000 \n", + "s1 0.000000 0.000000 0.000000 0.000000 0.999636 0.812438 0.000000 \n", + "s2 0.000000 0.000000 0.000000 0.000000 0.768708 0.997999 0.000000 \n", + "s3 0.000000 0.079495 0.000000 0.000000 0.000000 0.000000 0.999542 \n", + "s4 0.053803 0.104578 0.285713 0.000000 0.375567 0.613688 0.728683 \n", + "s5 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "s6 0.000000 0.000000 0.000000 0.053945 0.013372 0.000000 0.000000 \n", + "\n", + " s4 s5 s6 \n", + "age 0.000000 0.000000 0.000000 \n", + "sex 0.252741 0.182065 0.180895 \n", + "bmi 0.000000 0.000000 0.000000 \n", + "bp 0.000000 0.032632 0.000000 \n", + "s1 0.000000 0.000000 0.000000 \n", + "s2 0.000000 0.000000 0.000000 \n", + "s3 0.719799 0.157711 0.000000 \n", + "s4 0.998655 0.554312 0.285438 \n", + "s5 0.127121 0.999332 0.000000 \n", + "s6 0.000000 0.047138 0.999331 " ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RADTAXPTRATIO
RAD1.0000000.9102280.464741
TAX0.9102281.0000000.460853
PTRATIO0.4647410.4608531.000000
\n", - "
" - ], - "text/plain": [ - " RAD TAX PTRATIO\n", - "RAD 1.000000 0.910228 0.464741\n", - "TAX 0.910228 1.000000 0.460853\n", - "PTRATIO 0.464741 0.460853 1.000000" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[[\"RAD\", \"TAX\", \"PTRATIO\"]].corr()" + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correlation_cross_val(df, DecisionTreeRegressor)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pairplot_cross_val(df[[\"RAD\", \"TAX\", \"PTRATIO\"]], model=DecisionTreeRegressor);" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pairplot_cross_val(df[df.columns[:6]], model=DecisionTreeRegressor, figsize=(16, 16));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On regarde en pariculier les variables TAX, RAD, PTRATIO." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
RADTAXPTRATIO
RAD1.0000000.9213550.610248
TAX0.9908800.9999420.886053
PTRATIO0.9397020.9151630.999547
\n", - "
" - ], - "text/plain": [ - " RAD TAX PTRATIO\n", - "RAD 1.000000 0.921355 0.610248\n", - "TAX 0.990880 0.999942 0.886053\n", - "PTRATIO 0.939702 0.915163 0.999547" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(df[[\"s1\", \"s2\", \"bmi\"]]);" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
s1s2bmi
s11.0000000.8966630.249777
s20.8966631.0000000.261170
bmi0.2497770.2611701.000000
\n", + "
" ], - "source": [ - "correlation_cross_val(df[[\"RAD\", \"TAX\", \"PTRATIO\"]], DecisionTreeRegressor)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les variables sont toutes trois li\u00e9es de fa\u00e7on non lin\u00e9aire." + "text/plain": [ + " s1 s2 bmi\n", + "s1 1.000000 0.896663 0.249777\n", + "s2 0.896663 1.000000 0.261170\n", + "bmi 0.249777 0.261170 1.000000" ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Maximal information coefficient\n", - "\n", - "Cette approche est plut\u00f4t pragmatique mais peut se r\u00e9v\u00e9ler co\u00fbteuse en terme de calculs. Elle permet aussi de comprendre qu'un coefficient de corr\u00e9lation d\u00e9pend des hypoth\u00e8ses qu'on choisi pour les donn\u00e9es. On peut toujours construire un coefficient de corr\u00e9lation qui soit \u00e9gal \u00e0 1 mais il correspond \u00e0 toujours \u00e0 un ph\u00e9nom\u00e8ne qu'on souhaite \u00e9tudier. La corr\u00e9lation lin\u00e9aire recherche des relations lin\u00e9aires. On peut chercher une relation polynomiale. Les arbres de d\u00e9cision recherche une corr\u00e9lation construite \u00e0 partir de fonction en escalier. Plus la relation a de degr\u00e9 de libert\u00e9, plus le coefficient a de chance de tendre vers 1, moins il a de chance d'\u00eatre aussi \u00e9lev\u00e9 sur de nouvelles donn\u00e9es.\n", - "\n", - "Cela permet n\u00e9anmoins de mieux comprendre les avantages et les inconv\u00e9nients de m\u00e9triques du type [MIC](https://en.wikipedia.org/wiki/Maximal_information_coefficient) ou *Maximal information coefficient*. Plus de d\u00e9tails sont disponibles dans cet article : [Equitability, mutual information, and the maximal information coefficient](https://arxiv.org/abs/1301.7745v1). Le module [minepy](http://minepy.readthedocs.io/en/latest/python.html) impl\u00e9mente cette m\u00e9trique ainsi que d'autres qui poursuivent le m\u00eame objectif. L'information mutuelle est d\u00e9finie comme ceci pour deux variables discr\u00e8tes :\n", - "\n", - "$$MI(X,Y) = \\sum_{x\\in\\mathcal{X}}\\sum_{y\\in\\mathcal{Y}}p(x,y)\\ln_2\\frac{p(x,y)}{p(x)p(y)}$$\n", - "\n", - "La fonction $p(x,y)$ d\u00e9finit la distribution conjointe des deux variables, $p(x)$, $p(y)$ les deux probabilit\u00e9s marginales. Il existe une extension pour les variables continues :\n", - "\n", - "$$MIC(X,Y) = \\int_{x\\in\\mathcal{X}}\\in_{y\\in\\mathcal{Y}}p(x,y)\\ln_2\\frac{p(x,y)}{p(x)p(y)}dxdy$$\n", - "\n", - "Une fa\u00e7on de calculer une approximation du coefficient $MIC(x,y)$ est de discr\u00e9tiser les deux variables $X$ et $Y$ ce qu'on fait en appliquant un algorithme similaire \u00e0 celui utilis\u00e9 pour construire un arbre de d\u00e9cision \u00e0 ceci pr\u00e8s que qu'il n'y a qu'une seule variable et que la variable \u00e0 pr\u00e9dire est elle-m\u00eame.\n", - "\n", - "L'information mutuelle est inspir\u00e9 de la distance de [Kullback-Leiber](https://fr.wikipedia.org/wiki/Divergence_de_Kullback-Leibler) qui est une distance entre deux probabilit\u00e9s qui sont ici la disribution du couple $(X,Y)$ et la distribution que ce couple aurait si les deux variables \u00e9taient ind\u00e9pendantes, c'est \u00e0 dire le produit de leur distribution.\n", - "\n", - "Je reproduis ici le code de l'exemple propos\u00e9 par la librairie [minepy](http://minepy.readthedocs.io/en/latest/python.html#second-example) et j'y ajoute la corr\u00e9lation propos\u00e9e dans ce notebook ``DT`` pour *Decision Tree*." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[[\"s1\", \"s2\", \"bmi\"]].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pairplot_cross_val(df[[\"s1\", \"s2\", \"bmi\"]], model=DecisionTreeRegressor);" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
s1s2bmi
s10.9995500.8095050.00000
s20.7831780.9975550.00000
bmi0.0000000.0000000.99898
\n", + "
" ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from minepy import MINE\n", - "\n", - "\n", - "rs = np.random.RandomState(seed=0)\n", - "\n", - "def mysubplot(x, y, numRows, numCols, plotNum,\n", - " xlim=(-4, 4), ylim=(-4, 4)):\n", - "\n", - " r = np.around(np.corrcoef(x, y)[0, 1], 1)\n", - " mine = MINE(alpha=0.6, c=15, est=\"mic_approx\")\n", - " mine.compute_score(x, y)\n", - " mic = np.around(mine.mic(), 1)\n", - " \n", - " # d\u00e9but ajout\n", - " df = pandas.DataFrame(dict(x=x, y=y))\n", - " cor = correlation_cross_val(df, DecisionTreeRegressor)\n", - " dt = max(cor.iloc[1,0], cor.iloc[0,1])\n", - " \n", - " ax = plt.subplot(numRows, numCols, plotNum,\n", - " xlim=xlim, ylim=ylim)\n", - " ax.set_title('Pearson r=%.1f\\nMIC=%.1f\\nDT=%.1f' % (r, mic, dt),fontsize=10)\n", - " ax.set_frame_on(False)\n", - " ax.axes.get_xaxis().set_visible(False)\n", - " ax.axes.get_yaxis().set_visible(False)\n", - " ax.plot(x, y, ',')\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " return ax\n", - "\n", - "def rotation(xy, t):\n", - " return np.dot(xy, [[np.cos(t), -np.sin(t)], [np.sin(t), np.cos(t)]])\n", - "\n", - "def mvnormal(n=1000):\n", - " cors = [1.0, 0.8, 0.4, 0.0, -0.4, -0.8, -1.0]\n", - " for i, cor in enumerate(cors):\n", - " cov = [[1, cor],[cor, 1]]\n", - " xy = rs.multivariate_normal([0, 0], cov, n)\n", - " mysubplot(xy[:, 0], xy[:, 1], 3, 7, i+1)\n", - "\n", - "def rotnormal(n=1000):\n", - " ts = [0, np.pi/12, np.pi/6, np.pi/4, np.pi/2-np.pi/6,\n", - " np.pi/2-np.pi/12, np.pi/2]\n", - " cov = [[1, 1],[1, 1]]\n", - " xy = rs.multivariate_normal([0, 0], cov, n)\n", - " for i, t in enumerate(ts):\n", - " xy_r = rotation(xy, t)\n", - " mysubplot(xy_r[:, 0], xy_r[:, 1], 3, 7, i+8)\n", - "\n", - "def others(n=1000):\n", - " x = rs.uniform(-1, 1, n)\n", - " y = 4*(x**2-0.5)**2 + rs.uniform(-1, 1, n)/3\n", - " mysubplot(x, y, 3, 7, 15, (-1, 1), (-1/3, 1+1/3))\n", - "\n", - " y = rs.uniform(-1, 1, n)\n", - " xy = np.concatenate((x.reshape(-1, 1), y.reshape(-1, 1)), axis=1)\n", - " xy = rotation(xy, -np.pi/8)\n", - " lim = np.sqrt(2+np.sqrt(2)) / np.sqrt(2)\n", - " mysubplot(xy[:, 0], xy[:, 1], 3, 7, 16, (-lim, lim), (-lim, lim))\n", - "\n", - " xy = rotation(xy, -np.pi/8)\n", - " lim = np.sqrt(2)\n", - " mysubplot(xy[:, 0], xy[:, 1], 3, 7, 17, (-lim, lim), (-lim, lim))\n", - "\n", - " y = 2*x**2 + rs.uniform(-1, 1, n)\n", - " mysubplot(x, y, 3, 7, 18, (-1, 1), (-1, 3))\n", - "\n", - " y = (x**2 + rs.uniform(0, 0.5, n)) * \\\n", - " np.array([-1, 1])[rs.randint(0, 1, size=n)]\n", - " mysubplot(x, y, 3, 7, 19, (-1.5, 1.5), (-1.5, 1.5))\n", - "\n", - " y = np.cos(x * np.pi) + rs.uniform(0, 1/8, n)\n", - " x = np.sin(x * np.pi) + rs.uniform(0, 1/8, n)\n", - " mysubplot(x, y, 3, 7, 20, (-1.5, 1.5), (-1.5, 1.5))\n", - "\n", - " xy1 = np.random.multivariate_normal([3, 3], [[1, 0], [0, 1]], int(n/4))\n", - " xy2 = np.random.multivariate_normal([-3, 3], [[1, 0], [0, 1]], int(n/4))\n", - " xy3 = np.random.multivariate_normal([-3, -3], [[1, 0], [0, 1]], int(n/4))\n", - " xy4 = np.random.multivariate_normal([3, -3], [[1, 0], [0, 1]], int(n/4))\n", - " xy = np.concatenate((xy1, xy2, xy3, xy4), axis=0)\n", - " mysubplot(xy[:, 0], xy[:, 1], 3, 7, 21, (-7, 7), (-7, 7))\n", - "\n", - "plt.figure(figsize=(14,7))\n", - "mvnormal(n=800)\n", - "rotnormal(n=200)\n", - "others(n=800)\n", - "# plt.tight_layout()\n", - "# plt.show()" + "text/plain": [ + " s1 s2 bmi\n", + "s1 0.999550 0.809505 0.00000\n", + "s2 0.783178 0.997555 0.00000\n", + "bmi 0.000000 0.000000 0.99898" ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" + ], + "source": [ + "correlation_cross_val(df[[\"s1\", \"s2\", \"bmi\"]], DecisionTreeRegressor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les variables sont toutes trois liées de façon non linéaire." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Maximal information coefficient\n", + "\n", + "Cette approche est plutôt pragmatique mais peut se révéler coûteuse en terme de calculs. Elle permet aussi de comprendre qu'un coefficient de corrélation dépend des hypothèses qu'on choisi pour les données. On peut toujours construire un coefficient de corrélation qui soit égal à 1 mais il correspond à toujours à un phénomène qu'on souhaite étudier. La corrélation linéaire recherche des relations linéaires. On peut chercher une relation polynomiale. Les arbres de décision recherche une corrélation construite à partir de fonction en escalier. Plus la relation a de degré de liberté, plus le coefficient a de chance de tendre vers 1, moins il a de chance d'être aussi élevé sur de nouvelles données.\n", + "\n", + "Cela permet néanmoins de mieux comprendre les avantages et les inconvénients de métriques du type [MIC](https://en.wikipedia.org/wiki/Maximal_information_coefficient) ou *Maximal information coefficient*. Plus de détails sont disponibles dans cet article : [Equitability, mutual information, and the maximal information coefficient](https://arxiv.org/abs/1301.7745v1). Le module [minepy](http://minepy.readthedocs.io/en/latest/python.html) implémente cette métrique ainsi que d'autres qui poursuivent le même objectif. L'information mutuelle est définie comme ceci pour deux variables discrètes :\n", + "\n", + "$$MI(X,Y) = \\sum_{x\\in\\mathcal{X}}\\sum_{y\\in\\mathcal{Y}}p(x,y)\\ln_2\\frac{p(x,y)}{p(x)p(y)}$$\n", + "\n", + "La fonction $p(x,y)$ définit la distribution conjointe des deux variables, $p(x)$, $p(y)$ les deux probabilités marginales. Il existe une extension pour les variables continues :\n", + "\n", + "$$MIC(X,Y) = \\int_{x\\in\\mathcal{X}}\\in_{y\\in\\mathcal{Y}}p(x,y)\\ln_2\\frac{p(x,y)}{p(x)p(y)}dxdy$$\n", + "\n", + "Une façon de calculer une approximation du coefficient $MIC(x,y)$ est de discrétiser les deux variables $X$ et $Y$ ce qu'on fait en appliquant un algorithme similaire à celui utilisé pour construire un arbre de décision à ceci près que qu'il n'y a qu'une seule variable et que la variable à prédire est elle-même.\n", + "\n", + "L'information mutuelle est inspiré de la distance de [Kullback-Leiber](https://fr.wikipedia.org/wiki/Divergence_de_Kullback-Leibler) qui est une distance entre deux probabilités qui sont ici la disribution du couple $(X,Y)$ et la distribution que ce couple aurait si les deux variables étaient indépendantes, c'est à dire le produit de leur distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "rs = np.random.RandomState(seed=0)\n", + "\n", + "\n", + "def mysubplot(x, y, numRows, numCols, plotNum, xlim=(-4, 4), ylim=(-4, 4)):\n", + " r = np.around(np.corrcoef(x, y)[0, 1], 1)\n", + "\n", + " # début ajout\n", + " df = pandas.DataFrame(dict(x=x, y=y))\n", + " cor = correlation_cross_val(df, DecisionTreeRegressor)\n", + " dt = max(cor.iloc[1, 0], cor.iloc[0, 1])\n", + "\n", + " ax = plt.subplot(numRows, numCols, plotNum, xlim=xlim, ylim=ylim)\n", + " ax.set_title(\"Pearson r=%.1f\\nDT=%.1f\" % (r, dt), fontsize=10)\n", + " ax.set_frame_on(False)\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)\n", + " ax.plot(x, y, \",\")\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " return ax\n", + "\n", + "\n", + "def rotation(xy, t):\n", + " return np.dot(xy, [[np.cos(t), -np.sin(t)], [np.sin(t), np.cos(t)]])\n", + "\n", + "\n", + "def mvnormal(n=1000):\n", + " cors = [1.0, 0.8, 0.4, 0.0, -0.4, -0.8, -1.0]\n", + " for i, cor in enumerate(cors):\n", + " cov = [[1, cor], [cor, 1]]\n", + " xy = rs.multivariate_normal([0, 0], cov, n)\n", + " mysubplot(xy[:, 0], xy[:, 1], 3, 7, i + 1)\n", + "\n", + "\n", + "def rotnormal(n=1000):\n", + " ts = [\n", + " 0,\n", + " np.pi / 12,\n", + " np.pi / 6,\n", + " np.pi / 4,\n", + " np.pi / 2 - np.pi / 6,\n", + " np.pi / 2 - np.pi / 12,\n", + " np.pi / 2,\n", + " ]\n", + " cov = [[1, 1], [1, 1]]\n", + " xy = rs.multivariate_normal([0, 0], cov, n)\n", + " for i, t in enumerate(ts):\n", + " xy_r = rotation(xy, t)\n", + " mysubplot(xy_r[:, 0], xy_r[:, 1], 3, 7, i + 8)\n", + "\n", + "\n", + "def others(n=1000):\n", + " x = rs.uniform(-1, 1, n)\n", + " y = 4 * (x**2 - 0.5) ** 2 + rs.uniform(-1, 1, n) / 3\n", + " mysubplot(x, y, 3, 7, 15, (-1, 1), (-1 / 3, 1 + 1 / 3))\n", + "\n", + " y = rs.uniform(-1, 1, n)\n", + " xy = np.concatenate((x.reshape(-1, 1), y.reshape(-1, 1)), axis=1)\n", + " xy = rotation(xy, -np.pi / 8)\n", + " lim = np.sqrt(2 + np.sqrt(2)) / np.sqrt(2)\n", + " mysubplot(xy[:, 0], xy[:, 1], 3, 7, 16, (-lim, lim), (-lim, lim))\n", + "\n", + " xy = rotation(xy, -np.pi / 8)\n", + " lim = np.sqrt(2)\n", + " mysubplot(xy[:, 0], xy[:, 1], 3, 7, 17, (-lim, lim), (-lim, lim))\n", + "\n", + " y = 2 * x**2 + rs.uniform(-1, 1, n)\n", + " mysubplot(x, y, 3, 7, 18, (-1, 1), (-1, 3))\n", + "\n", + " y = (x**2 + rs.uniform(0, 0.5, n)) * np.array([-1, 1])[rs.randint(0, 1, size=n)]\n", + " mysubplot(x, y, 3, 7, 19, (-1.5, 1.5), (-1.5, 1.5))\n", + "\n", + " y = np.cos(x * np.pi) + rs.uniform(0, 1 / 8, n)\n", + " x = np.sin(x * np.pi) + rs.uniform(0, 1 / 8, n)\n", + " mysubplot(x, y, 3, 7, 20, (-1.5, 1.5), (-1.5, 1.5))\n", + "\n", + " xy1 = np.random.multivariate_normal([3, 3], [[1, 0], [0, 1]], int(n / 4))\n", + " xy2 = np.random.multivariate_normal([-3, 3], [[1, 0], [0, 1]], int(n / 4))\n", + " xy3 = np.random.multivariate_normal([-3, -3], [[1, 0], [0, 1]], int(n / 4))\n", + " xy4 = np.random.multivariate_normal([3, -3], [[1, 0], [0, 1]], int(n / 4))\n", + " xy = np.concatenate((xy1, xy2, xy3, xy4), axis=0)\n", + " mysubplot(xy[:, 0], xy[:, 1], 3, 7, 21, (-7, 7), (-7, 7))\n", + "\n", + "\n", + "plt.figure(figsize=(14, 7))\n", + "mvnormal(n=800)\n", + "rotnormal(n=200)\n", + "others(n=800)\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/dsgarden/discret_gradient.ipynb b/_doc/notebooks/dsgarden/discret_gradient.ipynb index 73877f9a..2ed9beb8 100644 --- a/_doc/notebooks/dsgarden/discret_gradient.ipynb +++ b/_doc/notebooks/dsgarden/discret_gradient.ipynb @@ -1,2342 +1,3913 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Le gradient et le discret\n", - "\n", - "Les m\u00e9thodes d'optimisation \u00e0 base de gradient s'appuie sur une fonction d'erreur d\u00e9rivable qu'on devrait appliquer de pr\u00e9f\u00e9rence sur des variables al\u00e9atoires r\u00e9elles. Ce notebook explore quelques id\u00e9es." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Un petit probl\u00e8me simple\n", - "\n", - "On utilise le jeu de donn\u00e9es *iris* disponible dans [scikit-learn](http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import datasets\n", - "\n", - "iris = datasets.load_iris()\n", - "X = iris.data[:, :2] # we only take the first two features.\n", - "Y = iris.target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On cale une r\u00e9gression logistique. On ne distingue pas apprentissage et test car ce n'est pas le propos de ce notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr',\n", - " n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n", - " tol=0.0001, verbose=0, warm_start=False)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "clf = LogisticRegression(multi_class=\"ovr\", solver=\"liblinear\")\n", - "clf.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Puis on calcule la matrice de confusion." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[49, 1, 0],\n", - " [ 2, 21, 27],\n", - " [ 1, 4, 45]], dtype=int64)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "pred = clf.predict(X)\n", - "confusion_matrix(Y, pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Multiplication des observations" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Le gradient et le discret\n", + "\n", + "Les méthodes d'optimisation à base de gradient s'appuie sur une fonction d'erreur dérivable qu'on devrait appliquer de préférence sur des variables aléatoires réelles. Ce notebook explore quelques idées." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Un petit problème simple\n", + "\n", + "On utilise le jeu de données *iris* disponible dans [scikit-learn](http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "\n", + "iris = datasets.load_iris()\n", + "X = iris.data[:, :2] # we only take the first two features.\n", + "Y = iris.target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On cale une régression logistique. On ne distingue pas apprentissage et test car ce n'est pas le propos de ce notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le param\u00e8tre ``multi_class='ovr'`` stipule que le mod\u00e8le cache en fait l'estimation de 3 r\u00e9gressions logistiques binaire. Essayons de n'en faire qu'une seule en ajouter le label ``Y`` aux variables. Soit un couple $(X_i \\in \\mathbb{R^d}, Y_i \\in \\mathbb{N})$ qui correspond \u00e0 une observation pour un probl\u00e8me multi-classe. Comme il y a $C$ classes, on multiplie cette ligne par le nombre de classes $C$ pour obtenir :\n", - "\n", - "$$\\forall c \\in \\mathbb{[}1, ..., C\\mathbb{]}, \\; \\left\\{ \\begin{array}{ll} X_i' = (X_{i,1}, ..., X_{i,d}, Y_{i,1}, ..., Y_{i,C}) \\\\ Y_i' = \\mathbb{1}_{Y_i = c} \\\\ Y_{i,k} = \\mathbb{1}_{c = k}\\end{array} \\right.$$\n", - "\n", - "Voyons ce que cela donne sur un exemple :" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/xadupre/vv/this/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2Y0Y1Y2Y'
05.13.51.00.00.01.0
15.13.50.01.00.00.0
25.13.50.00.01.00.0
\n", - "
" - ], - "text/plain": [ - " X1 X2 Y0 Y1 Y2 Y'\n", - "0 5.1 3.5 1.0 0.0 0.0 1.0\n", - "1 5.1 3.5 0.0 1.0 0.0 0.0\n", - "2 5.1 3.5 0.0 0.0 1.0 0.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
LogisticRegression(multi_class='ovr', solver='liblinear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], - "source": [ - "import numpy\n", - "import pandas\n", - "\n", - "def multiplie(X, Y, classes=None):\n", - " if classes is None:\n", - " classes = numpy.unique(Y)\n", - " XS = []\n", - " YS = []\n", - " for i in classes:\n", - " X2 = numpy.zeros((X.shape[0], 3))\n", - " X2[:,i] = 1\n", - " Yb = Y == i\n", - " XS.append(numpy.hstack([X, X2]))\n", - " Yb = Yb.reshape((len(Yb), 1))\n", - " YS.append(Yb)\n", - "\n", - " Xext = numpy.vstack(XS)\n", - " Yext = numpy.vstack(YS)\n", - " return Xext, Yext\n", - "\n", - "x, y = multiplie(X[:1,:], Y[:1], [0, 1, 2])\n", - "df = pandas.DataFrame(numpy.hstack([x, y]))\n", - "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", - "df" + "text/plain": [ + "LogisticRegression(multi_class='ovr', solver='liblinear')" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "clf = LogisticRegression(multi_class=\"ovr\", solver=\"liblinear\")\n", + "clf.fit(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Puis on calcule la matrice de confusion." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Trois colonnes ont \u00e9t\u00e9 ajout\u00e9es c\u00f4t\u00e9 $X$, la ligne a \u00e9t\u00e9 multipli\u00e9e 3 fois, la derni\u00e8re colonne est $Y$ qui ne vaut 1 que lorsque le 1 est au bon endroit dans une des colonnes ajout\u00e9es. Le probl\u00e8me de classification qui \u00e9t\u00e9 de pr\u00e9dire la bonne classe devient : est-ce la classe \u00e0 pr\u00e9dire est $k$ ? On applique cela sur toutes les lignes de la base et cela donne :" + "data": { + "text/plain": [ + "array([[49, 1, 0],\n", + " [ 2, 21, 27],\n", + " [ 1, 4, 45]])" ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2Y0Y1Y2Y'
4146.73.30.00.01.01.0
1255.52.50.00.01.00.0
3946.73.10.00.01.00.0
4116.02.21.00.00.00.0
957.63.00.00.01.01.0
645.82.60.00.01.00.0
3095.03.40.00.01.00.0
76.73.00.01.00.00.0
1826.12.81.00.00.00.0
494.73.20.01.00.00.0
\n", - "
" - ], - "text/plain": [ - " X1 X2 Y0 Y1 Y2 Y'\n", - "414 6.7 3.3 0.0 0.0 1.0 1.0\n", - "125 5.5 2.5 0.0 0.0 1.0 0.0\n", - "394 6.7 3.1 0.0 0.0 1.0 0.0\n", - "411 6.0 2.2 1.0 0.0 0.0 0.0\n", - "95 7.6 3.0 0.0 0.0 1.0 1.0\n", - "64 5.8 2.6 0.0 0.0 1.0 0.0\n", - "309 5.0 3.4 0.0 0.0 1.0 0.0\n", - "7 6.7 3.0 0.0 1.0 0.0 0.0\n", - "182 6.1 2.8 1.0 0.0 0.0 0.0\n", - "49 4.7 3.2 0.0 1.0 0.0 0.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Xext, Yext = multiplie(X, Y)\n", - "numpy.hstack([Xext, Yext])\n", - "df = pandas.DataFrame(numpy.hstack([Xext, Yext]))\n", - "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", - "df.iloc[numpy.random.permutation(df.index), :].head(n=10)" - ] - }, + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "pred = clf.predict(X)\n", + "confusion_matrix(Y, pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiplication des observations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le paramètre ``multi_class='ovr'`` stipule que le modèle cache en fait l'estimation de 3 régressions logistiques binaire. Essayons de n'en faire qu'une seule en ajouter le label ``Y`` aux variables. Soit un couple $(X_i \\in \\mathbb{R^d}, Y_i \\in \\mathbb{N})$ qui correspond à une observation pour un problème multi-classe. Comme il y a $C$ classes, on multiplie cette ligne par le nombre de classes $C$ pour obtenir :\n", + "\n", + "$$\\forall c \\in \\mathbb{[}1, ..., C\\mathbb{]}, \\; \\left\\{ \\begin{array}{ll} X_i' = (X_{i,1}, ..., X_{i,d}, Y_{i,1}, ..., Y_{i,C}) \\\\ Y_i' = \\mathbb{1}_{Y_i = c} \\\\ Y_{i,k} = \\mathbb{1}_{c = k}\\end{array} \\right.$$\n", + "\n", + "Voyons ce que cela donne sur un exemple :" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='deviance', max_depth=3,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=100,\n", - " n_iter_no_change=None, presort='auto', random_state=None,\n", - " subsample=1.0, tol=0.0001, validation_fraction=0.1,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2Y0Y1Y2Y'
05.13.51.00.00.01.0
15.13.50.01.00.00.0
25.13.50.00.01.00.0
\n", + "
" ], - "source": [ - "from sklearn.ensemble import GradientBoostingClassifier\n", - "clf = GradientBoostingClassifier()\n", - "clf.fit(Xext, Yext.ravel())" + "text/plain": [ + " X1 X2 Y0 Y1 Y2 Y'\n", + "0 5.1 3.5 1.0 0.0 0.0 1.0\n", + "1 5.1 3.5 0.0 1.0 0.0 0.0\n", + "2 5.1 3.5 0.0 0.0 1.0 0.0" ] - }, + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "import pandas\n", + "\n", + "\n", + "def multiplie(X, Y, classes=None):\n", + " if classes is None:\n", + " classes = numpy.unique(Y)\n", + " XS = []\n", + " YS = []\n", + " for i in classes:\n", + " X2 = numpy.zeros((X.shape[0], 3))\n", + " X2[:, i] = 1\n", + " Yb = i == Y\n", + " XS.append(numpy.hstack([X, X2]))\n", + " Yb = Yb.reshape((len(Yb), 1))\n", + " YS.append(Yb)\n", + "\n", + " Xext = numpy.vstack(XS)\n", + " Yext = numpy.vstack(YS)\n", + " return Xext, Yext\n", + "\n", + "\n", + "x, y = multiplie(X[:1, :], Y[:1], [0, 1, 2])\n", + "df = pandas.DataFrame(numpy.hstack([x, y]))\n", + "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trois colonnes ont été ajoutées côté $X$, la ligne a été multipliée 3 fois, la dernière colonne est $Y$ qui ne vaut 1 que lorsque le 1 est au bon endroit dans une des colonnes ajoutées. Le problème de classification qui été de prédire la bonne classe devient : est-ce la classe à prédire est $k$ ? On applique cela sur toutes les lignes de la base et cela donne :" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[278, 22],\n", - " [ 25, 125]], dtype=int64)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2Y0Y1Y2Y'
3815.52.40.00.01.00.0
526.93.11.00.00.00.0
1534.63.10.01.00.00.0
1895.13.40.01.00.00.0
3976.22.90.00.01.00.0
2395.52.50.01.00.01.0
1086.72.51.00.00.00.0
3985.12.50.00.01.00.0
224.63.61.00.00.01.0
134.33.01.00.00.01.0
\n", + "
" ], - "source": [ - "pred = clf.predict(Xext)\n", - "confusion_matrix(Yext, pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduire du bruit\n", - "\n", - "Un des probl\u00e8mes de cette m\u00e9thode est qu'on ajoute une variable binaire pour un probl\u00e8me r\u00e9solu \u00e0 l'aide d'une optimisation \u00e0 base de gradient. C'est moyen. Pas de probl\u00e8me, changeons un peu la donne." + "text/plain": [ + " X1 X2 Y0 Y1 Y2 Y'\n", + "381 5.5 2.4 0.0 0.0 1.0 0.0\n", + "52 6.9 3.1 1.0 0.0 0.0 0.0\n", + "153 4.6 3.1 0.0 1.0 0.0 0.0\n", + "189 5.1 3.4 0.0 1.0 0.0 0.0\n", + "397 6.2 2.9 0.0 0.0 1.0 0.0\n", + "239 5.5 2.5 0.0 1.0 0.0 1.0\n", + "108 6.7 2.5 1.0 0.0 0.0 0.0\n", + "398 5.1 2.5 0.0 0.0 1.0 0.0\n", + "22 4.6 3.6 1.0 0.0 0.0 1.0\n", + "13 4.3 3.0 1.0 0.0 0.0 1.0" ] - }, + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Xext, Yext = multiplie(X, Y)\n", + "numpy.hstack([Xext, Yext])\n", + "df = pandas.DataFrame(numpy.hstack([Xext, Yext]))\n", + "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", + "df.iloc[numpy.random.permutation(df.index), :].head(n=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2Y0Y1Y2Y'
05.13.51.1074610.1668930.0187651.0
15.13.50.1624641.1873590.1877210.0
25.13.50.0868760.1784721.1792010.0
\n", - "
" - ], - "text/plain": [ - " X1 X2 Y0 Y1 Y2 Y'\n", - "0 5.1 3.5 1.107461 0.166893 0.018765 1.0\n", - "1 5.1 3.5 0.162464 1.187359 0.187721 0.0\n", - "2 5.1 3.5 0.086876 0.178472 1.179201 0.0" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
GradientBoostingClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], - "source": [ - "def multiplie_bruit(X, Y, classes=None):\n", - " if classes is None:\n", - " classes = numpy.unique(Y)\n", - " XS = []\n", - " YS = []\n", - " for i in classes:\n", - " # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.1\n", - " X2 = numpy.random.random((X.shape[0], 3)) * 0.2\n", - " X2[:,i] += 1\n", - " Yb = Y == i\n", - " XS.append(numpy.hstack([X, X2]))\n", - " Yb = Yb.reshape((len(Yb), 1))\n", - " YS.append(Yb)\n", - "\n", - " Xext = numpy.vstack(XS)\n", - " Yext = numpy.vstack(YS)\n", - " return Xext, Yext\n", - "\n", - "x, y = multiplie_bruit(X[:1,:], Y[:1], [0, 1, 2])\n", - "df = pandas.DataFrame(numpy.hstack([x, y]))\n", - "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", - "df" + "text/plain": [ + "GradientBoostingClassifier()" ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "clf = GradientBoostingClassifier()\n", + "clf.fit(Xext, Yext.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le probl\u00e8me est le m\u00eame qu'avant except\u00e9 les variables $Y_i$ qui sont maintenant r\u00e9el. Au lieu d'\u00eatre nul, on prend une valeur $Y_i < 0.4$." + "data": { + "text/plain": [ + "array([[278, 22],\n", + " [ 25, 125]])" ] - }, + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = clf.predict(Xext)\n", + "confusion_matrix(Yext, pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduire du bruit\n", + "\n", + "Un des problèmes de cette méthode est qu'on ajoute une variable binaire pour un problème résolu à l'aide d'une optimisation à base de gradient. C'est moyen. Pas de problème, changeons un peu la donne." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
X1X2Y0Y1Y2Y'
2955.52.60.1976431.1999760.1807661.0
465.23.40.1783950.1906001.1597650.0
1876.73.10.1889471.0932880.1397231.0
2106.93.10.0954280.1826431.0375331.0
295.53.51.1314190.0772410.1774831.0
3156.43.20.0997380.1972911.0354311.0
1525.82.70.0690610.0453251.0612210.0
1686.52.80.0931641.1774130.0958901.0
3486.93.11.0941840.1969440.0839750.0
2616.32.80.1975580.0802731.0093791.0
\n", - "
" - ], - "text/plain": [ - " X1 X2 Y0 Y1 Y2 Y'\n", - "295 5.5 2.6 0.197643 1.199976 0.180766 1.0\n", - "46 5.2 3.4 0.178395 0.190600 1.159765 0.0\n", - "187 6.7 3.1 0.188947 1.093288 0.139723 1.0\n", - "210 6.9 3.1 0.095428 0.182643 1.037533 1.0\n", - "29 5.5 3.5 1.131419 0.077241 0.177483 1.0\n", - "315 6.4 3.2 0.099738 0.197291 1.035431 1.0\n", - "152 5.8 2.7 0.069061 0.045325 1.061221 0.0\n", - "168 6.5 2.8 0.093164 1.177413 0.095890 1.0\n", - "348 6.9 3.1 1.094184 0.196944 0.083975 0.0\n", - "261 6.3 2.8 0.197558 0.080273 1.009379 1.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2Y0Y1Y2Y'
05.13.51.0049200.0045320.0391571.0
15.13.50.0855631.1295750.1213370.0
25.13.50.1302750.1747631.0744600.0
\n", + "
" ], - "source": [ - "Xextb, Yextb = multiplie_bruit(X, Y)\n", - "df = pandas.DataFrame(numpy.hstack([Xextb, Yextb]))\n", - "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", - "df.iloc[numpy.random.permutation(df.index), :].head(n=10)" + "text/plain": [ + " X1 X2 Y0 Y1 Y2 Y'\n", + "0 5.1 3.5 1.004920 0.004532 0.039157 1.0\n", + "1 5.1 3.5 0.085563 1.129575 0.121337 0.0\n", + "2 5.1 3.5 0.130275 0.174763 1.074460 0.0" ] - }, + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def multiplie_bruit(X, Y, classes=None):\n", + " if classes is None:\n", + " classes = numpy.unique(Y)\n", + " XS = []\n", + " YS = []\n", + " for i in classes:\n", + " # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.1\n", + " X2 = numpy.random.random((X.shape[0], 3)) * 0.2\n", + " X2[:, i] += 1\n", + " Yb = i == Y\n", + " XS.append(numpy.hstack([X, X2]))\n", + " Yb = Yb.reshape((len(Yb), 1))\n", + " YS.append(Yb)\n", + "\n", + " Xext = numpy.vstack(XS)\n", + " Yext = numpy.vstack(YS)\n", + " return Xext, Yext\n", + "\n", + "\n", + "x, y = multiplie_bruit(X[:1, :], Y[:1], [0, 1, 2])\n", + "df = pandas.DataFrame(numpy.hstack([x, y]))\n", + "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le problème est le même qu'avant excepté les variables $Y_i$ qui sont maintenant réel. Au lieu d'être nul, on prend une valeur $Y_i < 0.4$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='deviance', max_depth=3,\n", - " max_features=None, max_leaf_nodes=None,\n", - " min_impurity_decrease=0.0, min_impurity_split=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, n_estimators=100,\n", - " n_iter_no_change=None, presort='auto', random_state=None,\n", - " subsample=1.0, tol=0.0001, validation_fraction=0.1,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
X1X2Y0Y1Y2Y'
2126.02.20.1490541.1555960.1094131.0
1166.53.01.0717600.0928020.0139110.0
3916.13.00.0841430.1373361.0636570.0
165.43.91.0982010.0643080.0328781.0
2295.72.60.1269991.0655820.1274801.0
384.43.01.1646210.0507790.0092771.0
2136.12.90.0619901.0348180.0470331.0
3344.93.10.0317130.1412051.0431950.0
546.52.81.0661180.1582710.1877640.0
3795.72.60.0334430.0558181.0087790.0
\n", + "
" ], - "source": [ - "from sklearn.ensemble import GradientBoostingClassifier\n", - "clfb = GradientBoostingClassifier()\n", - "clfb.fit(Xextb, Yextb.ravel())" + "text/plain": [ + " X1 X2 Y0 Y1 Y2 Y'\n", + "212 6.0 2.2 0.149054 1.155596 0.109413 1.0\n", + "116 6.5 3.0 1.071760 0.092802 0.013911 0.0\n", + "391 6.1 3.0 0.084143 0.137336 1.063657 0.0\n", + "16 5.4 3.9 1.098201 0.064308 0.032878 1.0\n", + "229 5.7 2.6 0.126999 1.065582 0.127480 1.0\n", + "38 4.4 3.0 1.164621 0.050779 0.009277 1.0\n", + "213 6.1 2.9 0.061990 1.034818 0.047033 1.0\n", + "334 4.9 3.1 0.031713 0.141205 1.043195 0.0\n", + "54 6.5 2.8 1.066118 0.158271 0.187764 0.0\n", + "379 5.7 2.6 0.033443 0.055818 1.008779 0.0" ] - }, + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Xextb, Yextb = multiplie_bruit(X, Y)\n", + "df = pandas.DataFrame(numpy.hstack([Xextb, Yextb]))\n", + "df.columns = [\"X1\", \"X2\", \"Y0\", \"Y1\", \"Y2\", \"Y'\"]\n", + "df.iloc[numpy.random.permutation(df.index), :].head(n=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[299, 1],\n", - " [ 10, 140]], dtype=int64)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
GradientBoostingClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], - "source": [ - "predb = clfb.predict(Xextb)\n", - "confusion_matrix(Yextb, predb)" + "text/plain": [ + "GradientBoostingClassifier()" ] - }, + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "clfb = GradientBoostingClassifier()\n", + "clfb.fit(Xextb, Yextb.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "C'est un petit peu mieux." + "data": { + "text/plain": [ + "array([[295, 5],\n", + " [ 9, 141]])" ] - }, + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predb = clfb.predict(Xextb)\n", + "confusion_matrix(Yextb, predb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est un petit peu mieux." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparaisons de plusieurs modèles\n", + "\n", + "On cherche maintenant à comparer le gain en introduisant du bruit pour différents modèles." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Comparaisons de plusieurs mod\u00e8les\n", - "\n", - "On cherche maintenant \u00e0 comparer le gain en introduisant du bruit pour diff\u00e9rents mod\u00e8les." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:01<00:00, 6.17it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
err1err2model
100.3333330.333333AdaBoostClassifier
30.0488890.000000DecisionTreeClassifier
40.0488890.000000ExtraTreeClassifier
60.0488890.000000ExtraTreesClassifier
80.3333330.333333GaussianNB
10.1044440.044444GradientBoostingClassifier
90.1044440.091111KNeighborsClassifier
00.3333330.333333LogisticRegression
70.3333330.333333MLPClassifier
20.0533330.002222RandomForestClassifier
50.3333330.053333XGBClassifier
\n", - "
" - ], - "text/plain": [ - " err1 err2 model\n", - "10 0.333333 0.333333 AdaBoostClassifier\n", - "3 0.048889 0.000000 DecisionTreeClassifier\n", - "4 0.048889 0.000000 ExtraTreeClassifier\n", - "6 0.048889 0.000000 ExtraTreesClassifier\n", - "8 0.333333 0.333333 GaussianNB\n", - "1 0.104444 0.044444 GradientBoostingClassifier\n", - "9 0.104444 0.091111 KNeighborsClassifier\n", - "0 0.333333 0.333333 LogisticRegression\n", - "7 0.333333 0.333333 MLPClassifier\n", - "2 0.053333 0.002222 RandomForestClassifier\n", - "5 0.333333 0.053333 XGBClassifier" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelerr1err2
10AdaBoostClassifier0.3333330.333333
3DecisionTreeClassifier0.0488890.000000
4ExtraTreeClassifier0.0488890.000000
6ExtraTreesClassifier0.0488890.000000
8GaussianNB0.3333330.333333
1GradientBoostingClassifier0.1044440.022222
9KNeighborsClassifier0.1088890.097778
7MLPClassifier0.3333330.333333
0OneVsRestClassifier0.3333330.333333
2RandomForestClassifier0.0533330.002222
5XGBClassifier0.3333330.000000
\n", + "
" ], - "source": [ - "def error(model, x, y):\n", - " p = model.predict(x)\n", - " cm = confusion_matrix(y, p)\n", - " return (cm[1,0] + cm[0,1]) / cm.sum()\n", - "\n", - "def comparaison(model, X, Y):\n", - "\n", - " if isinstance(model, tuple):\n", - " clf = model[0](**model[1])\n", - " clfb = model[0](**model[1])\n", - " model = model[0]\n", - " else: \n", - " clf = model()\n", - " clfb = model()\n", - " \n", - " Xext, Yext = multiplie(X, Y)\n", - " clf.fit(Xext, Yext.ravel())\n", - " err = error(clf, Xext, Yext)\n", - " \n", - " Xextb, Yextb = multiplie_bruit(X, Y)\n", - " clfb.fit(Xextb, Yextb.ravel())\n", - " errb = error(clfb, Xextb, Yextb)\n", - " return dict(model=model.__name__, err1=err, err2=errb)\n", - "\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier\n", - "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier\n", - "from sklearn.neural_network import MLPClassifier\n", - "from sklearn.naive_bayes import GaussianNB\n", - "from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier\n", - "from xgboost import XGBClassifier\n", - "\n", - "models = [(LogisticRegression, dict(multi_class=\"ovr\", solver=\"liblinear\")),\n", - " GradientBoostingClassifier,\n", - " (RandomForestClassifier, dict(n_estimators=20)),\n", - " DecisionTreeClassifier,\n", - " ExtraTreeClassifier,\n", - " XGBClassifier,\n", - " (ExtraTreesClassifier, dict(n_estimators=20)),\n", - " (MLPClassifier, dict(activation=\"logistic\")),\n", - " GaussianNB, KNeighborsClassifier, \n", - " (AdaBoostClassifier, dict(base_estimator=LogisticRegression(multi_class=\"ovr\", solver=\"liblinear\"), \n", - " algorithm=\"SAMME\"))]\n", - "\n", - "res = [comparaison(model, X, Y) for model in models]\n", - "df = pandas.DataFrame(res)\n", - "df.sort_values(\"model\")" + "text/plain": [ + " model err1 err2\n", + "10 AdaBoostClassifier 0.333333 0.333333\n", + "3 DecisionTreeClassifier 0.048889 0.000000\n", + "4 ExtraTreeClassifier 0.048889 0.000000\n", + "6 ExtraTreesClassifier 0.048889 0.000000\n", + "8 GaussianNB 0.333333 0.333333\n", + "1 GradientBoostingClassifier 0.104444 0.022222\n", + "9 KNeighborsClassifier 0.108889 0.097778\n", + "7 MLPClassifier 0.333333 0.333333\n", + "0 OneVsRestClassifier 0.333333 0.333333\n", + "2 RandomForestClassifier 0.053333 0.002222\n", + "5 XGBClassifier 0.333333 0.000000" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*err1* correspond \u00e0 $Y_0, Y_1, Y_2$ binaire, *err2* aux m\u00eames variables mais avec un peu de bruit. L'ajout ne semble pas faire d\u00e9cro\u00eetre la performance et l'am\u00e9liore dans certains cas. C'est une piste \u00e0 suivre. Reste \u00e0 savoir si les mod\u00e8les n'apprennent pas le bruit." - ] - }, + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def error(model, x, y):\n", + " p = model.predict(x)\n", + " cm = confusion_matrix(y, p)\n", + " return (cm[1, 0] + cm[0, 1]) / cm.sum()\n", + "\n", + "\n", + "def comparaison(model, X, Y):\n", + " if isinstance(model, tuple):\n", + " clf = model[0](**model[1])\n", + " clfb = model[0](**model[1])\n", + " model = model[0]\n", + " else:\n", + " clf = model()\n", + " clfb = model()\n", + "\n", + " Xext, Yext = multiplie(X, Y)\n", + " clf.fit(Xext, Yext.ravel())\n", + " err = error(clf, Xext, Yext)\n", + "\n", + " Xextb, Yextb = multiplie_bruit(X, Y)\n", + " clfb.fit(Xextb, Yextb.ravel())\n", + " errb = error(clfb, Xextb, Yextb)\n", + " return dict(model=model.__name__, err1=err, err2=errb)\n", + "\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier\n", + "from sklearn.ensemble import (\n", + " RandomForestClassifier,\n", + " ExtraTreesClassifier,\n", + " AdaBoostClassifier,\n", + ")\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.multiclass import OneVsRestClassifier\n", + "from xgboost import XGBClassifier\n", + "from tqdm import tqdm\n", + "\n", + "models = [\n", + " (OneVsRestClassifier, dict(estimator=LogisticRegression(solver=\"liblinear\"))),\n", + " GradientBoostingClassifier,\n", + " (RandomForestClassifier, dict(n_estimators=20)),\n", + " DecisionTreeClassifier,\n", + " ExtraTreeClassifier,\n", + " XGBClassifier,\n", + " (ExtraTreesClassifier, dict(n_estimators=20)),\n", + " (MLPClassifier, dict(activation=\"logistic\")),\n", + " GaussianNB,\n", + " KNeighborsClassifier,\n", + " (\n", + " AdaBoostClassifier,\n", + " dict(\n", + " estimator=LogisticRegression(solver=\"liblinear\"),\n", + " algorithm=\"SAMME\",\n", + " ),\n", + " ),\n", + "]\n", + "\n", + "res = []\n", + "for model in tqdm(models):\n", + " res.append(comparaison(model, X, Y))\n", + "df = pandas.DataFrame(res)\n", + "df.sort_values(\"model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*err1* correspond à $Y_0, Y_1, Y_2$ binaire, *err2* aux mêmes variables mais avec un peu de bruit. L'ajout ne semble pas faire décroître la performance et l'améliore dans certains cas. C'est une piste à suivre. Reste à savoir si les modèles n'apprennent pas le bruit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Avec une ACP\n", + "\n", + "On peut faire varier le nombre de composantes, j'en ai gardé qu'une. L'ACP est appliquée après avoir ajouté les variables binaires ou binaires bruitées. Le résultat est sans équivoque. Aucun modèle ne parvient à apprendre sans l'ajout de bruit." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Avec une ACP\n", - "\n", - "On peut faire varier le nombre de composantes, j'en ai gard\u00e9 qu'une. L'ACP est appliqu\u00e9e apr\u00e8s avoir ajout\u00e9 les variables binaires ou binaires bruit\u00e9es. Le r\u00e9sultat est sans \u00e9quivoque. Aucun mod\u00e8le ne parvient \u00e0 apprendre sans l'ajout de bruit." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:01<00:00, 5.83it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
err1err2modelerrACP1errACP2modelACP
100.3333330.333333AdaBoostClassifier0.3333330.333333AdaBoostClassifier
30.0488890.000000DecisionTreeClassifier0.3333330.000000DecisionTreeClassifier
40.0488890.000000ExtraTreeClassifier0.3333330.000000ExtraTreeClassifier
60.0488890.000000ExtraTreesClassifier0.3333330.000000ExtraTreesClassifier
80.3333330.333333GaussianNB0.3333330.333333GaussianNB
10.1044440.044444GradientBoostingClassifier0.3333330.224444GradientBoostingClassifier
90.1044440.091111KNeighborsClassifier0.3355560.340000KNeighborsClassifier
00.3333330.333333LogisticRegression0.3333330.333333LogisticRegression
70.3333330.333333MLPClassifier0.3333330.333333MLPClassifier
20.0533330.002222RandomForestClassifier0.3333330.024444RandomForestClassifier
50.3333330.053333XGBClassifier0.3333330.315556XGBClassifier
\n", - "
" - ], - "text/plain": [ - " err1 err2 model errACP1 errACP2 \\\n", - "10 0.333333 0.333333 AdaBoostClassifier 0.333333 0.333333 \n", - "3 0.048889 0.000000 DecisionTreeClassifier 0.333333 0.000000 \n", - "4 0.048889 0.000000 ExtraTreeClassifier 0.333333 0.000000 \n", - "6 0.048889 0.000000 ExtraTreesClassifier 0.333333 0.000000 \n", - "8 0.333333 0.333333 GaussianNB 0.333333 0.333333 \n", - "1 0.104444 0.044444 GradientBoostingClassifier 0.333333 0.224444 \n", - "9 0.104444 0.091111 KNeighborsClassifier 0.335556 0.340000 \n", - "0 0.333333 0.333333 LogisticRegression 0.333333 0.333333 \n", - "7 0.333333 0.333333 MLPClassifier 0.333333 0.333333 \n", - "2 0.053333 0.002222 RandomForestClassifier 0.333333 0.024444 \n", - "5 0.333333 0.053333 XGBClassifier 0.333333 0.315556 \n", - "\n", - " modelACP \n", - "10 AdaBoostClassifier \n", - "3 DecisionTreeClassifier \n", - "4 ExtraTreeClassifier \n", - "6 ExtraTreesClassifier \n", - "8 GaussianNB \n", - "1 GradientBoostingClassifier \n", - "9 KNeighborsClassifier \n", - "0 LogisticRegression \n", - "7 MLPClassifier \n", - "2 RandomForestClassifier \n", - "5 XGBClassifier " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelerr1err2modelACPerrACP1errACP2
10AdaBoostClassifier0.3333330.333333AdaBoostClassifier0.3333330.333333
3DecisionTreeClassifier0.0488890.000000DecisionTreeClassifier0.3333330.000000
4ExtraTreeClassifier0.0488890.000000ExtraTreeClassifier0.3333330.000000
6ExtraTreesClassifier0.0488890.000000ExtraTreesClassifier0.3333330.000000
8GaussianNB0.3333330.333333GaussianNB0.3333330.333333
1GradientBoostingClassifier0.1044440.022222GradientBoostingClassifier0.3333330.231111
9KNeighborsClassifier0.1088890.097778KNeighborsClassifier0.3355560.302222
7MLPClassifier0.3333330.333333MLPClassifier0.3333330.333333
0OneVsRestClassifier0.3333330.333333OneVsRestClassifier0.3333330.333333
2RandomForestClassifier0.0533330.002222RandomForestClassifier0.3355560.020000
5XGBClassifier0.3333330.000000XGBClassifier0.3333330.262222
\n", + "
" ], - "source": [ - "from sklearn.decomposition import PCA\n", - "\n", - "def comparaison_ACP(model, X, Y):\n", - "\n", - " if isinstance(model, tuple):\n", - " clf = model[0](**model[1])\n", - " clfb = model[0](**model[1])\n", - " model = model[0]\n", - " else: \n", - " clf = model()\n", - " clfb = model()\n", - " \n", - " axes = 1\n", - " solver = \"full\"\n", - " Xext, Yext = multiplie(X, Y)\n", - " Xext = PCA(n_components=axes, svd_solver=solver).fit_transform(Xext)\n", - " clf.fit(Xext, Yext.ravel())\n", - " err = error(clf, Xext, Yext)\n", - " \n", - " Xextb, Yextb = multiplie_bruit(X, Y)\n", - " Xextb = PCA(n_components=axes, svd_solver=solver).fit_transform(Xextb)\n", - " clfb.fit(Xextb, Yextb.ravel())\n", - " errb = error(clfb, Xextb, Yextb)\n", - " return dict(modelACP=model.__name__, errACP1=err, errACP2=errb)\n", - "\n", - "res = [comparaison_ACP(model, X, Y) for model in models]\n", - "dfb = pandas.DataFrame(res)\n", - "pandas.concat([ df.sort_values(\"model\"), dfb.sort_values(\"modelACP\")], axis=1)" + "text/plain": [ + " model err1 err2 \\\n", + "10 AdaBoostClassifier 0.333333 0.333333 \n", + "3 DecisionTreeClassifier 0.048889 0.000000 \n", + "4 ExtraTreeClassifier 0.048889 0.000000 \n", + "6 ExtraTreesClassifier 0.048889 0.000000 \n", + "8 GaussianNB 0.333333 0.333333 \n", + "1 GradientBoostingClassifier 0.104444 0.022222 \n", + "9 KNeighborsClassifier 0.108889 0.097778 \n", + "7 MLPClassifier 0.333333 0.333333 \n", + "0 OneVsRestClassifier 0.333333 0.333333 \n", + "2 RandomForestClassifier 0.053333 0.002222 \n", + "5 XGBClassifier 0.333333 0.000000 \n", + "\n", + " modelACP errACP1 errACP2 \n", + "10 AdaBoostClassifier 0.333333 0.333333 \n", + "3 DecisionTreeClassifier 0.333333 0.000000 \n", + "4 ExtraTreeClassifier 0.333333 0.000000 \n", + "6 ExtraTreesClassifier 0.333333 0.000000 \n", + "8 GaussianNB 0.333333 0.333333 \n", + "1 GradientBoostingClassifier 0.333333 0.231111 \n", + "9 KNeighborsClassifier 0.335556 0.302222 \n", + "7 MLPClassifier 0.333333 0.333333 \n", + "0 OneVsRestClassifier 0.333333 0.333333 \n", + "2 RandomForestClassifier 0.335556 0.020000 \n", + "5 XGBClassifier 0.333333 0.262222 " ] - }, + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "\n", + "def comparaison_ACP(model, X, Y):\n", + " if isinstance(model, tuple):\n", + " clf = model[0](**model[1])\n", + " clfb = model[0](**model[1])\n", + " model = model[0]\n", + " else:\n", + " clf = model()\n", + " clfb = model()\n", + "\n", + " axes = 1\n", + " solver = \"full\"\n", + " Xext, Yext = multiplie(X, Y)\n", + " Xext = PCA(n_components=axes, svd_solver=solver).fit_transform(Xext)\n", + " clf.fit(Xext, Yext.ravel())\n", + " err = error(clf, Xext, Yext)\n", + "\n", + " Xextb, Yextb = multiplie_bruit(X, Y)\n", + " Xextb = PCA(n_components=axes, svd_solver=solver).fit_transform(Xextb)\n", + " clfb.fit(Xextb, Yextb.ravel())\n", + " errb = error(clfb, Xextb, Yextb)\n", + " return dict(modelACP=model.__name__, errACP1=err, errACP2=errb)\n", + "\n", + "\n", + "res = []\n", + "for model in tqdm(models):\n", + " res.append(comparaison_ACP(model, X, Y))\n", + "dfb = pandas.DataFrame(res)\n", + "pandas.concat([df.sort_values(\"model\"), dfb.sort_values(\"modelACP\")], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Base d'apprentissage et de test\n", + "\n", + "Cette fois-ci, on s'intéresse à la qualité des frontières que les modèles trouvent en vérifiant sur une base de test que l'apprentissage s'est bien passé." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Base d'apprentissage et de test\n", - "\n", - "Cette fois-ci, on s'int\u00e9resse \u00e0 la qualit\u00e9 des fronti\u00e8res que les mod\u00e8les trouvent en v\u00e9rifiant sur une base de test que l'apprentissage s'est bien pass\u00e9." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:02<00:00, 5.40it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0266670.0000000.2266670.2066670.2733330.313333
4ExtraTreeClassifier0.0266670.0000000.2533330.2133330.2533330.273333
6ExtraTreesClassifier0.0266670.0000000.1400000.2000000.2133330.220000
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0800000.0133330.1766670.1866670.2466670.240000
9KNeighborsClassifier0.0700000.0766670.0733330.1600000.1600000.166667
0LogisticRegression0.3333330.3333330.3333330.3333330.3333330.333333
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0266670.0000000.1566670.2066670.2666670.213333
5XGBClassifier0.1066670.0366670.3333330.1933330.2800000.346667
\n", - "
" - ], - "text/plain": [ - " modelTT err_train err2_train err2b_train_clean \\\n", - "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", - "3 DecisionTreeClassifier 0.026667 0.000000 0.226667 \n", - "4 ExtraTreeClassifier 0.026667 0.000000 0.253333 \n", - "6 ExtraTreesClassifier 0.026667 0.000000 0.140000 \n", - "8 GaussianNB 0.333333 0.333333 0.333333 \n", - "1 GradientBoostingClassifier 0.080000 0.013333 0.176667 \n", - "9 KNeighborsClassifier 0.070000 0.076667 0.073333 \n", - "0 LogisticRegression 0.333333 0.333333 0.333333 \n", - "7 MLPClassifier 0.333333 0.333333 0.333333 \n", - "2 RandomForestClassifier 0.026667 0.000000 0.156667 \n", - "5 XGBClassifier 0.106667 0.036667 0.333333 \n", - "\n", - " err_test err2_test err2b_test_clean \n", - "10 0.333333 0.333333 0.333333 \n", - "3 0.206667 0.273333 0.313333 \n", - "4 0.213333 0.253333 0.273333 \n", - "6 0.200000 0.213333 0.220000 \n", - "8 0.333333 0.333333 0.333333 \n", - "1 0.186667 0.246667 0.240000 \n", - "9 0.160000 0.160000 0.166667 \n", - "0 0.333333 0.333333 0.333333 \n", - "7 0.333333 0.333333 0.333333 \n", - "2 0.206667 0.266667 0.213333 \n", - "5 0.193333 0.280000 0.346667 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0466670.0000000.5666670.2200000.3000000.553333
4ExtraTreeClassifier0.0466670.0000000.2633330.1866670.1733330.266667
6ExtraTreesClassifier0.0466670.0000000.2133330.1666670.1866670.193333
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0933330.0233330.3066670.1733330.1866670.246667
9KNeighborsClassifier0.1033330.1066670.1233330.1333330.1466670.146667
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
0OneVsRestClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0533330.0066670.1833330.1733330.1933330.153333
5XGBClassifier0.0533330.0000000.2100000.2066670.2066670.233333
\n", + "
" ], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "def comparaison_train_test(models, X, Y, mbruit=multiplie_bruit, acp=None):\n", - "\n", - " axes = acp\n", - " solver = \"full\" \n", - " \n", - " ind = numpy.random.permutation(numpy.arange(X.shape[0]))\n", - " X = X[ind,:]\n", - " Y = Y[ind]\n", - " X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=1./3)\n", - " \n", - " res = []\n", - " for model in models:\n", - " \n", - " if isinstance(model, tuple):\n", - " clf = model[0](**model[1])\n", - " clfb = model[0](**model[1])\n", - " model = model[0]\n", - " else: \n", - " clf = model()\n", - " clfb = model()\n", - "\n", - " Xext_train, Yext_train = multiplie(X_train, Y_train)\n", - " Xext_test, Yext_test = multiplie(X_test, Y_test)\n", - " if acp:\n", - " Xext_train_ = Xext_train\n", - " Xext_test_ = Xext_test\n", - " acp_model = PCA(n_components=axes, svd_solver=solver).fit(Xext_train)\n", - " Xext_train = acp_model.transform(Xext_train)\n", - " Xext_test = acp_model.transform(Xext_test) \n", - " clf.fit(Xext_train, Yext_train.ravel())\n", - "\n", - " err_train = error(clf, Xext_train, Yext_train)\n", - " err_test = error(clf, Xext_test, Yext_test)\n", - "\n", - " Xextb_train, Yextb_train = mbruit(X_train, Y_train)\n", - " Xextb_test, Yextb_test = mbruit(X_test, Y_test)\n", - " if acp:\n", - " acp_model = PCA(n_components=axes, svd_solver=solver).fit(Xextb_train)\n", - " Xextb_train = acp_model.transform(Xextb_train)\n", - " Xextb_test = acp_model.transform(Xextb_test) \n", - " Xext_train = acp_model.transform(Xext_train_)\n", - " Xext_test = acp_model.transform(Xext_test_) \n", - " clfb.fit(Xextb_train, Yextb_train.ravel())\n", - "\n", - " errb_train = error(clfb, Xextb_train, Yextb_train)\n", - " errb_train_clean = error(clfb, Xext_train, Yext_train)\n", - " errb_test = error(clfb, Xextb_test, Yextb_test)\n", - " errb_test_clean = error(clfb, Xext_test, Yext_test)\n", - " \n", - " res.append(dict(modelTT=model.__name__, err_train=err_train, err2_train=errb_train,\n", - " err_test=err_test, err2_test=errb_test, err2b_test_clean=errb_test_clean,\n", - " err2b_train_clean=errb_train_clean))\n", - " \n", - " dfb = pandas.DataFrame(res)\n", - " dfb = dfb[[\"modelTT\", \"err_train\", \"err2_train\", \"err2b_train_clean\", \"err_test\", \"err2_test\", \"err2b_test_clean\"]]\n", - " dfb = dfb.sort_values(\"modelTT\") \n", - " return dfb\n", - "\n", - "dfb = comparaison_train_test(models, X, Y)\n", - "dfb" + "text/plain": [ + " modelTT err_train err2_train err2b_train_clean \\\n", + "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", + "3 DecisionTreeClassifier 0.046667 0.000000 0.566667 \n", + "4 ExtraTreeClassifier 0.046667 0.000000 0.263333 \n", + "6 ExtraTreesClassifier 0.046667 0.000000 0.213333 \n", + "8 GaussianNB 0.333333 0.333333 0.333333 \n", + "1 GradientBoostingClassifier 0.093333 0.023333 0.306667 \n", + "9 KNeighborsClassifier 0.103333 0.106667 0.123333 \n", + "7 MLPClassifier 0.333333 0.333333 0.333333 \n", + "0 OneVsRestClassifier 0.333333 0.333333 0.333333 \n", + "2 RandomForestClassifier 0.053333 0.006667 0.183333 \n", + "5 XGBClassifier 0.053333 0.000000 0.210000 \n", + "\n", + " err_test err2_test err2b_test_clean \n", + "10 0.333333 0.333333 0.333333 \n", + "3 0.220000 0.300000 0.553333 \n", + "4 0.186667 0.173333 0.266667 \n", + "6 0.166667 0.186667 0.193333 \n", + "8 0.333333 0.333333 0.333333 \n", + "1 0.173333 0.186667 0.246667 \n", + "9 0.133333 0.146667 0.146667 \n", + "7 0.333333 0.333333 0.333333 \n", + "0 0.333333 0.333333 0.333333 \n", + "2 0.173333 0.193333 0.153333 \n", + "5 0.206667 0.206667 0.233333 " ] - }, + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "def comparaison_train_test(models, X, Y, mbruit=multiplie_bruit, acp=None):\n", + " axes = acp\n", + " solver = \"full\"\n", + "\n", + " ind = numpy.random.permutation(numpy.arange(X.shape[0]))\n", + " X = X[ind, :]\n", + " Y = Y[ind]\n", + " X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=1.0 / 3)\n", + "\n", + " res = []\n", + " for model in tqdm(models):\n", + " if isinstance(model, tuple):\n", + " clf = model[0](**model[1])\n", + " clfb = model[0](**model[1])\n", + " model = model[0]\n", + " else:\n", + " clf = model()\n", + " clfb = model()\n", + "\n", + " Xext_train, Yext_train = multiplie(X_train, Y_train)\n", + " Xext_test, Yext_test = multiplie(X_test, Y_test)\n", + " if acp:\n", + " Xext_train_ = Xext_train\n", + " Xext_test_ = Xext_test\n", + " acp_model = PCA(n_components=axes, svd_solver=solver).fit(Xext_train)\n", + " Xext_train = acp_model.transform(Xext_train)\n", + " Xext_test = acp_model.transform(Xext_test)\n", + " clf.fit(Xext_train, Yext_train.ravel())\n", + "\n", + " err_train = error(clf, Xext_train, Yext_train)\n", + " err_test = error(clf, Xext_test, Yext_test)\n", + "\n", + " Xextb_train, Yextb_train = mbruit(X_train, Y_train)\n", + " Xextb_test, Yextb_test = mbruit(X_test, Y_test)\n", + " if acp:\n", + " acp_model = PCA(n_components=axes, svd_solver=solver).fit(Xextb_train)\n", + " Xextb_train = acp_model.transform(Xextb_train)\n", + " Xextb_test = acp_model.transform(Xextb_test)\n", + " Xext_train = acp_model.transform(Xext_train_)\n", + " Xext_test = acp_model.transform(Xext_test_)\n", + " clfb.fit(Xextb_train, Yextb_train.ravel())\n", + "\n", + " errb_train = error(clfb, Xextb_train, Yextb_train)\n", + " errb_train_clean = error(clfb, Xext_train, Yext_train)\n", + " errb_test = error(clfb, Xextb_test, Yextb_test)\n", + " errb_test_clean = error(clfb, Xext_test, Yext_test)\n", + "\n", + " res.append(\n", + " dict(\n", + " modelTT=model.__name__,\n", + " err_train=err_train,\n", + " err2_train=errb_train,\n", + " err_test=err_test,\n", + " err2_test=errb_test,\n", + " err2b_test_clean=errb_test_clean,\n", + " err2b_train_clean=errb_train_clean,\n", + " )\n", + " )\n", + "\n", + " dfb = pandas.DataFrame(res)\n", + " dfb = dfb[\n", + " [\n", + " \"modelTT\",\n", + " \"err_train\",\n", + " \"err2_train\",\n", + " \"err2b_train_clean\",\n", + " \"err_test\",\n", + " \"err2_test\",\n", + " \"err2b_test_clean\",\n", + " ]\n", + " ]\n", + " dfb = dfb.sort_values(\"modelTT\")\n", + " return dfb\n", + "\n", + "\n", + "dfb = comparaison_train_test(models, X, Y)\n", + "dfb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les colonnes *err2b_train_clean* et *err2b_test_clean* sont les erreurs obtenues par des modèles appris sur des colonnes bruitées et testées sur des colonnes non bruitées ce qui est le véritable test. On s'aperçoit que les performances sont très dégradées sur la base d'test. Une raison est que le bruit choisi ajouté n'est pas centré. Corrigeons cela." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les colonnes *err2b_train_clean* et *err2b_test_clean* sont les erreurs obtenues par des mod\u00e8les appris sur des colonnes bruit\u00e9es et test\u00e9es sur des colonnes non bruit\u00e9es ce qui est le v\u00e9ritable test. On s'aper\u00e7oit que les performances sont tr\u00e8s d\u00e9grad\u00e9es sur la base d'test. Une raison est que le bruit choisi ajout\u00e9 n'est pas centr\u00e9. Corrigeons cela." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:02<00:00, 4.58it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0333330.0000000.1433330.1933330.2733330.206667
4ExtraTreeClassifier0.0333330.0000000.1433330.2266670.2333330.180000
6ExtraTreesClassifier0.0333330.0000000.1233330.2000000.2133330.193333
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0833330.0133330.2033330.1933330.2266670.280000
9KNeighborsClassifier0.1066670.1066670.1000000.1800000.1800000.193333
0LogisticRegression0.3333330.3333330.3333330.3333330.3333330.333333
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0400000.0000000.1766670.2066670.2400000.253333
5XGBClassifier0.0800000.0633330.1700000.1866670.2200000.240000
\n", - "
" - ], - "text/plain": [ - " modelTT err_train err2_train err2b_train_clean \\\n", - "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", - "3 DecisionTreeClassifier 0.033333 0.000000 0.143333 \n", - "4 ExtraTreeClassifier 0.033333 0.000000 0.143333 \n", - "6 ExtraTreesClassifier 0.033333 0.000000 0.123333 \n", - "8 GaussianNB 0.333333 0.333333 0.333333 \n", - "1 GradientBoostingClassifier 0.083333 0.013333 0.203333 \n", - "9 KNeighborsClassifier 0.106667 0.106667 0.100000 \n", - "0 LogisticRegression 0.333333 0.333333 0.333333 \n", - "7 MLPClassifier 0.333333 0.333333 0.333333 \n", - "2 RandomForestClassifier 0.040000 0.000000 0.176667 \n", - "5 XGBClassifier 0.080000 0.063333 0.170000 \n", - "\n", - " err_test err2_test err2b_test_clean \n", - "10 0.333333 0.333333 0.333333 \n", - "3 0.193333 0.273333 0.206667 \n", - "4 0.226667 0.233333 0.180000 \n", - "6 0.200000 0.213333 0.193333 \n", - "8 0.333333 0.333333 0.333333 \n", - "1 0.193333 0.226667 0.280000 \n", - "9 0.180000 0.180000 0.193333 \n", - "0 0.333333 0.333333 0.333333 \n", - "7 0.333333 0.333333 0.333333 \n", - "2 0.206667 0.240000 0.253333 \n", - "5 0.186667 0.220000 0.240000 " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0333330.0000000.2566670.2000000.2933330.260000
4ExtraTreeClassifier0.0333330.0000000.1866670.1866670.2000000.180000
6ExtraTreesClassifier0.0333330.0000000.1433330.1466670.1533330.106667
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0966670.0166670.1533330.1400000.1666670.146667
9KNeighborsClassifier0.1133330.1100000.1000000.1733330.1400000.146667
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
0OneVsRestClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0433330.0066670.1833330.1533330.1933330.153333
5XGBClassifier0.0433330.0000000.1933330.2066670.2000000.186667
\n", + "
" ], - "source": [ - "def multiplie_bruit_centree(X, Y, classes=None):\n", - " if classes is None:\n", - " classes = numpy.unique(Y)\n", - " XS = []\n", - " YS = []\n", - " for i in classes:\n", - " # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.1\n", - " X2 = numpy.random.random((X.shape[0], 3)) * 0.2 - 0.1\n", - " X2[:,i] += 1\n", - " Yb = Y == i\n", - " XS.append(numpy.hstack([X, X2]))\n", - " Yb = Yb.reshape((len(Yb), 1))\n", - " YS.append(Yb)\n", - "\n", - " Xext = numpy.vstack(XS)\n", - " Yext = numpy.vstack(YS)\n", - " return Xext, Yext\n", - "\n", - "dfb = comparaison_train_test(models, X, Y, mbruit=multiplie_bruit_centree, acp=None)\n", - "dfb" + "text/plain": [ + " modelTT err_train err2_train err2b_train_clean \\\n", + "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", + "3 DecisionTreeClassifier 0.033333 0.000000 0.256667 \n", + "4 ExtraTreeClassifier 0.033333 0.000000 0.186667 \n", + "6 ExtraTreesClassifier 0.033333 0.000000 0.143333 \n", + "8 GaussianNB 0.333333 0.333333 0.333333 \n", + "1 GradientBoostingClassifier 0.096667 0.016667 0.153333 \n", + "9 KNeighborsClassifier 0.113333 0.110000 0.100000 \n", + "7 MLPClassifier 0.333333 0.333333 0.333333 \n", + "0 OneVsRestClassifier 0.333333 0.333333 0.333333 \n", + "2 RandomForestClassifier 0.043333 0.006667 0.183333 \n", + "5 XGBClassifier 0.043333 0.000000 0.193333 \n", + "\n", + " err_test err2_test err2b_test_clean \n", + "10 0.333333 0.333333 0.333333 \n", + "3 0.200000 0.293333 0.260000 \n", + "4 0.186667 0.200000 0.180000 \n", + "6 0.146667 0.153333 0.106667 \n", + "8 0.333333 0.333333 0.333333 \n", + "1 0.140000 0.166667 0.146667 \n", + "9 0.173333 0.140000 0.146667 \n", + "7 0.333333 0.333333 0.333333 \n", + "0 0.333333 0.333333 0.333333 \n", + "2 0.153333 0.193333 0.153333 \n", + "5 0.206667 0.200000 0.186667 " ] - }, + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def multiplie_bruit_centree(X, Y, classes=None):\n", + " if classes is None:\n", + " classes = numpy.unique(Y)\n", + " XS = []\n", + " YS = []\n", + " for i in classes:\n", + " # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.1\n", + " X2 = numpy.random.random((X.shape[0], 3)) * 0.2 - 0.1\n", + " X2[:, i] += 1\n", + " Yb = i == Y\n", + " XS.append(numpy.hstack([X, X2]))\n", + " Yb = Yb.reshape((len(Yb), 1))\n", + " YS.append(Yb)\n", + "\n", + " Xext = numpy.vstack(XS)\n", + " Yext = numpy.vstack(YS)\n", + " return Xext, Yext\n", + "\n", + "\n", + "dfb = comparaison_train_test(models, X, Y, mbruit=multiplie_bruit_centree, acp=None)\n", + "dfb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est mieux mais on en conclut que dans la plupart des cas, la meilleure performance sur la base d'apprentissage avec le bruit ajouté est due au fait que les modèles apprennent par coeur. Sur la base de test, les performances ne sont pas meilleures. Une erreur de 33% signifie que la réponse du classifieur est constante. On multiplie les exemples." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "C'est mieux mais on en conclut que dans la plupart des cas, la meilleure performance sur la base d'apprentissage avec le bruit ajout\u00e9 est due au fait que les mod\u00e8les apprennent par coeur. Sur la base de test, les performances ne sont pas meilleures. Une erreur de 33% signifie que la r\u00e9ponse du classifieur est constante. On multiplie les exemples." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:02<00:00, 3.96it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0400000.0000000.1200000.1800000.2093330.240000
4ExtraTreeClassifier0.0400000.0000000.0733330.2133330.2320000.220000
6ExtraTreesClassifier0.0400000.0000000.0666670.2133330.1680000.160000
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0866670.0873330.1666670.1733330.1920000.186667
9KNeighborsClassifier0.1100000.0946670.1066670.1133330.1586670.153333
0LogisticRegression0.3333330.3333330.3333330.3333330.3333330.333333
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0466670.0006670.0900000.1600000.1880000.226667
5XGBClassifier0.1233330.1086670.1733330.1533330.2040000.193333
\n", - "
" - ], - "text/plain": [ - " modelTT err_train err2_train err2b_train_clean \\\n", - "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", - "3 DecisionTreeClassifier 0.040000 0.000000 0.120000 \n", - "4 ExtraTreeClassifier 0.040000 0.000000 0.073333 \n", - "6 ExtraTreesClassifier 0.040000 0.000000 0.066667 \n", - "8 GaussianNB 0.333333 0.333333 0.333333 \n", - "1 GradientBoostingClassifier 0.086667 0.087333 0.166667 \n", - "9 KNeighborsClassifier 0.110000 0.094667 0.106667 \n", - "0 LogisticRegression 0.333333 0.333333 0.333333 \n", - "7 MLPClassifier 0.333333 0.333333 0.333333 \n", - "2 RandomForestClassifier 0.046667 0.000667 0.090000 \n", - "5 XGBClassifier 0.123333 0.108667 0.173333 \n", - "\n", - " err_test err2_test err2b_test_clean \n", - "10 0.333333 0.333333 0.333333 \n", - "3 0.180000 0.209333 0.240000 \n", - "4 0.213333 0.232000 0.220000 \n", - "6 0.213333 0.168000 0.160000 \n", - "8 0.333333 0.333333 0.333333 \n", - "1 0.173333 0.192000 0.186667 \n", - "9 0.113333 0.158667 0.153333 \n", - "0 0.333333 0.333333 0.333333 \n", - "7 0.333333 0.333333 0.333333 \n", - "2 0.160000 0.188000 0.226667 \n", - "5 0.153333 0.204000 0.193333 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0200000.0000000.0900000.3266670.2800000.253333
4ExtraTreeClassifier0.0200000.0000000.1833330.2266670.2040000.293333
6ExtraTreesClassifier0.0200000.0000000.0500000.2133330.1946670.180000
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0800000.0893330.1200000.1866670.1693330.160000
9KNeighborsClassifier0.0966670.0880000.1300000.1733330.1506670.146667
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
0OneVsRestClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0233330.0006670.0800000.2066670.1613330.186667
5XGBClassifier0.0333330.0000000.0766670.2266670.1880000.200000
\n", + "
" ], - "source": [ - "def multiplie_bruit_centree_duplique(X, Y, classes=None):\n", - " if classes is None:\n", - " classes = numpy.unique(Y)\n", - " XS = []\n", - " YS = []\n", - " for i in classes:\n", - " \n", - " for k in range(0,5):\n", - " #X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.3\n", - " X2 = numpy.random.random((X.shape[0], 3)) * 0.8 - 0.4\n", - " X2[:,i] += 1\n", - " Yb = Y == i\n", - " XS.append(numpy.hstack([X, X2]))\n", - " Yb = Yb.reshape((len(Yb), 1))\n", - " YS.append(Yb)\n", - " \n", - " Xext = numpy.vstack(XS)\n", - " Yext = numpy.vstack(YS)\n", - " return Xext, Yext\n", - "\n", - "dfb = comparaison_train_test(models, X, Y, mbruit=multiplie_bruit_centree_duplique, acp=None)\n", - "dfb" + "text/plain": [ + " modelTT err_train err2_train err2b_train_clean \\\n", + "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", + "3 DecisionTreeClassifier 0.020000 0.000000 0.090000 \n", + "4 ExtraTreeClassifier 0.020000 0.000000 0.183333 \n", + "6 ExtraTreesClassifier 0.020000 0.000000 0.050000 \n", + "8 GaussianNB 0.333333 0.333333 0.333333 \n", + "1 GradientBoostingClassifier 0.080000 0.089333 0.120000 \n", + "9 KNeighborsClassifier 0.096667 0.088000 0.130000 \n", + "7 MLPClassifier 0.333333 0.333333 0.333333 \n", + "0 OneVsRestClassifier 0.333333 0.333333 0.333333 \n", + "2 RandomForestClassifier 0.023333 0.000667 0.080000 \n", + "5 XGBClassifier 0.033333 0.000000 0.076667 \n", + "\n", + " err_test err2_test err2b_test_clean \n", + "10 0.333333 0.333333 0.333333 \n", + "3 0.326667 0.280000 0.253333 \n", + "4 0.226667 0.204000 0.293333 \n", + "6 0.213333 0.194667 0.180000 \n", + "8 0.333333 0.333333 0.333333 \n", + "1 0.186667 0.169333 0.160000 \n", + "9 0.173333 0.150667 0.146667 \n", + "7 0.333333 0.333333 0.333333 \n", + "0 0.333333 0.333333 0.333333 \n", + "2 0.206667 0.161333 0.186667 \n", + "5 0.226667 0.188000 0.200000 " ] - }, + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def multiplie_bruit_centree_duplique(X, Y, classes=None):\n", + " if classes is None:\n", + " classes = numpy.unique(Y)\n", + " XS = []\n", + " YS = []\n", + " for i in classes:\n", + " for k in range(5):\n", + " # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.3\n", + " X2 = numpy.random.random((X.shape[0], 3)) * 0.8 - 0.4\n", + " X2[:, i] += 1\n", + " Yb = i == Y\n", + " XS.append(numpy.hstack([X, X2]))\n", + " Yb = Yb.reshape((len(Yb), 1))\n", + " YS.append(Yb)\n", + "\n", + " Xext = numpy.vstack(XS)\n", + " Yext = numpy.vstack(YS)\n", + " return Xext, Yext\n", + "\n", + "\n", + "dfb = comparaison_train_test(\n", + " models, X, Y, mbruit=multiplie_bruit_centree_duplique, acp=None\n", + ")\n", + "dfb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cela fonctionne un peu mieux le fait d'ajouter du hasard ne permet pas d'obtenir des gains significatifs à part pour le modèle [SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cela fonctionne un peu mieux le fait d'ajouter du hasard ne permet pas d'obtenir des gains significatifs \u00e0 part pour le mod\u00e8le [SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 11/11 [00:02<00:00, 4.74it/s]\n" + ] }, { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0333330.0000000.1433330.2000000.2333330.193333
4ExtraTreeClassifier0.0333330.0000000.2466670.2333330.3200000.300000
6ExtraTreesClassifier0.0333330.0000000.1433330.2066670.2200000.180000
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0900000.0133330.1333330.2200000.2066670.186667
9KNeighborsClassifier0.1033330.1100000.1233330.2066670.1800000.186667
0LogisticRegression0.3333330.3333330.3333330.3333330.3333330.333333
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0400000.0000000.1466670.1800000.2666670.173333
5XGBClassifier0.1000000.0333330.2100000.2066670.2400000.246667
\n", - "
" - ], - "text/plain": [ - " modelTT err_train err2_train err2b_train_clean \\\n", - "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", - "3 DecisionTreeClassifier 0.033333 0.000000 0.143333 \n", - "4 ExtraTreeClassifier 0.033333 0.000000 0.246667 \n", - "6 ExtraTreesClassifier 0.033333 0.000000 0.143333 \n", - "8 GaussianNB 0.333333 0.333333 0.333333 \n", - "1 GradientBoostingClassifier 0.090000 0.013333 0.133333 \n", - "9 KNeighborsClassifier 0.103333 0.110000 0.123333 \n", - "0 LogisticRegression 0.333333 0.333333 0.333333 \n", - "7 MLPClassifier 0.333333 0.333333 0.333333 \n", - "2 RandomForestClassifier 0.040000 0.000000 0.146667 \n", - "5 XGBClassifier 0.100000 0.033333 0.210000 \n", - "\n", - " err_test err2_test err2b_test_clean \n", - "10 0.333333 0.333333 0.333333 \n", - "3 0.200000 0.233333 0.193333 \n", - "4 0.233333 0.320000 0.300000 \n", - "6 0.206667 0.220000 0.180000 \n", - "8 0.333333 0.333333 0.333333 \n", - "1 0.220000 0.206667 0.186667 \n", - "9 0.206667 0.180000 0.186667 \n", - "0 0.333333 0.333333 0.333333 \n", - "7 0.333333 0.333333 0.333333 \n", - "2 0.180000 0.266667 0.173333 \n", - "5 0.206667 0.240000 0.246667 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelTTerr_trainerr2_trainerr2b_train_cleanerr_testerr2_testerr2b_test_clean
10AdaBoostClassifier0.3333330.3333330.3333330.3333330.3333330.333333
3DecisionTreeClassifier0.0266670.0000000.1800000.2200000.3533330.173333
4ExtraTreeClassifier0.0266670.0000000.1633330.2066670.3133330.220000
6ExtraTreesClassifier0.0266670.0000000.1200000.2266670.2066670.206667
8GaussianNB0.3333330.3333330.3333330.3333330.3333330.333333
1GradientBoostingClassifier0.0633330.0266670.1633330.2133330.2466670.200000
9KNeighborsClassifier0.0933330.1033330.1033330.1733330.1933330.160000
7MLPClassifier0.3333330.3333330.3333330.3333330.3333330.333333
0OneVsRestClassifier0.3333330.3333330.3333330.3333330.3333330.333333
2RandomForestClassifier0.0333330.0033330.1433330.2000000.2333330.246667
5XGBClassifier0.0533330.0000000.1600000.2000000.2466670.193333
\n", + "
" ], - "source": [ - "def multiplie_bruit_centree_duplique_rebalance(X, Y, classes=None):\n", - " if classes is None:\n", - " classes = numpy.unique(Y)\n", - " XS = []\n", - " YS = []\n", - " for i in classes:\n", - " \n", - " X2 = numpy.random.random((X.shape[0], 3)) * 0.8 - 0.4\n", - " X2[:,i] += 1 # * ((i % 2) * 2 - 1)\n", - " Yb = Y == i\n", - " XS.append(numpy.hstack([X, X2]))\n", - " Yb = Yb.reshape((len(Yb), 1))\n", - " YS.append(Yb)\n", - " \n", - " \n", - " Xext = numpy.vstack(XS)\n", - " Yext = numpy.vstack(YS)\n", - " return Xext, Yext\n", - "\n", - "dfb = comparaison_train_test(models, X, Y, mbruit=multiplie_bruit_centree_duplique_rebalance)\n", - "dfb" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Petite explication\n", - "\n", - "Dans tout le notebook, le score de la r\u00e9gression logistique est nul. Elle ne parvient pas \u00e0 apprendre tout simplement parce que le probl\u00e8me choisi n'est pas lin\u00e9aire s\u00e9parable. S'il l'\u00e9tait, cela voudrait dire que le probl\u00e8me suivant l'est aussi." + "text/plain": [ + " modelTT err_train err2_train err2b_train_clean \\\n", + "10 AdaBoostClassifier 0.333333 0.333333 0.333333 \n", + "3 DecisionTreeClassifier 0.026667 0.000000 0.180000 \n", + "4 ExtraTreeClassifier 0.026667 0.000000 0.163333 \n", + "6 ExtraTreesClassifier 0.026667 0.000000 0.120000 \n", + "8 GaussianNB 0.333333 0.333333 0.333333 \n", + "1 GradientBoostingClassifier 0.063333 0.026667 0.163333 \n", + "9 KNeighborsClassifier 0.093333 0.103333 0.103333 \n", + "7 MLPClassifier 0.333333 0.333333 0.333333 \n", + "0 OneVsRestClassifier 0.333333 0.333333 0.333333 \n", + "2 RandomForestClassifier 0.033333 0.003333 0.143333 \n", + "5 XGBClassifier 0.053333 0.000000 0.160000 \n", + "\n", + " err_test err2_test err2b_test_clean \n", + "10 0.333333 0.333333 0.333333 \n", + "3 0.220000 0.353333 0.173333 \n", + "4 0.206667 0.313333 0.220000 \n", + "6 0.226667 0.206667 0.206667 \n", + "8 0.333333 0.333333 0.333333 \n", + "1 0.213333 0.246667 0.200000 \n", + "9 0.173333 0.193333 0.160000 \n", + "7 0.333333 0.333333 0.333333 \n", + "0 0.333333 0.333333 0.333333 \n", + "2 0.200000 0.233333 0.246667 \n", + "5 0.200000 0.246667 0.193333 " ] - }, + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def multiplie_bruit_centree_duplique_rebalance(X, Y, classes=None):\n", + " if classes is None:\n", + " classes = numpy.unique(Y)\n", + " XS = []\n", + " YS = []\n", + " for i in classes:\n", + " X2 = numpy.random.random((X.shape[0], 3)) * 0.8 - 0.4\n", + " X2[:, i] += 1 # * ((i % 2) * 2 - 1)\n", + " Yb = i == Y\n", + " XS.append(numpy.hstack([X, X2]))\n", + " Yb = Yb.reshape((len(Yb), 1))\n", + " YS.append(Yb)\n", + "\n", + " Xext = numpy.vstack(XS)\n", + " Yext = numpy.vstack(YS)\n", + " return Xext, Yext\n", + "\n", + "\n", + "dfb = comparaison_train_test(\n", + " models, X, Y, mbruit=multiplie_bruit_centree_duplique_rebalance\n", + ")\n", + "dfb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Petite explication\n", + "\n", + "Dans tout le notebook, le score de la régression logistique est nul. Elle ne parvient pas à apprendre tout simplement parce que le problème choisi n'est pas linéaire séparable. S'il l'était, cela voudrait dire que le problème suivant l'est aussi." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([[1., 0., 0., 1., 0., 0.],\n", - " [1., 0., 0., 0., 1., 0.],\n", - " [1., 0., 0., 0., 0., 1.],\n", - " [0., 1., 0., 1., 0., 0.],\n", - " [0., 1., 0., 0., 1., 0.],\n", - " [0., 1., 0., 0., 0., 1.],\n", - " [0., 0., 1., 1., 0., 0.],\n", - " [0., 0., 1., 0., 1., 0.],\n", - " [0., 0., 1., 0., 0., 1.]]), array([[1.],\n", - " [0.],\n", - " [0.],\n", - " [0.],\n", - " [1.],\n", - " [0.],\n", - " [0.],\n", - " [0.],\n", - " [1.]]))" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = numpy.zeros((9, 6))\n", - "Y = numpy.zeros((9, 1))\n", - "for i in range(0, 9):\n", - " M[i, i//3] = 1\n", - " M[i, i%3+3] = 1\n", - " Y[i] = 1 if i//3 == i%3 else 0\n", - "M,Y" + "data": { + "text/plain": [ + "(array([[1., 0., 0., 1., 0., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 0., 1.],\n", + " [0., 1., 0., 1., 0., 0.],\n", + " [0., 1., 0., 0., 1., 0.],\n", + " [0., 1., 0., 0., 0., 1.],\n", + " [0., 0., 1., 1., 0., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 0., 1., 0., 0., 1.]]),\n", + " array([[1.],\n", + " [0.],\n", + " [0.],\n", + " [0.],\n", + " [1.],\n", + " [0.],\n", + " [0.],\n", + " [0.],\n", + " [1.]]))" ] - }, + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = numpy.zeros((9, 6))\n", + "Y = numpy.zeros((9, 1))\n", + "for i in range(9):\n", + " M[i, i // 3] = 1\n", + " M[i, i % 3 + 3] = 1\n", + " Y[i] = 1 if i // 3 == i % 3 else 0\n", + "M, Y" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr',\n", - " n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n", - " tol=0.0001, verbose=0, warm_start=False)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf = LogisticRegression(multi_class=\"ovr\", solver=\"liblinear\")\n", - "clf.fit(M, Y.ravel())" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/xadupre/vv/this/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 0.])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
LogisticRegression(multi_class='ovr', solver='liblinear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], - "source": [ - "clf.predict(M)" + "text/plain": [ + "LogisticRegression(multi_class='ovr', solver='liblinear')" ] - }, + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf = LogisticRegression(multi_class=\"ovr\", solver=\"liblinear\")\n", + "clf.fit(M, Y.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A revisiter." + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "clf.predict(M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A revisiter." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/dsgarden/file_dattente_ex.ipynb b/_doc/notebooks/dsgarden/file_dattente_ex.ipynb index b9e8f912..3b0349ac 100644 --- a/_doc/notebooks/dsgarden/file_dattente_ex.ipynb +++ b/_doc/notebooks/dsgarden/file_dattente_ex.ipynb @@ -1,300 +1,303 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# File d'attente, un exemple simple\n", - "\n", - "Cet exemple vient illustrer le paragraphe sur les files d'attente et l'esp\u00e9rance de vie des ampoules." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0749720223112896" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import math\n", - "import random\n", - "\n", - "def generate_expo(mu):\n", - " return random.expovariate(mu)\n", - "\n", - "generate_expo(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les param\u00e8tres de la simulation." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# File d'attente, un exemple simple\n", + "\n", + "Cet exemple vient illustrer le paragraphe sur les files d'attente et l'espérance de vie des ampoules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "S = 10000\n", - "iteration = 500\n", - "mu = 1.0 / 100" + "data": { + "text/plain": [ + "0.0749720223112896" ] - }, + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "\n", + "def generate_expo(mu):\n", + " return random.expovariate(mu)\n", + "\n", + "\n", + "generate_expo(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les paramètres de la simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "S = 10000\n", + "iteration = 500\n", + "mu = 1.0 / 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On crée un tableau de ``S`` ampoules qui contient la durée de vie restante de chaque ampoule." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On cr\u00e9e un tableau de ``S`` ampoules qui contient la dur\u00e9e de vie restante de chaque ampoule." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "itération : 0 moyenne durée : 0.0 grillées : 10000\n", + "itération : 100 moyenne durée : 99.7184 grillées : 95\n", + "itération : 200 moyenne durée : 98.7154 grillées : 93\n", + "itération : 300 moyenne durée : 99.2155 grillées : 101\n", + "itération : 400 moyenne durée : 98.9101 grillées : 108\n", + "nombre moyen d'ampoules grillées : 99.88577154308618\n" + ] + } + ], + "source": [ + "ampoule = [0 for a in range(S)]\n", + "moyenne_grille = 0\n", + "stats = []\n", + "\n", + "\n", + "for i in range(iteration):\n", + " grille = 0\n", + " mean = 0\n", + "\n", + " for n in range(S):\n", + " mean += ampoule[n]\n", + " if ampoule[n] == 0:\n", + " # remplacement d'une ampoule grillée\n", + " grille += 1\n", + " # on détermine la durée de vie de cette ampoule\n", + " # on arrondit à l'entier le plus proche\n", + " ampoule[n] = int(generate_expo(mu))\n", + " else:\n", + " # on enlève une heure à la durée de vie de l'ampoule\n", + " ampoule[n] -= 1\n", + "\n", + " mean /= S\n", + "\n", + " stats.append(dict(i=i, mean=mean, grille=grille))\n", + "\n", + " if i > 0:\n", + " moyenne_grille += grille\n", + " if i % 100 == 0:\n", + " print(\"itération : \", i, \" moyenne durée : \", mean, \" grillées :\", grille)\n", + "\n", + "moyenne_grille = float(moyenne_grille) / float(iteration - 1)\n", + "print(\"nombre moyen d'ampoules grillées :\", moyenne_grille)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "it\u00e9ration : 0 moyenne dur\u00e9e : 0.0 grill\u00e9es : 10000\n", - "it\u00e9ration : 100 moyenne dur\u00e9e : 99.7184 grill\u00e9es : 95\n", - "it\u00e9ration : 200 moyenne dur\u00e9e : 98.7154 grill\u00e9es : 93\n", - "it\u00e9ration : 300 moyenne dur\u00e9e : 99.2155 grill\u00e9es : 101\n", - "it\u00e9ration : 400 moyenne dur\u00e9e : 98.9101 grill\u00e9es : 108\n", - "nombre moyen d'ampoules grill\u00e9es : 99.88577154308618\n" - ] - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
imeangrillegrille_sum
000.0000100000
1198.51429999
2298.552691190
3398.3991108298
4498.442594392
\n", + "
" ], - "source": [ - "ampoule = [0 for a in range(0,S)]\n", - "moyenne_grille = 0\n", - "stats = []\n", - "\n", - "\n", - "for i in range(0,iteration):\n", - " grille = 0\n", - " mean = 0\n", - "\n", - " for n in range(0,S):\n", - " mean += ampoule[n]\n", - " if ampoule[n] == 0:\n", - " # remplacement d'une ampoule grill\u00e9e\n", - " grille += 1\n", - " # on d\u00e9termine la dur\u00e9e de vie de cette ampoule\n", - " # on arrondit \u00e0 l'entier le plus proche\n", - " ampoule[n] = int (generate_expo(mu))\n", - " else :\n", - " # on enl\u00e8ve une heure \u00e0 la dur\u00e9e de vie de l'ampoule\n", - " ampoule[n] -= 1\n", - " \n", - " mean /= S\n", - " \n", - " stats.append(dict(i=i, mean=mean, grille=grille))\n", - " \n", - " if i > 0: \n", - " moyenne_grille += grille\n", - " if i % 100 == 0:\n", - " print(\"it\u00e9ration : \", i, \" moyenne dur\u00e9e : \", mean, \" grill\u00e9es :\", grille)\n", - "\n", - "moyenne_grille = float (moyenne_grille) / float (iteration - 1)\n", - "print(\"nombre moyen d'ampoules grill\u00e9es :\", moyenne_grille)" + "text/plain": [ + " i mean grille grille_sum\n", + "0 0 0.0000 10000 0\n", + "1 1 98.5142 99 99\n", + "2 2 98.5526 91 190\n", + "3 3 98.3991 108 298\n", + "4 4 98.4425 94 392" ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas\n", + "\n", + "df = pandas.DataFrame(stats)\n", + "df = df[[\"i\", \"mean\", \"grille\"]]\n", + "df[\"grille_sum\"] = df[\"grille\"].cumsum() - 10000\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
imeangrillegrille_sum
000.0000100000
1198.51429999
2298.552691190
3398.3991108298
4498.442594392
\n", - "
" - ], - "text/plain": [ - " i mean grille grille_sum\n", - "0 0 0.0000 10000 0\n", - "1 1 98.5142 99 99\n", - "2 2 98.5526 91 190\n", - "3 3 98.3991 108 298\n", - "4 4 98.4425 94 392" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas\n", - "df = pandas.DataFrame(stats)\n", - "df = df[[\"i\", \"mean\", \"grille\"]]\n", - "df[\"grille_sum\"] = df[\"grille\"].cumsum() - 10000\n", - "df.head()" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAHjCAYAAADsXbRvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREqaQpGIiIiIiJQ0hSIRERERESlp/x/+lOs/UR4RVQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(3, 1, figsize=(14,8))\n", - "df[1:].plot(x=\"i\", y=\"mean\", label=\"dur\u00e9e de vie moyenne restante\", ax=ax[0])\n", - "df[1:].plot(x=\"i\", y=\"grille\", label=\"ampoules grill\u00e9es ce jour\", ax=ax[1])\n", - "df[2:].plot(x=\"i\", y=\"grille_sum\", label=\"total des ampoules grill\u00e9es\", ax=ax[2])\n", - "ax[0].set_xlabel(\"dur\u00e9e\")" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAHjCAYAAADsXbRvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREqaQpGIiIiIiJQ0hSIRERERESlp/x/+lOs/UR4RVQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(3, 1, figsize=(14, 8))\n", + "df[1:].plot(x=\"i\", y=\"mean\", label=\"durée de vie moyenne restante\", ax=ax[0])\n", + "df[1:].plot(x=\"i\", y=\"grille\", label=\"ampoules grillées ce jour\", ax=ax[1])\n", + "df[2:].plot(x=\"i\", y=\"grille_sum\", label=\"total des ampoules grillées\", ax=ax[2])\n", + "ax[0].set_xlabel(\"durée\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/dsgarden/README.txt b/_doc/notebooks/dsgarden/index.rst similarity index 53% rename from _doc/notebooks/dsgarden/README.txt rename to _doc/notebooks/dsgarden/index.rst index 2426de1d..0d5b6509 100644 --- a/_doc/notebooks/dsgarden/README.txt +++ b/_doc/notebooks/dsgarden/index.rst @@ -1,16 +1,12 @@ - Le petit coin des data scientists ---------------------------------- +================================= Ce sont quelques notebooks sur des points particuliers qui surgissent au quotidien quand on traite des données. -.. contents:: - :local: - - - - - - +.. nbgallery:: + :caption: Notebooks Gallery + :name: rst-nb-gallery-dsgarden + :glob: + * diff --git a/_doc/notebooks/dsgarden/quantile_regression_example.ipynb b/_doc/notebooks/dsgarden/quantile_regression_example.ipynb index 82d02383..07532a2d 100644 --- a/_doc/notebooks/dsgarden/quantile_regression_example.ipynb +++ b/_doc/notebooks/dsgarden/quantile_regression_example.ipynb @@ -1,378 +1,239 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# R\u00e9gression quantile illustr\u00e9e\n", - "\n", - "La r\u00e9gression quantile est moins sensible aux points aberrants. Elle peut \u00eatre d\u00e9finie comme une r\u00e9gression avec une norme *L1* (une valeur absolue). Ce notebook explore des r\u00e9gressions avec des quantiles diff\u00e9rents." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Un jeu de donn\u00e9es non sym\u00e9trique" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000, 1), (1000,))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy.random as npr\n", - "import numpy\n", - "n = 1000\n", - "eps = npr.normal(n)\n", - "X = npr.rand(n, 1) * 5\n", - "X1 = npr.normal(size=(n, 1)) * 1\n", - "X2 = npr.normal(size=(n//2, 1)) * 10\n", - "X2 = numpy.vstack([X2, numpy.zeros((n//2, 1))])\n", - "eps = - numpy.abs(X1) + numpy.abs(X2)\n", - "Y = (0.5 * X + eps).ravel()\n", - "X.shape, Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAATwAAAEyCAYAAABnD2x2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztnXtwHMd957+/BRaERYJYEYQI8AFSNiifk6hE5RidGeUSJHJi2fErp0suvpylsxTTZUuK5cRxrFSlwrvclRzHsZizdTnDFnViXRLHFca2LEt+RojsHC4SJVKRbVkkbRMUBKwIgtwlQAnEY/v+2Olhb2MePTM9r53+VLFIgLszPT3d3/71r3/9a2KMwWAwGIpAKe0CGAwGQ1IYwTMYDIXBCJ7BYCgMRvAMBkNhMIJnMBgKgxE8g8FQGIzgGQyGwmAEz2AwFAYjeAaDoTB0JnmzjRs3sh07diR5S4PBUACeeuqpM4yxfr/PJSp4O3bswOHDh5O8pcFgKABENKHyOTOlNRgMhcEInsFgKAxG8AwGQ2EwgmcwGAqDETyDwVAYjOAZDIbCYATPYDAUBiN4BoOhMBjBM7QF4/U67pmYwHi9nnZRDBkm0Z0WBkMcjNfruOGZZ7DYaKCrVMK3rrkGe3p70y6WIYMYC8+Qe8ZqNSw2GlgBsNhoYKxWS7tIhoxiBM+Qe0YqFXSVSugA0FUqYaRSSbtIhoxiprSG3LOntxffuuYajNVqGKlUzHTW4IoRPENbsKe31widwRczpc0AZoXRYEgGXwuPiLoBPA5gjfX5v2OM/TERXQngcwA2AHgawLsYY4txFrYdMSuMBkNyqFh4FwH8EmPsGgC7ANxIRK8H8KcA7mWM7QRwDsBt8RWzfTErjAZDcvgKHmsyb/1Ytv4wAL8E4O+s3z8I4B2xlLDNMSuMBkNyKC1aEFEHgKcADAO4D8APAdQYY8vWRyYBbImlhG2OWWE0GJJDSfAYYysAdhFRBcAXALzO6WNO3yWivQD2AsDQ0FDIYrY3ZoXRYEiGQKu0jLEagDEArwdQISIumFsBTLl8Z5Qxtpsxtru/3/dQIYPBYIgNX8Ejon7LsgMRvQrAGwA8B+AxAP/e+tgtAL4UVyENBoNBBypT2kEAD1p+vBKAzzPGHiai7wP4HBH9NwBHANwfYzkNBoMhMr6Cxxj7FwDXOvz+RwCui6NQBoPBEAdmp4XBYCgMRvAMBkNhMIJnMBgKgxE8g8FQGIzgGQyGwmAEz2AwFAYjeAaDoTAYwTMYDIXBCJ4hEUxWZ0MWMGdaGGInqazO4/W6SbNl8MQIniF2nLI66xYkkyrfoIKZ0hpiJ4msziZVvkGFQll4ZsqTDklkdeaiyi08kyrf4ERhBM9MedJFzOocx8BjUuUbVCiM4CXhRzL4E+fAY1LlG/wojA/PnA6WDYyvzZAmhbHwzJQnGxhfmyFNCiN4gJnyZAEz8BjSpFCCZ8gGZuAxpEVhfHgGg8FgBM9gMBQGI3gGg6EwGMEzGAyFwQiewWAoDEbwDAZDYTCCZzAYCoMRPIPBUBiM4BkMhsLgK3hEtI2IHiOi54joe0T0Aev3+4joRSI6av15c/zFbR/MGQ8GQ/KobC1bBvB7jLGniagHwFNE9A3r/+5ljH08vuK1JyY3n8GQDr4WHmNsmjH2tPXvOQDPAdgSd8HaGZMiyWBIh0A+PCLaAeBaAP9s/eoOIvoXIjpARJe7fGcvER0mosMzMzORCtsumNx8BkM6EGNM7YNE6wD8I4D/zhj7eyLaBOAMAAbgTwAMMsZu9brG7t272eHDhyMWuT0w52sYDPogoqcYY7v9PqeUHoqIygAOAfgrxtjfAwBj7CXh/z8D4OGQZS0kRUiRZETdkDV8BY+ICMD9AJ5jjH1C+P0gY2za+vHXAHw3niKmh+mw4TELM4YsomLhXQ/gXQCeJaKj1u/+EMA7iWgXmlPakwDeG0sJU8J02GiYQ5MMWcRX8Bhj3wFADv/1iP7iZAfTYaNhzq4wZBGT4t0F02GjYc6uMGQRI3gumA4bnSIszBjyhRE8D0yHNRjaC5M8wGAwFAYjeAaDoTAYwfOgnTOa6Hq2dq4jQ/thfHgutHMcnq5na+c6MrQnxsJzoZ0zmuh6tnarI2Ottj/GwnOhnePwdD1bO9WRsVaLgRE8F9o5Dk/Xs7VTHZmdNeHI235zI3getHMcnq5na5c6aidrNSnyaBUbwTMY0F7Wqgo6LLM4rOK4LUYjeAaDhU5rNctTPV2WmW6rOAmL0QiewaCZrE/1dFlmuq3iJPyoRvAMBs1kfQFEp2Wm0ypOwo9qBM9g0EzWF0Cy6q9MolzKh/jowBziYygKSfnwsuwrTBKth/gYDIZgJBGuk2VfYVaF2AieoW3JaqfTRVZ9hVkWYiN4hrYky51OF1n1FWZViAEjeIac42bFZbnT6SKIkz9JazerQgwYwTPkGC8rLsudTicqvsKkrd2srgIDRvBC0+7+oTzgZcVludMlTRrWblb3WBvB88FJ2IrgH8oDflZcVjtd0sRl7eZx0DeC54GbsBXBP5QHjBWnRhz1lNdB3wieB27CVhT/UB6Iy4rLo/Xihe56yuugbwTPAzdhy6Jl0W4dNE3yar0kSV4HfV/BI6JtAA4CGADQADDKGPsLItoA4G8B7ABwEsBvMMbOxVfU5OHCdrBatX8nCsvd27enWLpLmA6ql6xYL1kexLI46KugYuEtA/g9xtjTRNQD4Cki+gaA/wzgW4yxjxLRRwB8BMAfxFfU9HjwpZew2GjgQLUKArDMWKaEJSsdtB0Yr9dxamEBnUSA9Z7TsF7yMIjlcVHI99Qyxtg0Y+xp699zAJ4DsAXA2wE8aH3sQQDviKuQaSKKyRJjWGQsc6d08elFB5Cr6UXW4CLzmelpMADvGRxMTWja7US4rBDIh0dEOwBcC+CfAWxijE0DTVEkoitcvrMXwF4AGBoailLWVBB9FR1ELRZeVoQlr9OLrCGKDBjDUHd3anWZVx9Z1lEWPCJaB+AQgLsYY+eJSOl7jLFRAKNAMz1UmEKmiSwmADIpLHmcXmQNLjIXGw0QEfrK5dTKYgaxeFDKh0dEZQAPA/gaY+wT1u+eBzBiWXeDAMYYY6/1uo7Jh2fIOqNTU7jj+HGsMIY1GfWdGVajmg/P14dHTVPufgDPcbGzeAjALda/bwHwpTAFNRiyxOzSEhqMoYHi+M7G63XcMzGB8Xo97aLEjsqU9noA7wLwLBEdtX73hwA+CuDzRHQbgFMAfj2eIiZLlkMBDPFTNN9ZHlaDdeIreIyx7wBwc9jdoLc46VK0l29YTdF8Z0ULaTI7LQSK9vINzhRpAahoFm3bC16QKWrRXr7BUDSLtq0FL+gUtWgvXyd59X3mtdw6KZJF29aCF2aKWqSXr4u8+j7zWm5DeHzDUvJM3FuudC7n5zk0IK/boPJabkN42trCi3OKqtM6yLulkVffZ17LHRQzbb9EWwseEN8UVeeKbt5Xh3UOLEl2ziL4bPM+mOqm7QUvLnRaB+1gaegYWNLonHn22aoMDnkfTHVjBC8kOq2DIlgaKujsnO0+jVMdHNphMNWJEbwIqJ4JqtLx8mxp6EJX5yzCNE51cBAH075y2V6Yabf6UMUIXowUoePpRJelW4RpXJDBgT+7U1tsd0tYxghejBSh4+lGh6VbhGlc0MHBLQSnaAOyEbyQqIyMeet47TLaF8UnGmRwcGqLRRyQjeCFQHWqmqeOF+f0Ow0h9RKDdhH2ILi1xTwNyDowgheCICNjXhYj4hrts+bHFMvTQYRbBwZw88BALt5RVOS2mKcBWRdtvbUsLtrxlLC4nint7Vvylr2W8jCGT09P44Znnsnllj4d7Ontxd3btxdC7ABj4YWiHUfGuJ4pTT+mk3XJy7PQaIABYCiO/0qF0akpHJqZwU39/di7eXPaxdGOEbyQ5GWqGoQ4ninNwcHJurx7+3Z865prcLBaxQPVauaO3NRFGD/l6NQU3nvsGADg6+fOAYB20Uvbf2oEr2BkbQEhTtysS16emwcG2spK54T1mx6amVn1c1TBE9sbkH4YjBE8RXQLRRrCk7UFhLjx22XQjlY6EH4B6qb+ftuy4z9HQW5vt2zalHoYTOEFT0V4dAtFWsJTxLgrr10G7Yqf39StzXNrLqoPj1//1MJCS3sD0g+DKbTgqQqPbqFIS3jyFgiti6IJvZff1K/N7928OdI0Vrx+JxE6iADLT3qzFQJkfHgpodoRdAuFfL2+chn3TEz4WplRG0o7ri6rUEShd5uuxy3+4vXBGN4zOIih7u6W9pZmuyu04Kl2BN1CIfuW7jpxwtPK1DkFTsNvlfbKXJaFPum6UW3zQcolfla+ftaCugsteEE6gm6h4Ne7Z2LCd8TN85RMl1hHFYYsLlCklfDUr80HKZfTZ7M6uAAFFzwg/Y6gMuLmeUp2sFq1g3zDinW7ri6nNZD5tfkg5XKLdczq+ym84KWNyoib5SmZF+P1Oh6oVsGsnzuIQol1ni1cL7I6kAUpV1afwQ1fwSOiAwDeAuA0Y+ynrN/tA/AeADxS8Q8ZY4/EVch2R8XKTNsSDcNYrYZl1pQ7AnBrSH+O2Kk6iHBqYQHj9Xru6kMmqwNZUFdPFp/BDWKMeX+A6OcBzAM4KAnePGPs40Futnv3bnb48OGQRS0maTv8o6D7KEt5O1i7TG0N0SGipxhju/0+52vhMcYeJ6IdOgoVhjx3eFXcnjHvviudo/+e3l7bYszL1Lad225eny2KD+8OIroZwGEAv8cYO+f3haDkvcOr4PWM7eC70jkVV/EXZaUjptF2k3r2PPfLsPnw/hLAawDsAjAN4M/dPkhEe4noMBEdnpE2J/vBV/jSyqUWBjn/mt//eeWL05WjzqtMukjiHtxi/JMrr/SMV/yjH/849Rx34ntdaDRwsFqNfE2/tpXUswfNcSiWO4l24kUoC48x9hL/NxF9BsDDHp8dBTAKNH14qvcYr9dxQFjh6wy5wpckXiOf2/95WS3ylBCA746MIGVK4rl142Uxih3xYqOBfSdPYt+OHanE/Y1UKugkwgpjYAAOVKuRgnD96jjJ2UCQlVk5wzQBqfpgQ1l4RDQo/PhrAL6rpziXGKvVsCKs8L07YxHbTniNfG7/52e17OltZqQFEGoETyLjcNpZjTm8I5YANAB889y5UNaODmtpT28v3j0wALJ+XmEsUr341XHY2UAYi8uvzbqVe4kxLEo+2KRRCUv5GwAjADYS0SSAPwYwQkS70EwYexLAe3UXzGmLStbxGvn8LDk/MQ87gicRJxXHdqUw8I647+RJfPPcOTQQztrRZS3dPDCAB196SUvd+9VxmAWiKJa5qm9WDikSLbw0Zmy+YSk6CRqWkhUHdBC8yhzleaI0ziTq0e8eSU57o95LdziNrrrX/R7vmZjAH/34x1gB0AHgT6680p5N6EROAhpH/1ANS8m04BlaydMAIJc1qc7ldv+kv58H0tjnHFcIlrY4PEN2yMtuC6/Dc5LaghS1rvJS11HQESfpJlROwpaFEKy2E7wijMxJEKUe3TaU52kLUlGIKuxuiylOwuYlakkNiG0leHkOiMwSUevR7/CcOMprhDQdnN61m7Dxz15sNFAiQl+5bF8nqT25bSV47bAzIQtErUevxhvHYUi/ePQoFhlDFxEe27Urc+9c1zNnUdjd3rXbgLd/eBi3Hz+OFcZw14kTuHrtWvs7SbgRciV4fi88b6lqsoqOenRqvHFY4AerVVy0Ft4uMoaD1WqiYpDU6nSWZi/yM8vv2mvAm11aAmMsdMhQVHIjeCovPCmzuN2Jqx7bzQJXaZO6njnIdeK0BFWF181aS9soyY3gqb7wIqyuRUWlQ8RRj3E09psHBnCgWsUSYygTJRqgrtImdT1zkODuG555xvaT3bdzZ+TDtEWiZrBO2yjJjeBFbThZ9H+kQZpTozga+57eXozt2pXKux2pVNBBhAZjrtmcdT2z6nXGajVcbDTQANBgDHccP97iJ4uCrv3taRoluRG8KA0nS/4PXp60xDftaWUcjT3NDkTS307oKp/KdUYqFZQsEQYu7eHVcf887m+XCZseKtPIG6Kzsrmdly3NFEa6Uk4ZLqWwZ2juD02jXcltfU9vL+7buRNlIpQArIn4jsXri22nOyf722VyY+GpWmlZiPL3Ik4LS9U3F5cPRb5/u7sRorQrHXXj1if2bt6Mq9eujeX6qm0nq+8+N4KnKhRZj/KPS3yDTNvjmALK998/POx7wHjeCTt46HKxePUJHe/YrS/5XTdrLiSR3AieqlAkHeUflHYN+ZDvf2hmxnaeX2yDEBTA2WoJ0650vSuntq7TsgozOI/X69h38qT97rMWfpQbwQOAWzZtAgDPzLFpL3urkJeQjyj337VuHb5+7hyAZjJOcRtRHtFpteh6V3JbB5z3sIYlaF8SQ2IaaC5skLSFLG1yIXhyY/NzlmbFmkuStIVevv9YrWZnHi6hGWGfZ3Ra0LyudJxzIbb1eyYmtFv5QfoSryNb7NBcJb7j+HEA0BoPGJZcCF7a0zWdxOnMDSv0usok339NRhaKdOBmlUWpO54N+cGXXtLi5wpqOepui+L9STjPY4kx3K4xHjAKuRC8tKdruvCbFqWxshUkn1kQ0rY4deP0PFGmuXEM4kHqPI6FBfH+feUy7jh+HEtW3F7DIx4wyXafC8Fza2x560xejTytla0g+cyC0g6uBaeN8pwooiWmStLp51Ktc52C61VHtx8/jgZjrvGASbf7XAgeALsSxmo1PHvhQqiQh7g3Vftd28tSTWvaHiSfWdHw64wqMw+3drGnt5kq6Q6XVElxo2vW5FVHPB7Qy1eZdFvLjeCJFVuy/ANey95OQbBBRxJVgQySQcJtypHWtN2tTHGXJeqBRklY936d0WmV9J6JCfSVy5hdWkJfuew4MPPyn1pYQCOlVEm6XA4qguXlq0y63edG8MSKZYyhRARyOe7NSYCCjiRBBDLItd2mHGn6vOQyuXVkXeWKegJbUlMglc7I604OySgB9p5WUdCAS+6CTiJ0EAEpHVuow+XgV0dBBw3jw7OQK3b/8DBml5YcK8mpkoOOJEFETGdcVVamjnJH1ikwUaYxSUyBRAtStTOKIRlAMxwHVhYVcWAWyw/G8J7BQQx1d+fKFy3iJ1hBBo0kyI3gBRkJnCo56EgSRMTabUVSxE9gwkwvowwQqt8NO+11EniV4yTFRQhu4a1xGZjF8nsF0et6prgQy+NWR1nrG217Lq2uzdlZeVFp4WXhpXU4eJxp1aOcn8vLxX14XuUL+uyjU1MtK55p70/N2n7Zwp9Lq8NMjsvUzpOQeo3QUaaXUerW77s6wkXCWJ9yuXhqJacV2iDPPjo1hfcdO2ZPl7OwNznOsJY4aVvBcyILQpOlkVG1Ptw66EjFP+NvGkQVLR1TMF1B5uP1etOyE35XykBd69rVYeLwYkKs2E4ivHtgwN6Tm6QIZiXGTVdDU8n4mzSqouUVIwfAXlUNUy+6gszHajU7ezHQ9A3et3OntjYzXq/bcXJB/IlBBgav581cHB4RHQDwFgCnGWM/Zf1uA4C/BbADwEkAv8EYOxdbKR0Iaq2JFbvCGD49PY0D1SoIzWy1SVlbacTbOdWVX4dUXZmUM/5mZYruN22MyzfJ0RVkPlKpYE2p5HsoT5jZCz/Tlx9zeaBaxSd37nT0P4ZNjeWXLiqLcXj/G8CnABwUfvcRAN9ijH2UiD5i/fwH+ovnTJgGySuWn7jENzXD+ncSIQ68gSS5auVWV24NLUjd5nmPs5fo6LA6vN6z7giAsAI9VqthUbAeF63MJg3JAHBK7uq1KCOXS1y5lp83c3F4jLHHiWiH9Ou3Axix/v0ggDEkKHhhGiSv2IPVKg5Uq1jhMVJoWiedRDi1sIDxel3rdMHpyLwk447c6sqtoQUNos5SyEEQvEQn7rjKoPUW1yLNSKWCLiLbwusAHHcwide/2Gjg9uPHwVxmReIAL8YmlgC84fLLsW/HjlVlS7I/hPXhbWKMTQMAY2yaiK5w+yAR7QWwFwCGhoZC3q6VsA1SrlTuw+Mi+JnpaW2peoB4j8xTxauunBraSKWCTmshQuUYviQbq068RCcJIddZb1H6w2O7dtk+vPWdnbh3ctLe+dFXLttb5fj1vbZ1OlmCYrmcxC7phcTYFy0YY6MARoFmHJ6Oa4ZtkE6JRPkItsKYdsfpSCW+I/OccPOzBK0rJv2dhdXtOPASnbCClEZdRRFo/py8b6xY2zbv3LKlZR8wn8aK+4PlWZFsac4uLbWki5IXgdKIWAgreC8R0aBl3Q0COK2zUCqEaZBupn9cvqg9vc0j83hGDDFFTpCOofJZp8bDn9krEl6Giz9DU6APVqv25u+0w2iSJOwiQJqHnEe5lzj9JMZwdH5+lXjxNsQzoIizov3Dwzi1sIBOaW8wL5NTvaQRsRBW8B4CcAuAj1p/f0lbiWLETdh0TGHcOojTkXlBOobqZ+XGE1ao5DqCdb20w2iSJNIigFRX/PdZso6d2qr83m/q78e363VXV4g4K7rYaNiLHR1EeM/gYEuIS9KGhhcqYSl/g+YCxUYimgTwx2gK3eeJ6DYApwD8epyF1IWf3yZsg/TrIPK1g4xsqp/VJVRyHQFoEc48rcSGJdIigPAO+srlUKubceLWVp36htfZtuKzkuDXA2MY6u5WWpVOY9FLZZX2nS7/dYPmsiRCHE52tw7iZvU5dQy39Euqo2AUoXIKnRHLkbWV2NGpKRyamcFN/f2hD4bxmrJGWQSQDzISVzedQj5UyhPmGdzwEnP5vfv5OEX/nOjvk+srLkMjDIXZaRGWsJmMvaw+r8Yi+96CjIJOQuV3MpbK9C1LK7GjU1N477FjAGAfAxlU9FQs8qiLABwnKyjKzgvVZ3BD5zRSfFYva1D+bJoYwfMgSiZjvyPzeAOQP+fmewvbWPxOxorTcRzHiuWhmZlVPwcVPJVn1tFB5YHtd44fx6JDuE+YdxD2vcU1jcyKoPlhBE/CKXAyTCZj1ZE0zkUClfIHjbtTJa7T0G7q77ctO/5zUJJ0lvN2MV6vrwr34fSVyyAilAJkPo7yDFkQp7RCnYzgCfgFTgZtVConrcW5SKDaKdw6YhTcViyjhm1way6KDy8NZ7kc7iP6ee86ccJe4dw/PBybpZaVeMo0w3eM4Al4BU6GaSTiSOrn04tjkUClU7h1RC/C+jV1TZ/3bt4c+RT7pK0ct8FHjn+bXVpa9V2vrC6q78rtQKE0SCP+jmMET8CpUQbtGG6NM8r0OAp+1wo6NYp6QlucU8kkLZig93KrD7/613XgETkcKJSW4KURf8cxgicQdarj1Th1v2RdnTvoM0cR7jinkklOk8Ley2nw8asTv/r2agfid0sOBwqlRRouBY4RPIko1pVfjJOul6y7cwd5ZhWLJI3whCRXm3Xfy6tOvOrbrx3I381C4DMnrYWTTAteVpysqviJga6XHLXDRalXL+FOwspSDeaOc7U57L3C1LtXffu1gyiDbJyHLOm4R1gyK3hhgzHTFMikTHW/Ud/r/mK9dhDhVivVfZCyugl33M5o1WBunXXv9Ex3b98eaoU07GDgVt8qwhtmkNXlN/T6blortZkVvKCdJ64KDOOcjvvFuXVuFTET65WnuteVAzBOK2usVsOphQXXNhHXYOe1DzTIfeIYDJIUedVre303bIyrTjIreEE7z8Fq1U7friqQfg0lSyeMyTh1OBUx4/UqprpPqgNGTbnUSYQOKf2Q/Bnd70mXqMQ1GOgaYMV3E6Wsbt/VGeMahcwKXpCGNl6v44Fq1Q6c9TsyMGzKpTSX8lVwEjP5DFNerwerVTxQrdoHGKk0OJUTrtw6oI6US2AM7xkcxFB3d0ubiOs9iSKgmk/QjTRXJv1wejdR9hKrHB0QNcY1LJkVPEB99BqrNU/PAprHBd7q4ZPyO0VJJKuhJG7wxvaxU6fwxdlZAEADza1L8uf29Pbi5oEB5fKM1+sYOXrUPvTlgWoVj+3apWWq44X8DpyENg7rKQ6rMa2VSU6QGNG7t29XstBVTzPTEeOqg0wLnipOncIJ+RQlAjz3j+oclXV0IBXB3NPbi+vWr8dDs7P24SlO0fv8s0EEi5/yBjRPuAoSExZFlG7ZtAmAt1Wp21rIm3XvhPg+APdtfSrvxi2jtmqbzoqFmxvB8+pMqpUpbuMBYE/7vK7vNUUL8vJ0hJIEOT5xjWaLZ6RSQZnItvC6pIEiSsolt7p0OoMkKdLcDaADue5u2bQpUoyoU/sFgiW6kPuSCUtxQVfONtnHBYQ7tyGMtRa1AwXd4SAnJHBLMKrKnt5ejAknXHmt/rqVz+kdedWl6jN7XUPcSxok6DYrFklY5LoDvLf1+fUft/Ybtk2bsBQPdE0vRIc9P5u2qxQ8JVOY8kTtQEEFkzfgqDFVYnm9OkVYQfeqS9Vrul1DdmGUAKzJSUxnVJzcPEF8tjJu7TdsmzZhKR7onF7wTnttT4+dYujqtWsDpWQKW54oTtqwghm2YQUVyrDl86pL1Wu6XUN2YahunB+dmmo5aS5L4UiquNWd12Ke+FnVxYiwbZq/M35IvbywFhe5ELw9vb3YPzxsC1TUxsdzkC02Gvh2vR54GV53eVQJ07jisLyClE9lb61X3as8s9s1xE7FLTy/Ohiv13H78eP2qr8c1pMnVNuLPLjduWUL7p2cxLKVFPZTO3dGTsflVLb9w8O43RpY7jpxIpFD6nMheLJARa0Yv2V4le1ZYcqTxjQpDstLlSCppKLWh5v1IaZYV/HhjdVq9sHpgH9MZxLE3W7E/nCx0cDHX3jBtoqXGMP7jx2LRYxml5bAEk5ZlQvBC2NthA2RUOmkYcuTpUOao1peMk7Xy0Joh/jsvIz8907wFW4+1frUzp2uU7wkSKLdiP2BHzYksoLmTiad9x2v13FqYcFx50ycZFrweCOrLS8HyvkfJURCpZOGsX6idH7dnU235eW0bWh2aQl95XJmQjuiJC5Nc7Aaq9XsKXlc02vZEr7rxAm80mi0fObp+XmM1+va21+nw8HdcZJZwXMLEt4/PAzAO8wibIgEoJ6/Y4bcAAAgAElEQVSBIug0MewUMY7OFkR8VcRWnhKJ569mJQdb0LCeoINgXPSVyy2LLmGc+2G2BL7/2LHmdj6LJ+bm8ItHjwbaXeOGWJ9OB3fHSWYFzylIuMEYjszN+ebmj+J/UhWzoH6npFdZvVCtH3kkfrdLKimnKRH3y8wuLUXeh6qDKG0izSDk2aUllADfXTNuhNkS6HaPi4zhYy+8gC8k1P7iILOC57bCBvjHzIUVF/H7cYw4Sa6yOiFaa0F2pvilknKaEkUtr+5pfNg2wcvhZqnG7duLumtGZUug0z3lAH3Ol8+ciTy1jdo/o5BZwXNbYQPUjjGMS7SSRlfjcJoa+1lecsP3SiUl1rd8Cn1QUYjLZya3iTDJUkVGp6ZwuzV9jyteL+r799sS6HXPg9UqPjs9jWXh/xigZZaRVv+MJHhEdBLAHJoLOcuMsd06CsVxq5S0t/wkvWLn1ThUyxJld4i8M0V1lwcvX1Dxkn2C+06exL4dO7SvEgZZjZctXAC4I6F4Pbf3r5pMwmtLoN89bx4YwMdOncKXZ2fB0NypknaYThR0WHi/yBg7o+E6yugcHXRaH6pJRXWJZdCEAuLUuK9cVtpfKzb8pLYQye6Mb547ZweI63rvQVbjnSxcAC3hG6WE4/WCvPso/WVPby++cPXVud9qx8nslDYJolofTvs2va4VdqomNrZnL1ywd3jMLi0FWnl087O5lVV1H60XYXyQvKz7Tp7EN8+diyUwNchqvFuyVB6v1yHE6yVF0IEkqmC1i4soquAxAF8nIgbg04yxUQ1lSowo1ofcUVRSzEcNWC4R2Q7or587hw9v2+bZad1E656JCbscC43GqqBSnT60sD6oPb292LdjB75dr8eymhd0Nd7Jwo0amB1FhIIMJH7vs12sNxWiCt71jLEpIroCwDeI6AeMscfFDxDRXgB7AWBoaCji7fQSxfqQnfIHhBTzbklFowYsyxHwR+fnQx2ZOFKpoNMKH2EADlSrLb4dFWH2ymGnsulchbhX84KUy+mzqt+PmjzTrTyqdeP1PpMKqs6KqEYSPMbYlPX3aSL6AoDrADwufWYUwCgA7N69W17lThW3RqOy7Ur8/VitZosRAXi3hsy8Yh43LpKihQcAN/X3u37fq5Hv6e3FuwcG8OnpaTA0hVT8fz9hdusk7ZgWXQdO7wIIlpLMCV434/W6pz/W630mEVSdpcOwQgseEa0FUGKMzVn//hUA/1VbyRJC7lBenVl1b65XZl6VDuy2VUv24V29dm3otN03Dwy4hvf4CbPbKmoW9s6miVsbcXsXOuIrVZPjur3POIKA5XrIUruIYuFtAvAFIuLX+WvG2Fe1lEqBuExkt9E47N5cHWUQdyvs6e21U/WIvjgnK84v7VLYtExuq6hRjt7T8T7TnDZ5CY9bXfMFkSioionb+9Tddp3qIc2dFTKhBY8x9iMA12gsizJBrbDRqSnbKvLL6+X0cuRGdbBaDe2nUumUqg3E73N+ZYoyXbxl0yY8PT+PJ+fm7I3tYY7e4/s8xTi/sAccpTlt8vOTudUJt7LDHoauQ0x0ug2c6uHu7dtbIgS4EZGGlZfLsJQgVtjo1BTee+wYgObKJgBP0XMb8Xij6iQK3TmDrJapriAmHYQtrxpzjyLf2B6k8/BriVuYwk550p42uQlPkDM7nAZSN4K2laRwqwderrR9ebkUPBUrjIvgn5061fLdQzMzvlae2GnlvZSnFhbwmenpUB0r6GqZyqb7MKNzlKmf+AwNxkBoxiaF2djOr8XFjuCfkdgNpzaR5BTXbfAZq7mndxLL3EHUEuvnJQZOKeizkKABiJ56LW5yKXh+VhjfScCtBxF5ZdOrU7iFEwQ5/0JEdbXMKTYuKuKqr1/QseozdFoWnuqWM79ruWVjUYG3Ce4Te/bChUjPGQanwcctvVPYgTQPKejdBmGv9p/U4JRLwQNWV6osgqL1QABe092N3x8aarHu/LaJ7Tt50h6d+ZRjqLs7dI43v9WyDiE27gEpNi4K4nMSERoB02rLjVF8BgChG6p4LZ4gQvV5/HxiVK36PqeuTuZ1Haf0TlEG0rGadwp6sSz880m6O/zCudzCwJKa6uZW8JyQRVAcTQ6+7nWe4RVip5CTj5YAzylHkEbmNQXdtW4dnpybAwOwbMXG+V1PBfE5S4yhgwgUMXu0PNiEJahvR9UnRtZ02+3gHl2dzO86I5XV6Z38HPteMaH8enIKerksHUQgQGmKrAOed2+JMZSJMOaSc8+p/Sc51c2V4AUZkVUc+m4mNn8BXOzecPnleHV3t+OUQ0cjEwWW+8PEaXnUTik/ZxALNYnGGOQeXp+Vw2WAS1my4+pkftdRcb+Ijn15Ecvp/av4yLgV6LXVMQxuffBgtWqnoFq0DrdXvZ/43ijmIxtzI3hhRmQ/h75b45EFYt+OHQCcpxw6GpmTwAYN5PUaDJymjvxzfoPISKW5Da1hHdkXRwxVkNAKr8/y5xSTDjQYc5wqRwnnEOtM5Tp+7peg4q7iI5MHX10BxXHlKdw/PGwvxMR5ZGNuBC8uS8Op8bg1SD9x9GpkQXZqiLnfVDqlarQ90Dp13D88rOTYZ9LfYfDbPK8aWuEnFnt61ZIOqIqO03PIdR10uyD/nN/gFfQQJJ3+VSe8+uDNAwN4wLLyuog8dxs5Mbu0FNi3HIbcCF6QEVmHM9pNCP3EEVjdyPwEya3zRbUE5DqRF2EOzcz4fm+sVrMXUuQ9t6qobp5XDa0Ia7l7XSdKIlXxTGM3VK0jr22FQevdr56C4mddP7ZrV+h+pyOAWoXcCJ5qI3brXE7TubjKGcZf5NY4dVgCToswXaUSburv97WEdEz9Ti0sOMZIxhmGE6SzqzrcgeD14VUHTveQ24rTIUhemWriXO1Usa5VfdZhB/eo5EbwALUKlRvMwWoVD770Uktn9zp/IGjWYsB/hbGvXG7uSrCmuqrZhr1wswSctu64+Qj39PauOn9CRsfUr4MInQ4HLscVhhOUIA73IPUh1kEnkdKh034B1IB7e0tigSmq1ei35zju958LwQsyRZXFBYDd2QH4xmUFzVp8y6ZNno1svF7HXSdOYIUxlIhw55YtLX6zsFMWJ0tgpFJxLL+Xj1ClkYVpiGL5wBjeMziIoe7ulue8VUhRtRxyupwGqvWhUgdO15ZdJNw67yDCnvXrXRPNJjUtjEKSIShOZF7wgpjpsrjsHx7G1WvXrrLw3BqDysuQPwN4LyyI1hUxhqPz8/b3LzYauP34cVucg0xBZGF3i+/inTNqdt6gyJ3PyXoTU1R1EuHUwgLG63pOtw9CVIe7Gyp14IQoqPdMTNhtt8EYHq/X7c/Jq+Yq7znJ7XZO95LrRMdsJwiZF7wwMVpcXGaXlloagZ8PT2WEdGrEXgfcyJ8X/WYl6dBq1dHOSdj597ycyqrX1uEHUul8/DM8W8pnXM69jZuoDnev60b1S41UmjtwxN0VgHuiWT+fr65ga96fjszNobq4CAAY6OqyRd3tXs9euIAru7vBALy1r0/LbCcImRc8HTFaqp1dFkfZFyZ/Rna6+l2Tf577zcIeWu0k7F5lC4LOKYfKgstIpYKh7m6sMJbaNCdOqyeqX2pPby8+tXMnbrdi1Hhg+hprsJXLztNtAauPZByruScyAPz3lYttVgzuFhmdnsaHtm3D0fn5loiAsVoNz164YGcuAoAfvPwygGa400KjgfcdO2Y/3/+86irfJB9hyLzgBenEOjo8/45fGEmQa8ufF3/2WzRwwi88IEoHS8IPJI/+d27ZAiJCKWKQbBjhSjuPngp7N29uGST54pSYR7CDCG/esAFfOXvWPgbgQLXasuLslsgA8N9Xzv2IAOwjK51oAPjYCy/YP4u7huTMRUz6N/95BcD7jx2LJfg484IHRD9sJShJOlbDlFeHsKdxbY5YvxcbDdw7OYmG1WmdtoGpEFa4wrzrIMLqZXEFgbcTfr19J09iybL4gGaM5BdnZ1u+syQtBDklMuCI9SCHCYmWYVB29/TgtsFB3HXixKrMRV6sWPctpOAlTR5Wu+Jcwo87PECsXxL8mOL0PKi1FnaQChNX5xbn6RQXN3L0qB3y8kC1isc8Yvxk3xgAewFlrFZDbXkZ905OYlkQOi8YWq24kcrqRAb8vqcWFlq+9+npaXz/wgV89DWvwUilgpLgR+TZh364sOBbjp9etw6zS0v2PnGRDutebjIYx55aI3gOJGHlpHn+QtrIvlLZjxnGWlMVLqftXSorm+LZE6IfjMd58njDN/f1YaBctheyxFPmLloxfkCrQI5OTeH+6WkcmZtr7skW7n3/9LR9Wl1QC0u24pye1SnrNNAUosfPn8f1R47gvYOD+A/9/fir06ft//t3/f345Isv2nVB1vc2lcuYWVpCA7BXvJ+9cKGl7ASgW1ik+OrsLB4/f96z7LpoS8GLa2uZDnSd4ZBVVFNlyX5MUVCCWGt++3FFq+nR2Vl8eXYWDJeCz93KKH7vd44fx0VLuPiUENbf1cVFu6wrjOGLZ84AaPrPPrlzJ8pEtoUHAJ+V3v2dW7a0+LxklgCAedtRhGZHXsZqv5hsJcntmk9X3e7AAPyv6Wl0WPfhiwpH5+dtwaotL+PPX3gBKwCq1rT57X19+PDQEPb0Nk8t49/lvHHDBttHN1KptFjCwGofoy7aTvCy7ITWeYaD6v2StCKdUmUtWb65T+3c6bnqJh5ms3942LYYCPC01tz24/KB5YFq1RYrkYVGA3edOIGj8/NYtsJ7fnfrVlQ6O1FbXsYnLL8iDx3iiJZKCQCI7OmeeJcla3p+19atuO/FF3HB8l8tC2nxFxoN/PVLL/nU6mo6APuePFM00BQmEQbgjmPH8OjsbEvIiIiqqKygdQrKT6rjg4xYLw0AXzl7Fh8eGgJwKeMOt3YZgC+dOYOvnT1r98+xXbuw7+RJfOPcudBHBqjQdoKXdiS3F7xsOs5w8CMN4RfrXkyV1WAMtx8/vmrVzW2f6aOzs1i2PrOMZrp2AC3OfwC2I5yLB3e0u03TRBiAJyw/GawyfuyFF1ZZIowxlNDs8E48Mjvr6FNjAL534YI9DRTh92AApq0YNhXEbYFA6/7wvnIZpenpVdPeJcBezLi/WsVtQhp9Hs8pf6e/sxMzy8uQeWtfH15uNOzUW7x/jVQqqyxZMdHEnt5efHDrVnz8hRfse/GdIvzgor5yGa/u7kbZGmDi6hdtJ3hZXnAQg0g7Adw2OOi6chfVOkta+MfrdXz17Fnbn1MmahECeevY6NQU3n/smG05AJdCGKYkEbh/erq5Q8XqUHw7mghDcxrJfWeLHmLnhdN1b7j8ctvyEFlbKmHOZeWRADuOU6TL8vN96cwZ+3q7LrsMR62YNBGeh1DcA35Tf78tEKcWFrDv5ElbID60bRv+7IUXXJ97iTF8Wgju5tNZudzXVyp46MyZVUJ41WWX4fzyMkpWvfD08txC+8gPf4jvWL64NULfG52awicmJ5vfQat1esDKIs7bTQlNYeXTYd20neAlseAQBT5VK1kOXTexi2qdqQi/U9Cq189O8KkjzwbN2b5mDX4orPwBwFdnZzFSqeDZCxfwvmPH7A4lfu9nenrw+vXrW6yvzV1deEKwHtw6NLcqxIGF4L4KqAo/3lPGTeyAZsd9lbWXm1Pp6MCfvuY1AICvzM7a8XPPvfLKqu+XLTcAt974325Bv4uNBiqdnfina6/FXSdOtNSfiGhZAZfao1juhy0xFq1dAnDv5KS9ou703Sfn55vXEMKLxut13CEcOsQA3DYwgKHubjxx/jy+ZPlU+f+tAHhYmA7rpu0ED3BfcEh7ZXSsVrOtHq/N8irWWZgDU7yybojJQDuJ8KYNG/DI2bOr/Fvi9jx+Daep43FJ7IDmqt/I0aMtnWbVZ+p1jNfr6LQsxE4ifNea0noh75HmnbFMhA9s3YqxWg1PW/46N3auWYMfXrwYSSAJl4Tix1Id1FZWcOfx42CwfJsA3tzXhy9bCx0iv7phwyqf5z0TEy2JMMR7lqzU6Ht6m9mDnd4Lt6TF81lKVgaXhvX7n+3paVkx5d8Rw4c4YmYZt90/Y7Vaiw+0RIRre3pwZG4OXxHETqQRYyKJthQ8J/yspiTEsK9cVtpRwK0znuO/tryMeyYmbLERneqdRPiJyy7DmaUl/EKlgp9cu7ZFlO7evh3j9Tre9/zzLYcQ3bJpU0t4hZgMVA5i5f4tkRKAn+vt9Vzhc0IMlnX9DJqdBmgODCccxJNDAH5/2zZUOjvtd3fPxETLwFLp7MQ7Nm7Eky5WD+cn163DDRs2rHL+q0BorkwOdHXZ1m4JTcEQLdiWYGEAsN7hiiTEj5w9uyqRgtgu+BSQC7ucGv1b11yDj5061bIq7XQkpJzF5WC12iJ4b924Edf19Dhal6IbwW1GwWP/eLaXD27d2uJ75XX3b9evx/+dm0ODsZbpsG4KI3heVlPYKaRTCIbbmRG8waxY06w7t2xxFVwAeOPll+PL1rTHK2xhkTEctSwg0UFOaHa2t/T14dGzZ7EodLTFRgPVxcWW8Ipd69bh2/U6XlGMhm8ALZk7VOErfW6LAPwzXv8vfs5pz6XY+XgWlvWdnS1Cy/1F4n0ePXsWH9i6dZX/0Ykda9Zg4uLFls9ct349RiqVlrNP9g8P48jcnD3YQLrn1OIi3rRhQ8vUDlidXZq3DzEU5BNWEDJHbtdfs/yOfAcLr6fxer2ljLJrRcwc8+Ft21ZtgxSnoryc/NQ1MbwI8D4+lb+H7lIJH7Wm+nEbHYURPK+0NG5iyANCN3d12U5Up03UHL7yxh3M4jSRTwn4Z+6dnMQ7Nm5sWVUUwzkWFSwhLxiaK5zydiO+MjzQ1dWyzYjHVd0/Pe3q/wkLn+IRgN8eHMT6zk5XEb+upwc/vW4dRh1WHDl8tfKm/n77nFd5r7KchYWseuV1+jM9PXhKes4lxux4Mj5gNODsM7xxw4aW5+Axb24+ZL6Y0lcu487jx21L7/DcHDqJ0CUEF4v7T7llL589MlargQliJ6/4u00xxfpxEpc9vf6ZY97U14evnTvn6B8Ww4vEIz3F64iDkXz4etyupkiCR0Q3AvgLNNvGZxljH9VSKh+COtfl0ZEHk/JR7H/s3Gm/hA4iPDE3h194+ukW0/7h2Vncd9VVdsPz2hLjNE0sSVOWZcv/wcMyxJxnUYROhTdefjmu7elB2RrJG2g65v/h3Dncd9VVeMZaEVUphxiIK4d08N/x1UZuTchWAPcTdZVKuG1wEEfm5lB22F1QFq5zU3+/6wFE/H0DsLOwlKypI//+bYODePbChZY8iYRL1hcfmH6mp2fVAFDCJQFz2psq7nsVc72JlpJ4stqKMK2UFyh4zkUuvGIoiNhmb5WEw2/Ryiuw3un/VM7acDMc5P6Z5qJiaMEjog4A9wH4ZQCTAJ4koocYY9/XVTgnnDJt8FG5BOBtfX14U1/fKse62DEOCsGoFxnDkbk5+/f3V6t2tLzIMoBDMzOesV0i4jRxsdFonmYm/D8D8BnLV1QiavGLhGENEQa6ujBx8aLn5xiaVp9s+cEq36Ozs/YI/8TcXEtdlAC89rLL8PzLL9ud/Od6e/Htet224MRpomjpip3jY1LWjOvXr8eNfX0tA1GndW1x2vxBa+GE+5qcMv+K51N0AC2p5eVyyBlInjh/vtV3ieZe0CPz8y1bxN7W12d3VKe9qYB/KnP5ZDVRrEanpvBnp045tjWe9NNPOHQLiyxmvB75wMJ3TDilp/c7yF2ssyxPaa8DcIIx9iMAIKLPAXg7gFgEzylI9ZVGoyXuqIFLnZl3tjdefvmqjuEEX2lyW8XrAHBa8tn4ceyVV3DLpk2oLi7iyw4Cw4VBdliH4SJjq8Ru55o1OBGwzFOLi3ZjO7WwgDVChDwAPP/yy81pt2UBjltiBzTr/7euuGLVwgnQGpM20NXVcs+fWLsWd2/fjvc9/7w9EC0B+JEUrnF0fh5fu+YajNfreKBabXkuvrDzxPnzdrzeMoC3bNhg+9bctreNTk1hrFbDrnXr0CkEPa+xQofkKfhXZmftabSbqLgJsnhvp++OTk215IwTkZN+ipaV+LP8fDpwcgmpHBLOV5b94kGTCpSPInhbAIiOmEkA/0b+EBHtBbAXAIZCxtaIlVEiWrVf0IkGmoL4ZcEZ3GEt3Z9aWEAnmoJTtho1zxjRKXVwTglwDA714qEzZ5oraUSOTninKaBOgoodgFWjMgH4V5ddhh9YVh0AO76NT/tEPj8zg38UFmScGrJqSvVXv+pVmBSCkHetW4d7JiZwamGhZWBaQTMHG5+Wigx0dXke/ygKzNfPncOHt23DeWuXAbe65L2gy7iUusht+ndAEGS3A8ydvntoZqbl561dXTi9tGSHkFzb09Nyn6R207gtPvgdEq66ESCpQPkogie3LcCh/zLGRgGMAsDu3btD9W+xMoJaQ6J/6fXr19unm5eJ8NuW3wNAy6LBQLmMqrSPb/VGG/V7O23+7kAzopyvdpUAvLq7uyUEg1ew/G0ezuq3nupUU6LPzYlKZ2dLfQPA9x2EXpzCiv5MeXXRqSGPVCr2/s9re3owVmtmw5Xr/Lc2bcJvbdqEQzMz2LVuHT754ou2s5sHFovPyKfZfLFB5XwKWWC4FclxGgjLkoDJUzE59uzVr3qVZxlEburvbwl0/iNrGxlvt2LoSdK7adwWH7yETHVqndQOqSiCNwlgm/DzVgBT0YrjDK8MN//ZYLmMaZeNxrxTlohsXxPQXAWtLi05Lhq8JF2rZP1xEz0/EXGCb58RV7t+f2gId1o+rBIuHWMoChzfesOnyATgdZddhudeftl+tivKZZyWnmHHmjW4ccMG3DwwgC+eOdOyr5FTxqWN+l77R/l9udXDp7gNXAqA5XhNhTqIQNWqY+ojvghw9/bt2Lt5c8vUiMeOAc04MDG2rWwtQqmejSALzE39/fa/5dXzd2zcaKd+8rJg+U4PLnrPvfwyRo4e9TzvVhTNT191FQ7NzOCm/n772fkWM1HYwuym0UUQH6HK1DqpxYwogvckgJ1EdCWAFwH8JoD/qKVUFuLL2j88jP2Tk7bDXGRbdzdOLy05dtAPbduGY6+80rJ3EbCc92fO2NNOcdFANl3f1teHs8vLrnFn77ziCjw9P4/nJEvIa8rK94uKeeGOzM3Z9yfAjmwXp2qdRKvCAj5gBXPyn//kyivtfaqcGzdswF++9rUAmo3rHRs3tmSnAIBftZzx4/X6qjrmFukjZ882YwmFfZ7EGN66caO9XUq0QuSGLPq2xAQDMvI0UO7cXHSu7elpedYVxgKlBuexaaLAcFosXcZwXU/Pqumxk5V19/btuHVgoCWAWc4+LOIkmmI53ITNTySSOJg7y9dzIrTgMcaWiegOAF9Dsz8cYIx9T1fB5EOMVxhztbAWV5xtkQ4Ax15+ucWPJ+NkmfH7ceF5aHbW04L73OnTzS060r2vX79+VWJDzuG5OdzwzDO4c8uW5rYnK/mjGIXPLUvxwOplK6bK7WAgseHz/aplh6kdXykcq9VsJ/+jVnT/WK3WUl/ioSpyHCLvTAPlsqMVwu/FhVRcbOA51mTbXNxBIJbXqXPPWskmOWFSg+/dvNkxdZWKBeX2Ge6nvOgyDRbxm5r6xc25PWvSU948ECkOjzH2CIBHNJWlBfFl+cWmuS0mrGB14K0fJcDetM234PhNV2Xf4nU9Pdg/PAwAdohEmQif3LkTh2Zm7PirhUbDcxdFA03x/eDWrbb/incquaHLP1+9dq3td3JytvLvOB2GLW8H4rns5OmRKLJAM0Kfb3dz6tziKjgPQgZas59wq1j1YO6RSgVdRErCEoZbNm0C4H4ehZsY8QBelfMsVIQ1jPWT5cxBaZHZnRby9iAvC4/jtF3Ii04APyvFen1o27ZVW3DE4NROn50QXdJBNGNWTBsP0xDPpVXx+zFrL2hQ/wZ3nIvbf5y+d21PDzqFDLxuMV5+8VRieIrb4OQ0LQXQchg3L69qXFsQYQmCfD+vxQ83MVIVqbj8V2kH+WaRzAqe/LKevXChuc1rzRq8acMGHJmbw/2WwxpoTo/+dU8PRioV/MXkpC1IcgS9CAPwE5ddhn+q1+28bOeXl+0YK7EMTnFlfeVyi8VGAG6VOhz/txylfmRublVKJRkxA0jQEV7Voe12oLd8P7+4MhWBdeuA4u/4teQO6jU9i8P3k/YKaNavm1cyK3hAq++H+4uevXABH7asML5Nqbq4iEfPnsXhuTkcmZ+3I/LFk9F5qiNxdbGrVML3X375ksMbzTTZXvsARXiw6mO1GmBleXCyBMZqrQcgzy4tYai7u+UzP79+PZ60Di8W0zGFHZlVRnev/ZYiKnFlqtMnp/p0mp7LBJme6ViZNNPB9iTTgifurnA6MZ3/ed/zz9sWXYMxfGJyEo/v2gUALTne9loZhoFLFtr7HaLa/XLQyalyCPA8U9XpAOSr165t6VBxZIvwG92DBIWuCL63dztMG+OePqleX9fKpJkOtieZFTx5d4XXiekHpG1GPIEgcOlIPcYYhrq7W6ZB90xMOAb1OnV+uTxiMkRmXf/I3FzLZnGO0wHIXs7upFDt1G6+N6frpT3t0zkVNdPB9iOzgiev0opHxMknpsu7L3gCQfE8TKdj38TVSL9ppFgexlozxfLFDLejF/l9nOKo0u5QKmXIk7WT1FQ07ezZhnBkVvDkVVq31TuvNDlu6Xs4QTqy3JHEVFNyFlknp3peBMONLIizCknUdZaPAjV4k1nBkxsu4Ozf8mrgbpaVfB8doQNyFln5XnkRjHYg7ro2Ab35JbOCB6it3vl9P+ho7zVV8epI7WDFGdQwK7j5hRjz2sOgl927d7PDhw9rvabO6UU7TFWMbykZTD1nCyJ6ijG22+9zmbbwVHCbXoRpkHmfqrSDYK65Ju0AAAPlSURBVOcF46LIJ7kXPKfpRdiOn/epSt4F22CIm9wLnpPvTDWttMq1dJHEFCjvgm0wxE3uBQ9YPb2I0vHjmKokNdU0CycGgzdtIXgyWev4SU41jW/JYHCnLQUPyFbHN1NNgyEbtK3gZYmsWZwGQ1ExgpcQWbI4DYaiUvL/iMFgMLQHRvAMBkNhMIJnMBgKgxE8g8FQGIzgGQyGwmAEz2AwFAYjeAaDoTAYwTMYDIXBCJ7BYCgMiWY8JqIZABMBv7YRwJkYipM07fIcgHmWrNIuzxLmObYzxvr9PpSo4IWBiA6rpG7OOu3yHIB5lqzSLs8S53OYKa3BYCgMRvAMBkNhyIPgjaZdAE20y3MA5lmySrs8S2zPkXkfnsFgMOgiDxaewWAwaMEInsFgKAyZFTwiupGInieiE0T0kbTLExYiOkBEp4nou2mXJSpEtI2IHiOi54joe0T0gbTLFAYi6iaiJ4joGes5/kvaZYoKEXUQ0REiejjtskSBiE4S0bNEdJSIDmu/fhZ9eETUAeAYgF8GMAngSQDvZIx9P9WChYCIfh7APICDjLGfSrs8USCiQQCDjLGniagHwFMA3pG390JEBGAtY2yeiMoAvgPgA4yx/5dy0UJDRL8LYDeA9Yyxt6RdnrAQ0UkAuxljsQRQZ9XCuw7ACcbYjxhjiwA+B+DtKZcpFIyxxwGcTbscOmCMTTPGnrb+PQfgOQBb0i1VcFiTeevHsvUneyO/IkS0FcCvAvhs2mXJOlkVvC0AXhB+nkQOO1Y7Q0Q7AFwL4J/TLUk4rCngUQCnAXyDMZbL57DYD+DDABppF0QDDMDXiegpItqr++JZFTxy+F1uR+B2g4jWATgE4C7G2Pm0yxMGxtgKY2wXgK0AriOiXLobiOgtAE4zxp5KuyyauJ4x9tMA3gTgdsslpI2sCt4kgG3Cz1sBTKVUFoOA5fM6BOCvGGN/n3Z5osIYqwEYA3BjykUJy/UA3mb5vj4H4JeI6P+kW6TwMMamrL9PA/gCmu4tbWRV8J4EsJOIriSiLgC/CeChlMtUeCxn//0AnmOMfSLt8oSFiPqJqGL9+1UA3gDgB+mWKhyMsbsZY1sZYzvQ7Cf/wBj7TykXKxREtNZaDAMRrQXwKwC0RjdkUvAYY8sA7gDwNTQd459njH0v3VKFg4j+BsA4gNcS0SQR3ZZ2mSJwPYB3oWlFHLX+vDntQoVgEMBjRPQvaA6u32CM5Tqco03YBOA7RPQMgCcAfIUx9lWdN8hkWIrBYDDEQSYtPIPBYIgDI3gGg6EwGMEzGAyFwQiewWAoDEbwDAZDYTCCZzAYCoMRPIPBUBj+P7JnfscYyWqZAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n", - "ax.plot(X, Y, 'c.');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R\u00e9gression lin\u00e9aire et r\u00e9gression quantile" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Régression quantile illustrée\n", + "\n", + "La régression quantile est moins sensible aux points aberrants. Elle peut être définie comme une régression avec une norme *L1* (une valeur absolue). Ce notebook explore des régressions avec des quantiles différents." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Un jeu de données non symétrique" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "clr = LinearRegression()\n", - "clr.fit(X, Y)" + "data": { + "text/plain": [ + "((1000, 1), (1000,))" ] - }, + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy.random as npr\n", + "import numpy\n", + "\n", + "n = 1000\n", + "eps = npr.normal(n)\n", + "X = npr.rand(n, 1) * 5\n", + "X1 = npr.normal(size=(n, 1)) * 1\n", + "X2 = npr.normal(size=(n // 2, 1)) * 10\n", + "X2 = numpy.vstack([X2, numpy.zeros((n // 2, 1))])\n", + "eps = -numpy.abs(X1) + numpy.abs(X2)\n", + "Y = (0.5 * X + eps).ravel()\n", + "X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "QuantileLinearRegression(copy_X=True, delta=0.0001, fit_intercept=True,\n", - " max_iter=10, n_jobs=1, normalize=False, quantile=0.5,\n", - " verbose=False)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlinsights.mlmodel import QuantileLinearRegression\n", - "clq = QuantileLinearRegression()\n", - "clq.fit(X, Y)" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n", + "ax.plot(X, Y, \"c.\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression linéaire et régression quantile" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n", - "ax.plot(X, Y, 'c.')\n", - "lin = clr.predict(X)\n", - "ax.plot(X, lin, 'ro', label=\"L2\")\n", - "qu = clq.predict(X)\n", - "ax.plot(X, qu, 'bo', label=\"L1\")\n", - "ax.legend()\n", - "ax.set_title(\"R\u00e9gression lin\u00e9aire et quantile\");" + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "clr = LinearRegression()\n", + "clr.fit(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diff\u00e9rents quantiles" + "data": { + "text/plain": [ + "QuantileLinearRegression(copy_X=True, delta=0.0001, fit_intercept=True,\n", + " max_iter=10, n_jobs=1, normalize=False, quantile=0.5,\n", + " verbose=False)" ] - }, + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlinsights.mlmodel import QuantileLinearRegression\n", + "\n", + "clq = QuantileLinearRegression()\n", + "clq.fit(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "clqs = {}\n", - "for qu in [0.1, 0.25, 0.5, 0.75, 0.9]:\n", - " clq = QuantileLinearRegression(quantile=qu)\n", - " clq.fit(X, Y)\n", - " clqs[\"q=%1.2f\" % qu] = clq" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n", + "ax.plot(X, Y, \"c.\")\n", + "lin = clr.predict(X)\n", + "ax.plot(X, lin, \"ro\", label=\"L2\")\n", + "qu = clq.predict(X)\n", + "ax.plot(X, qu, \"bo\", label=\"L1\")\n", + "ax.legend()\n", + "ax.set_title(\"Régression linéaire et quantile\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Différents quantiles" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "clqs = {}\n", + "for qu in [0.1, 0.25, 0.5, 0.75, 0.9]:\n", + " clq = QuantileLinearRegression(quantile=qu)\n", + " clq.fit(X, Y)\n", + " clqs[\"q=%1.2f\" % qu] = clq" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n", - "ax.plot(X, Y, 'c.')\n", - "for k, v in sorted(clqs.items()):\n", - " p = v.predict(X)\n", - " ax.plot(X, p, 'o', label=k)\n", - "ax.legend()\n", - "ax.set_title(\"R\u00e9gressions quantiles\");" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAATwAAAE/CAYAAADbkX+oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztnXmYHFW5/z/vLMmELDMwCZksZIFJIgpJiPlhIoKRTUAQvNwrKvcGQYmAiEFFwQWCy0XAq4jiEhEkCkRkNQQEBAKoQyAJAWXLBMjGTJMwZIYJMpOZ6fP7o7s61TVV1VXVVd3V3efzPHky3V1ddar6nO95z3ve8x5RSqHRaDSVQFWxC6DRaDSFQgueRqOpGLTgaTSaikELnkajqRi04Gk0mopBC55Go6kYtOCVOCLyCxH5frHLYUZEviki1xe7HMVCRJ4XkQXpv5eIyB+KXCRNmppiF0AzGBHZBIwFBoBdwF+A85VSuyzHLQJ6lVLfLnghXVBK/W+xy1AoROR3wDbzb6CUel/xSqRxQ1t48eUkpdQIYDZwCHCJ9QCl1FKl1IX5XEREqvP5vkZTSmjBizlKqQTwACnhA0BEhorIj0Rki4i8ISK/EpFhps+/LiLtItImIp8XESUizenPficivxSR+0TkHeAjbucTkdEicq+IdIrIWyLyhIhUpT/7hoi8LiLdIvKyiByVfj9rGCciH08P8zpFZJWIHGj6bJOIfE1EnhORLhH5o4jU5bq2FRE5RkReSp/j5yLymIh83qE8U9LPpCb9+kwReTF9H6+KyBdMxy4QkW0i8lUR2Z5+rmemP1sEnA58XUR2icgK0z0d7VDOeSLyj/Q9PWsMfdOffTZ9/W4ReU1ETnerGxr/aMGLOSIyETge2Gh6+0pgOikRbAYmAJemjz8O+ApwdPqzD9uc9jPAD4CRwN/czgd8FdgGjCE1zP4moERkBnA+8P+UUiOBjwKbbMo/HbgVWJw+x33AChEZYjrsk8BxwFRgJvBZt2vbXGM0cAfwbWA08ApwmM19O7EdOBEYBZwJ/ERE5pg+bwLqST2XzwHXicjeSqmlwM3AVUqpEUqpk9wuIiITgJXA94F9gK8Bd4jIGBEZDlwLHJ9+nh8E1vu4B40HtODFl7tFpBvYSqpBXgYgIgKcDVyolHpLKdUN/C/wqfT3PgncqJR6Xin1b+Bym3Pfo5T6u1IqCfTmOF8fMA6YrJTqU0o9oVILsAeAocB7RaRWKbVJKfWKzbVOA1YqpR5SSvUBPwKGkWrQBtcqpdqUUm8BK9hjzTpd28oJwAtKqdvT17gGSDg810EopVYqpV5RKR4DHgQONx3SB3w3XYb7SPlVZ3g9v4n/Bu5TSt2nlEoqpR4C1qTLD5AEDhKRYUqpdqXU8wGuoXFBC158OSXd0y8A3kPKcoGUtbMXsDY9LOokNakxJv35eFIiaWD+2+69XOe7mpR1+WB6uHUxgFJqIymrbQmwXUSWi8h4m2uNBzYbL9Iiu5WUtWRgFqd/AyPcru1wjcw9pUXR7r5tEZHjReTJ9LC5k5QAjTYd0qGU6ncoox8mA/9lPOf0tT4EjFNKvUOqczgHaBeRlSLyngDX0LigBS/mpC2O35GyjADeBN4F3qeUakj/q09PcAC0AxNNp9jP7rSmv13Pp5TqVkp9VSm1P3AS8BXDV6eUukUp9SFSDVmRGhpbaUt/DmQs1P2A1z3cu+O1LbSb79N0DYN3SIm6QZPp2KGkhsM/AsYqpRpIDbslV/mMYno8DlIi/HvTc25QSg1XSv0QQCn1gFLqGFJW7UvAb3ycW+MBLXilwTXAMSIyO20h/YaUn2lfSPmGROSj6WNvA84UkQNFZC/2+OJsyXU+ETlRRJrTIvI2qaHsgIjMEJEj04LRQ0o0B2wucRvwMRE5SkRqSfnleoF/5Lppp2vbHLoSeJ+I/Ed6IuICTKJGyhd2hIhMEpF6sme8h5Aamu8A+kXkeODYXGUz8Qawv8dj/wCcJCIfFZFqEalLT4pMFJGxkprcGU7q+exyuFdNHmjBKwGUUjuAZcB30m99g9RQ70kReRv4K2mfklLqflLO70fTx7Skv9PrcgnH8wHT0q93pc/1C6XUKlIi8UNSFmIC2JfUpIK17C+T8l39LH3sSaRCbnZ7uHWna1uv8SbwX+nydKS/93fT5w8BfwSeA9YC95o+6yYlkLcBO0lN6PzZQ9kMfkvKj9kpIne7HaiU2gqcTOo57SBl8V1Eqh1WkeoM2oC3SE02neejHBoPiE4AWt5IKgTkX8BQix+qrBGRVcAflFIVu+JDMxht4ZUhIvIJERkiInuT8qutqCSx02ic0IJXnnyB1JDpFVJ+oHOLWxyNJh7oIa1Go6kYtIWn0WgqBi14Go2mYihoeqjRo0erKVOmFPKSGo2mAli7du2bSqkxuY4rqOBNmTKFNWvWFPKSGo2mAhCRzbmP0kNajUZTQWjB02g0FYMWPI1GUzHoPS00mhjR19fHtm3b6OnpKXZRYkldXR0TJ06ktrY20Pe14Gk0MWLbtm2MHDmSKVOmkEoSozFQStHR0cG2bduYOnVqoHPoIa1GEyN6enpobGzUYmeDiNDY2JiX9asFT6OJGVrsnMn32WjB02g0oXDFFVfQ3NzMjBkzeOCBB2yP+fnPf05zczMiwptvvpl5XynFBRdcQHNzMzNnzmTdunWRlFELnqYsaOnq4orNm2np6ip2USqSF154geXLl/P888/zl7/8hfPOO4+BgcEJmw877DD++te/Mnny5Kz377//flpbW2ltbWXp0qWce240CX70pIWm5Gnp6uKoZ59ldzLJkKoqHp41i/n19cUuVkG4+5nXufqBl2nrfJfxDcO46KMzOOWQCbm/6MIPfvADli1bxn777ceYMWN4//vfz9e+9jXX79xzzz186lOfYujQoUydOpXm5maeeuop5s+fn3XcIYcc4vj9hQsXIiLMmzePzs5O2tvbGTduXF73YkULnqbkWdXZye5kkgFgdzLJqs7OihC8u595nUvu/Cfv9qUsqdc73+WSO/8JEFj01q5dy/Lly3nmmWfo7+9nzpw5vP/97+fqq6/m5ptvHnT8EUccwbXXXsvrr7/OvHnzMu9PnDiR11/PuU9Thtdff5399tuz75LxfS14Go2FBQ0NDKmqylh4Cxoail2kgnD1Ay9nxM7g3b4Brn7g5cCC98QTT/CJT3yCvfZKbfL28Y9/HICLLrqIiy66yPF7dnk1/Uww5Pt9r2jB05Q88+vreXjWLFZ1drKgoaEirDuAts53fb3vFTuhyWXhTZw4ka1b92wFvG3bNsaPt9um2J58v+8VPWmhKQvm19dzyeTJFSN2AOMbhvl63wtHHHEEd911F++++y7d3d2sWLECSFl469evH/Tv2muvBVKW4PLly+nt7eW1116jtbWVQw891PN1P/7xj7Ns2TKUUjz55JPU19eHPpwFLXixQM8waoJw0UdnMKy2Ouu9YbXVXPTRGQ7fyM2cOXM47bTTmD17NqeeeiqHH364p++9733v45Of/CTvfe97Oe6447juuuuork6V7YQTTqCtrQ0gYw1u27aNmTNn8vnPfz5zzP77709zczNnn302v/jFLwLfgxs597QQkTrgcVL7kNYAtyulLhORqcByYB9gHfA/ufYanTt3rtL58LKp5BlGzWBefPFFDjzwQM/HRzFLa2bJkiWMGDEi5yxtIbF7RiKyVik1N9d3vfjweoEjlVK70jvH/01E7ge+AvxEKbVcRH4FfA74pf/iVzaVOsOoCYdTDpkQqsCVOzkFT6VMwF3pl7Xpfwo4ktQu7QA3AUvQguebSp1h1JQGS5YsKXYRQsXTLK2IVANrgWbgOlL7nXaaNnfeBuhuJgCVOsOo0RQDT4KnlBoAZotIA3AXYOdksHUGisgiYBHApEmTAhazvJlfX6+FTqMpAL5maZVSncAqYB7QICKGYE4E2hy+s1QpNVcpNXfMmJybCmk0Gk1k5BQ8ERmTtuwQkWHA0cCLwKPAf6YPOwO4J6pCajQaTRh4sfDGAY+KyHPA08BDSql7gW8AXxGRjUAj8NvoiqnRaOKOl/RQp59+OjNmzOCggw7irLPOoq+vD4BVq1ZRX1/P7NmzmT17Nt/97ncjKaOXWdrngEEpDpRSrwLeQ6k1Gk3ZYk4P1dbWxtFHH82GDRsywccGp59+On/4wx8A+MxnPsP111+fSQV1+OGHc++990ZaTr3SQqMpZZ67DX5yECxpSP3/3G15n/IHP/gBM2bM4Oijj+bTn/40P/rRj3J+xyk9lJUTTjgBEUFEOPTQQ9m2bVve5fWDFjyNplR57jZYcQF0bQVU6v8VF+Qleub0UHfeeSdPP/00kEoeYAw3zf8uuOACwDm9kxN9fX38/ve/57jjjsu819LSwqxZszj++ON5/vnnA9+DGzpbikZTqjz8XeizZEbpezf1/sxPBjplodJDnXfeeRxxxBGZtbpz5sxh8+bNjBgxgvvuu49TTjmF1tbWQPfghrbwNJpSpcthOOj0vkec0kO5WXh+0jtdfvnl7Nixgx//+MeZ90aNGsWIESOA1LC3r68va8+LsNCCp9GUKvUT/b3vgajTQ11//fU88MAD3HrrrVRV7ZGfRCKRsRKfeuopkskkjY2Nge/DCS14Gk2pctSlUGvJfVc7LPV+QKJOD3XOOefwxhtvMH/+/Kzwk9tvv52DDjqIWbNmccEFF7B8+fJIMh7nTA8VJjo9lEbjjt/0UDx3W8pn17UtZdkddWlg/50dlZgeSqPRxJWZnwxV4ModLXiagtDS1aUzwpQgFZkeSqPJh0JlddaiqsmFFjxN5BQiq7NOla/xgp6l1USOkdW5GiLL6mwnqhqNlYqy8PSQpzgUIquzTpWv8ULFWHjGkOc7r73GUc8+q7dELDDmfWOj2JbSENXvTZ2qh7NFwkt6qM9+9rNMnTo1s1Jj/fr1QGpp2gUXXEBzczMzZ85k3bp1kZSxYiw8vTtYPIjS16ZT5RcPr+mhILVM7T//8z+z3rv//vtpbW2ltbWV1atXc+6557J69erQy1kxFl4h/Eia3GhfW7isfHUlx95+LDNvmsmxtx/LyldX5n3OKNNDuX1/4cKFiAjz5s2js7OT9vb2fG7DlooRPD3kiQe64wmPla+uZMk/ltD+TjsKRfs77Sz5x5K8RK8Q6aG+9a1vMXPmTC688EJ6e3t9fz8fKmZIC3rIEwf0tpTh8dN1P6VnoCfrvZ6BHn667qd8bP+PBTpn1OmhrrjiCpqamti9ezeLFi3iyiuv5NJLL/WdXiooFSV4mnigO55wSLyT8PW+V5zSQ918882D3j/iiCO49tprPaeHGjduHABDhw7lzDPPzAyX/aSXyoeKGdJqNOVG0/AmX+97Ier0UIZfTinF3XffzUEHHZT5/rJly1BK8eSTT1JfX58RxzDRFp5GU6J8ec6XWfKPJVnD2rrqOr4858uBz2lODzV58uRA6aFqamoGpYe6/vrrGT9+PKeffjo7duxAKcXs2bP51a9+lTnmvvvuo7m5mb322osbb7wx8D24odNDaTQxwm96qJWvruSn635K4p0ETcOb+PKcLwf239mh00NpNJrY8LH9PxaqwJU7WvA0Go0j5ZYeSk9aaDSaikELnkajqRi04Gk0moohp+CJyH4i8qiIvCgiz4vIl9PvLxGR10VkffrfCdEXV6PRaILjxcLrB76qlDoQmAd8UUTem/7sJ0qp2el/90VWyjIkihRJGk0x8ZIe6vDDD8+swx0/fjynnHIKAKtWraK+vj7zmbF9Y9jknKVVSrUD7em/u0XkRWBCJKWpEHQ6ck254TU91BNPPJH5+9RTT+Xkk0/OvD788MO59957Iy2nLx+eiEwBDgGMRFXni8hzInKDiOwdctnKFp0iSRMWXStW0HrkUbx44HtpPfIoutJLwfKhEOmhuru7eeSRRzIWXqHwLHgiMgK4A1islHob+CVwADCblAX4fw7fWyQia0RkzY4dO0IocumjUyRpwqBrxQrav3Mp/W1toBT9bW20f+fSvESvEOmhAO666y6OOuooRo0alXmvpaWFWbNmcfzxx/P8888Hvgc3PAUei0gtKbG7WSl1J4BS6g3T578BbG1RpdRSYCmklpblW+ByQKdI0oTB9p9cg+rJTg+lenrY/pNrqD/ppEDnjDo9lMGtt97K5z//+czrOXPmsHnzZkaMGMF9993HKaecQmtra6B7cCOn4Emq1L8FXlRK/dj0/ri0fw/gE8C/Qi9dGVMJKZL0pknR0u+QEdjpfa9EmR4KoKOjg6eeeoq77ror857Z0jvhhBM477zzePPNNxk9enQ+tzIIL0Paw4D/AY60hKBcJSL/FJHngI8AF4ZashigZ1KDozdNip4ah/RJTu97Ier0UAB/+tOfOPHEE6mrq8u8l0gkMlbiU089RTKZpLGxMfB9OOFllvZvgJ1tWtZhKHomNT/0pknRs++Fi2n/zqVZw1qpq2PfCxcHPmfU6aEAli9fzsUXX5z1/dtvv51f/vKX1NTUMGzYMJYvXx5JxmOdHsqBKzZv5juvvcYAUA18b+pULpk8udjFKhl0hxEMv+mhulasYPtPrqG/vZ2acePY98LFgf13duj0UBWC3tg5P/TETGGoP+mkUAWu3NGC54BusPlTCRMz5U65pYfSgueCbrAaTXmhs6VoNJqKQQueRqOpGLTguVDOcXhh3Vs5PyNN+aEFz4FyDpwN697K+Rlp/OMlPdQjjzzCnDlzOOiggzjjjDPo7+8HUkvTLrjgApqbm5k5cybr1q2LpIxa8Bwo54wmYd1buT0jba0Gx5we6i9/+QvnnXceAwMDWcckk0nOOOMMli9fzr/+9S8mT57MTTfdBMD9999Pa2srra2tLF26lHPPPTeScmrBc6CcM5qEdW/l9IxK1VrdsDrBTd/8O9ed8wg3ffPvbFidyPucUaWH6ujoYOjQoUyfPh2AY445hjvuuCPz/YULFyIizJs3j87OTtrzXBNshw5LcaCc4/DCurdyekaluBRuw+oEj978Ev27kwDsequXR29+CYDpH2gKdE5zeqj+/n7mzJnD+9//fsfkAfM/9CEu/b//Y9PWrRz+wQ9m3rdLDzV69Gj6+vpYs2YNc+fO5fbbb88kHHBKLzUuj3XBdmjBc6Gc4/DCurdyeUaluLKm5Z5XMmJn0L87Scs9rwQWPD/poXb197Ph3Xd5ffdudvb10WMZwlrXwooIy5cv58ILL6S3t5djjz2WmpqUBPlNLxUULXgaDaVpre56q9fX+1nH9PfTPTDAyOpqRtRky4DX9FB9SnHw/Pl87eqrGTNhAq9u2ZL5zCk91Pz58zNp3h988EE2bNgAkEkvZZRri0t6qXzQPjyNJs38+noumTw5FLErxATIiH2G+nrfwGyZbXj3XXalZ0rBX3qo1WvX8vWrrwZgwQknsOJPf8qZHmr79u0A9Pb2cuWVV3LOOecAKUvyxptu4uV//5u//O1vDB0xgpFjxvh/KDnQFp5GEzKFyhQz/+QDsnx4ADVDqph/8gGu3+seGMD4RjL92rDy/KSHGlFTw/Rhw+geGOA9c+aw4bTTcqaHuvrqq7n33ntJJpOce+65HHnkkZlj/rRiBSfPmkXdXntx6S9+kVWusNDpoTSakMkntZjf9FAbVidouecVdr3Vy4h9hjL/5ANy+u8MCy9Jaog3fdgwR2EpZHoor+XS6aE0mhhRyAmQ6R9o8j1BYbbM7Hx4xaIQ5YrHnWo0ZUQhJ0DcJh/cGFFT4+n4QqeH8lquoGjB02gioBDhOn6GpoUmqBBHTXxKotGETKnumqaU8hSD5jb5UEyiFOJ85xyK/3Q0mggo1T016urq6OjooLGxMafojayupgoywjIyPStabKISYqUUHR0dWbud+UULnqakcbLiSnGpGKQCcLdt28aOHTs8HV+VTLI7maSuqoqtVc5htb3JJD3p44a6HBcGvckkHbt3o0htd1gzZAidIV2zrq6OiRMnBv6+FjxNyeJmxZXiUjGA2tpapk6dGuo5i2Htmjui2THqaLTgBaRU/UPlhJsVV4pLxaKiGNZuXNdYa8HLgZ2wlap/qNzIZcXFtdEVmqis3VLs9LXgueAkbKXqHyo3tBXnjSieU6l2+lrwXHAStlL1D5UjUVlxpWi9uBH2cyrVTl8LngtOwhZHy6LcGmgxKVXrpZCUaqefU/BEZD9gGdBEKqxmqVLqpyKyD/BHYAqwCfikUmpndEUtPIawLUvsSZttFhavC8KjRjfQcImL9RLnTiyOnb4XvFh4/cBXlVLrRGQksFZEHgI+CzyslPqhiFwMXAx8I7qiFo+b3niD3ckkNyQSCNCvVKyEJS4NtBxo6epiS08PNSKQ/p2LYb2UQidWipNCOaMBlVLtSql16b+7gReBCcDJwE3pw24CTomqkMXELCZ9SrFbqdjt0lVOm+kUE0NkftPejgLOHjeuaEJTbjvCxQVfPjwRmQIcAqwGxiql2iEliiKyr8N3FgGLACZNmpRPWYuC2VdRLZJl4cVFWEp1eBE3zCKDUkyqqyvasyxVH1nc8Sx4IjICuANYrJR62+sGG0qppcBSSCUADVLIYmIVEyCWwlKKw4u4YYhMbzKJiNBYW1u0suhOLBo8ZTwWkVrgXuABpdSP0++9DCxIW3fjgFVKqRlu59EZjzVxZ2lbG+e3tjKgFENj6jvTDMZrxuOcPjxJmXK/BV40xC7Nn4Ez0n+fAdwTpKAaTZzo6OsjqRRJKsd3VogNh+KClyHtYcD/AP8UkfXp974J/BC4TUQ+B2wB/iuaIhaWOIcCaKKn0nxnpTAbHCY5BU8p9TdSWV7sOCrc4hSXSvvxNYOpNN9ZpYU06ZUWJirtx9fYU0kTQJVm0Za94PkZolbaj6/RVJpFW9aC53eIWmk/fpiUqu+zVMsdJpVk0Za14AUZolbSjx8Wper7LNVya4ITbXL7IhP1kqswp/NLOTSgVJdBlWq5NcEpawsvyiFqmNZBqVsaper7LNVy+0UP2/dQ1oIH0Q1Rw5zRLfXZ4TA7lkI2zkrw2ZZ6Zxo2ZS94URGmdVAOlkYYHUsxGmcp+2y9dA6l3pmGjRa8gIRpHVSCpeGFMBtnuQ/jvHYO5dCZhokWvDzwYh14bXilbGmERViNsxKGcV47B3Nn2lhbm5mYKbfn4RUteBFSCQ0vTMKydCthGOenczDu3a4ulrslbEULXoRUQsMLmzAs3UoYxvntHJxCcCqtQ9aCFxAvPWOpNbxy6e0rxSfqp3Owq4uV2CFrwQuA16FqKTW8KIffxRBSNzEoF2H3g1NdLKUOOQy04AXAT89YKpMRUfX2cfNjmstTLcJZTU0sbGoqid8oX6x1sZQ65LAo66VlUVGOu4RFdU/FXr5lXbKXVR6l+HV7O0c9+2xJLukLg/n19VwyeXJFiB1oCy8Q5dgzRnVPxfRj2lmXRnl6kkkUoKgc/5UXlra1cceOHZw6ZgyLxo8vdnFCRwteQEplqOqHKO6pmJ2DnXV5yeTJPDxrFssSCW5MJGK35WZYBPFTLm1r4wsbNgDw4M6dAKGLXrH9p1rwKoy4TSBEiZN1aZRnYVNTWVnpBkH9pnfs2DHodb6CZ65vUPwwGC14HglbKIohPHGbQIiaXKsMytFKh+ATUKeOGZOx7IzX+WCtb2eMHVv0MJiKFzwvwhO2UBRLeCox7sptlUG5kstv6lTnDWsuXx+ecf4tPT1Z9Q2KHwZT0YLnVXjCFopiCU+pBUKHRaUJvZvfNFedXzR+fF7DWPP5a0SoFoG0n3RhOgRI+/CKhNeGELZQWM/XWFvLFZs357Qy860o5Ti77IVKFHqn4XrU4m8+P0px9rhxTKqry6pvxax3FS14XhtC2EJh9S0t3rjR1coMcwhcDL9VsWfm4iz0hX42Xuu8n3KZj7WeP25B3RUteH4aQthCYZzvis2bc/a4pTwkC0us8xWGOE5QFCvhaa4676dcdsfGtXOBChc8KH5D8NLjlvKQbFkikQnyDSrW5Tq7XKyOLFed91Mup1jHuP4+FS94xcZLjxvnIZkbLV1d3JhIoNKvq0UCiXUpW7huxLUj81OuuN6DEzkFT0RuAE4EtiulDkq/twQ4GzAiFb+plLovqkKWO16szGJbokFY1dlJv0rJnQBnBfTnmBtVtQhbenpo6eoquedhJa4dmV9XTxzvwQlRSrkfIHIEsAtYZhG8XUqpH/m52Ny5c9WaNWsCFrUyKbbDPx/C3srSuhysXIa2mvwRkbVKqbm5jstp4SmlHheRKWEUKgil3OC94nSPpe67CrP3n19fn7EYS2VoW851t1TvLR8f3vkishBYA3xVKbUz1xf8UuoN3gtu91gOvqswh+Je/EVxaYjFqLuFuvdSbpdB8+H9EjgAmA20A//ndKCILBKRNSKyZodlcXIujBm+YuVSC4I1/1quz9zyxYWVo86tTGFRiGsYFuP3pk51jVf8zmuvFT3Hnfl37UkmWZZI5H3OXHWrUPfuN8ehudyFqCduBLLwlFJvGH+LyG+Ae12OXQoshZQPz+s1Wrq6uME0w1cTcIavkLj1fE6fuVkt1iEhkHNFhp8yFeK+w8bNYjQ3xN5kkiWbNrFkypSixP0taGigRoQBpVDADYlEXkG4uZ5xIUcDfmZmrRmmBYrqgw1k4YnIONPLTwD/Cqc4e1jV2cmAaYbvzJhFbNvh1vM5fZbLaplfn8pICwTqwQuRcbjYWY0NjIZYBSSBv+7cGcjaCcNaml9fz5lNTUj69YBSeT2XXM846GggiMWVq846lbtPKXZbfLCFxktYyq3AAmC0iGwDLgMWiMhsUgljNwFfCLtgdktU4o5bz5fLkssl5kF78ELESUWxXCkIRkNcsmkTf925kyTBrJ2wrKWFTU3c9MYboTz7XM84yARRPpa5V9+sNaTIbOEVY8SWMywlTPyGpcTFAe0HtzLncz/5VM5CPMdc1yjksDffa4UdThPWsw/7d7xi82a+89prDADVwPemTs2MJsLEmgQ0ivbhNSwl1oKnyaaUOgBrWQvVuJyuX+jvlwLFWOccVQhWaHF4mvhQKqst3DbPKdQSpHyfVak863wII04ONHDlAAAgAElEQVTSSajshC0OIVhlJ3iV0DMXgnyeo9OC8lJaglQp5CvsTpMpdsLmJmqF6hDLSvBKOSAyTuT7HHNtnhNFebWQFge739pJ2Ixje5NJqkRorK3NnKdQa3LLSvDKYWVCHMj3ObpV3ig2Q/rI+vXsVoohIjw6e3bsfvOw7jmOwu70Wzt1eNc0N/PF1lYGlGLxxo0cPHx45juFcCOUlODl+sFLLVVNXAnjOdpV3igs8GWJBL3pibdepViWSBRUDAo1Ox2n0Yv1nq2/tVuH19HXh1IqcMhQvpSM4Hn5wQtlFpc7UT3HcrPAvdTJsO7Zz3mitAS9Cq+TtVZso6RkBM/rD14Js2v54qVBRPEco6jsC5uauCGRoE8pakUKGqDupU6Gdc9+gruPevbZjJ/sumnT8t5M20y+GayLbZSUjODlW3Hi6P8oBsUcGkVR2efX17Nq9uyi/LYLGhqoFiGplGM257Du2et5VnV20ptMkgSSSnF+a2uWnywfwlrfXkyjpGQEL5+KEyf/h1GeYolvsYeVUVT2YjYgsfxvR1jl83KeBQ0NVKVFGPas4Q3j+qW4vt1KyQieH6yCUuxGbi1bMcW32D6UcsJISKpIrQ8tRr2ym0C4bto0zk/PhA7N8zfOtQWjHzasTvDQjS94Pn7vpmF8Zsl8v0V2pWQEz6tQxCHK340oxderby4qH4r1+uXuRsinXoXxbJzaxKLx4zl4+PBIzp+r7tz49Sf499t9QCqziJvlm4udiXe5ZUlLqKJXMoLnVSjiHuUflfj6sRyjGAJar39Nc3PODcZLnaCdR1hWvlubCOM3tjv/e+/sYNTLnawD1uX4fj5iZ7Az8W4IZ9lDyQieV6EodJS/X8o15MN6/Tt27Mg4z3vLIAQF7K2yIPUqrN/Krq7nazne85N1bHs5tTxsFHCN6TPhFbb5PmO8KBnBAzhj7FgA18yxxZ729kKphHzkc/3ZI0bw4M6dQCoZp3kZUSkSpu81rN/KWtfBfg2rlcdueYl/PdEGHhIlhWGlxYmSEDxrZcvlLI2LNVdIii301uuv6uzMZB6uIhVhX8qEaUEbzyqMfS7Mdf2KzZs5aXU/h78CkGTtLWtzDjvjzt5Nw0I9X0kIXrGHa2ESpSM/qNCHVSbr9YfGZKIoDJyssnyenZEN+aY33vBlMd6ypMXWtzUKOJzyscoqdpa22MO1sMg1LCrGrKaffGZ+KLbFGTZ295PPMNetE9+wOsHDy14kOeA/OW+cxM6u9Oby1dZVseiaBQWt9yUheE6VrdQak1slL1Z8np98Zn4pB9eCXZybQdCRx2O3vMSox9sGTQhcxyvh30CBsAtBkdG1LD4mSTIdD+i2tWah6n1JCB6QeQirOjv55zvvBAp5iHpRda5zu1mqxRq2+8lnVmnkaoxOv+eG1Qke+t0LGRPHKR4tTtZYUBrnjeas/Tsdn9Gcri5XX2Wh61rJCJ658lWl9/t0SzFjFwTrtyfxKpB+Mkg4DfOKNWx3KlPUZcl3Q6NCWPdOjfGxW17iX4+3AXA1hqAlWXfLWtZiY+lEVsL8yRUcfNAR4/nwZ97j+PkVmzez+7W3XAXLzVdZ6HpfMoJnrnxKKapEEIft3uwEyG9P4kcg/ZzbaZhXTJ+XtUx24Q5+NwB3I98d2KIeAhmCZheHZjfsFIe/S4GDcwhaLnIJVq62Ueh6XzKCZ32w1zQ309HXZ/uQ7B6y357Ej4iFGVcVl6GjUZYoBCafYUxYQyDzEig3Sk3ArOSy0PIll2B5aRuFrPclI3h+egK7h+y3J/EjYuU2I2kml8AEGV7m00F4/e7y3z3Hm0++WfKCZcU6BI1a0Nww//ZOW27GrW2U7b60YS3OjssPVSzcLLxibQ7e0tXF3/6+lb1WbocB+2PyXbgeF4xYtKVtbXyxtdV1xrOQFDvrj5WK35c2DDM5KlO7lITUrYfOZ3jp5dkuXbyKvp6k7Wd75Th/KYldLittaVsb527YgPEk4rA2OczZVR2HFxFxEJo49Yxen4eTOHnJ+OuEeaazXFFAbzXceajwo1PmBHIFtHR1pSw703tVATMNh4lft4TT/cYuDk9EbgBOBLYrpQ5Kv7cP8EdgCrAJ+KRSamdkpQwB84OtEeHMpqbMmtxCimBcYtzCqmhOGX/9JnssVUTg6M++l+kfyF7ffcXmzXzntdcYAKohcJD5qs7OTPZiSK1Lvm7atNDqTIspTs4tKYcVP745t/uNYxze74CfA8tM710MPKyU+qGIXJx+/Y3wi+eMX2vN/GAHlOLX7e3ckEggpLLVFsraKka8nd2zyrXqw8uzXfPNtfzI5ENbe2tpLla38/eZPdtD9x3K2d89zNc5wwoyX9DQwNCqqpyb8gQZvRh7+hrbXN6QSPCzadNsox+CpsZq6epiyaZNmVRh1vuNXRyeUupxEZlieftkYEH675uAVRRQ8IJYJ8aDNXZcUkBf+ocOugOT17JalyYVctbK6Vm5LYY3jv/GyiTrup3PXRVpyaPFLGgv7QsTPndAZqbRap19b+pE3+cPK8jcS30Jaq2v6uxkt8l63J3e9CdpMQDskrs6hYTZlcsQuyoYdL+lEoc3VinVDqCUaheRfUMsU06CmMHGg12WSHBDIsFA2u9kWHg1Imzp6aGlqyvU4YLdlnmFjDtyelbz6+u54dWGdOhGapXAOlJCcFVBSlY4jEXqZgaJhKkRRh1X6beR56ovQYeFCxoaGCKSsfCqwXYFk/n8vckkX2xtRTmMiswdvPE9Q+yO3ntvlkyZMqhsZRWHJyKLgEUAkyZNCuWcQSuk9aEaPjxDBH/T3u47VY8bqzqj2zLPCxtWJxh14yuuqwVKaRmUE1INRy8c7Edzw010CmF1hNnI82kPj86enfHhjaqp4SfbtkFazBpra7li82Yaa2sz53db1mlnCZrLZSd2hZ5IDCp4b4jIuLR1Nw7Y7nSgUmopsBRScXgBr5dF0Appl0jU6MEGlArdcbqgIbot88B+ptM2a0UoVysedhZaGLiJTlBBKkYkQD4Cbdyn0TYG0ss2vzRhQlaCDmMY21hbm3nfOiqyWpodfX2ZcjXW1mYy8YQRxxmUoIL3Z+AM4Ifp/+8JrUQeCVIhnUz/qByn8+udt8zz2jCMWDSvgbSlKm7HnOnPQouaoJMAxdzkPJ9rmYefohTrd+0aJF6Gj/Pg4cMHjYquaW5mS08PNSIZC9H87OyeSzEiFryEpdxKaoJitIhsAy4jJXS3icjngC3Af0VZyLBwErYwhjBODcRuyzw/EwMGpSpkBlFZaVGQ1yRAcnBuwWLHflqxq6vWtnHqmDE80dVlawRYR0W9yWRmsqNahLPHjcsKcSm0oeGGl1naTzt8dFTIZYmcXH6bfCLFnRqIOfOGsbVdKU4MGL4IO+GtHVrNgs/MiJWFlg95TQKYGnBjbW2g2c0ocaqrdm3DbW9b872Kya+HUkyqq/M0K13oGVoo47W0hWLp4lXstln+ZAhDuazp7G+s5tCvz46FlbK0rY07duzg1DFjbGPSvOA2ZA1rjfCqzs5MeEsVZFalhJXeP8h3BofcTHVc+O/1+ma/ntPzitq3WfFracOipauLp65aT02Hwyp13AWtFMTO6PL+0QxfOvv9sRA1J5a2tfGFDRsAMttA+hW9XIIWxiSAgZ0VlGt202ty2iCiHOYw0nyvbtag9dhiUvGCt2F1gr/+7gWcDF1FqicsVZTp/6mnTeHEj+yf9bm1xz84RMdxFL36HTt2DHrtV/C8DFnDaKBm4WysreWC1lZ2p2M+g668yOc71jKF+bvk87w2nngifRsHJ1atbT6A5nvvzbdoWZS94Jl3Ug9CKVhoYB+LNnj4Us2Jlu8taGigJj3UsjbEfIhqN7RTx4zJWHbGa78U0lluDvswdz5mGmtrERGqHDJ425HPPRTa2nr5iA+T3J4duWZ+Bk5trG/jK2w88cRQRa/kBa8cF6k7VYaJMxo4+cI5ns/jtVE4NcR8cJqxzDdsw7Dm8vHhFcNZbsxqKrLjMVu6uli8cWNmhvOa5ubILLWo/GhOFpobXg0Jv+fNRUkI3i/Pf4Rkf7FLkT+5ejUj2WOYG2PnahRODdGNoDu0hRV3tWj8+MCTFQaFtnKcOh9r/FtH3+C0807P28s9+JlYcKL98svpvHW595uNMbEVPK97DsQRp+wbO4fCpZ+oyjk7FmZjzHWuIHnN8tmhLcqhZCFXOfi9ltPzyPX8w9rwSNJuC6ed/jadeSbvtjzp/QGUKLEUvFIWu783w21zqxzToVeH1NgLaQWa8WOlWcU2yqFkIVc5BL2WXeeT65nket5u9cD83W/ffAufevzxrI74Rb83XgRqmw8I9XyxFLw4i53bEqgrNm/mtvQkgbVyhtnYw27cfixKLxZJMcITolymZL2nsK/l9kzcnre1Hjz9pQuo6u3NfH5y+p9BqUzAGehZ2gLgd2LATC4xCKux59vg8rEO3YS7EFaWU9mjmnm1u6eg1wry3M3P+4RvfZsha9ZkLLN6wC2MP+4CZ+fTHjZ/HlNuvDGya5ad4OW7k3o+FGr2L1ev7zVZZLUIZ6VT3ecTXGsQ9WJwN0GN6tnb3dMlkycHmiHN1Rl0rVhB28WXwEB2kHsDcIrNOeMoaHYz/VnlrKpi/JU/5IUjjihKooVYCt5eo2p55+0+3z+oIpW99oaja0J7gEGc01H/cE6N24uYmRuwkeo+rByAUVpZqzo72dLT4yioUU1YuK0D9XMd47nfvuRypqfzz5WCD80v7+6zDx/43x/YLl2zSw5a6L1dYil4Z151OL+66DH6u/f0dIb4OVlo5778Mr9ub0+tjPDwAL00kDjtMGbFrsF5ETOjAZtT3YdV4XJZWfmmXKoRodqSfsh6TNi/k1/Lsf3yy+lc/kesS3fM/rQ4WmZmXK20YcMY/93LqT/pJNvvtnR1McT0Wzj9RtbkoIXIlAIxFTyAc67+sK+t7G5MJDI/VK4tA702kGL1QkGxEzPrHqZGA16WSHBjIpHZwMhLhfOyw5WT5RNGyiWU4uxx45hUV5dVJ6L6ncz1z7BSgsakxVnkrAI3MHUqu5bfGngtsV0H4ZYctJDZY2IreOB92LCqs5P+dI8qwFkuPqlcuyiZCXuIFnWcmFHZrtqyhbs7OgBIklq6ZD1ufn09C5uaPJenpauLBevXZzZ9uTGR4NHZ3rOnhJVyyU5ow/ydDEFTpCYFDKvsBeItWl4wC9ven/4U4y67DHDIoGLT9uzqr9fdzOx+o0K4f6zEWvC8Ytco7LDuoiTgun40bqEkXgRzfn09h44axZ87OjKbp9hF7xvH+hGsPtMwbbfNqgy38uUjSmeMHQu4W5V+ficvQbYlLW4iNHzqNDYtXpx5JjB4Wd+49OFefhu7+mt3Trd6WQyLzkrJCJ5bY/L6MM3LeIDMsM/t/G5DND8/XhihJF4r14KG1F6mYfpHFjQ0UCuSsfCGWDqKfFIued2V3qkjsxJkbWepkStGzfrszhg71rH+eWk/Tmuj/dRpa1sqxv4fJSF4Xhq7F2vF6uOClHN/WSLBTW+84dn6ymdf3KAi5HeFg7kCQ2rYkk/Fml9fzyrTDldus79+Ui4F3ZXe7EuzDj93U+IWGoAI46+60nFyIBfWZwfuy/pytR+n+hu0ThdrQrAkBC8sp7TZYW/sTTukKrWdtJ/zBylPvia9X8E0KnBY2XuN87lZlUEqv9uz/Pip/8nJu3ZlHW8XylGK4hbFKgIzdm4ePz5bK071N2id1mEpLoTplDYa7SEjR2ZSDB08fHiWhZfr/EHLk4+TNqhgBq1YfoUyqB/NuvwJ9ohaVc5SxpuoVw244fR7uE3mmY/1OhkRtE4bbcjYpN46sRYVJSF48+vruaa5OSNQ+fYERg6y3ckkT3R18fCsWb53gg+zPF4JUrmisLy8lK9rxQravv4NzHum2FlicbfO3DYvMiYHjNnOuOG1vlg7ty9NmMBPtm2jP50U9ufTpuWdjsuubNc0N/PF9BamizduLMgm9SUheFaByvfBOC0XcptqD6M8pbRJsxehfHHWbDAtVrcj7oJmxRqTlqgfxbAHHyzarCJEX2/M7aE3meRHW7dmJvb6lOK8DRsiEaOOvj6US8qqKCgJwQtibQQNkfAylAtanjht0uwlq0nL0t9Qm95lTij9pVBeFqubQ5eqRLhu2jQWOQzxCkEh6o25PRibDZkZAJYlEqFet6Wriy09PbYrZ6Ik1oJnVLLO/n5fOf/zCZHwImZBhon5OGnDbmzG8zm65Uk+fuONjkG1Q/K+Ujxo+PSn2LR4ceDEpcXsrFZ1dmbiRq2rZsLCfM9GZuR3k9lbj67btYuWrq5Q65+xXNC6cXeUxFbwnIKEr2luBtzDLIKGSIA3MQsyTMwnpVDQxmb40azrOuvZk1ao1IacdniZHPjd5s2+wnr8doJR0Vhbmxle2q2a8UKQJYHnbdiAOWfLU93dfGT9el+ra5ywLhe0btwdJbEVPLsg4aRSPNPdnTM3fz6zul7FzO8EQlSzrFFuoBIbRo7kwKefyusU+dSJqLLAeKGjr48qyLlqxokgSwKdrtGrFFdt3cpdeYpTMZ9nbAXPPG1t/NheY+byjXnLJ3wk7PMe9+ulnHznnVnvlbovbRBDh3Lgs+uz3gp7GB+0ThjluKa5mY6+Ps+rRMIi31UzXpYE2l3TGqBvsOLNN/Me2ubbPvMhtoJn9SsYlQ3wFDMXlWhFgd2+nQZ1BS5LGFgnBxSwe/hw3n3k4UhiAL1irRNBkqWaWdrWxhdbW0kqxdCIfHv5ikOuJYFu11yWSHB9ezvmDQMVhDKkL1b7FKWsGu7jyyKbgG5SEzn9Sqm5bsfPnTtXrVnjlpTaG8WaMQt6/Rffd9CgLLaliNM2k7LXXoy7fIntMqgg4mXO3lEFHL333iyZMiX0WcJc5TKXA1L3XGdaOP/h9esz1lMV8H2XneiiwE/6tFw+PLfvXrVlCys6OlAQmbDni4iszaU/EI6F9xGl1JshnMczYfYOfsXLrqGMOf10+ja+4mk39VJFVVez5mtfZdp//IevZx/E4W91Z/x1585MgHhYv7uf2Xi7ZKlAVvhGlQfLKUz8dCT5tJf59fXcdfDBRTcywiK2Q9pC4KfSGEuhzDOcBoaLN+4i52bLC9mznXYV/L0BrhnEQW0MqZZs2sRfd+6MJDDVz2y8U7LUoWlRrk6vRiikEPjtSPIVrFJyEbmRr+Ap4EERUcCvlVJLQyhTwTBXmqNbnmTUF87hxRxD/DiLmnUDI6vFOeHqq6g/6aSsoZoAXxg3jl/OmJE5NkwfWlAf1Pz6epZMmcITXV2RzOb5nY23W3jv5768Js/0ip+OJNfvWS7WmxfyFbzDlFJtIrIv8JCIvKSUetx8gIgsAhYBTJo0Kc/LBccufMO6cD3OYmbgJsd7WSw0ayU/MF2ZFzQ0UJOOqFfADYlElm/Hi/XglsPOy6JzL0Q9m+enXHbHev1+vskzncrj9dm4/Z6FCqqOi6jmJXhKqbb0/9tF5C7gUOBxyzFLgaWQmrTI53pudK1YQftlS1D//rfn78RZ4Jwe1IamJj6xJLVY/dfTp3Pw8OGZivRej8Gy8+vrObOpKbPp0YAlVCGX9eDUSKJoPOUwlAojeaYdxrNp6epyDcR3+z0LEVQdp82wAgueiAwHqpRS3em/jwW+G1rJbOhasYL2b30btXt3lJcpKrXNB7Dj5psH7fBkhOVsf+cdjjWltXKqSLlEa2FTk2N4Ty7rwbrYfMmmTSyZMqWoKxLigN9NwsMIvvWaHNfp94wiCNj6HOJUL/Kx8MYCd4mIcZ5blFJ/CaVUuMemlTINps1TnPiTZRlUR19fJtxhfn19JlXPFS7LpXKJlpfPnSql0yxqPlvvhTHkKeawKcgm4caESD54FROn3zNst4HdcyjmygorgQVPKfUqMCvEsmSIu9jlCj9xy2bb0tXF73KkW/daQXIdl2s4mM9w8YyxY1m3axdPd3dnFrYH2XrPiBEzZ6AOusFRMYdNufxkTs/EsLKDboYehpiE6TZwSr1mXkRgDOmLYeXFMiyl2GJnt1LATOu4Jk697LJAKdO9zpZ5nUEs9BId8z1UiWSejbGw3U/jMc5lXsIUdMhT7GGTk/D42bNjWSIRaNY3DruBGTg9B6NcxfblxVLwik31vvsy4/HHgD0Vy1jetqWnh9+0twdqWH5ny7xE7QfpnfMZ+pnvIalUpkMIsrDdOJc5q3BQK8WuoRVyiOvU+bildzKXuVokK9bPTQyWtrVxfjpT8FAfdaUQuHXCxe6UoIwFz2uQrVujcAon8LP/hRmvs2U96d4+zMpgFu5c2Wa83kNN2sIbCJjA0XquM9MbzeQTwmL4xP75zjt53WcQ7Dofp/RO1qQEXjvSlq4uvtjamtl4Pqocefng1AkvaGigecu3qFFvA3DLZrjF5TwHjDqAuz9xd6hli6XgVe27b6BhrVnk3p40iQ9985JMcO0BdXVcNGlSVm5+t+FGS1cXSzZtyvTOxpBjUl2dY+aMXOSaLas2xcbdaImNywfzfYoISZ9pta2dgnULyHyy0lgTRHi9n1w+MUkkct5nWBag23ns0jvl05Gu6uwkaQqOr7bZHziM38YrZz9wNk8m3Dc1N+Mnm98rb7/CKXedEqroxVLwZjz+WM6JiyrTsNOOlq4uhpgq1bIDD3QNrzA3Cmvy0SpwHXL4qWRuQ9DZI0bwdHc3CuhPx8blOp8XzPdZpRTVIkie2aPNZcmnMfn17Xj1iUl6uG2kFfMaS+iXXOexS++Uy7HvthrDOJ+Rgt68pM2a3UXA0xDZzPef/D5/fPmPvp9DVLzydrgbqsdS8ABbMfPTI3tx6DsNMY0KaYjd0Xvvzf51dbZDjjAqmVlgDX/YkKoqGmtrQ2mU1vv0Y6EWwu/i5xpux1rDZWBPlmyvnV3YZXeqh06Ofesklt3v78VHZliBo3Y9zYSOm1n0Wuln6wmD2AqelSA9spewDLvKYxWIJVOmAPZDDrtKZs6q4aUR2Qms30Bet87AbuhoHJerEzGWoSXTW/ZFEUPlJ7TC7VjjPs1JB5JK2Q6V8wnnMD8zL+exs4i9zKw6/f5XPnIGr7z9iq3/6z0278V5RVGhKRnBi8rSsBNFpwqZSxytFp6Tb8V6PTuBdbMErHiNtgcGreDw4thXlv+DkGvxvNfQilxiMb/eW9KBoCE9ds/a63msz8A49pS7TnEcupkFLJeTH8pP3A4YdUCo5ysZwfPTI4fhjHYSwlziCIN9bkF3UcvXErA+E+skzB07duT83qrOzsxEinXNrVe8Lp73GloR1HJ3O4/XOuPkfzN/Z+WrK/nmE99kz8A6hdFh3Ix3YSo3AXPDGv9aMbO0dnitxE6Ny244F1U5g/iLnBqxW+M2h5p4Wexv3R/k1DFjclpCYQz9tvT0DLp/2LN4PoownFyiaC3ngnT24loRVlk2uVn56koufuLizGu/VpdBJYmXF2plKOsW7skuWYi4yZIRPPBWie2i1296442sxu6WptrLQ7fOyuYaTjbW1qZWJaSHuo21ta7ZLbxgFXZjIsJu6Y6Tj3B+fX1WthUnwc136FctQo3NhstRheHkYuWrK7nkiUtQJptimunzRZvcv6+Fy5nTZpzGt+d92/Fza701bwjkp5MKSkkInh/lt4oLkGnsQM64rFziZT3mjLFjXa23lq4uFm/cyIBSVInwpQkTsvxmQWP6rMJunMOu/G4+Qi+VLEhFNJcPpTh73Dgm1dVl3edZphRV/QGHy5ASsG//7dv0q/7cBzugRcyeeU3z+M1HfxPa+Yq92iL2gudndtYqLtc0N3Pw8OGDLDynoZmXH8N6DLhPLJitK1GK9bt2Zb7fm0zyxdbWjDj7CTuxCrtTfJchVvlm5/WLVWTtrDdziqoaEbb09GT1+B+8+YN093cHur7Gng9P+QQTpnyuIMvt7OqRtV401tby6q+OZWpi9eBOZ/R74PzVoZYp9oIXJEbLEJeOvj7XkAwrXvxVdg3ZLv230/Fmv1lVekjnd88GO2HPNavr1UoLKyDXKrIPv/gzFtkEtBpzcAp4YhM8gba2rDjNjgu5h5BmMr/ta6/lHWxttKdnurtJpPNTNg0Zwk/+fBJ177yBAual/xn3IOnX75hP9sieexnEmy/Bzz8QqujFXvDCiNHy2tit4mj1hVmPsc6o5jqncbzhN7Oua/U6IWAn7G5l84OfDmbusrn0qt6c5/Ti1Ncil8JuZtLY/9bwd5r90ECWP9htS8ZVnc6JDMBikW1+AO5chCG3ZtE1C5kTTr+n79/5zZf8fsOVvPal9UvQfWn9DLHCSiRZqDQ2QcobRfm+/+T3+dOGP5FUyZz5/jTeGVY9jMs+eBkf2/9jeZ3HmrXHsK6MPILVIpywzz6sfOutzF65Q8wzzj//AMpGPIzf104FYvPbL+nKeYjXfWlLQvAKjXlXr2rgewXeYNkLXoQybusi40oQgf/Mgns9d8BBN8F2Ot9+13+YCd2vef5ObIQrKCEKXuyHtMUgn9izKLHLTOE1BqySsG5XmYu3RxxOW+MnPVnLhnX9HZMfDOwTPBjxfbvTRsWNiQSPWmL8+PkHMsM2L6aHMZQseRHzgFLwquxHmGsttODZEIYvLBctXV189YFP0/3u1tDPXalksiZXN3HTSbd7jrPMZS2brTQgyw9mxHn+R/ujLH7pZyi1Z93uPKDHerJHnS3KShAxLxiDzpfUBI7vvZJNIZ67LAUvqqVlufATRmHO8qsZjFkUaqqGs/5/7HOu5UrNZfZ93d/RwYqODhTZTv9BdcXi77I66X9hKYPxWv+W2QTxlvWoar7R/wX+nPxQ+AWiDAUvTIf+95/8Pre9fFtWRH5YlGvjcAuhMLAGs9qtGvnihg30k6qgjx9yiO053dLiG1bZzMcu4xtt9yPA2dYTpEMizKETdmD6jvoAAA+ZSURBVOXVBBOvl9QEjt99dfiFyYOyE7xcYRV+M7RqcrNXzV5cOv9Snqs+KDPZY539qxHhcYv/ymmt7f0dHRhrJvpJpWsHqL/5VA7c/nTm+04xXdYYMC1e2fgVLwX8fuBoLus/K5LyuDFt3+Ghnq/kBc8utU7Qxd0aqKaa9Wes9/29lq4u/vLqq6nYQKBWhH61xza2Lh3buvSjzGt7MiNK1mFiFqv2/OlFvCpJ4IJYXk8k38fCvm+FX5iQqRF46CsLwj1nqGcLkaP+eBTbe4Jt11hJFd4Nc1uopppnLUJm9XUar43lXV4TKfSuuJAPb7zDrEv2rNpTpono38lKKVlefrDelrh8ZrBfYx1/u+io0MsSS8HLR+zKhVz+pHEjpvKRuT/znNXFKmBgnwz0yZbzOPjdrbZLg+zQw0Z7ysnyyrUDoN1xAlQLfPjIidxUuzMr5f5106bxTHc3v21vx27bpirgnKlT8yy1PbEUvHIWOychM6+JNOevqxLhKxMn0lBTk4my7+zv58fbtvHwa69RI8J799qLN/v6+HBDA+8bPjxz3GfXXs0lz/0exWC/Fgz2fxlOfS1e2ZSDeFljE906VOtnezfUct7Cg1i8cWPWfiGwZzUH2KdJa+nqYvmzXfSm04RdOHEiizduzNp8XYDDR43iH93dJNN77UYV+xpLwSsHhGquOPwH7NP4oax1sz3JJAJ8bb/9uPKAPSGVLV1dmTWRAB/de29WdHQwoBRXbd3K+tXnM9MUs/dDX2XRQDDhivOwMddyMPPnUyYO57HzFwC5tyf9SDpYeohIVqC0sQb8qbff5p50eI+RBdvYdc0crwiDY1qNSUWz2NVVVfHDdFuIdQJQETkO+CmpFVjXK6X8tMNY4SUk4YBRB/CNI2/KErDe5J7+zrCkjMXdr9c181+m/WCNxd/PrD6fmau2ZnpdO+vrTpvra+HKxq+AxSVMwmux7cSrfzjcfkGqEzVmxI1j60ziZV0eaR4iugXWz6+v59HZs12F5/jGRh7YudN2JZKR7uumN97I2tLTfB63zddjmwBURKqB64BjgG3A0yLyZ6XUC/kWat+6fSMf1ipg15DpJMZfwLXTpmWyllSLcEJjI2/t3s3jb7+dOb4G+NL06ZmeUZFKJvrOqv9gGA5b4D1iE/uVRkfY7yGI5dWH8LW+cyMLUPVKkNi9/RrrGH7s6MzOatWQSZBqTg5g1ElI1TWVPtYQI/PmUWdZhCPX8ki3wHq7z5wybJtF0SkkzG0j90Im/4T8LLxDgY1KqVcBRGQ5cDKQt+A9fNrDrhMX1kpmvH5r+If4wIzzOL6xMfNjwGDfwrJEgl+1t6dPpnimu5uHZ81i+i/msE/vm84FW2UvYJUuWFb8Ctg7qpaDdt8UTWF8YjejaPeeAOd9fDqX9bc7Dg3dAuBburqy9hMxi9XStjau3rIly89lYGyVmUs4whYWpwzb5hRqdiLrZSN38zOJ85B2AmBeCLoN+EB+xdnDw6c9nPXaHKRqbIgNgyvk3R0d3N3Rwc83/IoPpCPsrc75eQyO+zLOo8UrmyDWV5tq4LDdrpF1keLH6vrveZOYdGgjd+zYwewRI/jx1q2ZoOehaR/W3W++yVVb91T1WuCxQw5hfn09H+ra17aRLkskMoJlFwDvJEhL29r4woYNtmUV4Eyb4Z9d3kbjdVjCYZep2Msm4Vds3uwpv2KhUrLlI3h2dWlQ8xCRRcAigEmTJvm/ynO3kbzz7Cwfl9em5Fe8ylnsgsR4tanRXNX/yYIOG52sd+O13XEK6Jg5hJ7xtQhkORjOGTeOX86Y4Xg9s8A8uHMnX99vP97uT0meYXWt6uzMKks/ZKXPtxv+3ZBIZK0y8bJBN8AdO3ZkvZ44ZAjb+/roT2e3PmTkyKzrFCpvo9Pkg912AuYyeM08VKi9LvIRvG3AfqbXE4E260FKqaXAUkjlw/N89h+9B3alhp1VeRSyHAlidfWqKt6z+w/hF8aCnZXs1eIaPnYILxxSm3n96+nTATLW189efz3j7FZAnxq8yrmKlJ8rSSpkYmFTk2t5rQKzftcuHkgnFYCUqGzp6aFGJJNYs9YiYNahmLGXr8H+w4a5lsHMqWPG8ODOnZnX35kyBYDz0xmPF2/cyMHDh2euU8gNcZwmH9yEzOvQulAp2fIRvKeBaSIyFXgd+BTwmVBKZRK7cieui7KDpkt4Z79q/vcTM1k0fjxg7+w2TxAJKeFKWs5TBXT09XHJ5MksGj8+a2hk7IIGcEMikSV8tSJcO22a553grAJz6pgxmb+tW02eMno0TbW1Wf42OytrQUNDZgtKgBf//W8WrF8/aL9bM2bR/PX06dyxYwenjhmTufekzd4nXkQiKr+YHx+hl6F1oSYzAgueUqpfRM4HHiDVqd6glHo+lFKVqNjFTby8WFbWY2oEfn/xhzj8mWeyhobVwEmNjdz31lsMKIWIZBphNXDS6NGsTMcNmq0Qa0U2+7aSavB+CQbWYaDTLmiHjBzJeRs2ZMo6oFTm2l4whNksMAbWrSYPHTlyUOZrOyvrksmTOaupac/EGClR9+O/MpfDba8WN5GIesgbpo8wivPZkVccnlLqPuC+kMqy57wU358WRLyWRRSg6rYW0Y2+4dB++HBqRXjMxrqwZuQ1nPSrOjuzrlkF/GL6dBaNH5+VX868AVFTba2tFQJ7KnJLVxc3mnxb1el7sS4vMmZBzTg17o6+vizrcAB8D+0WjR+fJTAGQXaxM45Z2NTEjYkEvQ7DYDO5hqa54uac7rXYe8DGkXiutAhZ8fyK1wDCV0KO8TLfkp/iKOCDHxnHn4ft8tVTG2ImSjk+yvn19babYS9oaGBoVVVmOdDPp03LEjvr7mtGQ74xkSCplKOTflVnJ/3pH0OAz6eHpcb1jffNZcl1nwsaGhgi4klYgnDG2LGA834UTmJkBPB62c/Ci7AGsX7iulVBMYml4LWpBsbTidi01GKuawwSIQ8wYkQtRx42nnH7j8yyjIxgUjeqgVlTG/lywwG+/BuG49y8/Mfue4eMHElNeucro1HYNeJc8VQtXV2Ze3G6J7thKZC1GbdRXmsDdbu+V2Hxg/V6bpMfTmLkVaSi8l8VO8g3jsRS8A7b/Qv+PuQ8xtOZ9f5bagSX9y/M2/LyGvpgZUgtLPta6trGsO6OHTsyEfMCfMEmDGLPBshvZhz3z3R3Z8UT2lEFWSLkp8J6dWg7behtvV6uuDIvAuvUAM3vGeeyNlC34VkUvp9iz4DG/bylSiwFb+zIIRzW7S9wNZdomT9/Z79qOt9Xx5CqKv7fyJE83pW9Ddwwj8PGVZ2dzB4xgkc7OyGd5cHOErBugNzR18ekurqsY44YNYqnd+0alCElaM/spXd32tDbipe4Mq/DJ7sGaH3Prqx+hmdhzEzq4WB5EkvBW/2tY/jADx7ije7dmfesgjZ25BBWf+sYAM59+eUsP5CRThz2LCszL1SGPRbaeTZR7U49utVhb4hYKvdXtoVkprG2NuNYT6ZfHzx8eFaDiiJbRK7e3U9Q6IDJ93amzbAx6uGT1/OHNTOph4PlSSwFD+Ca8w/NVNwqU9AnpAJSzXFeZusDUuEOxnIbQ5SUUkyqq8saBl2xebNt4Kpd4zc3pKp0jJUhYip9/me6uzMpnswNpKOvjypSYmfEl7k5uwtF0KBQJ39W1MMnL+cPcyiqh4PlR2wFz1xxk+mZRiP1knnoZY1qBzIJBP/5zjuDLCsz5tnIXMNIc3lU2udFWvSqSFmVN5ic/2bLwriOXRxVsRtUnIJCw6BQQ9FCLHTXhE9sBc9ccd1m79zS5Kzq7BxkWZnx05CtDclIj2Ok8zEnNbBzqpeKYDgRB3H2QiGedSHXsGrCJbaCZ624YO/fcqvgTpaV9TphhA60dHVlwiv85h/ThEvUz1oH9JYusRU88DZ7l+v7fnt7t6GKW0MqBytO4w09g1u6iAoSyRuQuXPnqjVr1oR6zjCHF+UwVNG+pcKgn3O8EJG1Sqm5uY6LtYXnBa9ppfM5V6lQDoJdKmgXRWlS8oLnJ610kHOVEqUu2BpN1JS84Nn5zrymlfZyrrAoxBCo1AVbo4makhc8GDy8yKfhRzFUKdRQU0+caDTulIXgWYlbwy/kUFP7ljQaZ8pS8CBeDV8PNTWaeFC2ghcn4mZxajSViha8AhEni1OjqVT0DogajaZi0IKn0WgqBi14Go2mYtCCp9FoKgYteBqNpmLQgqfRaCoGLXgajaZi0IKn0WgqBi14Go2mYihoxmMR2QFs9vm10cCbERSn0JTLfYC+l7hSLvcS5D4mK6XG5DqooIIXBBFZ4yV1c9wpl/sAfS9xpVzuJcr70ENajUZTMWjB02g0FUMpCN7SYhcgJMrlPkDfS1wpl3uJ7D5i78PTaDSasCgFC0+j0WhCIbaCJyLHicjLIrJRRC4udnmCIiI3iMh2EflXscuSLyKyn4g8KiIvisjzIvLlYpcpCCJSJyJPiciz6fu4vNhlyhcRqRaRZ0Tk3mKXJR9EZJOI/FNE1ovImtDPH8chrYhUAxuAY4BtwNPAp5VSLxS1YAEQkSOAXcAypdRBxS5PPojIOGCcUmqdiIwE1gKnlNrvIiICDFdK7RKRWuBvwJeVUk8WuWiBEZGvAHOBUUqpE4tdnqCIyCZgrlIqknjCuFp4hwIblVKvKqV2A8uBk4tcpkAopR4H3ip2OcJAKdWulFqX/rsbeBGYUNxS+Uel2JV+WZv+F7+e3yMiMhH4GHB9scsSd+IqeBOArabX2yjBhlXOiMgU4BBgdXFLEoz0EHA9sB14SClVkveR5hrg60Cy2AUJAQU8KCJrRWRR2CePq+CJzXsl2wOXGyIyArgDWKyUervY5QmCUmpAKTUbmAgcKiIl6W4QkROB7UqptcUuS0gcppSaAxwPfDHtEgqNuAreNmA/0+uJQFuRyqIxkfZ53QHcrJS6s9jlyRelVCewCjiuyEUJymHAx9O+r+XAkSLyh+IWKThKqbb0/9uBu0i5t0IjroL3NDBNRKaKyBDgU8Cfi1ymiift7P8t8KJS6sfFLk9QRGSMiDSk/x4GHA28VNxSBUMpdYlSaqJSagqpdvKIUuq/i1ysQIjI8PRkGCIyHDgWCDW6IZaCp5TqB84HHiDlGL9NKfV8cUsVDBG5FWgBZojINhH5XLHLlAeHAf9DyopYn/53QrELFYBxwKMi8hypzvUhpVRJh3OUCWOBv4nIs8BTwEql1F/CvEAsw1I0Go0mCmJp4Wk0Gk0UaMHTaDQVgxY8jUZTMWjB02g0FYMWPI1GUzFowdNoNBWDFjyNRlMxaMHTaDQVw/8HxHZS7QdNbhYAAAAASUVORK5CYII=", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n", + "ax.plot(X, Y, \"c.\")\n", + "for k, v in sorted(clqs.items()):\n", + " p = v.predict(X)\n", + " ax.plot(X, p, \"o\", label=k)\n", + "ax.legend()\n", + "ax.set_title(\"Régressions quantiles\");" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/dsgarden/quantization_f8.ipynb b/_doc/notebooks/dsgarden/quantization_f8.ipynb new file mode 100644 index 00000000..f4dd0541 --- /dev/null +++ b/_doc/notebooks/dsgarden/quantization_f8.ipynb @@ -0,0 +1,836 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c6b4f765", + "metadata": {}, + "source": [ + "# Quantization\n", + "\n", + "La quantization consiste à discrétiser les paramètres d'un réseau de neurones afin de réduire l'espace mémoire et les temps de calculer en contrepartie d'une perte de performance. Comment estimer ces paramètres pour minimiser la perte ?" + ] + }, + { + "cell_type": "markdown", + "id": "9333e48c", + "metadata": {}, + "source": [ + "## Une matrice de coefficients\n", + "\n", + "On les prend d'un modèle de deep learning [MobileNet](https://github.com/onnx/models/tree/main/vision/classification/mobilenet)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e26ddb92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "already downloaded 'mobilenetv2-12.onnx'\n", + "model size 13964571 bytes\n" + ] + } + ], + "source": [ + "import os\n", + "import urllib\n", + "import urllib.request\n", + "\n", + "url = \"https://github.com/onnx/models/raw/refs/heads/main/validated/vision/classification/mobilenet/model/mobilenetv2-12.onnx\"\n", + "destination = \"mobilenetv2-12.onnx\"\n", + "\n", + "if not os.path.exists(destination) or os.stat(destination).st_size < 10000:\n", + " print(f\"download {destination!r}\")\n", + " g = urllib.request.urlopen(url)\n", + " with open(destination, \"wb\") as f:\n", + " f.write(g.read())\n", + " print(\"done\")\n", + "else:\n", + " print(f\"already downloaded {destination!r}\")\n", + "print(f\"model size {os.stat(destination).st_size} bytes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f75c988a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model size: 13964571\n" + ] + } + ], + "source": [ + "from onnx import load\n", + "\n", + "with open(destination, \"rb\") as f:\n", + " onx = load(f)\n", + " print(f\"model size: {len(onx.SerializeToString())}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d8581d42", + "metadata": {}, + "source": [ + "On prend une des plus grandes matrices de coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "358217f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(614421, '616')\n", + "(614421, '619')\n", + "(1228821, '625')\n", + "(1638421, '628')\n", + "(5120034, 'classifier.1.weight')\n" + ] + } + ], + "source": [ + "initializers = []\n", + "for init in onx.graph.initializer:\n", + " initializers.append((len(init.SerializeToString()), init.name, init))\n", + "\n", + "initializers.sort()\n", + "\n", + "for init in initializers[-5:]:\n", + " print(init[:2])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3cd9fc33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((960, 160, 1, 1), dtype('float32'))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from onnx.numpy_helper import to_array\n", + "\n", + "coef = to_array(initializers[-4][-1])\n", + "coef.shape, coef.dtype" + ] + }, + { + "cell_type": "markdown", + "id": "b044ce26", + "metadata": {}, + "source": [ + "## Distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "374652c6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "cf = coef.ravel()\n", + "cf01 = cf[(cf >= 0) & (cf <= 0.001)]\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n", + "axs[0].hist(cf, bins=2048)\n", + "axs[0].set_title(\n", + " f\"Distribution des coefficients\\nd'une matrice de coefficients\\n{cf.size} éléments\"\n", + ")\n", + "axs[1].hist(cf01, bins=2048)\n", + "title = f\"Même distribution entre 0 et {cf01.max():.4f}\\n{cf01.size} éléments\"\n", + "axs[1].set_title(title);" + ] + }, + { + "cell_type": "markdown", + "id": "083b75c0", + "metadata": {}, + "source": [ + "Et maintenant la distribution des float 8." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "544a61de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "254" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "from onnx.numpy_helper import float8e4m3_to_float32\n", + "\n", + "float8 = [float8e4m3_to_float32(i) for i in range(256)]\n", + "no_nan8 = [f for f in float8 if not numpy.isnan(f)]\n", + "len(no_nan8)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "217b5762", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = 3\n", + "gauss = numpy.random.normal(size=len(no_nan8) * 20)\n", + "scale1 = numpy.std(no_nan8) / numpy.std(gauss)\n", + "scalep = numpy.std(no_nan8) / numpy.std(gauss**p)\n", + "\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n", + "axs[0].hist(float8, bins=50, alpha=0.5, label=\"f8\", density=True)\n", + "axs[0].hist(gauss * scale1, bins=50, alpha=0.5, label=\"N\", density=True)\n", + "axs[0].hist(gauss**p * scalep, bins=50, alpha=0.5, label=f\"N^{p}\", density=True)\n", + "axs[0].set_xlim([-200, 200])\n", + "axs[0].set_title(\"Distribution des float 8\")\n", + "axs[0].legend()\n", + "\n", + "axs[1].hist(float8, bins=2000, alpha=0.5, label=\"f8\", density=True)\n", + "axs[1].hist(gauss * scale1, bins=2000, alpha=0.5, label=\"N\", density=True)\n", + "axs[1].hist(gauss**p * scalep, bins=2000, alpha=0.5, label=f\"N^{p}\", density=True)\n", + "axs[1].set_xlim([-50, 50])\n", + "axs[1].set_title(\"Même distribution avec plus de bins\")\n", + "axs[1].legend();" + ] + }, + { + "cell_type": "markdown", + "id": "cbbf27bc", + "metadata": {}, + "source": [ + "Les coefficients ont l'air distribués selon une loi gaussienne. Les float 8 un peu plus selon une loi gaussienne à la puissance 3. On se sert de cette observations pour estimer le paramètre $\\lambda$. Néanmoins, la normalisation choisie ne permet que de changer d'échelle." + ] + }, + { + "cell_type": "markdown", + "id": "09ea6c43", + "metadata": {}, + "source": [ + "## Estimation de l'échelle\n", + "\n", + "On veut également comparer avec une quantization classique avec des entiers sur 8 bits." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "8e38ff05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float32(0.00014669707), -0.0)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from onnx import TensorProto\n", + "\n", + "\n", + "def estimation_quantization_scale(\n", + " coef: numpy.array,\n", + " to: int = TensorProto.FLOAT8E4M3FN,\n", + " method: str = \"naive\",\n", + " threshold: float = 0.99999,\n", + ") -> tuple[float, float]:\n", + " \"\"\"\n", + " Estimates the scale parameter for the quantization to float 8 assuming\n", + " the distribution of the coefficients is gaussian.\n", + " \"\"\"\n", + " if to == TensorProto.FLOAT8E4M3FN:\n", + " float8 = [float8e4m3_to_float32(i) for i in range(256)]\n", + " quant_float = [f for f in float8 if not numpy.isnan(f)]\n", + " if method == \"naive\":\n", + " std_coef = numpy.std(coef.ravel())\n", + " std_quant = numpy.std(numpy.array(quant_float, dtype=numpy.float32))\n", + " elif method == \"power\":\n", + " cr = coef.ravel()\n", + " ca = numpy.abs(cr)\n", + " std_coef = numpy.std(ca ** (1.0 / 3.0) * cr / ca)\n", + " std_quant = numpy.std(numpy.array(quant_float, dtype=numpy.float32))\n", + " else:\n", + " raise ValueError(f\"Unexpected quantization method {method!r}.\")\n", + " zero = 0.0\n", + " scale = std_quant / std_coef\n", + " elif to == TensorProto.UINT8:\n", + " qu = numpy.quantile(coef.ravel(), [1 - threshold, threshold])\n", + " scale = 255 / (qu[1] - qu[0])\n", + " zero = qu[0] * scale\n", + " else:\n", + " raise ValueError(f\"Unexpected quantization type for to={to}.\")\n", + "\n", + " return 1.0 / scale, -zero\n", + "\n", + "\n", + "scale_f8, zero_f8 = estimation_quantization_scale(coef)\n", + "scale_f8, zero_f8" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a64efc70", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float32(0.002160199), -0.0)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scale_f8p, zero_f8p = estimation_quantization_scale(coef, method=\"power\")\n", + "scale_f8p, zero_f8p" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e86fa67a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.0007863875484906944), np.float64(123.14246096563787))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scale_u8, zero_u8 = estimation_quantization_scale(coef, to=TensorProto.UINT8)\n", + "scale_u8, zero_u8" + ] + }, + { + "cell_type": "markdown", + "id": "61fa93b5", + "metadata": {}, + "source": [ + "Vérification par un graphique" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7db067a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n", + "axs[0].hist(coef.ravel() / scale_f8, bins=512, density=True, label=\"coef_f8\", alpha=0.5)\n", + "axs[0].hist(no_nan8, bins=512, alpha=0.5, label=\"f8\", density=True)\n", + "axs[0].legend()\n", + "axs[0].set_xlim([-200, 200])\n", + "axs[0].set_title(\"Distribution des coefficients à l'échelle\\nfloat 8\")\n", + "\n", + "axs[1].hist(\n", + " coef.ravel() / scale_f8p, bins=512, density=True, label=\"coef_f8\", alpha=0.5\n", + ")\n", + "axs[1].hist(no_nan8, bins=512, alpha=0.5, label=\"f8\", density=True)\n", + "axs[1].legend()\n", + "axs[1].set_xlim([-200, 200])\n", + "axs[1].set_title(\"Distribution des coefficients à l'échelle\\nfloat 8 method='power'\")\n", + "\n", + "axs[2].hist(\n", + " coef.ravel() / scale_u8 + zero_u8,\n", + " bins=512,\n", + " density=True,\n", + " label=\"coef_u8\",\n", + " alpha=0.5,\n", + ")\n", + "axs[2].hist(list(range(256)), bins=100, alpha=0.5, label=\"u8\", density=True)\n", + "axs[2].legend()\n", + "axs[2].set_title(\"Distribution des coefficients à l'échelle\\nuint 8\");" + ] + }, + { + "cell_type": "markdown", + "id": "80f2a2c5", + "metadata": {}, + "source": [ + "Pas évident de choisir les bons paramètres." + ] + }, + { + "cell_type": "markdown", + "id": "13ce5145", + "metadata": {}, + "source": [ + "## QDQ\n", + "\n", + "On compare la perte avec deux opérations [QuantizeLinear](https://onnx.ai/onnx/operators/onnx__QuantizeLinear.html) + [DequantizeLinear](https://onnx.ai/onnx/operators/onnx__DequantizeLinear.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "04cd02c0", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.00821504, 0.00381412, -0.00010085, ..., 0.00880182,\n", + " -0.02112438, -0.00410752], dtype=float32)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from onnx.helper import (\n", + " make_node,\n", + " make_graph,\n", + " make_model,\n", + " make_tensor_value_info,\n", + " make_operatorsetid,\n", + " make_tensor,\n", + ")\n", + "from onnx.reference import ReferenceEvaluator\n", + "\n", + "X = make_tensor_value_info(\"X\", TensorProto.FLOAT, [None])\n", + "Scale = make_tensor_value_info(\"Scale\", TensorProto.FLOAT, [1])\n", + "Y = make_tensor_value_info(\"Y\", TensorProto.FLOAT, [None])\n", + "\n", + "model_f8 = make_model(\n", + " make_graph(\n", + " [\n", + " make_node(\n", + " \"Constant\",\n", + " [],\n", + " [\"Zero\"],\n", + " value=make_tensor(\"Zero\", TensorProto.FLOAT8E4M3FN, [1], [0.0]),\n", + " ),\n", + " make_node(\"QuantizeLinear\", [\"X\", \"Scale\", \"Zero\"], [\"Q\"], axis=0),\n", + " make_node(\"DequantizeLinear\", [\"Q\", \"Scale\"], [\"Y\"], axis=0),\n", + " ],\n", + " \"quf8\",\n", + " [X, Scale],\n", + " [Y],\n", + " ),\n", + " opset_imports=[make_operatorsetid(\"\", 19)],\n", + ")\n", + "\n", + "ref_f8 = ReferenceEvaluator(model_f8)\n", + "qu_f8 = ref_f8.run(\n", + " None, {\"X\": coef.ravel(), \"Scale\": numpy.array([scale_f8], dtype=numpy.float32)}\n", + ")[0]\n", + "qu_f8" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "459cf5fd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.0086408 , 0.00378035, -0.00010126, ..., 0.0086408 ,\n", + " -0.02160199, -0.0043204 ], dtype=float32)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qu_f8p = ref_f8.run(\n", + " None, {\"X\": coef.ravel(), \"Scale\": numpy.array([scale_f8p], dtype=numpy.float32)}\n", + ")[0]\n", + "qu_f8p" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "95fe22fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.00865026, 0.00393194, 0. , ..., 0.00865026,\n", + " -0.02123246, -0.00393194], dtype=float32)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_u8 = make_model(\n", + " make_graph(\n", + " [\n", + " make_node(\n", + " \"Constant\",\n", + " [],\n", + " [\"Zero\"],\n", + " value=make_tensor(\"Zero\", TensorProto.UINT8, [1], [int(zero_u8)]),\n", + " ),\n", + " make_node(\"QuantizeLinear\", [\"X\", \"Scale\", \"Zero\"], [\"Q\"], axis=0),\n", + " make_node(\"DequantizeLinear\", [\"Q\", \"Scale\", \"Zero\"], [\"Y\"], axis=0),\n", + " ],\n", + " \"quu8\",\n", + " [X, Scale],\n", + " [Y],\n", + " ),\n", + " opset_imports=[make_operatorsetid(\"\", 19)],\n", + ")\n", + "\n", + "ref_u8 = ReferenceEvaluator(model_u8)\n", + "qu_u8 = ref_u8.run(\n", + " None, {\"X\": coef.ravel(), \"Scale\": numpy.array([scale_u8], dtype=numpy.float32)}\n", + ")[0]\n", + "qu_u8" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e9e49f1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float32(0.21044867), np.float32(0.15230674), np.float32(0.09929135))" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "err_f8 = ((coef.ravel() - qu_f8) ** 2).sum() ** 0.5\n", + "err_f8p = ((coef.ravel() - qu_f8p) ** 2).sum() ** 0.5\n", + "err_u8 = ((coef.ravel() - qu_u8) ** 2).sum() ** 0.5\n", + "err_f8, err_f8p, err_u8" + ] + }, + { + "cell_type": "markdown", + "id": "797aac3e", + "metadata": {}, + "source": [ + "La quantization avec les float 8 fonctionne moins bien que la quantization avec des entiers." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "ee4b9887", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 3, figsize=(12, 6))\n", + "for i, bins in [(0, 64), (1, 512)]:\n", + " axs[i, 0].hist(coef.ravel(), bins=bins, density=True, label=\"coef\", alpha=0.5)\n", + " axs[i, 0].hist(qu_f8, bins=bins, alpha=0.5, label=\"qdq_f8\", density=True)\n", + " axs[i, 0].legend()\n", + " axs[i, 0].set_title(f\"QDQ float 8\\nerr={err_f8:1.3g}\")\n", + "\n", + " axs[i, 1].hist(coef.ravel(), bins=bins, density=True, label=\"coef\", alpha=0.5)\n", + " axs[i, 1].hist(qu_f8p, bins=bins, alpha=0.5, label=\"qdq_f8\", density=True)\n", + " axs[i, 1].legend()\n", + " axs[i, 1].set_title(f\"QDQ float 8 (power)\\nerr={err_f8p:1.3g}\")\n", + "\n", + " axs[i, 2].hist(coef.ravel(), bins=bins, density=True, label=\"coef\", alpha=0.5)\n", + " axs[i, 2].hist(qu_u8, bins=bins, alpha=0.5, label=\"qdq_u8\", density=True)\n", + " axs[i, 2].legend()\n", + " axs[i, 2].set_title(f\"QDQ uint 8\\nerr={err_u8:1.3g}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c0b88331", + "metadata": {}, + "source": [ + "## Et avec plusieurs valeurs d'échelle" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a6e504a8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5/5 [00:05<00:00, 1.12s/it]\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
scaleerrscale_f8pscale_f8
00.0001470.2104490.002160.000147
10.0004400.1527210.002160.000147
20.0007330.1527140.002160.000147
30.0010270.1517310.002160.000147
40.0013200.1528310.002160.000147
\n", + "
" + ], + "text/plain": [ + " scale err scale_f8p scale_f8\n", + "0 0.000147 0.210449 0.00216 0.000147\n", + "1 0.000440 0.152721 0.00216 0.000147\n", + "2 0.000733 0.152714 0.00216 0.000147\n", + "3 0.001027 0.151731 0.00216 0.000147\n", + "4 0.001320 0.152831 0.00216 0.000147" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas import DataFrame\n", + "from tqdm import tqdm\n", + "\n", + "a = 0.00014669707383747942\n", + "h = 0.00014669707383747942 * 2\n", + "\n", + "data = []\n", + "for scale in tqdm([a + h * i for i in range(5)]):\n", + " got = ref_f8.run(\n", + " None, {\"X\": coef.ravel(), \"Scale\": numpy.array([scale], dtype=numpy.float32)}\n", + " )[0]\n", + " err = ((coef.ravel() - got) ** 2).sum() ** 0.5\n", + " obs = dict(scale=scale, err=err, scale_f8p=scale_f8p, scale_f8=scale_f8)\n", + " data.append(obs)\n", + "\n", + "df = DataFrame(data)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "bc734544", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(x=\"scale\", y=\"err\", logy=True);" + ] + }, + { + "cell_type": "markdown", + "id": "10901f96", + "metadata": {}, + "source": [ + "Pas mieux." + ] + }, + { + "cell_type": "markdown", + "id": "ee4b3e10", + "metadata": {}, + "source": [ + "## Optimisation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea44014b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/_doc/notebooks/dsgarden/regression_lineaire.ipynb b/_doc/notebooks/dsgarden/regression_lineaire.ipynb index 628dd3cd..410415b5 100644 --- a/_doc/notebooks/dsgarden/regression_lineaire.ipynb +++ b/_doc/notebooks/dsgarden/regression_lineaire.ipynb @@ -1,2205 +1,2388 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# R\u00e9gression lin\u00e9aire\n", - "\n", - "Ce notebook s'int\u00e9resse \u00e0 la fa\u00e7on d'interpr\u00e9ter les r\u00e9sultats d'une r\u00e9gression lin\u00e9aire lorsque les variables sont corr\u00e9l\u00e9es puis il explore une fa\u00e7on d'associer arbre de d\u00e9cision et r\u00e9gression lin\u00e9aire pour construire une r\u00e9gression lin\u00e9aire par morceaux." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Un cas simple\n", - "\n", - "Une fa\u00e7on d'interpr\u00e9ter des r\u00e9sultats statistiques est de les calculer dans un cas o\u00f9 la r\u00e9ponse cherch\u00e9e est connue. On simule un mod\u00e8le simple $Y=\\alpha X_1 + 0.X_2 + \\epsilon$ et on cale une r\u00e9gression lin\u00e9aire. On suppose que $X_1, X_2, \\epsilon$ sont des variables al\u00e9atoires gaussiennes de m\u00eame variance et moyenne." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000, 3), (1000,))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy.random as npr\n", - "eps = npr.normal(1000)\n", - "X = npr.normal(size=(1000, 3))\n", - "alpha = 2\n", - "Y = alpha * X[:,0] + X[:, 2]\n", - "X.shape, Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -0.0312982 , 0.05188551],\n", - " [-0.0312982 , 1. , -0.00356494],\n", - " [ 0.05188551, -0.00356494, 1. ]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numpy import corrcoef\n", - "corrcoef(X.T)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from statsmodels.regression.linear_model import OLS" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 0.815
Model: OLS Adj. R-squared: 0.815
Method: Least Squares F-statistic: 2204.
Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00
Time: 10:34:12 Log-Likelihood: -1385.2
No. Observations: 1000 AIC: 2774.
Df Residuals: 998 BIC: 2784.
Df Model: 2
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 2.0519 0.031 66.347 0.000 1.991 2.113
x2 -0.0032 0.033 -0.097 0.922 -0.067 0.061
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 0.709 Durbin-Watson: 1.990
Prob(Omnibus): 0.701 Jarque-Bera (JB): 0.674
Skew: 0.063 Prob(JB): 0.714
Kurtosis: 3.010 Cond. No. 1.07


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.815\n", - "Model: OLS Adj. R-squared: 0.815\n", - "Method: Least Squares F-statistic: 2204.\n", - "Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00\n", - "Time: 10:34:12 Log-Likelihood: -1385.2\n", - "No. Observations: 1000 AIC: 2774.\n", - "Df Residuals: 998 BIC: 2784.\n", - "Df Model: 2 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 2.0519 0.031 66.347 0.000 1.991 2.113\n", - "x2 -0.0032 0.033 -0.097 0.922 -0.067 0.061\n", - "==============================================================================\n", - "Omnibus: 0.709 Durbin-Watson: 1.990\n", - "Prob(Omnibus): 0.701 Jarque-Bera (JB): 0.674\n", - "Skew: 0.063 Prob(JB): 0.714\n", - "Kurtosis: 3.010 Cond. No. 1.07\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = OLS(Y,X[:, :2])\n", - "results = model.fit()\n", - "su = results.summary()\n", - "su" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8153831029946165, 0.8150131292531227)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results.rsquared, results.rsquared_adj" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On v\u00e9rifie que le coefficient devant $X_1$ est non nul (P-value nulle, 0 n'est pas l'intervalle de confiance). Le coefficient devant $X_2$ n'est pas nul mais presque, la P-value est \u00e9lev\u00e9e, le coefficient $R^2$ est \u00e9lev\u00e9. Dessinons." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python370_x64\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAEICAYAAAB2/gEGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzs3Xl8XPV97//XZ0aSsY2xhRe8ymAwDrETqC2waSBAITRwndCylCXNDU3BoaULN8ltmqR1qdO0SbeQ+wstcUhuf2nZAjZhKSRAwhJSbGypECyMsRGWLGy8ygu2sTQzn/vHOWd05syZfTSbPs/Hww80M2fOfGXj47e+38/5fEVVMcYYY4wxpYlUewDGGGOMMY3AQpUxxhhjTBlYqDLGGGOMKQMLVcYYY4wxZWChyhhjjDGmDCxUGWOMMcaUgYUqUxEioiJyWoU/8ysicnclP9MYAyLyKRF5qoKf956IzKnA5/ybiPxNke8t2zVwuK+nInK+iGzK89i5IvIrETlluMZTTyxUmYalqn+rqjflc6yI3C4i/zHcYzKm0YjIye4/8k3ec6p6j6peOkyf95yIpPy9VtXjVbV7OD5vJFLVX6jqPO+xiGwVkUuCx4nIeOB7wNWq+nYlx1irmnIfYowxxhiTSlUPABdWexy1xGaq6oz7E8MX3enWAyLygIgc5752o4i8GDg+OU0sIv9DRP5bRA6KyDYRuT1w7P8UkR4R2Ssif+n/6UREIiLy5yLylvv6j0TkxCzj/N8iskNEtovIZwOvjRKRfxSRXhHZKSJ3icjoDOe5UUR+KSL/n/v9viEiF/teny4ij4rIPhHZIiI3+15Lzj75fpr+jPu5e0Tkq+5rHwe+AlzrLiO86vvsbhE5JCJvi8incv4BGVPH3L9Pq0Rkt/v//J/4XjtHRNa714+dIvLP7ksvuP/d7/79OTd4LXL/7v2hiGx2/z59TUROFZGX3PP9SERa3GNbReRxdwz97tcz3de+DpwPfMf9rO/4zu9d58aLyA/d9/eIyF+ISMR97UYRedG9/vS73+NlWX4/fk1EOt0xPwAcF3h9qYi8IiL7ReS/ROTDef4+Z70Whxxf1PVURC4UkT4R+YKI7HLP8Xu+914uIq+73987IvJF//vcr/8daAMec3/P/8x9fon7Pe8XkVdF5MJ8vveGp6r2q45+AVuBl4HpwInARuAW97UbgRcDxytwmvv1hcCHcML0h4GdwG+5r30QeA84D2gB/hEYBC5xX78NWAPMBEYB3wXuyzDGj7vnXgCMBe4NjOMO4FF3/OOAx4C/y3CuG4EY8L+AZuBa4ABwovv688C/4FzszgJ2Axe7r90O/If79cnuGL4HjAbOBI4BZwSPdR+PBQ4C89zH04D51f7zt1/2a7h+udeFDmC5ew2YA3QDv+m+/hLwaffr44El7tfe360m37lSrkXu648CJwDz3b97P3M/YzzwOvAZ99iJwFXAGPf68CDwY9+5ngNuCozdf335IfCI+96TgTeB3/eNaxC4GYgCfwBsByTk96MF6PFde6523/s37usLgV3AYvdcn8G5Po/K8Pub17U45H1FX0/dz4kBK9zv4XLgCNDqvr4DON/9uhVY6Htfn28MW3H/LXAfzwD2uueLAB9zH0+u9v/H1f5V9QHYrwL/wJz/uX/X9/jvgbvcr1MuZO5zyb98Iee6A/iW+/VyfCHJvaANMBSqNuKGFffxNPcC0xRy3h8A3/A9Pt0bByDAYeBU3+vnAm9nGOONwYseTqj8NDALiAPjfK/9HfBv7te3kx6qZgbOc13wWPfxWGA/zsV9dLX/3O2X/RruXzjhoDfw3JeB/+t+/QLw18CkwDHe361coeojvscdwJd8j/8JuCPDuM4C+n2PnyNDqMIJN8eAD/pe+xzwnG9cW3yvjXHfOzXkcz8acu35L4ZC1b8CXwu8ZxNwQYbvI69rcchrRV9PccLR0cCfzS6GAnGv+/tzQuAzLyR7qPoS8O+B9/wUNxiP5F+2/Fef3vV9fQTnp8acRGSxiDzrTosfAG4BJrkvTwe2eceq6hGcnzw8s4GH3ane/TghKw6cFPJRKefC+WnPMxnnQtbhO9dP3OczeUfdv7W+8013f+1T1UOB12ZkOVdev3eqehhnVuwWYIeI/KeIfCDLeY2pd7OB6d7fS/fv5lcY+jv++zj/oL8hIutEZGmB59/p+/poyOPjAURkjIh81126O4gT5iaISDSPz5jE0AyTJ3hNSF4D3OschF8HphN+7fHMBr4Q+P2a5b4vqxzX4rBxlHI93auqMd9j/3XvKpzZph4ReV5Ezs01dtds4JrA934ezg/bI5qFqsZyGOcvGAAiMjXw+r0408SzVHU8cBfOTzrgTAPP9L13NM40vGcbcJmqTvD9Ok5V3wkZxw6ci4unzff1HpwL6HzfecararZgOENExPe4DecnyO3AiSIyLvBa2Jhy0bQnVH+qqh/DuVC8gbN0aEyj2oYzw+H/Oz5OVS8HUNXNqno9MAX4JvCQiIwl5O9Oib4AzAMWq+oJODNGMHStyvZ5e3Bm0Gf7niv2mrCD8GuPZxvw9cDv1xhVvS+Pc2e7FoeNo5zX0yRVXaeqV+D8mf4Y+FGmQwOPt+HMVPm/97Gq+o18PreRWahqLK8C80XkLHGK128PvD4OZ2bnfRE5B7jB99pDwCdE5NfdgtG/JvUv+V3A10VkNoCITBaRKzKM40fAjSLyQREZA/yV94KqJnDCybdEZIp7rhki8ptZvq8pwJ+ISLOIXAOcATyhqttwpuP/TkSOc4tEfx+4J8u5MtkJnOwraD1JRD7p/qNxDKfeLF7EeY2pFy8DB0XkSyIyWkSiIrJARM4GEJHfFZHJ7t/h/e574jh1jAmc+qhyGIcTFPaLczPMXwVe35nps1Q1jnP9+bqIjHOvV58HimmX8hJOPdKfiEiTiFwJnON7/XvALe6sk4jIWLcAfVzo2VJluxYHlft6intcizj9xMar6iBODWmma1zw9/w/cP69+E33/5Pj3OL2mRneP2JYqGogqvomTkHiM8Bm4MXAIX8IrBCRQzg1VD/yvbcL+GPgfpyfjA7hrL0fcw/5Ns5PVk+571+DU4MRNo4ncWoEfg5scf/r9yX3+TXu9P4zOD+ZZrIWmIvzU9nXcXqieEuT1+PUdGwHHgb+SlWfznKuTB50/7tXRDpx/m58wT3vPuACnN8/YxqSG0g+gVPD9DbO37e7cQrJwSmY7hKR93CuB9ep6vvuEtrXgV+6S0FLShzKHTg3k+zBuc78JPD6t4Grxbl77/+EvP+PcWbtu3Gugffi1CUVRFUHgCtx6rD6ccoBVvteX49T8P4d9/Ut7rH5yHgtDhlHua+nfp8GtrrvuwX43QzH/R3wF+6f7xfdH2ivwFke3o0zc/W/sUzhFOAZEyQix+P8NDpXq9jUTURuxClKPa9aYzDGGGPyMeJTpRkiIp9wi0TH4rRUeA3nrg9jjDHG5GChyvhdwVAB+Fyc6X2byjTGGGPyYMt/xhhjjDFlYDNVxhhjjDFlUJUNlSdNmqQnn3xyNT7aGFMlHR0de1Q1W5PXumDXL2NGnnyvX1UJVSeffDLr16+vxkcbY6pERHpyH1X77PplzMiT7/XLlv+MMcYYY8rAQpUxxhhjTBlYqDLGGGOMKQMLVcYYY4wxZWChyhhjjDGmDCxUGWOMMcaUgYUqY0xOHT393PnsFjp6+qs9FGOMqVlV6VNljKkfHT39fOruNQzEErQ0RbjnpiUsmt1a7WHlJCLzgAd8T80BlqvqHb5jLgQeAd52n1qtqisqNkhjTEOxUGWMyWpN914GYgkSCoOxBGu699ZFqFLVTcBZACISBd4BHg459BequrSSYzPGNCZb/jPGZLVkzkRamiJEBZqbIiyZM7HaQyrGxcBbqtoQXd2NMbXJZqqMMVktmt3KPTctYU33XpbMmVgXs1QhrgPuy/DauSLyKrAd+KKqdgUPEJFlwDKAtra2YRukMaa+WagyxuS0aHZrvYYpRKQF+CTw5ZCXO4HZqvqeiFwO/BiYGzxIVVcCKwHa29t1GIdrjKljtvxnjGl0lwGdqroz+IKqHlTV99yvnwCaRWRSpQdojGkMFqqMqTPW3qBg15Nh6U9EpoqIuF+fg3NN3FvBsRljGogt/xlTR2qlvUFHT39d1FiJyBjgY8DnfM/dAqCqdwFXA38gIjHgKHCdqtrynjGmKGUJVSIyAbgbWAAo8FlVfakc5zbGDKmF9ga1EuzyoapHgImB5+7yff0d4DuVHpcxpjGVa/nv28BPVPUDwJnAxjKd1xjjUwvtDcKCnTHGmDLMVInICcBHgRsBVHUAGCj1vMaYdLXQ3sALdoOxRD33rTLGmLIrx/LfHGA38H9F5EygA/hTVT3sP8j6vBhTHtVub1ALwc4YY2pROZb/moCFwL+q6q8Bh4E/Dx6kqitVtV1V2ydPnlyGjzXGVMui2a3cetFpFqiMMcanHKGqD+hT1bXu44dwQpYxpoZYKwZjjBleJS//qeq7IrJNROa5G5heDLxe+tCMMeXiv2OvKSJc0z6LKxfOtJkmY4wpo3Ld/ffHwD0i8iucXeH/tkznNcaUgf+OvYG4cu/aXj519xqbtTLGmDIqS58qVX0FaC/HuYwZKSrZQNO7Y+/YYALFaSZXrT5XxhjTqKyjujFVUOkGmt4de6s6+3ioo4943NohGGNMuVmoMqYKSu2MXswsl9eK4aqFM60dgjHGDAMLVcZUQSkNNEud5ap2nytjjGlUFqqMqYJSGmjWwv5/xhhj0lmoMqZKip0xsm1ijDGmNlmoMqbKCq2Psm1ijDGmNlmoMqaKiq2PKmSWq5KtG4wxZiSzUGVMFQ13fVSlWzcYY8xIZqHKmIB8Z3byOS7XMWH1Uf73ACXNMllRuzHGVI6FKmN88p3Zyee4fI4J1kcBQ3v0RSOgSiyhRc8yWVG7McZUTrn2/jOmIYTN7BR7XL7nWjS7lVsvOo1Fs1vT3jMQV2e/vsHM78/GC22fv3SeLf0ZY8wws5kqY3zyndnJ57h8z+Vf7vO/RyJCLK4AJIDWMS1l+z6NMcaUn4UqY3zybVeQz3H5HBO2ROjt0df1zgF+1XcABSIC/UcGCv5+RnqhuohsBQ4BcSCmqu2B1wX4NnA5cAS4UVU7Kz1OY0xjsFBlTEC+7QryOS7TMfeu7eXJDTs4rjmatkS4ZM5EVnf2MRBLOIEKaCmyHsoK1QG4SFX3ZHjtMmCu+2sx8K/uf40xpmAWqowZJpnu/Lt3bS9fefi15OOmCAgklwj9QUiAj8ydxG2XnG7d14fHFcAPVVWBNSIyQUSmqeqOag/MGFN/LFQZU0ZekGod08KKx7tCl92e3JD67/X86eO5dP7UZPja9O4hEk4pFQpctmBa0bNL1n0dBZ4SEQW+q6orA6/PALb5Hve5z6X8IYnIMmAZQFtb2/CN1hhT1yxUGVMm/vqliAgJ1dBlt8sWTOMXm4dWo649u40bFg/9Q91/ZACB5NJfMbVUfsXuMdggPqKq20VkCvC0iLyhqi/4XpeQ92jaE04YWwnQ3t6e9roxxkAZQ5WIRIH1wDuqurRc5zWmFJXcosW/bIcqkYggaNqymxegntywg8sWTEsJVB09/byz/yjNTRHicVuyK5Wqbnf/u0tEHgbOAfyhqg+Y5Xs8E9heuREaYxpJOWeq/hTYCJxQxnMaU7Ry3fmWbzBrHdNCRARUaYoKF86bwqRxo7hq4cyU93X09NN/ZCCtTso/3qaIcN05bVwZeG85x9voRGQsEFHVQ+7XlwIrAoc9CvyRiNyPU6B+wOqpjDHFKkuoEpGZwP8Avg58vhznNKZU5bjzrZAO6yse7yKhioizfvTMxp20NEVYMH18aMf04Pn8440nlOkTRg/beEeIk4CHna4JNAH3qupPROQWAFW9C3gCp53CFpyWCr9XpbEaYxpAuWaq7gD+DBiX6QAr9DSVVo473/xB59hggtWdfaEhJXjHXiKuKDAQS7D8kQ0k1Nlq5sqFMzMGvXKPdwS3UABAVbuBM0Oev8v3tQK3VnJcxtSyRNcv8jouMv/8YR5JfSo5VInIUmCXqnaIyIWZjrNCT1Nppdz55r+LrykaSfaMenD9ttAlOX8gikYERIjHE4gI8YQTsAZjCQQyBqewfQDvfHZLysbKrWNa6D8yUNAGzcYYk0m+ISrT+yxcpRLnB7USTiDyd8CngRhwHE5N1WpV/d1M72lvb9f169eX9LnGDJfgEtr5cyfzzOs7USAq8PlL53HrRaclj/WHIP/Xqzv72HXoGM+/uTtZdH7PTUtSjssU9FLqq9yNlQfd2a+IkHMpshZrqkSkI9jRvB7Z9cvUu2KDVCYjIVjle/0qeaZKVb8MfNn90AuBL2YLVMbUOv8S2kAswZRxoxjVnD77E1a/5DXvbB3Twiq3K3pEYMGM8Vx7dlsy5OQKO8FlPBi6zz/X0t4Ib6FgjAlR7iAVPPdICFb5sD5VxgS0jmlJNt9MqNOc88qFM5NhaU33XiA9+Kzq7EtuLxPxLfslFF7tO8CmnV3Mmzour8DjX8aLRITB+NCMstd9vXVMS3J50EKUMSbMcIap4OdYsCpzqFLV54DnynlOYyotrPmmF1r8M1PLl85PqV8SGOpThdOnKp4YCkPHBhPc8cyboa0Ugst1/vqqd/Yf5b61vShOoDpv7iQuWzCNFY93cWzQCV2/8YEp3HLBqRaujDEVC1JhnzvSg5XNVBkTsGTOxNDlvuDMVP+RgbTC8lWdfU6xejRCW+totuw+nDyvAr/csod1W/cl66E6evq5fuVLDMaV5qhw37JzU4KVd8xq97zNTRFuu+R01nTv5digUzwfTyhPv76T5zftSnm/MWZkqVaYCo5hJAcrC1VmxAvOFGW6azB4h9/2/UcBkkXrAPfctITVnX08uH4b3XucQCWA2xM0pR4KYMVjXQy4S3sDcWXFY10s/8T8lGCUaTzRiBDzzYQNxnVEt1AwZiSqhSAVNJKDVaTaAzCmkjp6+rnz2S109PQnH3/q7jX801Ob+NTda5LPL5rdyq0Xncai2a3cu7aXT39/LZvePcQ9Ny3h2nPaQIT7Xu5NeY/3vukTRhNLaLJn1eyJY1h2/hxGNUeIylA91PXfW8OrfQdSxvdq3wE+dfca7l3bmzJO/3i8xyuuWEDEt3NdNIK1UDBmBKnFQOWp5bENJ5upMiNG2N16uZpl3ru2l688/BoAv9i8h0s/eBKTxo0iFk9/j7+3VUtThIHBBAmgd98R/u2lrSxfOj/ZY8oraA9zbDC1YWim1gnzpo4jEhES7kyX2zncGNPgRmpgqQcWqsyIERaggs0yvTvqvCabT3W9m3KOp17fSTQizmbJiaHNkoOBbfnS+Ty5YQe/3LInpQbLWypc1dmXcZxenZTXMDTTkt6a7r3EfXcFxmz5z5iGVm9haiQuA1qoMiNGWLdxf71S65gWVjze5buDz6mFCoonlKiQsuHxVx5+LVk47gWo2y45nXVb94X2txKgKQIZJquIRgRVzdoVfcmciTRHJVmTZR3UjWlcxQQq7d5Q8ufKnAUln2MksVBlRoxMBd9ecfqdz25JCVTgFJd7IkLyNVWYPmE0AF99+DUeWNebbM4ZjUhaYPMeBzulf+wDk/n5G7tSWi8A3HTeKYwb3Zy1B9Wi2a3ct+xcVnX2IcCVC2cCWO8qYxpIIWGqHCEq0zktXOXHQpUZUfxtCoLhY8mciTRFhmZ+guZMGsvbe4+gCaWlOcKho4Nc+92XUu7AA7hw3hRgKNz47w4MLkG+Pxjn5vNOYeUvuodmx4Bxo5tT3ucJu1PRG39YzZgFK2PqV76BajjCVNhnWLDKzUKVGVE6evpZ1dnHQx19xOLp4eOMaSek3ZHn2bL7MC1R4ZrFbcyfPp7lj2xIC1SeYLgBJ1AdOjo4NNsFvLjZ6Vu17Pw5fO8X3cQVmqMSuoyXKzTlKro3xtSPfAJVJcJU8PMsWGVnocrUpUI3DfaHqcFYIrlU5+8Zdf3KlzLOUnlicaV33xHe3HkoLVB528dMGjcq6/Y1fl4N1qFjMaLRCIlYIryQi9yhKaxmzBhTX2oxTJn8WagydcebsTk26DThXHHFAm5Y3JbX8f4Y5IWgJXMm8s0nN+YMVAAJnNYKfhHgk2dNZ+/hAeZPO4GDx2JEIoLGlWhEUravUQ0PYgrE4onkf8NmmbzQNDCYQERoHdOS8nqmmjFjTH3IFagKDlNb3nD+e9oHihyRKZSFKlN3/Fu0xBLK8kc2ZN2o2Jvh8ceZpghce7Zz996mdw/x8tb+tPd580W5olYkIjz2q+0kEk7gEv97RJg/fTxNUWcGyf+it4/fbZeczqZ3D6Vs4hwMTOCEpuVL5yd7WK14PH2DZn+NlTGmPpQlTHkBKt/XLGgNCwtVpu4smTMxZYuWhKb3Z/IvD/pneLwOBpFIJHm33MoX3kr7jKao8DvtszhhVBN3vdA99HxIG4TgMqD/UTyeoGv7ARLq9J3yB6pRzZHk5spruvembeIcpv/IAAlVq5sypgGUvNSXLUjlUoFZrJHWowosVJk6ddEHpvDzN3ahbtdxf/1QWEH3PTct4Y5n3kw244zFE6x4rIvXdxxkMLDsd87JrXzpsjOSdwl+/5dvJzc8/utPLuDJDTvSlgDDRNwtaXYfOkYs8BkisHzp/JQ7D8M2cfa+n2BAtLopY+pbSbNTpYSpsHPZrFXZWKgydSWlz1NEuObsoQacHn9B90AswR3PvMltl5yebMbpvRa8y+/Esc188dIPcMPitmTLhXf2H032kEoklP4jA1y2YFrOUCXA9ec4dwne/mj4xdE/G5WpHipTQLS6KWPqU82EqeB5yxysRuIsFVioMnXGH5jiCWX6hNFpwaJ1TAsRcTqSJ3SobYF/xurFzXvSaqUOHBlk3tRx3Lu2l+WPbCDubkPTFBGni3pE2L7/KO/sP5paNwWcNXM8r71zAG9CSt1f/UcGQtsuRDIUmge/l7A7/vwbKxtj6kMll/p08xZkbnqfu1Ll205hpAYqsFBl6kyu5a+Onn5WPN7lzC65yce/h96tF52WNmPlUZw9+X60blsyCMViCa5f3IYCD67fxr1re2mKprc8UGDa+OPo2/9+8jmBtHouL4zFE+GF5oV+vyYzEZkF/BCYinPj5kpV/XbgmAuBR4C33adWq+qKSo7TNLbhDlO6eUvO54cjYJlwJYeqfC5cxhQjrBdVpq1fvMcpd/ql9E8YmhnyzrGqs4/7X+5NBqsmt/2Bf8uYSES4cuFMvvv8W8naq8G4MmF0M/uPDiaP27D9YMr7ohGSy5LBei5vaPkUmlubhJLEgC+oaqeIjAM6RORpVX09cNwvVHVpFcZnGtxwLfVlClK5ji8lXNksVX7KMVOV74XLmLxl6x6eaWuWpmiEC0+fnLJHn8ebGQJ4dtMudh18n1FNkZTjzpo1gXGjmlKy2Nwpx/Pd59/i6dd3ppzPC1QicPbs1pCWDEOzWYtmt6bNjkXIfwNka5NQHFXdAexwvz4kIhuBGYBdm8ywyxaoiglThQapTOcoJlhZoMpfyaHKLlxmOKzu7Ev2ogqb0fFmp7bvP5pSlP5UIPz4HRtM8BcPv0Yiw+svb+1PC0cb3z3ExncPZR6owoSQnlKJRGqbB/+MU+uYFvqPDNjMUwWJyMnArwFrQ14+V0ReBbYDX1TVrpD3LwOWAbS1ZW40a0zRs1PDGKYqwQKVo6w1VTkuXMbk5d61vdz/cm9KA01/UXfwDkCvsWbufuhkDFTFikRg8rhRabNjSnoDT5txqg4ROR5YBdymqgcDL3cCs1X1PRG5HPgxMDd4DlVdCawEaG9vz+d/NTMCFTU7VeNhKp9ZKgtUQ8oWqnJcuOwnPZOXjp5+58473z9bwaLu1Z19vD/oxKNYQrnunFkI8MC63rTGnH7l+JcweNcfDHVMHwh8+IbtB7jz2S1pM1KF7ltoiicizTjXpXtUdXXwdf+1SlWfEJF/EZFJqpq7EZkxPgUHqiLD1OGN7+Qcy9gzZuQ8Jh8WqApXllCV68IF9pOeyc+a7r0pBd8e/8bH97/cm3w+obBg+njmTR3Hj9ZvAzRte5l8t5vJR/Acqk7vqqsXzeTetb0prz24fhvxhKbUhGWrFTPlJSICfB/YqKr/nOGYqcBOVVUROQen3G1vBYdpGkCmQFXI7FS2MJVPkAoeX1CwKrJHlQWqdOW4+y/nhcuYfHmdxZ1Ng5277xJuv6glcyayqrOP4L7H/UcGWN3Zl7w7Ly344HQ3V3W2n1FV4r5JpaiQds58NUWdcW1691DaEmAsrmk1YWF9p/IJVTa7VZSPAJ8GXhORV9znvgK0AajqXcDVwB+ISAw4ClynwV2vjcmioEBVQJgqNEiVW65ZKgtU4coxUxV64VLVJ8pwbjPCBFsIAClfd72T2gVdBJ7qepf9RwbTzuXnhZ14SHoqNlABXL3I2T9wxeNdeP8UC9AcFRAhHk/tL1VM3ymb3SqOqr6I/zbM8GO+A3ynMiMyjaaUQDXcYSrv2aqQWSoLVMUrx91/OS9cpvGVcyYlrKB7VWcfD3X0MeirW/JmhoLbzWRTzimIlqhw1cKZKb2xIgIfOW0St11yOkBefbZyKXZ2yxgzPAqqn8pzdqoSM1PlaAJqgSo766huSjacMyneljH+rV4iwEfmTmLH/qNs2X24LJ+TTVjfK4Br2mclv0//7NNtl5yeEqKCCr0L0LqqG1M7SglUhYapbVtz/8A46+TxOY/JqMBZKgtUuVmoMiXLNZMSnMXKd1bLuxMwuHdeNCpctmAayx95LeN7m6PCWbMm0H9kkO7d74WGomknjGLHwWNZv7eWqHD7JxfQf2SAtd17ecG3kfL86UMXsysXzkQgbXPncrCu6sbUhnIv92UKVPmEKf+xYcGqmDsALVCVzkKVKdmSORNpigiDcWfTYf9MSnAWa/nS+ax4vCuvWa1MdwLOPnEM/UcGUorNg2JxpaOnH28LwDC5AhXAhfOmcMPiNjp6+vnW028mnxecAvng93flwpk5z1kM63FlTHWVM1CFhalCglQx0pb+ArNUFqjKw0KVKQ9xOzhJaoQJzmKeXxLcAAAgAElEQVQ9uWFH3vVB3p2AXk8qz5zJx7NkzkSiEUmbxfIoJAvHS6mj+tkbO7nkn58H1cDefpKy16DVOxnTmErtP1ULgSpNAS0ULFAVJlLtAZj6t6Z7L7G4U6gdjw/1k4KheqCoOHvdXbZgWsrj4KzWnc9u4d61vdz5rHMhWr50fjCnMWfSWNZ07+Wm804hGhneeyTiCdiy6z227D6M4sxQNUWEm847hTXdezl0dJCISM69/LzvraMnuEegMaZWlRKodPOWlEB1eOM7aYFq29YDwxKogkt/uQrUM81SWaAqnM1UmZJlK6QOqweaN3VcWn1QR08/139vTbIruQCjmiN8eMZ4gl2DvvuLblSd/lJTJ4zmnf6jQPrjIK9XVSkzV+fNncRlC6YllzC95cVoRFi+dH7oLJW1RDCmvhS1f18gUPmFhamqyXPZzwJVcSxUmZJlKqT2F6TfetFpKccDKTNaKx7rStnmRYGBwQTrtqbP7HghK64kA1QEiEYjpCUw19QTRvEnF59O797DyVBWqKYI3HbJ6SlLft5Yvc7qYWyJ0Jj6UXCgKnC5L1ug6j74fujzc044LuuYsimmjYIFquJZqDJlESykzjY7E9wQGZG0ffMAxO2mno85U47n7d3v8c7+8IvSuweP8VePbiCR0JyBKuq2UPAfJsCKKz7EotmtbHr3UMrdhEL2pT9riWBM7St1dgqKD1SZwpT/9UzBKnjnn3/pr5TidFMcC1VmWGSbnUl5La4EF+QiOCGpe9d7eX/evveO5eyMPphn6/RFs1tZ39OfFr68magN24cujIKzJOjvTRV2PmuJYEztqlagyhWmCpW1jYIt+1WEhSozLLLNzvhfi7ozVbH40HJaAqc4vBD7cmxTU4hjsURaoGqOOnf7dfT081BHX8rz2QKVx1oiGFObCi5Gh4ICVbGzU/nI1vgz27KfBarhY6HKlEWwoWe22Zmw/f3ueOZNXty8p6zbyHhmtI7m0NFBDr4fy3lsNALnzpmYsvXN2Se38ueXncGi2a3c+ewWYvGhYnp/V3VjTP1olNkpTyHLfmEsUJWHhSqTU64O6Jnqp7xfXjuB4B54/hqrthPH0BwVYnElgXOnXlM0wpkzxztd0Xe9R5Zen1lluhswTDwB3XsO43bdIoLTANQba3AGbriafRpjhs9wz05BeQNVWD2Vf5aq1GU/C1TlY6HKZJVPOwB/jdRAoH4qrKN6/5GBlC1rkkXr0QgXf3BKsgO6As9t2pV3LVS5dO85zKjm7C0iVnf2DcusmjFmeJXazBOqP0OV97KfFaZXnIUqk1WmgnP/7FXrmJaheiiFQ0cHQ98/EEuw/JENxBNKRODiM04C4Nig0zh0MJbgmdd3Vj2snDimmW9elbo8GZxpW9XZx0AswerOPus7ZUydGO7u6MNRP5WrnULWZT8fq6OqDAtVJquwgvPg7NIZU8elvOfuF9/mY/Onsmh2a/L9XssEb1uZuMJTr+9MeV+lwpS3tJfJaSeNS1m6DM7UWd8pY+pPKXv3QfVnpzzFLvuFsUBVfhaqTFZhBed3PrslZfbpV32pF5OEakrQ+Ojcyfxs407ybDk1rCICf/NbH6Jr+wF2HTqWXGr8+Rs7iSecu/mu8tVJhQUo6ztlTH2pxmbI5QhUwVmqci77WaAaHhaqTE7BdgBeqPCW7YJZKSKwff9RvvHERu5+8e2Mmx5Xw5Rxo5g3dRw3LG5Lef7etb08uWEHly2YFvq9+gOU9Z0ypn4MZ6AaztmpXIEq47KfBaqqEi1mv44Stbe36/r16yv+uaZ8Onr6Wd3Zx4PrtzEY15RgVY499nJpighzpxzPxncPZT0uuNTn7dO34ooFyT0IW8e0JPfyE5xar89dcCpA8nV/cb0pjoh0qGp7tcdRKrt+1Y9KBqpyLvUVcrdftvYJVkdVPvlev8oyUyUiHwe+DUSBu1X1G+U4r6ld3uzVlQtnsuKxrpS+TvlMTAlw0gmjePfgsaI+/9dPncgLm/fkPC44FMWp6/rLRzYQFafLugRC4FOv7+TZTbsQ91jbBNmYBpclUFW6dqpcgSoTC1TDq+RQJSJR4E7gY0AfsE5EHlXV10s9t6kt3h1/wZmba89u49W+1wo6lwK73ztGVMi5vUyYN3dmn6HKJZ5Q4t5YQj5/MK7JWS4rRq9fuX7gE5FRwA+BRcBe4FpV3VrpcZryy3uWqoRAVe5CdOtHVf/KMVN1DrBFVbsBROR+4ArAQlUD8d8Fl1BnpmlUs9N36skNO3K+P+JWhPtnseIJOG3yWLbsPpz1fWEzXzuLmOEScZp5qkLEbTSa8jpDs1XNUUFwwpcVo9enPH/g+32gX1VPE5HrgG8C11Z+tKacRkKgsjqq2lSOUDUD2OZ73AcsDh4kIsuAZQBtbW3Bl02NyNQ93X8XHDjhw993KhcviAWdOLYF2X04tP5KyLyUWEy91tmzW5l70jjUPfe9a3uT52ly66w2bD+AQLJTuhWj17V8fuC7Arjd/foh4DsiIlqNYlNTFsMdqLKFqVcPp/+wd+bYURmP9wx3oDKVU45QFfZvZdoFSVVXAivBKfQsw+eaMsvWPd3fbyqhzoxPRIR4QvMKOEJ4f6iXt/ZnfE85/yeJRuCVvgOs7+knIsJN553CqGbn+4mIE6iCdwQCFqbqWz4/8CWPUdWYiBwAJgIpBXv2Q2F9qFagCgtT+apEoLJZqsopR6jqA2b5Hs8EtpfhvKbCsm03428j4NVUeXfNvT+Yuivf7BPH0LvvSEooCmu9UIxMy4G5nDLpeN7a9R6K00fr7hffZsUVC+yuvsaWzw989kNhgyjoTj//6yUEqlLCFJQ3UGVigaqyImU4xzpgroicIiItwHXAo2U4r6mw4HYzrWNaUl5fNLuVWy86jRsWt7FkzkT6jwywfOl8zpyZ2j+lr/8I4PzPFfYvVinyWZQ5++RWzjk5NSQdHYil/EsZTyhd28O3lDANI58f+JLHiEgTMB7YV5HRmbLJtv1MGt8sVbBtgl9NB6oQVkdVG0qeqXKnzP8I+CnOHTY/UNWukkdmyipTrZRf/5GB5BKdAA+s66Vr+wGuXDgz5T3eMuGxwQTRiLOU1rXjIDH3jjl//Xe5f6TP53z/3bufFVcs4JW+AwzGnAal7+xPvSiKwIPrt2VtmZDP75mpackf+IB3cH7guyFwzKPAZ4CXgKuBn1s9VX0paD+/kO1nPP5ZqkoHqmyNPSF36wSro6odZelTpapPAE+U41ym/LLVSvktmTPRqTMaTJAAXu07wKt9B3iwo4/7bh56z5ruvcklv1hC+e4L3clzBJf8hpsAp045nu5d7+EtQiZU6T8ywH03L0nroeXnNS0Na5mQ7++ZqV2ZfuATkRXAelV9FPg+8O8isgVnhuq66o3YFKqUQJVp2S/TpsieeglUNktVHeVY/jM1Lmz/ujBe3dRH5k5KWbYLvie4LFiueqlCTT6+hWhU6N79HtGoEBWn5qrFbYGw6d1DbNh+MHl8JLAWGY047wlrmZDv75mpbar6hKqerqqnqurX3eeWu4EKVX1fVa9R1dNU9RzvTkFT+woKVMHXs9RR+QVnqQoNVLnu/CsoUJ32AQtUdcD2/hsBCtkAeNHsVm675HTWdu9lwF3Hi0QkJUj5lwnzUcixhdj93sDQZySU685pY/qE0cnvL9ju4ZIzTuKFzbuTvw/Ll87PWKi+ZM5EmiLCYFyJRsT6VBlTQwoOVGWqoypEWKDyz1LlvZcfhBalW6CqTRaqRoBCNwBeNLuV+5ady13Pv8XP39iFqnL7Y11s2H6AqxbOpHVMC9GI5L1RcqGBqtAQJjizTV7tV0dPP3c882ZKoGqKCJ+74FQ+d8GpWX8f/F3jEXckUu5ye2NMsUoJVEHVWvazQNW4LFSNEN5efZAaHLq2H0CBq3zF6N7rU8aNIuH2oRqIJbhvbS8PdfSBKvGE0hQRln54Guu27mPf4UGODsYzD6AAmQJVprAVEVi+dH4yUHlF9F7BvbeBsr89RBh/HVVEhIQ633s8btvUGFPrCt0kuZBlv0IFZ6ksUI0cFqpGmGDo8PxoXS/Xnt3G/OnjWfF4lxss0gvPB2OJ5NcJVR59ZTupXaqGzxVnTWfsqCYeXL8tuTTp6T/iLAWu6d6b8r2ddMIoPjxzAvOmjst5/pSu8apEIoJg29QYUysK6kWVJVAFlXOWKlugCrJA1XgsVI0wXnAIzvjEEs62LdGIM0OT0PSeUN4ymzdTBVDo3FQp9VWPvrqdB2/5da5cOJPVnX08uH5b2t58rWNaUs7/7sFjvPv6Tp57c3fKHYxhgrVn2WqujDGVVVAvqhyyLfuVMkuVK1Bl6kNVSssEC1S1xULVCJPcbsZtm+CnQCIxNEMTjQiIEI8niEYjXL1oJle5e+Kt8kJNvPCIdOkHT2L/kYGsW9SESajzuX/72x9i0ezWZLjyj6D/yEBo1/WwtglBhdaeGWMqo9Q6qnyX/UphgcqAhaoRxauV8mZgXt22n6df35kMJQK0NKfO0Gx69xBPbtjBZQumccPiNjp6+vnmkxt57Z0DDBYQqLwZKgV+9sYuFrVNKOp7eGBdL1t2HmLf4QFOHNvCK9v2E0soqzv7uOemJWl7FHqamyK0jmnhzme3ZA1M/tozY0xtK/eyXzGzVLnu8gMLVCOJhaoRoqOnn+tXvsRgXGmOCvctO5clcyYmWwz4Z6L8BetefdXa7r08t2kXz2zcWdTee8EtYgqdpRp6r28T5t2Hk897M1G3XnRayh6FG7YfQCClVsyaeRpTP4Zr2a8QZ44dlVZXlakHVVGBqoD6KbBAVcssVI0Qqzr7ksXdA3FNLqMtXzo/ZSbK47UlSG6wHFeeen1ntYaflVfr5dVVhc023fnslrRmnhaqjKlt5Vz2K1WuRp4w/IHKwlTts1A1QgQ7LQlOcLr9sa5kyHh20y6mjBuVnNUJ3iGYzZiWKEcGytNSwTP1hFH8ycWn88C63oxbzUQjcO3ZbSkzbGEKaYBqjKm+cvajgvRZqlx3/BXKApUBC1UjQkdPPwo0R4VYXJONMld39jHgtkgYjCtPuzNR0Ygk+1Ple7deuQMVwG+dNYMN2w9w0gnH0RQ9SDyuRKPCwlkT2Hd4gFMmH88tF5ya14yTFaEb0xhybUGTPK6Ms1S5WKAyHgtVDayjpz/ZeiDmNuu8YXFbsvP4qs6+0PclEs6df6qKiBB3A1Y+yrUlzUfnTuIHv3w7uWTZHBWuXxw+I+UV4OcKS1aEbkx9KLiOKscs1XApZHPktKL0AAtUjcFCVQPq6OlnVWcfD3X0MejrSRVPKNMnjE4Gi6sWzuSh9dsYjKeGpuamCJ/99ZN5qXsvo5oi/Pe2/cTccJMrMJVrj7/efUdS7i6MxZVt+46kHefvgm4F6MbUv3Is+w33LFVYQ89SOqWHBSoLU/XJQlWDydQxHUjbGNjb48+7U+7ZTbt4e89hThzTzPde7CZeqVbppM9wnTVrAlv3DoUoBV7cvId1W/elBKfVnX3J79UK0I2pbwUHqjKbc8JxWdsqZOqOboHKeCxUNZhgx/SUsBKyMbC3JPaNJzbyzMadaV3Uh1M0Ar/xgZM4cGSAjt79xBNKRGDZ+XMYN7o5LWgFg1NHTz8Prt+WPCYatQJ0YxpRxkCV57JfIa0Usm0rE5QtTEFxNVQWqOpbpNoDMOXl3eUWFWiKOHvfebyNgYPuXdvLXS90FxyoopJ+V2EhNAG7Dr6fDFTgbI3TteMgh44OpgSqpqgQldTWCWu69xJz3yfA1Yuy3wFojKld5aqjynfpLxiICjHr5PEWqEyokmaqROQfgE8AA8BbwO+p6v5yDMwUx7vLzaup2nXIaVgngIjQOqYl7T1PbtiR9ZznnNzKqOYoL27ekxJ0IhFx9wEsbqwJ4Fd9B9Jmo365ZQ8vvTUU/iLA77TPYsaE0SnF6ME2Cd4WOsaYxlGJZb98hQWxrGEKLFCNMKXOVD0NLFDVDwNvAl8ufUimVItmtzJjwmhicafZpROoIKHKise76OhJ7WZ+2YJpKY/PmDqOJvf/DAEWtrVy2yWnM6o5kjIzFYsXH6i8c/sDlbgzXwl1xtoUcWanmqKCQNrdfV6A/Pyl86xA3Zg6lmmWqtBlv0IL1MNmnDIdY4HK5KOkmSpVfcr3cA1wdWnDMeXin8Xxt0UYGExwxzNvctslpydDiNdJ3d9Z/RtPbHSWBIG7XuimbeJYli+dzw9e7OatPYdRLe1Ov+aooJC8qxCchB9tihCLJ4iIcNN5p/DWnsP8/I1d3Lu2l1Xu/n7BYGVhypgRpIj2CWPPmJG1rqrQpcBgmILiA5VpLOUsVP8s8ECmF0VkGbAMoK2tLdNhxiff/kth/M0uW8e0JDukJ4BfbN7D2rf3cd/NS5IF3/1HBlKCVteOgynn+z8/e5M9hwdSQlDQWTPH80qGzudB17TPApx6Lk9C4eLTJ/PzN3YRTyg/+OXbxJVkvdWxQbu7z5hGU/AsVQaVaPaZV5gC2xh5BMsZqkTkGWBqyEtfVdVH3GO+CsSAezKdR1VXAisB2tvbK3iPWX0qtP9StgA2b+o4li+dz1cffi353EAswWq3+af3ORERVlyxgBsWt3HZgmn8YvOe5PHvHkzdTDTM6zsO8ltnTeeRV7bnnMU6YVQTH5s/lYc6hrq6N0eFSeNGkVBnVi3YP0shtCbMmKB86z1FZCtwCIgDMVVtr+Q4TYFKaPKZa7Yq2/vC5BOmwALVSJMzVKnqJdleF5HPAEuBi1UreUN+Y/NaI4RtABwMUMEAtnzpfB7+7z7W9/SDwqjmCOfPnZwWdDTwOQlVlj/i/HTYf2SAWz46h590vUvP3iN5LfUNxpW39xzO61hvSfG+m5ewurMPhWSh+epOp2lpNCIpM1XijstTykyeaXhPA19W1ZiIfBOn3vNLGY69SFX3ZHjNDKNyzVLlK59glSlEeTJ2RrdAZSj97r+P41yoLlDV9HbXpmiZNgAOm8HyB6OBWIKvPvxaSrA5Npjg52/sSjl/U1RYMH08D/93HwnfwbGEE6zi7lY1Sz88LaUJJ2TeikaBDe9kXv4b3Rzh6OBQZfsD63qZP2M8Ainbz/j36Nv07iGWP7KBREJpac7++2DBynis3nNkKWTpL1doyqSQMAUWqEaqUmuqvgOMAp4Wp7HkGlW9peRRmYwbAIfNYPkDGCIkAhOGirOfn+fMmeO59uw2/vLHrxFWIuX1foollEdf3V7QuL2PEeDUyWN5e+/h5B2CA4FbBTdsP8Crbg3Wgx193HfzkuT36H3Pi2a3Mm/quLx+HyxUmQyy1Xsq8JSIKPBdt0whjdWE1oAq7e9nYcoUotS7/7LvEGlKEnZnW9gMlj+AvbptP0+9vjPtXApEBGd58BPzWdXZFxqohtovuO9TaIoIiYSSIPeGyRG350JzU4RvXn0mqzv7uHdtr/MehUs/eBJHB+OMbo7ytG+cg26N16rOvpTZJyB0iS/TTJ4ZOcpU7/kRVd0uIlNwfjh8Q1VfCB5kNaHlV3CzzwrKuvlxEWEKLFCNFLZNTZ0Jm8Hy1xYtmTORn72xM7R/1IdmjGf5J+azaHZrskg9KBoVbvrIKdz94tskVJM1Wv1HBmgd08ID63qTs0uhRLj27Fkpy3leUBIRLpw3hRsWt9HR089zm3Yx4Ca75qaI0/LBN/sUFrK8c2aayTMjRznqPVV1u/vfXSLyMHAOkBaqTOUUU08lc08r+e6/rEEKMoYpsEBlhlioqkP+Gayw2qKvXfGhtLoqgAUzxidDmOJssBxPpB41pjnKoWMxVlyxgP4jAylbwkB4zVTEP7OVUGZMGJ0SfpYvnZ+s01rxeBfzpo5Lbua8qrOPPYeOMWncKBZMH58y+xQMWcElPutRZTLJp95TRMYCEVU95H59KbCigsM0VZYzSIGFKVMQC1V1Lqy2KEw0Ily5cGZKCAvbt+/g+zHuWdtLS1OE2z8xn9WdfTywfhvxuBJx78YLE3HXBf3F5J7+IwNDbRJ84cgLRME7F/1hzrsT0Jb4TIFC6z1FZDpwt6peDpwEPOy+3gTcq6o/qdaAR5LhWPrzB6Rss1Z5BSkoKUyBBaqRykJVnctUW9QcleTSmgjcfN4pLJrdylcefo333TvwhNRZJr+BWIK/dGeXPMFZLU9CnfN8yC2Az6cOzOMPhccGE2zYfoC//e0PJV+3JT5TjEz1nu5y3+Xu193AmZUcl8muXK0U8g5OYSxMmRJYqKpzmWqsrmmfxZs7D9HZu59EQvm3l7bSNnEsD67flnyvAmfOGM9r7xxIm4ESyRyiwiTU2Rx5086h5b1sY/QsmTORpogTABV4qKMvpR7LlviMaSw1W6BuYcqUgYWqBpCpxioigvqW3Z7csCNtm5lX+g5wy0fncPBYjF9u3kPPPqf8JNddft4xIiT3AVSc2aYVj3UlC+LDxhgc+zXts5J3CMbj1h7BmEZVk4EqS5gCq5syhYlUewCmvFI6pCeUiAhRce6uu2zBNJqb0v/IX+rey55Dx5KBCpyZp7CaK79IRFh2/hyuX9xGc9Q5WoFX+w5w/cqX6Ojpp6Onnzuf3UJHT3/G81y5cCajmiPJcVrtlDEjT86lvxzhpyCnfWDoVwYyZ4EFKlMwm6mqYx09/azq7ENwgsmi2a20jmkh4k4ftTSnF35fePrktD5WXTsOhrZJ8EJVphmruLuseM9NS9h96Fhq36m4sqqzj9UZWiL4WXsEYxpfWWapTvtA8U1A8wxlttRnSmGhqk7du7Y3pSP6j9Zv48J5U3j+zd0k1LlTb/nS+dyw2On+7C0Lvj+Y2sBqxoTj2HHg/dDP8NouJBKaXN4L8vpJPf/m7pTnm6OCkL0lgp/VThlj8pIrWBU5o2VhypSDhao61NHT7/R98qWcwbimzBQJmrL58OrOPo4NpncEnT99PHsPDzAYSzjb2fjOGY0IN53nNAKNBYrWI+LMZHn9pGLxoTsKPzzTaTIKTuNPa4lgzMiWa5aq4Lv+yrgUmE+YAgtUJj8Wqmqcv1u6f9+74P5+QSJC65gWwJnVuv/l3tCZpjmTxjJ53Ch2HTqGAM9t2kXM7UnlNQANfpbXwypTPyl/kbot6xkzstVkcToWpszwsFBVBWFBKdNxwW7pi2a3Jvs+eQ08F81u5ZVt+4kn1LkbD2dT5RWPdwGkzWp5BLj7xbeJu8t7EYGmaITrFg9tM3Pv2t6U2atLP3gSn7vg1LRxL186nyc37OCyBdOs67kxpiblG6Q8FqhMoSxUVZBXWP5QRx+xePbibQjvlu6FFP8MEMB3n3+LnQff56QTjuOZjTuT73lyw460flPNUadOSkSSnc7BWfqLxxMp28z0HxlItleIAGfOmpA23o6eflY83sVALMG6rfvS+lQZY0amWpilKjRIgYUpUzwLVRXizTodG0wkQ0yu4u1snci9cNXR08+13/0vYm65VEQOEI0IktBkG4WX3tqbrIkS4Jr2WcyYMJrWMS1OGBpMkMCZqQp+zpI5ExnVHD4GT6bwZ4wx1VBMkPJYoDKlsFBVIV7w8AKVV+SdrXg7n1YDqzr7koEKnNkmjSsfCyzTLX9kA4mE02bhqoUzk2O68dyT6dpxkPnTTmDc6Oa0z8k0Bv8SptcVfTCuRCNiBenGmIorJUiBhSlTHhaqKsQ/6xSNCNe0z0r2lsom2C09GG7CGnQq8PM3dvG5C04F4IbFbcybOi5ludCr1fKafK7bui9rHyn/88Far+VL5zut1VH3v8YYUxkWpkwtsVBVIaU2uOzo6ef6lS8xGFciAjefP4dxo5uZP308zVFnlsgvllBWPNbFtWe3Je/Su/UiZ5PRO5/dkgxUQHIbm3yX7YKbID+wrpdYPGHbzBhjKsaW+EwtslBVQbnuhMt2V+Cqzj4G3OAUV7jrhW4i4rQ3uHDelJQeVZ5X+w7wat9ryePC7h70Zqr8LRhyWTJnIk3RSHI5s2v7AZqiEeJx60dljCmczFmQd6+qYsOUBSlTCWUJVSLyReAfgMmquqcc5xxpghshr7hiQbIbOoQv83mF4VPGjUqGpDAJdTqb3/HMm9x2yekps2aHjg4m2yqseLwrrzv3Fs1u5epFM7nP3QRZFa5eNJMZE0ZbPypjTFH8YckfsGx5z9STkjdUFpFZwMeA3tKHM3Kt7uzj/UFn5iiWUJY/siFlE+IrF85Mblrs8e7Wu3LhTO67eQmnTR4bem7BCVYvbt7Dp+5ek3Leg8diyT5VA4PO0l0+rgpsgnzVwpncetFpFqiMMSXzNjMudYnPApWptHLMVH0L+DPgkTKcq6FlWt7r6OnnwfXbUo5NJDSlNmnR7FbuX3Zush/VuXMmJu/WA6fO6bPnzeH2RzcwGFcEuOSDJ3HhvCk8uWEHL27ek6ydWtXZx0Prtzn1WZGhPf0SkPcSoG2CbIypRRakTDWVFKpE5JPAO6r6quS460tElgHLANra2rIe24gydUcHJxAF99ZraQ6vTXph824GYgk27TzEPTctAUgWsDdHhds/uYCu7QdQSHZFnzd1HOu27kv2mtpz6NhQfZZvxTAipOwXmIt1SzfGZBOZf37FGoBamDK1IGeoEpFngKkhL30V+ApwaT4fpKorgZUA7e3t2Teuq3OZ9uvL1CAzpd1CNMLVi2YmA5H/nHc882ayeai3VPfKtv3JgDQQV57btCsZvFZ39iXDm39WaXVnX8p4vVXFQorM891qxxhjhpOFKVNLcoYqVb0k7HkR+RBwCuDNUs0EOkXkHFV9t6yjrCO59uvL1B0921Kad873B4emlRLAoaOD/Gxj6l1/3XsOZ9zaxn/eBzuGNkD2b46cT0DKNutmjDF+wzVbZWHK1KKil/9U9TVgivdYRLYC7SP97r989+sLhpBsS2neOf0iAl07DhJYNeTEMc30BcJbcFZp0exW7ru5+Hoo25bGGFOIcvnVoY4AAA23SURBVAYrC1OmllmfqjLLZ78+GFo+ax3TknOWKNlXyrdHX0tThPnTTuC/3tqbsmHyK9v2c/snFyTPCYTOKpVSD5XtezSmFojI7cDNwG73qa+o6hMhx30c+DYQBe5W1W9UbJAjjBeGiglXFqRMvShbqFLVk8t1rnqWz11xwc2Vg805w865fOl8ntywI7lHn7cZciLh3Onnxap4Quk/MhDaPb1cs0p255+pE99S1X/M9KKIRIE7cVrC9AHrRORRVX29UgMcifIJVxaiTL2ymaphkGsWKLi5crbA09HT77RA6OgjFk8k9+gLnsMT3NB4uGaV7M4/0wDOAbaoajeAiNwPXAFYqKoAC06mEVmoqoKw5bywwBOc0YKh8OWdw/+aANe0z0oJOzarZEawPxKR/wmsB76gqv2B12cA/gZxfcDisBON9JYwxpj8WKiqAn/QyVZTFZyNEobCl3eO1Z19PLh+G/GEJrurh31eMRs4WxAztSxHu5d/Bb6GszL+NeCfgM8GTxHy3tB2LyOpJYwxpngWqqokn6CT0r8qIlzTPov508cnt5LxznHlwpllDUDWMsHUg0ztXoJE5HvA4yEv9QGzfI9nAtvLMDRjzAhloapGeTNFy5fOH9Y7+cJYywRT70RkmqrucB/+NrAh5LB1wFwROQV4B7gOuKFCQzTGNCALVUUazuWxTDNFw3EnXxhrmWAawN+LyFk4y3lbgc8BiMh0nNYJl6tqTET+CPgpTkuFH6hqV7UGbIypfxaqilCu5bFMwSzTTFGlwo4Vt5t6p6qfzvD8duBy3+MngLT+VcYYUwwLVUUox/JYtmCWKTxVMuyELSla8boxxhiTmYWqImQKPYWEjmzBLFN4qmaoseJ1Y4wxJjsLVUUICz2Fho5cS3neTFFHTz93Prsl2UG9WqHGiteNMcaY7CxUFSm4PFZo6ChkO5uBWIKICAnVqoUaK143xhhjsrNQVSbFhI58t7NJKKBKJCIIWpVQY8XrxhhjTHYWqspkOEJHMKj5e1ZVI9TYfn/GGGNMZhaqyqjcd8zZ7JAxxhhTPyxUDaNMxeuFBC2bHTLGGGPqg4WqYRRWvA7hW80YY4wxpr5Fqj2ARubVREWFZHF5pqBljDHGmPpW8kyViPwx8EdADPhPVf2zkkdV4/JdvstUE2WtCYwxxpjGU1KoEpGLgCuAD6vqMRGZUp5h1a5Cm3wGa6Ks+NwYY4xpTKXOVP0B8A1VPQagqrtKH1JtK0dncSs+N8YYYxpPqTVVpwPni8haEXleRM7OdKCILBOR9SKyfvfu3SV+bPWE1UkZY4wxxuScqRKRZ4CpIS991X1/K7AEOBv4kYjMUVUNHqyqK4GVAO3t7Wmv14tClu+quQGyMcYYYyorZ6hS1UsyvSYifwCsdkPUyyKSACYB9TsVFSIYjvJZvvNqr44NJohGhBVXLOCGxW0VGrExxhhjKq3UmqofA78BPCcipwMtwJ6SR1VDCi1M96zp3suxwQQKxBLKXz6ygXlTx9mMlTHGGNOgSq2p+gEwR0Q2APcDnwlb+qtnxfaVWjJnIhEZehxPKKs7+4ZplMYYY4yptpJmqlR1APjdMo2lJgU3Nc63MH3R7FYuPuMknnp9Z/K5hkqbxhhjjElh29TkUEpfqc9dcCrPbdrFYFxpjgpXLZw5jCM1xhhjTDVZqMpDsX2lFs1u5b5l59odgMZUmIg8AMxzH04A9qvqWSHHbQUOAXEgpqrtFRukMabhWKjKoFztEKzRpzGVp6rXel+LyD8BB7IcfpGqNtQNNsaY6rBQFaLYO/6MMbVFRAT4HZy7lI0xZliVevdfQyr2jj9jTM05H9ipqpszvK7AUyLSISLLMp2kUXaEMMYML5upClHsHX/GmMrJttuDqj7ifn09cF+W03xEVbe7m8E/LSJvqOoLwYMaZUcIY8zwslAVopQ7/owxlZFttwcAEWkCrgQWZTnHdve/u0TkYeAcIC1UGWNMPixUZWAF5sbUvUuAN1Q1tOuuiIwFIqp6yP36UmBFJQdojGksNV1T1dHTz53PbqGjp7/aQzHG1J/rCCz9ich0EXnCfXgS8KKIvAq8DPynqv6kwmM0xjSQmp2pquQdeOVqn2CMqR2qemPIc9uBy92vu4EzKzwsY0wDq9lQFXYH3nAEHmufYIwxxphyqNnlP+8OvKgwrHfgWfsEY4wxxpRDzc5UlfMOvGzLe9Y+wRhjjDHlULOhCspzB16u5T1rn2CMMcaYcqjpUFUO+dRmWfsEY4wxxpSqZmuqyqVStVnGGGOMGdkafqZqOJb3rAWDMcYYY4JKClUichZwF3AcEAP+UFVfLsfAyqmcy3vWgsEYY4wxYUpd/vt74K9V9Sxgufu4oVkLBmOMMcaEKTVUKXCC+/V4YHuJ56t5VqNljDHGmDCl1lTdBvxURP4RJ6D9eulDqm3WgsEYY4wxYXKGKhF5Bpga8tJXgYuB/6Wqq0Tkd4Dv4+wMH3aeZcAygLa2tqIHXAusBYMxxhhjgnKGKlUNDUkAIvJD4E/dhw8Cd2c5z0pgJUB7e7sWNszc7I48Y4wxxlRTqct/24ELgOeA3wA2lzqgYtgdecYYY4yptlJD1c3At0WkCXgfd3mv0vLpmm6MMcYYM5xKClWq+iKwqExjKZptimyMMcaYamuIjup2R54xxhhjqq0hQhXYHXnGGGOMqa6G31DZGGOMMaYSLFQZY4wxxpSBhSpjTN0SkWtEpEtEEiLSHnjtyyKyRUQ2ichvZnj/KSKyVkQ2i8gDItJSmZEbYxqRhSpjTD3bAFwJvOB/UkQ+CFwHzAc+DvyLiERD3v9N4FuqOhfoB35/eIdrjGlkFqqMMXVLVTeq6qaQl64A7lfVY6r6NrAFOMd/gIgITtPih9yn/n/gt4ZzvMaYxmahyhjTiGYA23yP+9zn/CYC+1U1luUYwNm7VETWi8j63bt3l32wxpjGUJWWCh0dHXtEpCfkpUnAnkqPJ0QtjKMWxgC1MY5aGAPUxjhqYQxQ3DhmF/NB2TZ1V9VHMr0t5LngnqP5HOM86du7VER2Z7h+FaNW/jwrxb7fxteo33Ne16+qhCpVnRz2vIisV9X2sNcqqRbGUQtjqJVx1MIYamUctTCGSo8j26buWfQBs3yPZ+LsVeq3B5ggIk3ubFXYMWHjCb1+FaNW/jwrxb7fxjcSv2c/W/4zxjSiR4HrRGSUiJwCzAVe9h+gqgo8C1ztPvUZINPMlzHG5GShyhhTt0Tkt0WkDzgX+E8R+SmAqnYBPwJeB34C3Kqqcfc9T4jIdPcUXwI+LyJbcGqsvl/p78EY0zhqbZualdUegKsWxlELY4DaGEctjAFqYxy1MAaokXGo6sPAwxle+zrw9ZDnL/d93U3grsAKq4nfxwqy77fxjcTvOUmcGXBjjDHGGFMKW/4zxhhjjCkDC1XGGGOMMWVQs6FKRL4oIioik6r0+V8TkV+JyCsi8pSvsLWSY/gHEXnDHcfDIjKh0mNwx5Fxf7UKfPbH3b3btojIn1fys31j+IGI7BKRDdX4fHcMs0TkWRHZ6P5Z/GmVxnGciLwsIq+64/jraoyjEVX7mlcptXJdG261cO2qlFq5PtWCmgxVIjIL+BjQW8Vh/IOqflhVzwIeB5ZXYQxPAwtU9cPAm8CXqzAGyLC/2nBz92q7E7gM+CBwvbunW6X9G87+cdUUA76gqmcAS4Bbq/R7cQz4DVU9EzgL+LiILKnCOBpKjVzzKqVWrmvDpoauXZVSK9enqqvJUAV8C/gzMnQ3rgRVPeh7OLYaY1HVp3xbaKzBaU5YcVn2Vxtu5wBbVLVbVQeA+3H2dKsoVX0B2Ffpzw2MYYeqdrpfHwI2kmFLlWEeh6rqe+7DZveX3e1Suqpf8yqlVq5rw6wmrl2VUivXp1pQc6FK5P+1d4cgUkVRGMf/H6IgaFSwzYbFoqAGi01FRGSyICJo3WDVCSbBZDJYjKsgqAiKyAZtIguyq4J2FcRksh7De8Ii6+4o791zd973awMzvBPmfXO4c989GgNfI2K1glpuSPoMnCdnpWqtS8Dz5BpKm2Z+2+BIGgGHgTdJ198maQX4DixFREods6KmzEswq7k22OzKzqdsKedUbTTLC7gGnMquIyKeRMQEmEi6CiwA10vX0L5nQrO8utj19f+ljgRTz2YbCkm7gIfAlT9WU4tpD9E81O6FeSzpQESk7TfbCmrJvFJqybVEg8yuGvIpW9bsv3VneUk6CMwBq5KgWRZ+K+loRHwrVcc67gHP6KGp2qwGSReBs8CJ6PFQsf+cr9a3aea3DYak7TSBtRgRj7LriYgfkl7R7DdzU7WBWjKvlFpyLdHgsqu2fMpS1d9/EfE+IvZGxCgiRjRfzCMZ4SJpfs3LMfApoYbTNGM0xhHxs/T1K7AMzEuak7QDOEcz021w1Pzi3gU+RsStxDr2/H5aS9JO4CQJ98asqCnzShlIrg0qu2rJpxpU1VRV5qakD5Le0SzNZzwiehvYDSy1RzvcSajhr/PV+tZuZl0AXtBsfHzQznQrStJ94DWwX9IXSZdL1wAcAy4Ax9vvwoqkM5t9qAf7gJftfbFMs6fqaUIdtnVVkWt9qiW7Cqoln9J5TI2ZmZlZB7xSZWZmZtYBN1VmZmZmHXBTZWZmZtYBN1VmZmZmHXBTZWZmZtYBN1VmZmZmHXBTZWZmZtaBXzg1YNohn7LAAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn\n", - "fig, ax = plt.subplots(1, 2, figsize=(10,4))\n", - "ax[0].plot(X[:, 0], Y, '.')\n", - "seaborn.kdeplot(X[:, 0], Y, cmap=\"Reds\", shade=True, shade_lowest=False, ax=ax[1])\n", - "ax[0].set_title(\"nuage de points\")\n", - "ax[1].set_title(\"estimation de la densit\u00e9\");" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Régression linéaire\n", + "\n", + "Ce notebook s'intéresse à la façon d'interpréter les résultats d'une régression linéaire lorsque les variables sont corrélées puis il explore une façon d'associer arbre de décision et régression linéaire pour construire une régression linéaire par morceaux." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Un cas simple\n", + "\n", + "Une façon d'interpréter des résultats statistiques est de les calculer dans un cas où la réponse cherchée est connue. On simule un modèle simple $Y=\\alpha X_1 + 0.X_2 + \\epsilon$ et on cale une régression linéaire. On suppose que $X_1, X_2, \\epsilon$ sont des variables aléatoires gaussiennes de même variance et moyenne." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evolution de R2\n", - "\n", - "Dans la r\u00e9gression pr\u00e9c\u00e9dente, le coefficient $R^2$ transcrit en quelque sorte la part du bruit $\\epsilon$ par rapport au terme $\\alpha X_1$. Faisons varier $\\alpha$." + "data": { + "text/plain": [ + "((1000, 3), (1000,))" ] - }, + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy.random as npr\n", + "\n", + "eps = npr.normal(1000)\n", + "X = npr.normal(size=(1000, 3))\n", + "alpha = 2\n", + "Y = alpha * X[:, 0] + X[:, 2]\n", + "X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "alphas = []\n", - "r2s = []\n", - "for a in [0.1 * i for i in range(0, 50)]:\n", - " Y = a*X[:,0] + X[:, 2]\n", - " model = OLS(Y,X[:, :2])\n", - " results = model.fit()\n", - " alphas.append(a)\n", - " r2s.append(results.rsquared)" + "data": { + "text/plain": [ + "array([[ 1. , 0.02585011, -0.00808406],\n", + " [ 0.02585011, 1. , 0.00338766],\n", + " [-0.00808406, 0.00338766, 1. ]])" ] - }, + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import corrcoef\n", + "\n", + "corrcoef(X.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from statsmodels.regression.linear_model import OLS" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.803
Model: OLS Adj. R-squared (uncentered): 0.802
Method: Least Squares F-statistic: 2029.
Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00
Time: 11:29:03 Log-Likelihood: -1417.8
No. Observations: 1000 AIC: 2840.
Df Residuals: 998 BIC: 2849.
Df Model: 2
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 1.9922 0.031 63.680 0.000 1.931 2.054
x2 0.0041 0.032 0.130 0.896 -0.058 0.067
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 4.685 Durbin-Watson: 2.126
Prob(Omnibus): 0.096 Jarque-Bera (JB): 4.706
Skew: -0.167 Prob(JB): 0.0951
Kurtosis: 2.972 Cond. No. 1.03


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(alphas, r2s, '.', label=\"observed\")\n", - "ax.plot(alphas, [a**2/(1+a**2) for a in alphas], label='theoretical')\n", - "ax.set_xlabel(\"alpha\")\n", - "ax.set_ylabel(\"r2\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dans ce cas de r\u00e9gression simple, la valeur \u00e0 pr\u00e9dire est $y_i$, la valeur pr\u00e9dite est $\\hat{y_i}=\\alpha X_{1i}$ et la moyenne $\\bar{y} = \\alpha \\bar{X_1} + \\bar{\\epsilon} = 0$.\n", - "\n", - "$$R^2 = 1 - \\frac{\\sum_{i=1}^n (\\hat{y_i}-\\bar{y})^2}{\\sum_{i=1}^n (y_i - \\bar{y})^2}=1-\\frac{\\mathbb{V}\\epsilon}{\\alpha^2\\mathbb{V}X_1+\\mathbb{V}\\epsilon} = 1 - \\frac{1}{1+\\alpha^2}=\\frac{\\alpha^2}{1+\\alpha^2}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deux variables corr\u00e9l\u00e9es\n", - "\n", - "On ne change pas le mod\u00e8le mais on fait en sorte que $X_2=X_1$. Les deux variables sont corr\u00e9l\u00e9es." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 0.810
Model: OLS Adj. R-squared: 0.810
Method: Least Squares F-statistic: 4271.
Date: Mon, 27 Nov 2017 Prob (F-statistic): 0.00
Time: 12:06:03 Log-Likelihood: -1411.2
No. Observations: 1000 AIC: 2824.
Df Residuals: 999 BIC: 2829.
Df Model: 1
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 1.0288 0.016 65.349 0.000 0.998 1.060
x2 1.0288 0.016 65.349 0.000 0.998 1.060
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 8.165 Durbin-Watson: 1.944
Prob(Omnibus): 0.017 Jarque-Bera (JB): 6.024
Skew: -0.064 Prob(JB): 0.0492
Kurtosis: 2.642 Cond. No. 1.61e+16
" - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.810\n", - "Model: OLS Adj. R-squared: 0.810\n", - "Method: Least Squares F-statistic: 4271.\n", - "Date: Mon, 27 Nov 2017 Prob (F-statistic): 0.00\n", - "Time: 12:06:03 Log-Likelihood: -1411.2\n", - "No. Observations: 1000 AIC: 2824.\n", - "Df Residuals: 999 BIC: 2829.\n", - "Df Model: 1 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 1.0288 0.016 65.349 0.000 0.998 1.060\n", - "x2 1.0288 0.016 65.349 0.000 0.998 1.060\n", - "==============================================================================\n", - "Omnibus: 8.165 Durbin-Watson: 1.944\n", - "Prob(Omnibus): 0.017 Jarque-Bera (JB): 6.024\n", - "Skew: -0.064 Prob(JB): 0.0492\n", - "Kurtosis: 2.642 Cond. No. 1.61e+16\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "[2] The smallest eigenvalue is 7.69e-30. This might indicate that there are\n", - "strong multicollinearity problems or that the design matrix is singular.\n", - "\"\"\"" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } + "text/latex": [ + "\\begin{center}\n", + "\\begin{tabular}{lclc}\n", + "\\toprule\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.803 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.802 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 2029. \\\\\n", + "\\textbf{Date:} & Mon, 07 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 11:29:03 & \\textbf{ Log-Likelihood: } & -1417.8 \\\\\n", + "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & 2840. \\\\\n", + "\\textbf{Df Residuals:} & 998 & \\textbf{ BIC: } & 2849. \\\\\n", + "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n", + "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lcccccc}\n", + " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", + "\\midrule\n", + "\\textbf{x1} & 1.9922 & 0.031 & 63.680 & 0.000 & 1.931 & 2.054 \\\\\n", + "\\textbf{x2} & 0.0041 & 0.032 & 0.130 & 0.896 & -0.058 & 0.067 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lclc}\n", + "\\textbf{Omnibus:} & 4.685 & \\textbf{ Durbin-Watson: } & 2.126 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.096 & \\textbf{ Jarque-Bera (JB): } & 4.706 \\\\\n", + "\\textbf{Skew:} & -0.167 & \\textbf{ Prob(JB): } & 0.0951 \\\\\n", + "\\textbf{Kurtosis:} & 2.972 & \\textbf{ Cond. No. } & 1.03 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "%\\caption{OLS Regression Results}\n", + "\\end{center}\n", + "\n", + "Notes: \\newline\n", + " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", + " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "X[:, 1] = X[:, 0]\n", - "Y = 2*X[:,0] + X[:, 2]\n", - "model = OLS(Y,X[:, :2])\n", - "results = model.fit()\n", - "results.summary()" + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.803\n", + "Model: OLS Adj. R-squared (uncentered): 0.802\n", + "Method: Least Squares F-statistic: 2029.\n", + "Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 11:29:03 Log-Likelihood: -1417.8\n", + "No. Observations: 1000 AIC: 2840.\n", + "Df Residuals: 998 BIC: 2849.\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 1.9922 0.031 63.680 0.000 1.931 2.054\n", + "x2 0.0041 0.032 0.130 0.896 -0.058 0.067\n", + "==============================================================================\n", + "Omnibus: 4.685 Durbin-Watson: 2.126\n", + "Prob(Omnibus): 0.096 Jarque-Bera (JB): 4.706\n", + "Skew: -0.167 Prob(JB): 0.0951\n", + "Kurtosis: 2.972 Cond. No. 1.03\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" ] - }, + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = OLS(Y, X[:, :2])\n", + "results = model.fit()\n", + "su = results.summary()\n", + "su" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.rank" + "data": { + "text/plain": [ + "(np.float64(0.8026213180783517), np.float64(0.8022257696175868))" ] - }, + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.rsquared, results.rsquared_adj" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On vérifie que le coefficient devant $X_1$ est non nul (P-value nulle, 0 n'est pas l'intervalle de confiance). Le coefficient devant $X_2$ n'est pas nul mais presque, la P-value est élevée, le coefficient $R^2$ est élevé. Dessinons." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les variables corr\u00e9l\u00e9es n'ont pas l'air de d\u00e9ranger l'algorithme de r\u00e9solution car il utilise la m\u00e9thode [SVD](https://en.wikipedia.org/wiki/Singular-value_decomposition) pour r\u00e9soudre le m\u00eame probl\u00e8me dans un espace de moindre dimension. Le probl\u00e8me survient que les deux variables ne sont pas compl\u00e9tement corr\u00e9l\u00e9es. On \u00e9tudie le mod\u00e8le $Y \\sim X_1 + X'_2$ avec $X'_2 = \\alpha X_1 + (1-\\alpha) X_2$ et on r\u00e9duit la variance du bruit pour en diminuer les effets." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_21413/1827909711.py:6: UserWarning: \n", + "\n", + "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n", + "This will become an error in seaborn v0.14.0; please update your code.\n", + "\n", + " seaborn.kdeplot(x=X[:, 0], y=Y, cmap=\"Reds\", shade=True, shade_lowest=False, ax=ax[1])\n", + "/tmp/ipykernel_21413/1827909711.py:6: FutureWarning: \n", + "\n", + "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n", + "This will become an error in seaborn v0.14.0; please update your code.\n", + "\n", + " seaborn.kdeplot(x=X[:, 0], y=Y, cmap=\"Reds\", shade=True, shade_lowest=False, ax=ax[1])\n" + ] }, { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "X_ = npr.normal(size=(1000, 3))" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "ax[0].plot(X[:, 0], Y, \".\")\n", + "seaborn.kdeplot(x=X[:, 0], y=Y, cmap=\"Reds\", shade=True, shade_lowest=False, ax=ax[1])\n", + "ax[0].set_title(\"nuage de points\")\n", + "ax[1].set_title(\"estimation de la densité\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evolution de R2\n", + "\n", + "Dans la régression précédente, le coefficient $R^2$ transcrit en quelque sorte la part du bruit $\\epsilon$ par rapport au terme $\\alpha X_1$. Faisons varier $\\alpha$." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "alphas = []\n", + "r2s = []\n", + "for a in [0.1 * i for i in range(50)]:\n", + " Y = a * X[:, 0] + X[:, 2]\n", + " model = OLS(Y, X[:, :2])\n", + " results = model.fit()\n", + " alphas.append(a)\n", + " r2s.append(results.rsquared)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
c1c2r2rank
alpha
0.900.9763701.0289820.9973912
0.910.9731501.0322020.9974162
0.920.9691251.0362270.9974402
0.930.9639501.0414020.9974642
0.940.9570491.0483030.9974892
0.950.9473891.0579630.9975132
0.960.9328981.0724540.9975362
0.970.9087471.0966050.9975602
0.980.8604441.1449080.9975832
0.990.7155361.2898160.9976062
1.001.0012791.0012790.9976271
\n", - "
" - ], - "text/plain": [ - " c1 c2 r2 rank\n", - "alpha \n", - "0.90 0.976370 1.028982 0.997391 2\n", - "0.91 0.973150 1.032202 0.997416 2\n", - "0.92 0.969125 1.036227 0.997440 2\n", - "0.93 0.963950 1.041402 0.997464 2\n", - "0.94 0.957049 1.048303 0.997489 2\n", - "0.95 0.947389 1.057963 0.997513 2\n", - "0.96 0.932898 1.072454 0.997536 2\n", - "0.97 0.908747 1.096605 0.997560 2\n", - "0.98 0.860444 1.144908 0.997583 2\n", - "0.99 0.715536 1.289816 0.997606 2\n", - "1.00 1.001279 1.001279 0.997627 1" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alphas = [0.9 + i * 0.01 for i in range(0,11)]\n", - "res = []\n", - "for a in alphas:\n", - " X = X_.copy()\n", - " X[:, 1] = a * X[:, 0] + (1-a) * X[:, 1]\n", - " Y = X[:, 0] + X[:, 1] + 0.1 * X[:, 2]\n", - " model = OLS(Y,X[:, :2])\n", - " results = model.fit()\n", - " res.append(dict(alpha=a, r2=results.rsquared, rank=model.rank, c1=results.params[0], c2=results.params[1]))\n", - " \n", - "import pandas\n", - "df = pandas.DataFrame(res)\n", - "df = df.set_index('alpha')\n", - "df" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(alphas, r2s, \".\", label=\"observed\")\n", + "ax.plot(alphas, [a**2 / (1 + a**2) for a in alphas], label=\"theoretical\")\n", + "ax.set_xlabel(\"alpha\")\n", + "ax.set_ylabel(\"r2\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dans ce cas de régression simple, la valeur à prédire est $y_i$, la valeur prédite est $\\hat{y_i}=\\alpha X_{1i}$ et la moyenne $\\bar{y} = \\alpha \\bar{X_1} + \\bar{\\epsilon} = 0$.\n", + "\n", + "$$R^2 = 1 - \\frac{\\sum_{i=1}^n (\\hat{y_i}-\\bar{y})^2}{\\sum_{i=1}^n (y_i - \\bar{y})^2}=1-\\frac{\\mathbb{V}\\epsilon}{\\alpha^2\\mathbb{V}X_1+\\mathbb{V}\\epsilon} = 1 - \\frac{1}{1+\\alpha^2}=\\frac{\\alpha^2}{1+\\alpha^2}$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deux variables corrélées\n", + "\n", + "On ne change pas le modèle mais on fait en sorte que $X_2=X_1$. Les deux variables sont corrélées." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.803
Model: OLS Adj. R-squared (uncentered): 0.802
Method: Least Squares F-statistic: 4062.
Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00
Time: 11:29:04 Log-Likelihood: -1417.8
No. Observations: 1000 AIC: 2838.
Df Residuals: 999 BIC: 2843.
Df Model: 1
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 0.9961 0.016 63.736 0.000 0.965 1.027
x2 0.9961 0.016 63.736 0.000 0.965 1.027
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 4.681 Durbin-Watson: 2.126
Prob(Omnibus): 0.096 Jarque-Bera (JB): 4.705
Skew: -0.167 Prob(JB): 0.0951
Kurtosis: 2.971 Cond. No. 3.15e+16


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[3] The smallest eigenvalue is 2.06e-30. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular." ], - "source": [ - "fig, ax = plt.subplots(1,2, figsize=(10,4))\n", - "df[[\"r2\"]].plot(ax=ax[0])\n", - "df[[\"c1\", \"c2\"]].plot(ax=ax[1])\n", - "ax[0].set_title(\"R2\")\n", - "ax[1].set_title(\"coefficients\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le $r^2$ augmente quand la corr\u00e9lation augmente mais les coefficients sont moins fiables. Les r\u00e9sultats devraient \u00eatre sensiblement identiques en th\u00e9orie mais en pratique, plus le d\u00e9terminant devient proche de z\u00e9ro, plus l'ordinateur est limit\u00e9 par sa pr\u00e9cision num\u00e9rique. Pour en savoir plus, vous pouvez lire un examen \u00e9crit que j'ai r\u00e9dig\u00e9, en python bien s\u00fbr : [Examen Programmation ENSAE premi\u00e8re ann\u00e9e\n", - "2006](http://www.xavierdupre.fr/site2013/enseignements/tdnote/ecrit_2006.pdf). Cette pr\u00e9cision est aux alentours de $10^{-15}$ ce qui correspond \u00e0 la pr\u00e9cision num\u00e9rique des [double](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
c1c2r2rank
alpha_1
-1.000000e-10-2.898180e+082.898180e+080.8115192
-1.000000e-11-2.898201e+092.898201e+090.8115192
-9.999779e-13-2.898941e+102.898941e+100.8115192
-1.000311e-13-2.891422e+112.891422e+110.8115182
-9.992007e-15-2.915101e+122.915101e+120.8115082
-9.992007e-161.012789e+001.012789e+000.8113592
-1.110223e-161.012789e+001.012789e+000.8113591
0.000000e+001.012789e+001.012789e+000.8113591
\n", - "
" - ], - "text/plain": [ - " c1 c2 r2 rank\n", - "alpha_1 \n", - "-1.000000e-10 -2.898180e+08 2.898180e+08 0.811519 2\n", - "-1.000000e-11 -2.898201e+09 2.898201e+09 0.811519 2\n", - "-9.999779e-13 -2.898941e+10 2.898941e+10 0.811519 2\n", - "-1.000311e-13 -2.891422e+11 2.891422e+11 0.811518 2\n", - "-9.992007e-15 -2.915101e+12 2.915101e+12 0.811508 2\n", - "-9.992007e-16 1.012789e+00 1.012789e+00 0.811359 2\n", - "-1.110223e-16 1.012789e+00 1.012789e+00 0.811359 1\n", - " 0.000000e+00 1.012789e+00 1.012789e+00 0.811359 1" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } + "text/latex": [ + "\\begin{center}\n", + "\\begin{tabular}{lclc}\n", + "\\toprule\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.803 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.802 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 4062. \\\\\n", + "\\textbf{Date:} & Mon, 07 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 11:29:04 & \\textbf{ Log-Likelihood: } & -1417.8 \\\\\n", + "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & 2838. \\\\\n", + "\\textbf{Df Residuals:} & 999 & \\textbf{ BIC: } & 2843. \\\\\n", + "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", + "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lcccccc}\n", + " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", + "\\midrule\n", + "\\textbf{x1} & 0.9961 & 0.016 & 63.736 & 0.000 & 0.965 & 1.027 \\\\\n", + "\\textbf{x2} & 0.9961 & 0.016 & 63.736 & 0.000 & 0.965 & 1.027 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lclc}\n", + "\\textbf{Omnibus:} & 4.681 & \\textbf{ Durbin-Watson: } & 2.126 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.096 & \\textbf{ Jarque-Bera (JB): } & 4.705 \\\\\n", + "\\textbf{Skew:} & -0.167 & \\textbf{ Prob(JB): } & 0.0951 \\\\\n", + "\\textbf{Kurtosis:} & 2.971 & \\textbf{ Cond. No. } & 3.15e+16 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "%\\caption{OLS Regression Results}\n", + "\\end{center}\n", + "\n", + "Notes: \\newline\n", + " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", + " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n", + " [3] The smallest eigenvalue is 2.06e-30. This might indicate that there are \\newline\n", + " strong multicollinearity problems or that the design matrix is singular." ], - "source": [ - "alphas = [1 - 10**(-i) for i in range(10,18)]\n", - "res = []\n", - "for a in alphas:\n", - " X = X_.copy()\n", - " X[:, 1] = a * X[:, 0] + (1-a) * X[:, 1]\n", - " Y = X[:, 0] + X[:, 1] + X[:, 2]\n", - " model = OLS(Y,X[:, :2])\n", - " results = model.fit()\n", - " res.append(dict(alpha_1=a-1, r2=results.rsquared, rank=model.rank, c1=results.params[0], c2=results.params[1]))\n", - " \n", - "import pandas\n", - "df = pandas.DataFrame(res)\n", - "df = df.set_index('alpha_1')\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On fait un dernier test avec [scikit-learn](http://scikit-learn.org/stable/) pour v\u00e9rifier que l'algorithme de r\u00e9solution donne des r\u00e9sultats similaires pour un cas o\u00f9 le d\u00e9terminant est quasi-nul." + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.803\n", + "Model: OLS Adj. R-squared (uncentered): 0.802\n", + "Method: Least Squares F-statistic: 4062.\n", + "Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 11:29:04 Log-Likelihood: -1417.8\n", + "No. Observations: 1000 AIC: 2838.\n", + "Df Residuals: 999 BIC: 2843.\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 0.9961 0.016 63.736 0.000 0.965 1.027\n", + "x2 0.9961 0.016 63.736 0.000 0.965 1.027\n", + "==============================================================================\n", + "Omnibus: 4.681 Durbin-Watson: 2.126\n", + "Prob(Omnibus): 0.096 Jarque-Bera (JB): 4.705\n", + "Skew: -0.167 Prob(JB): 0.0951\n", + "Kurtosis: 2.971 Cond. No. 3.15e+16\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[3] The smallest eigenvalue is 2.06e-30. This might indicate that there are\n", + "strong multicollinearity problems or that the design matrix is singular.\n", + "\"\"\"" ] - }, + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[:, 1] = X[:, 0]\n", + "Y = 2 * X[:, 0] + X[:, 2]\n", + "model = OLS(Y, X[:, :2])\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
c1c2r2
alpha
0.907.601931e-011.293903e+000.796916
0.917.275372e-011.326559e+000.798417
0.926.867173e-011.367379e+000.799911
0.936.342346e-011.419862e+000.801399
0.945.642576e-011.489839e+000.802880
0.954.662898e-011.587807e+000.804355
0.963.193382e-011.734758e+000.805823
0.977.441878e-021.979678e+000.807283
0.98-4.154200e-012.469516e+000.808736
0.99-1.884936e+003.939033e+000.810182
1.008.512221e+13-8.512221e+130.811404
\n", - "
" - ], - "text/plain": [ - " c1 c2 r2\n", - "alpha \n", - "0.90 7.601931e-01 1.293903e+00 0.796916\n", - "0.91 7.275372e-01 1.326559e+00 0.798417\n", - "0.92 6.867173e-01 1.367379e+00 0.799911\n", - "0.93 6.342346e-01 1.419862e+00 0.801399\n", - "0.94 5.642576e-01 1.489839e+00 0.802880\n", - "0.95 4.662898e-01 1.587807e+00 0.804355\n", - "0.96 3.193382e-01 1.734758e+00 0.805823\n", - "0.97 7.441878e-02 1.979678e+00 0.807283\n", - "0.98 -4.154200e-01 2.469516e+00 0.808736\n", - "0.99 -1.884936e+00 3.939033e+00 0.810182\n", - "1.00 8.512221e+13 -8.512221e+13 0.811404" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import r2_score\n", - "\n", - "alphas = [0.9 + i * 0.01 for i in range(0,11)]\n", - "res = []\n", - "for a in alphas:\n", - " X = X_.copy()\n", - " X[:, 1] = a * X[:, 0] + (1-a) * X[:, 1]\n", - " Y = X[:, 0] + X[:, 1] + X[:, 2]\n", - " model = LinearRegression()\n", - " model.fit(X[:, :2], Y)\n", - " r2 = r2_score(Y, model.predict(X[:, :2]))\n", - " res.append(dict(alpha=a, c1=model.coef_[0], c2=model.coef_[1], r2=r2))\n", - " \n", - "import pandas\n", - "df = pandas.DataFrame(res)\n", - "df = df.set_index('alpha')\n", - "df" + "data": { + "text/plain": [ + "np.int64(1)" ] - }, + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.rank" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les variables corrélées n'ont pas l'air de déranger l'algorithme de résolution car il utilise la méthode [SVD](https://en.wikipedia.org/wiki/Singular-value_decomposition) pour résoudre le même problème dans un espace de moindre dimension. Le problème survient que les deux variables ne sont pas complétement corrélées. On étudie le modèle $Y \\sim X_1 + X'_2$ avec $X'_2 = \\alpha X_1 + (1-\\alpha) X_2$ et on réduit la variance du bruit pour en diminuer les effets." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "X_ = npr.normal(size=(1000, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
r2rankc1c2
alpha
0.900.99732821.0139740.986445
0.910.99735321.0154800.984939
0.920.99737921.0173630.983056
0.930.99740321.0197830.980636
0.940.99742821.0230110.977409
0.950.99745321.0275290.972890
0.960.99747721.0343060.966113
0.970.99750121.0456020.954817
0.980.99752521.0681930.932226
0.990.99754821.1359680.864452
1.000.99757111.0008611.000861
\n", + "
" ], - "source": [ - "fig, ax = plt.subplots(1,3, figsize=(12,4))\n", - "df[[\"c1\", \"c2\"]].plot(ax=ax[1])\n", - "df[[\"c1\", \"c2\"]].plot(ax=ax[2])\n", - "df[[\"r2\"]].plot(ax=ax[0])\n", - "ax[0].set_title(\"R2\")\n", - "ax[1].set_title(\"coefficients\")\n", - "ax[2].set_ylim([-5, 5])\n", - "ax[2].set_title(\"coefficients, \u00e9chelle tronqu\u00e9e\");" + "text/plain": [ + " r2 rank c1 c2\n", + "alpha \n", + "0.90 0.997328 2 1.013974 0.986445\n", + "0.91 0.997353 2 1.015480 0.984939\n", + "0.92 0.997379 2 1.017363 0.983056\n", + "0.93 0.997403 2 1.019783 0.980636\n", + "0.94 0.997428 2 1.023011 0.977409\n", + "0.95 0.997453 2 1.027529 0.972890\n", + "0.96 0.997477 2 1.034306 0.966113\n", + "0.97 0.997501 2 1.045602 0.954817\n", + "0.98 0.997525 2 1.068193 0.932226\n", + "0.99 0.997548 2 1.135968 0.864452\n", + "1.00 0.997571 1 1.000861 1.000861" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le second graphe est trompeur mais il ne faut pas oublier de regarder l'\u00e9chelle de l'axe des ordonn\u00e9es." - ] - }, + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alphas = [0.9 + i * 0.01 for i in range(11)]\n", + "res = []\n", + "for a in alphas:\n", + " X = X_.copy()\n", + " X[:, 1] = a * X[:, 0] + (1 - a) * X[:, 1]\n", + " Y = X[:, 0] + X[:, 1] + 0.1 * X[:, 2]\n", + " model = OLS(Y, X[:, :2])\n", + " results = model.fit()\n", + " res.append(\n", + " dict(\n", + " alpha=a,\n", + " r2=results.rsquared,\n", + " rank=model.rank,\n", + " c1=results.params[0],\n", + " c2=results.params[1],\n", + " )\n", + " )\n", + "\n", + "import pandas\n", + "\n", + "df = pandas.DataFrame(res)\n", + "df = df.set_index(\"alpha\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Indicatrices\n", - "\n", - "$X_1$ est une variable al\u00e9atoire gaussienne. On teste maintenant un mod\u00e8le $Y = X'_1 + X'_2 + \\epsilon$ avec $X'_1 = X_1 \\mathbb{1}_{X_1 < 0}$ et $X'_2 = X_1 \\mathbb{1}_{X_1 \\geqslant 0}$." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "df[[\"r2\"]].plot(ax=ax[0])\n", + "df[[\"c1\", \"c2\"]].plot(ax=ax[1])\n", + "ax[0].set_title(\"R2\")\n", + "ax[1].set_title(\"coefficients\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le $r^2$ augmente quand la corrélation augmente mais les coefficients sont moins fiables. Les résultats devraient être sensiblement identiques en théorie mais en pratique, plus le déterminant devient proche de zéro, plus l'ordinateur est limité par sa précision numérique. Pour en savoir plus, vous pouvez lire un examen écrit que j'ai rédigé, en python bien sûr : [Examen Programmation ENSAE première année\n", + "2006](https://sdpython.github.io/doc/teachpyx/dev/_downloads/f9f86ad8c2bcfcba777d6ed8caafb5f6/td_note_2006.pdf). Cette précision est aux alentours de $10^{-15}$ ce qui correspond à la précision numérique des [double](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , 0.47358312, -0.03083914],\n", - " [ 0.47358312, 1. , -0.01293737],\n", - " [-0.03083914, -0.01293737, 1. ]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
r2rankc1c2
alpha_1
-1.000000e-100.80665121.355493e+08-1.355493e+08
-1.000000e-110.80665121.355632e+09-1.355632e+09
-9.999779e-130.80665121.355997e+10-1.355997e+10
-1.000311e-130.80665121.357117e+11-1.357117e+11
-9.992007e-150.80664821.410632e+12-1.410632e+12
-9.992007e-160.80661621.008605e+001.008605e+00
-1.110223e-160.80661611.008605e+001.008605e+00
0.000000e+000.80661611.008605e+001.008605e+00
\n", + "
" ], - "source": [ - "X = npr.normal(size=(1000, 3))\n", - "X[:, 1] = X[:, 0]\n", - "X[X[:, 0] >= 0, 0] = 0\n", - "X[X[:, 1] < 0, 1] = 0\n", - "Y = X[:, 0] + X[:, 1] + X[:, 2]\n", - "corrcoef(X.T)" + "text/plain": [ + " r2 rank c1 c2\n", + "alpha_1 \n", + "-1.000000e-10 0.806651 2 1.355493e+08 -1.355493e+08\n", + "-1.000000e-11 0.806651 2 1.355632e+09 -1.355632e+09\n", + "-9.999779e-13 0.806651 2 1.355997e+10 -1.355997e+10\n", + "-1.000311e-13 0.806651 2 1.357117e+11 -1.357117e+11\n", + "-9.992007e-15 0.806648 2 1.410632e+12 -1.410632e+12\n", + "-9.992007e-16 0.806616 2 1.008605e+00 1.008605e+00\n", + "-1.110223e-16 0.806616 1 1.008605e+00 1.008605e+00\n", + " 0.000000e+00 0.806616 1 1.008605e+00 1.008605e+00" ] - }, + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alphas = [1 - 10 ** (-i) for i in range(10, 18)]\n", + "res = []\n", + "for a in alphas:\n", + " X = X_.copy()\n", + " X[:, 1] = a * X[:, 0] + (1 - a) * X[:, 1]\n", + " Y = X[:, 0] + X[:, 1] + X[:, 2]\n", + " model = OLS(Y, X[:, :2])\n", + " results = model.fit()\n", + " res.append(\n", + " dict(\n", + " alpha_1=a - 1,\n", + " r2=results.rsquared,\n", + " rank=model.rank,\n", + " c1=results.params[0],\n", + " c2=results.params[1],\n", + " )\n", + " )\n", + "\n", + "import pandas\n", + "\n", + "df = pandas.DataFrame(res)\n", + "df = df.set_index(\"alpha_1\")\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On fait un dernier test avec [scikit-learn](http://scikit-learn.org/stable/) pour vérifier que l'algorithme de résolution donne des résultats similaires pour un cas où le déterminant est quasi-nul." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
c1c2r2
alpha
0.901.1405990.8636750.791250
0.911.1557460.8485280.792821
0.921.1746800.8295930.794384
0.931.1990240.8052500.795940
0.941.2314820.7727910.797489
0.951.2769240.7273500.799029
0.961.3450870.6591870.800562
0.971.4586910.5455830.802087
0.981.6859000.3183740.803603
0.992.367526-0.3632520.805111
1.001.0086821.0086820.806575
\n", + "
" ], - "source": [ - "from pandas import DataFrame\n", - "names = [\"X%d\" % i for i in range(X.shape[1]-1)]\n", - "ax = DataFrame(X[:50,:2], columns=names).sort_values(names).reset_index(drop=True).plot()\n", - "ax.set_title(\"Repr\u00e9sentation des features tronqu\u00e9es\");" + "text/plain": [ + " c1 c2 r2\n", + "alpha \n", + "0.90 1.140599 0.863675 0.791250\n", + "0.91 1.155746 0.848528 0.792821\n", + "0.92 1.174680 0.829593 0.794384\n", + "0.93 1.199024 0.805250 0.795940\n", + "0.94 1.231482 0.772791 0.797489\n", + "0.95 1.276924 0.727350 0.799029\n", + "0.96 1.345087 0.659187 0.800562\n", + "0.97 1.458691 0.545583 0.802087\n", + "0.98 1.685900 0.318374 0.803603\n", + "0.99 2.367526 -0.363252 0.805111\n", + "1.00 1.008682 1.008682 0.806575" ] - }, + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score\n", + "\n", + "alphas = [0.9 + i * 0.01 for i in range(11)]\n", + "res = []\n", + "for a in alphas:\n", + " X = X_.copy()\n", + " X[:, 1] = a * X[:, 0] + (1 - a) * X[:, 1]\n", + " Y = X[:, 0] + X[:, 1] + X[:, 2]\n", + " model = LinearRegression()\n", + " model.fit(X[:, :2], Y)\n", + " r2 = r2_score(Y, model.predict(X[:, :2]))\n", + " res.append(dict(alpha=a, c1=model.coef_[0], c2=model.coef_[1], r2=r2))\n", + "\n", + "import pandas\n", + "\n", + "df = pandas.DataFrame(res)\n", + "df = df.set_index(\"alpha\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 1.000
Model: OLS Adj. R-squared: 1.000
Method: Least Squares F-statistic: 2.212e+33
Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00
Time: 10:56:29 Log-Likelihood: 33713.
No. Observations: 1000 AIC: -6.742e+04
Df Residuals: 997 BIC: -6.740e+04
Df Model: 3
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 1.0000 2.42e-17 4.14e+16 0.000 1.000 1.000
x2 1.0000 2.39e-17 4.18e+16 0.000 1.000 1.000
x3 1.0000 1.73e-17 5.78e+16 0.000 1.000 1.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 4.249 Durbin-Watson: 2.031
Prob(Omnibus): 0.119 Jarque-Bera (JB): 4.338
Skew: -0.107 Prob(JB): 0.114
Kurtosis: 3.242 Cond. No. 1.40


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 1.000\n", - "Model: OLS Adj. R-squared: 1.000\n", - "Method: Least Squares F-statistic: 2.212e+33\n", - "Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00\n", - "Time: 10:56:29 Log-Likelihood: 33713.\n", - "No. Observations: 1000 AIC: -6.742e+04\n", - "Df Residuals: 997 BIC: -6.740e+04\n", - "Df Model: 3 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 1.0000 2.42e-17 4.14e+16 0.000 1.000 1.000\n", - "x2 1.0000 2.39e-17 4.18e+16 0.000 1.000 1.000\n", - "x3 1.0000 1.73e-17 5.78e+16 0.000 1.000 1.000\n", - "==============================================================================\n", - "Omnibus: 4.249 Durbin-Watson: 2.031\n", - "Prob(Omnibus): 0.119 Jarque-Bera (JB): 4.338\n", - "Skew: -0.107 Prob(JB): 0.114\n", - "Kurtosis: 3.242 Cond. No. 1.40\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = OLS(Y,X[:, :3])\n", - "results = model.fit()\n", - "results.summary()" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+oAAAGKCAYAAACb9buhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACoV0lEQVR4nOzdeVxU9frA8c/MMAyLLLKjoii4ay4o5lJSklpm2WK7Ci5Zv6zMbl0tt7Ky7jW1W6ZZKlbXm9pii2W5ZqaJa67gLoqsouzMwMz5/TEwiqCCAmeA5/16fV8zc+YszxmZ43nmu2kURVEQQgghhBBCCCGEXdCqHYAQQgghhBBCCCEukURdCCGEEEIIIYSwI5KoCyGEEEIIIYQQdkQSdSGEEEIIIYQQwo5Ioi6EEEIIIYQQQtgRSdSFEEIIIYQQQgg7Iom6EEIIIYQQQghhRyRRF0IIIYQQQggh7Igk6kIIIYQQQghxmR9++IH33nuPwsJCtUMRVSwzM5M333yTP/74Q+1QrkkSdSGEEKKOKyoq4tVXXyUoKAitVsuQIUMAyMnJYfTo0QQEBKDRaBg/fjynTp1Co9EQExNTqWPExMSg0Wg4depUlccvhKgc+c7fnJ07d/LYY48RGhqKXq+v9PbBwcHce++9VRqTRqNh+vTpttd1+fOvbqNGjeKXX36hW7duaodyTZKoi3qj5IJWUhwcHGjcuDFRUVEkJiba1rNYLMTExHDfffcRFBSEq6srHTp04K233qKgoEDFMxBCiBuzePFi/v3vf/Pwww+zdOlSXnrpJQDeeecdYmJiePbZZ/niiy8YNmyYypFeW15eHtOnT2fTpk1qhyKEXZPv/I27ePEijzzyCO+++y4PPfRQjR1XbYcOHWL69Ol1PvH/4IMP2LdvHz/++CPOzs5qh3NNDmoHIERNe/PNN2nevDkFBQX89ddfxMTEsGXLFg4cOICTkxN5eXlER0dz66238swzz+Dn58e2bduYNm0a69evZ8OGDWg0GrVPQwghKmzDhg00btyYOXPmlFl+6623Mm3aNNsyRVHIz8+vdC3SsGHDeOyxxzAYDFUSc3ny8vJ44403AIiIiKi24whR28l3/sbt3buXyZMnM3LkyBo5nr04dOgQb7zxBhEREQQHB6sdTrUwmUzk5uayZs0afHx81A7nuiRRF/XO3XffbWvqMnr0aHx8fHjvvff44YcfeOSRR3B0dOTPP/+kV69etm3GjBlDcHCwLVmPjIxUK3whhKi01NRUPD09y13erl27Uss0Gg1OTk6VPoZOp0On091oiEKIKiTf+RsXEREhPwReh6IoFBQU2H2N9JUcHR157bXX1A6jwqTpu6j3brvtNgCOHz8OWL/ElyfpJR544AEADh8+XHPBCSHqpMTEREaNGkWjRo0wGAw0b96cZ599FpPJBMCJEycYOnQoXl5euLi4cOutt7J69eoy+zEajUybNo3Q0FAMBgNBQUG8+uqrGI1GAFvf040bN3Lw4EFb159Nmzah0Wg4efIkq1evti0/derUVfurxsXF8cgjj+Dr64uzszOtW7fm9ddft71/tf6Sv/zyC7fddhuurq64ubkxaNAgDh48WGqdqKgoGjRoQGJiIkOGDKFBgwb4+vryj3/8A7PZbDsXX19fAN544w1bzCV9NpOTk4mOjqZJkyYYDAYCAwO5//7763wzTmH/rvd9B/nO28N3/nqf7eW+/PJLwsPDcXFxoWHDhtx+++389ttvZdbbsmUL4eHhODk50aJFCz7//PMy61y8eJHx48cTFBSEwWAgNDSU9957D4vFckPnUZHP/0oxMTEMHToUgDvuuKPU3w1c6nP/66+/0q1bN5ydnfnkk0+Aiv3tlvz9rVixgrfffpsmTZrg5OREv379OHbsWJl4Fi5cSEhICM7OzoSHh/PHH3+U+QHlan9/Jce6srvE9u3bGThwIB4eHri4uNC3b1/+/PPPMsdOTExk5MiR+Pv7YzAYaN++PYsXL77m51ddpEZd1HslX/CGDRtec73k5GSAWtFURghhv86dO0d4eDgXL17k6aefpk2bNiQmJvL111+Tl5fHhQsX6NWrF3l5ebzwwgt4e3uzdOlS7rvvPr7++mvbj4YWi4X77ruPLVu28PTTT9O2bVv279/PnDlzOHLkCKtWrcLX15cvvviCt99+m5ycHGbOnAlA27Zt+eKLL3jppZdo0qQJL7/8MgC+vr6kpaWViXnfvn3cdttt6PV6nn76aYKDgzl+/Dg//vgjb7/99lXP9YsvvmDEiBEMGDCA9957j7y8PObPn0+fPn3Ys2dPqeaVZrOZAQMG0KNHD2bNmsW6det4//33CQkJ4dlnn8XX15f58+fz7LPP8sADD/Dggw8CcMsttwDw0EMPcfDgQZ5//nmCg4NJTU1l7dq1JCQk1NlmnML+Xe/77ujoSEpKinznVf7OV+SzLfHGG28wffp0evXqxZtvvomjoyPbt29nw4YN9O/f37besWPHePjhhxk1ahQjRoxg8eLFREVFERYWRvv27QFr0/6+ffuSmJjI2LFjadq0KVu3bmXSpEkkJSUxd+7cSp1HZT7/y91+++288MIL/Oc//+G1116jbdu2ALZHgPj4eB5//HHGjh3LmDFjaN26dYX/dku8++67aLVa/vGPf5CZmcm//vUvnnzySbZv325bZ9GiRYwdO5ZevXoxfvx4Tpw4wX333YeXlxdBQUGV+jxKbNiwgbvvvpuwsDCmTZuGVqtlyZIl3Hnnnfzxxx+Eh4cDkJKSwq233opGo2HcuHH4+vryyy+/MGrUKLKyshg/fvwNHf+GKULUE0uWLFEAZd26dUpaWppy5swZ5euvv1Z8fX0Vg8GgnDlz5prbR0ZGKu7u7sqFCxdqJmAhRJ00fPhwRavVKjt27CjznsViUcaPH68Ayh9//GFbnp2drTRv3lwJDg5WzGazoiiK8sUXXyharbbUeoqiKAsWLFAA5c8//7Qt69u3r9K+ffsyx2vWrJkyaNCgUstOnjypAMqSJUtsy26//XbFzc1NOX36dJl4S5RcY0+ePGmL2dPTUxkzZkypbZKTkxUPD49Sy0eMGKEAyptvvllq3S5duihhYWG212lpaQqgTJs2rdR6Fy5cUADl3//+d5lzFEJN1/u+K4oi3/nLqPWdr+hne/ToUUWr1SoPPPCA7d+lxOWfTbNmzRRA2bx5s21ZamqqYjAYlJdfftm2bMaMGYqrq6ty5MiRUvuaOHGiotPplISEBNuyKz+Hm/n8y7Ny5UoFUDZu3FjmvZLzWbNmTanlFf3b3bhxowIobdu2VYxGo23dDz74QAGU/fv3K4qiKCaTSfHz81M6d+5car2FCxcqgNK3b9+rnn+JkmOVnIfFYlFatmypDBgwoNS/UV5entK8eXPlrrvusi0bNWqUEhgYqKSnp5fa52OPPaZ4eHgoeXl51/gEq540fRf1TmRkJL6+vgQFBfHwww/j6urKDz/8QJMmTa66zTvvvMO6det49913y+3zJYQQFWGxWFi1ahWDBw8ud1oYjUbDzz//THh4OH369LEtb9CgAU8//TSnTp3i0KFDAKxcuZK2bdvSpk0b0tPTbeXOO+8EYOPGjVUSc1paGps3b2bkyJE0bdq0TLxXs3btWi5evMjjjz9eKj6dTkePHj3Kje+ZZ54p9fq2227jxIkT143R2dkZR0dHNm3axIULFyp4ZkJUr4p83wH5zl9Gre98RT/bVatWYbFYmDp1Klpt6TTqys+mXbt2tu6VYG290Lp161Lnt3LlSm677TYaNmxY6riRkZGYzWY2b95c4XO4kc+/Mpo3b86AAQNKLavo326J6OhoHB0dba9LPp+Sz2Tnzp2kpqbyzDPPlFovKioKDw+PG4p77969HD16lCeeeILz58/bPpfc3Fz69evH5s2bsVgsKIrCN998w+DBg1EUpdRnOGDAADIzM9m9e/cNxXCjpOm7qHfmzZtHq1atyMzMZPHixWzevPmaI5YuX76cyZMnM2rUKJ599tkajFQIUdekpaWRlZVFhw4drrrO6dOn6dGjR5nlJU0QT58+TYcOHTh69CiHDx+29eG8UmpqapXEXHIDda2Yy3P06FEA243uldzd3Uu9dnJyKnMuDRs2rNBNuMFg4L333uPll1/G39+fW2+9lXvvvZfhw4cTEBBQqbiFqCoV+b6DfOcvp9Z3vqKf7fHjx9FqtWUG5CvPlT9yQNnzO3r0KPv27auSf9PKfv6V1bx58zLLKvq3W+LKz6Sk22nJZ3L69GkAWrZsWWo9vV5PixYtbijuks9lxIgRV10nMzOTwsJCLl68yMKFC1m4cGG561XVd6yiJFEX9U54eLjtl+0hQ4bQp08fnnjiCeLj42nQoEGpddeuXcvw4cMZNGgQCxYsUCNcIYQol8VioWPHjsyePbvc92+0L19VKRkI6Ysvvij3xtnBofQtyM2OHj1+/HgGDx7MqlWr+PXXX5kyZQozZ85kw4YNdOnS5ab2LYQ9kO98aVX5na+Oz/Zq56coSqnj3nXXXbz66qvlrtuqVasKH6+yn39lVcUI7xX5TCrqaq07SgYjLFHyufz73/+mc+fO5W7ToEEDzp8/D8BTTz111aS+ZHyEmiKJuqjXdDodM2fO5I477uCjjz5i4sSJtve2b9/OAw88QLdu3VixYsVNX+CEEMLX1xd3d3cOHDhw1XWaNWtGfHx8meVxcXG29wFCQkL4+++/6dev3zWbo96sklqMa8VcnpCQEAD8/PyqbErL651nSEgIL7/8Mi+//DJHjx6lc+fOvP/++3z55ZdVcnwhKqMi33eQ7/y11NR3vqKfbUhICBaLhUOHDl016avscXNycqrk87rZz/9G/qYq+rdbmf2BtRb88pYBhYWFnDx5kk6dOtmWldTGX7x4sdQ+SmrlS5R8Lu7u7tf8XHx9fXFzc8NsNtvNNMzSR13UexEREYSHhzN37lwKCgoA6xRsgwYNIjg4mJ9++qnWzRMphLBPWq2WIUOG8OOPP7Jz584y7yuKwj333ENsbCzbtm2zLc/NzWXhwoUEBwfbmlw+8sgjJCYm8umnn5bZT35+Prm5uVUSs6+vL7fffjuLFy8mISGhTLxXM2DAANzd3XnnnXcoLCws8355I01fj4uLC1D2xiwvL892/S4REhKCm5tbuVMrCVETKvJ9B+Q7fw019Z2v6Gc7ZMgQtFotb775Zpnp026kVviRRx5h27Zt/Prrr2Xeu3jxIkVFRRXe181+/q6urrbjVlRF/3Yrqlu3bvj6+rJgwYJS0xfGxMSUiaskAb+8H7/ZbC7TbD0sLIyQkBBmzZpFTk5OmWOWfC46nY6HHnqIb775ptwfqW7k7/dmSRWhEMArr7zC0KFDiYmJ4cknn2TAgAFcuHCBV155pcxckCEhIfTs2VOlSIUQtd0777zDb7/9Rt++fW3TACUlJbFy5Uq2bNnCxIkT+d///sfdd9/NCy+8gJeXF0uXLuXkyZN88803tgGMhg0bxooVK3jmmWfYuHEjvXv3xmw2ExcXx4oVK2zz3VaF//znP/Tp04euXbvy9NNP07x5c06dOsXq1avZu3dvudu4u7szf/58hg0bRteuXXnsscfw9fUlISGB1atX07t3bz766KNKxeHs7Ey7du1Yvnw5rVq1wsvLiw4dOlBUVES/fv145JFHaNeuHQ4ODnz33XekpKTw2GOPVcEnIMSNud733dPTU77z13Cz3/mYmBiio6NZsmQJUVFRVz1ORT/b0NBQXn/9dWbMmMFtt93Ggw8+iMFgYMeOHTRq1Mg2HV5FvfLKK/zwww/ce++9tqnbcnNz2b9/P19//TWnTp2q8LTAN/v5d+7cGZ1Ox3vvvUdmZiYGg4E777wTPz+/q25T0b/ditLr9bz11luMHTuWO++8k0cffZSTJ0+yZMmSMn3U27dvz6233sqkSZPIyMjAy8uLr776qsyPG1qtls8++4y7776b9u3bEx0dTePGjUlMTGTjxo24u7vz448/Atbp4zZu3EiPHj0YM2YM7dq1IyMjg927d7Nu3ToyMjIqdT43rUbHmBdCRSXTOJQ3RYrZbFZCQkKUkJAQ5dixYwpw1TJixIiaD14IUaecPn1aGT58uG16yBYtWijPPfecbTqa48ePKw8//LDi6empODk5KeHh4cpPP/1UZj8mk0l57733lPbt2ysGg0Fp2LChEhYWprzxxhtKZmambb2bnapJURTlwIEDygMPPGCLqXXr1sqUKVNs719rqpwBAwYoHh4eipOTkxISEqJERUUpO3futK0zYsQIxdXVtUx806ZNU668Vdm6dasSFhamODo62qYrSk9PV5577jmlTZs2iqurq+Lh4aH06NFDWbFiRZl9ClHTrvd9VxT5zpeo6u/8hx9+WO60YuWp6GerKIqyePFipUuXLrb1+vbtq6xdu9b2fnmfs6JY/10un2JMUazTmU2aNEkJDQ1VHB0dFR8fH6VXr17KrFmzFJPJZFuv5NxL3MznfzWffvqp0qJFC0Wn05Wa4uxq56MoFfvbLZkybeXKlaWWX+1v7+OPP1aaN2+uGAwGpVu3bsrmzZvL/eyOHz+uREZGKgaDQfH391dee+01Ze3ateVOM7dnzx7lwQcfVLy9vRWDwaA0a9ZMeeSRR5T169eXWi8lJUV57rnnlKCgIEWv1ysBAQFKv379lIULF17386tqGkW5gXYaQgghhBBCCGHHHnnkEU6dOkVsbKzaoYibFBERAcCmTZtUjaMmSdN3IYQQQgghRJ2iKAqbNm2SwSRFrSWJuhBCCCGEEKJO0Wg0NT7vtRBVSUZ9F0IIIYQQQggh7Ij0URdCCCGEEEIIIeyI1KgLIYQdmTlzJt27d8fNzQ0/Pz+GDBlCfHz8NbeJiYlBo9GUKk5OTjUUsRBCCCGEqGqSqAshhB35/fffee655/jrr79Yu3YthYWF9O/fn9zc3Gtu5+7uTlJSkq2cPn26hiIWQgghhBBVTQaTE0IIO7JmzZpSr2NiYvDz82PXrl3cfvvtV91Oo9EQEBBQ3eEJIYQQQogaUG8SdYvFwrlz53Bzc0Oj0agdjhCiFlIUhezsbBo1aoRWWzMNkjIzMwHw8vK65no5OTk0a9YMi8VC165deeedd2jfvv1V1zcajRiNRttri8VCRkYG3t7eco0UQlSaGtfHmiL3kEKIm3Gj18d6M5jc2bNnCQoKUjsMIUQdcObMGZo0aVLtx7FYLNx3331cvHiRLVu2XHW9bdu2cfToUW655RYyMzOZNWsWmzdv5uDBg1eNc/r06bzxxhvVFboQop6qqetjTZJ7SCFEVajs9bHeJOqZmZl4enpy5swZ3N3d1Q5HCFELZWVlERQUxMWLF/Hw8Kj24z377LP88ssvbNmypVIX9sLCQtq2bcvjjz/OjBkzyl3nyhr1zMxMmjZtKtdIIcQNqenrY02Se0ghxM240etjvWn6XtJUyd3dXS6yQoibUhNNH8eNG8dPP/3E5s2bK107pdfr6dKlC8eOHbvqOgaDAYPBUGa5XCOFEDejLjYNl3tIIURVqOz1sW51IhJCiFpOURTGjRvHd999x4YNG2jevHml92E2m9m/fz+BgYHVEKEQQgghhKhu9aZGXQghaoPnnnuOZcuW8f333+Pm5kZycjIAHh4eODs7AzB8+HAaN27MzJkzAXjzzTe59dZbCQ0N5eLFi/z73//m9OnTjB49WrXzEEIIIYQQN04SdSGEsCPz588HICIiotTyJUuWEBUVBUBCQkKpUUMvXLjAmDFjSE5OpmHDhoSFhbF161batWtXU2ELIYQQQogqJIn6ZRRFoaioCLPZrHYoNUqn0+Hg4FAn+5UJUdtUZHzPTZs2lXo9Z84c5syZU00RXWI2myksLKz249gjuU4KIYQQV1df86gSer0enU5XpfuURL2YyWQiKSmJvLw8tUNRhYuLC4GBgTg6OqodihDCDuXk5HD27NkK/ZBQV8l1UgghhCirvudRYB0orkmTJjRo0KDK9imJOta5ik+ePIlOp6NRo0Y4OjrWm1oTRVEwmUykpaVx8uRJWrZsWapJrRBCmM1mzp49i4uLC76+vvXm+lhCrpNCCCFE+epzHlVCURTS0tI4e/YsLVu2rLKadUnUsf4KZLFYCAoKwsXFRe1wapyzszN6vZ7Tp09jMplwcnJSOyQhhB0pLCxEURR8fX1tA9rVN3KdFEIIIcqq73lUCV9fX06dOkVhYWGVJepSJXCZ+lxDUp/PXQhRMfXtF/IryXVSCCGEKF99/z+yOu6R6vcnKoQQQgghhBBC2BlJ1IUQ9VJ2QSFz1x2h0GxROxQhhKiUY6nZZOSa1A5DCCFENZJEvY7JyMjg+eefp3Xr1jg7O9O0aVNeeOEFMjMz1Q5NCLtRUGhmzOc7mbvuKBO/2a92OEIlb7/9Nr169cLFxQVPT0+1wxGiQg4kZjJw7h8888UutUMRQog6yx7uESRRr2POnj3LuXPnmDVrFgcOHCAmJoY1a9YwatQotUMTwi4UmS2MW7aHv05k0MDgQHTvYLVDEioxmUwMHTqUZ599Vu1QhKiwr3edpciisCvhAsai+jlfsRBCVDd7uEeQUd9ruYiICDp06ICDgwNffvklHTt2ZOPGjbb3Q0JCePvtt3nqqacoKirCwUH+yUX9ZbEovPrNPtYdTsHRQctnI7rRobGH2mGJamSxWJg1axYLFy7kzJkz+Pv7M3bsWF5//XXeeOMNAGJiYtQNUogKKjRb+PHvcwCYLQrHUnNo30iuYUIIcSPs/R5BsrarUBSF/EJ1fql21usqNXLg0qVLefbZZ/nzzz/LfT8zMxN3d3dJ0kW9pigKb/50iG93J6LTavj4ia7c2sJb7bBqpdp0fZw0aRKffvopc+bMoU+fPiQlJREXF1eNEQpRfbYcS+f8ZX3T45KyJVEXQtgVuUeoOpK5XUV+oZl2U39V5diH3hyAi2PF/2latmzJv/71r3LfS09PZ8aMGTz99NNVFZ4QtdJ/1h8jZuspAGYNvYXIdv7qBlSL1ZbrY3Z2Nh988AEfffQRI0aMAKytjPr06VOdIQpRbb7fkwiAVgMWBeKSs1SOSAghSpN7hKojfdTrgLCwsHKXZ2VlMWjQINq1a8f06dNrNigh7EjMnyeZs+4IANMHt+OBLk1UjkjUhMOHD2M0GunXr5/aoQhx0/JMRfx2KAWAh8Os17C45Gw1QxJCiFqrNtwjSI36VTjrdRx6c4Bqx64MV1fXMsuys7MZOHAgbm5ufPfdd+j1+qoKT4ha5bs9Z5n+4yEAXopsRVTv5ipHVPvVluujs7NzNUYiRM1aeyiFPJOZZt4uPNq9KSt2npVEXQhhd+QeoercUI36vHnzCA4OxsnJiR49ehAbG3vN9efOnWubLiwoKIiXXnqJgoKCSu9z27Zt3Hnnnbi6uuLu7s7tt99Ofn7+jZzCdWk0GlwcHVQplelbUZ6srCz69++Po6MjP/zwA05OTlX0qQhRu6w7lMI/Vu4DILp3MC/0C1U5orqhtlwfW7ZsibOzM+vXr6/GT0OImrGquNn7/Z0a0SbADYC0bCPnc4xqhiWEEKXIPULVqXSN+vLly5kwYQILFiygR48ezJ07lwEDBhAfH4+fn1+Z9ZctW8bEiRNZvHgxvXr14siRI0RFRaHRaJg9e3aF97lt2zYGDhzIpEmT+PDDD3FwcODvv/9Gq5XW+5crSdLz8vL48ssvycrKIivL2ofN19cXna5ytfVC1Fbbjp/n/5btxmxReLBrY6YManfTP4KJ2sXJyYl//vOfvPrqqzg6OtK7d2/S0tI4ePAgo0aNIiEhgYyMDBISEjCbzezduxeA0NBQGjRooG7wQlzmfI6RzUfTAbi/S2NcDQ4083bh9Pk84pOz6RVqUDlCIYSoXWrDPUKlE/XZs2czZswYoqOjAViwYAGrV69m8eLFTJw4scz6W7dupXfv3jzxxBMABAcH8/jjj7N9+/ZK7fOll17ihRdeKHWM1q1bVzb8Om/37t22zzY0tHTt4cmTJwkODlYhKiFq1v6zmYz5fCemIguRbf3510O3oNVKkl4fTZkyBQcHB6ZOncq5c+cIDAzkmWeeAWDq1KksXbrUtm6XLl0A2LhxIxEREWqEK0S5Vu9PwmxRuKWJByG+1hvE1v5unD6fx+HkbHqF+qgcoRBC1D72fo9Qqepok8nErl27iIyMvLQDrZbIyEi2bdtW7ja9evVi165dtqbsJ06c4Oeff+aee+6p8D5TU1PZvn07fn5+9OrVC39/f/r27cuWLVuuGqvRaLTVJl9eq1zXbNq0iblz59peR0REoChKuUWSdFEfHEvNYcSSWHKMRfRs4c1HT3TBQSctb+orrVbL66+/zqlTpzCZTJw+fZpJkyYB1rlRy7tWSpIu7I2t2XvnxrZlbQLdAYhLqpv3N0IIUd3s/R6hUnev6enpmM1m/P1LT2vk7+9PcnJyuds88cQTvPnmm/Tp0we9Xk9ISAgRERG89tprFd7niRMnAJg+fTpjxoxhzZo1dO3alX79+nH06NFyjztz5kw8PDxsJSgoqDKnKoSohRIv5jNs0XYyck3c0sSDT0d0w6mSgzMKIYQ9STifx+6Ei2g1MLhToG152+J+6vEpMqCcEELURdVezbRp0ybeeecdPv74Y3bv3s23337L6tWrmTFjRoX3YbFYABg7dizR0dF06dKFOXPm0Lp1axYvXlzuNpMmTSIzM9NWzpw5UyXnI4SwT+k5RoZ9tp2kzAJCfF2JiQ6ngUEmthBC1G7f77XWpvcO9cHP7dLgsK1LEvXkbMwWRZXYhBBCVJ9KJeo+Pj7odDpSUlJKLU9JSSEgIKDcbaZMmcKwYcMYPXo0HTt25IEHHuCdd95h5syZWCyWCu0zMND6C3K7du1KrdO2bVsSEhLKPa7BYMDd3b1UEULUTVkFhYxYHMuJ9Fwaezrz5egeeLk6qh2WEELcFEVRWLW3bLN3gGberjjptRiLLJw6n6tGeLXSu+++i0ajYfz48WqHIoQQ11SpRN3R0ZGwsLBSw9hbLBbWr19Pz549y90mLy+vzMjsJSOPK4pSoX0GBwfTqFEj4uPjS+3nyJEjNGvWrDKnIISoYwoKzYyO2cnBc1n4NHDki1HhBHrY/9yYQghxPQcSszielovBQcuA9qW7COq0Glr7X6pVF9e3Y8cOPvnkE2655Ra1QxFCiOuqdNP3CRMm8Omnn7J06VIOHz7Ms88+S25urm3E9uHDh9s64QMMHjyY+fPn89VXX3Hy5EnWrl3LlClTGDx4sC1hv94+NRoNr7zyCv/5z3/4+uuvOXbsGFOmTCEuLo5Ro0ZVxecghKiFCs0W/u+/u4k9lYGbwYGlI8Np4SvTagkh6oaS2vTIdv64OenLvF/S/F0GlLu+nJwcnnzyST799FMaNmyodjhCCHFdle7A+eijj5KWlsbUqVNJTk6mc+fOrFmzxjYYXEJCQqka9MmTJ6PRaJg8eTKJiYn4+voyePBg3n777QrvE2D8+PEUFBTw0ksvkZGRQadOnVi7di0hISE3c/6lKEr97eNVn89d1E4Wi8I/Vv7NhrhUnPRaFkd3p30jD7XDEkKIKmG2KPz49zkAhlzR7L1EmwBrt77DUqN+Xc899xyDBg0iMjKSt956S+1whBDium5opKVx48Yxbty4ct/btGlT6QM4ODBt2jSmTZt2w/ssMXHixHLnar9Zer31V+q8vDycnetnk9m8vDzg0mchhD1TFIXpPx7k+73ncNBqmP9kGN2DvdQOSwghqsy24+dJzTbi6aKnbyvfctdpE1hco54sNerX8tVXX7F792527NhRofWNRiNGo9H2uq5O8SuEsG8yJDLWPvOenp6kpqYC4OLigkajUTmqmqEoCnl5eaSmpuLp6WnrjiCEPZuz9gifbzuNRgPvP9KJO9r4qR2SEEJUqZJm7/d0DMTRofyeiiU16mcy8skxFslMF+U4c+YML774ImvXrsXJyen6G2Cd4veNN96o5siEEOLa5IperGSE+ZJkvb7x9PS86sj9QtiTz/44wX82HAPgzfs7lBkJWQgharuCQjNrDiQDV2/2DuDl6oifm4HUbCPxydmENZO+11fatWsXqampdO3a1bbMbDazefNmPvroI4xGY5lKikmTJjFhwgTb66ysLIKCgmosZiGEAEnUbTQaDYGBgfj5+VFYWKh2ODVKr9dLTbqoFb7edZa3Vh8G4JUBrRl2q8z6IISoe9YfTiXHWERjT2e6XSf5bhPoTmp2GnHJWZKol6Nfv37s37+/1LLo6GjatGnDP//5z3LvfwwGAwaDoaZCFEKIckmifgWdTidJqxB26NeDyfzzm30AjLmtOf8XUXUDSYr659SpU8yYMYMNGzaQnJxMo0aNeOqpp3j99ddxdHRUOzxRz12aO70RWu21u+K1DXBj85E0maLtKtzc3OjQoUOpZa6urnh7e5dZLoQQYD/3CJKoCyHs3tZj6Ty/bA9mi8Ij3Zrw2j1t6804EqJ6xMXFYbFY+OSTTwgNDeXAgQOMGTOG3NxcZs2apXZ4oh67mGdiU7y1G96QLtfv2nNpijZJ1IUQoirYyz2CJOpCCLv295mLjPl8JyazhYHtA3jngY6SpIsKs1gszJo1i4ULF3LmzBn8/f0ZO3Ysr7/+OgMHDrSt16JFC+Lj45k/f74k6kJVP+9PptCs0DbQnVb+btdd/9IUbVkoiiLXxwq4coYiIUT9ZO/3CJKoCyHs1tGUbKKWxJJrMtM71JsPHu+Mg6780Y9FDVIUKMxT59h6F6hEIjJp0iQ+/fRT5syZQ58+fUhKSiIuLq7cdTMzM/Hykmn+hLpKmr0P6dyoQuuH+LnioNWQXVBEUmYBjTzr5zSzQgg7IfcIVUYSdSGEXTqTkcewRbFcyCukc5AnC4d1w+Ag40fYhcI8eKdiSUSVe+0cOLpWaNXs7Gw++OADPvroI0aMGAFASEgIffr0KbPusWPH+PDDD6U2Xagq8WI+sScz0Gjgvgom6gYHHS18XTmSkkNccpYk6kIIdck9QpWRqikhhN1JyzYybNF2krMKaOXfgCVR3XGV+YFFJR0+fBij0Ui/fv2uuV5iYiIDBw5k6NChjBkzpoaiE6Ks74tr03s09yLQo+IJt635u/RTF0KICqkN9why5yuEsCvZBYVELYnl1Pk8mjR05otRPWjoKqNw2xW9i/VXa7WOXUHOztdPdM6dO8cdd9xBr169WLhw4c1EJsRN+36P9Xt1rbnTy9Mm0I0f/kZGfhdCqE/uEaqMJOpCCLthLDLzzJe7OHguC29XR74c1QN/dye1wxJX0mgq3LRMTS1btsTZ2Zn169czevToMu8nJiZyxx13EBYWxpIlS9BqpZGZUM/hpCziU7Jx1Gm5u2NgpbZtUzLye3JWdYQmhBAVJ/cIVUYSdSGEXbBYFF5e8Td/HjuPq6OOmOhwgn3s/0Iv7JeTkxP//Oc/efXVV3F0dKR3796kpaVx8OBBBg4cSEREBM2aNWPWrFmkpaXZtgsICFAxalFflQwid0cbXzyc9ZXatqTp+/G0XIxFZhnPQwghrqM23CNIoi6EUJ2iKMxYfYif9iWh12lYMCyMjk081A5L1AFTpkzBwcGBqVOncu7cOQIDA3nmmWdYu3Ytx44d49ixYzRp0qTUNoqiqBStqK8sFoUf995Ys3eAQA8n3J0cyCoo4nhqLu0auVd1iEIIUefY+z2CJOpCCNUt+P0ES/48BcCsoZ24raWvugGJOkOr1fL666/z+uuvl3kvKiqq5gMSohyxpzI4l1mAm5MDd7Txq/T2Go2GNgHuxJ7KIC45SxJ1IYSoAHu/R5AOeUIIVX296yzvrbHOWTl5UFvuv4HaJCGEqM1KRnu/p0MgTvoba7beJrCkn7oMKCeEEHWBJOpCCNVsjEvln9/sA2Ds7S0YfVsLlSMSQoiaZSwys3pfEgD3d7nxuYdL+qlLoi6EEHWDJOpCCFXsSbjA//13N2aLwoNdGvPPgW3UDskuzJw5k+7du+Pm5oafnx9DhgwhPj7+ututXLmSNm3a4OTkRMeOHfn5559rIFohxM3aFJ9GVkERAe5O3Nrc+4b3Y6tRT5KR34UQoi6QRF0IUeOOp+UwMmYH+YVm+rby5b2Hb0Gr1agdll34/fffee655/jrr79Yu3YthYWF9O/fn9zc3Ktus3XrVh5//HFGjRrFnj17GDJkCEOGDOHAgQM1GLkQ4kaUNHu/r3Ojm7oOtvK3Juqp2UYyck1VEpsQQgj1SKIuhKhRKVkFDF8Uy4W8Qjo18eDjJ7ui18mlqMSaNWuIioqiffv2dOrUiZiYGBISEti1a9dVt/nggw8YOHAgr7zyCm3btmXGjBl07dqVjz76qAYjF0JUVlZBIesOpwJwf+cbb/YO0MDgQFMvF0DmUxdCiLpA7o6FEDUmM7+QEYtjSbyYT3MfVxZHdcfVIJNPXEtmZiYAXl5eV11n27ZtREZGllo2YMAAtm3bdtVtjEYjWVlZpcr11Pdpy+r7+Yuqt2Z/MqYiCy39GtAu8OZHam8TUNL8XfqpCyFqVn3/P7I6zl8SdSFEjSgoNDPm853EJWfj52bg85HheDcwqB2WXbNYLIwfP57evXvToUOHq66XnJyMv79/qWX+/v4kJydfdZuZM2fi4eFhK0FBQVddV6ezjkJtMtXv5rR5eXkA6PV6lSMRdcWq4mbvQ7o0RqO5+e4/tkRdatSFEDWk5P/Ekv8j66uSe6SSe6aqIFVZQohqZ7YojP9qL7EnM3AzOLB0ZDhBxU00xdU999xzHDhwgC1btlT5vidNmsSECRNsr7Oysq6arDs4OODi4kJaWhp6vR6ttn79xqsoCnl5eaSmpuLp6Vml/wmL+is5s4BtJ84DcF+nm2v2XqJNoIz8LoSoWTqdDk9PT1JTrd14XFxcquSHx9rEYrGQlpaGi4sLDg5Vl15Loi6EqFaKojD1+wOsOZiMo07LwuHdaFsFTTzrunHjxvHTTz+xefNmmjRpcs11AwICSElJKbUsJSWFgICAq25jMBgwGCrWokGj0RAYGMjJkyc5ffp0hbapizw9Pa/5mQpRGT/+fQ5FgW7NGlbZD5clNepHUrIxWxR0MkinEKIGlPzfWJKs10darZamTZtW6Y8UkqgLIarVf9Yf47/bE9BoYO5jnekZcuPTD9UHiqLw/PPP891337Fp0yaaN29+3W169uzJ+vXrGT9+vG3Z2rVr6dmzZ5XF5ejoSMuWLett83e9Xi816aJKlTR7v79L4yrbZzNvV5z0WgoKLZw+n0sL3wZVtm8hhLiakh/0/fz8KCwsVDscVTg6OlZ5i0NJ1IUQ1WbZ9gTmrDsCwJv3teeejoEqR2T/nnvuOZYtW8b333+Pm5ubrZ+5h4cHzs7OAAwfPpzGjRszc+ZMAF588UX69u3L+++/z6BBg/jqq6/YuXMnCxcurNLYtFotTk5OVbpPIeqjY6nZHDyXhYNWw71VeF3UaTW08ndj39lM4pKzJVEXQtQonU4nP2pXoRtK++fNm0dwcDBOTk706NGD2NjYa64/d+5cWrdujbOzM0FBQbz00ksUFBTc0D4VReHuu+9Go9GwatWqGwlfCFEDfj2YzORV+wF4/s5QhvUMVjegWmL+/PlkZmYSERFBYGCgrSxfvty2TkJCAklJSbbXvXr1YtmyZSxcuJBOnTrx9ddfs2rVqmsOQCeEUM+qPecAiGjtS0NXxyrd96UB5aSfuhBC1GaVrlFfvnw5EyZMYMGCBfTo0YO5c+cyYMAA4uPj8fPzK7P+smXLmDhxIosXL6ZXr14cOXKEqKgoNBoNs2fPrvQ+586dW+8GKBCittlxKoMX/rcHiwKPdQ9iwl2t1A6p1qjI9B6bNm0qs2zo0KEMHTq0GiISQlQlRVH4/u/iZu+dq67Ze4nWAcUDyiXJyO9CCFGbVbpGffbs2YwZM4bo6GjatWvHggULcHFxYfHixeWuv3XrVnr37s0TTzxBcHAw/fv35/HHHy9VY17Rfe7du5f333//qscSQqgvPjmbUTE7MBZZiGzrz1tDOsiPa0IIUWx3wgXOZOTj6qgjsq3/9TeopLZSoy6EEHVCpRJ1k8nErl27iIyMvLQDrZbIyEi2bdtW7ja9evVi165dtsT8xIkT/Pzzz9xzzz2V2mdeXh5PPPEE8+bNk1F3hbBTiRfzGbE4lqyCIsKaNeTDx7vgoKtfU3kJIcS1lDR7H9AhAGfHqu/L2bo4UU/IyCPXWFTl+xdCCFEzKtX0PT09HbPZjL9/6V+A/f39iYuLK3ebJ554gvT0dPr06YOiKBQVFfHMM8/w2muvVWqfL730Er169eL++++vUKxGoxGj0Wh7nZUlTcCEqE4X80yMWBxLclYBLf0asGhEt2q5CRVCiNqq0Gxh9X7r+BJDqqHZO4B3AwN+bgZSs43Ep2TTtWnDajmOEEKI6lXtVV2bNm3inXfe4eOPP2b37t18++23rF69mhkzZlR4Hz/88AMbNmxg7ty5Fd5m5syZeHh42EpQUNANRC+EqIh8k5mRMTs4lppDoIcTS0eG4+lStQMkCSFEbffH0TQyck34NDDQqxqnqiypVY9LkubvQghRW1UqUffx8UGn05GSklJqeUpKylWbo0+ZMoVhw4YxevRoOnbsyAMPPMA777zDzJkzsVgsFdrnhg0bOH78OJ6enjg4OODgYG0I8NBDDxEREVHucSdNmkRmZqatnDlzpjKnKoSooCKzhXHLdrM74SLuTg4sHRlOI09ntcMSQgi7811xs/fBnQKrtVtQ20DrgHLxydKaUAghaqtK/S/h6OhIWFgY69evty2zWCysX7+enj17lrtNXl5emcnfS+bXUxSlQvucOHEi+/btY+/evbYCMGfOHJYsWVLucQ0GA+7u7qWKEKJqKYrCa9/tZ31cKgYHLYujutPK303tsIQQwu7kGItYeygZqL5m7yVKpmg7LAPKCSFErVXp6dkmTJjAiBEj6NatG+Hh4cydO5fc3Fyio6MBGD58OI0bN2bmzJkADB48mNmzZ9OlSxd69OjBsWPHmDJlCoMHD7Yl7NfbZ0BAQLk19k2bNqV58+Y3fPJCiJvz/m9HWLHzLFoNfPREV7oFe6kdkhBC2KXfDiZTUGihuY8rtzTxqNZjXWr6noWiKDLzhhBC1EKVTtQfffRR0tLSmDp1KsnJyXTu3Jk1a9bYBoNLSEgoVYM+efJkNBoNkydPJjExEV9fXwYPHszbb79d4X0KIezP0q2n+GjjMQDeeaAjd7WT76sQQlzNqr3WZu/3d25U7YlzqF8DdFoNWQVFJGcVEOgh3ZGEEKK20SiKoqgdRE3IysrCw8ODzMxMaQYvxE36ad85nv/fHhQFJtzVihf6tVQ7pBpRl68jdfnchFBbWraRHu+sw6LApn9EEOzjWu3H7D/nd46k5LAkqjt3tPGr9uPV5WtIXT43IUT1u9FriExwLISolK3H05mw/G8UBYbd2ozn7wxVOyQhhLBrP+07h0WBzkGeNZKkA7QOsN4MHpYB5YQQolaSRF0IUWGHzmUx9vNdmMwW7u4QwPT72kvfRyGEuI6SZu9DOjeqsWOWDCgXLwPKCSFErSSJuhCiQs5k5BG1JJZsYxHhzb2Y82hndFpJ0oUQ4lpOpufy95mL6LQa7u1Uc4l620CZS10IIWozSdSFENd1IdfEiCWxpGYbae3vxqfDu+Gk16kdlhBC2L3v9yYC0CfUB58Ghho7bknT9+NpORiLzDV2XCGEEFVDEnUhxDXlm8yMXLqDE2m5NPJwYunIcDyc9WqHJYQQdk9RFL4vafbepeZq0wEaeTjh5uRAkUXheGpujR5bCCHEzZNEXQhxVUVmC+OW7WZPwkU8nPUsHRlOgIeT2mEJIUStsO9sJifTc3HW6+jfLqBGj63RaGhbXKsenyIDygkhRG0jiboQolyKovD6dwdYH5eKwUHLohHdaOnvpnZYQghRa3y3x9rs/a52/rgaHGr8+G2kn7oQQtRakqgLIco1Z91Rlu88g1YDHz7ehW7BXmqHJIQQtUaR2cJP+9Rp9l6idfHI74dl5HchhKh1JFEXQpTx5V+n+c/6owC8NaQj/dvXbJNNIYSo7f48fp70HBNero7c1tJXlRjalDR9l7nUhRCi1pFEXQhRypoDyUz9/gAAL/ZryRM9mqockRBC1D7fFzd7H9QxEL1Ondutkhr1lCwjGbkmVWIQQghxYyRRF0LY7DiVwQtf7cGiwOPhQYyPbKl2SEIIUevkm8z8ejAZgCFdGqsWRwODA0FezgDESa26EELUKpKoCyEAOJKSzaiYHZiKLES29WfG/R3QaDRqhyWEELXO2sMp5JrMNPVyoWtTT1VjudT8XfqpCyFEbSKJuhCCpMx8RiyOJaugiK5NPfnw8S44qNRUUwgharuSZu/3d26k+g+ebQNk5HchRP2RYyzifI5R7TCqhNyJC1HPZeYVMmJxLEmZBYT4urJoRHecHXVqhyWEELVSRq6J34+kAXB/Z/WavZdoXVyjLk3fhRB13eYjafR8Zz13vv87Zy/kqR3OTZNEXYh6rKDQzJjPd3IkJQd/dwNLR4bT0NVR7bCEEKLWWr0/iSKLQofG7oT6NVA7HNtc6kdScjBbFJWjEUKI6vHFtlNEx+wg21hEZn4hb68+rHZIN00SdSHqKbNFYfxXe4k9lYGbkwNLR4bTpKGL2mEJIUStVtLsfYgd1KYDBHu7YnDQkl9oJiGj9tcwCSHE5YrMFqb/cJAp3x/EbFG4q50/Oq2GXw4k88fRNLXDuymSqAtRDymKwvQfDrLmYDKOOi0Lh3WzDTgkhBDixszfdJydpy+g0cDgTo3UDgcAnVZDK/+SfurS/F0IUXdkFxQy+vOdxGw9BcCrA1uzcFgYw3s2A2D6DwcxFVlUjPDmSKIuRD00b+MxvvjrNBoNzHm0Mz1DvNUOSQghai1FUXhvTRzvrYkD4MV+LfF3d1I5qkvalAwoJyO/CyHqiDMZeTw0fyub4tNw0muZ/2RX/i8iFI1Gw/jIVvg0cOR4Wi4xW0+qHeoNk0RdiHpmxY4zzPrtCADT7m3HoFsCVY5ICCFqL4tFYfKqA8zfdByASXe3YXxkK5WjKq1NoAwoJ4SoO3advsCQeX9yJCUHPzcDK8f24u6Ol+5nPZz1/HNgGwA+WHeUlKwCtUK9KZKoC1GPbIhLYdJ3+wH4v4gQono3VzkiIYSovQrNFl5asZf/bk9Ao4GZD3ZkbN8QtcMqQ2rUhRB1xfd7E3n80784n2uifSN3vh/Xm45NPMqs91DXJnRp6kmuyczMn2vnwHKSqAtRT+xJuMD//Xc3ZovCQ12b8MqA1mqHJIQQtVZBoZlnvtjF93vP4aDV8J/HuvB4eFO1wypXSaKekJFHrrFI5WiEEKLyFEVh9tojvPjVXkxFFu5q58+KsT0J9HAud32tVsOM+zug0cCqvefYfuJ8DUd88yRRF6IeOJ6Ww8iYHRQUWoho7cu7D3VEo9GoHZYQQtRK2QWFjFgcy/q4VAwOWj4d3s1uBo8rj3cDA75uBhQFjqTUr1r1mTNn0r17d9zc3PDz82PIkCHEx8erHZYQohIKCs08/789/Gf9UQDG9m3BJ0+F4WpwuOZ2HRp78ETxD6jTfjhIkbl2DSwniboQdVxKVgHDF8VyIa+QTk08mPdEV/Q6+eoLIcSNyMg18eRn29l+MoMGBgc+HxnOHW381A7ruupr8/fff/+d5557jr/++ou1a9dSWFhI//79yc3NVTs0IUQFpGYX8NjCv/hpXxIOWg3/eugWJt3dFq22YhVO/+jfGk8XPXHJ2fx3e0I1R1u1rv0zhBCiVssqKCRqyQ4SL+YT7O3C4qju1/31UQghRPmSMwsYtmg7R1Nz8HJ1ZGl0eLl9I+1RmwA3/jiaXu+maFuzZk2p1zExMfj5+bFr1y5uv/12laISQlTE4aQsRi/dSeLFfDxd9Mx/MqzSMxU1dHXklQGtef27A8z6LZ5BtwTi08BQTRFXLalWE6KOMhZZ+08eTsrCp4GBz0f2wLuWXJiEEMLeJJzPY+gnWzmamkOAuxMrxt5aa5J0gDYBJSO/168a9StlZmYC4OXlddV1jEYjWVlZpYoQomZtiEvh4flbSbyYTwsfV777v943PJ3wY92b0qGxO9kFRfyreBrN2kASdSHqIItF4eUVf7P1+HlcHXXERHenqbeL2mEJIUStFJ+czcMLtnImI59m3i6sfKYnoX5uaodVKW0CLzV9VxRF5WjUYbFYGD9+PL1796ZDhw5XXW/mzJl4eHjYSlBQUA1GKUT9pigKi7acZPTSneSazPRs4c23/9eL5j6uN7xPnVbDG/dZv/Mrdp5lT8KFqgq3Wt1Qoj5v3jyCg4NxcnKiR48exMbGXnP9uXPn0rp1a5ydnQkKCuKll16ioKD0fHbX2mdGRgbPP/+8bR9NmzblhRdesP0qKoS4RFEUZqw+xE/7ktDrNCwYFkaHxrWn1kcIIezJ3jMXeeSTbaRmG2kT4MbKsT0J8qp9P3yG+jVAp9WQmV9Ici2dU/hmPffccxw4cICvvvrqmutNmjSJzMxMWzlz5kwNRShE/VZotvD6qgPM+OkQFgUeDw/i81HheLo43vS+w5o15OGwJgBM/f4gZov9/2BZ6UR9+fLlTJgwgWnTprF79246derEgAEDSE1NLXf9ZcuWMXHiRKZNm8bhw4dZtGgRy5cv57XXXqvwPs+dO8e5c+eYNWsWBw4cICYmhjVr1jBq1KgbPG0h6q6Fm0+w5M9TAMwa2onbWvqqG5AQQtRSW4+n8+Snf5GZX0iXpp589fSt+Lk7qR3WDTE46GhRXCNVH5u/jxs3jp9++omNGzfSpEmTa65rMBhwd3cvVYQQ1Sszv5DoJTtYtj0BjQYmD2rLOw90rNIBkP85sA1uBgf2J2ayYqf9/wBX6TOfPXs2Y8aMITo6mnbt2rFgwQJcXFxYvHhxuetv3bqV3r1788QTTxAcHEz//v15/PHHS9WYX2+fHTp04JtvvmHw4MGEhIRw55138vbbb/Pjjz9SVCTzgQpRYtWeRGb+Yu17M3lQW+7v3FjliIQQonZaeyiFqCU7yDWZ6R3qzZejelRJrY6a2gQW91NPqj+JuqIojBs3ju+++44NGzbQvHlztUMSQlzh9PlcHvz4T7YcS8fFUcenw7ox+rYWVT6VsK+bgZfuagXAv9bEcTHPVKX7r2qVStRNJhO7du0iMjLy0g60WiIjI9m2bVu52/Tq1Ytdu3bZEvMTJ07w888/c88999zwPsE6GIi7uzsODuWPYC0DgYj65o+jafxj5d8AjO7TnNG3tVA5IiGEqJ2+23OWZ77chanIwl3t/Fk0om7MmHFpirb6c0/03HPP8eWXX7Js2TLc3NxITk4mOTmZ/Px8tUMTQgDbT5xnyLw/OZ6WS6CHE18/04vIdv7VdrzhPZvR2t+NC3mFvP/bkWo7TlWoVKKenp6O2WzG37/0h+fv709ycnK52zzxxBO8+eab9OnTB71eT0hICBEREbam7zeyz/T0dGbMmMHTTz991VhlIBBRnxxIzOSZL3ZRZFEY3KkRr93TVu2QhBCiVvpi2yleWv43ZovCg10bM//JrjjpdWqHVSVKEvX4etT0ff78+WRmZhIREUFgYKCtLF++XO3QhKj3Vu48w1OLtnMhr5BOTTz4/rnetGtUvV1NHHRa3ri/PQD/3X6aA4n2O+ZZtY/6vmnTJt555x0+/vhjdu/ezbfffsvq1auZMWPGDe0vKyuLQYMG0a5dO6ZPn37V9WQgEFFfnMnIszXP7NnCm1lDb0GrrdqmQqLmbN68mcGDB9OoUSM0Gg2rVq265vqbNm1Co9GUKVf7oVMIUT5FUZi38RhTvj8IQFSvYGY93AmHKuwfqbaSpu/HUnMwFVlUjqZmKIpSbomKilI7NCHqLYtF4b01cbzy9T4KzQqDOgayfGzPGhsD5NYW3tzXqREWBab9cNBuZ8KoVDsuHx8fdDodKSkppZanpKQQEBBQ7jZTpkxh2LBhjB49GoCOHTuSm5vL008/zeuvv16pfWZnZzNw4EDc3Nz47rvv0Ov1V43VYDBgMMic0aJuy8g1MWJxLOk51tGIPxkehsGhbtT81Fe5ubl06tSJkSNH8uCDD1Z4u/j4+FIDHvn5+VVHeELUSYqi8O6aOD75/QQAL9wZykt3tary/pFqa+ThhJuTA9kFRRxPy6FtoAySJoSoWXmmIiYs/5s1B60VCs/fGcpLka1qvJLptXvasu5wCrtOX+C7PYk82PXag0yqoVI/Ezs6OhIWFsb69ettyywWC+vXr6dnz57lbpOXl4dWW/owOp01kVAUpcL7zMrKon///jg6OvLDDz/g5FQ7R10Voqrkm8yMWrqDE+m5NPZ0ZunIcNydrv7jlagd7r77bt566y0eeOCBSm3n5+dHQECArVx53RVClM9sUXjtuwO2JP31e9oyoX/rOpekA2g0mnrZ/F0IYR/OZOTx6Cd/seZgMo46LXMe7cTL/Vur0hI0wMOJF/q1BOCdn+PILiis8Riup9Ijo0yYMIERI0bQrVs3wsPDmTt3Lrm5uURHRwMwfPhwGjduzMyZMwEYPHgws2fPpkuXLvTo0YNjx44xZcoUBg8ebEvYr7fPkiQ9Ly+PL7/8stTgcL6+vrb9CFFfFJktPP+/3exJuIiHs56lI7vjX0unDBJVo3PnzhiNRjp06MD06dPp3bu32iEJYfdMRRYmrNjLT/uS0Gpg5oMdebR7U7XDqlZtAtzZceoCh5OzGILMDCKEqD6mIgu7Tl/g9yNp/H4kjcNJ1vzNy9WRhcPC6BbspWp8I3s3Z8WOM5xIz2XuuqNMubedqvFcqdKJ+qOPPkpaWhpTp04lOTmZzp07s2bNGttgcAkJCaVqciZPnoxGo2Hy5MkkJibi6+vL4MGDefvttyu8z927d7N9+3YAQkNDS8Vz8uRJgoODK33iQtRWiqIw5fsDrDucisFBy6IR3Qj1c1M7LKGSwMBAFixYQLdu3TAajXz22WdERESwfft2unbtetXtjEYjRqPR9lpmxhD1Tb7JzP/9dxcb49PQ6zTMfbQLg24JVDusatcmsHjk93o0RZsQouacycizJeZbj6WTazLb3tNooHuwF7Me7kRTbxcVo7RydNAy/b72DF8cS8zWUzzaPYhW/vZzT61R7LX3fBXLysrCw8PDNq2bELXVB+uOMmfdEbQamP9UGAPalz8+hKh6NX0d0Wg0fPfddwwZMqRS2/Xt25emTZvyxRdfXHWd6dOn88Ybb5RZLtdIUR9kFxQyaulOYk9m4KTXsuCpMCJa149xHXadzuCh+dsIcHfir9f6Vdl+6/J9Vl0+NyFuVkGhmb9OnLcl5yfScku979PAkdtb+tK3tS99Qn3wbmB/Y4iN/WInvx5MoWcLb5aN6VHlXZ9u9BpS+ycFFaIe+So2gTnrrHM+vnF/B0nSRbnCw8PZsmXLNdeZNGkSEyZMsL3OysqSaSxFvXA+x8iIJbEcSMzCzeDA4ujudFe5+WVNKqktSs4q4EKuiYaujipHJISoTRRF4Xhari0x337iPMbLZpHQaTWENW1I39a+9G3lS7tAd7ufjWjyoHZsik9j24nzrN6fxL23NFI7JEASdSFqjfWHU3h91QEAxt0RyrBbm6kckbBXe/fuJTDw2k14ZWYMUR8lZebz1GfbOZ6Wi7erI0tHhtOhsYfaYdUoNyc9QV7OnMnIJy45m54h3mqHJISwc9kFhWw9XlxrHp9G4sX8Uu838nCyJea9Qn1q3eDGQV4u/F9EKHPWHeHt1Ye5o7Ufrgb102T1IxBCXNeehAs8t2w3ZovCw2FNeLl/K7VDEtUkJyeHY8eO2V6fPHmSvXv34uXlRdOmTZk0aRKJiYl8/vnnAMydO5fmzZvTvn17CgoK+Oyzz9iwYQO//fabWqcghN3JyDURs/UUS7eeIjO/kEAPJ74c3YMQ3wZqh6aK1v7unMnIJz45SxJ1IUQZiqJwOCmbTUdS+T0+jV2nL1BkudRb2lGnpUcLL/q2sibnoX4Nav1MGWP7tuDr3Wc4k5HPvI3HeHVgG7VDkkRdCHt3Ii2HUUt3UlBooW8rX2Y+2LHWXwzF1e3cuZM77rjD9rqkefqIESOIiYkhKSmJhIQE2/smk4mXX36ZxMREXFxcuOWWW1i3bl2pfQhRX529kMdnf5zkqx0JFBRam2a29ndjUVQ3mjRUfyAjtbQNdGPd4RTiZIo2IQRQaLZw9kI++xMz2VzcpD0t21hqneY+rrbEvEcLL1wc61Ya6aTXMfXe9oz5fCef/nGCh8Oa0ELlH3Pr1icsRB2Tml3AiCWxZOSauKWJBx8/2RW9TubHrssiIiK41hifMTExpV6/+uqrvPrqq9UclRC1S3xyNp/8fpwf/j5nqwXq2NiDZyNCGNA+AJ2d95esbm0CrIMZHZZEXYh6o6DQzNkLeZxKz+PU+VxOn7/0mHgxH7Ol9L2Hs15H71Bv+rby5fZWvjTzdlUp8poT2daPO1r7sjE+jTd+PERMdHdVK8ckURfCTuUYixgZs4MzGfk083ZhcVR3u+gvI4QQ9mrX6QzmbzrOusOptmW9Q715tm8ovUO9pTVSsdYB1gHljiRnY7Eodj/QkxCiYvJNZk5n5HIqPY/T53M5dd76ePp8Hucy87nWXF/Oeh0tfF3pHepDRCtfwoIbYnDQ1VzwdkCj0TB1cHv+PLaZ34+kse5wKne181ctHrnrF8IOmYosPPvlLg4kZlkHPIoOx8cOp7MQQgi1KYrCpvg05m86TuypDMA6V+/A9gE80zeETkGe6gZoh4K9XTA4aMkvNJOQkUewT92vKROirsgxFnEq/fIa8UsJeUqW8ZrbNjA4EOzjQjNvV4K9Sx6tz33dDPJjJtYm/mNub868jcd586eD3NbSBye9Oj9YSKIuhJ1RFIWJ3+zjj6PpOOt1LI7qLjdRQghxhSKzhdX7k5i/6bitr7Vep+Ghrk0Yc3uLejtQXEU46LS08ndjf2ImcclZ8n+MECozWxQu5pk4n2siPcdIRq6J8znW1+dzjJzPMZGaXUBCRh7pOaZr7svDWU+wz+WJ+KVHL1dHScYr4Lk7Qvl2dyJnMvJZ8PtxxkeqM4izJOpC2Jl//RrPt3sS0Wk1fPxUV6kNEkKIyxQUmlm58wyfbD7B2QvWKYJcHXU8eWszRvZuToCHk8oR1g6tA6yJ+uGkbAZ2uPZ0jkKIyrFYFLIKCosT7eJk25Z8G20JeElCfiHPhOUazdKv5O3qSDNvF4K9Xa1J+GW15J4ujtV3YvWEi6MDkwe147llu5m/6TgPdW1CkFfND0AqiboQdmTp1lPM33QcgHcf7Mgdrf1UjkgIIexDZn4hX/51msVbTnI+11qj5O3qSHTvYIbdGoyHS+2at1dtbYr7qcfLgHJClMtYZCa7oIicgiJyjEVkFRSSU1BkXWYsuyzjstrwjFxTqenMKsrTRY+3qyPeDQzFj454uxrwbuCITwMDTb1caOrtUuvmKa+N7ukYQK8Qb7YeP8+Mnw6xcHi3Go9BEnUh7MQv+5OY/uNBAP7RvxVDuwWpHJEQQqgvJauARVtOsmx7AjnGIgAaezoztm8LhoYF4exYvwY7qiptA60jv8clZ6kciRBVw2JRKCgyk28yk19opqDQTJ7J+vpSYl2SeBfakvCs4tc5xiLbsuyCIkxmy03H5ObkcNXE28vVmnyXPG/o4igz+9gRjUbDG/e15+4P/uC3Qylsik8looYr0CRRF8IOxJ7M4MXle1EUeLJHU567I1TtkIQQQlUn0nJYuPkE3+5OtN0wt/Z349mIEAbdEig3tDepZOT30xl55JmK6tycyMI+KIqCschSXMyYSp4XWjCZLRgLLyXV+YVm8k0W8kxFttd5puL3TFe8LllWvNy6j5tPrMvTwOBgLU4OuDlZn7s76csssybc1oTcp4GBhq76ejdqel3T0t+NqF7BfLblJG/8eIieId41+m8qV2UhVHYkJZvRS3dgKrLQv50/b97fQQb6EELUW/vOXmTB78f55UCybSqh7sENeTYihDta+8n1sYr4NDDg08BAeo6RIyk5dJbxUGo1RVEoNCsUmi0Umq1JcKFZobDoitdmC4VFV7w2WzAVWcpuX6RgMpsvS6qLk23b8yuS7yJr4n35+1VRK30jnPRanPU6nPU6nBx1uJUk1QY9DWzJdkmifSnpdndyoEHxOm5ODrg6OqCT6QvrtRcjW7Jq7zlOpueyeMspno0IqbFjS6IuhIqSMvMZsTiWrIIiwpo15D+Pd5H/EIQQ9U5mfiFbjqbzv9gEthxLty2PbOvHM31D6BbspWJ0dVfbQDf+OGokLilLEvUqtGpPIilZBRRZrIlvkVmh0GLBbFbKLrMo1udmS6n3zBbr+5e/V2R7VCiylE6sC82V7w9d0zQaMDhocdRpMeh11ucOlxJqZ8fSj056HS6XLyt5Xpx8u5SzjbOjDicHHVq5lxJVxM1Jz2v3tGHCir/5cMNRhnRpRKCHc40cWxJ1IVSSmV9I1OIdJGUWEOLrymfDu6k2T6MQQtQki0XhwLlMfo9P4/cjaew5cxFz8cBLOq2G+zs1YmzfEFvzbFE92gS48cfRdNv0dqJqLP7zJPvOZqodBo7FSbFep0Gv06LXWRPjUq91WvQOV7wued+WVGsxOFgT65Li6HDZMr0WR52ueL0r3rvstV6nkRYxolZ6oEtjlm1PYOfpC7zzcxwfPt6lRo4riboQKigoNPP05zuJT8nGz83A0pHhNHSV6TSEEHVXeo6RP46m8Xt8GpuPppORW3ou4BBfV/q19WfYrc1UmQanPmodIAPKVYeI1n6E+jVAr9XiUJz06rQa6/PiZQ5aDQ46LQ5a6/u2ZZdt41C8jcMV+7m0Xw2OOl25ibZOK0mxEFVFo9Hwxv3tGfzhFn78+xxPhDelZ4h3tR9XEnUhapjFovDyir/ZfjKDBgYHYqLDadJQbkqFEHVLkdnCnjMXbbXm+xNL1zC6OuroHepD39a+3N7SV5JzFZRM0RaXnI2iKJLYVZEJd7VSOwQhRBVr38iDp25txufbTjP9h4P89EKfah/UVBJ1IWqQoijMWH2I1fuT0Os0LBwWRrtG7mqHJYQQVeLcxXw2H7Em5luOpZNdUFTq/XaB7vRt7UvfVr50bdoQRwcZuV1NoX4N0Gk1XMwrJCXLSICHk9ohCSGE3ZpwVyt+2pfEnen/Zc2aiwwedF+1Hk8SdSFq0Kd/nGDJn6cAmDW0E71CfdQNSAghbkJBoZkdpzJsyfmRlJxS73u66Lm9pS+3t/Ll9pY++LlLImhPnPQ6mvu4ciw1h7jkLEnUhRDiGjxdHHm3h5H+W7/CHLuc9Fv+wieoTbUdTxJ1IWrI93sTeefnOABev6ct93durHJEQghROYqicOp8Hr/Hp/L7kTS2nThfau5irQY6B3nSt5UffVv70rGxh8xkYefaBLgVJ+rZRLT2UzscIYSwXxYLd516H4DDfvfQwj+0Wg8niboQNeDPY+n8Y+XfAIzs3Zwxt7dQOSIhhLi+IrOFE+m5HDqXxa7TF/j9SBoJGXml1vFzM9C3lS99W/vSJ9QHTxcZGLM2aRvozk/7kohLkgHlhBDimvb+F8253SiObnQYPgccqzeVlkRdiGp2OCmLZ77YRaFZYdAtgUwe1FbtkIQQoozsgkLikrM5dC7LWpKyiE/JxlRkKbWeXqehWzMvW1/zNgFuMghZLdba/9KAckIIIa4i/yKsmw6AJuKf4OZf7YeURF2IapR4MZ+oJbFkG4sIb+7F+0M7oZVmoEIIFSmKQnJWQamE/FBSFqfP55W7fgODA20D3WjfyIPeoT70DPGmgUFuH+qKNoHWRP14Wg6mIosM8FcVPr8fkvaBTg9aPegcrI9ah0vPS97T6squpyteV+twlX2UPDoWF30lnl/jffnBTYir2/Qu5KWDTysIH1sjh5T/aYWoJpl5hYxYHEtKlpFW/g34dFg3nPQ6tcMSQtQjhWYLJ9JyOZSUeSkpP5fFhbzCctcP9HCiXaA77Rq52x6DGrrID4x1WGNPZ9wMDmQbiziRnkObAJmJ5KblX4D8DLWjqDxb8n95Au8IDk7gYLisOF22/FrvGa54v/i57vLXjqB3Ab2z9VGnV/tTEKKs1MMQu9D6/O73rH+3NUASdSGqQUGhmTGf7+RYag4B7k7ERIfj4SL/+Qghqk9Fm64D6LQaQn0blErI2wa64+Uq/cvrG41GQ+sAN3aevkBcUrYk6lXhkS+gMA/MhWApBHMRWIoue15ofW2+/LHkecl6xa8vf//K/ZhNl9Y1m4rL9Z4XPxYZAaV03CXHK/93vJqh1V+WuDuDo+ul53rXSwm93hkcXUon+Zc/v/I9g5u1OBhUPDlRKykK/PIqKGZocy+E3Fljh76hRH3evHn8+9//Jjk5mU6dOvHhhx8SHh5+1fXnzp3L/PnzSUhIwMfHh4cffpiZM2fi5ORU4X0WFBTw8ssv89VXX2E0GhkwYAAff/wx/v7V3z9AiMqwWBQmrNhL7KkM3AwOxIzsTiNPZ7XDEkLUATnGIhLO55GQkceZDOvj6Yw8TqXnlhnkrURJ0/VLNeUetPRvIC18hE2bwOJEXfqpV42GzdSOoGIs5msn9UVG6/OiguLXBdZlRcbLnl/5XgEUlby+1ntGMBuhsACK8kEp/kHRUgjGTGupDjqDNWF3ci9O3t2Ly5XLih+vXObkDo5u1q4Ion449D2c3GxtATLg7Ro9dKX/ypYvX86ECRNYsGABPXr0YO7cuQwYMID4+Hj8/MpO67Fs2TImTpzI4sWL6dWrF0eOHCEqKgqNRsPs2bMrvM+XXnqJ1atXs3LlSjw8PBg3bhwPPvggf/75501+BEJUHUVRmLH6ED/vT0av0/DJ8DCpnRBCVJjFopCSXcDpK5Px89bn53NN19y+kYeTrXZcmq6Liir5fyouWUZ+r1e0OtAW11arSVGsCX1hHpjyoDDf+txW8q3FlHvFe/mXbXPZeoW55bxX/EOm2Qh5Rmtf45uhdymb4Lv4gGtxcfEBV9/iUrzM4C7jANQ2pjz4bbL1ee8XoWFwjR6+0on67NmzGTNmDNHR0QAsWLCA1atXs3jxYiZOnFhm/a1bt9K7d2+eeOIJAIKDg3n88cfZvn17hfeZmZnJokWLWLZsGXfeaW1usGTJEtq2bctff/3FrbfeWvkzF6IafPbHSZb8eQqA9x/pTK8QH3UDEkLYnTxTEWcy8jl9PteWjJ8uTsjPZuRjMpdtqn65hi56mnq50NTblaZeztbnXq60CXCjoTRdFzegbfGAcnFJUqMuVKDRXOrH7tyweo5hMYMxG4xZxY/ZUJBV/PrKZdmXlhdc9p4xy9oaAC4l/znJFY9B51g6mXf1vey172XLvK2Pjq6S2Kvtz7mQeQY8gqD3+Bo/fKUSdZPJxK5du5g0aZJtmVarJTIykm3btpW7Ta9evfjyyy+JjY0lPDycEydO8PPPPzNs2LAK73PXrl0UFhYSGRlpW6dNmzY0bdqUbdu2SaIu7MIPf5/j7Z8PA/DaPW24r1MjlSMSQtS0IrOF87kmUrIKSM0ykpJdQEpmAWculCTm+aTnGK+5DwethsYNSxLwSyXIy4Wm3i64O8l4F6JqtSqeoi05q4CLeSY8XeQHH1HHaHXg7GktN6PIdEXCX/xYkAl55yE3DXLTi0uateY+Nx1MOdZWA9nnrKUiHJxL19A38LPW6Hq1AK/m1sfq+mFDwIVTsGWu9Xn/t6zjHtSwSiXq6enpmM3mMv3C/f39iYuLK3ebJ554gvT0dPr06YOiKBQVFfHMM8/w2muvVXifycnJODo64unpWWad5OTyf8kyGo0YjZduhrKypDmXqD5bj6fz8oq9AET3DmbMbS3UDUgIUaXMFoXzOUZSsozWJDy75PGyhDzLyPkcIxbl+vvzcNZfSsK9SyfkgR5OOOhkiixRc9yc9DRp6MzZC/nEJWdzawtvtUMSwj45OIKDN7hW8jtSmG9N2PMuS+JtyXxJgp8GucXPi/KtJfOMtVyNc8PixP3KEgIuXlIjfzN+fd3aVaL57dDuflVCqPaREDZt2sQ777zDxx9/TI8ePTh27BgvvvgiM2bMYMqUKdV23JkzZ/LGG29U2/6FKBGXnMXYz3dRaFa4p2MAUwa1QyMXRiFqBbNF4Xyu0ZpsX5aAp2QZSStOvlOyCkivYAIO1hHVfRo44u/uhJ+bE37uBoIalk7GZRYIYW/aBLhbE/WkLEnUhahqemfwDLKWijDllq2dz0mGjFOQccJacpKtUwEm7rKWKxk8LtW8X1ka+EkSfy3HN0DcT6DRwd3/Uu2zqlSi7uPjg06nIyUlpdTylJQUAgICyt1mypQpDBs2jNGjRwPQsWNHcnNzefrpp3n99dcrtM+AgABMJhMXL14sVat+reNOmjSJCRMm2F5nZWURFFTBL4cQFXTuYj5Ri3eQbSwiPNiL2Y90lkGbhFCJoihkG4u4kGsiI9fEhTwTGbmF1td5piuWm7iQV8jFPFOFE3CtBnzdDPi5OeHvbsDP3Qk/NwP+7sWvi5Nyb1cDOrkOiFqmTYAb6w6nEJ8i/dSFUJ2jq7Vca/AyUy5knLyUuNvKScg6ax05P2mvtVxJ71q6CX1J8WkJbuXnVvWGuRB++af1efjT4NdWtVAqlag7OjoSFhbG+vXrGTJkCAAWi4X169czbty4crfJy8tDqy3dhE+ns04JoyhKhfYZFhaGXq9n/fr1PPTQQwDEx8eTkJBAz549yz2uwWDAYJC5EkX1ycwvJGpJLMlZBbT0a8Cnw7vJdEdCVBFFUcg1mbmYZ+JCbuFVEu3ix8veL6po1n0ZrQZ8GhiKa8CtCXhJ4u3vfmm5dwNJwEXd1aZ4QLnDMqCcELWDoysEdLCWKxXmw4XTVyTwx62PmWetI+On7LeWK/m2gdBICO0HTXuB3qnsOnXZ9k8g/Yh1XICIsgOl16RKN32fMGECI0aMoFu3boSHhzN37lxyc3NtI7YPHz6cxo0bM3PmTAAGDx7M7Nmz6dKli63p+5QpUxg8eLAtYb/ePj08PBg1ahQTJkzAy8sLd3d3nn/+eXr27CkDyQlVGIvMPP35To6k5ODvbiBmZLg0ZRXiMiWJdlZ+IVkFhWTlF132vJCsgqLS7xWUfp5dUIT5BpJuAFdHHQ1dHfFydaShy+WPeutyF0fb+54ueqkBF4JLU7QdScnGYlGkdZgQtZneGfzaWMuVioxwMaGcmvgT1gHU0uKsZdtH1gHtmt9WnLhHWmvd63KT+ewU2PSu9XnktJsffPAmVTpRf/TRR0lLS2Pq1KkkJyfTuXNn1qxZYxsMLiEhoVQN+uTJk9FoNEyePJnExER8fX0ZPHgwb7/9doX3CTBnzhy0Wi0PPfQQRqORAQMG8PHHH9/MuQtxQywWhQkr/mb7yQwaGBxYEhVOY0+V5yAVogYVFJpZuPlEtSXal3N00NoSa2/X4gTbRX+VRNyaeEvLFiEqL9jbBUcHLXkmM2cu5NHM21XtkIQQ1cHBYG3i7tOy7Hv5F+DEJji6Do6ts/aDP/qbtYC1KX5J0h58Gxga1GTk1W/9G2DKhkZdofNTakeDRlGUm7+TqgWysrLw8PAgMzMTd3d3tcMRtdiMnw6xaMtJ9DoNMdHh9A6VudLri7p8HanMuRUUmmkzZU2F9qvXafBw1uPupMfNWY+7kwPuJY9O+kvPi9dxd7603M3JAWe9TgZnFKKG3PvhHxxIzGLBU2EM7FC5fqpyfRSijlEUSDloTdiPrYOEv8BSeOl9rR6a9SxO3O+y9uWuzf9fn9kBi4qnAh+9Hpp0q7Jd3+g1pNpHfReiLvnsjxMs2nISgFlDO0mSLuolJ72OJ3s0pcFVkm2Py5Jtg4NWEm0haok2Ae4cSMwiLjmr0om6EKKO0Wgu9YHvM946X/zJP4oT97XW5vMnN1vL2qng1sjarz00ElpEqN5svFIsFvjlFevzzk9WaZJ+MyRRF6KCfvz7HG+tPgzAxLvbcH/nxipHJIR63n6go9ohCCGqWJsA64By8ckyoJwQ4goGN2hzj7UoCpw/fqm2/dQfkH0O9nxhLRodNOl+aVC6wM5wxeDidmXvl3BuDxjcIXK62tHYSKIuRAVsO36el1f8DUBUr2DG3t5C5YiEEEKIqlUyoFycJOpCiGvRaMAn1FpufcY6yvzprXBsvTVxT4+HM39Zy8a3wMUbQvpd6t/u6q32GVySfxHWvWF9HjHROse8nZBEXYjriE/O5ukvdmIyWxjYPoAp97aTprxCCCHqnJIp2k6dzyXPVISLo9wmCiEqQO9c3Oy9H/COtVl8SdJ+4nfIOw/7V1iLgzMM+Rg6PKh21FabZkJeOvi0ts6bbkfkCizENSRl5hO1JJbsgiK6NWvI3Mc6yzROQggh6iSfBgZ8GhhIzzFyNCWHTkGeaockhKiNPJtCt2hrMRfCmVhr0h7/C6Qdhq+j4fwxuP0VdQegSzkEsZ9an9/9Hujsa6plO+4sIIS6MvMLiVq8g6TMAkJ8XflsRDeZ9kkIIUSdVtJPPS45S+VIhBB1gk4Pwb2t85I/+yf0HGddvvFt+PZpKCxQJy5FgV9eBcUMbQdDyB3qxHENkqgLUQ5jkZmxX+wkPiUbPzcDS0eG4+niqHZYQgghRLUqSdQPJ0k/dSFEFdPqYMDbMPgD0DpYm8IvHQw5aTUfy6FV1kHwHJyg/9s1f/wKkERdiCtYLAr/WLmPv05k0MDgwJLo7jRp6KJ2WEIIIUS1axNYMqCc1KgLIapJWBQ89S04ecDZWPjsTmsz9JpiyoNfJ1uf9x4PDZvV3LErQRJ1Ia7w7po4fvz7HA5aDfOf6kr7Rh5qhySEEELUiMunaFMUReVohBB1Vou+MHo9eLWwDj63qD8cXVczx94yB7LOgkdT6xzxdkoSdSEus3jLSRZuPgHAvx6+hdta+qockRBCCFFzQv0aoNXAhbxCUrONaodTZebNm0dwcDBOTk706NGD2NhYtUMSQvi0tCbrzfqAKRuWDYXtn1TvMS+cgj8/sD4f8JZ1xHo7JYm6EMV+3p/EjNXWZjevDmzNg12bqByRqI82b97M4MGDadSoERqNhlWrVl13m02bNtG1a1cMBgOhoaHExMRUe5xCiLrJSa+jhW8DAA4n1Y3m78uXL2fChAlMmzaN3bt306lTJwYMGEBqaqraoQkhXLxg2HfQ+SlQLNYB3lb/A8xF1XO8X18HsxGa94W291XPMaqIJOpCALEnMxi/fC+KAsNubcazfUPUDknUU7m5uXTq1Il58+ZVaP2TJ08yaNAg7rjjDvbu3cv48eMZPXo0v/76azVHKoSoq9r7GXChgPjkujGg3OzZsxkzZgzR0dG0a9eOBQsW4OLiwuLFi9UOTQgB4OAI938EkW8AGtjxqbV2vSCzao9zbD3E/QQanXU6NjWnhqsAmUdd1HvHUrMZ8/lOTEUW+rfzZ/p97dHY+RdX1F133303d999d4XXX7BgAc2bN+f9998HoG3btmzZsoU5c+YwYMCA6gpTCFGH9XOK4wOnFzkcGw5916odzk0xmUzs2rWLSZMm2ZZptVoiIyPZtm2bipEJIUrRaKz9xb1D4dsxcHyDtd/641+BV/Ob33+RCX75p/V5j7Hg1/bm91nNpEZd1GspWQWMWLyDzPxCujb15D+Pd0GnlSRd1B7btm0jMjKy1LIBAwZc9wbUaDSSlZVVqgghBEBb7VkA0oucVI7k5qWnp2M2m/H39y+13N/fn+Tk5HK3keujECpqey9E/wJugZAWB5/1g9NV8KNa7Cdw/ii4+kLExJvfXw2QRF3UWznGIqKX7CDxYj7NfVz5bER3nPQ6tcMSolKSk5PLvQHNysoiPz//qtvNnDkTDw8PWwkKCqruUIUQtURz8ykAeve6Xd1AVCLXRyFU1qgzjNkAgZ0g7zx8fh/8/dWN7y87GTa9Z33eb5p1WrhaQBJ1US8Vmi08++UuDiVl4dPAkaXR4Xi5OqodlhA1ZtKkSWRmZtrKmTNn1A5JCGEnHNLjAND6t1c5kpvn4+ODTqcjJSWl1PKUlBQCAgLK3Uauj0LYAfdG1pr1toPBbILvxsL6GWCxVH5f696wjirfOAw6P1n1sVYTSdRFvaMoCpO+3c8fR9Nx1utYNKI7Tb1d1A5LiBsSEBBQ7g2ou7s7zs5Xn3LEYDDg7u5eqgghBOZCa3NTAP926sZSBRwdHQkLC2P9+vW2ZRaLhfXr19OzZ89yt5HroxB2wtEVhn4OfSZYX/8xC76OAlNexfdxJhb+XmZ9fve/QVt70t/aE6kQVWTOuqN8vessOq2GeU92oVOQp9ohCXHDevbsWeoGFGDt2rVXvQEVQohrOn8MLIXg6AYeTdWOpkpMmDCBTz/9lKVLl3L48GGeffZZcnNziY6OVjs0IcT1aLUQOQ2GzAetHg59DzH3WJuzX4/FDD+/Yn3e+SloEla9sVYxGfVd1CtfxSbwn/VHAXhrSAfubON/nS2EqFk5OTkcO3bM9vrkyZPs3bsXLy8vmjZtyqRJk0hMTOTzzz8H4JlnnuGjjz7i1VdfZeTIkWzYsIEVK1awevVqtU5BCFGbpRy0Pvq1rVU1T9fy6KOPkpaWxtSpU0lOTqZz586sWbOmzPgeQgg71vkJ8GwGy5+Ec3vg0zutI8IH3nL1bfZ8CUl7weBuTfZrmbpxBRaiAjbGpfL6qgMAvHBnKI+H142aAlG37Ny5ky5dutClSxfAWhPUpUsXpk6dCkBSUhIJCQm29Zs3b87q1atZu3YtnTp14v333+ezzz6TqdmEEDcm9ZD1sQ40e7/cuHHjOH36NEajke3bt9OjRw+1QxJCVFZwbxi9HrxbQlYiLB4IcT+Xv27+BVj/hvV5xCRo4FdzcVYRqVEX9cK+sxf5v//uxmxReKhrE166q5XaIQlRroiICBRFuer7MTEx5W6zZ8+eaoxKCFFv2GrUa/9AckKIOsg7BEavhRUj4OTv8NUTcNeb0Ot561zsJTbOtI4Y79sGwseoF+9NkBp1UeclnM9jZMwO8gvN3NbSh3cf6ohGI3OlCyGEEGWklNSoS6IuhLBTzg3hqW8gLBpQYO0U+PEFKDJZ3085CDs+sz6/+z3Q6VUL9WZIoi7qtIxcEyOWxJKeY6JdoDsfP9kVvU7+7IUQQogyCrIgs7hrTR1r+i6EqGN0erh3Dgx8FzRa2P05fPkg5GXAL/8ExQxt74MWEWpHesOk6buoswoKzYxeuoOT6bk09nRmSXR33Jxq5y9qQgghRLVLPWx9dGtkrbESQgh7ptHArc+CVwv4eiSc+gM+6g556eDgBP3fUjvCmyJVi6JOMlsUXvxqD7sTLuLu5EBMdHf83Z3UDksIIYSwXynWAVelNl0IUau0GgAjfwWPIGuSDtDnJWjYTN24bpLUqIs6R1EU3vzxIL8eTMFRp+XT4d1o6e+mdlhCCCGEfUuV/ulCiFoqoIN1RPgfngdLIfR+Ue2IbtoN1ajPmzeP4OBgnJyc6NGjB7GxsVddNyIiAo1GU6YMGjTItk5KSgpRUVE0atQIFxcXBg4cyNGjR0vtJzk5mWHDhhEQEICrqytdu3blm2++uZHwRR336R8nWLrtNABzHu1MjxbeKkckRB2UfhSyU6CwQO1IhBBVpWQgORnxXQhRG7n5w5MrYNh3oHdWO5qbVuka9eXLlzNhwgQWLFhAjx49mDt3LgMGDCA+Ph4/v7Lz03377beYTCbb6/Pnz9OpUyeGDh0KWGs/hwwZgl6v5/vvv8fd3Z3Zs2cTGRnJoUOHcHV1BWD48OFcvHiRH374AR8fH5YtW8Yjjzxim3NYCIDv9ybyzs9xAEwe1JZBtwSqHJEQdZDFDB91u/RaZwAnjwoWz+JH90vLHJxKT6kihKh5igKpxVOzSdN3IYRQXaUT9dmzZzNmzBiio6MBWLBgAatXr2bx4sVMnDixzPpeXl6lXn/11Ve4uLjYEvWjR4/y119/ceDAAdq3t/6CO3/+fAICAvjf//7H6NGjAdi6dSvz588nPDwcgMmTJzNnzhx27doliboAYOvxdP6x8m8ARvZuzujbWqgckRB1lCnHmnAXZAIKmI2Qm2otN0LnePXE3rkhuHhfUbysjwZ3SfCFqCpZidbvtEYHPq3UjkYIIeq9SiXqJpOJXbt2MWnSJNsyrVZLZGQk27Ztq9A+Fi1axGOPPWarKTcajQA4OV0a6Eur1WIwGNiyZYstUe/VqxfLly9n0KBBeHp6smLFCgoKCoiIiCj3OEaj0bZvgKysrMqcqqhl4pOzGfvFLgrNCvd0DGDyoLZqhyRE3eXkARNPg8ViTdoLMitQLpZdZswCxQJmE+SmWUtlaB3KT+CvtUzvIsm9EOUpafbu0wocDOrGIoQQonKJenp6OmazGX9//1LL/f39iYuLu+72sbGxHDhwgEWLFtmWtWnThqZNmzJp0iQ++eQTXF1dmTNnDmfPniUpKcm23ooVK3j00Ufx9vbGwcEBFxcXvvvuO0JDQ8s91syZM3njjTcqc3qilkrOLCBqSSzZBUV0D27I7Ec6o9XKjbgQ1U6rLW7C7g4EVX776yb6FyH/IuSdv6xkWB8Lc8FSBDkp1lJRDk5lE3hXP3ALALdAa/82t0Dra6mxF/WJNHsXQgi7UqOjvi9atIiOHTvamq8D6PV6vv32W0aNGoWXlxc6nY7IyEjuvvtuFEWxrTdlyhQuXrzIunXr8PHxYdWqVTzyyCP88ccfdOzYscyxJk2axIQJE2yvs7KyCAq6gRtJYdeyCgqJWhJLUmYBIb6ufDq8G056ndphCSEq4mYS/cL8S0n7lUl8ucvSrTX3RQXWJr5Zidc/ht4FGlyWuNtK8esGxa8NbpLQi9rPNpCcJOpCCGEPKpWo+/j4oNPpSEkpXXuRkpJCQEDANbfNzc3lq6++4s033yzzXlhYGHv37iUzMxOTyYSvry89evSgWzfrYEXHjx/no48+KtWPvVOnTvzxxx/MmzePBQsWlNmnwWDAYJCmW3WZqcjCs1/uIi45G183AzHR4Xi6OKodlhCiJuidwaOxtVSEooApt/wEPicFspMhO8k6kn12MhgzoTAPLpy0lmvG4lq6Jt4t8IoEPxA8moDe6dr7EUJNKSU16jLiuxBC2INKJeqOjo6EhYWxfv16hgwZAoDFYmH9+vWMGzfumtuuXLkSo9HIU089ddV1PDw8AOsAczt37mTGjBkA5OXlAda+65fT6XRYLJbKnIKoIxRF4Z/f7OPPY+dxcdSxJKo7QV4uaoclhLBXGg0YGlhLw2bXX9+UBznJlyXwyZeVpEvJvTHL2gw/44S1XItbI2gYDF7NrY+20hxcfaRWXqjHXAjpR6zPJVEXQgi7UOmm7xMmTGDEiBF069aN8PBw5s6dS25urm0U+OHDh9O4cWNmzpxZartFixYxZMgQvL3Lzmm9cuVKfH19adq0Kfv37+fFF19kyJAh9O/fH7D2Yw8NDWXs2LHMmjULb29vVq1axdq1a/npp59u5LxFLTfrt3i+25OITqvh4ye70qGxh9ohCSHqEkcX8GphLddiyi0ngb8isc86Z03ms89ZS8LWsvvRu149ifcMksG9RPVKPwqWQuu4DB7STVAIIexBpRP1Rx99lLS0NKZOnUpycjKdO3dmzZo1tgHmEhISytR8x8fHs2XLFn777bdy95mUlMSECRNISUkhMDCQ4cOHM2XKFNv7er2en3/+mYkTJzJ48GBycnIIDQ1l6dKl3HPPPZU9BVHLffnXaeZtPA7AzAc7EtHaT+WIhBD1lqMreIdYy9UoirWp/YVTl5rSXzgFF05Dxklrf/nCXOtgXiUDepWiAffGxYl88KUEvmFxUu/iJbXx4uaklvRPbyt/S0IIYSc0yuUjttVhWVlZeHh4kJmZibu7u9rhiBu09lAKY7/YiUWBlyJb8WJkS7VDEvVIXb6O1OVzs3tFRrh45rJE/tSlknHSmsRfi6ObNYH372AtAR2txcWr2kMXdcS66bBlDoRFw+C5N7SLunwNqcvnJoSofjd6DanRUd+FuBl7Ei7w/P92Y1Hg0W5BvNCv/Kn5hBCiVnEwgE+otVxJUSA3/bLk/YpEPisRTNmQvN9aLufe5FLSXlIaBkuNqSirZMR36Z8uhBB2QxJ1USucSs9l1NKdFBRaiGjty1sPdEAjN5tCiLpOo4EGvtYS1L3s+4UFcDEBzh+zjtqdvM+asF84CVlnreXIL5fWN7iXrnUP6Ght7ix94Ou3VEnUhRDC3kiiLuze+RwjUUtiycg10aGxO/Oe6Ipep73+hkIIUdfpncC3lbW0uWzMloKs4sR9/6XkPfWQdZT6hK2lB7TTOoBP67K179J0vn4oyITMM9bnfm3VjUUIIYSNJOrCruWbzIxaupNT5/No0tCZxVHdcTXIn60QQlyTkzs062ktJcyF1tG9L0/ek/dB/oVLA9nt++rS+lc2nQ+8BTybSdP5uqak2bt7Y3BuqG4sQgghbCTjEXbLbFF44as97D1zEQ9nPTHR4fi5OakdlhBC1E46Pfi3s5ZOj1qXKYp1+riSPu7XazrfMBhCI60l+DbrvPSidiuZaUCavQshhF2RRF3YJUVRePPHg6w9lIKjg5bPRnQj1E9uCIUQokppNODR2FpaD7y0vLym8ykHrQPY7fjMWrR6a419SeLu105q22ujkhp1v3bqxiGEEKIUSdSFXfr0jxMs3XYajQbmPNKZ7sHSV1IIIWpMeU3njdlw8g84ts5aLp6Gk5utZe1UcAuE0H7WpL1FhDSjri1SpEZdCCHskSTqwu78+Pc53vk5DoDX72nLoFsCVY5ICCEEBjfrgHVt7rE2mc84AUfXWpP2U1sgOwn2fGktGi006V5c294PAruAVgYBtTuKAqmHrc+lRl0IIeyKJOrCrmw/cZ6XV/wNQFSvYEb1aa5yREIIIcrQaMA7xFpufQYK8+H0Vji+wZq4p8XBme3WsvFtcPGGkDshpJ81cW/gp/YZCIDMs2DMLB75v5Xa0QghhLiMJOrCbhxLzWbM5zsxmS0MaO/PlHvbyVzpQghRG+idi5u994MBb8PFM3B8vTVpP/E75J2H/SutBSDglkt924PCrQPdiZpXMn+6TytwcFQ3FiGEEKVIoi7sQmpWASMW7yCroIiuTT354LEu6LSSpAshRK3kGQRhUdZiLoSzOy71bU/6u3iAun2wZTY4ukGLvtakveVd4NFE7ejrj5QD1kdp9i6EEHZHEnWhulxjESOX7iDxYj7NfVz5bER3nPQ6tcMSQghRFXR6aNbLWvpNhZzUS03kj62H/AyI+8la0EC7+6HPeGjURe3I676SEd/9JVEXQgh7I4m6UFWR2cJzy3ZzIDELb1dHYqK74+Uqze+EEKLOauAHnR6zFosZkvZaE/aja+FsLBxaZS0t7rAm7M37yrRv1aWk6bt/B3XjEEIIUYYk6kI1iqIwedUBNsWn4aTXsiiqO828XdUOSwghRE3R6qBxmLX0fRWSD8CfH8CBb+DERmtp1AX6vARt7rWuL6pGkQnSj1ifS9N3IYSwOzJXilDNRxuO8dWOM2g18OHjXekc5Kl2SEIIIdQU0AEe+hRe2A3dx4CDE5zbAyuGw7xw2P05FBnVjrJuSD8CliIweMi4AEIIYYckUReq+GbXWd5fa/0l/4372nNXO3+VIxJCCGE3GgbDoFkw/gDc9g9w8oDzx+CH5+GDTrD1QzBmqx1l7VbS7N2vrXQtEEIIOySJuqhxW46m889v9gEwtm8LhvUMVjcgIYQQ9qmBL/SbAi8dhP5vgVsgZCfBb5NhTnvY8BbkpqsdZe2UctD66N9e3TiEEEKUSxJ1UaMOJ2XxzJe7KLIoDO7UiH8OaKN2SEIIIeydwQ16PQ8v/g33fQjeoVCQCZv/DXM6wM+vwIXTakdZu6TKiO9CCGHPJFEXNSYpM5/oJTvIMRbRo7kXs4beglbmShdCCFFRDgboOhyei4VHPrcONFeUD7EL4T9d4NunL9UUi2sr+Zz8pEZdCCHskSTqokZkFRQStXgHyVkFtPRrwMJh3TA4yOi9QgghboBWZ51vfcxGGP69dSo3xQz7lsP8XvDfR+D0NrWjtF/5FyAr0frcr626sQghhCiXJOqi2pmKLDz75S7iU7LxdTOwJLo7Hi56tcMSQghR22k00CIChq+CpzdBuyGABo7+CksGwqIBEL8GLBZVw7Q7qYetjx5B4OypaihCCCHKJ4m6qFaKojDxm338eew8ro46lkR1p0lDF7XDEkIIUdc06gKPLIVxO6HrCNA5wpm/4H+PwoLe8PdyMBeqHaV9sDV7l/7pQghhryRRF9Xq/d+O8O2eRHRaDR8/FUaHxh5qhySEEKIu8wmF+/4DL+6DXi+Ao5t14LTvnob/dIVdMaAoakepLtuI75KoCyGEvZJEXVSbZdsT+GjjMQBmPtCRvq18VY5ICCFEveEeCP1nwEsHoN9UcPWFzAT48UVYM7F+N4e3zaEuA8kJIYS9kkRdVIuNcalM+f4AAC/0a8kj3YNUjkiI2mXevHkEBwfj5OREjx49iI2Nveq6MTExaDSaUsXJyakGoxXCjjl7wm0vw/j91oQdYPsC+P45MBepGpoqFOVSH3WZQ10IIeyWJOqiyu0/m8lzy3Zjtig8HNaElyJbqh2SELXK8uXLmTBhAtOmTWP37t106tSJAQMGkJqaetVt3N3dSUpKspXTp2VOaSFK0TtbE/YHPgGNDv5eBitHQJFR7chqVuYZMGaBVg8+8v+zEELYqxtK1CtT0xMREVGmpkej0TBo0CDbOikpKURFRdGoUSNcXFwYOHAgR48eLbOvbdu2ceedd+Lq6oq7uzu33347+fn5N3IKopqcycgjOmYHeSYzt7X0YeaDHdFoZK50ISpj9uzZjBkzhujoaNq1a8eCBQtwcXFh8eLFV91Go9EQEBBgK/7+/jUYsRC1SKfHrHOw6xwh7idY9iiYctWOquaU9E/3aQU6mYFFCCHsVaUT9crW9Hz77belankOHDiATqdj6NChgHVU8CFDhnDixAm+//579uzZQ7NmzYiMjCQ399J/nNu2bWPgwIH079+f2NhYduzYwbhx49BqpVGAvbiYZ2LEkljSc4y0DXTn4ye7otfJv48QlWEymdi1axeRkZG2ZVqtlsjISLZtu/q80Dk5OTRr1oygoCDuv/9+Dh48eM3jGI1GsrKyShUh6o2298KTK0HvCic2wudDrHOL1wcykJwQQtQKlc6iKlvT4+XlVaqWZ+3atbi4uNgS9aNHj/LXX38xf/58unfvTuvWrZk/fz75+fn873//s+3npZde4oUXXmDixIm0b9+e1q1b88gjj2AwGG7w1EVVKig0M+bznZxIyyXQw4klUd1xc5Jf6oWorPT0dMxmc5kacX9/f5KTk8vdpnXr1ixevJjvv/+eL7/8EovFQq9evTh79uxVjzNz5kw8PDxsJShIxpEQ9UyLCBj+PTh5wtlYiLkXslPUjqr6lQwkJ/3ThRDCrlUqUb/Rmp7LLVq0iMceewxXV1fAWqsDlBr4SKvVYjAY2LJlCwCpqals374dPz8/evXqhb+/P3379rW9Xx6pLao5FovCyyv/ZsepC7g5ORATHU6AhwxkJURN6dmzJ8OHD6dz58707duXb7/9Fl9fXz755JOrbjNp0iQyMzNt5cyZMzUYsRB2Iqg7RP8MDfwh5QAsGQgXE9SOqnqlyIjvQghRG1QqUb+Rmp7LxcbGcuDAAUaPHm1b1qZNG5o2bcqkSZO4cOECJpOJ9957j7Nnz5KUlATAiRMnAJg+fTpjxoxhzZo1dO3alX79+pXblx2ktqgmvbsmjtX7ktDrNHwyLIzWAW5qhyREreXj44NOpyMlpXTNXkpKCgEBARXah16vp0uXLhw7duyq6xgMBtzd3UsVIeol//YQ/Qt4NIWME7B4IKQdUTuq6lFkhPTic5Om70IIYddqtAPxokWL6NixI+Hh4bZler2eb7/9liNHjuDl5YWLiwsbN27k7rvvtvU/txTPdTp27Fiio6Pp0qULc+bMsTX3LI/UFtWMpVtPsXCz9YeUfz18C71CfFSOSIjazdHRkbCwMNavX29bZrFYWL9+PT179qzQPsxmM/v37ycwMLC6whSibvEOgVG/gk9ryEq01qyf26t2VFUv/QgoZjB4gHtjtaMRQghxDZVK1G+mpic3N5evvvqKUaNGlXkvLCyMvXv3cvHiRZKSklizZg3nz5+nRYsWALabzXbtSv/627ZtWxISym+iJrVF1e+3g8m88aN1UJpXBrTmgS5NVI5IiLphwoQJfPrppyxdupTDhw/z7LPPkpubS3R0NADDhw9n0qRJtvXffPNNfvvtN06cOMHu3bt56qmnOH36dKnWS0KI63BvZK1ZD+wMeedh6WA4vVXtqKpWymX902VGFiGEsGuVStRvpqZn5cqVGI1Gnnrqqauu4+Hhga+vL0ePHmXnzp3cf//9AAQHB9OoUSPi4+NLrX/kyBGaNWtWmVMQVWTvmYu88NUeLAo8Hh7E/0WEqB2SEHXGo48+yqxZs5g6dSqdO3dm7969rFmzxtbtKCEhwdY1CODChQuMGTOGtm3bcs8995CVlcXWrVvL/LgphLgOV28Y8SM0622da/yLB+HoOrWjqjqpMuK7EELUFg6V3WDChAmMGDGCbt26ER4ezty5c8vU9DRu3JiZM2eW2m7RokUMGTIEb2/vMvtcuXIlvr6+NG3alP379/Piiy8yZMgQ+vfvD1jnB37llVeYNm0anTp1onPnzixdupS4uDi+/vrrGzlvcRMSzucxKmYHBYUW+rbyZcb9HWSudCGq2Lhx4xg3bly5723atKnU6zlz5jBnzpwaiEqIesDJHZ76BlYMh6O/wf8eg4c+hfYPqB3ZzSuZms2vfiTqp06dYsaMGWzYsIHk5GQaNWrEU089xeuvv46jo6Pa4QkhxDVVOlF/9NFHSUtLY+rUqSQnJ9O5c+cyNT1Xzm0eHx/Pli1b+O2338rdZ1JSEhMmTCAlJYXAwECGDx/OlClTSq0zfvx4CgoKeOmll8jIyKBTp06sXbuWkBCpya1JF3JNRC2J5XyuifaN3Jn3ZFccZK50IYQQdYneGR79L6x6Bg58A1+PhIIsCBuhdmQ3x9b0vYO6cdSQuLg4LBYLn3zyCaGhoRw4cIAxY8aQm5vLrFmz1A5PCCGuSaMoiqJ2EDUhKysLDw8PMjMzpb/6DSooNPPUZ9vZefoCjT2d+fb/euHvLtOwifqjLl9H6vK5CXHDLGZY/TLsWmJ93f8t6PW8ujHdqPwL8F6w9fnEM9aWA1WotlxD/v3vfzN//nzbjEIVUVvOTQhhn270GlLpGnVRP1ksCi+v+Judp61zpS+J7i5JuhBCiLpNq4N751iT2j8/gN8mQ0Em3PF67RuMraQ23aNplSfptUlmZiZeXl7XXMdoNGI0Gm2vs7KyqjssIYQoQ9osiwp5d00cq/dfmiu9lb/MlS6EEKIe0Gjgrjeh3zTr683/hl9eheKpY2uNFBlI7tixY3z44YeMHTv2muvNnDkTDw8PWwkKCqqhCIUQ4hJJ1MV1XT5X+r8f7iRzpQshhKh/bpsAg94HNBC7EFY9C+YitaOqONuI7+3VjaMKTJw4EY1Gc80SFxdXapvExEQGDhzI0KFDGTNmzDX3P2nSJDIzM23lzJkz1Xk6QghRLmn6Lq7pyrnSh3RprHJEQgghhEq6jwaDO3z3DOz7CozZ8PBi0NeCrmAlTd/rwIjvL7/8MlFRUddcp0WLFrbn586d44477qBXr14sXLjwuvs3GAwYDIabDVMIIW6KJOriqmSudCGEEOIKtzwCBjdYMQLiV8OyofDY/8DQQO3Irs5igdTD1ud1oEbd19cXX1/fCq2bmJjIHXfcQVhYGEuWLCkzM5EQQtgruVqJcslc6UIIIcRVtL4bnvoaHBvAyc3w+f2Ql6F2VFeXmQCmbNDqwTtU7WhqTGJiIhERETRt2pRZs2aRlpZGcnIyycnJaocmhBDXJYm6KONCromoGOtc6e0CZa50IYQQoozmt8PwH8C5ISTuhJhBkG2nCWBJs3ffNqDTqxtLDVq7di3Hjh1j/fr1NGnShMDAQFsRQgh7J9mXKKWg0MzTX+zkRFoujTycWBLdnQYG6SEhhBBClNEkDKJ/gQYBkHoIFg+EC6fVjqqs1Po54ntUVBSKopRbhBDC3kmiLmwsFoWXV/7NjlPWudJjRobLXOlCCCHEtfi1hZFrwLMZXDhpTdbT4tWOqrQ6NJCcEELUF5KoC5v31sSxel/xXOlPyVzpQgghRIV4NYeRv1qblmefgy8eBHOh2lFdklJ3pmYTQoj6QhJ1AcDn207xSfFc6f96+BZ6hcpc6UIIIUSFuQdam8G7+kLWWTixSe2IrIqMcP6Y9bkk6kIIUWtIoi5YeyiF6T9Yf23/R/9WPNClicoRCSGEELWQixd0eMj6fN9ydWMpkRYPihmcPMFNBlETQojaQhL1eu7vMxd5/n+7sSjwWPcgnruj/kzbIoQQQlS5jo9YH+NWgzFH3VigdLN3mWZVCCFqDUnU67GE83mMWnrZXOlDZK50IYQQ4qY07gpeLaAwD+J/VjuaSyO+y0ByQghRq0iiXk+VzJWennNprnS9zJUuhBBC3ByN5lKt+r4V6sYCl0Z8l/7pQghRq0hmVg/JXOlCCCFENbqlOFE/vgFy0tSNJVUSdSGEqI0kUa9nZK50IYQQopp5h0CjrtZB3A5+p14ceRmQnWR97tdWvTiEEEJUmiTq9YzMlS6EEELUgJJa9f0qNn8vGUjOsykY5P97IYSoTSRRr0e+uGyu9PcekrnShRBCiGrT/kHQaOHsDsg4oU4MtmbvHdQ5vhBCiBsmiXo9se5QCtOK50p/+a5WPNhV5koXQgghqo2bP7SIsD7f/7U6MaTIiO9CCFFbSaJeD1jnSt+DRYFHuwUx7k6ZK10IIYSodpeP/q4oNX982xzqkqgLIURtI4l6HXcmwzpXen6hmdtb+fLWAzJXuhBCCFEj2t4LDs5w/igk7a3ZY1sskHrY+txPRnwXQojaRhL1OuxinokRSy7Nlf6xzJUuhBBC1ByDG7S+2/q8pudUv3gaCnNB5wje0pJOCCFqG8na6ihjkZmnv9jFibRcAj2cWBwlc6ULIYQQNe6WR62PB74Bi7nmjlsykJxva9DJ//9CCFHbSKJeB1ksCq+s3EfsyQzcDA4sie5OgIfMlS6EEELUuNB+4OwFOSlw8veaO65tIDlp9i6EELXRDSXq8+bNIzg4GCcnJ3r06EFsbOxV142IiECj0ZQpgwYNsq2TkpJCVFQUjRo1wsXFhYEDB3L06NFy96coCnfffTcajYZVq1bdSPh13vtr4/nh73M4aDXMfyqMNgHuaockhBBC1E86PbR/wPp838qaO64MJCeEELVapRP15cuXM2HCBKZNm8bu3bvp1KkTAwYMIDU1tdz1v/32W5KSkmzlwIED6HQ6hg4dClgT7yFDhnDixAm+//579uzZQ7NmzYiMjCQ3N7fM/ubOnSuDoV3D/2ITmLfxOAAzH+xIn5YyV7oQQgihqluKR38//CMU5tfMMW1zqEuNuhBC1EaVTtRnz57NmDFjiI6Opl27dixYsAAXFxcWL15c7vpeXl4EBATYytq1a3FxcbEl6kePHuWvv/5i/vz5dO/endatWzN//nzy8/P53//+V2pfe/fu5f3337/qseq7TfGpTF51AIAX+rVkaLcglSMSQgghBEE9wLMpmLIh/pfqP15hAZw/Zn0uTd+FEKJWqlSibjKZ2LVrF5GRkZd2oNUSGRnJtm3bKrSPRYsW8dhjj+Hq6gqA0WgEwMnpUh9qrVaLwWBgy5YttmV5eXk88cQTzJs3j4CAgOsex2g0kpWVVarUZQfPZfLcf3djtig82KUxL0W2VDskIYQQQvD/7d15fFT12ffx72RPhCRmIawhBCNbgoEEaNTeWIuGpdzGWqTeyhIVn/JIRdNiodaltRb7FHdRKw1LqTbII1hRG+UJUEoLQgipRFkioEFIJqxJDJhA5vf8ETIaSSAJmZmTyef9ep0XM2d+55zrgsxFrjlnfkeSzSYl1Z+g0E43XP5+ZLdkHFLw5VLXi//OBACwnlY16kePHlVdXZ1iYmIarY+JiVFZWdlFt9+6dauKiop09913O9cNHDhQsbGxmjdvnk6cOKHa2lr9/ve/1xdffKHS0lLnuAceeEBXX321brrpphbFOn/+fIWFhTmXPn289+zy4ZOndefSbaqurVNafKSevGUoXw8AAMBKks5d/l68Vjp13LXHarjsvduQ+g8JAAAdjltnfc/OzlZSUpJGjhzpXOfv769Vq1Zp7969ioiIUEhIiNavX69x48bJx6c+vLffflvr1q3Ts88+2+JjzZs3TxUVFc7l4MGD7Z2OJVR+dUZ3Lt0me2WNErp10StTUhTgx2T+AABYSreBUvckyXFG+uQt1x7LOZEcl70DQEfVqo4uKipKvr6+stvtjdbb7faLXo5eXV2tnJwc3XXXXee9lpKSosLCQp08eVKlpaXKzc3VsWPHFB8fL0lat26d9u3bp/DwcPn5+cnPr/5+oLfccouuu+66Jo8XGBio0NDQRou3OVPn0L2vFWh3WZWiuwZqSeYIhQX7ezosAADQlIaz6q6e/d05kRwzvgNAR9WqRj0gIEApKSnKy8tzrnM4HMrLy1NaWtoFt125cqVqamp0xx13NDsmLCxM0dHRKi4uVn5+vvMy97lz5+qjjz5SYWGhc5GkZ555RkuWLGlNCl7DGKOHVu/UP4uPKtjfV4unjVDvy0M8HRYAAGhO0o8k2aSSf0snS1x3HO6hDgAdnl9rN8jKytK0adOUmpqqkSNH6tlnn1V1dbUyMzMlSVOnTlWvXr00f/78RttlZ2crIyNDkZGR5+1z5cqVio6OVmxsrHbu3KnZs2crIyNDN954oyQ5Z4z/ttjYWPXr16+1KXiFF9d9qjfyv5CPTXrxf4YpqXeYp0MCAAAXEtpTirtW+uyf9ZPKffdn7X+M6mPSl+eufOw2sP33DwBwi1Y36pMnT9aRI0f0yCOPqKysTMnJycrNzXVOMFdSUuL8bnmDPXv2aNOmTfrggw+a3GdpaamysrJkt9vVo0cPTZ06VQ8//HAb0ukcVu/4Qk+t3StJ+vV/D9H3B8VcZAsAAGAJQ2+tb9Q/Wildm9X+k72VnzubfnmcFNi1ffcNAHAbmzHGeDoId6isrFRYWJgqKio69PfV/73vqKYt3qozdUb3/Fe8fjl+kKdDAjoNb6kjTfHm3ABLOX1SWnClVFcj/WRT/QRz7WnLK1LuL6QBE6TbXm/ffV+AN9cQb84NgOu1tYYwPXgHUmyv0v9avl1n6owmJPXQ3LFc0gYAQIcSHC5dmV7/+KM32n//9qL6P5lIDgA6NBr1DqK86itNX7JNVV+dVUrfy/XUrVfJx4d7owIA0OEMPTf7e9GbksPRvvt23kOdRh0AOjIa9Q7gVO1Z3bU0X4dOnla/qMu0aGqqgvx9PR0WABdauHCh4uLiFBQUpFGjRmnr1q0XHL9y5UoNHDhQQUFBSkpK0nvvveemSAG0WsKNUlCYVHlI+vxf7bdfh0Mq313/OCax/fYLAHA7GnWLq3MY3ffXHdp5qEIRlwVoyfQRirgswNNhAXChFStWKCsrS48++qgKCgp01VVXKT09XeXl5U2O//e//63bbrtNd911l3bs2KGMjAxlZGSoqKjIzZEDaBG/QGlw/S1otbMdL38/+Zl0plryDZQi4ttvvwAAt6NRtzBjjH695mP9v13lCvDz0aKpqYqLuszTYQFwsaefflozZsxQZmamBg8erFdeeUUhISFavHhxk+Ofe+45jR07VnPmzNGgQYP0+OOPa/jw4XrxxRfdHDmAFks6d/n7J3+Tzta0zz4b7p8ePUDybfWNfQAAFkKjbmF/+ucB/Xnz57LZpGcnJyul7+WeDgmAi9XW1mr79u0aM2aMc52Pj4/GjBmjzZs3N7nN5s2bG42XpPT09GbHS1JNTY0qKysbLQDcqO81Umgv6asKqbjp29e2mv3c99NjhrTP/gAAHkOjblHv7SzVE+/tkiT9ctwgjU/q4eGIALjD0aNHVVdXp5iYmEbrY2JiVFZW1uQ2ZWVlrRovSfPnz1dYWJhz6dOnz6UHD6DlfHykxFvqH7fX7O8N91CnUQeADo9G3YK2f35c968olCRNTeuru7/bz7MBAfA68+bNU0VFhXM5ePCgp0MCOp+G2d/3vl9/f/VLZWfGdwDwFjTqFnPgaLXuXpav2rMOjRnUTY9OHCKbjduwAZ1FVFSUfH19ZbfbG6232+3q3r17k9t07969VeMlKTAwUKGhoY0WAG4WkyhFD5LqaqRdb1/avs6clo7vO7dfzqgDQEdHo24hx6trlblkq06cOqOhvcP0/G3D5Mu90oFOJSAgQCkpKcrLy3OuczgcysvLU1paWpPbpKWlNRovSWvXrm12PACLsNmkoZPqH1/q5e9HdkvGIQVHSF1iLj4eAGBpNOoW8dWZOs34c74+O3ZKvcKD9adpqQoJYMZWoDPKysrSokWLtGzZMu3atUszZ85UdXW1MjMzJUlTp07VvHnznONnz56t3NxcPfXUU9q9e7cee+wx5efna9asWZ5KAUBLJZ1r1D/bJFUebvt+vjmRHFfiAUCHRydoAQ6H0c/e+I+2f35CoUF+WnbnCHXrGuTpsAB4yOTJk3XkyBE98sgjKisrU3JysnJzc50TxpWUlMjH5+vPWa+++mq9/vrr+tWvfqVf/vKXSkhI0FtvvaXExERPpQCgpcJjpdirpZJ/Szv/r3TNfW3bTzkzvgOAN6FRt4Anc3fr3Z2l8ve16ZUpKbqiW1dPhwTAw2bNmtXsGfENGzact27SpEmaNGmSi6MC4BJDJ51r1N9oe6NuL6r/k4nkAMArcOm7hy3f/Jle3bhfkvR/fjRUV/eP8nBEAADArQZnSD7+UtlOqXx32/bBPdQBwKvQqHtQ3i67Hn27/p6nP7vhSt08rLeHIwIAAG4XEiEl3FD/eGcbJpWrPipVl0uySdED2zU0AIBn0Kh7yM4vKjTr9R1yGOnW1N6adf0Vng4JAAB4SsOkcjtXSsa0blt7/Yf+ujxOCuzSrmEBADyDRt0DDp08rTuXbdPpM3W69oooPXFzEvdKBwCgMxswTgroKp0skQ5+2LptGxp1LnsHAK9Bo+5mlV+d0Z1LtulIVY0GxHTVS3cMl78v/wwAAHRq/sHSoIn1j1t7T/Xyc406E8kBgNegQ3SjM3UO/e+/FGiPvUrdugZqceYIhQb5ezosAABgBUPPXf7+8WrpbG3Lt2MiOQDwOjTqbmKM0UOrd2rTp0cVEuCrxdNHqFd4sKfDAgAAVtFvtNQlRjp9XNqX17JtHHVS+a76xzTqAOA1aNTdZOH6T/VG/hfysUkv/s8wJfYK83RIAADASnx8pcRb6h+39PL3E59JZ09LfkFSRLzLQgMAuBeNuhv8rfCQFnywV5L06/8eousHxng4IgAAYEkNs7/v+btUU3Xx8Q0TyUUPqG/0AQBegUbdxT7cf0xzVn4kSZrx3X6akhbn2YAAAIB19RwmRV5Rf5Z81zsXH1/e8P30RNfGBQBwKxp1F9p35Evds3y7auscGpfYXfPGDfJ0SAAAwMpsNinp1vrHO1tw+bu9qP5PZny/oJqaGiUnJ8tms6mwsNDT4QDARdGou8jRL2uUuWSbKk6f0bDYcD0zOVk+PtwrHQAAXETD7O/7N0hV9guPdc74TqN+IQ8++KB69uzp6TAAoMVo1F3gqzN1untZvkqOn1JsRIgWTU1VkD/fGwMAAC0QES/1HiEZh/TxqubH1Z6Sju+vf9yNGd+b8/e//10ffPCBFixY4OlQAKDF2tSoL1y4UHFxcQoKCtKoUaO0devWZsded911stls5y0TJkxwjrHb7Zo+fbp69uypkJAQjR07VsXFxc7Xjx8/rp/+9KcaMGCAgoODFRsbq/vuu08VFRVtCd+lHA6jB1YUqvDgSYUF+2tJ5ghFdQn0dFgAAKAjabj8/UKzvx/ZLclIIVFSl25uCaujsdvtmjFjhpYvX66QkJAWbVNTU6PKyspGCwC4W6sb9RUrVigrK0uPPvqoCgoKdNVVVyk9PV3l5eVNjl+1apVKS0udS1FRkXx9fTVpUv1lXcYYZWRkaP/+/frb3/6mHTt2qG/fvhozZoyqq6slSYcPH9bhw4e1YMECFRUVaenSpcrNzdVdd911Cam7xpO5u/X3ojIF+Pro1Skp6h/dxdMhAQCAjmbIzZLNVzpcIB3b1/SYhhnfYwbXf7cdjRhjNH36dP3kJz9Rampqi7ebP3++wsLCnEufPn1cGCUANK3VjfrTTz+tGTNmKDMzU4MHD9Yrr7yikJAQLV68uMnxERER6t69u3NZu3atQkJCnI16cXGxtmzZopdfflkjRozQgAED9PLLL+v06dP661//KklKTEzUm2++qYkTJ6p///66/vrr9cQTT2jNmjU6e/bsJaTfvpZv/kyvbqy/BO0Pk4ZqVHykhyMCAAAdUpdoqf/19Y+bO6veMON7J7vsfe7cuU1erfnNZffu3XrhhRdUVVWlefPmtWr/8+bNU0VFhXM5ePCgizIBgOb5tWZwbW2ttm/f3qjg+fj4aMyYMdq8eXOL9pGdna0f//jHuuyyyyTVX14kSUFBQY32GRgYqE2bNunuu+9ucj8VFRUKDQ2Vn1/TKdTU1Dj3Lcnlly2t223Xo2/Xf7L98xuv1E3JvVx6PAAA4OWG3ip9urZ+9vfr5p5/1vybZ9Q7kZ/97GeaPn36BcfEx8dr3bp12rx5swIDG38FMTU1VbfffruWLVvW5LaBgYHnbQMA7taqRv3o0aOqq6tTTExMo/UxMTHavXv3RbffunWrioqKlJ2d7Vw3cOBAxcbGat68efrjH/+oyy67TM8884y++OILlZaWNhvH448/rnvuuafZY82fP1+//vWvW5jZpSk6VKFZr++Qw0i3pvbWvd+7wi3HBQAAXmzAeMk/pH7CuEPbpd7funzbeQ/1znVGPTo6WtHR0Rcd9/zzz+u3v/2t8/nhw4eVnp6uFStWaNSoUa4MEQAumVtnfc/OzlZSUpJGjhzpXOfv769Vq1Zp7969ioiIUEhIiNavX69x48bJx+f88CorKzVhwgQNHjxYjz32WLPHctdlS4dPntadS7fpVG2drr0iSk/cnCQb3xMDAACXKrCLNPDc5Lvfvvz9y3Kp+ogkmxQ9yO2hdQSxsbFKTEx0LldeeaUkqX///urdu7eHowOAC2tVox4VFSVfX1/Z7Y3v6Wm329W9e/cLbltdXa2cnJwmJ4BLSUlRYWGhTp48qdLSUuXm5urYsWOKj49vNK6qqkpjx45V165dtXr1avn7+zd7vMDAQIWGhjZa2lvlV2eUuWSbyqtqdGVMF710x3D5+3LHOwAA0E4aZn//eJVU9415eRoue4/oJwW0bDZzAEDH0aquMiAgQCkpKcrLy3OuczgcysvLU1pa2gW3XblypWpqanTHHXc0OyYsLEzR0dEqLi5Wfn6+brrpJudrlZWVuvHGGxUQEKC333670XfaPeFMnUP3vlagPfYqRXcN1JLMkQoNav6DAwAAgFbr/z0pJLL+7Pn+DV+vd04k17m+n34p4uLiZIxRcnKyp0MBgItq9enfrKwsLVq0SMuWLdOuXbs0c+ZMVVdXKzMzU5I0derUJmfXzM7OVkZGhiIjz58JfeXKldqwYYPzFm033HCDMjIydOONN0r6ukmvrq5Wdna2KisrVVZWprKyMtXV1bU2hUtmjNGvVhfpn8VHFRLgqyXTR6hXeLDb4wAAAF7O118a8sP6xzu/cfm7veH76YnujwkA4HKtmkxOkiZPnqwjR47okUceUVlZmZKTk5Wbm+ucYK6kpOS875bv2bNHmzZt0gcffNDkPktLS5WVlSW73a4ePXpo6tSpevjhh52vFxQU6MMPP5QkXXFF44naDhw4oLi4uNamcUle2rBPK/IPyscmvXDbMCX2CnPr8QEAQCcy9FZp2yJp1ztSbbUUcJlkL6p/rZPN+A4AnYXNGGM8HYQ7VFZWKiwszHlbt7b6W+Ehzc4plCQ9ftMQTUmLa58AAVhee9URK/Lm3IAOzxjp+WTpxGfSLdnSkJul3/WUzn4lzdouRXn+bjPeXEO8OTcArtfWGsLMZ62w9cBxzVn5kSTp7mv70aQDAADXs9m+nlTuozek4wfqm3S/4PrJ5AAAXodGvYX2HflS9yzPV22dQ2OHdNcvx3MrFAAA4CZDzzXq+/KkzzbWP+42UPLx9VxMAACXoVFvgWNf1ihzyTadPHVGyX3C9czkZPn4cK90AADgJlEJUo9kyXFW2rigfl23IR4NCQDgOjTqF/HVmTrN+HO+So6fUp+IYP1pWqqCA/j0GgAAuFnDWfXKQ/V/MpEcAHgtGvULcDiMst4oVEHJSYUF+2vJ9JGK6hLo6bAAAEBnlHiLZPvGr27cQx0AvBaN+gX8Pne33ttZJn9fm/44JUVXdOvi6ZAAAEBn1bW71O+/vn7OPdQBwGvRqDfjjfyD+uPG/ZKkP/zoKn0nPtLDEQEAgE6vYfb3y6KlLtGejQUA4DJ+ng7Aqv4rIVqDe4RqXGJ3ZQzr5elwAAAApKQfSYcLpD7f8XQkAAAXolFvRvewIL0582oF+XPRAQAAsAi/QGnCU56OAgDgYjTqF8Ds7gAAAAAAd+N0MQAAAAAAFkKjDgAAAACAhdCoAwAAAABgITTqAAAAAABYCI06AAAAAAAWQqMOAAAAAICF0KgDAAAAAGAhNOoAAAAAAFgIjToAAAAAABZCow4AAAAAgIX4eToAdzHGSJIqKys9HAmAjqqhfjTUE29CjQRwKaiPANC0ttbHTtOoV1VVSZL69Onj4UgAdHRVVVUKCwvzdBjtihoJoD1QHwGgaa2tjzbjjR99NsHhcOjw4cPq2rWrbDZbi7aprKxUnz59dPDgQYWGhro4Qtfxhjy8IQeJPKykLTkYY1RVVaWePXvKx8e7vjnU2hrpDT8Dknfk4Q05SORhNa3Ng/r4tc76M2BV3pCHN+Qgdd482lofO80ZdR8fH/Xu3btN24aGhnboH6YG3pCHN+QgkYeVtDYHbztT1KCtNdIbfgYk78jDG3KQyMNqWpMH9bGxzvgzYGXekIc35CB1zjzaUh+96yNPAAAAAAA6OBp1AAAAAAAshEb9AgIDA/Xoo48qMDDQ06FcEm/IwxtykMjDSrwhB0/ylr8/b8jDG3KQyMNqvCUPT/CWvzvysA5vyEEij9bqNJPJAQAAAADQEXBGHQAAAAAAC6FRBwAAAADAQmjUAQAAAACwEBp1AAAAAAAspFM16gsXLlRcXJyCgoI0atQobd26tdmxZ86c0W9+8xv1799fQUFBuuqqq5Sbm3tJ+2wv7Z3H/PnzNWLECHXt2lXdunVTRkaG9uzZ4+o0XPLv0eDJJ5+UzWbT/fff74LIv+aKHA4dOqQ77rhDkZGRCg4OVlJSkvLz812ZRrvnUVdXp4cfflj9+vVTcHCw+vfvr8cff1yunLty48aNmjhxonr27Cmbzaa33nrrotts2LBBw4cPV2BgoK644gotXbr0vDGeeI97ijfUSOqjdeqj5B01kvpIfZSoj9TH9ucN9VHq+DXS0vXRdBI5OTkmICDALF682Hz88cdmxowZJjw83Njt9ibHP/jgg6Znz57m3XffNfv27TMvvfSSCQoKMgUFBW3ep1XzSE9PN0uWLDFFRUWmsLDQjB8/3sTGxpovv/yyQ+XRYOvWrSYuLs4MHTrUzJ49u0PlcPz4cdO3b18zffp08+GHH5r9+/eb999/33z66acdKo8nnnjCREZGmnfeecccOHDArFy50nTp0sU899xzLsvjvffeMw899JBZtWqVkWRWr159wfH79+83ISEhJisry3zyySfmhRdeML6+viY3N9c5xhPvcU/xhhpJfbROfXRVHu6ukdRH6qMx1EfqY8fIg98h28bK9bHTNOojR4409957r/N5XV2d6dmzp5k/f36T43v06GFefPHFRut++MMfmttvv73N+2wPrsjj28rLy40k849//KN9gm6Cq/KoqqoyCQkJZu3atWb06NEuLbSuyOEXv/iFufbaa10TcDNckceECRPMnXfeecExrtSSQvvggw+aIUOGNFo3efJkk56e7nzuife4p3hDjaQ+fs3T9dEY76iR1MevUR+pj9TH9uMN9dEY76uRVquPneLS99raWm3fvl1jxoxxrvPx8dGYMWO0efPmJrepqalRUFBQo3XBwcHatGlTm/d5qVyRR1MqKiokSREREe0Q9flcmce9996rCRMmNNq3K7gqh7ffflupqamaNGmSunXrpmHDhmnRokWuSUKuy+Pqq69WXl6e9u7dK0n6z3/+o02bNmncuHEuyKJtNm/efN7PSXp6ujNvT7zHPcUbaiT10Tr1UfKOGkl9pD5K1MfmcmgK9bFlvKE+Sp23RrqzPnaKRv3o0aOqq6tTTExMo/UxMTEqKytrcpv09HQ9/fTTKi4ulsPh0Nq1a7Vq1SqVlpa2eZ9WzOPbHA6H7r//fl1zzTVKTExs9xwk1+WRk5OjgoICzZ8/3yVxf5Orcti/f79efvllJSQk6P3339fMmTN13333admyZR0qj7lz5+rHP/6xBg4cKH9/fw0bNkz333+/br/9dpfk0RZlZWVN5l1ZWanTp0975D3uKd5QI6mP1qmPknfUSOoj9VGiPlIf25831EdX5mH1GunO+tgpGvW2eO6555SQkKCBAwcqICBAs2bNUmZmpnx8OtZfWWvzuPfee1VUVKScnBw3R3phF8vj4MGDmj17tl577bXzPqmzipb8WzgcDg0fPly/+93vNGzYMN1zzz2aMWOGXnnlFQ9G3lhL8njjjTf02muv6fXXX1dBQYGWLVumBQsWuOw/C7ifN9RI6qO1eEONpD5Coj5aCfXROvVRoka2VsepGJcgKipKvr6+stvtjdbb7XZ17969yW2io6P11ltvqbq6Wp9//rl2796tLl26KD4+vs37tGIe3zRr1iy98847Wr9+vXr37u2SHCTX5LF9+3aVl5dr+PDh8vPzk5+fn/7xj3/o+eefl5+fn+rq6iyfgyT16NFDgwcPbrTdoEGDVFJS0q7xN3BVHnPmzHF+IpqUlKQpU6bogQcecNun1S3RvXv3JvMODQ1VcHCwR97jnuINNZL6aJ366Ko8JPfWSOoj9VGiPlIfqY/N6aw10p31sVM06gEBAUpJSVFeXp5zncPhUF5entLS0i64bVBQkHr16qWzZ8/qzTff1E033XTJ+7RSHpJkjNGsWbO0evVqrVu3Tv369XNJ/A1ckcf3v/997dy5U4WFhc4lNTVVt99+uwoLC+Xr62v5HCTpmmuuOe/WJnv37lXfvn3bNf4Grsrj1KlT533q7uvrK4fD0b4JXIK0tLRGeUvS2rVrnXl74j3uKd5QI6mP1qmPrspDcm+NpD5SHyXqI/WR+ticzloj3VofWzX1XAeWk5NjAgMDzdKlS80nn3xi7rnnHhMeHm7KysqMMcZMmTLFzJ071zl+y5Yt5s033zT79u0zGzduNNdff73p16+fOXHiRIv32VHymDlzpgkLCzMbNmwwpaWlzuXUqVMdKo9vc/Wsna7IYevWrcbPz8888cQTpri42Lz22msmJCTE/OUvf+lQeUybNs306tXLeWuNVatWmaioKPPggw+6LI+qqiqzY8cOs2PHDiPJPP3002bHjh3m888/N8YYM3fuXDNlyhTn+Ibba8yZM8fs2rXLLFy4sMnba7j7Pe4p3lAjqY/WqY+uysPdNZL6SH00hvpIfewYefA7ZNtYuT52mkbdGGNeeOEFExsbawICAszIkSPNli1bnK+NHj3aTJs2zfl8w4YNZtCgQSYwMNBERkaaKVOmmEOHDrVqnx0lD0lNLkuWLOlQeXybOwqtK3JYs2aNSUxMNIGBgWbgwIHm1VdfdWkOrsijsrLSzJ4928TGxpqgoCATHx9vHnroIVNTU+OyHNavX9/kz3FD7NOmTTOjR48+b5vk5GQTEBBg4uPjm/yZ98R73FO8oUZSH61TH43xjhpJfaQ+GkN9pD62P2+oj67Iw9010sr10WaMMa07Bw8AAAAAAFylU3xHHQAAAACAjoJGHQAAAAAAC6FRBwAAAADAQmjUAQAAAACwEBp1AAAAAAAshEYdAAAAAAALoVEHAAAAAMBCaNTRKXz22Wey2WwqLCxs8TZLly5VeHi4y2ICACugPgJA06iP8CQadQAAAAAALIRGHQAAAAAAC6FRh9fIzc3Vtddeq/DwcEVGRuoHP/iB9u3b1+TYDRs2yGaz6d1339XQoUMVFBSk73znOyoqKjpv7Pvvv69BgwapS5cuGjt2rEpLS52vbdu2TTfccIOioqIUFham0aNHq6CgwGU5AkBbUB8BoGnUR1gVjTq8RnV1tbKyspSfn6+8vDz5+Pjo5ptvlsPhaHabOXPm6KmnntK2bdsUHR2tiRMn6syZM87XT506pQULFmj58uXauHGjSkpK9POf/9z5elVVlaZNm6ZNmzZpy5YtSkhI0Pjx41VVVeXSXAGgNaiPANA06iMsywBe6siRI0aS2blzpzlw4ICRZHbs2GGMMWb9+vVGksnJyXGOP3bsmAkODjYrVqwwxhizZMkSI8l8+umnzjELFy40MTExzR6zrq7OdO3a1axZs8Y1SQFAO6A+AkDTqI+wCs6ow2sUFxfrtttuU3x8vEJDQxUXFydJKikpaXabtLQ05+OIiAgNGDBAu3btcq4LCQlR//79nc979Oih8vJy53O73a4ZM2YoISFBYWFhCg0N1ZdffnnBYwKAu1EfAaBp1EdYlZ+nAwDay8SJE9W3b18tWrRIPXv2lMPhUGJiompra9u8T39//0bPbTabjDHO59OmTdOxY8f03HPPqW/fvgoMDFRaWtolHRMA2hv1EQCaRn2EVdGowyscO3ZMe/bs0aJFi/Td735XkrRp06aLbrdlyxbFxsZKkk6cOKG9e/dq0KBBLT7uv/71L7300ksaP368JOngwYM6evRoGzIAANegPgJA06iPsDIadXiFyy+/XJGRkXr11VfVo0cPlZSUaO7cuRfd7je/+Y0iIyMVExOjhx56SFFRUcrIyGjxcRMSErR8+XKlpqaqsrJSc+bMUXBw8CVkAgDti/oIAE2jPsLK+I46vIKPj49ycnK0fft2JSYm6oEHHtAf/vCHi2735JNPavbs2UpJSVFZWZnWrFmjgICAFh83OztbJ06c0PDhwzVlyhTdd9996tat26WkAgDtivoIAE2jPsLKbOabX5gAOokNGzboe9/7nk6cOKHw8HBPhwMAlkF9BICmUR/hTpxRBwAAAADAQmjUAQAAAACwEC59BwAAAADAQjijDgAAAACAhdCoAwAAAABgITTqAAAAAABYCI06AAAAAAAWQqMOAAAAAICF0KgDAAAAAGAhNOoAAAAAAFgIjToAAAAAABZCow4AAAAAgIX8fxDUtFtId1h9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n", + "df[[\"c1\", \"c2\"]].plot(ax=ax[1])\n", + "df[[\"c1\", \"c2\"]].plot(ax=ax[2])\n", + "df[[\"r2\"]].plot(ax=ax[0])\n", + "ax[0].set_title(\"R2\")\n", + "ax[1].set_title(\"coefficients\")\n", + "ax[2].set_ylim([-5, 5])\n", + "ax[2].set_title(\"coefficients, échelle tronquée\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le second graphe est trompeur mais il ne faut pas oublier de regarder l'échelle de l'axe des ordonnées." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Indicatrices\n", + "\n", + "$X_1$ est une variable aléatoire gaussienne. On teste maintenant un modèle $Y = X'_1 + X'_2 + \\epsilon$ avec $X'_1 = X_1 \\mathbb{1}_{X_1 < 0}$ et $X'_2 = X_1 \\mathbb{1}_{X_1 \\geqslant 0}$." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On d\u00e9coupe en trois." + "data": { + "text/plain": [ + "array([[ 1. , 0.48561838, 0.0042644 ],\n", + " [ 0.48561838, 1. , -0.01058737],\n", + " [ 0.0042644 , -0.01058737, 1. ]])" ] - }, + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = npr.normal(size=(1000, 3))\n", + "X[:, 1] = X[:, 0]\n", + "X[X[:, 0] >= 0, 0] = 0\n", + "X[X[:, 1] < 0, 1] = 0\n", + "Y = X[:, 0] + X[:, 1] + X[:, 2]\n", + "corrcoef(X.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , -0.00347584, 0.16846101, 0.06722762],\n", - " [-0.00347584, 1. , 0.00326437, -0.04707208],\n", - " [ 0.16846101, 0.00326437, 1. , 0.08754832],\n", - " [ 0.06722762, -0.04707208, 0.08754832, 1. ]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy\n", - "X = npr.normal(size=(1000, 4))\n", - "for i in range(0, 3):\n", - " X[:, i] = X_[:, 0]\n", - "X[:, 3] = X_[:, 2]\n", - "X[X_[:, 0] > -1, 0] = 0 \n", - "X[(X_[:, 0] < -1) | (X_[:, 0] > 1), 1] = 0 \n", - "X[X_[:, 0] < 1, 2] = 0 \n", - "Y = X[:, 0] + X[:, 1] + X[:, 2] + X[:, 3]\n", - "corrcoef(X.T)" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pandas import DataFrame\n", + "\n", + "names = [\"X%d\" % i for i in range(X.shape[1] - 1)]\n", + "ax = (\n", + " DataFrame(X[:50, :2], columns=names)\n", + " .sort_values(names)\n", + " .reset_index(drop=True)\n", + " .plot()\n", + ")\n", + "ax.set_title(\"Représentation des features tronquées\");" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 1.000
Model: OLS Adj. R-squared (uncentered): 1.000
Method: Least Squares F-statistic: 1.581e+33
Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00
Time: 11:29:06 Log-Likelihood: 33532.
No. Observations: 1000 AIC: -6.706e+04
Df Residuals: 997 BIC: -6.704e+04
Df Model: 3
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 1.0000 2.98e-17 3.35e+16 0.000 1.000 1.000
x2 1.0000 2.73e-17 3.66e+16 0.000 1.000 1.000
x3 1.0000 2.09e-17 4.79e+16 0.000 1.000 1.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 20.214 Durbin-Watson: 1.267
Prob(Omnibus): 0.000 Jarque-Bera (JB): 30.796
Skew: 0.179 Prob(JB): 2.05e-07
Kurtosis: 3.781 Cond. No. 1.43


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "from pandas import DataFrame\n", - "names = [\"X%d\" % i for i in range(X.shape[1]-1)]\n", - "ax = DataFrame(X[:50,:3], columns=names).sort_values(names).reset_index(drop=True).plot()\n", - "ax.set_title(\"Repr\u00e9sentation des features tronqu\u00e9es\");" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 1.000
Model: OLS Adj. R-squared: 1.000
Method: Least Squares F-statistic: 1.910e+32
Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00
Time: 10:57:27 Log-Likelihood: 32608.
No. Observations: 1000 AIC: -6.521e+04
Df Residuals: 996 BIC: -6.519e+04
Df Model: 4
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 1.0000 8.75e-17 1.14e+16 0.000 1.000 1.000
x2 1.0000 1.22e-16 8.23e+15 0.000 1.000 1.000
x3 1.0000 8.33e-17 1.2e+16 0.000 1.000 1.000
x4 1.0000 5.23e-17 1.91e+16 0.000 1.000 1.000
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 457.967 Durbin-Watson: 1.816
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1967.636
Skew: -2.198 Prob(JB): 0.00
Kurtosis: 8.282 Cond. No. 2.35


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 1.000\n", - "Model: OLS Adj. R-squared: 1.000\n", - "Method: Least Squares F-statistic: 1.910e+32\n", - "Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00\n", - "Time: 10:57:27 Log-Likelihood: 32608.\n", - "No. Observations: 1000 AIC: -6.521e+04\n", - "Df Residuals: 996 BIC: -6.519e+04\n", - "Df Model: 4 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 1.0000 8.75e-17 1.14e+16 0.000 1.000 1.000\n", - "x2 1.0000 1.22e-16 8.23e+15 0.000 1.000 1.000\n", - "x3 1.0000 8.33e-17 1.2e+16 0.000 1.000 1.000\n", - "x4 1.0000 5.23e-17 1.91e+16 0.000 1.000 1.000\n", - "==============================================================================\n", - "Omnibus: 457.967 Durbin-Watson: 1.816\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1967.636\n", - "Skew: -2.198 Prob(JB): 0.00\n", - "Kurtosis: 8.282 Cond. No. 2.35\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } + "text/latex": [ + "\\begin{center}\n", + "\\begin{tabular}{lclc}\n", + "\\toprule\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 1.000 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 1.000 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 1.581e+33 \\\\\n", + "\\textbf{Date:} & Mon, 07 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 11:29:06 & \\textbf{ Log-Likelihood: } & 33532. \\\\\n", + "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & -6.706e+04 \\\\\n", + "\\textbf{Df Residuals:} & 997 & \\textbf{ BIC: } & -6.704e+04 \\\\\n", + "\\textbf{Df Model:} & 3 & \\textbf{ } & \\\\\n", + "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lcccccc}\n", + " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", + "\\midrule\n", + "\\textbf{x1} & 1.0000 & 2.98e-17 & 3.35e+16 & 0.000 & 1.000 & 1.000 \\\\\n", + "\\textbf{x2} & 1.0000 & 2.73e-17 & 3.66e+16 & 0.000 & 1.000 & 1.000 \\\\\n", + "\\textbf{x3} & 1.0000 & 2.09e-17 & 4.79e+16 & 0.000 & 1.000 & 1.000 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lclc}\n", + "\\textbf{Omnibus:} & 20.214 & \\textbf{ Durbin-Watson: } & 1.267 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 30.796 \\\\\n", + "\\textbf{Skew:} & 0.179 & \\textbf{ Prob(JB): } & 2.05e-07 \\\\\n", + "\\textbf{Kurtosis:} & 3.781 & \\textbf{ Cond. No. } & 1.43 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "%\\caption{OLS Regression Results}\n", + "\\end{center}\n", + "\n", + "Notes: \\newline\n", + " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", + " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "model = OLS(Y,X[:, :4])\n", - "results = model.fit()\n", - "results.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R\u00e9gression lin\u00e9aire par morceaux\n", - "\n", - "On se place dans un cas particulier o\u00f9 le probl\u00e8me est lin\u00e9aire par morceaux :\n", - "\n", - "$$Y = -2 X_1 \\mathbb{1}_{X_1 + \\epsilon_1 <0} + 4 X_1 \\mathbb{1}_{X + \\epsilon_1 > 0} + \\epsilon_2$$\n", - "\n", - "La r\u00e9gression donne de tr\u00e8s mauvais r\u00e9sultat sur ce type de probl\u00e8mes mais on cherche une fa\u00e7on syst\u00e9matique de d\u00e9couper le probl\u00e8me en segments lin\u00e9aires." + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 1.000\n", + "Model: OLS Adj. R-squared (uncentered): 1.000\n", + "Method: Least Squares F-statistic: 1.581e+33\n", + "Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 11:29:06 Log-Likelihood: 33532.\n", + "No. Observations: 1000 AIC: -6.706e+04\n", + "Df Residuals: 997 BIC: -6.704e+04\n", + "Df Model: 3 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 1.0000 2.98e-17 3.35e+16 0.000 1.000 1.000\n", + "x2 1.0000 2.73e-17 3.66e+16 0.000 1.000 1.000\n", + "x3 1.0000 2.09e-17 4.79e+16 0.000 1.000 1.000\n", + "==============================================================================\n", + "Omnibus: 20.214 Durbin-Watson: 1.267\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 30.796\n", + "Skew: 0.179 Prob(JB): 2.05e-07\n", + "Kurtosis: 3.781 Cond. No. 1.43\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" ] - }, + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = OLS(Y, X[:, :3])\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On découpe en trois." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "X = npr.normal(size=(1000,4))\n", - "alpha = [4, -2]\n", - "t = (X[:, 0] + X[:, 3] * 0.5) > 0\n", - "switch = numpy.zeros(X.shape[0])\n", - "switch[t] = 1\n", - "Y = alpha[0] * X[:, 0] * t + alpha[1] * X[:, 0] * (1-t) + X[:, 2]" + "data": { + "text/plain": [ + "array([[ 1. , -0.0221138 , 0.15312241, -0.01158589],\n", + " [-0.0221138 , 1. , 0.02182757, 0.03734989],\n", + " [ 0.15312241, 0.02182757, 1. , 0.01263351],\n", + " [-0.01158589, 0.03734989, 0.01263351, 1. ]])" ] - }, + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "\n", + "X = npr.normal(size=(1000, 4))\n", + "for i in range(3):\n", + " X[:, i] = X_[:, 0]\n", + "X[:, 3] = X_[:, 2]\n", + "X[X_[:, 0] > -1, 0] = 0\n", + "X[(X_[:, 0] < -1) | (X_[:, 0] > 1), 1] = 0\n", + "X[X_[:, 0] < 1, 2] = 0\n", + "Y = X[:, 0] + X[:, 1] + X[:, 2] + X[:, 3]\n", + "corrcoef(X.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(X[:, 0], Y, \".\")\n", - "ax.set_title(\"Nuage de points lin\u00e9aire par morceaux\");" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pandas import DataFrame\n", + "\n", + "names = [\"X%d\" % i for i in range(X.shape[1] - 1)]\n", + "ax = (\n", + " DataFrame(X[:50, :3], columns=names)\n", + " .sort_values(names)\n", + " .reset_index(drop=True)\n", + " .plot()\n", + ")\n", + "ax.set_title(\"Représentation des features tronquées\");" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 0.094
Model: OLS Adj. R-squared: 0.093
Method: Least Squares F-statistic: 104.0
Date: Mon, 15 Oct 2018 Prob (F-statistic): 2.69e-23
Time: 10:59:28 Log-Likelihood: -2594.9
No. Observations: 1000 AIC: 5192.
Df Residuals: 999 BIC: 5197.
Df Model: 1
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 1.0252 0.101 10.197 0.000 0.828 1.222
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 3.882 Durbin-Watson: 1.092
Prob(Omnibus): 0.144 Jarque-Bera (JB): 3.834
Skew: 0.151 Prob(JB): 0.147
Kurtosis: 3.015 Cond. No. 1.00


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.094\n", - "Model: OLS Adj. R-squared: 0.093\n", - "Method: Least Squares F-statistic: 104.0\n", - "Date: Mon, 15 Oct 2018 Prob (F-statistic): 2.69e-23\n", - "Time: 10:59:28 Log-Likelihood: -2594.9\n", - "No. Observations: 1000 AIC: 5192.\n", - "Df Residuals: 999 BIC: 5197.\n", - "Df Model: 1 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 1.0252 0.101 10.197 0.000 0.828 1.222\n", - "==============================================================================\n", - "Omnibus: 3.882 Durbin-Watson: 1.092\n", - "Prob(Omnibus): 0.144 Jarque-Bera (JB): 3.834\n", - "Skew: 0.151 Prob(JB): 0.147\n", - "Kurtosis: 3.015 Cond. No. 1.00\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 1.000
Model: OLS Adj. R-squared (uncentered): 1.000
Method: Least Squares F-statistic: 3.030e+31
Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00
Time: 11:29:06 Log-Likelihood: 31722.
No. Observations: 1000 AIC: -6.344e+04
Df Residuals: 996 BIC: -6.342e+04
Df Model: 4
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 1.0000 2.07e-16 4.84e+15 0.000 1.000 1.000
x2 1.0000 2.87e-16 3.49e+15 0.000 1.000 1.000
x3 1.0000 2.01e-16 4.97e+15 0.000 1.000 1.000
x4 1.0000 1.3e-16 7.66e+15 0.000 1.000 1.000
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 457.510 Durbin-Watson: 1.879
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1715.476
Skew: 2.280 Prob(JB): 0.00
Kurtosis: 7.514 Cond. No. 2.20


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "model = OLS(Y,X[:, :1])\n", - "results = model.fit()\n", - "results.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "yp = results.predict(X[:, :1])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "text/latex": [ + "\\begin{center}\n", + "\\begin{tabular}{lclc}\n", + "\\toprule\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 1.000 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 1.000 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 3.030e+31 \\\\\n", + "\\textbf{Date:} & Mon, 07 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 11:29:06 & \\textbf{ Log-Likelihood: } & 31722. \\\\\n", + "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & -6.344e+04 \\\\\n", + "\\textbf{Df Residuals:} & 996 & \\textbf{ BIC: } & -6.342e+04 \\\\\n", + "\\textbf{Df Model:} & 4 & \\textbf{ } & \\\\\n", + "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lcccccc}\n", + " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", + "\\midrule\n", + "\\textbf{x1} & 1.0000 & 2.07e-16 & 4.84e+15 & 0.000 & 1.000 & 1.000 \\\\\n", + "\\textbf{x2} & 1.0000 & 2.87e-16 & 3.49e+15 & 0.000 & 1.000 & 1.000 \\\\\n", + "\\textbf{x3} & 1.0000 & 2.01e-16 & 4.97e+15 & 0.000 & 1.000 & 1.000 \\\\\n", + "\\textbf{x4} & 1.0000 & 1.3e-16 & 7.66e+15 & 0.000 & 1.000 & 1.000 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lclc}\n", + "\\textbf{Omnibus:} & 457.510 & \\textbf{ Durbin-Watson: } & 1.879 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 1715.476 \\\\\n", + "\\textbf{Skew:} & 2.280 & \\textbf{ Prob(JB): } & 0.00 \\\\\n", + "\\textbf{Kurtosis:} & 7.514 & \\textbf{ Cond. No. } & 2.20 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "%\\caption{OLS Regression Results}\n", + "\\end{center}\n", + "\n", + "Notes: \\newline\n", + " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", + " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", - "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", - "ax.legend()\n", - "ax.set_title(\"R\u00e9gression lin\u00e9aire sur un nuage lin\u00e9aire par morceaux\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Passons \u00e0 un arbre de d\u00e9cision qui n'est pas le meilleur mod\u00e8le mais on va d\u00e9tourner ses r\u00e9sultats pour revenir \u00e0 un probl\u00e8me de r\u00e9gression par morceaux." + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 1.000\n", + "Model: OLS Adj. R-squared (uncentered): 1.000\n", + "Method: Least Squares F-statistic: 3.030e+31\n", + "Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 11:29:06 Log-Likelihood: 31722.\n", + "No. Observations: 1000 AIC: -6.344e+04\n", + "Df Residuals: 996 BIC: -6.342e+04\n", + "Df Model: 4 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 1.0000 2.07e-16 4.84e+15 0.000 1.000 1.000\n", + "x2 1.0000 2.87e-16 3.49e+15 0.000 1.000 1.000\n", + "x3 1.0000 2.01e-16 4.97e+15 0.000 1.000 1.000\n", + "x4 1.0000 1.3e-16 7.66e+15 0.000 1.000 1.000\n", + "==============================================================================\n", + "Omnibus: 457.510 Durbin-Watson: 1.879\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 1715.476\n", + "Skew: 2.280 Prob(JB): 0.00\n", + "Kurtosis: 7.514 Cond. No. 2.20\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" ] - }, + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = OLS(Y, X[:, :4])\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression linéaire par morceaux\n", + "\n", + "On se place dans un cas particulier où le problème est linéaire par morceaux :\n", + "\n", + "$$Y = -2 X_1 \\mathbb{1}_{X_1 + \\epsilon_1 <0} + 4 X_1 \\mathbb{1}_{X + \\epsilon_1 > 0} + \\epsilon_2$$\n", + "\n", + "La régression donne de très mauvais résultat sur ce type de problèmes mais on cherche une façon systématique de découper le problème en segments linéaires." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "X = npr.normal(size=(1000, 4))\n", + "alpha = [4, -2]\n", + "t = (X[:, 0] + X[:, 3] * 0.5) > 0\n", + "switch = numpy.zeros(X.shape[0])\n", + "switch[t] = 1\n", + "Y = alpha[0] * X[:, 0] * t + alpha[1] * X[:, 0] * (1 - t) + X[:, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.tree import DecisionTreeRegressor\n", - "model = DecisionTreeRegressor(min_samples_leaf=10, max_depth=3)\n", - "model.fit(X[:, :1], Y)\n", - "yp = model.predict(X[:, :1])" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(X[:, 0], Y, \".\")\n", + "ax.set_title(\"Nuage de points linéaire par morceaux\");" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.107
Model: OLS Adj. R-squared (uncentered): 0.106
Method: Least Squares F-statistic: 119.3
Date: Mon, 07 Oct 2024 Prob (F-statistic): 2.56e-26
Time: 11:29:06 Log-Likelihood: -2555.7
No. Observations: 1000 AIC: 5113.
Df Residuals: 999 BIC: 5118.
Df Model: 1
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 1.0940 0.100 10.924 0.000 0.897 1.290
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 3.084 Durbin-Watson: 1.111
Prob(Omnibus): 0.214 Jarque-Bera (JB): 2.960
Skew: 0.088 Prob(JB): 0.228
Kurtosis: 2.801 Cond. No. 1.00


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", - "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", - "ax.legend()\n", - "r2 = r2_score(Y, model.predict(X[:, :1]))\n", - "ax.set_title(\"Arbre de d\u00e9cision sur un nuage lin\u00e9aire par morceaux\\nR2=%f\" % r2);" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABKAAAAHxCAYAAABas8RJAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzdf3RcdZ3/8deF4g/0mGHFBKimrrop1fWksCopiN1NUA7FmXpWgvmxEXGTOFmJW2z2bMGZ0y9ncijHnTmytruJmSJm4yRzmoq2s1B1yXACnjbtLppZtlZrWzuzlCUDHDMuh4OFcr9/ZO8lk0ySSTI/8uP5OGcOM5/7uZ/7vnPm0/a++fwwTNM0BQAAAAAAAOTJRcUOAAAAAAAAACsbCSgAAAAAAADkFQkoAAAAAAAA5NWaYgcAAACWt//+7//WyMhIscPAMnDxxRfL5XJpzRr+CQoAwGpjsAg5AABYjC9/+ct6+OGHix0Glokf/vCH+tznPlfsMAAAQIHxv58AAMCi/OEPf1BDQ4NCoVCxQ8ESZxiGXnnllWKHAQAAioA1oAAAAAAAAJBXJKAAAAAAAACQVySgAAAAAAAAkFckoAAAAAAAAJBXJKAAAAAAAACQVySgAAAAAAAAkFdrih0AAADAQhiGkfbZNM2CnAsAAID5YwQUAAAoKsMwpr0CgYB9PBqNTjs+mWma0xJIkUhELpdLLpdLkUhk2jUznbNUzBV7JslkUsFg0P5+wuFwxjper3fWOlNZbQIAACwWCSgAAFBU4+PjGhoaktPplCQNDAxo+/bt9vHq6mqNjo7K6XTqyJEjcyaOwuGwgsGg+vr61NfXp8cee0zBYDAvscdiMXm93py1t5DYU6mUmpubJU0k1sbGxtTf358WVzKZ1JkzZ+Tz+WSapgYGBlRfX5+W6JsqFouptbU1NzcGAABWPcNcqv/7DwAALAuNjY2SpFAotKh2ksmknUjp6+tTSUmJJCmRSOiuu+7S3r17VVpaate3RuZM/qdMIpHQunXrdOTIEVVVVUmaSKRs3LhRo6OjqqysTLtmpjayiTMajWp4eFibN29WdXV1WlwLNd/YLeFwWPX19RofH7e/M+u8oaEhVVdXa2RkxG7TMtu9p1Ip+f1+dXZ2zlhnIQzDUCgUUkNDQ07aAwAAywcjoAAAwJJQWloqn8+nSCSivXv32uW7du2alnyayeHDhyVJV111lV125ZVXSpKOHTu2qPis0U67d+/Whg0b1NXVpbq6upwkn6SFx97f3y9JdvJJkt7//vdLkgYHByVpWvIplUpJkjweT8Y29+7dq/b29vmEDwAAMCsWIQcAAEtGZWWlDh48KJfLpWuuuUanT5/Wfffdl3WSZ3h4WJJUXl5ul1nnRiIRtbS0zCueqaOd2tvbZ4wl27WSZhpNtNDYM60TZSWjuru71dXVlXYskUjY0/qampqmnRuNRnXDDTfkLLEGAAAgMQIKAAAsMU6nU36/XzU1NfrgBz84r0RId3f3jMeyXdB7srKyMp07d04PPPDAnKOdrIXN53rlOna32y1JOnny5Jz3Y03zs6bWTW03mUzq9OnT00ZMAQAALBYJKAAAsOTU1tbK6XTqwQcftKeLFcPY2JjWrl2rHTt2KBwOK5lMFi2Wmdxxxx2SpG9961v2dxWLxSRJfr8/rW55eblM09To6Kg8Ho86OjrSFjk/cODAvEeJAQAAZIMEFAAAWFKSyaROnTqlPXv2TFsPai7WTnqZWCOF5qO0tFR1dXXq6urShg0btHv3bnm9XjvBM5lhGFm9ch17VVWVhoaGdO7cOTkcDgWDQb300kuSpJtuuinjOZWVlfb0O2unu0gkoptvvnnG6wAAACwGCSgAALCkHDhwQNXV1SovL1dPT486Ojo0MjKS1blWEmfySKVEIiFJuvbaaxcVV2VlpXw+n9rb23XixAm1tbWljYpa7BS8xcReXV2tgwcPyjRNtbS06Be/+IU8Hs+MO+dJUkVFRdpnl8uldevWZUyWZbu+FQAAwExIQAEAgCUhlUopEAikTQFraWmR0+nU/fffn9UaR9YInjNnzthlzz33XNqxxco0KioXchV7OBzW8PCwOjo6Zq1nTdcbGBiQlDmBZpktcQYAAJANElAAAKDoYrGYmpqaMk4Z6+vrUyQS0fr16xWNRmdtxxo11dvbq1QqpVQqpd7eXvX09KTtLpcr1qioXMg2dq/XK6/Xm3ZuKpVSLBZTW1ubzp07p4MHD9o74UkTo5sCgYA9oiqVSsnv98vj8aiuri4n8QMAAMxmTbEDAAAAq9vk6V2RSERDQ0Oqrq6W9OaubZaamhpJs4/IaWlpUSQSkcPhkNPp1LZt2+z2lrqFxG59fz09PXK73Rmn3bW0tMjlctmjovx+v2699VZ2uwMAAAVjmIypBgAAi9DY2ChJCoVCBb2ulXhZzD9lctEGsmcYhkKhkBoaGoodCgAAKDCm4AEAAAAAACCvSEABAAAAAAAgr1gDCgAALGsLmUY3ed0pAAAA5B8JKAAAsCwtZt0m1nwCAAAoLKbgAQAAAAAAIK9IQAEAAAAAACCvSEABAAAAAAAgr0hAAQAAFEksFlMwGJTL5Zp1YfRUKiXDMDK+wuFwWr2RkRG7zdmuO7mNtra2nN4XAADAVCxCDgAAUASBQEDDw8NqaWnRnj17dPDgwRnrnjhxYsZj1dXV9nu/3y9J6uzsnPXax44dS/u8ZcuWbEIGAABYMBJQAAAABdbW1qbLL79cfX19KikpmbP+2bNnFY/HVV5ebpclk0nt3r1bpaWldpnP55M0dwLqiiuuYCdAAABQUEzBAwAAS1oymVQ4HLanlEUiEXvaWCKRkCSFw+FpZZZAICDDMBQMBpVMJqdNdUsmk3Ydl8ulaDSa1/vxer2SJpJF2SSfpIlRTpOTT5IUjUZ12223zfv6iURCLpdLXq9XIyMj8z4fAABgIUhAAQCAJa25uVn19fWKRCKKxWJyOp06cuSIuru7tWvXLo2MjKiurk7xeNwuswQCAdXW1so0Td1+++3avXt3WtvJZFLNzc1au3atTNPUtm3bVFNTo1gsljGWmdZhmvqaSSwWU2dnp7Zs2aJgMJh10mvyKCfL8PCwKisrZz1vphikiVFSmzZtksvlUjKZnHc7AAAA80ECCgAALGmT10ayEi5VVVWSpO7ubvu9NUKou7vbrt/R0aG3ve1tkqSSkhK1t7entR2NRhWJRFRXVyfpzfWU9u/fnzEW0zSzes3k8ccft2NtaWnR+Pi41q5dq5qamnmNRorFYtq8eXPW9SdzOp0aHx/X6OioPB6PIpGIDhw4sKC2AAAAskUCCgAArFhut1tlZWUKh8NKpVIqLS1NSxD19/dL0rTRS3OtobRQHR0dkt5MpJWUlMjtdkuSent7s25n//79aYuPz1dJSYkqKyvl8/nU09OjSCSy4LYAAACyQQIKAACsWHfffbecTqfq6+vlcDgUCATSjluJl2xHMS12Cl4mVjJq8sit2VjT5TJNy1uI22+/nQQUAADIOxJQAABgxaqoqNDBgwc1Ojoqt9utjo6OaUkoSTp58mRW7S12Cp412imVSk075nQ6s4phoYuPz2TyKCwAAIB8IQEFAABWLMMwlEqlVFlZqa6uLo2OjtrT4CSpp6dHktTX12cnhaxd8fKhtrZWknT27Fm7zLpuQ0NDVm0sdPHxmaRSKTsuAACAfCEBBQAAlrTJO7RNThJNPZ6pTJL8fr8SiYQk6bLLLpPf77ePbd26VdLEmk8Oh0OGYaisrCxvCZnq6mp5PB55vV47xn379snpdNoLoUuS1+uV1+uddn42i49PHl01daRVOBxO23EvkUjoySefXNR6UgAAANkgAQUAAJa0srIy+73D4ZhWZr3PVCZJ7e3tGhwclGEYGhwc1Pbt2+1jpaWlisfj8ng8kiamyMXjcXtHvXzw+XxyOp0qKyuz14vq6+vL6ty5Fh83DMP+jiTZSTXLO97xDtXU1MgwDHm9Xv3ud7/LeuofAADAYhjmbAsVAAAAzKGxsVGSFAqFihwJljrDMBQKhbKebggAAFYORkABAAAAAAAgr0hAAQAAAAAAIK9IQAEAAAAAACCvSEABAAAAAAAgr0hAAQAAAAAAIK9IQAEAAAAAACCvSEABAABMkkwmFQ6H5XK5ih0KAADAikECCgAAYJKdO3eqvr5ekUik2KHMKpVKaWRkRMFgcNZkWSQSkcvlksvlmvGeclUHAABgJmuKHQAAAMBS0tXVpe7u7mKHMSe/3y9J6uzsnLFOOBxWf3+/+vr6JEk7duzQ888/r5aWlpzXAQAAmI1hmqZZ7CAAAMDy1djYKEkKhUJFjiR3DMOQJC2HfybNFGsikdC6det05MgRVVVVSZJisZg2btyo0dFRVVZW5qzOfGINhUJqaGjIxa0DAIBlhCl4AACgKAKBgAzDUDAYVDKZtBMp0sT0smAwKMMwZBiGvF6vksmkpOlrNEUiERmGoba2NiUSCUkTI3amliWTSXsamSS7/ba2Np08eXLOeJPJpB2zy+VSNBrN+n6K4fDhw5Kkq666yi678sorJUnHjh3LaR0AAIC5kIACAAAFFwgEVFtbK9M0dfvtt2v37t1px3fs2KHW1laNjY0pHo+rs7NTO3fulCQ1NzfbazTFYjE5nU4dOXJE3d3d2rVrl0ZGRlRXV6d4PG6XSVJZWZm9ftHIyIhaWlo0Pj4uSVq/fv2sSahkMqnm5matXbtWpmlq27ZtqqmpUSwWy+p+prISa3O9FmN4eFiSVF5ebpeVlpZKkr2GU67qAAAAzIUpeAAAYFEWMgXPMAyNjY3ZiYxkMqmysjJ7GpnX69WLL76orq4uu7705jSzTNPOsinLVMeaTub3+7V9+/aM9cLhsOrr66e17fF45PP55ryffJppCl425bmqM59YmYIHAMDqxAgoAABQcG63W2VlZQqHw0qlUiotLU1LZPh8PnV1dSmRSCgQCOQ1FmsNo46Ojhnr9Pf3S9K00UnWAuBz3Q8AAMBqRwIKAAAU3N133y2n06n6+no5HI6MSaZgMKi77rpLTqezCBGms6aamaY57SVldz+TFWIK3mzfm9vtzmkdAACAuZCAAgAABVdRUaGDBw9qdHRUbrdbHR0daUmbcDis1tZW7dmzRxUVFQWJKZtkykzrRM11P1NlSmTNlNxaKCtxZC3eLslekP3aa6/NaR0AAIC5kIACAAAFZxiGUqmUKisr1dXVpdHR0bQpcPX19ZLSF77OFyuptGXLlhnr9PT0SJL6+vqUSqUkvbkrnjT3/RTDzTffLEk6c+aMXfbcc8+lHctVHQAAgLmQgAIAAEXh9/vtkTSXXXaZ/H6/fcwadZNIJNJGHSWTybSROJOTQZPrzFRmCYfD9vl9fX1yOp0ZR/pY77du3SppYs0nh8MhwzBUVlam2trarO4nX6z7n/pemkje9fT0qLe3V6lUSqlUSr29verp6bETe7mqAwAAMBcSUAAAoCja29s1ODgowzA0ODho70AnTSxCLk2sA+VwOOTxeOR2u/Xqq6+qrKzMrudwOCQprcx6n6nMsmHDBrlcLjkcDpWXl6uvry9jXet9aWmp4vG4PB6PpInpevF4PC0BM9v95INhGPb9S7ITY5O1tLRoy5YtcjgcampqUm1trVpaWvJSBwAAYDaGyRYtAABgERobGyVJoVCoyJHMzUrQ8M+f4jAMQ6FQSA0NDcUOBQAAFBgjoAAAAAAAAJBXJKAAAMCqMNuaUAAAAMgvElAAAGBVmG1NKAAAAOTXmmIHAAAAUAis+wQAAFA8jIACAAAAAABAXpGAAgAAAAAAQF6RgAIAACteMplUOByWy+UqdigAAACrEgkoAACw4u3cuVP19fWKRCLFDmVOiURCbW1tMgxDbW1tikajOTsvlUppZGREwWCQZBwAACgoElAAAGDF6+rqKnYIWUmlUorFYurq6tL4+Lg2b96smpqaORNn2Z7n9/v16KOPqrW1dVkk4wAAwMphmGwJAwAAFqGxsVGSFAqFihzJ7AzDkLS0d8OLRCJyOp1pZdnEPd/zivVdGIahUCikhoaGgl4XAAAUHyOgAADAkjUyMiLDMNJelkAgYJclEgmlUikFg0G7zOv1KplMZmw3U3uZyqSJ9aOsa7lcrqynxC3E1CSSxe125+U8AACAQiEBBQAAlqyqqioNDQ1JkjweT9qIne3bt8vj8Wh0dFTl5eXasWOHWltbNTY2png8rs7OTu3cuTNju2NjY9PK4vH4tLJkMqnm5matXbtWpmlq27ZtqqmpUSwWy9ju1GTZTK9spVIpSdKWLVuyPmcx5wEAAOQLCSgAALCkVVdXy+PxqLOz006sSG8mWSorKyVJl19+udxut0pLS1VeXi5J6u7uzthmaWnptDLrnMmi0agikYjq6ursWCRp//79Gds1TTOrV7aefvppOZ1OfepTn8r6nMWcBwAAkC8koAAAwJJ32223SZIOHTpklz399NN2uST5fD51dXUpkUgoEAjk5Lr9/f2Spk/P6+zszEn7c3nwwQd17733qqSkpCDnAQAA5AsJKAAAsORVVlbK6XTaCSFJeuKJJ+zRT5ZgMKi77rprxjWR5svaKS7bUUy5nIIXDofldDpVVVU1r5gXeh4AAEA+kYACAADLQkNDgyKRiEZGRpRIJPSJT3wi7Xg4HFZra6v27NmjioqKnF775MmTWdXL1RS8WCym48ePq6WlZV5xLvQ8AACAfCMBBQAAlgVr/aXe3l4dPnx42vpG9fX1kjKv5bRQPT09kqS+vj57zSlrV7x8SSaTevzxx+Xz+eyyWCymtra2vJwHAABQCCSgAADAslBaWiqPx6Pu7m6dO3du2vpG1rS7RCKRNmIpmUwqmUymfZYkt9st6c3RTSMjI3YdK2mzdetWSRNrPjkcDhmGobKyMtXW1ub69uzYmpub1dHRkTZlb+PGjWk72nm9Xnm93nmfJynjQu4AAAD5RgIKAAAsG9ai45nWeLJG/gSDQTkcDnk8Hrndbr366qsqKyuz61nv77nnHjmdTq1fv16RSERVVVVyOp0aGBjQfffdJ2ki6RWPx+XxeCRNJK3i8XhOR1lNtnPnTnvdqanWr1+/6PMMw5DD4bA/W0k1AACAfDPM+ewFDAAAMEVjY6MkKRQKFTkSLHWGYSgUCqmhoaHYoQAAgAJjBBQAAAAAAADyigQUAAAAAAAA8ooEFAAAAAAAAPKKBBQAAAAAAADyigQUAAAAAAAA8ooEFAAAAAAAAPKKBBQAAAAAAADyigQUAAAAAAAA8mpNsQMAAADLX39/v1577bWiXPv8+fN6y1veUpRrLzdvvPGGLly4oEsuuaTYoQAAgFWGBBQAAFiUurq6oiWfXn75ZUWjUV1zzTV63/veV5QYlpOjR4/qwoULuuGGG2QYRsGvX1dXp+rq6oJfFwAAFJ9hmqZZ7CAAAADmK5VK6YYbbtDb3vY2Pfnkk7r00kuLHdKSd/ToUVVXV6u5uVn/+I//WOxwAADAKsIaUAAAYNm5cOGCbrvtNqVSKf3oRz8i+ZSl6667Tt/97ne1e/dudXV1FTscAACwijAFDwAALDvt7e06fPiwnnzySb33ve8tdjjLyhe+8AWdOnVK7e3tWr9+PVPiAABAQTAFDwAALCv/9E//pPb2dj3yyCP63Oc+V+xwliXTNNXU1KR//dd/1cjIiK6++upihwQAAFY4ElAAAGDZOHTokJxOpzo7O7Vjx45ih7OsnT9/Xp/61Kf00ksv6fDhw3rPe95T7JAAAMAKxhpQAABgWXjmmWdUX1+vxsZGkk858Ja3vEWRSEQXLlxQbW2tzp8/X+yQbMlkUuFwWC6Xq9ihAACAHGEEFAAAK4xhGPOqvxz+KfDCCy+oqqpK73vf+/TTn/5Ub3nLW4od0orxzDPP6MYbb9TnPvc5fe9731twO9n+7rL5vbW1tam7uzvr+gAAYOljBBQAACvQwMCATNO0X5bJZQMDA0WMMHvnz5+Xy+XSRRddpMHBQZJPOfbRj35UAwMD+v73v68HHnhgwe2Ypqnx8fG0z5NfQ0NDWbfFDn0AAKw8jIACAGCFMQxj2qgRa3TK5PJUKiWHw7GkR5hMXiz7yJEj2rBhQ7FDWrH27Nmjr33ta/rhD3+orVu3LridTL+1ycey/b3N1g4AAFh+GAEFAMAKE4/Hs6pXUlKieDyuZDKpSCQil8ulVCqltrY2eb1eGYZhvyyZyqSJNXsCgYAMw5DL5VI0Gs3JvezatUvhcFiPPPIIyac8u+uuu+R2u9XQ0KCf//znOW17pgRoMBi0f09er1fJZHLWdqzfWDAYVDKZLNjvEAAALB4JKAAAVpjy8vJ51W1ubpbL5VIkEtGJEyfkdrv14osvamxsbFr9TMmtZDKp5uZmrV27VqZpatu2baqpqVEsFlvUfezbt08ej0e7d+9WdXX1otpCdnbv3q3rr79eW7du1bPPPpuTNhOJRMbyHTt2qLW1VWNjY4rH4+rs7NTOnTtnbCcQCKi2tlamaer222/X7t27047n63cIAABygyl4AACsAnNNZ7KOj4+Pq6SkZNbzppaFw2HV19dPq+PxeOTz+RYU79GjR1VdXa2//uu/1re//e0FtYGFSaVSuv7663XppZdqeHhYl1566bzOn2kx8qm/Pa/XqxdffNFe72nq7yrT57GxMZWWlkqaSDiVlZXl9XcIAAByhwQUAACrQLYJqGzWjppaZo2eymQh/8x49tlndd111+maa67RgQMHdPHFF8+7DSzOqVOndP311+uTn/ykfvCDH8xrZ8Wpv49EIqF169bN+FtIJBIaHBxUR0dH2nlT27F2xhsYGNAtt9ySliiVcv87BAAAuUUCCgCAVSCfCahcLhb9yiuvqKqqSpL01FNPTUsyoHCeeuop3XTTTfr617+uXbt2ZX3eTL+ZTL+PYDCoSCQiv9+v9evXp503tZ2TJ0+qo6PDTjL5/X5t37591usCAIClY02xAwAAACvDyZMnVVFRseDzL1y4oPr6ej3//PM6duwYyaciu/HGG7V371598Ytf1Ic//GE1NTUtuK1MSaFwOKzW1lbF4/Gs1i2rqKjQwYMHFYvF1N3dbY+YmpyEkhb/OwQAAPnBIuQAAGBRenp6JEl9fX1KpVKS3tyNbD7uvfde/eQnP1EkEtH73//+XIeJBWhqatKOHTvU3Nysp556Kqdt19fXS8p+0XzDMJRKpVRZWamuri6Njo7aSSgpd79DAACQHySgAABY4SZvbZ9pm/tMZRa32y1pYlSJJI2MjNjH2traJElbt26VJHV2dsrhcMgwDJWVlam2tjbrGPfu3at/+Id/0EMPPaTrrrsu6/OQf/fff79uvfVWff7zn9epU6dmrWslfqa+z8TpdEqaWAPK+n1JE7/HmX6zfr/f3lXvsssuk9/vt4/l4ncIAADyhwQUAAArmPUQbikrK5u2oPTk4y6XK+3YPffcI6fTqfXr1ysSiaiqqkpOp1MDAwO67777JEmlpaWKx+PyeDySJpJW2U6rkqRoNKqvfvWr+sY3vqHGxsYF3SfyxzAMff/739e6deu0devWGRNLhmHI4XDYn60k0EysnemCwaAcDoc8Ho/cbrdeffXVab9ZS3t7uwYHB2UYhgYHB9Om3y32dwgAAPKLRcgBAEDRnDp1SlVVVfr0pz+t/v7+ee22hsJ69tlntWnTJl199dX68Y9/zO6EAABgXkhAAQCAohgfH9fHP/5xvfvd71Y0GtWll15a7JAwh5///Oe68cYbdccdd+if//mfix0OAABYRpiCBwAACu78+fP6/Oc/r1dffVU/+tGPSD4tE9dee636+/vV3d2tPXv2FDscAACwjJCAAgAABfe1r31Nx44d02OPPaYrrrii2OFgHrZu3ar7779f27Zt06FDh4odDgAAWCaYggcAAAoqEAjo7//+7/XII49MW/Qcy8eXvvQl/ehHP9JTTz2lj370o8UOBwAALHEkoAAAQMEcPHhQf/mXf6kHHnhAHR0dxQ4Hi3D+/Hl95jOfUSKR0NGjR/We97yn2CEBAIAljAQUAAAoiGeeeUY33HCD6uvr9Z3vfKfY4SAHXnjhBV1//fW6/PLLNTw8rLe85S3FDgkAACxRJKAAAEDejY2N6WMf+5gqKip06NAhEhUryK9+9StVVVXps5/9rPr6+mQYRrFDAgAASxCLkAMAgLx65ZVXtHXrVr3tbW/T/v37ST6tMFdffbUeeeQRhcNh3X///cUOBwAALFEkoAAAQN6Ypqnm5mb95je/0aFDh3TZZZcVOyTkQXV1tXbv3i2v16t9+/YVOxwAALAErSl2AAAAYOXauXOnfvCDH+jQoUP60Ic+VOxwkEdtbW361a9+pTvvvFPr1q3TddddV+yQAADAEsIaUAAAIC9CoZCampr0ne98Ry0tLcUOBwVw4cIFbd26VT//+c917Ngxvfe97y12SAAAYIkgAQUAAHLu6NGj+vM//3O1t7frm9/8ZrHDQQGlUindeOONkqSRkRFdeumlRY4IAAAsBSSgAABATsXjcX384x/Xpk2b9Mgjj+jiiy8udkgoMH4DAABgKhYhBwAA8/byyy/rhRdemFaeSqXkdDp1xRVXqL+/n8TDKrVu3TpFIhH95Cc/0T333JOxzvHjxwscFQAAKCYSUAAAYN6qqqpUWlqqRx55xC67cOGC/uqv/kovvviiHnvsMb3jHe8oYoQotuuuu04PPfSQ/H6/9u7da5e//vrramlp0Z/+6Z/q0KFDRYwQAAAUEgkoAAAwLy+//LJ+85vfSJJuu+027dq1S5L09a9/XdFoVD/84Q9ZfBqSpMbGRnk8Hn31q19VNBrV7373O33605/Www8/LElpiSkAALCysQYUAACYl+9+97tqaWnRG2+8IUkyDEOf+MQndOzYMQ0MDOgLX/hCkSPEUmKaphoaGvTjH/9Y73rXu/Q///M/eu211yRJa9as0fPPP693v/vdRY4SAADkGyOgAADAvASDwbTPpmnqP/7jP7Ru3TrV1NQUKSosVYZh6Etf+pJeeeUVPffcc3byydLf31+kyAAAQCGRgAIAAFn7zW9+o6NHj9qjnywXLvRxVyEAACAASURBVFzQuXPn9Gd/9mc6ceJEkaLDUvTwww/rs5/9rC5cuKDXX3897diFCxfU3d1dpMgAAEAhkYACAABZ++53v6s1a9ZkPPbaa6/pueee08aNG/Xoo48WODIsNaZp6m//9m/15S9/Wa+//rouXLiQsc4vf/lL/eIXvyhChAAAoJBYAwoAAGTlwoULuuqqq5RMJuese/nll+uFF14oQFRYqn7729/qAx/4wJz1LrnkErndbn37298uQFQAAKBYGAEFAACy8tOf/nTW5NPFF18swzBUX1+v//zP/yxgZFiK/viP/1hPPPGErr76al100cz/5HzttdfU29urP/zhDwWMDgAAFBoJKAAAkJWHHnpoxul3F110kTZs2KAnn3xS/f39uvLKKwscHZaiP//zP9czzzyjBx98UO985zt1ySWXZKz38ssv68CBAwWODgAAFBJT8AAAwJxeeuklXXHFFdMWkb7kkkv09re/Xbt27dJXvvIVXXzxxUWKEEvdiy++qL/7u79Tb2+vLr744rTf0po1a/QXf/EX+ulPf1rECAEAQD4xAgoAAMypv78/7fOaNWtkGIbuvPNOnT59Wn/zN39D8gmzuvzyy/Xwww/r6NGj2rhxoy666CIZhiFJev311zU0NKRnn322yFECAIB8IQEFAADm1NPTowsXLsgwDBmGoWuuuUZPP/20vvOd7+jyyy8vdnhYRj7+8Y/r6NGjCgaDcjgc9rTOiy66SP/yL/9S5OgAAEC+MAUPAADM6umnn9bHPvYxSROjWAKBgJqamuzRK8BCjY+P6//9v/+n3bt364033pAkvfHGG/y2AABYgUhAAQCK6vnnn9fdd9+tCxcuFDsUzODEiRP6r//6L1VUVOjDH/7wjAtJ50NTU5OcTmfBrod09957r06dOpX36/z+97/XE088ofPnz+uWW27RO9/5zrxfE/PzoQ99SPfff3+xwwAALGMkoAAARdXf36/GxkbV1tYWOxTM4PXXX9f58+d16aWXFvS6g4ODamhoUCgUKuh18SZrJFKh+ufvf/97vetd7yrItZC9wcFBSRKPDQCAxci8lzIAAAW2b9++YoeAJaaxsbHYIUBSKBRSQ0NDscNAEVn/owAAgMVgEXIAAAAAAADkFQkoAAAAAAAA5BUJKAAAAAAAAOQVCSgAAAAAAADkFQkoAAAAAAAA5BW74AEAVgVrO3nLfLYTX8y5AOZG/wQAYOVjBBQAYFkxDGPaKxAI2Mej0ei045OZpjntATUSicjlcsnlcikSiUy7ZqZzlrJUKqWRkREFg0G5XK55nTvXdzFVMBic9h0vpA5WBvrnmxbTDyebqf9Y34thGHK5XAqHwwuqAwBAwZgAABRRKBQy5/PX0fj4uDk0NGQ6nU5TkjkwMDCtzujoqOl0Os0jR47YZZIyXmdgYMB0Op3m+Pi4OT4+brrdbrOnpyfjtWdqI1ujo6Omx+NZ8PnZ8ng8psfjmXe88/kuTHPifua6RjZ1ZtLQ0GA2NDTM+zzkjiQzFAplXZ/++aaF9sOpMWU63+/3m5LM0dHRtHp+v39edbI13z+nAQDIhL9JAABFtdAHm7GxMdPpdNoPp5Z4PG46nU5zbGwsrX6mh7h4PG5KSnsQth7SrIe2udrIJs6BgQHT7XabAwMD0+LKp/nEO9/vYnx8fM6H62zqzIYEVPHNNwFloX8uLi7TnL3/zFTmdDrnVSdbJKAAALnAFDwAwLJUWloqn8+nSCSivXv32uW7du3S3r17VVpaOmcbhw8fliRdddVVdtmVV14pSTp27Nii4ovFYvJ6vdq9e7c2bNigrq4u1dXVZRVXMcz3u9i7d6/a29tnbTObOliZ6J+LN1v/8fv9kqSRkRFJUiKRkCT5fL551QEAoJBYhBwAsGxVVlbq4MGDcrlcuuaaa3T69Gndd999WT9EDg8PS5LKy8vtMuvcSCSilpaWecWTTCYVjUY1PDyszZs3q729fcZYsl0TySzQ2jbz+S6i0ahuuOGGWb/nbOpgZaN/Ltxc/Wf79u0aHx/Xpk2bdOTIEZ09e1ZjY2Np9bOpAwBAITECCgCwrDmdTvn9ftXU1OiDH/zgvB6uuru7ZzyWzQLcU5WVlencuXN64IEH5hxNYf7fwslzvQol2+8imUzq9OnTqqqqmrF+NnWwOtA/5y/b/uPz+eR2u7Vp0yYdP35cb33rWxdUBwCAQiEBBQBY9mpra+V0OvXggw8qlUoVLY6xsTGtXbtWO3bsUDgcVjKZzOv1ZttNLF8OHDgw58iTbOpg9Vit/XOhsu0/gUBAmzdv1vj4uCSpqalp2vebTR0AAAqFBBQAYFlLJpM6deqU9uzZM229mbk4nc4Zj7nd7nnHUlpaqrq6OnV1dWnDhg3avXu3vF6vYrHYtLqZtqvP9CqUbL6LSCSim2++edZ2sqmD1YP+OT/Z9p9wOKyOjg7dcsstKikpUVNTkyKRiPbt2zevOgAAFBIJKADAsnbgwAFVV1ervLxcPT096ujosBfdnYv1gDt5JIS1UO+11167qLgqKyvl8/nU3t6uEydOqK2tLW3URS6m+ORyOlA234XL5dK6desyPoBb77Opg9VjNffPhci2/9TX10uSSkpKJE1ML5Sk1tbWedUBAKCQSEABAJalVCqlQCCQNlWlpaVFTqdT999/v06ePDlnG9ZIgzNnzthlzz33XNqxxco06mIpyua7mO0B3HqfTR2sfPTPhcm2/0wdHWYlmSaXZ1MHAIBCIgEFAFh2YrGYmpqadNNNN0071tfXp0gkovXr1ysajc7ajjUqo7e3V6lUSqlUSr29verp6UnbeStXrFEXhTB5nZdMa754vV55vV77c6G/C6xc9M83zbcfZmvbtm2SJqbZSbJHlVnl2dYBAKCQSEABAJYVwzC0ceNGRSIRbdy4Me0hNpFIyOFw2J9ramrmnPbV0tKiLVu2yOFwqKmpSbW1tct+AW3DMNK+B4fDkdX0t5X4XaCw6J9vWmg/zEZ1dbWGhoY0PDwswzDU29uroaEhVVdXz6sOAACFZJiMhwcAFFF/f78aGxvzPj3LevBbzHVy0Qay19jYKEkKhUJFjmT1MgxDoVBIDQ0Neb+ORP9cqgr15zQAYGVjBBQAAAAAAADyigQUAAAAAAAA8mpNsQMAAKCQFjJNJ1frtgCYHf0TAICViwQUAGBVWMzaJax7AuQX/RMAgJWPKXgAAAAAAADIKxJQAAAAAAAAyCsSUAAAAAAAAMgrElAAACwDyWRSXq9XhmHIMAyFw+GcnbeQtoPBIIs/A5PMp0/EYjG7vxmGoba2tml1IpGIXC6XXC6XIpFIxnasOoZhyOVyZf3nAgAAxUACCgCAJS6ZTOrMmTPy+XwyTVMDAwOqr69XIBBY9HkLaTsWi6m1tTVn9wcsd/PtE8eOHUv7vGXLlrTP4XBYwWBQfX196uvr02OPPaZgMJhWJxAIyOVy2X3X5/Nl9ecCAADFQgIKAIAl7syZM6qqqrI/19XVSZI6OjoWfd58206lUtq/f/887wBYuRbSJ6644gqZpmm/nE6nfSyRSKi+vl733nuvSkpKVFJSIrfbrdbWVsViMbue1UcrKyvT/js8PLzYWwIAIC9IQAEAVqRkMqlwOCyXyyVpYqqKNdUlkUhImhhlMLXMEggEZBiGgsGgksnktKk1yWTSruNyuRSNRvN2L5MTRNLEA68keTyeRZ8337b37t2r9vb2LKIG0q2kPjnZfPtEIpGQy+WS1+vVyMjItOOHDx+WJF111VV22ZVXXikpfeSU3++XJLsN6/vy+XzzvAMAAApjTbEDAAAgH5qbm+11U2KxmJxOp44cOaJNmzZJku644w7V1dXp+uuv17p16yRJXV1dkiYedGtra7V9+3alUin7Qc+STCbV3NyshoYGmaapaDSqmpoajY6O2qMQJst2XRjTNOesk0gk7Kk4TU1NWbWb7Xlz1YlGo7rhhhtUWlqa9XUBy0rskwvpE9Yops7OTnV2dsrpdGrv3r12G9YIpvLycvsc61gkElFLS4skafv27RofH9emTZt05MgRnT17VmNjY/RPAMDSZQIAUEShUMjM119Hkqa1nU2ZJHNsbMz+PDY2lnZ8YGAgYxsejyeX4U8Tj8ftWCWZfr8/Z+fNVWdsbMzs6emxP2f6HnOtoaHBbGhoyOs1MDtJZigUyml7K6VPLqZPjI+Pm6Ojo6bH4zElZdXOTOVut9u+1/Hx8QXcydzy+ec0AGD1YAoeAABTuN1ulZWVKRwOK5VKqbS0NG0kRH9/vySl7WIlTYxoyKfy8nKZpqnR0VF5PB51dHRMW5h4oefNVefAgQP2yAug0JZin1xMnygpKVFlZaV8Pp96enpm3OVuLoFAQJs3b9b4+LikiZGL1jRaAACWGhJQAABMcffdd8vpdKq+vl4Oh2ParlLWw6I5aRFh65XJ5Ifi2V7ZqqystKfIzWfnrWzOy1QnEono5ptvzvo6QK4ttT6Zyz5x++23pyWgJi9IPpXb7bbfh8NhdXR06JZbblFJSYmampoUiUS0b9++nMQFAECukYACAGCKiooKHTx4UKOjo3K73ero6Mi4tfnJkyezai/TQ3G2D8qzxbgQ2Zw3tY7L5dK6desyPpjPJ3EGLNRS65O57BPWLncWKwGVTCbtMmuB8WuvvdYuq6+vt8+XpLKyMknzS0oDAFBIJKAAAJjCMAylUilVVlaqq6tLo6Oj9pbnktTT0yNJ6uvrs6e7WDtwFYp13YGBgZyfN7XObA/m802cAQux1PpkLvtEKpVSbW2t/dkaWXXmzBm77Lnnnks7Jk0fKWUlomYbQQUAQDGRgAIArEiTRw9MfiCdejxTmTSxxbk16uCyyy5L23Vr69atkibWl3E4HDIMQ2VlZWkPkbnkcrkUCATseKxdwDwej+rq6ux6Xq9XXq93Xudl2zawWCupT2Zrap8Mh8OKRqP250QioSeffFLV1dV2WXl5uXp6etTb26tUKqVUKqXe3l719PSk7Yy3bds2u01JGhkZSSsHAGCpIQEFAFiRrOkokuRwOKaVWe8zlUlSe3u7BgcHZRiGBgcHtX37dvtYaWmp4vG4PB6PpIl1WeLxeNrDYS61tLSoo6PDnvKzd+9e3XrrrfL5fIs+b6FtA/O1kvrkQr3jHe9QTU2NDMOQ1+vV7373u4wjllpaWrRlyxY5HA41NTWptrZ22oLn1dXVGhoa0vDwsAzDUG9vr4aGhtKSWQAALCWGydh5AEAR9ff3q7GxkalcmKaxsVGSFAqFihzJ6mUYhkKhkBoaGoodCoqIP6cBALnACCgAAAAAAADkFQkoAAAAAAAA5BUJKAAAAAAAAOQVCSgAAAAAAADkFQkoAAAAAAAA5BUJKAAAAAAAAOQVCSgAAPIgmUwqHA7L5XIVOxQAok8CAFBsJKAAAMiDnTt3qr6+XpFIpNihzCkSicjlcskwDLlcLoXD4bTjiURCbW1tMgxDbW1tikaj09pIpVIaGRlRMBjkAR9L0nLpk3P1pVQqJcMwMr4m991kMqlgMJjxGAAAxUACCgCAPOjq6ip2CFkJBAJyuVzy+XwyTVM+n0/19fUKBAKSJh52Y7GYurq6ND4+rs2bN6umpmbaQ7zf79ejjz6q1tbWJf+Aj9VpufTJufrSiRMnZjy3urpa0kS/bW5uliSZpqmxsTH19/fL6/XmJ2gAALJAAgoAgFWso6NDklRZWZn23+HhYUnSk08+KafTKUkqKSlRXV2dJE0bmeHz+eTz+QoSM7CSzdWXzp49q3g8LtM07dfY2Jg8Ho9KS0slSYcOHVIkEtHtt98uSSotLZXP51NnZ2fGEYwAABQCCSgAwLIWCARkGIaCwaCSyaQMw7CPpVKptCkoXq9XyWRS0vT1YCKRiD3FLJFISJLC4fC0smQyaU9Zk2S339bWppMnT84ZbzKZtGN2uVzTHgZnu5988Pv9kqSRkRFJsu/TegC2kk9Tud3uvMaF5Ys+mV/V1dUqLy9PK4tGo7rtttvsz/39/ZImksaW97///ZKkwcHB/AcJAEAmJgAARRQKhcyF/nXk9/vNeDxumqZpjo+Pmx6PJ60tt9ttSjLHxsbMeDxuSjLdbrdpmqbpdDpNSaYkc3R01DRN0zxy5Ihd58iRI6ZpmtPOs86RZNcZHx+3r/XrX//avr5VzzI2NmY6nU5zYGDANE3THBoaSrv+XPcz1eRYZnvNxbrOkSNHzIGBAXNsbGzGuuPj46Yk8+DBg7PGlAsNDQ1mQ0NDTtrCwkgyQ6FQ1vXpk7npk5linY31Xcx17kL752L+nAYAwMLfJACAolrMg431IGsZGxtLa8vj8aQ9mE19+Mr0MJZNWaY6o6OjpiTT7/fPWG9gYCBj2x6PJ6v7ySfrYd3j8Zjj4+Mz1hsaGjKdTueMdUhArSzzTUDRJ3Mn2740OjpqJ9AsmZJv82lzKhJQAIBcYAoeAGDZcrvdKisrUzgcViqVUmlpqUzTtI/7fD51dXUpkUjYi2rni7V2krWmUibWtJjJu1ZJUmdnp6S57ydfAoGANm/erPHxcUlSU1OTUqlUxroPPvig7r333rSpPYCFPll4+/fvtxcft9xxxx2SpG9961t2X47FYpLenHYLAEChkYACACxbd999t5xOp+rr6+VwODI+0AaDQd11110zrmVUSNaOVuakxYOtl5Td/Uw201bsU1+zCYfD6ujo0C233KKSkhI1NTUpEolo3759Ges6nU5VVVUt8BvASkefXHyfnA9r/Sxr8XFLVVWVhoaGdO7cOTkcDgWDQb300kuSpJtuuiln1wcAYD7WFDsAAAAWqqKiQgcPHlQsFlN3d7c90mH79u2SJhImra2tisfj0xbtzZdsFuc+efKkKioqppXPdT9T5WIkRn19vaQ3FysuKyuTJLW2tqqlpcWuF4vFdPz4cXa6w6zok4UdHTV18fHJqqur00ZGBQIBeTwee2QYAACFxggoAMCyZRiGUqmUKisr1dXVpdHR0bTpNlZypRAPutZuW1u2bJmxTk9PjySpr6/PnhZj7cAlzX0/+TB1FIqViJpcnkwm9fjjj6cln2KxmNra2vIaG5Yf+mRhDQ8PZ5VQCofDGh4eXlKxAwBWHxJQAIBlze/329uxX3bZZWnrm1hJlEQikbYdezKZtKeuSEp78JxcZ6YySzgcts/v6+uT0+m0r5npvK1bt0qaWF/G4XDIMAyVlZWptrY2q/vJh23btqXdy8jISFp5MplUc3OzOjo60qYQbdy4cdqD/eR1o2ZaQworH31y8bLpS7FYTJs3b561DStRfO7cOR08eJC12wAARUUCCgCwrLW3t2twcFCGYWhwcDBtaow1YicYDMrhcMjj8cjtduvVV1+1p5pJksPhkKS0Mut9pjLLhg0b5HK55HA4VF5err6+vox1rfelpaWKx+PyeDySJqYGTZ2KNNv95EN1dbWGhoY0PDwswzDU29uroaEhe+rOzp077XVyplq/fr393jAM+3uUZD/MY/WhTy5Otn0p0+LjU9s4duyY3G533mMGACAbhrnUtvIAAKwq/f39amxsXHI7S83GehhcTjEvR42NjZKkUChU5EhWL8MwFAqF1NDQUOxQZkWfzK/l+Oc0AGDpYQQUAAAAAAAA8ooEFAAA8zDb+jMACo8+CQDA8kACCgCAeZht/RkAhUefBABgeVhT7AAAAFhOWAMFWFrokwAALA+MgAIAAAAAAEBekYACAAAAAABAXpGAAgDg/ySTSYXDYblcrmKHAkD0SQAAVhISUAAA/J+dO3eqvr5ekUik2KHMKZVKaWRkRMFgcF4P58lkUl6vV4ZhyDAMhcPhae1ax6a+ptaNRCJyuVwyDEMul2vacWCxllOfnCwWi9l90zCMOetbfcnlcmW810Qioba2NhmGoba2NkWj0TnbDAaDWV0bAIBCIQEFAMD/6erqKnYIWfP7/Xr00UfV2tqa9cN5MpnUmTNn5PP5ZJqmBgYGVF9fr0AgYNc5ceLEjOdXV1fb7wOBgFwul92Wz+eb1hawWMupT1oCgYC8Xq+uuOIK7dmzZ85F0sPhsILBoPr6+tTX16fHHntMwWDQPp5KpRSLxdTV1aXx8XFt3rxZNTU1s/b7WCym1tbWnN0TAAC5QAIKAIBlyOfzyefzzeucM2fOqKqqyv5cV1cnSero6LDLzp49q3g8LtM07dfY2Jg8Ho9KS0vtetY5lZWVaf8dHh5e2A0BK0BbW5vGx8fV19cnp9Op8vLyWesnEgnV19fr3nvvVUlJiUpKSuR2u9Xa2qpYLCZJevLJJ+V0OiVJJSUldr+daeRjKpXS/v37c3hXAADkBgkoAMCyNzIyMm26mCUQCNhliURCqVTKnppiGIa8Xq+SyWTGdjO1l6lMmhhdZF3L5XJlNUWm0CYnn6SJB1VJ8ng8dll1dfW0h+ZoNKrbbrstrczv90ua+O6liQdpSfNOimFlWo190uv1SproAyUlJVmdc/jwYUnSVVddZZddeeWVkqRjx45Jkp18msrtdmcs37t3r9rb27MLGgCAAiIBBQBY9qqqqjQ0NCRpIpkyecrL9u3b5fF4NDo6qvLycu3YsUOtra0aGxtTPB5XZ2endu7cmbHdsbGxaWXxeHxaWTKZVHNzs9auXSvTNLVt2zbV1NTYIximmmmNpZke2PMhkUjYSaSmpia7fPIoJ8vw8LA9wslifa+bNm3SyMiIDh8+rLGxsWn1sDqttj4Zi8XU2dmpLVu22Mm0bJJe1ojByUlfqw/ONMXOShxv2bJl2rFoNKobbrghYz8GAKDYSEABAFaE6upqeTwedXZ22g9o0psPa1Zi5PLLL5fb7VZpaan90Nfd3Z2xzUwPcZmm1ESjUUUiEXtqjLVW0kzTYCZPb5vtlS+JRELr1q1TZ2enpJkfdKWJB+vNmzdnPObz+eR2u7Vp0yYdP35cb33rW/MSL5an1dQnH3/8cTuWlpYWjY+Pa+3ataqpqbFHCWYy031KM/fLp59+Wk6nU5/61KfSypPJpE6fPj1tpCMAAEsFCSgAwIphTRM7dOiQXfb000+nTR/z+Xzq6upSIpHI2YLZ/f39kqZPBbISPEtNeXm5TNPU6OioPB6POjo60hY9nmz//v1pi49PFggEtHnzZo2Pj0uaGEk1OdEArJY+OXVNNGstJ0nq7e3N6bUefPBBe82oyQ4cOKCWlpacXgsAgFwiAQUAWDEqKyvldDrth09JeuKJJ6ZNCwsGg7rrrrtmXFtlvqyRCtmOmFgKU/Ckie/Lmn6Xaccsax2eTKNOwuGwOjo6dMstt6ikpERNTU2KRCLat29ffoPGsrKa+6R1j7ONcprtfjOt8RQOh+V0OqeNcopEIrr55pvnFR8AAIVGAgoAsKI0NDQoEoloZGREiURCn/jEJ9KOh8Nhtba2as+ePaqoqMjptU+ePJlVvWJPwZtstu8g0+Ljlvr6ekmyR2GUlZVJypzIwuq2GvqklSzKNAJwtiSTdWzyouvWgv7XXnttWt1YLKbjx49nHOXkcrm0bt26GRdpBwBgKSABBQBYUazpYr29vTp8+PC0dVKsxMlc26PPR09PjySpr6/PfgC1duBa6qx4BwYGph3LtPi4ZepDtZWIytUIFqwcq6FP1tbWSpLOnj1rl1nXbWhomPE8a9TSmTNn7LLnnnsu7Zg0Efvjjz+etstkLBZTW1ubpNlHehUqmQ0AwFxIQAEAVpTS0lJ5PB51d3fr3Llz09ZJsRIkiUQibXREMplMG4VgvbdGNlh1Jy8obD38bd26VdLE+jIOh0OGYaisrMx+KM2XTAs7T+b1eu2t4aWJURKBQMAeYZFKpeT3++XxeOzFmi2zLT4uSdu2bZM0MXpFevN7scoBy2rok9aC616v145z3759cjqdaX1rap8sLy9XT0+Pent7lUqllEql1Nvbq56eHjshZ+3o19HRkTbCaePGjRl3wgMAYKkiAQUAWHGsaWOZRuNYIwiCwaAcDoc8Ho/cbrdeffVVexqZ9OaUsnvuuUdOp1Pr169XJBJRVVWVnE6nBgYGdN9990maeMCOx+PyeDySJh6Q4/F4Tkd0TGUYhhwOh/3ZesieTUtLizo6OuypOnv37tWtt96aNqrCMtvi49LEA/fQ0JCGh4dlGIZ6e3s1NDQ06zlYvVZDn/T5fHI6nSorK7P7Yl9f35zntbS0aMuWLXI4HGpqalJtbW3aNLudO3fOuCPe+vXrcxM8AAAFYJiMywUAFMkvf/lLff3rX9dPfvITpolgmsbGRklSKBQqciSrl2EYCoVCs04jw8rX39+vxsZG/pwGACwKI6AAAAX361//Wg0NDfroRz+qX/7yl8UOBwCQhfb2do2NjRU7DADAMkUCCgBQMCdPntQXv/hFfeQjH1EsFtPAwIB27dpV7LAAAFn4wQ9+oA996EPauXOn/vd//7fY4QAAlhkSUACAvDt16pTuuOMOfeQjH9G///u/q6+vT88884xuv/12tggHgGXi9OnT+sY3vqFvf/vb+sAHPqAHH3xQ58+fL3ZYAIBlggQUACBvfvvb3+rOO+/Uhg0bdPToUX3ve9/T8ePHVV9fr4su4q8gAFhO3v72t2vHjh06ffq07rzzTt177736kz/5E/X29uqNN94odngAgCWOf/0DAHLu7Nmzamlp0fr16/Wzn/1MDz30kI4fP67GxkYSTwCwzP3RH/2RvvnNb+rkyZP6zGc+o+bmZlVWVs64Wx8AABIJKABADsXjcX3lK1/R+vXrFY1G1dPToxMnTuiLX/yiLr744mKHBwDIofe+970KBoN65plnVFFRoa1bt+qTn/ykfvaznxU7NADAEkQCCgCwaM8++6za2tpUUVGhf/u3f1NXV5d+/etf60tf+v/s3Xl8E3X6B/BPOOq9BYWCLqJA5VhkQXQpIodW5DShRYo9QBShFtd6QEWEVMCyBTRVFljBFFDp2IDPkwAAIABJREFUtqktK5Daqki7gtIDXU0V0EIXLAusjbAmigfSMr8/+E3skbSTq98cn/fr1dfKZDJ5ptvn+U6emfnOg+jQoYPo8IiIyIv69++Pf/zjHygrK0NISAhGjRoFtVqNL774QnRoRETkQ9iAIiIil508eRLJyckIDw/HO++8g3Xr1qGqqgqzZ89m44mIKMhERESgpKQE7777Lk6ePIkhQ4Zg1qxZqKmpER0aERH5ADagiIjIaadOncITTzyB8PBw7Ny5E2vWrMHhw4eRmJiIjh07ig6PiIgEGj9+PP71r38hOzsbpaWl6Nu3L5588kmcPn1adGhERCSQSpIkSXQQRETkH2pra7Fy5Uro9Xpcc801ePbZZzFnzhyEhIS4vM0dO3YgOjrag1FSIHnooYewZcsW0WEELZVKJToE8iGufG349ddfsWnTJqxYsQI//PADUlJSsGDBAlx55ZVeiJCIiHwZG1BERNQqs9mMF154ARs2bECnTp3wzDPP4JFHHsEll1zi9rbr6upgNBpRX1/vgUj93/Tp0/H4449j5MiRokPxCcOHD8f1118vOoygVVZWhhMnTogOQ6i1a9cCAB5//HHBkYjVo0cP3H777S6//+zZs/jrX/8KnU6Hjh07IjU1FY888ohbJzCIiMi/sAFFREQOffvtt3jxxRfxyiuv4KqrrsKiRYuQmJiIyy67THRoAUulUiE7Oxvx8fGiQyEiAAkJCQCA7OxswZEEhtOnT2PVqlV45ZVX0L17dzz//POIj49Hu3acGYSIKNCx0hMRUTNnzpzBokWL0Lt3b2zduhVpaWk4evQonnjiCTafiIjIZV26dIFOp8NXX32FO++8Ew8++CBuueUWFBYWig6NiIi8jA0oIiKy+d///getVotevXphy5YteO6553D06FE89dRTbDwREZHH9OzZE1u2bMHnn3+O3r17495778Xo0aNRVlYmOjQiIvISNqCIiAjfffcdnnvuOfTq1QuvvvoqlixZgqNHj+Lpp5/G5ZdfLjo8IiIKUH/4wx+wfft2lJWVoV27dhgxYgSioqJw6NAh0aEREZGHsQFFRBTErFYrli1bhl69euGVV17BokWLcOzYMTzzzDN8QhEREbWZ4cOH44MPPkBRURG+/vprDBo0CA899BCOHz8uOjQiIvIQNqCIiILQ999/j7S0NPTq1Qtr167FggULcOzYMTz77LNsPBERkTATJ07Ep59+iq1bt2Lv3r3o27cvFixYgNOnT4sOjYiI3MQGFBFREPnhhx/wl7/8Bb169cJLL72EJ554AseOHUNqaiquuuoq0eERERGhXbt2SEhIwJdffgmdTofs7GyEh4djxYoVOHv2rOjwiIjIRWxAEREFgbNnz2LVqlXo1asXXnzxRSQnJ+PYsWNYunQpQkNDRYdHRETUTEhICB577DFUV1dj/vz5eOGFF3DTTTfhb3/7G3799VfR4RERkZPYgCIiCmA//vgjXnzxRfTu3Rvp6elISkrC0aNHsWzZMnTq1El0eERERK268sorbU9ljY2Nxfz58/GHP/wBBoMBFy5cEB0eEREpxAYUEVEA+umnn5CRkYHevXvj+eefx5w5c3Ds2DGsWLECV199tejwiIiInNalSxe8/PLLOHLkCEaOHIkZM2bgtttuw7vvvis6NCIiUoANKCKiAPLzzz9jzZo16N27N5YtW4aHHnoIx44dQ3p6Oq655hrR4REREbmtZ8+eeP311/H555+jR48emDhxIu666y6Ul5eLDo2IiFrABhQRUQD45ZdfsHbtWoSHh2PJkiWYMWMG/v3vf2PVqlXo0qWL6PCIiIg8buDAgTAajfjoo49QV1eHESNGYOrUqTh06JDo0IiIyA42oIiI/Ni5c+ewfv16hIeHY9GiRbj//vtx9OhR6HQ6hIWFiQ6PiIjI6+644w58+OGHMBqNqK6uxuDBgzFnzhz85z//ER0aERE1wAYUEZEf+vXXX7FhwwaEh4dj4cKFmDZtGo4ePYqXXnoJ3bp1Ex0eERFRm7v33nthMpmwZcsW7N69G/369cPTTz+NM2fOiA6NiIjABhQRkV/59ddfodfr0bdvXzz11FOIiorCkSNHsGbNGnTv3l10eEREREK1a9cOM2fOxOHDh7Fy5Uq8/vrrCA8PR3p6On766SfR4RERBTU2oIiI/MD58+exadMm9OvXD8nJyZg8eTKqq6uxbt06/P73vxcdHhERkU8JCQnBE088gWPHjuGJJ57AypUrER4ejo0bN6Kurk50eEREQYkNKCIiH1ZXV4fXXnsN/fr1w5///GdMmDAB1dXV+Nvf/oYePXqIDo+IiMinXXnllVi2bBmOHj2KadOm4YknnsCAAQPw5ptvQpIk0eEREQUVNqCIiHxQXV0dtm7digEDBuCRRx7B3XffjaqqKmzYsAHXX3+96PCIiIj8SteuXbF27Vp89dVXGD58OOLj43Hbbbdh165dokMjIgoabEAREfmQ+vp6ZGVlYeDAgXj44YcxevRoVFVVITMzEzfeeKPo8IiIiPxar169kJWVhc8++wzXXnstxo8fj7Fjx+Ljjz8WHRoRUcBjA4qIyAdcuHABOTk5GDhwIGbPno0RI0agqqoKmzdvRq9evUSHR0REFFD++Mc/4u2338bevXvx008/ISIiAjExMaiqqhIdGhFRwGIDiohIoAsXLuDNN9/EoEGD8MADD2DYsGE4ePAgXnvtNfTu3Vt0eERERAFt1KhRKC0txfbt23Ho0CHcfPPNSExMxMmTJ0WHRkQUcNiAIiIS4MKFC8jPz8fgwYMRHx+PW265BQcPHsTWrVvRt29f0eEREREFlSlTpuCLL75AZmYm3nvvPdx0001YtGgRLBaL6NCIiAIGG1BERG1IkiS89dZbGDJkCGJjYzFo0CAcPHgQf//739GvXz/R4REREQWtdu3a4cEHH8Thw4fxl7/8xXYb/OrVq/Hzzz+LDo+IyO+xAUVE1AYkScLOnTsxdOhQxMTEoH///qisrEROTg769+8vOjwiIiL6f5dccgmeeuopVFdX47HHHsOKFSsQHh4OvV6Puro60eEREfktNqCIiLxIkiQUFBTgtttuQ3R0NPr06QOTyYS8vDzcfPPNosMjIiIiB0JDQ5GWloYjR44gOjoaycnJuPnmm7Ft2zZIkiQ6PCIiv8MGFBGRlxQWFmLYsGGYMmUKbrjhBphMJmzbtg2DBg0SHRoREREp1L17d6xfvx6HDh3C0KFDcf/99yMiIgLFxcWiQyMi8itsQBERedi7776L4cOHQ61W49prr8Unn3yCt956C3/84x9Fh0ZEREQu6tOnD3JycvDJJ5/gmmuuwdixYzFu3Dj861//Eh0aEZFfYAOKiMhDdu3ahREjRmDixIno2rUr9u/fD6PRiKFDh4oOjYiIiDzklltuwTvvvIN//vOf+OGHH/CnP/0JsbGxqK6uFh0aEZFPYwOKiMhNxcXFGDlyJMaPH4/OnTtj//79tnmfiIiIKDDdeeedKC0txT/+8Q988cUXGDBgAObNm4f//ve/okMjIvJJbEAREbnogw8+wJgxYzB27FhcccUVKC0tRWFhIf70pz+JDo2IiIjagEqlQnR0NCorK7Fx40YUFhYiPDwcixcvhsViER0eEZFPYQOKiMhJe/fuxV133YW77roLISEh+Oijj/Dee+/h9ttvFx0aERERCdChQwc8/PDDqKqqwvLly6HX69GnTx/odDr8/PPPosMjIvIJbEARESm0b98+3H333RgzZgzatWuHDz/8EO+//z7uuOMO0aERERGRD7jsssuQkpKC6upqJCUlYdmyZejXrx82b96Muro60eEREQmlkiRJEh0EEZEvKysrw7Jly7Br1y6MHj0ay5cvx5133ik6LAoAubm50Gq16NGjh23Z559/jp49e6JTp04AgDNnzmDEiBF49dVXRYVJFDSOHz+OcePGoXv37o2WAUDPnj1ty7755hvs27cP11xzTZvHSP7lv//9L55//nls2rQJN910E1asWIHo6GioVCrRoRERtTk2oIiIHKioqMCyZcvw7rvvYuTIkVi+fDkiIyNFh0UBZOnSpXj++ecVrcvhmsj7Dhw4gEGDBiled+DAgV6OiAJFdXU1tFot8vLyMGzYMKxatYons4go6PAWPCKiJj755BNMnjwZw4cPxw8//ID3338fH374IZtP5HHx8fGtrtOxY0csX768DaIhoptvvhn9+/dvdb3+/fuz+UROCQ8PR25uLj755BOEhobirrvuwsSJE/HZZ5+JDo2IqM2wAUVE9P8+/fRTaDQaDBs2DGfOnME777yDjz76CGPHjhUdGgWofv36YeDAgS3einH+/HlFjSoi8oxZs2ahY8eODl/v2LEjZs2a1YYRUSAZOnQo3nvvPRQXF+PMmTO47bbbEB8fj+rqatGhERF5HRtQRBT0TCYToqKicNttt+Gbb77B22+/jfLyckyYMEF0aBQEZs2ahfbt29t9TaVS4ZZbbkF4eHgbR0UUvKZPn97iZNF1dXWYPn16G0ZEgSgyMhIVFRV488038emnn2LgwIH485//jG+++UZ0aEREXsMGFBEFrS+++AJTp07F0KFDcfLkSRiNRuzfvx+TJk0SHRoFkdjYWNTX19t9rX379rzSgqiN9e7dG7feeqvdKxNVKhVuvfVW9O7dW0BkFGhUKhWmTZuGAwcOYP369di5cyduuukmpKamwmq1ig6PiMjj2IAioqBz4MABTJ8+HUOGDMGxY8ewfft27N+/H/fee6/o0CgIXX/99RgxYgTatWs+JNfX1yMmJkZAVETBzdGViWwKkzd06NABc+fOxZEjR6DVarF+/XqEh4fjpZdewi+//CI6PCIij2EDioiCxqFDhxAbG4vBgwejqqoK27Ztw6effoopU6bwccgk1IwZM5r9DbZr1w6jRo3CddddJygqouA1ffp0u0+elCSJt9+R11x22WV45plncOzYMTz88MPQarXo168fXnvtNVy4cEF0eEREbmMDiogCXlVVFeLj4zFo0CAcOnQIeXl5MJlMiI6OZuOJfMK0adOaLVOpVHjggQcERENEYWFhGD16dKOroNq3b4/Ro0cjLCxMYGQUDDp16oRVq1ahuroaEyZMQGJiIgYNGoSdO3eKDo2IyC1sQBFRwDp8+DAeeOABDBw4EJWVlcjJyYHJZMJ9993HxhP5lC5duuCee+5p9GVXpVLhvvvuExgVUXB74IEHGl0FJUkSm8LUpq677jq8+uqrOHjwIP7whz8gOjoaI0aMwN69e0WHRkTkEjagiCjgVFdXY9asWRg4cCA+/vhjZGVl4YsvvsD9999vd54dIl8wc+ZM25fdDh06YPz48ejUqZPgqIiC19SpU5tdATV16lSBEVGw6tu3L/Lz81FRUYHLL78cY8aMweTJk1FZWSk6NCIip/CbGBEFjGPHjuGhhx7CgAEDUFFRgddffx0HDx5EXFwcG0/k86ZMmYKOHTsCuDj5+MyZMwVHRBTcfve730GtVqNDhw7o0KED1Go1fve734kOi4LYn/70J+zevRu7du1CbW0thg4dipkzZ+LYsWOiQyMiUoTfyIjI73399deYO3cu+vXrh48++gibN2/GgQMHkJCQwMYT+Y0rrrgCGo0GAHDppZdCrVYLjoiI4uPjUVdXh7q6OsTHx4sOhwgAcM899+Djjz+GwWBAeXk5+vfvj8cffxxms1l0aERELeI3MyLyOZWVlfj+++9bXa+mpgaPPPII+vXrh5KSEuj1enz55Zd44IEH0KFDhzaIlMizZsyYAQC47bbbcPnllwuOhogmT55s97+JRFOpVJg+fTq+/PJL/PWvf8W2bdvQp08fLF26FGfPnnX4vqioKKhUKpw4caINoyUi+n8SkYctWbJEAsAf/tj9qaioaPHvZ9u2bRIAaerUqQ7X+c9//iPNmzdPCgkJkXr16iVt3rxZOn/+vKf/lKmNVVRUCP/75I/v/CxZskT0nyRJkhQSEiL8b4E/vvETEhIi+s+RWvDjjz9K6enpUqdOnaQuXbpIa9askc6dO9donVOnTtn+/wwPD5fMZrPD7fF4nj8A8548j5cIkMcdO3YMHTt2RHZ2tuhQyMdMnz4d1dXVGDZsmN3XCwsLERsbCwDYuXMnjh07hl69etleP3nyJFatWoXMzEx0794d69atw4MPPoiQkJA2iZ+8q7q6GgCQl5cnOBISLSEhgXOa+Ihff/0VUVFRvP0syOXk5GDHjh2iw6AWXH755Xj22WfxyCOPYOXKlXj22Wfx8ssv4/nnn8eMGTPQrl07PP/88+jYsSPOnz+PmpoaTJgwAXv27MGVV17ZbHs8nifmPXkDG1DkFTExMYiJiREdBvmR3bt3Izo6GvX19QBgO1B67bXXcOrUKaxevRp6vR5du3bFmjVrMHv2bDaeAhRrB/GA17dwTKfz588zL/3E1VdfjRdffBGPP/44li9fjocfftj278zMTNtx1vnz5/H5559j8uTJeP/99+0eUzH3gxvznryBc0ARkXAffvgh7r33XtTX19seQ3/+/Hls3boVc+bMQXh4ON566y1kZGSguroaSUlJbD4REREROXD99ddj06ZN+Pzzz9G3b188/vjjzR7MUldXh9LSUsTFxdkaU0RE3sQGFBEJVVFRgYkTJ6Kurg4XLlxo9Fr79u1RUVGBVatW4ciRI3j00UfZeCIiIiJSaMCAAdBqtTh37hzOnz/f7PW6ujrs3LkTjz32mIDoiCjYsAFFRMKYTCaMHTsWv/zyi90zb+fPn8ehQ4cwYcIEXHrppQIiJCIiIvJvTz/9dItPB66vr8err76KZ599tg2jIqJgxAYUEQlx8OBB3HXXXQ6bT7L27dtj+fLlbRgZERERUWAoKSlBcXGx3aufGpIkCatWrcKaNWvaKDIiCkZsQBFRmzty5AjuvPNOnD17FnV1dS2ue/78eeTm5qKqqqqNoiMiIiIKDAsWLHBq/fnz5/PJd0TkNWxAkd9SqVSNftrqveSeb7/9FqNGjYLVam21+dSuXTuEhITgwoUL2Lx5cxtFSMGA9YPItzAnibwjPT0dM2bMwK233oqrrrrKtrx9+/YICQlpljOSJGHWrFk4ceJEm8TH3CcKLmxAkXBNBw+VSoWMjAzb6yUlJS0OMJIk2Z6cJisoKIBGo4FGo0FBQUGzz7T3Hl9mtVpRXl6OzMxMaDQal7ZRWVlpe3/T32Frv6+mMjMzXR7on3zySdTW1touBQ8JCWk2sfjvfvc7DB48GPfddx/mz5+PV199FQsXLnTp8yiwsX405mwu22Mvv48fP4558+ZBpVJh3rx5KCkpafY+JetQ4GNO/sadsduZXFYyvqtUKmg0GuTm5jq9H+TfJk6ciKysLHzyySf4/vvvYTabsXfvXmzcuBHJyckYP348rr/++kZPyKuvr8fevXtx+vRpxZ/D3P+Nq7lvNpttY7BKpVKUr66O2UTCSEQeFh8fL8XHxyte32KxSMXFxZJarZYASAaDodk6JpNJUqvVUllZmW0ZAMnen7DBYJDUarVksVgki8UiJSUlSXq93u5nO9qGUiaTSdJqtS6/XymtVitptVqX49XpdJJarZaMRqNUU1PT6DVnfl+SdHGfXY1Dfh8A6cYbb5Tuuece6dFHH5V0Op30j3/8Q/rss88ki8Xi9HYpMGRnZzv9d8X68Rtnc9lRTE33y2KxSEaj0fbfBoNBAmBbpnQdZzg7jpD3AJCys7MVr8+c/I2rY7cz+9zS+K7T6SQAkslkkiTpt/zW6XRO74sr9Zn8y7lz56QDBw5I27Ztk9LS0qTOnTtLarVa8fuZ+79xJfctFoukVqtt+1hbWyup1eoW43J1zFaKeU/ewL8o8jhXvzjIhVYebGQ1NTWSWq2WamtrG61vr6jX1NRIABoNbHJxlg/AWtuGkjgNBoOUlJQkGQyGZnF5kyvxJiUlSVqt1m5jx9nfl8VicasRJh/4Xrhwwen3UuBz50An2OuHs7Hb4yi/7R20urKOM9iA8h3ONqBkwZ6TrsblzD63NL47+lwATjUVZPwiGnx4PO8+Z+KSG0UNf2fyPhcXFzdb350xWynmPXkDb8EjnxEWFoa0tDQUFBRg06ZNtuUrV67Epk2bEBYW1uo2SktLAQDXXXedbdm1114LANi/f79b8VVWViI1NRXr1q3DgAEDsGHDBsTGxiqKS5TU1FQAQFpaGkJDQ5u97uzva9OmTUhOTnYrpmuvvZb36ZPHBXv98ETsjvJbrVbbXT8pKcmpdSi4BHtOukrpPrc2vgOATqcDAJSXlwO4eFuO/B4ib2HuuyYnJwcAGuXzjTfeCADIz89vtr47YzaRUKI7YBR43D1zbTQabd1+vV7v8IwE7HTzk5KS7Hbq4eCMn71tNOTM2RF5W639uMOZbchnTYxGo6TX622/g4ZnUZz5fRUXF9vORLm6L3DxTDoFB0+caQvW+uFs7E05k98Wi6XVy/mVrNMSXgHlO9yt28Gak87E1ZCSfVYyvsvkKyTKysrcusqDV0IEHx7Pt23uO1rX3nJPj9mOMO/JG/gXRR7niS8O8rwF9g6mZPYKrjPFu6XlDV/X6XQ+My+RMwNZ07kf5Pvn5QPRlrbXdHltbW2j++7ZgCJv8NSBTjDWD1djlCTn81ue46Ol/VKyTkvYgPIdnqjbwZiTTT/Xk19ClYzvDcmvtXS7Xmv4RTT48Hjefa40n6uqqlrchjfGbEeY9+QNvAWPfFJMTAzUajXWrFkDq9UqLI7a2lr8/ve/x6JFi5Cbmwuz2ezVz/Pk42RTUlIAAIMHDwZw8ZJe+fLbN954w6lt7dy5E3PnznUrHqK2Eqz1w1XO5veaNWuwePFih7f9KF2Hggdz0rOcGd8zMjIwZswYWCwWAMDMmTOF/n9AwYW5r9ysWbMAAC+//LLtd1VZWQngt9tpAe+M2URtiQ0o8jlmsxnV1dVYv359s/vHW+PovmfAtXufw8LCEBsbiw0bNmDAgAFYt24dUlNTbQNCQ/YeP2vvRyT5YHXjxo0AlP2+CgoKMH78eO8HR+QBwVo/XI3d2fzOzc2FWq3G8OHD3VqHgkew5qSrXN3npuM7cDEXU1JSMHHiRISGhmLmzJkoKChAXl6e5wImcoC575zhw4ejuLgYJ0+eRKdOnZCZmYkzZ84AAMaOHQvAO2M2UVtjA4p8zs6dOxEZGYmePXtCr9cjJSXFNoFma+QBq+GZDXnSzaFDh7oV1+DBg5GWlobk5GR8+eWXmDdvXqOzKNLFW1pb/WmJM+u2Rh6g7Z1xkn9PSn5fGo0GN9xwg91BV3RDjaipYK0frsbuTH5XVlbi4MGDLZ55VbIOBZdgzUlXKdlnJeM7AMTFxQH4bVLjbt26AQASExM9HTZRM8x950VGRsJoNEKSJMydOxefffYZtFqtrcHs6TGbSAQ2oMhnWK1WZGRkNCqUc+fOhVqtRnp6Og4fPtzqNuSzAkePHrUtO3XqVKPX3GXvLIoviomJAQB8/fXXtmXywWp8fDwAZb+vlgZdbw3ARM4K9vrhauxK89tsNmP37t2Nnp5VWVmJefPmObUOBY9gz0lXKdlnJeM70PwqErkR1dLVJUTuYu57Rm5uLvbs2WO75Rbw7JhNJIwH5pEiasSVSQtNJpOkVqttE2o2JD+9AU0mMYSDSff0er2UlJQkWSwW28ScDSfra8jRNnxRw9+DvYkEtVqtpNVqmy1Tq9W2p33o9fpmTw9x5vclc/X3Bk5CTi1wdbJL1o+LlMRur0401XS/amtrJbVabfcpQPJTdZSs4wxOQu47XKnbzMnfuDJ2K83l1sb34uJiCYBkMBgkSZKksrKyVieEdoSTEQcfHs+7x5Xct1gskslkkpKSkiSdTqfoc1wZs5Vi3pM38C+KPM7ZAatpcWw4KNXU1NgtoA3fZ4/86FdHjyVu+tm+zt7voGncjr5Yyo9oBiDp9Xq7g6DS31fTeFzZDzagyBFXDnRYPxprLXZXGlDyk3ns/chP61GyjjPYgPIdztZt5uRv3Bm7leyzkvG9uLjYlp9JSUkuNZ8kiV9EgxGP513nSu43zGV7DbzWPkvmyfGYeU/eoJIk3kNDnpWQkAAAyM7O9urnyPc6u/Mn7IltkHIqlQrZ2dmNbhEgkuXk5CAhIaFN8pH1w7e11ThCrWurus2c9G1tWZ/JN/B4npj35A2cA4qIiIiIiIiIiLyKDSgiIiIiIiIiIvKqDqIDIHKXK5fdNn1UKREFJ9YPIt/CnCQKTsx9ouDABhT5LXfuR+a9zETBjfWDyLcwJ4mCE3OfKLjwFjwiIiIiIiIiIvIqNqCIiIiIiIiIiMir2IAiIiIiIiIiIiKvYgOKSBCr1Yry8nJkZmZCo9Eofp/ZbEZqaipUKhVUKhVyc3PtrldQUACNRgOVSgWNRtNsPaXbISLfcPz4ccybNw8qlQrz5s1DSUmJoveZzWZkZma2muuVlZW2deTPaMjVmkUUqDyVE3J+NtVaTipdh4g8y9XxuKHKykpb7WhpMnV79cETn08kChtQRILodDoUFhYiMTERBQUFit5jNptx9OhRpKWlQZIkGAwGxMXFISMjo9F6GRkZ0Gg0tvXS0tIarad0O0TkG6xWKyorK7FhwwZYLBaMGTMGd999d6u1w2q1Ys6cOQAuTtZaW1uLnJwcpKamNlt3//79jf49adKkRv92pWYRBTJP5ERlZSUSExPtvtZaTipdh4g8x9XxuKGMjAykpqaie/fuWL9+vcPJ1O3VB098PpFQEpGHxcfHS/Hx8aLD8BsAJKWpWFZWpuj9jpap1WqntuNpAKTs7Gyvfgb5r+zsbK//Dforo9HYbJmSnDUYDBIAyWKx2JaZTCYJgFRcXNzqZ9jTFrWC44jvYN1unas5YbFYJK1W6/D9SnJSad66i/U5+LAO2+fqeCxLSkqStFqTPEgUAAAgAElEQVRto3HZHkf1wd3PdwbznryBV0CR3zCbzcjNzbVd5l5QUGC79PT48eMAgNzc3GbLZBkZGVCpVMjMzITZbG52OavZbLato9FofPJy1uHDhzf6t9VqBQBotdpGy3U6HQCgvLwcAGy/i7S0NKe2Q+TPAqlmqNVqu8uTkpJafF9OTg4AIDQ01LbsxhtvBADk5+fblh0/fhwajQapqam2ukHkaYGUk56wadMmJCcn231NSU4yb8lfBFLuuzoeA7BdfZyWltZoXLbHUX1w5/OJfILoDhgFHm+dMVGr1bYOv8lkkiTp4pU8AKSkpCTbVT01NTW2ZTKdTifV1NRIktT4jIKstrZWUqvVksFgkCRJkoqLixt9TlNyHK39KOHMug3V1NTY9qOqqqrZ6/JrZWVlksFgkGpra13ajieBZ9KpBZ4+0xaoNUOOCUCrVz842m7T5UajsVEcarXaYc1wtWY5g2fefYcn63ag5qQrOVFcXGzbX3vvV5KTzuStu3glRPDxZB0O1NyXY1IyHstXHxuNRkmv19tytunVyPI+tFQfXPl8VzDvyRv4F0Ue580vDvaKsJJlABodlNXW1jZ6Xb5Npek2tFqtJ8O3y5UDV3mAln90Op3d9ZKSkmz7Ye9SX6Xb8RQ2oKgl3jjQCcSaIUkXD07VanWrl/DLNaBpc9ne78BisUgmk8l2cK/X6+1ukw2o4OLpuh2IOelsTtTW1jbKL0fvV5KTSvPWXfwiGnw8XYcDMfclSfl4rNPpGjXGLBaLbYxuODWG0vrg7Oe7gnlP3sBb8CgoJCUloVu3bsjNzYXVakVYWFijCf/k21QaPkkGAFasWCEk3tb07NkTkiTBZDJBq9UiJSUFmZmZjdbJyMjAmDFjYLFYAAAzZ8603WrnzHaIgpGv14w1a9Zg8eLFrV7CP2vWLADAyy+/bMv/yspKAL/dqisLDQ3F4MGDkZaWBr1ezwlNyaf4ek46Y+fOnZg7d26r6ynJSeYtBTpfz32l43FKSgoAYPDgwQAu5q5829wbb7xhW09pfXD284l8hsjuFwUmX7wCqqqqqtHlv02v9LG3DSVxtPbj6j45o6qqqtk2mk48LK/T0plRe9vxNPAKKGqBL10B5cs1w2AwOHWVg3x2VK4Brd2eIEm/XdLf0r54E6+A8h2ertuBmJPOrGs0Gm23Eznz/pZy0pl1XMUrIYKPr1wB5cu578x47Gi7DZc7Wx+cPR5wFvOevIFXQFFQ6Nu3L4xGI0wmE5KSkpCSkoKMjIxm6x0+fFjR9qSLt6+2+tMW+vbt22xZXFwcgN8mHu7WrRsAOHzUs6PtEAUrX60ZlZWVOHjwoFNnRyMjI2E0GiFJEubOnYvPPvsMWq3WdhbWnoZnZol8ga/mpLM0Gg1uuOGGZldrAGg2sXJDSnKSeUuByFdz39nxWM7NpncjAL9NLO5MfXDleIDIF7ABRUFBpVLBarVi8ODB2LBhA0wmk+1SWADQ6/UAgKysLNvAID9Rw9fJ8RoMBtuypk/IkBtRjp6c4Wg7RMHKF2uG2WzG7t27bU+zBC4egM6bN0/xNnJzc7Fnz55G+2KP1WpFTEyMy7ESeZov5qQrWvqi29KXXiU5ybylQOSLue/KeCzn5tdff21bJscbHx8PQHl98MTxAJEobECR3zCbzbb/bjjANH3d3jLg4nwn8mNdO3fu3Gj+kylTpgC4eL94p06doFKp0K1bN68fyDU8C2LvjEhqaqrtka3AxTMjGRkZtv2wWq3Q6XTQarWIjY21rffkk08CuPhlE4Dt8czycqXbIfJngVQzzGYz5syZg5SUlEZnRocMGYJJkybZ1mtaM4CL+y4fmJ48eRJGo7HRXBG5ubmNHll9/Phx7N27F5GRkc3iaK1mEbUkkHJS5uw4roSSnHQmb4lEC6Tcd3U8joyMhFarRWpqqm3f8vLyoFarnTr2Vvr5RL6KDSjyG/JtZADQqVOnZsvk/7a3DACSk5ORn58PlUqF/Px8LFiwwPZaWFgYampqoNVqAVy8TLampgY9e/b0zs7g4hkdeT8A2AbNlsydOxcpKSm2y3M3bdqEyZMnNzoDAlwc5IqLi7Fnzx6oVCq88cYbKC4uth2YKt0OkT8LpJqxdOlSh5ML9+vXz+H75Dqzf/9+JCUlNdoH2RVXXIG7774bKpUKqamp+O677+xeLelKzSJqKJByEvBeTijJSaV5S+QLAin3XR2PASAtLQ1qtRrdunWz1YqsrKw2+3wiX6CS2mqiGgoaCQkJAIDs7GzBkZCvUalUyM7Otl1qTNRQTk4OEhIS2mz+NPJdHEd8B+s2AazPwYh1mJj35A28AoqIiIiIiIiIiLyKDSgiIiIiIiIiIvIqNqCIiIiIiIiIiMir2IAiIiIiIiIiIiKvYgOKiIiIiIiIiIi8ig0oIiIiIiIiIiLyKjagiBQym83Izc2FRqMRHQoR+QHWDCLfwpwkIhnrAZEYbEARKbR06VLExcWhoKBAdCgtslqtKC8vR2ZmpqJBtbKy0rauSqVyeTtE1Ji/1IyGHNUDpesUFBRAo9FAo9H41X5TcPCXnFQ6/jqbb5mZmQ7zmijY+EM9sFqtUKlUdn9yc3MbrVtZWdno9Xnz5gmKmqhlbEARKbRhwwbRISii0+lQWFiIxMTEVgfVjIwMpKamonv37li/fj0kSXJpO0TUnL/UDFlL9UDJOrm5ucjMzERWVhaysrJQVFSEzMzMttwFohb5S04qGX+dzbfKykokJiZ6K2Qiv+MP9eDLL790+FpkZGSjf+/fv7/RvydNmuSVmIjc1UF0AETkWWlpaQCAFStWtLjevHnz0KVLF2RlZSE0NNTl7RCR/2utHrS2zvHjxxEXF4eysjLba0lJSRgyZAiGDRuGwYMHe30fiAJFa+Ovs/lmtVqxbds27wZNRB739ddfo6amBj179rQtM5vNWLduHcLCwhqt2717d7snjoh8Da+AIp+TkZEBlUqFzMxMmM3mZreFyZeQq1QqpKamwmw2A2h+L3dBQYHtEtTjx48DuHjGsOkys9lsu4wd+O0S9Xnz5uHw4cOtxms2m20xazQalJSUKN4fUVJTUwFcPMh19GWTyF+wZrhHST1obZ3S0lIAwHXXXWdbdu211wJoflaWAh9z0ruczbdNmzYhOTm5bYIjaoL1wHWRkZGNmk8AUFJSgmnTpjVadvz4cWg0GqSmpqK8vNxr8RB5hETkYfHx8VJ8fLxL79XpdFJNTY0kSZJksVgkrVYrNfwzTUpKkgBItbW1Uk1NjQRASkpKkiRJktRqtQRAAiCZTCZJkiSprKzMtk5ZWZkkSVKz98nvAWBbx2Kx2D6rqqrK9vnyerLa2lpJrVZLBoNBkiRJKi4ubvT5re1PUw1jaelHCUfrmkwmCYBkNBolvV4vAZDUarVUXFzs1HZcAUDKzs72yLYo8GRnZzv9t8aa4V7NUFIPlKwj77u9+NRqtcPPd8SdcYQ8y9m6zZz0/jjuTL4VFxfbfifujOeu1Gfyb56ow6wHnqsHDX9nTRmNxkbbU6vVUm1trVPbtYd5T97AvyjyOHcGLHkQktXW1jYqfFqttlHhbVq47RVyJcvsrSN/6dLpdA7XMxgMdret1WoV7Y83ORrUdDpdo8G04aAsD9RKtuNqTGxAkSOuHOiwZrhHST1Qso6jOuFq/WADync4W7eZk57jbF7Z+zKt1+tbfZ8S/CIafDxRh1kPPMtkMtmaY01ZLBbJZDLZmmINc99VzHvyBv5Fkce5M2DJX2oMBoNksVgcrldTU2P7UuStgUrJeg3PzjT9cWZ/vMGZA1d5ULZ3VoUNKGorrhzosGa4R0k9cHWdlpa3hg0o3+Fs3WZOeo67DaimX0DZgCJneKIOsx54llarVXRlk16vd+nq46aY9+QN/Isij3NnwKqqqmpU/BuepZDJRbWqqkr4QNXawZyS/bH3ea39KOHugWtry13BBhS1xJUDHdYM92qGkv1Qso4cs7117DW2W8MGlO9wtm4zJ70/jivJN6PRaLtVqLXtKcEvosHHE3WY9cBz9aC2ttZ2JVZrLBaLR/KVeU/ewL8o8jhPDFgmk8l2lqFhcZcvjZUPqtpioFJyaXDD+8md2R9vcrRPchxNz9wA9udqceeA1d622IAiR9w50GHNcI2SeqBkHXluqIZnZuV5OVy5DYANKN/hat1mTrrP0T4pyTdPfOFtiF9Eg48n6zDrgfsMBoPtVnglXDn50xTznryBf1Hkce7OAdXwS458m0fD1509c+HqQCWfiTEajQ7Xkw8CtVqtLe7a2lrbYNTa/niTo8G36YSKkvTbmRJ795WzAUVtxdU5oFgzXKekHihZR/7y23AeOXmy2KZXYSjBBpTvcLZuMyc9x9H462q+uTOe84to8PHUHFCsB57hTEPJYrE4fLiQM5j35A3tQORjdDqd7VGqnTt3hk6ns72mVqsBXHzcaMNHqZrNZttjW4GLj3WVlzdcx9EyWW5uru39WVlZUKvVts+0974pU6YAAFasWIFOnTpBpVKhW7duiImJUbQ/3iLvf9P/Bi4+0lWr1TZ61G1eXh7UajViY2MVb4fIV7BmuE5JPVCyTs+ePaHX6/HGG2/AarXCarXijTfegF6vb/YIaQp8zEn3tTT+Mt/In7AeuK+yshJjxoyx+1pubi5KSkps/z5+/Dj27t2LyMhIr8dF5BLRHTAKPJ54Cp48EWHTy1rlMw3yJHzy0zPks4ENf+TtObPMZDLZ7u3W6/WNznLYe58kXTwTKT9xQo5F6f54Q9M4m8Yrk8/y2NtXZ7bjbGy8AooccecpeKwZ7mmtHihdR34UtFqtduvsK6+A8h3O1m3mpPuUjr/O5ps74zivhAg+nnwKHuuBe1qafFyuA/Lv0Znb9FrDvCdvUEmSJIHIgxISEgAA2dnZgiNRTqVSAQCYDt6lUqmQnZ2N+Ph40aGQD8rJyUFCQoJf5CFrhnf54zgSqPylbjMnvcuf6jN5hj/XYdYDz2DekzfwFjwiIiIiIiIiIvIqNqAo6LV07zgRUVOsGUS+hTlJRDLWAyLfxgYUBb1u3brZ/W8iIntYM4h8C3OSiGSsB0S+rYPoAIhE433NROQM1gwi38KcJCIZ6wGRb+MVUERERERERERE5FVsQBERERERERERkVexAUUBzWw2Izc3FxqNRnQoROQHWDOIfAtzkig4MfeJAhMbUBTQli5diri4OBQUFIgOpVVmsxmpqalQqVRQqVTIzc1V/N6CggJoNBpoNJpW97WyshKZmZnQaDRQqVS25VarFeXl5bbXiIKRP9UMwLncl5nNZmRmZiquNY5qRsPPV6lU0Gg0TtUtIiX8LScbkvPMGS3lm6fXIfJl/pL7VqvVNp42/WltTFQ6HnviOJ/IV7ABRQFtw4YNokNQxGw24+jRo0hLS4MkSTAYDIiLi0NGRkar783NzUVmZiaysrKQlZWFoqIiZGZm2l03IyMDqamp6N69O9avX99ookadTofCwkIkJib6/GBP5C3+UjMA53JfZrVaMWfOHAAXJ2qtra1FTk4OUlNT7a7fUs3IyMiARqOx1a20tDTFdYtIKX/KyYYqKyuRmJjo1HtayjdPr0Pk6/wl97/88kuHr0VGRjp8Tel47KnjfCJfwafgEfmAo0ePYvjw4bZ/x8bGIi4uDikpKViwYIHD9x0/fhxxcXEoKytDaGgoACApKQlDhgzBsGHDMHjwYNu68+bNQ5cuXZCVlWVbt6G0tDQAwIoVKzy1W0TkJc7kfkPvvPMOCgoKkJWVBQAICwtDWloahgwZgrvuuqvRwXJrNSMlJQUAbJ8l/++ePXtarFtEgc5qtWLbtm1Ovae1fPPkOkTkOV9//TVqamrQs2dP2zKz2Yx169YhLCzM4fuUjMeePM4n8hW8Aop8Unl5ebPLWGUZGRm2ZcePH4fVam10+WpqairMZrPd7drbnr1lwMXBQ/4sjUaDkpIS7+ws0Kj5BFw8eAUArVbb4vtKS0sBANddd51t2bXXXgsA2L9/v22ZfDYlLS2NgxIFpGCrGUpzv6mcnBwAaFQHbrzxRgBAfn6+bZmSmqHT6QBc/N0DF5ti8nuIgi0nG9q0aROSk5MVr68k3zy1DpG3BVvuR0ZGNmo+AUBJSQmmTZvW4vuUjMc8zqeAJBF5WHx8vBQfH+/2doqLiyUAklarbfaaVquVTCaTJEmSlJSUJAGQamtrpZqaGgmAlJSUZFsXgCT/qdfW1jb6tyRJtvc0XFZbWyup1WrJYDA0ikX+zKbk97f2o0RNTY2k1WolAFJVVVWL68r7bi8etVotSZIkmUwmCYBkNBolvV5ve624uLjFffEGAFJ2drZXtk3+Lzs7262/vWCqGUpyv6XPbWm5MzVDrlVlZWWSwWCQamtrHX62Mzw1jpD73KnbwZSTDfe5rKysWdyOKMk3T63jDnfrM/kfd+pwMOZ+Qw33wREl47E3jvOdwbwnb+BfFHmcJ784yF9uLBaLbZnFYmk0oGm1WoeDlZJ/21tmMBjsrmNvIPWkhgMpAEmn07W4vpLBS6fTNRp4LRaLbUCTD5KVbNMT2ICilnjiQCdYaoaS3LdHzv2mzW13aob8mlarbfR7dwcbUL7D3bodLDkpSRe/+Or1+hbjbEpJvnlqHXfwi2jwcbcOB1PuN2QymWzNr5YoGY+9cZzvDOY9eQP/osjjPPnFQe7qNyzkxcXFds9i1NTU2Iqwu4OXWq32yBkQV5lMJtvA3fBgtiklA5O9deTfq70zNGxAkSieONAJlprhagOqrKzMlvvylwL5dyY3vJ2pGTqdTjIYDLYvFWq12iNNKDagfIe7dTtYclKSpGbjtZLPU5JvnlrHHfwiGnzcrcPBlPsNabVaRVcDuzoeN13uzdxn3pM3cA4o8mmDBw+GWq223ScNAP/85z+bTbCbmZmJxx57DGq12iOfKz8FTrrYpG30Y4+jx686uge+NYMHD8bMmTMBoMWn6LS0v0lJSS1uHwA2btyoOCYifxAsNcPV3B8+fDiKi4tx8uRJdOrUCZmZmThz5gwAYOzYsQ7fZ69m5ObmIiUlBRMnTkRoaChmzpyJgoIC5OXlOdwOBZ9gycmCggKMHz/eI7ErGaM9tQ6RtwRL7jckz1/V0uTjMiXjMY/zKRCxAUU+Lz4+HgUFBSgvL8fx48cxbNiwRq/n5uYiMTER69evR9++fT362YcPH1a0nr1BTunA54iSfZEHpoYTNsoTAQ8dOhTAbwOUPLG5vfcTBZJgqBlKct+RyMhIGI1GSJKEuXPn4rPPPoNWq7UdsCqtGXFxcQB+m0C1W7duAFpumlNwCoac1Gg0uOGGGxxOlOyIknzz1DpEbS0Ycr8hJZOPN9TaeMzjfApEbECRz5MfC/7GG2+gtLQUo0ePbvS6/CWo6RMo3KHX6wEAWVlZtoIuP1GjrcifazAYHK4jn209evSobdmpU6cavRYTEwPg4mNim247Pj7ecwET+YhgqBlKcl+J3Nxc7NmzBykpKbZlSmtG0wNbuRHFA15qKhhysqUvrC19eVWSb55ah6itBUPuN7Rnz55mV3gpZW885nE+BST37uAjas4bc3fI8yHZm5Rbvte7pqZGqqqqst0LXVtb2+iJGfL92E0n/ZPvwUaDe6Ubvq/hT01NjUf3q+E+6HQ62/bl+VSaTphob5ler7fdPy5PPNh0Hgp5bhb5d6DX6+0+Kctisdj21VOTCTcEzgFFLfDkXAOBXjMkSXnuN60ZFotFMplMUlJSksMHHSipGfKTheT5PeTfiyeevMM5oHyHp+p2MORkU/JnNuRobG8t3zy1jqs4F0zw8VQdDpbcb23ycVfHY08e5zuLeU/ewL8o8jhvfHGQJ9Nr+qSIhq/Jk/7JT9Ro+kQ5uYDW1NTYBjyj0ShJkmR7XGvDSQNrampsg6a8PW8xGo2N4tTpdHafXGFv8Gr4/pYeuyo/mhW4OLF50waTvcHa04MOG1DUEk8e6AR6zZC1lvtNa0bDGuDokdSy1mqGJF1sQslfCJKSkjz2yHc2oHyHp+p2sORkQ0obUJKkLN88tY4r+EU0+HiqDgdL7rc2+bg747EnjvNdwbwnb1BJkpMT0xC1IiEhAQCQnZ0tOBLyNSqVCtnZ2bwkmOzKyclBQkKC0/OlUeDhOOI7WLcJYH0ORqzDxLwnb+AcUERERERERERE5FVsQBERERERERERkVexAUVERERERERERF7FBhQREREREREREXkVG1BERERERERERORVbEAREREREREREZFXsQFFRERERERERERexQYUERERERERERF5VQfRAVBgys/PR1RUlNPvq6+vR/v27b0QERH5i/z8fNEhkGD5+fmIiYkRHQb9v/z8fHTs2FF0GCQQ63JwcvV4nnyTJElQqVSK12fekzewAUUe16tXL5w/fx7Tp08XHQr5oPDwcNEhkI+S/zZYOwi4OJaQeCEhIdixYwd27NghOhQSLCQkRHQI1IZ4PE8A8548TyVJkiQ6CApuP/74Ix588EEYjUa88sorePjhh0WH5DE5OTlISEgA04xIvOzsbMycORPp6elYtGiR6HA8bsqUKaivr8fbb78tOhQinzR//ny8++67OHTokOhQPKKiogKRkZGYNWsWXnnlFdHhEAUclUqF7OxsxMfHiw7FbT/88AOmTZuGffv2IScnBxqNRnRIFKQ4BxQJdeLECYwZMwYffPAB3n///YBqPhGR7ygpKcHs2bMxf/78gGw+AUBUVBR2796NH374QXQoRD7JaDRiypQposPwmIiICBgMBuj1erzwwguiwyEiH3bVVVehsLAQM2bMwNSpU7FmzRrRIVGQYgOKhPnkk08QERGBX375BeXl5Rg9erTokIgoAFVUVGDKlCmIiYnBiy++KDocr9FoNKirq0NRUZHoUIh8zoEDB/Dvf/87oBpQwMW8T09Px+LFi2E0GkWHQ0Q+rEOHDti4cSNWrlyJBQsWIDk5GfX19aLDoiDDBhQJkZeXh9GjR+OPf/wj9u3bhz59+ogOiYgCUHV1NdRqNUaPHo0tW7Y4Nfmmv7nmmmswZswYbN++XXQoRD7HaDSiW7duGDZsmOhQPG7hwoVITExEXFwcKioqRIdDRD7u6aefRn5+PjZv3gyNRoOzZ8+KDomCCBtQ1KYkScLy5csRGxuLxMREvP322wgNDRUdFhEFoBMnTuCee+5B7969kZ+fHxQTaUZFRaGoqAjnzp0THQqRTzEajVCr1WjXLjAPfdetW4eRI0ciOjoaJ06cEB0OEfm4qVOn4oMPPsCnn36KUaNGsW5QmwnMUZh80s8//4z4+Hj85S9/wYYNG7BmzRq0b99edFhEFICsVismTZqEyy+/HAUFBbj88stFh9QmoqOjcfbsWezevVt0KEQ+49SpU/j4448DetLd9u3bIy8vD126dMGkSZNgtVpFh0REPm7YsGEoLS3F+fPncfvtt8NkMokOiYIAG1DUJk6dOoU777wTu3btwrvvvotHHnlEdEhEFKB++uknqNVqfPfdd3jvvffQtWtX0SG1mR49euC2227jbXhEDRQWFuLSSy/F2LFjRYfiVaGhoSgqKsLp06cxffp0zu1CRK3q1asX9u3bh/79+2PUqFEoLCwUHRIFODagyOs+++wzRERE4Pvvv0d5eTkiIyNFh0REAaq+vh5xcXE4dOgQioqK0KNHD9EhtbmoqCgUFBTwyyfR/9u5cyfGjRuHyy67THQoXtejRw9s374dH330EZKTk0WHQ0R+QG5ex8bGYsqUKVi/fr3okCiAsQFFXvXWW29h1KhRGDBgAEpLS3HTTTeJDomIApQkSUhMTMTu3btRWFiIQYMGiQ5JiKlTp8JsNuOjjz4SHQqRcGfPnkVxcXFA337XVEREBAwGA/R6PVavXi06HCLyAx07dkRmZibS0tLw+OOP46mnnuKJLPIKNqDIKyRJQnp6OqZNm4ZZs2ahqKgInTt3Fh0WEQWwxYsXIysrCwaDAREREaLDEaZ///4YMGAAduzYIToUIuHef/99/Prrr5g8ebLoUNqURqNBeno6lixZAqPRKDocIvITzz77LPLy8rBx40ZER0fjxx9/FB0SBRg2oMjjfvnlFzzwwANYunQp1q1bh7/97W/o0KGD6LCIKID99a9/xerVq/Haa68F1ZUOjkRHR3MeKCJcvP1uxIgRCAsLEx1Km1u4cCESExMRFxeHiooK0eEQkZ+YNm0aSkpKUFFRgTFjxuDUqVOiQ6IAwgYUeVRtbS0iIyNRWFiIwsJC/PnPfxYdEhEFuOzsbDz11FNIT09HQkKC6HB8QnR0NGpqavCvf/1LdChEwtTX16OwsDCom9Lr1q3DyJEjERUVxcesE5Fit99+O8rKyvDjjz8iIiICX3zxheiQKECwAUUe8/nnn2PYsGE4ffo0SktLMW7cONEhEVGA27VrF2bPno1nnnkGixYtEh2Oz7j11ltx/fXX8zY8CmqlpaU4ffo01Gq16FCEad++PfLy8tC1a1dMmjQJVqtVdEhE5Cd69+6N0tJS9O3bF3fccQfeeecd0SFRAGADijzCaDTijjvuQJ8+fVBeXo7+/fuLDomIAlxFRQWio6MRExOD9PR00eH4FJVKhejoaLz11luiQyESZufOnejfv3/QH5PIT7g6ffo0YmJiOLEwESnWuXNnvPPOO7jvvvug0WiwceNG0SGRn2MDitz2wgsvIDo6GvHx8Xjvvfdw9dVXiw6JiALc4cOHoVarMXbsWLz++utQqVSiQ/I50dHROHToEA4fPiw6FCIhjEZjUF/91FCPHj2wfft27Nu3D8nJyaLDISI/EhISgtdeew3Lli3Do48+igULFuDChQuiwyI/xQYUuezcuXN46KGHsHjxYmRkZODVV19Fx44dRYdFRAHuxIkTuPvuu9G7d28YDAY+5MCBUaNGoUuXLpyMnILSV199hXEXCyMAACAASURBVCNHjgT1/E9NRUREwGAwQK/XY/Xq1aLDISI/s2TJEuTk5OCVV17BtGnT+IQ8cgkbUOSSb7/9FmPHjsX27dtRUFCAJ598UnRIRBQErFYrJk2ahM6dO6OoqAiXX3656JB8Vvv27aHRaHgbHgWlHTt2ICwsDCNGjBAdik/RaDRIT0/HkiVLYDQaRYdDRH4mNjYW77//Pj788ENERkbim2++ER0S+Rk2oMhpBw8eREREBP773/9i3759mDhxouiQiCgI/PTTTxg/fjy+++47FBUV8XZfBaKiovDxxx/j5MmTokMhalNGoxGTJ09Gu3Y81G1q4cKFSExMRFxcHCoqKkSHQ0R+ZuTIkSgvL4fFYkFERAQOHDggOiTyIxyVySlFRUW4/fbb0aNHD5SXl2PgwIGiQyKiIFBfX4+4uDgcPXoUu3btQo8ePUSH5BfGjRuHK664grfhUVAxm82oqKjg7XctWLduHUaOHImoqCicOHFCdDhE5Gf69OmDsrIy9OrVCyNHjsSuXbtEh0R+gg0oUuyll16CRqNBTEwMdu/ejS5duogOiYiCgCRJeOihh7B7924UFBRgwIABokPyG5dccgkmTpyIHTt2iA6FqM0YjUZccsklGDdunOhQfFb79u2Rl5eHrl27YtKkSbBaraJDIiI/c/XVV2PXrl1Qq9WYPHkyNm3aJDok8gNsQFGrzp8/j7lz52LhwoVYuXIlNm/ejJCQENFhEVGQWLx4Md58801s27YNERERosPxO1OnTsXevXvxv//9T3QoRG2ioKAAY8eO5RxxrQgNDUVRURFOnz6NmJgY1NfXiw6JiPxMSEgItm7ditTUVCQmJmLhwoWQJEl0WOTD2ICiFp05cwb33HMP3nzzTWzfvh1PP/206JCIKIjodDqsXr0aW7Zs4XxzLpo4cSLatWuHgoIC0aEQed3PP/+M999/n7ffKdSjRw9s374d+/btQ3JysuhwiMgPqVQqPPfcc9i6dSvWrl2L6dOn4+effxYdFvkoNqDIoa+++goRERGoqanBvn37oFarRYdEREEkOzsbCxcuhE6nQ0JCguhw/FZoaCgiIyM5DxQFhffeew/nzp3jMYsTIiIiYDAYoNfrsXr1atHhEJGfmjFjBnbt2oWSkhLceeedMJvNokMiH8QGFNm1a9cuDB8+HN26dUNFRQUGDRokOiQiCiKFhYWYPXs2nnnmGcyfP190OH4vOjoau3btwo8//ig6FCKvKigowLBhw9CtWzfRofgVjUaD9PR0LFmyBEajUXQ4ROSnRo8ejfLycnz33XcYPnw4Dh48KDok8jFsQFEz69atw6RJk6DRaFBSUoKwsDDRIRFREKmoqMD06dMRFxeH9PR00eEEhClTpuDcuXN49913RYdC5DUXLlzA22+/jSlTpogOxS8tXLgQiYmJiIuLQ0VFhehwiMhP3XTTTSgtLcXvf/97jBo1CsXFxaJDIh/CBhTZ1NXVYd68eXjyySeRlpaGrVu34pJLLhEdFhEFkQMHDkCtVmPs2LHYvHkzVCqV6JACQlhYGEaOHMmn4VFAKy8vh9lsZgPKDevWrcPIkSMRFRWFEydOiA6HiPxUly5dsHv3bkycOBETJ07Eli1bRIdEPoINKAIAfPfdd5gwYQL+/ve/Y9u2bXj22WdFh0REQebEiROYOHEiwsPDYTAY0L59e9EhBZSoqCgUFBTg/PnzokMh8gqj0Yjw8HAMGDBAdCh+q3379sjLy0PXrl0xadIkWK1W0SERkZ+65JJL8Pe//x2LFi3CnDlzsHjxYj4hj9iAIuDIkSOIiIhAVVUVPvzwQ0RHR4sOiYiCzLfffotJkyahc+fOeOedd/j4dC+Ijo6G1WpFSUmJ6FCIvGLnzp28+skDQkNDUVRUhNOnTyMmJgb19fWiQyIiP6VSqfD8889jy5YtyMjIQFxcHH755RfRYZFAbEAFueLiYkRERODqq6/Gxx9/jCFDhogOiYiCzE8//QS1Wg2LxYKioiKEhoaKDikg3Xjjjbjlllv4NDwKSIcPH8ZXX30FjUYjOpSA0KNHD2zfvh379u1DcnKy6HCIyM89+OCD2LVrF3bt2oXIyEh8++23okMiQdiACmIbN27EhAkTMGHCBHzwwQfo3r276JCIKMj8+uuviI2NxdGjR1FSUoIePXqIDimgRUdHY+fOnbhw4YLoUIg8qqCgANdccw3uuOMO0aEEjIiICBgMBuj1eqxevVp0OETk58aMGYPS0lKYzWbcfvvt+Oqrr0SHRAKwARWE6uvrkZycjEcffRTPPfccsrOzcemll4oOi4iCjCRJmD17NoqLi1FQUIDw8HDRIQW8qVOn4ptvvkF5ebnoUIg8aseOHbj33ns5d5yHaTQarFy5EkuWLIHRaBQdDhH5uf79+6OsrAxhYWEYMWIE9uzZIzokamNsQAUZq9WKSZMmYcuWLcjLy0NqaiqfMkVEQixatAj5+fkwGo2IiIgQHU5QGDhwIG666Sa89dZbokMh8phvv/0WZWVlvP3OS55++mkkJiYiLi4OFRUVosMhIj/XtWtXlJSUYNy4cRg3bhxef/110SFRG2IDKoj8+9//xvDhw3HgwAHs3bsX06ZNEx0SEQWpVatW4cUXX8SWLVtw9913iw4nqERFRWHHjh0AgLq6OuzevRvR0dF4+umnBUdG1Lrq6mrcfvvtWLt2Lb7++msAQFFRETp06IBx48aJDS6ArVu3DqNGjUJUVBROnDghOhwi8nOXXnopDAYDFixYgNmzZ+O5557jE/KChEri/9NBYc+ePbjvvvtw4403wmg04rrrrhMdUkAKCQlR9IjzF198ESkpKW0QEZE48+fPx/3339/s6qbs7GzMnDkTa9euxWOPPSYouuD1wQcf4K677oJarcYHH3yAH374wfYaDwnI1+Xn52P69Ono0KED6urqMGDAAADANddcg7179/Kqbi+yWq0YNWoUAODDDz9s9MCIjz76CMuWLcO2bdvQqVMnUSESuWzp0qX4v/buPkiOss4D+Lez2aBHYCWaQFKa84UXy7ecUFjBUsHhODBlT7iTaLKr5WlBatYCtSQWas0KmvhS5axyJ2VkdgsqLruzGBTdFeROdzWo2ZCzvFkLzAUxMotHOXOCM6hYkE2e+2N9Oj293dNPTz890z3z/VRNwfZ09zwz+83ze/aZfvnsZz/ru96aNWvw1FNPtaBF3eOOO+5AJpPBtm3bcMcdd+C0005rd5MoQjwCqkN8/vOfxx133OH63OjoKK644gqkUin85Cc/4eRThFQ/W15zizrd/Pw8vvKVr2Dz5s111w2ZmprC+9//ftx0002cfGqhP/7xj7jrrrtw9dVX46qrroJhGHjggQfqJp8uuOCCNraQSM3f/d3fAVg6eg8Ajhw5gsceeww//elPcc455+BDH/oQHnjgATz33HPtbGZH6uvrw/3334+nnnoK27Ztw4kTJwAAExMTeOtb34qZmRlMTEy0uZVEzVm7dq3SemeccUbELek+H/zgB/H9738f3//+9/GP//iP+MMf/rBsnampKVx00UV49tln29BC0olHQHWAQ4cO4ZJLLgEAfOtb38K//Mu/AFi62PjHP/5x3Hrrrchms/jMZz7DbwYjls/n8aEPfcgalLnp6enBk08+iXXr1rWwZUStdd1112Hfvn1YXFyEYRjI5/N43eteh1QqhYGBAdx+++3sj1qkWCzijW98IwBYR424ufjii3H48OFWNo0osAMHDuCyyy7zfH7VqlV4/vnnce655+LXv/516xrWRR566CGkUim8//3vx/r163HzzTdDCAHDMPCa17wGDz/8cLubSBRYpVLBhg0bfMfwX/va17Bz584Wtqx7PPLIIzBNEytXrsR9992H8847D8BSn7N582YAwGc+8xl8+tOfbmczKSROQCXcyZMnceGFF+KRRx7BiRMnsGrVKhw8eBDnnXcetm/fjtnZWdx5553Yvn17u5vaFZ5++mmsW7fOs3j19PTg7W9/O37wgx+0uGVErVOtVnH22Wfj+eefr1u+evVqpFIpfPvb3+adqlrosccew3nnnYcVK1bg5MmTnutt3rwZc3NzLWwZUXD2P0QayeVyuPHGG1vQou707W9/G9deey2q1eqyU3d//vOf46KLLmpTy4iad8UVV+BHP/pRw3F8pVLBmjVrWtyy7lGpVGCaJh577DHce++9eMUrXoFNmzbhmWeewYkTJ3Daaafh0UcfxcaNG9vdVGoST8FLuHw+j1/+8pdYXFyEEAInTpzAli1b8OY3vxm/+MUvcODAAU4+tdCaNWtwxRVXeP5xLYTA+973vha3iqi1RkdHXSc6/vKXv+BlL3sZVqxg6Wmlc889Fz/72c98jzizX8+FKK7kKXheent78c53vhMf+9jHWtSi7vP000/jy1/+Mp555pllk0+9vb0YHR1tU8uIwnnf+97neS3Enp4eXHHFFZx8iti6devw4x//GKlUCv/0T/+Et771rfjzn/9sTQqePHmSN01JOP4VkGBPPfUUPvGJT9R1lIuLi3j66afx7LPP4sEHH8Sb3vSmNrawO/kVr6uvvrrFLSJqnZMnT+Lf//3fXU/zEkJg79696O/vX3Z0FEXrzW9+M774xS82nPzjUWmUBKtXr/Z8rqenBy95yUuwb98+nuIbkd/85je46KKLcPjwYdejRI4fP46xsTFep4US6eqrr+aXyDHwwhe+EHfddRc2btyIJ598su4GT8ePH8f+/fvxk5/8pI0tpDA4AZVg2WzWtcAfP34cCwsLyGazvKNRG2zduhWrVq1atnzlypUwTRNnnnlmG1pF1BrT09N44oknPJ8/efIk9u/fj1e/+tX461//2sKW0Y033ogrr7wSvb29rs/zzlWUBI3ujiSEwDe/+U0eoRCRJ554Aueeey4ef/zxhnf8/etf/4p77rmnhS0j0uPMM8+0rkHktGrVKmzdurUNrepOH/nIR3Ds2DHXvqanpwfXX399w8sKUHxxAiqhfv7zn+P222/3HAAsLi7innvuUbqdKOl1+umnI51OLyteJ06cwHvf+942tYqoNb761a/6Hklz4sQJ/Pa3v0WpVGpRqwgADMPAXXfdhZe85CXLfkc8+omS4vTTT3ddvmLFCnz2s5/FW97ylha3qHv09fXhxS9+MQD4nkq9d+/eVjSJSLv3vve9y47uW7lyJdLptGf/Q3p98YtfRD6f97wW1+LiIh5++GHPO8BTvHECKoGEEMhkMr5/MJw8eRK33HIL/u3f/q1FLSNpYGBgWad5+umnY8uWLW1qEVH0/ud//gezs7OeA4aVK1fCMAyk02kcOXIEr371q1vcQlqzZg2+9a1vLTs9yTAMXgOKEsHtD8De3l685S1vwSc+8Yk2tKh7nHnmmfi///s/TExMYM2aNa5HiQBL48+HHnoIR48ebXELicLbsmXLsn7mxIkTGBgYaFOLusuPf/xjfPKTn/Q9i0cIgZtuugnPPPNMi1pGunACKoHuvPNO/OIXv/C8lTaAukHB6173ulY0i2yuuuoqnHHGGdbPvb29eNe73tXw1AGipPva177menqXnCy/+OKLcfDgQXz3u9/l5FMbXXLJJfjCF75QdwQDLwxPSbFy5cq6L+BWrFiB1atXY3JykkfytYBhGNixYwd++9vf4qabbsKqVatc+/2VK1fy6ARKpNNOOw3vete76nJ9xhln4Kqrrmpjq7rH+eefjze96U3o6enxnOQGliagnnnmGZ7tk0AccSZMtVrFrl27XJ8zDAM9PT3o7e3F1q1bcd9992FxcRGXX355i1tJq1atwnve8x6reB0/fpzfnFBH+/Of/4zR0dG6i4sbhgHDMPDKV74SU1NTOHjwoNLt0yl6N954I6666qq6ATavT0dJ8YIXvMD6fyEExsfHsX79+ja2qPusXr0ae/bswa9+9Su84x3vAFB/Ku/x48cxOjra8FpRRHE1MDBgZbe3txfvec97XK/vSvpt2LABDz30EJ544gns2bMHGzduBADXie7FxUXceuut+PWvf93qZlIInIBKmKGhIfzpT3+qOyxR/oN8/etfj1tvvRW///3vcc8992DLli38NrCNtm/fbhWvF7/4xUilUm1uEVF07rzzzrrJp56eHpx99tkYHR3FkSNHYJpmG1tHToZhYGxsDGvXrkVPT481WUiUBC984QsBLPUzu3btsiZAqPVe9apX4bvf/S5mZ2fxyle+su5oyj/+8Y+Ynp5uY+uImpNKpazrnR0/fhzbt29vc4u6z/r163HTTTfh8ccfx49+9CNcc8016O3ttcYs0ooVK/DhD3+4jS2loDgBlSDFYhG33XYbFhcXrUMSzzrrLFx//fX45S9/ifn5eVx//fW8+0tMXHbZZVi7di2ApQsacjKQOpUQAl/60pdw4sQJ9Pb2YvXq1fjc5z6HY8eO4YMf/CCzH1P260E999xzPAKKEucNb3gD9uzZ0+5mEIC3v/3t+NWvfoWvfvWr6OvrQ29vL4QQuP3229vdNKLAenp6rBsHrV27Fpdddll7G9TFDMPAZZddhomJCfz+97/HV77yFVxwwQUAlg7COH78OB544AH8x3/8R5tbSqoM4bjC1+LiIqampjwvIkvt8+53vxvA0kzvG9/4RqRSKVx44YWh/rjbvHkzXvayl+lqYp0nnngChw4dimTfSfGlL30J//Vf/4U9e/bg/PPPb3dz2qanp8f1zoC6zM3N4Xe/+10k+yZ/Dz/8sHUO/jvf+U788z//c9010FrppS99KS655JJI9t2p9fF73/sevvGNb2DLli3413/913Y3JzFYP9tHjoduu+02rFu3rs2tCacT6+Of/vQn3H333fjP//xPAMDXv/51fjnaAqx/ej366KPIZrO4+OKL8fGPf7zdzWmpJNS3xx57DLOzs3jwwQetI/DvvvtuHs0dI571TTjce++9AgAfXfL4wAc+4IyANh/4wAfa/v74iM/j3nvvjSxr7X5vfMTrERXWRz7sD9ZPPnQ9WB/50PWICutfdz1Y3/jQ9XCrb8u+bnn22WcBwPfWh5R8AwMDeO655yLb/3PPPYf+/n6Mj49H9hqUDIZhWH1LVMbHx9Hf3x/pa1C8TUxMRHqxf9ZHklg/SRfWR9KB9Y90YX0jXbzqG68BRUREREREREREkeIEFBERERERERERRYoTUEREREREREREFClOQBERERERERERUaQ4AUVERERERERERJHiBBQREREREREREUWKE1BERERERERERBQpTkD5qNVqOHToEEZGRpBOpwNtOz09jXQ6jXQ6jenp6dD7np+ft9Y1DCNQWyh+osyW6jrz8/MwDMN6DA4OBn4fFG8LCwsYHBy0fr+zs7PK26pkyG5kZGRZ3xQm55QslUoFQ0NDVn8yOTmpvG3Qvsgta82sQ8mgqx/xyoRK/lgvky8O4y6JY/rkCzpGqtVqdX2I/WGvl82M21jvkqvZcbrKdpVKxcpGo3FZq+sbJ6B85HI53Hfffdi5c6dS5yJNTk5iZGQEY2NjGBsbw/3334+RkZGm9z08PIyhoSGcc845uO222yCEaOr9UHxEmS2VdQDg8OHDdT9v2bKluTdDsVSr1TA/P4+9e/eiWq3i0ksvxeWXX66UN9UMSfPz89i5c+ey5c3mnJKlUqng2LFj2L17N4QQKBQK2LFjB4aHh5W2D9IXeWUt6DqUHDr6kUaZUMkf62XyxWHcBXBM3wmCjpEA4MiRI57PpVIpAM2N21jvkqvZcbrKdrVaDddeey0AQAiBcrmMiYkJDA0NLdtfy+ubcBgfHxcui7seAOXPpVQqCQBibm7OWlYsFgUAUSwWA+87k8mIbDYrqtVq8IY30N/fL/r7+7Xus5X77xS6sxUkf1NTUxregT8AYnx8PLH7Tyq3369K3oL2YdVqVWSz2Yb7DpLzZkVdv1gfvdmzIgX5nav2RSpZU1knLNbP9mj2d+qXCZX8RVUvWR9br53jrqjG9Kx/rRN0jCQVCgVRKpXqlpXLZZHNZq2fg47boqh3rG+t0+w4XWW7QqEgANT1NTKnMzMzvvvTwav+aD8CqlKpYHJy0jq0dXp62jqUa2FhAcDSrLFzmTQ8PAzDMDAyMoJKpbLscMJKpWKtk06nA51O0ioHDx4EAGzYsMFatn79egDLZxj9yFnK3bt3o6+vT1MLk4nZUsuWav4WFhaQTqcxNDSEQ4cORdvwBOmknJmm6bo8k8k03C5oHzY6Ooobbrih2WZ2rU7K2ubNm+t+rtVqAIBsNuu7bZC+SCVrzGNnZUuHRplQyV+31kvmSO+4q5vH9J2UpWb/zkulUti4cWPdstnZWVxzzTXWz0HHbd1Y7zopS82O01W2m5iYAIC6vublL385AGD//v3WsrbUN+eMVNgZbtM0rRk4OQs8NzcnAIhMJmPNFsvZ40wmY22by+WsmWH7jK5ULpeFaZqiUCgIIYSYmZlpONss2+H3UBFk3Uwm47ouAGGapvK+5Szl1NSUyOfz1vbOWctmJW2Gm9lSy5Zq/qampuraapqmKJfLSu0ICgn6hrdTcybbJPuURoL0YTMzM9Zn0qg9QdvajKR9A9ypWSuVSlZ7jh496ru+al+kkjXVPIYV9/rZqdlq5nfqlwmV/EVZL+NcH5kjfeOuqMf0ca9/nZSloH/nNWJ/n24ajduiqnesb/Efp6ts5/XazuXtqG+RnILn9oZVlgGoe8PlcrnueXkomXMf9kMXo6KjsAVdnsvl6kJfrVatTs/tdIeg4t7BuGG2/DMUJGfValUUi0WrA87n8028A7V2x3WA7bW/TsuZEEuF1DRN30P/VTNULpfrMtMoyzoHR17iPgB302lZkwM++cjlckrb+fVFKlkLksewklA/Oy1bbm31o5oJlVoYVb2Me33s9hzpGndFPaZPQv3rlCwF/XvOS7FYtCY6vHiN26Ksd6xv8R+nq2wn+xfnF4Gt/HvQq/7EagJKflCFQsH1g7fPeDofUdNdrJpdLr9B8ZsxV5GEDsaJ2dI7AWWXz+cDf3OjKu4DbLf9dVrO5OurDHRVM+QsUI3eSyveZxIG4E6dmrUwAxm3vkgla0HyGFYS6mcnZivo6zSTCZVaqLNexr0+dnuOdI27oh7TJ6H+dUqWmh1nO2WzWd8jTbzGbVHWO9a3+I/TVbazHxUm36vscxp9OdiK+harCaijR4/W/eKdH07QAHgFqJlABVlXvge3fbgVmaAdma5/CEnoYJyYLf9sBc2fJA/fjELcB9hu++u0nBUKBeWJAJUMTU1NLbuYZqP2tKKAJ2EA7tSJWbO3sZntnH2RStaC5jGsJNTPTsxWkHWbzYRKLdRZL+NeH7s9R7rGXV6vqaufSkL965QsNTvOtnNefNyN17gt6nrH+hb/cbrqdvLIKGDpy0C/UwqFaE19034R8jDOP/98TE1NoVgsIpPJYNeuXa63cH700UeV9ieWJth8H7rJC4NVKhVrmbwA2oUXXqi8H3khMXkxV7fXIDXdlK1m89fX1+d70TtqLK45m5+fxyOPPILrrrtO6XVVMpROp/H3f//3MAzDekjOCzqSfnHNmrONzXD2RSpZYx71SUK2VDSbCZVayHrpr1NypGvcxTF98+KWJR1/5zkvPu7UaNzGete8uGVJCjpOV90ulUphamoKQghcd911+O///m9ks1ls2rTJc5+tqG+xmoAyDAO1Wg2bNm3C3r17USwWsWvXLuv5fD4PABgbG7M6cHml+ji58sorAQDHjh2zlj355JN1z6nYtm0bAODxxx+3lsn33d/fH7aZXaWbstVs/mq1mpU5ak4cc1apVPDDH/4Qu3fvtpbNz89jcHDQcxuVDDUqsFH8EUD14pg1J/m6hUIh8Hb2vkgla8yjPknIlopmM6FSC1kv/XVKjnSNuzimb17csqTj77wDBw54TgL4jdtY75oXtyzJ/Qcdpzez3eTkJA4cOFD3ft20pL45D4kKe4ilvJgXAOt8Q/syea6r2zJg6SJf8rDCUqlUd2icfRv7w3kYom7yUDT7e7LLZrPLDqPM5/PWOZfyQoNeF7X027f9avQ6z8tMwiGWdszWEpVs+a1TKBTq7rxSKpUC33EhCHgcghnH/XdSzuTdPNxe0/77DtOH2cl9O/nlXJcknIJg10lZM03T9e4yzlw5lzXbF3llLeg6zYp7/eykbEnN1EsnZyZU8hd1vYxzfWSOlugYd8l9RzWmj3v967Qsqf6+3fqkRhcfVx23Oemsd6xv8R+nq24nLy6eyWRcr/vUrvqmfQLK+SEEXVYul607Rbh9UPbbO2cymcgLldsv1vn5eHUw8raGXrdZVdm3EMK6XSuwdP6mrj/g4t7BODFbp/hly28d+y03s9lsw3OBdYjzANttX52SM3mxRbeH/a4YYXJm55Zh1X5Oh7gPwJ06KWvO2/jmcjnXC2k6s9ZsX6SSoyizFvf62UnZcmtnkHrpth9JJX9R18s410fm6JSw4y4pqjF93Otfp2VJCP/ft1eWGl18XHXc5qSz3rG+xX+crrKdvZ/xqlvtqm/G3560TExMYGBggIfwdYGBgQEAwPj4eCL3T8lhGAbGx8cjO8w86v1TMkRdv1gfSWL9JF1YH0kH1j/ShfWNdPGqP7G6BhQREREREREREXUeTkAREREREREREVGkOAFFRERERERERESR4gQUERERERERERFFihNQREREREREREQUKU5AERERERERERFRpLpiAqpSqWBychLpdLrdTaEEY46oFZgzahVmjVqFWSNdmCXShVkiHZij4LpiAurmm2/Gjh07MD093e6mKJufn8fIyAjS6TQMw7CW12o1HDp0yHqOWicpOVLNyPT0NNLpNNLptOd7UlmH9EpKzpxGRkbq+ipJZsgwDKTTaUxOTi5bZ35+HoZhWI/BwcFWNLnrJSVrfn1arVary4/94cwbRUM6cAAAFg1JREFUs9YeSclapVLB0NCQZ37cePV9FI0kZClIn8RxVvskIUtunH1OkLyRfknJ0cLCAgYHB62xz+zsrOt6KuP2sLpiAmrv3r3tbkIgw8PDGBoawjnnnIPbbrsNQgjruVwuh/vuuw87d+6MfdA7TVJypJKRyclJjIyMYGxsDGNjY7j//vsxMjISeB3SLyk5s5ufn8fOnTuXLR8eHkY6ncbu3bshhMDu3buxY8cODA8P1613+PDhup+3bNkSaXtpSVKy5tenHTlyxHPbVCpV9zOz1h5JyFqlUsGxY8es/qpQKLj2V3ZefR9FJwlZUu2TOM5qryRkycmtzwlSA0m/JOSoVqthfn4ee/fuRbVaxaWXXorLL7982ZhKddweVldMQCXJ4OAgqtUqxsbGYJomNm7cWPf87t27sXv37ja1jpLALyMLCwvYsWMHPvWpT6Gvrw99fX3IZDLYuXMn5ufnldchApaK2j333OP63K5duwAAmzZtqvvvgQMH6tY755xzIISwHqZpRthiShq/Pu3xxx9HqVSqy1C5XEY2m8W6devq1mXWyMuxY8ewefNm6+ft27cDONWPOTXq+6i7qfRJHGdRUF59TpAaSN3pwQcftMY7fX19Vn1zHlWuOm4PS+sE1PDwMAzDwMjICCqVyrLDA+Uhg4ZhYGhoCJVKBcDycyenp6etw8MWFhYALH1L4FxWqVSsw8SAU4ckDg4O4tFHH/Vtb6VSsdqcTqeXHYrW6P1EYWhoCMDSYLuvry/S14oz5ihaBw8eBABs2LDBWrZ+/XoAp44OUFkn6ZgzPUZHR3HDDTe4PpfL5QAAhw4dAgDrs7BPJiwsLCCdTmNoaMhar9Mwa9FKpVLLvqyZnZ3FNddcU7eMWWPWGrFPPgFLnxcAZLNZ1/Ub9X2dgFlqnkqf1A3jLIlZ0sOrz1GtgUnHHDXP68u2TCZT97PKuF0L4TA+Pi5cFvvK5XKiVCoJIYSoVqsim83W7SeTyQgAolwui1KpJACITCYjhBDCNE0BQAAQxWJRCCHE3Nyctc7c3JwQQizbTm4DwFqnWq1ar3X06FHr9eV6UrlcFqZpikKhIIQQYmZmpu71/d6Pk70tjR5eisWiACCmpqZEPp8XAIRpmmJmZqbh64XR398v+vv7Q+1D9/6Zo3A5ctuXk3xfbuubpqm8ThAAxPj4eODtoto/c6YnZzMzM9Z78dpGtmVubk4UCgVRLpfrnp+amqp7TdM0l62jqtn6FeX+mbXo+zQ38rOw05k11s/OzlqpVLJez/4eJJW+T1Xc6qMQzJLOLNk/M+fPOsdZcax/QjBLrRxv2bnVQFWsb52bI6larQpgad7ByW/cHoRX/dE2ASV/6VK5XK7bTzabrfvH4Pyg3D44lWVu68jJnFwu57leoVBw3Xc2m1V6P7rlcrm6YNoDLkPvbGvY9sSxg2GO9PHKiMryoNuqtCVOA2zmLLxyuSzy+bxnm+1kX5bNZkW1Wl32fLVaFcVi0Sp69v0GEccBOLOmj2r/UywWrUGfk66ssX52btbkHyHyYX8Psh2qfZ+KuNVHuQ2zpI9bn6R7nBXH+icEs6RD0D6nUQ1UwfrWmTmym5mZEaZpuo7JhfAft6vyqj/aJqBkQwuFQsOGlkola7IlqmCorGefDXU+grwfXRoF3G0WO+yAR4h4djDMkT5BBzf25boHRs0MgKPcP3MWnvMPd6/3lsvlrHZls9mGBU/ut5lvf4WI5wCcWdNHtf/JZrNK39iFyRrrZ2dnTQjhOVGp2vepilt9FIJZ0s2tT9I9zopj/ROCWdIhaJ+jWgO9sL51Zo7sTNN0PcBFiODj9ka86o+2CaijR4/WfdjOb4yEODXYO3r0aNuD4fePV+X9uL2e38Nv+7DLg4hjB8MchctRo7ZLsj1u6zsPV220ThBeHZAuQffPnIXL2dTUlHXocKM2ym+AZOGSn2Wjo07kYcHNiOMAnFmLvk+zK5fL1jeMfsJkjfWzs7Nmb4N9O9W+L4i41UchmCWdWfLqk3SPs+JY/4RglsJmKWifE6QGemF967wc2RUKBc9xeDPjdr92RzoBJRWLRWtWz/5hyjck/xG1Ihgqh+K5nduv8n50k6/hnGEE3M8FDzvgESKeHYzEHIXn9Z7kNcbs347IUw5kB6OyTtC2xG2ALQRz1izVAuj8Wf7B79d3NXvtgrgOwIVg1nRQyU6hULBOZVfRbNZYPzs7a3b2dqv2fUH3H8f6KASzpINXn6R7nBXn+icEs9SsoH1O0BrohvWt83Jkf71GE5TNjtsb7S/ya0DZJ0/k6WP254P8rLrMbR05W2e/sJZzPdnx289tLJfL1i/f7/3o5rw4mRCnfulu5/GGCYMUxw6GOdLHKyNygGM/9FJejE923CrrBG1LnAbYzJl+bu/N7RteoPEFVqvVqufNF/zEcQDOrOmjUveCTCiFyRrrZ2dnTWo0DpPCjsfiVh/lNsySHl59ku5xVhzrnxDMUhQa9TlhLj4usb51Zo7sr29/XXtmmhm3N+JVf1ZAo1wuZ92u76yzzrJu5Qecuv3fwsJC3a0LK5WKdZtE4NRtb+3L7LdRdC6TJicnre3HxsZgmqb1mm7bbd26FQCwZ88evOhFL4JhGDj77LOxbds2pfejWyqVQjabrbtt5De/+U2Ypont27fXrSs/I+f/dwrmKLxGGdm4cSPy+Tz27duHWq2GWq2Gffv2IZ/PW7dxVVkn6Ziz6H30ox8FcOr9ytu62pfbb0u7sLCABx98EKlUqsUtjRazFp5K3Zufn8ell17q+hyzxqz5SafTGB4etl6vVqshl8shm80uG4d1A2YpvEZ9UjeMsyRmqTUa5a0TMEfNq1QquPbaa7Fr1y4YhmE9/uEf/gFbtmyx1vMbt2vjnJEKe5cDeeEvtxk2/G0mUJ6fmslklt1tRL520GXFYtGatcvn83Wzim7bCVF/m13ZFtX3ExU5Y+r2Ptzei/M9BRHXGW7mKBzVjMhbkpum6XkUgMo6qm2K0ze8zJl+XjmbmZmxDi/OZDJ1OZL5kp912MPG4/gNMLMWnmqf1ujCq7qzxvrZeVmzZ0S+ntdFWu288qgqbvVRbsMshadyMWhd46w41j8hmKUoNFMDg2B967wcyXG428N5mmCjcXtQXvXH+NuTlomJCQwMDMCxOLYMwwCAxLQ3TgYGBgAA4+Pjidy/TsxRtAzDwPj4OPr7+xO5f12Ys2hFXb+SVB+ZtWixfp7CrIXD+ngKs9Q81r96zFLzWN9OYY7C8ao/Wk/BIyIiIiIiIiIickr0BFSjczWJVDFH1ArMGbUKs0atwqyRLswS6cIskQ7MUXQSPQF19tlnu/4/URDMEbUCc0atwqxRqzBrpAuzRLowS6QDcxSdle1uQBg8H5N0YI6oFZgzahVmjVqFWSNdmCXShVkiHZij6CT6CCgiIiIiIiIiIoo/TkAREREREREREVGkYjEBValUMDk5iXQ63e6mUIdhtqgVmDNqFWaNosJskQ7MEenCLJEuzFK8xGIC6uabb8aOHTswPT3d7qb4qlQqGBoagmEYMAwDk5OTyttOT08jnU7DMAyk02nfbUdGRmAYRuh1ulmSsrWwsIDBwUEYhoHBwUHMzs4qbzs/P29lUm7v5Je/MNnudknKGXAqC+l0OlCb/XKmkqFKpWL1W8xZcEnKWrN9Wq1Ww6FDhzAyMtJwsKij36NTkpStqMZitVqtLlP2h1xPZZ1ulqQcAWr9iJugddRtvM562FhSshS2T/DLks6a2K2SkiVAX7/gNUegK2+hCIfx8XHhsjhyANryukGUy2UxNzdn/VwoFAQAkcvlfLfN5XICgCgWi0IIIYrFYsNt5fONPhOVdRrp7+8X/f39TW0bh/2rSkK2qtWqmJqasv5fZksu85PP56336badX/7CZFsFADE+Pq5lX+3Yv2ob4p4zIZZ+t6Zpimq1KqrVqshkMiKfzytt2yhnKhmqVqvCNE3r9crlsjBNU2SzWS3vLer61a766JSErIXp07LZrMhms77vM2y/F0an1s8kZCvKsdjc3FxdpuyPcrmsvE4QnVgfk5Ajya8fcRO0jrqN13XXw06tf0nIUpg+QSVLumqiKta39tHVL3jNEejMmwqv+sMJqADsAx5Jtd1u6wEQpmkuW7darfr+4lXW8dOpHYxTErLlViSCtNuvyPjlL0y2VXTiANutDXHPWalUEgDqft+ySMk/yBpplDOVDMk/FKvV6rLXn5mZUX0bnjp1AO6UhKyF7dNU1g/b74XRqfUzCdmKcixWKBREqVSqe75cLtcN/lXWCaIT62MSciQF/SM9aB31Gq/rroedWv+SkKVm+4SgWQpbE1WxvrWPjn7Bq8/RnTcVXvUn1Cl4hw4dWnaooTQ8PGwtW1hYQK1WqzucbGhoCJVKxXW/bvtzWwYsHaYmXyudTgc6bSmozZs31/1cq9UAANls1nfbXC4HYOkzA5ZOTQCA3bt3L1t3dHQUN9xwQ8P9qayTZN2WLdM0XZdnMhnfbRcWFpBOpzE0NGTly8kvf2GynWTdlrODBw8CADZs2GAtW79+PQDg8OHDDbf1y5lKhiYmJgAAfX191rKXv/zlAID9+/ervo1E6rashenTVOjo9zpFt2UryrFYKpXCxo0b67aZnZ3FNddcY/2ssk4SdVuOALV+xCloHfUar3dyPey2LDXbJ4QZkzk1k+Uk6LYs6egXvPocnXkLzTkjFXSGe2ZmRgBwneXNZrPWjFomk7EORZQzcJlMpm6GTL5uuVz2nLWzL5OHpRUKhbq2eH2TL7f3e6golUrW7OLRo0eVtpHrz83NiUKh4HpY5szMjDUz6dUelXVUxH2Gu1uzJcTS7DWgdvjs1NRU3WuYpumaLZX8CdFctv0gxt/wdlPO5Htw26/fUSGqOZPv1S1DXu0L04/Zxf0b4G7KmlOQPs3tfTrp7veCilv97NZsRTUWs7N/PmHW8RKn+thtOQpS16QgdbTReF13PYxb/eu2LDmp9htBxmQ6aqIK1rf2ZSlsv9Coz9GZN1Ve9UfLKXiymNsPF5OHf9nX8QqBys9uy+Rhas51dF1PxIs9oECw60nIX342m637vIRYCrn9PEy3z0BlHVVx62DcdFu2pJmZGescXRXValUUi0Xr8/K6FkGj/AkRLtuNBBkAt2P/3ZKzsIVNJWeNMiTz5/xDUUeREyJ+A3A33ZI1p6B9mhD+udDV7zUjjvWz27IV1VjMrlgsWn94hFmnkbjVx27LkWo/Ym+TSh31G6/rrodxrH/dliVJtU8IOibTVRP9sL61L0th+gW/Pkd33lR41R8tE1Dy/EH7P7aZmRnX2cFSqWRdBDJsKEzTrBt82B+tEPQfeS6XE4VCwfoH4xyMO/fh9l5U1lEVxw7GqVuzZZqm63UuVOTzedeZbL/82ekoYHZxG2A7dUvOdBYZr5xJbhmSF+rMZDJW9nReGDqOA3CnbsmaUzN9WpD26ej3gohj/ezWbOkei9lls1nfowhU1mkkbvWxW3MkhH9dE0K9jvqN13XXwzjWv27NkmqfEHRMpqMmqmB9a1+WwvQLfn1OlHnz4lV/tExACbH0C7IH3W1mUP5jOHr0qJZQBP1gvAIUJlBu78WN86JicjsZlqmpqWUXsHPuV2WdIOLYwbjptmwVCoVQkz7yVBfnPhvlz41qtlXEbYDtphtyJgup236DnkLiljMnt89JHgkj8+d3OHMQcRyAu+mGrNk126cFeQ1d/Z6quNbPbsuWpGssZqdyEeEwFx+X4lgfuzVHKnVNpY6qjtd11sO41r9uy1KQPiHomCxsTVTF+tbeLDXTL6j0OVHmzYtX/dE2ASWL+tzcnCiVSsuu8yCflx+OzlDouk5Ns1R+Qc51ZMcgl6kEVWeRFSK+HYxTN2VLfpMblrMj8cufFx2dj9xP3AbYTt2QM3mLXvs3c/I0lmb+KFeZtPLLUC6X03Yoc1wH4E7dkDUpTJ8WtP/R1e+piGv97KZsOekYi9kVCgXfiQCVdfzEsT52c4786ppKHW12vB6mHsa1/nVbloL0CUHHZGFroirWt3hkSVLpF1T6nKjz5tUu7XfBs0ulUgCAffv24eDBg3jb295W9/yOHTsAYNldAsLI5/MAgLGxMesuKPJK9a0iX7dQKDRcz3lHIHl1e7lcLE0G1j0k+f8q63SibslWpVLBD3/4w7o7NM3Pz2NwcDDQfmq1GrZt21a3zC9/XvsB/LPdKbohZ1deeSUA4NixY9ayJ598su45VW45c1sH8M7Q5OQkDhw4gF27dgV67aTrhqzJ/evo01To6veSrluy5aRrLGZ34MABbNq0qeH+VNZJom7OkV9dU6mjzYzXO7UedluWgvQJOsdkTipZTppuyxKg3i+o9DlR5i0w54xUmBl0eQ6+2zmK8rCvUqlUd1hcuVyuuxK9nJVzXoRLnhMJnDpMzL6d/eE8BE0X0zRFLpez9i+vH+CclXRbJg+fk+euyvczMzPj+XpQmHlUWcdLXGe43XR6tuRdFtxe0z7D78xWoVCoy5DbNwJC+OdPNdvNQgy/4XXT6TkTYukbEHluebVaFZlMZtk3H83kTDVD8iKZmUxG20Xupbh+A+ym07PWbJ8m2Y9McV6fR1e/F0ac62enZ6sVY7FWXHxcimt97PQcqfYjbjlSqaNObuN1nfUwzvWv07Mk+fUJYbKkoyaqYn1rb5ZU+gWVv9Hc+hwdeQvCq/5onYCSF8lyO0xNPicvzCavVO+8i4l87VKpZAVJ/iOSt0F0Hjomwyj3FxXnLS5zuZzrRVW9QjEzM2OFPZPJ+A6C3YLTzDpe4tzBOHV6tmQu3B729+zMlj2T9luRummUP9VsNyuuA2ynTs+ZJH/fpmm69kPN5EwlQ/K5fD6v5ZpPTnEegDt1etaa7dOE8D6UXNLV74UR5/rZ6dlqxVisFRcfl+JaH7spR436Ea8c+dVRJ2c/prsexrn+dXqWJL8+odks6ayJKljf2pcl1X6h2QkoIcLnLQiv+mP87UnLxMQEBgYGOvqULloyMDAAABgfH0/k/ik5DMPA+Pg4+vv7E7l/Soao6xfrI0msn6QL6yPpwPpHurC+kS5e9UfbNaCIiIiIiIiIiIjccAKKiIiIiIiIiIgixQkoIiIiIiIiIiKKFCegiIiIiIiIiIgoUpyAIiIiIiIiIiKiSHECioiIiIiIiIiIIsUJKCIiIiIiIiIiihQnoIiIiIiIiIiIKFIrvZ5497vf3cp2UBvs378f/f39kb7GxMQEjh8/HulrEAHAwMAAvvOd77S7GdRG+/fvb8nrsD4S6yclCetj52P9I11Y3yhqPbfccsst9gVr167F//7v/0II0aYmUau89rWvxcDAAC644IJI9r9q1SosLi5Gsm9Klje84Q0YHBzE6tWrI9n/888/j/Xr10eyb0qO1772tdi6dSsuv/zySPbP+kgS6yfpwvpIOrD+kS6sb6SLV30zBHsSIiIiIiIiIiKKEK8BRUREREREREREkeIEFBERERERERERRYoTUEREREREREREFKn/BwB7WvKlE0ROAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } + "text/latex": [ + "\\begin{center}\n", + "\\begin{tabular}{lclc}\n", + "\\toprule\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.107 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.106 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 119.3 \\\\\n", + "\\textbf{Date:} & Mon, 07 Oct 2024 & \\textbf{ Prob (F-statistic):} & 2.56e-26 \\\\\n", + "\\textbf{Time:} & 11:29:06 & \\textbf{ Log-Likelihood: } & -2555.7 \\\\\n", + "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & 5113. \\\\\n", + "\\textbf{Df Residuals:} & 999 & \\textbf{ BIC: } & 5118. \\\\\n", + "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", + "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lcccccc}\n", + " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", + "\\midrule\n", + "\\textbf{x1} & 1.0940 & 0.100 & 10.924 & 0.000 & 0.897 & 1.290 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lclc}\n", + "\\textbf{Omnibus:} & 3.084 & \\textbf{ Durbin-Watson: } & 1.111 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.214 & \\textbf{ Jarque-Bera (JB): } & 2.960 \\\\\n", + "\\textbf{Skew:} & 0.088 & \\textbf{ Prob(JB): } & 0.228 \\\\\n", + "\\textbf{Kurtosis:} & 2.801 & \\textbf{ Cond. No. } & 1.00 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "%\\caption{OLS Regression Results}\n", + "\\end{center}\n", + "\n", + "Notes: \\newline\n", + " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", + " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "from sklearn.tree import export_graphviz\n", - "export_graphviz(model, out_file=\"arbre.dot\")\n", - "from pyensae.graphhelper import run_dot\n", - "run_dot(\"arbre.dot\", \"arbre.png\")\n", - "from IPython.display import Image\n", - "Image(\"arbre.png\")" + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.107\n", + "Model: OLS Adj. R-squared (uncentered): 0.106\n", + "Method: Least Squares F-statistic: 119.3\n", + "Date: Mon, 07 Oct 2024 Prob (F-statistic): 2.56e-26\n", + "Time: 11:29:06 Log-Likelihood: -2555.7\n", + "No. Observations: 1000 AIC: 5113.\n", + "Df Residuals: 999 BIC: 5118.\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 1.0940 0.100 10.924 0.000 0.897 1.290\n", + "==============================================================================\n", + "Omnibus: 3.084 Durbin-Watson: 1.111\n", + "Prob(Omnibus): 0.214 Jarque-Bera (JB): 2.960\n", + "Skew: 0.088 Prob(JB): 0.228\n", + "Kurtosis: 2.801 Cond. No. 1.00\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" ] - }, + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = OLS(Y, X[:, :1])\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "yp = results.predict(X[:, :1])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On extrait tous les seuils de l'arbre et on ajoute les milieux de segments." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", + "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", + "ax.legend()\n", + "ax.set_title(\"Régression linéaire sur un nuage linéaire par morceaux\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Passons à un arbre de décision qui n'est pas le meilleur modèle mais on va détourner ses résultats pour revenir à un problème de régression par morceaux." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "model = DecisionTreeRegressor(min_samples_leaf=10, max_depth=3)\n", + "model.fit(X[:, :1], Y)\n", + "yp = model.predict(X[:, :1])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[-2.0,\n", - " -1.8018612563610077,\n", - " -1.6037225127220154,\n", - " -1.323736995458603,\n", - " -1.0437514781951904,\n", - " -0.3109976723790169,\n", - " 0.4217561334371567,\n", - " 0.678125374019146,\n", - " 0.9344946146011353,\n", - " 1.0011553764343262,\n", - " 1.067816138267517,\n", - " 1.2776717841625214,\n", - " 1.4875274300575256,\n", - " 1.7147845923900604,\n", - " 1.9420417547225952]" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "th = list(sorted(set(model.tree_.threshold)))\n", - "th += [(th[i] + th[i-1])/2 for i in range(1,len(th))]\n", - "th = list(sorted(th))\n", - "th" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", + "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", + "ax.legend()\n", + "r2 = r2_score(Y, model.predict(X[:, :1]))\n", + "ax.set_title(\"Arbre de décision sur un nuage linéaire par morceaux\\nR2=%f\" % r2);" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On fait une r\u00e9gression sur les variables $W_{i>0} = X_1 \\mathbb{1}_{X_1 > t_i}$, $W_0 = X_1$ o\u00f9 les $(t_i)$ sont les seuils." + "data": { + "text/plain": [ + "'tree_dot.gv.pdf'" ] - }, + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import graphviz\n", + "from sklearn.tree import export_graphviz\n", + "\n", + "dot = export_graphviz(model)\n", + "\n", + "src = graphviz.Source(dot)\n", + "src.render(\"tree_dot.gv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On extrait tous les seuils de l'arbre et on ajoute les milieux de segments." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "W = numpy.zeros((X.shape[0], len(th)+1))\n", - "x = X[:, 0]\n", - "W[:, 0] = x\n", - "for i in range(len(th)):\n", - " W[x > th[i], i+1] = x[x > th[i]]" + "data": { + "text/plain": [ + "[np.float64(-2.0),\n", + " np.float64(-1.85539710521698),\n", + " np.float64(-1.71079421043396),\n", + " np.float64(-1.3022041469812393),\n", + " np.float64(-0.8936140835285187),\n", + " np.float64(-0.2086283266544342),\n", + " np.float64(0.47635743021965027),\n", + " np.float64(0.6395495533943176),\n", + " np.float64(0.802741676568985),\n", + " np.float64(0.942541167140007),\n", + " np.float64(1.082340657711029),\n", + " np.float64(1.310522198677063),\n", + " np.float64(1.538703739643097),\n", + " np.float64(1.6980005204677582),\n", + " np.float64(1.8572973012924194)]" ] - }, + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "th = list(sorted(set(model.tree_.threshold)))\n", + "th += [(th[i] + th[i - 1]) / 2 for i in range(1, len(th))]\n", + "th = list(sorted(th))\n", + "th" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On fait une régression sur les variables $W_{i>0} = X_1 \\mathbb{1}_{X_1 > t_i}$, $W_0 = X_1$ où les $(t_i)$ sont les seuils." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "W = numpy.zeros((X.shape[0], len(th) + 1))\n", + "x = X[:, 0]\n", + "W[:, 0] = x\n", + "for i in range(len(th)):\n", + " W[x > th[i], i + 1] = x[x > th[i]]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 0.849
Model: OLS Adj. R-squared: 0.847
Method: Least Squares F-statistic: 346.9
Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00
Time: 11:07:03 Log-Likelihood: -1697.7
No. Observations: 1000 AIC: 3427.
Df Residuals: 984 BIC: 3506.
Df Model: 16
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 -1.8316 0.124 -14.806 0.000 -2.074 -1.589
x2 -0.0573 0.210 -0.273 0.785 -0.469 0.354
x3 -0.2440 0.235 -1.038 0.300 -0.705 0.217
x4 0.3291 0.218 1.508 0.132 -0.099 0.757
x5 -0.3610 0.206 -1.756 0.079 -0.764 0.042
x6 0.5528 0.197 2.811 0.005 0.167 0.939
x7 3.2628 0.395 8.253 0.000 2.487 4.039
x8 1.4566 0.449 3.244 0.001 0.576 2.338
x9 0.2701 0.311 0.869 0.385 -0.340 0.880
x10 0.7213 0.374 1.928 0.054 -0.013 1.456
x11 -0.4599 0.457 -1.006 0.315 -1.357 0.437
x12 0.4177 0.378 1.105 0.269 -0.324 1.159
x13 -0.2703 0.253 -1.069 0.285 -0.766 0.226
x14 0.5325 0.226 2.360 0.018 0.090 0.975
x15 -0.3703 0.229 -1.618 0.106 -0.819 0.079
x16 0.1996 0.194 1.026 0.305 -0.182 0.581
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 228.022 Durbin-Watson: 1.978
Prob(Omnibus): 0.000 Jarque-Bera (JB): 716.200
Skew: -1.110 Prob(JB): 3.01e-156
Kurtosis: 6.502 Cond. No. 36.3


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.849\n", - "Model: OLS Adj. R-squared: 0.847\n", - "Method: Least Squares F-statistic: 346.9\n", - "Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00\n", - "Time: 11:07:03 Log-Likelihood: -1697.7\n", - "No. Observations: 1000 AIC: 3427.\n", - "Df Residuals: 984 BIC: 3506.\n", - "Df Model: 16 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 -1.8316 0.124 -14.806 0.000 -2.074 -1.589\n", - "x2 -0.0573 0.210 -0.273 0.785 -0.469 0.354\n", - "x3 -0.2440 0.235 -1.038 0.300 -0.705 0.217\n", - "x4 0.3291 0.218 1.508 0.132 -0.099 0.757\n", - "x5 -0.3610 0.206 -1.756 0.079 -0.764 0.042\n", - "x6 0.5528 0.197 2.811 0.005 0.167 0.939\n", - "x7 3.2628 0.395 8.253 0.000 2.487 4.039\n", - "x8 1.4566 0.449 3.244 0.001 0.576 2.338\n", - "x9 0.2701 0.311 0.869 0.385 -0.340 0.880\n", - "x10 0.7213 0.374 1.928 0.054 -0.013 1.456\n", - "x11 -0.4599 0.457 -1.006 0.315 -1.357 0.437\n", - "x12 0.4177 0.378 1.105 0.269 -0.324 1.159\n", - "x13 -0.2703 0.253 -1.069 0.285 -0.766 0.226\n", - "x14 0.5325 0.226 2.360 0.018 0.090 0.975\n", - "x15 -0.3703 0.229 -1.618 0.106 -0.819 0.079\n", - "x16 0.1996 0.194 1.026 0.305 -0.182 0.581\n", - "==============================================================================\n", - "Omnibus: 228.022 Durbin-Watson: 1.978\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 716.200\n", - "Skew: -1.110 Prob(JB): 3.01e-156\n", - "Kurtosis: 6.502 Cond. No. 36.3\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.858
Model: OLS Adj. R-squared (uncentered): 0.855
Method: Least Squares F-statistic: 370.4
Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00
Time: 11:29:07 Log-Likelihood: -1637.5
No. Observations: 1000 AIC: 3307.
Df Residuals: 984 BIC: 3385.
Df Model: 16
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 -1.8183 0.119 -15.303 0.000 -2.051 -1.585
x2 -0.4155 0.260 -1.600 0.110 -0.925 0.094
x3 0.2157 0.320 0.673 0.501 -0.413 0.845
x4 0.1368 0.247 0.553 0.581 -0.349 0.622
x5 -0.2634 0.166 -1.589 0.112 -0.589 0.062
x6 1.0105 0.196 5.164 0.000 0.627 1.395
x7 3.3282 0.356 9.357 0.000 2.630 4.026
x8 1.3866 0.454 3.051 0.002 0.495 2.278
x9 0.3655 0.403 0.907 0.365 -0.425 1.156
x10 0.1177 0.334 0.353 0.724 -0.537 0.773
x11 -0.3147 0.307 -1.023 0.306 -0.918 0.289
x12 0.2972 0.255 1.166 0.244 -0.203 0.797
x13 -0.0456 0.197 -0.231 0.817 -0.433 0.342
x14 0.2807 0.252 1.112 0.266 -0.215 0.776
x15 -0.3102 0.303 -1.024 0.306 -0.904 0.284
x16 -0.0231 0.237 -0.097 0.923 -0.489 0.443
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 207.506 Durbin-Watson: 1.993
Prob(Omnibus): 0.000 Jarque-Bera (JB): 673.510
Skew: -0.999 Prob(JB): 5.61e-147
Kurtosis: 6.489 Cond. No. 37.1


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "model = OLS(Y,W)\n", - "results = model.fit()\n", - "results.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dessinons les r\u00e9sultats de la pr\u00e9dictions." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "text/latex": [ + "\\begin{center}\n", + "\\begin{tabular}{lclc}\n", + "\\toprule\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.858 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.855 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 370.4 \\\\\n", + "\\textbf{Date:} & Mon, 07 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 11:29:07 & \\textbf{ Log-Likelihood: } & -1637.5 \\\\\n", + "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & 3307. \\\\\n", + "\\textbf{Df Residuals:} & 984 & \\textbf{ BIC: } & 3385. \\\\\n", + "\\textbf{Df Model:} & 16 & \\textbf{ } & \\\\\n", + "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lcccccc}\n", + " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", + "\\midrule\n", + "\\textbf{x1} & -1.8183 & 0.119 & -15.303 & 0.000 & -2.051 & -1.585 \\\\\n", + "\\textbf{x2} & -0.4155 & 0.260 & -1.600 & 0.110 & -0.925 & 0.094 \\\\\n", + "\\textbf{x3} & 0.2157 & 0.320 & 0.673 & 0.501 & -0.413 & 0.845 \\\\\n", + "\\textbf{x4} & 0.1368 & 0.247 & 0.553 & 0.581 & -0.349 & 0.622 \\\\\n", + "\\textbf{x5} & -0.2634 & 0.166 & -1.589 & 0.112 & -0.589 & 0.062 \\\\\n", + "\\textbf{x6} & 1.0105 & 0.196 & 5.164 & 0.000 & 0.627 & 1.395 \\\\\n", + "\\textbf{x7} & 3.3282 & 0.356 & 9.357 & 0.000 & 2.630 & 4.026 \\\\\n", + "\\textbf{x8} & 1.3866 & 0.454 & 3.051 & 0.002 & 0.495 & 2.278 \\\\\n", + "\\textbf{x9} & 0.3655 & 0.403 & 0.907 & 0.365 & -0.425 & 1.156 \\\\\n", + "\\textbf{x10} & 0.1177 & 0.334 & 0.353 & 0.724 & -0.537 & 0.773 \\\\\n", + "\\textbf{x11} & -0.3147 & 0.307 & -1.023 & 0.306 & -0.918 & 0.289 \\\\\n", + "\\textbf{x12} & 0.2972 & 0.255 & 1.166 & 0.244 & -0.203 & 0.797 \\\\\n", + "\\textbf{x13} & -0.0456 & 0.197 & -0.231 & 0.817 & -0.433 & 0.342 \\\\\n", + "\\textbf{x14} & 0.2807 & 0.252 & 1.112 & 0.266 & -0.215 & 0.776 \\\\\n", + "\\textbf{x15} & -0.3102 & 0.303 & -1.024 & 0.306 & -0.904 & 0.284 \\\\\n", + "\\textbf{x16} & -0.0231 & 0.237 & -0.097 & 0.923 & -0.489 & 0.443 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lclc}\n", + "\\textbf{Omnibus:} & 207.506 & \\textbf{ Durbin-Watson: } & 1.993 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 673.510 \\\\\n", + "\\textbf{Skew:} & -0.999 & \\textbf{ Prob(JB): } & 5.61e-147 \\\\\n", + "\\textbf{Kurtosis:} & 6.489 & \\textbf{ Cond. No. } & 37.1 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "%\\caption{OLS Regression Results}\n", + "\\end{center}\n", + "\n", + "Notes: \\newline\n", + " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", + " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "yp = results.predict(W)\n", - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", - "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", - "ax.legend()\n", - "ax.set_title(\"R\u00e9gression lin\u00e9aire par morceaux\\nsur un nuage lin\u00e9aire par morceaux\\nR2=%f\" % results.rsquared);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le mod\u00e8le nous sugg\u00e8re de ne garder que quelques seuils. En s'appuyant sur les p-values :" + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.858\n", + "Model: OLS Adj. R-squared (uncentered): 0.855\n", + "Method: Least Squares F-statistic: 370.4\n", + "Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 11:29:07 Log-Likelihood: -1637.5\n", + "No. Observations: 1000 AIC: 3307.\n", + "Df Residuals: 984 BIC: 3385.\n", + "Df Model: 16 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 -1.8183 0.119 -15.303 0.000 -2.051 -1.585\n", + "x2 -0.4155 0.260 -1.600 0.110 -0.925 0.094\n", + "x3 0.2157 0.320 0.673 0.501 -0.413 0.845\n", + "x4 0.1368 0.247 0.553 0.581 -0.349 0.622\n", + "x5 -0.2634 0.166 -1.589 0.112 -0.589 0.062\n", + "x6 1.0105 0.196 5.164 0.000 0.627 1.395\n", + "x7 3.3282 0.356 9.357 0.000 2.630 4.026\n", + "x8 1.3866 0.454 3.051 0.002 0.495 2.278\n", + "x9 0.3655 0.403 0.907 0.365 -0.425 1.156\n", + "x10 0.1177 0.334 0.353 0.724 -0.537 0.773\n", + "x11 -0.3147 0.307 -1.023 0.306 -0.918 0.289\n", + "x12 0.2972 0.255 1.166 0.244 -0.203 0.797\n", + "x13 -0.0456 0.197 -0.231 0.817 -0.433 0.342\n", + "x14 0.2807 0.252 1.112 0.266 -0.215 0.776\n", + "x15 -0.3102 0.303 -1.024 0.306 -0.904 0.284\n", + "x16 -0.0231 0.237 -0.097 0.923 -0.489 0.443\n", + "==============================================================================\n", + "Omnibus: 207.506 Durbin-Watson: 1.993\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 673.510\n", + "Skew: -0.999 Prob(JB): 5.61e-147\n", + "Kurtosis: 6.489 Cond. No. 37.1\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" ] - }, + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = OLS(Y, W)\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dessinons les résultats de la prédictions." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 5, 6, 7, 13])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "keep = numpy.arange(len(results.pvalues))[results.pvalues < 0.05]\n", - "keep" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "yp = results.predict(W)\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", + "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", + "ax.legend()\n", + "ax.set_title(\n", + " \"Régression linéaire par morceaux\\nsur un nuage linéaire par morceaux\\nR2=%f\"\n", + " % results.rsquared\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le modèle nous suggère de ne garder que quelques seuils. En s'appuyant sur les p-values :" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "W2 = W[:, keep]" + "data": { + "text/plain": [ + "array([0, 5, 6, 7])" ] - }, + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "keep = numpy.arange(len(results.pvalues))[results.pvalues < 0.05]\n", + "keep" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "W2 = W[:, keep]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
OLS Regression Results
Dep. Variable: y R-squared: 0.846
Model: OLS Adj. R-squared: 0.845
Method: Least Squares F-statistic: 1094.
Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00
Time: 11:07:38 Log-Likelihood: -1708.6
No. Observations: 1000 AIC: 3427.
Df Residuals: 995 BIC: 3452.
Df Model: 5
Covariance Type: nonrobust
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err t P>|t| [0.025 0.975]
x1 -1.9504 0.066 -29.574 0.000 -2.080 -1.821
x2 0.3384 0.148 2.287 0.022 0.048 0.629
x3 3.2628 0.397 8.209 0.000 2.483 4.043
x4 2.0247 0.385 5.260 0.000 1.269 2.780
x5 0.4635 0.119 3.901 0.000 0.230 0.697
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
Omnibus: 248.807 Durbin-Watson: 1.984
Prob(Omnibus): 0.000 Jarque-Bera (JB): 829.417
Skew: -1.190 Prob(JB): 7.84e-181
Kurtosis: 6.774 Cond. No. 20.1


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.846\n", - "Model: OLS Adj. R-squared: 0.845\n", - "Method: Least Squares F-statistic: 1094.\n", - "Date: Mon, 15 Oct 2018 Prob (F-statistic): 0.00\n", - "Time: 11:07:38 Log-Likelihood: -1708.6\n", - "No. Observations: 1000 AIC: 3427.\n", - "Df Residuals: 995 BIC: 3452.\n", - "Df Model: 5 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 -1.9504 0.066 -29.574 0.000 -2.080 -1.821\n", - "x2 0.3384 0.148 2.287 0.022 0.048 0.629\n", - "x3 3.2628 0.397 8.209 0.000 2.483 4.043\n", - "x4 2.0247 0.385 5.260 0.000 1.269 2.780\n", - "x5 0.4635 0.119 3.901 0.000 0.230 0.697\n", - "==============================================================================\n", - "Omnibus: 248.807 Durbin-Watson: 1.984\n", - "Prob(Omnibus): 0.000 Jarque-Bera (JB): 829.417\n", - "Skew: -1.190 Prob(JB): 7.84e-181\n", - "Kurtosis: 6.774 Cond. No. 20.1\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: y R-squared (uncentered): 0.856
Model: OLS Adj. R-squared (uncentered): 0.855
Method: Least Squares F-statistic: 1481.
Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00
Time: 11:29:08 Log-Likelihood: -1642.9
No. Observations: 1000 AIC: 3294.
Df Residuals: 996 BIC: 3314.
Df Model: 4
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
x1 -1.9657 0.062 -31.604 0.000 -2.088 -1.844
x2 0.8316 0.163 5.106 0.000 0.512 1.151
x3 3.3282 0.355 9.363 0.000 2.631 4.026
x4 1.7842 0.327 5.455 0.000 1.142 2.426
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 225.520 Durbin-Watson: 2.011
Prob(Omnibus): 0.000 Jarque-Bera (JB): 775.424
Skew: -1.066 Prob(JB): 4.16e-169
Kurtosis: 6.750 Cond. No. 17.4


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "model = OLS(Y,W2)\n", - "results = model.fit()\n", - "results.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "text/latex": [ + "\\begin{center}\n", + "\\begin{tabular}{lclc}\n", + "\\toprule\n", + "\\textbf{Dep. Variable:} & y & \\textbf{ R-squared (uncentered):} & 0.856 \\\\\n", + "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared (uncentered):} & 0.855 \\\\\n", + "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 1481. \\\\\n", + "\\textbf{Date:} & Mon, 07 Oct 2024 & \\textbf{ Prob (F-statistic):} & 0.00 \\\\\n", + "\\textbf{Time:} & 11:29:08 & \\textbf{ Log-Likelihood: } & -1642.9 \\\\\n", + "\\textbf{No. Observations:} & 1000 & \\textbf{ AIC: } & 3294. \\\\\n", + "\\textbf{Df Residuals:} & 996 & \\textbf{ BIC: } & 3314. \\\\\n", + "\\textbf{Df Model:} & 4 & \\textbf{ } & \\\\\n", + "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lcccccc}\n", + " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", + "\\midrule\n", + "\\textbf{x1} & -1.9657 & 0.062 & -31.604 & 0.000 & -2.088 & -1.844 \\\\\n", + "\\textbf{x2} & 0.8316 & 0.163 & 5.106 & 0.000 & 0.512 & 1.151 \\\\\n", + "\\textbf{x3} & 3.3282 & 0.355 & 9.363 & 0.000 & 2.631 & 4.026 \\\\\n", + "\\textbf{x4} & 1.7842 & 0.327 & 5.455 & 0.000 & 1.142 & 2.426 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\\begin{tabular}{lclc}\n", + "\\textbf{Omnibus:} & 225.520 & \\textbf{ Durbin-Watson: } & 2.011 \\\\\n", + "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 775.424 \\\\\n", + "\\textbf{Skew:} & -1.066 & \\textbf{ Prob(JB): } & 4.16e-169 \\\\\n", + "\\textbf{Kurtosis:} & 6.750 & \\textbf{ Cond. No. } & 17.4 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "%\\caption{OLS Regression Results}\n", + "\\end{center}\n", + "\n", + "Notes: \\newline\n", + " [1] R² is computed without centering (uncentered) since the model does not contain a constant. \\newline\n", + " [2] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], - "source": [ - "yp = results.predict(W2)\n", - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", - "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", - "ax.legend()\n", - "ax.set_title(\"R\u00e9gression lin\u00e9aire par morceaux\\nsur un nuage lin\u00e9aire par morceaux\\n\" +\n", - " \"r\u00e9duction du nombre de segments\\nR2=%f\" % results.rsquared);" + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: y R-squared (uncentered): 0.856\n", + "Model: OLS Adj. R-squared (uncentered): 0.855\n", + "Method: Least Squares F-statistic: 1481.\n", + "Date: Mon, 07 Oct 2024 Prob (F-statistic): 0.00\n", + "Time: 11:29:08 Log-Likelihood: -1642.9\n", + "No. Observations: 1000 AIC: 3294.\n", + "Df Residuals: 996 BIC: 3314.\n", + "Df Model: 4 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "x1 -1.9657 0.062 -31.604 0.000 -2.088 -1.844\n", + "x2 0.8316 0.163 5.106 0.000 0.512 1.151\n", + "x3 3.3282 0.355 9.363 0.000 2.631 4.026\n", + "x4 1.7842 0.327 5.455 0.000 1.142 2.426\n", + "==============================================================================\n", + "Omnibus: 225.520 Durbin-Watson: 2.011\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 775.424\n", + "Skew: -1.066 Prob(JB): 4.16e-169\n", + "Kurtosis: 6.750 Cond. No. 17.4\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n", + "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" ] - }, + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = OLS(Y, W2)\n", + "results = model.fit()\n", + "results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le coefficient $R^2$ est quasiment identique pour un nombre de segments moindre. Je me suis amus\u00e9 \u00e0 rendre ce code plus g\u00e9n\u00e9rique pour comparer la premi\u00e8re \u00e9tape, le d\u00e9coupage en morceaux, via deux mod\u00e8les, un arbre de d\u00e9cision et le nouvel objet [KBinsDiscretizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html) qui segmente une variable sans tenir compte de la cible. La r\u00e9gression n'est plus n\u00e9cessaire lin\u00e9aire : [Piecewise linear regression](http://www.xavierdupre.fr/app/mlinsights/helpsphinx/notebooks/piecewise_linear_regression.html). Je me suis \u00e9galement amus\u00e9 \u00e0 faire de m\u00eame pour une classification par morceaux [PiecewiseClassifier](http://www.xavierdupre.fr/app/mlinsights/helpsphinx/mlinsights/mlmodel/piecewise_estimator.html?mlinsights.mlmodel.piecewise_estimator.PiecewiseClassifier). Celle-ci pose quelques soucis pratiques car toutes les classes ne sont pas forc\u00e9ment repr\u00e9sent\u00e9es dans chaque compartiment..." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "yp = results.predict(W2)\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(X[:, 0], Y, \".\", label=\"expected\")\n", + "ax.plot(X[:, 0], yp, \".\", label=\"predicted\")\n", + "ax.legend()\n", + "ax.set_title(\n", + " \"Régression linéaire par morceaux\\nsur un nuage linéaire par morceaux\\n\"\n", + " + \"réduction du nombre de segments\\nR2=%f\" % results.rsquared\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le coefficient $R^2$ est quasiment identique pour un nombre de segments moindre. Je me suis amusé à rendre ce code plus générique pour comparer la première étape, le découpage en morceaux, via deux modèles, un arbre de décision et le nouvel objet [KBinsDiscretizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html) qui segmente une variable sans tenir compte de la cible. La régression n'est plus nécessaire linéaire : [Piecewise linear regression](https://sdpython.github.io/doc/mlinsights/dev/auto_examples/plot_piecewise_linear_regression.html). Je me suis également amusé à faire de même pour une classification par morceaux [PiecewiseClassifier](https://sdpython.github.io/doc/mlinsights/dev/api/mlmodel.html#piecewiseclassifier). Celle-ci pose quelques soucis pratiques car toutes les classes ne sont pas forcément représentées dans chaque compartiment..." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/dsgarden/split_train_test.ipynb b/_doc/notebooks/dsgarden/split_train_test.ipynb index 5b611724..5e286259 100644 --- a/_doc/notebooks/dsgarden/split_train_test.ipynb +++ b/_doc/notebooks/dsgarden/split_train_test.ipynb @@ -1,1391 +1,1351 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# R\u00e9partir en base d'apprentissage et de test\n", - "\n", - "C'est un probl\u00e8me plut\u00f4t facile puisqu'il s'agit de r\u00e9partir al\u00e9atoirement les lignes d'une base de donn\u00e9es d'un c\u00f4t\u00e9 ou de l'autre. Lorsque le probl\u00e8me de machine learning \u00e0 r\u00e9soudre est un probl\u00e8me de classification, il faut s'assurer que chaque c\u00f4t\u00e9 contient une proportion raisonnable de chaque classe." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R\u00e9partition na\u00efve\n", - "\n", - "On consid\u00e8re une base de donn\u00e9es qu'on divise en 2/3 apprentissage, 1/3 test. On note cette proportion $t$. Deux classes 0 et 1, la proportion de la classe 1 est de $p$ qu'on choisit petit." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "40" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import random\n", - "def generate_dataset(n, t):\n", - " return [1 if random.random() < t else 0 for i in range(0,n)]\n", - "\n", - "ens = generate_dataset(4000, 0.01)\n", - "sum(ens)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et on divise en base d'apprentissage et de test." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2683 27 0.01006336190831159\n", - "1317 13 0.009870918754745633\n" - ] - } - ], - "source": [ - "def custom_split_train_test(ens, p):\n", - " choice = generate_dataset(len(ens), p)\n", - " train = [x for x, c in zip(ens, choice) if c == 1]\n", - " test = [x for x, c in zip(ens, choice) if c == 0]\n", - " return train, test\n", - "\n", - "train, test = custom_split_train_test(ens, 0.66)\n", - "print(len(train), sum(train), sum(train)/len(train))\n", - "print(len(test), sum(test), sum(test)/len(test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On recommence un grand nombre de fois et on repr\u00e9sente la proportion obtenue dans la base de test." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "tirages = [sum(test)/len(test) for train, test in [custom_split_train_test(ens, 0.66) for i in range(0,100)]]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(tirages, \"o\")\n", - "plt.ylabel(\"proportion classe 1\")\n", - "plt.xlabel(\"tirages\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On consid\u00e8re maintenant la moyenne, les valeurs extr\u00eames de la proportion en faisant varier $p$." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "ps = [0.001 * i for i in range(1, 50)]\n", - "tmin, tmax, tmean = [], [], []\n", - "for p in ps:\n", - " ens = generate_dataset(4000, p)\n", - " tirages = [sum(test)/len(test) for train, test in [custom_split_train_test(ens, 0.66) for i in range(0,200)]]\n", - " tirages.sort()\n", - " tmin.append(tirages[int(len(tirages)*0.05)])\n", - " tmax.append(tirages[-int(len(tirages)*0.05)])\n", - " tmean.append(sum(tirages) / len(tirages))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", - "ax.plot(ps, tmin, label=\"min\")\n", - "ax.plot(ps, tmax, label=\"max\")\n", - "ax.plot(ps, tmean, label=\"mean\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)..." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "import pandas\n", - "\n", - "ps = [0.001 * i for i in range(1, 50)]\n", - "tmin, tmax, tmean = [], [], []\n", - "for p in ps:\n", - " ens = pandas.Series(generate_dataset(4000, p))\n", - " tirages = [sum(test)/len(test) for train, test in [train_test_split(ens, test_size=0.66) for i in range(0,200)]]\n", - " tirages.sort()\n", - " tmin.append(tirages[int(len(tirages)*0.05)])\n", - " tmax.append(tirages[-int(len(tirages)*0.05)])\n", - " tmean.append(sum(tirages) / len(tirages))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", - "ax.plot(ps, tmin, label=\"min\")\n", - "ax.plot(ps, tmax, label=\"max\")\n", - "ax.plot(ps, tmean, label=\"mean\")\n", - "ax.set_title(\"train_test_split from sklearn\")\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "L'\u00e9cart entre les extr\u00eames para\u00eet plus petit. Le g\u00e9n\u00e9rateur pseudo al\u00e9atoire utilis\u00e9 par [scikit-learn](http://scikit-learn.org/) para\u00eet de meilleur qualit\u00e9. Nous y reviendront peut-\u00eatre un jour." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R\u00e9partition stratifi\u00e9e" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nous utilisons maintenant le param\u00e8tre *stratify* qui permet de s'assurer que les deux classes sont \u00e9quitablement r\u00e9parties entre les deux ensembles *train* et *test*." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Répartir en base d'apprentissage et de test\n", + "\n", + "C'est un problème plutôt facile puisqu'il s'agit de répartir aléatoirement les lignes d'une base de données d'un côté ou de l'autre. Lorsque le problème de machine learning à résoudre est un problème de classification, il faut s'assurer que chaque côté contient une proportion raisonnable de chaque classe." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Répartition naïve\n", + "\n", + "On considère une base de données qu'on divise en 2/3 apprentissage, 1/3 test. On note cette proportion $t$. Deux classes 0 et 1, la proportion de la classe 1 est de $p$ qu'on choisit petit." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "import pandas\n", - "\n", - "ps = [0.001 * i for i in range(1, 50)]\n", - "tmin, tmax, tmean = [], [], []\n", - "for p in ps:\n", - " ens = pandas.Series(generate_dataset(4000, p))\n", - " \n", - " traintest = []\n", - " excs = []\n", - " for i in range(0, 200):\n", - " try:\n", - " train, test = train_test_split(ens, test_size=0.66, stratify=ens)\n", - " traintest.append((train,test))\n", - " except ValueError as e:\n", - " print(\"Skipping: small class too small in one side\", e)\n", - " excs.append(e)\n", - " if len(traintest) < 100:\n", - " ex = excs[0] if len(excs) > 0 else \"no exception\"\n", - " raise Exception(\"Too few, you should check the implementation is ok.\\n{0}\".format(ex))\n", - " tirages = [sum(test)/len(test) for train, test in traintest]\n", - " tirages.sort()\n", - " tmin.append(tirages[int(len(tirages)*0.05)])\n", - " tmax.append(tirages[-int(len(tirages)*0.05)])\n", - " tmean.append(sum(tirages) / len(tirages))" + "data": { + "text/plain": [ + "39" ] - }, + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "\n", + "def generate_dataset(n, t):\n", + " return [1 if random.random() < t else 0 for i in range(n)]\n", + "\n", + "\n", + "ens = generate_dataset(4000, 0.01)\n", + "sum(ens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et on divise en base d'apprentissage et de test." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzs3XdYFMf/wPH3UEVBQBAR6YqiFBEVFRuxm6IxMcWfSdRoNDGmF9NMjEk0xfRuYmwpGjX6NbF3JfbeCwIKUqRI73fz++NOAghySId5PQ+Pdzuzs5+9Oz83N7s7K6SUKIqiKI2DUW0HoCiKotQclfQVRVEaEZX0FUVRGhGV9BVFURoRlfQVRVEaEZX0FUVRGhGV9BswIcQbQoifizwfJYSIEkJkCCG6CCFOCyFCbrPtSCHEoCoL9ub2xwohNlVX+3WNEMJdCCGFECb65+uFEOMqsH4rIcQuIUS6EOLT6ou0YoQQIUKI6IqWKdVHJf06QAgxUwjxayXbuOk/kJRytpRyUpFFc4FpUkpLKeVRKaWPlHJHZbZbRiwLhRDvV6YNKeVvUsohlYxjvBAitDJtFGmrWr/kSpJSDpdSLtJv25D9mAwkAs2llC9Ve4BKvaWSfj0gdKrivXIDTldBO5VyozerVCk34Iws42rLxvSaN6Z9vS1SSvVXQ3/AdOAqkA6cBwYCw4A8IB/IAI7r6+4APgD+BbKBdsAE4Kx+/XBgir5uM30drb6NDMAJmAn8Cpjrl0kgE7ikXy8SGKR/bAS8BlwCkoA/gRZFYn8UuKwve7PouiX2cbJ+X/L02/y7yLamAyeAXMCkyPbSgTPAqCLtjAdCizyXwJPAReA68C0gbvFadwRyAI0+jhT9cnN0v3iuAPHAD4CFvswe+AdIAZKB3frXZYn+tc3Wt/XqLbbbRP+aJ+nbOQi0KvKezgEOAKnA/268xoC7fh9NitSdVNZ+lNjmwhKv+SD9e79CH0uavi1z4AsgRv/3BWCubyMEiAZeBa4BscC9wJ3ABf3r8cYt9vtO/XuYju4z/nLRdovUe1Zfz7mUMidgJZAARADPFikLAvbqX9NY4BvArMTn42n95yPidj4zjeWv1gNoLH9AByAKcNI/dwfa6h/PBH4tUX8HusTkgy5BmgJ3AW0BAfQHsoBAff1i/4FKa1f/n6BdkeeR/Jf0nwf26f8zmgM/An/oyzrpk0k/fdlnQAGlJH19/YXA+yWWRQLHABf+S7IP6P+jGwEPoftCaq0vG8/NSf8fwAZw1SeGYeW85sXa0C/7AlgDtACsgL+BOfqyOei+BEz1f31vJAnK+JIrZZtT9G02BYyBruiGXG68p1cBX3Rf1CtvvD+UkfTL2o/yXnP9e5+PLnEbARbALP177AC0BPYA7xX5/BQAb+v3/Qn9a/y7/nXyQffl41nG9mOBvvrHtpTyuQRmAEeAlqWUGQGH9ds3AzzRdWyG6su7Aj3R/V9wR9f5eb7E52Oz/n21KLKsQp+ZxvCnhndqjgZdwuwkhDCVUkZKKS+Vs85CKeVpKWWBlDJfSrlWSnlJ6uwENqFLTFVhCvCmlDJaSpmLLmmM1v9UHg38I6XcpS+bga7nW1FfSSmjpJTZAFLK5VLKGCmlVkq5DF2PLOgW638opUyRUl4BtgMBFdm4EEKgS2YvSCmTpZTpwGzgYX2VfKA14KZ/vXdLffaogHzADt2Xq0ZKeVhKmVakfImU8pSUMhPd6/igEMK4gtsw1F4p5Wr965sNjAVmSSmvSSkTgHfR/YIrGvsHUsp8YCm6Xz5fSinTpZSn0Q0N+pexrXx0n+3mUsrrUsojRcqEEOIzYChwh37bJXVH92UwS0qZJ6UMB35C/97oX8d9+v8Lkeg6Jf1LtDFH/75mF1lWqc9MQ6SSfg2RUoah603PBK4JIZYKIZzKWS2q6BMhxHAhxD4hRLIQIgXdT2r7KgrRDVglhEjRt30W3RdVK3S98cJY9Akr6Ta2UXJ/HhNCHCuyTV9uvT9xRR5nAZYV3H5LdD3ww0W2uUG/HOATIAzYJIQIF0K8VsH2QTcUtBFYKoSIEUJ8LIQwLVJe9DW4jK5XXVXvYUlRJZ476bdZdPtFP4NJUkqN/vGNxBlfpDybsl/z+9F9Hi8LIXYKIXoVKbNBN+w3R0qZWsb6boDTjfdF/968ge7zhxCivRDiHyFEnBAiDd2XdcnXreT+QuU/Mw2OSvo1SEr5u5SyD7oPuAQ+ulFU1io3HgghzNENB8xFN0ZsA6xDN9RzqzYMFQUMl1LaFPlrIqW8iu6nu0uRWJqi682WxZD9cUPXk5sG2On35xT/7U9VKBlHIrrE5VNkH62llJYA+h7tS1JKT+Ae4EUhxMAy2ip9g7pfCO9KKTsBwcDdwGNFqrgUeeyKroecWMH9MFTJ9WLQffaKbj/mNtsuviEpD0opR6IbOlqN7pjQDdfRvQ4LhBC9y2giCt1YfNHPn5WU8k59+ffAOcBLStkc3RdCyc+KmjLYACrp1xAhRAchxAB98s5Bl3xu9KriAfdyztAxQzc8lAAUCCGGA0VPaYwH7IQQ1rcZ4g/AB/pkjBCipRBipL5sBXC3EKKPEMIM3djwrWKNRzcmeyvN0P0nTdBvbwK6nn5Vigec9TEjpdSi+6L5XAjhoN9uGyHEUP3ju4UQ7fTDQGno3p+i71F5+4QQ4g4hhJ9+yCYNXVLXFKnyiBCik/6Lcxawokjv2qD9qIQ/gLf07609uvHzSp0qDCCEMNNfV2GtHxq68doVkrpTg8ei+zXZo5RmDgBpQojpQggLIYSxEMJXCNFdX26lbzdDCOENPFXZuBsrlfRrjjnwIbpeXRy6HtEb+rLl+n+ThBBHSlkX/fjzs+h6UNeB/0N3QPJG+Tl0/6nD9T+Pyxs6KulLfXubhBDp6A749dC3fRrdmRG/o+v1X0d3pkdZ5qMb300RQqwuY3/OAJ+iOyMjHvBDd6ZSVdqGbhw6Tghxozc9Hd0Qzj79MMEWdAfZAbz0zzP0cX0n/7uOYQ66hJkihHj5Ftt0RPclmYZuiGwnxRPrEnQHXePQnenz7G3ux+14HziE7gyqk+gOqlbqeooiHgUi9a/pk8AjJStIKTejOwNtjRCia4kyDbpfVwHoztxJBH4GbnRiXkb3mU9H98W9rIribnRunJmgKEo1E0LsQHe2zs/l1VWU6qJ6+oqiKI2ISvpKvSaE+EHo5hIq+fdDNW5zbBnbrPWrnRWlPGp4R1EUpRFRPX1FUZRGpM5NTGRvby/d3d1rOwxFUZR65fDhw4lSypbl1atzSd/d3Z1Dhw7VdhiKoij1ihDicvm11PCOoihKo6KSvqIoSiOikr6iKEojUufG9EuTn59PdHQ0OTk5tR2KUkc1adIEZ2dnTE1Ny6+sKI1YvUj60dHRWFlZ4e7ujm4uLEX5j5SSpKQkoqOj8fDwqO1wFKVOqxfDOzk5OdjZ2amEr5RKCIGdnZ36JagoBqgXSR9QCV+5JfX5UBTD1JukryiKUhsKcrPZOvMBstNKu8tj/aOSfh0WGRnJ77//XuH1xo8fz4oVK6ohosqbPXt2sefBwcG1FImiGObwr+/gtPQU+755obZDqRIq6ddht5v0K6OgoKBa2pVSotVqb0r6e/bsqZbtKUpVSTymmyEg8/T5Wo6kaqikb6Bff/2VoKAgAgICmDJlChqNhsuXL+Pl5UViYiJarZa+ffuyadMmIiMj8fb2Zty4cfj7+zN69GiysrIAOHz4MP3796dr164MHTqU2NhYAMLCwhg0aBCdO3cmMDCQS5cu8dprr7F7924CAgL4/PPP0Wg0vPLKK3Tv3h1/f39+/PFHQJdQp02bRqdOnbjrrru4du1aqfsQEhLC888/T3BwML6+vhw4cACAmTNnMnnyZIYMGcJjjz1GTk4OEyZMwM/Pjy5durB9+3YAFi5cyMiRIxk2bBgdOnTg3XffLWz7s88+w9fXF19fX7744gtA96XVsWNHpk6dSmBgIBMnTiQ7O5uAgADGjh0LgKWlZeE+vPLKK/j6+uLn58eyZbobI+3YsYOQkBBGjx6Nt7c3Y8eORc0Mq9SoCN2wTrMrmbUcSBWRUpb7BwwDzqO7zdxrpZSbo7t9WRiwH3DXL3dHdy/YY/q/H8rbVteuXWVJZ86cKfb8wR/23PS3eE+ElFLKrNyCUsv/PHhFSillUkbuTWXlOXPmjLz77rtlXl6elFLKp556Si5atEhKKeVPP/0k77//fvnxxx/LyZMnSymljIiIkIAMDQ2VUko5YcIE+cknn8i8vDzZq1cvee3aNSmllEuXLpUTJkyQUkoZFBQk//rrLymllNnZ2TIzM1Nu375d3nXXXYVx/Pjjj/K9996TUkqZk5Mju3btKsPDw+XKlSvloEGDZEFBgbx69aq0traWy5cvv2k/+vfvLydNmiSllHLnzp3Sx8dHSinlO++8IwMDA2VWVpaUUsq5c+fK8ePHSymlPHv2rHRxcZHZ2dlywYIF0tHRUSYmJsqsrCzp4+MjDx48KA8dOiR9fX1lRkaGTE9Pl506dZJHjhyRERERUggh9+7dWxhDs2bNisV04/mKFSsK9yEuLk66uLjImJgYuX37dtm8eXMZFRUlNRqN7Nmzp9y9e3eZ75OiVCWNRiP3dfGWp7y95akO3jLl6rlKt5mbmVIFkd0MOCQNyOflnqevv8Hzt8BgdPdFPSiEWCN19zi9YSJwXUrZTgjxMPAR8JC+7JKUMqAS30u1buvWrRw+fJju3XX3aM7OzsbBwQGASZMmsXz5cn744QeOHTtWuI6Liwu9e/cG4JFHHuGrr75i2LBhnDp1isGDBwOg0Who3bo16enpXL16lVGjRgG6C41Ks2nTJk6cOFE4Xp+amsrFixfZtWsXY8aMwdjYGCcnJwYMGFDmvowZMwaAfv36kZaWRkpKCgAjRozAwsICgNDQUJ555hkAvL29cXNz48KFCwAMHjwYOzs7AO677z5CQ0MRQjBq1CiaNWtWuHz37t2MGDECNzc3evbsWe5rHBoaWrgPrVq1on///hw8eJDmzZsTFBSEs7MzAAEBAURGRtKnT59y21SUyrp8YhPNsyCyvSnuF/I5v+EXgh7/6LbbO79lPtnPzyVr6iCCp35dhZEazpCLs4KAMCllOIAQYikwEiia9EcCM/WPVwDfiGo8h27ZlF5lllmYGd+yvEUzs1uWl0ZKybhx45gzZ85NZVlZWURH6+4RnpGRgZWVFXDzKYRCCKSU+Pj4sHfv3mJlaWlpBsfx9ddfM3To0GLL161bZ/Api6XFBRQm7Bvbqcj6t6pftN1buVUb5ubmhY+NjY2r7biDopQUseMvWgNOo0egnbOSxMP74fHbb+/iz9/RtgDSlmwhb3wqZk2ty1+pihkypt8GiCryPFq/rNQ6UsoCIBWw05d5CCGOCiF2CiH6lrYBIcRkIcQhIcShhIS6d1rUwIEDWbFiReFYeXJyMpcv62YxnT59OmPHjmXWrFk88cQThetcuXKlMLn/8ccf9OnThw4dOpCQkFC4PD8/n9OnT9O8eXOcnZ1ZvXo1ALm5uWRlZWFlZUV6enphm0OHDuX7778nPz8fgAsXLpCZmUm/fv1YunQpGo2G2NjYwjH40twYKw8NDcXa2hpr65s/dP369eO3334r3MaVK1fo0KEDAJs3byY5OZns7GxWr15N79696devH6tXryYrK4vMzExWrVpF376lvtWYmpoWxl9ym8uWLUOj0ZCQkMCuXbsICgoqcz8UpSaknz5NvjF0uvd54lsKjMITb7utpPAjuJzMIs5e0PI67PqoEt8elWBI0i+tC1myW1ZWnVjAVUrZBXgR+F0I0fymilLOk1J2k1J2a9my3HsA1LhOnTrx/vvvM2TIEPz9/Rk8eDCxsbHs3LmTgwcPFiZ+MzMzFixYAEDHjh1ZtGgR/v7+JCcn89RTT2FmZsaKFSuYPn06nTt3JiAgoPDslSVLlvDVV1/h7+9PcHAwcXFx+Pv7Y2JiQufOnfn888+ZNGkSnTp1IjAwEF9fX6ZMmUJBQQGjRo3Cy8sLPz8/nnrqKfr371/mvtja2hIcHMyTTz7J/PnzS60zdepUNBoNfn5+PPTQQyxcuLCwt92nTx8effRRAgICuP/+++nWrRuBgYGMHz+eoKAgevTowaRJk+jSpUupbU+ePBl/f//CA7k3jBo1Cn9/fzp37syAAQP4+OOPcXR0rPB7pShVyexyKgkOAtPm9mS5WtLyqoaC3OzbauvAV9Mx04DzrOlEORvR7J8zZCRFlb9iVStv0B/oBWws8vx14PUSdTYCvfSPTYBE9PffLVFvB9DtVtsz5EBuXRcREVF4kLQu6d+/vzx48OBtr79gwQL59NNPV2FEVau+fU6Uui07J0Me9vOW6x/pLqWUctucR+SZDt7y3OafK9xWbmaK/Lebt1w/VJcXji59X57p4C3XPTewyuLFwAO5hvT0DwJeQggPIYQZ8DCwpkSdNcA4/ePRwDYppRRCtNQfCEYI4Ql4AeG38+WkKIpSky7sWYFFHlh1bAeA54DRAETtXlfhtvb9+DK26dB8xB0ABDz0JhFepjhsu0py5PGqC9oA5SZ9qRujn4auN38W+FNKeVoIMUsIMUJfbT5gJ4QIQzeM85p+eT/ghBDiOLoDvE9KKZOreifqGnd3d06dOlXbYdxkx44ddOvW7bbXHz9+PN98800VRqQoddfVPRsA8Ai+EwCXrneT2gxyzkZUuK2ctXtIsoagiR8XLvN64SWa5MP2tydzLa3mJgs0aGplKeU6YF2JZW8XeZwDPFDKeiuBlZWMUVEUpcZln7tEthl4B98PgJGxMcltTGl+pWJj+mfWfotLtJYrIzwwMbf4r8DnPs50/JwOh9NYvvJ3np5QMwd21RW5iqIopbCMyiDZ0Qhh9l+iNvZyomUKJFw8YHA74Yt/IdcUejz7KQDZeRo+3nCO4V/uZmn7sUjAbUPN/YJWSV9RFKWEpJQYWl+T4GZTbHmrHroz4y5sWmRQOwkXD+B6KovozlbYOHcE4JON5/luxyVGBrRh3lvPEt2rBR7Hszm/pfSz6aqaSvqKoiglnN/xByZaaOHjW2x5h6ETKTCClGOGHXw99M3rmGrA8f+mEJ6QAcBTIW1ZOrknnz7YGTtLc4Jn/ECOOVzSz1lV3VTSr0Jr1qzhww8/rO0wFEWppGsHdwLQrv99xZZbWDsQ5ygwjbhebht5WanY/BtDhJsRj+5tw4z/6U7uaGllTk9Pu8J6tm5+XBvggkdYAUeXvl+Fe1E6lfSr0IgRI3jttdfKr6goSp2muRhNejOJld8dN5XlutvQKlZLXkbKLdvY+Nkz2GTAbs9O9G5nx4f3+ZdZt99bC0mxhLjFSysde3lU0jfQjemSJ02ahK+vL2PHjmXLli307t0bLy8vDhw4wMKFC5k2bRqgO73x2WefJTg4GE9Pzzp7UxNFUYrTSi220dmktTZFmJjdVG7T2Q8zDZzfXPYY/IZTcZhuPUSCDfR/8lN+HtcdlxZNy6zfzM4Jyzcn0fP7P6pkH27FoFM265T1r0Hcyapt09EPhpc/LBMWFsby5cuZN28e3bt35/fffyc0NJQ1a9Ywe/Zs7r333mL1Y2NjCQ0N5dy5c4wYMYLRo0dXbdyKolS5yKiTtE6Ga4H2pZa3GzSW69/vImbfNvxGvVS4XKuVxKXl4GRjgWPYUsxjJZdHeDKss6tB2y3aVnVSPf0K8PDwwM/PDyMjI3x8fBg4cCBCCPz8/IiMjLyp/r333ouRkRGdOnUiPj6+5gNWFKXCLu3UTUro2LlrqeWOPv1IsoaCC9GFy87FpfHgj3t58Me9ZGRkEP/tT2SbQa/nP6uRmCui/vX0DeiRV5eiU/waGRkVPjcyMip1ut+i9aW625Oi1AspRw7iDHjccdP1pv/VcWmCXWQO6Vm5fL0jnPmhETRvYsLrwzuy+817cY/SEvtIANZOHWoucAOpnr6iKEoRxuHxJNtIzDzLnrLEvIMrNhnw1JyvmLcrnAe6OrPtpRA6Xl2B8+arhHcyJ+T1X2swasOppK8oiqKXU5CDQ0w+OU7mYGRcep18Dc59dDcy6p+znxVP9uLD+/0xyYgm5cN5pFlC8Je/YWRc+vq1rf4N79SSkpOoLVy4sNSy8ePH31QOurtqKYpSt507uwu7dEht63RTWV6Blp9Dw1n4bySrJz9IvOnXtL4WTTf3FgDsfHY0HqmQ985YbF18ajp0g6mkryiKondl12q8ANeuvYst3x+exFurT3HxWgZDfVphYm7JtdZGWFzW3ep019zHaXs8m4gBrbjz4bdqIXLDqaSvKIqil3XqJBojcOyvm1lTo5W8uuIEK49E42xrwfxx3RjYsRUABZ52tNmZQNj2xTRbspdoJyMGf/pPbYZvEJX0FUVR9JpEXue6ncTI0RsAYyOBkYCpIW15ZoAXFmb/jdPbBXbDeMd6El6Zg7mEDh99iKmFZW2FbjB1IFdRFAVIzEzAOUZDvpMFD/64j3NxuqGbj0f78+ow72IJH6DD0AkA2GRA2tggXLvfU+Mx3w6V9BVFUYCjB9fRLBcuW9hwKSGDuFTd3ayEEKXWt3Xz47KbMZe6NCNkumFTLdcFanhHUZRGb8OpOHauWMpjQNNOfdg2LQTrpqblrjdkXc3e37YqqKSvKEqjdyQqFq/oK+SZSh6cOAlhQMIH6uy5+LeihncURWl0cgs0fL31IjvOXwPARbOBHue0mPoZIVoYNkFafaWSvoEMmVr5wIEDBAcH06VLF4KDgzl//jwAn332GY8/rrvp8cmTJ/H19SUrK6s2d0dRGq09YYkM/3I3n26+QOjFRLILspG//E6BqaT9ky9BGWP4DUW9G9756MBHnEs+V6VterfwZnrQ9HLrlTe18uLFi9m1axcmJiZs2bKFN954g5UrV/L8888TEhLCqlWr+OCDD/jxxx9p2rTsubUVRal619Jz+GDtWf53LAY3u6YsnNCdkA4O/LX+U7qeKUDT3RyTPuNrO8xqV++Sfm26MbUyUOrUyqmpqYwbN46LFy8ihCA/Px/QzcK5cOFC/P39mTJlCr17977VZhRFqQY7ziWw/mQczw70YmpIW5qYGpOvySdn3iLyzSR+z79T5nw7DUm9S/qG9MirS3lTK8+YMYM77riDVatWERkZSUhISGH9ixcvYmlpSUxMTE2HrSiN1snoVK6mZDHMtzWjuzrT09MOV7v/fmVv3vgtXc7mkxVsiXHgqFqMtOaoMf0qlJqaSps2bYDiE66lpqby3HPPsWvXLpKSktStExWlmqXl5DNzzWlGfhvKJxvPo9FKjIxEsYSv0WrI+PEXss0lnV/6pMGP5d+gkn4VevXVV3n99dfp3bs3Go2mcPkLL7zA1KlTad++PfPnz+e1117j2rVrtRipojRMUkrWHI9h4Kc7WbQ3kkd7urHq6d4YG92c0Hds+Ba/8/nk9WiBic/NN0BvqERdu6NTt27d5KFDh4otO3v2LB07dqyliJT6Qn1OlBPRKYz45l/82ljzwShf/J1tSq0npWTVyM64Xc7Db9USzDy713CkVU8IcVhKWfadX/Tq3Zi+oihKUTn5Gg5GJtPXqyX+zjYsejyIPu3sS+3d37B37Vd0vJBPwsBWDSLhV4Qa3lEUpd7adSGBYV/sYsKCg8SkZAPQv33LWyZ8KSWJP/xMZhPo8cYPNRVqnaF6+oqi1DvxaTnM+ucMa0/E4mHfjIUTgnCysTBo3SOrP8MrrICYoU6Yt2l8w4EG9fSFEMOEEOeFEGFCiNdKKTcXQizTl+8XQriXKHcVQmQIIV6umrAVRWmsMnMLGP7lbjafiefFwe3Z8Hxf+njZG7RuXkYKCd//QoYF9Hr9p2qOtG4qt6cvhDAGvgUGA9HAQSHEGinlmSLVJgLXpZTthBAPAx8BDxUp/xxYX3VhK4rS2EQkZuJh34xm5ia8dVdHAl1tcbdvZvD6GUnR7H50GO5XtMT+nx9NHT2rMdq6y5CefhAQJqUMl1LmAUuBkSXqjARuTCi9Ahgo9JNQCyHuBcKB01UTsqIojUlqVj5vrjrJgE93sOtCAgD3BTpXKOFfjz7DnoeG4hqhIeb//Bjw9p/VFW6dZ8iYfhsgqsjzaKBHWXWklAVCiFTATgiRDUxH9yuhzKEdIcRkYDKAq2vDnuFOURTDSClZfewqH6w9S3JmHuOD3eniWvopmLdy7dweTk2eROtESfKUvgx8fl41RFt/GJL0SzsMXvLk/rLqvAt8LqXMKOvuMwBSynnAPNCdp29ATIqiNHBP/36EdSfjCHCxYeGEIHzbWFe4jajDa4l4+mVsMyH7lZH0nfBhNURavxgyvBMNuBR57gyUnECmsI4QwgSwBpLR/SL4WAgRCTwPvCGEmFbJmGuFIVMrZ2Zm8vjjj9O9e3e6dOnC//73v8J1+/btS2BgIIGBgezZsweAHTt2EBISwujRo/H29mbs2LHUtYvlFKUm5eRr0Gh1/wcGd2rFB6N8+eup4NtK+GHbFxM95WWa5oDxuxMJUgkfMKynfxDwEkJ4AFeBh4H/K1FnDTAO2AuMBrZJXfbqe6OCEGImkCGl/KYyAcfNnk3u2aqdWtm8ozeOb7xRbr3yplbu1KkTAwYM4JdffiElJYWgoCAGDRqEg4MDmzdvpkmTJly8eJExY8Zw46rjo0ePcvr0aZycnOjduzf//vsvffr0qdL9U5T6YPVHUzDbvJvUO/ow5s15jOrifNttRR1ey/UX5yCMwGruy3QYNLEKI63fyk36+jH6acBGwBj4RUp5WggxCzgkpVwDzAeWCCHC0PXwH67OoGtLeVMrR0dHs2bNGubOnQtATk4OV65cwcnJiWnTpnHs2DGMjY25cOFCYZtBQUE4O+s+3AEBAURGRqqkrzQq4eEXOPTqQ/idyiHPBDyW7OafM90J+eovLO1cym+gFMfnvIVzPrT86QPce91XxRHXbwZdnCWlXAesK7Hs7SKPc4AHymlj5m3EdxNDeuTVpbyplY2NjVm5ciUdOnQott7MmTNp1aoVx48fR6vV0qRJk1LbNDY2pqCgoJr3QlHqjj+/exvHxcvxSYGTPawZMHsx+9+ZgEdoMkc2BSQcAAAgAElEQVTuGoLt9En4jXqpQm2eXf8dbU/lEN7fngCV8G+ipmGoQkOHDuXrr78uHJc/evQooJtauXXr1hgZGbFkyZJiM3AqSmOUm5nKuqkh+Hy9HCMtJE1/gAcX7cO+TXvu+vlfcmc8iLEWxBs/s+7pEPKyUg1uO/Kr78hoAr1nNO6zdMqikn4VmjFjBvn5+fj7++Pr68uMGTMAmDp1KosWLaJnz55cuHCBZs0MP79YUeqy/OwM9v30ksFJOSUrj4+/+YJdd/XCY1s8Ef4W+K1eTb8Js4rVCxz7Lr6rVxPpa4HH1nh23dWLy/tXldv+oSVv4R6hIXmwOzbOjW+KBUOoqZWVBkN9TmrehheH4rbuCpHtTOi/ZDNNbR1LrSelZMXhaHYs+4xxWzYgJORO6Eef534sdxu7Pn+CpotCyTGH9ksW4NC+Z6n1tBoN2wb6YZEpCdyyEwtrh0rtW31j6NTKqqevKMptuR51GvutV0iwAdewAnY/OJDUmPM31QtPyOChH/exa+HbTNy4gTxTsP/uPYMSPkC/F36iybtTsMyEE1MeJzOp9FuOhn71JG3iJLkjAxpdwq8IlfQVRbkte2ZNwSIXHGa9SNITwThd1XJozCgSLh4oVk+jlXQ6/RWTduwjzQo8f/4Gj+DRFdqW78jnyZwSQutYyY6Jw9Hk5xUrz8tKhT9DibeHfq/8Uul9a8jqTdKva8NQSt2iPh81K+bEVpz3JhHhZ4H3kCfo99J8Ml+4E7skydnHxrH272W8949uTsari57h/q3HSLAX+C35DSf/gbe1zd7PfE/0CA88z+WxYeqAYmW75kyg5XUwGzsYE3PDplhurOpF0m/SpAlJSUnqP7ZSKiklSUlJxU6FVarXkTmvIiR0fu29wmU9n/iU9FcfolkW2MyaybWDK9jw5r20/GU/Mc5GBP2xBjvPwEptd/Ccv7nUzQrP3UlseUf3ayEjKQrLdWeJcjai5+TPK9V+Y1AvbqLi7OxMdHQ0CQkJtR2KUkc1adKk8CI3pXqFbV+Mx7EsInra4N/1LgDyNVrmh0bw5cWe9B6QxhM71zNh1R9Y5EGElykhizeVeZC3IoyMjRn60za23N8Tlz9Pc8BtOknHD+GeCRavP4qRsXGlt9HQ1YuzdxRFqTvWjfDHMTIft9W/FfbckzJyGfDpTnp4tOCdET7knVnLlZdnkO7alCG/bMOsacXnzrmVtNgwDj04AptUXf6KczflzjUnqnQb9Y26MbqiKFXu+IoP8biQT+QQJ9xb+fL11os8fUc77CzN2fB8X1pb68fTg0fjsadiB2sronnrdnh/8xWXH3+G5lnQ/gV1Uz5DqaSvKA3UuY3ziD0aSr+XfsbY1KzS7Wk1GuJ/WELzZpAy8gMGfLqDjJwC+njZ08XV9r+EX0OcOg8i/7vZxJ/eh88dj9XotuuzenEgV1GUiov46mscFx5k892BxBzfUun29v/0Ii7RWo51d+b1zUm0d7Bi3XN96eJqWwXR3h63HqMIevyjWtt+faSSvqI0UDbxBcTbQasYDTGPPcPOTybcdlsFudnk/LqZBBtY6DiNT0b7s2xKT9q3sqrCiJWaoJK+ojRACRcPYJMBub09aPHzbK7bGeEwfx9r7wvgepTht6uWUrL5TDw75k7EMVGSMbIH618ZzgPdXLjV3fCUukslfUVpgCJC/wLAzi8Qtx6jCFm7n8ihTriey+XsqNEcXPRmuW1EJWfx+MKDPLH4ELm7T3CtBQx9eR62zSp/fECpPSrpK0oDlHzqGACefXW3uTC1sGT4l1sx+ugp8s2g6Zy/CNu+uNR18wq0fLs9jEGf7eRARDJvDnajdYyG9LZWVXJAWKldKukrSgNUcDmeZCto4d652HKfe57F64dvMAIurV9a6rpvrjrJJxvPM8DbgS0v9Sc4ezsWedC8s28NRK5UN3XKpqI0QJaxOaQ6mpZa5uQ/kDBb0JyJKlyWmJELgL2lOVP6e3KnX2vu8NbNVHli9wZcAa9h6rTIhkD19BWlgcm6HkfLZMClRZl10jwtaXWlgNysdH7dd5kBc3cUTpDWzsGqMOEDaM5Fk2QNrX1DqjlypSaopK8oDUxE6J8YSWhe4l7NRVkF+NI0D2bNnslbq0/h42TNMwO8bqqn1Wiwi8ojxU3NXNlQqKSvKA1M3JFQAFx6DiuzTlLHe3V1og/wxUMB/P5ED9o5WN5UL/zfZTTPAgvfdtUTrFLjVNJXlAYm+1Ik2WbgHHhXseVSSlKz8wHoFzKM+Bbgdi2Fe7u0KfOc+8vbdPel9RhQffPoKDVLJX1FaWDMYjJJdDAqdnplZGImj/1ygPELDqDVSuwtzcnwtKJVdMEtb2qeffoSaU3Bvdf9NRG6UgNU0leUBkSTn4f9NS15Trqhmpx8DV9sucCQL3Zx9EoKIzo7cWMy9eaB/ljkwbkNP5XZns2VbBJdzdQ89Q2ISvqK0oBEHfoHizywaOdGeEIGw7/czRdbLjKkUyu2vtSfCb09MDbSDeV0vGcyAFd3byq1rZgTW7FLBVNvlxqLX6l+6jx9RWlAovZvwB5wDOyLk40FLi2a8u4IH/q1b3lT3ZZeQZy2B3EuptS2Lm78FQfAue/w6g1aqVEq6StKA6HRSmJPnMZWgFPP+2liaszix4NuuU6GZ3Ocj6aRl5GCmaVNsbL046ewMgP/QY9XZ9hKDVPDO4rSAJyITuHeb//FLCaFhBZQYFb2hVlF2XQNwDwfzm744aYyy8gM4tsYY2KuztFvSFTSV5R6LCdfw9v/O8XIb/8lPi0HhyQtma2b4NC8iUHrd7xnCgAxuzcXW54ceZxWiUD7VlUdslLLVNJXlHrMzNiIC/HpjOvlzor7rbFNBxN3R4PXt/MMJLalQJyPL7b83LqfAXDsdUeVxqvUPoOSvhBimBDivBAiTAjxWinl5kKIZfry/UIId/3yICHEMf3fcSHEqKoNX1Ean0sJGTyx+BDX0nIwMhL8OrEHM0f4EL9/NQB2fl0q1F5W2+a0jtaQm55UuOz6kSPkG4P38MlVGrtS+8pN+kIIY+BbYDjQCRgjhOhUotpE4LqUsh3wOXDjppWngG5SygBgGPCjEEIdPFaU25CTr+HTTecZ/sVu9oUncT4+HQATY91/4+RTRwDw6FOxC6lsuwViVgBn1n5fuMzs0nXiWhthYe1wizWV+siQnn4QECalDJdS5gFLgZEl6owEFukfrwAGCiGElDJLSlmgX94ECq8LUZR65+CiN7h++WStbHvH+WsM+XwXX28L404/R7a+1J++XsVPw8yPjOO6Jdi37VqhtjuNfBotEBu6DYCMpCgc4yX5bQ07GKzUL4Yk/TZAVJHn0fplpdbRJ/lUwA5ACNFDCHEaOAk8WeRLoJAQYrIQ4pAQ4lBCQkLF90JRqtnVoxuxnLOKf9+dUivb/9+xGEyMBb9P6sEXD3fBwermA7XN4nJJbVXxH9K2Lj7EtRIYX9CN65/95wdMtGDX7daneyr1kyFJv7SZmEr22MusI6XcL6X0AboDrwshbvq0SinnSSm7SSm7tWx580UkilLbTq/4DgCrc9cNX+fvr9jepxNH/nivwtsr0Gj5eXc4Z2LSAHh3pA/rn+tLcDv7Uutnp17DPkmidbGt8LYAstva4HhVS3bqNRIO/IsW6HiXGs9viAzpFkQDRa/DdgZKXsJ3o060fszeGkguWkFKeVYIkQn4AoduO2JFqQXaw5cAcEiGKwf/xrX7PeWuE7F0CW0TJXkf/M6+tGR6Tvm83HVOrvqUS1vXkpSRR16BlmNNTbncVDdxmpmtLf1e/QVTi5unQI4IXY6JFqxuMYf+rbTo3g2zPZs5+8+3GIUlEu8g8HG6vbaUus2QpH8Q8BJCeABXgYeB/ytRZw0wDtgLjAa2SSmlfp0oKWWBEMIN6ABEVlXwilITUmPO0+aKhoj2pnhcyOf8/+aXm/Q1+XnYncvgsrsxpjlaHL7cwI7U64S8urDU+rnpSWx6cSSeu5MoO9VGszn+bu78bsdNJXFHdtEacOkxtAJ79h+fkU8T/dVmYndto02MhuhA69tqR6n7yh3e0Y/BTwM2AmeBP6WUp4UQs4QQI/TV5gN2Qogw4EXgxmmdfYDjQohjwCpgqpQysap3QlGq0/HfP8JEC06PjCHRBvKPXSp3nbPrvsMmE5qEdKH7H2uIaWNEy1/2s/mte2+qe3HbIv69qy/tdidxvJM5a97+GIfQHXgeDC32d6lzU9y2x3N06fs3tZEVdpkcU3DtPuKmMkNYO3UgtpXAYX8i5vlg3cX/ttpR6j4hZd06oaZbt27y0CE1+qPUHf/cH0DLiFy67j3Chif643w0nQ6hO295OuPaJ/vhtjOBNhv+xNbNj6zrcex4bAgeF/OJHOLE0M83IbUa1r12L64bIsgxh7yJA7G/bzYdWzcvtc30+HCOjbwLIw10+msFti4+hWWbBvmAhCFbT9/2fq6d1BvPUN2orN2aBTi073nbbSk1TwhxWErZrbx66opcRbmFvKxUWoflktC+KSbmFtj37o15Ppxc+ekt12t6OpGrzkbYuvnpnts6MvjPPVzys8B9Uwxrx/di411daLc2gkg3YzyWLaTv09+UmfABrFp5YvPaZJpnwp7nxqLVaAD9fWyvaclzalapfbUL6gFAgi0q4TdgKukryi0cX/4RTXOhRb9gAPxGv0KeMVzbvavMda4c/BvHBAmB7sWWm1pYMvz3fZzrZkW7g+m0jtWyZ4gHvf44gINXD4Pi8bv3BaKHOON5Jpcdcx4BIPrIOprmQRNP19vbST2fkdMoMII096aVakep29TVsYpyC/HbtmFqAp0feh2AZnZOxLgaY3U+pcx1zq74AVfA+75JN5XtiUzlhTZvMbXFYgIHj2TiPY9WOKbBH61h6+luOCw7RsSAFcQc3koLoFWX3hVuqyirVp7kTL+Prl0HV6odpW5TPX1FKYNWo8H2bCoxHqY0s3MqXG4c4IFDMlw+8L/SVzwaSbw9uPXQTTWVr9Fy6qruPrS929oz98FAnv/sTwbdRsIHMDG3wG/ul2iM4dKbb5Ny6hRaAZ59H7qt9orqPu4DWvuGVLodpe5SSV9RynBhy3xapIF5d+9iy73uGacr/98vN61z/fJJ2kRpyfTVXWR4KDKZe74OZcy8fVzPzMPISDC6qzNmJpX7r+fUeRA5E/rRJlbiEppIQguKfTEpSllU0leUMoT/vRQt4Pfwi8WWewSPJtEGCo6F37TOsT8+wVhCywF3M33FCUb/sJe07HzmPtgZm6amVRpfn+d+5FKXZphqIMPRvErbVhoulfQVpQzmJ+O42sao1DNZUjtY0/pyAdmp14otz9p/nJRm8NRZH1YeiWZKP082v9ifoT6OCFHabCWVE/LNKiI9jbEN6VXlbSsNkzqQqyiliDmxFad4yeU7nUstt+/TG/P96zi5Yi5BEz/WrRMbQ+tLecT4WfJ4vw4M7OiAt2PZp2BWBUs7F4avO1Wt21AaFtXTV5RSnP7zawDa3Tu+1HL/0a+QZ6I7dTMzt4A5687y5QcvY5EH9v368PQd7ao94SvK7VA9fUUpRcHhMBJsoU/v0s+IaWrrSIyrCc3PpzL4s53EpObwccI5ckyh88Ov13C0imI41dNXlBLSYsNwvqwh3acFRsbGpdaRUhLr2oqW1yEgZz/LnwjC+VI2sZ5m6m5TSp2mkr6ilHDsjw91E6wNGn5TWYFGC4AQAm0P3W0JR+bto+nZpdhmgEUPn5vWUZS6RCV9RSkhbc9h0i3Ab9RLxZbvD09i+Je72X5ed8bOpAlPkWAL2uMRXF63Eq0A/4dfro2QFcVgakxfUYrIy0rF8WIOcd66CdYAkjJymb3uHCuPRONsa4GZ8X99pbQO1rgcSiUlJZ6rbYzw8QysrdAVxSAq6StKEfvnvYJ9Ltj20d0fdvXRq7yz5jRZeQVMDWnLMwO8sDD7b5y/ZZ++mO37R3dHrd6Vm/BMUWqCGt5RFL1/v32a5j/tJs5e0OWRdwDILdDg7WjF+uf68uow72IJH8Dv/pfI1Xed2o+aWNMhK0qFqZ6+ogDbZz9CyyWHiXUUHJk4h5gLuYztAQ92c+HBbi5lXk3b1NaRmLZmmKfkMzB4dA1HrSgVp5K+0uhteu1uXFZfItLNiC+C3+TCaRMmt8gCMGjqhH6//ENBdkZ1h6koVUIlfaXR0mo0bHhmIB7b4jnfzoQ3Or2Jm00bVo7yJdDV1uB2LO1cqjFKRalaKukrjZImP4/1k/rRdn8qF/wtmNnhbV4a2plxvdwwMVaHupSGSyV9pdFIjjzOufXzST56hCbnk2kbL7kUbMs9P+0mJFeLdRVPfawodZFK+kqDpNVoiDq0hktbl5N16gKWlzNplQS2gJURxLQSbO3rxMRv1mNkbIx109KnW1CUhkYlfaVByMtK5cLmBVzds5mC89HYReVhnQmtgUxzSHA24aSfPfubtGW7xUAeGdCVqSFtaWKqkr3SuKikr9R7h39/F/HhUizywBVIsoZEzyZkdPTEtd/ddOk/lqjUfAZ/vpMgjxasGOmLZ0vL2g5bUWqFSvpKvRe7aT3OWkiY3BuvIY/QUX9j77ScfDacjKO9qRnu9masmdYHb0erarmDlaLUFyrpK/Vek8tpxLc2YuiLPwO6aY//PhHLe/+cITEjl0A3G9o5WNGxtbqpiaKopK/Ua1nX42gVL7nS1w6AiMRMZqw+RWhYIn5trJk/rhvtHKxqOUpFqTtU0lfqtfMbf6aJFlp0CSSvQMvD8/aSlath1kgfxvZww9hIDeUoSlEq6Sv1WvyBXbgBbQePw8zEiM8fDKCdgyUOzZvUdmiKUiepSw+Veis+LYe88zFcs4VdKbpbFAa3s1cJX1FuwaCkL4QYJoQ4L4QIE0K8Vkq5uRBimb58vxDCXb98sBDisBDipP7fAVUbvtIYFWi0LPg3goFzt+EYoyHJyZy7/VvXdliKUi+UO7wjhDAGvgUGA9HAQSHEGinlmSLVJgLXpZTthBAPAx8BDwGJwD1SyhghhC+wEWhT1TuhNC7PLTvG2hOxjLE9jVU2WPt5qYusFMVAhvT0g4AwKWW4lDIPWAqMLFFnJLBI/3gFMFAIIaSUR6WUMfrlp4EmQgjzqghcaVxSs/PJyisA4NGebnz7f4EMLjgGgGu/u2szNEWpVwxJ+m2AqCLPo7m5t15YR0pZAKQCdiXq3A8clVLmltyAEGKyEOKQEOJQQkKCobErjYCUklVHoxn46Q6+3HoRgJ6edtzl35qsMxfJMgfPvmNqOUpFqT8MOXuntHPeZEXqCCF80A35DCltA1LKecA8gG7dupVsW2mkwq5lMGP1KfaGJxHgYsM9/k7FyptdzuRaG2OMTc1qKUJFqX8MSfrRQNG7RDgDMWXUiRZCmADWQDKAEMIZWAU8JqW8VOmIlUZhxeFoXv/rBBamxnwwypcx3V0xKnLOfUr0WRwSJZe7tKrFKBWl/jFkeOcg4CWE8BBCmAEPA2tK1FkDjNM/Hg1sk1JKIYQNsBZ4XUr5b1UFrdR/4aF/sm7aAPKyUostzyvQAhDgYs3IgDZsezmEsT3ciiV8gAsbF2AEtOzWs6ZCVpQGodykrx+jn4buzJuzwJ9SytNCiFlCiBH6avMBOyFEGPAicOO0zmlAO2CGEOKY/s+hyvdCqVeyrsdxZfo7eGyJZduMhwCITc3mqV8P88Iy3cHZdg5WzH2gM/aWpR/3Tzx8AK0A72ETayxuRWkIDLoiV0q5DlhXYtnbRR7nAA+Ust77wPuVjFFpYLY9O4K2SRDbUuC46TILf13IJxccKdBKnh3ohZSy3JkwjcITiW8p8GnlWUNRK0rDoK7IVWrUnu+eoe3BdMJ722I16wMArH+bS5BHC7a82J+n72hXbsIvyM3GIUZDlquaE19RKkolfaXGxJ3ehelPW4hpJRj0xd94dr+Tg93s8Y7Q8ETOr7i0aGpQO2E7f8UiD5r5eldzxIrS8Kikr9QITX4ex16aimk+7Bz+CCbNWmBnac5jX68h3g7kgs1kJEWV3xAQvXs9AJ4D7q/OkBWlQVJJX6kRa1+9B7dIDZuD2nCqeX9SsvIAMLe0pfm0/6NFGux8/f8MaivnXCSpzcClq7oSV1EqSiV9pVrl5Gv4/vOZeGy8wrm2xjg/8RMrngzGrshZOYFjZnCpc1NcQxO5uG3RLVrTsY7KJtnZFCNjNd+OolSUSvpKtcpMiqLj8mWkN4XOnyxgTC+Pm865B+g5ex65ZhA+52O0Gk2Z7V27sA/7FDDyUvP2KcrtUElfqXLR17N4/a8TZOUVsPflB2iZDGbP3Y9np+5lrmPftiup9/riGqVl50ePllnv4qbFALTu2b/K41aUxkAlfaXK5Gu0/LDzEoM/28XqozHsXPwebQ9lEBFsS7dHy79cI+TN34h2MqLZ8qMkRx4vtU7KseMUGEH7wY9XdfiK0iiopK9UiYORydz9VSgfrj9HHy971k8NpMmi5SRbQciHvxvUhrGpGS7Tn8MiF04//DDHV358Ux3TyBTiHAUW1urCbkW5HSrpK5UmpWTuxvNk5Bbw02Pd+Omxbpz/bByOCRIeC8HSwd3gtryHTqbg7bEICSZvLWDtU/0L5+fJTU+iVayWXHebatoTRWn41I3Rldui1UqWH44ipIMDDlbmfPFwANYWpjQ1MyHm+BYcNkQS2c6EoVO/qXDbXR5+i7T+D7Pr+Ydou/0au+/shdust8hKjMFMAzad/aphjxSlcVA9faXCzsamMfqHPUxfeZI/DlwBoLW1BU3NdH2IY2+/hJDg887s2z6tsnnrdty97DBJT/Wjeaokc+p7xPy4EIB2g8ZWyX4oSmOkkn4DoNVo+GdMN9Y/N9DgddJiw9g8wIf9P79i8DqZuQV8sPYMd38dSmRSFp+M9ue5gV7F5srZN+9FPM7nETuoDa7d76nQfpSmz3M/4vrbT8S4GuN2WUOSNTj69Kt0u4rSWKnhnQZgzzdTaXs0k5RmmWg1GoN61ydXfIpzjJb0b/8hPngkrTr1KbOulLqbmc3ddJ4F/0YyJsiFV4d6Y9us+B2rMpNiKJi/nmstYOD7Kyq3U0W06tSHoX8fZ+dH42jSouRdOBVFqQiV9Ou59PhwjJbsIt8YbDLh0s5f8Rowrtz1ru8/TDMTMMuHoy8+yeC/j5R628Go5CxyC7S0c7Dk6Tvacbd/a7q6tSi1zR1vPoxnKmS99QBmllV7sNXI2Jg73vi1SttUlMZIDe/UcztffwTrDEiZ2BuA8I1/GrRe87B0Yt1NSLzfB7dIDVvevLdYeV6Blm+3hzHos53MXHMaAHtL8zITftiu33HdlcAlPwu6PjKrEnukKEp1Ukm/Hju3cR7ue68T0dWSfi/+TIItaE9dKXe9qMNraZkCxr5uDHh7GREdTHH6J4Kz678DYO+lJIZ/uYtPNp5ngLcDnzzgX2ZbeRkpnPhrLuGzPiDXFHrM/qHK9k9RlKqnhnfqKU1+HlEffUlzC+g9+xcA0tpZ0eZYOnkZKbccXrnw90KcAM+hD2JkbEzPL5dwfvTD5L33NZea92Da33G4tLBgwfju3OFd/CKolOiznFv3E0lHDmFyKQnHWC1mBdAGiH8kgJZeQdW414qiVJZK+vXUjg/G4hyjJe6xrti66c5bt+3RjSYHt3Pq768JHDOjzHVzTlwkxRJ69NOd+mjj6k/ulHtp+elq0r99ktemLGVcL3cszP47IHx8xYdcXfw7bhfysQaaGUF8K0FUkA02nf1pP3QcPt7B1brPiqJUnkr69VDipcNYrz7FFRcjBk//bypi3/ueI+rb7cTu2gJlJH1Nfh4OEbnEe1lgZGzMqaupvLn6FInpg3m9z27ahiZhe2IuFv2/R6vRcODnl0lbvgmXaC0tm0Bkf3sc+95Bh+FP4GfnUlO7rChKFVFJvx7a98Zk3PKg7RvTi52eae3UgQOtjTA7l1Dmuuc2/ohlNqQGdGLW32dYuCeCFs3MePOujgye9jehd/em+fwdbL42CpPt53FMlGiaQ9SodgS//B3dVaJXlHpNHcitZ4788R5tj2dxpY897e547KbyfO+WOMVJrkedLnX9K1v/BuD7nB4s2BPBmCBXtr4Ywqguzphb2uLx/kxMNeC8/BwA8eO70X3HPobM+RtLlfAVpd5TPf16Iv5MKBc2LUb7526yrCFkzrJS67XuNwijbb9xZvU39H7m+2Jl2XkaxNkY4loKOgaGMKOrCwEuxQ/4evZ5kOMzI0lPSaT/+Dnq7lSK0sCopF9HhW1fTOSONeScDcf6Sjb2KWAP5JhC/osjaWbnVOp6PiOe4dTs37i+/zA8o1uWW6Dhhx3h/BV6lK+iNET1sOX9e8uetKzz/a9Wwx4pilIXqKRfB216/R5cVoXRBkhrCkkupmT1c8ap10B8Bk+85emYZk2tiXM1oXlYOgD/hiUyY/UpwhMzecFkE2YasO/Vq4b2RFGUukYl/TomLysVq81hRDkb0f699+gQNLLCQywmfu60XBXGrC+/4ZdYD9zsmrL48SDSP55NvjH43vd8NUWvKEpdpw7k1jH7vn8Rmwxo/sAQ3HvdV+GEL6XEc8gDADhfWsdzA73Y+Hw/+rVvicWF68S2MVIHZBWlEVNJv47JWbePRBvo/vhHFV73RHQKo/6/vTuPjqrOEjj+vamssiRIAoRFshDEoNhgBGQRBUQEZVF7xJnTogPtirs9iEgfsB2XPgexsRGbI6gwdAtiS0dHDjaKKDIsQRAMioZAIIYlIEYICUkqd/6o0hPoxBSpSr1U6n7OqcOrer9X715ecuuX9179fi9voCLjBn5oCe0Li3j4mu7ERrko/nYzyUeU6szkRojaGBMq7PROE5L77ly6fFfNgXHptY54WZeSskpmf7CbJRsLSGwZw7GyKkrS42j/TRnuygpcUdHsWjmfdkCXq16NuQ8AAA2gSURBVK9vvASMMU2e9fSbkL1LXqcsGvo9MNvnbd7bUcSw2ev4n40FTLwihQ8fHcLgjCRa9rmElmWeL2MBnNi6g9JY6DHyrsYK3xgTAnwq+iIyUkR2i0ieiDxey/oYEVnmXb9JRFK8r7cVkbUiclJEzn2y1DBy5JuNXJBbRtGvWhPf8UKft/vm8Ek6JcTyj/sGMXNMT1rHRgGQOf4ewPNlrGq3m/P3nOJwSjSRMXGNEr8xJjTUW/RFxAXMA64DMoFbRSTzrGaTgOOq2g2YA/x0QrocmAE8FrCIm6mcl6YT5YaLfvvwL7Yrr3Tzwge7Wfv1EQCmXN2Nv987kEs6x5/Rrl33/hxKEuSrIvZueIs2JyDm0m6NFr8xJjT40tPvC+Spar6qVgBvAmPPajMW+GnkrxXAMBERVS1V1fV4ir+pQ8XJH2izoYh9aS7SB0+os93Hu48wYs4nzP0oj417jwEQHRmBK0JqbV+akUDyATffrnwdgIzR/zpsgzEmvPhS9DsBB2o8L/S+VmsbVa0CSgCfJzMVkTtFJEdEcoqL6x4srLn6v/kPk1AKbcZfW+v6QyXl3Lt0K7e/toXICGHp5H5Mu+6iet83acBAot3QZm0BRxOga9+zP6uNMeHGl6JfWzdSG9CmTqq6QFWzVDUrKSnJ182ajYpVmyhuA5ff/myt6z/5tpgPvzrCo9d0Z9VDgxnYLdGn9714/MNUuqD1KShJbxnIkI0xIcqXWzYLgZrf5ukMFNXRplBEIoF44PuARNjM7Vw5h85FyoHxGWfcprlt/3GKfihndK9kbu7TmYHdEumUcG4XYVu07cjBThFcsL+ahL59Ah26MSYE+dLT3wJkiEiqiEQDE4Dss9pkAxO9yzcDH6mqzz39cFawdDFl0dD/gRcAKDlVyRPv7OTG+Rt4cc03uKuViAg554L/s94pnI6CnuOmBDBqY0yoqrenr6pVIjIFWA24gEWqmisiTwE5qpoNLASWiEgenh7+z1cjRWQf0BqIFpFxwAhV3RX4VELPka830DW3nIK+8fTukM7bWwt55v2vOH6qgjsGpPLIiO51XqT11dCZy/h+4vafp1Q0xoQ3aWod8qysLM3JyXE6jKB4f8rVpK45ROyiP/Bjl+GMnfcZvS9I4OlxF9OzY3z9b2CMMV4islVVs+prZ8MwOOTHg3kkfnaI/DQXowfcDMDSyf24Iq0tEX727o0xpi42DIND1vz+DlqVwcqMqzhYUgbAwG6JVvCNMY3Kin6AHC/YyRcrnqu3XdEPZUx96TXSNh5lV/dIJj04i+R4GxrBGBMcdnonAE4czmfbb24h+Yiy+cfj9K1jWOTS01WMmvspj+2Yi8sNl06dSbc0n7/DZowxfrOevp8qy06y/j/H0q5Y+b41VM/L5uierWe0yTvimbqwRUwkM7rv49Jd5RRc1opuA29yImRjTBizou+Hareb1XcOJWVPFYf+LZO2Tz9CXDlsfvB2qt1ujpdWMHXFDoa/8Akf7/YMkBa7/GXcLug7/UWHozfGhCM7veOHfz4xhvQtJ8gfdD6jZ70NwAdjskl9J4+3fncDzyU8xInyKu66Mo3LU84n9925pO06zd6h7flVjwEOR2+MCUfW02+gz+bdR+d/5JPfI5qR89f+/Prwp1eSlxJBjw/2MoINvPfAIKaNuogWMZEcmLeAE3EwaMarDkZujAlnVvQbIPfdubR45SMOJgtXLVyFKyqaUxVVniETXC4q73+e8mgY+c/ldI3zjCq9+bXH6brPzQ/XptM62ca1N8Y4w4r+Ofpu22pOzJxPaRz0+ssizjs/mdW5hxg+ex1/3VQAwI2jr0fvGUWHYuXDB8fgrqzgxOvZHIuHIdPfqGcPxhjTeKzon4ODX35M3n0PEVUF7Z+fxunEXkx+I4e7lmylVWwUmR1b/9y2/29ns6d/POkbS1h12wA6HlYqx19OTCu7RdMY4xy7kOujvRtWUPTQDFqVA9P+nc9bXs3v56wjQoQnRvXgjoGpRLnO/Awd9qdsNl5/FenbSjmYJAx51M7lG2OcZT19H3y9egGH759BVCXEPHMvfW6dQac2cQzpnsSaR4Zw55Xp/1LwAeLi29F51lQOJQoJd//6jPHyjTHGCdbTr8f25c/i/u/FVEfD2v+4HSKG0xPPODm+zGCVMXQiGUMn1tvOGGOCwXr6v2DTq7+DpxZz8jx4YdjtLDx6CTGR9l9mjAld1tOvwyezJ9Fm4QYOJ8LTl99N+9S+rBp3MRntWzkdmjHGNJgV/bNsX/4sRUv+Ruq3lezvHMGLg6Zy/w1DubFPJ0Rs2GNjTGizog+4KyvY9OpjnHz7Q7oUVpMYB3uHd2DIrKUMTehQ60VaY4wJRWFd9CvLTrJ+zt3I+1tpfxSqW8Pq/u35tOcDvH7/DbSIjXI6RGOMCaiwLvqrJ15J+o4yDibCyitTWd5uMlNG9mbZFV2JtN69MaYZCtui//HzE0nfUUbekCSeTJ7GoIz2rLo+kw7xsU6HZowxjSYsi/6Xn75D66WbOdA5gpEvvk+/qkjatbJib4xp/sKq6LurlaWffkW7mdNJAmL/ayZRcS1p53RgxhgTJGFz4npnYQk3vvwZp/9yB10PKsW3Xs6VI37tdFjGGBNUYVH0q6uVh5dvJyN/CQO2/cieXnGMnLbY6bCMMSbomm3RV1VW7TzIqYoqIiKE2cNaMn79J3wfD0NeWuF0eMYY44hmWfT3Hi3ltkWbuWfp5/x1036q3W4KZ00i/gQkTJ1Mq/ZpTodojDGOaFYXcssr3cz/eA/z1+0hxhXBrDE9ufHCGNY8OY60XafZd21Hrhv/qNNhGmOMY5pV0Z/+zpds2byOB12b6FKUT9yTJew7pHSphv1dIrjmj+85HaIxxjiq2RT93PdeYsycl5l83PO80gWHkiPYP+h82vTuw+AJTxAZE+dskMYY4zCfir6IjAT+BLiAV1X1ubPWxwCLgcuAY8AtqrrPu24aMAlwAw+o6uqARV9DUrfLKEiMouCKZDr0v4oLr51Er3i7A98YY2qqt+iLiAuYB1wDFAJbRCRbVXfVaDYJOK6q3URkAvA8cIuIZAITgJ5AR2CNiHRXVXegE2nXYwCj3t0R6Lc1xphmxZe7d/oCeaqar6oVwJvA2LPajAXe8C6vAIaJZ/D5scCbqnpaVfcCed73M8YY4wBfin4n4ECN54Xe12pto6pVQAnQ1sdtEZE7RSRHRHKKi4t9j94YY8w58aXo1zZdlPrYxpdtUdUFqpqlqllJSUk+hGSMMaYhfCn6hUCXGs87A0V1tRGRSCAe+N7HbY0xxgSJL0V/C5AhIqkiEo3nwmz2WW2ygYne5ZuBj1RVva9PEJEYEUkFMoDNgQndGGPMuar37h1VrRKRKcBqPLdsLlLVXBF5CshR1WxgIbBERPLw9PAneLfNFZHlwC6gCrivMe7cMcYY4xvxdMibjqysLM3JyXE6DGOMCSkislVVs+pr1ywHXDPGGFO7JtfTF5FioKCeZonA0SCE01SFc/6We/gK5/x9yb2rqtZ7+2OTK/q+EJEcX/6Maa7COX/LPTxzh/DOP5C52+kdY4wJI1b0jTEmjIRq0V/gdAAOC+f8LffwFc75Byz3kDynb4wxpmFCtadvjDGmAazoG2NMGGlyRV9ERorIbhHJE5HHa1kfIyLLvOs3iUhKjXXTvK/vFpFrgxl3IDQ0dxFpKyJrReSkiPw52HEHih/5XyMiW0Vkp/ffocGO3V9+5N5XRLZ7H1+IyPhgx+4vf37nvesv8P7sPxasmAPJj2OfIiJlNY7/Kz7tUFWbzAPP2D57gDQgGvgCyDyrzb3AK97lCcAy73Kmt30MkOp9H5fTOQUp9xbAIOBu4M9O5+JA/r2Bjt7li4HvnM4niLmfB0R6l5OBIz89D4WHP7nXWP828BbwmNP5BPnYpwBfnus+m1pPP5xn6Wpw7qpaqqrrgfLghRtw/uS/TVV/GrI7F4j1ztscKvzJ/ZR6Ji4CiKWW+SqaOH9+5xGRcUA+nuMeivzKvyGaWtFv9Fm6mjB/cm8OApX/TcA2VT3dSHE2Br9yF5F+IpIL7ATurvEhEAoanLuItACmArOCEGdj8ffnPlVEtonIOhEZ7MsO6x1aOcgafZauJsyf3JsDv/MXkZ7A88CIAMYVDH7lrqqbgJ4ichHwhoisUtVQ+avPn9xnAXNU9aQfHV+n+ZP/QeACVT0mIpcBK0Wkp6r++Es7bGo9/XCepcuf3JsDv/IXkc7AO8Btqrqn0aMNrIAce1X9CijFc10jVPiTez/gjyKyD3gIeEI8c3+Ekgbn7z2VfQxAVbfiuTbQvb4dNrWiH86zdPmTe3PQ4PxFJAH4X2Caqn4WtIgDx5/cU72FABHpClwI7AtO2AHR4NxVdbCqpqhqCvAi8Iyqhtrda/4c+yQRcQGISBqempdf7x6dvnpdy9XsUcA3eD61pntfewoY412OxXOlPg9PUU+rse1073a7geucziXIue/D0/s5iadnkBns+J3KH3gSTw93e41HO6fzCVLuv8FzEXM78DkwzulcgpX7We8xkxC8e8fPY3+T99h/4T32N/iyPxuGwRhjwkhTO71jjDGmEVnRN8aYMGJF3xhjwogVfWOMCSNW9I0xJoxY0TfGmDBiRd8YY8LI/wMwwoWaP7RTpgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", - "ax.plot(ps, tmin, label=\"min\")\n", - "ax.plot(ps, tmax, label=\"max\")\n", - "ax.plot(ps, tmean, label=\"mean\")\n", - "ax.set_title(\"stratified train_test_split from sklearn\")\n", - "ax.legend();" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "2605 26 0.009980806142034549\n", + "1395 13 0.00931899641577061\n" + ] + } + ], + "source": [ + "def custom_split_train_test(ens, p):\n", + " choice = generate_dataset(len(ens), p)\n", + " train = [x for x, c in zip(ens, choice) if c == 1]\n", + " test = [x for x, c in zip(ens, choice) if c == 0]\n", + " return train, test\n", + "\n", + "\n", + "train, test = custom_split_train_test(ens, 0.66)\n", + "print(len(train), sum(train), sum(train) / len(train))\n", + "print(len(test), sum(test), sum(test) / len(test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On recommence un grand nombre de fois et on représente la proportion obtenue dans la base de test." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "tirages = [\n", + " sum(test) / len(test)\n", + " for train, test in [custom_split_train_test(ens, 0.66) for i in range(100)]\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La proportion initiale est bien respect\u00e9e. Comment faire cela en pratique ? Le plus simple est sans doute de :\n", - "\n", - "* De trier les observations qui appartiennent \u00e0 chaque classe.\n", - "* De les permuter de fa\u00e7on al\u00e9atoire.\n", - "* Puis de prendre les premiers \u00e9l\u00e9ments pour la base d'apprentissage dans chaque classe et les derniers pour la base de test." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(tirages, \"o\")\n", + "plt.ylabel(\"proportion classe 1\")\n", + "plt.xlabel(\"tirages\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On considère maintenant la moyenne, les valeurs extrêmes de la proportion en faisant varier $p$." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def custom_statitied_split_train_test(ens, p, stratify):\n", - " strat = set(stratify)\n", - " train = []\n", - " test = []\n", - " for st in strat:\n", - " cl = [e for e, s in zip(ens, stratify) if s == st]\n", - " random.shuffle(cl)\n", - " i = int(len(cl) * p)\n", - " train.extend(cl[:i])\n", - " test.extend(cl[i:])\n", - " return train, test" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:09<00:00, 5.26it/s]\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "\n", + "ps = [0.001 * i for i in range(1, 50)]\n", + "tmin, tmax, tmean = [], [], []\n", + "for p in tqdm(ps):\n", + " ens = generate_dataset(4000, p)\n", + " tirages = [\n", + " sum(test) / len(test)\n", + " for train, test in [custom_split_train_test(ens, 0.66) for i in range(200)]\n", + " ]\n", + " tirages.sort()\n", + " tmin.append(tirages[int(len(tirages) * 0.05)])\n", + " tmax.append(tirages[-int(len(tirages) * 0.05)])\n", + " tmean.append(sum(tirages) / len(tirages))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "ps = [0.001 * i for i in range(1, 50)]\n", - "tmin, tmax, tmean = [], [], []\n", - "for p in ps:\n", - " ens = generate_dataset(4000, p)\n", - " tirages = [sum(test)/len(test) for train, test in [custom_statitied_split_train_test(ens, \n", - " p=0.66, stratify=ens) for i in range(0,200)]]\n", - " tirages.sort()\n", - " tmin.append(tirages[int(len(tirages)*0.05)])\n", - " tmax.append(tirages[-int(len(tirages)*0.05)])\n", - " tmean.append(sum(tirages) / len(tirages))" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", + "ax.plot(ps, tmin, label=\"min\")\n", + "ax.plot(ps, tmax, label=\"max\")\n", + "ax.plot(ps, tmean, label=\"mean\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)..." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", - "ax.plot(ps, tmin, label=\"min\")\n", - "ax.plot(ps, tmax, label=\"max\")\n", - "ax.plot(ps, tmean, label=\"mean\")\n", - "ax.set_title(\"custom stratified train_test_split\")\n", - "ax.legend();" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 49/49 [00:09<00:00, 5.42it/s]\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "import pandas\n", + "\n", + "ps = [0.001 * i for i in range(1, 50)]\n", + "tmin, tmax, tmean = [], [], []\n", + "for p in tqdm(ps):\n", + " ens = pandas.Series(generate_dataset(4000, p))\n", + " tirages = [\n", + " sum(test) / len(test)\n", + " for train, test in [train_test_split(ens, test_size=0.66) for i in range(200)]\n", + " ]\n", + " tirages.sort()\n", + " tmin.append(tirages[int(len(tirages) * 0.05)])\n", + " tmax.append(tirages[-int(len(tirages) * 0.05)])\n", + " tmean.append(sum(tirages) / len(tirages))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La m\u00e9thode est simple mais plus co\u00fbteuse puisqu'elle n\u00e9cessite un tri." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", + "ax.plot(ps, tmin, label=\"min\")\n", + "ax.plot(ps, tmax, label=\"max\")\n", + "ax.plot(ps, tmean, label=\"mean\")\n", + "ax.set_title(\"train_test_split from sklearn\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'écart entre les extrêmes paraît plus petit. Le générateur pseudo aléatoire utilisé par [scikit-learn](http://scikit-learn.org/) paraît de meilleur qualité. Nous y reviendront peut-être un jour." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Répartition stratifiée" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nous utilisons maintenant le paramètre *stratify* qui permet de s'assurer que les deux classes sont équitablement réparties entre les deux ensembles *train* et *test*." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10000\n", - "20000\n", - "50000\n", - "100000\n", - "200000\n", - "500000\n", - "1000000\n", - "2000000\n", - "5000000\n" - ] - } - ], - "source": [ - "import time\n", - "\n", - "ns = []\n", - "tn = []\n", - "ts = []\n", - "\n", - "ii = 5\n", - "for N in [10000, 20000, 50000, 100000, 200000, 500000, int(1e6),\n", - " int(2e6), int(5e6)]:\n", - " print(N)\n", - " ens = pandas.Series(generate_dataset(N, 0.05))\n", - " ns.append(N)\n", - " \n", - " tt = []\n", - " for i in range(0,ii):\n", - " t = time.perf_counter()\n", - " train_test_split(ens, test_size=0.66)\n", - " d = 1.0 * (time.perf_counter()-t) / ii\n", - " tt.append(d)\n", - " tt.sort()\n", - " tn.append(tt[len(tt)//2])\n", - " \n", - " tt = []\n", - " for i in range(0,ii):\n", - " t = time.perf_counter()\n", - " train_test_split(ens, test_size=0.66, stratify=ens)\n", - " d = 1.0 * (time.perf_counter()-t) / ii\n", - " tt.append(d)\n", - " tt.sort()\n", - " ts.append(tt[len(tt)//2])" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:12<00:00, 1.97it/s]\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "import pandas\n", + "\n", + "ps = [0.001 * i for i in range(1, 50, 2)]\n", + "tmin, tmax, tmean = [], [], []\n", + "for p in tqdm(ps):\n", + " ens = pandas.Series(generate_dataset(4000, p))\n", + "\n", + " traintest = []\n", + " excs = []\n", + " for i in range(200):\n", + " try:\n", + " train, test = train_test_split(ens, test_size=0.66, stratify=ens)\n", + " traintest.append((train, test))\n", + " except ValueError as e:\n", + " print(\"Skipping: small class too small in one side\", e)\n", + " excs.append(e)\n", + " if len(traintest) < 100:\n", + " ex = excs[0] if len(excs) > 0 else \"no exception\"\n", + " raise Exception(\n", + " \"Too few, you should check the implementation is ok.\\n{0}\".format(ex)\n", + " )\n", + " tirages = [sum(test) / len(test) for train, test in traintest]\n", + " tirages.sort()\n", + " tmin.append(tirages[int(len(tirages) * 0.05)])\n", + " tmax.append(tirages[-int(len(tirages) * 0.05)])\n", + " tmean.append(sum(tirages) / len(tirages))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import math\n", - "fig, ax = plt.subplots(1, 1)\n", - "dd = tn[-1] - (ts[-1] - tn[-1])*1.3\n", - "ax.plot(ns, [x * dd / ns[-1] for x in ns], label=\"O(x)\")\n", - "ax.plot(ns, [x * math.log(x) * ns[0] * dd / ns[-1] / (ns[0] * math.log(ns[0])) for x in ns], label=\"O(x ln x)\")\n", - "ax.plot(ns, tn, label=\"split\")\n", - "ax.plot(ns, ts, label=\"stratified split\")\n", - "ax.set_title(\"processing time for train_test_split\")\n", - "ax.grid(True)\n", - "ax.set_xscale(\"log\", nonposx='clip')\n", - "ax.set_yscale(\"log\", nonposy='clip')\n", - "ax.set_xlabel(\"N\")\n", - "ax.set_ylabel(\"time(s)\")\n", - "ax.legend();" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", + "ax.plot(ps, tmin, label=\"min\")\n", + "ax.plot(ps, tmax, label=\"max\")\n", + "ax.plot(ps, tmean, label=\"mean\")\n", + "ax.set_title(\"stratified train_test_split from sklearn\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La proportion initiale est bien respectée. Comment faire cela en pratique ? Le plus simple est sans doute de :\n", + "\n", + "* De trier les observations qui appartiennent à chaque classe.\n", + "* De les permuter de façon aléatoire.\n", + "* Puis de prendre les premiers éléments pour la base d'apprentissage dans chaque classe et les derniers pour la base de test." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def custom_statitied_split_train_test(ens, p, stratify):\n", + " strat = set(stratify)\n", + " train = []\n", + " test = []\n", + " for st in strat:\n", + " cl = [e for e, s in zip(ens, stratify) if s == st]\n", + " random.shuffle(cl)\n", + " i = int(len(cl) * p)\n", + " train.extend(cl[:i])\n", + " test.extend(cl[i:])\n", + " return train, test" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le co\u00fbt de la fonction [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) semble \u00eatre entre $O(n)$ et $O(n \\ln n)$. Regardons." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/25 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import math\n", - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(ns, [(x / tn[-1]) / (n * math.log(n) / (ns[-1] * math.log(ns[-1]))) for x, n in zip(tn, ns)], \n", - " label=\"split / n ln s\")\n", - "ax.plot(ns, [(x / ts[-1]) / (n * math.log(n) / (ns[-1] * math.log(ns[-1]))) for x, n in zip(ts, ns)], \n", - " label=\"stratified / n ln s\")\n", - "ax.plot(ns, [(x / tn[-1]) / (n / ns[-1]) for x, n in zip(tn, ns)], label=\"split / n\")\n", - "ax.plot(ns, [(x / ts[-1]) / (n / ns[-1]) for x, n in zip(ts, ns)], label=\"stratified / n\")\n", - "\n", - "ax.set_title(\"processing time for train_test_split\")\n", - "ax.grid(True)\n", - "ax.set_xscale(\"log\", nonposx='clip')\n", - "ax.set_xlabel(\"N\")\n", - "ax.set_ylabel(\"time(s) / (s ln s)\")\n", - "ax.set_ylim([0.5,1.5])\n", - "ax.legend();" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 25/25 [00:10<00:00, 2.41it/s]\n" + ] + } + ], + "source": [ + "ps = [0.001 * i for i in range(1, 50, 2)]\n", + "tmin, tmax, tmean = [], [], []\n", + "for p in tqdm(ps):\n", + " ens = generate_dataset(4000, p)\n", + " tirages = [\n", + " sum(test) / len(test)\n", + " for train, test in [\n", + " custom_statitied_split_train_test(ens, p=0.66, stratify=ens)\n", + " for i in range(200)\n", + " ]\n", + " ]\n", + " tirages.sort()\n", + " tmin.append(tirages[int(len(tirages) * 0.05)])\n", + " tmax.append(tirages[-int(len(tirages) * 0.05)])\n", + " tmean.append(sum(tirages) / len(tirages))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "C'est difficile \u00e0 voir sur ce sch\u00e9ma. Il faudrait tirer plus d'exemple, regader les quantiles plut\u00f4t que la seule m\u00e9diane. Le [code de scikit-learn](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py#L1048) est plus explicite, une permutation pr\u00e9c\u00e8de la r\u00e9partition en train / test." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(ps, ps, \"--\", label=\"expected proportion\")\n", + "ax.plot(ps, tmin, label=\"min\")\n", + "ax.plot(ps, tmax, label=\"max\")\n", + "ax.plot(ps, tmean, label=\"mean\")\n", + "ax.set_title(\"custom stratified train_test_split\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La méthode est simple mais plus coûteuse puisqu'elle nécessite un tri." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Streaming splitting\n", - "\n", - "Streaming veut dire qu'on traite les donn\u00e9es sous la forme d'un flux et qu'on ne sait pas combien il y en. Concr\u00e8tement, il faut commencer la r\u00e9partition train / test au moment sans savoir quand elle s'arr\u00eatera. Par cons\u00e9quent, il faut qu'\u00e0 tout instant, on soit capable d'interrompre la r\u00e9partition et celle-ci doit \u00eatre valide.\n", - "\n", - "Le premier algorithme qui consiste \u00e0 tirer un nombre al\u00e9atoire et \u00e0 le r\u00e9partir en train ou test selon le nombre tir\u00e9. Chaque observation est trait\u00e9e ind\u00e9pendamment. A tout moment, la r\u00e9partition peut \u00eatre interrompue. En pratique, on impl\u00e9mente ce type de processus sous la forme d'it\u00e9rateur ou de mapper. C'est une fonction qui accepte un it\u00e9rateur sur un ensemble et qui retourne un it\u00e9rateur sur les valeurs transform\u00e9es. Dans notre cas, on retourne l'observation, suivi de 0 si elle est class\u00e9e en *train* et 1 en *test*." - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:09<00:00, 1.15s/it]\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "ns = []\n", + "tn = []\n", + "ts = []\n", + "\n", + "ii = 5\n", + "for N in tqdm(\n", + " [10000, 20000, 50000, 100000, 200000, 500000, int(1e6), int(2e6)]\n", + "): # , int(5e6)]):\n", + " ens = pandas.Series(generate_dataset(N, 0.05))\n", + " ns.append(N)\n", + "\n", + " tt = []\n", + " for i in range(ii):\n", + " t = time.perf_counter()\n", + " train_test_split(ens, test_size=0.66)\n", + " d = 1.0 * (time.perf_counter() - t) / ii\n", + " tt.append(d)\n", + " tt.sort()\n", + " tn.append(tt[len(tt) // 2])\n", + "\n", + " tt = []\n", + " for i in range(ii):\n", + " t = time.perf_counter()\n", + " train_test_split(ens, test_size=0.66, stratify=ens)\n", + " d = 1.0 * (time.perf_counter() - t) / ii\n", + " tt.append(d)\n", + " tt.sort()\n", + " ts.append(tt[len(tt) // 2])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "def streaming_split_train_test(stream, p):\n", - " for obs in stream:\n", - " x = random.random()\n", - " yield obs, 0 if x <= p else 1" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import math\n", + "\n", + "fig, ax = plt.subplots(1, 1)\n", + "dd = tn[-1] - (ts[-1] - tn[-1]) * 1.3\n", + "ax.plot(ns, [x * dd / ns[-1] for x in ns], label=\"O(x)\")\n", + "ax.plot(\n", + " ns,\n", + " [x * math.log(x) * ns[0] * dd / ns[-1] / (ns[0] * math.log(ns[0])) for x in ns],\n", + " label=\"O(x ln x)\",\n", + ")\n", + "ax.plot(ns, tn, label=\"split\")\n", + "ax.plot(ns, ts, label=\"stratified split\")\n", + "ax.set_title(\"processing time for train_test_split\")\n", + "ax.grid(True)\n", + "ax.set_xscale(\"log\", nonpositive=\"clip\")\n", + "ax.set_yscale(\"log\", nonpositive=\"clip\")\n", + "ax.set_xlabel(\"N\")\n", + "ax.set_ylabel(\"time(s)\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le coût de la fonction [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) semble être entre $O(n)$ et $O(n \\ln n)$. Regardons." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "obs=0 train/test=1\n", - "obs=0 train/test=0\n", - "obs=0 train/test=0\n", - "obs=0 train/test=1\n", - "obs=0 train/test=0\n", - "obs=1 train/test=0\n", - "obs=0 train/test=0\n", - "obs=0 train/test=0\n", - "obs=0 train/test=0\n", - "obs=0 train/test=0\n" - ] - } - ], - "source": [ - "def iterate_data(n, t):\n", - " while n > 0:\n", - " yield 1 if random.random() < t else 0\n", - " n -= 1\n", - "\n", - "for obs, s in streaming_split_train_test(iterate_data(10, 0.05), 0.66):\n", - " print(\"obs={0} train/test={1}\".format(obs, s))" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import math\n", + "\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(\n", + " ns,\n", + " [\n", + " (x / tn[-1]) / (n * math.log(n) / (ns[-1] * math.log(ns[-1])))\n", + " for x, n in zip(tn, ns)\n", + " ],\n", + " label=\"split / n ln s\",\n", + ")\n", + "ax.plot(\n", + " ns,\n", + " [\n", + " (x / ts[-1]) / (n * math.log(n) / (ns[-1] * math.log(ns[-1])))\n", + " for x, n in zip(ts, ns)\n", + " ],\n", + " label=\"stratified / n ln s\",\n", + ")\n", + "ax.plot(ns, [(x / tn[-1]) / (n / ns[-1]) for x, n in zip(tn, ns)], label=\"split / n\")\n", + "ax.plot(\n", + " ns, [(x / ts[-1]) / (n / ns[-1]) for x, n in zip(ts, ns)], label=\"stratified / n\"\n", + ")\n", + "\n", + "ax.set_title(\"processing time for train_test_split\")\n", + "ax.grid(True)\n", + "ax.set_xscale(\"log\", nonpositive=\"clip\")\n", + "ax.set_xlabel(\"N\")\n", + "ax.set_ylabel(\"time(s) / (s ln s)\")\n", + "ax.set_ylim([0.5, 1.5])\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est difficile à voir sur ce schéma. Il faudrait tirer plus d'exemple, regader les quantiles plutôt que la seule médiane. Le [code de scikit-learn](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py#L1048) est plus explicite, une permutation précède la répartition en train / test." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming splitting\n", + "\n", + "Streaming veut dire qu'on traite les données sous la forme d'un flux et qu'on ne sait pas combien il y en. Concrètement, il faut commencer la répartition train / test au moment sans savoir quand elle s'arrêtera. Par conséquent, il faut qu'à tout instant, on soit capable d'interrompre la répartition et celle-ci doit être valide.\n", + "\n", + "Le premier algorithme qui consiste à tirer un nombre aléatoire et à le répartir en train ou test selon le nombre tiré. Chaque observation est traitée indépendamment. A tout moment, la répartition peut être interrompue. En pratique, on implémente ce type de processus sous la forme d'itérateur ou de mapper. C'est une fonction qui accepte un itérateur sur un ensemble et qui retourne un itérateur sur les valeurs transformées. Dans notre cas, on retourne l'observation, suivi de 0 si elle est classée en *train* et 1 en *test*." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def streaming_split_train_test(stream, p):\n", + " for obs in stream:\n", + " x = random.random()\n", + " yield obs, 0 if x <= p else 1" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La r\u00e9partition stratifi\u00e9e repose sur une permutation al\u00e9atoire et cela implique d'avoir acc\u00e8s \u00e0 l'int\u00e9gralit\u00e9 des donn\u00e9es au pr\u00e9alable. En *streaming*, ce n'est pas possible. Il faut donc penser \u00e0 autre chose pour obtenir une version stratifi\u00e9e de la version *streaming*. Rien n'emp\u00eache en version *streaming* de garder les derni\u00e8res observations en m\u00e9moire pour faire une mini-permutation. Nous allons introduire quelques changements :\n", - "\n", - "1. Le *stream* est maintenant un flux sur deux valeurs, l'observation et la classe \u00e0 laquelle il appartient.\n", - "2. La fonction va conserver les derni\u00e8res valeurs pour chaque classe.\n", - "3. La fonction va produire des observations de temps en temps quand elle sera s\u00fbre que les observations seront stratifi\u00e9es.\n", - "4. Nuos allons compter les observations distribu\u00e9es dans chaque base." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "obs=0 train/test=0\n", + "obs=0 train/test=0\n", + "obs=0 train/test=1\n", + "obs=0 train/test=1\n", + "obs=0 train/test=1\n", + "obs=0 train/test=1\n", + "obs=0 train/test=0\n", + "obs=0 train/test=1\n", + "obs=0 train/test=1\n", + "obs=0 train/test=1\n" + ] + } + ], + "source": [ + "def iterate_data(n, t):\n", + " while n > 0:\n", + " yield 1 if random.random() < t else 0\n", + " n -= 1\n", + "\n", + "\n", + "for obs, s in streaming_split_train_test(iterate_data(10, 0.05), 0.66):\n", + " print(\"obs={0} train/test={1}\".format(obs, s))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La répartition stratifiée repose sur une permutation aléatoire et cela implique d'avoir accès à l'intégralité des données au préalable. En *streaming*, ce n'est pas possible. Il faut donc penser à autre chose pour obtenir une version stratifiée de la version *streaming*. Rien n'empêche en version *streaming* de garder les dernières observations en mémoire pour faire une mini-permutation. Nous allons introduire quelques changements :\n", + "\n", + "1. Le *stream* est maintenant un flux sur deux valeurs, l'observation et la classe à laquelle il appartient.\n", + "2. La fonction va conserver les dernières valeurs pour chaque classe.\n", + "3. La fonction va produire des observations de temps en temps quand elle sera sûre que les observations seront stratifiées.\n", + "4. Nuos allons compter les observations distribuées dans chaque base." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "def streaming_stratified_split_train_test(stream, p):\n", + " n = max(1 / p, 1 / (1 - p))\n", + " if n > 10000:\n", + " raise Exception(\"Cette répartition train / test est vraiment déséquilibrée.\")\n", + " memory = {}\n", + " for obs, strat in stream:\n", + " if strat not in memory:\n", + " memory[strat] = []\n", + " memory[strat].append(obs)\n", + "\n", + " for k in memory:\n", + " v = memory[k]\n", + " if len(v) >= n:\n", + " # on permute aléatoirement\n", + " random.shuffle(v)\n", + " i = int(len(v) * p + 0.5)\n", + " for j in range(i):\n", + " yield v[j], 0 # apprentissage\n", + " for j in range(i, len(v)):\n", + " yield v[j], 1 # test\n", + " # on efface de la mémoire les informations produites\n", + " memory[k] = []\n", + "\n", + " # lorsqu'on a fini, il faut tout de même répartir les\n", + " # observations stockées\n", + " for k in memory:\n", + " v = memory[k]\n", + " # on permute aléatoirement\n", + " random.shuffle(v)\n", + " i = int(len(v) * p)\n", + " for j in range(i):\n", + " yield v[j], 0 # apprentissage\n", + " for j in range(i, len(v)):\n", + " yield v[j], 1 # test" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def streaming_stratified_split_train_test(stream, p):\n", - " n = max(1/p, 1/(1-p))\n", - " if n > 10000:\n", - " raise Exception(\"Cette r\u00e9partition train / test est vraiment d\u00e9s\u00e9quilibr\u00e9e.\")\n", - " memory = {}\n", - " for obs, strat in stream:\n", - " if strat not in memory:\n", - " memory[strat] = []\n", - " memory[strat].append(obs)\n", - "\n", - " for k in memory:\n", - " v = memory[k]\n", - " if len(v) >= n:\n", - " # on permute al\u00e9atoirement\n", - " random.shuffle(v)\n", - " i = int(len(v) * p + 0.5)\n", - " for j in range(0,i):\n", - " yield v[j], 0 # apprentissage\n", - " for j in range(i,len(v)):\n", - " yield v[j], 1 # test\n", - " # on efface de la m\u00e9moire les informations produites\n", - " memory[k] = []\n", - "\n", - " # lorsqu'on a fini, il faut tout de m\u00eame r\u00e9partir les \n", - " # observations stock\u00e9es\n", - " for k in memory:\n", - " v = memory[k]\n", - " # on permute al\u00e9atoirement\n", - " random.shuffle(v)\n", - " i = int(len(v) * p)\n", - " for j in range(0,i):\n", - " yield v[j], 0 # apprentissage\n", - " for j in range(i,len(v)):\n", - " yield v[j], 1 # test" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 2)\n" + ] }, { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(10000, 2)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
obstrain_test
000
100
201
300
400
\n", - "
" - ], - "text/plain": [ - " obs train_test\n", - "0 0 0\n", - "1 0 0\n", - "2 0 1\n", - "3 0 0\n", - "4 0 0" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
obstrain_test
000
100
201
300
400
\n", + "
" ], - "source": [ - "iter = streaming_stratified_split_train_test( ((i,i) for i in iterate_data(10000, 0.05)), 0.66)\n", - "df = pandas.DataFrame(iter)\n", - "df.columns = [\"obs\", \"train_test\"]\n", - "print(df.shape)\n", - "df.head()" + "text/plain": [ + " obs train_test\n", + "0 0 0\n", + "1 0 0\n", + "2 0 1\n", + "3 0 0\n", + "4 0 0" ] - }, + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iter = streaming_stratified_split_train_test(\n", + " ((i, i) for i in iterate_data(10000, 0.05)), 0.66\n", + ")\n", + "df = pandas.DataFrame(iter)\n", + "df.columns = [\"obs\", \"train_test\"]\n", + "print(df.shape)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
obs01
train_test
06346320
13174160
\n", - "
" - ], - "text/plain": [ - "obs 0 1\n", - "train_test \n", - "0 6346 320\n", - "1 3174 160" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
obs01
train_test
06316350
13159175
\n", + "
" ], - "source": [ - "df2 = df.copy()\n", - "df2[\"un\"] = 1\n", - "piv = df2.groupby([\"obs\", \"train_test\"], as_index=False).count().pivot(\"train_test\", \"obs\", \"un\")\n", - "piv" + "text/plain": [ + "obs 0 1\n", + "train_test \n", + "0 6316 350\n", + "1 3159 175" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Il y a juste un petit probl\u00e8me avec cette impl\u00e9mentation. On multiplie la taille du buffer par un r\u00e9el. Je sugg\u00e8re d'enlever le nombre 0.5 dans le code pour voir ce qu'il se passe. La somme des arrondis est loin d'\u00eatre un arrondi m\u00eame si $N$ choisi tel que $N = \\max(\\frac{1}{p}, \\frac{1}{1-p})$." - ] - }, + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = df.copy()\n", + "df2[\"un\"] = 1\n", + "piv = (\n", + " df2.groupby([\"obs\", \"train_test\"], as_index=False)\n", + " .count()\n", + " .pivot(index=\"train_test\", columns=\"obs\", values=\"un\")\n", + ")\n", + "piv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il y a juste un petit problème avec cette implémentation. On multiplie la taille du buffer par un réel. Je suggère d'enlever le nombre 0.5 dans le code pour voir ce qu'il se passe. La somme des arrondis est loin d'être un arrondi même si $N$ choisi tel que $N = \\max(\\frac{1}{p}, \\frac{1}{1-p})$." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
obs01sumratio
train_test
0634632066660.048005
1317416033340.047990
\n", - "
" - ], - "text/plain": [ - "obs 0 1 sum ratio\n", - "train_test \n", - "0 6346 320 6666 0.048005\n", - "1 3174 160 3334 0.047990" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
obs01sumratio
train_test
0631635066660.052505
1315917533340.052490
\n", + "
" ], - "source": [ - "piv[\"sum\"] = piv[0] + piv[1]\n", - "piv[\"ratio\"] = piv[1] / piv[\"sum\"]\n", - "piv" + "text/plain": [ + "obs 0 1 sum ratio\n", + "train_test \n", + "0 6316 350 6666 0.052505\n", + "1 3159 175 3334 0.052490" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Il faut corriger ces erreurs d'arrondi. On s'inspire de l'algorithme de [Bresenham](https://fr.wikipedia.org/wiki/Algorithme_de_trac%C3%A9_de_segment_de_Bresenham) et m\u00e9moriser les \u00e9l\u00e9ments r\u00e9partis." - ] - }, + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "piv[\"sum\"] = piv[0] + piv[1]\n", + "piv[\"ratio\"] = piv[1] / piv[\"sum\"]\n", + "piv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il faut corriger ces erreurs d'arrondi. On s'inspire de l'algorithme de [Bresenham](https://fr.wikipedia.org/wiki/Algorithme_de_trac%C3%A9_de_segment_de_Bresenham) et mémoriser les éléments répartis." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def streaming_stratified_split_train_test2(stream, p):\n", + " n = max(1 / p, 1 / (1 - p))\n", + " if n > 10000:\n", + " raise Exception(\"Cette répartition train / test est vraiment déséquilibrée.\")\n", + " counts = {}\n", + " memory = {}\n", + " for obs, strat in stream:\n", + " if strat not in memory:\n", + " memory[strat] = []\n", + " memory[strat].append(obs)\n", + "\n", + " for k in memory:\n", + " v = memory[k]\n", + " if len(v) >= n:\n", + " # on permute aléatoirement\n", + " random.shuffle(v)\n", + " if (0, k) in counts:\n", + " tt = counts[1, k] + counts[0, k]\n", + " delta = -int(counts[0, k] - tt * p + 0.5)\n", + " else:\n", + " delta = 0\n", + " i = int(len(v) * p + 0.5)\n", + " i += delta\n", + " i = max(0, min(len(v), i))\n", + " for j in range(i):\n", + " yield v[j], 0 # apprentissage\n", + " for j in range(i, len(v)):\n", + " yield v[j], 1 # test\n", + " if (0, k) not in counts:\n", + " counts[0, k] = i\n", + " counts[1, k] = len(v) - i\n", + " else:\n", + " counts[0, k] += i\n", + " counts[1, k] += len(v) - i\n", + " # on efface de la mémoire les informations produites\n", + " memory[k] = []\n", + "\n", + " # lorsqu'on a fini, il faut tout de même répartir les\n", + " # observations stockées\n", + " for k in memory:\n", + " v = memory[k]\n", + " # on permute aléatoirement\n", + " random.shuffle(v)\n", + " if (0, k) in counts:\n", + " tt = counts[1, k] + counts[0, k]\n", + " delta = -int(counts[0, k] - tt * p + 0.5)\n", + " else:\n", + " delta = 0\n", + " i = int(len(v) * p + 0.5)\n", + " i += delta\n", + " i = max(0, min(len(v), i))\n", + " for j in range(i):\n", + " yield v[j], 0 # apprentissage\n", + " for j in range(i, len(v)):\n", + " yield v[j], 1 # test\n", + " if (0, k) not in counts:\n", + " counts[0, k] = i\n", + " counts[1, k] = len(v) - i\n", + " else:\n", + " counts[0, k] += i\n", + " counts[1, k] += len(v) - i" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def streaming_stratified_split_train_test2(stream, p):\n", - " n = max(1/p, 1/(1-p))\n", - " if n > 10000:\n", - " raise Exception(\"Cette r\u00e9partition train / test est vraiment d\u00e9s\u00e9quilibr\u00e9e.\")\n", - " counts = {}\n", - " memory = {}\n", - " for obs, strat in stream:\n", - " if strat not in memory:\n", - " memory[strat] = []\n", - " memory[strat].append(obs)\n", - "\n", - " for k in memory:\n", - " v = memory[k]\n", - " if len(v) >= n:\n", - " # on permute al\u00e9atoirement\n", - " random.shuffle(v)\n", - " if (0,k) in counts:\n", - " tt = counts[1,k] + counts[0,k]\n", - " delta = - int(counts[0,k] - tt*p + 0.5)\n", - " else:\n", - " delta = 0\n", - " i = int(len(v) * p + 0.5)\n", - " i += delta\n", - " i = max(0, min(len(v), i))\n", - " for j in range(0,i):\n", - " yield v[j], 0 # apprentissage\n", - " for j in range(i,len(v)):\n", - " yield v[j], 1 # test\n", - " if (0,k) not in counts:\n", - " counts[0,k] = i\n", - " counts[1,k] = len(v)-i\n", - " else:\n", - " counts[0,k] += i\n", - " counts[1,k] += len(v)-i\n", - " # on efface de la m\u00e9moire les informations produites\n", - " memory[k] = []\n", - "\n", - " # lorsqu'on a fini, il faut tout de m\u00eame r\u00e9partir les \n", - " # observations stock\u00e9es\n", - " for k in memory:\n", - " v = memory[k]\n", - " # on permute al\u00e9atoirement\n", - " random.shuffle(v)\n", - " if (0,k) in counts:\n", - " tt = counts[1,k] + counts[0,k]\n", - " delta = - int(counts[0,k] - tt*p + 0.5)\n", - " else:\n", - " delta = 0\n", - " i = int(len(v) * p + 0.5)\n", - " i += delta\n", - " i = max(0, min(len(v), i))\n", - " for j in range(0,i):\n", - " yield v[j], 0 # apprentissage\n", - " for j in range(i,len(v)):\n", - " yield v[j], 1 # test\n", - " if (0,k) not in counts:\n", - " counts[0,k] = i\n", - " counts[1,k] = len(v)-i\n", - " else:\n", - " counts[0,k] += i\n", - " counts[1,k] += len(v)-i " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "ratio de classe 1 dans l'échantillon entier 0.04930\n" + ] }, { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ratio de classe 1 dans l'\u00e9chantillon entier 0.04980\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
obs01sumratio
train_test
0627232966010.049841
1323016933990.049721
\n", - "
" - ], - "text/plain": [ - "obs 0 1 sum ratio\n", - "train_test \n", - "0 6272 329 6601 0.049841\n", - "1 3230 169 3399 0.049721" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
obs01sumratio
train_test
0627532666010.049386
1323216733990.049132
\n", + "
" ], - "source": [ - "iter = streaming_stratified_split_train_test2( ((i,i) for i in iterate_data(10000, 0.05)), 0.66)\n", - "df = pandas.DataFrame(iter)\n", - "df.columns = [\"obs\", \"train_test\"]\n", - "df2 = df.copy()\n", - "df2[\"un\"] = 1\n", - "piv = df2.groupby([\"obs\", \"train_test\"], as_index=False).count().pivot(\"train_test\", \"obs\", \"un\")\n", - "piv[\"sum\"] = piv[0] + piv[1]\n", - "piv[\"ratio\"] = piv[1] / piv[\"sum\"]\n", - "print(\"ratio de classe 1 dans l'\u00e9chantillon entier %1.5f\" % \n", - " ((piv.iloc[1,1] + piv.iloc[0,1]) / (piv.iloc[0,1] + piv.iloc[0,0] + piv.iloc[1,1] + piv.iloc[1,0]) ))\n", - "piv" + "text/plain": [ + "obs 0 1 sum ratio\n", + "train_test \n", + "0 6275 326 6601 0.049386\n", + "1 3232 167 3399 0.049132" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pas trop mal. Le dernier inconv\u00e9nient est la taille de la fen\u00eatre. Dans l'exemple qui suit, elle sera de 3. L'algorithme va donc grouper les \u00e9l\u00e9ments par trois, les permuter al\u00e9atoirement et les laisser filer. Nous ne pourrons jamais avoir trois \u00e9l\u00e9ments de suite du m\u00eame c\u00f4t\u00e9 *train* ou *test*. On peut bidouiller comme suit (ligne marqu\u00e9es ``# changement``). La fonction qui suit ne permet toujours pas d'avoir de grandes s\u00e9quences r\u00e9partie du m\u00eame c\u00f4t\u00e9 mais ce sera l'inconv\u00e9nient de ce type d'algorithme. La taille du buffer limite cette possibilit\u00e9." - ] - }, + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iter = streaming_stratified_split_train_test2(\n", + " ((i, i) for i in iterate_data(10000, 0.05)), 0.66\n", + ")\n", + "df = pandas.DataFrame(iter)\n", + "df.columns = [\"obs\", \"train_test\"]\n", + "df2 = df.copy()\n", + "df2[\"un\"] = 1\n", + "piv = (\n", + " df2.groupby([\"obs\", \"train_test\"], as_index=False)\n", + " .count()\n", + " .pivot(index=\"train_test\", columns=\"obs\", values=\"un\")\n", + ")\n", + "piv[\"sum\"] = piv[0] + piv[1]\n", + "piv[\"ratio\"] = piv[1] / piv[\"sum\"]\n", + "print(\n", + " \"ratio de classe 1 dans l'échantillon entier %1.5f\"\n", + " % (\n", + " (piv.iloc[1, 1] + piv.iloc[0, 1])\n", + " / (piv.iloc[0, 1] + piv.iloc[0, 0] + piv.iloc[1, 1] + piv.iloc[1, 0])\n", + " )\n", + ")\n", + "piv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pas trop mal. Le dernier inconvénient est la taille de la fenêtre. Dans l'exemple qui suit, elle sera de 3. L'algorithme va donc grouper les éléments par trois, les permuter aléatoirement et les laisser filer. Nous ne pourrons jamais avoir trois éléments de suite du même côté *train* ou *test*. On peut bidouiller comme suit (ligne marquées ``# changement``). La fonction qui suit ne permet toujours pas d'avoir de grandes séquences répartie du même côté mais ce sera l'inconvénient de ce type d'algorithme. La taille du buffer limite cette possibilité." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "def streaming_stratified_split_train_test3(stream, p):\n", + " n = 2 * max(1 / p, 1 / (1 - p)) # changement\n", + " if n > 10000:\n", + " raise Exception(\"Cette répartition train / test est vraiment déséquilibrée.\")\n", + " counts = {}\n", + " memory = {}\n", + " for obs, strat in stream:\n", + " if strat not in memory:\n", + " memory[strat] = []\n", + " memory[strat].append(obs)\n", + "\n", + " for k in memory:\n", + " v = memory[k]\n", + " if len(v) >= n + random.randint(0, 10): # changement\n", + " # on permute aléatoirement\n", + " random.shuffle(v)\n", + " if (0, k) in counts:\n", + " tt = counts[1, k] + counts[0, k]\n", + " delta = -int(counts[0, k] - tt * p + 0.5)\n", + " else:\n", + " delta = 0\n", + " i = int(len(v) * p + 0.5)\n", + " i += delta\n", + " i = max(0, min(len(v), i))\n", + " for j in range(i):\n", + " yield v[j], 0 # apprentissage\n", + " for j in range(i, len(v)):\n", + " yield v[j], 1 # test\n", + " if (0, k) not in counts:\n", + " counts[0, k] = i\n", + " counts[1, k] = len(v) - i\n", + " else:\n", + " counts[0, k] += i\n", + " counts[1, k] += len(v) - i\n", + " # on efface de la mémoire les informations produites\n", + " memory[k] = []\n", + "\n", + " # lorsqu'on a fini, il faut tout de même répartir les\n", + " # observations stockées\n", + " for k in memory:\n", + " v = memory[k]\n", + " # on permute aléatoirement\n", + " random.shuffle(v)\n", + " if (0, k) in counts:\n", + " tt = counts[1, k] + counts[0, k]\n", + " delta = -int(counts[0, k] - tt * p + 0.5)\n", + " else:\n", + " delta = 0\n", + " i = int(len(v) * p + 0.5)\n", + " i += delta\n", + " i = max(0, min(len(v), i))\n", + " for j in range(i):\n", + " yield v[j], 0 # apprentissage\n", + " for j in range(i, len(v)):\n", + " yield v[j], 1 # test\n", + " if (0, k) not in counts:\n", + " counts[0, k] = i\n", + " counts[1, k] = len(v) - i\n", + " else:\n", + " counts[0, k] += i\n", + " counts[1, k] += len(v) - i" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def streaming_stratified_split_train_test3(stream, p):\n", - " n = 2 * max(1/p, 1/(1-p)) # changement\n", - " if n > 10000:\n", - " raise Exception(\"Cette r\u00e9partition train / test est vraiment d\u00e9s\u00e9quilibr\u00e9e.\")\n", - " counts = {}\n", - " memory = {}\n", - " for obs, strat in stream:\n", - " if strat not in memory:\n", - " memory[strat] = []\n", - " memory[strat].append(obs)\n", - "\n", - " for k in memory:\n", - " v = memory[k]\n", - " if len(v) >= n + random.randint(0, 10): # changement\n", - " # on permute al\u00e9atoirement\n", - " random.shuffle(v)\n", - " if (0,k) in counts:\n", - " tt = counts[1,k] + counts[0,k]\n", - " delta = - int(counts[0,k] - tt*p + 0.5)\n", - " else:\n", - " delta = 0\n", - " i = int(len(v) * p + 0.5)\n", - " i += delta\n", - " i = max(0, min(len(v), i))\n", - " for j in range(0,i):\n", - " yield v[j], 0 # apprentissage\n", - " for j in range(i,len(v)):\n", - " yield v[j], 1 # test\n", - " if (0,k) not in counts:\n", - " counts[0,k] = i\n", - " counts[1,k] = len(v)-i\n", - " else:\n", - " counts[0,k] += i\n", - " counts[1,k] += len(v)-i\n", - " # on efface de la m\u00e9moire les informations produites\n", - " memory[k] = []\n", - "\n", - " # lorsqu'on a fini, il faut tout de m\u00eame r\u00e9partir les \n", - " # observations stock\u00e9es\n", - " for k in memory:\n", - " v = memory[k]\n", - " # on permute al\u00e9atoirement\n", - " random.shuffle(v)\n", - " if (0,k) in counts:\n", - " tt = counts[1,k] + counts[0,k]\n", - " delta = - int(counts[0,k] - tt*p + 0.5)\n", - " else:\n", - " delta = 0\n", - " i = int(len(v) * p + 0.5)\n", - " i += delta\n", - " i = max(0, min(len(v), i))\n", - " for j in range(0,i):\n", - " yield v[j], 0 # apprentissage\n", - " for j in range(i,len(v)):\n", - " yield v[j], 1 # test\n", - " if (0,k) not in counts:\n", - " counts[0,k] = i\n", - " counts[1,k] = len(v)-i\n", - " else:\n", - " counts[0,k] += i\n", - " counts[1,k] += len(v)-i " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "ratio de classe 1 dans l'échantillon entier 0.04600\n" + ] }, { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ratio de classe 1 dans l'\u00e9chantillon entier 0.05000\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
obs01sumratio
train_test
0627032965990.049856
1323017134010.050279
\n", - "
" - ], - "text/plain": [ - "obs 0 1 sum ratio\n", - "train_test \n", - "0 6270 329 6599 0.049856\n", - "1 3230 171 3401 0.050279" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
obs01sumratio
train_test
0629730466010.046054
1324315633990.045896
\n", + "
" ], - "source": [ - "iter = streaming_stratified_split_train_test3( ((i,i) for i in iterate_data(10000, 0.05)), 0.66)\n", - "df = pandas.DataFrame(iter)\n", - "df.columns = [\"obs\", \"train_test\"]\n", - "df2 = df.copy()\n", - "df2[\"un\"] = 1\n", - "piv = df2.groupby([\"obs\", \"train_test\"], as_index=False).count().pivot(\"train_test\", \"obs\", \"un\")\n", - "piv[\"sum\"] = piv[0] + piv[1]\n", - "piv[\"ratio\"] = piv[1] / piv[\"sum\"]\n", - "print(\"ratio de classe 1 dans l'\u00e9chantillon entier %1.5f\" % \n", - " ((piv.iloc[1,1] + piv.iloc[0,1]) / (piv.iloc[0,1] + piv.iloc[0,0] + piv.iloc[1,1] + piv.iloc[1,0]) ))\n", - "piv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Streaming distribu\u00e9\n", - "\n", - "C'est possible mais c'est un peu plus compliqu\u00e9 parce que le hasard en distribu\u00e9, c'est compliqu\u00e9. On n'est jamais s\u00fbr que des s\u00e9ries pseudo-al\u00e9atoires soient tout-\u00e0-fait ind\u00e9pendantes lorsqu'elles sont g\u00e9n\u00e9r\u00e9es en parall\u00e8les." + "text/plain": [ + "obs 0 1 sum ratio\n", + "train_test \n", + "0 6297 304 6601 0.046054\n", + "1 3243 156 3399 0.045896" ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "iter = streaming_stratified_split_train_test3(\n", + " ((i, i) for i in iterate_data(10000, 0.05)), 0.66\n", + ")\n", + "df = pandas.DataFrame(iter)\n", + "df.columns = [\"obs\", \"train_test\"]\n", + "df2 = df.copy()\n", + "df2[\"un\"] = 1\n", + "piv = (\n", + " df2.groupby([\"obs\", \"train_test\"], as_index=False)\n", + " .count()\n", + " .pivot(index=\"train_test\", columns=\"obs\", values=\"un\")\n", + ")\n", + "piv[\"sum\"] = piv[0] + piv[1]\n", + "piv[\"ratio\"] = piv[1] / piv[\"sum\"]\n", + "print(\n", + " \"ratio de classe 1 dans l'échantillon entier %1.5f\"\n", + " % (\n", + " (piv.iloc[1, 1] + piv.iloc[0, 1])\n", + " / (piv.iloc[0, 1] + piv.iloc[0, 0] + piv.iloc[1, 1] + piv.iloc[1, 0])\n", + " )\n", + ")\n", + "piv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming distribué\n", + "\n", + "C'est possible mais c'est un peu plus compliqué parce que le hasard en distribué, c'est compliqué. On n'est jamais sûr que des séries pseudo-aléatoires soient tout-à-fait indépendantes lorsqu'elles sont générées en parallèles." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/image/README.txt b/_doc/notebooks/image/README.txt deleted file mode 100644 index 72f9644f..00000000 --- a/_doc/notebooks/image/README.txt +++ /dev/null @@ -1,9 +0,0 @@ - -Images ------- - -.. contents:: - :local: - - - diff --git a/_doc/notebooks/image/index.rst b/_doc/notebooks/image/index.rst new file mode 100644 index 00000000..546f80f3 --- /dev/null +++ b/_doc/notebooks/image/index.rst @@ -0,0 +1,9 @@ +Images +====== + +.. nbgallery:: + :caption: Notebooks Gallery + :name: rst-nb-gallery-image + :glob: + + * diff --git a/_doc/notebooks/image/segment_detection.ipynb b/_doc/notebooks/image/segment_detection.ipynb index 6813f3b5..18c26dd6 100644 --- a/_doc/notebooks/image/segment_detection.ipynb +++ b/_doc/notebooks/image/segment_detection.ipynb @@ -1,591 +1,473 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# D\u00e9tection de segments dans une image\n", - "\n", - "C'est une technique assez vieille et qui consiste \u00e0 d\u00e9tecter des segments comme des anomalies : l'alignement de points est un \u00e9v\u00e9nement assez rare dans un nuage de points mais rare comment ? Cette id\u00e9e m\u00e8ne \u00e0 la probabilisation d'une image pour quantifier ce qu'est un alignement de points n\u00e9cessairement rare." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Une image al\u00e9atoire\n", - "\n", - "On consid\u00e8re un bruit al\u00e9atoire uniforme dans une image et on ajoute des points al\u00e9atoires tir\u00e9s le long d'une ligne selon une loi gaussienne : uniforme sur la ligne, gaussien autour du segment." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.image.detection_segment import random_noise_image, convert_array2PIL\n", - "img = random_noise_image((100, 100))\n", - "convert_array2PIL(img, mode=\"binary\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'size': 36,\n", - " 'angle': 2.285619160431492,\n", - " 'x1': 23.597410654261072,\n", - " 'y1': 40,\n", - " 'x2': 0,\n", - " 'y2': 67.18753777770554,\n", - " 'nbpoints': 108}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.image.detection_segment import random_segment_image\n", - "random_segment_image(img, density=3., lmin=0.3)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAAAAABVicqIAAAF9ElEQVR4nJ1a2YLDIAgE//+fZx8UGA6TdPPQJiqnMGBahfRLRUT2hKZ5RR3JjzpxE1mH5fncFwSA3VYFkNemJwzDKrJ04KMQ3atUKksREWiMFupYjPheGBZD9HyCZjTmsAUNYiAiqkWz1dRUFRWArA36rIktqKairDQhvi54b4ULL7XNZQKYcs5B810JDoWK4BIkbf3brD0uV2urVGUkV6goehjQMkiyEW4S0iBqtD2rzssUvpw/haJr71cIVVLOlCUramzbvvCG4USRLD3sPO5tBbKrJO+3qRKyFUdP/9p3Q55AkDQpclBsQAR2VsLuVGRvfGh1MCOMAFNENoKMnjFGXen9sGmmDY4xD4hh2RwaPLq2B1Wg6lrYJJgChM0eFt2ELkMtTyAKgrQGFMf7acRYs/Smn4pgCzl7pjQ+hmuGWDVKDAFPnBJAIsY1aVaUTPRGm2vbEWocNVibGz/m+HxF2h8AgUjsSfDGsK3ZEIvohLu5QO5qAQ5JxdWM3+wboUuW7F2jxM813yc4hZ9lpFzdQiLDleUl1CZpHAWlDtTgIksQsZ6jRE2QJoefuGk8G9IlEiohvcuaH7gO7Upi9WSoSCkRDqe+w9c9rxM3YFvWx7m0XnuvcTVNODRoLFm7USPrg7lSRJ2drh1VqGJ7ZECTlL3cx5jlkD40MVMb6hiKYT8nx97UmgYG+g2QXuZ3TU4goSVnYgfnLRvM3UL23sMxyYp5ODo3XOqeL6g1XFq9Sa1TX4yo1T9eP3SkF6ictzFdVLRqO1MpUzWkYSrMtdCfgVoZj2o7XSgrzk4fgOo9h7RiKdEX3H3M/RAxKqWiiThpocx+RqvgHak0Ch0FtpElFLpCCHKuSFfQWF7hxKF86ToueqRQYn+9WDFecQjK1436UvumK9qRlfe+NXaU2o3J4yNX2TUdaaPmF2o/nZ61mh+7yLPbP6DE49qHOoB+jr9fec+tv2iG1H4WBvVptMVxd5wKDE9aCOda8QC5VAiHJPzotzj9TipvGETFQ7SbsNEJtVomfMQemkKugrW98xW7zTlhecA1v/n5UE/aOwBJiHdFAwKNApAjykS/clWos84MsmL/5PRM62dGc3/qTJy6RHU7uqpK78pJSN5G5hGMz4sHyr3SpAD2UbrrI8RmOhS31i+FTGpsJaCMAfZotXxFUvRIRl7fbTbubo/bwQ38KoS1nIMf9h0dn0eU1ZOTodbqXhIjpJaEnRSe56PfYQ4RnKCNEKLmOyVDK7NjeH5DsyHRblK+cLtosaTuLaJ/DFcyNk6vEhKHBPs7ml5V650hFYKjbfJ6c8IaXZ3uMcwghUI6mp8aQceQx8OC5OkoYwOIuzmz3fP99+sDVU3GgcmN+QhTI2VuJLwIGgdt2Sx6VoBLHxFUpXUy9sHxMfqWfJnLcuklxpHDp4yWzqA8ONzaGwkhEBR5P/89cUeboLicwu6Xq+VBeVU0K34DxJn9jbevIOzymkXFKm9Uan5FpCGcgl1/xkKVW//Ra/BsxCMKmiVNgwgOlDHJb/UNpvKbxUxQ9elYduns3jfNsXFFovqvfvVg2NFjdyVvdcXdsIItNrveokHCKWxEFRNOKtVhJcRBdifiZS3sXQhZVbcavr9cbEGefXg1ESuic/YT1ETXsieH8Md3AS0WeqPBjO1lAcvwRGzBm5sIykRuNPzGA2YNS86vbmyG3aOaMMBx76s31FeYT+SZRbsMiTTCwsGJhPAPmIewVjn3tD95tJtG8EJISuoREl7l4sTwr4Eenkn7N/VsZZIp9AN9boBz0ToB+3SsfG1f1dpvX2Np0Mv8RP0wUibjECTk3jOI6guuExVrd46qsKOvCn2ZbVWIXDpUVT4EafsaIXDQlOqOyyeaFRGina6hvintzxZRnGOKQqgQeg9eTaUSNoLbpNbAKTvzTvyxHZuXUXMXzuT/ueQYe8IX4pF0nP7m4whB9gfwtC2vod30UG0Z9PsZpTm7s+BfHRycujqzkpuMv1Qml9n55My6i/rfeYh+7h829E1q/e8I9+P1B5ip/xyA3pP0AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "convert_array2PIL(img, mode=\"binary\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "random_segment_image(img, density=5., lmin=0.3)\n", - "random_segment_image(img, density=5., lmin=0.3)\n", - "convert_array2PIL(img, mode=\"binary\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pilimg = convert_array2PIL(img, mode=\"binary\").convert('RGB')\n", - "pilimg" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Détection de segments dans une image\n", + "\n", + "C'est une technique assez vieille et qui consiste à détecter des segments comme des anomalies : l'alignement de points est un événement assez rare dans un nuage de points mais rare comment ? Cette idée mène à la probabilisation d'une image pour quantifier ce qu'est un alignement de points nécessairement rare." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Une image aléatoire\n", + "\n", + "On considère un bruit aléatoire uniforme dans une image et on ajoute des points aléatoires tirés le long d'une ligne selon une loi gaussienne : uniforme sur la ligne, gaussien autour du segment." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from PIL import ImageFilter\n", - "\n", - "\n", - "pilimg = pilimg.filter(ImageFilter.BLUR).filter(ImageFilter.BLUR).filter(ImageFilter.BLUR).filter(ImageFilter.BLUR)\n", - "pilimg" + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.image.detection_segment import random_noise_image, convert_array2PIL\n", + "\n", + "img = random_noise_image((100, 100))\n", + "convert_array2PIL(img, mode=\"binary\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from PIL import ImageEnhance\n", - "enh = ImageEnhance.Sharpness(pilimg)\n", - "final_img = enh.enhance(4)\n", - "final_img" + "data": { + "text/plain": [ + "{'size': 43,\n", + " 'angle': 0.45754951975081887,\n", + " 'x1': 8,\n", + " 'y1': 43,\n", + " 'x2': 46.576920763478554,\n", + " 'y2': 61.995293743669706,\n", + " 'nbpoints': 129}" ] - }, + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.image.detection_segment import random_segment_image\n", + "\n", + "random_segment_image(img, density=3.0, lmin=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gradient\n", - "\n", - "La d\u00e9tection des segments est bas\u00e9e sur le gradient." + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "convert_array2PIL(img, mode=\"binary\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from mlstatpy.image.detection_segment import compute_gradient, plot_gradient\n", - "grad = compute_gradient(final_img, color=0)" + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_segment_image(img, density=5.0, lmin=0.3)\n", + "random_segment_image(img, density=5.0, lmin=0.3)\n", + "convert_array2PIL(img, mode=\"binary\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot_gradient(pilimg.copy(), grad, direction=-2)" + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pilimg = convert_array2PIL(img, mode=\"binary\").convert(\"RGB\")\n", + "pilimg" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## D\u00e9tection de segments" + "data": { + "image/jpeg": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAfEUlEQVR4Ab3dWY4uWXXFcQNFZ/rOgBAC8QAzZQIIeIFJ8MIUkBATAAFCou9bQxWdfxH/yJVR3y0KsCxvyZt11lm7Ofs7EZk3b97yG/7+97//x/+pveENb3gx52uSlf1HW+MD/Np8Mf+2/i2wEq8fNdkbX1+33XuvI18T/LsnedCvED78IHjNov878l/MPNl1C370ox/97W9/e8c73qHqG994TRAD//KXv3zf+94Hv/nNb/7Vr371/ve/3xnSAEwuuwLtwizyne98J9l3v/vd9773vX/5y1/e8pa3/PrXv/7kJz9p901vehPZL37xC/6vf/2rXQYI+dOf/kT2zW9+8xOf+ISuiL/97W/Ta+Oll17Sw3892ac//ekYmvUMN7jagDEwAW+Zlw3fAXmHspUGzwr/zW9+o7TlBz/4QYKXIP/zve99z/bLL78swHZJy/XnP//5pz/9qV2N4h2pELjsZHXw7ne/G8YzjECCd73rXQ3CVgLhzNJWMspXXnnlZz/7meb++Mc/2tLD73//exUpDQcw6z/84Q/Af5729re/XRSlDKow4Ex8HAeIjwwTV5f3qUyDZ/WcMiynhn0eltmRFPrOd74jwIYuWYkwAviWGqWkQfLLS8AsmVGWFzYjge3yTisQUwZKp+WNyW1ilj4tE+GZEFuFV06Gt73tbQ2L9xw0RDn1rKL8Vc9bIu1aJoAxlvXWLt+R24ULt2R167NBXmP71re+9fGPf1y7b33rW7s7UjDqzl/hTot0Br6kfF0iCxmgySpZ+HxttRTSsJrdigo3IwbQGxazLKHSdWIXqB+4No4DPH35qqU1DNiiDCRbhmRNEE4GXMPyWvFienjppNvZAIy8JX2xp7biKw8XDlQbWBIfLzMd94hn3bUdjJiZyzmuY0A+YR+nC1W2VSQTdcqv/HA95Os5TVGdrmb4zFbHLFxFITEExzuLwgvPYwzMyk4BIJfiHrzmrlJPV2xLgWFKIA/cq0joHjU4wwJ2rUxE3W6TAWl9k8LXealgefgMCVSdJ06Wppbi64q4rgZa0hdYyHWzPvCBD+jMRqJAisqrx8JlTJmGj6RZ0/A9w/R1YCLMdBpQw8IsXD8NyLBcqGEjqxYB8UpXS+d2d8K1ard++DQrVGN3ZaleZJ4jNSpL5Sv2Or50Cs/uqQUm4PGMLMBXRaFjWqfBhdg1AuPoueNdpW4T0tbKhfm70SQL5BNUXT87lKLZuk2ToC3dARMcN8vCV0MbsgvQENKSh1PjU5YlGdwUbFXJUraJ4yvGZ6K6TV5Vvgj6XuH44vfyyy4XgbNJ4h5lWjKsvdEt7Zb27iPn5YH5unrwBVYITgnQ5xeYks+ux7AArWDDAlgiwAkrGdM41kTk/HZFIYsNnzfp+HbBaEzKjPjeU8lUF76bZWTdrHie6YdmoDYs1ar0OgFqXl1b9YA0qTRHuvNCLKQML+YheH5nSedDbkB5AVkZ+SfiuGWMLCVvK/IOwqfq+LZLr+c76vh+Cjiu02n4rpUMPTV7AH1+sJFVukNWlLj8bfE6RPLrJ7JlsppcbL3dSbJ2gRVKcA2rxdIVsJg+kJEVaFkflEAy3tbKJOZ9GAbU9wc+lfDx/cIrr9AzhzQL96gB9fQ1u6ZAc+9zJBDmz0xHdaB+1pvdmikJn3jLlFsSDweuYelJpA+QQo38XRpTB/y9sC32ILZESsV6ytwhc/HQ8YbFd8vsqi4hb0ybVMNSq90SrkrJLQnm08QcPT29g/QAzyeY+BQ+909mq13ecszzzSq1ROXSfQEx+SPH09O3GvVBIClfnjymC2Uuvcv5zLDaTdlENqze7j19OpFW3fIDVYncbo21JSejL0oPYSSsFn5WHlsxgZUAGoXd52EVsxQBJKOWIpy3i1n3ayXSLgZ2a5i5uFb/fZpJwbzxVaJW+G5WY2p2yJVLVt28/AymKdUwpq35gK5Y4XnLicvDM3qmDT8C8RH285hrWP6g40/Ypq4nolWFiyx1S0zHQN7NLqsbXrYm1bDOV/nlTKpaUmnIgHro+nOfN3qD45efkrXcEJVbw2fxw5ENBzTDxFoCd0HhRcVXokBjQvqDYOTzzSrX3Rcwb0tkYZETByJNAbhPyj3qWvUtldmlkU2vrNEYWY8hj8FXbg0A9wYq6vAJLBsEkN35B8bynrZRqoivKx7ZKXzklvTXnw0XWdK84DtfK7WLr15+y/SVcX26U72kegC7U6KMw3T6FsGfZtwpl6v7tWHR1KWEMC+Q36kqVw/t0jNMFvkgsxxPlp7vvIFK1KR+hFBe/0OR6CpyDh5OVMY0fCSfJYN9DppoHLxHrqtkTH358xERyFATTac/J+8B3KTuhSqBARwDyJ5aOE4iM+uQlgmAcOCO03fH+bugJBXCe2296p2FUjgfKPhO3luxu24ktTW9wk2qC7XXuUnREDMT2aSArpVbhmddKPkpeVHagFcCGA88NBxTyHbTC5R8+ZHKLTlciWkU9RNny/hrG+WQZbEhnslVizDSMnvAJaKRwUR2pwzLq6onsetGqUSv8wbkAexJ1Ch+Xa6QkKrXT6VXkQzPMN2UAmOQG/E9Yce0pWH+THAdsPzI6bdr6xpWVLXvAauKHA7w+gurykyKmU4D6itfYyLTQRMxHTNi3SlLY7LrDHymHLCOa4nPKlq3PBLDuvuLKoNdzDBZGLClKI/hu1nEfWZIANnuIfB/rJgdXqKVtNuy7HzAdGiE8E3KdIymN5SXFIAxvvRKGIoL1aTuj17V5TlzX29fddcD0JIfKK0th4HzKdO0xQ+UP+bM9HxtLe3aWmyyu/j5Zt2DF1BD1RhOyY8xsuNqnX/7sGsFdK0cozvlKm1ShtVLyqRYqapbZkytR97xXRzP7/OHWeFIWAYlAsMl6TYNH5FnKnqxhWNEsWtYp+ZVd7V46nQ8JjKmZd5QNqPe6xjWvbtPyt/KmJdJMbyeZFOlS13+chYbk4avbx5fz/mWNRZTSPg49NPUwikf/D0h/LBr+XyzNMdqNJ2Aes0n4OPdo8RAT5x5efp6AD19CYyjN7oZ9bdYxuSKuVZ92pLUZdnyqjgVXCcDa2yATCEZACRlaWEAaatstV2tlOF4TOKRU9rKXnWzbD/xz4+Y8hsQUGfIxoEBDKjXecDUruwuz/ldggHtATQp48NrTh5W2t5u9YBx1DUDxPMdKSZySlFsAiBclEKiKkcGqLg8lk2ZeFaIJRl7vlk2ZjbgvLwNKADrYBhQkvWGAgjEKkBmIm6Q6WR94WtMdo/6T7aGEMKlreP2Jy5tflv32KLGpJRQhk5U5qpMnL5lyoetaj3erDLyA+nm8eoxQzmndIxpj6H3FJ64R8CM3ClPX97lwhiWXSZVky0nz8QG8vdlmO8kAZ10VHogn8Cxp0xstyTAeYjrHhR4TwWXCtCwKPaqm1U80Sym8/PMgKQGukqm4wHce8quEHnNoleVMTEz2te+rsm9BCxKTqC+4c7JyzYMCC9DfLUEtmy3qOGWBAxZlXb5cmogWc108DQF8tewIKJZ0pFTJ7C7O3V8zXv6m1E8pTGp0XS6U4ZlUsz4aq7MVWlGJZRfxwyoaJjf8g7wEnZJC6mHNA+e4F6xpQxZSyGWWgJ4d8rvqmj7Yx/7GOb5ZpW6kvyAGDUsgdc0Wwpo+kj39Gsj3aa+SzC4vdEpy0xc60AZanptTIZpq0EbjeWitLScTeoeuNjpieVJWdqYlMXGD/vdBrvs8WahiJoOMNzBGparBPQY8m0J7EM2L5foePaerPcUnh01T5PZ/5a/DDFNxFY3dAcrqpDXxEiHbCvQmSe+k7By+erqwaESw0jH5DtmyuebZYNoqQEMkwLvMQF64sSHS5RSOsc7n7bDuVP8XlUbQeIlB+5WxTFyCmSmHLAF8HXPJ265wJETFMILVyV9uC1ntGxMBANIhiG7huXHyn4xhALVdOp7MQDeyMzLG92ytwxgRg6jPG86DcitanA9gNL2+FSVr0r58zUtT83RM3j8/eRIVrZwfsxAUTymQkB2D1xyW2UuxLKfuSOvYa1pzd3DGhBvQMbUVfKNwlXt/IjMyERENamGxcfzHThfoHJbaqK2YmCAjB/eqQpPDwc6W1tlS2/3RbuLC+Ejiy0kUp/3X/571Y+VDUWL+8xJu0oYYzIvPiBXR/Lhy84bSlfJmMyOHRfjNIL1IScstv7a2q6lrXaLhQtJr+59GbktILufP4xfLRiZyQbE8C3npwTY9WNlvwvrLWMQMjYsnjUa/v7HY1fsiDwfPUcS0qQ8eibFN6x4nmmIPys+O4xZbKsZ8XcwvPDO1pJnZXjwS7t6d+YhkMYuA0yqnJaAZ9Bx/EqWresx7AXk7qDMiI5HdpualO88k9lyhp4viXahgMZkqyGS1VYdSG7J1xYfiMwXeFfSFE6wPCXBlOFI9GQ0YBkAmib+Yt0YmglqGA/gNePITQp5PYYui19JrjaRekTI/mDcN+hGhjepI+z8JqALZUYuVGZYTUqZSsoJPHTcsnISpiGrPyDDZ3gMz5oXMOZSn/9Df19OFt9QjixneMoSwjRLThD2zOH91SHmegzN5Sc/+cl73vMeExFG9zAsV4wdRc55V1UWQ9l04H0yKWtRQss6i2/ZbiEYy/oDktVJOHGBpbqnLSRGkrtM/mUD7nksidmdpI9xnJ6VD33oQyU8HkPSr371q65PJYl82syryt84dL941sHyxJTsTt6PUSvlrNgdp8QwGQiWp606LsnS3qOKHQNU5UFsSXknC+Snr4EY2KHqp5x+4dY1wlzvLBfqhz/84Uc+8hEzclM02rBMELDroiE9ZU2Hpieua1UZgjpbvVpst874dQkQEDN8HimPJauNcOJi58tPABSl2wLT49uVPAYoisewtoD4KQU63Yc//GHvrJJc76wvfelLHkP/5MF0KIzGtpvFAHdKK+V9sdhR8LQ6o6xq3dtpyYcT8zLzZHKG71uLGqCB+UBYSAJk+IG556fc7l08LMkOaA7xviD6VkHs8c5Cfe5zn/PE/fa3v/3d737n7vT5GBkTbEmKZ5ZNgZ8lq0z1kt2bg5F2mYphILObwDIMUFYCYwmPxNzzLDONLR6jbQ2nLDYeExkIVzcvFunRsfTCisRcj6F/H/P9739/w3J4e42sATcm3pIX30l6kWmipTKMho+knMnJ6pg+wwTIwmPwxAzDAGmBRrC0V6KnhxF/Z87Qa7JhvlotyzOyrZJMH7iGpQ/H9tD1mu8qKak5Yc0LZk2tXGSiYMOVwRIm6NVW+U22egSzh1aUuzNkGL48baXJV05dRpZHapKAYQaEW87ryhZlae9VhgECniU7hiXs85//vGOblH+LVZl0vnvazVKAiVTVdM4k113AlweoaUkmsAVj0vCBlG21C7fL18B4IGyrYwP0O3CBmKxlGoxYvuXyHBmf+IXfA4cD183ypdGfGPtGwYejm2Mw5x1RQ0Mtd4A+wF7/iXlkD6DUgJBiF7XOAJqj06eZdn4htSWQtZuSgGH4acpsCdjKJysKyY7Ip2+npZ0+nJJ/TZvG7jEs6y9/+cu+53S5mpTU+labd2z3q595+nNf4xDCyITwsLvWNxOiJAnzdVA2W5S6R56nOBzGUpKU2+rkI18fFM4vobQyMIF8S7gGyOB8DTzk31ag3esx/MpXvuJgDtmpVDUU1rewhgWEkYsHhLQUwizrr+wwMoGesrWFt4vEAHynSjaMp0y2cuW0jF9IybdsFxkQdbdaxRRVoflCGohT44/BY/1zFN+nrhUMo2PHzE6DZc8SlLfyTcr9koR/0fBp+CxmWB64bOWvVnVj+LsMrv/1A2Cy4Yfd8WSVW92YwiPD+etmffGLX+wHwXzxLlr2NKvrf5EJlARWJlwxW86JsQRqbrNuWXgaMnnGIEWVCmZ2Z/dPtF2Zi71nFl6GeRnoT/raulecbIBeG/0z6Y9+9KP465312c9+VkmD8JdXqZtNTx9sC+55JMik2+0A1F5DMIZswwrwCmk6MUG4YwuPCYTHpOExwqWCS2vZrmW7CVrqP1LalABDshKmbBnTp8I//+2OYAqT8/L2tjaRRBWzVKlEdUNs2W5MhRtN/l7YLtJT2edvCxMGap2Hi4IrwT9YZ0Amrk+4WSSmKWfgRbKe8XeQTCokDPC+1susc4XKeT2GX/jCF/YTTiMrEYUDsJblgjGydOCq2pIUzgM0HVulEiLJ8C4psrTJKDMaBtc0XFF6BufrR2zhOgEKKRYmXh7L4bPCdZvgtiKXJD6yfzRQ8utLm9/G9Z8u8Hw6re8hzt6OL3NMK6x4S0BSjEcSYEidCZwHqmRLKlivBJb0ZQgYui2MLdYJ01sCm9HAktvNytA4+IAtIMGZ+8qP2bLmLQuJhyvRBw+3FHjcLOY/KNGbW0/9QIYUZoKZvHfvkFm8MokXVQEh5U+AbEZF2a1Row8TY3jKQIGSY0bWiSTlyYsiq1yeLDuFx+PfUs4Mc1c+4GkAgfw1LO8s3757k6nnDz0eHE8szzALs6yw43mdSQGYGrLz8LOieAwlXzcYy/LAgKZb8rYYvtGI2gcgvAPbTUCJhPlwmpjEE0TexfdwPCuEL0o/frjgR1q2MNewPFMU/p7VOKTgjcC8zKJ5afrMdqSD+eKJmxd9NewyUQSAo6YsnBfCNh1NwDWXBhY4/UCawvNKl6olJQ28bOGWPBsDVzcQdoSlwiwVIPk1LNfEwsEcksiYPJXOrxuGxBA0KcssAV4uUQygd9SySwgcPZ5dCm92QjCWQugT85YpY8iyI/7MkGYeYO1OgBFlWT/txryYbbEPsvGeNtbyGtYxkvP7LKyM6lkejTxNAb8RdObuHf4MPR7GxPmaO+LP1usSphdeFb7ZjQeaYIKi8hhG0AjuHjlLlhdIxtvFwJLD40uYnyYBjevS0fxA1JLsGpZETut+rQl3x3ZG6lQqZZ2wLYyMiwKegp6HLpyM14eJp+GRy19mgvH0jGAyu2whQAIAH+bDvMDxaYpFqkKp85h5ZIbpXH4AX8OY52GhzAtVjSlizA4v0SalZFuUMBPOLBmlXR5GmpFwuOS2DtHT92sEwi3xM0x4PIDshOFpHqJe5BtBvl0+KxYGEgQwuvX3EtpL8zwslL10tbJIR+1mAchODtDD69vSSdiagMmaRfUs7fKi0ifmCe64ZcpihQDzhRAg88lKEgnbYmqFi7IbyBOk70R8S/5u17DEMH04XnmJqgGYVAMqF8zwlkxgGf0ZQAaPekt8TZQ5mfztUrLmKGQaZEqxbLiouy+E5h6SoMAj/rSRWwLIF5cpZbbFs6PLpyv/fLPonDxPBDsYCzjVwiRqfGRNzRYslh7IIwGkWWymKWWwxbdMXwZMPdhlMGuLvxs+Tf4UXq5AXnu8KA0kK4MqlvWAKdUSJuYx9Vbe55tlvUSJqEl5kwKYGsxu6QKittUfvLW4R89uITT1J4pZFjh80tdc4M42UG9peMu25tvCCxxZkryeWXnyltoosBBKZCPmly3N87DOORzz7mx0gFyCndztMAjx7pSXPc9sMUoyYqALCMhehhLGJK4wpnklq2hReWKxieFt3QFBmZH3QmmKKsl8IVvSKG2ZWTJHk5bXoV8McXB/RyHnNSxoVnmeScQalu/yDUuuBi+7kMbne64pFcCL5Xe/Ese3xW/ExdqdbDgmX+A0LcWOBxjBNPLM4nk9k+F5PTgRjB9ZTr4O+88n0lzDWqJjsKfFVKkwl4vZ3M1aZ8c1O/9Fij+E981aV6zB8bJJIpYv51H76UcOYyq6tJa2LJnAgKOGbaWfjy/qvhsWbksPlvPImkTOyCbuaC2fhyVeH/xMMBHThIPhDUsi8V0oWwspqSvGlLcsiq97YglhBvMSAmz8uXk9evH1QJmGhxU9447qyxYgyBIQa+CJu45DOWYVI+9eBgdxcBb/PKzCZL9boo6tgC3xHcOWuSB5jC03DnC5eCGJ+9wwBTY7S+G88Oqu+4CtdvOigELCdz2+ZYI85khxWjjNGliIPmGelmcyLIktP+Z71TuLosviRoQtSycF0FGlMGb1MHg/JsRbJrZrXkgMozkf3Ot7qE0Nb1cVIBPYEmDnAY/RMIKWtVEgvK0EV6JTPIasNoCVG5ZB25bFWsJMiDPqlsF+JuoUaa6b1WvIOSUVlhUJVz5QgXDp7DbiMspg2XWrjDwBtcsJlIFn9GIBXrhdRllC4I5txRdC3+6APGeCqwQZAQZ/JLpdZxgpsISBZHz5gbDlNaw2DIKFRdZWGStm5Ee1p+tKyYRgeCFH/Pntxak6fi4mMFxsIcqTwUJgw1pDgJB9DMUWRWxZCE/Ds0rQlGQVCQrEtEW8hKuYZssAmSjWLUtzDcvvazlY30lRCJil4/G8LDVhCciF5IWzojo8pfeXXSS8qsuDt5ulIStD3haQPobAsgHF0yAZgCcrCgPHH9vn8l46ZeMLC8yIB+5b17D8hYX/tDGFD9ypUvNrFLAcY2lSzPOFFNKrSoayy2N2PYwYShrgnlyj6fE1vXA54ZaVLm0NhOutQDhAMCtQkpiqtCwDn1UoGczgwpcEeQzLxo9//GPFmOC2kec0jq99SDgAF8IzfH5i18qkGtZegnKmlIR+qaooJ31bMKO/y+B4Pp6Y7STtnic4nkpLu4mLvWdIlr9rKoopA4EofppjWBZf//rXHbIfdJ1tPH+wKwPUQQKHFwi7XEC1JakAHuNyVbJYE7F0uXgCy/rgA2VuyVvG5yWZ3Rmy8YE784BLi4xfHkBXzWtJLFlK5PUYWseeu8e24JgljRQDmFS7gCVcgcaBUTjcTGux2DIXSMkKD9OYeMnhzmArXGx6GSJf02uAlTNvKbyuzs3DYYSXgcyyWgu8a67X09e+9jV/tSNRf1i5d78UkbUbidmykjJgeObMxzv//Ms0eFUBgfUkKsCXHwOE+bq3m5071wczTUflZSYDMrEAJp63jIzPV+JeKF6gEtXFsOsb+W984xvG5LPK18e98GKWdEBUTyKmGXnQymBSkUAdywMIIcgDNLAtQE+YSvPhgF0gIwMwBBICWRMpZwwlEqM3TC8KOF6SUgHVuvvVomHHzZLC3+j7jp6upiNT8AT8nYxJgG+3cJPKLMm0xWh0nBJoTEa8csQlWSoAKWReNktmi9XDPMAaVrsphTestmji73nO0Ot5FGtLVAazo95u1qc+9SnD8tu3FEuXgr8HhPl7AWduBE1BkmJpTIq35BsijW8p9ryXsKkloyx/pZcK6MCFlDYl39kS54nxGgCI+8zcLCSGnleXGDPDw5HlqRB8veD92l//HyXu28UUBsuy/ixVStyWqmbhspiCh443ESE0vbDqkpg1NYDAGXgmOU+fr+5KnHGHaxe4N4Ps/DsYkMlQq1veUwnEz1c0AXylOD9sy2tYvmlwWiZM9zYYqbDwfMxDlpS2StIs8gZXiI5hE8QbpbEC9PyqlPZeWj+WBFlbleMtdVvUYpGMPpCfJn3ja76J6dW6+/FLdb2z/PC0X3TQvXR8rQBi6nhVAyrVASAkjyFuHP12XHmMKY1hNSAyVkMGF6ic5rLIB2/JKh04iYPZcrtTSgjzSlR61RfV7PJloPE2F/L8b3fKwrOOFOarEVg9iWBWf3cxUgHDal79vgkZ0rxSKgH75l4T6UvYMTDJNA20BawcYKvlxAQdvlgltpW4DO0mJljFxDIAYRqls36Yhblu1hN//G86MWHLxQ/oICuQJpnyxoQ0JmKaQjpJn4SXl0nhkfQMzguEs3KWNj++bGvPbm3gIykLD9z9eGTlFlWVMnQ6Deu20eOv77MMbyKgeCDdigVS8jLyM8UEGhaAhAFTAHpDmVEJBfb+wreFb8rF5jtPeTDsoYFK44GaOVXPSnrhZSg2wT3Pnb8nDNtlsMDrBa/XRqik1PZSVGbLyLLw62+AoOkIbAp5P6sxC1sAMfPyUp6eL78Q1hXjIymRNHdvydZGIGZ+wC6s0JH9tDInCKsS6IDbivfzKy905DUsJ/GLNb5IiZGQZybIOr/uaTobQVmWFFg3Ao+855+WecMSe//yZ0yulc+GNzvZaBQSBTcsIKZCco5PxjN8At6SZ5EyZBrTBgwwoECylIE8gQyTHQGnHcU2LL8MqDn/6pC0tgQDvEp5apVg4eG6EQLk8WUnYxtWXwTtIgmITYo1si61LQJFTXajqYTepGqXR/JMiFSsomWOtyUJJVIsjG8JCJkM7oOpN/pkS/uqX2az7V9m+u7BF/u15e7hkSoJ61+g++k9LFfpXEYaR9WKSv1lJIZphdWHlz2Nf47gC7Bvff03EVwld6pwnxC9e66EBohlYxWiUUu4EAmVUAhzpr+exI6qYUCUD57GP3KThNJ/ZYCHhZytHb2R8UJ4UfQAA2wl7s9/SpuAQFvXk5WUCJunqIwB+f6el9qWwjT0fJqG1e+L4xOkUVgN5+y0wt0av+Drov3gBz/g5fz5z3+O8RvAn/nMZ/yLdtZ3yGKlEquNPsL9Sjq+hiVnLTXTf/S4l4lh4Q2u48BamjUsIW6N3mSTnDeghlUt+o6sit3n7w8sXjRquvyLuzGvv/v6UWIJauUfKfH/eokpB14nbVsPyoflPfyfDOsu/T/H2nrI+U+n9qD/f17+D03lApuhQ/hIAAAAAElFTkSuQmCC", + "text/plain": [ + "" ] - }, + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import ImageFilter\n", + "\n", + "\n", + "pilimg = (\n", + " pilimg.filter(ImageFilter.BLUR)\n", + " .filter(ImageFilter.BLUR)\n", + " .filter(ImageFilter.BLUR)\n", + " .filter(ImageFilter.BLUR)\n", + ")\n", + "pilimg" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n = 1000 ... 82 temps 0.27 sec nalign 298\n", - "n = 2000 ... 82 temps 0.52 sec nalign 671\n", - "n = 3000 ... 164 temps 0.83 sec nalign 964\n", - "n = 4000 ... 164 temps 1.10 sec nalign 1357\n", - "n = 5000 ... 249 temps 1.39 sec nalign 1544\n", - "n = 6000 ... 252 temps 1.66 sec nalign 1924\n", - "n = 7000 ... 374 temps 1.95 sec nalign 2183\n", - "n = 8000 ... 375 temps 2.23 sec nalign 2460\n", - "n = 9000 ... 379 temps 2.56 sec nalign 2728\n" - ] - }, - { - "data": { - "text/plain": [ - "379" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.image.detection_segment import detect_segments\n", - "seg = detect_segments(final_img, verbose=1, seuil_nfa=1e-1)\n", - "len(seg)" + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import ImageEnhance\n", + "\n", + "enh = ImageEnhance.Sharpness(pilimg)\n", + "final_img = enh.enhance(4)\n", + "final_img" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gradient\n", + "\n", + "La détection des segments est basée sur le gradient." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from mlstatpy.image.detection_segment import compute_gradient, plot_gradient\n", + "\n", + "grad = compute_gradient(final_img, color=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.image.detection_segment import plot_segments\n", - "plot_segments(final_img.copy(), seg)" + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_gradient(pilimg.copy(), grad, direction=-2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Détection de segments" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## D\u00e9tection de segments sur une image" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "n = 1000 ... 81 temps 0.21 sec nalign 277\n", + "n = 2000 ... 152 temps 0.34 sec nalign 640\n", + "n = 3000 ... 180 temps 0.49 sec nalign 917\n", + "n = 4000 ... 290 temps 0.62 sec nalign 1312\n", + "n = 5000 ... 315 temps 0.81 sec nalign 1535\n", + "n = 6000 ... 330 temps 0.98 sec nalign 1859\n", + "n = 7000 ... 426 temps 1.14 sec nalign 2093\n", + "n = 8000 ... 457 temps 1.29 sec nalign 2425\n", + "n = 9000 ... 604 temps 1.53 sec nalign 2756\n" + ] }, { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from PIL import Image\n", - "egl = Image.open(\"eglise_zoom2.jpg\")\n", - "egl" + "data": { + "text/plain": [ + "605" ] - }, + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.image.detection_segment import detect_segments\n", + "\n", + "seg = detect_segments(final_img, verbose=1, seuil_nfa=1e-1)\n", + "len(seg)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On r\u00e9duit la taille de l'image." + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.image.detection_segment import plot_segments\n", + "\n", + "plot_segments(final_img.copy(), seg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Détection de segments sur une image" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "egl2 = egl.crop((0, 0, egl.size[0]//3, 3*egl.size[1]//4))\n", - "egl2" + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "\n", + "egl = Image.open(\"eglise_zoom2.jpg\")\n", + "egl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On réduit la taille de l'image." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grad2 = compute_gradient(egl2, color=0)\n", - "plot_gradient(egl2.copy(), grad2, direction=-2)" + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "egl2 = egl.crop((0, 0, egl.size[0] // 3, 3 * egl.size[1] // 4))\n", + "egl2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "490" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seg2 = detect_segments(egl2)\n", - "len(seg2)" + "data": { + "image/jpeg": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIgAAADnCAIAAABKeomKAAC6yElEQVR4AYX9D5CfV3UneB//UcGqYLV4VDAq2C4yvTBdsL04XTC9kA6kB0aB0YTVwGggCo4SokAciMFxxjFhTRyDwSE4DobEgTgkAqI4o4FVIBpnNGZ6zNtDpteZHvN2TPWY1cDbC68WXo0prV1aU1rj9/M99/drySRV++inXz/Pfc4999zz75577n2e3yV1001V56t29KeqzvXlzr5U/n3HKHHXMc7H964uGXWVwLaNYQD0/Xy55fi+wi7LFwwAYPMNZhuhknE5bo1y52e7fHd/O3dc3O7F531z0rrqA3IU+h4lGr2YvEGDdkfhAB7n2/DbGEY5GjSKHgCDNuVKHKPLA2wbJxgl270DNmg+d/mk0R3boDuC4fy52qnCjieRFMw7G//5i8rBp0LtcmtHc7IxKNnZYCgMMak8ITFtTY+LThtsZ50G1XV3dXU4wUCCHuc5puVOFQxxXIxzEJcuN+RgyzYBF7fY6J781VUmRc531FltOOlzmHU2BaOPF1cdALvr9PnafTqsBndu50TiehQuNf39NekFqHSt+xt4103fjro8uNOrCXgux/k20OTWAGiygDype6gE7dau7kBqNk5l2yWjJ0+qdoHRqYB0tXa3OM+kTZfnW32GPHY02mDuWyFHf7rnQyoDd5D4v7N2gj/X9Iw6U1Z+n/oGHkDjHN8Dj/M01Pc0DWewuhzQo5XUmxzuRmY7a8bZuYgkwqPZCs/Vrh11RsPOfaZV8ndnnQV/thtqpXfVjbRgXDhGhQkpTYeicSu3vw9jip50d6sxjG6PboQ4ZjpFAX+Qt+7QxO2GGlMq65jPcEsqnevWB1X6FsfQ1QfhQX0+MMpS7GRoXOvghf43zDap8A9dDlH+D+zTk0FSKOy7+W7OpqQhz2HClKrc3T6ajICdDf1nd4WwUXfox1kKhzVnUmHSSteN4pIiRRyUQN4qXjumggkrNTWV6oTiKeXbBDTJ09KmJrUc6e7ErUDlGMqu1dhnt4ogd5yOGmCG/ALdR/rc4lcXjHo5RsnA4LJPLlSkyyB3Ntq+NQBSv5sZjV7gBTkpb0q2kedE2ajed0dJEHDpMA3CprUacVMyOZuQDe1EgXana0TCRwXHrpjFnh5OIjDHkH2fE2R62iUK4rFTqwXT5a10uNC4QEwbdfOiY+AdBa0+zZLgmhlVupqeAByfwd902wdfxt+L2D1padCKSpQ1o9Orrpy6+skg+sQXVGmnPQypOHLZd/1NwcX4BzFN2ESiXdJ1gPbReskRDRGmicY3YKIxE1pyMvA3yAQuLbvRjSJSK7vQ1l2N29hZu87WOZdDfUFOCQADm3Yzkjlaz7q5y7vp0U8Vpg1AERKbLyndPrrtCd5Q0tzQzBiCmXAXDvDIGBItn2u6Q0JbVZentYFtAt2UdJsqjnr+jA67F6mMy4ZPdYcxZnTSzVF0Ec5ItMu7ap8bzP29CCalji5vuiYd6KrjXrPC9cA/eNI2PSmBDQ1NWzC4bC7gdZRmR0yHWWw5n0JeIKBxxkPA4PyCJU2jsgkJ/WcwF65xDHqmV03MNrq0mwPS01B32xNxdhsAjTFaHewbohn4L0brfFy2svT432gD2XzXivPBnIjK2O5WS8u4Gr/cdxUORNGqPh3kTTqshPIipkcst2hwhi4nwxkapbsh7i7dh9ld3821CaqGD/Y+FPqAD0zLIB6rWTFqZWA/V1t7WkHPTEh1y5Gv7gtfN2TZxSmLK8ufUZDrPnc5LKAZOeo39BSy6Qh2J619OhzGnQ2/Jkf3Ch7lGs7RdqqtBG/dAmqCA1Ma1QCI4qjDZEctA6bLAaOJRquhVAEzPAaNY5TT2IYSTKgF32dInZSgEKo9IXXHVp3fnc+5rQYjivSh727VWee4qZWB5ALGlDgUQB4taVLPwYzvaNZWUxLxNJG6H0n3yJ+7ytWHv7EYfuDpdpqfbT3nz18+uZ0/o0L+5hgVR50h4RBx0THBNUooS588GSRFg03pwxRnBo9dgU9hq6p729V1RpWwqMkdQ8s2Wo0qH2h9uxy31EJk1KlPlI7qAe1+pZaPsLXnGaSgaSGsinu6OXURllmIEWJXHMAFJGm1ZRB0fejvpOFgRfyEfmMGzvp0a0Nr3doOTVWeENaNDmqhGtgyILU4dxBMMGq1K+QUEV2STnbbmpkcTWsg+84AHLecX3zZ9S6UDHiFKe/x38lwcU5GxUh9kAGmOwZyoI2ouhnUh78NNtodMON8gE3I6K4OfboAo+LO2tO2hQUUGcCuM6Hk3Ezaou5bzs/UbnpDZtpqmsPf5hdUXRYVw5VxHiKbVNgiEtfjcpA1qTBV9C5MWfcCZiNQGoKu+Tyw7RyubFxM8ABCRLOGqwlHmqwQPm4Fa662mx8d6KLcSnU3GyyX42S06nz0rTEPxo1ud52u1a1Hg5riqAWGoWFiwkEZYGBQtZhDTBeGkkHt+Yn1jNYHCYFhED3Xix3DwKGdq9O7am1nLZmu99BFMGkUUiJsstUKkaMJOLoV5EQSyvsT5IOO0f1xNWgGOQTWA8RQ/ZDUTRjwYih9FZKIXLs7Lk3RGLsgyYd66hWIaTMwDFIUhIiQkO/tk3E9qT6wNUTTH5zoScOtxVyT4Tcj9qifyhP8wYgFKkzxg0GPI7aSv4EMOxrSJQVKuwrHrdEFl9y63rbYhgwGMbw/4NFQmA9yZ20t1vrZOrOSiHx3T0H4/dFKaAHfNXI6zpuU0NAyGADbd7fhw6sGCHi7+gxAg45mr3KqMHEPTXmq9Kh5nsW4HWD/W/4uoHMJiy6NuzBP2muvkv40WMrH0S2FSePoDuQUqik1oc8xbvUYQ2Ld7LiRc3SO+wM45309SMrVBEtXaRpwE0y+uq3AdBX0p7Qvn1ypi0czbu+uXUs1t1Fr99YeDm05sUm0gRoN77IN2cPhpOP6OwhLCwEOhc23MLrpSTPD6Lv5MYq4O4StbHx2K4HKRSuub1fnhmCCtPFP+j/Fu11+gePdBli3Umd6qWTScONRHoQNEGUf2FMt1KMM6SMeDUz36gKq7nNkP0U+GL0NMJAFpw/j6xYHcGoNAiZ/WmcHv0Z5K9BAOAZR5M0/pWYX671Vp9ZqZjHSSMfbpaByJDGDb0rnOPd9oXB6d2Lf47JbjLR0hHcCP8777qAhGNxtCulB7phsxsd0uOz20PXADHa0E4jwm+NR5O4zKlO/JcdXOHJLdRYGewrS0DgZ512WrxQ2ZWkBoa759KYp52k7H6gi4wBdhKjP0+0eBoZUomhDfohoceo5kNHnrh9sejTUCPigbODhBqVJNs1dvlMzL63559eOB+tsJ7tQdcZc52ztmukBroP+gTD4tdadDfHdd4WjPNyI3uU6zfV3TjpPNngb4htkAIy6ShKgq99qsfvsCJc7B0xM6oDLfxhz1i6PBPWtY5j0uWUYD66wfRHaM43wgdfd0RTI5houqKWs6Zl6gKaYP02hWqMvLjoWSt8gbxIDMaoP0+jE886eOqjuVrgMYEioG09jfIhiGKBugoNZiZCaFwVvEmOQn+lubtSul9fBPXX2oTIdKbf21M6ZOrtR5073rFnTyNjGMyUJQmwZt4bstZG4aUAOToSCPhpDHG+Tkb9o3qYTEwajSMHYI4kJiV5tB9pIn4hk4G0saQjeMd92PtiqpIv7Ts4nNLjuW7keVbqxoFXedcO4Pp+wVX+6urL0tGGGOIOXNjUA4t0SxkQ/usTlNkeADZwBm8rpQotdBXbiFCI7oVtQzZyqjZ11/OV1tenk02vX7jrV+Pe0DzjbShO0fQRHn4++5LSHolxOyR4AKRi1pjO8dFyJT6vIINVV+A+44+9gabC4stwI4/tQ2m2kvs847+9JeZeNOxPAwdOh+wOHGz6NAeahIwN9kIxbKOm2XOVom3Br3PdH7S7u7160kBbUpVEYAhoCkidhGLcV9oiapvxvrVR31BIup2mDXItnbbmOHqzHv1Kzd0cws/TC6JfxJ6Gd6oLXaRvdqUE2wQckZORkQvf07+Re35qeB2T0q0smxYQ0BjO3t48Mw5dHQ2Ef5aC3AZznM0FwgY4A+D9lZarQGrPogRehLqdYnA+hB02TNdqK01PQ94bnnbTT/cxd19Mm8HQ3TuEvjjdbt2+FkFGzawwStu9O7wTZOLiOzCvZ8ZmIZ3OhNpZq9tLaIyoD8Ujt2koIy92n/Tb3nE3Fn8Lub7ApdKFk2tlBTL6HAHKz8fSf7nGXDPiBa6JsKXekzF0fS8upkNb6Mz3N5Ti00c0POsLKAdx3YQlg6xQlHbfAp8oUQ2pfTCh2u+V7wAzgpno0NIwAyOh2hNHu4syUEWPInaCncfAM4Q+S+juUOKY0jLJRkrIxmM/Vnrk6cKZ4rdmZOprBpchn51JzpzGkTypMsaWuc91BTDeau+Po8glsk5pK4273cXLZrBhdc1+XneeWo9GOksnMf2B3exCRalAP6FGBYFthB6EBa2EEY+s1bqZCtzAV0Hb9CycTnF1ltBUEXTHSaqLT+BiBGh8dJ4lEgFN5A4gIfXgnI8FFUhkYcmsbVTeuwJF2pBphM84b1U/V4u5anKnN59bxPXXds+rkt5KPEcghabTgT2juy4n4R9TT7jGUNyET5K1AYUwTkOaGQag+BNDwgz+jCggw4V4f/rrbjXZUFtBR1vUDA9yhsOsM7UghRPF947SdZvOrobpGV8zl6NLQ5nF74AxUqucYHe7TFnTISMjEk7gVVzuRga6G4j5CavcnSPz3afwTnBOoCGCQ4c/FJ5vna07icmdtbtXaStWhOOGt9Xret2rrWbU+U2f4usHHHpBpzGgh7XZrk0ZdKPFxe6ocgQGvBIZu94KQXI5bfTKpMjwz8FHF96Q3LZgUdvDQTaehYO8WneJIWm9eJOcKsr8HRJY3ENZiQMR2rbTgomfRGD3YGkbnRiOcnqTR/O9bBDCVijqpZVRQZ/QcVXo1WhmUAHbZ5QPNaChjCcpbh0ZdAwY0Ev5nRMmbVYs9Adisk6dq9kV1w2p9u2pjuWo2YwwYVVPBbMa6gPOmMW36b+AMrpxrrgsmMIq3D4SNW6k7RJKzEKw8J/3tK4fLptmtvru9HtPDXcpGBa3nfhM3Wpt20txlsCy0IRl9wpg+V2PUyq0+d5LZYoONTkSD2iuGxcNljR4Oird5AOMQZ+sBoh2qBBvk7cRQgi/mJaPbA1sY5y6t16NuKHcVdl3iWTpfmx10HTxTx3bW/M7aqLpjq/Y+vfbO1KrBf77mDEK2Ue2qM1ZudnX6WcTfNKRfWvWBnK11cyBHv1CnwNFkTr7Hn9Sa3hh3+zpa7jI31Zx2MwtlQdT4fF2o0NBprI/8bWdCOsMhjuYVj0vToAGMC5CPu2mya+HLwBQ3Nc4GhPM+Cd+nE8A0SFTd4mh3lEzOGx6SCYLWWdi3L50Fa6BbHm1eCtO0otO1Z0+dmq0zR+vIcs7fcr4uUT5bH3ukFjfr7MHauZYsxqmZMIvsM4bJp7UqQB198oHfR1A+padbTaPjRLG+bx/AlAQ2N7aLWxJoa4QoDKOagZPNGC4uPkbFIeBg8WmAtNQnyArMlN0TmIFiyimFgR2Yt6tvU9XdizwaZ7o3RT6xiW2CpnUHpoBNkeSU4AcZXX3iJZR3D+lIYBp51wtDj+2opT21cLiOHavFu+u/fQJE1Vy+7vpc7V+onWdrY76ZdSoysCgAgYohoNsKhY1/4ByUh4eT63ZWqTMpGbciyxZPWuojCDszFsb2vCKQKYVhuLKGy7X/fSN2Os6bFAwcV6Pnqe+Y0pGrqaicuBz3G+hJ1ISCPpy0/2sdsQ6/jWza0hRw2km1FDXq3BrK5WT45YvaG1DuDOcw+uxiUmu+Zjfr/Mla3Vs3LNbmNfWNqnvgu5XN1HEtrNeZhaxyLkuj2XbUFePEtDhQNwfT4DYxU9KUPYnIXE+7P53PjbJ8h6D45HjaHkcnlLeqTXZiBsQx5NkVQkeXpHzYzpRl/m6rT+4O+IkBpiCqEWS5E+L7z5BlCsdlM9TtwE5I6So9Y0/9cTRJqkASyO2jmTIwK3OiIMdwpA2Pao2m203J6MR5aTEwN9SuE3XyWO349zUMpg5W3ZT5/knZZTHbqTojrzxXu8d2gJRNncyU5tFcvkcDOcuxTWeq9DUawsMndyGEKRl7ENx1qfnuSLozNvw1wklpSOgjAugT2Kdl3c+mY2I0UwAQgelb239DlgGc0nV97YU73TEF2Ro6MFuXCym58N2w3SW3uzy3ekVy3BrfQTUAtslzY1TvmCJTn+78BYzusotTdepIXb1Qdx6q8/+46kB98Z01M1vPrVq9qgM5edJdtb67ZsRmpjVMu0lFfRMymDw5v5ikJiewKRx/ukIUi2gG6EA1vods+nxwJrVIKyy6HI4co1bOGstEws4HA7ryRDEb3QX41HmSzo5GfYNBU5jeAPH4WNMIU9Jt5St05DP+pLxZ0EXjRgIkqCYOtusGrI9toboajeZkqg1NRLfVt3ewmIXadbqObmWk2Thbv/HOur7qjVdlM/vymQwwmztqZk8isdnTtdrbnRLvdXu+RrvjJAo3Ib1VVu+aOYPybWJCJnbjQ/c0lxedTChsPMHXznOygonjqdtt9leCDRLOcvTUzwzUfG4UiNE5hnqm5uRy4u16/A8fgXQOzdRkyBe2Qd8IbS+wb3S4qwTJUI6BeBDEvbgcDXV5ZAyyc/gTeroNMG5BYtxyjK5tUyiMXjtTyz27tEVml/CswU4lTVM71urM1TUjNtvswGw+32Q5GKo5aJ07Mf6zyBjBoNl3qMlXXCUanA+NnFKrZIRwE2K63XCjT0aV1HKtCbmyCc7Rqw5jxhoDvFn2wNOQHC0OL5i5CkYC6JyMr5Z/XJZrwPQLNlUA9cDenc65wjA3lXv3O5jWI5cTDirpI6iGWg3SexkmNFxUHsAh8i5XdfQKTGgeZDSFg7OBtzh2plbP1ex8SQEsna2TKc3I7+Tcw7VjMxowM1enz9TprZpFQ0cm0dpBG+Jb9lpImkox+26Fg0fTAut0vwkItWjzyVkj2D4BPBB2+QR/z73Cp7Eekz4AGiic94ZEGOhFmkRco8gldQgtaQYVQwUGrwPfDHKnOR8QikCDVO/udDdSnLaCZlA5WD/KL6rbUNPSLm/wlCDdkUtLjYOcgarPu2Ml6akslDfXUB6qLcDM1amtOrPZpm9OE0y1XCXrTySnd9b6Ri3ZkjGbaSb+Uk1IRo+DrNEyl3SKWox2FTZNbFdPaXZUoZveZgUywmSfUYQD0IbEBnbHuVrNjbiyIES6724+2Hiwbn7UGXi61oS+hr0IXh21gqLbGCdKgqvLB5UNE9hujsByNPAFvC0wBGzfahx9ibxthAqmzAprRmf6u0HDJv3a1tM0Aqfq7GC29vZqmNz+6lym/U/vvPJTh+xna896rZpXnqmZ07VTupPbkMWBQnWfcdhPo6QtJqKaEhN5ND0DKhyYVvHXrVyNktA0OQbucRGwnLVgiCFk9zom1C7pivvb42pAcbMNK40N5IMdgyaFU5ROQsE2HyfQqZWjqQCQT9dqOgY1uRu3PrrXwBMYQNNepf2+DLaxE2qgUOxE3QbIAADVuJU6Xb6rNrZqn+fW5mrxTN22YAmmXtu3gEihzZ2u+d11ksqfrtMSZQKBxoM/wY2GJmPiVIYnmPa3yUmnmoqmxNlgRZMx+jUhqW3Oee703ZDoGPRPUjLcqOZJAwSd7cxPcmIKBt6+nyabyoHIaUOMPxPIADjgIcVu27krNPkzaMqlK/+7MPBOHF0rJ3FGrcC56JvdefeDpAsnf9q20uEuHN85HRO6baUZN9TcXXNnst1y16k6MF9vfmVgFWsQD3jF86t1dE+i57CsbSU0757Yn/PRBbXScrtu9/PpycBgUWB8YG7CEuk0/IRyN8dZwzh31aepMo64stiKIbpxRYvdEVY1Bdu8UzOfNqbBtAmm0YAqcRwZUSakPLnJQE0DsyACu93DwbvG3zda48AMSoE59b9rBWB6B8Aoa9FMugrt9pF+dStICuXjBoPYUSu7auFYnXrfBHbcSkMmOfP1qbN17clsX/YRgsoCjPYT5jX+iZ/vqyDvhiYEtwsdwhu0OHcyGYG6wVE+2o5UcvtJHYRxx/lLm+LmWqgfttn9UWdQEESwT4d6FSK/KbBe+WTzFdlsH6Px8d2Nptvbly66VpprzEHo0x0YfQAwKNZQPtuYp9gax0V4uncTWaVyqII8nxFqjgpnMnM0rlgoe0/jNMDMAr+ivjZIemcMZ+4TibhsX7I2k7Q/DB0FBK2IdCS4ujproMRkNjwwQqPTELY2BGTQn751hQt/+7KBU+HiI4VjJ6bRnlV2cIxBaHLEjPyBri/dTXvnE+3kbn9y5uiS1IKxNQiSXPV5VKZpCpoBwCJ1mIEPYTQOIEHTkIaHSEXLU5F0C32zq4wWAzEC966rFWBdL+XSkhtnar49zNq5rIy5be2y5uu+1QS1n0v9jDFqnd1fl30iNGd1eSNIdszlm1ztPctlEtM9cWk8c+ey8Uxn3RqfQCC7Z345d9Xdd7uvBhdTOKkw7gZwVO2z/kLGZCdmNLdr40iYOGI+NQfK6awlOMWRWkJjNzzaVGOoSUhscrmAIATZseYwJjAJt8anJ7BB4r9q299dK6MdStoEI93gTUmAG0H/7a+ezMI5bk1gAMtUWlZRxZzxcJ4klsw/Ywfs2Vp4ZV0mMr5tiuNPIoPNmVpq1j+keCMx9Cw6d9Vshp1+vmd4+yaMiNBmzU13LtCusz7ipu5Czn26kcC0i9umMyXTu9oBmZIB3YLfYcNfLpsFGaN0AEfIqVXeHceAd5LKpjj+TDUlhdPGB+rYirOOemEbkGMilkamFdUKfBM3QXBRydCSgRAUsG1gGIIn1320QUwvLio3QTlT+87WymJmLXZcbM6Gmwun6q6qq+Uj/rcEY4zmhe+oFbsv9uYZ0vsGooeDHg2kwDK22lXYCMjY+BXN084NJjg8JNqmGqO2JoBkAc0xOja+R0kHC4MJAfZfCQyNJFUcepRO9cw/E8OWyqTDsGPT6Pz43m67saT+tFV/h8BHwaTVizg+cEITbQt0FzR0JDfwh9lTxcnpRN5omKBNvXFj/Ml3StBzoWAKpMgMf6s2DQbzNbMeC1jfVcurtWN/kvzxSj9bT/xVfRaCfXXr1bXzrvbbU1TgIZcjOH22zu7JNhra5pkm0qKX/LBJTDgWIXSdbSIsZ/T5kNYoDsj29q5pE5MaQ7GavdslrdAdlaWxZpF72oaI0JhYjqkij/NxeYFfU7zYE3TN38Eql4MgFWNDQ9J9MumO84bx1wE4xjcY3dSkuv8TOqZAKZ3QmopDBweKUZ7bjdmUZXc2pM3Qem7tdM1t1k/9fm5etlGPP1i/NhpdqA8bMNo+PpWbdWV/c31L8/XemWyaPaBRA7DBBnnyzRJ0renp6bbKdi3Uhl7wIH13r0Py1JUNqAA4wAy+OR8z675swVzaAD2+pZOjveh2nzcHAtH80eSkVdfNCyhC64SSLkOoj6IuDxQuD4HBiSAoxnDaRAfwohN1U2UI9aKqFwiYthgKG7ibSq0Jqi4MqfS6d8COTlk23vpAfQuiqv/7znzfNOYud9RuK5U2MV/fS2Y/V/8xO2esAdT5pbrv6rrfyVoedxIK8Wa+sUecFrY3fweTg7FLQgYiDQdTkkJM/g+QfAMIuc2rRpTLIMy97siIylLFjb4zeD1xO109mLpxqIANfR/nKXArF33SAC3wUZZyRwxgaJaWmiCURVhNfYOkeEQQChsk0k2z3bS/3Uhgc9LUuzVYc/G97SrymLyKUUHIu/N0vNnWy1O9Pl2XQPFYTt9W9ZOymafi8U7trLeeqQMfS/kSuczXyVfm3MgDfNdy5BEKTW2ovySN1nv4ARIyAPm42fSnMFeT73Gz70+K4kWGMMb3FHrKz8tT2QUwjJgwnV0LEzFxQA98U86mrMsxd1sJJk32ne3zSb3+E8xOWhJDD1Ic4Uz6M3AlZAc1BrwmrCEa7zakq9H5tp6JbCZwkz84iJHSXBYihR4eDjLGzFd9Rt031WvfVHZgRARP1NI1tXFXLe1tRq/WsSaHDHbt7dCZ32sysUi4xZvhO+RGHXsBcGBQkq45plT1xUVfzS5fPsNWQjNgtcanYWPZ4yTflwaax8SiTETaReKOtk2v0Dc5WioKh+SCVgm32DFS6vYnOj5wqwbApxtOWDkwK2kHPSm/gD4Y0n57DJSo57wpauI1pNXRkSGPpgHYGCD75rSbgwYJsX4UNuayO+FvcPa2iye+Vw//QL010qlzL6/V83V8PYI5ezjceE3lIab9OzizHI9XregdAeOS0cg7QM7kOzOHbhXl24eCQbPv0dz4dpFgAbXdQTQzPO7RRyEJkXribEfzLYJR0yeFZNj78+iFJqO8zdAANNA4YYNYphuqEFWEMa0eQhtv/o5b4x4W+6jYCFM2ylu0EZJ+qKiLY/sWRz7t7hAhADWEQzrmzqTQOe0x6+rRsW+kM6j1YSX28+vLbYvJFttcgdGi1ud19//H/1z1paqfqWv/bm3O1Z1btWVxbLU+0ncfbAq/1ue+EsidDBOJB0LuxLzHSfijLXcR12yk9RPKUzo53PRREF2XUOjsMEoiPPBwYrXu62O7ioa/vPHC0IgUkWROO80wLU5J308L4y7WhHV9mRI1fTVQysEPyD4fXw2S6i2FnICnOwOS2NgEgU2cb58HtFH51h/AkAx431AQldBXfzSqLoHmLtSna80AMFuLh2v3eu16Yx17TWDeGXz1Dy/N8H76WM3tqXcsZkb5+FZ9cKues6fuen37O1uZOL6Lj+O1c4HC14zptslcO5OwVYvd68GBkDSthfacN83uZuwZzn/IEqRbIPS0WTH446Lxddp/MLQbyVfUOQhTJ0fXnDTfBfna1pQBM4jbLh/UNUzuwwDAx8WA76uxNjqsB0CMvQkN8ml/JohHr6Y9aXkFJzFQ5CG/NNHiVCCggunsnXVmtv7q1fX+VyWHf/fn6+4fC+2Of+n/3qjh6Y06eraumq+jR+sb6/WNmQwq3NdHO1J4Yy+ggT31SCgmeNTyaekF2VzUownBwd0HZ+AuQru/vkKVul0pndKXbf5sF04q+9MWE7hxpHZ/2snk3AFFdziX7fdT0ud9s5seetrw+Rp3L1AxISvYWo+QSM3RzQtBnstpywCcbx/jfAKgSssmwH1+QX6tjGm2aZA0WtxTuz2UdGddwgks1+d+JUPFgwPvJfXjVX/8RH3PxPPFtfsXa3W1rpmv3WYtqxnzPwXM+PNEHW5iuB1Yx1OZkGW0a58zLCYkTfnTjQc4hb6UuyCA4WzbngCnBI42jsFJZSmafPk7HWO6MMWB8L+5Bu9gUvrftyBFFoY70mKgJ8hG4ShQKwhHlSnIuDVh3EW60xxu+IYcPXE6bTxn2qWtmhh30ZBs7oDv79xrCka7BoDNtQynx/606sVV767NS+qvwur+PJGF5Ev+Vv39jgX27c1ccvWmOne0lg/Ujs/Vg09tpO+v9T+pT7Z5LYuhreXIxHQUrvVQtU1keDEpCQGDMn+7HJfoXwzMANPDUkRiXOlZI976TPrfPe22L530KLHNkGd3UM8zogLJ/3ynch9p1P8wKeWD0U4GZyZ3p7SNu1Gorp6741DRRctvIJ7c6ovIu1scmpGmSc8BfoicwrrUpFtQdVRJlxPnDIfTj1iuXZPpSs9MEmixAcejVV8UehHRw3XfNyNvz2BcPVPXvbl2PVRbq/X7r60XPFZPPFGfWq6ZN8aqHIzGeAaasD3ykPcoTiju24MwxPRJSGrCxmU07yJtJiElhtJoudogJ19TJgfzGPzHvVaBPk2f4eJtxmUYtX02CII3CCdH/BKqDIzT0vxtmNTzH8XTS7e6oFsZYH2NUDATeDANd7H8hlS0pTVtgQQvhSWKTQ8H/VQKHu+m3FHXPlNpLZCD9L6ho+o3/qB2/HSc1dUS0PfU195TW9KXZ+rOE5nlnLuyznyqvmrzfy/SXPPSOvPauvNzHtmozedk2p/1/ybejoD4UqLqQ+Ho9+TEH8coGt9NIcJchfjx6cIRJUeQ7qK8b2UFc5yBDutHjebOtk6kvEsCQyubv+FXFyrJCQZ13ahGw/f9EBGP3DCjxGko67rRdMCuG/OQgRIF1Cq8ntxsurpXoQBwt6i6WVGgW1TpmDpdzdxl5oZ6SdX9VS+5tV74jXrwOVV/qw4v1fpPB0QQQBKry3V6peYlng/WxrGqB2rPZfV8Q/+rSoLzPSv1SDf3iqp9u/I0s9XPzGDYq5Oe9uuU83w3JNogD0XNJWFCeq1HagUo5Odvi8T5eP3TZBU/PZscO4wxEAUUiu6SBrQUfsUpdquNcQBAkSxe8yUt9dCd9obH7CbRNfge1GhSBbzT1uWoc9Mq/IUnh5IujIDyv2P2cd5KFJJ6ApSajm43SACbrxhvBvGQKNW6cpvHNuvPekT54GKdf3a9+xP1qYfr738uPBdhOzZfne2vkjEnmk3LssjPqmN7MyDFoNbqETayFMj7XD2YdQFPz3hyI35+CGO0pXqLIaBNv7QNdiFP33MQIZrR5hvPGj5d3t0d4iXddKuZoCi9IJgc3Qw20j/fmomaQ91Awabh9t158Un7EAREPIOO9pXqGqgmGAb3epY35B0cUy6nG01H6AoRg9XdrslXCiZOIiIZI1/ImoB3jcA4GTPW0N80wJpCjJtLFvK2qq+T8q/WQ4/W+95cm1fVA0+k4k1fT1R2AvBc+hvOrAfVxqFaurlecbjeulpPHKlPvbsePj7Je84KE/QX9MnIRjVf6WyzLozqHmmdwrlMeS8wTi7dGEQ6aeDAT+PvvhnCoHe4MxWMOxcf3YavwI1brYxpnvNpe3LLJyiad/iSz4AH1tgC36QHeAhvW3Ijymqm5+5AolYLlsoHVTcOw8CzLfIJtkao4rgb+OZFyIB2q3Z6LqmyVdytJ+6oz1fd8sngvOn9VT9Qd3+6znyn9qzUDo+T94LYpthssxZurX97R/3u7Rlk3vSlOtnMfSJbz+vIXO33+Fk7bWYq8IM6PfXd/An2LpmQ1O7E+TCj6LDzARzQPnQ8ZTkufKdwhMsD3ZMZMSroZ9ruY1gJXji20TlX0C12Yd8IZH+Gvxrn25ADHrQTZpe7nSnSU58hpOBqEwEUsI4yU3hRc7kY/ZyqAtCITfFsxvNrv1h/u+oqCeVD9donOlD+B/Wrv1L34PTO2vfeZAe8S2b+VJ2wZWkrQ8jZrfomqTj+XX3lpXXnB7Li+U3psudEZ85ZsZ6tzc2s8QxHFJE0uC8dGRSG5i5lNIhxAxhODtDuRACHP1BrIEk3p73Lmr9rEE86NKaH0HVLQaSlYOo2+jwNTxmX+20KEyShZUKZk1R3fF8TXbYt9Qs307muM6qNG02PGUD61uWam5xPRRKCL65yoGaeLR6eOKKr7qr7bmrMn6k/aN/1mp21slF7rqlrVmq3reXnanM9LyubvLmy6n/u3OXejdjTe1X9Ru0/W1tSZHtqbj0S9ajm+TFYNWKEpf22gFGA9sFYrKN/5OoAE066N7rQFplBsTVMYboWwB78c/LkIy6iUYDLWQt8KKOrgSUn4+iWotSTFsO14bsy0PXQGi2efrYrKQnOni2ie3wydPmMe0CnrfuL/txV2KIat4K3wYZVTWjYUX84nU6+1slN9fKex1zyunpzD/6HZuoT5+vUT9WOlbzi9W3X1aG5Wtio3V8z3GTGc0ub73wvBBxuytftwbD//2wtyluT0Fa3u/3VDNXxQY7ieFTfrpta5A33MFQ8dOpLDw2p0hUHh/Vr+m5/d0aHAzDhBffSEOn+OJm04XKATb9zyQk0S5z4G02ZSL4NvKv4Cqo0MM76pIFHWa5FdzowHWO2ywkvdccxxTy97r8NkK66Uu147XjL5P6Hvt4nT/T3Z+uFJ2v25trxgrrlQB1/e/3wlXn81arzgZ11an8WCO6+b1Lxwapf2pP9ZhtVv/P0rA5YUtt3qo5bRJhJvDDpywQ8jMbZ8N23LiDGOXXzLQU+OnAR8Sno/P+oEtJ9uvpkghk+DlFPPQlJJu87vQwGl5A22MDQPGg8TQF/Cn7SRuMHj1Ixz6St0URTNuQ3SAm2CUS6pCPRLLa/3Rb820gCkmNCQ1fMsNQOapBnoMLrOxrG+H/+h+vXu0pE4/p36+fQ9c1amq3Zz9UbV8K1e87XPWt5+P/pByuP/eH7T9ZVxpLOXn9CLPZIzRysnbdmmzlzsfF8m2ZNj09YRD+abCW6MGCGSwg3nnykwFxiu/tD+fA8FXuMCURLWwuT6v0iK2ydXHbLOVfNeRRj8knhFIw4XUZTmlMhqx+S13xqDTtwkjqTIWEioUlZYIJ7d18PkbpGVZMbJONwMvBA1S7CG98ig61MTRBgtOBpcPiEqcg36stVP1z1v0kS78pzMKoun6tTG3V2uT54oE6fqLPeTmZPzOm6cT6jfR2p/XeHkrq21jczwNwu33xHdnTcuVhzu/JGYONL+uF/d0ffY+6Q67uPS9xwrdfdqXigi49x2Utko18KfIZoM/OfHLjp7KLKmPl9R24ax0bb/a1gKAX+5maT2GdNojOvyp3CpMncCxInAd5udLTbJTrgymdgbvBcXnwMwes7GJN/PD1zKsKg6SjxSNi52SjDqbRQr5tmyY5yPy+IRTp2n6wd19Wdp+vmE3V8V3agecX7+WN5IPbqG+uVrOey+vhcrXmS7RsJ5z7Pod1Sp+5JPLb7zuxS88hg9o91FyZd0xhx9DdzGb0YpHdZUxOQUDU5umOjSsSJM+m5f70eM4V60t8LlS8ubq6pN5FBXwaS5/mbK0SQOZqCbcltV//rlcL07lNilWlFfy9ANqoo46QPbR9GckYvE6NwVx1fzvrkZxrBRz/Yo8Sn6471mv1AzT2lTn+3jtlsdmkdPlfruOBFsljsucvlutOD5JfVK1X8Uu16aS3+VhTrv6r6V1T/3jpzog7tqPUD2YvqAejM8Acxg07nYyxp05lQ3BJCagM2QRfOcjmRyjYePSWeyZvKG/5JX0+ufPGt3NlmUvcqrXbeZdunXYAfXg78oG8ioPyZCOwC6OQM/mEQYLbbiSptQ05RDRGOMJq5SCx6B0x+22B/nX32NnQviF1fr/pU/eFb80Dlu15cB55StwmRv1n7vOfHkprAjGA8GTtTh7wIYFjYjnqZiafHyXtHzSuq7r2r1mZqBf4zNcvCwEllagdEmNLK+jf1C8y2Rg6y0seuFPUa7OzrXI2enr+k3n1To22THA1sVx4nT/oeFGBqI5KecWjVJ4zuy4tY2HfzlSNVm46cTZrvG5N7fZdHdguehkwHRoujYFSfVspf4WYrKahZYVU/iLy4WLMvj6a/9Mv13RfVP7agcsm0zhMJhR+s+tFv14L0pWeX99aO1cSBa55UWqnZ3XXgQP3wVn1rq946U6c8tmGKc3eW/Q0zlmpMdxY8isZhmthvBztNpDb8xWs0j5NRst3rARXOTeHdGrIBmV5Py/NwbM4D689Fx2DB9xX2/Qi/4YdUntTqVJcvQjQ9nSL8PkP56y0MwFTrHnYXI/WpsKYI+y9LhYFLOeOxStY5U7PHs4Hv1MuTP64X1VOqbvyLaZUHIpVnWc9/tHY+s5ZO1bn1DONBMZvlS5sl7lyv9SP1rw4kahAWLpuQ3V03Vj0jYVreZm5atumNJ6zlVFK3qjomNPef7cg+3B+9baAITFPECR6jRk2Vcz3Vv1EeNvb2JX/d1fOLP93iX/sarKfU23iBaH7KTn+np5O6AAPbVRKZXFyx9eWvV9muGVT+q9JAFzrTZcM/GKf1lhey7uCZ43nvVNormMrA8NXKPP+lL5vgu+RFOfmdqqNPq4Uv1+613nV+NhOSFS//8QsYe2vfDbXzVG3KVFpm3qxT9gJekTjtu51oBjk3X2etjYr55idUhbztTnWPXG5T3gNGE9BenWnmABCIPoGm4ScaP6alI1cGzZBnd7+Z0MmrwfALhVAMyObyoAY6AE/i+MXYxladxmZFKFQ1QToSJcDK1okJTU1q3++z7u9oPX0fFbsPBkwfG47PzWfvncB3eXcd7nhs92KdFYfZ5vqdfJ/KV45Lnsj3GzvRYnSYPdEezCuWlpr+zbprRyaSOzdq/+68CEiLe5azPXPr4SyUHVX5itBg88asMcxtYfT3iab9+VBZvQvNU10fnRqKlZl4H5FNQ/jKKleXk5xR8xzB5GhGK7/w6cwoHwp6NNBQuYy0FDkaVzjrsqP1AZnLAQBGLmiEKIOGqagn85IuDPhAAr4rXmi0y3Pp4a5uV1qXSKxzJClid/JGHRBNbdWxO+tVc3XtXL3KBLDHgxv/cd1Z9T7Oq+rj+cqBxR+l+99NylJ+7IRo3jsxpcj2JoPp/T9Hz9f6Uvi+eTyqc00vmUC4y5KMLJmnO7ivldDJberNWZ9WPqqJnvRPHGTsaX2d8DM6OOFtkIKZpq/A651hMv3WxhQyScxxNPzkPH9aJVuEXajVIdKRdHOjdSqtOW1uDuALVQIyAcNK5+MSgI8wP3+6YlAAGLJpa0tJV8gXMMeedpjeWYEdPRjggTyKGd+MSaJB4KUN9k8SIz9kOflNdf7f1s5f68Kqt7a53JL5Yoxm5ljGj9OckjUYD/x7eMz4Twa/WjN/UOfuqIWZkhnbdax2LNbpp9b5x8Zrs2rrdO0z8s/3nNFkFiu7H9F9HLPjoC9H4aTtvtjuXXoN5uKOt5C2Z29DKc+PMSadH/2fMGTKjml9N50OkEnZqDJMbWhHn1/M62iN1A6OD0MZlLocT2cxsu3mBvUtnigXVJrrJkZV53lJxe5+VRUxnU0g6w2K66fqOe+cSgWSP/Q/if5XXFLveH+976Zc/b2WipONv8zl/i/U1o46MbY1S0fyhBR2OU/Mnv+TOmh38lyd6feVbXbwPftYjHWOQstgrteGXEY/ACU3w3x9hA/Uhi8a6drI4KJ+jXP9iftlEq1/bRjDPFqtFXZnJ8CpPixGwTiaxYO5ICdHs8n5KIl77bPtSuAnwG0ZT6rl8RS6jtfDFNowyQl9cU2qXcDS9QaqCbqpZrm0aZhGi528beRUT1lma32h1j+UWrfbYvkH9Zc/XaefVvXBuu36LAafffeEkPv77+c8z//ieutXMoU8tbfOrydBsAu/Mdeq2sjfeDHKnloXbTPE81kXmJ2t/S+pd9yf7QP37Km1fpZs/3qt8H7teOAeNj2UyeWU9knrk5IuHbem/JsATC6741NzQdQQzHaNbRZ3rVHHTSfxhlodYw/Pul3lIshJU9M/4DMT9qflkRpDcgYJDbW0prAX/qbW9CC5rhoMFMIU0q/lot4Ac3oxT3wdOF1/9OxsSn7/T2ej/nVaOFmHH6ydL6x/2UjGmP+qqvd8t57+lCRv7nt1vebLebjSu2SsknkTk1Y8F+j52DxCTuqna1aOYCGvAzjxmdrxwiz831v187fXB75Uu48kH0Oxzou2ezE//Wqio2eT05z0Va4HH+K+Rped+Izb0zHbVcqaP30+5jFPnq+OKkMRLvB0NCU/6PZAk9YvNJ9ao2aXb39lOHFMSdF2CjqcnzRxcb2pJAKzbeDq8hVcHzH3Hn4vgTLgL63V8vk68qYYnw/VISEh747/YSKVL16Zio7lR+uup9WhR+vYbF3/F7V8snbtqZUDvSedi7clA55jdd2O2rLCpuRonTTU70/kdduDtXRFNqF9zULOP6pzv5d1a6+ZYcGTrmlgOGF0IsLhpGlOVwevm8Mp9n9oZwPiQ2p0rcGNUXWyfSksAN8cnIxLuICDShtXJNl3oYiyN/bcG6bQt3ogTNmTjg6U09iUGnUjG6nARh7gPhmtT7rRYpucA9juWw8/2RNkJPBs8Wqd/t36paoPdXBwVAT71bp6te54fELCK/5jTt5un9hazb+yzj+tFldr+XhmiJ6sRPDes9mUxP53K1mLAXmD2V+ZrSzUL6JzqyQRzpxOzu01vef/2ofrY6uRIgyGlrBo6FlzDzPwfYgGzdtH2DV6vc2E7XtYOL2rbMLkMOTygETyfdtXWN3t5UYg8jX8Caiw1TUAJ+5pySUS6bLLAZdK04Oxg+vAQ1Fmgo1EtJ6pFkYr9b+RDNkIFnJ0h3N32hzgM969Y6BuT4iVtsIQhoEXiCnIfoy2irVcS5+r/357mzKm/2XNksqjtXUuTy2f8GryhZrFX+EDpyTJdirTUtsEvP9n1+vTuA1mv/mi+vT/nBB2//46/olI8en9UoB33V43fCZSNH+y9Em97DRD7WwHzYiM6x5sQVg60A5KYZfnenrk5hDjuNvnU/aOecyYarjR3Q7zWvJpEYOwdUii+ZjGGpEvgAk2mJdbjSRVLv70UoS7gMPrkevsnWAZYxrPRMYAXBJwZwbH3UFAqqrbAR5ixLWyyGdtOFoIAbt7Fnmq352wOZ/njJU8qM5z/M+TrjdxQd+rxaeFdxB6lfLB9Qw25+XttWmS4uEYD9CaG53rpy9Htao3va+uoXO76tCV2es89wo3so/AE8+LOzKt2UPt7GbeVYunUt3bHWLNbUk47lZUzaVmprzCvTTqY0LWczIgkWUfJJAux7/1K0uUtyxSYSLCVJ1AB7Q/ricqsG0umm9EvMEIB0e98R2xqUNyvRUhNtsuFXODqjsAgDIm1mwW27cWGBzc7kBgW/CedO22/CaPBKKI1oZVUZVCvgeFOGyT37Jx4vZ+eGIhWeIHfjkN7Lyjzv1JHhKTIPjQSv30Qt1ss9KJDBWze1LI4gUUhD2mQ7/Wjfp62jvrhkN5GfOrXlb335eNAI7rCafDuZm1EJZXANpTuJRgWrZNFzJDwKLWvDiGbQ4Of9NIFEZIzalw1Wdwre/mOf8czYXcG0dXCE+nl/4GRR/EG49EFM3fyeWZRk2DwAw843sqwhQPjMrxfZDY56rEdEbFoV8pSsF2cS5Ymx3J7HIrkwmd8fQeQ3Hg0picz+PRRu29uXY9LznNj6Hwg3FzDy7kdX2LYveNevanMoTsfmdtzGb33sJ6ura6P2P+8pG8SP6ZjfMX/0n95r/I2dNeVFeb9GzU6pfqPc+qF34r5niVvYMfrLvwASW76tjpOiC069U51hxdQ3xrW/rhf/NBdyKD3OvvyZ3JXWDb/Z0kMQM9Kved+I22+lGMazpIi/PdPpR+5ekAGtHa7QYdCa9HhdHoKGmClKvr5nbLMV6fbii3umK+upwEfbapwri4Sj7dDlXtztcWHZfc7WcbjC6tvjEdb44xMq+v1N5Ha36pHrixrn1zXTFfTz9bD99RVx2pHz1mA3Ndck39RKeKD52I+1o/FC+3/0itPZYszqdl0l5Y526sf/JDE/Y997m1+cm67oP1+LdqsR2aG7deX7v35+eA53opaNOTngI5GbzmVaTSguEnhk5vdyeM6i4PnuiXywtHi2Ay+Ke02XTx7UB3jebVhcpcZ7yn0jCvz+PI+vN9bTS6ixEPVEM8IbRJzMkQScNvt6Q/2zTAP2MZfzMT9dNLxTLy4guPSfTg73t0ElP2LCTQWvfrVkfzTMX8wfqwgdPLx38ner1rpe5H7U/UhnGOmK/JaCHKTmr5UD84eG9ew3jH+WxlumZv/eCP1H/8tyHrTVV/Plu3X1nX3lf3X1YveTyjzqfuynYZzsqPAgrnVDHZEkSw6dHHoWep/319VDLte2CefDTHRtpfNZ8J2yZYGAGLjGX4jJFqqsisR++SAlAOaV8OUp7cRF81WFroVlw5RlO+IU+J/kwLXQH0P/D+slcfr0DGdDlED+d5Wky2cb5u35n93/dV/dN+Kwy3duJUJGcE2nVpLb8pG452vavOrtRttlueyR7zfYfr2n9TL+HEPlUvu6+eflv90lqtzNbaXJ3YWQd2Z/A/omvWmI/V0V1Z//+hnwwZjvnX5bHmKy+rn5jLe2gcV92dcW59Lo7EmhvDzS8CjK0XyRA3lyjNMJGusv3VnUtPx8l2+fT6krrlpmYJiG2QPhkGqMJ2cSob6/gl7j5MDbNySTCkqORJoAEfHIcZlSBjY+0YR/kF+FGxhZNT0JANWeUskaHKpg555c6pWl6q17wuMBcfLxRe/WQ2SCzN1MLvRvF3/FCSLnf+fp3/jTpwfa3enfD65mO192Dd6sn/rbr3eE9PnpVY+6k7a/+dyVd+BtK3N+I76/4b8zMzJw7XH3dU8E++WXt/qt56b739ZfXRLwXmCk8GfKzzEd68IRlh1sm3DufvdtS2dY6b0bG+VDKs5IKmTqPWQDuoqRfJ0X2cDRP83+bt0NOpixh8y+1uYBIUcmg93ySftNjcDEwfqvjAnLotQqoUCVGf4aMuhruouVBBAJ2pHVOEiFyizEyw99wsnal3bEvlC72hX5Wv1t7fyGZXZHxutdY/Wzu/WJu7Y1hzX62bbfLD6zeULa8Yv0q1z9TVZ+qR6+r62+s5h+vpp+qx1bp7R0sFtpO9PPB4veSm+qlr6owKfXzo2QnernxjHTmdrTOOh3Xzjvzk3I6FTIzOydPM5HVcYjM/gMIfLHWuROZt8HD0bnLe4TLW+RBbs7+R5vYldf1NccEJ+8ZEBH99IMW+gQDYRQ4nZQPHRY0p2IYN7gZIif9D6v5ebCtt78D6/kSDxkWiao7boq/NdnaLrWXdl6+QJdtoY/Uui6telEYo+v/Gff1cLfrRF6p6KHHz1lq952j8/tW/k40s5ijz+/KbCl5WNv/Jmvu1Wr+tzvyfdfoLtftVtXRTHTVQrdTCnuwrXz1W92+muYxdH0wTFw4P/z2lrx7NPOYn765Pav5AXfLRFN50VV6sxX94W/Pelbp1T90HiY6czt3XzNahfgG6y4SmHVLGB+h853MH5wPqCNcclwfdYBnxwhV5+LRsYkMTuP7bAO668OUY306mUF3a1+q6G9gGGgCo2q6rZNwadajMgKEN3BGlW5dO1zE/5cKJyZ1IopjTnayfb6mo9X9c4nVj9YZ/3xOZ3fHoP6aBgzW3UL90Z60frb1SLJ4EE1jbVqFHX8xEfaffWpK5saXmJXX6YJTSuouQgVwPL2Z5/7TZyVbt/lK99mTPTgd9T61Lnl6f+j/rTU/zvHN98u76kW/Vv/1IXXG0Hn647vpU7Zf59iqTzTpxQ903ZkMqorCd5for6kbzIT5Dh/q9J1RfT817fJub55hytS96U3nmJX0j4/x0qI+5DFa5NXjctjRhX98cIBd/6/7ko70eWsbdbmzylRLWg7Ju1HdkR5DxrflIEHiQLrlFMxXffTeFluKvy8sSHY91nzOS4NTL6+svyNtivsdkdtTiq+vq/bWgiZnMz9dW+/m83tK/vt4JMQKzYaOXJA56jxnuXBPZ7H5PzW9m34zcjCDinuvqZZ/rt/++sJt8pK66pC7x+U59+4kEHT/y8fov/yW3vmFt9Hg/t3eoProtlc9OxyoPHdxX597bfmh0trtpRMiE+iI2hzOTz+U5m4wuXRZbmx4KnnS0eDCu6+ROG8MEJMXjeuD5vtsTqOmfdmKRQVcJzm1sPd6IJvYwepmoHTXPbiQ9l6OSr31BIN9Y2f7i+JGP5DUKi/3TL7s2Ek/P7ktKZnkhPwkrTlLl7PG8kM944yfIxXV3mW2Qilmq98f7Yb+d2fdkQ5pV5HXhmdjPWgul2cjisWD64Iezlrx5R92uGbc3MuI/q1u/7631T95StzxR776krnqgPnqoln+sb8g8fDaJ0aP74mNvvymFp77WIa6z1tcooqARH6bm0jzYZunl0ccwx/8R9RKm874Mvj62r+AiuG0BKJ9g6irh7xT9MDioRsH43samVuo2fFyoI9c5aIZT9jEpR1/VnVKWK7XjIzl3/PEl+f5iIyAG4wIcdFwE5cGJvcv1Uiv5mHuuXk28h+rPzTrX6/RmwmKLOjtO1JqobCnyyE/F2FG2lXm732DYKbgySTSBtSXquXE1Gz9aH56rn/9QvdsmzcW67WR+tuHh05Ms6Wf+dn3mX4UYx9uvzTtRHqt6hx1Sx+qa36v9VqzPJsT7aPePiw5Dpq4i00HVWkL5ds7bKMjZ5TnDxMHuwSLgucydhnamWriVT8pTM0fjGadTPmq4a07g3ZwCTzD2XcWayNGXkfQUM5zCYnuREpKZ25tR7qv7jcZUtY/P/EJEstoTfgU7vlsbT4mVzO3JRnJKfdSrMCGURV6p39lRTztcHnx9NUe3s062DKx0EYxE9fmVPOt7Yi6/sni118mt5m2NwgQSlbyZeUPtk0CbrTsONPBP1eaB2nsgemn0OrFa96GQXf7woKu/L6mnPhGD9uKnd/xsHby6zh6oHbdHWnfqYvcIQ9hxOq7LtLD77jx82j632985Tc9tQKDFCS5JVclUgIpzq2u2Bk/OR1kuhNFE0mIfXE7Dmh6K0GKYIug7gw4QagXwwgkVE6XIxjsRifAqZ17eAM/N9xXfrNfdVf/1ZTX/eC7VJQwjy7wLP5yIj/vr5FydPJGV+Y21mnmQX8vWgGvW6tUH69XLCYoetdpmyXIrIrQwY1fGzmMRhlTCpgUkFiMRYBvNV7MkuiJ5Y8OGLYAfqBXDEm7elRBx/lDNLNZ1R+tWtWbqgZ/vVYGXWEytlfO1z6s2vIr+WC13pvVEc0D6DkfDbXRPPTaVunA0W9ycLJSRZDg4mOi8ZxsJW0G49Gebfe1nhsCGuWwrftA12CgZ010yTvXGENmPOhpqtzloApkqAxJ+j0MIWe27YPina3fvCXq4NW72L+oNL6s/ubYeOV0fvjtPwBDJXm6qLcZL+9X1KN78M2rOe5etz5/NNH7PgWzj/6Xbs7t1+cY6eT7B6/M0eSBzdf5toV9KykY32JOgw0x+ph7zhObP1ukb8zNlW4ypF/kPm5TsqcWlxHhgdv1wzexNmuCw9bc/rPeeyvu3vrGaDQXnluqASI9iGcyaATqbp9flFTspkD73i7vi0Hqi0lBhV7OxB38QYR9V9d2SpD5hluttkYx6o9z5kBDe9XnAVHAxDEqo7srdbj7ymIokDZGBEa9fNWapypwjTU/HQLbiNeJx9Hq+lqgMjs2qX1GRXXyj3vjshKEvZiFvTCtupTMWWiBJ72vme1nfFGXN3Vzr76k9d9aBmTpyRb33dB3BrOO19ua68e119oa8MesFs/W8xdjFlyVXdtRdm9GGQ4Yr5+v1S6u1b0fduZm1ToR5i/P5xRjxzNnAnF2su/Ym0Fj7+Vqwl+pwrPxqNHisSd6sdyV4cIeenex1PLouxYW34RXjaO6RVrjXfdCdSCV/xmMYSl30Qa/V0r1wsm1oemfcT/koGd+qOgaCbi7nadV3C3XbBw7IQDcdAhJJYoUSxv4gV7nW7cNLQDaTt/Fa60Uxcbysn6l43fn6h88OgifyVV/64/yKlQFJ19I03pGNrFqP/NSfgt+wnJBsgegPZ7+radB1Z+voU+sdpzo2O177T9bbbqivH0r1n8Y+3L+rnjZb9YHMVRmZtPF9ts6s1CGrYVqRdrMgxNcxrNm0vuA3gs2TbNt8XZ3+5drD0e2s25Zrnt/zxnoWPMjz7YECrlKvmxfEYxxNUnhoeReGCelcC4aIBpebqZPxJqrf7iUuaPsA2XxUMIqDbRRiKEMZwk/VKc7tuhedQIIassFHk0fuwkcdkxspyA36bvu2fSp7av/KJGN45rfqR96Z2cPHLrmAiK4LnPGLvRlvnbC/pEb4AOln9PsJco5oVy3tL/todvuwpIN1Cp0zmeTf+o06blpzvhZvrQ/vqecqPNWm+fL6kI2AMgtQKVnLb8lu0BgiEUn3C7EtDjGReR1n1rsTuO83txeYHIxm7zxaZxcyIUPVDTZ1tE0HsrmdMYIB6bSTMLFZl785Jk+UbSs1XjZMgCZ8d+2zLZupKShM2QXhDANsrF04wdmNpPQiyFzqTGcZJNDixHWY+1Ys17Qn3vmUX0GWH1yrn/rTgP8AzqxHKo63yMDLc/Rer5VOoOg5BFwfNhGModtqFZEf04Su+52YU7UwG5HbRCEZnJY2s3n8mtm66zm16scx6MTxuuMbdeDNWRs9PpM3Lrza4DBTb5vPy2ZFdII3rIzgdyZvbXcuu/FEuTfQJ6di2q9PfjKIB9tVHzibX9U82Rkz5OkLiW+wFfZBjdgKeUPSuqhTo3xiA1Huy8MisBcz3wUKguCiuBnUOKBzZ1SZFA1xDhRdS8X8DVww/w1HLyi5la6mQ+mkmYfzLbO5XfX2qzNp3HlD9rs6/sMldcUTOXmizeU1zl5S9z8RJcX5cSTdki7FYxhgrYtYydc9ozSDMEnUbU8bcXREo9bO47WwWAcOp8rhjUxibj2daELOxmi/+Mv17d11ZE/tXa8Xna1LF2txIRkXqzi2tM1ToPO1wbjPZEv7+NEsm6QQYDP0uRO1c192uTkRv+2c8LJfVGeNVZg+G1lKkzuiOo6mvM/CuriyCePGny4LkAr+X1w4KvX3KL6oYHrqBgw+Y+qEIsffCE0GHKtbZr8Uh0Pjf2yu3FUfw+x/1hV/qm7/SE4eJJUrcnL/T+b7H36srYaEfqHeJzr9SHzPGQTbzSzmaWsUNXnmb97jmecSaNksYSuM9/kYO4ZRitkYpfB3z2ItY/fJZCH9ZqxQcG8/Mw7nzx7Iz8zddVtevSAQRy08zztXz5tL1Ge3n8kQr7uiF6czGpnbUi/lJ3Zl1rJgZfNcndxMYmKzPxRiTNEoTJZijXxDg9OtiR4PT5OdmIN7udG3J6KZXoy/fef/6QvokErX+esV3bxQ2BkX495kLUAUtKtWPDj56mkrP14/8I3ssXO80P//UvXL9eJP5fKet+TbCuJT31h/wT98JGHPWb4Cf5vRzO60Ryw34r6YiyGHRsvxbDZ/7TmWj+FtbJcxv1nmtHdke5/nMWb5pTvyWwAz3oa9N7kAA/4JD9Bs5HkzL5U/fTyFvBL22Ut9YFe9YaG+zbOdqVul9dbCaD/zYBHoQabXHtbumbV+QceVhOG1qWLOHpnEBcwrDq1Fkm9CagY1F3uCmX62QNzLrcFfJy3PvjPhaVecnF9c7pwWxI2o3eOTy+0jkFA1CyaVMbKNEr9O7w6bPOmy/Oqw+HfM4b9Qz727/qsnAkMWVz1Y9Sd1ywdzOba83v9oHXljXsX7t56TFZHLqv6/VHKt1kl6f4TBHXmujCubsTriQSQDg5HAfgmbNw0w/I9BWPLtZOYunj+218IIP787T6C7ayjaL8w9mZm/WYvVF8947iOh03XM42RmTgK89bpZaMCtXV2vs+qzkdzEUZnQs3lWzcMhxjkWs9KPhXwrW9XqgDBvf2ZCbFyyhwVn5o9pF7EyfMtQsu3KXLu97emG6DpgyFCxbVbAxjFEtI3RCafJg00nQ0PSQTlQqdWyGfUAkwFRyeNmI1nPqJfeFOnSs1e/KhHxY71j+JXfrDdh5Z1ptceXeuL9mdS8+Kr6u5+q2T+KYBZsbfmZOvVhfIozZApzvSEve8wskfEggmDzDP7H2GbjgOfETci34m2syCWK9RMyzO5oNtkwHQzNLgAPY9hbs6+zqHfUzkP90v+V8HFuqVZ+tt51Qx1dquUfrT+erxcs1zf31w176po78kvaz+j+2vVspUdoc+8tof/WLyXtvdvDJBJxHS+QYtjS7MUrcuKK6XfeJYMXPs4Hy8LKZhlgJ0RCMAHtzzaXc6sBgA1IQwWkQ/gQppwu9IR0AMPDPkyGETOCY2jJZoaOquDVb/3g1jH7VH5gIoNXXlLf+fu9Igbglkjr7X8nL3r7XtV3Pln7PF95W7b6/6svh/XHfyzxN0mbXdoz7md5qbbGPOBqxDbGGF2M8OcO1Dn25I58pSXR3oKE7MWO9de/FUcn3WAjgDnmOilSf/OVxfzUn3nSHvYE/55aOhyAG2zWWa9fPpmXAvyoXbu3RajP0Pk99eju+o5Hq0x35urup4ddNGTuXdEb82tjDEiKgsuD//l2iScpuzwVXARgO0RWMkbRLnFr+wCTyy4aQmrp5f64BSkB5JZPCyTtjVp92aD5QtbEELtcvMgZMPmb+a7/HICHf7F+4bL66EP1iaqfvr9+5n9N4Ue+lm++64lv129WfftAcDzjZH39cNRCnzlxnsTEm30QP8U0td7iPHo+YUJn2mRcYSsSNlZlDNemI3SWhERKDloiuyz4lgKAh69jc/ZMzTEUeJiReEyCQLJZpLCUHOtJjzLP1vx6HTmaH0C1iQlZ+ucQNfgdwRc9Yl07x/uXoppZAKQQNkwb1egpAiZMHawFOMaYtomE0u4P8ViVcq7To0JLQqUJwOB7czwQjgHgZEilbyFdsehrAjCqA2ng3MWLbk447+nW9QZEs+PKj9cz+KX/T3a6Kvl71+btlldeklu/8URen5ic7tNr601xcq+craPPiyHq8+xGLZoJUnZeS8/hx24GgiRJFEp6IkPaokTZYpyYvWoci5jV3GIG4NPDepGV3uc3lWyF9Ui/3Akd5/FswfGMZ6+7k58dzGv7avFgPN75E3Xd+bw+AK+JxJBpx5pxa2mlDhxN646vk+sNtbgxiVZme+qGbIeG8t2c6a8eYyKOIZvBMiAUbXBwyCm1cqg5CaOHEYzScWuctyWmmdFGS2VIwn0nTUC+weAau0lsBtjqSG/cPtZvGH3x8foNQdqzJ9mXHb8ZwVBYh0B2serXWq9dEtujx+pXXle/zTt54Ejk6tZSXpZgvhnTb39lNUwAjSoJAi9R4jcPLeR1y/aYS5Hpl7UcTin7TCwSI1UEj/UmHPjrQ6hSCQg2gPWMnSkctb6wUdfN1j7vQLX04rnAvbXLaNTZcc+NmOp6lvP455LscfzTz9SSwEwEzwTl03q7c3i1zcztk/FmDK0Oze3qE+0fiu5ictKsnADA1cf0by4iyFE6PVHi2C6ciKpNKtTsTgGvHT+r+nIu99NTmygNxB+u659XP/BzufWFFsn1/VzSb/di5TJirP93EnCvkenu2vjjrDJn772X9umeVQMDvkgETw0zrJOQMJR4PFnBxVnvklc+lDmmBDBLEtdyXOIF/Q1PR7awt/GzPPSi2fSIf5NBoAFREwqyEpPyqPScAF1Wpre9mZbBaYeiwF1qlYv+SvJBtSBeX8kTa1QEwy2qOgzD8cJpYMKr1uN2ZeFMcyeAfQDTEQcgFVNjCPMisIkduOXQG51Hd1/lz5DnRfDuBNUwKfdbQpAYCcWO556RGrMmCZ7j2qyff1MQ/eeP5fsfvSYJEnx4ZkcHxiFOb99GMoCMTPB509sD5rAqbJ7BgWgWMQk9OrNgN5pc5MNn8yNYe+UccXZXHTlTK6t5CH3RIOSxSmuaJhn8FWIs2IjuRMlmiE2nqashgQZzUDjD1/mGalmqRqBhKagfS1sSHEtoehOKGIGAKYdcQ3cc/CKzk23qKZeQj+0us++mHB/CCiFiX05WMLFxfAYbNR8nq0gg29CAXTlcpqaLBuXT/R23/HHCKcEeATwZZy4nKHI3TbQfU5+LNyCvNEq3vHHSb+/g/si+/Mxn6oZ+DkZ/HIPCh2B7rOZfU/d0yJYZDZX8yeS+HJhokYogsdhgs0p0JjGn6lnWu/irjaTLRFlmnVsns3nz3OGYr1HE46/2IiMTSTgglvUTWSI9W6iMgonoxL5si4rj4FrEafvyXka5koay7sB6ZIA68cyPJe7q1CrTQrmHnq3L2cIAkoQ8LLAF7ULyrYM37NtJe6+2mMhKPSxPcYfVBKC9TrS5BcAhdAlDXbbpRSrg2/+mHqS8ARh30eFSLR0A1viDo5F3w+1hbcSey6Z9eyQMvFhPMGLTV1Q99bNB8oPmZa+rt76/5l8Qh7BkKvCylFslfFCqZm+igNV+Eubfe8qSjInXrM2vk2hovbao53LSjvOWMk3dccr+roXohHeU2eXEBdHu497fYO1rxK/dKX6SJFLlXBoNK/SI8JbTx7OrCb711GTojE1SYoEDYfwu8Te72J15lWkQV+n1Gia89dH2Y/TcapsEq6XSmWShBPQSCrNMBsfSQHqGA+HkzhYMfl1Q8xYDjdAwUF8B7VoYTSTccUQ1Aion41aLAZKISqFvf1sewtCByuVoeyiAMUDiSIrJtrGjO/IWK8dDhpBn5uT/en2+HzBx+eWaXatjv1Kzn897LR/5dyl/zY7YSl1bV78zj62e+e06+bZauK7WT/SzGd7e48d89iXNY/w/IFtlNBJrCGGNul5qKTTfk9He3IWCS1nyoYQ0OkOQYCgKKQot9MgklNuxxCkckOTXOaOOiubwydZIBXmYtkM7k2UKgUvnD6d3HkbkqljsMHfdl6gV9clJcxIkPTeCrAljBnvyPVlapkEO1zGdPiGMjAHOx3eXBoD/wX2sz0VqXThcXlQS2YDsJFUjypdPxOO7nw+G3PsMqeeXXnwBzTesgP3XubzkgXx/5IP1nXuSbrrhtbkcxx8uTzYQEd/1VgRO1LtEwLi5Xru5o8Va4ej7KUv5mNmTCQqUiwukf4BRFNoqBiMqzoo92fhp/kENKXX6Lj89lNJ6sy2DBOCWwcl8BUNMiQTl4oKFPPjBSjg3mzpIQlDHj3FQeUznXF4HKBMB8XKP2TBw9WZa0hCaMcxgd+wDzrBm2r2c9swfw5Sr4zv/DRIkpAPbwLkxsYBRHfXj7vZ3ILru5G9jQyhIoLkzvklLwZmYPKJt+jq7LZVv5436plYvfYQvy7uTxjBz+tVxDserfvvtkcdj78+i7/UvgTcvI/l1M/9/nnWwfVKTp/O6HmtWeQrQjgg/cyGBz/X3YMkKkkCUFabRNqqRk9nJVu3vZQJhGLMgKnaSdR0q1ZbB2cJmgGFANgis+i0n1uNRDU/7rySfZMP0no6wdcoQQtIHBGD2cszXPYfTaxYTYbfHElCQhKiMlpgt4AVu4HYY3mAg2cnkt5YjNJ1slgWYWYBrUOi2DzXHEfE6tq/7XC2f7XIwMY6hCxFOmkiNhnFi5OQWLIzfkps9X/mlVq3Hc3nJ/1Wv/05OPp+v7Klw/NVH620fSaKM1/Fo3cwP15m31k+u1sIn0217KrYOJjEF+Zln1szxmjlZM54lIw8NgWBD4mB6bU1zMyO5ju+W+yInAw/+4Ujn07gaSJwzIOzjdkRukj2rDKgXKeZNkkxjT+SFvxEMNTM94pn1YLV7ipLr6jlbtfyujFLKiXneoCJ9aZiBmRflM42LGNKsmTCn2R71HKxXmhuOZhy2khu6c7D3cbel1ffb9Pqmr9wdUpmgmPgrNxQM+IQMDZbLlpZcSGj6VUNjlgiTHftWR8S31h8cytvB/8Vz0sBP9GoYIj+cq37cq+cEv3trfe/huu2B2sNHPZrffbtrNWLGQQMGCxM1JQ9vhohtraeQ2JYvSSU1YGAzAqHh/MN1koXZHt1aHSGB6wEpqUZ975k/NWI3JoaP4DsfKPbtsZYfO2sAYRbGeW2dzHvMvJcL0zxJcQPpdux+pM3lQyQhNSAiN9qTpSANfs0pn3JY083Fnvlj6xhmUta+NbzDysHX/h5Wwgiw2N1xhNdTjk9QT+5c+BOYhk9FnzZqvoXJRzDvzzvd82s7H61vPzMzlW//p7yzejixv/WxuqZ1DdNe2u8fu6r3EtvHxK9f9iCHUk/sqEc/UudeXe/YXytWq7DJDj+1rLsYDIR8nX6P+fb+At+GXNGabZgLXNZT41EFYBuSY3qmG+0wDLHYR8pel7GyO/E3DhhFPKopny86sPHTzMYRvycN49Zc7Z2pu5YyeJiNHfvzOmgmZPXvW0mB+5w6XjsOZoqKD3ySg+AZsUtf2u2ylCe7nAsBHwpA9/cwFIM8wUQScXkNhvCWcP9tscHmQnFHZelM8z3DVQvMd+oqHx+gQvt2LHyC3Ab8v/23Y+q/9bbsE/v4mJf83eB07H5LLXwv6YDVvvz7/c0afvNoPfpnVS+oF36s/sFHs618nQ+5ufYKhLgpYwbj4NPsDde6mX+zALNWLZD0Ri/7vrKGiMWSj23BvBYpGkgEskaLSLQznjzY6QM1M1vLp/J4zV6P9OmykIwNgez+cn1y1SL6PUu1tFhLGwntFxnqXZH03MuabrpyT7Ol58VELqCQKBrSwKXtD5wZ/COYZqu/WgyjmWHf1L8xKRkSCoDONHAgIe2SIYPJuRKf/M/dYGhvkCa6PHOgDqAxxXjL24phfsGP6nU+Ro+ezlweStUfuSkP58m6r+Qqi5XfeHeS/Dv/YVzTnU9LPIbMewUFX8wgZNuxLV7JPHpw6Xv54etT+zJR53fo1srJTOwXxAgW0DYzVOgjj8TRWx/LZhrvH5dstkmDHQjtmtd2VpxYjmjnresQrxb1ej5qjg+EmvjNgxxuGO1PJZtpIGEW+P4BIaK1Nd7SFt9+I5dCOioqI3jbNpidxaF42sGu1g/MbJG0YJr4SWzg3AHSCZhIgu63vufcPf/bhqangeemYlvfL/ZgYIiwIWjSYiPRk+wUaeeQ9MZTwqPVOJsMD45HOwfzPOb/vIycuuTzrBOxHo8pfXtP3dqQCy/LEsDbN4PcVJEtzllHMYmRINkZx3LAnnHrK3Z5GSFurVnLIfbn3RBjssR55uGkakQHUpMMxU4wgw3WI481G2NOSRJfGpGwnmgY4vvtHGRpiIpp6rW2m1nZy3k+kb1ueF3NrF4fSMiw9u5sT/zNqFMAk0QQ+1notF2E4irFIMdwM85zeWm+fXDfxzGR2OR2SsLQ/J+cB3i4rGE6zjsNPkC+/3vUUueiI9iCpdWNV5Dd6inYVlYgQ/13zPAZ0JH8sruS5amQ5vfWf+dhmN31rI8EHYHt8yT4FTnb9dWkyKg5AVo+2WeAPVYHzDb218ZCbOgaj9TO1K376j2LWYT2Rpl9NOw5WR3g/dL97k6430pgOBEE85L7vlf7PJ8uvuqfn53BCYFD/zbjJArXG9bT7KY9pDLLSmwWaLkKRiD8i+7+5i3JDmQDBl1kkXSLubTiDpaC9EHXToLJnyEVptT1aTfBft8x4XgrSOtHjz0guwStQfTXj1HtbypPDeMNny5Q6d8EBfuIseHLWZdNEwfrppYKHSSDR8ns1xOYfnsx6P7fbV7yKPv21XPw4mRUihoaG/CLRs/J/PoZX5IzHhytNQm0a/Kk5M1bte9EdpHJi+zal4Sm58ey4sJc4DRy9Lwn5OG+wULMLZeFOSbFvZvALlm6ZWU6c22koL6rIyCrbZ0xs8XAEwS2BioE845+Ca0VVe/cstfQKIDJa+SnOs/mT4snHZscl0+EMf3TPmcinynMxX/7VtA5iNP38FHjvIuf9AW0XWfanh4xyumRMXYuVmJowYXnc+DvTZL85qr/5n0B2vdD9U//Xe35ekZ4Uvmrv6i/8+spf/YXauaVcX189Fw/XCEyph9bSpiOtUXPVPYuMspuOXJjMQvM1/n1vj110r6kE8nwU//M9Xg/6mXuIj42s4ENf8mAfzN2GAkoUPPUPN9M0+BvkZSfSFKADplXdoJDH7GHp8rqgLSYymINqFp2q55CXK/15QATHj9h+DGn6QEqsiGqgLYgJ2v+GEVZlE7uOe02gH3/0QKYuDIwDTYuW/rfDx6cXZZwYJz09WgxjFDYcSoF3/G9nqlZQWpreLzq9Y/mqX4e//CtecHu8ucTNX/Nj+79g/r8Rmg2kEquLOm8QVXA2s9pShR6NGlFeNarKQRm14tJosVEyeOjc9meIs1ltNB8uky0fAvZNB54KYq9TkrwVswmwRUVFDWcyoK0J57zxJO22K65C/o6icAU7C/go4QzpC65IKYwitAnjcBpU5V0wCzTJFfbP5ngkAfkg1F6DjS8ujzdy4ljlI5L546WtvJxlRLU+55UuKh8KoDAPPnQpajSaC+VwwuXuk3paLHUCKlo4mwPeLu+JhdTf/dLgdzz9Dp9fcKBuY9FhMB+KMW1/97aEO+qYnSB3BIyUhkfF0FnLVJ5aommW/gay/XSZbyKlJc4ijozjg49VJFGU5JIpAcDOPVOgjWzE0HaM/MkjWwe58PhO5G+zHC9mpmN55i8F4iJ4LJP+gUYfoSxG5SYIRHhU+v0YxHt5jeiDRwaMyI/4XtSOCinIup217p9X5e2JelNcxzffPqqv8fZ4Omo1lY1ikFCOOC3kU6h0kw+PT12BmwcqdJ5iDjo3s9w1Y7kwZZ7kFqx+tRwD700f6KL1mK7hPLu/rFJEhr7fsX7EuCfSWgnDSOhYgmZY5wnTn5cvGQjpMzYqcjJSD6WWMCEcYD6hc3GLnyHf6v3trmVw7XAmtYbe9zy0gyrXtyODbTtIfaJnk05hdQdv9gmqHtLxMPCdmf75zIh9MApNLelfc1mzxTkoB8cYHay9SIp2wqLLuZ5g01e8kPaEejg7hQqXq/5vs3TrjLl+AjG1NNPTtNJ2zs06Nv2hNTBHAKeHP7QDh/Q9F8ub7Ne+3t55tiYv68Zwjiea3hvcF+HQR3I5DF+oIdQhdd/M2L+7ZN5t9/mUkecvUksw7hgzCzPIphxSwXbiPGXNVBz/Tc/76dVLExZTOOjDO9nHovnQU7MmoDBk6J0Fp6IlRHmSSV21rm1hGp3JZW5si/ZexG27I7umC1xWeTtgZ5s2CR1mzROhOjd1wQMW+5vBksKEDaNxgcWRsNMkiacH+LBpYhq6sqME1P+NVcu0vHBZaWqjG+wLjSWv7lIf/pvzp0NyHE+gCd3u1bOLUtQ5N8PSI5LstllV6fFXB3tshf+eIKxmcMRAye20psZ3dnznLj+qz4UoNccjyKb5QGy655fWqWkazVrN5P8ozo62TMPMxVGI5tJjZKtGm6E97si3OdVQM6SUG+RMTHUKMmd+3bSnXvna2Uzk8FlUcBCnSBOG8xO1vm1qKA1/OwOtJFzPelqsyiGZeVbi8elrr2ofrl+6sFQy7mxD+ZK6na+2aAzdpdHTu2KcGZYbQQTNl1gZOrnaJd34ZazqRMMbNuEk+j+qBvQvwFNSlP1wjeg1KBuHE7Ph9f6dck/H85kdva+qnfdF/hDd6fi2qWBf2UnORU+wVaeVVvfyqLAW/FOXgCbBGMGfLNo+qvDZMCDU3z22u4+8sCONlbeX+7djZChU4RMXXrlKqrG6YOk1U20sE1d9merv8SwVwNQCnPPZSO8hUgmy7DknntzpSiDSFiqOM2mQH3MvvXVOnddLT6r7v9WUHrwjIC1biOgn6sBjGA3BhdDkfNo+eXOJ0RcuDnKBmzTl4JhE0NgutsHFBOkfZKWt1sYEKNku9DlIML0RZza8+EBOHdT5LTwc7l67OX53tei0otrcpUXLJGKY/MDte/NWSzguD0RsWSDHaPp511Na8y9cYr3YB8Uc/A96T7bXwwnYiSejXL0OEffsxWEqAQR5kBuiUdafyUO4KHdRpfEXVasRWX4aDF4I2PJmf1ZDUO0F6YzSskbcxfzGNNVq8XBSeS2aqi1lp8IPtJJ8as9OW2vUw9aUt023EiGhowWwvjGpEkSM/z1efIRa/C56FZOL7Ibl+nwtNZfQzC5ETVsTON6G4zWmtn/dpd++fbauTfUHz6dXXFXjCeV+nkfrDnsRy2eX4+8NqB/DyrPH7fMjvfAs2c1PwCbvO9yzRxIosYOlWM0WlRm3scGrFfK6qsohO2FlrhCvDbGwMhEdKrXILLBxQYzO1d61ZKzzYRGKCyt13E55yZtY/YuEzrWUTSBe3kEQAMCMI8ACBqF5oJ4Lzq1J12K+kjNSm5SONHcBzPye882b0nSmkPE4Mnke8KgYTF/TS5DhunWRVaWKmZYLZ4+9ZWSlIOcfOVk+4Cn77RSNLbcUsX+Epc31tu9q0DF3fVvNut/fGmCy7d0hlxPFzt7tpbXvtWfPeSJvvqfkiqs+Y8FB2VlMcu2DHgvCT5aPOa+Z2rf/hjBhuDYcG1Q6WkQ1jMpng03fTh6uS+05RC8gcFT3rUTi1SetmXrF/uwi6NHAjGOmZCKBuO4LOEGjsuT2ulC2Ba20SZiFlZIwWEaa9AvMDY0fauObta/7NZ4BMt0cqBsS9OISfA9VXcguELSO+z2/xuOppg1POnoy0lAOaxgdGwbbFxO66S4S4aR+R5hQord42rkUS4NNKjDV9XRB7LxxavAH01Z9E/HKIHvj/5CpFLX53Jvl3y2a83JnFtxsRu405c2rGbetzfv6sFT7sKIwKX4JFA03oiemwxiGFKJnLgv7PbDM6Sioj1KXCp/1bLE39OdMkk40KjQIEaH3/hh4LGzl1Wd/Eaka0k7Tsz+NP6w85skLLG/4/l1x4MZO3PcEhVB8FguS6YZldsDXnMsvso8JtRPeNg1+5zM3UYEcQImCGRhhAs+EWcTxelVlyd+bzCQigHrNvgIAIwq8AxgYGk1IROnT+sd1/1Jzb2kjr8o789fMPJ34cHPpxaAlapPfDRFX/z1jmc6tsG61f68l/IKsSRgKLlW4GQorEx8vDfhr2EmVAlniafpsZ0sAQJKegQKDJxWCvxQLyJlXLyLy5bJdnqBwVnICc+5cZG86bsJv71way3OAzX//GzPQBMuy16rZX+TxbcM7MQpdM7jb9nQIF6HmXWyZkE2eZsgm9K1pUz41iFuT7bT8LZwAKFAGMPDwmLmZXHUZu3TdUKGw/ZOiFg3VFJJfLe5AofOctsPSAQhhqDoBSrJA4+AiUSzVEc19DDyTA74eJiZ7h1lPSlLyY63pnD2tfU8/LoiEcFST/jv6A0xm92FvU/P1Ofkd0OY7MupE5khUhxd4tPWdtaxXrtETOwere46IRJNa2ihB2cuq6UlNan6nTZd6osHa4AZaQzLRoK2G1XIQxhmoj5ibhOdbLvph82YiDEsbrOfyuSl9RSeTG52Zws1A2L3Q8m8IIDrMx1etPNPgqfdoCoRfzxYmG/xIpsx0IxoTBn67iqGz2PKAFr2sXFtLrsm3rCcZY+776inL+cZhiXDIIcgayRLwR69/dbwawsdXhAbLTYVt/9RJ9eDUMCOdPJm41pidgvLmspmGL9hRJcJgBbPu1yO6cQ60XVdBhLMvFl+bJQ0jF1bVzKv3i2nVxyaQWWfFACFMNh0xBWb4Lh0Rz9NZmPIYbRySuoSQ2kJr2CzGcK+Zp5/NJsrRNuS0IzPPgK/FEBsjiwaxfBDfyyvHeCydzZIxtiDYWXFXNLdTqDwadRT7CfTY3k7BPS7O1er/hcLNlCtZUTcczAb/s6uxn+iDq3RM/P0iOPytBo5+LjRIkmJObAfsSGVpeSu32C8fSLF5n6P3JTHuu/7gcSzT52vg1KtZlt7s3537khyUxQNqqyNmz8bWi3NSj+wbm5XCU2UD2eOuyaPWJpDGPyXe15paDm+L93wYRzn/MR71R81dS/rwgOt/XaOHdL+SqLbldls8rN5nuv3eC3N0L4FLkuZvOUIOKOMRORDDv7yB4QELb1RZtl4pvZv1PH1utemJG6A/qK8Y2WpRuIcHwiGBqsW5CqjmInwbD2RsiJH8PRVp4w63s6BGjLeTQEfq8fBerLpQwFe7c2h+4UtZAJp600Qqp5Y4PJQG+r6SzfSE4e9RadrlV7sqZvfnoKv9HugXvifKu/Nx7OvZfvEYwYAn1ek+ZcxrJuj8h6z85I8fDfJiEnpAK9NQbgCHobdyEQx55cGreNzD9YrnpqHH4820zefFsNf71j5M25/Pcszf17173pHQJhl1tnTwIzb+4LfFiRea9WGMc8WGWOoMO9xtnvbc8xIRfdxExPNMHgwykRytMRQKrNyIhuXd3+49sG+GvVf7+3e/A82mVFSakxK+MDOYIOKtg0XZKjg852bDC0l6yymkIKTC+CuI2BtXVdPvSXsWvtGagk0DASnepch1wJfopKpVaA2byofggjhTURaZY+8xGw8/nXvys6V33hqhPqrbY+nPlBrR+uI1+AQ8koX3ZfvL+XHVntF5ep62dUZkyzkiW1OMz7Ord/VR784DY+V8uOvT4P1Mb9TpfZjec/Rgf5Vw4h2aEkD1P+QP8c8mNnfuEML75DGZxbG3sVkG4W2RBVzQZKptfSirrZGxzmoo08YKhDgoLCgk4/pppjKXduRPGBGZiYcUgaG2P0xa1NLu86zO8nbAbnrjuiAa4RgsE84R1Hg4Xxk6iQ9E2KZ0rNd+2AJnodw3xi8kK1MNzXDrOWYGFmPUV2yleMNFnCIbsvGoto1XFkzQhMODcegjFEsYKUWH6pvP73WBB5S8b232Cj2rOVMyxdhPFjHD9SXUOP8RCfqHkqahYS+9KyeiczVS2Zqb+8cp9TctPFGu6u/P1qLDJ5+ZT3yQP2U92B8Pdv78AHy0wwSFjmxR/KriQp/sHNlV7X8Vp+XjnhXhg1BVyPWPNyQZm7R9pFZoZrtr5zgYGTTbB1OLNptHPJN0w3v/KroY65OyYOtZd43t5pLt2RNIhJBQQ9I0WVoW8E1Zw7Lc0bTxaV+GxXH+64+Ig8PATMIqkkGeuq4VyLDT92hk4p0uMTyRmwSmrGR0wrtl3cfpkaUqjrgqzOmNvVyKbJyB/tHvfE8x5H61kP1mcvqM3szrXjOrnqzXwyzYHd1Hd1f92qGlz3Ru/c+lzzK/eT11Hq7kHFvryAZJHntxvSyqlf8ZX1prn74gWy72rg1mfxH2mLI2prYFf0LVcd6+vLujhQO/JPU1O1l42uPf4IlJ16cwPdyI7hAsYzDUd4WgP7QQd2OwsmqCQRw0wisp6xNbxV6NBB/2AoPbEsY5+M5zT2ZlyQp0DhxX7iFM8ZLzpB/y9GBX+LMfi7Hxo9F+tce20THAoQFUwIQo+++cpqFvb1mPtO2g1dcIl4Q5FQEyGkRtCvTT9dNYRssUtm1JnuqYblpdm98zuTAksXGe09ekPoNYwzjmE/hlXsyk8+PSO1LUsSbuyIBNR+rDaRYs1osb66UNofgiUb3i3Z5313/y6sy/5/9WHaTEMabvlJfeEVuL382LutAjzdvbIGZjpxue5rVoFHQAysmHx5ANcfkSebDzc2wK+zWLzkPGf4MKilIMEJmhiUXMxST2kIr3TJTx3XZnJxsPO9q/9/xjEZzxrDlzOcxTi0C1la21EAinNEQhnJrpgEGod158i8PjC3GqS6eqZMbmfRwnmKtpZvrqa/tYabTCoTHsqM0vrAemK51SIb202OMEc6DG1RSGfJnoUyY/13Stx117J+FTY6PfaWOLkbdzizWQzikh9jgw0TvrQeuqAc0ps58veJc7MyI6pVW7/5S6opSDI/z1p0sT32zvvPsjOrXPLfOPKtW7g19B3s3CUkIA47820jukndFKm8wqHjioh/2N1DvaxZvaXY5TsxII3llJPfk3NpaJ/aXM7uK2xS+a5eGcUSSWtSTMZEl7y/pixsas5JtAeJQ8lpUkFmYYez32L8I/mSy/fZyMD5s8Un4QJYclMCydTdpBYEMhnoo50DmfCa85+/MHEVeQMZMHgG7V1friJCnh+Gjnlww09iT/JOAja8jV3NkkYXBD34t3uq3lnFytJrJV3tM3xyCts99K7NFj958Koy11ye/RoCzu48n3DKVYeMkZHPpw3vbg13fea67M1G8z5CO2efqMnL6Ui6v2czGlJNeKGmJ99n1jzrUVO8tB+r2axJ4/Yt+/+iHnM7VJ7+YBbTX/qfafUl+llcHHCuya1+Kpp/pab915SVvOMA4a+m8PJ3llE7WzpPZ0KznOinwjWcb0VT7LuppDk9ixBNN90ox0a04paeZJob2cvjd3n2mJvvy3KwuRGtbGxiHcT4CpuDt1pJfYZEw0LlT0UXxyKbp80rEuWypjStbjMsxz1u6JaPv16A8mmdOY3xETr8d8JC05oj5UB06ennGVSaC1Li5jmXjncfs9LLa8Xjd9ZGu2W7J02mmXVryKgnwJiv6JnXo2VyZ143b88abx7EN0rW2p7vr8WdNqp/9Uu36nVq7q5boVI/wyHjLo/UHN9Zlz6x9/4NtzPXu3pr8uavqiWfVp/+PGAeVv5U1nsnL3X8gnjx1W0yJZ7z18mA/CmTDJqUzxgjNTaTwmqOThyew6CMSxtipOsfCjDrhxD6CjsPwxhfBt3GCpzXJkFAxmwFDWkbjtjZgsZWWdCqRFaZBLfTgQtAkUbaYeMzr/c8/VFuvyhYDmzc8hsAaPLuzSMGvSq1d99aZq/O2G2vPprfMzocr0wRNEoD8C65sEaA2qD/VYPXowG7qJqR7PAK7N6hybP1CXNPnlupZKwnbfWDhGWwuNcPiqextXKKDqDTM2Fh1pr6FXHL6VvzSL2p3pm7emVezzHf25QCkT6s3Py0jky10774/L7v6XLf1xTbW+e8E8s+q/rsPZDBbaq+566lhNMJETTLzlDpP4HX6S1OYjjDzJJwyrka1Ow7sXqd3pty0Pl3u+CcCEwhwTZyV8IE98eGEZMQCRanjUkJTZqNcEDBVAChXorz3C0RsZtBiELF7GzRIct1jNw/ByN+s1rtOTkbWX4Z5NruZ1rgytgKhPVMmElJBou1j9XcWLs80Ih3dU280DndqyCgKF13bZ1cqjraY55jBR2rrs3WtJw0J6VytkXMrI0dtUSTM2hsu+LAkwck+rlyG4/h4drVOX1bnTRQYu1dJdGiN6X876GvPp2vX1RkqPoPf/WKfl/cK/++syLImtXzjC7LCjwbMWuY/nX83FwzX7iyTD8phaTls3QzT2Yoeplu8GfaREE3nxMkDc6eTEtjAYLRmRf88ktyEIEqGkETxS+zA2zAykP6Tq0/E1CN/lFgxGHxowRhjaINfbt76XNRU5D2mKQsIXqz3vjG9c6zcUnNHc2JvO5LInniygaRfUXfjP69P3Hp5bmc5sH/W6+7nJwZ66s46aHf2u2rj2pQ7Ntq/H/hs7T8V9dnNs4eiaKXVVoOnfQgrhH8i+iiwMRPWvUSfcmL3Dxy169/XnqP13s264cN106Qsj8TcDv+JeOfr9kdmt309bKJTD5hR/kQYt95xAWH4HLRbQ7ZDP58S1tMnQ2iSOqZ4nq8gGMMYwSOSroiy+ORhOnSOxpgGNjfHaKEL3HiCIv1aiJ9wYPeI4nTBlhdTEF0eVhLpglCL2yTdjpgiJ36CNog4OtZiNDPMuhMQWW7xcIgU8N5Y//u7F4clx47n6Y40iGPtonTE9ihPE/YixxDMS3I76yEPRUiPdaJF3HrLb6oX6wN1CD0mYojEKjsNRG6cWO9/0Da9kGZg3SyJlq3r7WYYIVa5OjjCaJHewnxtrtbx56Xkg50tdnLuC0mDH70r7wFffkPt/HqGkA++MHHzzscS8d3mXTKpEQkdsJq5L1Z+y9eTJomnxKoejS1wAYZq5ytr31dbXThkWm/qhzbu26guBOJ4OQ1mhDyMls1kBzvzs/Ec4LJy2S3y2MjwJqAQ4EQjdF/HfQiJaHsdGit2gJkOPKQiVBO4c2gBxh+DluBitU54RV3nA1FIq+yr3tofnaAQ5JcZlULcs5/rXPr7RFjuWKzn9wsfv0ZxWIfaX+unIty6JE7nwy/OlHGfuRVt5RCMb1JJrT7Zm2NZu0caCks8ylFlKq4/J456qXqOLcH7RuYc4hONsEzddjhZX8tzLTbev+OeOL2Xz2R1+Zb5zGzmWpXB/1qPScstm7M3p+LWHdmmtGCDFtZzZYbQmRBP8BJW+0zxjNuGXNG/D0dno6wnYwgDH1u7DVHYipt08ty+uKCzt2Zw8EyTqRiXmC2D9K9Xm8wcAowkUgTPMjq/LijPuE3eHU/zhJTXEMv4ZNvMZKXYF23t3KpDvXX0QO7XylI4YsGG3sAbGlq9BITcwB69nQjmo/XQZT1JbI1IvM1AfjX9d5hM1L+um5xxDottyE0NOaM0CrvYL5GQcaJBBKzn/VK9/L7EI8Hg2PPuaNmKlXBdbXa/ucsP/WXNrLYJv6KO3ZfV40f/NFvLFj+c4BgLDvckn5AeaD+mhAdI3X4Z5S5D/UJy/jhocOaptnp6KM0V0+G17WYSC5gcYOXB7GKR2OZzRF9GDtbAHTEgzv2gvhtcpTwMS7J5vdyCZsB0P2A+xqfpXJIMdN3CmuXnLeTQepYkiOCgZDNl2ZWwHk1z8iCh6i7rgtWp6C4V52bd6jCKQw5JfmP1A/XRu4bFvL6jW6LUUXQ83G85Pl3P/92af6I+88XgW/xuHV8IZTAKNqBwQpuIgZwN9XlKsW1ZOUzsPWmSJuXzIPclZbtgx9dGMpLacVCG/XcmSD3K5E/X/6Jod/1oJ5JXn5m6ev7M/ry34aFdFCXulo3L3JBmrJm+kTNPhdd2cHOh7NVk0+jiZExWWutpkdn4fj6KZXAK/JX5BwfiQjNtnfEqpNiORbGQGu/oshjHVJ+YJf95OawU4BhBMxRxIQC4EAtObgkZpAloCceoSZh77Y7MuHcFWkQvzDTAJVxMLX6V5++34Cx8qBaeKQS9PL8QwPZl40+erceHYPgOPPhYPeTJ4DdloucRvOOa0LDIT2NGeyM8T60PAkfPuustW9aBbjhtCjPaOLS+1hTa7CMnb6f9Qm8nU/4fPl5njtVd/zxKfebayODnfi9TsK/0uHIDwrxXtzf+3AMa5l/L7PLpl6Z8sQNWLNNn034luLOhUfbaKQaTWWmS1syM/zqk55lp0lMCaG/cfIuy01M6x/tiqC74L2KGPJKDmf6p3yNH3o/RcyPicQvfWQYuZ2GX2DrsZkzclGcNleMME8QZkm7wnKRi5/GgolISBBY6MY3/XL2j/vR9bl8eiuWvTBKTrO4hxKTUfpwH3Lij7t1V1/5Yh4DePaDb7aPJgGbRUCAJPQmejTe56ZXDuV42vx7t7RPc/V5PqJjxmaK+bQDV3FvrKQ/WCd2wftMy2Lw0s0hifSS1g/kkub4x65U6Ywl5sZ2bcpsfJTKTzSUb4ZDbMmayWCIUw4lsxxjkyantAHNBJv7pETvfzRp3dUFLXnNpvIRKOodl0MJIkRISDi/U+6FViaZrkSLgI47TSO4I9c5ZmJxYj0zG6WDmCVBu1y4f29j0PP1qqnQTlzhbtTjABP3eI3g8KfYfIxgM9JJ1oFFbs5O2CfJ/s5zBQn3GJmgeYCtvcswP17bwpBmQTkFEEeZcHJpW4YkbNdFDPZ0jOV3tH0Ra5Sgsk9gXaq3wdL2m5fK5z2Zd+YmT9evvrL/nbdXXZ8rCVtnT8yaCyx9s4XZ8U6nT90dmzIBQMjJLeGx2xglHKCxXqbqVFdZvR90QHlWj0TSp1ZarQSdfB49yykRUiITUdjKWtKGV9loclA1KpgHkhDkEjwbeAqPzbTyDQV0dZxlcCPFATUENwJLcbLQnTMxx50on0wxUrWrplQiCbLCp17y9dIg+sVo9kq+ii68lGJ2xt5rMsdLKaDhrD+5GfeJ0vf5gvWQp767jhfSW+Ws+HBdsoJ7ZjnM9o/UMn6/XB+rmW6tSFKEigwlyj56qw9539ckUPCEt9roExIaL33pnLT4v/fxs/6bUcu4nLwernuLv2uFE8M7n+nOiXdxay96ictw9c2c0ZE9LZDB7xrB7Lf6H3mCTaDjenxp1IEQFIScVKp+OMJ3+GWbPzRBhRhGsZ3ZYaUWjh4E4pTYFAHF9+C713o81z0PbuxjYEx1dR73xr7e6KedmcUEvdJA2OMb3EDDj88guOdlAIVciMOF1KcckKjMhog6mIAz06uaHBIng8uTJ2iuy4hy8aWg2M7g8XdjdoKE8OP++uJ5fVdX2b6OAMuuGNvM3wtbGz8O5WKcXEi8euTpx+N25n4fH/4s/V2Q7+Q1fj6fqerXwixljVHepD07mnhvEDn4MGJyEh32yHfpDA8I7btojXkJVzh1VhgoRc88MWCoFwim44GRqCKNh4iX0j4GEB8NTzRiH9hiENAPGtwwIuxeP9YYhsbLBFVnRyJmk1Gw1oTEc2hba8Mfygakuu+ThRR+I0awpBNvC4u5RBOM/O3YiWWza2+teUnzLRinvC676tyyGUieAIQDGTg+17f0BNGU5XLCnncztmBFxmjMymjPWsVm3YMOPPK7Umz7VC1j/v3rb0+z3TtsU/5R1p/bgb2tu3vmlvMHdfp9dL84rr97wD+rH/3UgHV++LqrEFejYoS5ZuD0OLZz5bOjXsSWSa0GyGAS6u6+XzmwGMzGiYllGNBFZTHhjtPMwkdiP1pve4poYGruxXU+zlsEUDPXt0xh3hu5O65IQGsIx/o2rEVWOEINpwslQyJL89Eu6gZmapVNor1IGL80hJracsTcpVL/aRTPYJQ1I0/MZCDKKTpUvJ6ji5RQa/7klaI9lsPEQ2v4AXt66Kd4XV4CxvIFNuiRZ5DFf+qI/3SvpIwEGLhgMlw2tGHam3vmJ4IijUU0JOVS9zv8/Dxu+/XDGsXnS/UQe2HnX2fpQ79s3K0IPln3HUufVIcDsjzWw1ee0ya9cFj5KWmh9L/v4Th1/RvAcbYEpicbIhplCrkYSRjgGpJPyuHjEz+CD4RTqMQRa8+CvkqrwgF2/E5NzxgJ6xntkDkRUc6HJCBxnQD4WDuwW30hHkCEHnDmHCRPFpQo4gK0EI1Cy/QMEAVvpmU8YZQ1m5wPpCWETG/1A/NAM3zng1+3OOnNTaCDjPBfolvR2XthyeQ99DYwaH32mMkxbjnZ4WxJCnBST4FjSME4TLzz+++auJpZ+rNbvqvmfr31767lIIGFCel7C39vemJUYv+h5y3+ovXfW5s/V7O9m/qjw9UFTp5+RFjePxCye1iXuMhAO4/zJYNKF255Rt3RKDTxNeE6PMYTB0HGHkwmdpCUBwbdQI111qRdQ9zAreIMwGtp1+RlxrbAtuS/fjcHYu2+1ucErjKAA3/lDo4UhmtSFNoyghw3N5byHWAqAY0Yp3AsYit29rHMfFkREHyI0QtVQi4dsWCRFCZ1k3GaAYKQKqRz4fZxg+rxRawDhXTkNu+ygPk3qZI/q56zoSehawL/VC0Ny/MMrah+DtR3wdL19ta40wi+mDy9F7+G68Q113yV5VvzdV2Y7zAevq5//ei09NxVnvpxvrolI7ApbbR1ie98jp+vCoJ3fykrzz/XaNEhG8PsEc0Xy/2c+30MLO0D96f7hdkGO4LBFhQt5AJVzSAtRVSzBUGLxRYqwYxNZ6RoGcQ/ZLCCXs5IwzNZykHGJvgWEhv3VOHniSQkDMlZ1BhN/5bN5RXixjmASazTHBa4ykpSGHotKuBnE+DSDWwE13a0PkbgXPjfB/XV5f3dBvtg+vGr7JknzfGbeqheaaB9ToxeekrovFb/6sjq3PzLceFE7nVvqgcuyYTN+yu8WvyX8ffyJuoxsHB/LRv2tJzLsX+bytlDJCJbFHVZ3Hq7TL4s1rLeJMDm25/ASnnHs+J/y94P/pa7X4HujHHz6or2Qwlyuw4IQ1+8pFvLgERA6eNDCySCvTEfYFm3TzWYETohZ8RcwX3r24U6OAeDSjfC4yfXZFbWeIE3FjDSytB0r2WjhqTDTKfwxVmkw2Do1MFoxIC1oCKp2VgIw3EAOSgg7LrFnM5qGOaJtMvrL1aX+BykG+TAirRpRdY0CumFeBkumwa0InEvyB8eTAcXt55/LgxCrO40a9cTL676319MP9izjRJI67/rBmv9yZid3PxGeEgbKxieUfKqeneIQailiWb0DIebF/TIGgjlSeYT8k3MEmmPzc3k2U/lbXZiOyV2eSEXB4bptx95w3FMWWVTjJZzbB2YRg0P/9VQwRtuirUzHIXBgASZ3zp8Tvou52ZPJADzm8LJN+6X47HSxMdjPyXgzvUsy4DaNLp1x5xVtqDSrExA5IM4cZSMfip5ppqKLaRoiaj+EtBDTMnMerxg9ubxF3dYQlO0omWSkhOK+VJySriM7KTl45kAd19J9ySwefyA5gtkb6n2vrpd/pP7PANan319H3p0B8CeykSYiuT2/3ZnViNWWzYMdrYFE7SmfxyK/k+/M8sw4Fnqm+RTD4GIkcX2DPasHmA+o1f5H/phgTGIyozJo45Ee4oLhd2uiv+lnK7IuEAwZGLoZUM7FC+0AMdOYalKS8UYw1t7JJkJjqvEVhTnBSqk/EjU+kbTonD1hPXPpBDaflrZ6VqRuRNW5O+LPxK4VAqpmZCTBXMC7JsFRmI5fOLt8woiYNji3xr0xHeulvQER6vujA8t+WOW6On1ffjJ6/RP1+rN1y2tq/RV5EcKvHE5Y9qbr6k3vqS/O1StelNrL3219bB1a7iyLwqWOymgKEfNa32nkVwqg+xeKDfuvS9V6zu9GrroEHvCnWmdY80loJSJ7qphRgUMToYxhH9lMHKMZt343C6K5jL69kP6nryJmEbB2e5YzHDDZ0Em+EQGGZZNTiWqCxGh7uLMMuBRGQ7UMUjTYHIuAkUcwvQSZUWcugxyyVYFHSbQhbeYwFOGzcxqmyjgwviWQqwubytUkmIwi2gDdCoJGlzHDJkXDMX+zGU5jdxLyz/X56aQdf/bOuue+GMe7T9aVH67/iNMk9+tpw0+K3vCjmbQ7YFVrtX+rBxGYO4St/BnN/YM9POHRR/5O4H8rmwByYBZOzV8W9zD77fw4CJM4YmVQbIb72K1b7TGYAicjTtVhMU94NJSs8WfC0XxPsSgLX90VMszGJLk4Ty0ntWE3vXITeJlQYKAFCEYXdtlS58TwZNwFwAFmVZvnlyXiY+2P4fpki5HRtqvRU9xj6xZK8TnDkgiCzFgwm1NdP2kM6Qbs0u43yRKAOxZ8fDPYHqnondEvHhBlVNIAqCVpJUG9LvUD+X+QHZP1V3/WaRb6/Hg98Pa65BX1Q3xu+7EP9PLU0IWNK+s9HYnhg3bsKEPE+e9NNAXT9/x5gux7od6TMezafm38/dNA4NRSLA/r/XzL1UY7J8fjzSwc6OQYS4faNt4eSzoDpn9JFKprgYTmEScJUTLl+KvX6MNrM01hCLPoKYXNJDir1wTD1+GMOSxO4DWybQIxqMTJc4BGJg9UwjGfJSsz8XMne5SyWkMAJj1NtrqTj5jNWOh5o6Dr6HkYN4IgHIJBnlEOmxL8WSNqtwseRKTXl9aIzNF0XoAR52BMOxZzWUfKV4LoJ5+X71e9qX7gozlxfOmmuv6rmR7a1b/joQB/G0oT4/ZdBPNszR+OJ5FZwe7vdkZ55h3xaW9V/9+1pKt+6bfz5ljUgpR0oHpWar2J2dLkjRYoF1pt+wdjTB6NN/RdODTXemc2PmqyCTYX5etMvh7oRTpu0qPvvcVXdaok7UQ2BiqzUcKCIUzvsMjYozr+kGISM7QWfkrslNQpIsnRIa6+HaaZX5qQcWnHIFpx6VApQ13n8dgKydH7yK8XMhI6BubStBp/5cOP8dEtTPdyaRBDrfmBu5qXR+rkD8d06ltpQ6Ge/712/S+zwvhz9Ufn61N3T1fzn5/fEsFKpuNwsutA87f90lf4tOvS+pkT4d5TGmb2oV746ZDvjf27pHvenjSrDhOezYX6hjUrli+Jh9OwYoS5pvodxeobNoE22OCdfmWkcd5uJHxstzY6SyTUVgXW4KMV7kiX4mEE38TcCbQ4dp68ZRkOjk+HBomzUYWhY4QnMMlcMbQJ1li2AYB0BzJahBokp0zVyRJ7G1tGkI7FAcKv0hmC0VUXOoB9lEJ+AgqFasZQuqsA2hHED6zqpyfVujlVzj8v28YdC7+dl+SuUa6N+thsXf+qFH7pN+rclyeCAbzjxZHEIz3MONl6RqJkM3xMHwe2nn9h4N8qdfbZmM3BW3K5+9P59iA2RTEOz+PUsfwEkpdd6YapXPxw669LYRLnww6wE+E0bKgtpighj7h4XcY0vWuFlUQxIBEJieIyr4V9SWuqC6An2vGB7T/C5bbICJUU4UUPYDyVfei0P1VQbCZPorljx4xgunVUu6ilN9gb0dIVFtbWFqk3/Bnhsg7DEY2gI03lGM0QF4IaUPUMVurYNaKxE9Evx9ar4pkdbwcghWet+2AMy8zUvOyF5/PjdrxftDhaEAFAlfM/mIQD+zDasnz/Folydz1ccIPvdlmvdvdXktyUL9H6wosSL+w5Xu/cqr9YrFsXk0gW0WIZDJSUSGIrOgTagXH61UJCFX/Q/cydyX1ZkD4z0mhRfwGANyCDHQ5QGfrDd+SNb9LNVSwJfFiEOVS+WWRSZeRgnUzHeOymI4/LNBe0pu5kIkW6w1Dc6tihQVIlT5T5I1VOgwjQCqBRkQKa9DIdA6k+g6BK9EhlMpMLIgDq8FXsO5RIzLHrK5mCeepOtkomMdtED+Rta1cfzXsOlxpGzx04+EKx73sjp3t7VN84E/W849rcNf4f77iDWs2u1g82AEVTiyqcbD+w/6P16PPr5D9P0LngaWPOnZ5SKQ4EHM/GlaGcDrZgklzRf/1qc9EKAeAjRyfvSTDxXe15MvdkSRQ/Wt3uSK/DgHDTn1hbn6iOORkqFMNDnDCoC5W6lF3IwLot8ISiJE9ZngOeoGqb01C7plRPzKJdkmmwXQTzEXBL9RWvcabyPdBRLowPImEYFLrXDo2QLCtZPpIqfnb/sNPmm9LY8zV0smeOFMS2JvVkOGziOhKO2m4yH6gA4DuJzqLna/EkDsSfeyzUnz04GeStdCH9172j/ObaeE2derSWnlZ1U2RzV+8re6UE3eG65yl1+0q64XW6Jz1xym5ORxXm5IwFKe2axtgAm1bGQfFJwrc+xh3hoO+ORTOi8GZsrqMs5zgwGbSmJtJyCLXqYlKk2+JxGSU2vHMPuZ0W6RNBDtNxS0pFFZyPWbSMxQuqayjjRfOZAvVN9y9vgt+UjOTsV/O6InDSOLTMkgxJEkl81zAdg6RErMtHUuvO8YCZudS301jc5Vo/AUM7SAAV+2tjpXbdle44dv5AhEFsS6E8jH5qU7nc6bK7Wnov7zXKaxr+/KtT8RGLK0pbKvdX/XNO7/l1zz+r13y+Zu7KC3z3zdfGgdp/PPv8bWmTL1nwy+Y9/sv1YodJKE91prmZFCS2wDuUtLP3hKGn8oy+bUcS2q0jrOeAlJphMSkd9MFzHl7PkqzEHG5zqvvR2haVqdWqlCiEPYTYg8n9yMp73H7oIiQOwtCuAFLcwRtZvMlkuTlForsM/n/ZcF+nzs+LaUfyiENiT6wIBikowspoYo+KePpd+cRvpearOBlisC8JgFd4wCptY57FzkwAvQ//fN7Y5zjzgWQKeCTadLC/IWZAPk5OPaXOPRowG52e8TM5cWgIk472+KSR1/aU9mf/sK78Xn3gzjoh9SsYweJ2cbsPhYkbt+VnrvSWizdTsb3B047e+evJrkVPqLBgaHvUYdyYKw0s5mQZeLL1WHrt/Y0gCTUy4JoMTjrfRkeiMJMKLJYTWUPCAc2TkxV7wrBn2K3lSBdCy9tYTx7WbEDRvZn+zjJEc1J1aQsEHB+abY+8gDA8uTT76n7D75ph62AQzWorRhaCojK4gyDfLI7GrXfC0eUzkyDZj0jUyPK2Cujhhsi1xwnnFo7mHgKaw8YqwiOG+9tiSJCQ9rUaIho1S7cGbGOrvnMkJ7qx35/lRIDj/HceTS7g7EvruvfktXmmclm54sGYOFaeS5hAsXRb3ggNdEJW0XijZFvPFsx+hEzYx2WRh8BX636sFObLElN5XTb2+e/ZJSLg9FxCGzdoqEcrD2a6Q/B9Hgn0MrYpp32wJ5ULmm3ZUbFXH2QQOKE1e4MrWxVfr6czefaMzxTxG94WDtSyy5NxAKpg9QxXBjmR4L6TNI9Drk02e6o8/GBCgLZ36cJTR8LEZl1yzLhm84MFlex88AhBe4x2HK0yFoJ0sldZZg5lx77jyh5soNQjfXbytBbM3J1JlK3aFf94BLZEEUn+dO19UWh7td/1fVpyAR/8fCYcR3bUYYrGyfCWhj1hjxc5NKP5K85dOJC2ffO9fE77LisF1E50A6HyDOaU18yD6QhY2jRtIrReaWGGVKwt+v0xsoHGbWqaiv6Db9w57w+YEfXZ5J/1UHJuWVr2BZzZ5VKexMgGcZjeWrMP1M6fjdPzQISXQPsxzUUzlmP9W1GZwF0eajZahXd8NcaYQQXpxA6BSAPGHi3B6QbGz30tVL7r7jTwwJ/nQYJ9J2vZM0SibbLmA1uydETFmHkfx2n9U6Iy+1uWLGD1OQlekJo+t6uYezjOff7SSMXJMpG/qa76g9r5l9lL/p3K3sTXkKXFmOvymv3jFhaNeSfzcBC90xFqxPNgKB4ZXRKVsn5KhjbbUNlx2wFJGI2z/m+bhFyInL9pgLwOFByL9IfHw2yl7CFERxJ9kUq7jUTMfEOPu1gPuU94ZYQwf8IhCipnYw9F75/OvkPZT/3ZUYf+qO68Mgvx7/GT0idq53V1J7VbzctA/I7OG9F9uOq9zY4hmM34zN551lE5AHT45gc1yYwQE4Hptm6EhmzLd3LqWO33RhIrzZjCpNxt9Yl60hp86Rm1pkJxt7vW53Mur47SHfvyZA/GN9p6ThHM1/M+vyMdHKPhAEpOJMX5d94ein/n83XnUvLti96GhbyVDPgekeE3CMOWItskQp8XhSHbZqX2xtgSufUt4f6IygRj+mBN1uQ6j5O3NUTx0bqaMMe5AcAyjH7BmRk+kWBCS4jA4CTLdLbZou9aJqfER6RFV3r5zsKoQo8PHF+rP7yi7no4nnnlWJ09XEtbCd7MTD71L9K7HFeNP5fnD5p5JPEYAlCrYUxUmOx004GauGMfD/Z1B5d7U8txsanQorVMhUx9wKCJf0ArFD3oaYJRutrfyZpXdSvHDqYtEoDwq07CpRjK3B2Tc5fHRBXrtesT0ZKvfaQ+aD/GbYkcZvnrXXXIeCadSjBiZWO4d03QVtImdi4InT10t6qE6Ch+P3VF3XTWMCMoYNYimkShpKhl6uWP5RlO5lw2Wy9tBHkE01sD4jbwnbWxITvZ9Mv2mtSMGUGBHiMuoyRaHNvXM01EeZkLsB0Pp5tqLV8dFrFje2he9c5UnxzXVv24LlweYXRbIYsSDFPNdzslwo+hDBXgFtbTa82owkq8TN1jD/xYskOogqtJJBsY2D6nTOQOpABY6/Olhjrx3KCSHnZr7crcONX7YG6+PedX91LmAZpFH3+grv9aXfsL9c8+Uj/4gTp8R7KZ8sSr/K2d0GS/1lq12A8rIcv2sLOZjQ4tiWDGPINNyPK5tqvUOfaxuWafyaBiPQWZAZmMuXH7T1mkYJeejvAMK2DDX5lKPtOIK/AR6bYbiCX1mCrsxlaSszk2OioyWojIZRp3vKTuuT8d/NRbO3l+tl74vlw6nvPlumEtj1zdeN7O+h5jmpkJYPiBfNMgvOuBNEoHJ1MQ+TD8Zq2+IxVDbePzkgYvCuVGeHDKAthARUDcerp6IiL8rs48fzKioGDHx3s22sP77q+1QuzL+EFOHBpB/kZHayCvcWm783V1//Nr10fryx+p/S+trXf1j4aZK8jh20nDNDyqQn64aajDGl3g4hVHd0K5v7gpbDvhNZetNNTcUEowdN8TGgRp2oEUCZ7w15ZdHkzIx556/iH3ozu6BiGdcwtn2ByrJ8jcag9miZN2zhNV560Z6BpeWNBre0WYR+Cfs7u+cU8kUa2CD/5+Tl/Bhk7kfUx/9BN16mBdfaZzZUjSYrYpNbs9ckBliBoRjNGUGN3JsmjsoXR4JQpk43P92Ll6v7frzOfZnNgpw4LLPUHaeieIHgnfmcLOD6TWY/3ksUeNGcRdPby7+1Ii2ZNB5QVpP9/HWgF/vHME2T93PmtxIK/sx5r5Pv6HxdhVnJ8axRUxLh49s2b219qxOrZRj++oH+gNeXgxNxNhnFmps0vZVJa9gLaW4RGNFgXomohZ9dl0PnniZqIHw+xykmsnbCNQnAEbEgG3o4aW2yQbjEuqmBRFEyZGnVCAk2iRLbTDQ35SIJAtCfY1LtfeD9QnbuywB1/47p+Jc77RL+J4KdCP1tp84tL9J1owVJVgtGJWKFxJUBgdC1kOrPafNaAGDAbtuKJ2vb/1+Zl1+n0194uRHFIyJiGUN+i6wh4G9KLem+l5UcScDJ214xMZzJFEAI6XIvJtIeDR6eMZfJ1GDUu+fbzsU7vP6wX/c19JPLa+v4fWHs/kQMnpRL+s5RpZvhvqJA3VZ5M7L2u3mdg44fdHFzL8Hj5bx80fvXiWkfVAyN+K32zASF+pIw4YHng2H1GcCQDxeKSY1IyadgcaSsXizFQfYUB3j7LGIR0nbPkq3k9hmOnnnKG1MdG0acwIpTitxGM1PsrULdTJPfmxGeZ+41J+Le2oQnEmV0bh8vlO6zuukYo6vJn4xCVN8cECbXW2Eb+W5TRP1497kV6/4m3JL4xyZVSD2Hx3hMrUEqSG8yHuxFb9/gvC682Ozb7dja7A34v8b+wl5KdN3/arX2492KLXDXzHl2OviJD2Hs+wfIw19Kwlu/0ouJEfV7+YB7Gfa4O2hMb5+q6M6t6YwvqRqAP/LCd/20I2T5+6I88d4EXe2TAXX+fxTK3Qa8pH9/GUqLIQZdMaxfecgndZtTVgRoZ9JDIjEm05ibbh0WUsgkEho6SC9FvQ6BZGCNIyvHH4EHZ0vsV2YfPcrOFAGLk/L1Cgdhi+0y/HOtzOHioVml8w+qBSS8wwvHai/56VagDKvufmWrk0b0Y79+n8/IBWhbAGSRX9T1StB2TcfE9nNutPXjDZTk5OyrV1uOX0opBQ93+x/4jQLquTB2NbDhWPed0EDyn93LutDQwnrom3sUdC8DPX8XEol5I4HieWfcAsVdP9EJe29rMJ3PHj7ofj8b1ZaW13Mh+ZgQrePClJJPwV4i196hrboVBjYtQ7XfSIsrI/0UTmQ7yCfTnY0pMbYtCcSlwfAdPsVRuJ7L7DBHS2ctuBJgQQoKtCon5mjmbw0rJc8Gx4e92pvNZE0/wzzSiCiZr79PDujBZwRy6pTLSghz5+DDB201xdlQ7S6gee0a6pXyV5SHAiyMagZiLxJFLw6SH9Nt/LGd4dvk+0gx0yxohx3J5doZH0R3fVsQNZTHv+mNnQzSNxdzfui1l4GkTW6wZR70I/wL7WYwwXz4i36gT3YppNKlzWTD2IFyfyTgiTx43V/O7bfrMT0cRSXqAlvYiJZE/ARKWPnj8xcpABjSQtbMLKvfpA1fSlBUBBo1bdQVVyqwc8VfgMA55na3WbohA5qsxhE1AoE/4RDGe4GV8yb1CDk30z+v3xfusmpG1txrkslKEM081XjfZD3yMwQCabrfIRYBtTqOmQdxFBsD83QjK0LBjn3bNJxYyXCfdcjyJwPao+pfGffUY64/jQF/Oe2E919tPbRm8epXadLWSH5tXwP1yP/FhKIaBuxu2Tj0Qf51+eS2594ViiKXnSQ1y/NeYeexd5IZeMo7mwsbdmpeloxO1112uzOOT1LXwaSzjul7G9dXYliS9RpfF5nzeqtd0Iatc3Iyd5Nty0eGEzvzDPiyWMKeCNN7gRlRq+AccwihJ74MQDx6JtZNBmuQOzH7kcvxYj08plGbe4O5MKsQaaBXWt69nSbm7bT+cs2UbazolunfaSH7xzmKjjBOh4QKy0HoOUDuEj7Ybh0Kgv7hzgOrkRxuvSlNNg2M3E6UHD7405cAf1qkL1id7293uCMftI+tz3qTtz6yevTdrYjPc/dyv/0z2Z/PJyt47IzQDWRravRUXNDX63UvytTgrMRfEtDFMICM2rsDB5X79Ctlr7FurIW+tW3fYjYzzYl+q2A7XzUO14T35c+ejeOkJ1jtWNHlneUyuLSZEZS5KJEUFRWB5psW7D1s1s9iR1PiBTH65M9435dJf70vfWxRgQB9ujVCJbowAeho/hG+ZQC5Ya7rMMJfjZgwVlyr1+4IJzlpDcTTCzlPGbiUOSSFDNjeHQ2j9SAR8EpaKYp7m87k+nCTSMRPpl9cz2Q5SRNhfMRaAMFKH+wdj10huRfsm8ZGfCs7nLEjjYXe/bD+8eaJH4+m+fXrteXTf/QW38dDTgGu34OZaW0+nvZI8Ar+2l+gvwvrcfclNdF1vXMn6IeTYCI3W/tZKHFI0r+RFsC0VyiPMZzPfdFS1cFRZ6bSG3dlfd+VCtfbQ+sVwvPFaHV8Ijof/BrXrxbH1lrj6s+yTNvRg8xpK+vR92NuFVi0RYkRGIyxAjSIV4SEy2Qpjak8L4G36yX5vGuTEvSkyo6q6xDGEb+c0lNqMTeaTbRnAPSXFlBCNvzwuTNobGYqhGWyXBEjsU+MDDMmTi4egNlUYzUiFpYrDbysoY4RvfaALZUB02h1gnbx5M/2ydf13aWvz7wWZ/DFi8V/JLnpqs+tPeGcNSvvF36x03Z6bpWPh2kO8dD04/I9h4IRHbot4uZ3u7oXKVqzHn4EDQTxuW85hVhkyvWVvLj1HTaIsu1Ev2jIxD84He1e/FD173spwfrT+yUC8k1PfmqeDNG7Kos3ZvXkmYoYXlSQGgZm8dXExox1sYBiysUT2OiNMjDA+zWbCgELv3R65eziIlI1wUrQhDxM2iR83LYZ+VJWOR1ySOcL6FCfuSFDCz43J3XpM5+yLBYJDKmU62PAjGQQVcmsGiDD9SSEgGt+FVvhVjBKIuF+j5P8aUDrORnpFZkGB5qswEWR+vr9ufqOsNId47+aV4A8fmg2H6w4fqTy/JbN+TmR+7pO56RbaN3f2GSPS45UuxSu9JO9lxhNjJcaKHd4QJecOfTtO5w0SoEZEYurED+6wSIdQI6i4tjvWrfzp5NpoEQ/ze3jq5Xh/wDrilhA/imr27kgi/Y732jnc2WAQ72O+Mt8d8IaM9ByPE4MxV505EazxYXpLuUTp525Va+EZoo7VJFiBmNjMqKZL5dlqmt56Q8gIX1e3DWvJCpKU66lHIjVo+ljennN9zeTrWDi+GQgyo5QrSUg8bkUuLClS61NzxbTKhGN+F/26IFDhHnlBEn9mZvvOeAgSbMe+rn3hFgB8ZwdiuLF8aGG6zk79/hPCWtqnrn4jJvMUbzObq0eviBiEhP51f6Uc8eLb0qqlCAAGMBAnyaAzZsBLNGHhjQC0D/oeQUu6OAIz8KK+nvCDvwAQYhBmixT6YtZzVF+P2svDp9bE8kd6h83Vc7ET/dverTNdraV8d9ZijuIC0kSSU8LC89ActkWXgV+wWagrjA3EVg3f33kepM+bb8182ZHbpnemm296JjSle2GjrhHnLyRGVYaw5beLrlm3UijXgKOvtlzwoH86Nf9MG5xOuicHHiQ63/zVR4g+Nh/lIbJDx5wKMTf/hVfXce+sHm7+ePXunF90fyvt8HUskQSKkMp32v19A/OxU3HL3eL2aCeZmlEdv0TaCcnJjtdqydhn621aYvphH8p+ujPx/8pgUXO8Gp5x0zhH1pq4kGi/Xcw4OyoOoVE0kJm/mnTeHOf3VyENi2HtV5g+FpvPHwkRkJwuwlG5ueuuMl0xZQiVsFdtKNjDHJfdOMOgmb9qDZoMfI5P76cw0+GheGzcy4jDJlX6fuzSkUBbqGQHQPqoHrjUrDi1amOY1o/MKiE/HJZ3SB+c99aXaSVQo4dGESW3dcK7znIt1573JYz4QntT+b9UX5GbO1ys/m4fH3mIG2hbj5Ru/2e/EOLK35r6XngOe+2j+ODkxyMAZIYk36k8Xj03C03kprHa8lvTxEYWJX8cqEeo7TKJbep6phokOdpii9/BJ6uaqVoVXyZgdCPPoGSUz5zcyU7uFusYmCMPJ2TrssWE/r3k6k9aEvyRHnHviWo8yoI2kgrTuacozl2VQYXAJpnBcIIA/RiZc82p1POf/zTQpzWX5cUz9SvZBWlbGzFhljNFtNeOanegGvJ0u00KGPtfk4V4HBf4y6jVXU5nFlXWqJkamPVIhXaQIOfLUWH1mT8Lf/yY3E/je3KL9T7fVb52bvJjpyt/PrSfemu/bbqkDJ5I++aUXhx4d+IKHYNt60hm0mK9h3CCMfXT6Ei9QTo2SgGgLwA5THJELLFE1ENolRWrUiujSfCVP8mvGlitG0xHXvPfQ9rqL4coOCjmuxc16seb29l4AAdWh7LvQkN05NpJLMEv80Midq53xNJ4b89kEN8htcJs0wCCHjNaMRIydaHDLB7XyzTq1JMQwhTLJRZjZGMEgNBR335h2fKJgjmwICUGjS9Oe68Vy2wFmda/zM1TcYKoLSHy3PKBP0iJhV73MPEs5I+u6x3po+Qd/VP/6ijBrOLF7Lsn5l7HsV2rff18nfz0vYLyp9wKsvDquzAcGOsArCNATmreD4qliykJB7nfMupoFoEk3DsAHDPg2HToOCzrxjnRRaKinQwRsMJdKWbWoDKAng/pllZ6Kf9zYo3fn6hgvvSdBmnko6yQ2QykHqMQypRhMDG2HmxWQTHeIhxpxmJ1iAJndOYjsXnCPdm5QdwkkUbJGHaZNRqlkW85cmmugZqQRDziU9wDLm+FyPopQrxPai3rV+mWtKSMQsOWwo3INu0XawlOzPyGj18J8yLZ942pLZX9b2yf7bQDzV6Td//qJfL/5t7LR4ttmlC+p1d+KohGeY+HRfDv3WMwDrQc7vh0nQwdj+D2YiYU47oTmHQustJtVy2BjFBVPw+ZWesTt4EiHBixJpoS2LhhpOB+9E84s5H25wHgktkIM3BTprlvDljHjqTq1dboXs+W41siVJIwWW1khxiW/smj2HjZa6hYxG2+YAmEkyuolalmutmPDu4XLhEbUy/oNPe53SNsgEA8UyV0aXmeGT6cU+EajwaYXvnCa6Qwti0p2uMyPpW7PY5yYDWETkZivGv3YiHQQ/5tg9GvhuP+Lb/cnCzBv+HhOQBlOPv6SxGmXeRb52hS+x9TS2+jn6x3rSdg4dv3TvDfjoRa2haRFdK5HtobrTQ9+WoPy2jx614kZyuFWmGIu2XotJ8aOvbnCwSasvWKBMIkCrcEgtSFotMSHfuED9Zfd8tCv6Q6Z2cjZmS5aP+bd/J4f6st8wOvRhgtFIRXkr9YTpluJMIFntVm4I1pOTJ6URzVdnUua0vih5wzXPJezIc4dT8lC1F4WdqId4HKS05icXzzjylDP0jOKaK9dWbxXPywKQkt6K7RgAfrWMq79LNRUvyVEwkY21gqDfFx2/RjuuDAqHJ5k2D/9kbr3SBvFW/MD6m/q8rf+r/nzf1+S78++NhHbxw7UfWfqFTSjj5ffM14BWfOP9fOehG6cp4M9FQ/DO7KKN2ibprNS9M75bg5AVGYYx3QqZf0xeUxy4gAMLTRMX+iQjoiCDAOEpzv0S3BlVqiiPAKf4SU/cPZjQ8Awa0bkSZDGLbZC3mI2z2O2R7Ftw6xTgip6aYGH2Nrf4o9L3pjNopRbMvNfY8TfrCU5VqHNYk+WMR+RyrOxogWTcSn2kkO18Y0LbAVZ+m/8SGB61Gn7OgJ4Sk6oqbb11iK8qQwTZnDoEPDRF3cdx9KddNjPMJDKS7twDC1PDKm8vo7cGBN84kfzWzFP/d9TcbHB/tufzn6lHa+INax+NluTZzazO9m8LK03Q5NuYs1pI9+MOHLi8TgTLtvwzntAIs9BhzoRoIp8K/HgLIWMHTSAVFM4K9BCgVq6Zl7SWss3poTKg5Qlo9T4yP/jDGCSxqWmCgZu0NBl9M0Es2cO3CDLa0YED9+L5xTdm1zWKMp8GBu3JnJBj8CMYLS0fZAKLud7vELXmNabgAGMdItuU1XBA9OBS136iFD+wXK3tjOH4LWNt50cUxEkZbnnaxORKBlSuepvpdlvS+r/XjbtXff6XH78BfV/5W/9Qn9/IxKp42frtYzARPpkXrScme9CuMY1p4c+GkM2LcG4ph+1qxysrgLGUMqLgxZFWDY8bIImMQsDCeLIg9ZbTOS+jvbbxjvTmmGylYw8yN5EzTcNZlvU0XSq76ezCrUSps7GoXGD2RVlmrnQg5woi7SMCz0JQzkUJOsV2dh7gjDMeHiw7rIkU95gZuYPzBFxth+ImrhAPSo10E6WCqi86/GgW21D4cdQguk5qIy0OZHTc3tWUGCcPJHH+xx3NMEf6HNfQypesPTJh1N07Pr603P16aPB9iNvroXvZp3gLwnDwswbM9hwiqRuM8r+laQVbF0U2sYmTuUJKeNtPA+CsQb9bTH8mKDLQgA+KtdBoyDGkYpgQaFARtCYcXQ9NkRUm3vrnpfnIZ1HvAjJctmhrEUhiaFwLIQqos0mJn3pxDPZ6DUyyCK8a9Yp4YsM+wJ6RIrXMcaHbi2IG40f8HCqTdU8B6tp231aY0zYKZlcn193zFSUxeRA91Q+vIGKeiPcztgDF1HxUc1f3D/71ISAwH1Ofy+1CdK5eMxJwj5U8mPHcuvrnZ38mbxNpv61V5E+kULHEy/M93evr7cdzyLMwr+oW3+oDqzX8bvq1w7XylNy9+l315v+OO9yMsU7YCe/GYAFLpPwDlUMafIcxtjEo3iEgyjvWYsxRry+ZE5u2ViJvWdIQicBo65zVkYduRPOTbnfzTpDKpbmiOJbkYcfcvCK/mTsyV4g13N41SkoRiXkE3ySE5kxHU3wV21J8ZYI7jAEFaJTLm6ZTtusbGQiqo7RxZbRHrNaioUGqtwTdlaVNVCrPgRD5tG1EBbha16F+GVy0wh2oKxn9WBOI+ZQnV/NSS7bncEAGIOifRxaxypzjwQf4hx7+xctnHyuL8fQ8iPe5fPB/Ab1+nOjUzecq1v35a2MR9sWVfnDXjI4YvflapL2J0Ymw5ihIWMD2mgZD4MOOks3W3+JR6EQ6KQtZGzCBKUjeDw1UFNGFiN3gOMYirenJOoXotevkZWQA35OHf9GbT2Y3xmdWe4ttZSyvQj8+oyD4gU7p/irNKtkIX4FyszbDfu0XASBvGaLwcwoYqHIuGv/W9hrCiVCg0EgvsNOpcmEhi1mT6thKWZ4eepHHt03p2SQtnuGrFwowr4225b5scjDyuBqVE919hgFlCmiCARJmxBkWLN3uxG/oH/u9Zo+v+Rr+TOkcskV9RpqdV/dJuz5Zo8fN4T03X9Z514chwn5hhWBnpOeeiCPrvEnlkO4IF3i7ika1eY0BMdRLApIZ/WCLjjn3BezKJA1LgIw0uDc7OT9bpkUi55lgo26c72ErC/fTUVPUMx/KsRbZNyzkMFAcjaJRDcFC41q3pRIr3XZokMbCsrRY6euaIL1YAhVw1LkWbT2Lf4WRCSNRjl4UWyUVbJyakuN0Wh/tCqmY7RWPbJowQCaMHJkL0i4FTMejJBMHXi2EzFSvBDam3iTkJ7agMFLsm6rFPROBBKtYW2LE3xaIMIjH892ZGPOLb/tT/33n0lW5w8/mIBNrMnCdlxaJ1fq8P76xy9OZkEV/Medh3tm6sQMiVM17di7lj6IuCIS7/420owghyaqhhcYgb88OLdgzrsY7aa2xCzAFTGilsMhD2aCEVhGilvPTgTloRE/UrCv9Y/i07P4Mf20YMpKhE/refADJTFWk0E4eCHDIKpMLaxMm1o2QzJ/UktWVO9YZ7ce8sSKLL6jDzAaPU4ViK3Da4nnWbLXo/MjXNarVooImcx9KAJ294nxikGI8Yc68gbxti0k4tEB8T7+0iZ99uErLBTORwT1xebyNVt5o5xj+e353v26euL1OXn7B8PK16/U6kIIuGNvouTfqzyEPvOdCL6hIiE+mqXO35UYRG+xyfrSzEo2ZhhCiCG0YRxetwExOGTrBWrnqLZlD6TqBa1ni2CIzQBg+DFLfUGrAvmJo1bDxOgfJzEb+/NOrEC2vpO6W1rxx0gjsF4y1Gm0zdfyvrmnRUl56xG4Z4BpLaEBWV7Dz86GIY9nMptBSUZ7UrQQzCUKMQQmej52yYRJDt3jpodgaAp33KIwvkWz1A+dmbIZUYnEeTI/IJ0Nl9JBJGtD/Tje87Hsvtx3S/KeL+lY6x95FeY/yM2P3xu9k7a58e5673PTga2jdeO5etpbIhKb3jQXkXSAbuVjb6vwSgesNgplHnc6I2oIRoBvRPqoRSStIoSEvMSy9AgLOrbEhFhJD058Dqfp5hJXJo1Ecv2uUDZg3MZf4glG4wFe2jQD2CTXXQudHCZZ2hE4RmVDiIeV9MK8lRnxKJqWJ2whmd5SVtoAWRwMBqEQtk54U+6VTikJF2PNcUjTeYwKkOkPirtzcak6RsViRqzBqmX7fT3Bi/BOFUZHWbSdi4m5mAPpBVfhuO8t+T6JDGnwfkBgxvVK/cLba/aVueU488ZaeEPWqpd6rkCEX8OEO9PcSssOjB9xi4c0+ZDqsA5orww/M5/5s3xXokfM6sE/vpeQZOl1RBTQU25d0MEEQsLolnd23qpFnzifb0d5QYytdGclh3SwF/bH/FEiAPGRMf7oS0cQDFHcAYxRqosGas2G+C7dl4kJaL97JtmHDrrCTMrRhEUnkNdLq34OiFa5ZLKo4iR3nL088o8rUOoWWvXAJYFTN7rQU0V3HTovmjAjE9ETDBZrCaiYPfrkNkJbkDZTGzi/25ta4ad2nob5Ru9LfoEx5uo6uBEMjlt6hId2cS1Ppu3dG9u6DPL7auvpIWnmCyHGma7yYLptyM2ACS9HpCbt4zeh6/FfF9Csn/wGU9YdPHULeDjip4dwWVzULkHaRizEGUhnUXzdhIa0Eh8jGjeFD3yRWxSx3Yl2OW3RR4jqCDhTS+EyjjOgFk+4rAwBsLEzZtERV8yuLZ4F4xbKmVRyMLbsBnQicmTPSmI6IhgYWwxIQIRL3eDNKJEG0EQM0O58avyAUQ4xwGAfXsLzbWlGG4DcuC9fnIbekdCrZTAP1/VpKpPHBdoqMO3LX7GA9qxgXn68TqzUieMRBmuDf+NgjNLPwMB91NjbS1WasxMlGSrKK+3Ry00cAvJ0LYrVFoMMTWcDOK/d1oxWzsAEQt7FqKB3Ro7EnLjsMTNGQMaYLpsJDz9DctI/+sjfDvm1I0rM7ReQQlmnnwG0OCFURV2MNqUj4AhMo/wVS2o7yNyrHRraRGhcq6hanCZ65pYjSDd0IZ7w0uDiH1gWRSBSzcRlawaHHG2YmTa3UkrZYpBhanLoufgnEu7RVX/azkgF3zdbMKsN+okX1amP5+yf+i/gNuHq8r+wuelw/Zv/uZafWv/8mlQ52U5Sc+O91qjc+ZL4bqllIQ39xaxlqUyDpxBDCWKRqj88BoHpBS4LwDoGw0SpAWLDhSiZDop/PAorDYx9YtMOZwVCjI+LlhvWF9bP1Na8xLuZRYU1gVHrdlxgVBs7CnVcehg3SSXzJMjpAfp7AI+9IqYZjUrZT5kLozKF5dAMh1gt0kvmF/fMYKAlRfWVG2OUgqCzSnw7777mBBKM1ivq6QBgZVQMakpBY6LaFjaonodIhEyYshrvYZWCVJ6H7mn+BvcdO26rPzF+XFn7/nF+kVW7V7Wc/uzW2vF79Y7zdWiurntdkjFrjXy8BIsqGUXsxbI1gq7gKW+mstUOPo1/o+/DyyuJr2tNPMm5U2R9Bqy33TUnfICtKuaGtJCErZcosXBg8DBaMM2AY6Utd8vxaWsn4yadRBb47tOQwgS9i9VqAjcG03rjK64Nf64Jvo60pDVJ1GunZ0mdDzf7YRmyvTIXImZsXcjoYuyMOfKicXSXpwECGFkBbegJkdAXwRVxGC28DcyhbzkWE1DuuDGOAjFcByMjKl0159KfLMZQ6T5U11Udg+AVVR9+KJd366J9J+bbbYJurTxeO346b4g5+fN1zQ21+OroJrFtPDNs8pwN1tu8aoYh8KeAK94D0gEI10wtUJ5Qnh20++VU/aKM2RXF1Bz3wqp8b9Bf3DEJo4+bfbme1xBYBDr/3Dr/lxmW6KwR3hGDExQcjHtZ32BuKad/YWKHT1aXuRlcYEnii6gpWmVThPXyhB0cIjKeBuUSYua564k7kgvGEQDCGUaGD9BaS2xb1660jbtnd18eg2ABeAEiJgYdqRjKAOA4cpkhwfbIP6KX05dOdDCpdYrJS8jOUlWyIUJ63YK5bbKMkm1KBKDLSiyqWyvcfzhPKqFhbxf+71+t/cxurt7jUa6m/MXhRgDo8qk+422EUjy4OZPRBTsou25iCgPFOPh9mwcg2NTKBit0GBGNfB5vCG3WJekZzVtqidroazazEUcfHhG27jMXmi5kwIHN+Ew6JDpIjEDwPkyEaeK+9qgF9XSi3R6nmSmw2JaQwX2NWhliWPILaGUu0PLAhpxuCLUqEqpLnoCF0byeGF3eZtHGOKLvEQhgLNkkfJRUx+y1yCmFbVKIcQzuqx+3DlJQlClrqBy31HO88BezmH+kY+XPoNmura/V2nU1/7y6+dF0ap+FsnfU2QN5Y++BZ8eVKfxOehGmn3s4HokTNwrGM3BibRnaDQswotMYmmXl1q90NdMFk3mJslMR2snWMNt6sED0xTIIlXuEISM2KrGPMVFHCDWqL7jGelCwr5Z6pxx+cVBEjg9JtFMLGIwTrcT6HjwMtFnB9UWn3IK5PRuPYjKQcb6D+9iZ++LMllxiFpSkRo9zib8uj0N3W4cTZaK2IeJn1dF5khAyPpKGdDMbrjrkQBU9xpQ0j6KOW0KH5nsJQNnpN4bWf3MqqeXHu/ojRp2v1Wm/3PnSVH/lkQAwVj+78UIe9rb6yG92nqYjus1+ylm7VJhdaoVGI5VF+k5nuisMyNBChOmWLxaMF4Z3G++NB9bKlHgvot71NGhRVMlXdC/SWXhM3SHsCCos0xLZoL/xJKgjAJfw0AlRQ2sevU7fGUfLLI0PHsLWboMI1RqKjofrLtmZ5tylwQRv8CNINECieuvKwJNcGT9A/kpjE/67g0RCa8GQUNxiOJ+KbmEKcLVwVqmbnIBznVQroT1OdzJm8+o84yK/BOZVrf5Psxf8+uSCDvbk8ZvHgvPa19QtHNRKzd1bH//NAM8HQWjhAL0JiBeini71AQFGtXShHU661A9gCEOSwmqeAhDkgDGV0XluDZjh2rHY0UoCO/7AcKinKY6+47vORq6tmtrjBgS+SnSZCoZJ+NeYNzUavQ67YjQtABSOE0yXwMc3YoBZj4xtdBp+8NQr81/fvFEKogrBBs/ApmsmmLrDNccY1ekGNDfIjR4ZMJr7wNJxlHVQEIDvpVUnmlFTMy4ERfy4jq8yhYVwVoev6Vnkgipq28Z4Ze34fS/MrsWNVL/pntq/kc3BtzUAse5rAvQn9NAM5GqopZ5kRncglKTtxKZmdhgd16wLyMNcKokv6F8NQw3IlgAMFZgUFydUofVNtriOAMTZMAdVOyFoyTLzaO/34ja0BXnjJ3DNclAxXxxBWytKYFqi7g7NZaCxadUbm0zBBJjL1QWt4xgxIxWUEQ4P+wNB1mMgh9Oh85Gbey23YFGDM9WzAXNF6KOYg2U8OCKiv2wLVXgkluV/ew//CuxPS5Mr36r39U8vPLvqh/w+r2D6OZnNvEKU+HAq/pgxZn+tHKjPnc6S5VUE9t1MaCA/hXj972y5+VMGZ62kg92ZQVZrD3abkUAnOztnLKWSVrtxvPeVi+CpF8W3ErqII3JIsu6MTxyh1lMiRZzlpnSf6wcSbjILW/pM98SvRN4TA+DRwg6CwaAj42szzZWDJiGdbeGMaQpcsTOUu6dyS1esMeZ/rMreCkzG89BFYNBHwD2PydSk67unyVBJtf0ulGhddG+3Y3sVu564LAAMAstojT4k7o5OpmNWuZG48+GYi9CQoRxtiX5xT/LEX+haz/hH9Z4PRzH3p1LAfvmyOndjguNrj2fAd9gtr0chUfXH2wMYSEzUmbVGWXBHLzrjIDb9yWSey+7nhhbUFbZK44NvqzVLEOyqxc1ijhVG2I2Fhp+M4Y+2+EfSjwDwrqXo7qLoDg34juP4YMbTlmRUQwYAt9Cq12FZ08Ne8UeaisB0QA4Qr5xTlIxeMKNEcCjgtnBgAc3ivXJjOcpItAWxe0dHZXEFULfpUSXxHBaIhUa3TX929tqRsUvkxqoiFUioksjdVIAwmDZm6QYzempd/VhenwzmSG8h+7WepcL3//J2+UP10f2R04nuCLacvC655Jtn6+V76EmOuV8KI9DpgzRoaR8ti7sY04geculYJtJ4pIdW70XDXphmFBGVEkxnHuULspsZr3seg+lruNnZI7P0jAQ6wuaosKQ4J2FW1Oxzmbb5g4UeqFFL/O0eKDjmUtwYHLlwKghrqYDnJx1qpQu299OG+Q7HyaMTCsZnvfZ7nfRe4EeKeifvkKBmNi16QO4cwagPle8hcrxwYUTNVhgKyLl5D2bnV2wcpWUefjzWeXIEZeKteYRqiqV7CkQ+/L761dA22QnuxJZX3XxeJ8quelu9/Xu5XM+u+TzPtyTLciL++uN/hKoWxoMRKnpQ5aCVdM2QQAo24mCfG5kt6arZIq3v6VQgvTKp/Uw0WqJXRECv243gvnNOicoHQTveMTi55vE5BjFSNtDoiy0lKPH8badzMj3CxOFRO/aBitQYDQmlXYQOWpPmqhUbOaR622Ea20idd1IdebGtDgp8L3Q5eXO/aukItdBQpo9UFGQo8x3XlsZyyUehTKxiLGXM3S51U2IP9cMBid5Ruvh9TsA1v2FcNX+8L3swTvREBNmfbl7PfTWu7F0i40frmvfW5q9HomnaN8GcqaNX19E92VL73ph1rb8sYvD2n92XxQUvUmR2yQ50BuNwik+jgD3dgytMR3mLKkhTuScKzVDUoZi2SUyYD8meSQdonWIZnBBJLeLZhOxdrhUakF2sVmUEb01q/HZ7MF3O/LG9d7oPc+MPDzHEEweyD8wXneLDFoPIYq21J2NwCwnSyFtNSxicsKW8PZ2Ia4TTFUxkT2UT7H0YLWPnfN/x/NVTSEy+1p6Sd1kyQIyItPkT2/tEzxREGtUMrkOyI13F+dHeYOZnu17Z7/b7s7vyyiTOEM6fasuzWuxRrh0vr0OfzgvKVOH0xAJsVz7G+eqpSM47XTQk8YOwqIHlP2ZtGNB/rOHi6B2lQ6VLURZBGhL4me4aMVAsP0Bk9ZfW0CHpkKygtztSy0q+1R0c56sT9ch3nKslFqBfAhaNdnSaipS6OxvZUIig7ihLQ7wQDD10xcL4WCk+FC1miPIoKNECpyIZ54WaPdStc6R0br4h+eH4unZlxknscIzvnHT3MI/pzTwQMVDwBGCiiObpru8Ge3pOobTaqVwjnpaWeyKCrfdn73H9y/7thEPY0b/nd+pobf5eogMAezsEsEvx+L7a9Wh+ZPu27B/K4VfYNGqEOGAribdbtPbRdx5ckGPay1aY/NzJJFoStooOiLppi9PgqVRpMyIz6h+T4kl21IFOywvPEM9xGSqUZ+zk+rAG7/TU+MEi21Vgrl7zb1y6chiovKZpKqngZX+1m3JlvkL9BcFCIQM+FukF4/AbRDiIctCw6Xy3G/X0WmR51XYbTEd+yCCydaoF05JKr8aBUEf6D2tn6Lqg+2+JqYE8dhVF4dmAUKL+yQFkeVnpUi+IHegk8f6/CPS1iLklHdj1+vpH++u6FyXnr/rv9jz0HQZ/dK5lM/V9jRxLt14Qwew4Uk/hdg7Xqo1e6GmrzQIwVdUTg5+Xr/QIBJs+cw4k5zyaCJUzptPxPWYxNRlYIeiOpQy2siP2aVDwXU8JoBBrlnyxr5+vwEQSXe0pRIyvpy8Io8yJqgUOw0qa4AweCEBhp+lIzliQ4YB995L8oCXydob+1qFU4QPsaqNnLOFk+yeLZpLQpy6pm27q7gaqG/E1dKBRxH/dMi3/XCv5U/ryt0Ji7vrokKGce1roy080wFub8f+wfxHjCTswu/D6DnVu6Ff/fKHzAYrva5rmp5vPlABWZZw8v+o/9XuC1sLlCwfG+9ACGn6iiddbJPlg82L3AknIU8u3cwBI5RxdEoDqTva3gQ8MSo42TkgAuEXvwKiixDGagAoeaBVSH+d3Nf59TQwwLIZKj9R1gMTScY42SCBUokcKVXdoCDZ9cQlg8/8PtUYXdhZ53zoAAAAASUVORK5CYII=", + "text/plain": [ + "" ] - }, + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grad2 = compute_gradient(egl2, color=0)\n", + "plot_gradient(egl2.copy(), grad2, direction=-2)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.image.detection_segment import plot_segments\n", - "res = plot_segments(egl2.copy(), seg2)\n", - "res" + "data": { + "text/plain": [ + "490" ] - }, + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seg2 = detect_segments(egl2)\n", + "len(seg2)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Il faudrait fusionner les segments mais cela a l'air de marcher." + "data": { + "image/jpeg": "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", + "image/png": "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", + "text/plain": [ + "" ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" - } + ], + "source": [ + "from mlstatpy.image.detection_segment import plot_segments\n", + "\n", + "res = plot_segments(egl2.copy(), seg2)\n", + "res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il faudrait fusionner les segments mais cela a l'air de marcher." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/index.rst b/_doc/notebooks/index.rst new file mode 100644 index 00000000..2fab6bcc --- /dev/null +++ b/_doc/notebooks/index.rst @@ -0,0 +1,11 @@ +Galleries de notebooks +====================== + +.. toctree:: + :maxdepth: 2 + + dsgarden/index + image/index + metric/index + ml/index + nlp/index diff --git a/_doc/notebooks/metric/README.txt b/_doc/notebooks/metric/README.txt deleted file mode 100644 index 74759aab..00000000 --- a/_doc/notebooks/metric/README.txt +++ /dev/null @@ -1,9 +0,0 @@ - -Métriques ---------- - -.. contents:: - :local: - - - diff --git a/_doc/notebooks/metric/index.rst b/_doc/notebooks/metric/index.rst new file mode 100644 index 00000000..834cadf1 --- /dev/null +++ b/_doc/notebooks/metric/index.rst @@ -0,0 +1,9 @@ +Métriques +========= + +.. nbgallery:: + :caption: Notebooks Gallery + :name: rst-nb-gallery-metric + :glob: + + * diff --git a/_doc/notebooks/metric/pvalues_examples.ipynb b/_doc/notebooks/metric/pvalues_examples.ipynb index ac8c97a7..a25e3476 100644 --- a/_doc/notebooks/metric/pvalues_examples.ipynb +++ b/_doc/notebooks/metric/pvalues_examples.ipynb @@ -1,1346 +1,1265 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# p-values\n", + "\n", + "Compute p-values and heavy tails estimators." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## p-value table" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# p-values\n", - "\n", - "Compute p-values and heavy tails estimators." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "norm.ppf(0.025) -1.9599639845400545\n" + ] }, { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
-0.200-0.100-0.020-0.010-0.002-0.0010.0010.0020.0100.0200.1000.200
0.001NaNNaNNaNNaNNaN76767676191977201.01.0
0.002NaNNaNNaNNaN3834.015336153363834154392.01.0
0.050NaNNaN913.03650.091235.036493936493991235365091337.010.0
0.100NaN70.01729.06915.0172866.06914636914631728666915172970.018.0
0.150NaN98.02449.09796.0244893.09795729795722448939796244998.025.0
0.20031.0123.03074.012293.0307317.012292671229267307317122933074123.031.0
0.25037.0145.03602.014406.0360137.014405481440548360137144063602145.037.0
0.30041.0162.04034.016135.0403354.016134131613413403354161354034162.041.0
0.35044.0175.04370.017479.0436966.017478641747864436966174794370175.044.0
0.40047.0185.04610.018440.0460976.018439011843901460976184404610185.047.0
0.45048.0191.04754.019016.0475381.019015231901523475381190164754191.048.0
0.50049.0193.04802.019208.0480183.019207301920730480183192084802193.049.0
0.55048.0191.04754.019016.0475381.019015231901523475381190164754191.048.0
0.60047.0185.04610.018440.0460976.018439011843901460976184404610185.047.0
0.65044.0175.04370.017479.0436966.017478641747864436966174794370175.044.0
0.70041.0162.04034.016135.0403354.016134131613413403354161354034162.041.0
0.75037.0145.03602.014406.0360137.014405481440548360137144063602145.037.0
0.80031.0123.03074.012293.0307317.012292671229267307317122933074123.031.0
0.85025.098.02449.09796.0244893.09795729795722448939796244998.0NaN
0.90018.070.01729.06915.0172866.06914636914631728666915172970.0NaN
0.95010.037.0913.03650.091235.0364939364939912353650913NaNNaN
\n", + "
" ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" + "text/plain": [ + " -0.200 -0.100 -0.020 -0.010 -0.002 -0.001 0.001 0.002 \\\n", + "0.001 NaN NaN NaN NaN NaN 7676 7676 1919 \n", + "0.002 NaN NaN NaN NaN 3834.0 15336 15336 3834 \n", + "0.050 NaN NaN 913.0 3650.0 91235.0 364939 364939 91235 \n", + "0.100 NaN 70.0 1729.0 6915.0 172866.0 691463 691463 172866 \n", + "0.150 NaN 98.0 2449.0 9796.0 244893.0 979572 979572 244893 \n", + "0.200 31.0 123.0 3074.0 12293.0 307317.0 1229267 1229267 307317 \n", + "0.250 37.0 145.0 3602.0 14406.0 360137.0 1440548 1440548 360137 \n", + "0.300 41.0 162.0 4034.0 16135.0 403354.0 1613413 1613413 403354 \n", + "0.350 44.0 175.0 4370.0 17479.0 436966.0 1747864 1747864 436966 \n", + "0.400 47.0 185.0 4610.0 18440.0 460976.0 1843901 1843901 460976 \n", + "0.450 48.0 191.0 4754.0 19016.0 475381.0 1901523 1901523 475381 \n", + "0.500 49.0 193.0 4802.0 19208.0 480183.0 1920730 1920730 480183 \n", + "0.550 48.0 191.0 4754.0 19016.0 475381.0 1901523 1901523 475381 \n", + "0.600 47.0 185.0 4610.0 18440.0 460976.0 1843901 1843901 460976 \n", + "0.650 44.0 175.0 4370.0 17479.0 436966.0 1747864 1747864 436966 \n", + "0.700 41.0 162.0 4034.0 16135.0 403354.0 1613413 1613413 403354 \n", + "0.750 37.0 145.0 3602.0 14406.0 360137.0 1440548 1440548 360137 \n", + "0.800 31.0 123.0 3074.0 12293.0 307317.0 1229267 1229267 307317 \n", + "0.850 25.0 98.0 2449.0 9796.0 244893.0 979572 979572 244893 \n", + "0.900 18.0 70.0 1729.0 6915.0 172866.0 691463 691463 172866 \n", + "0.950 10.0 37.0 913.0 3650.0 91235.0 364939 364939 91235 \n", + "\n", + " 0.010 0.020 0.100 0.200 \n", + "0.001 77 20 1.0 1.0 \n", + "0.002 154 39 2.0 1.0 \n", + "0.050 3650 913 37.0 10.0 \n", + "0.100 6915 1729 70.0 18.0 \n", + "0.150 9796 2449 98.0 25.0 \n", + "0.200 12293 3074 123.0 31.0 \n", + "0.250 14406 3602 145.0 37.0 \n", + "0.300 16135 4034 162.0 41.0 \n", + "0.350 17479 4370 175.0 44.0 \n", + "0.400 18440 4610 185.0 47.0 \n", + "0.450 19016 4754 191.0 48.0 \n", + "0.500 19208 4802 193.0 49.0 \n", + "0.550 19016 4754 191.0 48.0 \n", + "0.600 18440 4610 185.0 47.0 \n", + "0.650 17479 4370 175.0 44.0 \n", + "0.700 16135 4034 162.0 41.0 \n", + "0.750 14406 3602 145.0 37.0 \n", + "0.800 12293 3074 123.0 31.0 \n", + "0.850 9796 2449 98.0 NaN \n", + "0.900 6915 1729 70.0 NaN \n", + "0.950 3650 913 NaN NaN " ] - }, + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import norm\n", + "import pandas\n", + "from pandas import DataFrame\n", + "import numpy\n", + "\n", + "\n", + "def pvalue(p, q, N):\n", + " theta = abs(p - q)\n", + " var = p * (1 - p)\n", + " bn = (2 * N) ** 0.5 * theta / var**0.5\n", + " ret = (1 - norm.cdf(bn)) * 2\n", + " return ret\n", + "\n", + "\n", + "def pvalue_N(p, q, alpha):\n", + " theta = abs(p - q)\n", + " var = p * (1 - p)\n", + " rev = abs(norm.ppf(alpha / 2))\n", + " N = 2 * (rev * var**0.5 / theta) ** 2\n", + " return int(N + 1)\n", + "\n", + "\n", + "def alphatable(ps, dps, alpha):\n", + " values = []\n", + " for p in ps:\n", + " row = []\n", + " for dp in dps:\n", + " q = p + dp\n", + " r = pvalue_N(p, q, alpha) if 1 >= q >= 0 else numpy.nan\n", + " row.append(r)\n", + " values.append(row)\n", + " return values\n", + "\n", + "\n", + "def dataframe(ps, dps, table):\n", + " df = pandas.DataFrame(data=table, index=ps)\n", + " df.columns = dps\n", + " return df\n", + "\n", + "\n", + "print(\"norm.ppf(0.025)\", norm.ppf(0.025)) # -1.9599639845400545\n", + "ps = [0.001, 0.002] + [0.05 * i for i in range(1, 20)]\n", + "dps = [\n", + " -0.2,\n", + " -0.1,\n", + " -0.02,\n", + " -0.01,\n", + " -0.002,\n", + " -0.001,\n", + " 0.2,\n", + " 0.1,\n", + " 0.02,\n", + " 0.01,\n", + " 0.002,\n", + " 0.001,\n", + "]\n", + "dps.sort()\n", + "t = alphatable(ps, dps, 0.05)\n", + "dataframe(ps, dps, t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## p-values in 2D" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## p-value table" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "norm.ppf(0.025) -1.9599639845400545\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
-0.200-0.100-0.020-0.010-0.002-0.0010.0010.0020.0100.0200.1000.200
0.001NaNNaNNaNNaNNaN76767676191977201.01.0
0.002NaNNaNNaNNaN3834.015336153363834154392.01.0
0.050NaNNaN913.03650.091235.036493936493991235365091337.010.0
0.100NaN70.01729.06915.0172866.06914636914631728666915172970.018.0
0.150NaN98.02449.09796.0244893.09795729795722448939796244998.025.0
0.20031.0123.03074.012293.0307317.012292671229267307317122933074123.031.0
0.25037.0145.03602.014406.0360137.014405481440548360137144063602145.037.0
0.30041.0162.04034.016135.0403354.016134131613413403354161354034162.041.0
0.35044.0175.04370.017479.0436966.017478641747864436966174794370175.044.0
0.40047.0185.04610.018440.0460976.018439011843901460976184404610185.047.0
0.45048.0191.04754.019016.0475381.019015231901523475381190164754191.048.0
0.50049.0193.04802.019208.0480183.019207301920730480183192084802193.049.0
0.55048.0191.04754.019016.0475381.019015231901523475381190164754191.048.0
0.60047.0185.04610.018440.0460976.018439011843901460976184404610185.047.0
0.65044.0175.04370.017479.0436966.017478641747864436966174794370175.044.0
0.70041.0162.04034.016135.0403354.016134131613413403354161354034162.041.0
0.75037.0145.03602.014406.0360137.014405481440548360137144063602145.037.0
0.80031.0123.03074.012293.0307317.012292671229267307317122933074123.031.0
0.85025.098.02449.09796.0244893.09795729795722448939796244998.0NaN
0.90018.070.01729.06915.0172866.06914636914631728666915172970.0NaN
0.95010.037.0913.03650.091235.0364939364939912353650913NaNNaN
\n", - "
" - ], - "text/plain": [ - " -0.200 -0.100 -0.020 -0.010 -0.002 -0.001 0.001 0.002 \\\n", - "0.001 NaN NaN NaN NaN NaN 7676 7676 1919 \n", - "0.002 NaN NaN NaN NaN 3834.0 15336 15336 3834 \n", - "0.050 NaN NaN 913.0 3650.0 91235.0 364939 364939 91235 \n", - "0.100 NaN 70.0 1729.0 6915.0 172866.0 691463 691463 172866 \n", - "0.150 NaN 98.0 2449.0 9796.0 244893.0 979572 979572 244893 \n", - "0.200 31.0 123.0 3074.0 12293.0 307317.0 1229267 1229267 307317 \n", - "0.250 37.0 145.0 3602.0 14406.0 360137.0 1440548 1440548 360137 \n", - "0.300 41.0 162.0 4034.0 16135.0 403354.0 1613413 1613413 403354 \n", - "0.350 44.0 175.0 4370.0 17479.0 436966.0 1747864 1747864 436966 \n", - "0.400 47.0 185.0 4610.0 18440.0 460976.0 1843901 1843901 460976 \n", - "0.450 48.0 191.0 4754.0 19016.0 475381.0 1901523 1901523 475381 \n", - "0.500 49.0 193.0 4802.0 19208.0 480183.0 1920730 1920730 480183 \n", - "0.550 48.0 191.0 4754.0 19016.0 475381.0 1901523 1901523 475381 \n", - "0.600 47.0 185.0 4610.0 18440.0 460976.0 1843901 1843901 460976 \n", - "0.650 44.0 175.0 4370.0 17479.0 436966.0 1747864 1747864 436966 \n", - "0.700 41.0 162.0 4034.0 16135.0 403354.0 1613413 1613413 403354 \n", - "0.750 37.0 145.0 3602.0 14406.0 360137.0 1440548 1440548 360137 \n", - "0.800 31.0 123.0 3074.0 12293.0 307317.0 1229267 1229267 307317 \n", - "0.850 25.0 98.0 2449.0 9796.0 244893.0 979572 979572 244893 \n", - "0.900 18.0 70.0 1729.0 6915.0 172866.0 691463 691463 172866 \n", - "0.950 10.0 37.0 913.0 3650.0 91235.0 364939 364939 91235 \n", - "\n", - " 0.010 0.020 0.100 0.200 \n", - "0.001 77 20 1.0 1.0 \n", - "0.002 154 39 2.0 1.0 \n", - "0.050 3650 913 37.0 10.0 \n", - "0.100 6915 1729 70.0 18.0 \n", - "0.150 9796 2449 98.0 25.0 \n", - "0.200 12293 3074 123.0 31.0 \n", - "0.250 14406 3602 145.0 37.0 \n", - "0.300 16135 4034 162.0 41.0 \n", - "0.350 17479 4370 175.0 44.0 \n", - "0.400 18440 4610 185.0 47.0 \n", - "0.450 19016 4754 191.0 48.0 \n", - "0.500 19208 4802 193.0 49.0 \n", - "0.550 19016 4754 191.0 48.0 \n", - "0.600 18440 4610 185.0 47.0 \n", - "0.650 17479 4370 175.0 44.0 \n", - "0.700 16135 4034 162.0 41.0 \n", - "0.750 14406 3602 145.0 37.0 \n", - "0.800 12293 3074 123.0 31.0 \n", - "0.850 9796 2449 98.0 NaN \n", - "0.900 6915 1729 70.0 NaN \n", - "0.950 3650 913 NaN NaN " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import norm\n", - "import pandas\n", - "from pandas import DataFrame\n", - "import numpy\n", - "\n", - "\n", - "def pvalue(p, q, N):\n", - " theta = abs(p-q)\n", - " var = p*(1-p) \n", - " bn = (2*N)**0.5 * theta / var**0.5\n", - " ret = (1 - norm.cdf(bn))*2\n", - " return ret\n", - "\n", - "def pvalue_N(p, q, alpha):\n", - " theta = abs(p-q)\n", - " var = p*(1-p) \n", - " rev = abs(norm.ppf (alpha/2))\n", - " N = 2 * (rev * var**0.5 / theta)** 2\n", - " return int(N+1)\n", - "\n", - "def alphatable(ps, dps, alpha):\n", - " values = []\n", - " for p in ps :\n", - " row=[]\n", - " for dp in dps :\n", - " q = p+dp\n", - " r = pvalue_N(p,q,alpha) if 1 >= q >= 0 else numpy.nan\n", - " row.append (r)\n", - " values.append (row)\n", - " return values\n", - " \n", - "def dataframe(ps,dps,table):\n", - " columns = dps\n", - " df = pandas.DataFrame(data=table, index=ps)\n", - " df.columns = dps\n", - " return df\n", - " \n", - " \n", - "print (\"norm.ppf(0.025)\",norm.ppf (0.025)) # -1.9599639845400545\n", - "ps = [0.001, 0.002] + [ 0.05*i for i in range (1,20) ]\n", - "dps = [ -0.2, -0.1, -0.02, -0.01, -0.002, -0.001,\n", - " 0.2, 0.1, 0.02, 0.01, 0.002, 0.001, ]\n", - "dps.sort()\n", - "t = alphatable(ps, dps, 0.05)\n", - "dataframe (ps, dps, t)" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## p-values in 2D" - ] - }, + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import random\n", + "import math\n", + "import matplotlib.pyplot as pylab\n", + "\n", + "\n", + "def matrix_square_root(sigma):\n", + " eigen, vect = numpy.linalg.eig(sigma)\n", + " dim = len(sigma)\n", + " res = numpy.identity(dim)\n", + " for i in range(dim):\n", + " res[i, i] = eigen[i] ** 0.5\n", + " return vect * res * vect.transpose()\n", + "\n", + "\n", + "def chi2_level(alpha=0.95):\n", + " N = 1000\n", + " x = [random.gauss(0, 1) for _ in range(N)]\n", + " y = [random.gauss(0, 1) for _ in range(N)]\n", + " r = map(lambda c: (c[0] ** 2 + c[1] ** 2) ** 0.5, zip(x, y))\n", + " r = list(r)\n", + " r.sort()\n", + " res = r[int(alpha * N)]\n", + " return res\n", + "\n", + "\n", + "def square_figure(mat, a):\n", + " x = []\n", + " y = []\n", + " for i in range(100):\n", + " x.append(a * mat[0][0] ** 0.5)\n", + " y.append((random.random() - 0.5) * a * mat[1][1] ** 0.5 * 2)\n", + " x.append(-a * mat[0][0] ** 0.5)\n", + " y.append((random.random() - 0.5) * a * mat[1][1] ** 0.5 * 2)\n", + "\n", + " y.append(a * mat[1][1] ** 0.5)\n", + " x.append((random.random() - 0.5) * a * mat[0][0] ** 0.5 * 2)\n", + " y.append(-a * mat[1][1] ** 0.5)\n", + " x.append((random.random() - 0.5) * a * mat[0][0] ** 0.5 * 2)\n", + "\n", + " pylab.plot(x, y, \"ro\")\n", + "\n", + " x = []\n", + " y = []\n", + " for i in range(100):\n", + " x.append(a)\n", + " y.append((random.random() - 0.5) * a * 2)\n", + " x.append(-a)\n", + " y.append((random.random() - 0.5) * a * 2)\n", + "\n", + " y.append(a)\n", + " x.append((random.random() - 0.5) * a * 2)\n", + " y.append(-a)\n", + " x.append((random.random() - 0.5) * a * 2)\n", + "\n", + " xs, ys = [], []\n", + " for a, b in zip(x, y):\n", + " ar = numpy.matrix([[a], [b]]).transpose()\n", + " we = ar * root\n", + " xs.append(we[0, 0])\n", + " ys.append(we[0, 1])\n", + "\n", + " pylab.plot(xs, ys, \"bo\")\n", + " pylab.show()\n", + "\n", + "\n", + "def circle_figure(mat, a):\n", + " x = []\n", + " y = []\n", + " for i in range(200):\n", + " z = random.random() * math.pi * 2\n", + " i = a * mat[0][0] ** 0.5 * math.cos(z)\n", + " j = a * mat[0][0] ** 0.5 * math.sin(z)\n", + " x.append(i)\n", + " y.append(j)\n", + " pylab.plot(x, y, \"ro\")\n", + "\n", + " x = []\n", + " y = []\n", + " for i in range(200):\n", + " z = random.random() * math.pi * 2\n", + " i = a * math.cos(z)\n", + " j = a * math.sin(z)\n", + " x.append(i)\n", + " y.append(j)\n", + "\n", + " xs, ys = [], []\n", + " for a, b in zip(x, y):\n", + " ar = numpy.matrix([[a], [b]]).transpose()\n", + " we = ar * root\n", + " xs.append(we[0, 0])\n", + " ys.append(we[0, 1])\n", + "\n", + " pylab.plot(xs, ys, \"bo\")\n", + " pylab.show()\n", + "\n", + "\n", + "level = chi2_level()\n", + "mat = [[0.1, 0.05], [0.05, 0.2]]\n", + "npmat = numpy.matrix(mat)\n", + "root = matrix_square_root(npmat)\n", + "square_figure(mat, 1.96)\n", + "circle_figure(mat, level)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## p-value ratio" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy, matplotlib, random, math\n", - "import matplotlib.pyplot as pylab\n", - "\n", - "\n", - "def matrix_square_root(sigma) :\n", - " eigen, vect = numpy.linalg.eig(sigma)\n", - " dim = len(sigma)\n", - " res = numpy.identity(dim)\n", - " for i in range(0,dim) :\n", - " res[i,i] = eigen[i]**0.5\n", - " return vect * res * vect.transpose()\n", - " \n", - "def chi2_level (alpha = 0.95) :\n", - " N = 1000\n", - " x = [ random.gauss(0,1) for _ in range(0,N) ]\n", - " y = [ random.gauss(0,1) for _ in range(0,N) ]\n", - " r = map ( lambda c : (c[0]**2+c[1]**2)**0.5, zip(x,y))\n", - " r = list(r)\n", - " r.sort()\n", - " res = r [ int (alpha * N) ]\n", - " return res\n", - " \n", - "def square_figure(mat, a) : \n", - " x = [ ]\n", - " y = [ ]\n", - " for i in range (0,100) :\n", - " x.append( a * mat[0][0]**0.5 ) \n", - " y.append( (random.random ()-0.5) * a * mat[1][1]**0.5*2 )\n", - " x.append( -a * mat[0][0]**0.5 ) \n", - " y.append( (random.random ()-0.5) * a * mat[1][1]**0.5*2 )\n", - "\n", - " y.append( a * mat[1][1]**0.5 ) \n", - " x.append( (random.random ()-0.5) * a * mat[0][0]**0.5*2 )\n", - " y.append( -a * mat[1][1]**0.5 ) \n", - " x.append( (random.random ()-0.5) * a * mat[0][0]**0.5*2 )\n", - " \n", - " pylab.plot(x,y, 'ro')\n", - " \n", - " x = [ ]\n", - " y = [ ]\n", - " for i in range (0,100) :\n", - " x.append( a ) \n", - " y.append( (random.random ()-0.5) * a*2 )\n", - " x.append( -a ) \n", - " y.append( (random.random ()-0.5) * a*2 )\n", - " \n", - " y.append( a ) \n", - " x.append( (random.random ()-0.5) * a*2 )\n", - " y.append( -a ) \n", - " x.append( (random.random ()-0.5) * a*2 )\n", - " \n", - " xs,ys = [],[]\n", - " for a,b in zip (x,y) :\n", - " ar = numpy.matrix( [ [a], [b] ] ).transpose()\n", - " we = ar * root\n", - " xs.append( we [0,0] )\n", - " ys.append( we [0,1] )\n", - " \n", - " pylab.plot(xs,ys, 'bo')\n", - " pylab.show()\n", - "\n", - "def circle_figure (mat, a) :\n", - " x = [ ]\n", - " y = [ ]\n", - " for i in range (0,200) :\n", - " z = random.random() * math.pi * 2\n", - " i = a * mat[0][0]**0.5 * math.cos(z)\n", - " j = a * mat[0][0]**0.5 * math.sin(z)\n", - " x.append ( i )\n", - " y.append ( j )\n", - " pylab.plot(x,y, 'ro')\n", - " \n", - " x = [ ]\n", - " y = [ ]\n", - " for i in range (0,200) :\n", - " z = random.random() * math.pi * 2\n", - " i = a * math.cos(z)\n", - " j = a * math.sin(z)\n", - " x.append ( i )\n", - " y.append ( j )\n", - " \n", - " xs,ys = [],[]\n", - " for a,b in zip (x,y) :\n", - " ar = numpy.matrix( [ [a], [b] ] ).transpose()\n", - " we = ar * root\n", - " xs.append( we [0,0] )\n", - " ys.append( we [0,1] )\n", - " \n", - " pylab.plot(xs,ys, 'bo')\n", - " pylab.show()\n", - "\n", - "level = chi2_level ()\n", - "mat = [ [0.1, 0.05], [0.05, 0.2] ]\n", - "npmat = numpy.matrix(mat)\n", - "root = matrix_square_root (npmat)\n", - "square_figure (mat, 1.96)\n", - "circle_figure (mat, level)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9487\n" + ] + } + ], + "source": [ + "def densite_gauss(mu, sigma, x):\n", + " e = -((x - mu) ** 2) / (sigma**2 * 2)\n", + " d = 1.0 / ((2 * math.pi) ** 0.5 * sigma)\n", + " return d * math.exp(e)\n", + "\n", + "\n", + "def simulation_vector(N, mu, sigma):\n", + " return [random.gauss(mu, sigma) for n in range(N)]\n", + "\n", + "\n", + "def ratio(vector, x, fdensite):\n", + " under = 0\n", + " above = 0\n", + " fx = fdensite(x)\n", + " for u in vector:\n", + " f = fdensite(u)\n", + " if f >= fx:\n", + " above += 1\n", + " else:\n", + " under += 1\n", + " return float(above) / float(above + under)\n", + "\n", + "\n", + "x = 1.96\n", + "N = 10000\n", + "mu = 0\n", + "sigma = 1\n", + "\n", + "v = simulation_vector(N, mu, sigma)\n", + "g = ratio(v, x, lambda y: densite_gauss(mu, sigma, y))\n", + "print(g)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## p-values and EM\n", + "\n", + "See [Applying the EM Algorithm: Binomial Mixtures](http://statisticalrecipes.blogspot.fr/2012/04/applying-em-algorithm-binomial-mixtures.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## p-value ratio" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "------- sample\n", + "ave 0.38\n", + "mea 0.373\n", + "min lk -3393.2292120130046 0.373\n" + ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9487\n" - ] - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
averagepipqlikelihood
00.3730.0003240.3418770.373010-9358.705695
10.3730.8637470.2847880.932204-4531.967709
20.3730.9360830.3461010.766941-4490.512057
30.3730.1230230.2909640.384508-3563.557269
40.3730.5388350.0535840.746213-3487.438442
50.3730.3463510.0578800.539974-3302.391944
60.3730.7975400.3764910.359248-3144.938682
70.3730.3925200.5925630.231131-2902.915478
80.3730.3902410.4594880.317648-2778.903072
90.3730.6091270.3380620.427447-2764.987703
\n", + "
" ], - "source": [ - "import random, math\n", - "\n", - "def densite_gauss (mu, sigma, x) :\n", - " e = -(x - mu)**2 / (sigma**2 * 2)\n", - " d = 1. / ((2*math.pi)**0.5 * sigma)\n", - " return d * math.exp (e)\n", - " \n", - "def simulation_vector (N, mu, sigma) :\n", - " return [ random.gauss(mu,sigma) for n in range(N) ]\n", - " \n", - "def ratio (vector, x, fdensite) :\n", - " under = 0\n", - " above = 0\n", - " fx = fdensite(x)\n", - " for u in vector:\n", - " f = fdensite (u)\n", - " if f >= fx:\n", - " above += 1\n", - " else:\n", - " under += 1\n", - " return float(above) / float (above + under)\n", - " \n", - "x = 1.96\n", - "N = 10000\n", - "mu = 0\n", - "sigma = 1\n", - "\n", - "v = simulation_vector(N, mu, sigma)\n", - "g = ratio(v, x, lambda y: densite_gauss (mu, sigma, y) )\n", - "print (g)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## p-values and EM\n", - "\n", - "See [Applying the EM Algorithm: Binomial Mixtures](http://statisticalrecipes.blogspot.fr/2012/04/applying-em-algorithm-binomial-mixtures.html)." + "text/plain": [ + " average pi p q likelihood\n", + "0 0.373 0.000324 0.341877 0.373010 -9358.705695\n", + "1 0.373 0.863747 0.284788 0.932204 -4531.967709\n", + "2 0.373 0.936083 0.346101 0.766941 -4490.512057\n", + "3 0.373 0.123023 0.290964 0.384508 -3563.557269\n", + "4 0.373 0.538835 0.053584 0.746213 -3487.438442\n", + "5 0.373 0.346351 0.057880 0.539974 -3302.391944\n", + "6 0.373 0.797540 0.376491 0.359248 -3144.938682\n", + "7 0.373 0.392520 0.592563 0.231131 -2902.915478\n", + "8 0.373 0.390241 0.459488 0.317648 -2778.903072\n", + "9 0.373 0.609127 0.338062 0.427447 -2764.987703" ] - }, + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import norm\n", + "\n", + "\n", + "def average_std_deviation(sample):\n", + " mean = 0.0\n", + " var = 0.0\n", + " for x in sample:\n", + " mean += x\n", + " var += x * x\n", + " mean /= len(sample)\n", + " var /= len(sample)\n", + " var -= mean * mean\n", + " return mean, var**0.5\n", + "\n", + "\n", + "def bootsample(sample):\n", + " n = len(sample) - 1\n", + " return [sample[random.randint(0, n)] for _ in sample]\n", + "\n", + "\n", + "def bootstrap_difference(sampleX, sampleY, draws=2000, confidence=0.05):\n", + " diff = []\n", + " for n in range(draws):\n", + " if n % 1000 == 0:\n", + " print(n)\n", + " sx = bootsample(sampleX)\n", + " sy = bootsample(sampleY)\n", + " px = sum(sx) * 1.0 / len(sx)\n", + " py = sum(sy) * 1.0 / len(sy)\n", + " diff.append(px - py)\n", + " diff.sort()\n", + " n = int(len(diff) * confidence / 2)\n", + " av = sum(diff) / len(diff)\n", + " return av, diff[n], diff[len(diff) - n]\n", + "\n", + "\n", + "# generation of a sample\n", + "\n", + "\n", + "def generate_obs(p):\n", + " x = random.random()\n", + " if x <= p:\n", + " return 1\n", + " else:\n", + " return 0\n", + "\n", + "\n", + "def generate_n_obs(p, n):\n", + " return [generate_obs(p) for i in range(n)]\n", + "\n", + "\n", + "# std deviation\n", + "\n", + "\n", + "def diff_std_deviation(px, py):\n", + " s = px * (1 - px) + py * (1 - py)\n", + " return px, py, s**0.5\n", + "\n", + "\n", + "def pvalue_(diff, std, N):\n", + " theta = abs(diff)\n", + " bn = (2 * N) ** 0.5 * theta / std\n", + " pv = (1 - norm.cdf(bn)) * 2\n", + " return pv\n", + "\n", + "\n", + "def omega_i(X, pi, p, q):\n", + " np = p * pi if X == 1 else (1 - p) * pi\n", + " nq = q * (1 - pi) if X == 1 else (1 - q) * (1 - pi)\n", + " return np / (np + nq)\n", + "\n", + "\n", + "def likelihood(X, pi, p, q):\n", + " np = p * pi if X == 1 else (1 - p) * pi\n", + " nq = q * (1 - pi) if X == 1 else (1 - q) * (1 - pi)\n", + " return math.log(np) + math.log(nq)\n", + "\n", + "\n", + "def algoEM(sample):\n", + " p = random.random()\n", + " q = random.random()\n", + " pi = random.random()\n", + " iter = 0\n", + " while iter < 10:\n", + " lk = sum([likelihood(x, pi, p, q) for x in sample])\n", + " wi = [omega_i(x, pi, p, q) for x in sample]\n", + " sw = sum(wi)\n", + " pin = sum(wi) / len(wi)\n", + " pn = sum([x * w for x, w in zip(sample, wi)]) / sw\n", + " qn = sum([x * (1 - w) for x, w in zip(sample, wi)]) / (len(wi) - sw)\n", + "\n", + " pi, p, q = pin, pn, qn\n", + " iter += 1\n", + "\n", + " lk = sum([likelihood(x, pi, p, q) for x in sample])\n", + " return pi, p, q, lk\n", + "\n", + "\n", + "# mix\n", + "p, q = 0.20, 0.80\n", + "pi = 0.7\n", + "N = 1000\n", + "na = int(N * pi)\n", + "nb = N - na\n", + "\n", + "print(\"------- sample\")\n", + "sampleX = generate_n_obs(p, na) + generate_n_obs(q, nb)\n", + "random.shuffle(sampleX)\n", + "print(\"ave\", p * pi + q * (1 - pi))\n", + "print(\"mea\", sum(sampleX) * 1.0 / len(sampleX))\n", + "\n", + "lk = sum([likelihood(x, pi, p, q) for x in sampleX])\n", + "print(\"min lk\", lk, sum(sampleX) * 1.0 / len(sampleX))\n", + "res = []\n", + "for k in range(10):\n", + " r = algoEM(sampleX)\n", + " res.append((r[-1], r))\n", + "res.sort()\n", + "\n", + "rows = []\n", + "for r in res:\n", + " pi, p, q, lk = r[1]\n", + " rows.append([p * pi + q * (1 - pi)] + list(r[1]))\n", + "\n", + "df = pandas.DataFrame(data=rows)\n", + "df.columns = [\"average\", \"pi\", \"p\", \"q\", \"likelihood\"]\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## p-value and heavy tail" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------- sample\n", - "ave 0.38\n", - "mea 0.373\n", - "min lk -3393.2292120130046 0.373\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
averagepipqlikelihood
00.3730.0003240.3418770.373010-9358.705695
10.3730.8637470.2847880.932204-4531.967709
20.3730.9360830.3461010.766941-4490.512057
30.3730.1230230.2909640.384508-3563.557269
40.3730.5388350.0535840.746213-3487.438442
50.3730.3463510.0578800.539974-3302.391944
60.3730.7975400.3764910.359248-3144.938682
70.3730.3925200.5925630.231131-2902.915478
80.3730.3902410.4594880.317648-2778.903072
90.3730.6091270.3380620.427447-2764.987703
\n", - "
" - ], - "text/plain": [ - " average pi p q likelihood\n", - "0 0.373 0.000324 0.341877 0.373010 -9358.705695\n", - "1 0.373 0.863747 0.284788 0.932204 -4531.967709\n", - "2 0.373 0.936083 0.346101 0.766941 -4490.512057\n", - "3 0.373 0.123023 0.290964 0.384508 -3563.557269\n", - "4 0.373 0.538835 0.053584 0.746213 -3487.438442\n", - "5 0.373 0.346351 0.057880 0.539974 -3302.391944\n", - "6 0.373 0.797540 0.376491 0.359248 -3144.938682\n", - "7 0.373 0.392520 0.592563 0.231131 -2902.915478\n", - "8 0.373 0.390241 0.459488 0.317648 -2778.903072\n", - "9 0.373 0.609127 0.338062 0.427447 -2764.987703" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import norm\n", - "import random, math\n", - "\n", - "\n", - "def average_std_deviation(sample):\n", - " mean = 0.\n", - " var = 0.\n", - " for x in sample:\n", - " mean += x\n", - " var += x*x\n", - " mean /= len(sample)\n", - " var /= len(sample)\n", - " var -= mean*mean\n", - " return mean,var ** 0.5\n", - "\n", - "def bootsample(sample):\n", - " n = len(sample)-1\n", - " return [ sample[ random.randint(0,n) ] for _ in sample ]\n", - " \n", - "def bootstrap_difference(sampleX, sampleY, draws=2000, confidence=0.05):\n", - " diff = [ ]\n", - " for n in range (0,draws) :\n", - " if n % 1000 == 0: \n", - " print(n)\n", - " sx = bootsample(sampleX)\n", - " sy = bootsample(sampleY)\n", - " px = sum(sx) * 1.0/ len(sx)\n", - " py = sum(sy) * 1.0/ len(sy)\n", - " diff.append (px-py) \n", - " diff.sort()\n", - " n = int(len(diff) * confidence / 2)\n", - " av = sum(diff) / len(diff)\n", - " return av, diff [n], diff [len(diff)-n]\n", - "\n", - "# generation of a sample\n", - "\n", - "def generate_obs(p):\n", - " x = random.random()\n", - " if x <= p : return 1\n", - " else : return 0\n", - "\n", - "def generate_n_obs(p, n):\n", - " return [ generate_obs(p) for i in range (0,n) ]\n", - " \n", - "# std deviation\n", - "\n", - "def diff_std_deviation(px, py):\n", - " s = px*(1-px) + py*(1-py)\n", - " return px, py, s**0.5\n", - "\n", - "def pvalue(diff, std, N):\n", - " theta = abs(diff)\n", - " bn = (2*N)**0.5 * theta / std\n", - " pv = (1 - norm.cdf(bn))*2\n", - " return pv\n", - " \n", - "def omega_i (X, pi, p, q) :\n", - " np = p * pi if X == 1 else (1-p)*pi\n", - " nq = q * (1-pi) if X == 1 else (1-q)*(1-pi)\n", - " return np / (np + nq)\n", - " \n", - "def likelihood (X, pi, p, q) :\n", - " np = p * pi if X == 1 else (1-p)*pi\n", - " nq = q * (1-pi) if X == 1 else (1-q)*(1-pi)\n", - " return math.log(np) + math.log(nq)\n", - " \n", - "def algoEM (sample):\n", - " p = random.random()\n", - " q = random.random()\n", - " pi = random.random()\n", - " iter = 0\n", - " while iter < 10 :\n", - " lk = sum ( [ likelihood (x, pi, p, q) for x in sample ] )\n", - " wi = [ omega_i (x, pi, p, q) for x in sample ]\n", - " sw = sum(wi)\n", - " pin = sum(wi) / len(wi)\n", - " pn = sum([ x * w for x,w in zip (sample,wi) ]) / sw\n", - " qn = sum([ x * (1-w) for x,w in zip (sample,wi) ]) / (len(wi) - sw)\n", - " \n", - " pi,p,q = pin,pn,qn\n", - " iter += 1\n", - " \n", - " lk = sum ( [ likelihood (x, pi, p, q) for x in sample ] )\n", - " return pi,p,q, lk\n", - "\n", - "\n", - "# mix\n", - "p,q = 0.20, 0.80\n", - "pi = 0.7\n", - "N = 1000\n", - "na = int(N * pi)\n", - "nb = N - na\n", - "\n", - "print(\"------- sample\")\n", - "sampleX = generate_n_obs(p, na) + generate_n_obs (q, nb)\n", - "random.shuffle(sampleX)\n", - "print(\"ave\", p * pi + q*(1-pi))\n", - "print(\"mea\", sum(sampleX)*1./len(sampleX))\n", - "\n", - "lk = sum ( [ likelihood (x, pi, p, q) for x in sampleX ] )\n", - "print (\"min lk\", lk, sum (sampleX)*1. / len(sampleX))\n", - "res = []\n", - "for k in range (0, 10) :\n", - " r = algoEM (sampleX)\n", - " res.append ( (r[-1], r) )\n", - "res.sort ()\n", - "\n", - "rows = []\n", - "for r in res:\n", - " pi,p,q,lk = r[1]\n", - " rows.append( [p * pi + q*(1-pi)] + list(r[1]))\n", - "\n", - "df = pandas.DataFrame(data=rows)\n", - "df.columns = [\"average\", \"pi\", \"p\", \"q\", \"likelihood\"]\n", - "df" + "data": { + "text/plain": [ + "[357621, 148, 18, 1812876449, 36150]" ] - }, + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import norm, zipf\n", + "\n", + "\n", + "def generate_n_obs_zipf(tail_index, n):\n", + " return list(zipf.rvs(tail_index, size=n))\n", + "\n", + "\n", + "def hill_estimator(sample):\n", + " sample = list(sample)\n", + " sample.sort(reverse=True)\n", + " end = len(sample) / 10\n", + " end = min(end, 100)\n", + " s = 0.0\n", + " res = []\n", + " for k in range(end):\n", + " s += math.log(sample[k])\n", + " h = (s - (k + 1) * math.log(sample[k + 1])) / (k + 1)\n", + " h = 1.0 / h\n", + " res.append([k, h])\n", + " return res\n", + "\n", + "\n", + "# mix\n", + "tail_index = 1.05\n", + "N = 10000\n", + "\n", + "sample = generate_n_obs_zipf(tail_index, N)\n", + "sample[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## p-value and heavy tail" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "[9999, 55186871.0339, 233342554.46156308]\n" + ] }, { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[357621, 148, 18, 1812876449, 36150]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import norm, zipf\n", - "import sys\n", - "\n", - "\n", - "def generate_n_obs_zipf (tail_index, n) :\n", - " return list(zipf.rvs(tail_index, size=n)) \n", - " \n", - "def hill_estimator (sample) :\n", - " sample = list(sample)\n", - " sample.sort(reverse=True)\n", - " end = len(sample)/10\n", - " end = min(end,100)\n", - " s = 0.\n", - " res = []\n", - " for k in range (0,end) :\n", - " s += math.log(sample[k])\n", - " h = (s - (k+1)*math.log(sample[k+1]))/(k+1)\n", - " h = 1./h\n", - " res.append( [k, h] )\n", - " return res\n", - " \n", - "# mix\n", - "tail_index = 1.05\n", - "N = 10000\n", - "\n", - "sample = generate_n_obs_zipf(tail_index, N)\n", - "sample[:5]" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas\n", + "\n", + "\n", + "def graph_XY(\n", + " curves,\n", + " xlabel=None,\n", + " ylabel=None,\n", + " marker=True,\n", + " link_point=False,\n", + " title=None,\n", + " format_date=\"%Y-%m-%d\",\n", + " legend_loc=0,\n", + " figsize=None,\n", + " ax=None,\n", + "):\n", + " if ax is None:\n", + " import matplotlib.pyplot as plt\n", + "\n", + " _fig, ax = plt.subplots(1, 1, figsize=figsize)\n", + "\n", + " smarker = {\n", + " (True, True): \"o-\",\n", + " (True, False): \"o\",\n", + " (False, True): \"-\",\n", + " # (False, False) :''\n", + " }[marker, link_point]\n", + " for xf, yf, label in curves:\n", + " ax.plot(xf, yf, smarker, label=label)\n", + " ax.legend(loc=legend_loc)\n", + " return ax\n", + "\n", + "\n", + "def draw_variance(sample):\n", + " avg = 0.0\n", + " std = 0.0\n", + " n = 0.0\n", + " w = 1.0\n", + " add = []\n", + " for i, x in enumerate(sample):\n", + " x = float(x)\n", + " avg += x * w\n", + " std += x * x * w\n", + " n += w\n", + " val = (std / n - (avg / n) ** 2) ** 0.5\n", + " add.append([i, avg / n, val])\n", + "\n", + " print(add[-1])\n", + " table = pandas.DataFrame(add, columns=[\"index\", \"avg(n)\", \"std(n)\"])\n", + " return graph_XY(\n", + " [\n", + " [table[\"index\"], table[\"avg(n)\"], \"avg(n)\"],\n", + " [table[\"index\"], table[\"std(n)\"], \"std(n)\"],\n", + " ],\n", + " marker=False,\n", + " link_point=True,\n", + " )\n", + "\n", + "\n", + "draw_variance(sample);" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[9999, 55186871.0339, 233342554.46156308]\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAEDCAYAAADz4SVPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deZwcdZ3/8denjzkzmVyTg4SQcIUEApoMEAzGCIKEU1jkUFlx3c26HivuevFz/QVW96HygH2gP1fcCIgHAnJ4RUU5gkLkcBKEhFwEQsLknEySyUzm6uP7++NbM9MzmaMnmZ7pmryfj0c/urvq29Xf6up+17e+VV1lzjlERCS/RYa6AiIi0jeFtYhICCisRURCQGEtIhICCmsRkRBQWIuIhEDOwtrM7jWz3Wa2JouyU81suZm9bGavmtnFuaqXiEgY5bJlfR9wUZZl/wP4uXPuncB1wPdyVSkRkTDKWVg75/4M7M0cZmYnmNnjZrbSzJ41s1PaigMjg8flwPZc1UtEJIxig/x+S4FPOOdeN7Oz8S3o84BbgD+a2WeAUuB9g1wvEZG8NmhhbWYjgHcBD5tZ2+DC4P564D7n3B1mdg7wEzM7zTmXHqz6iYjks8FsWUeA/c65d3Qz7uME/dvOuefNrAgYB+wexPqJiOStQTt0zzl3ANhsZh8EMO+MYPRW4Pxg+EygCKgZrLqJiOQ7y9VZ98zsAWAhvoW8C1gCPA3cBUwC4sCDzrn/NLNZwA+AEfidjV90zv0xJxUTEQmhnIW1iIgMHP2DUUQkBHKyg3HcuHFu2rRpuZi0iMiwtHLlyj3OuYqexuckrKdNm0ZVVVUuJi0iMiyZ2ZbexqsbREQkBLIKazP7nJm9ZmZrzOyB4DhoEREZJH2GtZlNBv4VqHTOnQZE8SdbEhGRQZJtn3UMKDazBFCCTrQkIv2QSCSorq6mubl5qKsy5IqKipgyZQrxeLxfr+szrJ1z28zsdvy/DJuAP3b3hxUzWwwsBpg6dWq/KiEiw1t1dTVlZWVMmzaNjHMDHXWcc9TW1lJdXc306dP79dpsukFGA1cA04FjgFIz+0g3lVjqnKt0zlVWVPR49ImIHIWam5sZO3bsUR3UAGbG2LFjD2sLI5sdjO8DNjvnapxzCeAx/NnzRESydrQHdZvD/RyyCeutwDwzKzH/LucD6w7r3fqy+VnY83pOJi0iEmZ9hrVz7kXgEWAVsDp4zdKc1OZHl8J3K3MyaRGRgXDTTTfx5z//udcyy5YtY8mSJQP6vlkdZ+2cW+KcO8U5d5pz7gbnXMuA1kJEJAT27t3LCy+8wIIFC3otd8kll/DrX/+axsbGAXtv/YNRRI4KH/jAB5g7dy6nnnoqS5cu5a677uKLX/xi+/j77ruPz3zmMwB87Wtf45RTTuGCCy7g+uuv5/bbbwfgkUce4aKLOq4DPm3aNJYsWcKcOXOYPXs269evB3y/9MKFC1m2bNmA1X+wr8EoIke5W3/zGmu3HxjQac46ZiRLLju11zL33nsvY8aMoampiTPPPJOnnnqK+fPnc9tttwHw0EMP8ZWvfIWqqioeffRRXn75ZZLJJHPmzGHu3LkArFixgquvvrrTdMeNG8eqVav43ve+x+23387dd98NQGVlJc8++yzXXHPNgMyjWtYiclT4zne+wxlnnMG8efN4++232bx5M8cffzwvvPACtbW1bNiwgfnz5/Pcc89xxRVXUFxcTFlZGZdddln7NHbs2EHXQ5OvuuoqAObOnctbb73VPnz8+PFs3z5w/x9Uy1pEBlVfLeBceOaZZ3jyySd5/vnnKSkpYeHChTQ3N3Pttdfy85//nFNOOYUrr7wSM6O3C7IUFxcfcox0YaG/7nc0GiWZTLYPb25upri4eMDmQS1rERn26urqGD16NCUlJaxfv54XXngB8K3iX/7ylzzwwANce+21AJx77rn85je/obm5mYaGBn7729+2T2fmzJls2rQpq/fcuHEjp5122oDNg8JaRIa9iy66iGQyyemnn85Xv/pV5s2bB8Do0aOZNWsWW7Zs4ayzzgLgzDPP5PLLL+eMM87gqquuorKykvLycsAf5fHMM89k9Z7Lly/nkksuGbB5yMk1GCsrK91hXXzglvLgvm5gKyQiQ2rdunXMnDlzqKuRtYaGBkaMGEFjYyMLFixg6dKlzJkzB/At72XLljFq1KgeX79r1y4+9KEP8dRTT3U7vrvPw8xWOud6/KOJ+qxFRLpYvHgxa9eupbm5mY9+9KPtQQ1wxx13sHXr1l7DeuvWrdxxxx0DWieFtYhIFz/72c96HHf22Wf3+fozzzxzIKsDqM9aRCQUFNYiIiGgsBYRCQGFtYhICCisRURCQGEtIkelO++8s8dTmN533318+tOf7lT2xz/+ca/TW716NTfeeONAVrEThbWIHJV6C+tMyWSSe++9lw996EO9lps9ezbV1dVs3bp1oKrYSZ/HWZvZDOChjEHHA//XOXdnTmokIsPb778MO1cP7DQnzoZF3+xx9MGDB7nmmmuorq4mlUrxwQ9+kO3bt/Pe976XcePGsXz5cn74wx/yjW98g0mTJnHyySe3n6Dp6aefZs6cOcRiPi4XLlzI2WefzfLly9m/fz/33HMP7373uwG47LLLePDBBzudJ3ugZHNZrw3OuXc4594BzAUagV8MeE1ERHLk8ccf55hjjuGVV15hzZo13HTTTRxzzDEsX76c5cuXs2PHDpYsWcKKFSt44oknWLt2bftrV6xY0X4+6zbJZJKXXnqJO++8k1tvvbV9eNs5rHOhv/9gPB94wzm3JReVEZGjQC8t4FyZPXs2n//85/nSl77EpZde2t4SbvPiiy+ycOHC9nNVX3vttWzcuBHw57Dueh6PwTqHdab+hvV1wAO5qIiISK6cfPLJrFy5kt/97nfcfPPNXHjhhYeUMbNuXzuU57DOlPUORjMrAC4HHu5h/GIzqzKzqpqamoGqn4jIEdu+fTslJSV85CMf4fOf/zyrVq2irKyM+vp6wJ/v45lnnqG2tpZEIsHDD3fE3FCewzpTf1rWi4BVzrld3Y10zi0FloI/ReoA1E1EZECsXr2aL3zhC0QiEeLxOHfddRfPP/88ixYtYtKkSSxfvpxbbrmFc845h0mTJjFnzhxSqRQAixYt4oYbbsjqfQb6HNaZsj6ftZk9CPzBOffDvsrqfNYikils57Pu6sorr+S2227jpJNO6rFMS0sL73nPe3juuefajxzpyeGczzqrbhAzKwEuAB7LpryIyHDyzW9+kx07dvRaZuvWrXzzm9/sM6gPV1ZTdc41AmNzUgMROSo453rciZfvZsyYwYwZM3otc9JJJ/Xa8m5zuFfn0j8YRSTnioqKqK2tPeygGi6cc9TW1lJUVNTv1+pKMSKSc1OmTKG6uhodKeZXXFOmTOn36xTWIpJz8Xic6dOnD3U1Qk3dICIiIaCwFhEJAYW1iEgIKKxFREJAYS0iEgIKaxGREFBYi4iEgMJaRCQEFNYiIiGgsBYRCQGFtYhICCisRURCQGEtIhICCmsRkRDI9rJeo8zsETNbb2brzOycXFdMREQ6ZHs+628DjzvnrjazAqAkh3USEZEu+gxrMxsJLABuBHDOtQKtua2WiIhkyqYb5HigBvihmb1sZnebWWnXQma22MyqzKxKl+4RERlY2YR1DJgD3OWceydwEPhy10LOuaXOuUrnXGVFRcUAV1NE5OiWTVhXA9XOuReD54/gw1tERAZJn2HtnNsJvG1mM4JB5wNrc1orERHpJNujQT4D3B8cCfIm8LHcVUlERLrKKqydc38DKnNcFxER6YH+wSgiEgIKaxGREFBYi4iEgMJaRCQEFNYiIiGgsBYRCQGFtYhICCisRURCQGEtIhICCmsRkRBQWIuIhIDCWkQkBBTWIiIhoLAWEQkBhbWISAgorEVEQiCriw+Y2VtAPZACks45XYhARGQQZXtZL4D3Ouf25KwmIiLSI3WDiIiEQLZh7YA/mtlKM1vcXQEzW2xmVWZWVVNTM3A1FBGRrMN6vnNuDrAI+JSZLehawDm31DlX6ZyrrKioGNBKiogc7bIKa+fc9uB+N/AL4KxcVkpERDrrM6zNrNTMytoeAxcCa3JdMRER6ZDN0SATgF+YWVv5nznnHs9prUREpJM+w9o59yZwxiDURUREeqBD90REQkBhLSISAgprEZEQUFiLiISAwlpEJAQU1iIiIaCwFhEJAYW1iEgIKKxFREIgP8PauaGugYhIXsnPsE6nhroGIiJ5JT/D2imsRUQy5WdYp5NDXQMRkbyisBYRCYE8DWt1g4iIZFJYi4iEQNZhbWZRM3vZzJblskKAukFERLroT8v6s8C6XFWkE4W1iEgnWYW1mU0BLgHuzm11AgprEZFOsm1Z3wl8EUj3VMDMFptZlZlV1dTUHFmtXI9vIyJyVOozrM3sUmC3c25lb+Wcc0udc5XOucqKioojq5Va1iIinWTTsp4PXG5mbwEPAueZ2U9zWiuFtYhIJ32GtXPuZufcFOfcNOA64Gnn3EdyWiuFtYhIJ3l6nLXCWkQkU6w/hZ1zzwDP5KQmmdLawSgikkktaxGREFBYi4iEgMJaRCQE8jOsdfEBEZFO8jOsddY9EZFO8jSs1Q0iIpJJYS0iEgL5GdapxFDXQEQkr+RpWLcOdQ1ERPJKXoV1ypl/kGwZ2oqIiOSZvArrVuL+wRNLhrYiIiJ5Js/COjhVSUvd0FZERCTP5FlYx4e6CiIieSmvwjpJdKirICKSl/IqrN1QV0BEJE/lVViLiEj3srlgbpGZvWRmr5jZa2Z262BUDKd2tohIm2xa1i3Aec65M4B3ABeZ2bzcVgv9MUZEJEM2F8x1zrmG4Gk8uOW+2dt6MOdvISISFln1WZtZ1Mz+BuwGnnDOvdhNmcVmVmVmVTU1NUdeM4W1iEi7rMLaOZdyzr0DmAKcZWandVNmqXOu0jlXWVFRceQ1SzQe+TRERIaJfh0N4pzbj7+6+UU5qU2m1oa+y4iIHCWyORqkwsxGBY+LgfcB63NVoa3poFXeqpa1iEibbFrWk4DlZvYq8Fd8n/WyXFWo/S/nNTlbH4iIhE6srwLOuVeBdw5CXQDYQzknsh0ifVZNROSokXf/YNzjyv2DlgNDWxERkTySd2Hd6AohEoemfUNdFRGRvJF3Ye0wKB6lsBYRyZB3YQ1A8Who2j/UtRARyRt5HNZqWYuItMnPsC5SN4iISKb8DGt1g4iIdJLHYb13qGshIpI38jOsyyb4c4O06PwgIiKQxT8Yh0TZJH9fvxMKTzzy6dW+Af9vjn98zqdh7AkwYgKccsmRT1tEZBDkeVjvgHEZYZ1KwOY/wQnng5kf9vDHYOyJcN5Xep7eyvs6Hj//3Y7HF30L5n1iwKotAyDZAjUbINEE42dC0cihrpHks2QLWBSiQZSlUxCJBo/TvjvVIlAwAvZshAPb/C1WBPFi2PcWNO6FZDOkk3540Sj/X4+icogWgEtDXbW/j8ahsMyfDqP2DWjYBQ27oXCEf23pOLjw6zmZ1fwPa4A3lvsfcOEI+NWn4IzrYfYH4cTz4bXHfJmz/9l/UN0pCv7CfskdsPpR2PoX//zxL/nzZr/73+BPt8GMRTBxdu7mS7qXaPIXm9j8J3jkHzqPG3M8TDwdJpwGo4+DcSf74a0NfljxKP9jqVkPf73b/3DHHO/LjTq247u0/rd+eNNeaKn3P766tyFeCqVjYeRk/8Nt2O2/dxb1P+YJp/ppjTvZNxyKR/u6xks6GgzZqH0DGmth+8swepqvy6ipECvsCJgdr/jpjpoKO9fArtU+LMomwoiJvmzNBv++sSJfrvxYiBX0/zPf9xY0H/CfX902KCiF0gp/X1jWv3kbTAd2wK7X4LVf+O9L3dt+eEEZuJT/PceKAeeDPNuLWkVi/maR/p1LP1YM5ZOD1zVB4Ui4sL8zleVb5WayR2hkl7D+yQf8/WXf8fevPOBv8z/b8ZoVd/a8Rlv9sL+fc6O/PXUrTHs3rP65f7xztQ/95f8F194PMy8d6DkKlzWP+uCYPBfGz4LXfgl/+Y5vWcSK4KQLYcIsH4Qv/xQqToEplTB5DoyeDnvf9NMZMd6X373OB05BqQ+fh2+ETU9C+RSo3dR9Hc79Nygo8fXY/jKs/WX35UZP88HTJlroW0gulf38RgsOveZnJOan8+Zyf9+VRWHkMT7YRkzwK5IRE2HTEz7wS8bAqON8K27Xa77lduhE6D5MehreHfP1iMaheEywgjrGr8xc2tejab9fBttX+VBrOdD79KMFfp5GjIfS8T7QLQqJg74V2trgQ6l8sn+vdNI3pErHB6+p8Cu1RBPsXuvrkGj2y7+u2r9HyRi/gt293u+jKhnb0aqNFXacG6hum18W21f5Fd6eDZ3reu7n/DJvrvO3gtKO0G2b/8KRfgWdbPIr/omz/fjWBj+vFTM6TzOVDKa33y835/z3adzJ/nvS0uCnN2K8n/4gyauwbl+XF5b5zZYDQViXjA1aJas6v2DFtzsev/QDOP06/yW5/2q4+HaYNt+Pq9vm7yNR32K48Gv++Ynn+y/Iyz/pmM5DH/b35/2HD4y2Tarhzjm/j6D6r4e2bjOVjoNXftZ52JvLof1Cb/0ImsKR/sdevx3eeYPfAjpuPpxy8aFlmw/4H/qejT7gIzH/A9rxiv9Rlk+Fs/4JTv0AJFth32Zf/sA23xqNFcKMS/yKpGxix4qkdJxvzTbuhcY9UDIO4kV+GPhW7/4t/n33bIQ9r8PW5+GE83wIth70jYp1y/zr24yc7AOydlPH57HoNh8mo6f7FuHezX7aNeuhYqYPttOv8UGwdzNMXwBTzvTTrd/puwHTCR9o0QL/m9j3lp9G/Q5f112vwYbHIdXigznZ5MO07Bj/OU2e40PWOT8PLfX+sykYAQdrOkKqYbffxK97G3at8a9NJXxDKtnqQ2v3Ol/mSC/JGon7+epL6Xhf55Mv8oE78XS/khho0Zjf2iode+i4eHHHlvogy6uwhozFPmqqD47WRv/Fb6zt3Pec6aq74bF/hO/Ph3mf8l/6+y6GL272f65prfctha6bdpFo0Fp3voW4+E++m2XXGnj66/529ifggv/0P47eNg1TSf/D3LYS3noOjnsX/O7z/os487KOgDv1Sjjrn2HqvPza1PzhxR3dQwCnXwtz/t7/IOt3+hAcNdV/UZ2DA9t9GB7cDSe93wfOtpU+GBt2+tZ2OuUf793sf1g163041myAG37Rv1ZJ0UgomuVb9H2JBa2lri0mgClzu39NTz/OaMzvkB57gu8m601L0JrN7Gd3bgCW88n9K+6cr0ck6vtt08nD6yrJRrLVrxAicf9daKjx9037fQDHin3LOZ30LWCL+JVFY61fQU441W+hNdf5FUDzAf+bLSj1W1bFo/00kk1DFpL5wpwb+AuVV1ZWuqqqqn6/bseS6fwpdTrXff1X8MD1sOF3MG7GoZs+7/0PwPluC4Bb6uCPX/Wb6plOvABOfr8PzbZyPWk+0PEjS6d9t8ijH+9cpnAkfPhhH7Rtki3w4Id9a6s/lyKbcBpUfgxmXzM0O9GSLfDmn3xL6dF/gpp1fvisK3xIT1/YsdNGRHLOzFY65yp7Gt/nr9HMjgV+DEwE0sBS59y3e3/VABg93d93DWrwLdXxp8CWFX7zDXzXxtRz4MHr/fNZV8DaX/k+RIAP3tf7+2UGZiQCs6+Gky7wm7e/+qQf3nIA7n2/D9qZl/tyL/+0c5/p6Om+NR6N+9bDzMv94y0r4NSrfCtr9cPw13vgt/8OTyzx/efbV/nNu7q3/Q7UE87reYfpQPj6+M7PLQqfehHGnZS79xSRw5ZN0ykJ/LtzbpWZlQErzewJ59zanNZszPTOz2df43cIQscOnxt+2XkT85SL4aofwJO3wt/dAwdrYctzftzUc/pfh6JyeOeH/W3Hq35Hy0s/8C34XWs6l73wv3zQnfz+7qc19oSOx3NvhDkfhW2roOoe+Nv9fviqH/n7N54GzB+SCL7P/rS/gxe+57uEdr7asQMmEvMriJGToPZNOFDtV2DHzfddMccv9F0QkYz/Pz15a8fji77lNzHP/Vz/Px8RGTTZXNZrB7AjeFxvZuuAycDghnU0Du/5EvzpW37PO3TfF3j6Nf4GcOMy351Rs9H3WR+JSaf7+wu/5ndkvfqQ33E59kS/khg9vX99k2a+/3TKXH8Uy+51MPE0v3fcIv5oiTWPQu3r/vb2C/51+zZ3nk7FTH+IV+3rHcNaG3y51//QMWzEBN99M/5UeO6//bAPP+K3HkQk7/WrU9LMpuGvx/hi7yUHwLiuO4cM3vt//C1bZr5FOtBGTYUFXxi46ZWM6ThyZerZ/v7YM2Hhl/1OOvBhvOs1362SaPQt6sw94a0H/eFRmTvJ6nf6fum3nvWt+G0v+64hgCuXKqhFQiTrsDazEcCjwE3OuUMukGhmi4HFAFOnTj3ympVP6fIGRz7J0DHr2Mk3fqa/Qfd79gtK/S1T2UQ441p/a3NgB2yr8oexiUhoZHUiJzOL44P6fufcY92Vcc4tdc5VOucqKyoqjrxmZvC51+Bd/+qfN+sCugNi5CS/gzaSn+fwEpHu9fmLNTMD7gHWOef+O/dVylA+xf9bDuDtlwb1rUVE8kk2zav5wA3AeWb2t+DWzV/McmRKcNhhf/4+LCIyzGRzNMhzDGWPcbzYn68j89A3EZGjTDj+ona0n1hJRI562sskIhICCmsRkRBQWIuIhIDCWkQkBBTWIiIhoLAWEQkBhbWISAgorEVEQkBhLSISAgprEZEQUFiLiISAwlpEJAQU1iIiIZCXYe2cG+oqiIjklbwM62RaYS0ikimvwtrwIZ1IpYe4JiIi+SWbazDea2a7zWzNYFTIYSy4bflgvJWISGhk07K+D7gox/XoZE9D62C+nYhI3uszrJ1zfwb2DkJdRESkBwPWZ21mi82sysyqampqBmqyIiLCAIa1c26pc67SOVdZUVFxxNNrTqQGoFYiIsNDXh0NEjFrf/xmzcEhrImISH7Jq7DO9Mn7Vw51FURE8kY2h+49ADwPzDCzajP7eO6rBSOKYoPxNiIiodBnIjrnrh+MirSZPq4UdsLOupbBfFsRkbyWd90gkaDbek+DwlpEpE3ehTXAx8+dTjRiJPW3cxERIE/DesbEMlJpx4Zd9UNdFRGRvJCXYV1SEAXgiu+u6LPs9v1NHGxJ9lkunXZsqT3Y3r2ik0WJSJjk5SEXl8yexKd5mWTasbq6jt31zazeVsen3nsidz+7mW89vh6ARadN5PdrdgLwm0+fyymTyohHI6TTjkik45jt6n2NnPutQ08OddWcyXziPSdw8oQyaupbMINxIwoHZybzWCrt2F3fzJjSAuKRCMm0w+FoTqQZURgjYmDBMfGNrX5FWRyPtg9rOx95Ku2IRSM0taYojEWIRAznHHsaWnmjpoGG5iRF8Sh1TQkONCc42JJk695Gtu1rIpl2lBXFKC+OU1YUp7w4zsjiGMXxKPFohHg0QkHMKIpFiUaM5mSarXsbaUmkKIhFKC2IUVoYo6woRsSM1lSa1qS/NSdS1DUlKIpHcTiKYlFiUcM5qG9O0Nia8uOco7QwxsjiOCOL/PuPLIpTFI9SGI/Qdtr1eNTXIxIx0mlHfUuSptYURfEIhbEokQhsrW2kviVJIpkmHotQEI1QFI9QEI2STKcxM9LOEYsYpYUxDKhrShCNGKm0Ix6NEI0YiVSaVNpRXBClJB6jpNB/Hv1V15Rg38FW0s6RSjuiEaMg5utbGPf1K4h2LLNU2tGYSNHYkiLlHBGDoliU4oIohbFI+7LvL+ccaQdp53DBPXR+7gCXBofjYGuK5oS/1dS30JxIcbAlhQuWQyziP6eWpC/T2JoilXZEzGhKpGhsTdLYmmqfdlNriuZkmmhQfTMjHrX271jaOdLOkUj6+1jwHolUmvrmJI2tSZoTaSIR/z+RkUVx/ufDcw7rs+hLXoa1mfG+meN5ct1uLvvuc+3D73zy9U7l2oIa6FQOoCge4acfP5t3HDuKi7/9bPvwWZNGsnbHAf/61Tt5bNU2Jo8qZtv+JsAv8ETKsXjB8RTFIkwsL2ZieSHjy4oYU1rAMaOKe6x3SzLFnzfuYeeBZqaNLWHlln2MKo4zeXQJG3fVUxyPMrI4zvEVpZxQMYLy4vjhf0i9cM71+ePZe7CVqrf2UhiPsn1/Eys27WFHXTONrSk27qonFZxTPGLQ9fTisYhRHGz91Df7sC6MRSguiFIQjVDT0NIeZGbgnL+PRyO0JnvfoolFjCmjiymKR9m6N01dU4L65gSJVP6f47wg5n/Eg33tjHjUKApWYtGIETUjYpBIOwqiEZxztKZcEDCJQ5Znb6IRaw/O3hTGIhTG/ErDOXB0E7iHPD/cOT58ZlBaEMPMz1vbCieV9mEcMb9CTKTStCTTRCNGxPwtHjWSaUcylSYWjVBWFKMkWGkmUo6U8593ruRlWAMsXnACT67b3eP4L7x/Bs+9vofn36zlsU++iwdf2srPq6rbxzcn0lz9/ec7vWbzNy7uFGJ7D7byyMq3+dFftgAw97jRrNyyD4Clf36z2/c9cfwIxpQU+Nc3thIx2Lir4bDmsaKskBMqSqkoK2J/YyvHjinxrbkC/yUYVVJAY2sy+ILDmu11NCdSjC4pYNv+JvY0tFAY8y280sIY+xpbaWxNse9gK0XxKGVFseALFSMWMUYWxymOR3nhzVpqDx56ZsNjxxQzpqSA980cz4wJfiulOZmiNem/nPHgh59MOxqakz4YIsbokjh1TQlakmkaW1OMKIxRWhglGomQSqcpiEZJpf2XPx71oT59XCmTyotoTaYpiEUYN6KQ0sIYhbEIpYWdv5bOOVqSPribEykSqTStSUdrKk1LIkVjIkVBNEJ5cZypY0toTaY52JKkoSVJQ3OStPNBWhCN+PtYhNKCKC3JNJFgR3bbymBkUay9RZVMO1qD9z3QnOBAU5IDzQlaEilaMlY6yXTQQkukKIxHKSv0Ld6WRJrmZIp02lFR5lf4saiRTPn5aWvtu6DFlg4m2dZaKy+Og0FBNNLeopuJk98AAAflSURBVC6IRTJaiSkaW5I0JlI0BS3IZNqRSqdJpX2It6bS4KAwHqUgapQU+u9CSUGMirJC4lHzrfpgXluSft5akun20IqaEYtGKI5H25eNmT8lRFMiRXPCb620rYjNfCvTgEjE35tZMBwMa98661oWgucZZS0oGzG/HEcU+mVUXhxnVEmcEYUxDCORTpNMOZLpNEXxKEXxKMXxaPDZ+s+upCBvI69PlotLaFVWVrqqqqp+v273LdN5a/S7OOuz95NIpTnpK7/vNH7K6GKq9zWx/msXURSPdjuNv7yxh2TKccaUUXzp0Vd5/DXf+l72mXM5bXJ5j+99sCXZ/kVsax1t3XuQbfubeXtvI6/vqqclmWbb/iZera6jrinR6fVtrfNPLjyBGRPLGFkUZ+2OA8yaNJJk0F9+9vSxFMUjbKltZFNNA2/sbmBTTQPrd9TTFGy+J1M+0Fq7aaEVRCNMHVvCgaYE5cVxjh1T0r6J39yaYkSwph83ohAzaGhOUt+cpL4lQTLlaGhJ0pRIcbAlydnTx3Lp6ZMYU1pAUTzKrEkjO3UdicjgMrOVzrnKHsfna1iDX3NHI8aV31vBdWdO5SPzjhvoqh6RbLobsp1O2vnNsrZpptKO2oYWzIzy4nh7d0RBLC/3CYvIEeorrPN6m6Ct9bzsM+8e4pp0byCCum06mTs4wAf3+JFFAzJ9EQk/NdNEREJAYS0iEgIKaxGREFBYi4iEgMJaRCQEFNYiIiGQVVib2UVmtsHMNpnZl3NdKRER6Syby3pFgf8BFgGzgOvNbFYuKpOLP+iIiAwH2bSszwI2OefedM61Ag8CVwx0Rer27maC7aMpoVOXioh0lU1YTwbeznheHQzrxMwWm1mVmVXV1NT0uyJl5eN4fcIiJiz4WL9fKyIy3GXzd/Pu/lN9SH+Fc24psBT8uUH6W5FINMJJ//Jgf18mInJUyKZlXQ0cm/F8CrA9N9UREZHuZBPWfwVOMrPpZlYAXAf8OrfVEhGRTH12gzjnkmb2aeAPQBS41zn3Ws5rJiIi7bI6Rapz7nfA73JcFxER6YH+wSgiEgIKaxGREFBYi4iEgMJaRCQEcnLBXDOrAbYc5svHAXsGsDphoHke/o62+QXNc38d55yr6GlkTsL6SJhZVW9X+B2ONM/D39E2v6B5HmjqBhERCQGFtYhICORjWC8d6goMAc3z8He0zS9ongdU3vVZi4jIofKxZS0iIl0orEVEQiBvwno4XZTXzI41s+Vmts7MXjOzzwbDx5jZE2b2enA/OhhuZvadYN5fNbM5GdP6aFD+dTP76FDNUzbMLGpmL5vZsuD5dDN7Maj7Q8EpdjGzwuD5pmD8tIxp3BwM32Bm7x+aOcmemY0ys0fMbH2wvM8ZzsvZzD4XfKfXmNkDZlY0HJezmd1rZrvNbE3GsAFbrmY218xWB6/5jpl1d5GXzpxzQ37Dn3r1DeB4oAB4BZg11PU6gvmZBMwJHpcBG/EXG74N+HIw/MvAt4LHFwO/x1+VZx7wYjB8DPBmcD86eDx6qOevl/n+N+BnwLLg+c+B64LH3wf+JXj8SeD7wePrgIeCx7OCZV8ITA++E9Ghnq8+5vlHwD8GjwuAUcN1OeMv57cZKM5YvjcOx+UMLADmAGsyhg3YcgVeAs4JXvN7YFGfdRrqDyWo+DnAHzKe3wzcPNT1GsD5+xVwAbABmBQMmwRsCB7/L3B9RvkNwfjrgf/NGN6pXD7d8FcQego4D1gWfAn3ALGuyxh/bvRzgsexoJx1Xe6Z5fLxBowMwsu6DB+Wy5mO67GOCZbbMuD9w3U5A9O6hPWALNdg3PqM4Z3K9XTLl26QrC7KG0bBpt87gReBCc65HQDB/figWE/zH6bP5U7gi0Db5enHAvudc8ngeWbd2+crGF8XlA/T/ILfEqwBfhh0/9xtZqUM0+XsnNsG3A5sBXbgl9tKhv9ybjNQy3Vy8Ljr8F7lS1hndVHesDGzEcCjwE3OuQO9Fe1mmOtleF4xs0uB3c65lZmDuynq+hgXivnNEMNvKt/lnHsncBC/edyTUM930Ed7Bb7r4higFFjUTdHhtpz70t/5PKz5z5ewHnYX5TWzOD6o73fOPRYM3mVmk4Lxk4DdwfCe5j8sn8t84HIzewt4EN8VcicwyszarkaUWff2+QrGlwN7Cc/8tqkGqp1zLwbPH8GH93Bdzu8DNjvnapxzCeAx4F0M/+XcZqCWa3XwuOvwXuVLWA+ri/IGe3bvAdY55/47Y9SvgbY9wh/F92W3Df/7YK/yPKAu2Mz6A3ChmY0OWjUXBsPyinPuZufcFOfcNPyye9o592FgOXB1UKzr/LZ9DlcH5V0w/LrgKILpwEn4HTF5yTm3E3jbzGYEg84H1jJMlzO++2OemZUE3/G2+R3WyznDgCzXYFy9mc0LPse/z5hWz4a6Ez+jk/1i/FETbwBfGer6HOG8nIvfrHkV+FtwuxjfX/cU8HpwPyYob8D/BPO+GqjMmNY/AJuC28eGet6ymPeFdBwNcjz+R7gJeBgoDIYXBc83BeOPz3j9V4LPYQNZ7CEf6hvwDqAqWNa/xO/1H7bLGbgVWA+sAX6CP6Jj2C1n4AF8v3wC3xL++EAuV6Ay+AzfAL5Ll53U3d30d3MRkRDIl24QERHphcJaRCQEFNYiIiGgsBYRCQGFtYhICCisRURCQGEtIhIC/x/+K066/q1KSgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas\n", - "\n", - "\n", - "def graph_XY(curves, xlabel=None, ylabel=None, marker=True,\n", - " link_point=False, title=None, format_date=\"%Y-%m-%d\",\n", - " legend_loc=0, figsize=None, ax=None):\n", - " if ax is None:\n", - " import matplotlib.pyplot as plt # pylint: disable=C0415\n", - " fig, ax = plt.subplots(1, 1, figsize=figsize)\n", - "\n", - " smarker = {(True, True): 'o-', (True, False): 'o', (False, True): '-',\n", - " # (False, False) :''\n", - " }[marker, link_point]\n", - " has_date = False\n", - " for xf, yf, label in curves:\n", - " ax.plot(xf, yf, smarker, label=label)\n", - " ax.legend(loc=legend_loc)\n", - " return ax\n", - "\n", - "\n", - "def draw_variance(sample) :\n", - " avg = 0.\n", - " std = 0.\n", - " n = 0.\n", - " w = 1.\n", - " add = [] \n", - " for i,x in enumerate(sample) :\n", - " x = float (x)\n", - " avg += x * w\n", - " std += x*x * w\n", - " n += w\n", - " val = (std/n - (avg/n)**2)**0.5\n", - " add.append ( [ i, avg/n, val] )\n", - " \n", - " print(add[-1])\n", - " table = pandas.DataFrame(add, columns=[\"index\", \"avg(n)\", \"std(n)\"])\n", - " return graph_XY([\n", - " [table['index'], table[\"avg(n)\"], \"avg(n)\"],\n", - " [table['index'], table[\"std(n)\"], \"std(n)\"],\n", - " ], marker=False, link_point=True) \n", - "\n", - "draw_variance(sample);" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def draw_hill_estimator(sample):\n", + " res = hill_estimator(sample)\n", + " table = DataFrame(res, columns=[\"d\", \"hill\"])\n", + " return graph_XY(\n", + " [\n", + " [table[\"d\"], table[\"hill\"], \"Hill\"],\n", + " ],\n", + " marker=False,\n", + " link_point=True,\n", + " )\n", + "\n", + "\n", + "draw_hill_estimator(sample);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def draw_hill_estimator (sample) :\n", - " res = hill_estimator(sample)\n", - " table = DataFrame(res, columns=[\"d\", \"hill\"])\n", - " return graph_XY(\n", - " [[table['d'], table['hill'], \"Hill\"],],\n", - " marker=False, link_point=True)\n", - "\n", - "draw_hill_estimator(sample);" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\python372_x64\\lib\\site-packages\\pandas\\core\\series.py:679: RuntimeWarning: divide by zero encountered in log\n", + " result = getattr(ufunc, method)(*inputs, **kwargs)\n" + ] }, { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python372_x64\\lib\\site-packages\\pandas\\core\\series.py:679: RuntimeWarning: divide by zero encountered in log\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def draw_heavy_tail (sample) :\n", - " table = DataFrame([[_] for _ in sample ], columns=[\"obs\"])\n", - " std = 1\n", - "\n", - " normal = norm.rvs(size = len(sample))\n", - " normal = [ x*std for x in normal ]\n", - " nortbl = DataFrame([ [_] for _ in normal ], columns=[\"obs\"])\n", - " nortbl[\"iobs\"] = (nortbl['obs'] * 10).astype(numpy.int64)\n", - "\n", - " histon = nortbl[[\"iobs\", \"obs\"]].groupby('iobs', as_index=False).count()\n", - " histon.columns = ['iobs', 'nb']\n", - " histon = histon.sort_values(\"nb\", ascending=False).reset_index(drop=True)\n", - " \n", - " table[\"one\"] = 1\n", - " histo = table.groupby('obs', as_index=False).count()\n", - " histo.columns = ['obs', 'nb'] \n", - " histo = histo.sort_values('nb', ascending=False).reset_index(drop=True)\n", - " histo.reset_index(drop=True, inplace=True)\n", - " histo[\"index\"] = histo.index + 1\n", - " \n", - " vec = list(histon[\"nb\"])\n", - " vec += [0,] * len(histo)\n", - " histo['nb_normal'] = vec[:len(histo)]\n", - " \n", - " histo[\"log(index)\"] = numpy.log(histo[\"index\"]) / numpy.log(10)\n", - " histo[\"log(nb)\"] = numpy.log(histo[\"nb\"]) / numpy.log(10)\n", - " histo[\"log(nb_normal)\"] = numpy.log(histo[\"nb_normal\"]) / numpy.log(10)\n", - " return graph_XY ([\n", - " [histo[\"log(index)\"], histo[\"log(nb)\"], \"Zipf\"],\n", - " [histo[\"log(index)\"], histo[\"log(nb_normal)\"], \"Gauss\"], ],\n", - " marker=False, link_point=True) \n", - "\n", - "draw_heavy_tail(sample);" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } + ], + "source": [ + "def draw_heavy_tail(sample):\n", + " table = DataFrame([[_] for _ in sample], columns=[\"obs\"])\n", + " std = 1\n", + "\n", + " normal = norm.rvs(size=len(sample))\n", + " normal = [x * std for x in normal]\n", + " nortbl = DataFrame([[_] for _ in normal], columns=[\"obs\"])\n", + " nortbl[\"iobs\"] = (nortbl[\"obs\"] * 10).astype(numpy.int64)\n", + "\n", + " histon = nortbl[[\"iobs\", \"obs\"]].groupby(\"iobs\", as_index=False).count()\n", + " histon.columns = [\"iobs\", \"nb\"]\n", + " histon = histon.sort_values(\"nb\", ascending=False).reset_index(drop=True)\n", + "\n", + " table[\"one\"] = 1\n", + " histo = table.groupby(\"obs\", as_index=False).count()\n", + " histo.columns = [\"obs\", \"nb\"]\n", + " histo = histo.sort_values(\"nb\", ascending=False).reset_index(drop=True)\n", + " histo.reset_index(drop=True, inplace=True)\n", + " histo[\"index\"] = histo.index + 1\n", + "\n", + " vec = list(histon[\"nb\"])\n", + " vec += [\n", + " 0,\n", + " ] * len(histo)\n", + " histo[\"nb_normal\"] = vec[: len(histo)]\n", + "\n", + " histo[\"log(index)\"] = numpy.log(histo[\"index\"]) / numpy.log(10)\n", + " histo[\"log(nb)\"] = numpy.log(histo[\"nb\"]) / numpy.log(10)\n", + " histo[\"log(nb_normal)\"] = numpy.log(histo[\"nb_normal\"]) / numpy.log(10)\n", + " return graph_XY(\n", + " [\n", + " [histo[\"log(index)\"], histo[\"log(nb)\"], \"Zipf\"],\n", + " [histo[\"log(index)\"], histo[\"log(nb_normal)\"], \"Gauss\"],\n", + " ],\n", + " marker=False,\n", + " link_point=True,\n", + " )\n", + "\n", + "\n", + "draw_heavy_tail(sample);" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/metric/roc_example.ipynb b/_doc/notebooks/metric/roc_example.ipynb index 7969065a..c472e9ff 100644 --- a/_doc/notebooks/metric/roc_example.ipynb +++ b/_doc/notebooks/metric/roc_example.ipynb @@ -1,1078 +1,950 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ROC\n", - "\n", - "A few graphs about ROC on the iris datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Iris datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn import datasets\n", - "iris = datasets.load_iris()\n", - "X = iris.data[:, :2]\n", - "y = iris.target" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "clf = LogisticRegression()\n", - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy\n", - "ypred = clf.predict(X_test)\n", - "yprob = clf.predict_proba(X_test)\n", - "score = numpy.array(list(yprob[i,ypred[i]] for i in range(len(ypred))))" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ROC\n", + "\n", + "A few graphs about ROC on the iris datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Iris datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "\n", + "iris = datasets.load_iris()\n", + "X = iris.data[:, :2]\n", + "y = iris.target" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.70495209, 1. ],\n", - " [ 0.56148737, 0. ],\n", - " [ 0.56148737, 1. ],\n", - " [ 0.77416227, 1. ],\n", - " [ 0.58631799, 0. ]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = numpy.zeros((len(ypred), 2))\n", - "data[:,0] = score.ravel()\n", - "data[ypred==y_test,1] = 1\n", - "data[:5]" + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "clf = LogisticRegression()\n", + "clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "ypred = clf.predict(X_test)\n", + "yprob = clf.predict_proba(X_test)\n", + "score = numpy.array(list(yprob[i, ypred[i]] for i in range(len(ypred))))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ROC with scikit-learn\n", - "\n", - "We use the following example [Receiver Operating Characteristic (ROC)](http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py)." + "data": { + "text/plain": [ + "array([[ 0.70495209, 1. ],\n", + " [ 0.56148737, 0. ],\n", + " [ 0.56148737, 1. ],\n", + " [ 0.77416227, 1. ],\n", + " [ 0.58631799, 0. ]])" ] - }, + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = numpy.zeros((len(ypred), 2))\n", + "data[:, 0] = score.ravel()\n", + "data[ypred == y_test, 1] = 1\n", + "data[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ROC with scikit-learn\n", + "\n", + "We use the following example [Receiver Operating Characteristic (ROC)](http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.metrics import roc_curve\n", + "\n", + "fpr, tpr, th = roc_curve(y_test == ypred, score)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from sklearn.metrics import roc_curve\n", - "fpr, tpr, th = roc_curve(y_test == ypred, score)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAENCAYAAADzFzkJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQwmYSGkIIIWz2/x0resRi4Ro4AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.plot(fpr, tpr, label='ROC curve')\n", - "plt.plot([0, 1], [0, 1], linestyle='--')\n", - "plt.xlim([0.0, 1.0])\n", - "plt.ylim([0.0, 1.0])\n", - "plt.xlabel('False Positive Rate')\n", - "plt.ylabel('True Positive Rate')\n", - "plt.legend(loc=\"lower right\")" + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(fpr, tpr, label=\"ROC curve\")\n", + "plt.plot([0, 1], [0, 1], linestyle=\"--\")\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.0])\n", + "plt.xlabel(\"False Positive Rate\")\n", + "plt.ylabel(\"True Positive Rate\")\n", + "plt.legend(loc=\"lower right\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
fprthresholdtpr
00.0000000.9107120.032258
10.0000000.8697940.096774
20.0000000.8631740.161290
30.0000000.8058640.258065
40.0000000.7909090.387097
50.0000000.6505100.612903
60.0526320.6344990.612903
70.0526320.6203190.709677
80.1052630.6150150.709677
90.2105260.6079750.741935
100.2105260.6044960.774194
110.2631580.5863180.774194
120.2631580.5841720.806452
130.3157890.5614870.838710
140.4210530.5564990.838710
150.5789470.5254490.838710
160.5789470.5225790.870968
170.6315790.5225510.870968
180.6842110.5208350.903226
190.7368420.5166870.903226
200.7368420.4538260.967742
210.8421050.4179410.967742
220.8421050.4127031.000000
231.0000000.3755731.000000
\n", - "
" - ], - "text/plain": [ - " fpr threshold tpr\n", - "0 0.000000 0.910712 0.032258\n", - "1 0.000000 0.869794 0.096774\n", - "2 0.000000 0.863174 0.161290\n", - "3 0.000000 0.805864 0.258065\n", - "4 0.000000 0.790909 0.387097\n", - "5 0.000000 0.650510 0.612903\n", - "6 0.052632 0.634499 0.612903\n", - "7 0.052632 0.620319 0.709677\n", - "8 0.105263 0.615015 0.709677\n", - "9 0.210526 0.607975 0.741935\n", - "10 0.210526 0.604496 0.774194\n", - "11 0.263158 0.586318 0.774194\n", - "12 0.263158 0.584172 0.806452\n", - "13 0.315789 0.561487 0.838710\n", - "14 0.421053 0.556499 0.838710\n", - "15 0.578947 0.525449 0.838710\n", - "16 0.578947 0.522579 0.870968\n", - "17 0.631579 0.522551 0.870968\n", - "18 0.684211 0.520835 0.903226\n", - "19 0.736842 0.516687 0.903226\n", - "20 0.736842 0.453826 0.967742\n", - "21 0.842105 0.417941 0.967742\n", - "22 0.842105 0.412703 1.000000\n", - "23 1.000000 0.375573 1.000000" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
fprthresholdtpr
00.0000000.9107120.032258
10.0000000.8697940.096774
20.0000000.8631740.161290
30.0000000.8058640.258065
40.0000000.7909090.387097
50.0000000.6505100.612903
60.0526320.6344990.612903
70.0526320.6203190.709677
80.1052630.6150150.709677
90.2105260.6079750.741935
100.2105260.6044960.774194
110.2631580.5863180.774194
120.2631580.5841720.806452
130.3157890.5614870.838710
140.4210530.5564990.838710
150.5789470.5254490.838710
160.5789470.5225790.870968
170.6315790.5225510.870968
180.6842110.5208350.903226
190.7368420.5166870.903226
200.7368420.4538260.967742
210.8421050.4179410.967742
220.8421050.4127031.000000
231.0000000.3755731.000000
\n", + "
" ], - "source": [ - "import pandas\n", - "df = pandas.DataFrame(dict(fpr=fpr, tpr=tpr, threshold=th))\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ROC - TPR / FPR\n", - "\n", - "We do the same with the class this module provides [ROC](http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/mlstatpy/ml/roc.html?highlight=roc#module-mlstatpy.ml.roc).\n", - "\n", - "* TPR = True Positive Rate\n", - "* FPR = False Positive Rate\n", - "\n", - "You can see as TPR the distribution function of a score for a positive example and the FPR the same for a negative example." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from mlstatpy.ml.roc import ROC" + "text/plain": [ + " fpr threshold tpr\n", + "0 0.000000 0.910712 0.032258\n", + "1 0.000000 0.869794 0.096774\n", + "2 0.000000 0.863174 0.161290\n", + "3 0.000000 0.805864 0.258065\n", + "4 0.000000 0.790909 0.387097\n", + "5 0.000000 0.650510 0.612903\n", + "6 0.052632 0.634499 0.612903\n", + "7 0.052632 0.620319 0.709677\n", + "8 0.105263 0.615015 0.709677\n", + "9 0.210526 0.607975 0.741935\n", + "10 0.210526 0.604496 0.774194\n", + "11 0.263158 0.586318 0.774194\n", + "12 0.263158 0.584172 0.806452\n", + "13 0.315789 0.561487 0.838710\n", + "14 0.421053 0.556499 0.838710\n", + "15 0.578947 0.525449 0.838710\n", + "16 0.578947 0.522579 0.870968\n", + "17 0.631579 0.522551 0.870968\n", + "18 0.684211 0.520835 0.903226\n", + "19 0.736842 0.516687 0.903226\n", + "20 0.736842 0.453826 0.967742\n", + "21 0.842105 0.417941 0.967742\n", + "22 0.842105 0.412703 1.000000\n", + "23 1.000000 0.375573 1.000000" ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "roc = ROC(df=data)" - ] - }, + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas\n", + "\n", + "df = pandas.DataFrame(dict(fpr=fpr, tpr=tpr, threshold=th))\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ROC - TPR / FPR\n", + "\n", + "We do the same with the class this module provides [ROC](https://sdpython.github.io/doc/mlstatpy/dev/c_metric/roc.html).\n", + "\n", + "* TPR = True Positive Rate\n", + "* FPR = False Positive Rate\n", + "\n", + "You can see as TPR the distribution function of a score for a positive example and the FPR the same for a negative example." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from mlstatpy.ml.roc import ROC" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "roc = ROC(df=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Overall precision: 0.63 - AUC=0.850594\n", - "--------------\n", - " score label weight\n", - "0 0.375573 0.0 1.0\n", - "1 0.385480 0.0 1.0\n", - "2 0.412314 0.0 1.0\n", - "3 0.412703 1.0 1.0\n", - "4 0.417941 0.0 1.0\n", - "--------------\n", - " score label weight\n", - "45 0.863174 1.0 1.0\n", - "46 0.863174 1.0 1.0\n", - "47 0.869794 1.0 1.0\n", - "48 0.903335 1.0 1.0\n", - "49 0.910712 1.0 1.0\n", - "--------------\n", - " False Positive Rate True Positive Rate threshold\n", - "0 0.000000 0.032258 0.910712\n", - "1 0.000000 0.193548 0.828617\n", - "2 0.000000 0.354839 0.790909\n", - "3 0.000000 0.516129 0.737000\n", - "4 0.052632 0.645161 0.627589\n", - "5 0.157895 0.741935 0.607975\n", - "6 0.263158 0.838710 0.561487\n", - "7 0.526316 0.838710 0.542211\n", - "8 0.684211 0.903226 0.520835\n", - "9 0.842105 0.967742 0.417941\n", - "10 1.000000 1.000000 0.375573\n", - "--------------\n", - " error recall threshold\n", - "0 0.000000 0.02 0.910712\n", - "1 0.000000 0.12 0.828617\n", - "2 0.000000 0.22 0.790909\n", - "3 0.000000 0.32 0.737000\n", - "4 0.047619 0.42 0.627589\n", - "5 0.115385 0.52 0.607975\n", - "6 0.161290 0.62 0.561487\n", - "7 0.277778 0.72 0.542211\n", - "8 0.317073 0.82 0.520835\n", - "9 0.347826 0.92 0.417941\n", - "10 0.380000 1.00 0.375573" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "roc" + "data": { + "text/plain": [ + "Overall precision: 0.63 - AUC=0.850594\n", + "--------------\n", + " score label weight\n", + "0 0.375573 0.0 1.0\n", + "1 0.385480 0.0 1.0\n", + "2 0.412314 0.0 1.0\n", + "3 0.412703 1.0 1.0\n", + "4 0.417941 0.0 1.0\n", + "--------------\n", + " score label weight\n", + "45 0.863174 1.0 1.0\n", + "46 0.863174 1.0 1.0\n", + "47 0.869794 1.0 1.0\n", + "48 0.903335 1.0 1.0\n", + "49 0.910712 1.0 1.0\n", + "--------------\n", + " False Positive Rate True Positive Rate threshold\n", + "0 0.000000 0.032258 0.910712\n", + "1 0.000000 0.193548 0.828617\n", + "2 0.000000 0.354839 0.790909\n", + "3 0.000000 0.516129 0.737000\n", + "4 0.052632 0.645161 0.627589\n", + "5 0.157895 0.741935 0.607975\n", + "6 0.263158 0.838710 0.561487\n", + "7 0.526316 0.838710 0.542211\n", + "8 0.684211 0.903226 0.520835\n", + "9 0.842105 0.967742 0.417941\n", + "10 1.000000 1.000000 0.375573\n", + "--------------\n", + " error recall threshold\n", + "0 0.000000 0.02 0.910712\n", + "1 0.000000 0.12 0.828617\n", + "2 0.000000 0.22 0.790909\n", + "3 0.000000 0.32 0.737000\n", + "4 0.047619 0.42 0.627589\n", + "5 0.115385 0.52 0.607975\n", + "6 0.161290 0.62 0.561487\n", + "7 0.277778 0.72 0.542211\n", + "8 0.317073 0.82 0.520835\n", + "9 0.347826 0.92 0.417941\n", + "10 0.380000 1.00 0.375573" ] - }, + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roc" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.85059422750424452" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "roc.auc()" + "data": { + "text/plain": [ + "0.85059422750424452" ] - }, + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roc.auc()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAENCAYAAADzFzkJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfVYAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "roc.plot(nb=10)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function draws the curve with only 10 points but we can ask for more." + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "roc.plot(nb=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function draws the curve with only 10 points but we can ask for more." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAENCAYAAADzFzkJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "roc.plot(nb=100)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also ask to draw bootstropped curves to get a sense of the confidence." + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "roc.plot(nb=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also ask to draw bootstropped curves to get a sense of the confidence." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAENCAYAAADzFzkJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1PZf3U34zQDgmhoAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "roc.plot(nb=10, bootstrap=10)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ROC - score distribution\n", - "\n", - "This another representation for the metrics FPR and TPR. $P(xs)$ is the probability that a score for a negative example to be higher than $s$. We assume in this case that the higher the better for the score." + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "roc.plot(nb=10, bootstrap=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ROC - score distribution\n", + "\n", + "This another representation for the metrics FPR and TPR. $P(xs)$ is the probability that a score for a negative example to be higher than $s$. We assume in this case that the higher the better for the score." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAENCAYAAAD0eSVZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "roc.plot(curve=ROC.CurveType.PROBSCORE, thresholds=True)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When curves intersects at score $s^*$, error rates for positive and negative examples are equal. If we show the confusion matrix for this particular score $s^*$, it gives:" + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "roc.plot(curve=ROC.CurveType.PROBSCORE, thresholds=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When curves intersects at score $s^*$, error rates for positive and negative examples are equal. If we show the confusion matrix for this particular score $s^*$, it gives:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
True PositiveFalse PositiveFalse NegativeTrue NegativethresholdP(+<s)P(->s)
00.00.031.019.00.9107121.0000000.000000
11.00.030.019.00.9107120.9677420.000000
26.00.025.019.00.8286170.8064520.000000
311.00.020.019.00.7909090.6451610.000000
416.00.015.019.00.7370000.4838710.000000
520.01.011.018.00.6275890.3548390.052632
623.03.08.016.00.6079750.2580650.157895
726.05.05.014.00.5614870.1612900.263158
826.010.05.09.00.5422110.1612900.526316
928.013.03.06.00.5208350.0967740.684211
1030.016.01.03.00.4179410.0322580.842105
1131.019.00.00.00.3755730.0000001.000000
\n", - "
" - ], - "text/plain": [ - " True Positive False Positive False Negative True Negative threshold \\\n", - "0 0.0 0.0 31.0 19.0 0.910712 \n", - "1 1.0 0.0 30.0 19.0 0.910712 \n", - "2 6.0 0.0 25.0 19.0 0.828617 \n", - "3 11.0 0.0 20.0 19.0 0.790909 \n", - "4 16.0 0.0 15.0 19.0 0.737000 \n", - "5 20.0 1.0 11.0 18.0 0.627589 \n", - "6 23.0 3.0 8.0 16.0 0.607975 \n", - "7 26.0 5.0 5.0 14.0 0.561487 \n", - "8 26.0 10.0 5.0 9.0 0.542211 \n", - "9 28.0 13.0 3.0 6.0 0.520835 \n", - "10 30.0 16.0 1.0 3.0 0.417941 \n", - "11 31.0 19.0 0.0 0.0 0.375573 \n", - "\n", - " P(+s) \n", - "0 1.000000 0.000000 \n", - "1 0.967742 0.000000 \n", - "2 0.806452 0.000000 \n", - "3 0.645161 0.000000 \n", - "4 0.483871 0.000000 \n", - "5 0.354839 0.052632 \n", - "6 0.258065 0.157895 \n", - "7 0.161290 0.263158 \n", - "8 0.161290 0.526316 \n", - "9 0.096774 0.684211 \n", - "10 0.032258 0.842105 \n", - "11 0.000000 1.000000 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
True PositiveFalse PositiveFalse NegativeTrue NegativethresholdP(+<s)P(->s)
00.00.031.019.00.9107121.0000000.000000
11.00.030.019.00.9107120.9677420.000000
26.00.025.019.00.8286170.8064520.000000
311.00.020.019.00.7909090.6451610.000000
416.00.015.019.00.7370000.4838710.000000
520.01.011.018.00.6275890.3548390.052632
623.03.08.016.00.6079750.2580650.157895
726.05.05.014.00.5614870.1612900.263158
826.010.05.09.00.5422110.1612900.526316
928.013.03.06.00.5208350.0967740.684211
1030.016.01.03.00.4179410.0322580.842105
1131.019.00.00.00.3755730.0000001.000000
\n", + "
" ], - "source": [ - "conf = roc.confusion()\n", - "conf[\"P(+s)\"] = 1 - conf[\"True Negative\"] / conf.loc[0,\"True Negative\"]\n", - "conf" + "text/plain": [ + " True Positive False Positive False Negative True Negative threshold \\\n", + "0 0.0 0.0 31.0 19.0 0.910712 \n", + "1 1.0 0.0 30.0 19.0 0.910712 \n", + "2 6.0 0.0 25.0 19.0 0.828617 \n", + "3 11.0 0.0 20.0 19.0 0.790909 \n", + "4 16.0 0.0 15.0 19.0 0.737000 \n", + "5 20.0 1.0 11.0 18.0 0.627589 \n", + "6 23.0 3.0 8.0 16.0 0.607975 \n", + "7 26.0 5.0 5.0 14.0 0.561487 \n", + "8 26.0 10.0 5.0 9.0 0.542211 \n", + "9 28.0 13.0 3.0 6.0 0.520835 \n", + "10 30.0 16.0 1.0 3.0 0.417941 \n", + "11 31.0 19.0 0.0 0.0 0.375573 \n", + "\n", + " P(+s) \n", + "0 1.000000 0.000000 \n", + "1 0.967742 0.000000 \n", + "2 0.806452 0.000000 \n", + "3 0.645161 0.000000 \n", + "4 0.483871 0.000000 \n", + "5 0.354839 0.052632 \n", + "6 0.258065 0.157895 \n", + "7 0.161290 0.263158 \n", + "8 0.161290 0.526316 \n", + "9 0.096774 0.684211 \n", + "10 0.032258 0.842105 \n", + "11 0.000000 1.000000 " ] - }, + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conf = roc.confusion()\n", + "conf[\"P(+s)\"] = 1 - conf[\"True Negative\"] / conf.loc[0, \"True Negative\"]\n", + "conf" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## ROC - recall / precision\n", + "\n", + "In this representation, we show the score." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## ROC - recall / precision\n", - "\n", - "In this representation, we show the score." + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAENCAYAAADHURCIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH+rirEZXwbMAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(14,4))\n", - "roc.plot(curve=ROC.CurveType.RECPREC, thresholds=True, ax=axes[0])\n", - "roc.plot(curve=ROC.CurveType.RECPREC, ax=axes[1])" + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(14, 4))\n", + "roc.plot(curve=ROC.CurveType.RECPREC, thresholds=True, ax=axes[0])\n", + "roc.plot(curve=ROC.CurveType.RECPREC, ax=axes[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/_doc/notebooks/ml/README.txt b/_doc/notebooks/ml/README.txt deleted file mode 100644 index 0063392e..00000000 --- a/_doc/notebooks/ml/README.txt +++ /dev/null @@ -1,8 +0,0 @@ - -Machine Learning ----------------- - -.. contents:: - :local: - - diff --git a/_doc/notebooks/ml/benchmark.ipynb b/_doc/notebooks/ml/benchmark.ipynb deleted file mode 100644 index a1db7bc7..00000000 --- a/_doc/notebooks/ml/benchmark.ipynb +++ /dev/null @@ -1,818 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "# Benchmark\n", - "\n", - "Ce notebook compare diff\u00e9rents mod\u00e8les depuis un notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "Si le message *Widget Javascript not detected. It may not be installed or enabled properly.* appara\u00eet, vous devriez ex\u00e9cuter la commande ``jupyter nbextension enable --py --sys-prefix widgetsnbextension`` depuis la ligne de commande. Le code suivant vous permet de v\u00e9rifier que cela a \u00e9t\u00e9 fait." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a4f30591a01b4404ad63f2e1507bd5f3" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "072625ddefdc4d6e963fc6c703cb9910" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "92eef01d3a614aa284dea6cb591e2d85" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "37fe02c008ce4cfc9249e5abdf74483a" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from tqdm import tnrange, tqdm_notebook\n", - "from time import sleep\n", - "\n", - "for i in tnrange(3, desc='1st loop'):\n", - " for j in tqdm_notebook(range(20), desc='2nd loop'):\n", - " sleep(0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Petit bench sur le clustering" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### D\u00e9finition du bench" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "import dill\n", - "from tqdm import tnrange\n", - "from sklearn.cluster import AgglomerativeClustering, KMeans\n", - "from sklearn.datasets import make_blobs\n", - "from mlstatpy.ml import MlGridBenchMark\n", - "\n", - "params = [dict(model=lambda : KMeans(n_clusters=3), name=\"KMeans-3\", shortname=\"km-3\"),\n", - " dict(model=lambda : AgglomerativeClustering(), name=\"AgglomerativeClustering\", shortname=\"aggclus\")]\n", - "\n", - "datasets = [dict(X=make_blobs(100, centers=3)[0], Nclus=3,\n", - " name=\"blob-100-3\", shortname=\"b-100-3\", no_split=True),\n", - " dict(X=make_blobs(100, centers=5)[0], Nclus=5, \n", - " name=\"blob-100-5\", shortname=\"b-100-5\", no_split=True) ]\n", - "\n", - "bench = MlGridBenchMark(\"TestName\", datasets, fLOG=None, clog=None,\n", - " cache_file=\"cache.pickle\", pickle_module=dill, \n", - " repetition=3, progressbar=tnrange,\n", - " graphx=[\"_time\", \"time_train\", \"Nclus\"],\n", - " graphy=[\"silhouette\", \"Nrows\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Lancer le bench" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f658c02f930b4b9099a601652a7807a7" - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0/|/2017-03-19 20:11:11 [BenchMark.run] number of cached run: 4: 0%|| 0/4 [00:00\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
_btry_date_i_iexp_name_span_timeNclusNfeatNrowsds_namemodel_nameno_splitown_scoresilhouettetime_preproctime_testtime_train
0km-3-b-100-32017-03-19 20:11:11.13213500TestName0:00:00.1476100.14759432100blob-100-3KMeans-3True-175.3969440.7006180.0091540.0031950.044693
1km-3-b-100-30:00:00.14761001TestName2017-03-19 20:11:11.1401410.14759432100blob-100-3KMeans-3True-175.3969440.7006180.0060680.0028030.037633
2km-3-b-100-32017-03-19 20:11:11.14014102TestName0:00:00.1556200.14759432100blob-100-3KMeans-3True-175.3969440.7006180.0062300.0026300.035106
3aggclus-b-100-32017-03-19 20:11:11.31728310TestName0:00:00.0960810.09670032100blob-100-3AgglomerativeClusteringTrueNaN0.6623450.0081470.0025080.026997
4aggclus-b-100-30:00:00.09608111TestName2017-03-19 20:11:11.3252880.09670032100blob-100-3AgglomerativeClusteringTrueNaN0.6623450.0095110.0041560.016807
5aggclus-b-100-32017-03-19 20:11:11.32528812TestName0:00:00.1060880.09670032100blob-100-3AgglomerativeClusteringTrueNaN0.6623450.0070180.0032520.018227
6km-3-b-100-52017-03-19 20:11:11.45268820TestName0:00:00.1451300.14501252100blob-100-5KMeans-3True-466.8292000.7905110.0075870.0027480.033610
7km-3-b-100-50:00:00.14513021TestName2017-03-19 20:11:11.4631990.14501252100blob-100-5KMeans-3True-466.8292000.7905110.0074710.0022780.036098
8km-3-b-100-52017-03-19 20:11:11.46319922TestName0:00:00.1531360.14501252100blob-100-5KMeans-3True-466.8292000.7905110.0115760.0044630.039103
9aggclus-b-100-52017-03-19 20:11:11.64035130TestName0:00:00.1015730.10076552100blob-100-5AgglomerativeClusteringTrueNaN0.6362410.0094830.0024180.020562
10aggclus-b-100-50:00:00.10157331TestName2017-03-19 20:11:11.6473550.10076552100blob-100-5AgglomerativeClusteringTrueNaN0.6362410.0115320.0016340.021456
11aggclus-b-100-52017-03-19 20:11:11.64735532TestName0:00:00.1125810.10076552100blob-100-5AgglomerativeClusteringTrueNaN0.6362410.0096430.0025010.021430
\n", - "" - ], - "text/plain": [ - " _btry _date _i _iexp _name \\\n", - "0 km-3-b-100-3 2017-03-19 20:11:11.132135 0 0 TestName \n", - "1 km-3-b-100-3 0:00:00.147610 0 1 TestName \n", - "2 km-3-b-100-3 2017-03-19 20:11:11.140141 0 2 TestName \n", - "3 aggclus-b-100-3 2017-03-19 20:11:11.317283 1 0 TestName \n", - "4 aggclus-b-100-3 0:00:00.096081 1 1 TestName \n", - "5 aggclus-b-100-3 2017-03-19 20:11:11.325288 1 2 TestName \n", - "6 km-3-b-100-5 2017-03-19 20:11:11.452688 2 0 TestName \n", - "7 km-3-b-100-5 0:00:00.145130 2 1 TestName \n", - "8 km-3-b-100-5 2017-03-19 20:11:11.463199 2 2 TestName \n", - "9 aggclus-b-100-5 2017-03-19 20:11:11.640351 3 0 TestName \n", - "10 aggclus-b-100-5 0:00:00.101573 3 1 TestName \n", - "11 aggclus-b-100-5 2017-03-19 20:11:11.647355 3 2 TestName \n", - "\n", - " _span _time Nclus Nfeat Nrows ds_name \\\n", - "0 0:00:00.147610 0.147594 3 2 100 blob-100-3 \n", - "1 2017-03-19 20:11:11.140141 0.147594 3 2 100 blob-100-3 \n", - "2 0:00:00.155620 0.147594 3 2 100 blob-100-3 \n", - "3 0:00:00.096081 0.096700 3 2 100 blob-100-3 \n", - "4 2017-03-19 20:11:11.325288 0.096700 3 2 100 blob-100-3 \n", - "5 0:00:00.106088 0.096700 3 2 100 blob-100-3 \n", - "6 0:00:00.145130 0.145012 5 2 100 blob-100-5 \n", - "7 2017-03-19 20:11:11.463199 0.145012 5 2 100 blob-100-5 \n", - "8 0:00:00.153136 0.145012 5 2 100 blob-100-5 \n", - "9 0:00:00.101573 0.100765 5 2 100 blob-100-5 \n", - "10 2017-03-19 20:11:11.647355 0.100765 5 2 100 blob-100-5 \n", - "11 0:00:00.112581 0.100765 5 2 100 blob-100-5 \n", - "\n", - " model_name no_split own_score silhouette time_preproc \\\n", - "0 KMeans-3 True -175.396944 0.700618 0.009154 \n", - "1 KMeans-3 True -175.396944 0.700618 0.006068 \n", - "2 KMeans-3 True -175.396944 0.700618 0.006230 \n", - "3 AgglomerativeClustering True NaN 0.662345 0.008147 \n", - "4 AgglomerativeClustering True NaN 0.662345 0.009511 \n", - "5 AgglomerativeClustering True NaN 0.662345 0.007018 \n", - "6 KMeans-3 True -466.829200 0.790511 0.007587 \n", - "7 KMeans-3 True -466.829200 0.790511 0.007471 \n", - "8 KMeans-3 True -466.829200 0.790511 0.011576 \n", - "9 AgglomerativeClustering True NaN 0.636241 0.009483 \n", - "10 AgglomerativeClustering True NaN 0.636241 0.011532 \n", - "11 AgglomerativeClustering True NaN 0.636241 0.009643 \n", - "\n", - " time_test time_train \n", - "0 0.003195 0.044693 \n", - "1 0.002803 0.037633 \n", - "2 0.002630 0.035106 \n", - "3 0.002508 0.026997 \n", - "4 0.004156 0.016807 \n", - "5 0.003252 0.018227 \n", - "6 0.002748 0.033610 \n", - "7 0.002278 0.036098 \n", - "8 0.004463 0.039103 \n", - "9 0.002418 0.020562 \n", - "10 0.001634 0.021456 \n", - "11 0.002501 0.021430 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = bench.to_df()\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAELCAYAAADz6wBxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df.plot(x=\"time_train\", y=\"silhouette\", kind=\"scatter\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dessin, Graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[,\n", - " ],\n", - " [,\n", - " ],\n", - " [,\n", - " ]], dtype=object)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAukAAALECAYAAAC19q7jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQbqIiIiISInRIF1EREREpMT8PzsO1msZeOaWAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bench.plot_graphs(figsize=(12,12))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/_doc/notebooks/ml/index.rst b/_doc/notebooks/ml/index.rst new file mode 100644 index 00000000..cc6c1bac --- /dev/null +++ b/_doc/notebooks/ml/index.rst @@ -0,0 +1,9 @@ +Machine Learning +================ + +.. nbgallery:: + :caption: Notebooks Gallery + :name: rst-nb-gallery-ml + :glob: + + * diff --git a/_doc/notebooks/ml/logreg_voronoi.ipynb b/_doc/notebooks/ml/logreg_voronoi.ipynb index fbeec6da..1714a946 100644 --- a/_doc/notebooks/ml/logreg_voronoi.ipynb +++ b/_doc/notebooks/ml/logreg_voronoi.ipynb @@ -1,1905 +1,1966 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vorono\u00ef et r\u00e9gression logistique\n", - "\n", - "Le notebook \u00e9tudie la pertinence d'un mod\u00e8le de r\u00e9gression logistique dans certaines configurations. Il regarde aussi le diagramme de Vorono\u00ef associ\u00e9 \u00e0 une r\u00e9gression logistique \u00e0 trois classes. Il donne quelques intuitions sur les mod\u00e8les que la r\u00e9gression logistique peut r\u00e9soudre." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R\u00e9gression logistique" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import load_iris\n", - "data = load_iris()\n", - "X, y = data.data[:, :2], data.target" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "clr = LogisticRegression()\n", - "clr.fit(X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-2.49579289, 4.01011301],\n", - " [ 0.49709451, -1.63380222],\n", - " [ 1.15921404, -1.77736568]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr.coef_" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.81713932, 1.22543562, -2.22516119])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr.intercept_" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 6.34157245, -1.54507432, -4.6206785 ]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy\n", - "x = numpy.array([[1, 2]])\n", - "clr.decision_function(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "A = clr.coef_\n", - "B = clr.intercept_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On v\u00e9rifie que la fonction de d\u00e9cision correspond \u00e0 la formule suivant." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 6.34157245, -1.54507432, -4.6206785 ])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(A@x.T).T.ravel() + B" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def draw_border(clr, X, y, fct=None, incx=1, incy=1, figsize=None, border=True, ax=None):\n", - " \n", - " # voir https://sashat.me/2017/01/11/list-of-20-simple-distinct-colors/\n", - " # https://matplotlib.org/examples/color/colormaps_reference.html\n", - " _unused_ = [\"Red\", \"Green\", \"Yellow\", \"Blue\", \"Orange\", \"Purple\", \"Cyan\",\n", - " \"Magenta\", \"Lime\", \"Pink\", \"Teal\", \"Lavender\", \"Brown\", \"Beige\",\n", - " \"Maroon\", \"Mint\", \"Olive\", \"Coral\", \"Navy\", \"Grey\", \"White\", \"Black\"]\n", - "\n", - " h = .02 # step size in the mesh\n", - " # Plot the decision boundary. For that, we will assign a color to each\n", - " # point in the mesh [x_min, x_max]x[y_min, y_max].\n", - " x_min, x_max = X[:, 0].min() - incx, X[:, 0].max() + incx\n", - " y_min, y_max = X[:, 1].min() - incy, X[:, 1].max() + incy\n", - " xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, h), numpy.arange(y_min, y_max, h))\n", - " if fct is None:\n", - " Z = clr.predict(numpy.c_[xx.ravel(), yy.ravel()])\n", - " else:\n", - " Z = fct(clr, numpy.c_[xx.ravel(), yy.ravel()])\n", - "\n", - " # Put the result into a color plot\n", - " cmap = plt.cm.tab20\n", - " Z = Z.reshape(xx.shape)\n", - " if ax is None:\n", - " fig, ax = plt.subplots(1, 1, figsize=figsize or (4, 3))\n", - " ax.pcolormesh(xx, yy, Z, cmap=cmap)\n", - "\n", - " # Plot also the training points\n", - " ax.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=cmap)\n", - " ax.set_xlabel('Sepal length')\n", - " ax.set_ylabel('Sepal width')\n", - "\n", - " ax.set_xlim(xx.min(), xx.max())\n", - " ax.set_ylim(yy.min(), yy.max())\n", - " \n", - " # Draw lines\n", - " x1, x2 = xx.min(), xx.max()\n", - " cl = 0\n", - " if border:\n", - " for i in range(0, clr.coef_.shape[0]):\n", - " for j in range(i+1, clr.coef_.shape[0]):\n", - " delta = clr.coef_[i] - clr.coef_[j]\n", - " db = clr.intercept_[i] - clr.intercept_[j]\n", - " y1 = (-db - delta[0] * x1) / delta[1]\n", - " y2 = (-db - delta[0] * x2) / delta[1]\n", - " ax.plot([x1, x2], [y1, y2], '--', color=\"white\")\n", - " cl += 1\n", - " else:\n", - " for i in range(0, clr.coef_.shape[0]):\n", - " delta = clr.coef_[i]\n", - " db = clr.intercept_[i]\n", - " y1 = (-db - delta[0] * x1) / delta[1]\n", - " y2 = (-db - delta[0] * x2) / delta[1]\n", - " ax.plot([x1, x2], [y1, y2], '--', color=\"yellow\")\n", - " cl += 1\n", - " \n", - " return ax\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(10,4))\n", - "draw_border(clr, X, y, ax=ax[0])\n", - "draw_border(clr, X, y, border=False, ax=ax[1])\n", - "ax[0].set_title(\"Fronti\u00e8re entre 2 classes\")\n", - "ax[1].set_title(\"Fronti\u00e8re entre 1 classe et les autres\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quelques diagramme de Vorono\u00ef" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "points = numpy.array([[1, 2], [3, 4], [4, 1]])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.spatial import Voronoi, voronoi_plot_2d\n", - "vor = Voronoi(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(4,4))\n", - "ax.ishold = lambda: True # bug between scipy and matplotlib 3.0\n", - "voronoi_plot_2d(vor, ax=ax)\n", - "ax.set_xlim([0, 5])\n", - "ax.set_ylim([0, 5])\n", - "ax.axis('off');" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3, 1, 2], dtype=int64)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vor.point_region" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2.75, 2.25]])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vor.vertices" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib.patches import Circle\n", - "from matplotlib.collections import PatchCollection\n", - "points = numpy.array([[1, 1], [2, 4], [4, 1], [6,3]])\n", - "vor = Voronoi(points)\n", - "fig, ax = plt.subplots(figsize=(4,4))\n", - "cs = []\n", - "for i in range(vor.vertices.shape[0]):\n", - " v = vor.vertices[i, :]\n", - " d = (v - points[2, :])\n", - " r = (d.dot(d) ** 0.5)\n", - " circle = Circle((v[0], v[1]), r, fill=False, ls='--', edgecolor='g', visible=True)\n", - " ax.add_artist(circle)\n", - "for i in range(points.shape[0]):\n", - " for j in range(i+1, points.shape[0]):\n", - " if i == 0 and j == 3:\n", - " continue\n", - " ax.plot(points[[i, j], 0], points[[i, j], 1], \"g-\")\n", - "ax.ishold = lambda: True # bug between scipy and matplotlib 3.0\n", - "voronoi_plot_2d(vor, ax=ax)\n", - "ax.set_xlim([0, 7])\n", - "ax.set_ylim([0, 7])\n", - "ax.axis('off');" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "n = 5\n", - "a = math.pi * 2 / 3\n", - "points = []\n", - "for i in range(n):\n", - " for j in range(n):\n", - " points.append([i + j * math.cos(a), j * math.sin(a)])\n", - "points = numpy.array(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "vor = Voronoi(points)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(4,4))\n", - "ax.ishold = lambda: True # bug between scipy and matplotlib 3.0\n", - "voronoi_plot_2d(vor, ax=ax)\n", - "ax.set_xlim([-1.5, 4])\n", - "ax.set_ylim([-1.5, 4])\n", - "ax.axis('off');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Un diagramme de Vorono\u00ef proche\n", - "\n", - "On applique la formule d\u00e9finie par [R\u00e9gression logistique, diagramme de Vorono\u00ef, k-Means](http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/c_ml/lr_voronoi.html) et on r\u00e9soud le syst\u00e8me lin\u00e9aire d\u00e9fini par :\n", - "\n", - "$$\\begin{array}{ll}\n", - "&\\left\\{\\begin{array}{l}\\left + B_i - B_j = - \\left\\{ \\left + B_i - B_j \\right \\} \\\\ P_i- P_j - \\left \\frac{L_i-L_j}{\\Vert L_i-L_j\\Vert }=0 \\end{array} \\right.\n", - "\\\\\n", - "\\Longleftrightarrow & \\left\\{\\begin{array}{l}\\left + 2 (B_i - B_j) = 0 \\\\ P_i- P_j - \\left \\frac{L_i-L_j}{\\Vert L_i-L_j\\Vert}=0 \\end{array} \\right.\n", - "\\\\\n", - "\\Longrightarrow & \\left\\{\\begin{array}{l} \\left + 2 (B_i - B_j) = 0 \\\\ \\left - \\left \\left<\\frac{L_i-L_j}{\\Vert L_i-L_j\\Vert},u \\right>=0 \\end{array} \\right.\n", - "\\end{array} $$ \n", - " \n", - "O\u00f9 $u$ est un vecteur unit\u00e9 quelconque. On cherche \u00e0 r\u00e9soudre sous la forme d'un syst\u00e8me lin\u00e9aire $LP=B$ o\u00f9 le vecteur $P$ est l'ensemble des coordonn\u00e9es de tous les points cherch\u00e9s. D'apr\u00e8s la page cit\u00e9 ci-dessus, dans le cas d'un diagramme \u00e0 trois classes, ce syst\u00e8me a une infinit\u00e9 de solutions." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((6, 6), (6,), 2.0281820935727704e-16)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy\n", - "matL = []\n", - "matB = []\n", - "L = clr.coef_\n", - "B = clr.intercept_\n", - "for i in range(0, L.shape[0]):\n", - " for j in range(i + 1, L.shape[0]):\n", - " li = L[i, :]\n", - " lj = L[j, :]\n", - " c = (li - lj)\n", - " nc = (c.T @ c) ** 0.5\n", - " \n", - " # condition 1\n", - " mat = numpy.zeros((L.shape))\n", - " mat[i,:] = c\n", - " mat[j,:] = c\n", - " d = -2*(B[i] - B[j])\n", - " matB.append(d)\n", - " matL.append(mat.ravel())\n", - "\n", - " # condition 2 - cache plusieurs \u00e9quations\n", - " # on ne prend que la premi\u00e8re coordonn\u00e9e\n", - " c /= nc\n", - " c2 = c * c[0]\n", - " mat = numpy.zeros((L.shape)) \n", - " mat[i,:] = -c2\n", - " mat[j,:] = c2\n", - " \n", - " mat[i,0] += 1\n", - " mat[j,0] -= 1\n", - " matB.append(0)\n", - " matL.append(mat.ravel())\n", - "\n", - "matL = numpy.array(matL)\n", - "matB = numpy.array(matB)\n", - "matL.shape, matB.shape, numpy.linalg.det(matL)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
012345
0-2.9928875.643915-2.9928875.6439150.0000000.000000
10.7805160.413897-0.780516-0.4138970.0000000.000000
2-3.6550075.7874790.0000000.000000-3.6550075.787479
30.7148790.4514720.0000000.000000-0.714879-0.451472
40.0000000.000000-0.6621200.143563-0.6621200.143563
50.0000000.0000000.0449020.207088-0.044902-0.207088
\n", - "
" - ], - "text/plain": [ - " 0 1 2 3 4 5\n", - "0 -2.992887 5.643915 -2.992887 5.643915 0.000000 0.000000\n", - "1 0.780516 0.413897 -0.780516 -0.413897 0.000000 0.000000\n", - "2 -3.655007 5.787479 0.000000 0.000000 -3.655007 5.787479\n", - "3 0.714879 0.451472 0.000000 0.000000 -0.714879 -0.451472\n", - "4 0.000000 0.000000 -0.662120 0.143563 -0.662120 0.143563\n", - "5 0.000000 0.000000 0.044902 0.207088 -0.044902 -0.207088" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas\n", - "pandas.DataFrame(matL)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le d\u00e9terminant est tr\u00e8s faible sugg\u00e9rant que la matrice est non inversible et on sait qu'elle l'est dans ce cas. On remplace la derni\u00e8re \u00e9quation en for\u00e7ant la coordonn\u00e9e d'un point." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "42.07770646874508" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "matL[-1,:] = 0\n", - "matL[-1,0] = 1\n", - "matB[-1] = 3\n", - "numpy.linalg.det(matL)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On v\u00e9rifie que le syst\u00e8me lin\u00e9aire est celui attendu." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
012345B
0-2.9928875.643915-2.9928875.6439150.0000000.0000000.816593
10.7805160.413897-0.780516-0.4138970.0000000.0000000.000000
2-3.6550075.7874790.0000000.000000-3.6550075.787479-6.084601
30.7148790.4514720.0000000.000000-0.714879-0.4514720.000000
40.0000000.000000-0.6621200.143563-0.6621200.143563-6.901194
51.0000000.0000000.0000000.0000000.0000000.0000003.000000
\n", - "
" - ], - "text/plain": [ - " 0 1 2 3 4 5 B\n", - "0 -2.992887 5.643915 -2.992887 5.643915 0.000000 0.000000 0.816593\n", - "1 0.780516 0.413897 -0.780516 -0.413897 0.000000 0.000000 0.000000\n", - "2 -3.655007 5.787479 0.000000 0.000000 -3.655007 5.787479 -6.084601\n", - "3 0.714879 0.451472 0.000000 0.000000 -0.714879 -0.451472 0.000000\n", - "4 0.000000 0.000000 -0.662120 0.143563 -0.662120 0.143563 -6.901194\n", - "5 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas\n", - "df = pandas.DataFrame(matL)\n", - "df['B'] = matB\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[3. , 4.12377262],\n", - " [5.03684606, 0.2827372 ],\n", - " [5.48745959, 0.18503334]])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numpy.linalg import inv\n", - "points = (inv(matL) @ matB).reshape((3,2))\n", - "points" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 9.86655487, -4.02070972, -6.07697098],\n", - " [-10.61997713, 3.26728747, 3.1110941 ],\n", - " [-12.13641872, 3.65091377, 3.80710713]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = points[0, :]\n", - "c1 = (L@x.T).T.ravel() + B\n", - "x = points[1, :]\n", - "c2 = (L@x.T).T.ravel() + B\n", - "x = points[2, :]\n", - "c3 = (L@x.T).T.ravel() + B\n", - "numpy.vstack([c1,c2,c3])" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = draw_border(clr, X, y, incx=2, incy=2)\n", - "ax.plot(points[:, 0], points[:, 1], 'ro');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R\u00e9gression logistique dans un quadrillage\n", - "\n", - "On s'int\u00e9resse un probl\u00e8me de r\u00e9gression logistique o\u00f9 le probl\u00e8me est tr\u00e8s facile mais pas forc\u00e9ment \u00e9vident du point de vue d'une r\u00e9gression logistique." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((240, 2), (240,))" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Xs = []\n", - "Ys = []\n", - "n = 20\n", - "for i in range(0, 4):\n", - " for j in range(0, 3):\n", - " x1 = numpy.random.rand(n) + i*1.1\n", - " x2 = numpy.random.rand(n) + j*1.1\n", - " Xs.append(numpy.vstack([x1,x2]).T) \n", - " Ys.extend([i*3+j] * n)\n", - "X = numpy.vstack(Xs)\n", - "Y = numpy.array(Ys)\n", - "X.shape, Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(Y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On v\u00e9rifie que le nuage de points est tel qu'indiqu\u00e9." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(6,4))\n", - "for i in range(0, 12):\n", - " ax.plot(X[Y==i,0], X[Y==i,1], 'o', label=\"cl%d\"%i, color=plt.cm.tab20.colors[i])\n", - "ax.legend()\n", - "ax.set_title(\"Classification \u00e0 neuf classes\\ndans un quadrillage\");" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "clr = LogisticRegression()\n", - "clr.fit(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAHwCAYAAAC/n0kWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzs3Xd8FNXawPHfSe+9kBBCCb1XASlKUxCwXkUpgqIiXvXargV77+VaeO2KvaJ0UBEEpPfeCaQnpPdsds/7xyyYCiEkmZTn+/mgyczsmWdnN8mzZ55zjtJaI4QQQgghhCjNwewAhBBCCCGEqI8kURZCCCGEEKICkigLIYQQQghRAUmUhRBCCCGEqIAkykIIIYQQQlRAEmUhhBBCCCEqIImyEE2UUmq2Uuo5s+MoSSk1Syn1cS20O00ptaYG2olUSuUopRyr8dj3lVKPn28M50sp9ZRS6iuz42gIlFKfn+lnRCk1RCl1oMT30Uqpkfav5ToL0Qg4mR2AEKLmKKWigVDACuQAS4E7tdY5ZY67DSjUWj9W50Gegdb6BbNjOBOt9QnA62zHKaWmAbdorQeXeOzttRiaMIHWejXQwew4hBC1R3qUhWh8xmutvYCeQC/gkbIHaK0/1Frfez4nqU6vqhCNhVJKOpqEaAIkURaikdJaJwLLMBJmAJRSrkqp15RSJ5RSSfZyAPcS+x9USiUopeKVUrcopbRSqq193+dKqf9TSi1WSuUCw87UnlIqSCm1UCmVoZRKU0qtVko52Pc9pJSKU0plK6UOKKVG2LeXul2tlLpcKbXH3sZKpVSnEvuilVIPKKV2KqUylVLfK6XcqnJtlFIXKqU22R+3SSl1YYl9rZVSq+yx/aGUeu9UTEqpVvZr4mT/fppS6qj92GNKqUn2GN8HBtrLNDJKXL/nSpznvyWu9c1lrvVKpdQtJY4tVTqilOqolPrdfl0PKKWuO8Nzba2U+sse4+9AUJn9PyqlEu3XYpVSqkuJfZ/bn/8i++M3KKWi7PuUUupNpVSy/bE7lVJdK4nhdEmC/fvTr3OJazrV/j46qZR69AzPJ1ApNV8plaWU2qiUevbUtSn7+pS9lkqpKKXUn0qpVPt5vlZK+ZU4tpdSaqv9uX4PuJXYd7FSKtb+3k0EPju1rbJYz+E6ByqlFtif0yal1HPVfb2FEDVLEmUhGimlVAQwBjhcYvPLQHuM5Lkt0Bx4wn78aOA+YKR930UVNDsReB7wBtacqT3gfiAWCMYoB5kFaKVUB+BOoJ/W2hu4FIiuIP72wLfAPfY2FgMLlFIuJQ67DhgNtAa6A9OqcF0CgEXA20Ag8AawSCkVaD/kG2Cjfd9TwJRK2vG0tzHG/jwuBLZrrfcBtwPrtNZeWmu/Ch47GngAGAW0w7jmVWI/7+/2OEOAG4DZJROvMr4BtmAkyM8CU8vsX2KPIQTYCnxdZv8NwNOAP8Z76Xn79kuAoRivvx8wAUit6vOowGCMMoYRwBOqxIeiMt4DCoAw4Gb7v6pSwItAONAJaIHxGmN/X/0KfAkEAD8C15R5fDP7vpbAbedwXjjzdX4PyLW3P5USr1E1Xm8hRA2SRFmIxudXpVQ2EAMkA0+C0QMI3Arcq7VO01pnAy8A19sfdx3wmdZ6j9Y6DyM5Kmue1vpvrbUNKDxLexaMZKal1tqitV6ttdYY9dOuQGellLPWOlprfaSCc00AFmmtf9daW4DXAHeMhPSUt7XW8VrrNGABJXrPz2AscEhr/aXWulhr/S2wHxivlIoE+gFPaK2LtNZrgPlnaMsGdFVKuWutE7TWe6pwfvjnWu/WWudiT9aqaBwQrbX+zB7/VuBn4F9lDyzxfB7XWhdqrVdhXKfTtNafaq2ztdaF9jh6KKV8SxwyV2u9UWtdjJHcnbrGFowPTB0BpbXep7VOOIfnUdbTWut8rfUOYAfQo4Ln44iRvD6htc7VWu8G5lT1BFrrw/b3U6HWOgXjQ9KpD4QDAGfgLfv79SdgU5kmbMCT9sfnn8uTq+w6l3hOT2qt87TWe8s8pyq/3kKImieJshCNz5X2Hs6LMZKYU7fagwEPYIsyShkyMAb7Bdv3h2Mk16eU/LqibWdr71WMHsjflFGe8DAYyQpGL/FTQLJS6julVHgF5woHjp/6xp6cx2D0Wp+SWOLrPKow0K5su3bH7e2GA2n2DwqnVHQdsCe4EzB6jxPs5Qkdq3D+UzGUbLdsPGfSEuh/6prbr/skjN7Iis6Tbo+13LmUUo5KqZeUUkeUUln807Nfsjyjwmustf4TeBejNzRJKfWhUsrnHJ5HWVV5LYMxBqFX69oppULs77c4+/P9in+eazgQZ/8wV1nbKVrrgqqer8R5z3SdK3pOJb8+l9dbCFHDJFEWopHSWv8FfI7REwtwEsgHumit/ez/fO0D/wASgIgSTbSoqNkSX5+xPXvv2f1a6zbAeOA+Za9F1lp/Y58RoqW9zZcrOFe8fT9wuke8BRBX9atQoVLt2kXa200AApRSHiX2VXQdANBaL9Naj8LoOd8PfHRq11liSCjTbmSZ/bkYH0JOKZkUxQB/lbjmfvYSj5mVnMfffvu+onNNBK7AKP3wBVrZt6uzxA+A1vptrXUfoAtGCcZ/Kzn0TM/nXKQAxVR+7U59IKjsXC9ivDbdtdY+wGT+ea4JQHP7+6yituHsr2tlznSdTz2nyn72zuX1FkLUMEmUhWjc3gJGKaV62ntkPwLeVEqFACilmiulLrUf+wNwk1Kqkz1RfKLiJg1na08pNU4p1daeeGRhlFxYlVIdlFLDlVKuGLWm+fZ9Zf0AjFVKjVBKOWPUPBcCa8/jeoBR69xeKTVRKeWklJoAdAYWaq2PA5uBp5RSLkqpgRhJfjlKqVBlDDb0tMeVU+J5JAERZeqpyz63aUqpzvZr/WSZ/duBq5VSHsoY4De9xL6F9vinKKWc7f/6VVTTW+L5PG1/PoPLPB9ve+ypGMlllafns5+zv/21ycV4LSt6HU89n+vtsfalmmUDWmsrMBfj9fFQSnWmRD2vvZwiDphs78W9GYgq0YQ3xuuUoZRqTunEfh1Gwnq3/X1xNXBBdeKsQKXXuYLn1BG4scRjq/x6CyFqniTKQjRi9sThC+DUQhcPYZRDrLffAv4D+zywWuslGIPTVtiPWWd/TOEZTlFpexgDl/7ASEzWAbO11isx6pNfwuiRTsQYoDSrgtgPYPT4vWM/djzG1HdF53INKmg3FaPu836MxOVBYJzW+qT9kEnAQPu+54DvqfgaONjbiAfSMGpd77Dv+xPYAyQqpU6WfaD9Wr9lP+6w/f8lvQkUYSTccygx8MteC34JRi14PMY1fBnjulZkItDfHuOTGO+HU77AKC+IA/YC6ytpoyI+GB+U0u1tpPLP3YuyHsdIWNMxat+/OYfzlHUnRllGIsYdk8/K7L8VIwFOxejpLvnB6mmgN5CJMaBz7qkd9vfV1RgDQtMxymrmUjPOdp3vxOhpTsQYTPgt9vdcNV5vIUQNUqXLsYQQwmDvsdoNuNoHcjVJypgmbL/Wumyvb02fRwPt7DXcoopUBYu7NHRKqZeBZlrrsjOUCCHqmPQoCyFOU0pdZb9F74/Ra7WgqSXJ9tvaUUopB/s0bldgTBsmRK1QxjzJ3ZXhAoxSm1/MjksIIYmyEKK0GRiDi45g1Js2xQFDzYCVGCUjbwMztdbbTI1INHbeGGUeuRj1668D80yNSAgBSOmFEEIIIYQQFZIeZSGEEEIIISogibIQQgghhBAVcDI7gJKCfD10q1Dfsx8oao9DMXidhHxfsLibHY0QNSLHFnhOxzs65eLskk5BXnOquPaGKZxcMnB1T0ApG9ZiDyyFgRRbvKnPMZ9NVl6a2SGc5hIYfPaD6pgDFlwdM8m3Bp39YCFEpY7v3nlSa33WH/J6lSi3CvVl8+xpZofRtLmnw6APYPcQSOxqdjRC1Ii1uVOqdJxyKELbKlsjpH7w8tuJpciPwrxIvPx2ERY1h4QjN5KT0d3s0GrEb9t+MDuE01pOus3sEM4owHU37bx/YOPJx9E4mx2OEA3Kze2bl12ivkL1KlEW9YiSQZ6i4atqggzg4JhP18FTSY0fRdyhW2sxquqwEhD2J+FRc/AJ3EbisQkc3fkEORndOLSlsjU+RHXU9+S4pHD31XT2/wRf14OsTPiAIpvckRWipkmNsiij4d6yFaL6NG17Po6n717ystqZHUwpIZE/0XvkWDpecA8ubskc2/Uw0XvvNzssUQ/szpjJ6qQ3aOa+nrEtLsfLqUodZEKIcyA9ykKIJq95u48IiljC8T33kp50sdnh4OyagqUwCFB4+h6gqCCQ6D33kZYwAnA0O7xaUZ9KLhqSw1kTyLZEMjzsFsa3GMei2F/IsrQ1OywhGg1JlEVpRe6wbzRkhpsdiRB1wr/Zn7Ts/D9SYsYSd3i6qbEYNcdfEBS+jD1rPyErtR/Ru/+L1vW7blqYKyl/IItiFtDF/wOyLS3NDkeIRkUSZVGa1RXiepodhRB1xsk5k6zUnhzZ/gzmlB5ZCQhbYa8/3kqxxYv4o1MoyG0BIEmyqJIsSxvWJb8MgJtjCm29f2Z3xm1IhaUQ50cSZVGasoJXMhT4gsXD7GiEqJaqDeLTgCIl5ipSYq6g7hMK4/zKoZg23Z/BZnPl2K6HSD5xNdZirzqOxTxSclHz2vr8SL+g5wly287qpDexapnqU4jqkkRZlOaSC/3nwN4xEN/D7GiEqBVKWejQ7x5SYi8nNf5S6jJJdnFLJKzN1/gGr2PnX9+jba7s+fsz8nNbgpZfyeL87U6fCUDfwBfwco5lefyn5FtDTI5KiIZJ7skIIZqcVl1fJSBsJQ6O+XV2Tk/fPbTr/SC9R11KeNvPKciNxMk5G4D8nChJkkUNUuxOv4M/Ez7C32Uf41qMw9f5sNlBCdEg1fpvZqWUI7AZiNNaj6vt84maIvMoi8YppOWPhLX5mrjD00iJubJOzukTuImug6dRbPEk8ehEEo5OpjC/eZ2cWzRdJ3LHsDj2F/oFPUOBNcDscIRokOqiC+M/wD7Apw7OJc6bKvU/IRoT74AttOn+HOlJgzi+575aO4+DYx4hLeaB0iQem0hWam+O7HiCk7GXYS32rrXziuprSAuNnIvUwu4sjfsJAAdVSKTnb0TnjDc5KiEajlpNlJVSEcBY4Hmg9v4qiZojHcmiEfMLWUthXgQHN79GbcxH7OKWRLPW3xDa6gecXbJITxxK4rGJgCNJ0RNq/HwNmQziq3sdfL5hQMhj7MtYz4aUp9EyTEmIs6rtn5K3gAcB6UJpKIrdYNflkBVmdiRC1LiY/XcRf3harfTqNmv9Na26voJSNtISRhB/eCrZ6TLVoqg/9mfeiJdzDF39P8Db+TgrE/8Pi03+PAtxJrWWKCulxgHJWustSqmLz3DcbcBtAJEhUp1hOpszJHU2OwohqqXiaeE0rbq+RErMleRmdqrBJNmGf+gq8rLbUJgXSU5GVxKPXW/UH+e1qKFzCFFzNI5sOvkEmUVRDAyZxdiIK/gjfg45xfJ+FaIytdmjPAi4XCl1GeAG+CilvtJaTy55kNb6Q+BDgL7tw+TGv9mUFfxiIS8ACqWnQTR8Ee3fJzzqKwrzw8jN7HTe7Tk45hPcYj7hUV/g7hVN3KGbOL73AXLSe5CTLlMqivrvYNYksi2RDAp9ACeHPLPDEaJeq7VEWWv9CPAIgL1H+YGySbKoh5wKoM+3sP8SiO1tdjRCnJeAsD+I7PQuyTGXk3Bk6nm317z9B4RHzcHZJZOc9C4c2PwqafGjaiDSpkNqk+uHhPwhzI1ejQ0XQBPkuoOThVIqJERZUskvKiGd+6Jh8/A+SLveD5Od3o0j25+iulO5uHkdoyCnNQCubklknexH/JEbyU7rXe02Rf3RWGe7qAojSYbWXvO5OOwOtqY+wI60e5D3tRD/qJNEWWu9ElhZF+cS50t+QYrGIbztHIot3uzf+Dba5nqOj7bhH7qasKg5+AVvYOeqr8lJ78nRnY8h6zSJxuZ47mgOZV1L78DX8HU+yt/Jr2LVbmaHJUS9ID3KomJKepRFw1HRIL4j25/CxT0BS0HVl+5VDkWEtPiVsKgv8PA+RmF+M6L33E9+dhv7EZIkV5eUXNRfNu3KmqQ3ySyKom/QS3g5n2B5wqcUWgPNDk0I00miLMqQHmXRsIW1+ZKTsZdhKQqkMC+yag9SxaCdUMpKZOe37HMtv0Jq/CVo7Vy7AQtRLyh2pd9FlqU1Q0LvJch1B3F5w80OSgjTSaIsSit2ge3/gpwgsyMR4pyFtvqe1t1ewsEpj7iDM856vIfPAcKjvsDTbw87VszFZnVnx4qfKSpohnxoFE3R8ZxxJOYPPN2b7O6YSL61mclRCWEeuY8oStOOcLItFPiZHYkQ58QncBOtu71AeuJQ4g7ecoYjbfiFrKLzhdPpOexqAsOXkXWyHw6O+QAUFYQhSbJoyk4lyaHu6/hXqwvp4DvH5IiEMI/0KIvSlBUCj0JuEOT7mx2NEFXi6h5Hh373UpAbycEtr3Cm5an9Q1fRacC/KcwP4fiee0k8fi1Wi2/dBStEA5Fa0I2E/MFcGDILX+ejbDr5BLoWln4Xoj6TRFmU5mCBnj/DweFw4gKzoxHijE4N4utwwd0oZWX/hnfKrbzn7HqSZq2+o9jiS8LRKaQnD+bAptdJSxiO1i5mhN1kyAC+hq1Ye7E8/jP6BT1NF/+P8XaO5q/E9yjWXmaHJkSdkURZCNHgHdn2DG5exynIbXV6m4f3IcKiviA4YgHKoZjkE1cbO7QTqfGjzQlUiAZG48jGk8+QaYliQPDjtPH+lYNZsnaYaDokURZlnKrNlOnhRP3nH7qCjORBFFv8yEn/p64+osNsIju+h7XYjaQT15BwZEqpJFqIprzQSHUcyJxKcn4/0ouMZeAdVYHMtSyaBEmUhRANU8h+OnV/ieN77yH+yI0ERywg8+QACvMiyEweyHGbM0nR11JskYGpdU1KLhqn9KLOAPg4H2F08+vYcPJpjueMMzkqIWqXzHohypDR/qIB8EqCLovITu+Cg2M+fS8ZSdteTxLUfDEA2em9iDt0qyTJQtSCQpsfOcURDA+bQTf/d5A7kKIxkx5lUZrVCbbcAHky44Wop5xzocdPoMHT5yDe/ntIS7yI+CNTyTopA1CFqG2F1kCWxX3PoJAH6Bv0Er4uR1ib9Ao2ZHCsaHwkURZlOEB6S7ODEKICGnzjodVacMmHlLYk5/cg/ugUCnJamx2cEE2KVbuxKukdMi1R9A58jeyiVuxIv8fssISocZIoizJsELoPckIgN9jsYIQAh2JotgciN4HXSfZveg2OupCWOMLsyIRo4hQ70u4ltaA7CfkX2rdppIRPNCaSKIvSHGzQbQEcukgSZWEux0IjOY7YCq55kBsAe8aSnjgCbZNbvPVRQxjEJ7Nd1LzYPONDq4tDJiPDp7It9QES8gebHJUQNUMG84mKSYeAMItD0T9fR26CrDA4OAzc08DBIkmyEPWUs0MOLg6ZXNJ8Eu18vjE7HCFqhCTKojRd7gsh6oCGgGjo+QNc8IXxvdUV/r4dDo6CVusgLwgSu5gdqBCiErnFzVkUO4+EvEEMDv0vfYOeRWE1OywhzouUXogypCtZ1CFVDM32Gj3H3ilQ6AmxvUFZQTuBdjBmuFDAjmuM5FkIUW9ZbD78Hv8F/YOfoJv/+2jtyJbUWWaHJUS1SaIshDBPyCHoshiyg2HPZZDY2UiQAdDQZRF4pMK2Caw9ebepoYrKNYTaZFF3NE6sT3metMLOxObKoFvRsEmiLErTDrBhGhR6mR2JaIw8Uo3e49xAiOkHye1hy/X2KQnL3s1QEN8NUltDeisTghVCVJ/iYNZk+1dWBoY8woHMKaQWdjM5LiHOjSTKogwF2c3MDkI0Khr8jxsJcvARsDrCif72XY4VJ8FO+VDsDifb1WmkQoia5+GUSHOPlbTxnsuqxPc4kXup2SEJUWWSKIsyNDTfDlnNIDvM7GBEY9Dhd2ixFQo94MhgiO0FFs/Kj/dOhD7fwt4xkNyx7uIU50xKLkRV5BY3Z2HMQkaE38zwsOlsPvkYuzNmIGNiREMgibIor9MyODpIEmVRPc750HybMUNFga9Rd5wdanxvO8uvHJdc6DEXil0ho0XdxCuEqHX51hCWxP7IkNB76Rf8LK6OaTLITzQIkiiLMuQTvqgmj1SI3Axhu8CxGCzuENcLMiOMf2ejiqH7XHDOg82TocjodV6bO6WWAxdNgSw0Yj6rdmdl4mwyitoRmzvM7HCEqBJJlIUQ50lD918g5KBRf5zYFU70PceVHTV0/A384mDnlVInL0Sj5cD2tPtPf9fZ72NickeQbWltYkxCVE4WHBGVkAVHxBkoKwQePfUNFPgY9cdr7oB9Y6q3/Hm+n1HyI3XJQjQJro6p9PD/H+NajCPUbb3Z4QhRIelRFuVJjiwq45QPEduhxRZwzYF1042k+ODI6repbMa0hNEX1lycolbJID5REwqtgSyMXcDI8KlcGnE9a5Ne4XD2dWaHJUQpkiiL8tbdatSXCnGKcx60WQPhu8DRYsxtvOcyyA06v3bd06HXD0ZbmTJ4T4imJtvSikUx8xgWNoMhze7Fy/kE29MeMDssIU6TRFmUlxdodgSiXtDGDBYWD7A5QOg+SOxkrz8OOf/mHQuN5amd8qHI+/zbE0I0SEU2P36L+4oBIY+RbWlpdjhClCKJsigvciNkhcn0XE2VskLofuN9oGyw4Wawuhn1xzbnGjqJDbrOB4802Ha9UZ8shGiyNM6sS3759PfNPf4krbAL+dZQE6MSQhJlUZG2K+F4f0mUmxqnAmOxmRZbwC3bWGb6RD+MonVVg0kyELXKWKVv/yX25avLk2nh6p+GWJss08I1PE4qh6HN7qbY5s4f8Z+TXtTF7JBEEyazXohKyIi+psP+WgcfgnYrjQR527Ww7haI60nN/5rQ4J4JsT2NVfqEEKKEYu3FsrjvUNgY2+IqIjx/Nzsk0YRJj7KogJJ1Rxo9DX4xELkJ0iMhpp9Rf5wdAjm1fatTwe7LQdl7qoUQooy0wq4siFnEyPCbGBF2M5tOPsHejFvNDks0QdKjLMrTp/8jGhtlhdA9cMEc6PsN+MVyOlnVTrWbJLvkQK/vjZkuUMaUcEIIUYl8azOWxP7MidzReDglmh2OaKKkR1lUQHr5Gq0uC6HZPsgNgH2XQkLXmq09roxDMfSYC14p4FhU++cTQjQKxdqDFQkfoOydN74uB8kvDqXI5mtyZKKpkERZlPf37WCtg+RJ1D73dGixGY4PgEJviOkLCV0gNYq6+0CkoeNS8I03lqc+S6+1DOKrXxriAD7R2DigAQeKGBU+BavNnd/j55BTLFPJidon9z5FeUVeYHU1OwpRbRp8Y6D7XLjwA4jYBr6xxq7M5pDaljq9axC5CcJ3G0tcy/LUQohqsuHCmqQ3cXdKYXyLcYS4bTI7JNEESKIsymu1FgKOmh2FqA5lhb5fQb+vwe+EsSz0mjsguZNJ8diMhUqSOsCxQebEIIRoNBLzL2RhzHwKbb6Mbn4dbbznmh2SaOSk9EKU12qdMS1YWhuzIxFV4VQAAceMZFg7QkaEUXtcV/XHZ6IdYMtETs/FLBqMhlxyIXMnN25ZligWxixgeNitRHnP5Wj2VcjvF1FbJFEWFZBfOA2CWwZEbobwneBUBGvCoMAPDg8zOzJwLICo1XBkqJTxCCFqXJHNn9/ivsFBFQEKV4c0irU7Vu1udmiikZFEWVRMyfRw9ZZbJrT7E0IOglaQ1MlYQa+gviwDbYNu8yEg2ogtM6JKj5JBfEKIc2HDBZt2AWyMDJ8KwPKETymwBpsbmGhUpEZZVEB6lOsdZQPXLOPrYhdjBonoAfD3TNgzHrKbmRtfSW3/gqCjcGBUlZNkIYSoPgd2p88kwHUv41qMw89lv9kBiUZEEmVRnnQm1x+OBRC5ES5835jFAg3F7rBmJhy5yJjyrT5pthtabYCY3hAny1MLIerG8dzLWBw7F0dlYWzEFTT3WGF2SKKRkNILUd6af4NNPkOZyi3TmP+4+Q6j/ji9hVFecVo9fH1UMUStgrRIODjC7GhENTXkQXyiaUst7MGCmIWMDJtGv6BniD8xBC1pjjhP8g4S5VldzI6gCbPPDhEQDS22QFJHI0HODjM7sLPTTrB5MticjNk3hBCijuUVh7M49hdcHdPROOGgCtHaURJmUW3yzhHltVkF2aGQ0sHsSJoGZYPgg8bCHEmdjNXzErtAamso9DE7urNzsED4Lojt1TDiFY2WTAsnAIq1J8XFnoBmUMgDuDlmsDJxNhZbPStVEw2CJMqivIitkNRZEuXa5lholFa02AzuWZDnB0X2qY1sTg0k6dTQaSmE7YHskGoN3pPZLuoHKbkQjY8iKX8AA0NmMTbiSn6Pn0NusQwwFudGEmVRAZn1ok50m2fMDpEeAQdHQkpb6mXt8ZlEbjSS5CNDZIYLIUS9czBrEtmWSIaF3cb4FmNZnvApKQV9zA5LNCAN7K+yqDsy9UWN84mHrvPBJdf4/uhg2DgVtkyGlPY0uB/HwCPQboVRR33sQrOjEUKICiXkD2FRzAIsNk+Gh92Co8o3OyTRgEiPsqiAkk7lGmMzFgaJ3AR+cWBxBa9ukNYassLNDq76HCzQZRHkhMCey5A3jBCiPsu0tGVhzEJ8XI7aV+871Rkkv7vEmUmiLMqTzuSa4WCBAZ+AR4ZRf3xgJMR3axxLOtucYcfVUOANNpklpSGT2mTRVBTaAkgpCACgq98HBLju4e/kV7FqN5MjE/WZJMqivNV3IZ+yq8k1EwKjIb6HkUwmdrbPINKOBldaURFlA78TkN5KapKFEA2WUhaifObi5RzD8oRPKLQGmh2SqKckURYVkCT5nPkkGAPbQvYDCk62hSJPODrU7MhqVtsV0HITbJh23stmy2wXQgiz7Eq/i2xLS4aE3sv4FuP4PX4OmUXtzQ5L1EOSKIvy2q6AnGBI7Gp2JPWf50nouBT8Y6HYFWL6QUwfI0lubMJ2GknyiT7nnSQLUZNk/mRRHdE5l5NjiWBE+M1cFnGJJDlJAAAgAElEQVQVP0f/TZHNz+ywRD0jibIoL2wPnIySRLkyjkXgkgP5AVDkAc75cGAExHdvHPXHFfGNg07LILUlHJLlqYUQjcPJwt4sjFlIqPtGSZJFhSRRFuXp0/8RJblmGctKN98OuUGweQpYPGD9LTTqchXHAug+1xi4t/tK0I2g1lrIID4h7HKLIziabYy5iPBYTpjH32w++SgaR5MjE/WBJMqiAo046asOryRouQFC94PSkNwBTvQrcUAjv15WNzh8EWSFgcXd7GiEEKLWhLhvpKv/B/g4H+WvxPco1o2wjE6cE0mURcVUU+9RthnXQDsa8x8HHzZqj2P6QEFTuT2nwT0D8v0hobvZwQghRK3bmvoIecVh9A9+gstaXMny+M/JLW5udljCRJIoi/KsLmBtom8NhyII3wWRm+H4BRDXy5j7OKFL460/rkzL9dDmb9g4zSg1qQEy04X5pORCiDPbnzmNLEsrhjW7nXEtxjP/xGLyrTKAualqotmQOKN1t5odQd1zzYIWW436Y+cCyAiHfF9jn83Z3NjMEHQI2v4FSZ0gV+YXFUI0LfF5F7Modh5tvH8h3xpqdjjCRJIoCwHQdb5RYnGq/jizCd9q8zwJXRcYU8DtleWpRf0l08KJ2pRR1IGtqQ8D4Ot8mAjPP9iTMQP5ndi0SKIsymv/O+QFQmzvOj91kcXKi9+tY85vu8jOL2J03zY8O20IrZrVZF2whqDDRg/y7vHGzBUHR4LFrQnVH1fCqQB6/ARW+xLVTbE3XQghymjn8x3dAv4Pf9cDrE16GRsuZock6ogkyqK84MOQUWBKojzt1UVk5hbyy1NXE+jjzidLdzLkvq/Z/v7NBPqc54wLDkUQvhtabALPdCjwAfd0I1GWBTQMVhdjue3kDlDoY3Y0QghRL2xOfZRi7U6vwDfwdjrBnwkfUWgLMDssUQckURblmTThxYGYVP7cfpzjX83E1cV4az45ZTDHEjL4ZMkOHpww4Kxt2GyaZZuPsu1wEm3C/LhqUHujLacCuPADcMmHzDDYdYWRDMqcwP9wLDQGLNbCgiIyiO/s8gvymLfsB9ZtXYOHuydjLr6cIf2Ho1TN3OaVQXxCnA/F9rT7ybK0YXDIfYxrMZ6lcT/IjBhNgCTKogLm1F/tOpbChZ2bn06STxnRqxVLNx896+Oz8woZM+tH8ossjOzVipVHN3FA/c7UjlON0o0TF0B6C3v9sdSYlRK+A9qsgc2TarT8ZE90Cm/M3camY0tpGRHFhMtvpE1k2xprv7EoLCrkP0/eQoBvINeNm0xWTiYffPUW+4/s4bZJd5sdnhCNWlFBPkopnF3dznrs0eyryLFE0NX//yiwykDnpkASZVGJuu9Wbhvuz9bDiVitNhwd/+np3XQwgXbN/c/6+Oe+XkubMB8+f7kTDq02QUACBXmOTBm3lB9nXQ/RA2sz/IbLNxY6LoP0yBott1i/N46xj//KdZffxO2D+7Jz31buemwaL896l64de9bYec5VemYaq9b/QZGliIF9hhAR1tK0WE75fdUiPNw8efGRt0/3IA/sM5Tr77iMq8ZcT3BAiMkRCtH4JB0/xjfPPsH+DWtRCroOGcakx5/Fv1nYGR+XXNCPPxOMRadcHDJp7rGSYzlX1EXIwgRy31mUV+QJxWf/ZF1dhUXFvP7jBgbe/QUD7/6C13/cQGFRMT3bhtK+eQAz3lpKSkYeRRYrny3dyfcr93HLmB5nbfdA1m4++jEOh94/g0c6HBqGXn07i9fFkZNfVGvPp0FzzbQvT+0Lu2p2eeqHPl3Lv2+axZR/zaBnlz7c+K9bueumB/ng6//V2DnO1ar1y7nh32PZtmczx04cZsZDk5jz4wemxXPKtt2bGDF4dKkyCz8ff3p16ceufdtMjEyIxqkgN5fXpk6g08BBvLNpN2+t20nz9h147aYbKLZYqtxOF78PuDjsDvoGPg/Yai9gYRrpURblba69elKbTXPlU3NxUIrnbxoKwGs/bmT5tuMsev5afnziSh74YAWtp/wfFquNCzs3Z8kL1xERXElPp0uOMe9xbhBxcQ5YC11h1+X2+mNHlLUYAK2b+kqDFXCwQI+54GiFLdfU+IejtbujeWrWJaW2DRt0KS+881iNnqeqsnOyeOHdx/jf05/QIaozALdMvJPp90+gX4+BdG5v3uqD/r4BJKbEl9qmtSYhOQ4/n7PfTamM1CULUbGNi+cT2akro6fffnrbVf/5L/vXr2XnyuX0HjW6Su1sT7sPV8d0ugXMxsflGKsS36ZYe9RW2MIEkiiLOrVi+3FiUrLY/v7NONnLK4Z2j6Tn7Z/y57bjjOjdio/uG8P//edSiq023FwqeYt6JUHkJmi2FzIiYOtELurQiX/fmM+nD3Q63TP37rwtDOkagbdHE1tVryocrFDkAUeGQl7NrLxXUrCfN3FJsaVqkuMSYwjwr/lzVcX6ravp0bnP6SQZIMAviPGjruHPv5eZmiiPHXkVdz52E4MvGEantl2x2Wz8vPhbiixF9OzS17S46iuZP1mcr5QT0bTqVv5nvlW37qTEHK9yOxon1qe8QGZRWy4IfooxEVfzR/znspJfIyKJsiiv41LI94PjZ59l4lyt2xfH+AFtTyfJAE6ODozr35b1++IZ0bvV6W0ljznN/zi0WguBx6HYGWJ7QYyRSDw++UIuefh7Bt79JZf0acXWw0nsjk5h+Ss31PjzaPi00YO8/Tpqa2Dj7eN68OZHz/P8g2/i4+1HZlYGb370PFdeel2tnO9sbDYbjg6O5bY7OjphtVlr7Bw5edl4unvh6Fj+XJVp3aItD8x4jAefu4NA/2Cyc7Lw9fbjpUfewcFBKuSEqGktOnZm5XdfMm7mf053rNhsNvat+5sJDz9xjq0p9mVOJ9vSkr5BzyODxRsXSZRFef4njOnUakF4oBcL1x8pt/1AbCqXD2hX8YMcLEbtrHYEr2TwTINDF0Ncz1LlAr6ebvz91hQWbTjM9iPJXHdRR3547Eo83GTRjFKCD0LkRth5tTGHdC2ZdUN/dqfGc93tYwgPjSA+KZZLLx7P5GtuqbVznkn/3oN586MXOB53jJbNWwOQk5vNwj/m8ujdz513+78u+4E5P3xAbn4Ori6uXH/FNCZeeVOVp3cbduGlDO43jANH93H0xCHmLfuBiXeOI8AvkGsum8Tkq6dXOfmuqZKLwsJCCgsL8fb2rrFp6oR5CvPzSTkRjV9IKF7+TXsO4N6jxrDw/Xf46ulZXDLtVqzFVha9/zYe3j50Gji4Wm3G5o0k7sQwNI4oigl220ZyQb8ajlzUNUmURQVUrX0gvnZoRx79bBVzftvFlJFdAfji911s2JfAlw+NL32wSw5EbIWIbXD4YojvAXG9jIVQdMUJg5OjA1dc2J4rLmxfO0/ARDabNuaZTsqkX4cwurepxkwIninQZSHkBhqr79UiJ0cH7rnlEW66bibxSTGEh7bA18e8lQ/9fPy5e/pDzHx4MiOHXIaHuye//bWAiy+8hB6d+5xX20tXzOe7eXN4+dF3ad+mE8djj/L0Gw/h6OjI9ZdPrXI7zs7Gal8ffvU2D858kkH9LuZE/DFe/b9nyCvIZeaUe88rzqoqKipiyZIl7Nu3DycnJ1xcXLjkkkvo2LFjnZxf1CytNcs+/YBFH7yDT2AwGclJ9L10LJOffK5KU6I1Rk4uLjz4xQ/Me+cNXplyLQ6OjvQbM57JT714XndxNMbfps5+n9Av6Fk2nXxMlr1u4FR9GuTUt32Y3jx7mtlhiAEfQW4Q7LqqVprfcSSJm19fTHxqDmD0Mn96/2X0iAo1DvBK/qf+WFmNleKiB0JWeK3E0xDEncxmzKwfcHZyoFurYJZvP86gLhF8+dA4nJ2qeIvfOR/6fW4M3ts4FQq9azVmqJ8LjcQnxbJ8zRIKiwoZ1O9iOrXtet5tTr3nau6++UH6dP+nXOlw9AEeeHYmv3y8/Jx6Yx975V76dOvPVWOuP73tZFoKk++6nLkf/4GHu+dZ2zjfHuUff/wRJycnRo8ejZubGydOnOCnn35iwoQJREREnFfbNUFqlM/N+oW/suC9t/jPB3MIiWxJXlYmn816AN/gECY/+bzZ4TVKjiqfIaH/obX3Ig5kTmJd8vNo5O5mfXJz++ZbtNZnHQQiPcqiArX7ybdHVCib35tGdGImAK2a+VJosbLjSBIhfp6EXbDIKK+I6wEn+kJ+075FCDDjraVcNag9T904GKUUhUXFjH/8J96au5n/Xtf/7A0oK3T7BVxzYMukOkmS66vw0AimXHNrjbYZnxRL+zadS22LatmetIxUiostp3uKq+JEXDQ3/qt0IhgUEIyfrz8pqUm0jGhTIzFXJisri2PHjnHvvffi7Gz8YW/ZsiWDBg1i06ZN9SJRFufmz68+47oHHyMk0pgz3MPHlylPv8gjlwzh2gcfw9Xd3eQIGx+rdmdl4vtkWV6lR8DbeDtHsyLhQ4ps5t1VE9Ujo0REefm+xlzKtUgpReswP1o392R12kL2hr7OzNnz6Hrrx9xzpxuZS6fDgUskSQbSswtYtSuGh68fcLpn0tXFiSemDOLr5Xuq1ohLLrhlw74xjaJnft+hXbzx4fM8//ajLF+zlGL7NIBmade6I5t3riu1bdvuTbQIizynJBmgdWQUO/ZuKbUtKSWBjKx0QoJqfyR9VlYW/v7+p5PkU0JDQ8nMzKz184ual5GcTLPWpT9geQcE4uziQl6WvKa1x4GtqQ+xKvEtAl134e18wuyARDVIj7Iob8e1tX8Ol1yI2EpRs80M9SgkPzWQte9fSV6qH/e9v5ybX/qTn5+8uvbjaACKiq04OTrgUqbEwtvdhQJLFRPEQh9YPx1sdfMjX5slFz8t+pqv5n7CVaOvx9fHj+/mz2HZXwt44eH/4eRozq+0mybM5Jk3H6LIYqF3twvYe3An//v4Rf497b/n3NbEK2/mvmdm4OPly+D+wzkec4Q3Pnqea8dOxt3tzIMva2IQX1BQEGlpaWRlZeHj88/85QcPHiQ83NwPWVJyUT1te/dly29LuOy2f5/ednjrZlzcPfANllUfa9uR7GuJyb2EIpsvAF5OMeQUtzA5KlFVkiiLuueSA4P+DxytbF/jSdqunoxudzGg8HCDN24fQYuJ7xF/MpvwoKZbInBKqL8nUWF+fP/XPiYO7wIYg3NmL9jGuP5tz/xgvxhotgcOjKyzJLk2ZWSl8/E37/L5mz/TLMRI2saNuIqZs25k1frlDB90abXbPpmWwuHo/TQLDqdVi6hzemy/HgN56v5X+eKnj3j3s1doEd6K+2c8zqB+F59zHB2iOvPCQ2/x8bfv8vLspwgODOGayyZy7bjJ59xWdbi5uTFgwAC+/vprRowYgb+/P7t372b37t3ccos5M5aI8zN+5n94efI1WAoL6TZ0GLEH9vHr269zw6NPyfSDdeRUktzaax5DQu/h7+RXOJJdB51S4rzV2l9OpZQbsApwtZ/nJ631k7V1PlGDOi8yaliPDK2hBjUERIN3kjE3c5EXHB4Gqa25+9ZFvD6jHSXroj3cnAkL9CI5I08SZbvZd1/KuMd+ZOWOE3RrHczijUeJTclm5esTK3+Qm315aos7OBZDccNPlLft3kj3Tr1OJ8kATk7OXDb8StZtWVWtRNlms/HOZ6+wZMU8OkR1ITrmCFEt2/H0/a/h7VXJipAV6NOtP326VaFevAp6dO7DO89+ViNtVcfQoUPx8/NjzZo15Obm0rJlS26++WZ8fX1Ni6k6bDYb6379ib9//o6i/Hx6jhzNiBun4+7lZXZodSosqi2PfPsLSz95ny+feoSg5i2Y8ca7dLhgoNmhNTlxeReRVNCPoc3uwdflCFtTH0SqYOu32vzLWQgM11rnKKWcgTVKqSVa6/W1eE5RE7wTa2YeZYdiozczchN4nYQCH4jpAzbn04uEDO4awc9rDjCo6z8DhPYdP0lyRh4dIwPPP4ZGol+HMHZ+OJ3Pl+1i7/FUrh3agRuGdcbdtZJR1I5F0OMnUDbYUfPLU5vF3c2DrJzyNZVZ2RlVmg2iIvOW/cCegzv54f2l+Hj5Umwt5o0Pn+eNj57nyXtfPt+Q60RNL1WtlKJHjx706NGjRtuta18/NYuDa/9i0IABuLm5sfXPpWxZtohZP8xvctOihbZqw9RnXzE7jCavyObHb3FfMzBkFj0C3sHH+Sirk/6HVcuAyvqq1hJlbcw7l2P/1tn+r/7MRSfOQIE6z5cqIBq6zAfXPMgOhj1jIbET6NJvuXuv6cfAu78E4OrB7Tkcl87TX/3Ns1OHVL58dQOnta5wurD8Qgvr98Xj4epMvw5hODiUPibU35OHrq/KaonauCvgdRK2XQt5jecDR59u/Xn5vaf4a/0fXDRgJAAJSXHMXfIdLz3ydrXaXPDHXP499T58vIzeUidHJ+648T6uumUEufm5eFYzARfmSjp+jI2L53HXHXfg6mosYd+qVSu++vY7Ni5ewKCr5La3MIfGmbXJr5BZFEXfoOc5nH0tsbmjzA5LVKJWMxGllCOwBWgLvKe13lDBMbcBtwFEhlT9NqeohzxPGtOQ5YRCboAxu8KJvpDeksqmnGse5M26t6fwxk8beeCDFTQL8OT9uy/l0n61OwVWXcsrsPD456v4/LddZOUVcUmf1rxy68V0aRUMwPcr9/Hvd36jfUQAGTkF2LTmx8evpFvragy08UiDwGNwaBik1e11rO15k52dXXjhkf8x68X/8O2vn+Hj5cvOfdu4ddJddGrXrVptZudkERRQ+jp7enjh5OREQUGeJMoN1JFtW2jTJup0kgxGT3nHdm05uHGdJMrCZIo9GbcTkzuSLIsx1sRJ5VKs5fdNfVOribLW2gr0VEr5Ab8opbpqrXeXOeZD4EMwFhypzXhEFWlF1Tv/7fXHkZsg6CikRBmzZhT6wI5/VamF5kHevH77iOpG2yBMemk+zo6ObJk9jWBfDz5btosRD37H1tnTyMwt5K73fuePl6+nZ9tQtNZ8+cduxj/+E4c+n1H1BUVOyQuEdbc02rmSO7Xtyo8fLGXLrg3k5efx6N0vnNeKf/16DGDJivncPuWe09vWbVlFkH8IAX5BNRGyMIFvcAhpaWnltqdlZBDcqp0JEQlR3qkkOcRtEyPCb2J14lvE5o00OSpRUp3c29ZaZyilVgKjgd1nOVyYLTfIGAB2NiH7oc3f4JUChZ5weCjE9az9+BqYfcdPsmFfAtFfzcTF2Uh677yyD/tjUvlw8XbyC4u5dUwPerY1ViZUSnHjqG68v3A7f2yNZswFVZyBwSvZmOUitrfxQaURc3Jypn+vwTXS1tRrZzDzkclkZKUxoPcQjkQfZO7S73jqvlfOaUU9Ub90GjCIYmDd+vX0v+AClFIcO3aMXbt388Szr5sdnhClZFtakGNpwYjwm9h08kn2ZkxHlr2uH2pz1otgwGJPkt2BkUDDGBnT1O0ZX/k+5zwodgXtCO6ZRsdzJfXHwrA/JpV+HZqdTpJPGdQlgnnrDuHt7kIve5JcUkSQN2nZVRxU6Zz3z+C9xC6NZvBeXQgNDuOT13/k16Xfs2TFPJoFh/Pec5+f8xRxon5xcHTk3k+/5cN7Z7Lu7XdwcXUBB0dmvPU+wS0iq9VmWmI8GxfNpyAvl+5Dh9O6e0/5MFWPHd62mY2L56OtNnqPGk3HAYPq7euVb23GktifGdrsLvoHP4mP8xE2pDyLlll8TVebr0AYMMdep+wA/KC1XliL5xO1yeOkUV4Rtgf2jYbErkb98fELkE+9Z9YpMoiNBxIoslhLJcurd8fQKTKQDhEBvDtvKzPG9sTR0ZgmKCUjjz+2RfPG7cPPfgJlhe6/GIu4bJksSXI1+PsGcNOEmXVyrtUb/+TXpd+TlpFK9069mXjlTYQGh1WrrZqe7aK+qu5CIyGRLXns58UknzhOUUE+4W3bV3ve4E1LFjDn0f/SqVNH3Fxdee+bL+g5agyTn36x3iZfTdn8d99k9U/fctGEyTg6OfHFkw/TdfDFTHriObNDq1Sx9uDPhI/oG/gi3QJmk1rYjUNZZ5gCVNSJ2pz1YifQq7baF7WoywKj1/jAKPA/Di03GvXHVidI6PrPEsj6HGtnm6iOkYEM6hLBDS/M4+VbhhHi58Gny3byy98H2Tr7JoJ83fl4yQ5Gz/qBW8b0ICOngNd/2sjdV/YhIrgKJRTt/wD/GNg9HrKql3Cdr9oexNdY/LjwK35a9DW3TryL8NAIVq77nRkPT+KjV74lOLD8XQVRM0IiW57X4/Nzspnz6ANMmTSJZs2MZcSHDB7MJ599zp5LxtB18EU1EaaoIUnRR/njy095bvEKfAKNcQYX3zCFJ8aPZMAVVxPVo7fJEZ6JA5tTHyU+fzDxeUPs22zIXMvmkT59UZ5HKljsS+V2WA7OuXBkCMT2+md7I2K12ohLzcbfyw1vD9ezP6AavnxoHE99sYZB93xJZm4ho/u14c9XbiAs0Fj4YNFz1zLn9918u2IvHq7O/O+OkVWrTfZKgohtED3AKLkQ9VZBYT6ffj+bj175jogw49Z/5/bdKbYW8/38L7jzpnNf7lrUjb1/r6Z5RIvTSTKAq6srvXp0Z/Pi+ZIo1zM7V62g96gxp5NkAHcvbwaMv4qdK5fX80TZEJ9nvKc8neIYGT6VdckvklzQz+SomiZJlMU/nPOg+XZjcF5GBKBgx1XGwLBGsPxxRb5bsZeHPl6JpdhGboGFCRd35K2ZI/Fwq2Qhj2pyd3Xm5VuH8fKtwyrc7+rixG1je3Lb2HMcDJkTCpunQKY5Pcmi6o7HHiMkMPR0knzK0P4jeP/LN8+praZSclFfKAcHbDZbue1aa5QsAV3vuLq5U5CTXW57QU4OfiEN686NgyrCURUwuvl1rEl+naPZV5sdUpMjP+HC6EHuuAwGz4a2q4yk2MFq7MsPaLRJ8sodx7n/gz/5/tEriP/+To5+eTtZeUXc8fYys0M7O7cMY1o+gMzmyI9y/RfgF0hyahKFhaUHaB6PPVpuHmdRv3QZNJSEhHhiY2NPb8vPz2fr9h1cMO5KEyMTFek9ajR7/l7FsZ3bT2+LP3yIDQt/5YKxV5gY2bnLtrRmUcx8Ugp6c1Gzu+gZ8BqydlvdapwZkKg69wwY+BHYHP8ZoNdpSaNNjkt659ctPH3jEAZ0bg5AoI87H94zmpaTZ5OSkUewXz0tM3EshJ4/g0sO/D0TrC5mRySqIDgwlN5dL+DNj1/g7psfwsPdk4NH9/H5Dx/w+D0vmh2e6Q4dOsSWLVvIzc0lMjKSgQMH4uXlZXZYALh6eHDra+/y0QN3EhUVhZurK/v3H2DQv66nY/8LzQ5PlOHlH8D0l9/kjVsm0aZ7bxydnTi4aQMTH3um2jOemKnQFsCyuG+5MPQhegW+icXmzZ6MGWaH1WQ0/mxIlKas0GwvuGXBsUGQ7wf7xkBKW7DYVwTKbtYkEuUTyVl0bVV6QQkfT1fCA71JTM+pp4mypqjDfJw8UnjnyShcs/YwaUTnWqutPldxiTGkpCYT1bId3l6Ney7n6njkzmd55f+e5upbRuLr40d+QT63T7mH3t0uMDs0U23cuJF169Zx0UUX4e/vz549e/jsq695/Jel+AYFmx0eAD2GjeTF39ey5bdFFOTmcsWTLxPetnEsXJKXncXmJQvJTkulQ/+BRPXs0+Bn8ug5/BJe+XMDu1evwGq1Mv2lN/H0rf7CRGaz4cKapDdIzu/L0eyG1Sve0DX+bEgYnPOh+TZosRVccyA7BKIHgnaA+B6ljz1wiTkx1rF+HcJYtPHI6R5lgCPx6SSl5xIV5m9iZJVLCfiN4PAjfDW7GU4Zbfht2zFe+2kDq16fRHiQeSvxZeVk8uxbj7Dv0G4iwloQHXOUCZffyLTrbm/wf3BrkpenN8888BrpmWlkZqUTERaJk1PN1sOfC601sbGxnDhxAi8vLzp16oSLS93eobBYLKxcuZLp06cTGBgIQMuWLVm8dCm/f/YR//rvrDqN50y8AwK4+PrGNcPLke1bePv2m+hwwQACwyP4+L//oXX3ntz66ts4ODbsmY3cvbzoN+YM6wJUQmvN7tUr2bhoHsUWC71GXELf0ePqwfVQHMyaBICTymNI6H/YlvYAGUUdTI6rcZNEuSkI2Q9dFoJjMaS2hj2XQVprmvr8xw9c258L//Mlrs6OXD24A4fi0njo45XMumFgjQ/mqxG+cQT33sa+deFMbj8F2iv+fUUfHv54JY/PWc0n919W5yGdmhbu1dn3ERLUjBceegtnZxdS0pK596lbiQiLZNTQsXUeV33n7xuAv2/AOT+uJgfxWa1Wfv75Z5KSkmjfvj0nTpxg+fLlTJw4sdTsDrUtOTkZX1/f00nyKZ06dGDthrV1FkdTZLPZ+OiBu5n67Cv0HjUagKvvfZBXp05g7byfGHz1BJMjNMdPr73I1j+WMHLKdJxdXVn26Yds/X0pM96cXW8++Hs6xRPivoWxEVewIvF94vMuNjukRktGADVK2pj/2DvR+DarGSR2hnXTYdsESGvDGZPkrvOhc+NfG6ZNmB+r35jEkfgMrnzyZ974aRPPTB3Cff+qn7fBC1JCmD5d0SbzOkq+fndc3ouF6w+bFldmVgYbt6/l31Pvx9nZ6I0MDgjh1ol3Me+3H02LS5zZli1bKCgo4I477uDSSy/lhhtuYPjw4fz6669oXXeDhby8vMjKyqK4uLjU9rS0NHxDZJBjbYrZtwcHR0d6jbz09DZnVzdGTb2FzUsa/9+AiiRFH2XNz9/x6PfzGTF5GkOvvYGHv51L7MH97F272uzwTsu0tGVhzAKyi1swKvxGOvp+bnZIjZYkyo2JskKz3dD/M+jzLbTcYGwv8IN9l0FuFWv9XLOMGuYmoF1EAJ/9dywHP5/BX29M4rqLOpkdUnnOueCejk0EeG4AACAASURBVKODA99+7UhuXukkJjuvCDcX824OZedm4enphYe7Z6ntzYLDyczKMCkqcTZ79+5l4MCBOJa4ndyjRw/y8vJIS0urszh8fX1p3rw5v/322+lkOTk5mdVr1zH8xunn3f6mJQt47prLuO/Cnrx9241E79553m02FqemtyvbS6ocHKjDz0r1yt61q+kxfBRefv+U3zm7uNJ//JXsWfOXiZGVl1vcnMUxvxKbO4yBIY/S1X+22SE1SlJ60Vg03wat/wa3HMgJhL1jjF5kcVY7jiTx+9ZofD1d+deQjvh716MloJUVevwCbpk4r53BNYM78PSXa3jrjpEopSi22njqizVMGm7eYiNhIc1RKHbt30a3jv8sxvn76sVNfpBaTaitOZO11hUu5+zg4FCnPcoAV111FfPmzeONN97Ay8uL3Nxcrpv1NJ0GDDqvdld88wWLZ7/JqOHDCR1+MYcOHeL1aRN44Isfadm5a80E34BFdurC/7N31nFV3W8cf18u3d1ISQmI2ArW7NndNWNT5+bKuXLp3IzZzvZnOze7uxu7W0npzhvn98dVNqbOAs4Fzvv1MvjeEx/gxuc85wlFfj4XDuyhWpPmACgLCti7dBH1O3UTWZ04GJmZk5mc9NR6RlIS5ra2z9hDXJSCCfvjFlPNegoPs0o//a4iIBnlsoxRKuSZa0ZJ6+VCtq0mcpz8pvnHMpCV/3CCIAh8MHsPG47dpnO4LwlpOXyx6BB/fNWBptU9xJYHCOC/Gyyj4XIHUOsydXhT2n79F8HDFlHL14lDlyLxd7Phm77itaiSy+W8P+gzvvp1NL07DsLDzZujpw9w/Oxh5k5YIZouif/G39+fkydP4unpWWiYr127hr6+/lP5wiWNkZERPXv2JCMjg5ycHGxtbfHu3vuNjqlSKtk0YzK9u3fDwUEzZKJWrVqoBYGts6cycvai4pBeptGRyxkycTqzRg4mMKwhti6unN29A1c/f8IqqFGu9lYLVo//lkuH9lO10VsAPLx6mVNbNjBu/Q6R1T0bATnnU8YUfhVqPYlbGb3JVrqKqqu8IBnlMocAllFQ6QzY3YYrHSA+QNPB4oHUz/NV2HbqLgcuRnJt4RDMTTTt1Q5djKTH+E08XDEcAxHTGQBwPQcuF+F+fc3vGLC1MObEjH4cuRzF7ZhURravTk2/0p3K96SA7580qd8CRzsn1u9Yw4lzRwn0rcrCSWuwttS+CIyEhlq1anH37l3mz5+Pn58fKSkp3L9/n169eolWsGRubo65efG0FUxPjAdBXWiSn1DZ25uIdeuL5RzlAd+atfl512HObN9MZkoKA36aiG/NOlpTtFbaGJqYMHLWQuaOHo6VoxP6hobE3LpB/x9/xdbVTWx5L8RM7yEBlkvwtVjFvtjFJOVr/7hubUcyymUGNThcB/czYP4ICow0Bir1yQu3GN/U0lypCJN//jx8g/fb1yg0yQCNQirh5WTJ4ctRNK/hKZ44i2jw3avpb323QZGHZDIZDatWomFV7WqcH+ATzFc+wWLLkHhJdHV16dOnD/fu3SMyMpJKlSrRpk0bDA21KPXoDTC1skZRoCAzMxMzs79bJz569AgbF+03PKWJqaUVTXoPEFuG1uBbszYTD5zkdsQplAoFvrXqYmBkJLaslyJT4cG2qM00d+lPa9duHImfyoOs9mLLKtNIRlnbkak1vY6RgddxQIDrrSAuENQl1MLsbqOSOa6WoVYL6Og8fYEh15GhVot8oZDpAA/raO4UVPA2fhWVkspN/icymQxvb2+8vb1L/Fwvi3ufYcVyHH1DIxr16M3GLVto36YNFhYWREdHs3f/AQZM+K1YziFRftHV0yOgXrjYMl6LdIUPW6K20tRpME2chnM6MU6a5PcGSEZZWzFKBbcIsL8FJ4ZqxhSf6wH5ZkjGqXjoHO7L98uP0bdpYGHf5JPXYrgZnUKjEJGitfJ8zb8qA7jbWBwNEhLlhC6ffsV6mZz5CxcCYGhiStex4whp0kxkZRISJUu+yoadMX9Q334sKflSYf+bIBllrULQ3HJ3PwN2tzSR5EdVQF6gMcr5pTQSOHgj6CjhYtfSOZ9IdKjvy5aTdwgetojujfxJSMth47FbLPu8rUjt1gQI2gKG6XB6oKZIU0KrUKvVz+wUIaGdyHV16fb513T86DNyMzMxtbKWfn8SFQa1YMDR+KmFX3uZbSAmuxH56lcfdlSRkYyyNmGaCLVWQoGhpjAvqjoUmJa+Dt1czRS/co6OjoxFn7zNyeux7D57n2BPO8YPaoijtQg/cwDvw2B3B24010qT/KwivorC8YhDLFw1i1v3r2Nn7UD3dv3o0b6/ZLrKCHr6BujZGLx4QwmJcoqRPJ4w+0/JUTqwN3YZ6YrKYksqM0hGWUx08zRdDeT5cK8hZNnDpY6Q5F1y+ccvRcVJ7ZDJZNSr4kK9Ki7iCnG4Bp4nIDoEoqUqZW3i7OVT/DJ7HGNH/kDd6g24F3mbX2aPI78gj4Hd3xNbnoSEhMQLyVU5sDNmLU2d3qGNWzsOxM0nLrfBi3eUkCbziYJRGvjugfDZ4HNA08XiSZeJBH+RTfITyn/XC63B7BFU2Q6prnCzBRXpQqUssGrDEob3/5j6NRuho6NDZQ8/vv9kMmu3LKdAUVDs59t9fm2pFPJJvDlnd+/gx86t+aBWIBN7d+bGqeNiS5KQeC6JeTXYGrWVHKUzLVz64mO+SmxJZQIpolzaOF+AgJ2a/OP4AIisBZmOYqv6FzLJq5UmBcaQ7AXXW2plykVFJzLmAUF+1YqsuTi6oaurR1p6Cva22vb6LZsUV7eL0uL4xr9YN/FHWjZrhkvL5ty/f585I4cwfNaCN54o+KZE37zO1t9ncO/SBaydnGnW/x1qtmwjqqbS4v7lixzf8Cd52VkENmhMzZZt0NXThuCTdpCldGNb9EYaOw5HV5YvtpwygRRRLmlkj/sfmz3SfJ3qrmn5dWw4XG2nhSYZSKkEyR5iqyj/yFSAWlOkeakzKEzEViTxDDwreXPx2tkia5ExD1CpVVhZSEUxFRFBENg4bSKd2rfHz88PU1NTgoODadGsKZumTxJVW8ztm0wa0AOvkFA+WbySFgOH8tfknzmwaqmoukqDA6uXMWP4IMzt7PAOrcn+FUuY8d4glAqF2NK0CoXajD2xy7iePggAO8Oz6MqyRValvUgR5ZJC/jj/2O0sGGVAdCjccIRcK+3vU/ywntgKKgCC5s6CXg5c7IJ0zaq99O08mC9/GY2RoTH1ajTg3sPbTJr7A707DkJPT7/YzqNN6Rbx8fE8evQIKysr3NzcKuyUtueRn5NDelIirq5FRwR7eXmxa89ekVRp2D5vFq2HjqDFIE2E3sHDCwcPTyYP7EmDrr3Q1S++56w2kZ2exropvzBu/Q7sK7kD0KBrT37t25WInVup266TyAq1Dc1njr5OOs2d+5KlcGNv3P/IUTqLrEv7kIxySeB5DNxPgW6BJjp7szkkSRWm2kaBQsXWk3d4mJBOLT8nwgJdi90QqNUCRy5HEZ2USW0/J3xcH0cg3SLA+TLcq49kkrWbqgHV+f6TiSxaM4efZnyJo50zPdr3p0OLbmJLK3ZUKhXr168nOjoad3d3Hj16hL6+Pr169cLExITo6GiOHz9OUlISdnZ21KtX7ymzWBHQNzLCyMS08OfwhLi4OGxdxP153L98kbfffb/ImouPH7p6eqQ8iis0keWNW2dO4RUSWuT7k+vqEt6lB5cO7ZeM8nMoUFtw6NEcGju+Rzu3tuyNXUJyfojYsrQKySgXF+axkOEI6IAgg0QfLc0/fgmqrtdEOs/2FVtJiXE/Lo3mY9fgZmdOsKcd87ZdwMvRkvXfdS62HsoxSZm0+epP1IJAFXdbPpq7jw71fJj7gx9y3/2Q4AP3tL/quCK3hXtCjap1qVG1rtgySpxjx46hUCj44IMPkMvlCILA7t272bFjB9WrV2f9+vU0btyYRo0aERkZyerVq+natSueniKOexcBHR0dWg5+l01rV9KxXTtsbW2JiYlhx67ddP/6B1G12bq6EXXzOi4+foVrGclJ5GRmYm5jK6KyksXQxITstLSn1rPTUjE0kdLa/ouYnCZsi95EM+cBvO3amcOPZvIw+22xZWkNklF+E2RqzWCQSqfBMhYudoJEP00P5LKMjqrM91HOyVPw15Eb3I5JJdjTjo71fdHX+7tQbti0nbzbJpTPutcBQKlS0/m79Uz58zRf9Sme39/gKdvpFO7LuL5hyGQysnMLGPz7ShQBV5Fn2Wpy1MtB1aRSpSS/IB9jQ+NXjsjn5+ex+/A2LlyLwMbSljbNOuPuUrFMlzZx6dIlOnfujFyuea3IZDIaN27MlClTSE1NpU2bNgQEBADg4OCAkZERBw8erHBGGaDV0JGoBVi2eC4FubmYWFjS4aPPqf12e1F1NR8whGXjPsehkgeeVauRlhDP/77+jPodu5Zrw+hbqy4ZKUmc3r658HeQHBvD3uWLGT59rsjqtJ+0An+2Rm2lqdM7uJgclIzyP5CM8usgU2lyj90iNPnHOZaaIREp5eTDQij8q0zy4FEab322moBKttT2d2LO5nP8tPI4+yf1wtbCmOSMXE7fiGPrj39PHtSV6/Blr3oMm7azWIxyfGo2p27Esun7LoXm0cRIn9Hdg4mJOYh3QhfNtMUyjEJRwLyVM9i6Zx0KpQJXJzeG9/+YutVfLkqenZvNB98MwsLMkib1WxLzKIoRX/Zn7MjvaVD7rRJWL/EsFAoFBgZFB3PoPe4YEBsbi6+vb5HH/Pz82LBhQ6np0yZkMhlt3n2f1kOGk5eTjaGJqVYMoAlu2IROo8cwe9RQlAoFivx8wrt0p9unX4ktrUSR6+ry/uxFzBrxDruXzMfM2oZbEafp+MEnVA6tKba8MkGeyo6dMWtRCxpraKIbQ67SDjVl+7PqTZGM8qugUwBqfU1qhes5yDOHW80gsTJSnqn28NHcfQxuFVJoeMf1DWPUrD2MW3qEOR+0RKVSI5OBXF70d6avJ0epUheLhpw8BUb6ev+IYguADJ0sZ7q8bcmFuZbFch4xmbrgZ5JSElg6bT12Ng6cPHeUn6Z/xaSvZxPgE/zC/ddtW4WzvSs/fDal8GKifs2GjJv8KfVqNERXXjHenrSpiM/Hx4eIiAhatmxZuHbp0iXs7e1JTk5m8+bNuLu7ExQUhL6+PomJiZibm4uoWHx05HKMzbTrZ1C/Y1fqtutEelIiJhYW6BsaiS2pVHCvEsSEPce4efoEuVmZvDNhKmbWUmeaV0ElaJ4rclkurVy6ka10Zn/cAgrUViIrEw/J3b0M5rEQtBHC5oGOAtCB0wM0ObyJvpS/H6MMZGUzoqxQqthx+h4fdv47giCTyfika23WHbkJgL2VCVXcbVm6+3LhNoIgMHXdGTrW933qmK+Dh6MFlqYGbD15R7PgfQjB+wDztp3n7Vo+xXIOMUnLSGXf0Z18M/oXHOyc0NHRoX7NhgzoNow/tix/qWOcOn+Udi26FknXqBpQHRMjE+49vF1S0iX+g8aNG3Pr1i3Wrl1LREQEW7ZsYffu3WRmZuLm5oa9vT03btxg7ty5REdHs337dmrXri227GeSEPmQ09s3c/vsGQShbL6fvSyCIHD/8kUuHthLRkoyoDHwVg6OFcYkP0FXT4/AsIbUbNnmuSb5/L7dTOjViY/CQvltcB9unTlVyiq1H5VgxPmUT7E3PEtbt3aY690VW5JoVIyQzWuhBvtbUOkMWMaAwgBiqmnyd9V6oCzHbz7JXqCXK7aK10Ymk6FWF/1gVKkFdHT+NmTzRrek5di17Dn3gGAPO3acuYdCpWL2qBbFpuH3D1rS9ccNTPnWkf7N7rNjvTmnbxhwaErvYjlHSfNfRXwJSY9wtHfCzLRoJM2/ciB7jmx/qeMbGhiRkZVeZE2lUpGZnYmxkfGrC5Z4Y0xNTRk2bBiXLl0iLi4OS0tL3NzccHFxoWHDhgCEhYWxd+9eVqxYQd26dalb982KHIt70IharWbFuM85vX0Lnl5eJCYmYGBuweiFK7FyKIPF1S8gJS6GmSOHkJeVia1rJe5/doEWA4fSbuRoqa3fMzi5ZQPrpkyg11c/4BkcwrUTR5nzwTCGT5+LX22pNeo/uZfZmSyFG02d3qGtW3v2xy3gUW4Zr8F6DSSj/Dws4qDqRk3+8c1mEFu1zOeUvjTR1cVW8Nro6cppV7cyE9ee4qdBmg92QRD4Zc0Jujf0L9wu2NOe64uHsGr/NR48Smd055p0qO+Dnm7xTcZrFFKJS6vfxrbpBm5cMiH2eF1OzgjCxKjsP49cHN2IT3xEYkoCdtb2hetnLp6gsvvLReVbN2nP8r/mU7tafcxNLRAEgbVbluNk74KrU/lsYVUWMDAwoFatWoDmtTN+/Hg6d+5cZJt69epx5swZGjduLILC/+bw2pXcPXWcD94fiYGBAYIgcPDQIRaP+ZBPlv4htrxiZ/6nH1C9WUvaDv8QmUxGemICE/t1w9UvgOrNW4ktT6sQBIGNMyYzdPIsfGtq7oSEdeqGjlzO5tnT+Ewyyk+RkFeLLVFbae48gGrWU9kZU4/yUIT+KkhG+QkG6ZoCPUEH7jaGdGc41wNS3Cl/qRUv4kk0tmy+GKYNb0qzz9dw+HIktf2cOXgxEl25DjsndC+ynYWJIcPbleBFgX4WTuG7QGWGf+oA/FuVnyipibEpXdr05vPx7/PBO2NwcarEoRN7WLd9NXN+XvZSx2ga3pqbd6/RY3hrQgNrEfMoCqVSQcdWPZi2cAK21na0atwBW2u7Fx+sDKJNucn/hY6ODkqlskiRn1KpLOyMoW0cXbuKhuFhhXplMhkNwsP5bdo00pMSsbAtP8+nhMgHxD+4x2dL/yiMHlvY2dN2+AccXbdGMsr/Ij8nh7T4R/jUqFVkPTC8Eat/GieSKu0nS+nOtuhNyFABMnRl2SgFIyqKN5KMsnmsJr3C/obm67gnRUiy8tPF4lWpugGMUuHUYLGVvBbOtmZcnPcOW0/e4XZMKj8ObEDLml5FUi9KBfM40FHCue6gKD8m+QlDer2PrbU9v80fT3JaElUDqjP9+4Uv3d5NJpMxcuCndG3Th8s3L2BiZMriP+Zw4PhuGtZ5i8iYBwwY3Ymfx04npEqNEv5uJJ6FTCYjMDCQQ4cO0bp1a2QyGYIgcOjQIQIDA8WW90zyc3IwMiqaGieXy9HT0yc/J0ckVSVDTkYGplbWyHWLfpSb29qRk5kpkirtoCAvlzvnItDV08c7tAZyXV30jYwwNDUj7u4dnCv/XSsSefUytq6VRFSr/RSoLQCQoaSZ80Dy1ZYcfjSjsPivPFOxjbL7CfA5pMk/jqwNUdUh30JsVdpB2QwmF6KnK6dTuN+LNyxJknzg2HBQGbx42zKITCajU6sedGrV442O42DnhIOdE0v/nIeDrSM/fvZbYXSsXs2G/DrnO1bO3CzlW4pEixYtWLFiBfPmzcPNzY3IyEgMDAzo3Vs7c+2D32rG+QsRuLi4FK7duXMHIzMz7NzKlxly9fUnKzWFB1cu4RFUFdCkFxzb8CeBYdo/zKikOLtrO0vHfY6jpzcFeblkp6cxfNpcvEJCaTFwKIvGfsSwyTNw8PDi4dXLrPjhKzp9OEZs2WUCATmR2S2obfs9b7t2Zm/sEnJV5S/3/59ol1HWKeEhF/J8zdjgNFfNxLxEX01hXmxwuTUzr0/5rhIvUdwioMAI4gOl59UrcPT0AUYM+KSIIQ6v1YSp88cTFfuQSi4eb3T8jKx0jp4+QIGigLrVw3G0c35Dxa9HWUm5eIKRkRFDhgzh/v37JCUlUaVKFTw8PLT2wuXtoSOZ0KM9a/9ah4+3F0nJKVy8fIkRMxdorebXRVdfn15ffse0Yf1p1u8d7Nzcidi5lfiH9+k77iex5YlCYlQkS78Zw8eLVxVePJzfu4uZI97h133HaTVkOIJazYRenVAqFBiamNBuxGjqtO0gsvKygoxraUPJUHjQ2HEE7dzasDduKSn5QWILKzG0yyibpGhu+ecWc78+gwxN/rHLBdDLh3v1NUY5x0bzR6IoQvn6MCkJ0rLyWLnvKg/i0wmt7ECXcD8M9HXB5h747oMEX4ivQpkPzb+AAkUBi9fMYdu+9WRlZ1IrpB7v9fsIL/dXb4Gnp6dPXn5ekTWVWkWBQoG+3psVQB6POMSP07+gelBtjAyNmbdiGn07D6FPp3fe6LgVBZlMhpeXF15eXq+1v1Kp5Pjx41y5cgW1Wo2vry8NGjR4KkWiODC1suabDbs4tuFP7p07jVWVEL75fiL2lV6uQLQgL5ebp08iCAL+deppfXu12m064ODpzeG1q3h47TK+terwzi9TMTI1FVuaKJzYtI66HToXmmSA0GYtObB6GRf276H22+1p894oWg0ZTm5mBsYWlloxKKasEZ3dnG1RG2nuPICGDqPYFLkXAe2sW3hTtMsoI0D11XC2D+QVUwqE325wOa/5f4I/RNaCDHEiSWULKaL8PK4+SKT553/QqKob1bwdWLjjIpPWnuLAnFZYBm2CLDu42oayapL/qy3cv/l55tfk5GYze/wybKxs2b5/Ex9+O5hFk9dib/tqt+OaN2zDsr/mUz2oFgYGhgBs2LEGVyc3HO1f/zWbnZPFj9O/YPI3cwn01Xx4JqYkMOTTHtQIroN/Ze3MtS0vCILAn3/+CUD79u3R1dXl9OnTrFq/kW827iyRcxqamNC070Ca9h34SvtdOrSfhZ+OwtbOFoDEhESGTp5J1cZNS0Bl8eFeJYh+3/0stgytIDsjDUs7h6fWLezsycn4ux2lXFcXUytpGMmbkFoQyJaobejL0xGQI0OFgA5l9bPveWiXUc6xBt2sx2a5N+S/zrQjNdjc1/QCRgb5ZhBVU/OnuMx3eSepMmQ4ia1Caxk5czfj+obxXrtQAMb0qMPoBdsoCFwLghwudtFMcCznxDyK4szFE6xfsBcDfU2KSbe2fYh5FMnGXX8wrM+Hr3S89s27cuXmBboPb03d0HAiY++TnJrEb9/OeyOdJ84eJtivWqFJBrCztqdd8y7sPbpDMsrPIS8vjwMHDnDt2jUEQSAgIIAmTZpgbPxqhamxsbEkJiYycuTIwk4Z7dq1Y8WaNUTs3Ea99p1fcITSISM5ifkfj6Bnt264ubkBEBUVxfxPRvLz7qOY29iSnZ7GvuVLuHH8CGa2tjTuPYCAumEiK395FPl5HPxjJRf27UZPX586bTtSp12nchVRDazfkHW//UKLQcPQfTx+PTs9jUsH99FuxKu9J0m8mFyVA7kqB0Cgjt036MiUnEgYj4Ce2NKKDe0yyio9ON9TY5Sr7IDzr1AkJC8Ap8uaDhbGaXC+GyR7wwOpL+IrE/fi8cMVlbSsPM7ejmfPrz0L12QyGWPet8TSLg8uFePdEC3nYfQ9/L2rFJrkJ4RUqcHuQ1tf+XhyuZxvPpzA3Qe3uHzzAg3rNqVOaBi6um/2hqtUKtHXfzpXXF/PgKzsit0Z4Hmo1WpWrFiBnZ0dgwYNQiaTceLECZYtW8bQoUOf2xpOoVBw9OhRrl27hlqtxt/fHxMTEzw9PYvsI5PJ8HZ35/7F81pjlM/s2IpPZZ9Ckwzg5uaGT2UfzmzfQt32nfipSxscbaypHuBPWloaCz4aTrtRn9Ck9wARlb8cKqWSacMGINfVo/mAwRTk5bFz4Vxunz1N/x9+FVtesRHUsAmH1q5iYr+uNOrRl4K8XPYuXUR4lx7YV/IQW165pkBtToj1TMz0HnIgbh4FakuxJRUL2mWUQRPJPNcD8l8yv0peAJ7HNekVevmQ5gx3Glfc1m7FgY5CM8K6ogxYeQV0HrfHUijVRYaTxF7ypuvHESgy9pGalUfTUHe+7lOfSvbl1zRXcvHg5t3r5BfkFzHLl2+cx9319XJZAbw9fPH2KJ5R4gB1QsOYtnAC0XGRuDppuh5k52azbd96xoz4rtjO81+UtQK+u3fvolarad++fWEBXOvWrVm6dCk3btx4Zms4QRBYvXo1RkZGdOqkiVKePHmSW7duFenB/ISEpGQCampPNDYvKxNjI8On1o2NDMnNymTf8iU42ljTsX27wse8vLxYPGUC9Tt2w+AVI+2lzcUDe8nLzuarPzah8/iipWqjpnzRsgFN+w/GpXLxvebEREdHhxEz5nFq60Yu7N+Nrr4BPcaOI7jRW2JLK+fIOJc8lowCL+o7jKGNW3v2xi4jU+EhtrA3Rjvvt2S4PG7TJoDHcdDNe3obvcf9MNW6mh7IKZ5wph9E9NfkIgva+a2VCapsh9r/E1uFVmJuYkCTapWY/OcpzYL9DZQmMQyYtJWCdAsmDm3Mtp+6YWdhTPjolSSkZosruARxdXInNKgW3/82htj4aPLz89iw8w92H9r2xi3jihMrSxtGDPiYd8f2YdaSSSxeM4eBo7tQu1p9qgfVFlueVpKQkPBUZwuZTIaHhwfx8fHP3OfBgwdkZWXRpUsXnJ2dcXR0pEOHDhgaGpKRkcGhQ4dQKBSo1WrOnz/PvQf3qd+xS2l9Sy8kMLwR127cID8/v3AtPz+fazduENywCdePHyaoSkCRfWxsbLC2tiHqxtXSlvvK3Dxzgpqt2hSaZNDkcgc3bMKtMydFVFb8yHV1qd+xKyNmzGfY4xzz8tbxRFu5k9mdXdFrMJQn08qlOzqy/BfvpOVoX0T5n5gmgNcxsLsN53pqIpy2d8D9NBinaHrUqnXh5GBNmzeJYkR7ivkEQeBhfDp6unJcbM3ElsOcUS1pMfYPogtuMntZEkcPyYlMkBG1aiRWZpqI1Ph3GpGUkcvcrecZ1y9cZMUvx6sU8T3h6w9/ZsGqmQz+tDvZ2VnUqlaPqd/Nx8FOu3Lc27foRkhgTfYd2UF+QR7fjP6ZYP9Q6cPzOVhbW3P79u2n1mNjMA7T5gAAIABJREFUY587aCQuLg5vb+8i+a4ymYzKlSuTk5NDTEwMkydPRiaTYW9vzydL1mhVMZVHUFVCm7VmydJl1AitBsDZ8xcIbdYa98BgzKxtSEtLK7KPWq0mIz1dq76P52FubUtSdORT68nRUYQ0en6x4qVD+9m3fAlpCY/wCgml9dARZSaFQRAE6TUuAvF5ddkatRVzvQeohbLfIlW7jXKWA1zuAMEboM4SzZpxGuSaw8M6FJo5ySQXMzKtKVo9fSOWoVN3kJCWg0Kppoq7DYs/aUNll2JuIfgKuNmbc3lZd5Q1lpCTbsi1HTWoGxBVaJKf0LqWF4t2XhRJZelgoG/A+wM/5f2BnxbLh5JSpWT1xiVs3r2OzKx0alStw9Deo/Bw835jre4unrzTc8QbH+dVKGspF0/w9fVl//797N+/n7CwMGQyGSdPniQxMZEqVao8cx9LS0vu3Lnz1Hp8fDyVK1emdevW5OXloVarMTY2plIV7eu72u/HX7l0cB9ntm1CEAR6fPczIY2bAdCkz0AWfjwST09PbGxsUKvVHDp8GAcvbxw93/z5WdLU69iF79q3oEaLt6lSv8HjwSRriX94n6pNnm2UD69dxda5M+g0egxOXpU5t2cn47u356u1m7XWLAuCwMHVy9ixcC5J0ZG4+gXQYdQn1GjRWmxpFYpMhSeZCk0KrK/5Ckz1YjiX/BnamsjwX2i3UUbQDAW5Xx+8j4FSH6601fSnlVIrSg6h8C9RSUzLoe03fzHr/eZ0beCPWhCYveksLb/4g+uLhqKvJ1LPRh0FuqEb0TVSY3ilH00Ddfh56QUUSlWRvOVL9xLwcCi/Ocr/pjgiN7/NH0907EN+GvMbdjb27Dq4lVHfDGLhpD+0LkpdnpHL5fTv35+dO3cyadIkAHx8fBgwYAB6es8OTPj5+bF3716OHj1K3bp1kclknD17lujoaDp00AxzMDR8OgdYm5DJZIQ0aUZIk2ZPPRZQL5y3R45m8dRfsbaxJSMtDQdPb4bPnC+C0lfH2tGZd6fOYcmXn6BvZIwiPw9DE1NGz1+G3jOKXZUFBayfNpFPl6zG1U+TcuIRVBVBUDNzxGC+37xHK7tl7F22iCN/rWH4tN9xDwzm2omjLPniY/T09bW+zV95xcbgCv6WyzHXu8eR+Gllbuy1dhpls0ea7hX5JnDnLbgfrhmC4XlS00JOMskljHaEk1fsu0Kb2t50b6R5k9ZBxoeda7Hh2C22n75LxzCRik9cz4FFHFzsDNl2+FeCEC97Rs7czcShTbAwMWD76bvM2nyOg5O1c8xvSSEIAtv2bWDbvg1k52RRK6QefTsPxsryxYN9EpPj2X9sJ+sW7MXEyASAXh0HkpD8iPU71zC830clLV/iH5iZmdGtWzdUKhXAcztdPEEul9OvXz+2b9/OkSNHAHB2dqZfv35Fivnc+wwrOdElTNO+gwjv3IPI61cxtbLCyauy2JJeicCwhvy67wRRN64h19PDxcfvuRe4STFRGBgZFZrkJ9Rs2YZDf6wkYudWar/dvjRkvzRqlYodC37n48UrcfX1ByAovBG9v/6R7QvmSEZZJE4kTiBT4U5N2/GY6sWwL3YxuSp7sWW9NNpllHXzoPoqsI7URI+jaj5+QAYPwiE2FApMHq8JaIuhK3ck+GpFH+WYpCwCKj1tsAIq2RKdKGJbr6hakGVfpLPK6q/aM3LmHtx6z0FfVwcnG1NWfdGeKu624ukUgVlLJnH+agRDer2PlaU12/dvZPiX/VgwcQ1mpv/dF/1+1F18PQMKTfITagTXYeOuspnCUB54YpDz8/M5d+4ckZGRGBsbU6NGDZydiw6CsbKyok+fPuTl5SEIQolM3hMbA2NjfGrUElvGa6Mjl+Me+OIWoGbWNmSmppCTmYGx2d+v3bh7d7BxduHM9i1aZ5TzsrPIy84qNMlPqFy9Jsu+HSuSKgmQcSVtOBkKTxo6vk8bt3ZsfLgPpVA2pkdql1E2TgNjNdxqAjEhoPrXbbonJtn5Ilg/hKttpehySZDoJ7YCAOr4OzN9QwSfda9TGPXIL1Cy48xdBreq+oK9SwDLSM0djQLTp9oPWpgYsmJsOzJz8snOU+BgZVJmikhep4jvWSQmx7Nt/wbWzt2Juakm5SSgchDfTfmMLXvX0bvjoP/c39WpEncf3nqq3dy125dxc3658cOvQ35+HkfOHCA9I5XQoNp4VSpbUcLSIDc3lyVLlmBvb09QUBCpqamsXr2ali1bEhT0dK6xtqdYvCqZKSns+d98bpw4ipm1DU36DiKoQWOxZZUoJhaW+Nasy+IvPuadCb9hbGZO9K0brJ86kZqt2hB//57YEp/C0NQMI1NTIq9fpVLA30WntyJO4eJTPtrflWUis1uxI3oDDkanyoxJBm3Lqs61gGPvQmSdp03yP9HNA8drUGUb2pBLW+7QzQP9LLFV0DHMB7Ug0HP8Jo5eiWLvuQe0+fpPavs5U9OvlCPexklQbR0E/PfIXTNjAxytTcuMSS5Obt69RpBftUKT/ITw2k24duvSC/d3dnClenAdfpr2BYnJ8ShVSnYd2sqmXWvp3LpXsetNSUviz60r6TKsOdv3beDO/ZuM/nYIk+f+gCC82fvK7vNry2wh37M4deoUzs7OdO3alcDAQMLDw+nduze7du0qTM0or2SlpvBTl9bERZykXtUg3MyM+d/no9m7dKHY0kqcoZNncvPUCT5tWIuxzcKYPLAnLQe/y+2zp6kuYnFcfk4OR9b9wbopEzi5dSOKAk0LMh0dHdq8N4p5H4/k1plTFOTlcn7fblaN/5a33x0lml6Jv0nOD+Za2hAAHAxPEmCxGG33cdoVUVYYaUYAv4jIOiBXgvcRUOnCjVZIaRjFiO9esIqEY6XbIeDf6OnK2f1LD6auO8OHc/airyunZ+MARrSvXrpCdPM0JlklhxstSvfcZQh7W0ceRt9DrVYXKfK5H3UXOxuHlzrG1x+M5/flU+kzqj35BXlU8Qnm1y9nFWtEWRAE5q2YzoYda9DR0eGT976hWbjmQz87N5v3vxrAvmM7C9ck4P79+zRq1KjImpOTE8bGxsTHxz+VglGe2Lt8Ma6OjrRt83bhmru7O4tmTCa8ay8MTUz+Y++yjYm5Oe9N+525H76Hq38ATl6V2b98CS5+/tRt10kUTYlRkUwa0B1XX388q1bjyNpVbJs7k8+WrcXc2oYmvQegZ2DI0nGfkxgViZtfAAN/nEhQeKMXH1yiVKls/ie+Fmuw0L/DqcQfELTMkj5BO1W9DPfDQEcJnic0vZRvNUMyy+UPUyN9vukbxjd9RZrgJVND8EYwTIezvSH/v/NsKzI+nv7YWtszZ9lvDO31Pvr6BkRcOsnGXWuZM37pSx3DwMCQ0UO+YNSgMajUKvT1in865O5DWzlx9jA/fT6V3+aPp2lYq8LHTIxM6Nl+APuO7JCM8j8wNDQkK6voXSaVSkV2dna5zEP+JzdPHKOmf9F0NGtra6xtbIm6ca1M5yu/DIFhDflpx0FObd1IVnoafb/7Gf869UW7a7b6529p1L0Pbd7TRIjbDv+QVT+NY+O0ifT/4VdkMhkNuvakQdeeouiTeHmOJUwiX2VFsPXvmOk95OCj31Gote8ztuwaZYC7DTVmWWGEZJKLE+3poyw67ifB5gFcaw3prmKrAeDPQzeYsTGC6KRMavs58VXv+lT1Er+CWCaT8fPn0/hlzrd0eKcJhoZGGOgb8M2HP7/ySGu5XP7CLguvy5a96xjS632MDI3R19N/6gNfX18fhVJRIucuq4SGhrJ37148PT0xMzNDEASOHDmCnZ0dVlbi9TQvDSzs7EhNTS2yplKpyEhLxdy2YhTrWtjZ02KQ+N1KlAUFXDl8kHenzC5ck8lktBw0jJ+6taX/D7+KqE7i1dEhIvlr0hVe1Lf/gjauHdgRvY58tXYN8CnbRhkZ3H6LQlenn6UptJIoBrQ7Z6jUiK4OCmOIDRFbCQCzN51lxsazTB7WhCrutmw5eYdmn6/hwKReBHrYiS0PK0sbfv1yFqnpKeTkZuNk76J1vVazsjOxsbLF1yuAtIxUzl85Q2iQJiqoVClZt301LRq2ea1jl6e85H/i7+9PQkICs2fPxtnZmbS0NIyNjenR4+VHlZfVtnBN+g5i3gfv4uHhgZ2dHSqViv0HD+Ls44eDu+eLDyBRfMhkyHR0UKmURZZVKiUyuRy1Ws3N0ydIePgAV/8AvKpK0zfLArczepOlqISn2Sby1ZZiy3mKMm6UodAkG6VA7aXwoC48rCeupLKOIL2xYJysKS5VGkJMNbHVAFCgUPHDimMcnNybgMdt50Z3roVCqebXP06y7PN2L32s4up08TysLKyxstCuqMATaobUY8eBTVTxrcqXo37kq19H06huM5wcXNl/bCe21va0btJBbJlaR8OGDalZsyaxsbGYmpri4OBQIUyIX+16dPx4LEsn/oS5uTmZGRlUCgjkvRllY9BIeUKRn4+Lrx9b58yg++dfA497t8+dSUiTZozv1g5FQT4ewSHsWDAHe3cPRs5aiEE5Tw8qD8TlhhOXGw6AiW40dobneJClHe0Hy4FRfkyuJSR5g88hTc5yVPnOGytR4v21oo+yaBhkQI1VkOIBV1/efJY0UYkZGBnoFprkJ7Sq5cmSXS/uKiGhoU/HQbz3RV9+mDqWBnWa8PZbHdm4ay21q9VnWJ8PqVs9XOui4NqCsbExlStXvPZ5Dbv3pl6HzkTfuomZlTW2rm5iSypV1CoVB9cs5/jGv8jPzaVqo7doPWQ4plaldzGcnZ7GL707Y+PiwpmdW7hx6hheIaHcPH0SQ1NTrJ1c8K5eg15ffo9MJkOtUjH34xFsmjmFzqPHoKtf/PUOEiVDiPUM/CxWcj75DhdSPkLsXNDyY5TRgWttQUcFfvs0ZjkmVGxRZZOUV8snLVfoKCBkHcgVcF/8OxMqlZqsvALMjAywtzQmLSufhNRs7K3+rrS/eLdijcp+U6wsbVgwaQ1bdv/F7kPbsLWxZ94vK/H2kPqsSmhIS4jn7K7tFOTnEdK4Gc6VfdAzMMQzWDtSsEqbZePGEnfvDp1Gj8HY3ILDa1fyS58ufP3n1jfq+qHIz+Pq8SMo8/MJqBeOicXzb7vv+d9CPIJDGPzLVFRKJZcO7efqsUOkJyXy1dotjK5XlSlHzhbe5dCRy+kw6hN+6tKGvUsXUSWsAb2//gH7Sh6vrVeidDiZ+CM6sgJCbaZgrnePYwmTUQni9WYvX2ETQQeutIdEbwjYBdba1xC9TKCfBUapL95OC4lNyuS3v07z/fKjnLwW84r9cAWosh3M4jXPoxzxCnUEQeC3v07j2ns2rr3m4Nnvd/44eIP+zYMYNHk7ccmaDgSnrsfy5eLDjO4s3UF5FcxNLejYuif9uw1jQNd3JZMsUcjpbZv5qlVDrm3fwMPDe/mlVwc2TK24RWLxD+5xft8uPl60ksCwhngGhzDgx4k4uHtyYvO61z7uzTMn+axJXXYtmsexDX/yedP6HP5z9XO3v3r8MOGduwMg19UltGkL+o4bj4mlJY/u30GtUqP/r0E3hsYm6BkaMjPiGn616zGpf3fysrNfW7NE6aAWDDgaP5WIpLF4m2+glUt3DHRSRNNTjiLKjxHkcLkTVDoNqSU3zatc43UU7G7DkbLVoH3T8Vu8M3k7ncP9sLUwoveEzTSv4cncD1u+XC6l+2lwvA63G0OSuLeXp2+IYNams/i5WpOZW4CfqzXjVx/nu37h6Mp1qDJkAXq6ckwM9ZgwuBEtakpFRS+LIAisWL+QlRsW42TvwqPEWOpUC+Pzkd9jZGj8Wscsr0V8FY3s9DSWfv0ZA/r1xcFB0/u7YXg4C5csoepbzfEOKeUe7lrA/csX8a9THwPjoq+NkCbNuHM+gia9+r/yMfNzc5kzahjDpswiMKwhoDHkE3p1wrtadVx8np4Oa2RqRnpSYpE1pUJBTno6Zta2+Netz6E/VtGs/zuFj+9f+T9Cm7bEwMiI1kOGc+dcBKe2bqRRjz6vrFmitJFxOXUUmQoPAi0XoBLES50pf0YZNGkXD+pr/q+Xo4kQpkhGojyTnVvA4Ck72DWhR+HUvq9616feB8vZduoubeu+hPFNrAx6ufCwTgmr/W8EQeDHFcewNDXk3TaheDlZ8teRG+TmK5m09iRXFg7l53cakZaVh72lCTo65b+gqjjZc3gbuw5t5X9T1+Fo50xuXg4Tf/+eaQsm8MWoH8WWJyEilw7tx8PTs9AkA5iYmFAtOJgz2zZXSKNs7ehM7J1bCIJQJOAQc+cmVo6vV8ty+dA+KlUJKjTJAA4eXjTo2pOTm9fT5ZMvntonvHN3tv4+A/+6YZhb2xQW8bn6+mPj7ELPL75j8sCe3L90Ho+q1bh8+AAJD+/z+Yq/o95eVauREPngtTRLiMODrHY8yGoD6KAry8HG4BLxeXVLVUP5NMr/xOcAOF6Fi50hueIVobw+Zas93IGLkYR42RcZbW1qpM97baux7sjN/zbKejmaXtw5NnCnccmLfQGpmbnk5Cs4PWsA3s6aHrV1ApwpUKpYtENTtGeor4uj9au3QizpbhdlgQ07/2B4v49wtNNMkzMyNOajoV/SdVgLPhgyFhOj8jtp7d9kZGRw+PBh7t27h6GhIdWqVaNWrVoVopvFM/mXGXyCTCZDENQiCBIfn5q1kevpsXHGZNq+NwpdfQMu7N/DiU3rGbd+x2sdMz8n55n5yMYWlqTExT5zn1pvtyf61g2+bNEAr5DqJEY9wNjMgvdna0aJO1f24Yetezm+8S9unjpO9M3r/LT9IMZmZoXHuH7yGGGP0zckyhKaLOFqNlMItFzAycQfuZk+oNTOXv6N8s2mYJIIVTfAxa5SZPmlKHsDR3RkMlTqpz/IVGrhvyOuerlQaxkk+j7uyS0+d+PScLU1KzTJT+gc5sefh26IpKr8kJqejLNj0eExZibmGBoYkpWV8UpGuSynXOTk5LB48WKCgoLo1asXWVlZHDhwgOTkZFq3Lp6phGWtd3Jwwyas+O4LEhMTsbPT9CXPzc3lwuXLDH/3Q5HViYNMJuPDeUtZ+vVnjK4fip6BAWZW1oycOR9bl9cbwlSlfgPWTPie1PhHWDk4AprCvuMb/6L7mK+fq6PzR5/TtO8g7l++gIWtPR7BIUUubEwtrWgxcCjN+g9mYt+urPzhK9qN+BBdPX12/28BaQnx1Gz59jOPL6H9XEj+CEv929S3/xILvbucSfoWgZIZTPVPyr9RVhnC+R6adl8h6+B8d0irJLYq7UYo/KvM0KRaJQZO3saxK9GEBWnevNOz85iz5RzThzd79k4y1ePx1JmQ8HROnFg4WZuSlJFLbr4CIwO9wvXrUcn4u9mIqKx8EBJQgwPHduPZ8++7DJeun8NA3xA7G4f/2LN8cfbsWTw9PWnWTPP6sLOzw8nJienTpxMWFoa5ufaNki1pTK2s6fvdBJZ89yVVqgSgr6fH1WvXCevak8rVK27BrJWDI6MXLCcjJRlFXh7WTs5vdNfBytGJt999n/Hd29GkV38MTUw58tdqXHz8CAxv9J/7WtjZU+2tFv+5jY6ODh/OX8ammVOY1L87KqWS6s1bMWb5n+gZiNc9QeLNUAqm7ItdQi3bHwi0Woi5/gMOxs1BKZTsoLnyb5QBlEZwrqfGLPvug9MDKXMh09LkURXIcBZbxSthZKDH8jFtaT/uL1rU8MTWwoh1R27Rs0nA8wvdfPaD9UO4+jaku5Su4P/A1c6cJiHuvD9rDzNGNMPESJ8Ld+L5YfkxVn2pPX2dyyr9ug5lxJf9yMvPpX7NRtx9eIulf87no6FfVKj+ybGxsQQFBRVZMzQ0xMXFhUePHlVIowxQr0MX/GrX5fT2LSgK8mk19nvc/KuILUsrMLcuvgv11kOG41uzNic3b0CRH0mHUZ8Q8lbzYnsNGpma0vOLb+n5xbfFcjwJ7UBAzumk70lXeFPNehqG8lSylCVrlGWv1j6rZKnp6yREzBlYcifQzwIEKDB74aYSZZPkjFzWHblJRk4+rWp6EeT5nLHOzhehyg54WAtuNy1dkS9BenYeQ3/byZ5z97GzMCYzt4BfBjdmQIvgNzqulKOs4VFCLH9sWcaNO1dwsHWiS5veBPu/et/1spx6sWvXLvT09Hjrrb9TjtRqNdOnT6dPnz7Y29u/8TnKWuqFhIRE2UFXlo1SMAHUmOvdJ0Ph/Ur7v+PrclYQhJovPM/rCiyTFDy+6pCpwXevZjRx1pt/GJQ7DNJBPxcyHcVW8srYmBsxrM1LjJzON9VMILzTpORFvQYWJoas/aYjCanZJGXkUtnZCn2918/FkgxyURztnflw8NjX3r8sG+Qn1KhRgyVLluDi4oKvry/5+fns27cPW1vbYjHJEhISEiWJxiRDoOV8qttM4kj8NB5kFf9d14pllJ+gl63pE+xwAyJ6izpYQitxPw1OV+DQR2IrKX5kas1gmmRvzR8tx97KpMgUPgmJ4sLW1pauXbuyc+dONm3ahEqlwtfXl65du4otTUJCQuKluZPZDXfTnTRxeo+zSfe4lPoBxZleWzGNcoEZnOsFNVZCjTUQ0QdyrV68n0TZRqdAk6ceWxViKl4/VAmJf+Pp6cl7771HdnY2enp6GBgYiC1JQkJC4pXIV9mwK2YNYfafUcN2Ihb69ziWMBG1UDzvZxWncuXf5FhrCvxkKqi+GgzTxVYkUaIIELgdzB9BnoXYYiQktAaZTIapqWmxmmT3PsOk/GQJCYlSQyUYcjh+BueSP8XTdDPW+teK7dgV1ygDZNs9NsuAYYbYarSIstdH+YV4Htek2txpUiZSLiQkJCQkJCReBRkXUz5i3cMjJOVrirP1dd48CFoxUy/+SZYDHBsGwuMfhUz59/8rNNrTDeWNsbsF3kcgLhAe1hZbjUQZpTwU8ElISEiUd7KVmlkK7ibbCXP4jANx84jLDX/t41XsiPITnhhjl3NQZ6lmpHFFJi4IrpWj6UWG6ZDmDNdbU/5C5f/N8ex+UscLCQkJCYkKR3J+MDlKB1q49MHHfNVrH0cyyv8kxwaMUqH6GtDNE1uNeGQ6QoK/2CqKcOleAn0n7iR4+Eq6jt/G6RuxL79zVC042xfU0p0CCXFQqVRkZWWhfsaYdQkJCQmJ4idL6ca26I3E5jQg3OEzatr+iAzVKx9HMsr/JNUdLnUGk2QI/QPk+WIrEgejVLCMFFtFIaeux9J4zF/o+rWj/5cLsKreg9bjNrPn7P3n7yRTQdX1YH1P87UgPdWLm+i4hxyPOEzMoyixpZQou8+vfe20C0EQOHHsGDOnTmXR3LnMnDqV0ydOoE2DniQkJCTKKwq1OXtj/8e1tEEEW83FxfjgKx9DCrH9m2QvuNwBgjdCtT/hXO+KZ7JczoPbOTjwqdhKAPhy2Ul6v/89Tdv2AqByQDVs7F0Ys+Rbztd4znhq331gfwsSfEtRacUgPz+PCdPHcv7KaUIqVeL8w4fUDg3j81E/o6+nL7Y8reLMqVPE3bjB6TGf4uvgwPW4ODrPX4iuvj7Va9QQW56EhIREuUdAl1OJP/Egsx3xeXUAXimyXMEc4EuS6AtX2kOCX8UzyYC25fGeuhZFnYati6zVDG/B5TvRKJTPeLI/MfoP6sCjoFJSWXFYuHI6pnkpRI//gb3vDyd6/A/opMWy9I/fxZamdUScOsXS/v3wdXAAIMDJiUV9e3P21CmRlUlISEhULJ6YZGv9K3Ryb/zS+1VEF/hyJPhrclsBTBI13TAkRMHB2pzYqHtF1uJjHmBpZoKu/F9PYctI8NsDSV5wp1EpqtQuSqqITxAEtu3bwNQuHTHQ0wPASF+fKZ07sH3vumI/X1lGEAQS09IIcXUpsl7NzY3E1FSRVJUsUv9kiTdBrVajKKigKY8SpYYaXV6ls5eUevEi9HKg1gpIqQSXO4IgF1tRKaE9OZQfdqjKnCmf8ekvK7CxdyI9NYmFEz9mRLtqyGT/in7b34RcS7jcHuk6sPgRBIHMnCwWHD3GxosXEQToElqNdxs2ID07U2x5xYYgCKRnpmFkaPxS28fHx3MhIoLsjAxsnZ2pUbMmJiYmuDs5sevadVoHBRZuu/PqVSo5O5eU9P8kLi6Oi+cukJuVg5O7C6GhoWVmGl9mSgoRu7aSl51NcIPGuPoFiC1JophQKhSsnzGZA2tWoMjOxsEvgN6ffkFgWEOxpUmUQ9IK/Nnw8ADg8VLbl5hRlslkbsAywBFQA/MFQZheUucrMRTGcKch+O+BwC1wtX0FSMfQroEjozpUJyE9j0/7hmNlbUtKchIDWwYzrk+9pze+1Qz08kBlWPpCKwh2FlZcjYtjSf9+yGQyJu3eQ9Op06kTUldsacXC8YjD/L7kFxKS41EjUDU4mLeat0DvcQT939y6dYsdmzfzSdO3CK5Zg42XLvO/hQvpN2gQ9Ro1ov+y5Uzv2oX6Xl4cvnObj9Zt4O0OHUr5u4Irl6+wf9dehtTojoezMxtv7GX5+aX0HdQfQ0Ptfr1cOrSfRR+9T2OvOlgbmDFj3lxqtO9I96+/e/piWUKrUSmVbJk3iwNrV5GXkYZP7foYGhtzMzER09nLkTs4kXPiMLM+Hsnni1biEVRVbMkS5RCBZ7+fPwtZSVVfy2QyJ8BJEIRzMpnMDDgLdBQE4blzBWv6OgkRcwaWiJ43ptIp8D2gGVpxtS1a5SSLG+MkMEqD5MpiKylCVm4BDx6l42ZvhoXJPz/YBfA5ADHVNKPJJYot7eLC1QiWrJrO1TtXcbRxoEa1MK5dPMLlr8aio6O5YFSr1QR8/yPtOg6jc+uexXJesbh+5wpjfxjGyoH9aB4QQGJmFsPXrOGhUkXbTp2e2l4QBObNns2qfn15y9+vcP3jv9ZxMiOTFq1bc+/ePSKOHycxKQl7OztqhYXgWMF8AAAgAElEQVTh4eFRit8VKJVKZk2byequUwh29CvUPmLr9+Q6QHjDBsVynpJIu8jPzWVsg1osbT+eGi6amoP0vEzarB5Ol19+ISi84qZYlUUWfj2Gi/fuYvDuR8ht7cndup6s5fOxW7sLHXOLwu1y/1qJT9RdRk6ZJaJaifLMO74uZwVBqPmi7UosoiwIQhwQ9/j/mTKZ7DrgAhTfAO7SJLIOyJWaCW9prhATKraikiPHVvNHyzA10ifI0+7pBzyPgftpyDOXjHIxcu3WJcb98gEzunWm7ZA+XI2Lo9O8hfSrU6vQJAPo6OjQtXoo8ekpIqotHjZsXc4XLZrRokoVAOzNzVg2oD/OX3xFZmYmZmZmRbZPT09HpVDQxK9od5UBdeuwdv4CALy8vPDy8iqdb+A5JCQkYGtiVWiSAWQyGb2C2vDdqTlQTEa5JLh+8ih+dl6FJhnAwtCMAUHtObxls2SUyxCpj+KI2LkVi1Xb0DE2AUC/dn3kuzYXMckAcv9A4o7sFUOmhEQRSiVHWSaTeQChQNku9b4fBtk2mq4Y5RnjZDBOgSQfsZW8GPsb4H0UYoMhSmq3VZwFfKvXzefHdq3pXVtT1FrPy4sxzZqy6dLFp7a9EPuIUB/tNVsvS9yjKKqHNimyZmJgQCUbazIyMp4yygYGBuQWFJCVn4/ZP9IXYlLTMDIyKhXNL4OBgQFpORko1Up0df5+20/MTsHA4OXSLgRBIDo6muzsbFxdXTE1NS0puf8+cZELsyfo6sgRtKiWQkJD/MP7pMTG4OIXgLm1TZHH4u7dwdDbt9AkA8idXFCnJKFKSUJu/XeARnnxLO6vmYdekJfL7qWLOLFrOzKZjLC329Gs3yD09MtGPr6EdlHiybYymcwUWAeMFgQh4xmPD5PJZBEymSwiMb0MjI5O8NfkKOtnQaXTaFPRW7HhdBVC1out4sWYxkPgtsfjqVtSrtNhROB+1B0aVS56sTS8UQPORkYxff8BCpRKFCoVsw4e4lxUDE3qtxBJafHh4x3E9qtFb3rFpafzICkZGxubp7Y3MjKisrc3n63fgEKlaVWYmJnJ55s2E1hNe+462djYYGFlyeyTK//P3nnHVVW/cfx97uXCZQ8ZIoqoCOJCUHEg7r33yNwjM00tc1T+LCszR2ZlmpaVaY5cuTX31lREFPcEAVmy973n98dVjFBk3AF43q8XiV/P+X4fEi+f+5zn+Tw5w04eJ8fw9elfqe1b55X3x8fHs+rHnzj0137Cz99l+dJlHD10RC+DU7waN+Nq5E0uR97IWUvJTOXXK9vx6dRF5+dLFIzUpEQWjBnKJwN6snzxAj5o68/6BXNzfY84Vq5C+r3biBnPJ9/KLCxRuHuQOHMimSGXUSfEk7pzM1lb/qDLyLcKHYdarWbhW8PZd/oU6SMnkDbsbXYfPsTid0ZLg34kioROM8qCICjQiOS1oii+UHmJorgCWAGaGmVdxqNVXII0ZRhGGXC39GfSciHm/KdkU+UUZCk10xRFycBF21Sq4MaZe/eoWcE5Zy0mORmZ3Ijfg28za8cuBEGgupsHX89ZVWCHiJJMv+7DGP1+b2zNTBnYoD53Y2KYsmkLDRs2fGnDW4cuXdixZQsuMz/C3cmJ4LAw6tevj7e3t56jz5/ufXqwYcMm1l/ZTUWb8gRH3KBxk8Z4enq+8t4dW/7ijRpdmNDoTQRBIDY1nl7r3uG6sxNeXl46tYQzMTNj2LxFDJr+Ph2rB1DOxIptNw/j1aYNdVq01tm5EoXjl08+JNTCCut1uxEUCpTxcZyYOZEKld1o3v8NAOxdKlLbvwU3536EcvxUZOXsST+0Dx7co90bwzi18FPio6OoWr8hA35eSwX3wj/VDDl5jPDoaCyWrUWQa1yqFHV9eDBmIDf/OYOn3wuawCUk8kGXrhcC8DNwTRTFr3V1jsG41xSUCVD1JKiN4L70j0/vXO0KyiTI1NMj4NeMAb3GMHP+JBwtLelcuxbXIiMZvXY9fbsMZvTgScQnaryAbaxsDRyp9nB2cmHwsGH8eewYiw4fwcLcnLq+vvj4+r70HlNTU/oPHkxMTAyJiYn4Ozlhbm7+0usNhbW1NSPGjCQiIoLk5GQCKrbFzOzVb27i4uJIeJLA236DchwmypnZMKXJcH4M3ISXl+5t2nzbdaJKXR/+2b2diORkRk/9iSp16+n8XImCkZacxOVDf2PzVCQDyGzsMB45gf1rVuQIZYC35i1i49dfcXzMALLSUnH1qc+bP/1ONW9f+kyZVuxYbgeeR2gckCOSAQQjBfLGzbgdeEESyhKFRpdpOH9gCBAsCMKlp2sfiqK4W4dn6hEBrnUEWTa4HwWV0fMBJWWBElvFIGom70XW1FjASc17OqNerfp8MHEu09YupseyH7G3tqVPtyG80WsUULYEMsD+wI0A2Nvb071370Lfb29vj719yWuC/TeCIFChkB7OGRkZWCktctU2A5QztSEzI1Ob4eWLrVN52o+QhpmURNKTkxGMTRDMcyct5A6OpMTnHq6jMFEyeOZs3pjxP9QqFXIj7coQG8fyyIKv5FkXQu9jK42NlygCunS9OEEJllvaQQYhXUGm0pRiPPLRZJdLPSX4r61iINTYD/IsjROJHklNz+LPY9e5Hf4EX3cnujWpnncyYBGJjk/lXmQ81SrYUs6q5DSBNW3QnKYNmpOtykYuk0ueta8hjo6OPElPJDA8BJ8KGjcQURRZF7wT12qVDRydREnAxqk8FtbWZF44i0mD537qGQf34N30xaWJgiBoXSQDNOrag03fLiR9/05M2nYGUSR933bE2zep30GqaZcoPGVB1RkWUQZXuoNR5lORLFKihWZBCK8DcZUpcV+L7QPw+Buiq8FD/Wbv74Q/oeW0TVR0r41rjSZs3XKAOevPc3heH2wsij6sIStbxcQfjrDucAjOLpWIeBTK0Ha1+eatFsi1JMK1gZFceql4XZHL5bTr1J6hm6cx3Kc3bjYubLtxgNtJoQzurv0x6RKlD0EQGPrRpyybPhlV70HIq1RHdfYEnD1Ozw3b9RqLqYUl035aw/KZ7/Fk5RJEtYi9S0XGrVqLSQlyopEoPehs4EhRKNEDRwqCoNJM74uuDo9rvfp6iYJjGg8Nf4NMM/hnKKj0a/PT9sMtVGw0gB6D3wE0GbXlcydR0+QB34xr9Yq7X87/Vp9k13UVkz9fhYWVDUkJT/h65lD6+Zjz0aCCT7rTpi3c68qz0ovXnaioKB48eIC5uTkeHh4YPc36RUVFEXTxEqnJqVRwdcG7njfGxsaAbgaNSJQ+Hl67yt9/rObxo1A869aj7eDhWDs4GiQWURSJCQtFkMmwd6lokBgkSjZaGzgiCIIJ0AfNUOyc60VRnFOcAMskghqMU6HWThDlGiu50oh5NFhGaeqAS0pGucY+EEQI6qt3kZySlsnJy/f5dcGonDVBEOg2eALzJ/cqllBetvMys5ftwsLKBgBLa1uGvfcVi6b2K5RQft24F3qbU+ePYWJsQqum7Sln+4JBNBKFQhRF9u7czZ2bd2hdtTEPE29xYO/fDBg8ECcnJxwdHWnXsfRbAEroDlevWoz67CtDhwFoXqMdKrm+9M8j7txm968reXjrBhWrutN5xBhcqr/aAUbi9aMgz1P/AhLQjKDO0G04pRy1QiPkfDZA7e1w2QhiStYY6ALheFNjfffYC8QSIpSvdgbTBEh73kCmVovs/ecu+wMfYmtuzNC2tajibKOzEERRnev3KpWqWDW7oigSl5CEY4XcL+ZOFSoTHZfHclziKSvXfMOuv/+kv68PMekZDPnjWz5457Mi+zhLmWQNwcHBJIXHc3LMOsyNNW4Ym67sZf6mVYwZP1aqT5coM9wLDmL+qMEY9xyA0ZtjuRISxMXBfXhv2S9Ur1+GmvIlXsjMsKhCXV8QoVxRFMWORQvnNURlDIH9oP56qLsVLvWFuCqGjqqIlICyHLt7mnrpTEvNx1OyVWp6fbaTa5GZNGk/gLuxEXwzcS0/TWpLnwDtZgXMTY1p6VONneuW02fEe4DG1P6v1d/Qv3nRpxcKgkDj2lU4dWgHLTr0yVk/dWg7/nVL6/eMbgkKucChI9u4NutDyj2dDHcpNJSW38yioXcTLMwtX7FD0UhISODu3bsoFAo8PDxySg7KEjeuXGeC3+AckQzQp1YHFpz8maioKJycnHR2dtTDB5zduY2M5GTqtGyNR8PGkjCX0BnrFs3DZPRETDv3AsC4XgMEpwr8sXAus9dtNXB0EiWNggjlU4Ig1BFFMVjn0ZQVVEoIHAB1N4NKYehoCk9JySI7XoO6f8GNthCau4xo/eEQ7iUaM/eXvSgUGtHSvNMAxk7uS2e/qpiaaPf/+/IJLWk1/RdCLhzBrYYvV84dppxJBrPf6VWsfeePaEK3T2YSE/GQGnUbce3SafZs/JF9XxRv37LK0ZP7GOvfJEckA9SrVAl/d3fOXDxO24DOWj/z1IkTnD19mg61ahKXksoPe/fSs29f3NzctH6WIVGrVCiNcpc1CYKAiZExqqdTB/+LNmqTz+zYyobZH9PLqy22Jpas2/4ebk0aMWz+YkksS+iEexfOYvfh3FxryhZteTD3I9Rq9QtHpksYnsJmgrXFS4WyIAjBaFKKRsAIQRDuoim9EABRFMW6+gmxlJJlChcGk1Pjq0iBrJI3hKDEYhn5dDx1RQjLO1hgy+kHtOk1NkckA1Sr4Y2La1VOXn1EW183rYZT2cma6yuH89epW9yNuM5bI7xpV78KMlnxfpD7167IsQX9+HrrAXae2kRtVxtOLuqPV+VX+/FutZic87lTSmyx4igtiIjIXiCeBEHIdzxt08ijL/2z/fmcFxoaSvCFC9yYPYvy1tYAHLx+nT4/rWLCpEk5jW6lidu3b3P10hWyM7Nwre6Gj48PRkZGVPGsxs+Bm2hTrQlymWZYw4n7F0jITKZ8+fI6iSUtOYl1sz9kS/9vqeFQFYC3/QbRZd14go4coF6rdkXeWxRFBEHgSWQEibExlK/qLrkeSABgZmePKuIRsmoeOWuqyHBMbWwlkawjDCVytUF+r/Jd9RZFmeXpD/QKQVD9EFwcBEm6+YGjGwxUemGcAt5bIMsMLvd64XhqY4WMjPS0POsZ6WmYKOR51rUSlkJOvxbab9CsXcWBVe8VXRC8TrRo2oEF30xjXEAAtuaaEoHgR484fvMWE6dqf5T8teBg3m3ZIkckA7SpUYOa5ctz9+5dPDw88rm75HHi6HFuBIUwwe9NbJRWrAnezvorfzBwyBv4+vqy6cZGOv4+ml6ebbmX8IhdN47Qo09PnYmHkFMnqFuhZo5IBjBVKBlaqwsH9uwutFBWq9Xs//lHDv/6MzExkZSztic1LQUXexceJ0bTbdL7tBk6UttfRplDFEVuXfiHG+dOYWlXjoadumFurbv+D33TdtAQ9i9dgPyThcisrFEnJ5H+3XxaDZLcgyTy8lKhLIriAwBBEH4XRTHXd48gCL+jmbonURBiq0AVJfish4tvQLJh7HIKTHhdiK2qce7QO6LGYk+RCuffhMwXZ+GHtKrOOyuX4t+me45jxLnj+0hNjKVpTRd9Bqxz/p05fhmPncoB4PS49GeW88v+NrETuV+vDrXnfMaghg1ITE9n08VAfhw0kA7JgZBc+PM+cdbU3n4S8TjPn6lUKixM8tYjm5sYk52dXfjDDEhycjJnzpzh+Og/cDDXTLTs5NmcPusncvXqVby9vek/eCC3bt3i6P1gzMqZMnrcGKysrHQWk1xhRJYqK896hioLuaLwdeA7v/2auzv3s7brXKrbV+bgndNM37uQr1pOxt7MhkHLp1GuUqViZarLOqrsbJa+9w43rgYj92+FcDmYjYvnM+WHVWWm0a3LmPE8iY7m5JDumLhUIuNRKH6du9PznSmGDq1EUZqzwNqkIM8NcxkCC4IgB6Q5kIUhwwouDIIGa8F3PZx/A1JL8KjbTAvNh0EQ4E5zMEnKN/ve2a8afS+H827/RjTwb0NCTCT3b11hx6c9S9SgDgntIggC8/v0ZnAjP3ZeDqaCjQ2XZ31ERVvdjNOu6uHBD8eOMaZZM5QKTd37zcePOXP3HuO7lK6Hbg8ePMCvUt0ckQwgE2T08+rIxnsH8fb2RiaT4enpiaenfmyyajZpxm8x73H64SWauGpKrOJS4/nl8l+8uWRJofbKTE/j4Oqf+XvIz7hYad78dKgeQGxKPMvPrePn3nOZ1ngEv63+TRLK+XDqr83cevQIy5UbEZ42rWacPsYPUyey6MBJZHJDJFC0i0wuZ+isOfR6ZzJRofdxqFQZK7tyhg5LooSSX43yTOBDwFQQhGdeVQKQCazQQ2xli3QbTelF/adi+fQYvfsBFxiLx2Adrsks6zOrbBoHaXaQWOGVlwqCwMIxzRnXuQ4HA+9j6+tA11ljMFOWwubJ/1CQDPLLMFRmOb8ssC7wrlgR74raHSLwLLOc8/uIx3h4eHAzJIQ6X8xlZOPGxKamsurUKdq0a4dpKat3NTU1JTIpJs96eHIUJkrDvBYZK00Zs+QHRk8ch1+lutiaWPH37ZO0GDwUT78mhdorIToac2OzHJH8jAYV67Dinw0AuNm4kHgzWmvxl0VO7t6BUa9BOSIZwLhxAMkrl/Ag5ApV6ngbMDrtYmlnh6Wd3asvLIVI2WDtkV/pxZfAl4IgfCmK4kw9xlR2SbWDiwM1IrSkimSAcveg+hGIqKU/oWx3H+ptgJAuEFm7wLe5u9ji7qKbbKI+KI4ozo+XCWZ9C9rSTI5w7tmTu3fvsvP2bRQKBYOGDsPBofQNOHFzc2Nvxh7WBu3gjbpdEQSBa9F3+C1wKwOHvlGovbQ5ia9m0wDmHjlN4IF9pKekMD1gBk6VC2+PaOPoSGpWOqEJEVSyds5ZPxcWRHX7ygDsvHWUqn5+Wou9tBP18AFBh/9GbmSEb7tO2DjmYwEoIrmQ6AlJ5JYs8sso+z799M9/fZ6DKIoXdRZVWSbFQfMBYB0G6Vaa0owShZDrF51j+gTqbIPUcprx3xISJQhBEKhWrRrVqlUzdCjFQiaT0W9Qf77983d+OLcWa1NL7j95RNsO7XTqkVwQzCyt8O/Vr1h7KEyUtB0xhtFbZvNV6yl42lfhwO1TzDu6gmnNRzP78Hdsv3ucGQt2aCnq0s3un5ezffl3mAS0hqwsNi6ez7D/fY5/525sXLsasWmLnKxy5uljKFTZuNYseBJDQqKskF+N8qKnvyqBBkAQGulUFzgLNNNtaGUcWZZmIEm2CVx4w4A1wS9AzPOJ7pBngPdmzVEGGE+tb3SVQX4ZUga5+OTX7FfacHBwYPTbY4mIiCAzM5NuLi4oFKW/XOkZXd6ZhKmlJWN/mUtMVDgVK7vj7FWD1Y+OULWhHzMX7cLWqTQ5DxWO7KwsTm/fwrmD+zFRmtKiZx9qB7TMkwkOu3GNHT8tw2rFBuQOmuZy4/t3+G3SSObtOcKl40e4PrqfppkvKoKsS+d5b9kqyTqtAEjZ4LJHfqUXrQAEQVgPjH02cEQQhNrAVP2EV4ZRKzTWZz4bNDXLF97Q2KG9VohQeweiWSxcHICQVnbshyQkSiqCIFChwqv7AEojgiDQZtgo2gwbZehQ9I5apWLRuOGEJSYh79IbMTWFa3M+pmWX7vSbMj3Xtef27kTRrmuOSAYwcquG0s+fy0cOMmHxD9wJPM/1s6exrF2Lhgu+wczK+r9HSjwlJSGe+OgoHCpWMnQoEjqgIK4XNf49lU8UxSuCIOSdACFReBIqarKo9f58LpazlYaOSm+ERiWx7ad0bjwS+HXVNvq1rMnC0QGUsypdTVL5oe8MsoTu+G+zH5SNLLNE2SDw4H7CYmIw//YXBLnmR7u6RVsODutF6wFvUq7Cc9tMtUoNL3CvEOVy1GoVgiDg7tsQd9+yYQeXH8XJAIuZmSQtnU/6oX3I7OxRJ8RjPmgEZv2HSPXcZYiCCOVrgiD8BKxB84D8TeCaTqN6nXhSGYJ6Q73NUOk83CsBFS3hdSGmuk7Hb6dmJxMwdSONO71JlwHjqN85i82/LKTNzM1c/G5wsSfeFYXtp27x2foL3AyNxtPVgf8NakDXxu6F2kMSxq8XZaksQ6JwJMREs+eHbwk5chiluQWNBgyg1aChBrNPu3zyGEKrjjkiGUBmbYuyYVOunTlBs94DctYbtO/EoXEjUPcdjMxa0wydHR5GxpnjeM/6VO+xFxVDlzkkrfgGdWwM9mt3IrOyJjs8jPiPJiErZ49p284GjU1CexREKI8A3gYmPf39MWCZziJ6HYmrCv8MgaQSMogk21TzoSssI5HXWUvPgTVo0X9GzvLI9+Yxc3gr9l+4R8eGVfPZQPtsPn6D8ctOMPKDhUys25BrQecYtXAqP6rU9PQvXdPXJEoO2dnZ3Lhxg6SkJFxdXctsycPrRmpSIvP79aBDhYZMbfc/4tLimb/6VyJCrvHm3PkGicnC2gZi81rfiXExmFvndgZyq12X1n0HcHDsQBStOkJWFpmH99L//ZlluoZbm4iZGaTv20G537Yie1qWYlShIpbjppDy+wpJKJchXimURVFMBxY//ZDQFc+Ga5gkQtWTcKOtpo7ZEFhGgu0DCKsP6oK8lyoExsngvZnUJIFMo9a5/kgQBDzqNuZ6aKjehfL/1pxj3MffU8+vBQB+zTtipDBm9rLpLxXKUvZYQ7ZK86hW/ho3+rzIgzk6OpoNa9dSy7k8Xk5O7Ny8GXtnZ7r37l0im6JGp7cp+MU/3wHg71Gl2wmkqJzYtAEfW3c+a/1uztra8l40/mkgHd6egEMlV73HFNCrLwcH9MC4TScU7pqBMelH9iM+CqV2QIs81/edPI1GHbty8e+9yBUKGo7fUSRbvqJi6GxwcRFTUkAuR2abe1CJvJIbqhjJq7sskZ893EZRFPsLghDMC+wPRFGsq9PIXlesw6FCkGYyXVBvELUsVAuCTSh4HNaUYGhTKAvZUHcLKNI5vqYRF04E0q738z8WRZEbQaeZMEK/hvaiKHLtXjh1fP1zrdep78+8u+F6jaU0cTsqivc2bWbv1RDkMhn9fH34ul9f7C0M4+CiVqs5ffcesSkpNKlaBQdLS4PEAZrvqd1//cUXXbvwVoCmnCqjbxatv/2OCxcu0LBh2a/9LMuEBgbSt3KjXGsWJmY0cPXmQUiwQYRy+SrVGDl7Lr9OexuFqxvq1BQU6em8/+OvKIxf7CZUqUZNKtWoqedIywaCtQ0yCyuyggMxrvvcQTfj5GEUtSR5VJbITwU9K7UoXXNaSztRNeBaJ6i5B+r8BcE99TsdD9CNj7IIXvvAJhwu96Rt9WpMfriaP5Z9QddB48jO0tQoW8pSaVdff1kN0GSy3VwcuBUSSI26zwXM7WuXcHN5Pljidc4gi6LI6bt3OXXnLhVsbGjj6UnrxUuY2Kol60aNJD0rizm7dtPpu+85O32a3jOmd6Kj6bFsOQCutnYM/fU3prVvx4edOuo1jmcMUxixMjmZMf5Nc9ZMFAo+7tCByTt3FVsoFyr7q0PaleLMsiiKXD97isA9uxEE8O3SDc+GjQt0r03Fily7ei/XmlpUE/zoGim7dnD7zGl8OnfFo0EjvTZ1+XXpTr027bh98TzGSiVVvX21WjOtjSxwdtgDMs6cQFAoMAlog7yUjo4WZDIsxkwk4bMZmA8fh8Ldk4xzp0jdug67RSsNHZ6EFsnPHi7i6adtgOOiKN7ST0gShHuDLBtq/A21dsDV7iAa4lGtln2Us0zhrj9E1cBMCccX9mfqT8cY32sFcrmc/i29WPtlH4M08s3sV58v5r7LhE+WU62GN7evXeKHue/Ta+gMtloM1Hs8JYkslYqBP/3M5bBHdKlTm0M3bjJpw0Z8XV35oH07AMxNTPimfz98537JoRs3aetVQ2/xiaLIgJU/M9rfn0mtWyEIAhEJCTRf+DU+lSrRqXYtvcXyjGy1CiOZLI9IMjaSY5VtWmKErrZ4JpifURqE859ffMqV3XsYUrsralHNmnffxadXT3pP+/CV9wYMHMyXvbrQyLkO7dz9SctKZ9Cf7yOqVDRPc0YIgzWT3qVe9270mTFLZ1+DKIo8DLlCekoybnXqYWJqirHSlJpNA/JcWxJKHZJ/X0nq1nUoA1ojpqWTvGopVu/PQtm8raFDKxLKFu2Q2diSunkdaX/9iZFHDey+WYWRq5uhQ5PQIgV5ru4GvCkIQmXgAnAcjXC+pMvAXnvC6mvEsvNVkGfq1zZO1LZQFQEBbrXm3+Lbxd6SdTM6AZ20fF7hGd2pLtlqkS9mvEH0k0Qcba3oPmImrbu83iIZYPmxYySkpXF19iyMjTQvGStPnOTLvXsRRTFHDAqCQCM3N24+fqxXoRz86BFxqSm82+r5YAVna2umd2jPr6dPG0QoV3d0xMLEhK2XLtHbxwfQlIYsOXiUDu5560Ul9MuDkCtc3LGdQ0NXYa3UlOgM9u5Oq9+G06h3X1zc82/gdXStzLhlK/l01oe8v38BGVnpmMgUHBvzB7amVjn7tVk9Ar+efXRS3hBx9zZLJo4lKT0duZUNWY8eMuSjT2nao4/Wz9IGWdevkrZzM+V++jMni5x1+wZP3huDsY8fMsuSNqG2YBh7N8DYu4Ghw5DQIQVp5vsfgCAIpsAY4APgG8AwHjivEw8bPW+oE1RPs8qlzJvRLE5Tl3y169OGxZIZvyAIlB/4K98OEMnMSMPYxLTAj0xFUeTgjnXs2/YbCXEx1KzXmH4jpuBSuXDWckUlPi6akwf+IjUliXp+LaheK8/E+WKx8cJFPu7UKUckA4xq2oQZW7dxLyaWqg72gEYIHrt1mzf89Ft/m5iejr25RZ5yD0dLSxLS0vUayzMEQeDnoW/SY9lytl0KwtPJia2BVzCRWTGsXckUMtqkpJdkXD5ygJ4erXJEMnXU0n0AACAASURBVICtqRVdPVpw+cjBVwplAE+/Jszee4j4x5Ec2bAG+3OPckTys/26e7Ti8pGDxRbK/80GiyoVsaOHYtbvTSy79kEQBLLu3mLVB2+zxcYRRTXPYp2nC9KP/o2yU49cpRYKd0+MPGuStnMz5oNGaOWcrFvXSf1zDdkP72JUuSpm/YbkNDdKSBSFVz7PFwThY0EQ9gD7AXc0U/kq6jowiac8E8l1t4DHAfQyVjq8DhybAFnFzGLL08F7ExinFH8vPSEIAiZKs0LVFW74eSF7Nq3izXEfMWfpFlyr1mDW+F5ERTzUYaQazp/8m3cHNefuzWDSUpJYNOstls37AFHU3veJSq3GSJ77pUIQBARg3r59PHoSz+2oKIb/thonK0sC3PXzBuEZ9V1duRcbS/CjRzlroijyy6nTdKjppddY/k2TqlUJmf0/6ru6Ev7YgvENx/BH/28wVZTtMe2lAWOlKQlZKXnWEzNTMFYW3BpTEARsyztjYWtH4gv2S8pKRaHU/mtfVnAggtIUs259c16rFFWrY9qjH+l7tmv9PK2gViHI8ubXBBMlaft2auWIzKALPJk+HqPqNbCa9CFG7p48mfY2mcGBWtlf4vWkIKUXvYFsYBdwFDjz1DJOQl+IMki1g8r/aITz7ZboNDOrNoZM41de9jAqgQ9+PsWuMzcxURgxqJUXX47wx9LMBFBDne1gGg8XB0J6yRpP/aLGvLD7N9mxfgUP7lzDxbUaXQeMpYpH7Xz3SUlOZNfGn1iy9ih2DhqLv95DJ5KanMiO9SsYNeVzncQPkJGexvefT+Kjhb/jWUfz6K/fyPf5cGxXzp/4m4YB7bVyTu969fjm4CFaenjkWMBtvHABR0tLMrOzqfv5FxjL5Qxq2IClgwbqfSKVqbEx3/TrS/sl3zGhZQtc7exY9895YlNSGNPM/9Ub6BB7CwsmtWlN2L38v4/KKv+tXYaSkWVu2Lkbc75fwijv3ng5auIJjrzBgdunmNNxXuH369iV2YsXcrXeLWo5VQcgJOo2e24ew2TGRA5ruT5YnRiPzCGv777cwYnMsFCtnqUtTJq1JuGTqZj1GphTZpH98D5Zly8iZmWiTk1BZmZerDOSVy3FasI0lK01TbwKr9rIbMuRvGopdot/KvbXIPF6UpDSC19BECyBZkA7YKUgCI9FUSwBI+ReF57W98pU4HZWI5bv5m3W0BpWEWB/Gx74gerF2a+k1Ayavb+Rpp2H8P3UtaSnpbBx5Ty6zN7O0fl9EaofBfu7cK0DxOvfKum/vMqx4va1S3z+3ht0HTCWlp36c/PqBT6dPICpn6+gtu/LxVb4g9uUd6mcI5Kf4dO4FX+s+Eorsb+Mq4GnqejmkSOSAUzNzOnYezinD+/QmlCe0Kole0NCaPjlV/TwrsuNx485eP0GO995m4Zublo5o7gMbuRHTWdnfj55iuDwcHp412VI40aYGb/6DZ8ueV0Fcn4YuvEvISaaPUu/w8hIQZ8/JuBo5YCzXXmCHoUw9KtFzEsXodDCVkD2/sf0WDgZn0p1kCFwIfQyyvc/Qv4CQVtcFLV9SFw4B1VcDHI7TemTKIqkH9yDsk3JHHShqF0PURSJHdkXZbsuiGmppB/ej8WYd0latgihmE45oiiSdTUIk4U/5lpXBrQm8avZxdpb4vXmlUJZEITaQADQAmgAhKJp6JPQKwLcaKdp8Kt6ErKNNTXMusAyUnNGmM9LhfLvB69SuUZ9BozRTNaztrXnnVlLeW9QY06FhOLvEw2hPvDIRzcxapl1K75i8LgPadfjTQC8vP1wKF+R33/4gq9+2v3S+8o5ufD40UPSUlMw/Vc25O7NKzg6V9JpzIIgvLDEQi2qtZrVVSoU7H93IvtCrnHyzh2aVavG9wMHYGdevOyPtvFxrcT3rgNefaHEa0t6SgoL+vekY4WGzOr7DYkZycw/tYpoZTbzjp3D1MKSTUXM/ipbtsO4QWOu/XMKRBErvy+RWejGy1tuVw6z/kN5MmkkZv2HIrO2IW3PX4iZmSjbGMYS8VUIgoBZ1z5khlxGMDZGZmlFuWVrSNu9FZOGTREKUfLysv1ldvaowh5gVOV5+Vd22ANk5eyLG77Ea0xBSi++QlNy8S3wjyiKWboNSeLlCHCtoyaj/KSyHs57eZ1r8P0nePr0yLUmk8moUbcRV+/H4X+pLwh6qKd+CYX1PL52+RxTPs09mb1Ri858/b9xZGdnYWT04imJdvZO1Pdvy9IvJjP6/blY29pz+Z/jbP39O2YuWF3k+AtCLZ8mLJkzgWtBZ/Hy1rxpSktJZu/mXxjyjnYtqWQyGZ1q1zKIg0RZ5lzYZfbePIJMkNHFszU+FV6/4Q/abPx7lQVa6l8b8bVw5dNWE3PWfuv1JfVXDmTWxcsoqlYv1vkyC0uUrToUa4+CYvHmaBQeXqTt24GYlopJo2aYduqB8JLhIiUB88GjyJr9PukH92Ds60f8nOmQlYnNV0u1sr9Zz/4kfjMX69kLkNuVQxUXS9K3X2HWU3oDLVF0ClJ60UUfgUgUFBnc+NcjdYvHkOz08suLxKsHjnhVsmbL5VPQf3TOmolRBO9N2IX1/faaOA2nkwuNrZ0jkY/u425VL2ctOiIUcwtr5PL8/5mMm76AX7/7hAn9myKTybG2c2D8zEVUr6nbbLqxiZJJ//ueL6cNx6dxK6xt7Tl9eCd+AR2o37R0+pK+Tsw98gO7bx1klH9j1KLI+B0fMaB2dyb7a6f7XyIv8hvX6eCa25XFWK6gkWs9Tt++XmyhrG9M/Pwx8TNsHX5hEEyU2Hz5PVlXg8i+cxMT/1YY12+MoKWhKGYDhqNOSiJ2eC9k5RxQx0Zj2rUPZv2HamV/idcTA8xHltAaTiGahrmrnSFCiyMzxTyf5GFo29rM2/gbW1d/S8e+o8jOfIJX1Q641kzD1MoW8jaA64xXZY9vBJ9n65rvCb13ExfXavQYPJ5aPk1yXdOh9zB+/vpjpn/1CzZ2DiQnxrNi4Qw69Br2yjIGE6Upb33wFcMnfkJaajLWtvZ6a2jzadyK7zec4PShnaSkJPLhgtWvbECUMDxXHt/kr+v7ufK/D7E1NwNgfIsAan06l2412lCtnOHr+vXNs8xygw66Gz2uquDCxcBbDPnXmiiKXI26jdxJyjrqA0EQMK5dD+Pa9V59cWH3lsuxfGsy5oNHoYqKRO7kjMzcQuvnSLxeGGLcm4S2iPaA2CpQczc4XdXixq8WeTYWSo4t6M+Ty9sY3sEDo1g/anjEor7cBSFF2xnuohN84QTzpg+nftO2zJz/G41admbRx2O5ePpgruu6DhiLV71GTBjgz/tD2/J230aUd3Gj/6j3CnyWidIUGzsHvbs+WNmUo0PvYfQeMlESyaWEA7dP8UbD+jkiGcDB0pJe9epw4PZJA0ZWtjHp3INdd47zZ/AestXZJGek8snRZSRYmqKoq13/cQnDIbOwRFG1uiSSJbSClFEuzaiNIKg31PsTau0EUQ5RWpiIFlEbHnuB6sV1uc9wd7FlxyfdUVc6g8zzCNwJwCJZ90KtMPXHG35ayOj3vsC/raaeuqJbdaysy7Fu5Xx8mzwfIyyTyRj6ziz6DH2XyEf3cXSuhKW1ndZjlyjbXHkUzi+nTxMWJadRxfr08PLAxCiv84bSyITYjIw8609SU/kn8jBvNRqkj3BfO+R29pjOX8qni+cx4+ASRETMG/qj/Oo7vb/BlZCQKB28VCgLgrCDfJ69i6LYXScRSRQOtQKC+oLPBqi1AxIqQEYxR4GKclAVsGZMyEbmEgyPa8C9psU79yUUtjHv39wKCeTjr9fmWvNt2oZ504ehVqvzTHMzt7SmWg3vIp+nS9RqNZfOHibon2NYWNrQomMfHJ3L9iP61MxMzt67h5XSFF/XSiVazGw8f4GJGzbyVkAz2tW1Y925bay//Bdr+3+DqSL30ImuNVrR8dffeLd1C7ycnQEICgvjwPXrKGQKrkXfwcvB8H7DZRGFhxeKZb9hnJSIYKRAMC2e24KEhETZJr+M8kK9RSFRPFTGENgPbEOLL5IBrMLB6ZpG+Ga/4oeIaATnh4C6ZI7Xtndy4eHdG3j8a6xz6L0b2Dk45xHJJRlVdjYLPhrD4/AHBLTrRVxMJB+M6Mj4mYto1KKTocPTCavPnGHKn5up4eRETEoyxnIjNr81Bg8n/Zb2iKLI7aho1KKIh5PjC8V6elYWEzZsYN/Eifi4amwBR/k3pdvSH1kXtIORDfrlur6idXnqOdfEb9582tf0QqVWc/TWLVYOHsyeK9c5H3ZFEso65tnQCwkJCYn8eKlQFkXxqD4DkSgmKiXEPO3YtrsHannRB32Yx2imAIbWf7lQNkqHymfgXjPI1u6I1uJkkP9L1/6j+XH+dD74YiXlK7oRFfGQZfOm0uVfbh2lgeN/byXhSQwLftmXY1XXokMfvvhgCD6NW2FsUvi/g8iEBD7dtZudwcEojRS82ciP6R3ao1TkX3KjDwIfhjJtyzaOvT+FWhUqIIoiPx4/TvcflhMye5be3uQEhYUx7NfVxCQnIxMErE1N+WXYEBpUzm3PeOHBQyrb2eWIZNA0LY1p1oQlf5/OI5QBmrrWx9XBiPa1PBEQ+HXYUGzMzFhy6DhNnaWyH4nSjyomiqwbIcgdHDGq7lWinwhJvF7M9/icYQW89pU/bQRBqC4IwiZBEEIEQbj77KOYMUroDBHcj2jqlq0f6egMNdT+CyqfA4toHZ2hHTr2GYF/m+7MGNOZMT18mTq8A75NWtN90DhDh1Yozh3fS8few3L5OVev5YuzSxVuBJ8v9H7J6ek0X7QYM2NjDk+ZzMYxo7n4MJSBP/2szbCLzKpTp5jYsgW1KlQANKLzrYAAlAoFJ+7kHYusC5LT0+n03VLeb9uGh3M/58Hcz/m4c0e6fP8DCWlpua61UJoQl5KaZwBMbEoK5ooXv9nsU7sDu6+EYGNqxht+DbFUKll65CgPY+NpXbXJC++RkCgNiKJI0vLFxI7qR9rOzSTMmcGTicNQxcUYOjQJCTzM7jDUZXOBry9IM98vwGxgMdAKGEFJfMYu8RQBLvWDBmvBZyNcGAhJzoXf41+/5KH6EbC/ByGdILGwe+dGm9njFyEIAr2HTqTbwLE8iY3Cxs6hSNnXl5GVlUlSwhOsbOxeOpSkqGRnZyGXGyEIAgojYzIz0vNck5mRjlERMsBrz/1DLWdnFvXtk7O26a0xuM+aTeDD0FyZUUMQl5JKfdfcT0QEQcDFxprYZP14D24OvETDypUZ0vj5BMwBDRqwJfAS6/85z1vNn4+Rr+vigqVSybKjxxnfsjkAUYlJfLXvIDMDJr1wfycLe5b3/JzJG75iwrpNpGdn4WJVntV9F6F4hXe3hERJJn3/DjID/8H+9+3IrKwR1WqSf/6exAWfYvvld4YOT+I1Z1yl30lVKYG8P1NfREFejU1FUTwoCIIgiuID4BNBEI6jEc8SJZFMC7gwSCOWfTdoPi/SUJIX9HI6B2syyaG+EF74pjddC+OXoTA20epIaVEU2bL6O3asX44gyBAEgV5DJtJ1wJhiP148cWAbG39eRPjDO9g5ONNj8Nv4t+vJ2mVf0LR1N8wtrQE4d2wvyUnxeNRuUOgzLoWF0dYrt0OKQi6nlYcHl8LCDC6UW9fwYO25fxjWpHHO/8/w+HhO3b3Lr8P0MzwgIiEBDyfHPOvVHR2JSEjItSYIAhvHjKLTkpWsPH4a13J2HLt5kxH1+9Km2suzw40r1ePQqLXciX2IsZExlW0qaP3rkJDQN2l7t2M+dCwyK81rlSCTYTHsLaL7tUcVF4PcThopLWEYHI2j6VN+N3+E9wI2FuieggjldEEQZMAtQRAmAI+AvD89JEoWGVbPxbLzVbhVCKEsvkToyTI1ZR1xleFmmxdf85qwY/2PnDm6i3krd1O+ohth92+y8KOxKE3NaNfjzSLve/boHlYv/Yx3Z31LLZ+m3Lt5he8+n0TrLgPxadyadwY0pUHTdjyJfcy9m1eYMf835EWYalXV3p6LDx/mWhNFkQsPHzKiaeMix68tBvv58cupM3Rd+gMjmjQhOjmZhX8fYEaHDjhY6m4gxb/xr1aN0WvW8EWP7hgbaV4qs1Uq/gq6zNf/ysQ/w8PJiUOj1nIm9BJP0hKZ1awO5S1fLQhkgozq9m7aDl9CwmCIKSnIbP9TZ68wRjA1Q0xNAUkoSxiI4S4bMRJULA99k4IKZeG/NXV5LhCEhsA1wAb4DLAG5ouieKZ44ealgYezeP6H4dre9vXGOBkyzdFatYxZHGSZaj4KgKEyyLpmTA9fPlq0Bjf3mjlr14LOsmzeVL5dd7zI+84c05XeQ9+lYcDzMeUPbl/js/feYMW2C0SG3SP4/AnMraxp2Kw9JkqzfHaDXkFbXrgenZREnc++4PPu3RjWpDGpmZnM2bWbY7ducW7G9BLRdJOWmckvp06zNyQEK6WSYY0b066ml97OF0WR3stXkJSRzvtt2yKXCXx94BAyQWDnO2+/sKEw7J408KW46HIyn4R+SFqxBDElCaspH+esZV48R+Kizyj3+18IpchxSKJsYSFPxt/2H/bFtOJxa58Loii+8pHsKzPKoij+A/A0q/yuKIpJWohVQl9kPp1MpIyHWrvgahdItyncHrIscLquGUSSKnXjq1Qq4qIjcK2au3TBzb0W0ZFhxdo7PPQu1Wv55Fqr7O5FSlI8YfdvcWjnOh7evY5zxSq4udeiolv1Ip3jYGnJvncnMGnDn7y7YSOCINCtbh12T3jHoCI5LTOTk3fuopDL8K9WjfEtWzC+ZQuDxCIIAhvHjmb5sWN8uXcfIiJ9fHx4u3lAqbIWlJAoKmJWFumH9pB54SyCuSWmHbuh8Kz1yvvMBwwl7t2RxM+Zjol/S1QP75G6YxPW0z6VRLKEQUlWWbAvplWh7nmlUBYEoQGahj7Lp79PAEaKonihKEFKGAijDLCIAt91cGFw/n7LVuHgEgR3AjTZaK89UD4Ekh0gqfxLbyur2eP/IpfLca3mxaWzR/Bt0jpn/eLpg1T1rFusvStV8SAk8DRN2zyf53MrJBBzC2tmT+hDq8796TpgjGaQyvieTP/yF7y8/Yp0lnfFihx5fwpJ6ekYyWSYGuedIKdPtl26xJg1f+Dp5ER6VhZRSUlsGDOKJlWrGiwmhVzOxFatmNgq/xdWKZNcfLSRSc66dZ2sq0HIyjlg0jgAoQRYHZZWxMxMnsx4BwDTdl1Qx8US//FkzIe/jVmX3vneK7O2xW7patL2/EXGicPIHRyxW/wTRpUN929Z4vXGSMhidZ3J/Bj6JkefFM5VqCA1yquA8aIoHgcQBKEZGuFcPEUgoV+SnSBwoEYo+66HC288zzb/F9MnGqH8wA+cr4BzCNxunkckvy7C+EUMGjudpXOnMGzCbDzr1Odq4Gl+X/o5Uz5dVqx9+w6fzLefvYtMLqduw+bcDrnEjwumY+foTEC7XnQbOBYA3yZtcHF1Z/XSz/hyxY5inWmp1K4PdlF4EBvL6N/XsmfiOzR0cwNg5+Vgei77kbufz8HcxMSwAUqUaERVNhlz/4c88CLtqzbi1pNQbn+/CNMFSzFydTN0eKWS9AO7EGQybOYvy8kCm7RoS9w7Q1G2bI/M/CU/P54is7DEvF/R+zUkJLRJD8f9tC53ilWPBhb63oI8A0l6JpIBRFE8AUjlF6WRRGcI7A8mSRqxrEh9yYVPH73bPtA070V6wf3n78BEUeTy3SiuBZ19oWXZ64BfQAfenfUdh3dvYNY7vTl1aAcfzP2Jug0DXn1zPtRr1JIJH33DX38sZ3Q3b3797hP6j3qfiNC7NO+QO4vTpFVX7ly7RFZmRrHOLAmsOXuOQQ0b5IhkgK5169CgsivbL182XGASpYK0nVuofC+Cs6PW8E27qezqv4QZPgPI+mKWoUMrtWScO4WyY49cpRJGFStjVM2TrKtBBoxMQqKwiIx3/Y0bKVU5GOtf6LsLklE+JwjCj8A6NH5hA4AjgiD4AoiieLHQp0oYjoSKENQXqpwE8RXvk6ofgSQntj/chcpC0zQWEXqPhR+PJTUlEUuri0RFhjJq8ucEtO+l+9hLGN5+zfH2a671fX2btM5V0gGwYeUCYqLCsbZ93i3+JC4KY6Upci37NxuC+LQ0ylvlLQdytrbmScrL3tBJSGiQ79/L1AZvoDR6/uRhqHd3Fpz+DVXEI+TOLgaMrnQimJujjn+Sa00URcSEJwhm5gaKSkKi8LSwPUMti1tMvvYJYoHyw7kpyB31AA80vsmfAF5AU2ARsLDQJ0oYnieV4eIgzehpWTbI/5uRfJpRvtkagvqgUmtEslqtZu4HQ2nTdSA//HmG+av2MnvJRlZ9M4t7t67q92t4zWjbYzC/ffsJKcmJAGSkp7Jq8SzadB1UJhrL2taowfrzF8jMzs5Zi09NZcflYNrUqJHPnRISQHY2Jka5a+xlggwjuQLxX99TEgXHtH03UjetQRUVmbOWvm8HYlYWippS5aVE6WG8629EZtiz5XGnpysivlYFf1JZENeLwrUHSpQSBECEuls1jX6B/UFlzDaLCbhbLcMz+yBHMz8kSeGRc8e1oLMojI3p1HdkjjNCleq16NhnOId2rmPUlM8N9LXojsT4WHZuWMnl88exsLKhbbc3aNyyi97j6PHGeGIfhzOud0MqV/Mi9N5N6jVqyeBxM/Ueiy5o51UDTycnWixazNstAkjLzGLJocMMaeSHZ/miDMvRD1ITX8lA1bwly05uobFrPWSC5o3jvlsnyDIzxayi6yvulngRxt71MevzBrGj+qGoXQ91XCxiSjI2ny2WnCskShEif0Z2RUAkUzSmqc155nnMxcP8XoFNcwvieuEEzAUqiKLYSRCEmkATURR/Lk7oEiUBgbNx0/GrPo4Y37Ocur6G2q5zqFb+Jw5ePkRSukeuq5MSnmDvWCGPfZhD+YpEhN7TZ+B6ITkxng/f6k7Neo0ZMv5j4qIjWbt8HmH3b9F3uH4bGeVyOWOmfknfEVMIu3+T8i5uOJSvqNcYdIlMJmPDmFFsOH+BbZeCMDaSs6BPLzrXloRoaSM9O4M9N45yJy6U6uXc6OgRkCfbq21Mew/iwunjdFj3Dr2rBXA1PpR9d05i+sXiEuEJXlox7zcE0/ZdyQy+hMzCEkUdH4QiDDgqDYiZmaQf3EPmxbMIFpaYduxeICs8iZJPeEZ54rI0UyJjM21xyg4n5Jo9EFOg+wsycGQPGpeLj0RR9BYEwQgIFEWxTrEifwHSwBH9s9ViMpXsN1G/2kQS0zyxNrvO7YixBD+Yk+fa+LhoJg5sxnfrT2Bj5wBoatY+mzKIJq270a77YH2Hr1O2rP6Oh3evM/mTpTlrcdGRTBrcgh/+PIOlta0BoysYLxs4IqEdpIxybiKSohmwbhKVrJ2p71KLc2GXiUqOZcOgJTiY5+/BXlx7OFGVTcbJo6iDL4G9AybtuiC3K1esPSVeD8TMDJ5MG48gN0LZrgvquGhSt67HYsR4TDu/fv03ZQF7RRxjKq5lcIUt2BvHsz6iO5OvfwrAWlEzWbVtm7vaGTgC2IuiuFEQhJkAoihmC4KgKkb8EgbkRZZuoTF9sVRex7Pi96RlOvEophsN3Mdx9eHHpGU+z1ra2DnQbeBbfDSuB73enICVjR0Hd64jNSWJ5u3z99UsjVy/fI7WXQflWrNzKI+be03u3gjWSSOfhERp5vPDP9CtRms+aD46Z+2zQ0uZd3QFizrP0OnZgtwIZfM20LyNTs+RKHuk7d+JYGyCzbzvn1vhNWtD3MRhmLRsj0xqXiyxPBO9/8ajegwuLonIZCCKcOOmHU7hl194bUEoSKFRiiAI5dA4XiAIQmMgoUinSZRIjI1iqey4gfRMe87dXIHS5DGV7LehMErMc23/ke8xcvIcLp09zN4tv1GnfjM++XYjJsqCjbQuTdiUcyQyLHdJiUql4nHEQ2zKORgoKgmJgqNSq1h1fhPtV42g8bJ+TN09j7CEyFffWAREUWTfzeOM9RuQa32s3wD23DiqkzMlJLRB5j+nUHboltsKz9UNo6rVyboq2VOWdIyNs6lUMYGnMpWMTCPCwy1RqQQiIy0IC7NBrS56XX1BMsrvAduBaoIgnAQcgL5FPlGi2LgdaFaMu8+/YE1kZERPjt70ZYJ/IyrY7ny6/uIHB/WbtqV+07bFiKF00L7nEOZ+MJTa9f1x96pHVlYmG35aiGP5SlSu5mXo8IrN7uArfLlvHyERkXiVL8+MDu3pWlfrFVVlktJScjH74LeERN3hs3aTcTS3Y9OVffRe+w67h/2Evbn2S4cEQUAtqnOtqdVqZFKdsEQJRjAzR0yIz7UmiiLq+CcI5lI2Wd8ULPMrYmeXhkuFROztU5HJIDHRhIREJQ8e2OBW+QlyucjDh9bFjqcgrhcXBUFoAXiisUq4IYpiVrFPfk0pnsjVNiLlrWKJTLRn1ckeAEzZdp6JrQ/TyBMyhP8xZduH8J/e0MU9X1nSUyZw96rHyMlz+HLaMMzMrUhKiKWqpzdTv1hp6NCKzfagy7z9xzq+HziAZu7VOHnnDuP+WMd3KhW9fOoZOjwJLRCRFM22kAOcGrcBKxPNFLUPmo8mJvUJay5tZ7L/MK2eJwgCXTxb8v3pNXzcajyCICCKIkvPrqVrDck8SaLkYtqhOwlf/Q+TZq2QO5ZHFEXSdm8FUURRo3S8KS7pFLXs4UUolVn4+kRgappNZqaM0FBrwiMsSU3VNA3LZGoqVkwkNtaU5JTiT3V9qVAWBKEhECqKYuTTuuT6QB/ggSAIn4iiGFfs0yUMyviWfzKuxWa6fLeE0Ljn46lvRLoB0LnOaWZ3W8GnO8byX7H8OqDKzqaBfzsat+xC2P1bWFhaY+9UNgYXfLprFyvfHEznOpofAj3r1cNUYcz08wiPoQAAIABJREFUrdskoVxGuBZ1G+/yNXJE8jNaVGnIlqt/6+TMj1q9zaD1U7gYfpX6LrU5GxpEtjqbPwZ8rZPzJMoO6sQE0nZuIuvaFWT2jph27YOimserb9QCxvUaYNZrILGj+6OoWRd1XAxieppkhVdiEClXLg2FkYrIx5akpxuRkGDC7Tt2REebI4q59YlCoSYlxZgHD220cnp+GeUfgbYAgiA0B+YBE9EMIFnBa1h+UbKywcWjXc0zTOu4mq2BLQmNy+1Tm55lzONEO47f8maE/w4yshXM2zOCZ2J5yrYXlW/kpjRnnTPS01izbC6Hd60nKysT12o1GDbhf7i51zR0aFrjctgj2tTwzLXWpoYnlx89QhRFyVKrDFDJ2pkbMffIUmWjkD9/qb/6+DYVrcvnc2fRcTC3Y++Inzl45zR3Yh8yqekwWlVthFz2ckux4rpdSJR+VHGxPJk4HEWdeijbdSX7wV2efDAO66mzMWnaQi8xmPcfimmH7mQGByKztERRx1cSyS9Bm9nh/DAxyaaCcxIVKiSiVKpISjIm8rEFIHA15OX++hkZRgRectZaHPkJZfm/ssYDgBWiKG4GNguCcElrEUjoHQ+n+ywesIig0OrM2DyR/2aLj92qT6O5qwGR1EwzxgRsY/ulloREVDVIvPpm2bypZGams+SPY9iUc+TcsT0s/Hgsc77fjGvVsjElrpqDA/88eEAzd/ectfMPHuLu4CCJ5DJCdXs3ajpW46P9X/Nhq7exMjFn/62TrLn0F1vf/EFn5xrJjOhQPQCq6+wIiTJG6oZfMW4SgNWEaU9X2mBc14fE+Z9i3DhAb4JVZm2DsplUJlQScHWNx72aRoLGxZly85YlMTHmvOrptrl5JtnZMjIyCtKCVzDyFcqCIBiJopgNtAHGFvC+EklZygYXB2vTJFYO/ZzUDCVjf/+IjOz86ncEZm9/i80XWhdaJBck6wwlL/McExXOxdOHWLHtPEpTTRNHk1ZdefTgDrv/XMW46fMNHKGG5MR4srIysbErmrCd1r4dY9as5Y+RI/FxrcSl0FBG/b6GD9qV/SbNolJaGvj+zffdZzP7wBIaL+uLDBmuNhVY3nMOVe0qGTq0Eo2YmUn6sQMIwUGoHRww6dAduYOjocMqs2ReOIvVB7NzrSnq1kdUZaOKCMPIJf/piplXLpFx8gjI5Shbtkfh7pnv9RIvRl+Z4hehVGZRwTmJyMcWpKYak5Cg5P4DG8LDLUlPVxR4nxqe0Rgbqzh9phLaKhnNT/CuA44KghADpAHHAQRBcEeyhyu1pGeZcOpOXTb+057HifYvvMa74g3ebbOez3aO5n6sC0FhmhedNl5nqWT7mF9PdddnyHolKvwhLpXdc0TyM9y9vLly4YSBonpOXHQky7/6gKuBp5HLjXBycWXs1HlUr+VbqH1GNG1ClkpFj2XLiU1JoZy5OTM6tGd0M38dRS5hCKxMLFjc5SPmtn+ftKx0bE2ty9QTA1X0Y41AUqsx8W+J3Kn4j1vVqSmkT3mLqtnG9KrajGtBoWzfOADlZwsx9q5f/KAl8iBYWqGO/c+UtIx0xNQUZOYWL77pKUnLF5N+7ACmHbpDdhbxMydg1ncI5gOG6jBiCW0gCCL29qlUqJBIObs0QFM28UwoJyQoC7WflVU6NjYZ3LxZDm32Vb1UKIui+IUgCAcBZ2C/+HyEnwxNrbJekDLB2sPEKIOMbJP/s3eWcVKVbRy+znTtbHfQsHR3twIKGIhgYADqaysqdmChr2KjlIgo6qvYiqh0h3TXst0xOx3n/bCyuAIb7MzO7HKuT/wO5zznntmZM//nfu7nfzPzm3srPS/cUMzQ1tt464+KzTau7LiWsZ3W4HIr+HTLKK/EFGj1zvGNmpN26gim4sIKnff27lhPIz/XKHs8HmY9NJlufUfw0KwPUSjVbPzje16ccSNvfPInYREXrtn6N4IgMH1Af6b264vZ4UCvUiGT6vEaLFqlBq2yZj86gY7tp+U4PpjDiBb9QBD4bdFcVLf9B824CbUa1/6/pfRWRjN//DPlk4qRjXty72svoFyyvEFNNP6JKy0F+/pVgIB6wFAUcQlVXuMttJePpfTjD1C27YAsOBTR7aZ04XuoOnZFFnLhjo7OQ/uxrf6N8HlfIAsylo115QTyp05AM3AY8pi4unoJZ2M6cRTn7h0IxmA0fQch+LnHgD+zxJUj0qtnKjqdC5tNzslTIWRkGGtVMtEoqQinU0ZGpnf3PVQakSiKm89z7IhXI/gHe00GSRj7iIndf2XqgOVc/9FL5Jgqb+t6ZgepQMX25g9/dT86lZVZ49/H7lbw1fYRPov3n1Qlpr0ppINDIxg86jpeeXQKN939NJExCWxY+S1//riMVxf87LX7XAz7d25EQOD6aY+U/1j3HzGe/X9tYtVPX3D1zZVPgM6HTCYjSNOwBJREw8ednYlj7hxW3DCXJqFlgu50zxsZvmA6ih59aiXyZOvWMLXnHRUE8bBmfVD+MQd32mkUiY1qHT+UCSrX4QPIo2NRdurm141j5q8+xfzZAjSDR4IoUnDXjRim3IFu3HVVX+wFNMPH4Dp9irwbx6Js2RpX2mnkcQmEPFN5qZt942q0w8eUi2QAeWQU6r6DsG9eV2fxA4geD6a3XsK+cS3qPgNwZ2dR+sEbhLz0FsqW3k2yBK74vTCCIBIZaSY0xMbhIxGAQGpqMFabgoIC3TnOFTVFq3UQGWnhVEoIbrd3v0v1rtZYouZ0b7yP58fOZePxjuSVVm2XckYe/ztx4nQrufuzx5h30wu8etU72J0qvt89yOvx+psp9z7LT1/M491Z92MqzqdN5948/97XRMVWXifna3KyUklqlnxORqtR89acOnrAT1FJSNQ99vWruLzFgHKRDJAUEsfY5MH8sPZ3FBOnXPTYglqNxWGtcMwtunG6HGhUqose9wyiy4l91pMIu/+ib6MuHMg9Tr5ahua1d5H7oeOnKzUF8+cLCf9oGfLIslUp/XU3k3/HJNS9+tdJVlYQBIJuvwfdVZNwHT+MPCIKRZPmVV+oVCKazu0gK9psoKh+Xas3sK/+DeehA4R/8i0yrQ4A2+rfKJ41k/CPl1+yDhparYP4OBOxsSZUKg9WmwLlSTdOp5y09No3AzlDSIgNt1sgLdVY9ck1RBLKDZz4kBw+uOFlUgujuffzGXjEC9s0lXOBjDKA3aVi2pInWDTlOTolHgkIoezt8g2ZTMYV10/niuun1yYsr9O8dSc+/2g2TocdpersJsydG/+gx8DLz3vN8o5XATB+9zd1EmNDpD5u4mvoiB43ivNYzillCvCcv6NodXGNuJzXvvmU3kmdy8tVFuz4BlliI6/UQFu/+pTW6YV8dvvnqBUqRFHk5fXz+fz1F5G/PKfW49cU+7o/0AweWS6SAeQxcWgGDMO27k/0195QZ7HIw8KRh/Wp9vmaQSMpuHcK2nETy1cRnMcO49i+CeO9j/oqzPNi+/NXdBNuLBfJAOqBwyld9AGuY4cqzSrXxwxxdQgPt9CpYxYeD+Tl6UjPMFJQoMUXfRkyM43k5upxuaqhcWqIJJQbMFqljXk3vYBK7mLq4qcosVW+KeIMZoeG47nx2F3nz57YnBqmLHoOu6tsxq6QuXB5Lp2PksfjIS87DZ3eiMHoHUPz6tCoWWvadurFiw/fyIRbH0SrD2LFN4vJyUplwIjxdRaHhIS/UfcZxE9LFvJwzxuJNZa5UWSX5vHNoT9Q/2dBrcbWjr6KtP376D5/Ev2adONYYSppThOa2e96I3RkK35hZv/7USvKnq+CIPBArxtZ8N44lKUmZIYA8ZUWxXOXFQMMRWIjDLfdTcEdk1D36IvocuLYuRXjw08jC/Z+i/bKEN1uBGXFLLYgCAhKJaLLVaex+Audrix7bDYrycg0Ulio4dixMDKzDDgcvtMICoUbl0vuE5EMklBu0OhUNiwODfd8/ggn8qpfs7ftVDuG/vfDSs85I6IbhWew+JZneOq7O1l3tGbOC3WJt7LOW9f+yqK3n8HpcGCzlNKlz1Cmz3gVfZD3lpAq456n3+bHL+ax4I0nsdutdO07jFnvL0et0VV9sYREA0ERn4hy8q0M+XQq17Qejgz46uDvyCfcgCKpSa3GFuRy1DOfw3XyOOsO7kEWPhp9t14Icu/8XHocNvTqit9XlVxZ1pTF6fTKPWqCuv9QCu67Bf11NyOPKmtE485Mx7buD8In31bn8dQU3ZirUfcZiGPLepDLMT74FDJj3TyP/4m672As336Jus9AhL/LPhx/bUNXks6yls8ir2UNbqAik3mIijQTF28iNMSGxwOpqWXvv8cj81p3vMru37tXKqlpwZw65ZvJkXDWzML/qGNbiLE31/3SU8NEBAQEwYMo+q42Klhr4vOpj9MkIoMpi55ly8n2PruXv7k7WcmsByfz8IvzaNOpFzaLmY/feY7CvCwef32Jv8OrFKn04uKRSi98Q6ndwvIDK3lVm46nUWM0w0dXaQX2b1ynT2Jb8zuIIpr+Q1E0aeajaL2H9Z3XGJVi4bXhD5YfW77/N5489A3aj5b6JSbL159R+uk8NINGgMeDbc3vGG65E93Y2jmINDQqK5FwuURmPlPE0dwgxIFXIM9OwbHud557IoSu3RpuIqND+ywiIy1YLArSM4xkZRpwOOsuB5uQUEyrlvls3x5HcUnNNqYPG3pihyiKVWbIJKHcABnZdiMTu6/gns8fpdRe8y9o+/ijPDl6AU9/dweHsxtXeX6Yvpgvpj1GXEguNy54gZ2nW19E1IFP8/1LiW/cgnGT7yo/5nQ6mD6uKy99+AMxCY39F1wVSEL54pGEsvdJK85iwuf30j66Fd0T2rM2ax9bcg6he2s+8tj4ao/jPHoIx67tyIJDUPcfUqE+NFDxFBdivec2OuhiuLxRT3bnneCn4+vQvvIOyuS2fovLlZ5aZg8ngGaAf6zVAp2qaok9HpHt26zs3O0gJBiGDTUQEdFwFu5lMg/RUWZi40zs2xeFw6Eg2GhDJhMpLNLgi9rjyhAEkd69UrHb5ezYWf3nxhmqK5Qbzl9QAoDkmJO8MeENjmYn4XRf3J83WFtKz6b7CNKYq3V+gTmYSfNf5Mvpj/Lxrc8wYe6rHMqq3fJnILLj8FF6DxlT4ZhSqSIkJonnvlmFJql9wHUalLh4JIHsO15e8yFXtx/FQ32nAHA7E5izaQkL3nsT+azXq7xe9Hiwz34e2dbNXNm8L6dM2fz1wZtoX37br2KzOsiCQ9F9tJS9v//CgUMHcHdOQv/El8jDzt8Aqq5QxCeiuASbdHhzI51MJtCjp44ePQN/wlYTDAY78XEmoqNLUSo9mM1KNGoXDoeixllcbxIZaUardXHkaOWWt7VFEsoNiDB9MfNvfgGTTfd3e+qLszIS/54VCkL1VxtyTWFMmvcSj1y2mLTC6je+qE8Ikc3ZtGYFXXoPLT9WmJ9DxqkjRA1r7L/AJCTqGSuPbWDL8PsrHLuty1XMefdKVKJYZVMP25+/EnPgCD/euqTcmeKnQ6t56IXHUSwJfCsuQaNFN+YqGHOVv0ORkKgUlcpFj+7peDwCObl6MtKDKCqu++zx+UiIL8FsVpKX59uJiSSUGwhKuZMPJr9MpKGQaz98tcqmIpVxphqnphueM4sjeeCLh4Eyx40oYwEp+Q1n+U7feTQblj6ETm9g0GVXk5+dzqL3XsLQ9Qrk2rKd6nXZHEVCor6ikivLW2qfweK0Iqum96185a/c23lChW6Do1oN5LmN87EfO4yyZcMs/5KomoZqtVZXBAXZiYsrQanwsG9/NA6Hgj17oykq0vjMVeJi2bM3Go3Gha9FuySUGwhxIbk0Cs/gka/vZU9ay1qNVZ5RPo+PcnV57do59Gi8nwkfvsKp/JrXDgUickMo4de/yvqtX7Pqj9uQ64wo2o7C0HZwtccItJbdEhL+YFybYbyycRFzRj6CTJAhiiKvbFiEbvBl1WoRLXg85/goC4KAXK4Ad+18lCUCD0n8+ha53ENMdClxcSUYjQ7cboHsbD1nTAHy8vT+DvE8iLhcckpLfS/eJaHcQEjJj2Pof+didtR+CaLUpmNPWnPMjovvUf/W79ezbNpMlk59kus+fKXBlGMojJEYh93h7zAkJOo1jwyYSu/fnqDPx1PoEd+OrRn7MIUFo77znWpd7xw4mA+++YYRLfqh/Nu2bd2p7RQ4zRikbLKERDUo78FLfHwJLZoXYDKpOHw4nKxsQ8Blj/+JXu+gTescDhyMwmyufbfMqpBcL+o5PZvsZXDyNmb/enP1uu7VIW1iT/D5tJkUWwxM+PBVskr8u1mloVGTzLPkelF9pE18dUPXEQace//ClXICRVJjlB26ViubDH+3gX5qBsaUVK5pNpATpdmsOL4B7bOzUXXp4ePIJbyBlCX2DwqF++/ssYmU08FkZwehVLrRapyUmNQEQu1xVbRpnUNkpJkNG5NqJeir63oR2DseJColITSLD254mWGtt6JT2f0dzjkcyGzKjQteIFRfwlvXvwa1KOWQkJBoWAiCgKpDF3RXXIOqY7dqi2QAQaFE/eIbmB6cwbxGAn/0aY3h468lkSwhcV5EjEYbrVvn0K/vaVq1ykcE3K4yCeh0yikxBcYGvapQq11ER5eSmRlUZ1lvqfSinqJTWZl30yzkMje3L37qovySL0TbuGO8evU7PLH8LnantarVWHvSWnLzwucpthqoD1/C+oRU7yxxKSPIZKi79ULdrZe/Q5H4B1KmOHAQBBHx746ArZNz0WhcZGUZSM8wYjKp/RzdxZGYUAzA6dS6677oM6EsCMJCYAyQI4qitJbpRQTBwxsT3qBl9GmmLHrW65vl9Gob7eKPo1dbvTLe2QYkIrf0/Z6vdw6lxFqzDlwSF8cZMT2+4dlaS0hI+BhJ9NZHREKCbcTFmwgPs7JxUyJut4x9+6Ox2RS43fW3kEAu9xAfX0JOrh6brXoOOd7Al+/Yx8BlPhz/kqV1zCkGt9rOiz/dxrqjXbw+fmX2cG5bKfbMI7gtxTUet3lUKo9dvohPbnkag9pSyyglJCQkJCQkoKz2ODGxiF490+jaNZPICDM5OXpksrIfdLNZVa9FMoDHI3DkaDgpp0Lq9L4+yyiLorhWEITGvhr/UuZAZlOGv/k+pwtifDL+maWaf9rDiaKHotUfU7p7BYqQGFxFWeha9yds2B0I8up9jI7lJPGfpTP54IaXWHTLs9y88DkstXDWkKg+jU8mAXCqyWk/RxK4SJv4JC4FGlKW2O0W2bbVwv79dsLC5QwZYiA4OLA2tfsWEblcxO2WodG4aNmigKJiNQcORpKdrcfjqd/C+N+IokBmprHO79uw3sUGTpvYE4zv/CcApwti8VXN7/k685m2fYc9/SBx0z4idspbxN+xAFdJLkXrl9Zo7N8P9uS+ZTPoknSI+Te/gFoReJsQJSTqOx7Rw6oTW5i16n3e37yUTFNuta8tsBSxP/soZoe06iMRuNjtHh59JJNPPilErRY4fMjObbemcuiQzd+h+Ryl0k1SUhG9e6XRqmUeAKWlajZuSmDHjngyM4ManEiOjDCTmFBco47B3sLvm/kEQZgGTAOQGyP9HE3gEq4v4qObXkAmiKzY39unmViTTcem4+3/3oD397FdPxNxxQzkurICepnGQNjwO8la/AAhA26q0Y71n/f2QyV38uo1b9E56TCbT3Tw+muQOD9SZrnu+O3oehbs+x9ZpXl0j2nP3d1uoHGo75vvON0ubl/+GEfzj9E2IYJ9+S7e3byE9658jsFNe17wOpvLzswVr/HLkbWE6Q0Ums1M63Ed9/e5pUbf76roNjLIa2NJlNGQssTV5dvlJWi1Ml6dHYtcXvb5XLOmlNdfy2Xe/ASvfmYDhZBgKwkJJURGmpHJoKhIQ17+2Y38VqvvPYX9g0jTpgWIokBqWt1nlP0ulEVR/Aj4CMp8lP0cTkCilDt5/4aXiTAUc83c2T4vVziS3Zjr571c4ZjbXIwipGKph8IYicdhAY8L5DUrrP9212A2n2gveSv7iTOCGS5N0bzt1CnmbtpAttnEsCYtub1vH6+Ov3jXct7d+yljZo5gcNNI9qzYz7jFd/LdxLk0CvFtW/ev968g1ZTC3cN6IpeVZZU6JEXxwE+z2HbX8vIGHf9m1qr3OFq0n8dGDUCrUlJksbJ4ww/EGqKY2HGMT2OWOD+XogCuLhs2mJlyS1i5SAYYMEDP3A/ySU93kZBQd5u9fIlK5cLhkAMCEZEWwsKspKUFk54RhMXSUIVxRcLDrRgMTvYfiMQf7lkNKzffIBF57sq59Gyynxlf3ce+9OZ+iUKT2BbL4Q0VjlmObkYV3RyhhiL5DGdE8uXt1vPmda8jl9X/1rOi6MFVkovHLi1bByqLN2/m8nlzKeoZTOzN7VhaeIheb/4Xk93slfFtLjuvb1zAHZ/cTLcxHUlsE8foB4bTa3JX3t9es1Kli+Gnw3/Qs2lcuUgGaBYZjkGjYlfmwfNeY3c5+Grfr4ztnIxWVfZ9DtFpubxDcxbu/NLnMUtI1BSZTMDtrphb83jKOpjL6r2yEQkLs9C+XRZ9+5wmNLTMgerUqRDWb0ji6LHwS0YkAzRKKsJmk5Od7R+3LF/aw30ODAIiBEFIA54RRXGBr+7XUOmSdIhJPVfw3qpr+WHPwDq5Z+vYE7w/+WUe+/petpxsD0BI/xvI/vJp3OYiNEntsWccpmTrN0Re+Wit7xcXksv4zqsRRYGHv7o/4DoMVhfL4Y0Url6I6LTjcdrRtexD2PDpyFSBvWHxUirHsDmdPPDN19z9xTTik2MB6Dq6I4vuWsaS3d9yV4/Jtb5HalEmumAtMc2iKhxvP6INy3/4qdbjV4VCpsDtcVY4JooiLo8bhez83y2L04qASJCmordqhEFHnrnIZ7FeSkjZ4Yvn8GE7u3dbCQmW06+/Hp1OxqDBer78oohOnbQolWVZxhW/moiIVBAXVz+zyTKZh6SkYuJiTWi1LhwOGampwVitZa8nkNtK+4qgIDuhoTaOHg0rNxqoa3zpenG9r8a+lNh5ujWT5s1iUx3W8aoVTppEZKJVnd0UoYpuRsykVynZ/h1FaxajCI0n+roXUEU1rfX9Fqwfj0bpYMbIJdhdSh5ffjeiWPOUgMdpx3piO6LdjKZRRxTB0bWOrbrY0w9RsPIDIsY+ijqhLR67mcLfPyT/l7eJHFv7yYSEd/grNZWwuNBykQxlHeK6T+jEny9v9opQjtCHUlxgwmqyoQ3SlB/PPJZNnMH3+zCubns5L699m7bxMWiUZY/4felZCKKCjrHJ570mRGMkQh/GsZx8WkSfLYfal5ZNt3jJDUTCP7jdIq+/lsuePTb69NGxb6+Njz4qYNaL0YwZY2TPbhu33pJKz1460tKcpKQ4ePnl2KoHDihEtFoXVqsSURSIjyvBYlFx7HgYubl6v4nDwEEkL09Lekbd1yafwe81yhLnJykskzB9CbtSW7HxeKc6vXe5j/K/jivDEwgf+R+f3PO9VdehUTq4Z8gX2F0qnv1++nkiuDD2jMPkfjMLZWRjZLpgCv5cgCqmBcE9xqNp0hlBqN1anCiKWE9sx7x/FaLLgbZZdwzthpSXnZh2/oix9wQ0iWWiQq4xEDbyP6S/fwsuUx6KoMCvxb4UMsuhOh0lBaV43B5k8rOfiZIcEyFq7zyIQ7XBjGjZj2WPLue6V8ahM2pJPZDBz7N/581Bj3vlHpUxutUg1qds47+/rqZNXBQlVjvpRSYWX/Masgt8DwRB4OnB9/DwLy8xqHUj4kOCOZKdy8ZjKdwxqBdfnHzVa/HNmHv+46/dMctr96grpCyxb/njj1JSU50sXJSAWl322V27tpSXX8ph0ceJPPV0FIcO2dm/z0bbNhr69NWVnxfoqNUu4uJKiIs1AbBxUxKiKLB5S2K99zv2JiaTht17/Dv5kYRyAKJXWZh30wuE6EoZMHs+dlfd1iKdzx6uLvjvbzegVjjw1HAGLXrc5H77CmEj70bXomxXv9tSTObi+8lf+QEKQxhR1zyLTH3xbb6L1i3Bcngjxu7jkKm0mHavwHJ4A1HXPIMgk+MqycXQYUSFa2RKDYqQaNym/HohlM8Q6IL5w5GltbjaQND3wfz24WpG3jkYQRAozCrm17mr+G8372X+Xx3yMDNX/Zener+KPkiHx+7hsT5TGdikh1fGr0q4dm4JMdFdOJ6TR+NII9f3as9h2/ccPvn9hS9SwOTeHVh/9CQ7TqUTF2LkP0P6EBmk90rMVTFj7pMX/L+6FNGS+A0c1q4xc/U1wRXEb//+ehYtLOT4MQctWqpp3VpD69aaSkYJLIxGG00aFxIeXlZ3XFCgJT3jrBOMJJLPEhFupsSkxuHwr1SVhHKAIQge5kz8L80i07hp4fN1LpLhHw1H6tyvUOCln28t/3eorphCS9X93O2p+5HrQ8pFMoBcF4yxx1U4so4helwUb/6S0IFTLioqV3E2pbt+JW7qXOTasqyjLrkfWZ/OwHp0C7pWfVDFNMd6YjuaRmdLZFwlebgKM1GGJ17UfSV8w+1vT2LuHZ+w4atthMeHkrInjcvuGsxgenntHlqlhjkjnqB4gIkCazEJxpgLuk34itjgIGKDa2bF1jgilMYRoT6KSEKiZng8IvJ/leUKgoBcDm5P/THJ0miceDwCDocChcJDUJCDU6dCyMgMqtNWzPUJpdJNu3Y5ZGUbOHTIv9bBklAOMB4cvpThbbbwzPfT67zk4gwlNj0rD/Qgz1S3bSLLKBPpccE5fHf3gyzacCXvr55Q6RWi24mgVJ9zXKbUILqdBPe6htzlL1+0ULad3oumcedykQwgyOTo2wzEmrILXas+GLuPI2vJQwhKNbrk/riKsylasxhjj6tqlcn2J2cyyzPvOODnSLxLWHwoM7+/l9Pxy2fDAAAgAElEQVT70iktMNO4YyL6EB0/sbnS60bPrb6Q/nfGd6u0H65WXCjbXJ1Ms5Qhrr/07afn2+Ul9O6tL9+wt22bBatVpEWLc5/5gYQgiEREWIiPKyEszMrp08EcOx5OQYGWDRuTpNrjKkiIL0EuF0k9XXWyzNdIQjmA6N10D/cM+YJl20aweKP/fEtT8uOY+snTfrs/QFZJOOuPdeKRyz7B7lKxYP24C56rTmiLM/d1HDknUUU1AUB0uyjdvYKgrmNAkAEXn32QaYJwl+afc9xtykemKcvYKYyRRE+eTcmmL8n95gXkulCMPa9G32bQRd9XwncIgkCj9gn+DkPCz7jdIps2Wdi4zopKLTBshJ527erPMn5dUlDgYvk3JRw8aCMiQsGVY420aePb92rkyCC2brFwx/Q0+vfXk53tYutWC089HV3BPznQaNK4kPj4EtRqNzabnJOnQsgo34wmlO8Dkjg/MpmHhIRicvN0mAPABk8QA+gvpo5tIcbePMffYfgNmeBmcs9f+GLbSBxuaTlGLnPz9sTZjO6wgSe/vZNPN4++4LnmA2so+P1D9G0GIQ8Kx3xgDYrgKCLGPkbBr28j14UQOvjWC15fGaLbSfqHUwkddAu61gMQBAF71jFyvnyamBteQxnm+25roseNqyADQa2t83rnhpZR9galj67xdwgS/6DT9PP7Q1eGxyPywrO5nDoC3RIb4XC52HTqFOOv0XP9ZH+spgUuubku7r03nT599PTpreP0aSfLlhVx513hDBrkW29bURT56y9buT3c4CEGQkICyyZNEERCQ60UFJStHrZunYNS4SE9w0h+vhZ/NMmoz8THlZCcnMeOnbEUFfnOXnXY0BM7RFHsVtV5UkY5AIgwFCKXuckuiWDJZv93wGoZfYqltz/JjK/uZ/WRKj9DPsPtkXPfshmoFE5mjfuArOIIfj94/ha8+jYDUcU0p2TH9xRv/gplSCzK8CSylz4KiIQNm37RcQhyJVFXP03ud69QvOkLBJUWV2EmYSPuqhORbDm+jYKVcxEEAY/dgiq6GeGj7kcRFO7ze0tINFS2bbNy4rDIPQP7o/i7ELZrowT++/lqho8wEBEp/TyeYdmyIgYPMjBtetkzp2s3aJWs5oUXsunfX+/T7K4gCHTpoqVLl5oJJqvVw5o1ZtLTnDRtqqJf/7PlG95Cq3UQH2ciNtaESuVh85YEzGYVBw/6p4NcQ0Grc1JUrKaoKDBWd6QngZ9RyZ3MveElIgxFDHvjA1we//9JFDIPkUFFqJUOf4eCy6PgP0tncveQZWw41rHSc5Vh8YQPv5PQQbdgObQBV3E2wb0noG3WDeECjRaqiyq6KXFT5+LIPIrosqOOS0ZQ+H5JyJmXSv7Pc4gc+xiapPaIbifFG78k95tZxNz0BoLgu4exlEm+MIZXy5r/SJnl+suWTVY6xyeVi2QAo1ZDq9gIdu60MmSogS+WFbHiFws2m4cePbRMuTXkkhTQe3bbmPFIxQ1VbdpokAkCmZmB1y46M9PJww9l0rSpilbJan7+uYSlnxXy+utxXslGa7VOWifnEhpqw+OBvDwd6RlGzOYz74MkkmvDsWPhf5sJBMb7eOl94wMKkVnj36Nb44PctfSxgBDJUJtqXt/gcCt5Y+WNABjUFjokHKl0o6NMqcHQfqjX4xAEGeq4Vl4ftzJMu38lqNPlaJLKOiQKciXB/SZhPrQWR+aROo9HQiKQuJiSizNotQJ5Tvs5xy0OBxqtkldfyiPzuIpr2nVDp1Ky9eRp7r07lY8WxGEwBNbSv68JCZGRne2iZcuzG+gsFg+lpR6CggLPzuz99/K54gojE68vK6GZPDmEd9/N5+OPC7j//otzUNDpHCiVHoqLNTgcchQKD8eOh5KZGeR3+7KGg4hG48JmUwbUZsfA+4RfQtzS93smdPudt/6YyM97+/k7nHL8Zw9XNTNGLmbxrc8wtPUWf4dSJ7jNhSj+Vd4hCALK0DjcpQV+ikpCov4zfKSBbSmp5JrO+nIfyswhs9hEXJySHdtt3NyzO0lhIUQY9Ixq35oEYzi//mLyY9T+YfQYI4sWFpCT4wLA4RD56MN8unXXEhwcWJMGp1Nk+3YLY8f9w6VIELjmmmA2rLfUaCyZzENMjImuXTLo3SuNli3ygDKv463bEkhJCZVEshcJCbHRt08q4eFmf4dSAekv7Cd6NtnLE6MWsGJ/L+b8Psnf4VSgXCgHXG4ZXl9xE50Sj/D+5Je5ffHTrDvaxd8h+RR1XDLWI5swtB1cfsxtNWFPO0CYj7okSlQfqQSjdoiiSGphMbklpUQZDSSGVW8TXW0yyWdo3FjF1DuCeef99TSJDMHucpFXaubZF6JITXXSNCoU5b9MfFuER3H08Kla37u+MXCgnowMJ9OmppGQoCQry0nr1hoefcy//rbnQxDKhLHHU/G42yUiq0FqMCG+mKZNC1EqPVgsCo4eCyMzs2a+5BI1IympGIdDRmGh7zbwXQySUPYTBzKasmTzaF5fcSOiGFiJ/RKbnu92DSSzOPAegia7npsWPs/nUx/noxtfZMqiZ9lysr2/w/IZhvbDKN31K3k/v4Whw3A8lmKKNy7D0HFkver2JyHxb2xOF4s37qCg1EJSeAgr9h8hMsjATb27oFbWzU/T5aOM9OuvZ9cuG2qVkk6dw1CpBA4dspFeWIBHFJH9Yx9ARkkRjVpdfAY1LdXBil9LKS4W6dpdTb9+vt0I5y0EQWDSpFDGjg3m1CkH4eFyYmICqy75DAqFQK/eOr5YVsStt4UBZROyzz4rYuCgC3eZlMk8REeZycvX4XTKcTrl5OdrycgwUlikIVDqZRsqep2DyAgLJ06E4vEEliaS7OHqGL3Kgssjx+4KbLP0QCdMX8yyaTORCx5GzHkPtyewlv+8idtqwrTtW6wndyJTa9G3H46+zSCfbeSTNvHVHCmjXHOW79yHzeniuu4dkckEPB6RZVt3oVerGNu5baXXeiOjXBmiKHL/PdmEuCMY2SYZtULOrtMZ/HRgPx/OjyPyIjb0rVlTypzXC+jWKBGjWsuerDSCo1y89Go0KpUkwrxJfr6LRx/JRG+QkdxKw67dVjRqgZdejkWvryjCDAY78XEmYmJMKBQihw5FkJ5hvMDIEr6idXIu0dGlbNiYhNNZN7/n1bWHk4RyHSIT3My7aRbB2lImfPgKHrHhiru6IDKoAI3SQWpBjL9DaVBIQvnikQRz9Xly+QoeHjmAEN3ZZdYCs4U5K9fz/LgR573G1wL5n5SUuHlnTgEbN5kRRWjaWM3d94eSnFxzyyqHw8PEa9O4tXcvEkLLOo15PCLzN21m9AQZo8dIwsxbmM0eli8vZusWM3Y7JCYpGTnSQNeuOmSysxMSQRDp2iWD4GA7brdATo6ejIwgioql7HFdI5N56Nf3NNnZBg4fqbuVUslHOQB5eMQShrbexpPf3hnQIrlpRBo/3HM/D331AL/u6+vvcC5IrqlsWU0QPMwY8Qk/7BnAwcymfo5KQkKiKkRRxOl2o1FWXL7XKpU43O4aj7dxo5lPFpZwOs1OXIyK6280MnRo7RphGI1ynng6Ers9HKcTDIaLXw4+eNBOmEFbLpIBZDKBHkmN2LjuqN+EcmGBixUrSsnKctKipZohQwxotYG17F0T7HYPDz+UQUKCkltuDcdkcvP5Z0Vs22ale3c9QUF2go020tKDEUUBk0lNVraBrCwDLlfg/iY3dDweGZs2JwbkviiQhHKdcWXH1dw1+H8s3XwZn24e5e9wKkUQRPRqG0qZy9+hVItwfTHjOq9mQveVTPzoZY7lJPk7JAkJiUoQBIHkmCg2H09hUHKz8uObjqfQOjbqnPMryyRv3GjmzdmFjO/YgWadwkkpKGLee7txu0RGjKz95iu1Woa6lpVyarWAzeFCFMUKJVN2pwu1xj/Zy6NH7MycmUXvPjqaN1OxaaOFr/9XzBtvesdr2B/8+UcpISFyHn8iqvx97tFDw+8rU+nUyUp4mBOXSyAzKwi3W1an2UuJC1Emjuuq3OJiqL9Tx3pE+/ijzL7mbbacaMezP0ynvizr+LCXhVfJKw1l8vwX8XhkLL39SRqHp/s7JAkJiSoY07E1a4+e5PMtu9h8/DSfb9nFhmMpjO6QXKNxPv24hPEdO9AmLhq1UkHL6Aiu69qZJYuLfRR51WRnu5jzRj633ZzBw/dnk5PjQq7ysPP02WeT2e5g3YnjjLhM55cY33knj2nTw3jooUjGjgtm1osxdO6iZemnhX6Jxxvs22ejf399uUiOCDdz2chU3nxTxGEXOXQ4nA0bk3C7JekTKCQmFtOtawZyuafqk/2E9GmpA0w2HZtPtOfOpTNxugNzp/A/OWv0HZjLIOfjZF48k+a/iELm4rOpT5AQmuXvkOodM+84INUn1xLDqwPLLeMkKicySM9DIwYQG2LkdEERcSFGHhrRnwjDhZ0JzkdKqp2mkWEVjjUODyUrx4HLde4zzOMR2bXLyurVpeTmen/VLDfXxT13ZWI5Gc745G60M7Rk7tsm+vTTsPLoAT5Yv57Ptm9n9m+rGDRcTc9edS+US0rcnDrlOKc85corjGzeXDOv4UAiPkFG27alhIaWvQaTSU1Wlp6rrlbw2ecRpKcHSyUWAYQgiCQlFuPxCAE9eZFKL3yIQubC5ZFzKj+eKYue83c41UYkcBuOVMaxnCRuWDCLxbc8Q7PIdNIKpU1+EhKBjF6tYlCr8+8rqO7GvfhYFSn5RbSKOWtnmVpYTGS4CoWi4rJYRoaTxx/NAaeCcL2ON7PTGT0miKnTQ73mIvPVlyW0j4lnVLvWACSEBZMQGsy7P69jyWcJ7N9vp6TEzUMdY4mO9k/iRKEQEMWyxiFa7dnXbbF6UKvryVJiOSLBRjtx8SV8ONeMQiGya1cJoMNilfPc8yr+2mnnzjsDy5tXAqKjStFo3Bw6HNglMJJQ9hkiL1/1LnKZmwe/fJD6Um4BUGLV89mWy0jJj/N3KDXmYGZTBrw2D5uzbGe6XOZu0NZxlwLHd5xi63e7cDlcdBzWhnZDkpHVpHOARINm0k1GPnh7NxNknWkaGUZqQTFf/vUXk246d4PcrOfy6BLdlP7NmyAIAha7g7l/biS5tZkBA2u3+e8MB/Y4GJxYcZIeYdAToteQmemilx8yyP9Gp5PRtauWpUuLuO22skmC0ymy5JNChtRyE2Rd0759NlGRFlwugaxsAyt/UzFzZjEKxWksFpH4eCWzXoyp4HghEQiIJCUVYzYryc/3/3eiMiSh7CNu6/cd13b7nTdXTqI+iWSAfHMIjy+/299hXDRnRPLYTqu4Y+DXTJ7/IgXm4CqukghEfn3/T9Yu3czAG3uj0qj47r8r2PHTHqa8cZ3PfKS9gdSx7+KpqQXcoEEG3C745OO/yFhjJypCxaSbjYwaXXEjX2qqg9xsN7d3alL+2dGpVQxq3oJffjzmNaEcESUnt6SUZpHh5cccLjeFpXbCwgJn0n7vfRE8PjOLrVssNGumYtcuK8nJGq69tnrdEf2DSEiwjZjYUo4cCcfjkZGTbSA/T0d2jgG3W0ZSI/hkiZHTp51oNAKxsYFf7ngpEhZqJSjIwYGDkQS6RpKEsg8Y0GIHj49ayM97+/D2nxP9Hc5F8M+Si8o/wB6HjdI9K7Cd2oWg1mFoPxxt406VXuMqyaNk69fYTu9FrjNi6HgZuuT+Xhc+2SXhNInIYMltT3H9vJcosdavTMmlRmmBmT1/lomk9oOTcdqcrJy3lqd/e4jgyDLR03diD1664i0ObThG634t/BmuRAAxdJiBocMMuN3iBTvdWa0iWrXinMyiXq3CWuK9jUTjrzEw65kjJISFkBAajM3p4oe9++jcWUtEROD85IaFKXj/g3j27LaRle3immuDadYsMBthKZVuYmJMxMeZ0OvLnCsyMoIoKdGQnXPuc10uF2jSROWHSCWqS1GxhoOHIsjKCvzf5cD51jYQmkSk8+6k2RzOasRDXz4YcO2pq0NiWDbrHrmdB798gG92Dr3geR6njexljyPXh2JoPxy3pYj8X9/B2GU0xh5Xnfcat7mQrE9noE/uR/io+3GX5FC09lNchZkE97nOq69j84kOTPvkCebd/AKf3PoUN8x/kVJ7YCzxuEx5mPevwm0uQpPYDm3zHggy/2SbAmED35Zvd/LFM9+R3K8FggD/e+EHOl/ejnaDkstFMoBKo6TX+C7sW31IEsoS51BZO+imTVVYnU5S8gtpFB4KlPk5bzudQq9hNW8iciE6dtQy7a5gPpq7Bbkgw2Jz0b27jgdnhFd9cR0jkwl06hzYtbsajZPevVKRyaCoSM2BA5Fk5+gDrs2xRM3weGRk1JMOiJJQ9jLRxnxyS0OY+slTWJ3ee/j6g6ryu+Z9fyLXGom86snybLC2WXcyF96Nvv1w5NpzPUxLdvyItnl3QofcVnYgpjmqmJZkLvwPQV1GI9N4d3a59mhX7lo6k7k3vMSiW55l8rwXcfjZecR6ahd5389G16ovipBoijd/iWnnj0Rd8wyC4tLLghRmFvHFs9/x8P/uIq5FNACZx7KZfdV7JLWPP+d8c7EVta5+vE9SCUb18XXXPYVC4P6Hwnj91a30aNyIMK2OfVkZuNRmxo7z7sbf4SOCGDzEQFamkyCjnOBg/5VcuN0iO3daKSpy07athri4wC5FUCrdxMaakMtETp4KxWZTcOJkKHl5eszm+vG9l6icZk0LsFiVZGbW3ue8LpCEspfZfKIDI958v15vIDvT1bwq1wtbym70bQdVKJlQGCNRxbbEnnEIXbPu51zjyDiMscf4CscUxggU4QnYs4+D24XocqBJau810fzHwZ7c+/kMWkSn4nD77iMviiLW41uxHFyH6HGja9ELXXK/Cpli0eMm/5e3ibjykfISFWP38eT873lMu1dg7HqFz+ILVHb+spfOl7UvF8kAsc2j6TKqPdt+2M2hjcdI7tMcgOwTuWz+ejsPfXGnv8KVqCWiKFJstaGQyTBo6napv29fPYnvKvnlp3wK8nMZNVDFkKExqNXez04qFAIJif4VdmlpTp54PJOgIDmxsQrmfpDP0KEG7rwrPMBq/EXCQq3ExZuIjDAjk0FenpayMkCBlJRQfwco4SU0GidJSUWkptWffUOSUPYSj172MUUWAx+uvbpei+Qy/raHq8JHWaYJwmXKr3BMFEXcpjzkmvPPFOVB4Tjz09A27Xr2GpcTV2Emed/PRhkah6DSkv/zHEKH3I6hw/BavpYyftnXj1/2lf27cXg6GUVRXs8sF/45H9vJvwjqdgWCTEHJ9m+xHNtKxBUPl/8oOXJOIlOqK9RxCzI5QV1GY9r+3SUplF0ONyrtuX8LlVZF9ys7seDez4hrGY1Kq+T49hSueeoKYpqd271NIvA5lVfI1zv2UmKz4/Z4aBQeyjNvaomKqrufoqQkFdPvDKv6xHqOKIq89FIOV10VzNhxZaKktNTDQw9lsGqVmSFDAqc2tGmTQpo0KcLplJGaFkxGRhAWi38mGSaTG0EQatWyXOLCJCaUNQJKTZWE8iXF+M5/cueg//HJptEE+u7N6lDecKSKjLKhw3Byl7+IrlkPlBGJiKKIacf3IMhQxbU67zVBnUeR+80s1HGtUMcn43FYKVy1EDwewq94qDwL7SxIJ2vpo6hiW6KKbOS112bUlvL1nTPYkdKGu5Y+hsvjna+AI+80loNriZs6F5m6rGGCvs1AMhbdi/30XjSNOgAgyBWILsc5rWxFlwNXcQ5p709BpjFg6DCcoC5j/Fa3XJd0GNaaN6//kFF3DyUovOzH25Rfyo4fd3P/Z9OZ+OxYDq47itPh4pY3r0dnDOyaSonzU2y1sWjDdq7q0o72CTF4PB5WHTrBYzNSmL8oTrLv8jJpaU4KC1xcceXZOlCDQcbEiSGsXGnyo1AWCQuzEh9XQmpqMEXFWrKyDZgtKnJzdX6rPU5JcfD2W3kcOWIHoF07DffdH0FMTGCXqtQnFAo3cXEmsnMM2O31R37Wn0gDlE6Jh3nlqnfYdLw9z/8w1d/heAWTXcdHa8dzJKtygaqOa0VI/xvIWvoIyrB43JZiBJW2Qs3y+a4JHTad3O9eBUHAYzejimyMMqpxhVINZVg8ho4jMO9fhWrQFK+9thKrgTm/T+KFcXOZM/F17ls2wysrALZTf6Ft0atcJAMIChX61gOwnvqrXCgrIxohqLSY9/2JoX3ZRkmPw0rR+qUoIxsROuR23KX5FK1ZjKswk7Dhd9Q6tvMRCBv4zhDbPJoBN/TmxTFv0efabgiCwMavttF/cq/ycowOw9r4Ocra8c9ufZdqvfL2k2l0SIihY2IsADK5nGFtmnNwTQZ//WWla9fA2GhbE5xOkX37bIgitG+vQakMHLFvs4lotbJzJiAGgwybre6bSalVLmLjTMTFmdBqXDgcMjZtVrJooRmlUmDwEAOtWvlHJJvNHh59JJPrJ4XwyquxeDwiX39dzKOPZDJ/QWJA/V3rM/HxJSgUIqdP159sMkhCuVZEG/P48MYXyS4J82p20t+UWA289PNt1TrX0GEEuuQBOLKOIFPrUUY1rbL2TZ/cD13L3pRs+QbTnt+wpx9EpgvBcnQzuha9ys+Ta404CzNq9VrOx5LNY1ArHTw5eiF2l5KHv3qg1u4kMrUBt7nwnONucyGKkLO1t4IgEHHFw+R89Szm/X+gCI7BcnQzckMYkeMfRxBkKENiiLrmGdLn3oax17UoggJvt7y3GXPfcNoPTmbnL3sBmP7BTTTumOjnqCS8SaHFSmxwxZIsQRCIDgrySStpX7Njh4WXZ+URqisT+IWWXGY+EUHXboEh+Js2VWG1edizx0qHDmWrMKIo8tOPJX5oeiLSvXs6arWb/AItR4+G8cTjFvbssXDZ5UHYbCJPP5XFtdcGc40ffJxXrSqlTRsNY8eeEXACkyaFsmO7lc2bLfTvX7O26hLnp9SkJuV0MKWlgWlDeCEahrLzEz2b7EertHPjgucptNSvGVJlCIIHvcqG3aXEWY06XplKgyapQ43uUbpnJaX7/iBi1H2oY1thS9lN/q9vgyBD17wHotuJef8qgvtNvtiXUSnz112FWuFkxsgl7EtvzqINY2s1nq5lbwpXL8R6Ykd5/bU94zCWQ+uJveWdCueqIhsTP20e1uPbcFuKcBZmYOw2FkE4K9Zlaj2qmBY4c09dEkIZoFGHRBp1kMRxQyUpPITdqZn0ad6ofDLtdLs5kp3H9OToKq4OLIqL3bzwXC43du9Os6iy7+fxnHxeeG4bHy9JICTE/yVTcrnA/fdH8tyz2QwfHkRsnIK1a8w4nSJXXOFbWy612kVcXAlhoTZ27IwFBA4ejMRiVWK1KvnrLyu7d9v5YG48Gk3Zc+/yy4OYNjWNQYMMRETWrTTJynLRtNm5NdHNmqnIznLWaSwNmfwCHfkFgTGRrAmSUK4F3+8eyJojXSi21g+Lk+oSFVTAlsen8NjXd7Ns22VeH18URYo3fUnkuJmoY8u8cLXNuhE2/E4K/5yPuzSf0t2/oQiORtusm9fvf4b3Vl1HemEUv+zrW+uxZGodkeNmkvf9bORB4QgyJc6CNMJH34/CeG4fe0GhRNeqDwDO/DQc2SfQtexd/v+i24UzLwVFiHdtqyT8z6VqGdc5MY51R07y1c7d9G7SGLvLxR9HjtClm4bGjeuX7dfq1aW0io4qF8kAzaLCaRUTxZrVpeWb5/xNz5463nk3nhUrTBw/5qBNWzUx0UrS0500b+7drJ4giISHW4iPKyE83ApAfoEWpdKD0ymvIJA2b7IwfIShXCQDREYq6N5Dx7btFi6/vG79dVu2UPHNNyVMnhxSPolzu0V27LByz73nPr8laopIYmIxWVlBOJ3+n0TWFEkoXwQ39/mBlPwYVh/u3uBE8j/xlXuQ6LLjNheWi+QzqBPb4i7Nx55+EGPPq9G17F0hy+oLvt01GCjb5Deu0yo+2TSGmm7IdBZlUbRmMdbj2xCUahTB0ejbDEbbpHO1fJGDOo8ia+kjqKKboG3RC4+tlMJVC1FFN0MZdq6PsERg4bA6cDnd0ibDKlAq5Nw5uDf7VFtZvmEHKpXA8LE6xo2vf0LEXOrBoDrXJz9IpaHUbPNDRBcmLk7JuHHBPPlEFnaHh+bN1Xy6tJBWrdQ8/ng0KlVtH/RlFm5hYVY6dsjGbpdz6lQIGZlB2GznX5FUqwUslnPrpC1mj0+s+qqiT189y74o5vXXcrn66mBcblj2eRFh4Qo6dqzf/RACgYgICy1bFOBwKMjODhy3leoiCeUaMqjVNp4Z8xE/7unP6sPn+gQ3BM64XlRlD3exCAo1cn0I9qxjqGOalx+3px1EFdmEiNEP+uS+lTGx+woeH7WIcEMxb668odrXuW2lZH82k6BOlxE24k48dgtFa5dg+utndC16VmsMZXgCkeNmUvjnfPJ+ehMAXau+RFwx46Jei0TdUFpgZtkz37L3z4MgQHyrWK575kqpfKQSdCol06aHMW26vyOpHV26avnufxmMaNMStaLsZ9TucrE3I4NrugZeqdS77+bRoYOGadPDEAQBp1PkuWez+fLLIm64oeYexYIgEhFhJj7OREmJmhMnwygo0LJ7dzT5BbqzzkkXYMhQAw8/lMmoUUHlDVD27rFy4ICNx5+oe+tHhUJg9uwYPltaxHPPZSOTCQwcqGfi9SEB5jddP2mUVIzVqiAnp37WektCuQY0i0zl7etf42BWEx79+l5/h+MzxDM+ylXYw10sgiAQ3Ota8n/8L+GX34sqtiW2lN0UrPyAsOH+aSQxb914mkSkc9/QZdidKt5fPaFa15n3/o4msW15+2251kjEmAdJ/2jaOROBytAktSfm5jl4bKUIChUyZf3a7HCpIYoi792+iCadk3hl85OotEq2fb+Ld29dyAWJNs4AACAASURBVBM/3U9IdGAsvUv4huRkDd17qflg7Xp6N24KwKaUk3TrqSY5ObAykDabhy2bLXzx5dnacKVS4MabQnnl5ZwaCWWt1klcrInYWBNqtRubTU5efllJhSgK5OVXTwg1bqxiypRQ7rwjnc5dNNhsIkcO23niiSh0Ov84XxgMcqZND2fa9MCb6NRnjEYbISE2jhwJr3ICFahIQrmaGLWlzL/5eRwuJVMXP1nv21NXSnlG2XcYOl0OMgV5P72JqzATZVRjwoZOq3YW1tuIoownlv8HjdLBI5d9gt2lYsH6cVVe58w7jTqxbYVjgkyOJr41zryUagtlKJtAnK/ttzcJJFu4usbtdLPxf9vZ9ds+ZDIZXUd3oMe4zshkNf9hPrEzBUuxlWufvKJcfPS6qisn/jrNhi+2MfreYVWOcanVKvu6RXVd8+CMcDZutLDq91MA3PYfPX37Bt5GJdffhiJqdcUnul4nYLN7qrxeEMRygdOsaQGRkWby83WkZxjJz9dysb8UY64w0q+/nh3bLSiVAs88E41WKzX5aGg0SiprJJNRT9pVnw9JKFeTa7r8QXxILpPmvUhGccPuCmZ2aHhz5ST2pFdf5NUUQRAI6jiCoI4jzmm+4S88opyHv3oAldzJzb1/4POtI7E4Kq87VYQlYE8/RFCny8uPiaIHe8Zhgi7BLnuBisfj4cO7lmA12Rh8c188bje/z1/H4U3Hufm16q0e/JO81AIS28ad87lNahvHib9Oeyvsek9DE8f/RCYT6NdPT79+gb2cbDDIaN5cze+/l3LZZWfFyg8/mOjV68Kxa7UO4uPKssc7dsZhsag4fiKMo0fDsTu8Ix1CQuQMHVZ/BZRE5QiCCAKkpRlxu+vvJEgSytVk4YYrWX+sI0eyG/s7FJ9jcWh5649JdXa/QBDJZ3B75Nz/xcOE6ExVimQAQ4dhZC68m5KtyzF0uhyPw0LR2k+QGyNRxbasg4glqsOhDcfISy3giR/uQ64s23XdYWgbnhn2Oqf3pZHULqFG4yW2ieObl3/CaXehVJ99jB5Ye4QWPZp6NXYJidryn7vDmflYFvv322jRQs2O7RZOnXIyZ05shfMEQSQqykx8XAmhoTY8HsjL05XnjK1WqUudRPURRYG9e2PAR/ud6or6K/HriMvabaBZZCogXBIiGcp8lKONeehUVn+H4hecbiW5pjBkgpuXxr/LlR1XX/BcudZI9MSXsKXuJfWt68iYNx1BoSKqku6EEnXPkc0n6HxZu3KRDKDSqug4rA1Htpyo8XhxLWNo0bMpc6cv5uSu02SfyOV/L/5I6oEMel3dtUZjGV4dWKFzn4SEt2nRQs28efHExyk5cdxOt+465n4YT2hY2SRPJisrwZDLPbROzkWtcXHseBgbNiaxd18MZkv9su+T8D8KhRut9owHdf3+LZQyypXQJekgb018jVWHunPHp0/4O5w6I0RrYsvjU3j6u+l8sunSLR9Qyl00iUhnQrffcLiV/HoBv2VleAJRVz+NKJbNmiWBHHgEhetJO5h5zvGC9MKL7gA45fXr+H3+WhbP+BKHxUG7wck8/OWdaIMa8P4FiXpLaJiCidef7Xonk3mIijIRH2dCkIls3x6PyyVn27Z4zBYl9V3ceBO3W2TXX1by890kt1aTlCRNHKoiKbGYRo2K2LAxCYeXSnX8Rf2O3ofEGPOYe8NLZBZF8tg39/g7HL9wqes9u0vNbYuf5pNbn+ad62dzx6eP88fBC282DGSBfClv4gPofkUnfn73Dw6sO0Kb/i0RRZGdv+zl1O5Ubn3r4sqMFCoFl901hMvuGuLlaOs/Dbk2ub6j0zlIiC8hJqYUpdKDxaIkPSOIM37IUva4IllZTh6fmYVGKyMxQclHHxXQu4+OBx6IQCYL3Ge+P5HJPCQklJCfr6v3IhkkoXxe1Ao7H900C63KxuT5syiy1G2XIH9Tbg9Xz+uKvIHFoeWWRc/y6e1P8v7kl7l98dOsO9rF32H5DGuJlV0r92O3OGg7oBWRjRqGVZIxMohp793A4oe/RGNQ43Z5ED0id82/BY0+MKz4LjUXDIm640xphccjIyTYRnx8CTk5etIzjBQVaZCyxxdm9uxcRo4M4rqJZdl4q9XDY49m8ssvJkaPvrS0QXWJizWhVHpIOR1S9cn1AEkon4fpA7+hXdxxpi55kqM5jfwdTp1T3nDERz7K9Q2TXc9NC59n4ZTn/B2KTzmw7ggL7v2MFj2bogvW8uOclQy6qQ9j7hvu79C8QqvezZm19jFO70tHJhdIaBN3UdZwEhL1BYPBTnyciZgYE8dPhJGWFkxWtoHcPH29bCVc1+TkuEg55WD27LObHrVaGZMmh7Ls8yJJKJ8HQRBJSiqmqFhNcXHDKEOThPJ5mPt/9s47MIoy/eOf2d6yKbtpm0IILVRBOipFARVRROxd1LOf5fSnZ+96epaznBU9K3YUG3ZARZAaektCettsNtvrzO+PDRsiLYGEtP38A5mdmX23zXzf532e77P4TDaU9eWXbtp572A0RZRj7KbBG8ecl55k97sSr3V2q/blAW+AN2+ez9WvXEK/Mb0BcNa5ePz05xkwvm90W1dHJpcdck5ydyAYDrO5oga3P0CflCRSjW37HY6lXHQGJCzpTiwZTuKNfsJhgZoafVS0iKIM8eD2yUeUcFjC4RCJi5OhUHSeO08gIKFWy5D/ZU6h08nwB2KBpH1hMARQq0Ps2NE9ViMhJpSbMbLXZnbUZOPwGnqsSAbwBdQ89NXl/Llr8MF37lFELuCnDlvCw7P/y4WvP8yG8n4dPKa2YctvO8jIS28miONMBiZeMJ5VX67rNkK5J1Ne38C831aSaowjUaflh807GJKRyhlHD+nU+fUxWoZGE8TnixThpaW5kMtEtm03UVVlIBTqnNFjSZL4fIGD+fPthEISMhmceWYC55wb3ym+kxkZClQqgT//9DJ27O4OhBJffulg3DgdW7f6ePPNejZt9JGYKOfU04yceWZ8j85ddjrVjQV8nfM7dyh0KqGcZrRy3phF7LJaKLRaqHaYOFJxzb4pJfzvsvtYsm0U18+//Yg8Z2clEFYy77fZHT2MTsuq4kE4vAbeufweznvtUbZUdl7f3JYW8YVDYjPrtN0o1ArCoXBbDyvGAWiPXGVJknhv+VpmDhvI0b0yAPAFQ7z0yx+sK61kRLblsM4fiyR3DHK5SFqqC0uGA4M+wO+/ZxMIKli/IZVQSEZnXxdc9K2Tr7928MST6eTkqCgrDfDoozUoVQJz5nR8G3hBELjpZjMPPVjNlOMNZGUqWbbMg9MZZvZsI3fdWcUVVyZxzz2pVFYEeeFFKw32MFf+rftEU1uDTCYiirJuUcC3J8JuS6vOwMiRMmn16qbxeAJqJj/5KjVOE6NzNpJjqqTIamFXnQWrK4G2ugjEa518cd0t6NVeTn3+Waoc5jY5b1dFEERyzeVYXQmHlF4QrK+gfvGbeAtWIlOo0Q+eQsLEi5GpO19710MlM7GKj666A5UiyLmvPsbOmuyOHtI+aalQ9jq83HXc49z6ybVY+qUC4PcE+Nfs55l9xwyGThnYnsOMcQDaQjCX1zfwzh9ruf3kSc0idWuKy8kvreSyY0cd1vljQvnIotEE6Z1jJzXVhVwu4XSqKK+Io6oqrtN2QJMkiT//9LJksQtJgmOP0zPv9TpuvjmZocOamjvt3OnnnnuqmD+/89QH1dSE+G6Rk7q6EAMHaphyvJ7nn6vDkqHkvD0s9+rrw1x2aSnvvpeFwdB9Iqot5ahhlYRFGRs3pnb0UFrE1BMKV0uSdNCLX6eS/Rsr+nDM43eSY66gt6mCnEaxBjB7xGLOH7souq/Dp6OwJpPZL/0bSZIxNGMHcplIkdXSKnEnl4V58fzHSU+o5bxXH+vxIhlAp/Tx0z+u4ZGv5/Lar2e06tiw10n1+/8kbuSpmGfcjOh3Y1/6NrULHiXlnIc6xXJaW1BWn8b5rz3CR1fdwftX3MW0Z/7bpXOWtUYt5zwwi6fOeYkxs0agi9fy5xdr6T8mlyGT8zp6eF0Kd4OHDT9tIRwSGTJ5APEpHV/wExYllHLZXr8/pVxOqLMlrMbYJwpFGIVCbEyvgJQUF1XVBirK43A41XT26PF/X6xj9Wovs043IpMJ/O/NempqwvTp29yOLjdXhbU2TDgsIZd3jteUkqLgoosTm20r2hXgpJObX/MTE+WkpiqorAzRr1/PEsp6fQCz2UtBQeLBd+5idCqhDFBuT6HcnsLvO4c3237PF9fw8pI55JrLyTFXkGOuIE7jRpIis+dbpr3LlLzVANS74yiyWlhbmsdDX10JQFZSFfVuIy5/86jm30+Yz7H98rn145tYUxKLmkFTMd+h4N7wI5pew4gfdyYAMrUO04ybqHjtagKV21FbBrTVMDucXXUZnP/6Ixzbd12XFsm7GXv60eSO6MXKhevwewNc/K+z6Ts6p9tMbo4E677fyNv/9zH9x/VBoZLz6SNfcfr/ncTEC8Z36LgyEo14AkEKaurokxJZFg6LIssKihmakXbI541Fktsbifh4PxaLg9QUN/X1WvLXp+HzKfn1t16IYueMHv+VggI/S5e6eePNLPT6yJinTjVwycWlfP65g/PPbxJXa9Z46d1b1WlE8v7IylSyeZOPwYObnB0aGsJUV4dITe100qrdyc62EwoJlJV3fGCgrekyn2ZYlFNiS6fElg7b9378voVX8+6KEnqbyxuj0RWkxtmij7928UPkpRVT60ygyGqhyJrB8sKhvLt8BlZnAgvXxVrI/pVDsYcL1pWizhyEGPDh2b6MsKsOdfoAVJYBBOvKupVQBthZkx1NuxhsKaDOFd+lVyWSe5mYccMJHT2MLomr3s3b//cxN75zJb2GZgJgLbXxr9nP039cH9L6pHTY2OQyGWePHsZby1ZzVFY6iTod68sq0atVjOmd2eLzWF1udlbXoVYqGGTpuNfTE0hLddKrlx2DIUgoJFBZZaBiDxHSVUQywKpVXiZO1EdFMkRs1qZPN/D+e3aSkxUMG6Zh82Y/L79Ux99v7PzX0DPmxPPPOypJSVVw7LF6KiuCPPdcHVOnGTAae1Y0Wa0OkZbqorzc2GkLRw+HLiOUD0ZURO+HJ7+7mH4ppfRujEhPHbgCjdLPgrXH887yU8i/71w8AQ27rJaokP6zaDD5Zd1L2LWEqI/yITQcUZqz8e5cScPvH6BKzUVpyqLuh5cQ3XbiRsxo66F2GpTyIK9c9DD+kIpzX3mcWlfHLj/19E58HUH+D5sZeGy/qEgGMGclMXb2SFZ9lX/IftS7i/ug9fnKwXCY9aWVlNsdJOm1XDN5HJsra3D5/Uwb1I+B6SktqtCXJIlFG7exvLCUgekpeMJevtq0gYcmpzBocPfwSu14JBISfDQ0aJAkAbUmRDgsY/MWMzU1hk6be9wS9DoZOxv2Lgq220VOPDGOH3908cY8G5lZKm77v2RGjer89Sz9+qm5+55U3phn45GHazAaZZx6mpELL+x+qQcHIzPDAUBJaccXYLYH3UYoH4yftoyNth+2xNew8IabcfsiF3i5TOS1X2dHI9EnDv4Dk8HBS4vPJL9sAHqVh0U33RAtJNwtpjeW9+1wQdQe7JbHh7Lirh9yAvbf55M4+VLihp8MQMKkS6j+8F785VvQZA5qu4F2IoJhJTd+cBtvz72X9668i3NffQybu3teNGLsm3AwhEq7d/tflVZJKBA64uNx+wO8vHg5Bo2aAalmiuvs/LylgCsnjSU9vnWpQturreSXVvF/J01Cr468xs0V1TxwXz7vfZDZqbxvuxpKZZj0NCcWixO9PsiGjSnU1BgoLk6guLh73F+Om6hn3jwb69d7GdZYuLdli4/ffnOTlaVk2zY/er2M/v1V0ce7AsOHa3nu+QzC4Yi1XU9NU9tVnIDdronmz3c3eoxQ3o1W6eO1Sx5GpQgy7/eIBVpYlPPCz+c228+odSEXIjNgrcrPmpI8ckwVnD58MUatG4B7Pr+ad5bPJDupkvtPeyUqoHfVRf6tsCcjSl1vGSIYVvLPz64nv7R/q4+Vgl4ADMOmR7cJMjkJE87B9tNrxI+d02bj7GysLh7E5W/dy/8uuz9qHefwGjp6WDGOEEMm5/HFv7+jvtJOYnpju1uHl+WfreGK588/4uP5cfMOcsyJzXyS/ygoZsGajVw7pXU502tLKjiuX05UJAMMsqTy0w4NGzb4GDGi64ibzoJcLpKXV0tKshuZDOx2NZs2J2O17o6mdh/RFR8v5+67U3jwgRoyM5XI5FBUGEAUJWbNMjJ5ioHa2hAvvlDHf56t5bb/61ppPZ09n7q9CYdl1Nk6/yrAodLDhLLEk2c9y8C0Iua+dS8Ftfvv0LWnwLG6Ernxg9ui50jSO8gxVVBuTwYgXusizVjHuNwN6FT+6HFXvn03P2wex6D0QmYNX9wsGl3tTIoWInY2wqKc+X+edEjHBuurAGnvcLRMhuh3Hf7gOjnLC4fxt7fv4rVLHuLaSR/z+KLLOnpIXRZJksj/YTOrvlpHOChy1LRBjD5tOHJF55x8JmUkcvJ1x/PYrOeZcNZolGo5f3yymhEnDSF3RNtYXbXGY3lTRTVzjx3dLMo1OieLL/O34A0E0apaHv0JiyLKfbzvKoWcYLDzWIx2dpTKMHEGP7Z6HeGwgEYdoqzcSEWFEbd779WI7sTIUTren5/FunU+kOD3390kmeRMnRZZ3UhPV3LX3Smcf14J1toQ5uQeJk+6IIIgMfyoSopLErDFhHL34NrJHzNz2G889s2lLD7kznsCNnd8s2X1DeX9mPHc84BESpyN3o2uHOsaI7L9Uou5dMKXqJXB6DHegJpTX3iGnTXZDM3YQV7arnbxiD4UBEFkiKWAKoeJWmdSq46VxDCCXIl74y8Yhp4Q3daw4lMEZc/IZVy6YyTnvvI4Gyv6dPRQujQfP/wlW3/bwfGXHYtSrWTJe3+Q/8MmrnzxQmSyzjnJnHrFRPIm9GXV1/n4XH4uffpc+ozsGD9YuUy2l/VbWIr8LWvlEvEgSypLtxdxdHYGCnnkvS+vb6DK7mTYsISDHN3TkUhK9GLJcJJsdiOKQtSxYvUaC90pcnwwVCoZY8ZEBNUnnzRw7HH6Zo9rtTKyeympqAzGhHIXIC3VRVKSj+KSjh5J62mNWUGP+ib+umMEiXoHryxtr+V/gRqniRqniRVFQ6Nbv1g3hYX5k7DEW5t5RFfaI5W9Jw/5nWunfBLd3+nTsstq4ZxXH8cT0JKXVoRW6aeozoLd0/7WKwpZmC9vuJknv7uIF385p1XHqlNykIJ+6pf8D+/OFShMWXgL/kT0e9DnTWynEXc+1pZGvIeT9A3cNPV9Hvl6Lv6Qut2ft7sU8VXurGbVwnU88NNtaI2RZf2RpwzjsdOeY+tvOxk0sfVpQS1FkiRWLlzH4reXYa92kHt0NjOuPwFL/5bZqGUOspA56PA63bUFw7Ms/Lh5BxeNPxp548Ri8dZC+qWYUStbfukfftUWhoYliu8P8fySpQxLz8Ad9LOmpJyb/mFCo+mck5bOQFKSh7wBVrTaEIGAjNKyeCoq4vZwrOg5Ivmv5OQo2bDex+jRTZFIl0ukeFeQzMzumevavZDIzrbjdKqw2Tpn6lViogedLohOG0KrDaLVBqmv17J9h5nW9NrrEUI5Tu3G6dezobwfG8r7dcgYJEm2X4/op364iA9XTY8WE+aYK0iPt+IJRCKwV036lNkjFgMRj+hddRa2VOZw54IbgEhxosNn2Msj+tDHeugXb7k+kbjhJ+Mt2YA8PgXJ70aZlIWvdCPGUae1yfi6EiN7beHCsd/QK6mSK9++h0A4dgNoCduWFTBs6qCoSAZQqBSMnHkUW37fwaCJ/Qn6Q2z5dTtel4+8CX3brLHHj68vZdnHq5hz5ymk9k4m/4dNPHP+K/zjw2sOaPHm9wTYubIIuVJOv9G999kS/GCUbCzjz8/XEvCHGHp8HoMnDdhn9LwlKRjH5/XhrWWreXLREvqmmKmwO/CHQvxt4thWj0suF7j3gWRWr/ay8s9qkg0yrrgrHYsl9n1ujoQpyYvPr8DtVhEMyvH6FBQUJFFTqz+sa2t34/TZ8fz9hnLMyQqOP15PTXWIl162MeV4A0lJPUKadGlMJi8GQ5BNm5PpqAmfThdArwtGRbBWF8LvU7BlayQtNi/Pik4bIhwW8HoVeL3KPVKcWj7mbv9tTNA5WHjdzXy0etpeBXudhbAop7jOQnGdZZ8e0U99fyFfrT+OHFMFvc0V9DaXk5lYE338mXOeZmzuxqhH9K46C2uK8/hgZSTPWCELERJb/1Efio8yQMKUy1CsW4Rr/feIXieaXkeRftG/kRu6RwV3a/hh8zhu/+wGnjzzOV684HGuefefh/RZ9DR08Voaahx7bW+odpCYHk/RuhJevuotUnonE5ek58P7vuDEa6Zw4tWTD+t5A74gi176hTsW3EByr0hjjqlXTCTgC/LDq0u46F9n7fO41d+s5/27P8PSL5WAL0RDjYMrX7iAPiNzWvzcP7/5G9+9/AvHnT+OhDQ1C/71LasW5nPp0+ccUjW9UiHn8uNGU1xnp9zewOCMVAakJrfIDm5fyGQCo0frmkUAD0Z1dYhPP2pg65Ygaelyzjgrjry87peCpVaFSLdEnCu0mhClZUa2bzfjdKpZu7bjVxc6IxaLkn89kc4b8+p59ZU6jEY5p8yM49xzY6k8XYFe2XZ8PjnV1e1XsK5QhNE1CuHdUWEE2Lw5ErDIG2AlMdEHQDAow+NV4go1BRbWr08jGJQRCMg5HDHfre/YClmI/17wOKlGG7/tGNHRwzlkyurTKKvf/7Lvi7+cxS/bRkXTOib3X02yoT4qlH+85RrUykBjIWEGRVYLa0ryWF28b6u23Z35DsVHGUAQZMSNmNGtfZNbw8erpqNRBHjo9Jd59tx/c+MHtxEWO2dBWmfhqGmD+fihL8n/YRNHTRsMQOHaYlZ9nc8dn9/A0+e+zHkPncHw6ZHH7NUNPHHGi+Qe3Yt+Y3of8vNaS20YEvVRkbybwRP78+6dn+7zmNqSOt6/+zNufOdKsgdnALDhly28fNXbPLz0DtS6gxdpOWqdfPXsD9z9zU0kZUQmlBMvHM/jpz/P5qXbGTzp0PzcBUEgx5xIjrn1k9TD7bpXXh7kxusrGZGRxcT0FCrsDu68fSe33p7EhAn6g5+gizBwYA3paS4EAepsWnbuSKLW2n1eH0TSkbZvD1BRHiQ3V0WvnLYpPOzTR80jjx56Z8gYHYVEebkRhMNbgQYJtTrcKISDaLUh1JoQmxuj1P3715Ge1mQC4PPJce1R9LpjZxIQiRbvq9FJWxXIdmuhfPfM15nQZz23fHQz60q7b+OQpTtGsnTHyGbb5LImc/cPV06nb0ppo0f0MkwGB++vOJHVxYMQBJEfb76Wcnty1JVjV13kwtVDLSHbhXeWz0StDHD+mEUkaJ3UuWNRkwOh1qm45rVLeP369/jyme9RqpVYS21c8sTZWEtsJKTGR0UyQEJqPJMvOYY/P19zWEI5PiUOR60Tt92DPqEpclq2pTIqYP/Kn5+vYezpR0dFMsDQKQPJHprB+p82M/rU4fs8bk82/7qdvGP7NXsOlUbJ+DkjWf/j5kMWyh3JO/9rYFyvXKYOjKS79U0xkx5v5OUX1jJ+vK7Les6q1SFSU1yNzRUEvF4lxcUJVFTG4fW2XypKYWGARd86sDeIDBumYdo0A2p1++eHu1xh7r+vmurqEP36q3n55ToGD9Fwxx0pqFRd8zOMcbgIVNe0LJIsCFJjakRTnnBhYRLhsIzc3Hp659ij+4oi+HwK5HKJcFigrMxITbUer1eJ16fYqxul03lkVqe6rVA+b8wiLp3wFa8sOYPP1vS8lrx7RixfWtJ8udiodaGSRxw4dEofG8r70Nvc3CP6m/XHsGjjeJL0DTx11tPRSHTEIzqDCru5S3pEHwhf8XrcW5YiiSF0/cah7TsGQWi7G9Hrv57Be8tn4A1qkAlhJIQ2swjsrEV8DquLHSsK0cZpGDChT6us3XJH9OLhpXdQuLaEcDBE7tE5KNUK8n/YhDZu7wukzqgh4A3u40wtRx+vY9TMo3jrto+44NE5GM0GClbtYuEz3zP32fP2eYzX4cNo3vumYTTH4XX6WvS8Kq0K3z729bn8qA4QkW6NXVxLOdxI8m7y833MHdO8W2rfFBOOlSI2WxiTqevcfgRBwmTykGFxYDJFvOJt9VpcLjW7drV/StnixS5eeL6O02YZ6dNHzZIlLr75xslTT6Wj1bavWH7ppToyMpQ88WQ6MplAICDx4APVzJ9fzyWXtM4VKUbXR6MJkprqoqwsPtotUi4XoyJYpw1RVW3A71eQluZk0MDaZkG3UEigvNyIx6PCWqvD71Pgacwf9vsVzSLUDkfnSNPqOleqVuL2a/l24wT+teiSjh5Kp2NPj2h3QMdNHzb3iO5tLqfGmUSpLY1ccxkpxnrG9N6EXt10I7/t4xv5ePU0epkquGDst5G0jjoLRbUZndojen/Yl76De8sS4kacgqBQYv/1XTzbl2GacXObRr52i+Snz36aBq+B+xZezcFypzwFK3GuXECooQZVSi7G8WejTuvbZmNqL75/dQmLXvyZvmN647S6ePdOJ9e+ekmrHCFkchl9R+U029Z/bC5v3foR1YW1pOZGijbCwTC/f7iS4+cee9jjPvu+WXz2+Nfcf8KTyBQydEYtZ99zGgPG7dvub+Bx/fn0sa+ZesVEFKrIJdVt97Dh5y3MuL5lk/Qhkwcw/57P2LpsJ3kTIp+ttczG4neWceM7Vxz2a+oIEuLl2NxekuOarjeeQJCwKKHTdZ3rg04X4OgRlajVYXx+Obt2RaLHR6oLWSAg8cLzdTz6WBr9+0ecc6afaODBYHwcrQAAIABJREFUB2r48ksHZ5/dfqtToZDEksVu3ns/O5rbrlIJXDY3kfvuq44J5R6DhEoZRqsN0TvXRmKCj8rKOAyGAMOGVqNSNW9P7nKr8PsVuJwqinYlRCLCXiVej4JAsClf2OHU4DhCUeHDodsJZUEQkSQZC/MnsTB/Ij3Zfqf1NHlEj89djyRBoTWTU557jr96RC9vtL/LNZdzyfiv9vKIvuD1h1lTMpB+KcUMz94WSemwZlDbwR7R+yJYX4EzfxGWy/+LXBfxx9YPnUbl//6Ov3QjmuyhBzlD6xAlGTXOJP42cQG+oJrHvr2M/b0nrk2/YF/6NomT56JKzcVbtIaaj+4l5ewHO7VY3rGikMVvL+Oe724hMS3ynv75xVpevuZtHvz5/5DJD10oaY1a5tw1k6fOeYljzx2LIUnPis/XkJAaz9EnH/5npVQrOOe+Wcy+fQY+pw+DSX9A3+aBx/Uj7cNk/n32Sxx3/jiCviC//O83jjlnzF65zvtDpVVxxj9P4cXL3yRnWCb6RB1bf99JnNnAyoX5ZA/OPOzXdaSZNcfAB29uJi1+LPFaDYFQmC/Wb2DSRH27R0EPB0GQMJsj3fKqqw14vUps9VpqavTU1ekO27nC6QxTUREiNVVBQsLBV1gKCvyYzPKoSI6MUeCkk+P45JOGdhXKoigRDu89sTEa5fi84n6OitE1kdBoQmi1Ifx+OR6PCo0myLCh1Wi1QRSKppqlhgY1gYACmUyi1qrD61Hi9SrwNAri3ZFml1uNq6j9bVHbm24llLVKH/OvvJM3l53KF+um0NkEWVdi/t/u5D8/ncszP1zYuGXfHtG/bBtN3r2fkm5s9IhudOUotUXynKfkreLOGW9G99/tET33rfuodSaRay4jXus6Yh7R+8JXtAZd37FRkQwgU6rRD5qMt3BVmwtlEHj0m7moFQGumvQZvpBqj/e5CUkSsf/6Lsmz7kBtieSoKpMyEGRyGpZ9QMoZd7fxuNqO5Z+t5oS5x0ZFMsCYWSP44dUlFKwupt+Y3kiSxM4/i7CW2cgalEHmwPQDnLE5x5w9mpxhmSxfsIbKndWcfN3xDDth0GEJ8L+i0ihRaQ4eNZTJZFzx/AWs/mY963/cjFwp5+z7ZrXa67lg1S6m/W0SWYMsBH1Bzn844vd+/wlPctK1U9DH799toi1SMNoq5WI3J55ooLoyxFOfLibZqMPq8DJqlJbr/t6yycORRqsNYrE4sKS7UKnC2O1qqqsNSJIQrbI/HERR4rVX6vn6Kycmo5Y6h5fJk/XccJMJpXL/9yqdVobDEUYUpWaOJQ32MHpd+97jVCoZgwZp+PEHJyfPaLo+f/O1I9o4JEbXQSYT0WhCSBJ4vSpkMpGhQ6vRaUNoNEF2xwN2FcdTUGAiGJTj9yuw2zV4vEqMRj/paS62bo30gPD5lGxttGLrznQboSwIIk+d/TRDM3dS7+4YwdWdEEWhxa4XkiSjoiGFioYUlhU0L1ya99vpLNo4IRqJ3m1xZ/dE2pZeNP5rLjvmSwDsHgO7rBYKrRnc+vFNiJKclLg6PAFtm3lE7wtBpSXs3duKTPQ6kOvaK1ojcP+XV6FWBrnxhA/w+DW8svTM5s/vcSD5PVGRvBtt7igaln3YTuNqG/yewD6FnS5Bh9/jx2F18eLlbxL0BcgalMHCp76j9/Bs5j57XjR94WBk5KUz55+ntPXQDwmZXMboU4e3qHBvf1Rsr2bcGSPpO7p5MaIpKxFrcR36YV1LmAiCwCWXJTLnrHhKSgKkJCd22m5rvXvbyO1tRxShrk5HeYWRurq2baKw4FMHq36VuG36FOI0aryBIPNXreF/b9Rz5VX7T2HI7qUkKVHBZ581MGdOPIIgUF8f5oMP7Fx+RfunPlxzrYk7bq9i+44AAwaoWbfWy/r1Pp55tuUT2xhHDoUijFwu4fdHfmv9+1kxGAJotUHU6jCCAFVVBjZtTkEUBeRyCadLRU3t7siwErc7EiAIh2Xkr48EvWQykd459VitOlzurh8lbg2d86p1CNxw/IfMGLqMh766fC8HiBitR2qjaHxYlFNiS6fEls6S7Xt/Lq8tnc1vO4dHBXSOqYLc5LJooeCDs17mpCF/UOtMiLpybKrI5X/LIs1L5LLwYVut6fqNp/7nefiK16PpNQyAQO0u3JuXkHbx04d17gMhSTLu/Ow6wmFZtJPfnsjUEYupkKMWhbFp1h6oLUIRn9ppC/gABk0awO8fr2TM6SOiUd7qwlpKN5XTZ2QO7/7zE/qN6c2cO09BEARCgRAvX/0237+6pMV5vd2N1NxkCteWNBPK7gYPdaX1mDLbTxC1dST5rxgMkahkZ0KnC2CxOCkvN+L1Kqmv11IgClRUxhEItM9t8YsFLs4cMoo4TURkaFVKTj9qKP/5aimXX5m4X39rQRC46+4U7r2niu8WuUhNVbBxo4/Zs41MmND+k6e+fdW88moG337jJH+dlz59VFx3vYm4uO5VzN11kFAoxKgdWmZmA/FGH1ptCJ0uiFIpUl+vYU2jf7deH0AQJOrrtXi9SjxeBS7XbqErsGZNy2pGlEoRh1NNcUn8wXfuZnQLoXzi4GXcMu09Pll9AvN+O72jh9NtOBLuTbsj0fvj7T9OYV3pgKhH9MT+a+ibUhoVyp9ecyupRlujR3TElWN9WT+WFw5r8Rhkah3mWbdTu/AJlKZMBLmSQNUOkqZdgzKhfT0+RUnOXZ9fH/27T3IpBbVZAAgKJYbhJ1H3zbOYTrkZRZyZQE0R9T+/TsLEzl2kOua04az8Yi1PnfsyY2cfjdPqZMm7y5lz50xkchkbf9nKv1bcHS2UVKgUnHrTNN685cMeK5SPv+xYnrv4NUwZiQw/cTB1ZfV8cO/njD5tOIak7uXL2xHIZCLJyRHnisREH6IILpcKr1eJ3a7Fbm/fNrz2hhAmQ3Nhm6DV4vOJhEKgOoDlq8Wi5NXXMtm0yU9DQ5hb/mE+ot3rTCYFF17U8xpGdRwSu1NHzSY3CYm+Rp/hYGM7dDnL/sgGICnRi8EQwONVUl0TsVJzuZq+TGvXtU3DG79fQX5+z1xF6BZCeUBaMWuKB3DXguuI5SW3DZJ06A1H2pJlBcP3SudQyELR/3+ZP4lB6YWNHtF/YDI4+HbjhKhQ/vy6m3H69M2arWytyqGyoXlelbbXUWRe8wa+4nwkMYwmexgy9ZFd6j5h4Apeu+hhbv/sBj5eNR2AhOMuxL70bSrnXYegUIEgEH/MeejzjgU6b0RZoVJw/RtzWfPtejb/ugNtnIbr35xL9uAMPI6IvZbyL/m/GoMGvzfQEcPtFGQNsnDlCxey4Ilvef3v76GL1zLpgvGc8vepLT7HoeQqS5LEzh0BbPVhBgxQt6jArKshCBLjx5eiUYfxeBXsLEiistLQbtHjfTF4sIb80kom9O0V3bapoprcHE2L/IhlMoGhQztXZD7G4RNn8JOQ6I12ntNqg6hUYZYszQEEzGYPaWmuaAtmm02Hx9N07Vy/IZX21j0Gg59QSHbEnF46G4IkdbwY2o06vZ+Ufsmzh3SsSh4kEO6ZH2J7MGXASkpsadHoZlfBqHGhVfmodpiRCWGeOvsZepvL6W2uIL7RI/qN307jwa/+hkoe5OWLHqG4Lp3C2oxGj2gLFfbkDvGIVsmDvHbxQxzXby03f3RLY0FqBDHoR/Q5kesTEWSRsXXm1IuD8eRZ/+WYc8Yw4cxR0W0fP/wlIX+I8x6a3YEj6xyEQ2FkctkhWxO2RCgPv2oLVmuIe++qpd4qYYrTUlLrYPYcI5deltBlG4JAJHqckuLGaPSzfXuk8CgzswG3O5Jm0REBlR07/Nx+azXje+fQx5xMSX09S3bs5N4Hkhkxon2j2TE6DrU6REKCr5nPsFYb5M+VGQQCCnJy6umTW08wKGu0UYu4R+zalYAoypDJRERRoOOCgBKjRlagUIgsX5HZgeNoe6aeULhakqRRB9uvy0aUlfIgz5z9NG/8fhprSgbGRHIb88u20R09hEPC4TPg8EV8W0VJzs0f3tr4SMQjOsdUQUOjj3SCzkGq0cbY3hubeUQ//PVcXv/1DJLjbFx53IIj5hEdCCu56t07efPSB3j67GcIhJR8uzHiCyxTqpEpO08BhaPWyZfPfs/6HzejUCkYfdoIZlx/PCpty1qGnnP/LJ6/ZB6Fq3eRPSSDTUu3U7m9mn98dE07j7xr0JrGLIfDYw9b6aW2cMW0AcgEAafPz6vfLqNPHzcTJ7Ws89bh8OefHr750o3LKTJmvJqZpxoPy2NZrw+QYXGQluZCqRRxu5XI5SLhsIyyso7NrezXT81/Xkjj4w9q+HlnBZnZCp58OpW+fTvP77orUlwcYMFnDZSUBunVS8UZZxjJymqb1sUtQS4XiYvzNxPBWm2QLVuTcbnUJCZ6GTyoFgCfX47Xq6TOpo2mNpaVGSkrM+6zBTOwVze6I018vJ/4eD/btpnoTiK5NXTZiPLDp7/IheO+5e/zb2Vh/uT2HVgP5Pi8PymxpbGzJrujh3IEkEiOqyfXXE6OuYI1xXnsqOnFyF6bee+Ku9Eom9IBvAE117z7TxZvH0VWUhXjcte3uUe0TuXl7bn3MixzB1OffokSW/O8sI6OJAe8AR6Z+R+GTMlj8sUTCHiDfPXsDwS8Aa5/c26LI5ENtU7++GQVdaU2sgZbGDNrBBpDbGm5LdlXZHl38V51dZCrr6jk7hnTkO/hE722pIKt/u3869+p7Tq2+e/ZWfiZh8l9+xGnUbO6tASX4ODZF9IOyWc5JcXF0CE1iCLU1Bgor4jDbtfQU2/ubUEgILL4FzcbN/kwJck58aQ40tI6T1Bq82Yf99xdxezZ8QweomHjBh9ffNHAI4+mM2BA20xABCHiL9yUIxzJEy4ti6e+XktiopejR1QCTS2YvV4lBYVJOJ1qFIowanUYr3fvFsxdgWFDq4iP9/H7suwuOf4D0a0jyheO/YYLx33Lf385MyaS24mXLniMN36fxb8WXdrRQzkCCNQ6k6h1JjXziF5dPIiB936yl0d0UV2kOGJ87nqeOPO56P67PaL//sH/UWTNIDOximSDvdUe0Z6AlsvevJ8peav2EsltjSRJbF9RGIkMK+WMOnU4WQfpnLdy4TpSckycdfep0W1XPH8+90/9N0XrSsgd0esARzcRnxzHSddMOfiOMdoFt0tEr1E2E8kARo0al7V9m0k0NISZ/34D/5g6mXhdZHI0MD2Ft1b8yXffOTn99INHfw0GPxkWJw0ONVVVcdTV6di+I4nKyrj9RuditBy3W+S22yox6GUcc6yO8vIg111bzp13pTByZOewKpz3uo2rrjYxfXrEbnTECC1ms5w35tn41xMtv3Y2tWBuigjX1emwWvVoNEEmjC+L7hsOC3i8ShTyyG/E4VCzdm0ann20YAYIheRd9vuo0wVITvZQWJTQ7URya+hyQnl87nruO+0Vftwymn9/f1FHD6db0xmK+TqaA3lEf7rmBJYXDm3mEZ1rLsfuiSxZzzn6Z26e9j4Q8YjeXUx49+fX4gloMentBEJKnP69HQ2cfj0L8yOFWUdnb0GlCLbKyaNlr03ig3s/Z/Nv25lw5miCviDPXzqP6VdNYurlE/d7XNmWSvKO6ddsm1whp/+4PpRvqWyxUO6JhAIhrKU24kwG9AkdKzaye6nwh0MU19XTy9TkaLC6tJTR49o3sr91q5/sZGNUJEPEBu0oSyZrVxZw+n7Mi+RykdRUFxkWJ0ajn3BYIBCIiJBwWEZp6aH5nouihCiCQtH1o89OZ5jvv3exa1eA7Cwl00+MIz6+9UJtwYIGMiwK7rwrJbpKNGaMjv88a+V/b2Xt187uSCFJEhs2+Hjs8eaCePIUA889Z/3r3iiVYrM8YY9HSXWNAZlMZPKkXc32DgRkeBsL5nw+JZs2J0fzhyPft6bXHg7LsNV3jolDWxMf7ycUEjo8bamj6XJC+cyRP7LLauGmD27rkIKrnoKEAEJMKB+Ig3lEf7ByOhvK+0aLCXNMFQzP2oY3GFkSvO2ktzl39PfNPKJ31mRGG4/IhDCiJOOema/TP7WYi994kNXFg9ps/DtX7mLT0m3c/fVN0ZSH484fy0MnP8PIGcNITN+36DD3MlGcX9psmyRJFK7eRVVBDQ21TiaePw5jclybjbU78Ov8FXz59Heo9WpcNjdHTR/MeQ/ORq1rv3zK3S4YAH0LX272mEIhcMONifzn6ZVM6N0bk0HPxsoKrAE7d5zZvraIRqOMercPSZKaperUe7wkZOw/cjV0aDWmJC8ul5Jt201UVRkOK1rn9Yq88lI9P/7oJBiUGDxIy7U3JHbZvOGKiiD/+EcFQ4dqGTZUw6ZNPv52ZRlPPZVOZivzdpcv93DllUnNPp+RI7WIEpSWBOmVc+TygPeFIAjEx8uprg425iRHUiR8Pi/XXy+QmuqkujpyDRo/vhSdtsktSZKgojKO6hoDoihj+3YT/oAcr0eJZ48WzJF9Baqqeua1rLIyjpoafbP3oyfS5YTybZ/cSJLe0a6d2mJELg5dP7bSsVQ7zFQ7zPt9/NPVx1NUayHHXEGuOeIRPSpnU1Qov3npAwxI20W5PYWwKOO9K+7ika8vB3LbZHzrf9rM+Dkjm+UFJ6YnMHTKQDYu3sZx543d53HjZh/N9y//wuK3l3HMOWMI+YN8+ewP+Nx+TrlxGtuXF/DIqf/h1g+vIblX52xXfKTZ8MsWFv33Z256729Y+qfhdXh5/54FzL93AZf++5wOG9fESQYyMpV8+UU1BbUiY09UMuOUdPT69r0x5uWp0RokluwoZGK/XGSCQGWDk98LC3n06oivulwukpbqIj3dSf76NIJBOUVFiRQWJuJwqGmL3OMH76tFqjfyf9NHoVMpWV1czu23buHl1ywkd9Iuggdi3jwbM2caueCCyArBzFONfPyRnVdetfHQQ62b/GjUAm538xQcUQSfV0Kt6Zi7gyBI0RQJuVxk5sw4XnihjoULBdJSPdEWzKfMgIYGR1Qol5cZkSQBT6PFms/XPF+4tIdHTPeFUhkmGJT3eJEMXUQoC4LIrdPf4Z0/TqHKYcbqihmftzexWHL7s3LXEFbuGtJsm0oejP7/+83jqHUlkmOqICTKMSo93D/rVT6r+Du2oIUZqa8jINEQNNMQMtMQNGMLpOEKt+z3oVQr8HuCe233ewMo1fu/NOgTdNz47t/46IGFfProV4iiRO/hWfxz4Y0YzQZGzTyKhNR4vnr2By575twWvhvdm8VvL2PWrSdh6R8RK1qjlvMfms2dxz6G2+7p0DSMPn3U3HTLkY2gCoLAw48l88A9u/jj+yLiNCrqXF6uuS6B0aMlMiy1pKa6Iu11nSrU6hDBoJyGhrZLCdm1K8DO7UHuOHF4NE97bG42VU4HXy10cNnlXe8+s2K5hxtvbD45n3mqkddft+0VvT8YU6fF8f57doYP10adSD77tIHMTGW7FvTJ5SI6bRC1OoS1LpKWlptrIz3NGW3BDBAIyLngwmxeeMHKC8+7yM2Vs369SEKClqnTkggGm8ZYcogpOT0VuVxk/LhSSkvjKdrV9X4HbU2XEMo3TX2f66Z8TKU9mXdXzOjo4fQILnnjAaodHRcN9Batxf7buwQqdyA3JmMcdRpxI0/r0t6uLWFPm8P3VszgvT2+70/e8CMzUucx2PgHv9bNwR2KJ0lVRV/9OtTySBOP7a6j+cV6LiAxLfkd3OH4iJAOJtMQMuMKJSARuemNOnU4z5z3ChMvGIc5K9IiuWhtCTtWFHLxE2cdcJyWfqnc9O6VOKxO7jzmUW6Zf3W0VTXAuDkjeXzWcwc4Q8/CXuUgrc9fmtwYtRiS9DjrXB2er9wRpKUp+e+r6RQXB3G5wvTrZyI+PszoUaWEQgJV1QbKy+OorVXy5rwGfvyhGn9AZPQoPVdenYDFcnhirawsSKbJuFcxY3ZiIiXFtsM6d0ehbowCG41N6Shut4ha3frr5vTpBrZu9XHxRSWMOFpLeXkIr0fk0ccONy1HQqUKo9WGcDjUSJJAepqTjAxHY7ONSBRbkmDxkhxEUUYgIKfero003WhMj/B6lSgUAjfdlEy9LZGKyhATJylJTJQT3Hv+H6MVWCwOlEqROlvM3xvaWSgLgnAS8B9ADrwuSdLjrT3HjKG/ceMJH/DRqqm8u+LkNh9jjH3z10jnkcRXtgnrV09hmn4t2r6jCdQWY/vuRcSAl4QJPTdCaQtaWFB5A55wZDlxSd1uMSuhkXmIV9YSFCORQaXgx6iwkaXdjlLWZG+3xn48K+0noRR8nDFmJdlv9+XTJ55GMOVSXg47V5Vw6VPnoDO27AKpM2qRK+W47R7iTE2+u45aJ9oWnqMnkHt0Nuu+30T2kMzotvKtlfjd/ugkpechkZDgZ9AgBzIBNm3W4vPJyF+fSn29Nrrk+8C91YTq4rjm2OFoVUqWFxVz89+LeO0NSzNB2FpyeikprrURDIdRypvOU1hnpf+EzmOB1hqmTo3jjTds3HFHCnK5gChKvPmGjROmxrU6yCCTRUTomWcmsHmzj5NOlDN8hBa5/ODnEQQJtTrSalkUZSQmeMnMbECrC6HVBFEoImuWvy/LinR7EyTCYRm1tfqoCI7YqUWe62DFZIlJChKPYEvv7owgSGRnNVBv1+BwxOw6oR2FsiAIcuBFYBpQBqwUBGGhJEktNoEdbCngqbOeYXVxHnfH2lMfUU4Z+ivFtnQ2lvc94s/tWPEZCRMvRjdgAgDqtL6YZ91O1Vs3Ez/mjEgr5x6KOxxZQtTJHUw2f8SvdWfgDCXhE/X49nDPCEoaPq28CZDQyZ3EK6wYlVbqApEKcYPCzmDjMo6aEeLyGQBb8Qfl/FR5BqXiQHRyB1nabdG0Dm/YwL5+fwqVgtGnDufTx77mosfORK6U43P7WfDEt0w4q2s2rWkPpl81mSfP/C8AR00bTNXOGhY+/R2n3nIiClX73eD/WsDXGVAowqSnubBYHBgMQUIhgcrKOCIJXwJWa9P3eMcOPwU7Qtw+fUQ08ntCXj+sbieLvnVy9jmHvqSemaVixNEa3vlzFScPGoRBrWLlrlK21lRzy6kHtkjsrFx6WSIPPFDNJZeUMmSwhi1bfKSkKLj/gUOPAmdmKsnM3HviIJNFIr+iKEOnC5CV6Yi6Smg0IWQyWL0mHbtdi1whotcH8XiV1NdrIkLYoyQYjExQKiuNVFa23EIzRvuRkuJCowmzbdv+62t6Gu05BRsD7JQkqRBAEIQPgFlAi4XybSe+Tb0njqvfuSvWee8I89ic5/l41bQOEcrBulISJ1/abJsyIQ1BqSbstqOITzniY+psaGRuklWlzEx9lYVVV0cF9N4IeMJGPGEjlf6mIsD6YBrzih/GIG8gXhkR0fEKKx5FBgQgRV3CZPPH0f0DopqGoImldWdiDWSikzcQp7BjD5o5866ZzLtxPnce+yiZAy3syi9l+IlDmHrFce38LnQdkrNN3PbJtfzw6hLeuf1jEtLiOe+h2QyZnNfRQztC7K56EMjKdJCbW09Dg5rNW8zU1Bj2WzBUUhwgx5ywV3pETqKZooLiwx7V7Xeaef9dO299+wcer8iokTqeeS6NxMSu6aik1cp4/PF0tm3zU7wrwKzTjeTlqQ8jZS0yeVEqw1gsjsamG6FGMRxm06ZkqqrjUCgitn1erwKnU01NjQGPV4Gn0WLNatU3mwDF6LxkZDhxu5VY63peOtj+aE+hnAHs6SFVBuxVRi8Iwt+AvwHIjc1z+K5//3ZSjXXUxor3OoSO8lFWmrLwlW1BacqKbgvaq5BCAeT6WFEGgC2YztfVVzAz7TVmpr3Kl1VX4wm3NiIjwxVOxBVOpNzX3Be52DOQ+WW3R0S0wkp8o5D2i5F0it66jRxr+gIAX1jLud+YqbZl8v7iozBmnYElW4UkBQhIsfSL3SRnmzj/4TM6ehhHFKUyTHqaE4vFSUFBErVWfSTv2KrD5Tp4AWFmloqSujpEUWrm21titzF0+OHfvlQqgUvnJnLp3O51jxkwQN2qznSCIJGW5mr0GG4Sw8UlCRQXJyCTSfTtU4+/sQWzrV6L16vE2fgZOhxqlv7ai9iqb9cnPz8NjSZE7LNsoj2F8r7e5b2UlyRJrwKvQqSFNUicNeoHFq6bhMuvw1Ubm9V0CJKA0EE+ysaxc6hd8ChyjaFZjnLcqNN6dNrFX7EGsvi2ei6npL7OKamv8WXVVfhEw8EPbAESchwhE46QCRiw1+NFnqE4Q4mNArqOeGUtvZMr6TfhfEKSmuHxXzM8fgnesB57MBlHYwrHuoZJSMgBEYjZDnVPJBITfWRYHCQnu5HJwG5XEwpHbgmBoIJAsGW3nv79VWT2kvHJ2nWcOGggWqWSFUXFbK+t5raTM9rzRXQ7kpI8zUSwVhukzqZj585I0XbegFoAfP6IhZqjWo/TGbne+v1yflmcc4DubDFR1T2I5Iq73bH77J60p1AuA7L2+DsTqDjYQReP/4oHZ72CRhHgneUz221wMQ5MR9rDaTIHYZ75Dxp+e4/aLx5HYUwmrtH1oifyz6v3n61U7c/h25rLGJf49RGd2HjCRkq8g8C778eL3EPxhfURIa20kqndRi9hM2sbjgfgePMHWDSFUVu7hqAZWzCVUu/AI/YaYrQtMpkYFVJ5A2pRKkXKyo1UVBgP+cYrCAIPPJzCay/X8+/vfyEQlDh6hI5/P5NGQkLXTI9oL3S6AHpdMCqCtboQXq+CbdsiK7UD82rRaMKEw0LEPcKrjKZGSJLAH8uz9tmCOYIQLayL0T0xGPwMGlTLpk0pMaH8F9pTKK8E+gmC0BsoB84Fzj/QAQa1h3tnvsYPm8fGbOA6mH1fLI8c2t4j0PYe0aFj6CpU+vqwoPJ6QIZAGIUQJCh1bLVyTSCbmkB2s21yocmzqczXHxE58QorObpFuGMoAAAgAElEQVRNaOVu6gLpUaF8QvJ7aGSeZh7R9cFUnKGe6hDRWZFISvKSYXESH+9j2R9ZiKKM9evT8P6lqcOhotfLuOkfJm68JQlJosNbJ3cUCkUYna6pBbNWG0QCtmyJ1GwMzKslIcEPQDAoi3SYCzW9//n5aQSC8r1aMO/G54vVAfVksrMb0GqC+P2xCehfaTehLElSSBCE64HviNjDvSFJ0qYDHZOdVEVBbSY3f/gPJCm2LNuRnPfao9g9PbNtZ9ck8nuZYv4Qg8LON9WXE5I6VxvesNR0I97uGsV216jo3yqZF43ME/3bGzZgVNiaeUQXe/JYVDMXgMnmDwmImv16RMdoX3YXd2VYnGi1IQIBGZVVcchkEqIIbk/bR6QEQaB726hLqNXhxvSISIqESh2KCuG8AVZSU93RvX0+ebM87x07TUhSJFq8r7beLnfnuh7E6DxoNEFSU1yUlcUfVkv47kq7Gg9KkvQN8E1rjrny7Xti7ak7AVsq26ZNcowjS5FnCFOT3+OklP/xbc3cZuK0MxMQtQTEpsK/ZbZZjf9r8ogWpcgFXEAkSVlFgrK2mUf0Jsc4frOdgYDI2MRvaAiZIiI6aMYdNhLLiT5cJOTySA6jVhukb596bPUaCgqSqKnVd/gqVFdgzxbMuwvndhYkIYoy+vax0atXQ3RfUYxEeXentJSWxVNVbcDrUe4zWh/zvI1xqGRlRr53JaWxVt77olM5dO+qSyfelt7Rw4gBzDn6J0psqR3aeCRG6ynyDOMXa4jjzR9yYspbLKq+FLFz/cxbibCXR7SEjM8qb2RPj+h4ZS0NwUguplbuZHDcMhSyUPSYkKhgme00trjGoZJ56a3bgCNoxh5K3q9HdIwIanUIS7qTdIuTujot27Yl43Com5pFxGiGXC425QlrQ1RVGQgEFKSnOxiYZ20WFQ+FBErL4vF6ZdTU6iN5w435wz6fgj2/l23ZvjtGjN0oFGEsFifVNQb8/q58r2g/OtW74gloic1nOgf/nPEGizZOiAnlDuRARXwHYqf7aORCmMnmj5ls/oifrQcsDejC7Nsj2hOOZ17Jw+jlDhKUtVGP6PpgKgAmZSWTzZ9E9w+IKhqCZv6wnUqlvw8amYt4ZR0NQTM+UUdPFdFJSR4yMxyYzR4EAepsWuqi3qpCDxbJEkqlGEmR0AVpaIg00EhI8DJ0SHW0BfNuXC4VNlvEX7hoV0K02YbXqyAQbMoXdjhindBiHHnCYRnbtptxOmOpOfujUwnlGJ2H2DJq12abazQCIrbgoXfk6trIcIcTcIcT9vKIrvLn8H7Z7Y2R6CaP6EBjAWSmdjsnJH8AgD+sxR4y4wia+dN+Iq5QEiohkjPdHT2i1epQYzGPQLLZg9Hop7g4gYrKOLzeniSMJTSaEFpt5P3weFRotUGGDqlGq21qwQywZasZr1eJ36+g1qpvFMKKaCvm3c1UXC51i7yjY8Q4kkiSQFVVrB7pQMSEcoz90lENR2K0DVtdTf19srTbKPP2ixW7EUndcIZMOEMmynx7e0SXefvzbfWle3hEW0nTFEULjAfGLWdc0rd4w/pmxYQbHMcSklR0NY9oQZAwmTxkWByYTF5Wr7HQ0KChoDCR7TtM3XbSLJOJaDShxgK4SC7w0CHV6HQhNJogu5sBFhUlUFiURDAow+9XYLdroiJ4t80agNerZOvW5AM8Y4wYnYuUZBcabYjS0vhu+ztvC2JCOcZ+6d4V5j2HVHUxM1LnscU5hqV1Z9CVRFxH4BMNB/SILvf1Y7lN2MMjejt95WvIb5gEwDFJC+mt29jMI9oeTKbYO/gIvoqDI5eL9Oplx5LuRK0O4/PL2bUrAa83clvoDtXvCkUYuVyK5l4O6G9Frw+g1QZRq8MIAlRUGtiyJQVRFFAoRJwuFTW1usb0CCWuRk/ZUEhO/vqeukITo/shkZtbT1gUKCmJJb0eiJhQjrFPYrPL7kO1vxer7ScwMuEnwpKC322z6Kl5t22BNZCJNZDZbJtCCCASEZZV/hyUMn8zj2hXKJ7isohQPs70KUZFXWMk2tQopFMbuyC2L4IgodEE8XpVSBJkWJw0ONRUbI2jzqbrgr97CYVCjIr6rMwGjEZf1FVCpRKx2bSsXRcpEjcYAoBEfWMLZo9XsUc6hMDqNbFufzF6BmazB70+yMZNKcTuBwcmJpRj7JM5Lz+JNxDLp+sIDrWI70Cssk9HIQQ5Kn4pYUnB8vpTiF0c245IykWEAvdwCtzDo3+rZF60Mmf0b39Yh1JV0cwjutqXzedV1wNwTNLnSAjRaHTEIzqxVWkzfQtfbva3VhvEYnFgSXcRCgv80dgY5PdlWW3SFKR9kdj9XTWb3SQk+Jp5Dft8CpaviDSBTTJ50OuCeLxKamr0zSLCAKvXWDriBcSI0enold2A16ugpkZ/8J17ODGhHGOflNpiS4zdC4Hl9acgF0IcFb+UQs9Qavy9OnpQPYK/ekT/aT8Z7NDkEW1FoMkpIUlVhVlVhmoPj+id7qP4qfYCAEYlfIc7bGyRR3RCgpfeOfUkJfkQRbDW6aioMEYf72wiOS7OT2KCNxoR1mqDKJUiS3/tRaTA0E1qqjuaG2yz6XB7mooM8/PTiE0AY8Q4MEajj4QEH9u3d98ahLYkJpRj7JPzx3xLuT2FJdtHdvRQYrQZAr/bTqPQPSwmkjsFe3tEA3xZdTUgoZW7SFBE7O3coUgOoUIIcJRxSTOP6KCoZLV9GvmOyciFIEONS9GYXTgcGhSKMFptiIKCRCoq4wgEOvaSr1aHSEjwRUXw7lbMy1dkEgrJMZvd5Pa2EwzK8HqVOJ1qPF4lggCSBNu2m9myNZn9i+HYTT9GjIMhSQK1tToqKmNuFy0hJpRj7JPrj/+Q33YOjwnlbocs6jmcrinApKxko/PYDh5TjL0R8Ibj8IbjmnlEhyRV1CM6UkxYS7zCij1opq9+DUONv5KiLofExv1D4PEqcbrUBAIK5HIRvT6A16skGJTR1sJSLheJi/M3E8FabZDNW1Jwu1WYkjwMHGgFwOeX4/UqqbP9f3t3Ht/2Xed5/PXVZZ0+JN9x7NyJ2yQ0PRJ6t0BLbyi0UJjhLHRgF3Zmh2FuBmZnl92ZYVlg2Rko0GHozHBDKT0p0NAjadKkSZq7bQ7fp2Trvn7Sd//4KbJdyzkaJ5Ltz/PxyCOxrOOjyJY++ur7e39cWCxmwk5PTw09PTOP0a20FXAh5qJotIqX98qnxqdLGmVRkpaVmXlvtXcHq7070VjYH72i3OWI0zaREd2fWs6b6x7luvof47QmCWcD7A5fi7XnAA57HrfbHIqRy5m/zzU1KTZcNAhANmshkbCTSNo5fryWRMKB1ZpHKT1jo3riYMDJTbDLZdDdU8P4uGvK9ZsjmM0tEkqZjfDIqIfwNifJ5PQRzDA/kjaEqGQNDXEikSqZwncG5H9KzEha5fPrXBzEdzLPjN5FlSXJVYGHMLSNw7GN5/X2xRtjUxlanEfpSa4BFG5rhN7UKg5FN9KXWg5YWDHcU/KykUgVu/c043ZnzGbXnaW2NoWl0Mg2NcXoXDNKNqvIZKwYOQtoc6V3aNiH253hzZv6iteXyykSSTs2a754/bt2NZMoDOB4/f7HbNZKNivNsBDlYLfnuPCCYQYHvRw6LJnfp0saZVGS1hRXgcT8lMfKU8O/z01N3+XawE/JaRuvxS8ud1liBn77AJ2+baz0vkSVJcX3e/+UiFHPb0ffx+m+rTUMK8Ggi0ikqrA9wlwRdruzxOJVRKNmQoTdrrHbJ/ZBj4ya/25qipHNmgM64gk7sZiDZMLBaGG0tWFYCY25p9+wEKLsFreFsVg03T2Sm3wmpFEWM5D15IUgj41fDX+QmxofoM31qjTKFajOPsi1gZ/Q5OzG0DaOxddyMLppUu5yqd9VTVVVztx64cpiGBaGh70AXHlFN05nbuKcGvr6fQyPeIlGnbzyqp90YctEImlH64m9wZGIk5GqHG5XloA/SUtznFxOsfl3SwBYvjxItS9T2NJhM/9OOBbY+GshKo/FkqetLcLoqJtEwnHqC4giaZRFSe/4+pfJ5uTHYyEwtIMnhj9KTpuPtyIvo67LLODow6ayDKWXkMhVY1E5toRu55XYxaTzZkqGBYNqe4hq2yhWZXAssR6Atzf+C+3tx4ojmAHGx53FRrm3r4Z8TpEoRKylUlP3C/f01M5Y1+ioh9HRiZQOq9UcA32iWc/lLFiteZqaYtjt5naMeNxezDletiyEzZov7o1OJMzblzfmQpxbrS1R7PY8Xd0z/36L0qQTEiUF4/LLtJCcGJjhsY5zc9MDvBC6jd7UqjJXtbDYVYoVnt2s8W2nsaqX/tRSnhz6MD5biB3jN9Kd7ARgY91jrPDsxmsNF7dHxYyaYqM8mFoCI/0kk/bCH1uhGTV1dc3e73YuZyE+aaDH8eN1HD9eB2js9jwuVxardWILl9eToa4uic02cVow5GJ3YXJeR8c4RtZSbKLTaSvSRAtx9pxOg7FxJ+Gws9ylzDnSKIuSPnLlLxgI1/PEvivLXcq8dr4P4DsVQzvQKG5s/FceH/4oA6nl5S5pnjPzki+u+TWrvTuxWzLEjGoi2Trq7EN8pOPzAOS1he90/Q/yWEnmvAyklhHJBogY5p9wdmL89Z7I9cSPHi7XHSpQJQ/cMyOpNA7HpG0hxfNoOtrHiyvRYB4s2N1Tw9GjfkDT0hI1t4Qk7GQy0kQLcbpeOxKQ447eIGmURUkfuvwRdveskkZ5gUnn3Tw6+HFub/4mNzf+C48OfYyh9JJylzWnKXJ4beMkctXktJ1FzldZ63uO+qo+qixJ7JYsAK/F1rM3ejUBez/LPXsJG4EpzfCJyMa9kWvKeXdmgSKTsZHJ2Bgfd005/ZlnO4p7q90uM9ouVjjA0OHIcUEhgxnAMMzEja6uWoaHvVgseXzeDIlzlBEtxNykcbmyJJMOmcL3BkmjLEqS950LVyrv5ZGh+7ij+Z+5uekBHhm8j9FMW7nLqmhWZTa7OW2nzj7Ihb6tVNuDVNuCeG1jWFWehwY+icbChprf0uo8glLQnVhFd7KTiBFgILUMQzsYTndwMHb5G6pjxdFvzObdKgNFOm0jnbYxNuaa8p1MxsrzWxYXm+gTK9L5vPni7/VkuPTSfqCQEZ20k0zY6e6pIRqtwmLJY7HMnBEtxHxUV5vi4osH2L2niWDQc+oLiGmkURYzUvLmc8FK5nw8MngfV/ofJmbUlbucinDiIEeXJcoa33aqbcFCMxzCawvz6+H3cyRxEVWWJCu8u4hkA4xmFnE0vp6YUcO1gZ9S5xgmm3dwKLaRg9FNjGTakJXP06VIpeykUnZCJb4bTzjMjOgTTbQ7S01NCmu/OaY3EEiwft0wmczEoJVkwj5ptLdGHgsx37S3j5PJWKe98RSnTxplUZLWCiXrygtaPFfLr0Y+CJgJCx5bhKjhL3NV555VZVnh2W02wsVmOMiu8Ft4OXItNkuWjXVPEjd8RIwAfakVRLIBQllzJOxgegnf7f4CTVXdBBz9HChMPfQ7BtkbuYrX4heR1XJAzWzL5SwEg26CM3w/FnPw6mv+YiPt9ydxtsQYKqSBtLVFWNIxPiWRI5kwR2zL6GwxF3k8Gerrkxw5Wic/w2dBGmVRkoywPrcq7SC+U7k68HMWuw7x8OAniBhzf6JTm/MVauyjUxrhrmQn28duQWvFtYGfoFFEjToiRoAj8cUEM60ARI1avtP134tJIZNVWeKs8r5Ep3cbdY5hkjk3h2OXkdN2ngu963zfTTFJMumgu3vqY2ax5ItbNxKFptjtytJQH8fhMA8qfHrzEsBM5PDXJUkkbBONdOFvWYkWlai9fZxcTtHbW13uUuY0aZRFSbd97avktbwDFaY9kWvocB/g9ub7eXjwkxW/slxrH6LWPjJlVTicbeD50DsBuK7+h3hsUbJ5u5kaYdQTztYD5hCW/+j7MxJGDXlK7We1lGySl7n3cH3DD7Epg6F0O5tH7+ZIfD05LcM2KtXkVbZQyE0oNDFV0GrNF/ZAWwrnpZARHS8mc2QyFp59bgkAHe3jVFUZU5roVGr6GG8hzgeLJU9DfYL+fp/syz9L0iiLkpJZ+WhYTBjPNvFIIQ3jtqb7eXjwE8Rz5cvarrIkqLGNFleDq+1B8trKM8G7ALi+/kc0VvUAkMq5iRgBsvmq4uUfG7qXVN5LIuej1Gpg7DTeCDgtMVZ5dzKSaWMgtZyRzGIORc29x6Fsy+zcUVE2uZyFWGziZ6anp7YwjGUiI3pylJ3Hm6GhPj4lIzoScfDiDvNA2La2MDqvJCNanBf5vIUtWxfLsUazQBplUdIfXPMTRmN1/PSlt5a7FFEhQtlWHh36GLc1f4sbGh/koYFPce5e6PN4rJGJ7RG2EE5rvNgIXxP4Ccs8+4rnjhk1hQPjTFtCd5DTFiJGgEzePe3aQ9nWN1zXIucR1vi2s9S9D6vKsTt8HQOp5UQNf3HFWsxnpTOiDxxopJgRXYi2m7xi3d4exuU0il/ncoqBAR+HXzE/yWhqipFOWyUjWpw1pTRaIyvJs0QaZVHSuy/5La8Nt0mjLKYYzSzm8aET467P7oXcgoHPFiomR9TYR3khdCt5bFxe9wjra54rnjenLUQNPxYM8tjYG7maV2KXEjYCRA3/tO0NQ+mOs6ptJrc2fZs212ukci4ORC/nYHQjY4WD+ISYkhEdnpoysGXL4mkZ0SemGloseS68YLi4+mcYimTSTm9vNf0D1YCmpjotGdHitCxeHKapMcZLu1rJ5WQL5dmSRlmUpCXw4pyYawfxlTJ5AMlq73aOJ9aSLrFqC+ZY5mp7iGqbuTJ8OHYZqbyXTt9WrvY/NGVSVDbv4OXI1cQMP8cS6xjPNhaHbcSMGvSk/cKD6aXn7P5NyNPmfI0Vnt08E3wXeWwcjl3K4dilHEusk73H4gzNnBGdzyu2bC2REV3Y3+xyGSUzovv6fIyHXSilsVrzsoIoUEqzeHGYRNwuTfIskUZZzEj2NomTqbYFuTrwMy70bWH72M24rDEG0kuJGX4WOV/lrQ3/gcsan3KZkcxi+lMrGEm38VL4rYRPTJ7LBkjmvZxYKRtMLz1PzfB0bmuY1d4drPFup9o+RjLnpjZyNaFsC6/FLy5LTWK+O3lGdCZjLZkRPTxiDpCoqUlxycUD0zKiB4e8pFJ2JCN64WhuiuGsynHw4NxPJ6oU0iiLkjSSoyxMijwe2zg1tiBRo5aI0UCNbZi3Nf47WltoqOrn1ubvALB59G4Ox/zEjBqOJy6caIQLzfCJ/ODRzGJGM4vLebdKqrMPclfrV7CoPH3JFWwfv5lj8bXk5alSlNGpMqJTKVvJjOjQmItUyk5zU4wVK0JTmuhEwk5ozCWrjvOKpr19nGjMQSgkA0Zmizz7i5Ik0mhhsaosPluIvLYRMQLYVYq3Nfw71fYgPtsYVpUD4MWxG3gpfAPpvJu4UcNAahl2lTbTH9JtHI2vBSBsNBYPvKtkHus4q70vorGwK/xWxrJNvDj+do7G186pvOi5P7panI1Uyk5399QUGoslX3weT6VsBIMu3O4sDfUJHA7z9/m559rJ5Swsao3Q2Bib2khLRvSc4/cn8Xqz7D/QgDxus0caZVHSzV/9erlLELPMYUliV+lCrJvm6sDPilnDXlsYgIPRjTwTvIusdlBlTRDMtHAsvo6I4SdiBAhlzAPXUnkvTwx/tHjdfamVXF//Q5qdx+lJdpbj7p02RY5212E6fdtY7DqERWmOxNcXv7s7fH1Z6xNiNkxO3BgPu6YcXGi15nG7s6Qz5p5mDVitekpGdD4Pm3+3FK1h0aIwHndWMqIr3Pi4k/0HGhga8pa7lHlFGmUhzoPzcxCfxmFJkcmbL4jrqp+hwdFLTSFr2GlN0J1YzePD9wKKgH2APJbCCGazER7NLCpcl4WHBj592rf8WnwDQ+l2okZg9u/WLHtz3aOsr3mOuOFjd/h6DsUumxN1CzFbcjkL0ehERnR/fzX9/eb0NpvNTOaocuSKjbDXk6W5OTolIzqRtLF1azsALS0RLBZzuqFkRJdPPm9hcNBX7jLmHWmURUmffsv3CSe9fG/r7eUuRUyiyKMxV4qWuPfRXHV8yhjmqOHnx/2fAaDDdQifLUTECHA0sY5INsBoZiI/+KHBT81qbSeazTbnYVZ6d7F59O4pSRXlYCFHu/sgnd5t7Bi/0RwKEtvIQHoZ3YnOGSbvCbFwGYaVSGTq78XhV+o5/EoAhz1XPJhw8sHei9si+HyZ4te5nGJ4xFPIloaGhriZ1iEZ0efMihVBEnF7IU5QzCZplEVJN63dSv94vTTKZdTg6KHFeZTqYtbwKA5Liu/1fB5QLHXvY5n75eLBcn3JlYxlG4uXf2To45TjBanOMcwq70sAbB59T7GxP598thBrvNtZ7X0Rjy1KzKjBbY0AMJZtluxjIc6YIpO1kQlPz4je/uIiMyN6UipHKnWivdBc0DlcXI0+kRE9MOCjp7cGgOrqFEnJiH7DnM4s7YvDdHfXlLuUeUkaZVGS1mpKxq2YfV7rGE3O48VhG+YEuiA/6vsTMtrFUvc+NtQ+TSrnImIEGMksJpINYCFPHivPBt/F06PvgRkb0fK84OyNXI1NZdlY9wQ5beOZ4LuYucbZZ8HgXS1fw2FJ0pNcw7PBTXQnV5d9dVuI+WtSRvT49LSFbdvbpmVEn3h1sdlyXDYpIzqZtJNI2BgY9BEKuSUj+jS0Lw6jNfT0SKN8LkijLErSWt7Xny1fVZwLWo/SHhhgY+3e4vaI34y8j7DRSLv7IFcHHgIgblQTMQL0JFdjUTnQsCdyDXsi1844zMPQjvN5d87IrvBbsKosl9T+hpy28XzoHZyrn6hq2widvhdprOril4OfII+N347eQyjTXDhwUQhRPifPiM7nVYmM6HRxKIvbneXNm3rNjOhJiRxDQx6Sycp9DjxfbLYcra1RBoe8pDPS0p0L8r8qStLIivKpWC052v2DdPgHaA8M0BEYpCPQzz9vvpudXRewcdk+vvOhvwPMEcwxo46IEShGrR2Nr2cgtYyo4S/Z9KbznvN6f2bbjvEbsaksDkuK2R54YMFgqWcfnd5tLHIdIa8tdCU6iwcz9iTXzNptCSHOnXz+5BnR2axlSkZ0XV2SlpYY0WgVyaSDgD9BZ+fItGi7sTHngliFbmuLYLXqafGAYvZIoyxKSmScpLLybt3tSNIRGDD/+M1m+Ff738zmVy5ldVMXj/3hfymeN5GpoivYgs9pTqPb2dXJI4MfK4xgrp320X8q7yWVn88xPooXxm7FbJItOFSSjD7bEPw8YKHDfYC3NfwHkWwd28du4nDsEhI5+dhRiPkmk7GdNCM6k7WUzIh+YVsbhmGlqTFGa2tk3mZER8JVHD9eSzwur9fnijTKoqR77v9f5S7hPNH4PRGWBPrN1eHAAIcGl/Dk/iuocUXZ8/n3TTl3MFbNwYElABwdbeUzP/qvdAWb6Qq1MBKtY/IT73iimr7UqvN4XyqRAhQua5Q7W77OwegmdoXfckbXYFVZlrr30unbRm9yFbvCb6UrcQGPDn6M3tQKzuf+ZyFE+U3OiI5GnRw85Cx+fSIj2myEATU9Ixrgd890YBhWmpujVPvSczYjOjTmJjRWenuemB3SKIt5z6JytNQEze0R/gHCSS+P77sK0Gz/yw/SWD1WPG8+r3jwhVt4cv8VhJNevvjYR+gda6Ir2Ex3sIVoemI7RCrr5KcvvbUM92juSeU8DKSWsrHuCQxtY2/kmlNeps4+SKdvOys9O3Fak4SzARI5MyM0j43eBf8mRAjxeq/PiB4a8hYHcJzIiHa5jOK2DI8nQ0vL1IzobNbCM892AIqmxhg2e55EwkYi4aigjGhNe3uYwUEvGdmbfE7J/64o6Y9veJBUtop/2vyecpdyWhzWLIv9g7T7B3HYsjy5/woAvvuRz3P58j1U2YziebceWVdolBXf3XI7yay5ZaIr2ELvWBNp48RHWIr7n3n3+b8z85DGwubRu7Eqgyv8j5DXNvZHr5h2PgsG+cLT0sa6x1nseoVjibUcjG6iP7UMWT0WQrxRJzKiI5GJ044cCXDkiB+Hw4y3c7mz2KwTx1QsWhShri5VPH8upwiFXLy814yYDAQS5HLqvGdEN9QnWLkiRCppY3hkPm/hKz9plEVJV6/cTTTlrqhG2VcVpz0wQIN3nM2vXArAX9z8ALeuf5bWmlEsFnNFoDvYVGyUXzx+AYcGltAVajG3SARbGQhPTGGrpPs332ms/HbkfViVwVWBh0jkfBxLrAPAbx+g07eNFZ5d/GzgD4kafraGbud3eec838cthCg/RSZjI5OZnhH90q6WaRnRRnbiDfua1SM4nea+6BMZ0cPDHo531QHg86VJpWyznBGt6egYJ5G0MTwytw/6ngukURYl6bIEXmgavOO0BwbY2dUJKN6/8XHuvvTXtPsHCHjNZYC0YaPzcz8lr62E4tVsP7a2uCLcHWqmK9hSvMb/9/R7y3FHxAzyWHlq+Pe5rO5JBtMdrPa+SKdvG01V3RjaxrH4OhTmPsKIUV/maoUQ4uQZ0Ttfap3SRLtdE1MLldJcekkfFsvUjOihYS+jox5AY7OdeUZ0TU2ampo0hw8HqIxtIPObNMqipHMVD2e15GipGWE4EiCTs3PNyp383psfp72wbcJTZX7Eddl/f5CRWB0WS5542smT+68oHjTXHWxBF54cvvnMXbNe42z4i08cKHcJFcuqDLaN3YrTEuPqwM+IG9U8H7yDV+MXz5gZLUpbcfQb5S5BiAWtmBE9Vvr7L++dnhEdjZl7qKscOa66qntaRvTIiOekKRYd7eNkMhb6B3zn4i6J15FGWZSktULxxhrlKlsaUKQNB6ubjvP+TY+zJGBmDbfVDWO35rjj61/m5d5V1LqjLJiIirgAABeESURBVKvvoyvUzNYj6zkebCkcNGe+c/+3F27l3164dRbvmSgHu0qxwrObTt82DO3g4cFPksp7eTW2gZXenUQNvzTJQoh5RWs1Q0a0+dqayytefdVvTi2clBGdStmIxx1U+1KsXz80kciRsJNI2tBAb2/NlPQPce5IoyxKCsVryBj2Gb+vVB6tLTT4Qrzn0qcKOcPmwI3mmhB/8OBf8uT+Kwh4x7lzw2aOB1vY37eCx/ZeRVewhf7xBgAe3nMdD++57jzdK3G++e0DrK1+nhWe3dgtGYKZZo7E1nNiAMnW0O34HUPc0PggTwx9mN7U6nKXLIQQ55j5iahhWOnumZ4RfUIufyIj2qA+kKCq1dwLvfOlFsbHnfj9CTrax1/XSJsRd3Ml3m4ukEZZlHTfg38NgNOe4vb1zxaHbpzIGv7Kr9/Pd7fcgbcqyWff/iBDET9dwWaee20DXcFmjoy0AbDlyJtY/7c/QPZRLRwOlSSHjZy20+I8ygrPLo7EL+JgbCPD6XYm/yxktIvHhu7ltuZv8vbGf+Wx4XsZSC0vX/FCCFFGk1eJ43EHBw81Fr92OrP4fOlC/J3CorSZEd04NSN6y9bFJJN26uvj+OuSU7Z1zKWM6EohjfKCp7lm5Uu0BwZZEuinIzBIu3+AJ/dfzpef+gAA/3j3VzFyFvrGG+kKtvDLPddweKgDgOPBFtZ87iekss4Zrl9+IRcGTWNVN53ebSz37GFL6A4OxTZxOHYZr8YuPulEvnTezaODH+f25m9yXeBH/LDvs8WIOCGEEKZFrVHa28d5fks7uZyF0aCH0aCZenEiI9rtzpJKmc+fpTKi83nY/LulaK2or49TVZUjkbAVB63Ia/Z08mq0AKxo7GZ5Qy/tfrMZbg8M8upQO//tkfsAxVfu+d/4PRGSmSq6QmaE2trWI/zZTd/l75/4MFf9/bcZDNdj5Kf/uGhtOUmTvPAsvIP48lzo20qnbxsBxyDZvIPX4hsKK8dg6NMbq5rKe3lk6D6clrg0yUII8TpWa55FiyKMjnpKDhiZyIieeD3u6qqjq6sWhz2Hy23gdmdxOIziinJLc5TGxkTx/LmcIhKp4qVdrQDU1SXRmvOeEV1p5BVpHqh1R1ga6DcnzwUG6fAPkMg4+dwv/hMAX73nS1zYehSAsbiPrlAzsfTECt8HvvN3jERrGY76OfGL8MP7/pwN7YcA6B1rPr93SFQ4TY1tlLDRAFhY6d1FTtv43ei7ORJ/E1n9xt44JXM+kjkfoLmo5mm6E2sIZVtntXIhhJiLWlui2O15urprzvCSikzWRiZsIxye+ty8d19TMSP6RLTd5EP4V64I4vNlgImM6GDIxZEj5iwCrydNOjPbGdGVRxrlOUCpPM3VwWJyRId/gBpXjL966FMAfOnu/8PbOl8snr9/vJ7dPRPjfT/30CfJ5Ox0B5uJpKYPb9jfP31P6MRcIiFMVZY4q7w76fRux2cP8W89f0067+axwXtPurXizG8nyYW+rayvfpaHBz/BeLZp1q5bCCHmGqU0i9vDjI07p6wYz8I1nzQj+uW9TdMyoi2TGoMNGwZwOPJTMqJHRz0MDZt9htWaJ5eb+8kc0ihXCIc1S1vdUHFVuN0/yBcf+yi5vJUv3P5NPnTFo8XzZnNWukPNWC05cnkr3372Tr6//Sa6gi30hJpIG1VTrvul7s4zrkdrhZp09K1YuGpsI1xS+xTLPHuxqhxDqXaeC96Joc1UlNlsksHcs/zLwfu4o/kb3NZ0Pw8PfoKI0TCrtyGEEHOF15vBYc/xyuHAqc88i06eEa05cLBxWkZ0Km0OVLFY8lx37fFpGdHBoLtwMOLcIY3yeeRxJMwmODDAksAAP3zxBsYSNXzw8l/yhdvvL45gBoinnXzrmTsZjNTz6N6rOTw4MYZ5INxALj8xyeeFo+tnvVbNG89RFnOf0xLDbkkTNQKApt11mIPRTRyMbiKUbTnl5c9WxGjgkSGzWb69+X4eHvwkUcN/zm9XCCEqTTRaxfNb2gtbHCrFyTOilaKYEe2alBGdMyxEo1W4XFkuubh/ShOdSNgJj1eRyVZWa1pZ1cx5mnrveDFCbdvRtfSHG7l21U6+dPf/ocE3PuXcO453sqOrhpd7V/F/f3sPXaFmjo+20h1qZjRWy4nND9uPrWX7sbXn9Z4MjNdjs+bO623OVfPnAL48i5xH6PRtY4l7P8cTF/DrkQ8QNhr5Xs/nzvtBduPZJh4Z/Di3NX+LekefNMpCiAXH3L6gyGbPbMx1+Zh9Sy5nKZkRfWK8t9YQDLpxu7MEAglaq8x+4+W9jYyMeKmuTrF8eWhKPnQiUZ6MaGmUz5BF5WitHaXdP0DvWBPdoRZWNHbztXv+kfbAIN6qZPG8f/SDz/DQ7kb6x+t56sAmugsrwubfLcTS5iSy3T2r2d1TWYMWPvPjPy53CeI86vS+wJtqNlNjD5HKudgfuZyDsU3F75criSKUbeX7vX9WPEBQkUdTSasqQghx7qxbN0TOUOzdN/cPqp+cEZ1K2Tl4aGJLndWax+3OkkyarzVWi8Zq0TQ2xHE4JraBvvhiK5Gok7q6JA0N8SmN9LnKiJZGuYQqW4bF/kHShoOeUDM1rihfvedLtPvNEcwOmwHAPzzxQf5p83sIJ70MhOvZdmwtXUGzCe4ONdMTMn+wXx3u4C9//uly3iUhXifPIudr9KeWo7HisYWJ52rZMX4jxxLryOmZpzKebyea5A7XAS6p/RWPDX2MVH76QakLyYqj3yh3CUKIc8zrTRPwJ3ntyPz/NC2Xs0zZuzw27mLHzkXApIxoV5Z4wowcdbsztDRPz4h+7vkOslkrdXUJ3O5scRX6bDKiF2yjXO2M4XKkGIrUA5ov3vl1ljX00e4fpLk6iMWi+d7WW/mbX3ySeNqF3xPm4MBSntx/BceDLXQHW4pDN0aifu7918+X9w7Nsr+85Tu47OlixJyYH9zWMKu9O1jj3U61fYwnhj5EV/JCdozfABW+UpvRVdTZh7m1+Vs8MvgHpPPucpckhBDnTHt7GMNQ9PX5yl1KWZXKiO7rq6Gvrxq7PV8ctOJyZov7uJua4ixqjRbPn8sp4gk7L764CFDU1KRO+/bncaOs8VUliKbNqTUfv/pnrF10hI5APx3+Qeo8UZ46sJGPf+9vAMXaRUdIGw62HllPV6iF46Mtxdg0I2/jjq9/pYz35fxb03wcb1Xi1GcUc0KVJcG1gR/T4T6IReXpS65g+9jN9CRPbPmp7CYZYCC1nCeHP8xNTf/CLU3f5tHBj8964oYQQlSCqiqDpsYYvb01GMZc2Z98vpl7t8Nh67SM6EOH6jl2tM6MtSusRlutE8G3y5aGTvtW5nSjrFQerc0X+JvWPs8lHQfp8JupEu3+QY6NLuLWr30NgLddsI2W6lG6Qi08tvdKukItU/KDF1ojfDqUBCmfVKUfxOexjlNnH6Y3tYp03onbGmVP5BoORTcSMerLXd4b0ptaxVPDH+CGxu9xc9MDPDr08dOe/ieEEHNF26IIAN09ZzpgRJgU6YyNdMbGeImM6AMHG4Hu07qmOdMoX7T4MJcu2U9HIVGi3T+I25Fi4xcfBOC2dc/xtgu20R1qpivYwnOvXlTcGgHw3m/+L2SExunTWuLh5iJFjnbXITp921nsOkQ67+bBnr9GY+WhwU+Vu7xZ0ZW8gN+M/B7NVccw9Jx5ChNCiNN27HgtY2NO0ml5jjsXzuT/taIegVpXlE+/5fuFgRtmM3zdl+4nlXVyy7rnuO+anzOe8NIVbGFv3wqOB1uxqBx5beVPf/pfSP6gqrjCPJ00yWdCo0BJozyXLHXv5Ur/L/DYIsQNH7vD13MothHN/PvY7lhiHccS6wDwWsdI5HxlS+YQQojZls9bCI3JcRiVoKJeWRb7h/jMjf/OYNhPV6iFZ1+9GJc9TSrr5J8338X/e/o9hJOlN7UnMrJXcTYdG22l2ikf+VQyCzna3QcZyzQSNhpJ5HwEMy08F3on3YlO8vOwQX49m0rzjpZ/YjTTylPDH5BmWQgxpyml2bBhgK6uWoJBaZQrQUW9qrwy1M6az32ZVHb6LPOxhDRt59Pf/vIPyl2CmIHPFqTTu51V3h14bFF2h69l29itDKWX8PjwveUu77wydBW7wtdzdeAh3tLwfX4z8v55uYIuhFgYmpuj1NWmOHas3JWIEyqqUU4bjpJNshCVpJwH8d3Q8D2WefaR14ru5BqeDW6iO1lZw2rOtwPRK7Aqgyv8j5DXP+Lp0ffKUBIhxByk6WgPE4k6GBuTT8krRUU1yqJy/M1t9+P3hPmjH3623KUsaNW2EZa697Mnci2gCGWbCY61cjh2KfFc7Skvv1DsjVyDTRlsrHuCYKaZPZHry12SEEKckfpAAo8ny779jchxVZVDGmVRUntggObq088ZFLPHgsFSzz46vdtY5DpCXls4nriQsNHAzvEby11exdoVfguJnI+j8XXlLkUIIc5Ye0eYZNLG8LCn3KWISaRRFiVprVCSenHe1Tt6uaXp27isCSJZP9vHbuJw7FISuepylzYnHI5dBoBNZVjpeYmDsU3Mp5UZGV0txHyl6empRmG+/orKIY2ymIHkKJ8PVpVlmXsvOW3laOJNjGWb6Emu4ZXYxfSlVjAXJuZVojXe7VwZeBiPLcyO8beXuxwhhDgFxciIt9xFiBKkURYlyYrydLN5EF+dfZBO33ZWenbitCbpSa7iaOJN5LSdp0fvmbXbWaj2Ra/A7xjkktrfkNM2doXfWu6ShBCiJJcrS3NzlJ4eGVddiaRRFiUdGuxgLFE6s1qcnTfX/ZI31TxLTls5Fl/Lwdgm+lPLyl3WPGPh2eC7sCqDjXVPktM2Xo5cW+6ihBBimvbF47S2Runrky12lUgaZVHSl5/6QLlLmDf89n46fdvZHb6OeK6WnuQaErlqXoldQiovH7WdKxoLm0fvxqoM1lc/w6HYRjJ5iVwSQlQOuz1HS0uMgUEfmYy0ZJVIHhUhzgGbSrPcs4dO3zaaqnowtI3+1HKOJWrpS62kL7Wy3CUuCBorvx15H25rRJpkIUTFWdwWxmrVdHfLULVKJY2yKOkLd3yDDv8AH/nu35a7lDnHpjL8XtsXcVqThDJNPB+8g1fjF5POyzjScshjJZarAzSb6h4jlGnh1fjF5S5LCLHAWSx52toijIy4SSQc5S5HzEAaZVFSoy/EorqRcpdREU51EJ9dpVjh2U2dY4gtoXdgaAc7x29gJNPGULqD+RRPNpdZyNHg6GN99TMY2saxxPpylySEWMDs9jzj4066umV4VCWTRlmUpLXEw52cpsHRS6dvGys8u7FbMoxmWrCpDIZ2sC96VbkLFK+Tx8YTwx/mlqZv89aG/+CpYRtdyQvKXZYQYoFKp228vLe53GWIU5BGWZSkkXi4k1nt3cF19T8mm7dzJH4RB6ObGM4sRlaPK5uhHTw+9FFua/4WNzQ+yBNDH6Y3tbrcZQkhFhifL41hWEgm7eUuRZyCNMqiNK2k5SvSNFV1s8a7jd7USo7EN9CVuIBng3fyWuwiMloOEptLstrJY0P3ckvTd7BbMuUuRwix4GjWrB7Fas3zwrY2ZIGlskmjLEp6uW8F48mFHV1W7Ypx54anuav1FwQcg2TyDsayTQCk8h4ORC8vc4XijUrn3Tw08J/RhcmHdpUiq51lrkoIsRDU1qaork5z6FA90iRXPqV15Xy8rpQaAbrKXYeYUT0wWu4ixGmRx2rukMdq7pDHau6Qx2ruKNdj1aG1bjjVmSqqURaVTSm1Q2t9abnrEKcmj9XcIY/V3CGP1dwhj9XcUemPlaXcBQghhBBCCFGJpFEWQgghhBCiBGmUxZm4v9wFiNMmj9XcIY/V3CGP1dwhj9XcUdGPlexRFkIIIYQQogRZURZCCCGEEKIEaZTFKSmlblJKHVZKvaaU+vNy1yNmppR6QCk1rJTaV+5axMyUUouVUk8rpQ4qpfYrpf6w3DWJ0pRSTqXUdqXUnsJj9bflrkmcnFLKqpTapZR6pNy1iJkppY4rpfYqpXYrpXaUu56ZyNYLcVJKKSvwCnAD0Au8CLxPa32grIWJkpRS1wAx4Hta67XlrkeUppRqAVq01i8ppXzATuCd8ntVeZRSCvBorWNKKTvwHPCHWusXylyamIFS6o+BS4FqrfVt5a5HlKaUOg5cqrWu6LxrWVEWp7IReE1rfVRrnQF+ALyjzDWJGWitnwFC5a5DnJzWekBr/VLh31HgILCovFWJUrQpVvjSXvgjK0wVSinVBtwKfLvctYj5QRplcSqLgJ5JX/ciL+hCzBql1BJgA7CtvJWImRQ+yt8NDANPaa3lsapcXwH+FMiXuxBxShr4lVJqp1LqvnIXMxNplMWplBpEL6spQswCpZQX+CnwR1rrSLnrEaVprXNa64uANmCjUkq2NVUgpdRtwLDWeme5axGn5Uqt9cXAzcB/LmwdrDjSKItT6QUWT/q6DegvUy1CzBuF/a4/Bf5da/2zctcjTk1rPQ5sBm4qcymitCuBOwp7X38AvEUp9W/lLUnMRGvdX/h7GPg55lbPiiONsjiVF4GVSqmlSikHcA/wcJlrEmJOKxwg9h3goNb6y+WuR8xMKdWglKot/NsFvA04VN6qRCla67/QWrdprZdgvlb9Vmv9+2UuS5SglPIUDmRGKeUBbgQqMq1JGmVxUlprA/gU8CTmAUc/0lrvL29VYiZKqe8DW4HVSqlepdS95a5JlHQl8AHMFa/dhT+3lLsoUVIL8LRS6mXMhYOntNYSOybE2WkCnlNK7QG2A49qrZ8oc00lSTycEEIIIYQQJciKshBCCCGEECVIoyyEEEIIIUQJ0igLIYQQQghRgjTKQgghhBBClCCNshBCCCGEECVIoyyEELNAKfVXSqn9SqmXC3Fvm2b5+q9TSk2LJZvp9Fm4vXcqpS6Y9PVmpdSls307QghRyWzlLkAIIeY6pdTlwG3AxVrrtFKqHnCUuayz9U7gEeBAuQsRQohykRVlIYQ4ey3AqNY6DaC1Hj0xnlUpdYlS6ndKqZ1KqSeVUi2F0zcrpb6ilNqilNqnlNpYOH1j4bRdhb9Xn24RhWlXDyilXixc/h2F0z+slPqZUuoJpdSrSql/mHSZe5VSrxTq+ZZS6utKqSuAO4B/LKyOLy+c/W6l1PbC+a+ejf84IYSoZNIoCyHE2fsVsLjQQP6TUupaAKWUHfi/wF1a60uAB4D/MelyHq31FcB/KnwPzPHI12itNwB/A3zxDOr4K8yxvZcB12M2up7C9y4C3gusA96rlFqslGoFPge8GbgBWAOgtd6COar+s1rri7TWRwrXYdNabwT+CPj8GdQlhBBzkmy9EEKIs6S1jimlLgGuxmxQf6iU+nNgB7AWeEopBWAFBiZd9PuFyz+jlKpWStUCPuBflVIrAQ3Yz6CUG4E7lFJ/UvjaCbQX/v0brXUYQCl1AOgA6oHfaa1DhdN/DKw6yfX/rPD3TmDJGdQlhBBzkjTKQggxC7TWOWAzsFkptRf4EGZDuV9rfflMFyvx9d8BT2ut71RKLSlc5+lSwLu11oennGgeWJiedFIO8/lfncF1M+k6TlxeCCHmNdl6IYQQZ0kptbqwAnzCRUAXcBhoKBzsh1LKrpS6cNL53ls4/SogXFjxrQH6Ct//8BmW8iTwaVVYvlZKbTjF+bcD1yql6pRSNuDdk74XxVzdFkKIBUsaZSGEOHtezO0SB5RSLwMXAF/QWmeAu4C/V0rtAXYDV0y63JhSagvwDeDewmn/APxPpdTzmFs1zsTfYW7VeFkpta/w9Yy01n2Ye6C3Ab/GTLgIF779A+CzhYMCl89wFUIIMa8prV//yZ8QQohzTSm1GfgTrfWOMtfhLeyxtgE/Bx7QWv+8nDUJIUSlkBVlIYRY2L6glNoN7AOOAQ+VuR4hhKgYsqIshBBCCCFECbKiLIQQQgghRAnSKAshhBBCCFGCNMpCCCGEEEKUII2yEEIIIYQQJUijLIQQQgghRAnSKAshhBBCCFHC/wd0lyGjGSGNgAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = draw_border(clr, X, Y, incx=1, incy=1, figsize=(12,8), border=False)\n", - "ax.set_title(\"R\u00e9gression logistique dans un quadrillage\");" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6958333333333333" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr.score(X, Y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On copie les features en les mettant au carr\u00e9. Le probl\u00e8me est toujours aussi simple mais la r\u00e9gression logistique a plus de variables non corr\u00e9l\u00e9es sur lesquelles s'appuyer." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def create_feat(X):\n", - " X2 = X.copy()\n", - " X2[:, 0] = X2[:, 0] * X2[:, 0]\n", - " X2[:, 1] = X2[:, 1] * X2[:, 1]\n", - " XX2 = numpy.hstack([X, X2])\n", - " return XX2\n", - "\n", - "clr2 = LogisticRegression()\n", - "clr2.fit(create_feat(X), Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def fct_predict(clr, X):\n", - " return clr.predict(create_feat(X))\n", - "\n", - "ax = draw_border(clr2, X, Y, fct=fct_predict, incx=1, incy=1, figsize=(12,8), border=False)\n", - "ax.set_title(\"R\u00e9gression logistique dans un quadrillage avec X2\");" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9583333333333334" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr2.score(create_feat(X), Y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Du fait que ce probl\u00e8me de classification est \u00e9quivalent \u00e0 un diagramme de Vorono\u00ef, il a \u00e9t\u00e9 construit comme tel, le fait que la r\u00e9gression logistique semble \u00eatre provenir d'un probl\u00e8me de convergence num\u00e9rique plut\u00f4t que du mod\u00e8le th\u00e9orique. Pour v\u00e9rfier on joue avec les param\u00e8tres d'apprentissage. Tout d'abord, l'algorithme de descente de gradient." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr_t = LogisticRegression(solver='lbfgs')\n", - "clr_t.fit(X, Y)\n", - "clr_t.score(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = draw_border(clr_t, X, Y, incx=1, incy=1, figsize=(6,4), border=False)\n", - "ax.set_title(\"R\u00e9gression logistique dans un quadrillage avec L-BFGS\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ensuite, on change la fa\u00e7on de r\u00e9soudre le probl\u00e8me. Plut\u00f4t que de r\u00e9soudre *n* probl\u00e8mes de classifications binaires, on r\u00e9soud un seul probl\u00e8me avec une erreur de classification \u00e9gale \u00e0 la [Multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9875" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr_t = LogisticRegression(solver='lbfgs', multi_class='multinomial')\n", - "clr_t.fit(X, Y)\n", - "clr_t.score(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", - "draw_border(clr_t, X, Y, incx=1, incy=1, figsize=(6,4), border=False, ax=ax[0])\n", - "draw_border(clr_t, X, Y, incx=1, incy=1, figsize=(6,4), border=True, ax=ax[1])\n", - "ax[0].set_title(\"R\u00e9gression logistique dans un quadrillage\\navec L-BFGS + multinomial\")\n", - "ax[1].set_title(\"R\u00e9gression logistique dans un quadrillage\\navec L-BFGS + multinomial\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les fronti\u00e8res entre une classes et les autres n'ont plus l'air d'avoir de signification g\u00e9om\u00e9trique. L'approche une classe contre toutes les autres marchent bien si celles-ci ont des fronti\u00e8res convexes sans angles aigus et si elles ne sont pas born\u00e9es. En gros, cette approche rapide fonctionne bien si toutes les classes sont dispos\u00e9es autour de la boule unit\u00e9 ou d'une boule unit\u00e9 compos\u00e9e sur un sous-ensemble des dimensions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## R\u00e9gression logistique autour d'un cercle" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((240, 2), (240,))" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from math import cos, sin, pi\n", - "Xs = []\n", - "Ys = []\n", - "n = 20\n", - "for i in range(0, 12):\n", - " x1 = numpy.random.rand(n) + 2.3*cos(i/ 12. * 2 * pi)\n", - " x2 = numpy.random.rand(n) + 2.3*sin(i/ 12. * 2 * pi)\n", - " Xs.append(numpy.vstack([x1,x2]).T) \n", - " Ys.extend([i] * n)\n", - "X = numpy.vstack(Xs)\n", - "Y = numpy.array(Ys)\n", - "X.shape, Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(6,4))\n", - "for i in range(0, 12):\n", - " ax.plot(X[Y==i,0], X[Y==i,1], 'o', label=\"cl%d\"%i, color=plt.cm.tab20.colors[i])\n", - "ax.legend()\n", - "ax.set_title(\"Classification \u00e0 neuf classes\\ndans un quadrillage\");" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9833333333333333" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr_c = LogisticRegression()\n", - "clr_c.fit(X, Y)\n", - "clr_c.score(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = draw_border(clr_c, X, Y, incx=1, incy=1, figsize=(6,4), border=False)\n", - "ax.set_title(\"R\u00e9gression logistique autour d'un cercle\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rien n'est prouv\u00e9, ce ne sont que des observations. On peut se poser la question si le probl\u00e8me pr\u00e9c\u00e9dent n'\u00e9tait pas justement choisi pour montrer que dans un cas, l'approche une classe contre les autres dans le cas d'un quadrillage est particuli\u00e8rement malvenue. On accro\u00eet l'espace entre les classes." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((240, 2), (240,))" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Xs = []\n", - "Ys = []\n", - "n = 20\n", - "for i in range(0, 4):\n", - " for j in range(0, 3):\n", - " x1 = numpy.random.rand(n) + i*3\n", - " x2 = numpy.random.rand(n) + j*3\n", - " Xs.append(numpy.vstack([x1,x2]).T) \n", - " Ys.extend([i*3+j] * n)\n", - "X = numpy.vstack(Xs)\n", - "Y = numpy.array(Ys)\n", - "X.shape, Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7875" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr_q = LogisticRegression()\n", - "clr_q.fit(X, Y)\n", - "clr_q.score(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = draw_border(clr_q, X, Y, incx=1, incy=1, figsize=(6,4), border=False)\n", - "ax.set_title(\"R\u00e9gression logistique autour d'un cercle\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A priori non mais on pr\u00e9f\u00e8re l'approche une classe contre les autres car elle est beaucoup plus rapide. L'approche multinomiale requiert de changer d'algorithme de descente de gradient." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.25 ms \u00b1 148 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 100 loops each)\n" - ] - } - ], - "source": [ - "clr_q = LogisticRegression()\n", - "%timeit clr_q.fit(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "55.4 ms \u00b1 1.18 ms per loop (mean \u00b1 std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "clr_qmn = LogisticRegression(multi_class='multinomial', solver='lbfgs')\n", - "%timeit clr_qmn.fit(X, Y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pousser les classes sur la boule unit\u00e9\n", - "\n", - "Puisque le mod\u00e8le est plus facile \u00e0 apprendre lorsque les classes sont r\u00e9parties sur la boule unit\u00e9, l'id\u00e9al serait d'avoir une transformation qui le fait, comme d'ajouter des dimensions. La r\u00e9gression logistique ne peut mod\u00e9liser que des classes convexes. Cela veut dire que le barycentre, sous cette hypoth\u00e8ses, appartient \u00e0 la zone que le mod\u00e8le attribute \u00e0 une classe donn\u00e9e. On calcule ce barycentre pour toutes les classes et on ajoute comme variables la distance \u00e0 chacun de ces centres. On reprend le probl\u00e8me du quadrillage." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((240, 2), (240,))" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Xs = []\n", - "Ys = []\n", - "n = 20\n", - "for i in range(0, 4):\n", - " for j in range(0, 3):\n", - " x1 = numpy.random.rand(n) + i*1.1\n", - " x2 = numpy.random.rand(n) + j*1.1\n", - " Xs.append(numpy.vstack([x1,x2]).T) \n", - " Ys.extend([i*3+j] * n)\n", - "X = numpy.vstack(Xs)\n", - "Y = numpy.array(Ys)\n", - "X.shape, Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(12, 2)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bary = []\n", - "for i in range(12):\n", - " b = X[Y==i].mean(axis=0)\n", - " bary.append(b)\n", - "barys = numpy.vstack(bary)\n", - "barys.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(240, 12)" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics.pairwise import euclidean_distances\n", - "dist = euclidean_distances(X, barys)\n", - "dist.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "Xext = numpy.hstack([X, dist])" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9916666666666667" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr_ext = LogisticRegression()\n", - "clr_ext.fit(Xext, Y)\n", - "clr_ext.score(Xext, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def fct_predict(clr, X):\n", - " dist = euclidean_distances(X, barys) \n", - " Xext = numpy.hstack([X, dist])\n", - " return clr.predict(Xext)\n", - "\n", - "ax = draw_border(clr_ext, X, Y, fct=fct_predict, incx=1, incy=1, figsize=(6,4), border=False)\n", - "ax.set_title(\"R\u00e9gression logistique dans un quadrillage\\navec des distances aux barycentres\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cela r\u00e9pond \u00e9galement \u00e0 une question : **Que faire lorsque les classes ne sont pas convexes ?** Une id\u00e9e consiste \u00e0 effectuer un [k-means](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) par classe jusqu'\u00e0 ce que chaque classe soit \u00e0 peu pr\u00e8s converte par un ensemble de cluster appris sur cette classe." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cas presque hexagonal\n", - "\n", - "Pour tester quelques id\u00e9es et parce c'est joli. L'id\u00e9al serait de se rapprocher d'un pavage de [Penrose](https://fr.wikipedia.org/wiki/Roger_Penrose)." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import math\n", - "n = 4\n", - "a = math.pi * 2 / 3\n", - "points = []\n", - "Ys = []\n", - "for i in range(n):\n", - " for j in range(n):\n", - " dil = ((i+1)**2 + (j+1)**2) ** 0.6\n", - " for k in range(0,20):\n", - " x = i + j * math.cos(a)\n", - " y = j * math.sin(a)\n", - " points.append([x * dil, y * dil])\n", - " Ys.append(i*n+j)\n", - " mi = 0.5\n", - " for r in [0.1, 0.3, mi]:\n", - " nb = 6 if r == mi else 12\n", - " for k in range(0, nb):\n", - " x = i + j * math.cos(a) + r * math.cos(math.pi*2/nb * k + math.pi/6)\n", - " y = j * math.sin(a) + r * math.sin(math.pi*2/nb * k + math.pi/6)\n", - " points.append([x * dil, y * dil])\n", - " Ys.append(i*n+j)\n", - "X = numpy.array(points)\n", - "Y = numpy.array(Ys)\n", - "set(Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(6,4))\n", - "for i in range(0, max(Y)+1):\n", - " ax.plot(X[Y==i,0], X[Y==i,1], 'o', label=\"cl%d\"%i, color=plt.cm.tab20.colors[i%20])\n", - "ax.set_title(\"Classification \u00e0 16 classes\\ndans un quadrillage hexagonal\");" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9919354838709677" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr_hex = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=200)\n", - "clr_hex.fit(X, Y)\n", - "clr_hex.score(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = draw_border(clr_hex, X, Y, incx=1, incy=1, figsize=(6,4), border=False)\n", - "ax.set_title(\"R\u00e9gression logistique dans\\nun quadrillage hexagonal\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Diagramme de Vorono\u00ef approch\u00e9\n", - "\n", - "On pousse l'id\u00e9e impl\u00e9ment\u00e9e dans le cas de trois classes pour un nombre de classes quelconque. Il n'existe pas de fa\u00e7on g\u00e9n\u00e9rique de diagramme de Vorono\u00ef \u00e9quivalent. On r\u00e9soud le syst\u00e8me lin\u00e9aire avec une r\u00e9gression quantile et d'autres astuces de calculs \u00e0 d\u00e9couvrir dans le code de la fonction [voronoi_estimation_from_lr](http://www.xavierdupre.fr/app/mlstatpy/helpsphinx//mlstatpy/ml/voronoi.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((240, 2), (240,))" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Xs = []\n", - "Ys = []\n", - "n = 20\n", - "for i in range(0, 4):\n", - " for j in range(0, 3):\n", - " x1 = numpy.random.rand(n) + i*1.1\n", - " x2 = numpy.random.rand(n) + j*1.1\n", - " Xs.append(numpy.vstack([x1,x2]).T) \n", - " Ys.extend([i*3+j] * n)\n", - "X = numpy.vstack(Xs)\n", - "Y = numpy.array(Ys)\n", - "X.shape, Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(6,4))\n", - "for i in range(0, 12):\n", - " ax.plot(X[Y==i,0], X[Y==i,1], 'o', label=\"cl%d\"%i, color=plt.cm.tab20.colors[i])\n", - "ax.legend()\n", - "ax.set_title(\"Classification \u00e0 neuf classes\\ndans un quadrillage\");" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "clr = LogisticRegression()\n", - "clr.fit(X, Y)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[voronoi_estimation_from_lr] iter=1/20 score=0.0953 tol=3.48e-10 del P2,9 d=3.19\n", - "[voronoi_estimation_from_lr] iter=2/20 score=0.0939 tol=3.48e-10 del P1,9 d=2.72\n", - "[voronoi_estimation_from_lr] iter=3/20 score=0.089 tol=3.48e-10 del P2,6 d=2.5\n", - "[voronoi_estimation_from_lr] iter=4/20 score=0.0892 tol=3.48e-10 del P0,11 d=2.46\n", - "[voronoi_estimation_from_lr] iter=5/20 score=0.0894 tol=3.48e-10 del P2,10 d=2.42\n", - "[voronoi_estimation_from_lr] iter=6/20 score=0.0882 tol=3.48e-10 del P1,10 d=2.44\n", - "[voronoi_estimation_from_lr] iter=7/20 score=0.0889 tol=3.48e-10 del P0,10 d=2.3\n", - "[voronoi_estimation_from_lr] iter=8/20 score=0.0877 tol=3.48e-10 del P5,9 d=2.29\n", - "[voronoi_estimation_from_lr] iter=9/20 score=0.0869 tol=3.48e-10 del P1,11 d=2.18\n", - "[voronoi_estimation_from_lr] iter=10/20 score=0.088 tol=3.48e-10 del P2,3 d=2.2\n", - "[voronoi_estimation_from_lr] iter=11/20 score=0.089 tol=3.48e-10 del P0,8 d=2.14\n", - "[voronoi_estimation_from_lr] iter=12/20 score=0.0884 tol=3.48e-10 del P1,6 d=2.2\n", - "[voronoi_estimation_from_lr] iter=13/20 score=0.0871 tol=3.48e-10 del P2,11 d=2.07\n", - "[voronoi_estimation_from_lr] iter=14/20 score=0.0874 tol=3.48e-10 del P0,5 d=2.1\n", - "[voronoi_estimation_from_lr] iter=15/20 score=0.0868 tol=3.48e-10 del P0,2 d=2.1\n", - "[voronoi_estimation_from_lr] iter=16/20 score=0.087 tol=3.48e-10 del P0,9 d=2.06\n", - "[voronoi_estimation_from_lr] iter=17/20 score=0.0876 tol=3.48e-10 del P8,9 d=1.99\n", - "[voronoi_estimation_from_lr] iter=18/20 score=0.0878 tol=3.48e-10 del P2,7 d=1.93\n", - "[voronoi_estimation_from_lr] iter=19/20 score=0.0889 tol=3.48e-10 del P9,11 d=1.93\n", - "[voronoi_estimation_from_lr] iter=20/20 score=0.0875 tol=3.48e-10 del P1,7 d=1.97\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0.59042773, 0.41675379],\n", - " [0.19276405, 1.61586254],\n", - " [0.38750542, 2.34848342],\n", - " [1.70510075, 0.5341869 ],\n", - " [1.69940467, 1.50388896],\n", - " [1.66571087, 2.15827251],\n", - " [2.23834543, 0.6114512 ],\n", - " [2.14600591, 1.3636044 ],\n", - " [2.08762755, 2.04091816],\n", - " [2.5732091 , 0.170076 ],\n", - " [2.81087731, 1.40217985],\n", - " [2.49984364, 2.02978587]])" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Voronoï et régression logistique\n", + "\n", + "Le notebook étudie la pertinence d'un modèle de régression logistique dans certaines configurations. Il regarde aussi le diagramme de Voronoï associé à une régression logistique à trois classes. Il donne quelques intuitions sur les modèles que la régression logistique peut résoudre." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression logistique" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "\n", + "data = load_iris()\n", + "X, y = data.data[:, :2], data.target" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "clr = LogisticRegression()\n", + "clr.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-2.49579289, 4.01011301],\n", + " [ 0.49709451, -1.63380222],\n", + " [ 1.15921404, -1.77736568]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.81713932, 1.22543562, -2.22516119])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr.intercept_" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 6.34157245, -1.54507432, -4.6206785 ]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "\n", + "x = numpy.array([[1, 2]])\n", + "clr.decision_function(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "A = clr.coef_\n", + "B = clr.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On vérifie que la fonction de décision correspond à la formule suivant." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 6.34157245, -1.54507432, -4.6206785 ])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(A @ x.T).T.ravel() + B" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def draw_border(\n", + " clr, X, y, fct=None, incx=1, incy=1, figsize=None, border=True, ax=None\n", + "):\n", + " # voir https://sashat.me/2017/01/11/list-of-20-simple-distinct-colors/\n", + " # https://matplotlib.org/examples/color/colormaps_reference.html\n", + " _unused_ = [\n", + " \"Red\",\n", + " \"Green\",\n", + " \"Yellow\",\n", + " \"Blue\",\n", + " \"Orange\",\n", + " \"Purple\",\n", + " \"Cyan\",\n", + " \"Magenta\",\n", + " \"Lime\",\n", + " \"Pink\",\n", + " \"Teal\",\n", + " \"Lavender\",\n", + " \"Brown\",\n", + " \"Beige\",\n", + " \"Maroon\",\n", + " \"Mint\",\n", + " \"Olive\",\n", + " \"Coral\",\n", + " \"Navy\",\n", + " \"Grey\",\n", + " \"White\",\n", + " \"Black\",\n", + " ]\n", + " del _unused_\n", + "\n", + " h = 0.02 # step size in the mesh\n", + " # Plot the decision boundary. For that, we will assign a color to each\n", + " # point in the mesh [x_min, x_max]x[y_min, y_max].\n", + " x_min, x_max = X[:, 0].min() - incx, X[:, 0].max() + incx\n", + " y_min, y_max = X[:, 1].min() - incy, X[:, 1].max() + incy\n", + " xx, yy = numpy.meshgrid(\n", + " numpy.arange(x_min, x_max, h), numpy.arange(y_min, y_max, h)\n", + " )\n", + " if fct is None:\n", + " Z = clr.predict(numpy.c_[xx.ravel(), yy.ravel()])\n", + " else:\n", + " Z = fct(clr, numpy.c_[xx.ravel(), yy.ravel()])\n", + "\n", + " # Put the result into a color plot\n", + " cmap = plt.cm.tab20\n", + " Z = Z.reshape(xx.shape)\n", + " if ax is None:\n", + " _fig, ax = plt.subplots(1, 1, figsize=figsize or (4, 3))\n", + " ax.pcolormesh(xx, yy, Z, cmap=cmap)\n", + "\n", + " # Plot also the training points\n", + " ax.scatter(X[:, 0], X[:, 1], c=y, edgecolors=\"k\", cmap=cmap)\n", + " ax.set_xlabel(\"Sepal length\")\n", + " ax.set_ylabel(\"Sepal width\")\n", + "\n", + " ax.set_xlim(xx.min(), xx.max())\n", + " ax.set_ylim(yy.min(), yy.max())\n", + "\n", + " # Draw lines\n", + " x1, x2 = xx.min(), xx.max()\n", + " cl = 0\n", + " if border:\n", + " for i in range(clr.coef_.shape[0]):\n", + " for j in range(i + 1, clr.coef_.shape[0]):\n", + " delta = clr.coef_[i] - clr.coef_[j]\n", + " db = clr.intercept_[i] - clr.intercept_[j]\n", + " y1 = (-db - delta[0] * x1) / delta[1]\n", + " y2 = (-db - delta[0] * x2) / delta[1]\n", + " ax.plot([x1, x2], [y1, y2], \"--\", color=\"white\")\n", + " cl += 1\n", + " else:\n", + " for i in range(clr.coef_.shape[0]):\n", + " delta = clr.coef_[i]\n", + " db = clr.intercept_[i]\n", + " y1 = (-db - delta[0] * x1) / delta[1]\n", + " y2 = (-db - delta[0] * x2) / delta[1]\n", + " ax.plot([x1, x2], [y1, y2], \"--\", color=\"yellow\")\n", + " cl += 1\n", + "\n", + " return ax\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "draw_border(clr, X, y, ax=ax[0])\n", + "draw_border(clr, X, y, border=False, ax=ax[1])\n", + "ax[0].set_title(\"Frontière entre 2 classes\")\n", + "ax[1].set_title(\"Frontière entre 1 classe et les autres\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quelques diagramme de Voronoï" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "points = numpy.array([[1, 2], [3, 4], [4, 1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.spatial import Voronoi, voronoi_plot_2d\n", + "\n", + "vor = Voronoi(points)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "ax.ishold = lambda: True # bug between scipy and matplotlib 3.0\n", + "voronoi_plot_2d(vor, ax=ax)\n", + "ax.set_xlim([0, 5])\n", + "ax.set_ylim([0, 5])\n", + "ax.axis(\"off\");" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 1, 2], dtype=int64)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vor.point_region" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.75, 2.25]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vor.vertices" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.patches import Circle\n", + "\n", + "points = numpy.array([[1, 1], [2, 4], [4, 1], [6, 3]])\n", + "vor = Voronoi(points)\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "cs = []\n", + "for i in range(vor.vertices.shape[0]):\n", + " v = vor.vertices[i, :]\n", + " d = v - points[2, :]\n", + " r = d.dot(d) ** 0.5\n", + " circle = Circle((v[0], v[1]), r, fill=False, ls=\"--\", edgecolor=\"g\", visible=True)\n", + " ax.add_artist(circle)\n", + "for i in range(points.shape[0]):\n", + " for j in range(i + 1, points.shape[0]):\n", + " if i == 0 and j == 3:\n", + " continue\n", + " ax.plot(points[[i, j], 0], points[[i, j], 1], \"g-\")\n", + "ax.ishold = lambda: True # bug between scipy and matplotlib 3.0\n", + "voronoi_plot_2d(vor, ax=ax)\n", + "ax.set_xlim([0, 7])\n", + "ax.set_ylim([0, 7])\n", + "ax.axis(\"off\");" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "n = 5\n", + "a = math.pi * 2 / 3\n", + "points = []\n", + "for i in range(n):\n", + " for j in range(n):\n", + " points.append([i + j * math.cos(a), j * math.sin(a)])\n", + "points = numpy.array(points)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "vor = Voronoi(points)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "ax.ishold = lambda: True # bug between scipy and matplotlib 3.0\n", + "voronoi_plot_2d(vor, ax=ax)\n", + "ax.set_xlim([-1.5, 4])\n", + "ax.set_ylim([-1.5, 4])\n", + "ax.axis(\"off\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Un diagramme de Voronoï proche\n", + "\n", + "On applique la formule définie par [Régression logistique, diagramme de Voronoï, k-Means](https://sdpython.github.io/doc/mlstatpy/dev/c_ml/lr_voronoi.html) et on résoud le système linéaire défini par :\n", + "\n", + "$$\\begin{array}{ll}\n", + "&\\left\\{\\begin{array}{l}\\left + B_i - B_j = - \\left\\{ \\left + B_i - B_j \\right \\} \\\\ P_i- P_j - \\left \\frac{L_i-L_j}{\\Vert L_i-L_j\\Vert }=0 \\end{array} \\right.\n", + "\\\\\n", + "\\Longleftrightarrow & \\left\\{\\begin{array}{l}\\left + 2 (B_i - B_j) = 0 \\\\ P_i- P_j - \\left \\frac{L_i-L_j}{\\Vert L_i-L_j\\Vert}=0 \\end{array} \\right.\n", + "\\\\\n", + "\\Longrightarrow & \\left\\{\\begin{array}{l} \\left + 2 (B_i - B_j) = 0 \\\\ \\left - \\left \\left<\\frac{L_i-L_j}{\\Vert L_i-L_j\\Vert},u \\right>=0 \\end{array} \\right.\n", + "\\end{array} $$ \n", + " \n", + "Où $u$ est un vecteur unité quelconque. On cherche à résoudre sous la forme d'un système linéaire $LP=B$ où le vecteur $P$ est l'ensemble des coordonnées de tous les points cherchés. D'après la page cité ci-dessus, dans le cas d'un diagramme à trois classes, ce système a une infinité de solutions." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((6, 6), (6,), 2.0281820935727704e-16)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "\n", + "matL = []\n", + "matB = []\n", + "L = clr.coef_\n", + "B = clr.intercept_\n", + "for i in range(L.shape[0]):\n", + " for j in range(i + 1, L.shape[0]):\n", + " li = L[i, :]\n", + " lj = L[j, :]\n", + " c = li - lj\n", + " nc = (c.T @ c) ** 0.5\n", + "\n", + " # condition 1\n", + " mat = numpy.zeros((L.shape))\n", + " mat[i, :] = c\n", + " mat[j, :] = c\n", + " d = -2 * (B[i] - B[j])\n", + " matB.append(d)\n", + " matL.append(mat.ravel())\n", + "\n", + " # condition 2 - cache plusieurs équations\n", + " # on ne prend que la première coordonnée\n", + " c /= nc\n", + " c2 = c * c[0]\n", + " mat = numpy.zeros((L.shape))\n", + " mat[i, :] = -c2\n", + " mat[j, :] = c2\n", + "\n", + " mat[i, 0] += 1\n", + " mat[j, 0] -= 1\n", + " matB.append(0)\n", + " matL.append(mat.ravel())\n", + "\n", + "matL = numpy.array(matL)\n", + "matB = numpy.array(matB)\n", + "matL.shape, matB.shape, numpy.linalg.det(matL)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345
0-2.9928875.643915-2.9928875.6439150.0000000.000000
10.7805160.413897-0.780516-0.4138970.0000000.000000
2-3.6550075.7874790.0000000.000000-3.6550075.787479
30.7148790.4514720.0000000.000000-0.714879-0.451472
40.0000000.000000-0.6621200.143563-0.6621200.143563
50.0000000.0000000.0449020.207088-0.044902-0.207088
\n", + "
" ], - "source": [ - "from mlstatpy.ml import voronoi_estimation_from_lr\n", - "points = voronoi_estimation_from_lr(clr.coef_, clr.intercept_, max_iter=20, verbose=True)\n", - "points" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "text/plain": [ + " 0 1 2 3 4 5\n", + "0 -2.992887 5.643915 -2.992887 5.643915 0.000000 0.000000\n", + "1 0.780516 0.413897 -0.780516 -0.413897 0.000000 0.000000\n", + "2 -3.655007 5.787479 0.000000 0.000000 -3.655007 5.787479\n", + "3 0.714879 0.451472 0.000000 0.000000 -0.714879 -0.451472\n", + "4 0.000000 0.000000 -0.662120 0.143563 -0.662120 0.143563\n", + "5 0.000000 0.000000 0.044902 0.207088 -0.044902 -0.207088" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas\n", + "\n", + "pandas.DataFrame(matL)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le déterminant est très faible suggérant que la matrice est non inversible et on sait qu'elle l'est dans ce cas. On remplace la dernière équation en forçant la coordonnée d'un point." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "42.07770646874508" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matL[-1, :] = 0\n", + "matL[-1, 0] = 1\n", + "matB[-1] = 3\n", + "numpy.linalg.det(matL)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On vérifie que le système linéaire est celui attendu." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
012345B
0-2.9928875.643915-2.9928875.6439150.0000000.0000000.816593
10.7805160.413897-0.780516-0.4138970.0000000.0000000.000000
2-3.6550075.7874790.0000000.000000-3.6550075.787479-6.084601
30.7148790.4514720.0000000.000000-0.714879-0.4514720.000000
40.0000000.000000-0.6621200.143563-0.6621200.143563-6.901194
51.0000000.0000000.0000000.0000000.0000000.0000003.000000
\n", + "
" ], - "source": [ - "ax = draw_border(clr, X, Y, incx=1, incy=1, figsize=(8,5), border=False)\n", - "ax.plot(points[:, 0], points[:, 1], 'ro', ms=10)\n", - "ax.set_title(\"Diagramme de Voronoi approch\u00e9\");" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [] + "text/plain": [ + " 0 1 2 3 4 5 B\n", + "0 -2.992887 5.643915 -2.992887 5.643915 0.000000 0.000000 0.816593\n", + "1 0.780516 0.413897 -0.780516 -0.413897 0.000000 0.000000 0.000000\n", + "2 -3.655007 5.787479 0.000000 0.000000 -3.655007 5.787479 -6.084601\n", + "3 0.714879 0.451472 0.000000 0.000000 -0.714879 -0.451472 0.000000\n", + "4 0.000000 0.000000 -0.662120 0.143563 -0.662120 0.143563 -6.901194\n", + "5 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 3.000000" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" + ], + "source": [ + "import pandas\n", + "\n", + "df = pandas.DataFrame(matL)\n", + "df[\"B\"] = matB\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3. , 4.12377262],\n", + " [5.03684606, 0.2827372 ],\n", + " [5.48745959, 0.18503334]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy.linalg import inv\n", + "\n", + "points = (inv(matL) @ matB).reshape((3, 2))\n", + "points" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 9.86655487, -4.02070972, -6.07697098],\n", + " [-10.61997713, 3.26728747, 3.1110941 ],\n", + " [-12.13641872, 3.65091377, 3.80710713]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = points[0, :]\n", + "c1 = (L @ x.T).T.ravel() + B\n", + "x = points[1, :]\n", + "c2 = (L @ x.T).T.ravel() + B\n", + "x = points[2, :]\n", + "c3 = (L @ x.T).T.ravel() + B\n", + "numpy.vstack([c1, c2, c3])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = draw_border(clr, X, y, incx=2, incy=2)\n", + "ax.plot(points[:, 0], points[:, 1], \"ro\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression logistique dans un quadrillage\n", + "\n", + "On s'intéresse un problème de régression logistique où le problème est très facile mais pas forcément évident du point de vue d'une régression logistique." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((240, 2), (240,))" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "Xs = []\n", + "Ys = []\n", + "n = 20\n", + "for i in range(4):\n", + " for j in range(3):\n", + " x1 = numpy.random.rand(n) + i * 1.1\n", + " x2 = numpy.random.rand(n) + j * 1.1\n", + " Xs.append(numpy.vstack([x1, x2]).T)\n", + " Ys.extend([i * 3 + j] * n)\n", + "X = numpy.vstack(Xs)\n", + "Y = numpy.array(Ys)\n", + "X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On vérifie que le nuage de points est tel qu'indiqué." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEXCAYAAABWNASkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJztnXt8FPW5/99PNlcIECABw00sF+VSEMtBreKlKNpaq3i8tR6LVn/UHm2pVVtbT22qpWj1VKVaKz1awXK0WO+lLdiCihwVUbxhJEBFE4xyDRBICEm+vz9mJ2w2M7szuzO7M5vv+/XiRXZmduY7C3nmu8/383weUUqh0Wg0mtwiL9sD0Gg0Go336OCu0Wg0OYgO7hqNRpOD6OCu0Wg0OYgO7hqNRpOD6OCu0Wg0OYgO7t0EEakSkT/6eP51InJK9GcRkT+IyC4RWS0iU0VkvQ/XHCYijSIS8eHcx4nIehHp6fW5XY7jBBHZEL3Pc1287zIRednPsWmCjQ7uOYSIfENE1kQDQb2I/E1ETszEtZVS45RSL0RfngicDgxRSk1RSq1USh2Z7jVEZLOInBZzzY+VUqVKqbZ0zx2PUupV4F7gNq/P7ZJbgHuj9/l0lseiCRE6uOcIIvID4G7gl8BAYBjwW+CcLAzncGCzUmpfFq7tGUqp3wDVItIji8M4HFiXxetrwopSSv8J+R+gD9AIXJDgmCrgjzGvHwc+BXYDLwHjYvZ9BXgf2AtsAa6Pbi8H/gI0ADuBlUBedN9m4DTgCqAZaIuO6efAKUBdzPmHAk8C24AdGDNTgBHA8ui27cAioCy67xGgHWiKnveHwHBAAfnRYwYBz0bHthH4f3H3vxhYGL2vdcDkBJ/XPUAtsAd4A5ia4NiHgfuAJdFzvwaMiNl/FPB8dFzrgQtj9r0AXBnz+jLg5ejPm+Luucji2nafZcd5kt0PMAVYE933GfDr6PZi4I/R8zYArwMDY/7PPQjUR/+P/AKIRPeNBF7E+L+1HfhTtn9HuuMfPXPPDY7H+EV8ysV7/gaMAgYAb2IEUpMHgW8rpXoB4zECLsB1QB1QgfHt4CcYwbUDpdSDwFXAK8pIJfwsdn80P/4X4COM4DwYeMzcDczFCNJjMAJXVfS8lwIfA2dHz/sri3t6NDq+QcD5wC9FZFrM/q9Fr1WG8RC4N8Hn8zpwNNAP+F/gcREpTnD81zEeZH0xHixzovfbEyOw/y/GZ/114LciMi7BuQBQSo2g8z0fiN2f5LN0cz/3APcopXpjPGAXR7fPxAjiQ4H+GP+uTdF9C4BWjEA+CZgOXBnddyuwLPpZDAF+k+xeNd6jg3tu0B/YrpRqdfoGpdRDSqm90YBRBUwUkT7R3QeBsSLSWym1Syn1Zsz2SuBwpdRBZeTS3ZoTTcEIvjcopfYppZqVUi9Hx7RRKfW8UuqAUmob8GvgZCcnFZGhGLn+H0XP+RbwP8ClMYe9rJT6qzJy9I8AE+3Op5T6o1Jqh1KqVSn130ARkGjd4Eml1Orov8EijEAK8FWMFNUfoud6E3gC4+GTLrafpcv7OQiMFJFypVSjMtYbzO39gZFKqTal1BtKqT0iMhD4MvD96HW3AncBF8e873BgUKIxafxFB/fcYAdQLiL5Tg4WkYiI3CYim0RkD0ZKBYy0C8C/Y6RmPhKRF0Xk+Oj2OzBmpctE5F8icmMKYx0KfGT1IBKRASLymIhsiY7rjzFjSsYgYKdSam/Mto8wZrMmn8b8vB8otvvMROQ6EakWkd0i0oAxg000lvhzl0Z/Phw4VkQazD/AJcBhju4qMbafZTxJ7ucKYDTwgYi8LiJfjW5/BFgKPCYin4jIr0SkIHpPBUB9zD09gPHNBIyUmQCroyqqb3lwrxqX6OCeG7yCked2KpX7BsZC62kYv+TDo9sFQCn1ulLqHIxf1qeJfk2PzvSvU0p9Djgb+EFc2sMJtcAwm6A6FyPNMyGaIvgPc0xREn1L+AToJyK9YrYNw8gHu0JEpgI/Ai4E+iqlyjDyx5LwjdbUAi8qpcpi/pQqpb4T3b8PiF2wdRP0E32WHSS7H6XUBqXU1zH+vW8H/iwiPaPfzn6ulBoLfBHjW8g3o9c9AJTH3FNvpdS46Pk+VUr9P6XUIODbGGmokS7uS+MBOrjnAEqp3cDNwH0icq6I9BCRAhH5sohY5aZ7Yfxy7sAILL80d4hIoYhcIiJ9lFIHMRbZ2qL7vioiI0VEYra7lSGuxliEu01EeopIsYicEDOuRqBBRAYDN8S99zPgczafQS3wf8Dc6DknYMxIF1kdn4ReGPnkbUC+iNwM9E7hPGDkxEeLyKXRf5MCEfk3ERkT3f8WcF7032xkdMxOSfRZOr4fEfkPEalQSrVjLJwCtInIqSLy+Whufw9GuqVNKVWPkVP/bxHpLSJ5IjJCRE6Onu8CERkSPc8ujIey53JVTWJ0cM8RlFK/Bn4A/BfGL3EtcA3GzDuehRgpiy0YqphX4/ZfCmyOpkauwphBg7EA+w+MAPwK8Ft1SNvudJxtGLP+kRiLhXXARdHdPweOwZhVLsFQgcQyF/ivaCrgeovTfx3jW8gnGIvLP1NKPe9mfFGWYiw412B8Ts0Yn6drommi6Rj56E8w0je3Y+S8wchVt2A8uBbg4mGU5LOMJdn9nAmsE5FGjMXVi5VSzRjfIv6MEdirMRQwZiHcN4FCjP8/u6LHVUb3/RvwWvR8zwKzlVIfOr0vjTeI+/UwjUaj0QQdPXPXaDSaHEQHd41Go8lBdHDXaDSaHEQHd41Go8lBdHDXpIyIPCwiv8j2OMKAiLwgIlcm2H+JiCyLea1Mbbj+nDWpoIO7RhMAlFKLlFLTsz0OTe6gg7tGk2Wc2kZoNG7QwV3jGBGZJCJvisheEfkThhOlua+viPxFRLaJ0YHpLzFVimZa4lYRWRV9/zIRKY/uKxaRP4rIjmiB0utRcyqrMXSkK6KvO1IWInKKiNRFfVS2itGw5PIE93OEGN45e0XkeRG5V6LdqsxzxR3f0SxERKaIyCvR8dZH31sYc+zpIvKBGF4u9xJjXSBGl6RVInKXiOwEqsRh5yQHn/MRIvJS9J7+ISL3SUwHLjE6TP1fdNxvS7R7lib30MFd44ho4Hoaw0yqH4Yf/L/HHJIH/AHDVGoYhjVsvKXuN4DLMTxMCgGzyjSRtaxbDoueazBGKf99ItLX5tj/xfA2L8ewqZ3p4jptwLXR9x4PTAP+EyD60HoCo1q4HMOXPd4W4FjgXxifxRwX1032Of8vhi1Bfwy3zw5XzKilwxIM7/V+GJ//EyJS4eL6mpCgg7vGKcdhOAHeHTWU+jOGRzgAUTvZJ5RS+6Ml93Poatf7B6VUjVKqCcOMzLTFtbSWTXGcB4FbomP8K4ZVQherXhEZhlEm/9OoxfBLwHNOLxId46tRC93NGK6I5v1+BXhfKfXnqD/P3XR2jQT4RCn1m+j7HT/IEn3OMfd0s1KqJWq1+2zM2/8D+GvU9rg9as2wJjpeTY6hg7vGKYOALXH+7R+ZP0SNrx4QkY+injQvAWXSuXm1nS2unbVsKuyIs8CNvU78/exSnVsBfmRxnCUiMjqaEvk0er+/5JCF7iBivFuin1m8N01KXjVJPmfT9ni/zXUOBy6QzvbDJ3LIE0aTQ+jgrnFKPTBYRGJtb4fF/Hwdxgz52Khd70nR7UltchNYy1qxn9QtcmOpB/qK0SnJJPZ+OlnxRoNnbPrifuADYFT0fn/CoXutx0gxme+V2NdRUjV1SvQ512PYHsd+PrHXrQUeibMf7qmUynYTcI0P6OCuccorGLax3xORfBE5D6MTkEkvjPxvg4j0A35mcQ5LxMZa1ubwt4BviNFw5EwcdmqKRyn1EUZK4udi2ByfiOGwaFKD0czjrOi3iP/ikJMjGPe7B2gUkaOA78TsWwKME5HzxFDCfA9vmnOY17X8nGPuqSp6T8fH3dMfgbNF5Izo51ccXTgegibn0MFd4wilVAtwHkbj5V0Y1rKxlrx3AyUYDZFfBf7u4vSJrGXjmY0RsMyORlaWxk75BsbC5k6MILnQ3BH1yP9PjFZ9WzBm8rHqmeuj798L/B74U8x7twMXALdheOaPAlalMc5Ykn3Ol2As8O7AWDj9E4Z3v+l5fw7GtwzTFvoGdBzISbTlr0YTRUSqMBZ1/yPZsWFBDMnqByquUbkm99FPbI0mhxCjy9MIMbojnYkxU0/n240mpOjKOI0mtzgMI13WHyON9B2l1NrsDkmTDXRaRqPRaHIQnZbRaDSaHCRraZny8nI1fPjwbF1eo9FoQskbb7yxXSmV1DIia8F9+PDhrFmzJluX12g0mlAiIo4qqXVaRqPRaHIQHdw1Go0mB9HBXaPRaHIQHdw1Go0mB9HBXaPRaHKQ7hfc31kMd42HqjLj73cWZ3tEGo1G4zndy37gncXw3PfgYLTxze5a4zXAhAuzNy6NRqPxmO4V3P95y6HAbnKwydhuFdzfWWzs210HfYbAtJv1QyDEbGh8n9W7VtLYtofSSG+m9J3KqNKx2R6WRuML3Su4765zvl3P8jvIhaC4ofF9XtqxlNZoB77Gtj28tGMpQOjuRRMMtm6s4eM1r3BgXyNFPUsZNvl4Bowcne1hdZD7wT129i15oCwa/PSxaETjdpafo2QqKPr9AFm9a2XHPZi0qlZW71qpg7vGNVs31rDp5RW0txn/pw7sa2TTyysAAhPgc3tB1Zx9764FlHVgLygx0i3xuJnl5zCJgqJXmA+QxrY9wKEHyIbG9z27hnlup9s1mkR8vOaVjsBu0t7WysdrXsnSiLqS28HdavYNIBFAoM9QOHue9UzcajafaHuOkomgmIkHSGmkt6vtGk0iDuxrdLU9G+R2WsZulq3aoaoh8Xun3dw55w72s/wcpjTS2zKQexkU032AOEnpTOk7tVN6yeRgewsbGt/3JDVT89qnvPLMJhp3HqC0XxHHnzOC0cd61Rc7Nwl63tqOop6lloG8qGdpFkZjTW7P3NOZfU+40JjV9xlK0ll+psiCRn9K36nkS+c5QL7kM6XvVM+ukc6s2mlKZ1TpWE7qfwZFeSWdth9QzZ6kgGpe+5QViz6gcecBYxw7D7Bi0QfUvPZpWufNZcy8tRkkzbz11o01WR5ZcoZNPp68SOffi7xIPsMmH5+lEXUlt4P7tJuN2XYsbmbfEy6Ea98zZvnXvpf9wB67fmCqd3wO8GZQNANtaaQ3J/U/w9NFyHQeIG5SOqNKx1IgBV22e5ECeuWZTbS2tHc+b0s7rzyzKa3z5jJhyFvbMWDkaEaceGrHTL2oZykjTjw1UN86cjstYwbjf95iBEOJHFK8xO4PA1lU74wqHZtyMHeSMjFfp6KWcZvS8WsNwZyxO92uCUfeOhEDRo4OVDCPJ7eDOxwKfGHXrIdQveNGRpnqA8TtmoBfawil/YosA3lpv6K0zpvLhCFvHWaSpmVEpFhEVovI2yKyTkR+bnFMkYj8SUQ2ishrIjLcj8GmTKJZb1gIoXonVRXMhsb3WVT7AA9svoNFtQ8kzIe7Ten4tYZw/DkjyC/s/OuUX5jH8eeMSOu8bql57VMW/GQV9121nAU/WRXonH8Y8tZhxknO/QDwJaXUROBo4EwROS7umCuAXUqpkcBdwO3eDjNNQjjr7UK66wdZIJUUiFvNu9s1Ab/WEEYfexinXnJUx0y9tF8Rp15yVEbVMmFb1A1D3jrMJE3LKKUUYH53Koj+UXGHnQNURX/+M3CviEj0vdmnz5DoQqTF9mxj519jtf3seaHyukklBZJKJanblE46awiJGH3sYVmVPiZa1A2qJDPoeesw4yjnLiIR4A1gJHCfUuq1uEMGA7UASqlWEdkN9Ae2x51nFjALYNiwYemN3A1B1azb+dd8/Cq8/b9dt589z1DthAQrbXmyFIiuJE0dvairicWRFFIp1aaUOhoYAkwRkfFxh4jV2yzOM18pNVkpNbmiosL9aFMliJp1sF8LeOPh8K8RkFoKRFeSpo7d4q1e1O2euFLLKKUaROQF4EwgdgpZBwwF6kQkH+gD7PRqkJ4w4cLsB/N4bCtoLTxwEh0fYNymQFKZ7duRC26Wbjj+nBGsWPRBp9SMk0VdXVmbmLBW0TpRy1SISFn05xLgNOCDuMOeBWZGfz4fWB6YfHuQscv5S8Td8TmEVwuemTAjCxqpLOqGbRE204S5itbJzL0SWBDNu+cBi5VSfxGRW4A1SqlngQeBR0RkI8aM/WLfRhwk0m3mYbcWMPEbnXPu5vZsrxFkCC8WPLurxa/bRd0wLsJmkkRVtEGfvTtRy7wDTLLYfnPMz83ABd4OLeB40cyjUwVt3ANi2HGhUsYEDb0w6wy9CJuYMFfR5n6Fql94ZQdgtxYQxDWCEJEJN0s/yVQeXFfWJibMVbQ6uKdKNgujMtDbNeyLkV4uzGYaMw9upkvMPDjgeYBPdRHWT4K0gDls8vGdOi5BeKpoAxXcDx48SF1dHc3NzdkeSnK+/AS0t3bdnpcP1dWWbykuLmbIkCEUFHR1JnRMBnq75kK/0XTMyJzi1+w6k3lw83xBUcsErX2deU0nD5sgPZQgYMG9rq6OXr16MXz4cESspPMBYv9AI7CqmF9CyTM09D36dTlcKcWOHTuoq6vjiCOOSP26GXCHzJXFSL8qUcHf2XWm8+DZrqyNJYgLmE6qaIP2UIKABffm5uZwBHY4FMD31kNbC0QKoVelZWAHEBH69+/Ptm3b0rtuBtJBejEyOX7OrtPJg4ddsx7WBcwgPpQC16wjFIE9RTy5twy4Q+oq0eT4ObtO1WEyFzTrdguVQV/ADOJDKXDBPTTs32mkZdpajNdtLcbr/T4X5mbAHTITrfXCjp+l/qk6TOZCN6iw2gAH8aEUqLSMW55eu4U7lq7nk4YmBpWVcMMZR3LupMGeX6eqqorS0lKuv/56du7cyUUXXcTmTTUMH1LJ4t/dTt+y6IxWtRtpGpvUjCck0sZ7RCYWI8OO3yqTVPLguaBZd7OAGSSCqKoJbXB/eu0WfvzkuzQdNHxYtjQ08eMn3wXwJcCb3HbbbUybNo0bF/yK2+79A7fd9wduv2n2oQPMmbyfZEAD7+diZC4QNJUJ5I5mPYw2wEF8KIU2uN+xdH1HYDdpOtjGHUvXpx3cFy5cyJ133omIMGHCBEaMODQbe+aZZ3jhhRcgbyczL/gqp5w/q3NwjxSmdW2NPUHT3gdJZQLB1Kx3J4L2UAptcP+kocnVdqesW7eOOXPmsGrVKsrLy9m5cyfz5s3r2P/ZZ59RWVkJ+4uoPKyVrTticuySZyhmNJ6TC9p7vwnitwlN9ghtcB9UVsIWi0A+qKzE4mjnLF++nPPPP5/y8nIA+vWzyZ935NWjCpgkUkhNeuSK9t5v0v02EXYppeYQoQ3uN5xxZKecO0BJQYQbzjgyrfMqpRJKFgcOHEh9fT2VlZXU7z7AgIGHwaAuvmoaj9Hae//JlO1B0Co57QjLOO0IrRTy3EmDmXve5xlcVoIAg8tKmHve59POt0+bNo3FixezY8cOAHbu7Cxt/NrXvsaCBQsAWLBgAeecc05a19M4Q2vv/ScTUsqw+KOHZZyJCO3MHYwA77UyZty4cdx0002cfPLJRCIRJk2axPDhwzv233jjjVx44YU8+D+/Z1hlBY//7jb4bF1qKZkMGIDlCkE3AotPZwwf35/N7+0IVXojE1LKIFZyWhGWcSYi1MHdL2bOnMnMmTMt9/Xv359/Pvd4Z18Zs4AJnAf4DBiA5RJB1t5bpTPee+mTjv1+ujp6SSaklEGs5LQiLONMhA7uydi/s6t/zN76zoZh4L6AKQMGYKngRm6YaWliULX3VumMeMLQ3SgTUsqw+KOHZZyJCG3OPSPYWQzYFSq5KWDKph+8DW76jnbHHqV2OE1bBL1SNFXbAzeExV4gLONMhJ65J8Juhm6HmwKmPkMOpXLit2cJN3JDLU08hF06w+o4P/FCxuhXYVas8iRSWER+fj6tB5oDq0JJVHEaFhWNDu6JSDQTl7yuXu5uCpjsmmNnsQm2G7mhliYewiqdYXecX2Sye5Nb4r3O21oOkBfJZ9TJpwcyKJpYVZwG0bfdDp2WSYTdTDxSaDTlMPebr92oZSZcCGfPM96HGH+fPS+r+XY3ckO7YwXpdqmZ+HRGouP8IsiOkImUJ2EjTPeiZ+6J6FVp3W3JlD2mW40asCbYbuSGVscCKFS3tAUw0xkLfrIqK+ZdQXaEDLPyJD4FE6Z7STpzF5GhIrJCRKpFZJ2IzLY45hQR2S0ib0X/ZCS3ULutib+/uY2nXv2Mv7+5jdpt6fnKdKFHP+gzlKpf/547f7cQIoU8/s83GfdvU8nLy2PNmjXeXi/LjCody0n9z+iYlZdGenNS/zMsg7R5rNC1mtfMvTthQ+P7LKp9gAc238Gi2gdCP+tPtdFGuvjpL58uQfQ6d4JVIZMdQbwXJzP3VuA6pdSbItILeENEnldKxf8WrlRKfdX7IVpTu62JtR/uoS06qW5qaWfth0a+d2hFev4ynejRD0oHQGkpDBzH+C/k8eSTT/Ltb3/b2fvjpZQtHj+Assio0rEs377Ecp+T3HsumoG5Ne/yysvFqYwxG94xQfQ6d4JVCsaKoN5L0uCulKoH6qM/7xWRamAwkNUp1rraxo7AbtLWbmxPN7gnsvwdM2aM8xOZUsrYYqemnUYBU4DSMSapBNvSSG/LQO7EFiDXFDfxgfP0y8cmDJxeLoI6eajYXa9+U4Ov1bTJvM6Dqj5JNlMP2njjcZVzF5HhwCTgNYvdx4vI28AnwPVKqXUW758FzAIYNmyY27F2oslGmWC33SnJLH9dYSmlVFkvVLIjlWCbji1ALilunAbq2AdAvOAK0it2SiZjtFt0zUQ1rZ3XeZDVJ4kKmSZfbF3BHiQcq2VEpBR4Avi+Uir+t+9N4HCl1ETgN8DTVudQSs1XSk1WSk2uqKhIdcwAlBRaD91uu1McW/46wU5KubsW7hpvzOADRCrB1k2ePp5cMgNzolaJb2BtVzLh1yKo0/NmUmUTZPVJ2AuZHM3cRaQAI7AvUko9Gb8/Ntgrpf4qIr8VkXKl1HbvhtqZcUNLO+XcASJ5xvZ0SGb564pIYeIAHzAvmVRTLKnaAgTdDMwNTtQqTmwKwL9FUKfFVpA5lY1T9Uk2UjdBbJ3nBidqGQEeBKqVUr+2Oeaw6HGIyJToeXd4OdB4hlaUMOmI3h0z9ZLCPCYd0dtdvn3/TsPR8ZO1xt/7dya1/HVFr0pDOmmH6SUTEKb0nUq+dH7eOw22qahe0pn1Bw0nahUnAdNPZY2VkseOTKlsnChptm6sYePKf3ZSrWxc+c9Q2e9mAycz9xOAS4F3ReSt6LafAMMAlFK/A84HviMirUATcLFSSvkw3k4MrShJffHUarFzdy3jjhia0PL3qaee4rvf/S7btm3jrLPO4uijj2bp0qXW1zB18KZaxoosesnEE++8WJRXAkqxfPsSVu9aaWsMlo7qJahmYG5xolaxmzmbuXe/1StWi67Dx/fng1c/zVrfVSdKmg9fXYlq7/yNR7W38+GrK32dRQd5PcAJkoEYbMnkyZNVvE68urranRolHT5bZx1wI4UwcJxv16v+aCtjlsakYfoMhWvf8/56aRIfsMGYxVvNrBfVPmCbzrlkqEPJaA4Qu1ha1DOCIDTva+0I2oDlA8Brcy63ZLu1XrKUy6oH77N97wlXXO3buNY8tiCQC6oi8oZSanKy47pvhaoXzo5uMKtdY8myl0wi3ChnvFS9ZNpG2EtMtcoh5YzRAtJUoJx6yVGceslRgepRmu3ADvZKmmwTpmpUK7pvcLdb7HTj7OgGM0WTtx3DSybYnZfcBOx0tO6xrNz+PO83vtXxOqxFTYmUMzN/eULWjbxMgmw2FkuksIi2lq7prEihd+sCVt8ewu7p3n2Nw6wWO906O7qlRz/oPQiqGoxUTEADO7iTKaazEGuyofH9ToHdxI2VQVAIss9LLEE2G4vlc8efBPEKNhFjuwfY9UstGzo81FLI7hvco74xaTk7Zot3Fhs6+aoy3/TybgK2F6qXRAE8bEVNQfZ5iSUsD6EBI0cz6qTTOmbMRT1LGXXSaZ6lcuy09g21mxlx4qmdrjvixFMDmUKyovumZcAbZ8dMk6Heq257lqarekkUwMNW1JSJdnVekImeqV7hZ14+UW59w4vPGw+TgHvPW9G9g3sYyWDv1UzKFO3y9kDoiprcmodli7A8hEz8KmTKLyqm9UCz7f6wSSBNwh3c31lsBLXddb4uUFZVVVFaWsr111/PDTfcwHPPPUdhYSEjRozgD3/4A2VlZZ5fsxOx94mNdDVAevlUsPOHH1t6dKgWU038alfnBbEKmeKe+UQKhAP72gL7EIL0NOeJHgpbN9bQarFYG097WysbXnyej9e8Epoq1fDm3M30xO5aQB1KT3iVf46tXm3cCi37ATj99NN57733eOeddxg9ejRz58715np2/OUH8OSsQ/dpRxZ7r3rBqNKxjO45vsMfXhDGlh7N1PLTszyy3CLe36Z5XyttBxWnXz42UEqeeFL1oLFbLDWrWz9e84ph5ueQ+PcHmfAG90TpiTRZ+PvfMmHycUw8dQaXfve/QLVBcwPs38n06dPJzze+8Bx33HHU1fk4Y35nMax5iIRBHQKtl3fKhsb3qdn3Hip6rwpFzb73Qt+8I2iERSETT6qa82QPhVQ060ExNktGeNMydmmINNMT69atY87td7Dq6Qcp79eXnbt2M++hRwFl2AjELMA+9NBDXHTRRWldLyH/vIXEgT34enmnhNHbPQgFQG4Ji0ImnlQ158keCola56Vy3iAR3pm7XRoizfTE8uXLOf8rX6K8X18A+vXtc2hnTNHTnDlzyM/P55JLLknreglJ9KDqMzQUenmnhM3bPT69YRYA1bz2aZZHlpiwyDTjSdV+N5kxmd15Bx41PuGDIwyFTOEN7tNuNtIRsXiQnlBKIRGbLzRRTfyCBQv4y1/+wqJFi7yzB7bC9kEloU/DxBM2b/ewpjey1eM1XQaMHJ2S5jzZQ8HuvCNPOJnJF89k1Mmnh7aQKbxpGXO26rFaZtq0acyszEsUAAAgAElEQVQ4dx7XXvkN+vftzc5du6N7BHpV8ve//53bb7+dF198kR49eqR3D0kHc3NnTbs5jsnfyonZeixh83YPa3ojLDJNK1LRujvxZE903jB7uoc3uIMR4DwOcuPGjeOm//opJ19wFRFpZ9K4Ixk+bAgUl0GPflxzzTUcOHCA0083VBzHHXccv/vd7zwdQwc+PcCCiNuiqWwTpgKgeIIs0/SDdAuggmpslozua/mbJbrDPXYH4k23IBj2vZrcR1v+ajQ+Eub0hqZ7oIO7RpMi3S29oQkX4VXLaDQajcaWbj9zbznYTlNLG+0K8gRKCiMUFuhnXphZ8q8l3PPmPXy671MO63kYs4+ZzVmfOyvbw9JoMkq3Du4tB9vZ39LWYS3RrmB/tDVadwzwuRAUl/xrCVX/V0Vzm+HyV7+vnqr/qwII3b1oNOnQ/SJYDE0xgd1EKWN7d8MMivX76lGojqC45F9Lsj00V9zz5j0dgd2kua2Ze968J0sj0miyQ7hn7vVrYdMyw9SruAxGTIfKSY7f3m6jAo3fHmv5+9Of/pRnnnmGvLw8BgwYwMMPP8ygQYPSuIlgMPe1ubZBMcgz3vhvG/X76i2P+3SfR7YAG2pg9WvQ2AilpTDlWBgVPg10d6R65QpWPraQvTu206t/OVMv/iZjpp6a7WH5RtLgLiJDgYXAYUA7MF8pdU/cMQLcA3wF2A9cppR60/vhxlC/FqqfgvaDxuvmBuM1OA7weWId4PMSOArccMMN3HrrrQDMmzePW265xb8ipgyx5F9L2N2y23KfZ0ExBZKliaxSMHYc1tMDVcuGGnjpRWiNVtE2NhqvITMBXj9YXBEbzIt6lnKwuYn26L/d3u3bWDb/XoCcDfBO0jKtwHVKqTHAccDVIhJfNvhlYFT0zyzgfk9HacWmZYcCu0n7QWO7Q0oKI1Z9d3niT4uYMGECEydO5NJLL+20v3fvQ14n+/bt89dbxmeW/GsJ0/88nRtX3mh7jCdBMQWcpImsUjBWFEeKmX3M7PQHtfq1Q4HdpLXV2O435oOlMepGaD5YNgTfVzwbVK9cwbL597J3+zZQigONezsCu0lrywFWPrYwSyP0n6Qzd6VUPVAf/XmviFQDg4FYo+1zgIXKKHd9VUTKRKQy+l5/aG5wt90Cc9E0Vi3z4YYPuP32uaxatYry8nJ27tzJvHnzOr3vpptuYuHChfTp04cVK1akfAvZJH7Wa4cnQTEFEuXOzdl7om8VlT0rvV8YbrSxebXb7iWJHix69t6FlY8tdNRhae+O7Qn3hzmV4yrnLiLDgUlA/FRlMFAb87ouuq1TcBeRWRgze4YNG+ZupPEUl1kH8mJ3Le8KC/I6KWNWrnyB888/n/LycgD69evaQHvOnDnMmTOHuXPncu+99/Lzn//c3dgDgJNZb1lRWdby7XaBO3a7XY69smcly853/g3OMaWl1oG81ML+1esUSjYfLAHHKgAnC9omvfqXJzzvsvn3djwkwpbKcayWEZFS4Ang+0qpeJNtq9xEl2y2Umq+UmqyUmpyRUWFu5HGM2I65BV03pZXYGxPA6WU41TLN77xDZ544om0rpctkuXSiyPF3DjFPl3jN3bpoNjts4+ZTXGkuNN+z1IwVkw5FvLj5kP5+cb2WPxIoVg9QBJt7ybEp1/MAOzEbz2/sIipF3/Tdr/V7D9MqRxHwV1ECjAC+yKl1JMWh9QBQ2NeDwE+SX94CaicBGNmHJqpF5cZr12oZayYNm0aixcvZseOHQDs3Lmz0/4NGzZ0/Pzss89y1FFHpXW9bJEol17Zs5KqL1ZlVSXjJHCf9bmzqPpiFZU9KxHE/3GPGg0nnXwooJaWGq/jZ+R+5OadPli6GXYBWMQI3rFIJEJxr14gQq/yCqbPuibhDNxu9u/0W0G2caKWEeBBoFop9Wubw54FrhGRx4Bjgd2+5ttNKielHczjGTduHDfddBMnn3wykUiESZMmMXz48I79N954I+vXrycvL4/DDz/cVimTTuVr7bYm1tU20tTSTklhHuOGljK0oiT5G10w+5jZXXLuxZHirAd1E3MMyYqqzvrcWZkd76jRydMrfqRQzGtqtUwn7AJtc2MjX7n6B2nly3v1Lze+EVhsDwNOcu4nAJcC74rIW9FtPwGGASilfgf8FUMGuRFDCnm590PNHDNnzmTmzJmW+5ykYRJVviajdlsTaz/cQ1vUSbappZ21HxpZMC8DvNPgmU0yHri9wk1u3g1OHizdjEQBeMzUU9PKjU+9+Judcu6QPJUTJJyoZV7GOqcee4wCrvZqUGEnncrXdbWNHYHdpK3d2O717N3v4JkLdgaOiF88HTYMamo6p2Z0CsUX/AzA5oPBavYfBhVNuCtUfcALIzGnla9WNMX15Uy2Pah0G48Xq8KmmhoYPRo+/linUHwmUQD26vzx5wqLikYH9xi8MhJLpfLVpKQwzzKQlxSGywbIiU7dDYH9FmC3ePrxx3DJpdbvSYauRHVFuukXtyRS0ejgHlASpVPcBPeSwkinhwQYla8lhZGk7x03tLRTzh0gkmdsDxNOdOpOCdS3gPjA6/XiabYtDjRJCYuKRgf3GNJJp8RiVfnqNL1j5tX9Vsv4jV2BUSp2Bl5/C0gZq8BrR6qLp6lWosY+dESMWUkOzPqDmNsOi4omXN/1fcYubeIknRJPYUEefXoW0Le0gD49C1zN/IdWlHDmMRXMOG4gZx5TEbrADt4WGHn5LSAtrAKvFeksnqbyTSC+aMr8yhhy/xm7AqXqldm1/Jh68Te7aOiDqKIJdXA3ja8mLJjA9D9PT9t73M5I7M7bfsGdd97Zafudd96JiLB9e7C+igUFLwuMnFSrZgQnM3W7wianpFKJmuihkyljMx8IaoXomKmnMn3WNfQqr3BcEJUNQpuW8SMPa5dOiUQ6R/za2lqef/759P1xchyvpJZ2BVcZNzVLpF9PdfE0ninHdk79QPJvAsny+yHxn4lPwVilPiAYue1ML+KmQmhn7n513CksyOOZJx7l5C9+gZO+OJkrvtW1mOnaa6/lV7/6VajtfsNExm0G7MiEBYBTi4NYkuX3A+Q/U71yBfOvvpz/vvhs5l99eUeKxSoFY0fQcttBJbQzd7/ysOvWrWPOnDm2lr/PPvssgwcPZuLEiWldR+OOQFSrZsoCwG0lqtVs3yRAxVOJ9OFOLXqDmNsOKqEN7m7UGG4Kk5YvX25r+bt//37mzJnDsmWd7WS9KHzShIQgWgDEP3QCqpZJlENPlGrpVV4RKLVMKmRD9RPa4O40D+u2MCmR5e+mTZv48MMPO2btdXV1HHPMMTz/wioGDDjM0fk1Gl/w4qHjc/FUIn24rbywvIJZ9/3BszFkg2xVtIY2+jjNw7r1eUlk+fv5z3+erVu3snnzZjZv3syQIUN4YeVrHYHdyfk1/uK1gqrbkIE2fna5cnMmGwZ5YSpkS/UT2pk7OMvDui1MSmb5m+75g46fZf5+WwgEqpI1bGSgjV8iky+/PWKySbYqWkMd3J2Qis9LIsvfWDZv3szufQdT9pEJArEBt09RHxpbGmlVxi+5l8ExE4E3MJWsQcNJuiVR8dSiRzxJ0SQL4F7LC4NS3ZqtitacD+7p+LwE4fx+Eh9wGw507UnrVXDMROANTCVrkHDqVZPMJycNf5tsBNnqlSv4++/uoT1633u3b+PvvzNk0pkO8Nnyhc/54J6Oz4vX5zdVNU+9+pknnjHpdmxy0iQbvAmOmQi8XvrZ5AxO0y2J5JR273FAthYTly+Y3xHYTdpbW1m+YL7j63r1UMpWyim0wd2N/LCwIC/rypV41U66HZa86NjkNLB6ERwzEXgDU8kaJJx61cTLKd2cKwHZssdt3rvX1fZ4vH4oZaOiNZRqGTNQtsfJG1sOZr6hhdOxWKl2zA5LqZCoY5NTnARWr4Kjl0ZidgSmkjUobKihi1mSiVXV6qjRho1CKv42NoTFHjeeoPrauCGUM3evfNczORY79UyqHZa86NhkNdMtyCugR34P9rTs8VTRkqmerYGoZA0CZq49/j8nJK9aTcXfxoZsLSYWlfbiQGPXWXpRaS9H7w/rQymWUAb3IMkPnY7FTrWTaoclLzo2ZbpJdrYCb2C7OPmJnVOkSHKvGg9tFrK1mDjtsln87f67UW2H6k0kEmHaZbMcvT8snu2JCGVwNwPlR80f8O7+Vexv30uPvF5M6HkCfUs/7/n1qqqqKC7pyXeu+T6/nHMLjzz8EBUVFYjATTffwmnTv2w5xlhMVU0s6XRY8qpjU67PdLut9t0uP66UsyDtkc1CthYT071uth5KXhLK4F5SGKF6zzrWNP6DNozZyf72vbze+A96FEUYVTrW0+u1tSkOtrV3zLyvuvp7fO/7P6BHVO7oRApppmjMFGi6aplc6djkBYlm5t1W+57InjjDZMseN53r5kJRVdLgLiIPAV8FtiqlxlvsPwV4BvgwuulJpdQtXg4ynsKCPN7dv6ojsJu0qVZW71qZdnBfuHBhRzOOCRMmMHjocAps8up9ehYAzqSQhQV5lBRGmDFxYErjspI+nnlMRUrn8oNspD+SzcytFDrmcTmNh3nz7koYPNsT4SRB+zBwZpJjViqljo7+8TWwm+xrs5Y0NbbtSeu8puXv8uXLefvtt7nnnnuIT5X/z/z7OfG4Y/jPq/4fu3btSqulnlNM6aOZZzelj7XbmpK+NxN+K2aQrd9Xj0J1BFmvrmV3D8l8/fPE+t/CbnvOkIovvCanSDpzV0q9JCLD/R+KO0ojvS0DeWmkd1rntbL8jU2ff+vKb3PDj25CRJh768+47rrreOihh9K6phMSSR8TpWIylXP2M/2R6B6SFUe1K2v1kN32nCKI9sQ+EhS7gaDg1fTleBF5W0T+JiLj7A4SkVkiskZE1mzbZt9pxQlT+k4lXzo/m/Ilnyl9p6Z1XivL34JIXkeufMCAgUQiESKRPGbNmsXq1avTup5TUpU++tWxKh4/K1AT3UOy/qqVPSst99tt14SToDbTziZeBPc3gcOVUhOB3wBP2x2olJqvlJqslJpcUZFernhU6VhO6n9Gx0y9NNKbk/qfkXa+3cryNxIRCiJ55Al8+mk9eQI9CiMs+cszjB/fZRmiCy0H29m97yC7Gg/S1NLmKJUCRirm729u46lXP8OmFCWp9DFTfit+NrFOdA/JiqMyUTylyT65UHTkNWmrZZRSe2J+/quI/FZEypVSvqv9R5WO9VwZY2f5mx8R+vQs4Jqf38Rbb72FiDB8+HAeeOCBhOeLtx1QCkc2AfH2AlZyeifSx0z5rfhZ+p/oHpJp9TOt5ddkh1woOvKatIO7iBwGfKaUUiIyBePbwI60R5ZFEln+PvLII67Olch2IFFwt8qxxyLAsPLipNLHTPitmCqZ5rZm8iSPdtVOZc9Kz4JosntIptXPdS1/ruMkl54LRUde40QK+ShwClAuInXAz4ACAKXU74Dzge+ISCvQBFyslFXNc/ckVduBZPsV8PH2Zvr3KkwY4P2eucYvdrar9o7A69U19Ow7CT63x8smTg28cqHoyGskW3F48uTJas2aNZ22VVdXM2bMmKyMxy/im3n8a+N6Pm6uoKQwL6FG/e9vbnPkE5PsPH4z/c/TLVMmlT0rWXb+Mot3aDwl3q8dDD17jsge5199uePeqt1FLSMibyilJic7LnQVqm6sfoOAVTMPJ7lyK3sBK1I1HvMK3SAjy2SgPV42cZNLD3vRkdcENypaECSrXztilTG79x0EDGWN6TUjApOO6J00Vz60ooRJR/TuUMOkqpbxGz9VMhoHeOi9HkQSNdXWJCZUM/cgWf1aEa+MMR8+PQojHTYFnxZGHPu/DK0o6Tg2Xj0D6RmPeYVukOESr/PjAfKQ8YOg59KDnAoKVXAPktWvFX4+fIJqFKYXO13gtJ+pG3LcQybIBl7ZaiHolFAF93hP9P1/W0LjffNo/+xTtldWMuDa79Pn7LM9v25VVRWlpaVcf/31APzmN7/h3nvvJT8/n7POOotf/epXgP8Pn9iZfJDoFlJDL2bcfuTHPfReDypBzaVnq4WgU0IV3GMXJ/f/bQl75vwcmo10QOsnn1D/05sBfAnwJitWrOCZZ57hnXfeoaioiK1bt3bss2vIEe/tnmm6ZbMKL/Fqxu1XfrybecgEhaAXTmU/Ue2CwoK8jsXJxvvmdQR2E9XczNa77k77OgsXLmTChAlMnDiRSy+9tNO++++/nxtvvJGioiIABgwY0LGvpDDSpWWllbd7JvHbrbFbkGjG7QYPe5Nqsk/QF3tDFdyBDnvd9s+spXat9en5dFtZ/sZSU1PDypUrOfbYYzn55JN5/fXXgUMSzdicu+lBk83F3kwZh+U0Xs24pxxr5MNjyaH8eNCpXrmC+Vdfzn9ffDbzr748bVOxqRd/k/zCok7bgrTYG6q0TCz5lZW0fvKJ5fZ0sLL8jaW1tZVdu3bx6quv8vrrr3PhhRfywfqNNB1st+zGlG0Vj9ahe4BXipRukB8PKn4sfgZ5sRdCHNwHXPt96n96MyomNSPFxQy49vtpndfK8jeWIUOGcN555yEiTJkyhby8POo++Yx+5Z2rRP2SaFp1Y0q0yJop47CcxktFShDy4zlsV2CHm8VPN/LGoC72QgjTMiZ9zj6byltvIX/QIBAhf9AgKm+9Je3FVCvL31jOPfdcli9fDhgpmpaWFvra5Ni8lmim0o1JW956QBC7Gm2ogUWPwAP3G39vqHH+vpdePPRNxFwcdvr+DOB1+gScL37mki98aGfuYAR4r5Uxdpa/Jt/61rf41re+xfjx4yksLGTBggVE8iQjKplUujFpHbpHBGHGbZKOescLOaaPM3+/tONOXSP9ljdmsugp1MHdLxJZ/hYWFvLHP/6x07b4ylTwRyWTajembqFD706kE6DTXRz2oxArBr+Cq9NKVz/ljZkuegptWiZIxEo0wT+VjJ2PTLb9ZTQZJp0Ana4c0ytZqA1+BdcxU09l+qxr6FVeASL0Kq9g+qxrLH3hrfBC3pjpblF65u4RhQV5XYK5lYNlOlg5RQbBX0aTYdJR76S7OOyzUZmfTTecLH766WWT6aInPeXzCTsHy9a21FdZ450iSwrzHDlMakKC00XSdPTy6S4O+1yIlW3tuNMZfipkuuhJz9x9ws5E7GAyg/YkBNVfRpMmbnLZo0bDp/VQXW38pxKB0S4WfNNZHPbZqCwI2nG/5I2ZdrjUwd0n7GSQugGhxhI3i6QbaqCmhk5d12tq4LBK/xU9GSjECrJ2PB0y/eDSwd0n7EzEEtRHabozbnLZ2e6+FCRZaMjI5IMr3ME9Q5V2sZa/F110EevXrwegoaGBsrIy3nrrrS7vsWqvJwIFEb3MobHAzSJpjndf0nhDeIO7z3pbO/70pz91/HzdddfRp08fy+NM5Uy8WiY/oqfuGgvc5LJzvPuSxhvCO430UW+byPLXRCnF4sWL+frXv257HtPBsm9pAX16FmTdREwTYNyoWLS7pMYBSWfuIvIQ8FVgq1JqvMV+Ae4BvgLsBy5TSr3p9UC74NNXU9Pyd9WqVZSXl7Nz507mzZvX5biVK1cycOBARo0aldb1NJoOnOaytbukxgFO0jIPA/cCdmVUXwZGRf8cC9wf/dtzdu1v4bPdzbS0tTOmpAcFTfu7HpTmV9Nklr8mjz76aMJZu0bjK3pRU5OEpMFdKfWSiAxPcMg5wEKllAJeFZEyEalUSqXXNSOOXftb2LKrifboCmX9kRMZ8u5q8traDh3kwVfTZJa/YHi6P/nkk7zxxhtpXUsTfHY/9xxb77qb1vp68n3s06sJBttqG/i4eisHmlopKsln2JgBVAwty/awUsKLJPBgoDbmdV10WxdEZJaIrBGRNdu2dS0xTsRnu5s7AjtAw+Dh1H1+CgdLehgbPLJhTWb5C/CPf/yDo446iiFDhqR1LY3H1K+Fl2+Hf/zY+Lt+bVqn2/3cc9T/9GajKYxSHX16dz/3XJfjNnxpGtVjxrLhS9O67NeEg221DWx6u54DTcZa3oGmVja9Xc+22oYsjyw1vFDLWE1zLUt1lFLzgfkAkydPdlXO02JR2dkweDgNg4czYYh3T9Zklr8Ajz32mE7JeE39Wti0DJoboLgMRkyHyknu3l/9FLQfNF43Nxivwd15Yth6192dmsHAoT695uzdfACoDDdq13jPx9VbaY+zB2lvU3xcvTWUs3cvgnsdMDTm9RCga/+7NCmM5FkG+EIfdOOJLH8BHn744ZTOu2t/C5/ubuYrNy5hUFkJN5xxJOdOsvyS073wIjBvWnbo/SbtB43tKQZ3u368sdudPAA0wSQ+BWPO2OOx2x50vIiMzwLfFIPjgN1e59sBBvYpJi8uF54nwsA+xTbvSI9d+1v4oH4P79Q18EH9Hnbtb0n7fFt2NdHarlDAloYmfvzkuzy9dos3Aw4ziQKzU5ptvjrbbXeAXT/e2O1OHgCa4GGVgrGjqCSc5UBJg7uIPAq8AhwpInUicoWIXCUiV0UP+SvwL2Aj8HvgP/0YaN8ehQzuW9IxUy+M5DG4bwl9exR6fi0zEJvfFFra2qnduZ8tuyzUOQnOEftwqG9o6rRmANB0sI07lq73dOyhxIvAXGzztdluuwMGXPt9pLjz5CG+T6+TB4AmeFilYKzIiwjDxgzIwIi8x4laJmFyOaqSudqzESWgb49CX4J5PPGLtyY79rXQoyg/6RjilT1W6SSTTxrs+592G4rLrAO5m8A8Ynrn1A5AXoGxPUXMtEoitYxfjdo1/pJspp4Laplwft/wmUTB+LPdzUmDu93DwYpBZdq+15PAbObV01mUtSBZn14nDwBN8LDLsReV5POF6blRP6CDuwV2i7eQOPC7OQagpCDCDWcc6WpsOYlXgblyUtrBPBX8aNTuJbmi1fdSgz5szAA2vV3fKTUT5hSMFTq4WzCwTzG1O63z607UOXYPh0iekJ8nCLhWyzy9dgt3LF3PJw1Nuam0yVJgznVyRappLoCawdjUoAMpBXjzPckeFmEuagp1cK957VNeeWYTjTsPUNqviOPPGcHoYw9L+7x9exSy/0ArO/YZCpn7f30bPXr05PLvfI+tm9dz2Yzv0tzcTH5+Pr/97W+ZMmVKp/cP7FPcKecOhrJnUFkJebuL+fC2s1yN5+m1W/jxk+/SdNCoxjWVNkBuBXiN59hJNT+b88tQzeb90KBXDC1L+F6vHyiZJrQ2hTWvfcqKRR/QuNNoWdW48wArFn1AzWufenL+wX17MLRfj46ZeiRPGNy3hF/87CZ+9rOf8dZbb3HLLbfwwx/+sMt7vVb23LF0fUdgN9FKG40T7CSZbQ0NSStvg0Q2NOiJHihhILTB/ZVnNtHa0jn10drSzivPbEr73Kbl7ynH/xtzfng1A3sXM6B3MX17FCIi7NmzB4Ddu3czaNAgy3P07VHIUZW9mTCkjKMqe6el8rFT1GilTe7htZWBU0mmWXgVVOy05n5q0MNe1BTa4G7O2J1ud4pp+bt8+XLefvtt7rnnnk777777bm644QaGDh3K9ddfz9y5c9O6nhPsFDVaaZNbOPWycYOVVt8OJ4VX2fLRGTZmAHlxjW78XgDNxgPFS0Ib3Ev7Fbna7pRklr/3338/d911F7W1tdx1111cccUVaV3PCTeccSQlBZFO27TSJvdIZGWQKn3OPpvKW28hf9AgECF/0CCkzDpfnGyW78fDxykVQ8sYMbGyI7AWleQzYmKlr7nvsgFd7cPDpKgJxyPIguPPGcGKRR90Ss3kF+Zx/Dkj0jpvMsvfBQsWdMzmL7jgAq688sq0rucEc9E0p9UympSsDJzIHOOlmvEKGnBWeJUJH51E6pRkC6Besq22gW11u7tsrxjSJxSLqRDi4G6qYrxWy0ybNo0ZM2Zw7bXX0r9//y6Wv4MGDeLFF1/klFNOYfny5RnrxHTupME6mOc4+ZWVxqzYYrsVqcocUy288ttHJ0jqFDt7goat4WlCHtrgDkaA90L6GEsyy9/f//73zJ49m9bWVoqLi5k/f76n19d0X9xaGaQzk3ZaeBX7zYC8PGhr63KMVz46QbLcDftiKoQ8uPtFIsvfE088UXdg0viC2xl1spl0upWpXdI3FoHdSx+dIAXURPYEYSE8I9WEg3SbbnRz3FgZJErjeFGZavXNAIBIBNrbPS9+ClJAzQV7glAH99iG2YWRPAb2Ke6iJ3dyjCYNYoN5QQ842AxEF7k96IaksSdRGseLxU/bXHp7O2Oq30953HYEKaA6tScIMqEN7la2ult2GUU9ZvB2ckxQCKV3THwHpYMWfjxpdkPS2JMojfPJD39k+R43i59uF3jTJWgBNZPqHD8IbXC3stVtV6qTJa+TY4JAaL1jrDooWZFGNyRNYuzSOF4EZr+96u1kj2EOqEEitEVMTix5Ex3jReu8VNjf0soJty3niBuXcMJtyztm7KH0jnEatNPohtQd8KPq00kXqWRYFUBV3nqLJzl2qzZ3m96uZ1utngh4RWhn7k4aZifzZc90imbX/hYa9h9kS9QTxpyhxwd2ky0NTRwR5Gbadh2UYkmzG1Ku45clr1dNRPzyqg+S7DFdgmoLHNrgPrBPMR9u3M6BLXtRLW1IYYSiwb0YOrK80zHx1ruxOE3RVFVVUVpayvXXX8/bb7/NVVddRWNjI8OHD2fRokX07t3b0ZiNNFHnbU0H24iI0GYzxthm2hCwNI1VByWJQKQQWpu0WsYBflZ9ug3MmWzqkUnZo5/BN0iFV/GENi3TumM/Bz7ajWoxZr2qpY0DH+2mdcehRT2j52pBwvM47ZpkcuWVV3Lbbbfx7rvvMmPGDO644w7H77W7VptSXbxj4glkmqZyEoyZcSjtUlwGY/8dTrkZTpsLJ/5IB/Yk+F316ZRM+8ZkypTL7/RPkG2BQxvcP67eioqbBkQzry8AAA/iSURBVKv2rh/q3ubEMwGrzkqm5e/EiRO59NJLO+1bv349J510EgCnn346TzzxhKPx7trfgtGDqSuDy0qYe97nGVxWYnOEQSAtfisnGUFcB/OUsFvg9EuRYocfpmWJsHJ5BGuzrnTwO/gGqfAqntAGd6cfaqKZeZ4IA/t0XnRKZvk7fvx4nn32WQAef/xxamtrk47VlGQquqZeTHfHcycNZtWNX+LD285isLb4TU79Wnj5dvjHj42/69dme0Qp4cXCpxdk+htExdAyKob06bJ9W91uTxdV/Q6+QbYFdhTcReRMEVkvIhtF5EaL/ZeJyDYReSv6x3erRKcfql3PU0EsuyMls/x96KGHuO+++/jCF77A3r17KSxMvhhrJckEiIgw97zPd8mja4vfJJj6enMx1yyWCmGA91OR4oZsfIOwMuHyOqXhd/DNhs+8U5LeoYhEgPuA04E64HUReVYpFV+i9iel1DU+jNESp9Vsdv1M7dreJbP8Peqoo1i2bBkANTU1LFmyJOlY7b49tCtluUDa7Sx+3VoWWOnrQ1ws5ZcixQ1+a9qtyERKw++q16AVXsXi5PE1BdiolPoXgIg8BpwDeF9/7AKnH2psQZMTC4Jklr9bt25lwIABtLe384tf/IKrrroq6VjtJJmJ0izdxuI3vsrViWWBnfxSF0uljFfSSTdkwksmE8E3qIVXTj7FwUBsYrkOONbiuH8XkZOAGuBapVSXZLSIzAJmAQwbNsz9aONw+qEaqhlnWvZklr+PPvoo9913HwDnnXcel19+edJzWn170GmWKKnMwu309bpYKi0y/Q0iU14yQQ2+fuMkuFvlKOITyM8BjyqlDojIVcAC4Etd3qTUfGA+wOTJk62F3QEgkeXv7NmzmT17tqvzxX57EAx1TE6nWdyQyizcSl+vi6VCR5BTGukQlKImJ8G9Dhga83oI0Mm0Qim1I+bl74Hb0x9abmF+e1ANJay68ZjsDSRolrypzMLN8QbpPjQpkWuz6iAVNTkJ7q8Do0TkCGALcDHwjdgDRKRSKWVqpr4GVHs6So03pJLf9ptUZ+GVk3Qw1wSOINkqJA3uSqlWEbkGWApEgIeUUutE5BZgjVLqWeB7IvI1oBXYCVzm45g1qRJElUnIZuGZLNHXeEemUiWJFED/98z7GU3TOFqWVkr9Ffhr3LabY37+MfBjb4em8ZygqkxCMgv3y+RL4y+ZTJXYKYBMMpmmyX4ZlUfojksO0CqTtPDT5EvjLbEzdYQuEhC/UiVWCqB4MpWmyYngHqaOS1kll1UmGVgoDorJlyYx8TN1C9cPwB//l3gFkB2Z8J4JrbcMQPXKFcy/+nIeuvw8Xrz1e9S/sapjX7tSnhltVVVVceeddwKGn8y4cePIy8tjzZo1nY6bO3cuI0eO5Mgjj2Tp0qWeXNtTrFwcx8wIRUokIRmyI3BTou9HAw6NM6wWNa3wy/+lYmgZX5g+mi+eMzar3jOhnblXr1zBsvn30tpyAIDmXTt4//EHAaj8wgkAtLUrdu1v8XT2Pn78eJ588km+/e1vd9r+/vvv89hjj7Fu3To++eQTTjvtNGpqaohEElv5ZpyQ5Ldd4fNCccciqkXbOqsSfZ2bzy5OZsWZ8n/JZtPv0M7cVz62sCOwm7QfbGHj3xZ32lafwuw9keXvmDFjOPLIrpWlzzzzDBdffDFFRUUcccQRjBw5ktWrV7u+tiYFfFwo7uRzbhL1HrIz+cq0fa6mM05mxRVD+mREsVIxtIwREys7xlRUks+IiZXBUcsEkb07tltub961o9Pr1vjWR0kwLX9XrVpFeXk5O3fuZN68eUnft2XLFo477riO10OGDGHLli2urp0TZKNIyseFYqtAjVLkDxrEqOX/tHyPzs1nFyeLmlaOlH6RrUKt0Ab3Xv3L2bt9W5ftxX37p3XeZJa/digLS99E7pI5iR9FUk4eFj4uFKcSqPMrKy1TOJluwNFdcbKoGYRmGn4T2rTM1Iu/SX5hUadteQWFjPzyhZ22RfLcBdhklr92DBkypFPjjrq6OgYNGuT6PKEmUe47FZwulLpZKHbZ5CMVn/OgNODozpiLmkFupuE3oQ3uY6aeyvRZ19CrvAJE6Nm/grEXXNmxmApGQw633YumTZvG4sWL2bHDSO/EW/7a8bWvfY3HHnuMAwcO8OGHH7JhwwamTJni6tqhJ1HuO5WOSU4eFmawXhddaxl3oX27vxRUNakEaqcNOLSixn+C3EzDb0L9+Boz9VTGTD2147UXhUzJLH+feuopvvvd77Jt2zbOOussjj76aJYuXcq4ceO48MILGTt2LPn5+dx3333BU8r4jV3u28RtmibZQqnbNFAKqppUfc6T2edqRY07UrUPyFXnSSeIVa44E0yePFnF68Srq6sZM2ZMVsaTKm4fKGG8R8fEB1s7isuM2XUyXr7dfqH0xB8l3x/PPxI4ZJw2N/l4PGTDl6ZZ5+UTLNSGCS+9XLoUJWHMvjOlOgkaIvKGUmpysuNCPXPPNpmsjH167Zbstd1zqoCJNwGzw6lEMdlCqVsJZIDsF3JZUeO1l0uQnBbDRGhz7kHAqvF1u1J8trvZ5h2p8fTaLfz4yXfZ0tCEArY0NPHjJ9/l6bUZkFq6zVNXTjJmzafNtQ+aToNpsoVSt+fvb9P5ym67j2SjIbUXOFknSBSMU6E7K17SQc/c08Cu8bXd9lS5Y+l6mg62ddrWdLCNO5au93/2bpenXv9cZiSKiSpq3Z5/x3p3230kGw2p08XpOoHXwTgTvVZzET1zT4PCiPXHZ7c9Vew8crzyzkmIXYqjtclbiWIquD1/gCyPnSpqgoTTyluv5YfdWfGSDvrRlwZWja/zRBjYpzjBu9wzqKyELRaB3K3MMyWSKWBM7FQnfnvZuDl/gHLukPmG1OnidJ3Aaz+V7qx4SQc9c0+Dvj0KGdy3pGOmXhjJY3DfEs8XU28440hKCjrLKksKItxwRgZyxSOmG6kOJ2S76UcyrO4ljUrW7qZTd7pO4IefSqzT4hemj9aB3QGhnrlv3VjDx2te4cC+Rop6ljJs8vEMGDna8+tUVVVRWlrK9ddfz+OPP05VVRXV1dWsXr2ayZMn07dHITt27OD882fw+uuvc9lll3Hvvfd6dn0zr+67WiaRKiZ2e1sLHNzf9f1Bb/rhYUs/v3TqQW7j52adIKyNrzPVji8ThDa4b91Yw6aXV9DeZiy0HNjXyKaXVwD4EuBN7Cx/i4uLufXWW3nvvfd47733PL/uuZMG+7t4mqwgKDYAWunZw9L0w6M0kR9dmYJe2JRqQVe6ZCrgZrIdXyYIbXD/eM0rHYHdpL2tlY/XvJJ2cF+4cCF33nknIsKECRMYMWJExz67AqSePXty4oknsnHjxrSu7RvJtOpuqjdD1tTaD/zQqYehjV+66wRuA3UmA26u6elDG9wP7LO27LTb7pRULX8DjZMy/URKkvq1mV8oTRefrYf9cH70s7ApCOmeVAJ1JgNurunpQ7ugWtSz1NV2p6Rq+RtonBhwJcqX+9Cyzlcy0HbPD+dHvwqbOjUcUaoj3ZPpBeBUipsyGXBzzUHSUXAXkTNFZL2IbBSRGy32F4nIn6L7XxOR4V4PNJ5hk48nL9L5Q8+L5DNs8vFJ37trfwsf1O/hnboGPqjfw679LR37UrX8DTRO9N2JVDHp2PZmA6+thy3wQ6ee6IGRjjInKJ2hUgnUmQy4uaanT/oJiUgEuA84HagDXheRZ5VS78ccdgWwSyk1UkQuBm4HLvJjwCZmXt2tWiaZH8y0adOYMWMG1157Lf3793ds+RtonOi7zZTFusVdj4PgyxxjyVCxktc6dbsFSyCthdag+NikUmmayR6kuaand/L4mwJsVEr9C0BEHgPOAWKD+zlAVfTnPwP3iogony0nB4wc7XrxNJEfTN8ehSlb/gIMHz6cPXv20NLSwtNPP82yZcsYO3Zs2veZNk7L9Csn2Zt+BV3mGEvAipXcYPXA2PClaWkttAalM1QqgTrTATesEk4rnAT3wUBtzOs64Fi7Y5RSrSKyG+gPdGp0KiKzgFkAw4YNS3HI6eHED2bmzJnMnDnT8rgZM2YwY8YMy32bN29Oe3y+4Ebd4mPLuoyRC/cQQ7oz76D42KQaqHMp4GYSJ8HdKgEdPyN3cgxKqfnAfDD83B1c23MKI3mWAd5rP5jA4VTdkgsyx1y4hxjSnXlnS59uhQ7UmcNJcK8Dhsa8HgLE/08zj6kTkXygDxDIZHWm/GBCTdBljk7IhXuI4sXMO2w+Npr0cTJdfR0YJSJHiEghcDHwbNwxzwJmHuN8YHmq+Xa/O0Nlyg/Gimx1vdKEmzA6SGqyT9KZezSHfg2wFIgADyml1onILcAapdSzwIPAIyKyEWPGfnEqgykuLmbHjh3079/fVzli3x6FGQnmsSil2LFjB8XF+huCxj165q1xS6B6qB48eJC6ujqam73tZBQUiouLGTJkCAUFDl0WNRqNJo5Q9lAtKCjgiCOOyPYwNBqNJvTkuEREo9Fouic6uGs0Gk0OooO7RqPR5CBZW1AVkW3ARym8tZy4yldNB/qzsUd/NvbozyYxQft8DldKVSQ7KGvBPVVEZI2TleLuiP5s7NGfjT36s0lMWD8fnZbRaDSaHEQHd41Go8lBwhjc52d7AAFGfzb26M/GHv3ZJCaUn0/ocu4ajUajSU4YZ+4ajUajSYIO7hqNRpODhCa4J2vS3Z0RkYdEZKuIvJftsQQNERkqIitEpFpE1onI7GyPKSiISLGIrBaRt6Ofzc+zPaagISIREVkrIn/J9ljcEorgHtOk+8vAWODrIhKA5qSB4WHgzGwPIqC0AtcppcYAxwFX6/87HRwAvqSUmggcDZwpIsdleUxBYzZQne1BpEIogjsxTbqVUi2A2aRbAyilXiKgna+yjVKqXin1ZvTnvRi/qIOzO6pgoAwaoy8Lon+0wiKKiAwBzgL+J9tjSYWwBHerJt36F1TjChEZDkwCXsvuSIJDNO3wFrAVeF4ppT+bQ9wN/BDo2nQ5BIQluDtqwK3R2CEipcATwPeVUnuyPZ6goJRqU0odjdEbeYqIjM/2mIKAiHwV2KqUeiPbY0mVsAR3J026NRpLRKQAI7AvUko9me3xBBGlVAPwAnrtxuQE4GsishkjDfwlEfljdofkjrAEdydNujWaLojRjPdBoFop9etsjydIiEiFiJRFfy4BTgM+yO6ogoFS6sdKqSFKqeEY8Wa5Uuo/sjwsV4QiuCulWgGzSXc1sFgptS67owoOIvIo8ApwpIjUicgV2R5TgDgBuBRj5vVW9M9Xsj2ogFAJrBCRdzAmUM8rpUIn+dNYo+0HNBqNJgcJxcxdo9FoNO7QwV2j0WhyEB3cNRqNJgfRwV2j0WhyEB3cNRqNJgfRwV2j0WhyEB3cNRqNJgf5//cDle9S7HmSAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "for i in range(12):\n", + " ax.plot(\n", + " X[i == Y, 0], X[i == Y, 1], \"o\", label=\"cl%d\" % i, color=plt.cm.tab20.colors[i]\n", + " )\n", + "ax.legend()\n", + "ax.set_title(\"Classification à neuf classes\\ndans un quadrillage\");" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "clr = LogisticRegression()\n", + "clr.fit(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = draw_border(clr, X, Y, incx=1, incy=1, figsize=(12, 8), border=False)\n", + "ax.set_title(\"Régression logistique dans un quadrillage\");" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6958333333333333" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr.score(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On copie les features en les mettant au carré. Le problème est toujours aussi simple mais la régression logistique a plus de variables non corrélées sur lesquelles s'appuyer." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def create_feat(X):\n", + " X2 = X.copy()\n", + " X2[:, 0] = X2[:, 0] * X2[:, 0]\n", + " X2[:, 1] = X2[:, 1] * X2[:, 1]\n", + " XX2 = numpy.hstack([X, X2])\n", + " return XX2\n", + "\n", + "\n", + "clr2 = LogisticRegression()\n", + "clr2.fit(create_feat(X), Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def fct_predict(clr, X):\n", + " return clr.predict(create_feat(X))\n", + "\n", + "\n", + "ax = draw_border(\n", + " clr2, X, Y, fct=fct_predict, incx=1, incy=1, figsize=(12, 8), border=False\n", + ")\n", + "ax.set_title(\"Régression logistique dans un quadrillage avec X2\");" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9583333333333334" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr2.score(create_feat(X), Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Du fait que ce problème de classification est équivalent à un diagramme de Voronoï, il a été construit comme tel, le fait que la régression logistique semble être provenir d'un problème de convergence numérique plutôt que du modèle théorique. Pour vérfier on joue avec les paramètres d'apprentissage. Tout d'abord, l'algorithme de descente de gradient." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr_t = LogisticRegression(solver=\"lbfgs\")\n", + "clr_t.fit(X, Y)\n", + "clr_t.score(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = draw_border(clr_t, X, Y, incx=1, incy=1, figsize=(6, 4), border=False)\n", + "ax.set_title(\"Régression logistique dans un quadrillage avec L-BFGS\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ensuite, on change la façon de résoudre le problème. Plutôt que de résoudre *n* problèmes de classifications binaires, on résoud un seul problème avec une erreur de classification égale à la [Multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression)." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9875" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr_t = LogisticRegression(solver=\"lbfgs\", multi_class=\"multinomial\")\n", + "clr_t.fit(X, Y)\n", + "clr_t.score(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "draw_border(clr_t, X, Y, incx=1, incy=1, figsize=(6, 4), border=False, ax=ax[0])\n", + "draw_border(clr_t, X, Y, incx=1, incy=1, figsize=(6, 4), border=True, ax=ax[1])\n", + "ax[0].set_title(\"Régression logistique dans un quadrillage\\navec L-BFGS + multinomial\")\n", + "ax[1].set_title(\"Régression logistique dans un quadrillage\\navec L-BFGS + multinomial\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les frontières entre une classes et les autres n'ont plus l'air d'avoir de signification géométrique. L'approche une classe contre toutes les autres marchent bien si celles-ci ont des frontières convexes sans angles aigus et si elles ne sont pas bornées. En gros, cette approche rapide fonctionne bien si toutes les classes sont disposées autour de la boule unité ou d'une boule unité composée sur un sous-ensemble des dimensions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression logistique autour d'un cercle" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((240, 2), (240,))" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from math import cos, sin, pi\n", + "\n", + "Xs = []\n", + "Ys = []\n", + "n = 20\n", + "for i in range(12):\n", + " x1 = numpy.random.rand(n) + 2.3 * cos(i / 12.0 * 2 * pi)\n", + " x2 = numpy.random.rand(n) + 2.3 * sin(i / 12.0 * 2 * pi)\n", + " Xs.append(numpy.vstack([x1, x2]).T)\n", + " Ys.extend([i] * n)\n", + "X = numpy.vstack(Xs)\n", + "Y = numpy.array(Ys)\n", + "X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "for i in range(12):\n", + " ax.plot(\n", + " X[i == Y, 0], X[i == Y, 1], \"o\", label=\"cl%d\" % i, color=plt.cm.tab20.colors[i]\n", + " )\n", + "ax.legend()\n", + "ax.set_title(\"Classification à neuf classes\\ndans un quadrillage\");" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9833333333333333" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr_c = LogisticRegression()\n", + "clr_c.fit(X, Y)\n", + "clr_c.score(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = draw_border(clr_c, X, Y, incx=1, incy=1, figsize=(6, 4), border=False)\n", + "ax.set_title(\"Régression logistique autour d'un cercle\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rien n'est prouvé, ce ne sont que des observations. On peut se poser la question si le problème précédent n'était pas justement choisi pour montrer que dans un cas, l'approche une classe contre les autres dans le cas d'un quadrillage est particulièrement malvenue. On accroît l'espace entre les classes." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((240, 2), (240,))" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Xs = []\n", + "Ys = []\n", + "n = 20\n", + "for i in range(4):\n", + " for j in range(3):\n", + " x1 = numpy.random.rand(n) + i * 3\n", + " x2 = numpy.random.rand(n) + j * 3\n", + " Xs.append(numpy.vstack([x1, x2]).T)\n", + " Ys.extend([i * 3 + j] * n)\n", + "X = numpy.vstack(Xs)\n", + "Y = numpy.array(Ys)\n", + "X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7875" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr_q = LogisticRegression()\n", + "clr_q.fit(X, Y)\n", + "clr_q.score(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = draw_border(clr_q, X, Y, incx=1, incy=1, figsize=(6, 4), border=False)\n", + "ax.set_title(\"Régression logistique autour d'un cercle\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A priori non mais on préfère l'approche une classe contre les autres car elle est beaucoup plus rapide. L'approche multinomiale requiert de changer d'algorithme de descente de gradient." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.25 ms ± 148 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "clr_q = LogisticRegression()\n", + "%timeit clr_q.fit(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55.4 ms ± 1.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "clr_qmn = LogisticRegression(multi_class=\"multinomial\", solver=\"lbfgs\")\n", + "%timeit clr_qmn.fit(X, Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pousser les classes sur la boule unité\n", + "\n", + "Puisque le modèle est plus facile à apprendre lorsque les classes sont réparties sur la boule unité, l'idéal serait d'avoir une transformation qui le fait, comme d'ajouter des dimensions. La régression logistique ne peut modéliser que des classes convexes. Cela veut dire que le barycentre, sous cette hypothèses, appartient à la zone que le modèle attribute à une classe donnée. On calcule ce barycentre pour toutes les classes et on ajoute comme variables la distance à chacun de ces centres. On reprend le problème du quadrillage." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((240, 2), (240,))" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Xs = []\n", + "Ys = []\n", + "n = 20\n", + "for i in range(4):\n", + " for j in range(3):\n", + " x1 = numpy.random.rand(n) + i * 1.1\n", + " x2 = numpy.random.rand(n) + j * 1.1\n", + " Xs.append(numpy.vstack([x1, x2]).T)\n", + " Ys.extend([i * 3 + j] * n)\n", + "X = numpy.vstack(Xs)\n", + "Y = numpy.array(Ys)\n", + "X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(12, 2)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bary = []\n", + "for i in range(12):\n", + " b = X[i == Y].mean(axis=0)\n", + " bary.append(b)\n", + "barys = numpy.vstack(bary)\n", + "barys.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(240, 12)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics.pairwise import euclidean_distances\n", + "\n", + "dist = euclidean_distances(X, barys)\n", + "dist.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "Xext = numpy.hstack([X, dist])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9916666666666667" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr_ext = LogisticRegression()\n", + "clr_ext.fit(Xext, Y)\n", + "clr_ext.score(Xext, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def fct_predict(clr, X):\n", + " dist = euclidean_distances(X, barys)\n", + " Xext = numpy.hstack([X, dist])\n", + " return clr.predict(Xext)\n", + "\n", + "\n", + "ax = draw_border(\n", + " clr_ext, X, Y, fct=fct_predict, incx=1, incy=1, figsize=(6, 4), border=False\n", + ")\n", + "ax.set_title(\n", + " \"Régression logistique dans un quadrillage\\navec des distances aux barycentres\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cela répond également à une question : **Que faire lorsque les classes ne sont pas convexes ?** Une idée consiste à effectuer un [k-means](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) par classe jusqu'à ce que chaque classe soit à peu près converte par un ensemble de cluster appris sur cette classe." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cas presque hexagonal\n", + "\n", + "Pour tester quelques idées et parce c'est joli. L'idéal serait de se rapprocher d'un pavage de [Penrose](https://fr.wikipedia.org/wiki/Roger_Penrose)." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import math\n", + "\n", + "n = 4\n", + "a = math.pi * 2 / 3\n", + "points = []\n", + "Ys = []\n", + "for i in range(n):\n", + " for j in range(n):\n", + " dil = ((i + 1) ** 2 + (j + 1) ** 2) ** 0.6\n", + " for k in range(20):\n", + " x = i + j * math.cos(a)\n", + " y = j * math.sin(a)\n", + " points.append([x * dil, y * dil])\n", + " Ys.append(i * n + j)\n", + " mi = 0.5\n", + " for r in [0.1, 0.3, mi]:\n", + " nb = 6 if r == mi else 12\n", + " for k in range(nb):\n", + " x = (\n", + " i\n", + " + j * math.cos(a)\n", + " + r * math.cos(math.pi * 2 / nb * k + math.pi / 6)\n", + " )\n", + " y = j * math.sin(a) + r * math.sin(\n", + " math.pi * 2 / nb * k + math.pi / 6\n", + " )\n", + " points.append([x * dil, y * dil])\n", + " Ys.append(i * n + j)\n", + "X = numpy.array(points)\n", + "Y = numpy.array(Ys)\n", + "set(Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "for i in range(max(Y) + 1):\n", + " ax.plot(\n", + " X[i == Y, 0],\n", + " X[i == Y, 1],\n", + " \"o\",\n", + " label=\"cl%d\" % i,\n", + " color=plt.cm.tab20.colors[i % 20],\n", + " )\n", + "ax.set_title(\"Classification à 16 classes\\ndans un quadrillage hexagonal\");" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9919354838709677" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clr_hex = LogisticRegression(multi_class=\"multinomial\", solver=\"lbfgs\", max_iter=200)\n", + "clr_hex.fit(X, Y)\n", + "clr_hex.score(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = draw_border(clr_hex, X, Y, incx=1, incy=1, figsize=(6, 4), border=False)\n", + "ax.set_title(\"Régression logistique dans\\nun quadrillage hexagonal\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diagramme de Voronoï approché\n", + "\n", + "On pousse l'idée implémentée dans le cas de trois classes pour un nombre de classes quelconque. Il n'existe pas de façon générique de diagramme de Voronoï équivalent. On résoud le système linéaire avec une régression quantile et d'autres astuces de calculs à découvrir dans le code de la fonction [voronoi_estimation_from_lr](https://sdpython.github.io/doc/mlstatpy/dev/api/ml.html#mlstatpy.ml.voronoi.voronoi_estimation_from_lr)." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((240, 2), (240,))" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Xs = []\n", + "Ys = []\n", + "n = 20\n", + "for i in range(4):\n", + " for j in range(3):\n", + " x1 = numpy.random.rand(n) + i * 1.1\n", + " x2 = numpy.random.rand(n) + j * 1.1\n", + " Xs.append(numpy.vstack([x1, x2]).T)\n", + " Ys.extend([i * 3 + j] * n)\n", + "X = numpy.vstack(Xs)\n", + "Y = numpy.array(Ys)\n", + "X.shape, Y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "for i in range(12):\n", + " ax.plot(\n", + " X[i == Y, 0], X[i == Y, 1], \"o\", label=\"cl%d\" % i, color=plt.cm.tab20.colors[i]\n", + " )\n", + "ax.legend()\n", + "ax.set_title(\"Classification à neuf classes\\ndans un quadrillage\");" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "clr = LogisticRegression()\n", + "clr.fit(X, Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[voronoi_estimation_from_lr] iter=1/20 score=0.0953 tol=3.48e-10 del P2,9 d=3.19\n", + "[voronoi_estimation_from_lr] iter=2/20 score=0.0939 tol=3.48e-10 del P1,9 d=2.72\n", + "[voronoi_estimation_from_lr] iter=3/20 score=0.089 tol=3.48e-10 del P2,6 d=2.5\n", + "[voronoi_estimation_from_lr] iter=4/20 score=0.0892 tol=3.48e-10 del P0,11 d=2.46\n", + "[voronoi_estimation_from_lr] iter=5/20 score=0.0894 tol=3.48e-10 del P2,10 d=2.42\n", + "[voronoi_estimation_from_lr] iter=6/20 score=0.0882 tol=3.48e-10 del P1,10 d=2.44\n", + "[voronoi_estimation_from_lr] iter=7/20 score=0.0889 tol=3.48e-10 del P0,10 d=2.3\n", + "[voronoi_estimation_from_lr] iter=8/20 score=0.0877 tol=3.48e-10 del P5,9 d=2.29\n", + "[voronoi_estimation_from_lr] iter=9/20 score=0.0869 tol=3.48e-10 del P1,11 d=2.18\n", + "[voronoi_estimation_from_lr] iter=10/20 score=0.088 tol=3.48e-10 del P2,3 d=2.2\n", + "[voronoi_estimation_from_lr] iter=11/20 score=0.089 tol=3.48e-10 del P0,8 d=2.14\n", + "[voronoi_estimation_from_lr] iter=12/20 score=0.0884 tol=3.48e-10 del P1,6 d=2.2\n", + "[voronoi_estimation_from_lr] iter=13/20 score=0.0871 tol=3.48e-10 del P2,11 d=2.07\n", + "[voronoi_estimation_from_lr] iter=14/20 score=0.0874 tol=3.48e-10 del P0,5 d=2.1\n", + "[voronoi_estimation_from_lr] iter=15/20 score=0.0868 tol=3.48e-10 del P0,2 d=2.1\n", + "[voronoi_estimation_from_lr] iter=16/20 score=0.087 tol=3.48e-10 del P0,9 d=2.06\n", + "[voronoi_estimation_from_lr] iter=17/20 score=0.0876 tol=3.48e-10 del P8,9 d=1.99\n", + "[voronoi_estimation_from_lr] iter=18/20 score=0.0878 tol=3.48e-10 del P2,7 d=1.93\n", + "[voronoi_estimation_from_lr] iter=19/20 score=0.0889 tol=3.48e-10 del P9,11 d=1.93\n", + "[voronoi_estimation_from_lr] iter=20/20 score=0.0875 tol=3.48e-10 del P1,7 d=1.97\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0.59042773, 0.41675379],\n", + " [0.19276405, 1.61586254],\n", + " [0.38750542, 2.34848342],\n", + " [1.70510075, 0.5341869 ],\n", + " [1.69940467, 1.50388896],\n", + " [1.66571087, 2.15827251],\n", + " [2.23834543, 0.6114512 ],\n", + " [2.14600591, 1.3636044 ],\n", + " [2.08762755, 2.04091816],\n", + " [2.5732091 , 0.170076 ],\n", + " [2.81087731, 1.40217985],\n", + " [2.49984364, 2.02978587]])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.ml import voronoi_estimation_from_lr\n", + "\n", + "points = voronoi_estimation_from_lr(\n", + " clr.coef_, clr.intercept_, max_iter=20, verbose=True\n", + ")\n", + "points" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = draw_border(clr, X, Y, incx=1, incy=1, figsize=(8, 5), border=False)\n", + "ax.plot(points[:, 0], points[:, 1], \"ro\", ms=10)\n", + "ax.set_title(\"Diagramme de Voronoi approché\");" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/ml/mf_acp.ipynb b/_doc/notebooks/ml/mf_acp.ipynb index 9bc2366e..dbb9f11b 100644 --- a/_doc/notebooks/ml/mf_acp.ipynb +++ b/_doc/notebooks/ml/mf_acp.ipynb @@ -1,485 +1,350 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Factorisation et matrice et ACP\n", - "\n", - "Un exemple pour montrer l'\u00e9quivalence entre l'ACP et une factorisation de matrice." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Factorisation de matrices" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def erreur_mf(M, W, H):\n", - " d = M - W @ H\n", - " a = d.ravel()\n", - " e = a @ a.T\n", - " e ** 0.5 / (M.shape[0] * M.shape[1])\n", - " return e" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On cr\u00e9e un nuage de points avec que des coordonn\u00e9es positives pour satisfaire les hypoth\u00e8ses de la factorisation de matrices." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.81960047, 0.63887134, 0.74019269, 0.96110175, 0.0685406 ,\n", - " 0.11103301, 0.06033529, 0.67913157, 0.10460611, 0.98860048,\n", - " 0.50497448, 0.26893866, 0.73143267, 0.32617974, 0.1332449 ,\n", - " 0.83328515, 0.3775355 , 0.69163261, 0.53095348, 0.15601268],\n", - " [ 2.48031078, 2.2279066 , 2.85929872, 3.27833973, 0.27323095,\n", - " 0.53806662, 0.48019992, 2.09428487, 0.40521666, 3.94539474,\n", - " 2.36639105, 1.66857684, 3.14027534, 1.94032092, 1.22602705,\n", - " 3.09679803, 1.696636 , 2.69144798, 1.84350664, 1.16862532]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numpy.random import rand\n", - "M = rand(2, 20)\n", - "M[1,:] += 3 * M[0,:]\n", - "M" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.19729615330190822" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import NMF\n", - "mf = NMF(1)\n", - "W = mf.fit_transform(M)\n", - "H = mf.components_\n", - "erreur_mf(M, W, H)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "wh = W @ H" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Factorisation et matrice et ACP\n", + "\n", + "Un exemple pour montrer l'équivalence entre l'ACP et une factorisation de matrice." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Factorisation de matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def erreur_mf(M, W, H):\n", + " d = M - W @ H\n", + " a = d.ravel()\n", + " e = a @ a.T\n", + " e**0.5 / (M.shape[0] * M.shape[1])\n", + " return e" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On crée un nuage de points avec que des coordonnées positives pour satisfaire les hypothèses de la factorisation de matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0, 4)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8f/bTjxZL3VRIAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(M[0,:], M[1,:], \"ob\")\n", - "ax.plot(wh[0,:], wh[1,:], \"or\")\n", - "ax.set_xlim([0,1])\n", - "ax.set_ylim([0,4])" + "data": { + "text/plain": [ + "array([[ 0.81960047, 0.63887134, 0.74019269, 0.96110175, 0.0685406 ,\n", + " 0.11103301, 0.06033529, 0.67913157, 0.10460611, 0.98860048,\n", + " 0.50497448, 0.26893866, 0.73143267, 0.32617974, 0.1332449 ,\n", + " 0.83328515, 0.3775355 , 0.69163261, 0.53095348, 0.15601268],\n", + " [ 2.48031078, 2.2279066 , 2.85929872, 3.27833973, 0.27323095,\n", + " 0.53806662, 0.48019992, 2.09428487, 0.40521666, 3.94539474,\n", + " 2.36639105, 1.66857684, 3.14027534, 1.94032092, 1.22602705,\n", + " 3.09679803, 1.696636 , 2.69144798, 1.84350664, 1.16862532]])" ] - }, + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy.random import rand\n", + "\n", + "M = rand(2, 20)\n", + "M[1, :] += 3 * M[0, :]\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ACP : analyse en composantes principales" + "data": { + "text/plain": [ + "0.19729615330190822" ] - }, + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import NMF\n", + "\n", + "mf = NMF(1)\n", + "W = mf.fit_transform(M)\n", + "H = mf.components_\n", + "erreur_mf(M, W, H)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "wh = W @ H" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PCA(copy=True, iterated_power='auto', n_components=1, random_state=None,\n", - " svd_solver='auto', tol=0.0, whiten=False)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components=1)\n", - "pca.fit(M.T)" + "data": { + "text/plain": [ + "(0, 4)" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "projected_points = pca.inverse_transform(pca.transform(M.T))\n", - "pj = projected_points.T" + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(M[0, :], M[1, :], \"ob\")\n", + "ax.plot(wh[0, :], wh[1, :], \"or\")\n", + "ax.set_xlim([0, 1])\n", + "ax.set_ylim([0, 4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ACP : analyse en composantes principales" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0, 4)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(M[0,:], M[1,:], \"ob\")\n", - "ax.plot(wh[0,:], wh[1,:], \"or\")\n", - "ax.plot(pj[0,:], pj[1,:], \"og\")\n", - "ax.set_xlim([0,1])\n", - "ax.set_ylim([0,4])" + "data": { + "text/plain": [ + "PCA(copy=True, iterated_power='auto', n_components=1, random_state=None,\n", + " svd_solver='auto', tol=0.0, whiten=False)" ] - }, + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "pca = PCA(n_components=1)\n", + "pca.fit(M.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "projected_points = pca.inverse_transform(pca.transform(M.T))\n", + "pj = projected_points.T" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les r\u00e9sultats ne sont pas exactement identiques car l'[ACP](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) centre le nuage de points par d\u00e9faut. On utilise celui\n", - " de [statsmodels](http://www.statsmodels.org/dev/generated/statsmodels.multivariate.pca.PCA.html) pour \u00e9viter cela." + "data": { + "text/plain": [ + "(0, 4)" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Principal Component Analysis(nobs: 20, nvar: 2, transformation: None, normalization: False, number of components: 1, SVD, id: 0x1c01a2861d0)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from statsmodels.multivariate.pca import PCA\n", - "pca = PCA(M.T, ncomp=1, standardize=False, demean=False, normalize=False)\n", - "pca" + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(M[0, :], M[1, :], \"ob\")\n", + "ax.plot(wh[0, :], wh[1, :], \"or\")\n", + "ax.plot(pj[0, :], pj[1, :], \"og\")\n", + "ax.set_xlim([0, 1])\n", + "ax.set_ylim([0, 4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les résultats ne sont pas exactement identiques car l'[ACP](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) centre le nuage de points par défaut. On utilise celui\n", + " de [statsmodels](http://www.statsmodels.org/dev/generated/statsmodels.multivariate.pca.PCA.html) pour éviter cela." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "pj2 = pca.projection.T" + "data": { + "text/plain": [ + "Principal Component Analysis(nobs: 20, nvar: 2, transformation: None, normalization: False, number of components: 1, SVD, id: 0x1c01a2861d0)" ] - }, + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from statsmodels.multivariate.pca import PCA\n", + "\n", + "pca = PCA(M.T, ncomp=1, standardize=False, demean=False, normalize=False)\n", + "pca" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "pj2 = pca.projection.T" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0, 4)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6RIK+CIiXUIBX0SkS/x/NZLryEDpFOkAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 1)\n", - "ax.plot(M[0,:], M[1,:], \"ob\")\n", - "#ax.plot(wh[0,:], wh[1,:], \"or\")\n", - "ax.plot(pj[0,:], pj[1,:], \"og\")\n", - "ax.plot(pj2[0,:], pj2[1,:], \"oy\")\n", - "ax.set_xlim([0,1])\n", - "ax.set_ylim([0,4])" + "data": { + "text/plain": [ + "(0, 4)" ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On retrouve exactement les m\u00eames r\u00e9sultats." + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(M[0, :], M[1, :], \"ob\")\n", + "# ax.plot(wh[0,:], wh[1,:], \"or\")\n", + "ax.plot(pj[0, :], pj[1, :], \"og\")\n", + "ax.plot(pj2[0, :], pj2[1, :], \"oy\")\n", + "ax.set_xlim([0, 1])\n", + "ax.set_ylim([0, 4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On retrouve exactement les mêmes résultats." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/ml/neural_tree.ipynb b/_doc/notebooks/ml/neural_tree.ipynb index 15e26298..9457d7e1 100644 --- a/_doc/notebooks/ml/neural_tree.ipynb +++ b/_doc/notebooks/ml/neural_tree.ipynb @@ -1,1538 +1,1829 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Un arbre de d\u00e9cision en r\u00e9seaux de neurones\n", - "\n", - "L'id\u00e9e est de convertir sous la forme d'un r\u00e9seaux de neurones un arbre de d\u00e9cision puis de continuer l'apprentissage de fa\u00e7on \u00e0 obtenir un assemblage de r\u00e9gression logistique plut\u00f4t que de d\u00e9cision binaire." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from jyquickhelper import RenderJsDot\n", - "import numpy\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.colors import ListedColormap\n", - "from tqdm import tqdm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Un exemple sur Iris\n", - "\n", - "La m\u00e9thode ne marche sur un probl\u00e8me de classification binaire." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import load_iris\n", - "data = load_iris()\n", - "X, y = data.data[:, :2], data.target\n", - "y = y % 2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6052631578947368" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.tree import DecisionTreeClassifier\n", - "dec = DecisionTreeClassifier(max_depth=2, random_state=11)\n", - "dec.fit(X_train, y_train)\n", - "dec.score(X_test, y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.tree import export_graphviz\n", - "dot = export_graphviz(dec, filled=True)\n", - "dot = dot.replace(\"shape=box, \", \"shape=box, fontsize=10, \")\n", - "RenderJsDot(dot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## M\u00eame exemple en r\u00e9seau de neurones" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.ml.neural_tree import NeuralTreeNet\n", - "net = NeuralTreeNet.create_from_tree(dec)\n", - "RenderJsDot(net.to_dot())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On consid\u00e8re une entr\u00e9e en particulier." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.27906977, 0.72093023]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n = 60\n", - "dec.predict_proba(X[n: n+1])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "RenderJsDot(net.to_dot(X=X[n]))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.75 , 0.25 ],\n", - " [0.75 , 0.25 ],\n", - " [0.27906977, 0.72093023],\n", - " [1. , 0. ],\n", - " [0.27906977, 0.72093023]])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dec.predict_proba(X_test)[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.79156817, 0.20843183],\n", - " [0.73646978, 0.26353022],\n", - " [0.29946111, 0.70053889],\n", - " [0.94070094, 0.05929906],\n", - " [0.24924737, 0.75075263]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.predict(X_test)[:5, -2:]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1. , 0. ],\n", - " [0.75, 0.25],\n", - " [1. , 0. ],\n", - " [0.75, 0.25],\n", - " [0.75, 0.25]])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dec.predict_proba(X_test)[-5:]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.93247891, 0.06752109],\n", - " [0.86338585, 0.13661415],\n", - " [0.98219036, 0.01780964],\n", - " [0.98352807, 0.01647193],\n", - " [0.73646978, 0.26353022]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.predict(X_test)[-5:, -2:]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 0, 0, 0, 0], dtype=int64)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy.argmax(net.predict(X_test)[-5:, -2:], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python372_x64\\lib\\site-packages\\ipykernel_launcher.py:16: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", - " app.launch_new_instance()\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAEICAYAAABViZKWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAB6eElEQVR4nO3ddXhURxfA4d9sNkqCB3d3d3d3KVahpaVeqLdfBQoVqAvFirRIC4UWSiltgSLF3d0dgsQ9u/P9MZsSyAYSsklIct7nyUN2M7l37oacncydM0dprRFCCCGEECK7sWR0B4QQQgghhMgIMhAWQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyERYoopf5USj2SjHZhSqky6dGntKaUaqWUOp+Gx5+slHonweOnlVJXHK9hvqz0Wgohsjel1Gil1JyM7ocQ8WQgnEUppU4rpSKVUqFKqSCl1Eal1FNKqVT9zLXWnbXWPySjna/W+mRqzqWUOuAYBIYppWxKqagEj/+XmmM7OVcDpdQyx2t1Qym1VSn1qCvPkRSt9VNa67GOfrgDnwMdHK/hdVe8lkKI7MvxfnBFKZUjwXOPK6XWZGCfmieI5+FKKZ3gcZhSqoQLz6WVUvsSvv8ppd5XSn2fzO9fo5R63FX9EfcXGQhnbd211n5ASWAc8DowPWO7lHxa66qOQaAvsA54Lv6x1vrD+HZKKWtqzqOUagysAtYC5YB8wNNA59Qc9x4VBLyAA6k9UGpfFyFElmIFRqTlCVISc7TW6xLE96qOp3MniPFn7+W4d1AEGOiC44gsRgbC2YDWOlhrvQQYADyilKqmlPJUSn2qlDrrmCmYrJTyjv8epVRPpdRupVSIUuqEUqqT4/n//jJWSpVTSq1VSgUrpa4ppeYn+H6tlCrn+DyXUmqWUuqqUuqMUurt+L/MlVJDlVLrHX0JVEqdUkrdcQCqlCrlOP4wpdRZzCAWpdRjSqlDjuP8rZQqmeB7KimlVjhme48opR5IcMhPgB+01uO11te0sUNr/QBOKKXecLwmoUqpg0qp3gm+5vQ1UcYXSqkAx9f2KqWqOb72vWN2ogJwxHGoIKVU/HUlfC2T/LkpxxIOpdTrSqnLwMw7vY5CiGzlE+AVpVTu279wp/h4+2xofMxO8FgrpZ5VSh0Djjme+0opdc7x/rFDKdU8JR1VZvnEQqXUHKVUCDDU8T4yXSl1SSl1wREz3RJ8T5Lx3+Fj4L2kBtVKqUbK3DkNUkrtUUq1cjz/AdAcmKDMTPWElFyLuP/JQDgb0VpvBc5jfqnHAxWAWphZ0KLAu2CWCQCzgFeB3EAL4LSTQ44FlgN5gGLAN0mc+hsgF1AGaAk8DCRcdtAQMwDMjwlW05VSKhmX1BKoDHRUSvUC/gf0AfwxM8g/Oa4nB7AC+BEoAAwCJiqlqiqlfIDGwMJknC/eCcxrmAt4D5ijlCrs+FpSr0kHzOtYAfOaDgCuJzyo1voot86MtHFy7iR/bg6FgLyYuwDDU3BNQoisbTuwBngl4ZN3io8pOHYvTByv4ni8DROj8jqOu0Ap5ZXC/vbExOXcwFzgByAOE/dqY2Jq/KRML5KI/wn8CoQAQ28/kVKqKPAH8L6jz68Avyil/LXWb3HrHcnnUngd4j4nA+Hs5yLmF/0J4EWt9Q2tdSjwITdvGw0DZmitV2it7VrrC1rrw06OFYsZcBXRWkdprdff3sDxF/sA4E2tdajW+jTwGfBQgmZntNbfaa1tmGBXGLNE4G5Ga63DtdaRwJPAR1rrQ1rrOMf11HLMCnQDTmutZ2qt47TWO4FfgH6YAasFuJSM8wGgtV6gtb7oeG3mY2ZBGtzlNYkF/IBKgHL0M9nnBDOrzJ1/bgB2YJTWOtrxugghRLx3geeVUv4JnrtTfEyujxwxKRJAaz3Hkd8Qp7X+DPAEKqawr5u01ou11nYgJ2ap2khHzA8AvuBm7LtT/I+ngXeAd5VSnred60FgmdZ6mSOur8D84dAlhX0WmZAMhLOfopi1Yj7ADsdtoCDgL8xf0gDFMbOed/MaoICtyiS2PeakTX7AAziT4Lkzjn7Euxz/idY6wvGpbzLOfy7B5yWBrxJczw1H34o6vtYw/muOrw/BzJ4GYgaPhUkmpdTDyiwbiT9WNcd1QhKvidZ6FTAB+Ba4opSaqpTKmdxzOvhz558bwFWtdVQKjyuEyAa01vuBpcAbCZ6+U3xMroSxGKXUy45lCsGO4+XiZoy8l2OWBNyBSwn6OAUzgx3/9aTi/3+01suAsyS+W1YS6H/ba9CMFLwviMxLkmmyEaVUfUxgWIxJnKuqtb7gpOk5oOzdjqe1voyZoUQp1QxYqZT6V2t9PEGza9ycJT3oeK4E4Oy8KaVv6/MHWuu5tzdyzAqs1Vq3d3YQpdQmoC+w+m4ndBzrO6AtZsbCppTajQm6d3xNtNZfA18rpQoAP2OWnrzj5DRJuQZEkvTPDW59TYQQ4najgJ2YO3NgYmeS8REIx/wBHs/ZAPm/uONYD/w6JkYe0FrblVKBOGJkCtwe36OB/I4Z39slGf+deBuYh1mykfD7Z2utn0hGX0QWIzPC2YBSKqdSqhvml3+O1noPZjD3hWNQhlKqqFKqo+NbpgOPKqXaKqUsjq9VcnLc/kqpYo6HgZhgYUvYxrHc4WfgA6WUn2Mg+RLg6n0kJwNvxq9rcyRW9Hd8bSlQQSn1kFLK3fFRXylV2fH11zDJGK8qpfI5vr+mUmqek/PkcFznVUe7RzEzwjgeO31NHOdrqMz2aOFAFLe9VnfjuEV4p5+bEELckWOiYj7wguOpu8XH3UAfpZSPMkm7w+5yCj/MWt6rgFUp9S5maUNq+nwJk3vxmeP9zKKUKquUaulocqf4f/ux1gD7gIT74c8BuiulOiql3JRSXsokH8fH8iuYHBeRBclAOGv7XSkVivlr9y3M/rTxSWqvA8eBzcpk5a7EsYbLkVT3KGYNVjBmW7HbM3AB6gNblFJhwBJghNb6lJN2z2MGfyeB9Zi/xGe44gLjaa0XYRLJ5jmuZz+O7c8ca2k7YNaTXcQsxRiPWbeG1noj0MbxcVIpdQOYCixzcp6DmJmUTZjgWB3YkKBJUq9JTswgNhCzNOQ68Ok9XGqSPzchhEimMZg/6u8aHzHvAzGYePcDJnHtTv4G/gSOYmJdFLctnbhHD2OW2R3ExNGFOJYu3Cn+J+FtTK4Mju8/h0nO+x9mAH8Oc8cufoz0FdBPmR0pvnbBtYj7iNJaZvyFEEIIIUT2IzPCQgghhBAiW5KBsBBCCCGEyJZkICyEEEIIIbIlGQgLIYQQQohsKcP2Ec7vp3Qp/7u3E0KI+9GOU1zTWmebKCYxWwiRmSUVszNsIFzKH7a/n1FnF0KI1FFDbqmWmOVJzBZCZGZJxWxZGiGEEEIIIbIlGQgLIYQQQohsSQbCQgghhBAiW5KBsBBCCCGEyJZkICyEEEIIIbIlGQgLIYQQQohsSQbCQgghhBAiW5KBsBBCCCGEyJZkICyEEEIIIbIlGQgLIYQQQohsSQbCQgghhBAiW5KBsBBCCCGEyJZkICyEEEIIIbKlZA2ElVKnlVL7lFK7lVLbnXxdKaW+VkodV0rtVUrVcX1XhRBCJIfEbCGESB5rCtq21lpfS+JrnYHyjo+GwCTHv0IIITKGxGwhhLgLVy2N6AnM0sZmILdSqrCLji2EEMK1JGYLIQTJHwhrYLlSaodSariTrxcFziV4fN7x3C2UUsOVUtuVUtuvhqa8s0IIIZJFYrYQQiRDcpdGNNVaX1RKFQBWKKUOa63/TfB15eR7dKIntJ4KTAWoV0Yl+roQQgiXkJgthBDJkKwZYa31Rce/AcAioMFtTc4DxRM8LgZcdEUHhRBCpIzEbCGESJ67DoSVUjmUUn7xnwMdgP23NVsCPOzIRG4EBGutL7m8t0IIIe5IYrYQQiRfcpZGFAQWKaXi2/+otf5LKfUUgNZ6MrAM6AIcByKAR9Omu0IIIe5CYrYQQiTTXQfCWuuTQE0nz09O8LkGnnVt14QQQqSUxGwhhEg+qSwnhBBCCCGyJRkICyGEEEKIbEkGwkIIIYQQIluSgbAQQgghhMiWZCAshBBCCCGyJRkICyGEEEKIbEkGwkIIIYQQIluSgbAQQgghhMiWZCAshBBCCCGyJRkICyGEEEKIbEkGwkIIIYQQIluSgbAQQgghhMiWZCAshBBCCCGyJRkICyGEEEKIbEkGwkIIIYQQIluSgbAQQgghhMiWkj0QVkq5KaV2KaWWOvlaK6VUsFJqt+PjXdd2UwghREpIzBZCiLuzpqDtCOAQkDOJr6/TWndLfZeEEEK4gMRsIYS4i2TNCCuligFdgWlp2x0hhBCpJTFbCCGSJ7lLI74EXgPsd2jTWCm1Ryn1p1KqqrMGSqnhSqntSqntV0NT2FMhhBDJ9SUSs4UQ4q7uOhBWSnUDArTWO+7QbCdQUmtdE/gGWOyskdZ6qta6nta6nr/fvXRXCCHEnUjMFkKI5EvOjHBToIdS6jQwD2ijlJqTsIHWOkRrHeb4fBngrpTK7+rOiqwlNg5emwsVX4L6b8EfuzK6R0JkCRKzRZqZsQZqvg5VX4VPfgetM7pHQqTOXQfCWus3tdbFtNalgIHAKq31gwnbKKUKKaWU4/MGjuNeT4P+iizk9Z9g13H45UF4ry08NgW2ncjoXgmRuUnMFmnl160w9hf4tifMegDmroNvl2d0r4RInZTsGnELpdRTAFrryUA/4GmlVBwQCQzUWv5OFHf26zZY8TiU94dqhWF4Q/h9J9Qvm9E9EyLrkZgtUuvXrfBOO2hW2jz+uAt8tA6e65ix/RIiNVI0ENZarwHWOD6fnOD5CcAEV3ZMZH05POFiiBkIg/m8gtycFcJlJGYLV8rhBZdCbj6+GGLiuBCZ2T3PCAuRWqP6wKDZ8FwTOBsEq07CuEczuldCCCGceakLNB8DQZHg7Q6TNsPilzK6V0KkjpRYFhnmgcbw0/MQqKBoEdgyBvyT2vpfCCFEhqpYBDa9B165IdYbVv4PmlbM6F4JkToyIywyVMvK5kMIIcT9r2xBGPtARvdCCNeRGWEhhBBCCJEtyUBYCCGEEEJkSzIQFkIIIYQQ2ZIMhIVLxdngf/NMtbg6b8KibRndIyGEEHcyZ52pFlf5ZfhgMdjtGd0jIdKPJMsJl3p3AWw8aKrFXQqFh2eYnSCaSWaxEELcd5bthv/NhzkDIacXPLEQPNzg1e4Z3TMh0ofMCAuX+nUbfNPLVIprX8HsEbxkR0b3SgghhDO/boU3WkGLslCrKHzWzcRxIbILGQgLl4qvFhdPKg8JIcT9y9fL3L2LdzEEfCVmi2xElkYIl3q3Dzwy3cwEXwyB3w/B1sEZ3SshhBDOvNAJmoyCsGjI5QUTN8H8FzK6V0KkH5kRFrc4cB7avA9lRsCAr+Ba6N2/J6Ge9eDXFyHUCoUKw9axUDhP2vRVCCGyuxthMGSCidktx8CeMyn7/jIFYPMYyJkfor3grzegddW06asQ9yOZERb/uR4K7T+EUe2gdTn4diP0/BTWjwalkn+cJhXMhxBCiLTV70uomBv+GgbrT0HHcbBnHBTMlfxjlPKH9/qlVQ+FuL/JjLD4z6ZjUK0QPNkYKvjDF93h8CUICLn79wohhEhfwRGw9SRM6G1i9mMNoEFxWH8ko3smROYhA2HxnxyecCUUbI49JAMjISoWfDwytl9CCCES87SaeH093Dy2203im69XxvZLiMxElkaI/zSvBAXyQI+Z0KI0/LQHnmsPft4Z3TMhhBC38/KA17pCm6nwUG3YeAZ8vKFNlYzumRCZR7JnhJVSbkqpXUqppU6+ppRSXyuljiul9iql6ri2myI9WN1g6avQqT5c1fBGbxg3KP37sekYNHkXyo6EYVMgNDL9+yBEZicxO3sY3Q9G9YerQOva8Pcb4J7OU1wXbph8kjIjoONHcOJK+p5fiNRIya/LCOAQkNPJ1zoD5R0fDYFJjn9FJuPpDs93zLjznwqAHp/ChF5QuyiMWQFDJ8MvL2Zcn4TIpCRmZwNKQf+G5iMjxMZBp3HQuzJ82gGWHDBJ1/vGQw5ZoiEygWTNCCuligFdgWlJNOkJzNLGZiC3Uqqwi/oospF/DkCXSjCglkn++K4//L7r5rplIcTdScwW6eVEAEREw3sdobw/vNwK8vnA7hRu4yZERknu0ogvgdeApIYjRYFzCR6fdzx3C6XUcKXUdqXU9qsp3J9WZA/xlem0No8vhYCXO1hSsH2bEEJitkgfOTwhOBLCY8zj6Di4Fi4JeyLzuOtAWCnVDQjQWu+4UzMnz+lET2g9VWtdT2tdz98vBb0U2UbPunA9CgbOhXGroMN38F7flO1jLER2JjFbpKfi+aBPfWg3Fcavgk7ToG5pqFEio3smRPIkZ0a4KdBDKXUamAe0UUrNua3NeaB4gsfFgIsu6aG4b83fCEWehlzDoOJLrkmQ8PGEf0dBvUpwDfjyEXixS+qPK0Q2IjFbOHXoApQbaWJ2kafht+2uOe7kYfB0JwjQMLAFzHtBJi9E5nHXZDmt9ZvAmwBKqVbAK1rrB29rtgR4Tik1D5NwEay1vuTaror7yf5z8NhU+KY3NC8NH6+G5u/BxYmpP7avF7zaPfXHESI7kpgtnLHbodUYeKAmvNAMVh6DId/Crg+hfCpXh1ss8EgL1/RTiPR2zwU1lFJPKaWecjxcBpwEjgPfAc+4oG/iPjZzLTQpZSoZlfeHyf3gehicvZbRPRNCOCMxO3s7eMGs4/2qp4nZTzeBmkVg7oaM7pkQGStFuw1qrdcAaxyfT07wvAaedWXHxP0tl49JZLPbzWzAtXCwa8jtk9E9E0LEk5gt4uX1hRgbBEVBXh+zE8+VUMjjm9E9EyJjSWU5cU9e6QITl0PnadC6HEzdDM0rQk4ZCAshxH2nSB6oXxoafwOP1oflRyAyDp5ul9E9EyJj3fPSCJG5xcSYBLdcwyD3MGj7fsq+38cLDn8GOXLCosPQuyGs/F8K+xAHr8wxyRt13oSlO1P2/SJjnL4KXcZD6Reg63g4czXptieumEpTpV+AHp/A+evp108hsppen0OeYZDrMVPFLSyFVTfXjYKOtWHxYciXD458Dh4pnA6btgqqvQqVXobxS25udSnuX+FR8OQ0U6214duw9lDSbUMjTUXXMiOg8buw8Wi6dTPDyEA4m6r9Fngo+PcZ+P0x2HsWBk9I2TFy54BfX4QtY+GzB80SiZR4/UfYdxJ+HwofdYRhU2HriZQdQ6SvqBjo8BE0LwornoCmRaHjOIiOTdw2PMpUmOpQyrSt7Q+dx5tKVEKIlBk5G9YcgAUPw8bnoaAP1HgjZcewWODrR2DzWFgwIuV7/f6yBT5cDFP7wE+DYN56+ObvlB1DpL/h0yDwBvz5GLzeAvp9CUeS2CNm6GSICYe/hsGLjaHnZ3AyIF27m+5kIJxNXQ6CiX1MskTzMvBeBxNk09Oi7aaUcuWC0LEiDG8os8L3u4MXwNMCb7aFcvnhf23BDbMt0+32nDUVpl5uZdqO7mAqUJ3I4kFViLTwyxZ4rTW0qwBVC8GUfnAjLH37sGgbvNPWJErXLgrju5jnxP1t8Q6Y2s9Ua+1THfpWh+X7Erez2U0l1/i2D9SCrpXhn/3p3uV0JQPhbEoBF4JvPj4XnPIZ3dSKryIX70KwVCO63+XwhBsREOmYAY6MNY9zOPm55fA0SZTRjhng8BhTgcrXM/36K0RWYXWDs0E3H18ITv+Kmzm84MLtMVt+n+97OTzhYoL3+6Teay3KVHK95PgZa5093pclWS6bGtgEHl8AB69AYCRM3wozh6dvH0b3hUGz4JnGJsCvPgXjH0vfPoiUqVAY2lQ1Ff+6VYLfD0H76lCuYOK2NUpAvTKm0lSnCrDogKlAVSxf+vdbiMxu6jCzRthuh6K54PO10KlW+vbhpS7QYgwERoC3O0zZAr+9nL59ECk3ph90mQFPNoT9V+B0MPRvmLidUqZt++/g8fqw+xLciDYVX7MypTNopXu9MkpvT2GClnAdux3af3RzIfzw1vDVUOdtbXYYvRDmbQJvD/hfTzOQ3n0aXvgBzl2HRuXg28fMFj0pse4wLN0FOb1heBvwz5maqxLpwWaH7/81yyEqF4VHWyR9NyHOBjPWwtFLUL04PNQs/e88pBU1hB1a63oZ3Y/0IjE7441aCB8vBTTUKgHrRpuZYmfmbYQPf4OoWBjQCEY7llI8MwO2n4SS+WHCo1CtuPPvT8rJAPjhX/O7PaCxlFLOLP7ea5Y45PeDp9reeYenP3bBmoNQMBc81S7rzAgnFbNlIJxNjV8Cv26G7/pCcBQM+QkmDYOutRO3fW8hrNgNk/uYW91DfoIvH4bnf4BxnaFZafj8XzgSCP+8ne6XIkSGkIGwSE/rDsMDX8GcQVDYD55dDI0rw4cDE7dduR+GToK5g8w6/Sd/hQ61zWCoURF4pgmsOg7vrYS94yCfX7pfjhDpLqmYnUXmZkRKLdoGn3aFGo5kuTdaweIkkh4WbYcvukO1wtCqHLzcwswINiwBQ+ubRKhvesGWkxAckZ5XIYQQ2cOSHfB8U2hbHqoUgq96JJ2o9tt2eKk5tCxr4vYX3U2y3ZFL8Gk3E7OHN4KqBWHz8fS9DiHuNzIQzqZyeN6aLHchJOnbHzk8b02QOO9YPB9fWQ7MTLHNbhbaCyGEcK1EMfsOSUzOEpH9vM0yiaAo85zNDpdDs85tbyHulSTLZVPv9IH+X5lkueAo+HkfbBztvO27feGhb2H3BbgWYZKe1r0Lj06BXj9Ak5Iweye83g08ZSAshBAu92RbaPgu2H8xSyMmbobvnnDe9tkO0OhdM/DNnwMmboI5z5o1oq0nw6BasPYkFMoDzSqm62UIcd+RNcL3kVMBJpHh8EWoUhQmPgYl/ZP//VrDuCUmkcFqgZGd4fE2JqnpuZkmyaF2KXPcQrlh12lYsNkMXh9tCSXyJ33sbSfg120mWW5YKyia1xRXmLrKkSxX3uwIoNJ5Ox9hfL8WPl0KsTZ4qLlJaMwqSWn3K1kjLMKjYOQs+OeASUL6dAi0qJyyY/y+E95dYCp69awHHw00+20//z1sOAqFc5ucjPplzf7v01ZDuCOTv1H5pI974QZMXwORMSY21y9r3iN+3uxIlvM3CcoprSwnXGPzMXhpNlwOhhaVTKGTOyWwidSTZLn7XGQM1HwDHqsL/WrA/N0wezfsGZf8Wdav/oQf1sCM/mZ/1yE/wXv94X/zzRrgjhVh2hZYeQq2jpWBUlbx+054fqZJovHzNNviDWgGr3TL6J5lbTIQFoO/AXsUjOkI+y/Dk7/AhtFmm8Hk2HIcenwKswZCidww4jeoVsZMhhTxgtdbw9Zz8OLvsPMD2XowqzhzFeq/A1/3gLrF4IN/IMgOi2UrujQlyXL3uYMXwMsN3mhjEhneamd+OIeTKIPozKJtptJPraLQuBS83RbmrIcyeeHZpua4H3WBS4FmFldkDYu2wputze4dNYvAJ12l2pMQaU1rWHRbxa4+1ZxX7ErK0p1mb9eOFU2FzW97w69b4Z+DMLkvlPeHIXWgVRlYcyjtrkWkr1UHoUMFGFjb/Iyn9IM/90r5+YwiA+H7RA5PuB5+s2JXRAzcCE9ZIoOvV+JkCj8vuBJqbpmDWQ8cFu28EpjInBL93O+Q+CiEcA2lTFW1+N89rR2JxCmotOYsEdnP21T4uhJ287gXQkwsF1lDfOJj/A35yyHg7pb0ntAibcnqoPtExcLQuiq0nwrdKsOSg9CpJpQpkPxjvNXb3GY7fg0iYmHuLlj7Drz6I3SZDm3LwYK9Zj1wftk3MssY2QWajIKQaLM0YvJm+GVkRvdKiKxvbH/oPB2GNzRLI86FQj8nFbuSMqw1NHgHnlhglkZM3ATfDIXjl6HNFBha1yyN0G7m/UBkDT3qmJyO/rOhblGYvg3e6ys5NhnlrmuElVJewL+AJ2bgvFBrPeq2Nq2A34BTjqd+1VqPudNxZb1ZYvEVuw5fgCrF4JHmKV/Hu+8szN9skuUeaQGlC8Di7TD8O5NB7OcFy16HqkWh03iTNOFmgb4NYerj5vuf+x7OXIP6ZUxiXUqqvcXGwds/wy9bzV+97/aFvg1Sdg0i5c5cNf93YuJM6cxapTK6R1nf/bpGWGJ2+vp7L6w6APl9zc4OKU14uhoC362C0ChT0KhZRfP73HKsqQTnYTVFM4a3gVfnwsw1EGeHqsXgn7fM3cNnZ8Cm41A8rxlIp/T3/4e18MlSiLHBg83g7V6SQ5LWwqNgyiqTANmiEnSrk9E9yvruOVlOKaWAHFrrMKWUO7AeGKG13pygTSvgFa11stNzJKimj7PXoMqrMK4LdK4EkzfBzO1mkHvxGsweZGYS+3wPQ1vCnA3wXntoXQ6+3QhbLprkj+T+pfrGT7D1MEzoBZdC4cGf4OcR0LxSGl6kEBngPh4IS8zO5Io/C+3KmVyRLWdh+AJTFnf6GvjlEbN92rAF4OZhbqlXyQsvtoD1p+CNP02SdcFcyTvX0p1mID13MOT0hCcWQt8m8Fr3NL1EIdLdPSfLacOxWgl3x0fGbDUhUmzeJqjkD881g7L54eNupgjGluMwobepLNesNIzuYLbVqV4YnmhkEus+62YqEQWE3P088RZvh696mspHbcubSkhLdqTd9QkhbiUxO3O7EWaKFX3X38ThIXWgTTkzSfFa65uV5ab0hQPnYNsp+LqXaTu0PjQoDuuPJP98i7fDG45k2xqSbCuyoWTd/FBKuSmldgMBwAqt9RYnzRorpfYopf5USlVN4jjDlVLblVLbr4bee6dF8uXNAQHhtybLRcaCh9utCVbngs02bZdCzBINgMBIs5zCxyP550uYPAI3kz+EEOlHYnbm5esFiluT5c4Hg5cHnA282e5CsEmustlNZU8wkxwXU5gs6zTZNgUJf0JkdslKltNa24BaSqncwCKlVDWt9f4ETXYCJR234roAi4FEW31rracCU8HcZktl30UyDG0BY36FNpOha2WYtR2qFYMhzeDxn+HAFQiMgJnbYOmr8NFv0G0GtCgNP+2B5zukbCA7qh8M/Q6eaQQXQ2HZEdg6JO2uTwiRmMTszMvDCu2rQeNv4OnGZrnDhRBY/BJ0GGcGvkVzwRf/wgudwU2ZxLoHa8PGM+DrA22qJP98IzpBk9EQGg25vGDSZlg4Mq2uToj7T4qWw2utg4A1QKfbng+JvxWntV4GuCul7lCnTDhzKgA6fQSlXoAu403CRFJW7INiz0Kux6DU8ybpzRmrFT5/ELafg/eWm5mF7x6HFzpC51owcSP8vBde7mp2rRg3CM4Ew+frwM8HXuqSdB9+2giFnzZ9qPAiHLsE3evAopcg0hOKFTWFOwrlTs2rcmdrD0GlVzzxf8qDId+6Ex6VdNsV+6Du/6DMCHhmuiliEhYFw6ZA6Regwduw7nDa9VWI9CYxO22FR8ETU038qP+WiUdJOX8dqrwCuYZBwSdhyj9Jt530GITFwnsrYOUxGNUXmlSEMf3gt4Pw9QaTVDeqDzzXAfLnMjH72A0Tw92TmOI6cB7KjjAxu/DTZglE6QKw+T3IWwBsPrD8TWiZwup4KXHmKrQY44H/kx40e8+DUwFJtz1xBTp8aN4Tu31s9r/XGsYvgYovmddzysq066vIHu46I6yU8gditdZBSilvoB0w/rY2hYArWmutlGqAGWBLyYYUiIyBjuNgWF34thvM32MeO6ssdzkI+nwB73WAnlXNbG6HD+HiRHP7LKGz12DoFPi0O3SqCFM2Q7uP4OUucPYyrHnaLJcY8iNUKgqvzDHJcm0cyXK9PneeLLf3DDw+Fb7tA81Lw8droMUYuDQJGpc3H2ntyEXo8rE7ETEDgEL8um0pEdHHWPRSbKK2+8/BkG9hej+oWABe/8OUnQ6PBksMLH8c9l02r+vG96B8obTvvxBpQWJ2+nniO1NZbvnjZvu0fl8mXVmu+XvQuDgseQR2nodHZ0PlIs5LMjcbDT0rm2S5rWfhiR/NTkCfLIV5Q6BwTnh2EbzzM2w+DtXzw4zesO6UidnOkuXsdmg9FgbXguebwcrj8NBE2PUhlC9sBttpLToWmo1x51JQa2z2Wtw4tofmY/7h+Oexid67wqOgw0fwTEOY1B3m7oRO48zuGT+th/mDITrOVFDNkwMeaJz2/RdZU3JmhAsDq5VSe4FtmPVmS5VSTymlnnK06QfsV0rtAb4GBuqMqt2cScVXlnu9jUlq+1/bpCvLLdpm9px8qaVpO7aTWfO7+mDitvHJcs82NW3HdzUBcd4m+LSbSY5rVhpebwVz1t1Mliub33w9qWS5H9ZBk1ImOaNsfpjYxxQAOXvNta/LnazYD3ZdA6gO+BMVO5A/dtuctv1zj3kD6F7VVIGa3BcWbYfFjspQ5R2VoXpVNTPHQmRiErPTQcLKcuX9ofcdKstFxcDZGzBjgElqe6AWdK9idoG43e3JcoPrQJvyZqut55uazysXhC97mPeCbafg654mDt8pWe7gBTPh8oWj7ZONTCXKuRtc/cok7dAFCI7wwWZvB+THrtsSEunLgQuJ2+47B7m94OVWpr/vtIeYWPhpE3zUyVRQbVgS3mlrEv6EuFd3nRHWWu8Fajt5fnKCzycAE1zbtezFN0FlOW/3O1eWy+dnkiNi4sx6srBoCItxvt9v3hwm6SLWZrbZiU+W8/M2yyTinQ825zp8zaxBc7PAjTsky+XJYZIy7Haz3+S1cLBryJ3CPTRTw9cTLJYE2SME4+VuAeyJ23o5v16lzOeVCtxMSmkuFZxEJiYxO33EV5Y7H2R2cYiPH42dJJp5WE1MvRQCpfOZtmeDoF7BxG0TJssVzeU4bhD4+SWuHOrrZeL11XAo6HfnZLm8vhBtgxsRkC+H+b7LIZDH1zWvR3L4ekGcPRqIATyAWOLsUU6r5vl6wbUw8x7k5Q7hMRAUaZZyJKzGJ5U0RWpJZbn7RIXC0CaZleX6NYA3f4IWE83SiB93QdkCUK9M4rZDW8DYRdB6kiNZbodJlnv/Aej/FRy8YgbHC/bBunfhmRnQdQa0vEuy3EudYcLf0GkatC4LUzdDi4op30w+Nfo1hPcXn+P8jR+IjiuGj8c6xg10PiM8uAl8/Rc8/BNU9DfV18Y+ABHRpjLUEw1g7yW4FA5966ffNQghMq+x/aHLDFNZbt9lOB/mvLKcxQLdakPTb+G5pmZv4CNXYen/ErdNKlluw0vQ9kOw/WL2EZ64CaYNhx0nofVkeKgObDgDfjmcJ8sVyQP1S5vjPloflh+FaDs83c71r0tSyhaE7rXj+GP3BMKja+HjuYdONeKcLkWrWgwaV4CO06BTBbM2ulddeLwNdPsETl43SyPm7IK176bfNYis564FNdKKbM6emM0OP/xrbh9VLQYP36GyXFQMDJ9mli7ULgUTH0267c6TZu1rUCQUzgUr34LCuc2gd+FWM1Pxend4qauZZf5ulaksV6+MqVKWVDGNoHB4/Ds4f8Osc/t4kCtehZQJiYivzqPoWEPToUbSbYPCYeoqc+uxQw3zh8flIKj/DlwPNdc5ug+86oKN5E8FmPVsl4PNDP/4wabSn8g67teCGmlFYrZzy/fCPwfA389UlrvTLjvv/Qp/7YECOU3ScoEkil4EhUOb9+HUVfDxhO+fgvbVTWx+ZwHExZlk5xlPmsS4X7fB5mNQPJ9ZQ3t7Xkk8ux1engubjkGJ/DBjePrPptrtMGsd7DmrqFFC37GCqs0OM9eafJDqJeDBpub5FmNg5ymzOXb76rDkldT3KybO5NrsOQsWBQMam8qqIuu458pyaUWCavoIjoBqr8GbraBjRZi2FZafMGWPl2yF7/pBSBQM/hG+fSz7lXks/YKidmEY31Wz7zI89CMsftkE19Qo9ix0KG/WesdXhlr7rvNZe5E5yUBYpJXO46B4DnitlUmWG/m7mex44QeYM/BmslzDSvBRBkxAZKR+X8LeU/DzQ2ZGuPcPJlHuy4dTd9wOH8L1IPhhoNlDv/f38GJXeKtX6vss7g/3XFlOZG7bTkKZvPCMI1nuw85mFnTBZlNBqHphaOpIlvstmyUc2O1wLlAza5D+L1mudzXlNIElJa6FwJUQk0QTXxmqVVkz2y+EEHcSGQOrDsHE3jeT5VqVMTOjzze5NVkuOyaJbTyq+LrXzWS5jzrD0p1J3LZMgd1nzC5I1QpD8zImOW/B5rt/n8j8ZCCcxfl6wuXQWyvLhUUnnSyXnVgsZkui+NchPoElZyor4cW/jgGOylB2uzlH7hypO64QIuvzsJpb8wkry10IMXEpu8dsMEnfCV+Hs0Hg6YJsJ6vFJCX+d9xA8E5BVVWReUmyXBbXoCxULAKdp0HbciYp7rGW0Ku+ucWUMFlu4+iM7m3661UXWnwLI5rD1nOKvZc1C19N3TG9PKBdVWj0DTzVGNadNH+MvO6CtcdCiKzNzWJyFVpPhqH1YOs5wA3GDzJ7EcctNMlykzbD9OEZ3dv0N7qf5rmZcOyamT2fthV+GZn6JZ4vdoWh82HPJbML0qwdsPJNF3RY3PdkRjiNnb0GXcdDyefNGqQTV5JueyoAOn5k2nYeZyrw2O3Q41PI8zjkfRwGfp2y81ss8P4AOBsCn6wBixVe7WYqBy1/E3QOyF/QVIAr7WSHivtFVAwMm2rF/ykPyr3oyV97XHPceS/AiC7w+zGwe2gOfmISWN77FfI9ATkfg9pvKkIiTGLeIxNNlaNG75iEk6T8+QY82AJ+PwrefnDoMzN7s2AzVH8Nyo6EN36COBtcC4UBX5mfe/PRsPu0OcbsdVDyBU8KPe3Baz+5YUu8K9x/Zq4xVZbKvwhjfzX/b4QQKRcRDU9NM9Xi6v4PVu5Pum1YlCksVOq2ynJjfoX8w00VuTpvmuS3lHiyLRTJB5+uNQOzjwaZ5LYtY6FEcYjxMgli93tOx1d/Koo+50mRZz0Yt0ThipSkR1vCj8/Bxguw5zose80kDv65Gwo9pfB7DIo/p9h5ysTBsb+auFjlFRMnk/JqN5g8DNachaPBsOYts2vF7tMmLpd8Hh740sTrOJuJ32VHmnj+8yZzjO0nofrrnvg/6UHPz9wJvMPPfdMx8z5S6gV4eKLJ5xEZQ5Ll0lBMHNR6AwbVMOtEF+2DiVtg33iTCZxQZAzUeB2eqA/9asD83fD9TrMjxI4TJjEgzgZ9Z0G3usnPZg0Kh+qvm2S5TpXguy0mWW7b+0ln6t6PHp5kZeGW0kTG9gau4+Mxm/WjYqhdyvXnmrMenp4OCx6GMvng6V8UV6OgtL8mrxXeamsqQz272LyOpfyTd9w1B2HwBJg7CAr5wTOLoHFls/l9rQIwohn8exLe/Mskfgyb6kNEzCOADz4eP/JSlyuM7Z94hLtkB7zwPfw4GPw8YdgC6N/ENbtfiKRJslzW9OhkCAmCcV3MFmeP/gyr34ZqxRO3HfQNEA1jO8K+SzD8F3ijB4xeaGJ22fzw3CIIiIDd4xN/f1Lik+Veb2WSbUf+Djs/gGL5XHSR6WDWOnh6Ri5HDFP4eM7mk0GBPNPe9WOO89eh0ivwQWdTqGTKZrOl52vdYNEWmN4fQqNNUvhXj0DPZP7WBgRDjTdMAY+WZeGr9bDriilvvfGgWcd9JcxUt/vqEXhsqpWwqH5ASTys/1Cv9B42jE5c6fT0VWjwDnzTE+oWgw/+gRtx8JsLdr8QSZNkuQxw7DLYbGbRfZl8pkJObi/YezZx24MXwMcKr7U2bd9sC24K/j0En3eH2kWhfgkTnFfsTX4ftp+6mSxXJt/NZLlzmayY6m87FJGxDwD+QCWi4xryp4tmhW83Y61Z0tCpkqlCN+MBzYkAzZ97TUW6+MpQnSrCqgPJP+7vO2FEU2hdziS7fNHdVIbafdYkvpTND482gHrF4LvVFiJi2gFlgEJExPTlp03O90RavA3ebG0q/VUvDB93yZ5JNEK4wuIdMKmPqRbXrQoMrg1/OYm5Wpu2UxwxIb6y3HerYXhj6FzZxI/pD8CxO9wJvF3CZLmyCZLl1hxy3TWmhznrPYmI6QYUB4oREd2DOevTZtHtwq1QJq9iRHPzPjeui3n//GmTiYfVC5v4+GbrlCWFbzhq4vGjDcxxv+hutlf7dZuJ2VUKmXg+oin8sBYUpYG6QH5i4vqx9YSN8KjEx119EDpWgAG1zP+dyX3hr30QG+ea10OkjAyE05CvJwRGmOQ0MBVyroY532fS19OsS4p0/PEYHm0qy7lb4VzQzXbngsAjiT0ik+qDs2S5HJksycLbQwE3MyTc3a7j66SCkyvk9IZTN25mIZ8PNokUHlZTGQpuVpFKSbKKs+p2fl6O5RGOW2g2R2Wo3D4at9uq5jmrvpTUcbNjEo0QruDr6eT3yUmsSVhZDm7GhByecOZGgu8PAqtb8s/vYTWDuNuT5ZL6/b9f5fKxo0gYw4LI5ZM2d6Dz+sK1cE2MYyAZEmUq0eVKZYKhr5eJx/HL0q6Fm/dSP2f/R7xBE8LNyqZhoJzv6Rwfs+NvyF8KMUmAKfl/IlxHkuXSUEl/6FkX2k6FnlXgryPQtCJUKZq4bXxluXZToVslU1mucy3z3FPT4XSg+QWcuhl+ej75fYhPlusUnyy31yTL5fdz2WWmi08Hx/Dk9OlExDTDwxpAfr9jPNw8bc712RCo9aZm0BxFJX/Nl+vg4RZQxt/8LB+rBzsvQLgNeqRgjd5TbaHhuxBrh0K+Jtll5lOw5Ri0ngJDasP605DHDz4drFl9cBOhkTHE2X3x8VjHp4NjnB53ZBdoMsr8kZPTE6ZsgV9fdM1rIUR2M7Y/9PwenmwEhwPg0FWY2cR52/f7m8qUCSvL/fis+X0cPBcq+cOX6+DBZsk/v5sFRjlJlutcywUXl45G943lr73LiYgORWPBx2MjHzyQeJmAKwxuDG/PV7SYCD2qamZtN+97Hw40xaQOBZilEQv2wYbRyT9u6yomHnefCc1Kwdxd8EpXaFwehk6GpxqZP1iWHYF/34Wjl29w+MI0ImPL4uOxide7Ox/cdq8Nny41Sx3rFoWZ22FM36SLV4m0JWuE05jdDnM2wP5z5hdzaAsT6Jw5fhl6fQ4Xb0DRfLDkJShdEJbuhE+WAgre7Q1tq5llF09PN/9WL24W+RfJA13Gw5YTplZ9tzow6xlzu2X6GjgZYNYcD2ycOX/h1h6CpbsU+Xw1w9uYWYC0MvUfeHeB2bC9dkmT/ObpDn/sgn8PQ6HcMLx10jPru09Dmw8sxMTZcVOKkV007/Uzy1KmrzEJOT3rmT9UNh2Dbh+bNeVuFpO08VZvuHDD0TbGVPirWzrp/p65Cj+sM8fo3xBqlnT5SyJuI2uEs65/9sOKfSbGDG+T9NaHYVFm952tJ8DHA6Y+Dl1qm6ToZ2dCaKQp9jCik0m2ff57WHsYCuaEzx8yEyNv/QxTVpg/kCsWhlVvmxnDJTvMrfkieUwfMuNWXscvm7ikNQxpCpWdTAK5yt4zJrE8KAIK5oZ//mfWVO85Awu2mJn2R5qbCSpnImOg8iuK66EapUzxo1VvQ3SsWe5y7rqJ133qQ3gUVHwZQqPMVnd1y8A/b5ljTF0F564rmlfUd1yLHB4FU1eb94QWlaBr7bR4VURCUlnuPpdUstze8YlvrYRFmQS4kU1NYsCs7fDLQShXCI6cg3kPmiUWvX+AIc3gkyEZckmZ1o5TJlll1kCzLuyl36FEIZg4LPnHyDVM0ac6vN1Ws/ksPLEAFo6ELrVubWezQb7hZh3iK61g7QlTMWrZa9CqigsvSricDIRFwmS5/ZfhiYWwfpSZ9Lhd788glwXeaWfuKD2zCF7uAh8tgYUPQ4nc8NQvEG6HrfI6p0hIBFR7HV5rAV0qw4yt8Mcx2P5B0hNPtys7UuHnrpk72FSW6zkTutc15a1vV/oFKJQDZgyAK6HQ5wcY3BQmPOra6xKuJcly9zlnyXJWBYcvJm675wwUyMF/iQGjOpgZxi3H4KteUKOIqbjzQSczmyxSZtkueKz+zWS5b3uZpJjkOnsNImI00/pryjoqyzUvrfjqz8Rt952DqDiY0Nv8LB9tAI1KwFd/uexyhBBp4PZkuV7VTLLcCifbrcXZ4I89JimqbH7oXxO6VDKJuSOaQfsKULGAqUZ5xEnMF3e287T5Q+K5ZiaOju0E10PhzLXkH+N6qGZyX6haCJqVhlHtYdlu57dOA8NhUl+T9NyqHLze2iQ+i8xJBsL3CWfJctfDnS/s9/UyVcuiHYkBYdEQFGnWIiWsjHMm0PlCfXFnvrclWJxLYYJFXsdt1Cuh5l+7HS4kUVnOP6dJxEiYLHchJG2XfQghUs9Zsty5JBLr3Czg5W4SrxK29fE0+R/xzgVJwtS98PUy8TZhslxIlPOfRdLULe+fp26Au5vzO+YWdet7xOlAsErGVaYlP7r7RIXCZu3v7clyZZwUuahRAuqXhQ7fmS1YFu2HAY2gVil4bpbZ+zIiFqZvgcUvp/eVZH5DW8DE5TB0ntl6bvIW+PzB5H+/rzeU9lc0+FrzdGNYc0JxJlDz75jEbYvmhZL5oOHX8EQj+OeYCehfPeS66xFCpI3bk+UuhJk1+rdTCsb2M/F9WH3YdRFCY2HW09B0NDzyE5TOaxLrhrdL98vI9OqUMvs8d5oO7crBL/vMeuACuZJ/jAcaa4bOh10X4Go4zNlhCi4507WOSYYc2dz8cfPTLlj8kksuRWSAu84IK6W8lFJblVJ7lFIHlFLvOWmjlFJfK6WOK6X2KqXu83o3aWPSCqj8MlR4Ccb9Zv7qP3vNJLCVfB7af2iSB5xRyqxFerw9BFrgqU6mfKazpDalTHWdQ1fgo3/MLMLQFvB4K+hVD77ZADO2wTPtoX11OHgeWo81fej1GVwKTHzMe7HzFNR8w5P8T3rQ7RN3rocm3Xb6GvB6xAvLEA8KP+PJqQDz1/vTM9wo8JQHZUd63nF/x6BwqPmGIudjpuLbmF/N82sOQr3/meo8j08166evhUDVV2+2/fj3lF1XnhymglPlshDpCfNfgIFNTMJjoafA71HIMwxe+9G0X7wdyoz0pODTHjwz042YOPj7DU1kLIxbBVvPasb0N7O8P2+6WVnu9R9NIuORz6BmGZi4CW7EwrHPzWDamSvB0Odz87NsOcb0CWDGmpuV5ca4qLKc1vD1X1DpZaj4Enz2h3kuYQXETh+Zx+L+ITE7+dYeMhXhSr0Aw6aY+JGwslydN03SXFKebAfTnoRQK9SvYtYHJ5VA27cBaGD8alh13ORv1CgB4wfCr/vhk7UmIfb9/ubW+5AJpl+N3zXJeK4QEAwdx3mQ/0kPav/Pg31O9rSPt/MU5B3uiWWIBzke9fpvmd3EFYriz3tS+BkP3vvVkmS1OLsd+n0BuYeZj64fm+dOXzU5GCWfN3HkZMDNCqrxbXt/kbIYZrHAgpEwsAUEu8ErPU1RopgY836c6zFz3OaO34T956DOW+Z16DDOgyvBMGGouUP39XqYt9vkaPSoaxKfm42+tbLc7GdgRGeTx7PmNPz+CrSv4bxvMXHw0mwT82u+fnMJxcajaVNZbuV+UxGx9Avw5DTz/zlhBcR6/4PVKdj/Pju4a7KcUkoBObTWYUopd2A9MEJrvTlBmy7A80AXoCHwldbayd/FN2W1xIt5G+Gdn03VME+rqVn+SCuTQTq4pkmGWrwfJmyC/R8nriyXEpeDHKV0O0GPqvDDNvh6A7zZE+avh5kPmCUWg3+Cd/vC2z/D/1pDx4owbQv8fcIkYyQ3iSCpPlR42UpoVB+gDO5uq6lVchdbxybe4mvLcWg0yh3oj9lc/R9yeu9jYCMbs9eXIDK2LxCEj8csVr0VTcNyic9X9VVFUV+Y0Ftz4ho8MBtG94NxS2BaP7Ou652/QXnC9lOKinnh8x6awwEwcK7Z0ii51YSSku8Js65vdAeT7PLIPHirJ3ywxJPImIeB3Hh7LGBo83Ms2manU0WTLLfFkSz3+UPw3i/w4yAo6GeS4hpVNlv8JIfW0OhdaFnCFPz45xiMXgnjB938v+fKynI/rDV/0M0ZZHYheWQ+PN0BvvjzZlLngj0wYwfsGQdemTCrPTXu12Q5idnJc/SSmY39rh9UKwTv/g12D7M7Q2gwfNT57pXlUqL0C9CkBIzpaGYdH5kHnw4x5dznDITiuWHEEqhU0uSGlHBUltt6zjyf2spyWkPNN905dLEBcbbmwDFy+yzh+Oex5LttO82YOPB9zINYW1PMf48jKJYy4ZFYXv3Jj4iYoYAVH485jOl3jZe7Jh5HDJ8Gf+2CXx8x7zX9Zpk7mbvOwKN14YGasHAvfLfN7NCw5ahJGtRAn++hfS2Y9sS9Xy9AtdfAYoO5Q8wSwt7fQ4PysP6oO0Hh3dFUwOq2gYqFNlMgZyxBwTDbkSzXayY83ga+/xfGdYYWZW5Wllv7bvJ3XnppNhw4BV/3NMsphvwEEx+FJ6eb/JP4ynLXYmHJq6m73gPnodVYmNHfrF9+80/w8TVbr2pHUueBy/D4Qlg3Cio5SerMyu45WU4bjq29cXd83P6/vicwy9F2M5BbKVU4tZ3OTH7bDu+2gwYloGYR+LATzNtkdgV4u51j94GWkMfbeWW5lFi0DUrkgZEtzHFHdzSD79nrTEWd+GS5t9uY58rkhaebmLYfOCrLnU9lZbn1R0CpkkB9IB+xtj7sPmNz+lft9/8ClAdqA/mBfoRExrBwm4XI2P5AAaACkTFN+T2J5L7TVzUzHtBU8DcVm55oZLa06VsdelYzySpT+prklTPXNDMHasr7Q/eq8GAdxYy1qbve4DCzDnvaAzeTXbpWhkn/QFRMc6ACUIDImP78vFlxLexmstzgOtC8jGLKP6YCUav4ynI94LcUJOFdDjKzJ+O7mp/lE42gSgGYux7eaHWzstwnXV1TWe63HfBeexOo6xQzQfTHDeCdIKnz9TbgboFDkuBz35CYnTwr90PvaibJLWH8WLzDVHWLryw3JInKcikRFQNnb5gqc2XzQ7+aZsefSf/Akw2hnSNZ7puepmrZ6gSV5QbVhtZlU19ZLiAEjl1WxNl6AvmARth1YTYfT9x263GItbkBnR1tm6ApwLcrPYiI6QyUAIoQEdOLuRucz+qs2Kv4uCvUK24qo37Rw2w96W6BN9qY+PFaaxNPVh+Az3uYOFO3GHzSDVbuTf0en5cD4eteJi42LgVjOpmkcpu9IJrGQD7ibN05EWBh7xmYmCBZ7t328PNmU1luaP1bK8sFhie/D7/tMIPgigWgbXl4upG5g9epoqlQWtaFleWW74OBNc37Xrn8MLmP+f/8207zebn85v2yX4073+nIbpK1Rlgp5QbsAMoB32qtt9zWpChwLsHj847nLt12nOHAcADfvMV4IiiNauRmgP1uT1I16Bfi32/OBcNVjwbciNhLWHQUvp6mstyFUG8+jV1JnqBK93yuk5bFXAsfRnScGQCHRZsPD4/qnAu6+b/7TJCF09a2xF3dQKwtAnc3U3QhKNqDt6P34xV079ML5+L+IcL+OqaKjgUII0678WL4Odxibg2MW93GAAsxr40CQgFFtHsxIAgzOAblFsZyNYorQYkXZlks/pwLslMst+M1uK64Zq3B8aBjaB2BUo5EE09fVHQ454I0+R3JaaduwKlc/XkiaPI9X6/NZsNNFeBSCJTKa2ZXzgRCqLUsyi0MbYtvGUSkRymIPM6VUCiSy9ziOx8M13M05HTQDsBEu3NBEOpenieCNjs/6W1iokIIi6nAjYhY8uUwmegXQ6BK8cTVB11RWc7XK/Fxc3rDyavmjoO3u9nj+Hp45qt6ldVJzL67Y/Z5WIJe/S9+nA8Gd88cKHcPzgUHUsAxS3o8yJvtRcdyJOje98ay2+1YLf5cDDEDqvj4ccVan1NBuwGTJX0uCGI9ioG6wuXQWIrlNm2PB+Vglm0ya4O63HMfYqJCibFXBCKAHICNCLtmUtwSFgc1vaVtkDoGNAOiAG/ABoRxzaMeqKAEf1YFccm9MU8ELUx0vjBLTc4Env/v8dlAiLbk4VJYJBExUfh4mPhxMcwbu5s3ZwNvluM7EwgRlsI8EZS80dp3uZ2/lyll3osTHhcFdh3muCY3IAKbPQ43N8W5IE0jxx7sp26YZPMLwSaJ2c1ys7JcSvZ09vU0P9eKjnyfc0GQ0wfOXzU/W6VMHPewpj5R0tfz1uuNfy+Is5nPc3k7EjWDoIHE7P8kayCstbYBtZRSuYFFSqlqWuuEm8Q4+9Mt0b0SrfVUYCqAf8laGbOBcRqp1O4VPvn8L66GReJptTNlqzetn/2AE+un0mzKHzxQNYLFh33IW7YluQtXTNW5StXuwb7f8tP822v0qgY/7oSc/qWp3fcTXpzUh6PXIgmPtTBrtw8dXhzL7kWv0+a7rXQuH8mcvT5UaDwQL99U3GMDilZqQZ5CBbhxcTa22GJYPfZSrc1I3NwTzw7U6vgSB1Z9jy1uOlAa2ESRiq2p3Pwh1vzwIrbY+ljcgvHMcZGKjZ1vely8wSN0mT6Tkc3gUIBi5XELHV+dxMYZQ+g39yK1CkUzcYs3tbqN4srpHbSbMo8RzWDPJcXGs270eGdUqq7Xzc2NvIUr0OSbozzbFDadMVWn2r/8A39+M4DoiF+w23Li5r6VJv2/5ui/02jw9TqebqxZc0JxLtSLDsMnMP+bjsQsDKGoXxzfbPKm4UNjk90HD++cVGnxGE0nz+ahmhGsPQklC5ilFS3GQEi0WRoxdQssckHixmvdoc0HcCnUZEnP3A7L34Av/4S2U6BbZVh6CDrVhLIFU38+4ToSs++uVK3uLP/nM/rOuUDtwtFMcsQPN3cvOs98necaRXLgqgfbr+anY72+qTqXxWKhePVONJ3wF881hc1n4dA1Dzq+OollE7ryyM83KJ07jm82eVF74PuEBRyj6ZQvGF43gvXnvLiqSlOzauqy6Dy8/KjS8gkOr59OXExV3NzPkr9YGQqWbZSobe6C5cldqDJBl78B6gKH8fDxpvngz1jyWRfiYiLRdnesHjto0Gu+0/PV7jOO92Y8yKUQM8CbuBEaDPqQa0f+ptmU5fSrEsGvh3woWKkdBap04425wzkTCHYNUzZDk6HjUnW9AA+1gKcWwsErZheJmdtgyuMwfU04205MJiKmEjk8t/F4KyieT/PofNjtSJabuxOWvwmjFprKck1Lwo+74dWuKRsIv9cfHpxuZoLPBcE/J00Vuv5f3qwsN2O7SaZMbaGrgY3N1ptDfjRVDaduMWvOY23QdQY80dDsd30uFB6440Ko7CXFBTWUUqOAcK31pwmemwKs0Vr/5Hh8BGiltb6UxGHwL1lL93p91b31+j4Vcu00x7fMA7uN0vX6kqdwJUKvnWX1lH6E3ziPd65CtBo+n9yFyqf6XHFxMayfO5KwgMPkKV6Hxg98jMVi4cbFQ5ze8SvKzZ1yDQfil68EdlscRzbOIfzaCfIUr02Zur1RLigtZ4uN5vCGWYTduEDBsvUpVbNrkm3PH1zFP9OGERsTTc78Jej52l9YPXz4Z9pwLhz+Fzd3Lxr3G0X5hgOSPMb+1VM5tX0h7j65aTLgE3LmL8nZ/ctZO2sE9tgw8pWsT8enZqEsFpZ81p3gSwfAzYPmgz+jXP3+nD+0mg3z3iYmMoji1drRbOBHaLud7T+P4NLRtfj45qVm3y8oXL4pZ/cvZ9PPo4mNDqFE9Y40GfABVncvVs98kqvH1uLmk5+Oz8zDN28xjm76kU0LR2O3xVC0cmvaDpuCm9WDbUve5/KRNXjlKkrzB7/Cyyc3EcFXOLppLraYcIrX6EKBUnVT9JprrTm18zfqBQyjlL9JkHS3mqTMH/51VJZrZJJwLgaaZIk9Z6FsAZg0LOVrwo5fhjnrzQhpcBNTKMBuh1nrzHKIykXg4eYmWSW7uV/XCN9OYnbSYqJCObx+FjFhARSq2IpilVsTFxPB2mkPEnByC27u3tQf8CWla3Vzyfl2LvuEiwf+xiNnIZoN+gKfnP5EhV3nyIbZxEWFUKx6JwqWaQDAmX1/cfXERrxyFaVS04exeiSRQZsCWmtO7VrC1dO78MtfgkpNH8Li5nyPzdAbF/j90y5EhATg4e1H5xEL8C9Wk53LPmPfyilobadcw740HTAuyfeTC4fXsuvPT1BoqrcfQYlqHQi5epo/vx1EdPB5PHMWodOzP5HTvzT/zh7Bie0LACjfcBDNBn9GyNVTrPnhBUICTpK7cCVaD/2aHHmKsv/vTzm1aQbKYqF86xdZ1+N1Dl+EByd6cCrAxL+5z8ZQJI9J8v3mbzOj++kQ6F0fdp2GHp+5cTVEUaGwZvkbNgrlhrkbYNJKc6d1/CCzdjk6FqbFV5YrZyrLpdSmY2ZPf18veLy1ScqLiDY5RJeDoEVlU2wpOhZemWva5vSGDwaYCrEpERxhlg1eDzXJ8m2qmudX7INVByC/n6lU6Jf6/06Zzj1XllNK+QOxWusgpZQ3sBwYr7VemqBNV+A5biZefK21bnCn42bFoHo7W1wMf3xYj2dqX+bh2jYW7rcwfmM+ur+9E6uHT0Z3L92EBV5g4dgWxEV3AUpgcVuLfyl3chcqz/Gtm7DFdgMCcXNfQNcR8ylQOnlji6Arx1g8riNxMb2AQrhZV1K0clEsVnfO7TuJLa4DEIDV/VdaDf2G1d8/jy22L+CPm/vflKxeHncdRA3+5aOO0ey9BEMX+lB/0ETW/DACW2x/IC9u7n9Spm5tWj70RaI+XDm5lWVfD3K0zY2b+x+Ub9SMZgM/ctXL51RStwLj2ewmc7hbBRjWAJYdhvFrYd94yJV9/uulqft1ICwxO3U2zRpGmYg/+bxrNEcCYPB8b9qNWE7eotmn1KPdbmPh2JaEXiuKttcHjuCZYz0Ner/Dpp/fJy7mAcCK1eNX6nR5nBrtn0nWceNio/h5VBMiQ6qidU2U2ot3zn3U7PAs2377mriY/oDG6rGABn1eYtcfXxAZVhd0VZRlNzlyHaF664cJ2vIxPw2IIDoO+v/oTclO49m66AOiI5qAroSybMMv3zn6vbsOi+XW9Qbjrfko91J8slxFrG7rqVBoM/vGxWb4H/TPTIezl81a5JM34OF5sOx1s5uISL3UVJYrDKxWSu0FtgErtNZLlVJPKaXiiw8uA04Cx4HvgOT9VmRxwQEn8LIHM6qdjdL54NWWdgp4R3HjQvbau+TS0fWYpYq1gXzYbT0JOLmJ07uWYovtDvgDFbDF1uPsvuXJPu6FQ6vR9mpANSA/trhenD/4N+f2/YUtridm7XEV7PaaHNk0B22vCVQ2bWN7cnrvn5zYu4oZ/aIpmx96V4de1exmxjauDlAR8McW250ze5Y57cOZPX9hi21AfLKcLbYrp3ctddo2PZ25ZmYExnaC0vng2aZQMrfZEklkeRKzU+HU3r+Y1ieacvmhaxV4qE4c5w9l/T8AEgoPvEB44BW0vQOQF2iM3ZaHIxvmERfTkvhkubiYDpzYviTZxw26fJTYaDtatwbyonUrYqPhyMb5xMW0wyxTL0ZcTBuObJhPXKwH6Oamrb0N0RGRXNz1E593jqBmEZOcPrZdJBe2z8JuywW6iaNtByKCrxN241yiPmw5njBZLi9xtu6cDLBwOThR03S3eIfZSaK8v9nl6bH6ptKpSFt3XSOstd6LGcHc/vzkBJ9r4FnXdi3zc/f0JTgilrBo/kuWux4WRxVPv7t/cxbi7uULBJMwWU5ZLFg9fYmOCCI+Wc7iFoK7d/JLqrl7+qIsCY8bhJvVB2WxYosLwiSEgMUSjIeXP8oSYPIjAAjE3TMHVm3nXFAElQs69n0OsuDulxOL2xns/2XwBuHm7nwa1cPbD4tbCPYEyXJWz4wvC+frCaHRZl1cLm+zZOJyqGuS6MT9TWJ26nh6enMuOOq/ZLnTQVbcizopC5mFuXvmQNujSJgsp+0heHiXSZQsZ+J7co/ri90WBsQAHkAMdlsoHj5lMInTN4/r6Z0TbT+FSSR0B6Kx28KxehW/JYn3TJDCzTMX2n4Ok4hsBaKw2yNx90z8c5sYt5QI+5MkTJaLsStypGJLU1fx8zLJbiXzmsfngqBuNtviLCNIZbk05JevOMVr9qDZlD/oXyWC3474kM8FyXKZTfGq7cmZ/0uCA+Zgiy2M1WMPNTu+Ru6CZZOdLOdM6To92f33BMID52GL88fNYwf1e72NxWpl089jsMXWxWK9jnfOcOr3Hs2Vkz2JCJmPPS4vbu7badh7DPbYCNpOG8XTDSPZedGTI+GFafXwe1w+3pWosAXY43Lh5r6dRn0/dtqHik0f4sCamURHLHQky22jUd+vXfXS3bMCuUxlpdZToG81WHnc7IMqt9iEuLMa3cbQeeZrPNcokv0BrkmWy2y8fPNRockQjm+ZRlxMFdzcT1GwTDUa9R3Fb590TlaynDM5/UtTsno7zu6fQVxMeawexyhetS01OzzL0i96ERdrJjasHnto2O93di79lAuHZxIXUxarxxFK1epOlZaP8MrEXhy7Hkl0nIXvd3nTfuT7RMSM5sqJ74mLKY3V4xBl6w/E288/UR8KlGlA/hIVuHb6B+JiS2L1OEDFJsPI5fOtC1/BezOmv9kj/8mGZteKLefh6yczuldZnwyE01jDQRM5vm0BCy/ux69JJao3GuiSRLXMxM3qQeP+o9k293GiIo6St0gVqrQYirJY8fLNQ3jgWux2C2XqPIyXbz4uH9/Mnl9GEh56jcLlm1H3ga/x8M6Z6Ljunjno9cbfHF4/i8jQaxSt9ATFKrcmKuw6O5Z8SmTsWuw2N8o1fB7f3EVo2GcUe34dSWz0fvzLNKJM3Z5ou52Lexfz2bqdeHjloM6AD8mRuzBl6/Xg3IZvsbnZ8c5dhKJV2hATGcLa2S9x+fgmvHzz0+LBTyhYpgF93lrFkQ2ziYkOo2T1l/5LdnHmxI5FbF30EbbYSMrW60XDPu8mmaySWl8+bPay3n0GBrWEx1qapLZJK0xmsc0Oj7WCN3qkLFv5VAA8Nf1mstzkYVDaSSlwITKj8o0HkyNvMRYd+gePkvnpOOQRp/Enq6vV8QXCzv5LyNXV+OT0p2Gfd8ldqDyFyjXk3P41AOQqVJX8JesSFniB7T8+yY2Lh8lVoDT1Bk0mV4GyiY6plKL1oxM5tnU+Ny4cJm+RrpRvOBAN5CtejSsnzFaS+Us2JE/hStTv9SYx0wYQHrQav3zFqdvtVXzzFqNUg8FM2/4jSlmo0PIZ8hSuQJUWDxNxdiixHMHDy5tKTQebZOil4zmycR4WNyt1uoygUtOH6fL8TxzZOIfggNMUKNWf0nV6As4HwrtOwyOTPbhwAxqW08x6Opb8aXRTd0BjKJIH/txtKpt+/oSpSLr2ELw8G66GmuS3rx9JWbJbWBSM+MHsnZ3fzyQNtq6aNteQGaV41whXyS6JF8Ikyy0b15gpPcPNmq5V7qwLqcu1G6EEX4kC+gKBwGzq9XiVI/98xsy+EdQuCu+s8GB7TBNaPPlLss/34//qEhHsA3QHAoC5NOo3moN/juXHAZFULAAv/uHJKZ/OxMWE/5cst+8yPLLAmwrtXuXwX2NY+LApRvLsIjgQXhqdowxXToZhj2sNnMfq8Tt9316DX74SyerXxaPr+Xvio9hiHwD8cHNfQuXmHWjUd3QKX9G7J8slZf4meHu+qULnZYWhP8PQ1vBCp+R9f2QM1Hgdhjsqy/28B6bvgL1SWS7Lk5idfdjtNv4a35iHKp7myQY2lh1RvLMqNwWr9uLwhl+ARzDLFeZQrEptYm8c5MkaFxla18big4r31+Wj+1s7kr1sYvWMpzixYxXwEGbdxSzK1u9A0MlVvNH0On2r2pm1y42JuwpTtunjBG0ez7yBkUTHQb8fvSnWdjR7Fr3BuC6arpXhuy3w7SYr5dq+xt7lPxEX0weIxs1jPm2Gfk7Jmknvx5wwtl4JNhVUQyL7AGVxd1tDtWLb2fFBbKq3OkuuY5ehySiY2hdqFIZRyyHGDX4emfxjDP7GVJZ7vyMcuGKqj0pluZuy4aZHIr1dOrqe1uUUA2ubzeS/6xPLmaPbCL5yDOhHfLIcNOXIhjl0rQx9a5i20/vGcPLAv9hvLsK9q4jgc8ADxCfLQV0Or/+eh+rE0rWKqa4zvU80p/b+dUuyXK9q0Kua5uimOTzd2CQrlPc3JViDr57i8rG12ON6YSot1QQqcvHIumT36/SuZdhiGwFlMYl1XTi1849kf78rLN5mKg42KGEqEH7U2VRFTK6DF0wlqFdbmyS819uAh1SWEyJLCQ+8QGzoRT7qZBK9n22iKZvXxsmdfwCdMMlyhYGeXDy6GWvsDUa3N21fbK4pkiOa6ylICj+7fy3QjfhkOejKmT0rKegdxSst7JTOB6Pa2fC0B5pkuS6R1CgC9R3Jcuc2z6Bcfs1zzUxc+qAzuKs4RxJeR6AQUBJbTAuOb/892f3acBSgOKaCal5ibb04cEGnqLJcaq3YB72qmmTu+Cp0v+0yOS3JtXiHqSxXNj/0qCqV5W4nA2GR5ty9fDkfpP/7xb0UajaYNytzghK0DMTdOwdng9R/bc8Hg7u7B0ql5L+q223HvYG7py9ngm4uQTgbZJJiPD09/0u8iE+Ws3r6cupmkSPOBYObxYKyuAMhjmc1qGCnyRhJ8fDxQ1lCEjwThDUF3+8Kft63Vh46G5iyBDo/L1NdKSLGPI6IgWthUllOiKzE3TMH4dE2gqPM41gbBITGYfXwxNy9ixeExc2dkMg4wqLNMyYp3Jai2Ojm4cHt7wVWD0+uh8URZYruERYNIRFxWL1yJUqWs3jmIiDMJAWDqaAaEQue3rluPa4KxuMuCdlPBF3/78PPC+w6BFNBFSACre0pKqiRWvEJdAnfE309U7aczS9BlVCtUx73szpZIyzSXPGq7TmyojRdvj9O0+JRfLfdhzpdXyIsNJD9/0zHlPK8AeogrR5ewZY5w+gx6wwNikYzZas3dbq9laJ11eUa9uX4lqlAU+AyynKOlg+v4t+p/XlgbgBV/aP5dos3NXqMxRZjkuWeahDJrkuOZLlHJvHXp80ZPNdOhfzw1Xoo1nAYvnlLsPOPCcTF1MLN/TJ+eT0pUSOZawqAKi0e49C62cRG/YrdlsORhDclpS9nqrzSDZq/ZwazXlaYvg3+eC3531++EHSoDu2mQtdKprJc51pSWU6IrMTLNx8VGj9Ik8nzGFI9gmXHvfEs1IDmLZ7h72+HAOGYXR820KjvxwSf3UrTKb8xoGoES476kLNUc/IWTf4i1Mb9RrF65jOYQbYd2EKTgdO4tGseLaaupWfFCH4+4EOJWt0o0/QJXvm2J8euRxIZZ2HWLm/av/AFayd3p8XEG3SvAnN2Qp5CFajTbyx/fTuYuJgAlCUGd89D1OwwPtn9al0FqhULYc/ZyUTGVCCH5xae75CyynKp1bcBfLEMBsyB6oVMzH6/f8qO8f4DprLc4w3M0ojzYVJZLiFZIyxucf3CAdb+8CLhgefJX6IWrYZ+5TTzNqUuHd3AqhlPERcdRO7C1ej03E94+uTm0LrvObp5Hh7eOWnS/yNyFSzLyqmPcOPwUuwalLLQ+oW/KVDKeXmdi0fXsf7HN4mOCKRopZY0H/wx7l6+7F05gZM7f8fbLx9NB3yMb95ixESGcHjDLGLCr1GkcjuKVGhGTFQoa78bzNUzO3D39KXR4IkUr9qOw+tnsfXXN1H2WPKVqE2H53/D6u7FuQMruXh0AzlyF6JS04dSXBjFVJb7kdiYCErV7Ix/yRSWDXK41zXCYJLdZq83yXIDGkGVYin7frvdfP+hC1C5KDzUTCrLZQcSs+9PUWHXWTNrJFdP7cAndxFaPvQZ+UvUTPVxwwIv8teEgUTcOIV7jgJ0ePoH8hWtxuXjm9mxdBx2Wxw12j1DyZpdOLR+Nvt+HYlNm3LsFdq9Se0urzg9bui1M6z+YQTBl4+Sq2A5Wg39mpz5S3HuwEp2//0VSilqd36ZopVaYrfbOLZ5HqEBR8hVpBrl6psR4NaFb3Bq64+gFJVaPUetLq9x7fxeVkzoQ1xUMF5+Bej04t/45S3G9QsHOLXjNyxWDyo0Gohv3uQHvO9y5yM6FqavgVNXFU3Ka3rfQ2W51AqLMtXirgabJLf21VN+jJX7YdV+yJ8TnmgtleVueV4GwiJeVNh1fh7dmJjINkA5lGUTuQsF0ed/K1O108XNynJdgeJY3P7Fv5SV7i8tStR236pJ7F3yNrMHYZLl/oKlhxQPfHYtUdubleV6AwWxWFdSrHJROjw1M9l9WzdtIDXVv3zY4WayXP1Bk9g+7yl+HhRF6bzw3O9eXM7biwaDMn57nXipGQgL15CBsLgfLB7fmRsXPLHbmgGncPf6m/7vbsAn173fprHbbSwc04LQ68VuqSz3wOjNePrkuqVtVNg1Fr1TkZdbwtB68NsBeOtP6PjKWvIVq3ZLW1NZrjGRIdXRugZK7cM75x4eGL0p2SWkD62eROCmD/+rLNfvR29KdhzHvj9G837rILpU0kzb5sbMQyXo/MaWRJXl7pXE3MxPkuXEXV05tQ2tCwENMNV5uhAScILI0KupOu7NynK1MJXlehBwchNxsVGJ2h5YM5UeVRMkyz0AYdGaqKiwRG1vVparCuTH7qgsl1xaa07sXcX0vrcny81leP0YOjiS5ab2iuLsXueV5YQQIqNERwRz48I+7LZumApwdYGSXDm5JVXHDQ+8QETw1USV5a6d3Z2o7b413+HrAaM7mES1kS2gRB7Y/feXidqaynIarVthKsu1JDZaEXT5SLL7dnnvAj7rHHFLstyFHbMpk8fGs001pfPB+x1t2MOvOK0sJ8TtZCAs/uPu6YtOlBgQk+y/1JM87i2V5SC+spyz/XPdPf04E3hrYoDVAlZr4pX9t1aWg/jKcsmllHKaLOfunZPTCRLrzgWDh1fKlkAIIURas7p7obUNiJ8osKN1EO6prG7p7pkDuy0SU1kO4ivLOUuAy5WvBGEx3JosFw4+ORPPSN9aWQ7iK8ulpL9unjlvS5az4OaZi4CQuFuS5cKi4lKUsCeyLxkIi/8UKteY/MXK4Ob+PbASq8c0qrZ6Cg+v1O0eXrxqe3L6++HmPsdx3OnU6fq601tW7Z+cw/5L0H0GjF0Bzb4FL7+CWK2J8zpL1+mJT6443KzzgBW4uf9Ag97vpKhvtbuPps00b8asgD6zPTkaXpiGvd9j9fm8DP7Jg9HLFb1me1Ot2/v3ePVCCJE23Nw9qdlhJFaP6cBK3NxnkadwYYpUbJGq43r55qNikwexekwD/sHqMZOCZao7zWmo0HgIys1Ko2/gg5XQ/FuIirNQv/d7idrm9C9NyRrtsXrMcBx3BiWqtyWnk+IbSanc+V1e/tOH1/6w8Pxvbny63oeaPT7Ap1gjWn3nzQcroelkH8o3GuyS/BaR9ckaYXELW1wMRzbOIfT6OQqUqk2pWt1TtD7Ybotl88JRnNy5BDd3Hxr0fpOydXsTFxPJ4Q2zCA+6TOFyjShRvSO22Gg2LniH07v/wN3Tj0b93qFUza4c3TyfdXNGYFGxWH0KMGD0liQrO8VEhXJkw2xHZbkWFK3UitjocHYsfImLh/7ByycXNXp/SrHKrZPs8/lDq7l8dC2efgWp1OQh3L18iQoP5MjG2cRFBFG0agcKlWuU4tcyLWXkerWTAfDUtJuV5aY8nj0ry8kaYXG/OL3nD66c3I5f3qJUbPIQbu6eKfr+wxtms3PZl9jjYqnYZAD1ur8JSnFy52KundlDroJlqNBoMBY3KwfXzmD33xOw221UbjaEOl1fJfjaWRZ90Bxs4WjlTfdX/sA/iYQ9bbdzfNvP3LhwmDxFKlK+wQBQigMrv+Lk+imgFGVbPEOV1s8m+d4TeOkwp3b8irJYKddgAH75S2K3xXF084+EXT1BnmI1KVO3t0uruGbmNcJhUfDC97DyAOT3hU8fNBXqshtJlhPpYuPPb3Nk42pssd2BUNzc59Pp2R8oXL5porbr5r7K8W2bscV2A4Jwc/+ZVo98wdpZI4mL6QEUxWJdRaGyuejywrzk9+GHoVSIWs6nXaI5HACD53vTbuQK8hap7LLrzGgZFZQTVpbrX9NUlpu2XSrLZQcSs7OmM3v/ZNWMFx0VL72weiyiRvtB1OnyUqK2J3YsYt2ct4iLeQBwx+rxC7U6Pcrh9bMJDyqHttcBdRCvHFsZ8N7mZFeWO7JuBpfXjOLnQRHYNfT/yYfi7cdRvvEQ116si2S2QfGgb0BFw/udYP9lqSx3+/OyNEK41Oldf2CL7QoUAMpii23I6T1/OW+7Z5ljwOwPlMcWW4+jG38EKgM1gHzY43px6ejqFFWWO7VnOd85EuC6VoEhtW1cOLQ61dcmbq0sVyovvNZaKssJkZmd2P47tthmQCmgEHExHTmRRPW1E9t+Jy6mJfGV5eJiOnBsy0KiwsPQ9rZAXtDNsMV5p6iy3OW9C/m4UwS1ikKdYvBR+wiu7F2Q+osTAPy2Ayb1MQnoUlkuMRkIC5eyevmSsJKPxRKCh7fzNcZWjxy3trWG4O6TE1QQNxPggrFYPVNUWc7Ly/uWZIpTgW6SNOEiUllOiKzF0ycnqFsrXnokMZPr4ePniM8J2nr7oW0RgCNbjtgkE+uSYvXKxdkEhz3rqBYnXEMqy92ZVJYTLtWo7zv8M+1pbLH1UG5heHifoXLz6c7b9nuHNd+PxBZbH4tbMJ4+F2nYawY3zg8i9PpcbLEFcfPYRf3uKassV6P7GDrNfJ1nGkSyL8CTXTcK0LFeH1ddYrYWX1mu7RToVhmWHoYutaSynBCZVY32z3Bie3tio6PQdk+sHjto2Geu07a1O73AmT2diIuJRNutWD120rjfzxxeP5eTO6cTF1MRq8dJilRsnKLKcpU6vsXbX6/nTGAUNq2YudOLdiPecNUlZnvvPwBdZsDj9eFAAFyQynK3uOtAWClVHJgFFMLsqzVVa/3VbW1aAb8BpxxP/aq1HuPSnoq70nY7+/4ez6nNP2CxuFG+zctUav6Y07Z2u43tSz7i6Kb5WKwe1On6IpWaPJjqPhSr3JoSVVtz9sBKLG7u1O3+Nj45CxBwahv/znmVyJAACpZtSMuHv6BE1Q6Urd6YK0dWY/XwpFr3j8iRpzC9Xl/G4fWzCA++QtGKT1CsSpsU9aF84yHkyFeCxYdW4VE6Px0ffCjJnS/Cgy6x4stORAZfwGq1UqnzKKq3eTrVr0NUeCDb5z1DwMlt5MhVgNoPfEOBUnVTfdyMphTMePJmZbmnO8GDTVNW916kLYnZmcvpPX9w4Pe3iIkOp3iNbtTuNS7JZLcTOxaxddGH2GIjKVOnB436ved0G8qU8MtXgpodn2PXn1+i7TbK1OlFwTINCQ+6xJrvX+D6hf345S1Jq6FfkqdwJWq2H87JtROwazvF6wzAv1Rd/EvVpUilX7h+bj+5C3WlfKOBKZq8yFesGp1eXs2abQtAWej4cn9yFSjjtK3dbmfFhN4End4ISlGgcldaP578IkpJsdtt7Pn9Pc7smIfV6knFDv+jfKNBqT7u/eCJNlCmAPyzHxpWhWmtIYfMCP/nrslySqnCQGGt9U6llB+wA+iltT6YoE0r4BWtdbfknlgSL1zvwD/fELp1PD8NiCQyFvrO9aZ8zwmUqdsrUdudyz5j74p5xMX0ASJxc/+ZNsO+pGT1Tqnqw81kuR5ACG7u82n58Gf8O+clR2W5Eljc1uJfyo1CxUqR/8oCJvWM4mwQ9P/Rm+ZPLqJA6fSrYbnonUq0KnqNj7tp9l2CwXOhxfAFKR583+6frzvSLu8e3mwVy+az8NRvOej6xiZy5Cnqkn5ntmSNrOh+TZaTmJ15XDm5jfVTe7NgcCQlcsNTv3lxo9AD1Ov/RaK2l45t4K9vH8EWOwDIiZv7Eio1bUvj/qn7++VmstwATLLcr1RvN4AT2xYlqizXoPfbnP7zdX4ZEom3Owya70POhq9Tpc1zqepDSvwz9UHcL/7Fwoc10XHQcybkrjWMJgM+TtVx9y77AH1gEj/0jyQwEvrM8abW4O8pXrVdorYSfzOne06W01pf0lrvdHweChwCXPNuLlzq8p4FfNElkmqFTcWd99pGcmXfL07bnti+hLiYTkBBoBS22Gac3L401X24mSznT3yy3JGNc7lZWS6vo7LcZs7tXcrkXlGU94e25eGZhlGc37881X1ILrvdTlDgVb4fqCmTD3pWg17VFEc2Ob8tmFxxMRGcP7GLSb1jKZ0PBtWGFmXg8onNLuq5EEmTmJ15nN//F881iqRteVPFcnLPKM7tdR6HT+/+E1tsI6As4I8ttgundv2R6j6YZLnmQEmgIHExHTm+dbHTynIXts9mTLtI6peAaoXh8y4RXN6zMNV9SImQU2v5qqememGoVxw+6gJXD6T+vevinl/5pnsklQtCk1Lwv5aRXNr3W+o7LO57KUqWU0qVAmoDzuo3NlZK7VFK/amUcro4SCk1XCm1XSm1PSrsesp7K+7I6n1rwsHpIEuSCQfuXn4kTFRDBZtEiNT2wWmyXE7Hc7dWlvPwzHFrUluQO26pLN6REhaLBavbrUkEpwNJcs/iZB/XzQOU4nKoeWy3m3NYJWFPpDOJ2fc3q1dOTgfd3HfwXBB4JBEnPLz9sFhuTWpzRRKwSZYLvuW4Ht5+TivLuXnm4nTgzWHD2UBw80pdvEwxi+et7xs3APfUVT8F856Y8P3zVJAbFs90vjaRIZKdLKeU8gV+AUZqU4c3oZ1ASa11mFKqC7AYKH/7MbTWU4GpYG6z3WunhXOVO43ixUm9OXw1ivBYxZw9PnR8KfFekAANe/+Pvyc+SFzMZZQlGnfPI9Ro93mq+5AwWc7iSJZr2HsywQFDCb4yG1tsYawee6nV6Q1yFyxD37lP8VyjSE4FevD3qTx07p/6dcopUbxmb1pMXMRzTWDbecXeK+70evrtVB3T4malbpdXaTL5K4bViWDDOS8C3ctRt0pbF/VaiLuTmH3/q9jkIZZ9PIWH5gVSOk8MEzZ7U3eg8yqWlVs8yqF1s4mJ/AW7zRc39+006js51X1wlizXuN9cTuz4nWObpxEXUxmrx2kKlqlOrZ5j+eKLdlyNiCCHu2bKVi9aPf1uqvuQElW7j2XEvOc4chUiY2HmNmg5/JNUH7dy17E8Pm0QOy9GcT3CjQUH/ej06jMu6LG43yWroIZSyh1YCvyttb7raEkpdRqop7W+llQbWW/metpuZ8P81zm+9WeUslC93XPU6fJyku2vXzjAqZ1LcLN6UKHRQJesX42LieCX99sQev0UoKjQZCAthnxJXEwkRzbOJjzwMoXKN6JEtQ6AWSN3fv9fWL1zUbHxg3j55k11H1Jqz/KvOLtvGV6+BWg68FN8crlmC4Sz+/4m4OQmvHMVpWLTh7C6uy47QdaoZbz7dY0wSMzOTI5unsfmhe+ibVEUrtiatsOmJpksFxl6lSMb5xIbHU6pmp2dljy+FyunPsLpPX8DmjyFK9P7jZUoNzdO7fyNq2f3kKtAGSo0GoTFzUro9XMc3zoPbYujdN3e5ClcySV9SIkz+/5m38pvsFjcqNP1TZdV/rx2bi9n9yzFYvWifKNB5Mhd2Gk7ib+Z0z1XllMm9fMH4IbWemQSbQoBV7TWWinVAFiImW1I8uASVF1v74pv2blsBnExfYFYrB7zafHgh06T5dLKLx+2I/BCENAfCAF+oFG/d6jW+sl060N2IIE4492vA2GJ2ZnHlZPbWPb1QEeiWm7c3JdSrkEjmg9OXeJXSmxb8j57/v4OGAp4AT9SuHwFuo50nl8iJP5mVqmpLNcUeAhoo5Ta7fjoopR6Sin1lKNNP2C/UmoP8DUw8E4BVaSN49t+Jy6mPWbXpOLExbTkhAsS4FIi8OIRoA/xyXLQkqObf07XPgiRzUnMziTO7vsbW2x9TDJxfmyxXTm9O/UJcClxfOvvQAfik+WgJ1dO7krXPgiRke66RlhrvR6444aAWusJwARXdUrcGw/vWxPVUEF4+hRK1z5YLG7YbYGYwTjADTyTqCwnhHA9idmZh4e3Hxa3YG5WkA/C3TN946W7lzcQmOCZoFTvTSxEZiIllrOQBr3ewOqxHNQylOU33D23U7Pj8+nah1odnwHmAn8B84A9NOr/Ybr2QQghMoOKTR7CM8dFLG4LgOW4uf9Mo76pS9ZNqWaDxgMbMHmVy4BfqN/zlXTtgxAZSUosZwCtNQdXTeDEvxPRaMo2e5Kq7UamqBKPMwVK16Pnq8s4sXMxFouV8g0n4JevOIEXD7PmhxGEXj9DvmLVaTX06ySTAFKrVqcXuXR8C5eOrcNicaNu9zfJV7RKmpzLFWKiQvl3zstcPLIOrxz5aDZ4HEUqNMvobgkh7jPnD61m76JXiAoPpkjlNtTt/0Wqty/z8s1L37dWc2TTHGIiQylR7WUKlqlPdEQQa34YwZWTW/H2K0DLhz5Ns0JDhco2pGbHZ9m7chLabqdEtfZUaTEsTc7lClprdi77lEPrZqOUGzU7PEO11k9kdLdEJiYzwhng2Ka5BGz4mL8fCmDFw1e5vvlzjm743iXHzlOkEvW6vUGdLq/gl6840RHBLP2iN9fPlyIm8gkun3Djjy/7Yb95L86lNv/yHgGnroAeid02lJ1/fMWlYxvS5FyusGrGM5zdd5aYiGGEXG3I8kkPE3TlWEZ3SwhxH7lx8RCbvn+I7zqdZtvTgZQLX8q2ec+65Nhevnmp2f4F6vd4i4JlzGD370lDuXAokJiIYQRfqcWybwYSFnjBJee73Zm9f3Jg9Ry07QnQL3DxyCF2/ZW4st39Yv+qKexb+RNRoQOJDOnN9iVfcnybJPaJeycD4QxwZe8CPuoQQa2iULMIjO8YwZW9aVOd5+qZndjteYGGQB60vT3hQQGEp1FQPbXrD2yxXTDJcmWwxTbi9J6/0uRcqaW15sKhFdjjemKqJ1VF66pcPLw2o7smhLiPnD/4D0NqxdGlMpTJB9/1jebU3rSpghkXE8nVU5ux27pj4lJNoGyaTSic2P47cTHNgRLEV5Y7sf33NDmXKxzftoS4mHaYPJRijqTw+7e/4v4nA+EM4OaVmzOBN5dBnAlSWL1yp8m5PLz80PZgIH4GOBJtj8Ld0zfNzpcw8cJiCTKVi+5DSinc3H24mWCoUSrIUXVPCCEMDy8/TgfdXEl4NhC8vHzS5FwWqwfKYgEcpSmxA8GO2Op6zivL3Z8xGxz9va0q6v36HiMyB1kjnAEqd3iTsV+t5nxIFArN9B3etHvhzTQ5l3/JOhQsU50rJ2cSF1MSq8chKjR6KM0KVzTq+w4rpz11S2W5Ss1mpMm5XKFBr3fYsmgctpg6uFkDyJEHStfuntHdEkLcR8rU68Pfa76i35zLVCsQw8QtXtToPiZNzmWKRLzB7r+mEBdTAzf3S+Tyz02xNKpMWaP9s5zY3u6WynINe89Nk3O5Qv2er/HHl/2Ii72OUjasHgeo3enPjO6WyMSSVVkuLWT3zdlDrp7ixLYFaK0pW78fuQqUTbNz2W1xHN38EyEBJ8lfshala/dIdWLenVw9s5PTe/7E3TMHFZsMwdvPP83OtXflBA7/9T42WxzeuYvSYeTyJCvDnTvwDxvnv01MZDDFqral+eDxWD18uHB4DReOrMPHz5+KTR9KdQJMepAN3TPe/VpQI61k95gdExnCkY1ziAm7RuHKbdI8qfbc/hVcOr6JHLkLu7wy5e3CAy9wdPM8bHExlKnTk7xpmOB86dhGNs4YTExUGO6eOWjy6ByKVGzutG1wwAlWz3yBkKsnyF24Eq2HfmMSwC8f4cT2RVgsbpRvOAC/fCXSrL/OSPzNnO65slxaye5BVaTe2f3LWT9tEPMehGqF4JWlirUX/Ok99lCittcvHGDJJ92xxfYD/HGz/k2J6mVp+/iU9O+4C0ggzngyEBYiZaIiglj0dgXeamNjcG2YvwfGrLTQe8yRRHcpY6PDmT+qEVFh9UFXRVl24ZPrMA+M3oib1SODrsCQ+Js5paaynBD3paObfqR/TUX3qlA6H8wcoAkOCnDa9vzBf7DbagKVgHzY4npwdv/9mcQnhBBZ0fkD/5DP286bbaFkXnitNfjn0Jw/uDJR2xsXDmKL9QTdDJPo3YboiChCAk6mf8dFliYDYZFpefjk4uQNiL+pcSYQrG5JtPUyFZxuCsTqkTYJg0IIIRLzyVmAoChNeLR5HBEDgREaLyfL50yidygQ63gmCm0Lw10qlQoXk4GwyLTqdX+bXRfd6T5DMepvaD0Jitfu77Rt2fr98M4Zjpt1HqaC0xwa9X03fTsshBDZWKHyTfHIWYKGXyvGroCGXyvc/YpTpGLLRG1zF65I0crNsXrMAFZg9ZhBmbq98M1TNP07LrI02TVCZFo+Of3p8e5uNsx7hR0nrlK2YzdqtHvOaVsPLz/6vLmCIxvnEBV2g2JVXqBw+Sbp3GMhhMi+LBYLPd7axsYFr/Pt0X3kKVuNHg98jMWSeE5OKUXbx6dwfMt8gq4cJ2/RvpSt1zcDei2yOhkIi0zNJ1dB2j85O1ltPbxzUr3tM2ncIyGEEEmxWK00G/RZ8tpa3KjQeHAa90hkd7I0QgghhBBCZEsyEBZCCCGEENmSDISFEEIIIUS2dNeBsFKquFJqtVLqkFLqgFJqhJM2Sin1tVLquFJqr1KqTtp0V9yLGxcP8euHHZj1SkWWftmP8KCLGd0lIUQakZid+UWFB/L3pIeZ9UolFoxpwZWT2zK6S0JkWcmZEY4DXtZaVwYaAc8qpW6vv9gZKO/4GA5McmkvxT2Ljgjmjy/6cONCaWIih3PlhJU/vuyP3W7L6K4JIdKGxOxMbvmkR7hwKIiYyCcIvlKbPycMJCzwQkZ3S4gs6a4DYa31Ja31TsfnocAh4PaN/HoCs7SxGcitlCrs8t6KFLt6Zid2e16gIaY6T3vCgwIID5RZYSGyIonZmVtcTCRXT2/FbusO5AFqAmW5dGxDBvdMiKwpRWuElVKlgNrAltu+VBQ4l+DxeRIHXpRSw5VS25VS26PCrqewq+JemOo8wZhJIoBItD0Kd88cGdktIUQ6kJid+VisHiiLBQh1PGMHgvDwkopqQqSFZA+ElVK+wC/ASK11yO1fdvItOtETWk/VWtfTWtfz8s2Xsp6Ke+Jfsg4Fy1bH6jETU51nGpWaPYKXb96M7poQIg1JzM6cLBY36nR9A6vHNGAFbu6zyVUgD8WqtM3orgmRJSWroIZSyh0TUOdqrX910uQ8UDzB42KA3Hu/DyiLhY5Pz+bYlnkEB5zEv8QgStXuntHdEkKkIYnZmVutji+Qr1gVLh3fhG/utlRo8iBuVo+M7pYQWdJdB8JKKQVMBw5prT9PotkS4Dml1DzMYtRgrfUl13VTpIbFzUrFJg9mdDeEEOlAYnbWULxqO4pXbZfR3RAiy0vOjHBT4CFgn1Jqt+O5/wElALTWk4FlQBfgOBABPOryngohhEgOidlCCJFMdx0Ia63X43w9WcI2GnjWVZ0SQghxbyRmCyFE8kllOSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyEhRBCCCFEtnTXgbBSaoZSKkAptT+Jr7dSSgUrpXY7Pt51fTeFEEIkl8RtIYRIHmsy2nwPTABm3aHNOq11N5f0SAghRGp9j8RtIYS4q7vOCGut/wVupENfhBBCuIDEbSGESB5XrRFurJTao5T6UylVNalGSqnhSqntSqntUWHXXXRqIYQQ9+CucVtithAiq3PFQHgnUFJrXRP4BlicVEOt9VStdT2tdT0v33wuOLUQQoh7kKy4LTFbCJHVpXogrLUO0VqHOT5fBrgrpfKnumdCCCHShMRtIYQwUj0QVkoVUkopx+cNHMeUe2hCCHGfkrgthBDGXXeNUEr9BLQC8iulzgOjAHcArfVkoB/wtFIqDogEBmqtdZr1WAghxB1J3BZCiOS560BYaz3oLl+fgNmmRwghxH1A4rYQQiSPVJYTQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLMhAWQgghhBDZkgyEhRBCCCFEtiQDYSGEEEIIkS3JQFgIIYQQQmRLdx0IK6VmKKUClFL7k/i6Ukp9rZQ6rpTaq5Sq4/puCiGESC6J20IIkTzJmRH+Huh0h693Bso7PoYDk1LfLSGEEKnwPRK3hRDiru46ENZa/wvcuEOTnsAsbWwGciulCruqg0IIIVJG4rYQQiSP1QXHKAqcS/D4vOO5S7c3VEoNx8w+AIRNezbfERecPy3kB65ldCfSQFa9Lshm1zYtgzqSBjLzz61kRncgFZIVtyVm3xfk2u4zyYi/mfK6kikzX5vTmO2KgbBy8px21lBrPRWY6oJzpiml1Hatdb2M7oerZdXrArm2zCorX9t9LllxW2J2xpNry3yy6nVB1rw2V+wacR4onuBxMeCiC44rhBAibUjcFkIIXDMQXgI87MhCbgQEa60TLYsQQghx35C4LYQQJGNphFLqJ6AVkF8pdR4YBbgDaK0nA8uALsBxIAJ4NK06m47u+1uB9yirXhfItWVWWfnaMkw2jNtZ+f+RXFvmk1WvC7LgtSmtnS7nFUIIIYQQIkuTynJCCCGEECJbkoGwEEIIIYTIlmQgfBullJtSapdSamlG98WVlFKnlVL7lFK7lVLbM7o/rqSUyq2UWqiUOqyUOqSUapzRfXIFpVRFx88r/iNEKTUyo/vlCkqpF5VSB5RS+5VSPymlvDK6TyJzkpid+UjMzpyyatyWNcK3UUq9BNQDcmqtu2V0f1xFKXUaqKe1zqwbYSdJKfUDsE5rPU0p5QH4aK2DMrhbLqWUcgMuAA211mcyuj+poZQqCqwHqmitI5VSPwPLtNbfZ2zPRGYkMTvzkZid+WTluC0zwgkopYoBXclShbuyNqVUTqAFMB1Aax2T1QKqQ1vgRFYIqA5WwFspZQV8kD1sxT2QmJ35SMzO1LJk3JaB8K2+BF4D7Bncj7SggeVKqR2OsqlZRRngKjDTcXt0mlIqR0Z3Kg0MBH7K6E64gtb6AvApcBZT0jdYa708Y3slMqkvkZid2UjMzoSyctyWgbCDUqobEKC13pHRfUkjTbXWdYDOwLNKqRYZ3SEXsQJ1gEla69pAOPBGxnbJtRy3DnsACzK6L66glMoD9ARKA0WAHEqpBzO2VyKzkZidaUnMzoSyctyWgfBNTYEejnVZ84A2Sqk5Gdsl19FaX3T8GwAsAhpkbI9c5jxwXmu9xfF4ISbIZiWdgZ1a6ysZ3REXaQec0lpf1VrHAr8CTTK4TyLzkZidOUnMzpyybNyWgbCD1vpNrXUxrXUpzC2NVVrrLPHXjlIqh1LKL/5zoAOwP2N75Rpa68vAOaVURcdTbYGDGdiltDCILHSLDXNrrZFSykcppTA/s0MZ3CeRyUjMzpwkZmdaWTZu37XEssgSCgKLzP9drMCPWuu/MrZLLvU8MNdxO+okmb9c7H+UUj5Ae+DJjO6Lq2ittyilFgI7gThgF1mwbKcQqSAxO5PKijEbsnbclu3ThBBCCCFEtiRLI4QQQgghRLYkA2EhhBBCCJEtyUBYCCGEEEJkSzIQFkIIIYQQ2ZIMhIUQQgghRLYkA2EhhBBCCJEtyUBYCCGEEEJkS/8HpkostjcCGXEAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def plot_grid(X, y, fct, title, ax=None):\n", - " \n", - " cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])\n", - " cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue']) \n", - "\n", - " h = .05\n", - " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", - " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", - " xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, h),\n", - " numpy.arange(y_min, y_max, h))\n", - " Z = fct(numpy.c_[xx.ravel(), yy.ravel()])\n", - "\n", - " Z = Z.reshape(xx.shape)\n", - " if ax is None:\n", - " _, ax = plt.subplots(1, 1)\n", - " ax.pcolormesh(xx, yy, Z, cmap=cmap_light)\n", - "\n", - " ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,\n", - " edgecolor='k', s=20)\n", - " ax.set_xlim(xx.min(), xx.max())\n", - " ax.set_ylim(yy.min(), yy.max())\n", - " ax.set_title(title)\n", - "\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", - "plot_grid(X, y, dec.predict, dec.__class__.__name__, ax=ax[0])\n", - "plot_grid(X, y,\n", - " lambda x: numpy.argmax(net.predict(x)[:, -2:], axis=1),\n", - " net.__class__.__name__, ax=ax[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Le code qui produit les pr\u00e9dictions du r\u00e9seau de neurones est assez long \u00e0 ex\u00e9cuter mais il produit \u00e0 peu pr\u00e8s les m\u00eames fronti\u00e8res except\u00e9 qu'elles sont plus arrondies." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interm\u00e8de de simples neurones de r\u00e9gression\n", - "\n", - "Avant d'apprendre ou plut\u00f4t de continuer l'apprentissage des coefficients du r\u00e9seaux de neurones, voyons comment un neurone se d\u00e9brouille sur un probl\u00e8me de r\u00e9gression." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAewAAAEICAYAAACd/8f0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqqklEQVR4nO3de7wdZX3v8c+XnQ3ugLqhxNpsEsAWQSlgNIJ9pbaCUhAspPQiKFVbLaWnWOVgarzUQqWHtOlReR0vHKrIq0cqWMAYBRttA7ZFoSQGxAixyC3ZQQ1KsECAXJ7zx8zaTFZm1nXWXNb6vl+vvNh7rdlrZtZazG+e3/N7nkchBMzMzKza9ir7AMzMzKw9B2wzM7MacMA2MzOrAQdsMzOzGnDANjMzqwEHbDMzsxpwwLaRJulmSe8oeJ+vlrShgP28RtKmxO/rJb2my9eYkPRNSad08TePS3pRN/sxs/YcsK1vkh6Q9CNJ+yYee4ekm0s8rMoKIfx7COHwTrZtDrp97vfIEMLNXf7Z/wX+LoRwYxf72S+EcF+X+zGzNhywLS+zgHeVfRCWrxDCW0II1+f1epLG8nqtHvY9q+D9lXauNpwcsC0vy4H3SJpsfkLSIZJC8oKZTEVL+kVJqyX9RNIjkq5Kvo6kl0taJ+m/Jf2TpGskXZx4/g2S7pC0NU7fHp11kJJOlHSPpMckfRxQ0/N/KOluSY9KWiXp4IzXaZzTOZI2S3pY0gWJ5/eR9LH4uc3xz/vEzzWnqh+Q9B5J34mP6xpJz4kzFl8F5sZp5sclzZV0rKQ1kn4WZzY+kv2x7HbMD0h6XfzzhZK+IOkf4vd1vaSFiW3nSrpO0hZJ90v6s8Rzx0r6Vvx+Pyzp45L2TjwfJP1S/POVkj4l6UZJTwDHt3rtlGO+UtJlkr4eH+c3kp+JpEslbYzfi7WSXp147kJJ10r6nKSfAW/LeP1PSvpq/P7eIumF8ef1aPxdWZDY/iXxd3dr/J6d1vRazec6T9L18bn+JP7ONbbP/K61Oa8rtfv3f+b7pOj/pZ9Kennic3xEXXaFWDU5YFte1gA3A+/p4W8FXALMBV4CzAMuBIgDwReBK4EDgM8DvzXzh9GF6Qrgj4GfI0rhrmwEx912Ih0IXAd8EDgQ+AGwKPH8YuD9wBnAHODf4/21cjxwGPAbwNJGQAQ+ALwKeBlwDHBsvN8svwecDBwKHA28LYTwBPB6YHOcZt4vhLAZuBS4NITwPOAXgS+0OcYspwFXA5PASuDjAJL2Ar4M3AlMAa8F3i3ppPjvdgLnE72HvxI//z9a7OdNwF8DzwW+2ea107wZ+HC8vzuAqxLP3U70Hh8A/CPwT5Kek3j+dODa+ByTf5f0ezz7nXga+Bbw7fj3a4GPAEgaj4/9a8ALgHcCV0lKdm8kz/VbwFeAB4FD4vO9On6txbT+rrU7r1QhhB8A742PazbwWeDKHrpCrIpCCP7nf339Ax4AXgf8MvAY0QXoHcDN8fOHAAGYlfibm4F3ZLzeYmBd/POvAdOAEs//B3Bx/POngA83/f0G4NdTXvctwK2J3wVsahwHUWv27Ynn9wKeBA5Oea3GOR2ReOxvgc/EP/8AOCXx3EnAA/HPrwE2Nb1/Zze9zmVp28aP/RtwEXBgm88lbT+vi3++EPiXxHMvBbbFPx8HPNT0Wu8DPpuxn3cDX0z8HoBfin++EviHxHPdvvaVwNWJ3/cjumGYl7H9o8AxiXP8tzbv0ZXA3yd+fydwd+L3o4Ct8c+vBn4I7JV4/vPAhRnn+ivAFhLf+8RzHX/XUs7rSuLvf4vvyErgLuA7wD6t3gP/q88/t7AtNyGE7xK1KJZ283eSXiDpaknTceryc0StG4ha3dMhvgrFNiZ+Phi4IE5RbpW0laiFPjdlV3OTfxu/ZvNrXZp4nZ8SBfWpFoef/PsHE/udG/+e9lyaHyZ+fpIoMGV5O/Bi4B5Jt0t6Q4ttW2ne53MUdVscTJSGT76n7wd+HkDSiyV9RdIP48/rf/Hs55Wm+T3OfO12fx9CeJzoc5kbH8sFcVr5sfi1nt90LMl9Z/lR4udtKb83Pou5wMYQwq7E8w+y+/cjub95wIMhhB0p+2z5XevgvNr5e6Ib6P8TQni6i7+zCnPAtrz9JfBH7H4ReyL+7+zEYy9M/HwJUavs6BClec/m2b7lh4EpScm+5nmJnzcCfx1CmEz8mx1CSEtlP5z82/g1m1/rj5teayKE8M0W55v8+/nA5vjnzUQX5bTnurHHcnohhP8KIZxFlJb9G+BaJSr0c7ARuL/pfXhuCKExtOtTwD3AYfHn9X6aagFanEO7106T/Mz2I0oTb477dd9LlNLeP4QwSZThSR5LnssRbgbmxV0GDfOJMkBp+9sIzFd6sVvmd62D83qC7P+XGu/Rx4DPABdKOqDL87SKcsC2XIUQ7gWuAf4s8dgWoova2ZLGJP0hUd9rw3OBx4GtkqaAJYnnvkWUAj1P0ixJpxP1Bzf8PXCupOMU2VfSqZKem3J4NwBHSjojvoj+Gbtf7C4D3ifpSABJz5f0u21O+S8kzY7/5g/ic4coVfpBSXPivvMPEWUOuvUj4OckPb/xgKSzJc2JW3pb44d39vDaWf4T+Jmk9yoahz0m6ZclvTJ+/rnAz4DHJR0B/EmOr53mFEm/GtczfBi4LYSwMT6OHcRpZ0kfAp7X5bl24zaiYPnnksbjQq7fJO6XTvGfRDeJy+Lv5XMkNWomWn3X2p3XHUTvyQGSXkjUJZF0KbA2hPAOou/8ZT2er1WMA7YNwl8BzS2+PyIKxD8BjiQqPmq4CHg5USviBmBmGFEI4Rmiwpy3EwWns4nS7k/Hz6+JX/vjRP1895JSDRxv+wjwu8Cy+DgOA25JPP9Fohbr1XGq97tERV+tfCPe578SjVf+Wvz4xUSFeN8h6kv8dvxYV0II9xAF//vi9OlcouK09ZIeJ7o4nxlCeKrb126xz51EgehlwP3AI8CnidKyEBUWvgn4b6Ibpmv2fJWeXzvNPxJlbn4KvIKoCA1gFVFf8PeJUtNP0VkKvCfxd/E0ou/EI8AngbfEn1Ha9o1z/SXgIaJ6iTfGz7X6rrU7r/9HVLT3AFEB3Mz7H9/QngycGz/0P4GXS3ozVnvavWvQrPok3UZUlPXZEo/hEKKAM57RR2k5kHQlUUFVqwp7s5HgFrZVnqRfVzQ2dpaktxINe/rnso/LzKxIhc78Y9ajw4nGGu9HNFzqd0IID5d7SGZmxXJK3MzMrAacEjczM6uBSqfEDzzwwHDIIYeUfRhmZmaFWLt27SMhhDlpz1U6YB9yyCGsWbOm7MMwMzMrhKQHs55zStzMzKwGHLDNzMxqwAHbzMysBhywzczMasAB28zMrAYqXSVeRyvWTbN81QY2b93G3MkJlpx0OIsXtFpO2czMrD0H7BytWDfN+66/i23bo5UOp7du433X3wXgoG1mZn1xwM7R8lUbZoJ1w7btO1m+asNAArZb82Zmo8MBO0ebt27r6vF+uDVvZjZaXHSWo7mTE1093o9WrXkzMxs+Dtg5WnLS4UyMj+322MT4GEtOOjz3fRXZmjczs/I5YOdo8YIpLjnjKKYmJxAwNTnBJWccNZAUdZGteTMzK5/7sHO2eMFUIX3Ixx8xh6tufYjkauaDas2bmVn53MKuoRXrprlu7fRuwVrAb7+imJsFMzMrngN2DaUVnAXgpnu2lHNAZmY2cE6J10DzeOvpARaceWy3mVk1OWBXXNp4a8Fu6fCGfgvOPLbbzKy6ckmJSzpZ0gZJ90pamrHNayTdIWm9pG/ksd9RkJX+VtN2eRSceWy3mVl19d3CljQGfAI4EdgE3C5pZQjhe4ltJoFPAieHEB6S9IJ+9zsqstLcgWjYWJ6pa4/tNjOrrjxS4scC94YQ7gOQdDVwOvC9xDZvAq4PITwEEEL4cQ77HQlZfdZTkxPcsvSEQvblsd1mZuXLIyU+BWxM/L4pfizpxcD+km6WtFbSW3LY70gocva0bva1Yt00i5at5tClN7Bo2WpWrJvO/XjMzOxZebSwm7tTYc+aqFnAK4DXAhPAtyTdGkL4/h4vJp0DnAMwf/78HA6v3hpp7iIqtzvdl4vTzMyKl0fA3gTMS/x+ELA5ZZtHQghPAE9I+jfgGGCPgB1CuBy4HGDhwoVpxdAjp6jZ0zrdV9HLiJqZWT4p8duBwyQdKmlv4ExgZdM2XwJeLWmWpNnAccDdOezbSuDiNDOz4vXdwg4h7JB0HrAKGAOuCCGsl3Ru/PxlIYS7Jf0z8B1gF/DpEMJ3+923lcPFaWZmxctl4pQQwo3AjU2PXdb0+3JgeR77s3ItOenw3fqwwQuPmJkN2kjMdObpNvNVZCGcmZlFhj5gu6J5MIoshDMzsxEI2K5ofpYzDWZm9TX0AdsVzZFuMw0O7mZm1TL062FnVS6PWkVzNwt7NIL79NZtBJ4N7p7NzMysPEMfsIuc2rPKusk0eNUuM7PqGfqAvXjBFJeccRRTkxOIaNGMS844auTSu91kGtyNYGZWPUPfhw2uaIbuxk57YhQzs+oZ+ha2RbrJNLgbwcysekaihV11RVVkd5pp8MQoZmbV44BdsqpO7NIctBsFZw7aZmblcEq8ZFWtyPbQLjOzanHALllVK7KreiNhZjaqHLBLVtWJXap6I2FmNqocsEtW1Yrsqt5ImJmNKgfsklV1Ypeq3kiYmY0qV4lXQBkTu2QNJUs+Pjl7nH1m7cVj27Z7aJeZWckcsEdQ1lCyNQ/+lOvWTs88/uiT25kYH+Ojb3yZA7WZWcmcEh9BWRXgn79toyvDzcwqygF7BGVVeu8MoavtzcysOA7YIyir0ntM6mp7MzMrjgP2CMqqAD/ruHmuDDczqygXnY2gVot7LDz4AC768noefXI7APvM8j2dmVkVOGCPqFZDyZ7avmvm563btldiMRIzs1HngF2iopbV7EarOcTLPjYzs1HmgF2Sqi6rmfcc4lW8KTEzqyN3UJakqqth5TmHuJfoNDPLjwN2Saq6GtbxR8zp6vFWqnpTYmZWR06Jl2Tu5ATTKcG57DHPN92zpavHWynypiSP1LvT92ZWZW5hl6Sqq2HlGWSLWqIzj9S70/dmVnUO2CUpalnNFeumWbRsNYcuvYFFy1a3DUB5BtmibkrySL07fW9mVeeUeIkGvaxmL5XoS046fLe/gfZBNiuV3GqCljzlkRWoak2BmVmDA/YQ62VMdbdBtt1NQRFrfedRD1DVmgIzswYH7CHWa6uxmyBbhYlWeskKDOI1zMwGyQF7iA261bhi3XTq60OxqeQ8Uu9Fpe/NzHrlgD3EBtlqbKTCsxSdSs4j9V5E+t7MrFe5VIlLOlnSBkn3SlraYrtXStop6Xfy2K+1NshK9LRUeINTyWZm+eu7hS1pDPgEcCKwCbhd0soQwvdStvsbYFW/+7TODarV2CrlPYjhaWZmoy6PFvaxwL0hhPtCCM8AVwOnp2z3TuA64Mc57NNKlpXynpqccLA2MxuAPAL2FLAx8fum+LEZkqaA3wIua/diks6RtEbSmi1bup8O04pR1ZnazMyGVR4BWymPhabfPwa8N4SQ3umZ/MMQLg8hLAwhLJwzp/sFJ6wYRc3UZmZmkTyqxDcB8xK/HwRsbtpmIXC1JIADgVMk7QghrMhh/1aSXvrHvcCGmVlv8gjYtwOHSToUmAbOBN6U3CCEcGjjZ0lXAl9xsB49vUyVamZmkb4Ddghhh6TziKq/x4ArQgjrJZ0bP9+239qG04p101z05fU8+uR2ACQITZ0lRc+KZmZWV7lMnBJCuBG4semx1EAdQnhbHvu0aluxbpol197J9p3PRujmYN3gBTbMzNrzTGfWt7R+6eWrNuwWrFvpZVY094Wb2ahRyGr2VMDChQvDmjVryj4Ma6G5Xxqi4V1Zs6A1mxgf45IzjgJ6XyEs+ToO2mZWZ5LWhhAWpj3nFrb1JWu1rjGJnRk3g43nxiS2bd/JhSvX88QzO2Za5O2K0aqwQpiZWdFymUvcRldW//POEBgf23OI/vhe4qzj5jExPjYT0Ldu275H+rwRgLvZp/vCzWyYOWBbX7L6n/efPc7y3zmG/WePzzw2OTHO8t89hpvu2dJRyjwrAGfts+gVwszMiuSUuM3opZBryUmH71ENDvD4UzsAWPeh39jjb86/5o6OjmcyEeyb9zmoZUPNzKrKLWwDni3kmt66jcCz/cgr1k3PPL9o2WoOXXoDi5atnnl88YIp9t17z/u+7btCZkq705bw40/tmNlPkqdFNbNR5Ba2Aa0LuYCWM5Q9tm176mtmpbTTWshpGkE/LRAPatlQM7OqcgvbgNaFXO2Cebd9ymkt5G6Py8xs1DhgG9A66Laryu5lqc3FC6a4ZekJ3L/sVJacdHjqkm8Q9WOnpeLNzEaNU+IGtC7kWr5qA9MpQbsR5Bup6U4K1rJmRcuavufxp3bMzEXuxULMrFPDOBuiA7YB7YNuu6rsTvqUs1bratWXvX1X+vjsuv+PZ2aDM6wrAzpg24ysoNtNC7qVXmZFS+N+bTNrZVhnQ3TAto7kUZXdala05vnHJ8bH2GfWXmxNqUD3BClm1sqwzoboojMrTFagbYyjbh5XfeFpR3ZdzGZmNqyzITpgW2GyqsmPP2JOarrdE6SYWS96GblSB06JW2HS+sKPP2IO162dziwO8QQpZtatvOpuqsbrYVupFi1bnTpkbGpygluWnlDCEZVnGIehmFl3vB625SbvoFKH4pAiAumwDkMxs/w4YFvHBhFU5k5OZE7K0k2gHFRQLSqQDuswFDPLj4vOrGNZQeWiL6/v6fVWrJvmiad37PF4oxCt1ephza/T6bbdajePel7qkGkws3I5YFvHsoLHo09u7zo4NoJs8zjr/WePc8kZR3HTPVs6DpSDDKpFBdJhHYZiZvlxwLaOtQoejeCYtW522vZpU5LO3nsWixdMdRUoBxlUiwqkwzoMxczy44BtHWsVPDZv3dZVarpdkG0VKJtvCiZnj2du26+iAqnHnJtZOy46s44tXjDFhSvXZ04X2k3hVKtiM8hePazRt50sAkuTV1Atcjynx5ybWStuYVtXWk0X2k1qul3LNavFmda33WxyYjzX1mly7e5blp7goGpmpXAL27rSqsXZbt3sTl8nuU1zcDz/mjvaHuO++8xyUDWzoeOAbV3LSt1mpbGzUtO9pICzUulJHgplZsPIKXHLTRGFU2mp9GYeCmVmw8gtbMvVoAunkqn06a3bEJCcDd9DocxsWDlgW1/KWLAieVPgBTPMbFQ4YFvPqrBghYdCWV345tL65YBtPfOCFf3zRXw0VOHm1urPRWfWMy9Y0Z9BLlpi1VLUIjI23BywrWdesKI/voiPDt/cWh5yCdiSTpa0QdK9kpamPP9mSd+J/31T0jF57NfK5QUr+tPvRbzThVasfL65tTz0HbAljQGfAF4PvBQ4S9JLmza7H/j1EMLRwIeBy/vdr5XPC1b0p5+LuNPpz6rDjYtvbi0PeRSdHQvcG0K4D0DS1cDpwPcaG4QQvpnY/lbgoBz2axVQdpV2nYu2up0ZLskFf5G6FHMVuYiMDa88AvYUsDHx+ybguBbbvx34ataTks4BzgGYP39+Dodnw6ouF+ss/VzE3ScaqdONS9k3t1Z/eQRspTwWUh5D0vFEAftXs14shHA5ccp84cKFqa9jBvW6WGfp9SLebnnSUeEbFxsleRSdbQLmJX4/CNjcvJGko4FPA6eHEH6Sw35txI3yxdp9ohEXc9koySNg3w4cJulQSXsDZwIrkxtImg9cD/x+COH7OezTbKQv1i74i/jGxUZJ3ynxEMIOSecBq4Ax4IoQwnpJ58bPXwZ8CPg54JOSAHaEEBb2u28bbf0UbQ0D94m6mMtGi0KobjfxwoULw5o1a8o+DKuwOleJF8nvU7358xsdktZmNWg9l7jVRtZFyxeu1upeTT/q/PlZg6cmtVrwRCG98xSo9ebPzxocsK0WfNHq3ShX0w8Df37W4IBtteCLVu+yquYDVHYqT3vWKI+GsN05YFst+KLVu7ShTw1Fdy3UYd7vVso4fg9dswYHbKsFX7R6lxyznaaoroW61yGUdfwec28NHtZlteGhLf07dOkNqfMGC7h/2aldvVa3n8eiZatTp1OdmpzglqUndLXvMtT9+K0ePKzLaiktIPjC2J+85iDvZahR3esQ6n78Vn9OiVsl1T19WlV5dS30UrVf9zqEuh+/1Z8DtlWSh3ENRl79ob20Nuteh1D347f6c0rcKsnpx8HJY3a4XlLrdZ/3u+7H3w/Xj1SDA7ZVktd7rrZeF16p+1Sy3Rz/sAS5YZoate6fiVPiVklOP1abhxq1Nkw1GMPSPTUMn4lb2FZJo5x+LEu3rY+6t5YHqVWQq9t7NizdU8PwmThgW2U5IGTLO7U3TGnPKhiWIAfD0z01DJ+JU+JmNZOW2jv/mjv44Iq7en7NYUl7VsUwDQEblu6pYfhMHLDNaiYtuAbgqlsf6rk/bhhaH1UyLEEOqlWv0M9c7mmfiYDjj5iT81EOjlPiZjWTFUQD9NwfV1baMyu1X/dq3mGrwahC91S/3TaLF0yx5sGfctWtD81MzxuA69ZOs/DgA0o/v044YJvVTFZwhc5axGnBsNdhWv3IugCvefCnXLd2uvb96VUIclnqeEOUR9HYTfds2WMu/ToVnjklblYzS046HGU8t5fUMl2YNbQFKDztmXUB/vxtG92fnpO0FHJdhzfl0W1T964ft7DNKqZd6ycttdewM159L6tV2qqVcsvSEwptZWRdJHdmrCBYl4tqVWRlMPaZtVcthzfl0W1T94p3t7DNKqTT1s/Fi4/io2982UyLeEx7trnTWqVVamFkXSTTzqXV9pYu6+Zs67btqdtX/YYoj0K+uhcDOmCbVUg3w6sWL5jilqUncP+yU9nVYat0cvZ46nZlBMO0iydELeyxvXYP2nW6qFZFtwG46jdEeVSrV6nivRdOiZtVSK8t4E5SfSvWTfP4Uzv22GZ8TKnBcNCFSY3X+sAX7+KJZ3a/Sdm5K7Dv3mM8+czO2hRFJVWhqCvrO7H/7HGe2r6r0ALDvORRyFflYsB2HLDNKqTbPrZGYJjeug3Bbn3azRfh5as2sH3Xni3xffeetccFrKiZzxYvmOKCL9yZ+txT23dx/7JTc9tXUYp679rdFGRV/v/lbx4JDM+Qs1HigG1WId0Mr2oODAFmgvZUykU4q5X+WEqfZpHzLmcVmWU9XnVFvHed3BS0GwvuAF0/DthmFdLNhBtZM55NTU5wy9IT9ti+m9Z7kcVpY1JqcM4qPhukPFLZ3bx3ve6v05uCOqd/bU8O2GYV0+lFttug2k3rvcjhL2cdN4/P3fpQ6uNFyiuV3el718/+qlTtb8VxlbhZTXW7mEE3FbJFDn+5ePFRnP2q+TMt6jGJs181n4sXH5W6fT/zSbeS1wIonb53/exvkAtZDOr9tf45YJvVVFpgGB8TTzy9I/Vi2036tejhLxcvPoofXHIKDyw7lf/9e8dw0z1bMs9hULN05dVq7fS962d/g7qhKmIWNN8Q9M4pcbOaau7vnpw9zuNP7ZiZGCOZYgW6Tr82p+YbF9pBVha3SxMPsqCr1wr9rIKudsfTT7fDoBYXGXTBnNdd748DtlmNJQPDomWrefTJ3Su+kynWfi7ERV1o2wWMQfbd9lOh38v70e+CK90WlHWSYRl033iRow+GkQO2WQk6TU93k8bu5WLb6YW4qAttu3MYZDFcvxX63b4fRS7B2ekNxqCLDV0s1x8HbLOCdXrx7LYV1+5i28+FuKgLbbtzGPQyoIOq0O93f/3q9AZj0O9v3RffKJuLzswK1ml1cLdVxK0KkfotUhpkVXJSu+PMqxiu38Knot6PvHR6gzHoYsO6L75RNrewzVoYxJzQnV48u23FdZJi7fVcBt3y6uYc2rVK231mVeh/Llo3LdtBFhvm0Q3wwRV38fnbNkaLxEicddy8zCGAwyaXgC3pZOBSYAz4dAhhWdPzip8/BXgSeFsI4dt57NtsULq5sHcT2Du9ePaSPmwVzPpJvxbZ39rPcXbymdWt/zkPvd5gDKLYsJ/P94Mr7tptkp2dIcz8PgpBu++ALWkM+ARwIrAJuF3SyhDC9xKbvR44LP53HPCp+L9mldXphb3bi1qnF8+8WnF5ZQmyLrRVWJmqoZPPrG79z3no9QajalXdn79tY+bjzQG7k0xLVb63ncqjhX0scG8I4T4ASVcDpwPJgH068A8hhADcKmlS0i+EEB7OYf9mA9Hphb3bi1qnF888WnGDHo71wRV3cdWtD82sElb2uNpOPrNRLXzq5QajalXdnS4U0+57X9fx4HkE7CkgeduziT1bz2nbTAF7BGxJ5wDnAMyfPz+HwzPrTacX9l4uap1ePJPbNVoE519zRyVaSCvWTe8WrPN+/V508pnVrf+5TP3c3AyiBdvpQjHtvvdVyxx0Ko8q8bQldZrf0U62iR4M4fIQwsIQwsI5c+b0fXBmveq0orWIiuFep4wcZAvpoi+vT/+fOKfX70Unn1nR067WWdr7CfDE0ztafvcGNcVp1oIwzY+3+95XLXPQqTxa2JuA5Lt1ELC5h23MKqXTlHQRLbZeWwRZLaS9JFasm+6ruKt5VrXm/Ta2K7KfsJvuBgfo1hqf3bbtO9lLsCtxd7Z12/aWKeRBtWAb/dTtqsTbZQbq2i2SR8C+HThM0qHANHAm8KambVYC58X928cBj7n/2uqgkwt7ERXDvbYI0m4mIOrz66fPrtWKUor3u2LdNEuuvZPtO6Mr/fTWbSy59s6e99mpUQ/GedwkNffx7kpJpbQKwINswV68+Ki2FeHtbqLr2i3Sd8AOIeyQdB6wimhY1xUhhPWSzo2fvwy4kWhI171Ew7r+oN/9mlXJoINEry2CxjFd8IU79+j766fF0+rC++ZXzWfxgikW/NXXZoJ1w/adgYu+vH6kA+og5VVMldZCTpP1PcirBdvrzUe7m+i6DctryGUcdgjhRqKgnHzsssTPAfjTPPZlNor6aREsXjDF+dfckfpcry2erAvy5MT4TOsnK2XeKpVu/ckrFd3p9yIrAB9/xJzdxksnH09qFZD7vflodxNdx0yMZzozq4F+WwTtWjzdtmSybiAuPO3Ibk7LcpZXKjrr+5LUuGFM++7cdM+W1L9JPl7mUqp15YBtVhP9tAhatdB7acl0cgMxOTE+szZ30uTEeE/nYO3llYpO+76Mj4l9957FY9u2z3zekL7OelY6PXnjUOZSqnXlgG02AloF2EXLVvfUkml3A3HhaUey5J/uZHuiYml8L7kVPkDHHzFnj7HxvRRTdZrRyfruZI2XTt44lLmUal05YJuNiKwAm3XhbJcS7WR/UL/CnrpasW6a69ZO7xasBfz2K3rLzHSS0cn67uwMgYnxsZY1F2UvpVpHDthmIy7rwinoa6w21LOwp67SUswBMvuT85D13ZmKb85a3ay1C8iNbS9cuX6ma+U546O9IrQDttmIW3LS4Zx/zR17zFoWYKQLfOqm3z7fXoZQtQq6nVRpQ/sMzNM7ds38/OiTrSdsGXYO2GYjbvGCKd6d87AvK16/8373MoSq326PdkHdleK7c8A2M6Zc4FN7nfT5ZrWi+wmMg+z2cKX47ka7Q8DMgM4XOrHqareoSasFOaoaGItYWKdO3MI2s6Gs6C564ZEqaNXabdWKruoQKleK784B28yA4arozmtO7WHSqhX90Te+LHWilCee3sGhS28o7YZnGG8k++GAbWZDZxSKlbrNILRqRTcHxsnZ4zz+1I6Z4VRl3vAM041kv9yHbWZDp6p9snlp1R+dpV2dwuIFU9yy9ATuX3Yqs/eetdsMdfDsDY+Vxy1sM8tFXn3GebxOVftk89JLBqG5Ff38iXEkOP+aO1i+asNu73O7G55RrA+oAgdsM+tbv33GjQAwvXUbgplJXHpNxQ57sVKvGYRGernd59Xqhqefz9qBvj9OiZtZ31q1+NpJpneBPWZc6yUV226IU931O9yp3efVKn3e62fdSxq/ylasm2bRstUcuvQGFi1bXch5uIVtZn3rp884LQD08jrNhrlYqd8MQrvPq1V19vk9zoo3TIWAZY1CcMA2s77102fcSTAelr7nvHQy3KlV+rmTzyvrhqfXz3qYCgHLuvlwwDazvh1/xBw+d+tDqY+3kxUAGoap77mdbvp4W2UQ2rUA+2mh9/q3w1QIWNbNh/uwzaxvWUs4drK0Y1p/qeL/Dlvfcyt59vG262fup4+/178dpulvy5oy1S1sM+u7ereTFkfWPjybVSTPNGsnn0c/ffzJavPlqzakDg1L+xsYjs+5rFEIDthmIy6PApp26c52+xjmArFO5ZlmHWT6uZ8heMPyOZd18+GUuNmI62dIVkO7dGce+xh2eaZZB5V+HsQQvLpKzgx3y9ITCrkRccA2G3F5tOza9WsOU4XwoOQZZAc1Dn1QQ/CsM06Jm424vNKnrdKdw1QhPCh5p1kHkX72ELxyOWCbjbgiCmiGfarQvAyqjzevKUE9BK9cTombjbgipvEc9qlCqyzP4WIeglcuhdBcNlAdCxcuDGvWrCn7MMzMamvRstWpreKpyQluWXpC16/nBTwGS9LaEMLCtOecEjczG2J5F/wNy9CsOnJK3MxsiJU1K5flzy1ssyE3yBSm06PV54K/4eGAbTbEBrkMYFlLDFp3hmlK0FHngG02xAa5DOAwrW887NzvPBzch202xAY5w5hnLzMrlgO22RAbZMGRi5nMitVXwJZ0gKSvS/qv+L/7p2wzT9JNku6WtF7Su/rZp5l1bpBrEA/T+sZmddBvC3sp8K8hhMOAf41/b7YDuCCE8BLgVcCfSnppn/s1sw4McoYxz15mVqy+ZjqTtAF4TQjhYUm/ANwcQmh5ey3pS8DHQwhfb/f6nunMzMxGSauZzvptYf98COFhgPi/L2hzIIcAC4DbWmxzjqQ1ktZs2bKlz8MzMzMbDm2HdUn6F+CFKU99oJsdSdoPuA54dwjhZ1nbhRAuBy6HqIXdzT7MzMyGVduAHUJ4XdZzkn4k6RcSKfEfZ2w3ThSsrwohXN/z0ZqZmY2oflPiK4G3xj+/FfhS8waSBHwGuDuE8JE+92dmZjaS+g3Yy4ATJf0XcGL8O5LmSrox3mYR8PvACZLuiP+d0ud+zczMRkpfU5OGEH4CvDbl8c3AKfHP/8Gza5ybWY15sQ8Dfw/K4rnEzawjXuzDwN+DMnlqUjPrSKvFPmx0+HtQHgdsM+uIF/sw8PegTA7YZtYRL/Zh4O9BmRywzawjXuzDwN+DMrnozMw60igocnXwaPP3oDx9Lf4xaF78w8zMRskgF/8wMzOzAjhgm5mZ1YADtpmZWQ04YJuZmdWAA7aZmVkNVLpKXNIW4MGyjyPhQOCRsg+iRD7/0T3/UT538Pn7/Is7/4NDCHPSnqh0wK4aSWuyyu1Hgc9/dM9/lM8dfP4+/2qcv1PiZmZmNeCAbWZmVgMO2N25vOwDKJnPf3SN8rmDz9/nXwHuwzYzM6sBt7DNzMxqwAHbzMysBhyweyDpnZI2SFov6W/LPp6iSXqPpCDpwLKPpUiSlku6R9J3JH1R0mTZx1QESSfH3/d7JS0t+3iKJGmepJsk3R3///6uso+paJLGJK2T9JWyj6UMkiYlXRv/v3+3pF8p61gcsLsk6XjgdODoEMKRwN+VfEiFkjQPOBF4qOxjKcHXgV8OIRwNfB94X8nHM3CSxoBPAK8HXgqcJeml5R5VoXYAF4QQXgK8CvjTETt/gHcBd5d9ECW6FPjnEMIRwDGU+F44YHfvT4BlIYSnAUIIPy75eIr2UeDPgZGrVgwhfC2EsCP+9VbgoDKPpyDHAveGEO4LITwDXE10wzoSQggPhxC+Hf/830QX66lyj6o4kg4CTgU+XfaxlEHS84BfAz4DEEJ4JoSwtazjccDu3ouBV0u6TdI3JL2y7AMqiqTTgOkQwp1lH0sF/CHw1bIPogBTwMbE75sYoYCVJOkQYAFwW8mHUqSPEd2g7yr5OMryImAL8Nm4W+DTkvYt62BmlbXjKpP0L8ALU576ANF7tj9ReuyVwBckvSgMyfi4Nuf+fuA3ij2iYrU6/xDCl+JtPkCUKr2qyGMriVIeG4rvejck7QdcB7w7hPCzso+nCJLeAPw4hLBW0mtKPpyyzAJeDrwzhHCbpEuBpcBflHUw1iSE8Lqs5yT9CXB9HKD/U9IuoonhtxR1fIOUde6SjgIOBe6UBFE6+NuSjg0h/LDAQxyoVp89gKS3Am8AXjssN2ltbALmJX4/CNhc0rGUQtI4UbC+KoRwfdnHU6BFwGmSTgGeAzxP0udCCGeXfFxF2gRsCiE0sirXEgXsUjgl3r0VwAkAkl4M7M0IrGITQrgrhPCCEMIhIYRDiL7ILx+mYN2OpJOB9wKnhRCeLPt4CnI7cJikQyXtDZwJrCz5mAqj6O70M8DdIYSPlH08RQohvC+EcFD8//uZwOoRC9bE17eNkg6PH3ot8L2yjsct7O5dAVwh6bvAM8BbR6SlZfBxYB/g63GW4dYQwrnlHtJghRB2SDoPWAWMAVeEENaXfFhFWgT8PnCXpDvix94fQrixvEOygr0TuCq+Yb0P+IOyDsRTk5qZmdWAU+JmZmY14IBtZmZWAw7YZmZmNeCAbWZmVgMO2GZmZjXggG1mZlYDDthmZmY18P8B5d7LN+pjDn8AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "regX = numpy.empty((150, 1), dtype=numpy.float64)\n", - "regX[:50, 0] = numpy.random.randn(50) - 4\n", - "regX[50:100, 0] = numpy.random.randn(50)\n", - "regX[100:, 0] = numpy.random.randn(50) + 4\n", - "noise = numpy.random.randn(regX.shape[0]) / 10\n", - "regY = regX[:, 0] * -0.5 * 0.2 + noise\n", - "regY[regX[:, 0] > 0.3] = noise[regX[:, 0] > 0.3]\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.scatter(regX[:, 0], regY)\n", - "ax.set_title(\"Nuage de points lin\u00e9aire par morceaux\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On cale une r\u00e9gression avec *scikit-learn*." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "lr = LinearRegression()\n", - "lr.fit(regX, regY)\n", - "\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.scatter(regX[:, 0], regY)\n", - "ax.scatter(regX[:, 0], lr.predict(regX))\n", - "ax.set_title(\"R\u00e9gression scikit-learn\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et maintenant un neurone avec une fonction d'activation \"identity\"." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "NeuralTreeNode(weights=array([0.60837151]), bias=-1.1294931047949746, activation='identity')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.ml.neural_tree import NeuralTreeNode\n", - "neu = NeuralTreeNode(1, activation=\"identity\")\n", - "neu" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0/20: loss: 929.2 lr=0.002 max(coef): 1.129\n", - "1/20: loss: 3.077 lr=0.000163 max(coef): 0.1235\n", - "2/20: loss: 2.876 lr=0.000115 max(coef): 0.1247\n", - "3/20: loss: 2.919 lr=9.42e-05 max(coef): 0.1275\n", - "4/20: loss: 2.825 lr=8.16e-05 max(coef): 0.131\n", - "5/20: loss: 2.869 lr=7.3e-05 max(coef): 0.1339\n", - "6/20: loss: 2.84 lr=6.66e-05 max(coef): 0.1343\n", - "7/20: loss: 2.894 lr=6.17e-05 max(coef): 0.1357\n", - "8/20: loss: 2.875 lr=5.77e-05 max(coef): 0.1361\n", - "9/20: loss: 2.839 lr=5.44e-05 max(coef): 0.1377\n", - "10/20: loss: 2.828 lr=5.16e-05 max(coef): 0.1369\n", - "11/20: loss: 2.831 lr=4.92e-05 max(coef): 0.1378\n", - "12/20: loss: 2.818 lr=4.71e-05 max(coef): 0.1389\n", - "13/20: loss: 2.819 lr=4.53e-05 max(coef): 0.1364\n", - "14/20: loss: 2.829 lr=4.36e-05 max(coef): 0.1358\n", - "15/20: loss: 2.819 lr=4.22e-05 max(coef): 0.1351\n", - "16/20: loss: 2.829 lr=4.08e-05 max(coef): 0.1335\n", - "17/20: loss: 2.83 lr=3.96e-05 max(coef): 0.1328\n", - "18/20: loss: 2.826 lr=3.85e-05 max(coef): 0.1349\n", - "19/20: loss: 2.825 lr=3.75e-05 max(coef): 0.1363\n", - "20/20: loss: 2.819 lr=3.65e-05 max(coef): 0.1369\n" - ] - }, - { - "data": { - "text/plain": [ - "NeuralTreeNode(weights=array([-0.05102527]), bias=0.13690028978271412, activation='identity')" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "neu.fit(regX, regY, verbose=True, max_iter=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.scatter(regX[:, 0], regY)\n", - "ax.scatter(regX[:, 0], lr.predict(regX), label=\"sklearn\")\n", - "ax.scatter(regX[:, 0], neu.predict(regX), label=\"NeuralTreeNode\")\n", - "ax.legend()\n", - "ax.set_title(\"R\u00e9gression et neurones\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et avec d'autres fonctions d'activation..." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3/3 [00:01<00:00, 1.74it/s]\n" - ] - } - ], - "source": [ - "neus = {'identity': neu}\n", - "for act in tqdm(['relu', 'leakyrelu', 'sigmoid']):\n", - " nact = NeuralTreeNode(1, activation=act)\n", - " nact.fit(regX, regY)\n", - " neus[act] = nact" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(NeuralTreeNode(weights=array([-0.11921977]), bias=-0.06480161783085508, activation='relu'),\n", - " NeuralTreeNode(weights=array([-0.10546549]), bias=-0.004911010378026508, activation='leakyrelu'))" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "neus['relu'], neus['leakyrelu']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.scatter(regX[:, 0], regY)\n", - "ax.scatter(regX[:, 0], lr.predict(regX), label=\"sklearn\")\n", - "for k, v in neus.items():\n", - " ax.scatter(regX[:, 0], v.predict(regX), label=k)\n", - "ax.legend()\n", - "ax.set_title(\"R\u00e9gression, neurone\\nactivation\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rien de surprenant. La fonction sigmo\u00efde prend ses valeurs entre 0 et 1. La fonction *relu* est parfois nulle sur une demi-droite, d\u00e8s que la fonction est nulle sur l'ensemble du nuage de points, le gradient est nul partout (voir [Rectifier (neural networks)](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))). La fonction leaky relu est d\u00e9finie comme suit :\n", - "\n", - "$$f(x) = \\left\\{\\begin{array}{l} x \\, si \\, x > 0 \\\\ \\frac{x}{100} \\, sinon \\end{array}\\right.$$\n", - "\n", - "Le gradient n'est pas nul sur la partie la plus plate." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interm\u00e8de de simples neurones de classification\n", - "\n", - "Avant d'apprendre ou plut\u00f4t de continuer l'apprentissage des coefficients du r\u00e9seaux de neurones, voyons comment un neurone se d\u00e9brouille sur un probl\u00e8me de classification." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "\n", - "\n", - "clsX = numpy.empty((100, 2), dtype=numpy.float64)\n", - "clsX[:50] = numpy.random.randn(50, 2)\n", - "clsX[50:] = numpy.random.randn(50, 2) + 2\n", - "clsy = numpy.zeros(100, dtype=numpy.int64)\n", - "clsy[50:] = 1\n", - "\n", - "logr = LogisticRegression()\n", - "logr.fit(clsX, clsy)\n", - "pred1 = logr.predict(clsX)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def line_cls(x0, x1, coef, bias):\n", - " y0 = -(coef[0,0] * x0 + bias) / coef[0,1]\n", - " y1 = -(coef[0,0] * x1 + bias) / coef[0,1]\n", - " return x0, y0, x1, y1\n", - "\n", - "x0, y0, x1, y1 = line_cls(-5, 5, logr.coef_, logr.intercept_)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAEICAYAAACDNvdHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABISUlEQVR4nO3deXzU1bn48c8zWcgCJEAIBMJOZEsCQWqttm5UaEUFuZRardLaVv21tlavVFxLe2ulat26XK+91mqrdStCEVuworfVuiEgYRUEZEnYk0A2yGTO74+ZDDPJTDLLd+Y7k3nerxcvyZmZ7/dkiHnmnPOc54gxBqWUUkrZw2F3B5RSSqlUpoFYKaWUspEGYqWUUspGGoiVUkopG2kgVkoppWykgVgppZSykQZi1W2JyEIR+VMMr79RRM7z/F1E5EkRqRGR90XkCyKyNQb3HCoi9SKSZvW1lVL20ECskpqIXCEiqz3BqVpE/iYin4/HvY0xE4wxb3q+/DxwIVBsjDnDGPMvY8yYaO8hIrtE5Is+99xtjOlpjGmN9tph9uMPIvKzeN5TqVShgVglLRG5GXgY+DkwABgK/BaYaUN3hgG7jDENNtw7pYhIut19UMpKGohVUhKRPOCnwPeMMYuNMQ3GmBZjzDJjzPwgr3lRRPaLSJ2I/FNEJvg8dpGIbBKR4yKyT0Ru8bQXiMgrIlIrIkdF5F8i4vA8tktEvigi3wL+F/icZ2T+ExE5T0T2+lx/iIgsFpFDInJERH7taR8lIqs8bYdF5BkRyfc89kfcHy6Wea77IxEZLiKmLRiJyCAR+aunb9tF5Ds+91woIi+IyNOe72ujiEzp5D0dKyKvea61VUTmetqvBa4EfuTpx7Igrzcicr2IbPNM0f9GRMTn8WtEZLPnsRUiMszT7vc9edreFJFve/7+DRF5W0QeEpGjwEIRyfN8X4dE5FMRudPn3+UbIvKWiDzguddOEfmy78+OiDzhmUHZJyI/a5vqF5HRIvJ/np+RwyLyfLD3SymraCBWyepzQBbwchiv+RtQAhQCa4BnfB57ArjOGNMLKAVWedr/E9gL9Mc96r4d8KsLa4x5ArgeeMczbfxj38c9v+RfAT4FhgODgefaHgbuBQYB44AhwELPda8CdgOXeK57X4Dv6c+e/g0C5gA/F5GpPo9f6rlXPvBX4NeB3hgRyQVeA571vD9fA34rIhOMMY973qv7PP24JNA1PC4GPgNMBOYC0z3Xn4X7vZuN+738l6fvofossMPTt3uAXwF5wEjgXOBq4Jvtnr8VKADuA57w+VDwFOAERgMVwDTg257H/gtYCfQBij33USqmNBCrZNUPOGyMcYb6AmPM740xx40xJ3AHu4mekTVACzBeRHobY2qMMWt82ouAYZ4R979M+AXaz8AdKOd7Ru7Nxpi3PH3abox5zRhzwhhzCHgQd2DpkogMwb02favnmutwj8yv8nnaW8aYVz1ryn/EHSADuRj31PqTxhin5/v/C+7gHo5FxphaY8xu4A1gkqf9OuBeY8xmz7/Zz4FJbaPiEFQZY37lee1J4KvAbZ5/z13AL/H/vj81xvzO830/hfvfcICIDAC+DPzQ829xEHgIuNzzuhbcywyDfP+dlIolDcQqWR0BCkJdLxSRNBFZJCKfiMgxYJfnoQLPf/8DuAj41DM1+TlP+/3AdmCliOwQkQUR9HUI7sDQ4UODiBSKyHOeKdJjwJ98+tSVQcBRY8xxn7ZPcY+42+z3+XsjkBXkPRsGfNYzBV8rIrW4p6MHhtiXYPfr6XP9R3yufRT3bMBgQrPH5+8FQCbu77VN0O/bGNPo+WtPTz8ygGqfvvwP7pE2wI88/XrfM5V/TYj9UypiGohVsnoHaAZmhfj8K3AncX0R95TmcE+7ABhjPjDGzMT9C3kJ8IKn/bgx5j+NMSOBS4Cb2039hmIPMDRIALwX91R3uTGmN/D1tj55dDb6rgL6ikgvn7ahwL4w+9fWx/8zxuT7/OlpjPl/IfQj1Otf1+762caYfwNtCW45Ps9v/wHA9/6HOTVybRPq970HOAEU+PSjtzFmAoAxZr8x5jvGmEG4R/G/FZHRIX+XSkVAA7FKSsaYOuBu4DciMktEckQkQ0S+LCKB1lJ74f4FfAT3L/yftz0gIpkicqWI5BljWoBjQKvnsYs9CTzi0x7u1qH3gWpgkYjkikiWiJzt0696oFZEBgPtE80O4F4HDfQe7AH+DdzruWY58C38175D9Qpwmohc5XkfM0TkMyIyrqt+hOgx4DbxJMh5Eqa+4vk+DuEOol/3zFxcA4wKdiHPdPMLwD0i0sszvX0z7tmEThljqnGvAf9SRHqLiEPcCXPnevr1FREp9jy9BvcHgLhuFVOpRwOxSlrGmAdx/wK+EziEe7RzA+4RbXtP456+3AdsAt5t9/hVwC7P9PD1uEem4E7u+gfuYPkO8FufvcOh9rMV92h6NO7kq7241zgBfgJMBuqA5cDidi+/F7jTM416S4DLfw336L4Kd+Laj40xr4XTP08fj+NOWrrcc639wC+AHp6nPIF7Db1WRJZEcP2XPdd7zvMeb8C9VtvmO7g/hBwBJuD+gNGZ7+MeSe8A3sKdZPb7ELtzNe6p7U24g+1LuNeQwZ1o9p6I1ONObrvRGLMzxOsqFREJP+9EKaWUUlbREbFSSillo6gDsWdt6n0R+ciTZfgTKzqmlFJKpYKop6Y9SSy5xph6EcnAvV5zozGm/RqcUkoppdqJumarp7hBvefLDM8fXXhWSimlQmBJ8XRPCb8PcWeF/sYY816A51wLXAuQm5t7+tixY624tVLdRm1jM60t28h0nPRvb03nWOup/1UzHBmc1ue08C7eVAO1u8G4TrWJA/KHQnafaLqtotBaV0fLvn3g8hm7OISMwYNJy8sL/kKVdD788MPDxpj+gR6zNGta3MXqXwa+b4zZEOx5U6ZMMatXr7bsvkp1B2cvWkVD0x7uOvMW+mUf9ra7DPz+0BA+asojKy2LhWctZMbIGeHfYP0L8PpPoW4v5BXD1LuhfK6F34EK17YLpuKsqurQnj5oECWrXrehRypWRORDY0zAQ1csPU7MGFMrIm8CX8K9T1ApFaKq2iYMBTz04Y+548wfkZ3eBIBD4OqCvfzpWBFfLb87siAM7qCrgTehOKurw2pX3ZMVWdP95dSxbdm4Swhuifa6SqWaQfnZAOytH8Gv195Gq+vU/56Ffc/niRkrIg/CKnGsfwEeKoWF+aTnBp6RTC8qCtiuuicr9hEXAW+IyHrgA+A1Y8wrFlxXqZQyf/oYsjPSANh4ZDJPbbwBgBMZcykv/xsZGfk29k5ZYv0LsOwHULcHMBSW1iBp/sFYsrIovOmHtnRP2cOKrOn1uM/0jEpLSwt79+6lubk52kupEGVlZVFcXExGRobdXVHArAr34UH3r9hKVW0TnzTMpD73bGZM+QqnjtJVSe31n0JLk/fLvOHuvx/c0Adng5BeVEThTT8k75LOjnxW3Y2la8TR2Lt3L7169WL48OH6SycOjDEcOXKEvXv3MmLECLu7ozxmVQz2BmTVDdXt7dCUN7yJvOHNsLA2/v1RCSFhSlw2NzfTr18/DcJxIiL069dPZyCS2MmTh+zuggpXXnF47SolJEwgBjQIx5m+38nr6NF/8N57JVRXh3rgUPdRt2wZ2y6YyuZx49l2wVTqli1LyGsGNPVuyMj2b8vIdrerlJVQgTiVvPDCC3z66ad2d0MloerqJ6ms/DKtrXV8/PF1HD0a9qmHSatu2TKq77rbvffWGJxVVVTfdXdUgTMW1wyqfC5c8ijkDQHE/d9LHtVtZSlOA7GPRx99lHHjxnHllVdGfa1du3bx7LPPer9evXo1P/jBD7xfX3DBBSxYsIBDh6KbXty1axelpaUd2r/xjW8wYsQIJk2axMSJE3n9dS0O0B2cOLGPbdu+izFOAIxxsnHjHOrrK23uWXwcfOhhTLvlFNPczMGHHk6oa3aqfC7ctMG9JnzTBg3CSgOxr9/+9re8+uqrPPPMM37tTqcz7Gu1D8RTpkzh0Ucf9X5dUFDAn//8Z/r3D1jxLKJ7tnf//fezbt06Hn74Ya6//vqor6fs16PHYMaN+xNwalmhtfUYlZUzOHGiY4WmRBXWVLDPvttAVaggugIYVhbViNsUt+pWEiZrur0334xs/bJnz8lMmfJh2K+7/vrr2bFjB5deeinXXHMNdXV1VFVVsWvXLgoKCrj33nu55pprOHToEP379+fJJ59k6NChfOMb36B3796sXr2a/fv3c9999zFnzhwWLFjA5s2bmTRpEvPmzaOiooIHHniAV155hYaGBr7//e9TWVlJS0sLCxcuZNasWfzhD39g+fLlNDc309DQwLJly7zPczqdLFy4kJkzZ4b9vX3uc59j3759Yb9OJab+/f+DUaPu55NPbvG2nTixh8rKi5k06Z+kp/e0sXdda5sKbhuFtk0FAx237bTtu/Vs+UnPceJs7PhrK+wCGD7lPtNzi3DWB3iOw0HdsmUhbyUK6/tSyoeOiD0ee+wxBg0axBtvvMFNN90EwIcffsjSpUt59tlnueGGG7j66qtZv349V155pd80c3V1NW+99RavvPIKCxYsAGDRokV84QtfYN26dd7rtbnnnns4//zz+eCDD3jjjTeYP38+DQ0NALzzzjs89dRTrFq1invuuYcLLrgg4PPC8fe//51Zs2ZF+M6oRFRcfDODBn3Xr62+fi2bNn0Vlyv62ZRYCmsquN2+28Ly40iay+8pYRfACKGoBgCtrWGtFcd9ilt1GxqIO3HppZeSne3OcHznnXe44oorALjqqqt46623vM+bNWsWDoeD8ePHc+DAgS6vu3LlSh577DHOO+88LrvsMtLS0tizZw8AF154IX379vU+b9GiRUyaNInzzjuP5uZmdu/eHXL/58+fz8iRI/n617/O7bffHvLrVOITEUaPfoS+ff1LXh49+irbt38fKw9zsUSk08vt9t3mDW+i6DN1pOc4QYT0QYMo+q+fhjfiDFBUo+gztb6z/V7hBFKtG60ilbBT04kgNzc36GO+W3969Ojh/XsovwCNMTzxxBO0Pwry3Xff9bunMYa//OUvjBkzJpxue91///3Mnj2bRx99lHnz5vHhh+FP2avE5XCkM378c6xbdy719Wu87VVVj5GVNYqhQ2/p5NVxFM30cl6xZ+Tq0zS8ibyJBe5Ep0gEKapR9W4+gaJxp4E0hClurRutupKwI+LzzjMR/YlkfTgUZ511Fs899xwAzzzzDJ///Oc7fX6vXr04fvx4wMemT5/Or371K2/QDhYg2z9v7dq1Yffb4XBw44034nK5WLFiRdivV4ktPb0nZWWv0KPHEL/2HTvmc/Dgizb1qp1oppdjse82SPGM9JzWwO3BAqnWjVYWSdhAnGgeffRRnnzyScrLy/njH//II4880unzy8vLSU9PZ+LEiTz00EN+j9111104nU7Ky8spLS3lxz/+ccBr3HXXXbS0tHifd9dddwV83tatWykuLvb+efFF/1/AIsKdd97JfffdF8Z3rJJFjx5FlJW9Slpab7/2zZuvoq7u3zb1yodnBFq3K5ttfy10jzwdhrTM1q6nl2Ox73bq3QQa+RaWH0faDdQ7DaSeDxj+35eLtB5EPm2uUpLYsZY0ZcoUs3r1ar+2zZs3M27cuLj3JdXp+959HD36Dyorv+zdYwyQkVHA5Mnvk51tYz3xh0qp++gw1R/kYVpPffaXdCi69z57AtXCvIDNdbuyObh7As7q6q4PYFiYT92urI7fV5qLokUPpHQArlu2jIMPPRza+5giRORDY8yUQI/piFipbqJv3y9y2mmP+7Xl5Z1LZuZAm3rkMfVuDlb29gtWAMaJfRnFeUMCN08soGTV64zbvImSVa93Hjzyijm4vlfH76vVkdKZ0nGtVNZNaCBWqhspKvomw4a5lzCGDLmFCRNeIC0tu4tXxUhbpvTia3E2BP5VY1tGsRVrz1PvxtmYFvAhZ1VVygYe3cYVPs2aVqqbGT78J+TlfYG+fS+0rxMdMqVbrSnEYZW2NWZPxjN5xe4gHMras1+m9ACcDYGLD6VqMQ/dxhU+HREr1c2IiL1BGGJTiMNqkdR8bp8pXVYXuBgIKTYK9Nknnp4bJO/IGC37GYQGYqVSTEvLkdjfJBaFOOIsYN3oYMVAgkiJUWColcpA14uD0ECsVAo5ePBF3n13OEePxnhPeYC9unnDmyi5qkdoiVA2C5pw9NHhDs/NG97k/oARQEoU8wjy4SRYyfOUmikIkQbiTixcuJAHHngAgKNHj3LhhRdSUlLChRdeSE1Njc29Uyp0xhh2776fTZvm0tpa7zk68aPY3TAWhTjiKGjC0YY+AZ/vnnq3r5iHrac+BalUVnJx8NPAUmKmIAwaiEO0aNEipk6dyrZt25g6dSqLFi2yu0tKhayhYT07dtzq/bq1tZ7162fQ3Nzxl6glYlGII46CJhw10PEDBu1GgXGeerd9u1CQSmVAas8UhCFpA/GStfs4e9EqRixYztmLVrFkbfTH/D399NOUl5czceJErrrqKr/Hli5dyrx58wCYN28eS5Ysifp+SsVLz54TGT36Yb+2kyf3UVk5A6fzWGxuGkkylJ1CSDhKLxrk/kARgHsUWB33qXfbtwsFmv3wSMgkvQSUlIF4ydp93La4kn21TRhgX20Tty2ujCoYb9y4kXvuuYdVq1bx0UcfdShheeDAAYo8n+KKioo4ePBgNN+CUnFXXPwDBg++0a+toWE9GzfOxeVqsalXCSKcutHlc4MWBOlsdGipSE+zigW/2Q9/yZikZ4ekDMT3r9hKU4t/gfamllbuX7E14muuWrWKOXPmUFBQAOA9ilCp7mT06F/Sr99Mv7aamhVs2/a9xDs6MZ46SzgKFEDsXANv96EhIaZ/22Y/ggTjZEnSs0tSBuKq2qaw2kNhjPE72rC9AQMGUO35hFldXU1hYWHE91LKLiJpjB//DL16fcavvbr6d+zZk8KHggRNOPKfavYmRX11Idv+Npy6g4OJ+xp4CHu0EaHnuefEvi/tJXmSnl2SMhAPyg+8HhGsPRRTp07lhRde4MgR9x7Lo0eP+j1+6aWX8tRTTwHw1FNPMXPmzA7XUCoZpKXlUla2jKys4X7tO3Ys4ODB5+3pVBx0mlkcbErZp71DUtThOqr/3YO60/8Y3zXwAHu084Y3Aj4zGsZQ9/KS+O/XTdIkPVuzzknSQDx/+hiyM/xrvGZnpDF/+piIrzlhwgTuuOMOzj33XCZOnMjNN9/s9/iCBQt47bXXKCkp4bXXXmPBggUR30spu2VmDqCs7FXS0/P92jdvnkdt7Vv2dCqGAmUWV/3oVjaPHef+xZs5s8uRnO1JUW0CfGior86i/dGOtu3XTbIkPduzzrHgGEQRGQI8DQwEXMDjxphOD+u14hjEJWv3cf+KrVTVNjEoP5v508cwq2Jw+N9AitNjEFNbTc0brF8/HWNOJWulp/dl8uR3yckpsbFn1tp2wdSgSU3gTsQq+s5F5J1cGrT29OZx4yHQ70sRxm3eFFI/LDkesF0db4DNzxUR6IzlcPqWqoL9bKQPGkTJqtctu09nxyBaceiDE/hPY8waEekFfCgirxljYvqvP6tisAZepaLUp8/5jBnzBFu2XO1tczqPsn79lzn99PfIyOhnY++s01UGsWlu5uBf3iVv1QZvW92yZRz87pk4D7uzfiUzHXOi42tDTYpqG3m1jarbRl4Q5sEQ5XNh97uw+ve0TUd3daiGng8cXCIcUhH11LQxptoYs8bz9+PAZkAjpFJJYuDAqxg+/Cd+bX36TCUtLc+mHlnEZ4tPWo6ry6f7/uKtW7aM6jvuwHm4zv1YYzq0GJDIq2dZOrW9bSW+a8Kd7ddNhKnXhBPSnvH4ZZ1bukYsIsOBCuA9K6+rlIqtYcPuYuDAbwAwcuQvOO20x3A4kviU1HZbfAaU1QY9iKCN7y/egw89jDnpv7fauIS0DFfE1bMsHXmFcahGwqxtJ4pw9ozHiWX/p4lIT+AvwA+NMR1K9YjItcC1AEOHDrXqtkopC4gIp532PxQWXknfvl+0uzvRC7AvGKB6fT6uRsHgPwpp/4s3WHBsPelg3OxqdyJSKPzOLi7CWd/xKRGNvPKKPYHEp2l4E3kTC9wJUutfgNdvg4VX4awKfP2Urfcc5Gfj4IY+OBvElql7SwKxiGTgDsLPGGMWB3qOMeZx4HFwJ2tZcV+llHUcjszuEYQh6L7gXsOaGXniGc7b8yHf2PQ3BjTXnfrFO6zJPV3ZWdDMaQ29ela7pKrC0hqqP8jHtJ5Kqop45DX17g4JW94s73b3Tc9xdrp+nHKC/GzkDW8O/QOWxaKemhZ3FYwngM3GmAej75JSKhG1tER34lhc92oGCZZVxp189uaQ07njq/ecKtYxrKnr6co0F4UVzaEXpwi3Wlc4OtuvG0LBj5Su9xzCnvF4s2KN+GzgKuACEVnn+XORBde1ne8xiC+++CITJkzA4XDQfuuVUt1dVdX/8O67Izh+fG1Er497wlCACk+NJpP7nO7tSB3qDgQLmrnuAJae46To3DTybvxl6PtiQ6zWFbFg+3XDWD9OSQlY/SvqqWljzFsE3MDWvZSWlrJ48WKuu+46u7uiVNwY42LHjtu85S8rK2cwefJ7ZGUFOfQgiM4ShmISENqCkmd9tjF7IPe1fJVlJ85gcKC6A7GYrgywjuttj6Ug68fk9OXgliKc1dXeRK2UDMbtfjYC7RmPt+RNi/RJgrDqjXz66ad54IEHEBHKy8sZNWqU9zEteqFS0dGjK/xqUJ88WU1l5UVUVLxFenro25ts2atZPtf7OyEHWOj5E1AsgmZn67ixFOC+dXt6U/1BOuaku3BFxHuYuwufn41EkJQlLtunn1O3x/31+hcivmRXxyAqlYr69fsyxcU3+bU1NGxg48avdH10YoLt1exULKYr7aq7HOC+B7cO7rgdK5W3MCWY5BwRt1vPAdxfv/7TiH/I9RhEpQIbNep+mpt3cfjwy962mprX+Pjj/8eYMb8LfGpZLDOGYyFW05URjLwsqYLV7r7Ox8cHfFrKbmFKMMkZiAOs53TaHoKujkFUKlWJpDFu3J9Yt+58jh9/39u+f/8TZGePZNiw2zu+KAH3anYpAaYrLSuDCbHbw6wsl5xT0zFIP+/qGESlUllaWg5lZX8lK2uEX/vOnXdw4MCzHV8Q64zhbsqyKlieGYm6jw6z7a/9cdYb/I5JJMFmJFJccgbiGKzndHUM4ssvv0xxcTHvvPMOM2bMYPr06RHfS6lkdOroxD5+7Vu2fJPa2n/6PzkB92omg6BJbVVV4e2/fv2n1G2D6g/yPMU8BN/NLSm/hSnBRH0MYiSsOAYxFlnTqUiPQVThqq39Jx99dCHGnPS2paf3YfLkd8jJ8ezNDXBUHxnZMU1WStoThnx+l217JfAUsi/Jyuo6iC7Md4+EA1XUsvh4PxWazo5BTM4RMSTd4dNKdRf5+ecwduyTfm1OZw3r11/EyZMH3Q1xzhhO2hOGQjiAoL2QpqrzinE2pgV8SBO0Ek9yJmsppWw1YMAVNDfvZOfOO71tzc07qKy8lEmT3iQtLSuuyU9xLxhilWBJbet742x0EKxWUtBg6h1d7yE9p1BrTCeJ5B0RK6VsNXTo7QwceI1fW58+X8ThyIx7XxLhcPeIBEtqu/QA4y6vdpelDCBgMPUbXQeuMQ3Q2tiY+DMFKUYDsVIqIu6jEx+jT58vAmmcdtrvGDnyZ4jE6ddKAhQMifogiy6S18I6sCFgvew60nr4P83U1ibHtH0K0UCsVIws37GcaS9No/ypcqa9NI3lO5bb3SXLORwZTJjwEpMmvc6gQd+O340T4HB3S9alA+0A8RHWgQ1BRteS1nFUrVW1EouuESsVA8t3LGfhvxfS3Opet6xuqGbhvxcCMGPkDBt7Zr309Dzy88+N700ToGCIJevS7St6iQNMq99T8oY3kTexwJ2U2pkg9bI1aSvx6YjYR8+ePTu0LVy4kMGDBzNp0iTGjx/Pn//8Z+9jR48e5cILL6SkpIQLL7yQmprozmtV3ccjax7xBuE2za3NPLIm9WqYO5113r9bNktgccGQSKaYLVuX9t0BctljkddICDK6Ts9pDfBkTdpKJBqIQ3DTTTexbt06li5dynXXXUdLi7t4+qJFi5g6dSrbtm1j6tSpLFq0yOaeKjstWbuPsxetYsSC5VTXB/5lvL9hf5x7ZR9jDJ9+eg/vvz+O5uZPvbME1Q3VGIx3liCiYBxGwZCugmxYU8yxXpeOZttX22vFfwQc1jqzskXSBmI71t9KSkrIycnxjnyXLl3KvHnzAJg3bx5LliyJeR9UYlqydh+3La5kX20TBnC15Ad83sDcgXHtl11crha2bv02O3feycmT1axfP4PH1j5g3SxBiNX1QgmyIZeVjNG6dIcPCp9mR14joXwuGP+gG9Y6s7JFUq4R27X+tmbNGkpKSigsLATgwIEDFHk+/RYVFXHw4MGY3VsltvtXbKWp5dQU4IlD08kqWow4Th09l5WWxY2Tb7Sje3G3f/8f2L//996vGxs3cnFOLv9dP4zWdp//I5olCPG0pFDWcUOeYo7BurSlhzx4O9ZxrTjkdWZli6QMxJ2tv8UiED/00EP87ne/Y8eOHfz973+3/Poq+VXV+h/L6TxWQTPQo/8K0jLrGJg7kBsn32hLotbyHct5ZM0j7G/YH7d+FBV9iw07lpLpPDVTNSa7gcv7VfHMkcH4FqqIeJYghIIhwYJsS3U1Zy9aRVVtE0/n5FPQ0DG/o8MUc5B16bzhze7RawRiUohk6t2By4tGc7ayiqmknJoO9gk6VutvN910E1u3buX555/n6quvptnzP86AAQOo9vyPXl1d7R0pq9QzKL9jkozzWAX5R37C+nnrWTlnpW1B2LJ12TCIOLjn7e+xrWasX/uZPWuZnnfI+3VMZglCWMc9lJ3vXUZ4YuyXaE7L8O9/oCnmGBxkEZNCJHEuL6qil5SBONgn6Fivv82ePZspU6bw1FNPAXDppZd6//7UU08xc+bMmN5fxUck+Qfzp48hO8M/SSY7I43508fE7J6hXPP2t263LXt7d42LR9fcxYEG/5HlxfkH+UxuLUW5RSw8a6G1H1BCWMc9kZbBk+O+5P36zSGn88ikORzO7dP5GqpVp77FoxCJ1uJPKkk5NX3j5Bv91ojBmk/WjY2NFBef+nTb/ihEgLvvvpsrrriC73znOyxYsIC5c+fyxBNPMHToUF588cWo7q/sF2n+wayKwYB7rbiqtolB+dnMnz7G2x6LewazZO0+7vm/Z2jKew5xdCxxCPHJ3h6Un82+Wnjww4XcdeYt9Mw87n1sXv8DlJf/kT59zrP2piGs495XdC5vDjnd72VvDjmd/xtyOjsXdfJ+h7gu3al2p1IVltZQ/UE+pvXUVL1mNKeepD0G0Y51r+5Ij0H0N+2laVQ3dJwWLMotYuWclQl/z7bsbcfQe3Bk1gZ9Xiy/n/Z9aWpppSR/Iz864w4yHKeqPKWn51NR8W9ycy38+VuYDwT6nSbeddyzF61iX7s1fYDB+dm8veCCDu2WHq/4UGmHRKq6XdlxLUSi7NHZMYhJOSIG90hBA6+yWrzyD5as3ecdPfccWx3wkJ2w77n+Bc5cejsbHYeYlDEEE+Tknnhlb/vOEmyvncBfPrmVy0vu8T7udNZSWXkRkye/S2bmAGtuGqS6lO867vzpY7wfENoEW0awPKs5BglfKvklbSBWyiq+QbFXST6kd8ygtTL/wHekCO49x4FGr2Hd0zPlOZAmEBjobKU6o+P/3g5xWL8u24lZFYN9pudn8OmnPdm58zbv483Nu6isvMRzdGJO9DcMIWM4nGUEy7OaQ/igoFJPUiZrKWWV9oU4Gg9Mw7j8M2itHkEG2nMc9T3brY3eWFNLlst/fTgrLYuff/7nts4kDR16K0VF3/FrO378AzZtugJjAq9nhyXEjOFZFYN5e8EF7Fw0g7cXXOAfhH2SqZxVVQFvE3FWs1UJX6pb0RGxSmntg2Lb/t+cASshvTYm+QeR7DnuMiei3ZTnjIZGAB7pk09Vejr5mYXcdubNti/niAglJb+huXk3NTUr2lrJzz+PgPPzkQhhf3FQ7ZKp0nOcOBs7/pqMOKvZioQv1e1oIFYprX1QBHdgPH6sovMM2ii4s4k7BuMBjrMCJguFlFUdYMpzRkMjpzfk8u7Mf4SUvR0v7qMTX2Dt2i/Q1PQx48Y9Q//+s+3ullu7mYXC8uNUf5CHaT01eRh1VnM0HxRUt6RT0yqlBSrE0Vm7FcLdcxzSSU5BpjwHzv55QgXhNunpvSkrW86kSW8mThCGDjMLWqdZxYMlgVhEfi8iB0UkqQuZhnsM4osvvsiECRNwOBy0346lkkO0hTgiMatiMPfOLmNwfjaCe9vMvbPLggbMkDK5k7CaUlZWMb17f9bubvgLkDSVN7yJkqt6RHS8olKhsGpq+g/Ar4GnLbpeQrnpppu45ZZb2LZtG6effjpz5swhIyOD0tJSFi9ezHXXXWd3F1WEoinE4Svcfe3+2cQdRZTJ3c2mPJ3OY6Sn947vTbVOs7KBJYHYGPNPERluxbVCZekm+xD5HoNYWFiYkoUwumMhla6CYldiURnLd3tT44FpSXeSUzQ/J8a0sn37f1JTs5KKirfJyOgT49760GQqZYO4JWuJyLXAtQBDhw6N6loxOTosBO2PQUw1dh0/mYh8A42I4Gq39Saa08DsyOQG6z5kRfNz0trayObNV3L48BIANm6cTXn5Cv6267X4fQDsZjMLKvHFLRAbYx4HHgd3ictorhWTo8M6occgusX7+MlE9bN3f8bzW5/3fh2sTGyk1bjsyOS28kNWND8nu3f/whuEAWpr3+T11V9i4cfHaG49EXXflEpESZk1HZOjwzoR7BjEVBPv4ycT0fIdy/2CcGcircZlRyZ3SJnZIYrm52To0Nvo3ftsv7aMxjc4v+fuTvtWt2wZ2y6YyuZx49l2wVTqli0Lu99K2SUpA3GwzfRRHx3WhfbHIKaCJWv3cfaiVYxYsByc+QGfE+vjJxNJqIEpmjVcOzK5o/2QFe3PSdsxkBV/OoOf7EzHlT7I7/GL8g9xRq5/wlpb39qWqpxVVWCMd6lKg7FKFlZtX/oz8A4wRkT2isi3rLhuMIU3/RDJyvLvgwVHh7Udg9j258EHH+zwnLvvvpsHH3wQl8vFyy+/THFxMe+88w4zZsxg+vTpUd0/0dhR/jFRtQWaqvpOZl2MA0GiPmc33O1NkbLqQ1a0Pydt0+LVDdUYDDvqj7Bodx8anP61p6/ot4/Tsuo79K2zpSqlkmG2xKqs6a9ZcZ1Qta0DW5017XJ1Xev29NNPZ+vWrQBcdtllXHbZZVHdM5HZlTSUaO5cUskz7+7GALn98pEABzQYAyeqvsK22++05J5dZXJHm1hlZWZ2tD8ngabFq1uE/64ezY2DNpOR5u5TmsC3+u/mof0jqXXle/sW76UqlTzsSuwNV9KWuMy75JKEeiO7IzuShhLNkrX7vEEY3Ac0tA9YxkBLzZkUOs6KS5+sSKyy8kNWJD8noRwDuavVxePr/5PvVSzytuU4XNwwYC8ZaTO4aOmtUHcl6blFOOs7vj7WS1Uq8cU7sTdSSRuIVewFqonc1p4q7vm/Z8gZtQzJqMW05HPi0HSaq2fTo/8Kv7aMpinMnx27NVxfVmSvW/khK9yfk1CPgTQt+Xxw4PM8v+WbfHXsk972vLRmetb/Bmd9NukIhaU1VH+Qj2k9Fc2tWKpKVHbUUEgq61/w7gN3VgX+MJZosyUJlawVbBuIio2u3m87koYSyfIdy2nKew5HZi0i4MisJatoMQANnyygaesvaPxkAQMcZ8VkDbeN71ru2YtWUW1B9rqVmdnh/pyEcgykcWVw4pA75+Jvu2bz+u6L/B6v7+lk87gmDMZTD7qW9J50+3rQmpjWhbbTs+r2AMZdIzyARJstSZgRcVZWFkeOHKFfv36IWHQcmgrKGMORI0fIapf05suq8o/J6pE1j/hNQQOIo4Ue/VfQeqyCX86dGPP3ov3ocV9tEz0L8pCM2g7PDSd7ff70MX7Xhcg/ZIX7c9LVMZC9M/pzdM9UnMcmep4hLN7+Xc4Z2UyGc5X3dUcKnGwf3UzJ9mzyhjeRN7wZFtaG3f9kkixTrbaJx+lZMZAwgbi4uJi9e/dy6NAhu7uSMrKysigu7ljk3le05R+TWbARpmTUcuWZQ+PyvrQfPQI0H5xOdtFiiKLkZTQfsgIlis2qmBHy+xHKMZC+a8htfTu/bCnr1p1Dff1aAMQFvY77jMQDHNjQ3WhiWhcCnJ4FcHB9L5xNGQk7lZ8wgTgjI4MRI0bY3Q2V4kI5bCE/s5CfzSqLS3+CreU2AyNO+2dUJR8j+ZBlRaJYKKPxYH0rK3uFNe+V4zx5lNIN2fSp9fwK664HM/isd5JXTHq/3jgP13V4WqJNtdomwLncecObyJtYADcl7uGACROIlbJbqFt6bjvz5rj1KVgiVKHjLFbOsWarVDisSBSLZjTeo8cgyia/iWxbSa75PdCND2ZoW+9sm2qt20PhmN5UH+uDOXnqZzIRp1ptk6SnZ2kgVsojEfdNW7mWawWrypxGs+TRs2cpVJRCRfw+EHUlJpnM7dY7AfKGHIMevTi4ZZBmTQeSpKdnaSBWyiMR903bnTDXfq2299D+1LUc7PC8RClzaozB5WokLS03bveMWdGIduudbfIKq8j77abIr9vdJeHpWRqIlfJI1H3TdiXMBcrYzpGpZBUtpsWc8D4vUcqculwn2Lr12zQ372bixJU4HD3ict+YZTIHWO/0tqtuJaH2EStlp1TfN91eoIztxpqJpB2dS1FukSV1ta3S0lLD+vVf4sCBP1FX90+2bLkmtnUJ1r8AD5XCwnz3nt4Aos5knnq3e33TVxKsd6rw6YhYKQ+7p4ETRdv2pLqB1eT2c1cOcx6r8D5+eP8EPvjhj2zsYUeffHILtbVver8+ePBZFu96h4qxv7L+Q0K7JKr0HCfOxo6/SqPOZE7S9U4VPg3ESvlIlX3TwQ6N8N2eJALiqSbWDN5gbPdUfSCjRt1P1eGVOJyn1lU/n7OTF9bdAIS+tSok8SwakYTrnSp8OjWtVIppf+xg217gtuDcfntSWzUxSNyp+oyMvjx2cBjHW/2XFv6jzy5eXHertTcLUDSi6DN17nKK3bzEpooNsaO+85QpU8zq1avjfl+lFEx7aRrVDR3XL4tyi9jfsB9Dx98JxkDe/kcSb6rep+BF+fBihvVo4vsDdpLpOPU9NLkcmEGPctHY71lzz4dKgyRRDUnoohHKXiLyoTFmSqDHdESsVIoJtue3qr4anPkBHxvUs4i3F1yQeEHYp8D/QGcru07m8PSRYlw+nyWyHS5a9t3CiROBk6rCpklUymIaiJVKAb4nOAULtqYln8YD0zqchJQo25M6aLdWe2NNLRjDR415LKnx39ecl9ZMZeXFOAMdXByu8rlwyaPuETDi/u8lj+paroqYJmsp1c2FUrqz7djBRKgmFrJ2a7UzGhq5t28f6tLTeON4PwrST3JO76Pex+vr17Jp0+WUli7B4YjyV58mUSkLaSBWqpvrrHSnK60G0+K/RSmUamLBsq7jKkDBi9uO1vDjgn6ccAh/qSmiT3oLZTnHvY8fPbqc7dt/QEnJb/S4VZUwdGpaqW4uaOnObbeSt/8RGj5Z4LdPGDrfotRZ1nVcTb2bRpPp13R+vZOR+ytwnczH4ODvjVNozRjl95yqqv9mz55fxrOnSnVKA7FS3VywoNpWsCTcamKdncAUV+VzuS/ju+x1FeAywl5XAQtavs37dZeTf+QnrJ+3nuVzVvH5Kf+iR48h3pc5HLnk5o6Lb1+V6oROTSvVzXV2glMk1cSsOoHJCpNmXMuFiz9H08ngp1P16FFEWdmrrF17NmlpuZSVvUKvXpPj3lelgtFArFQ311WwDaWamO8pTL1K8iG9psNz7DiBKdQPEj17llJW9gpZWcPIyhoa9n1icsyhUh4aiJVKAdGU7gwl6zreW5zaJ4t9+bxz+Ofef7K/YT+//WQgGXkdk8fy878Q0b1idsyhUh5aWUulnITI+E0iZy9a1eF4yPTea23b4uRbDzuYrLSssE6FcrmcQGvAoxO3XTA14AlL6YMGUbLq9ZD7rVJbZ5W1dESsUkr7X+JtGb9g8cEA3UjQrOsutjjFSqBksfbaksdC+Td1OuvZtOmrpKX1Yvz4ZxHxz2ENdpxh1MccKuWhWdMqpSRMxm8S6SzrOm58zv/dXx9aqcpQksdOnKhi3bpzOHr0VQ4dep6dO+8A/CuRHc7JD/jaqI85VMrDkkAsIl8Ska0isl1EFlhxTaViIZKM3+U7ljPtpWmUP1XOtJemxX+/rM0i2eJkqQA1pUMRSvLYli3XUF+/1vv17t2LePW9X3Db4kr21TZhgCfGfonmNP+yn5Ydc6gUFgRiEUkDfgN8GRgPfE1Exkd7XaWsEkqd5WC/tBOmeIWNZlUM5t7ZZQzOz0aAwfnZ3Du7LH4HQASoKZ3lcvk/p12uS6jJY6ed9hgZGYV+bT0ab2d03vver98ccjqPTJrD4dw+esyhiomok7VE5HPAQmPMdM/XtwEYY+4N9hpN1lLx0j7jN7332oAZv8ESezo7MnDlnJWx67g6ZWE+tDuacXluDo/0yWd/ehoDna2c09jIP3v2Yn+aI+zksWPHPmDdunNxuU4F+yZnNj9/7xfsOT7S2yZgy5q46h5inaw1GPAt+LoX+GyATlwLXAswdGj4+/iUikRndZZDyfhNpOIVXQmWDZ70WeIBakrPaGhkRkOj//OO1sHC2rAv37v3Zxg//s9s2HAZbQE/O72Jm07/Cf/1zi+pOVEAxHlNXKUUKwJxoMrpHYbZxpjHgcfBPSK24L5KdSmSjN9ELV7RmWDZ4GsPrmXp9qUxzRKPeaCferd7jbil47+ln7ziiG9RUDCT0aMfZvv2U9PZfbOOcNPpC/n5e/chjl7xWxNXKceKZK29wBCfr4sBi07gVio64Wb8tk1ltyXqJMv5vMGywV/8+MWYZonHZQ29/fm/2X0hzf+wBzKy3QE7CsXFP2DwYP9/16G9d3HzZ+7n3svGxm9NXKUcKwLxB0CJiIwQkUzgcuCvFlxXqaiFm/EbcCq7ejbi7IMgFOUWhVUoIhRWZGUHmyp3GVfAdqum1uO2Hax8Lty0wT31fOtOmPmbU4E5b4g7UFtwPvDo0b+kX7+Zfm2n5X/AuNx7sKP4kUoNUU9NG2OcInIDsAJIA35vjNkYdc+UskC4hxrEu3hFNAVGQplCxzhAOgZjq6bWbVtDL59rSeBtTySN8eOfYd268zl+/ANve3X1/5KVNYphw3R3prKeJZW1jDGvAq9acS2lrBZOneVB+dkdyjm2tcdCZyPKzgJxKPWfjSuDltrTycj/sEOW+DnF5zDtpWkRresm4xp6ONwnNC1jzZozaW7e5W3fufM2srKGM2DA5fZ1TnVLWllLKR/xLl4R6Ygy2BS6qyUfY8B1Mp/m6tmcODCrw9T6zNEzWbp9aUTruj9Z9Ufu/PBy6gbeSM6oRZw4NiYp1tDDlZk5gLKyV0lPz/e2paf3IytrSPAXKRUhrTWtlI9IzucNlxUjymBT6M5jFQHbfafWp700LaJR+PIdy3np04eQDPfoWjJrycj/kJba0+nRe6stB0DEUm7uOCZMWMz69dPJyhpGWdmr5OSU2N0t1Q1pIFaqnWiODOyKVUcKBptCTxOhNUBSUX5OBmcvWkVVbRM9x1YH3HTY1Sj8kTWPgE8/AcTRQnrPLRzftqBbFrvo0+d8SkuX0KvXGWRmFtjdHdVN6dS0UnFkVVZ2sCn0r312SIf2jDShvtnp3ZLlaskPeM1Ao3Df8qDV9YFPG5KM2m5d7KJfv4s0CKuY0hGxUnFkVVZ2Z1PoU4b19WtvOOGktunUSPbEoekhjcLbj95dLfk4Mms7dsaZn7LFLlpbm3E4MjscnahUODQQKxVArKpFWZmVHWwKvX37iAX+SVhtZT579F9BWmZd0O+v/eg9UADHlcFXRl6bksUuTp48zIYNM8nL+zyjRv3C7u6oJKaBWKl2otnb25X508f4jTIhcFa2lR8EAgV/57EKBjjO4u0FFwR9XfvRe6gBPBU0Nm6jsvIimpq2c+zYv8nKGsHgwdfb3S2VpHQ+Ral2YlktKpQjBSMtGxmsQlekW7ICjdKdxyrIP/IT1s9bz8o5K2MehBPxLGhjXGzcOJumpu3etm3bvseRI1pKQUVGR8RK4b+lKNKs4lB1lZUdSZGPzkbxsyrcrwl3S1aoo/dYieXMRDREHIwZ86Tn6MS2E6BcbNw4l4qKf9GrV8ctZEp1RgOxSnmhJiXFqlqU74eAQfnZHCsKv8hHV8E7ki1Z8dhT3ZlIq47FQ+/eUxg//jk2bJgFuEuIulwNVFbOYPLk97TwhwqLBmKV8kJJSopVtaj2HwL21TbRsyAPyajt8NzOPgjEquZzJAE8mvXteM5MRKug4BJKSh5l27YbvG0nT1ZTWXkRFRVvkZ6eZ2PvVDLRNWKV8gImJVXPxnUyP2YnLrVp/yEAoPngdAihbKTvHl+c+QGvH++az9Eci9j+CMpw9jvbZfDg71FcfLNfW0PDBjZu/AouV0uQVynlTwOxSnl2JiUF21fcXD2botyioB8EEvXc5EgT3ZbvWM7da75G2qj55I5aRHrvtZw4ND0hvqeujBp1PwUFs/3aampe4+OP/58enahCooFYpbx4H/TgK9j+4ULHWaycszLoBwE7zk0ORSRT5G2jaJNegwg4MmvJKloMELeZiWiIOBg37o/06vVZv/b9+59g9+57beqVSia6RqxSnp1JSZFmJsf73OTORHuIRaBRtDha6NF/BQ2fLOhyv3MiSEvLoazsr56jE3d623fuvMNzdOIVNvZOJToNxEoR24MeurovhP8hIN7nJgdjxSEWwUbLklEb1+1S0crMLKSs7FXWrj0Lp/PUh5EtW75Jjx7F5OefY2PvVCLTQKyUzSL5EGD3Ht82AafIgZwBKzs9FjGUUbSjtQ8/a1fsJNHl5o6ltHQJH310IcacBNwBOj29j809U4lMA7FKKbGqIR1vdu/xbeM7RZ7eey09+q9AMmppbcnnvi/cG/C9DXUUvfALtzJjZPIE4Tb5+ecwduyTbN58JT17TqKsbDk9egyyu1sqgWkgVikjUSs1Rcqu6XRfbVPk6b3X+gVTyawN+t5GOopOJgMGXIFIGn37XkR6ei+7u6MSnGZNq5QRyxrSqaot47xH/xX+pzIR/L0Nmmi27da41bCOh8LCr2oQViHREbHq1pKpUlMyahuR3/VRbcDHA723iZJoZqfW1mbS0rLs7oZKEDoiVt1WMlZqSiZtlb1uen4d0ho4GSnQe2vnvu1E0Ny8hzVrzmDv3l/b3RWVIDQQq27JzkpNiXh0n9WiqewVylGQ3dXx42tYs+azNDRUsn37jRw+vMzuLqkEIHaUYJsyZYpZvXp13O+rUkP7pCwA48qgudpdhjCWB9sHundWWlZCVoSKxtmLVnWYXk7vvbZbJVxZrbW1iffeG8nJk6em6x2OHCoq/kmvXqfb2DMVDyLyoTFmSsDHNBCr7mbaS9Oobqju0O46mU/DJwsYnJ8ds0pNwe5dlFvEyjkrY3JPO4xYsJxAvzkE4l7ZK5kcObKcyspLaTs6ESAzcyCTJ79LVtYw+zqmYq6zQKxT06rbsbNSU6yOI0w0wRKr4pVwlazT//36zaCk5Fd+bSdP7mf9+hm0tNTa0yllOw3EqlsI5UhAR2ufmKxFJuJxhNEIJcjZmXAVzVGLiWDw4O8yZMgtfm2NjRvZuPE/cLlO2tQrZaeoArGIfEVENoqIS0QCDrmVirVQE4fuPf/WmAThRDyOMFKhBjk7E666w37wkSN/QUHBf/i11dauYuvWa/XoxBQU7T7iDcBs4H8s6ItSEbGzUlN3qxLVWZBr/z3YVdmrO0z/tx2d+NFH+zh27F1v+4EDT5GdPZLhw++2sXcq3qIKxMaYzQAiAaokKBUndh4JmEjHEVohUYNctEctJqK0tGxKS//KmjWfo7n5E2/7rl0/JitrOAMHXm1j71Q8xW2NWESuFZHVIrL60KFD8bqtSgF2Jg7ZnbRkhURf4+5u0/++MjP7U17+Kunpff3at279NjU1b9jUKxVvXQZiEfmHiGwI8GdmODcyxjxujJlijJnSv3//yHusVDt2Jg4le5WoZAhyAaf/q2cjzj4IQlFuUVLv087JOY3S0qWIZHrbjGlhw4bLaGjYZGPPVLx0OTVtjPliPDqiVKTsPBIwUY4jjFQyrHF3t+n/QPLzP8/YsU+xefPXvG2ZmQNxOLQedSrQQx9Ut2DnkYCJcBxhpJIhyKXKIREDBlxOc/Mudu68jby8cygtfZmMjL5dv1AlvWi3L10mInuBzwHLRWSFNd1SSsVDMqxxJ/v0fziGDr2VMWN+z8SJKzUIp5CoArEx5mVjTLExpocxZoAxZrpVHVNKxV6wIDftjH0JU7kqlQ6JEBGKir6Jw9HD7q6oONKpaaVSWKA17mln7OOVqke9+4nbinoAtq0VJ/P0v5VcrhMapLshPfRBKeUnVQ6uSDYNDZuprLyY0aMfpKAgrE0rKgHooQ9KqZAlalGPVFZT8yZr155Fc/MONm36GseOfWB3l5SFNBArpRK+qEcqO3nyIJWVM3A6awFwuZqorLyYpqad9nZMWUYDsVIpLhmKeqSyzMxCRo9+yK+tpeUglZUX0dLSsdSnSj4aiJVKYlacy9vdK1d1B4MGXcvQoQv82hobt7Bx42xcrhM29UpZRbOmlUpSbUcWRpvdnAxFPRSMGHEPTU07OXToeW9bbe2bbN36bcaOfVoP30liOiJWKklZdS5vMhT1UO6jE8eO/QO9e5/t137gwJ/YtWuhPZ1SltBArJQFrJgiDoVvUlV1fcctRhB+dnMqVa5KdmlpWZSWLiE7u8Sv/dNPf0p19R/s6ZSKmgZipaLUNkVc3VCNwXiniK0Oxu2Tqlwt+QGfF252s9WVq+L1oSRVZWYWUFb2Kunp/fzaP/74O9TUvG5Tr1Q0tKCHUlGKVwGMsxet8jv8IL33WrKKFiOOFm9bVlqWrYlV7detE6FP3VVd3b9Zt+4CjDmVrJWW1puKirfp2bPUxp6pQLSgh1IxFK8CGO2Tqtqym10n8xMmu9mqdWvVtby8sxg37o9+ba2tx6isnMGJE4GXLVRi0qxppSKwZO0+b33mXiX5kN5xP6fVBTACHQfoPFbBAMdZvL3gAkvvFSmtyhVfhYVfobn5Pnbs+JG3LTNzACI6xkom+q+lVJjsKoCRqElViVSVKxXXp4cMuYVBg64HoF+/mUya9CaZmQNs7pUKh46IlQpTwAIYQM6AlZBey8Dcgdw4+UbLp4gDnZQ0f/oYW08lavtQ0vZ+NB6YFnDdOh5VuazaV51sRITRo39Fz54VFBV9C5G0rl+kEoomaykVphELlhPo/xqBlCuA0T6BDNxJZLH+UBKInhqlEllnyVo6IlYqTIHWatvaU00iVeXS9engXK6TOByZdndDBaFrxEqFKVHXau1gd1WuRFqfTlTHjr3Pe++dRl3du3Z3RQWhgVipMFldACOZ2fmhRE+N6tqhQ0tYt+48Tpz4lA0bLqWp6RO7u6QC0DVipVRUfLdyxTOBLJHWpxNRQ8MmPvigFHwyGrKzT2Py5HfIyOhrX8dSVGdrxBqIlVJJSZPmurZjx53s3n2PX1te3heYOPE1HI4eNvUqNWllLaVUt2P3+nQyGDHivygsvMKvra7uX2zZ8k2McdnUK9WeBmKlVFLSpLmuiQhjx/6evLwv+LUfPPhndu6826ZeqfY0ECulkpImzYXG4ejhOTrR/wPK7t33UF39hE29Ur50jVgppVJAU9MO1qw5k5aWQz6taZSXv0rfvtNs61eq0DVipZRKcdnZIykt/SsOR5ZPaysbN86hvr7Stn4pDcRKKZUy8vLOZNy4Z3Dnlru1th6nsvIiTpyosq9jKS6qQCwi94vIFhFZLyIvi0i+Rf1SSikVA/37z2bUqF/6tZ04sZfKyhk4ncdt6lVqi3ZE/BpQaowpBz4Gbou+S0oppWKpuPiHDB58g19bRkYBoFua7BBVIDbGrDTGOD1fvgsUR98lpZRSseQ+OvFh+vW7BICBA79JWdmrpKfn2dyz1GTl6UvXAM9beD2llFIxIpLG+PF/5sCBZygq+g4i0vWLVEx0GYhF5B9AoONL7jDGLPU85w7ACTzTyXWuBa4FGDp0aESdVUopZZ20tFwGDbrW7m6kvC4DsTHmi509LiLzgIuBqaaTTcnGmMeBx8G9jzjMfiqllIozl8uJw6HH1sdatFnTXwJuBS41xjRa0yWllFJ2O3r0Nd5/fyyNjdvt7kq3F23W9K+BXsBrIrJORB6zoE9KKaVsVF39eyorL6K5+RMqKy+ipeWI3V3q1qKaczDGjLaqI0oppexXU/M6W7d+y/t1U9M2KitnMnHiP0hLy+rklSpSWllLKaWUV37+BQwY8HW/tmPH3mbLlm/o0YkxooFYKaWUl4gwZswT5Oef59d+6NDz7Nx5hz2d6uY0ECullPLjcGQyYcJicnLG+rXv3r2IqqrHbepV96WBWCmlVAcZGX0oK3uVjIxCv/aPP/4uR4783aZedU8aiJVSSgWUnT2CsrJlOBzZPq2tbNr0FerrP7KtX92NBmKllFJB9e59BuPGPYv/0Yn1rF8/g+bmvfZ1rBvRQKyUUqpT/fvPYvToh/zaTp7c5zk68ZhNveo+NBArpZTqUnHxjQwe/AO/toaG9WzcOBeXq8WmXnUPGoiVUkqFZPToB+nX71K/tvT0XhjTalOPugcNxEoppULiPjrxWXr1mgLAkCHzGT/+ea24FSU9VkMppVTI0tJyKS1dRk3NCgYOnGd3d7oFHRErpZQKS48eAzUIW0gDsVJKKUu5XE67u5BUNBArpZSyzMGDz7N69SROnjxkd1eShgZipZRSUTPGsHv3L9i06XIaGzeyYcNMWlub7O5WUtBArJRSKmoHDvyJHTsWeL8+duwdtmy5Wo9ODIEGYqWUUlErLPwq+flT/doOHXrJLzirwDQQK6WUipr76MSXyMmZ4Ne+Z8/97Nv33zb1KjloIFZKKWWJjIx8ysuXk5k50K9927YbOHLkVZt6lfg0ECullLJMVtYwyspeweHI8Wl1sXHjXI4fX2tbvxKZBmKllFKW6tXrdMaPfw7fEONyNVBZOYPm5j32dSxBaSBWSilluYKCSygpedSv7eTJaiorL8LprLOpV4lJA7FSSqmYGDz4exQX3+zX1tCwgY0b5+jRiT40ECullIqZUaPup6Bgtl9bTc0/+Pjj6zHG2NSrxKKBWCmlVMyIOBg37o/06vVZv3aHIxPQYh+ggVgppVSMpaXlUFb2V7KyRgAwcuR9lJT8FpE0m3uWGPQ8YqWUUjGXmVlIefnfqK+vpLBwjt3dSSgaiJVSSsVFTs4YcnLG2N2NhBPV1LSI/JeIrBeRdSKyUkQGWdUxpZRSqcWYVru7YIto14jvN8aUG2MmAa8Ad0ffJaWUUqlm377/Zu3ac2ltbbS7K3EXVSA2xhzz+TIX0Fx0pZRSITPGxSefzGfbtu9y7NjbbN789ZQbGUu0+7hE5B7gaqAOON8YcyjI864FrvV8OQbYGtWNk08BcNjuTiQ5fQ+jp+9h9PQ9jF4qvofDjDH9Az3QZSAWkX8AAwM8dIcxZqnP824DsowxP46mp92ViKw2xkyxux/JTN/D6Ol7GD19D6On76G/LrOmjTFfDPFazwLLAQ3ESimlVIiizZou8fnyUmBLdN1RSimlUku0+4gXicgY3HXKPgWuj75L3dbjdnegG9D3MHr6HkZP38Po6XvoI+pkLaWUUkpFTmtNK6WUUjbSQKyUUkrZSAOxDUTkFhExIlJgd1+SjYjcLyJbPKVVXxaRfLv7lCxE5EsislVEtovIArv7k2xEZIiIvCEim0Vko4jcaHefkpWIpInIWhF5xe6+JAINxHEmIkOAC4HddvclSb0GlBpjyoGPgdts7k9SEPd5c78BvgyMB74mIuPt7VXScQL/aYwZB5wJfE/fw4jdCGy2uxOJQgNx/D0E/AgtBxoRY8xKY4zT8+W7QLGd/UkiZwDbjTE7jDEngeeAmTb3KakYY6qNMWs8fz+OO5AMtrdXyUdEioEZwP/a3ZdEoYE4jkTkUmCfMeYju/vSTVwD/M3uTiSJwcAen6/3okEkYiIyHKgA3rO5K8noYdyDEZfN/UgYeh6xxTorCQrcDkyLb4+STyhlVUXkDtxThc/Es29JTAK06axMBESkJ/AX4IftDr5RXRCRi4GDxpgPReQ8m7uTMDQQWyxYSVARKQNGAB+JCLinVNeIyBnGmP1x7GLC66qsqojMAy4GphrdCB+qvcAQn6+LgSqb+pK0RCQDdxB+xhiz2O7+JKGzgUtF5CIgC+gtIn8yxnzd5n7ZSgt62EREdgFTjDGpdgJJVETkS8CDwLnBTvpSHYlIOu7ktqnAPuAD4ApjzEZbO5ZExP0J+ingqDHmhzZ3J+l5RsS3GGMutrkrttM1YpVsfg30Al4TkXUi8pjdHUoGngS3G4AVuJOMXtAgHLazgauACzw/e+s8IzuloqIjYqWUUspGOiJWSimlbKSBWCmllLKRBmKllFLKRhqIlVJKKRtpIFZKKaVspIFYKaWUspEGYqWUUspG/x/EpEXxEzkUpwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "h = 0.1\n", - "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", - "ax.scatter(clsX[clsy == 0, 0], clsX[clsy == 0, 1], label='cl0')\n", - "ax.scatter(clsX[clsy == 1, 0], clsX[clsy == 1, 1], label='cl1')\n", - "ax.scatter(clsX[pred1 == 0, 0] + h, clsX[pred1 == 0, 1] + h, label='LR0')\n", - "ax.scatter(clsX[pred1 == 1, 0] + h, clsX[pred1 == 1, 1] + h, label='LR1')\n", - "ax.plot([x0, x1], [y0, y1], 'y--', lw=4, label='fronti\u00e8re LR')\n", - "ax.set_ylim([-3, 3])\n", - "ax.legend()\n", - "ax.set_title(\"Classification et neurones\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Un neurone de classification binaire produit deux sorties, une pour chaque classe, et sont normalis\u00e9es \u00e0 1. La fonction d'activation est la fonction [softmax](https://en.wikipedia.org/wiki/Softmax_function)." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "clsY = numpy.empty((clsy.shape[0], 2), dtype=numpy.float64)\n", - "clsY[:, 1] = clsy\n", - "clsY[:, 0] = 1 - clsy" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "NeuralTreeNode(weights=array([[-1.13305547, -1.03218058],\n", - " [ 0.04768595, -2.2063421 ]]), bias=array([ 0.32667765, -2.10119485]), activation='softmax')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "softneu = NeuralTreeNode(2, activation='softmax')\n", - "softneu" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0/20: loss: 745.5 lr=0.001 max(coef): 2.206\n", - "1/20: loss: 174.1 lr=9.95e-05 max(coef): 4.91\n", - "2/20: loss: 167.3 lr=7.05e-05 max(coef): 5.769\n", - "3/20: loss: 167.3 lr=5.76e-05 max(coef): 6.11\n", - "4/20: loss: 165.5 lr=4.99e-05 max(coef): 6.398\n", - "5/20: loss: 165.6 lr=4.47e-05 max(coef): 6.635\n", - "6/20: loss: 165.3 lr=4.08e-05 max(coef): 6.839\n", - "7/20: loss: 166.1 lr=3.78e-05 max(coef): 7.014\n", - "8/20: loss: 166 lr=3.53e-05 max(coef): 7.175\n", - "9/20: loss: 165.9 lr=3.33e-05 max(coef): 7.328\n", - "10/20: loss: 165.4 lr=3.16e-05 max(coef): 7.479\n", - "11/20: loss: 164.6 lr=3.01e-05 max(coef): 7.618\n", - "12/20: loss: 164 lr=2.89e-05 max(coef): 7.753\n", - "13/20: loss: 164 lr=2.77e-05 max(coef): 7.872\n", - "14/20: loss: 164.2 lr=2.67e-05 max(coef): 7.995\n", - "15/20: loss: 163.8 lr=2.58e-05 max(coef): 8.111\n", - "16/20: loss: 163.5 lr=2.5e-05 max(coef): 8.226\n", - "17/20: loss: 163.6 lr=2.42e-05 max(coef): 8.331\n", - "18/20: loss: 164.1 lr=2.36e-05 max(coef): 8.421\n", - "19/20: loss: 163.6 lr=2.29e-05 max(coef): 8.518\n", - "20/20: loss: 162.8 lr=2.24e-05 max(coef): 8.622\n" - ] - }, - { - "data": { - "text/plain": [ - "NeuralTreeNode(weights=array([[4.04165198, 3.20293386],\n", - " [7.45384894, 6.02257915]]), bias=array([8.62194837, 4.28542524]), activation='softmax')" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "softneu.fit(clsX, clsY, verbose=True, max_iter=20, lr=0.001)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[9.12184881e-01, 8.78151188e-02],\n", - " [9.99603939e-01, 3.96061397e-04],\n", - " [2.42055866e-01, 7.57944134e-01],\n", - " [9.99969480e-01, 3.05196263e-05],\n", - " [8.40757221e-01, 1.59242779e-01]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred = softneu.predict(clsX)\n", - "pred[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "pred2 = (pred[:, 1] > 0.5).astype(numpy.int64)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "x00, y00, x01, y01 = line_cls(-4, 4, softneu.coef[:1, 1:], softneu.bias[0])\n", - "x10, y10, x11, y11 = line_cls(-4, 4, softneu.coef[1:, 1:], softneu.bias[1])\n", - "xa, ya, xb, yb = line_cls(\n", - " -5, 5, softneu.coef[1:, 1:] - softneu.coef[:1, 1:],\n", - " softneu.bias[1] - softneu.bias[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", - "ax.scatter(clsX[clsy == 0, 0], clsX[clsy == 0, 1], label='cl0')\n", - "ax.scatter(clsX[clsy == 1, 0], clsX[clsy == 1, 1], label='cl1')\n", - "ax.scatter(clsX[pred1 == 0, 0] + h, clsX[pred1 == 0, 1] + h, label='LR0')\n", - "ax.scatter(clsX[pred1 == 1, 0] + h, clsX[pred1 == 1, 1] + h, label='LR1')\n", - "ax.scatter(clsX[pred2 == 0, 0] + h, clsX[pred2 == 0, 1] - h, label='NN0')\n", - "ax.scatter(clsX[pred2 == 1, 0] + h, clsX[pred2 == 1, 1] - h, label='NN1')\n", - "ax.plot([x0, x1], [y0, y1], 'y--', lw=4, label='fronti\u00e8re LR')\n", - "ax.plot([x00, x01], [y00, y01], 'r--', lw=4, label='droite neurone 0')\n", - "ax.plot([x10, x11], [y10, y11], 'b--', lw=4, label='droite neurone 1')\n", - "ax.plot([xa, xb], [ya, yb], 'c--', lw=4, label='fronti\u00e8re neurone')\n", - "ax.set_ylim([max(-6, min([-3, y10, y11, y11, y01])),\n", - " min(6, max([3, y10, y11, y11, y01]))])\n", - "ax.legend()\n", - "ax.set_title(\"Classification et neurones\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ca marche. On v\u00e9rifie en calculant le score. Le neurone a deux sorties. La fronti\u00e8re est d\u00e9finie par l'ensemble des points pour lesquels les deux sorties sont \u00e9gales. Par cons\u00e9quent, la distance entre les deux droites d\u00e9finies par les coefficients du neurone doivent \u00eatre \u00e9gales. Il existe une infinit\u00e9 de solutions menant \u00e0 la m\u00eame fronti\u00e8re." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9788" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.metrics import roc_auc_score\n", - "roc_auc_score(clsy, logr.predict_proba(clsX)[:, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9776" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "roc_auc_score(clsy, softneu.predict(clsX)[:, 1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Apprentissage du r\u00e9seau de neurones\n", - "\n", - "Maintenant qu'on a vu les diff\u00e9rentes fonctions d'activations et leur application sur des probl\u00e8mes simples, on revient aux arbres convertis sous la forme d'un r\u00e9seau de neurones. La prochaine \u00e9tape est de pouvoir am\u00e9liorer les performances du mod\u00e8le issu de la conversion d'un arbre de classification avec un algorithme du gradient. On construit pour cela un nuage de points un peu traficot\u00e9." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.76" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clsX = numpy.empty((150, 2), dtype=numpy.float64)\n", - "clsX[:100] = numpy.random.randn(100, 2)\n", - "clsX[:20, 0] -= 1\n", - "clsX[20:40, 0] -= 0.8\n", - "clsX[:100, 1] /= 2\n", - "clsX[:100, 1] += clsX[:100, 0] ** 2\n", - "clsX[100:] = numpy.random.randn(50, 2)\n", - "clsX[100:, 0] /= 2\n", - "clsX[100:, 1] += 2.5\n", - "clsy = numpy.zeros(X.shape[0], dtype=numpy.int64)\n", - "clsy[100:] = 1\n", - "\n", - "logr = LogisticRegression()\n", - "logr.fit(clsX, clsy)\n", - "pred1 = logr.predict(clsX)\n", - "logr.score(clsX, clsy)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "x0, y0, x1, y1 = line_cls(-3, 3, logr.coef_, logr.intercept_)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python372_x64\\lib\\site-packages\\ipykernel_launcher.py:16: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", - " app.launch_new_instance()\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n", - "plot_grid(clsX, clsy, logr.predict, logr.__class__.__name__, ax=ax)\n", - "ax.plot([x0, x1], [y0, y1], 'y--', lw=4, label='fronti\u00e8re LR');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "M\u00eame chose avec un arbre de d\u00e9cision et le r\u00e9seau de neurones converti." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dec = DecisionTreeClassifier(max_depth=2)\n", - "dec.fit(clsX, clsy)\n", - "pred2 = dec.predict(clsX)\n", - "dec.score(clsX, clsy)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "net = NeuralTreeNet.create_from_tree(dec, 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.9315, 0.9)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(roc_auc_score(clsy, dec.predict_proba(clsX)[:, 1]),\n", - " accuracy_score(clsy, dec.predict(clsX)))" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8328, 0.7533333333333333)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(roc_auc_score(clsy, net.predict(clsX)[:, 1]),\n", - " accuracy_score(clsy, numpy.argmax(net.predict(clsX)[:, -2:], axis=1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python372_x64\\lib\\site-packages\\ipykernel_launcher.py:16: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", - " app.launch_new_instance()\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", - "plot_grid(clsX, clsy, dec.predict, dec.__class__.__name__, ax=ax[0])\n", - "plot_grid(clsX, clsy,\n", - " lambda x: numpy.argmax(net.predict(x)[:, -2:], axis=1),\n", - " net.__class__.__name__, ax=ax[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et on apprend le r\u00e9seau de neurones en partant de l'arbre de d\u00e9part." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1., 0.],\n", - " [1., 0.],\n", - " [1., 0.]])" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.ml.neural_tree import label_class_to_softmax_output\n", - "clsY = label_class_to_softmax_output(clsy)\n", - "clsY[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0/30: loss: 715.7 lr=1e-05 max(coef): 1\n", - "1/30: loss: 688.7 lr=8.14e-07 max(coef): 1.149\n", - "2/30: loss: 699.1 lr=5.76e-07 max(coef): 1.179\n", - "3/30: loss: 703.4 lr=4.71e-07 max(coef): 1.193\n", - "4/30: loss: 706.6 lr=4.08e-07 max(coef): 1.204\n", - "5/30: loss: 709.2 lr=3.65e-07 max(coef): 1.214\n", - "6/30: loss: 710.5 lr=3.33e-07 max(coef): 1.223\n", - "7/30: loss: 712.6 lr=3.08e-07 max(coef): 1.231\n", - "8/30: loss: 715 lr=2.89e-07 max(coef): 1.239\n", - "9/30: loss: 717.3 lr=2.72e-07 max(coef): 1.246\n", - "10/30: loss: 718.9 lr=2.58e-07 max(coef): 1.253\n", - "11/30: loss: 720.9 lr=2.46e-07 max(coef): 1.26\n", - "12/30: loss: 723.2 lr=2.36e-07 max(coef): 1.267\n", - "13/30: loss: 724.6 lr=2.26e-07 max(coef): 1.274\n", - "14/30: loss: 726.3 lr=2.18e-07 max(coef): 1.28\n", - "15/30: loss: 727.8 lr=2.11e-07 max(coef): 1.286\n", - "16/30: loss: 729.3 lr=2.04e-07 max(coef): 1.292\n", - "17/30: loss: 730.7 lr=1.98e-07 max(coef): 1.298\n", - "18/30: loss: 731.5 lr=1.92e-07 max(coef): 1.304\n", - "19/30: loss: 732.8 lr=1.87e-07 max(coef): 1.31\n", - "20/30: loss: 734.2 lr=1.83e-07 max(coef): 1.315\n", - "21/30: loss: 735.2 lr=1.78e-07 max(coef): 1.321\n", - "22/30: loss: 736 lr=1.74e-07 max(coef): 1.326\n", - "23/30: loss: 736.8 lr=1.7e-07 max(coef): 1.332\n", - "24/30: loss: 737.2 lr=1.67e-07 max(coef): 1.337\n", - "25/30: loss: 738.5 lr=1.63e-07 max(coef): 1.342\n", - "26/30: loss: 739.2 lr=1.6e-07 max(coef): 1.347\n", - "27/30: loss: 740.1 lr=1.57e-07 max(coef): 1.353\n", - "28/30: loss: 740.9 lr=1.54e-07 max(coef): 1.358\n", - "29/30: loss: 741.9 lr=1.52e-07 max(coef): 1.363\n", - "30/30: loss: 742.7 lr=1.49e-07 max(coef): 1.368\n" - ] - }, - { - "data": { - "text/plain": [ - "NeuralTreeNet(2)" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net2 = net.copy()\n", - "net2.fit(clsX, clsY, verbose=True, max_iter=30, lr=1e-5)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\python372_x64\\lib\\site-packages\\ipykernel_launcher.py:16: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", - " app.launch_new_instance()\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", - "plot_grid(clsX, clsy,\n", - " lambda x: numpy.argmax(net.predict(x)[:, -2:], axis=1),\n", - " \"Avant apprentissage\", ax=ax[0])\n", - "plot_grid(clsX, clsy,\n", - " lambda x: numpy.argmax(net2.predict(x)[:, -2:], axis=1),\n", - " \"Apr\u00e8s apprentissage\", ax=ax[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ca ne marche pas ou pas tr\u00e8s bien. Il faudrait v\u00e9rifier que la configuration actuelle ne se trouve pas dans un minimum locale auquel cas l'apprentissage par gradient ne donnera quasiment rien." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.8328, 0.6666666666666666)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Un arbre de décision en réseaux de neurones\n", + "\n", + "L'idée est de convertir sous la forme d'un réseaux de neurones un arbre de décision puis de continuer l'apprentissage de façon à obtenir un assemblage de régression logistique plutôt que de décision binaire. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import ListedColormap\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Un exemple sur Iris\n", + "\n", + "La méthode ne marche que sur un problème de classification binaire." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "\n", + "data = load_iris()\n", + "X, y = data.data[:, :2], data.target\n", + "y = y % 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6052631578947368" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "dec = DecisionTreeClassifier(max_depth=2, random_state=11)\n", + "dec.fit(X_train, y_train)\n", + "dec.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "digraph Tree {\n", + "node [shape=box, fontsize=10, style=\"filled\", color=\"black\", fontname=\"helvetica\"] ;\n", + "edge [fontname=\"helvetica\"] ;\n", + "0 [label=\"x[1] <= 2.95\\ngini = 0.454\\nsamples = 112\\nvalue = [73, 39]\", fillcolor=\"#f3c4a3\"] ;\n", + "1 [label=\"x[0] <= 7.05\\ngini = 0.429\\nsamples = 45\\nvalue = [14, 31]\", fillcolor=\"#92c9f1\"] ;\n", + "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n", + "2 [label=\"gini = 0.402\\nsamples = 43\\nvalue = [12.0, 31.0]\", fillcolor=\"#86c3ef\"] ;\n", + "1 -> 2 ;\n", + "3 [label=\"gini = 0.0\\nsamples = 2\\nvalue = [2, 0]\", fillcolor=\"#e58139\"] ;\n", + "1 -> 3 ;\n", + "4 [label=\"x[1] <= 3.25\\ngini = 0.21\\nsamples = 67\\nvalue = [59, 8]\", fillcolor=\"#e99254\"] ;\n", + "0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n", + "5 [label=\"gini = 0.375\\nsamples = 32\\nvalue = [24, 8]\", fillcolor=\"#eeab7b\"] ;\n", + "4 -> 5 ;\n", + "6 [label=\"gini = 0.0\\nsamples = 35\\nvalue = [35, 0]\", fillcolor=\"#e58139\"] ;\n", + "4 -> 6 ;\n", + "}\n" + ] + } + ], + "source": [ + "from sklearn.tree import export_graphviz\n", + "from mlstatpy.render_js_dot import RenderJsDot\n", + "\n", + "dot = export_graphviz(dec, filled=True)\n", + "dot = dot.replace(\"shape=box, \", \"shape=box, fontsize=10, \")\n", + "RenderJsDot(dot)\n", + "print(dot)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'arbre de décision est petit donc visuellement réduit et il est perfectible aussi." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Même exemple en réseau de neurones\n", + "\n", + "Chaque noeud de l'arbre de décision est converti en deux neurones :\n", + "* un qui le relie à l'entrée et qui évalue la décision, il produit la valeur $o_1$\n", + "* un autre qui associe le résultat du premier noeud avec celui le précède dans la structure de l'arbre de décision, il produit la valeur $o_2$\n", + "La décision finale est quelque chose comme $sigmoid(o_1 + o_2 - 1)$. Un neurone agrège le résultat de toutes les feuilles.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" ], - "source": [ - "(roc_auc_score(clsy, net2.predict(clsX)[:, 1]),\n", - " accuracy_score(clsy, numpy.argmax(net2.predict(clsX)[:, -2:], axis=1)))" + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.75580743, 0.24419257],\n", - " [0.78688454, 0.21311546],\n", - " [0.77486773, 0.22513227],\n", - " [0.78826818, 0.21173182],\n", - " [0.91909152, 0.08090848]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.ml.neural_tree import NeuralTreeNet\n", + "\n", + "net = NeuralTreeNet.create_from_tree(dec)\n", + "RenderJsDot(net.to_dot())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On considère une entrée en particulier." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.27906977, 0.72093023]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = 60\n", + "dec.predict_proba(X[n : n + 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les sorties du réseau de neurones :" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.12536069, 0.87463931]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.predict(X[n : n + 1])[:, -2:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et on trace les valeurs intermédiaires." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" ], - "source": [ - "net2.predict(clsX)[-5:, -2:]" + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.54085131, 0.45914869],\n", - " [0.53771359, 0.46228641],\n", - " [0.62108042, 0.37891958],\n", - " [0.49166938, 0.50833062],\n", - " [0.61319735, 0.38680265]])" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RenderJsDot(net.to_dot(X=X[n]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On poursuit la comparaison :" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.75 , 0.25 ],\n", + " [0.75 , 0.25 ],\n", + " [0.27906977, 0.72093023],\n", + " [1. , 0. ],\n", + " [0.27906977, 0.72093023]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dec.predict_proba(X_test)[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.79156817, 0.20843183],\n", + " [0.73646978, 0.26353022],\n", + " [0.29946111, 0.70053889],\n", + " [0.94070094, 0.05929906],\n", + " [0.24924737, 0.75075263]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.predict(X_test)[:5, -2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 0. ],\n", + " [0.75, 0.25],\n", + " [1. , 0. ],\n", + " [0.75, 0.25],\n", + " [0.75, 0.25]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dec.predict_proba(X_test)[-5:]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.93247891, 0.06752109],\n", + " [0.86338585, 0.13661415],\n", + " [0.98219036, 0.01780964],\n", + " [0.98352807, 0.01647193],\n", + " [0.73646978, 0.26353022]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.predict(X_test)[-5:, -2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numpy.argmax(net.predict(X_test)[-5:, -2:], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On compare visuellement les deux frontières de classification." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_grid(X, y, fct, title, ax=None):\n", + " cmap_light = ListedColormap([\"orange\", \"cyan\", \"cornflowerblue\"])\n", + " cmap_bold = ListedColormap([\"darkorange\", \"c\", \"darkblue\"])\n", + "\n", + " h = 0.05\n", + " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", + " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", + " xx, yy = numpy.meshgrid(\n", + " numpy.arange(x_min, x_max, h), numpy.arange(y_min, y_max, h)\n", + " )\n", + " Z = fct(numpy.c_[xx.ravel(), yy.ravel()])\n", + "\n", + " Z = Z.reshape(xx.shape)\n", + " if ax is None:\n", + " _, ax = plt.subplots(1, 1)\n", + " ax.pcolormesh(xx, yy, Z, cmap=cmap_light)\n", + "\n", + " ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor=\"k\", s=20)\n", + " ax.set_xlim(xx.min(), xx.max())\n", + " ax.set_ylim(yy.min(), yy.max())\n", + " ax.set_title(title)\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "plot_grid(X, y, dec.predict, dec.__class__.__name__, ax=ax[0])\n", + "plot_grid(\n", + " X,\n", + " y,\n", + " lambda x: numpy.argmax(net.predict(x)[:, -2:], axis=1),\n", + " net.__class__.__name__,\n", + " ax=ax[1],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le code qui produit les prédictions du réseau de neurones est assez long à exécuter mais il produit à peu près les mêmes frontières excepté qu'elles sont plus arrondies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intermède de simples neurones de régression\n", + "\n", + "Avant d'apprendre ou plutôt de continuer l'apprentissage des coefficients du réseaux de neurones, voyons comment un neurone se débrouille sur un problème de régression. Le neurone n'est pas converti, il est appris." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "regX = numpy.empty((150, 1), dtype=numpy.float64)\n", + "regX[:50, 0] = numpy.random.randn(50) - 4\n", + "regX[50:100, 0] = numpy.random.randn(50)\n", + "regX[100:, 0] = numpy.random.randn(50) + 4\n", + "noise = numpy.random.randn(regX.shape[0]) / 10\n", + "regY = regX[:, 0] * -0.5 * 0.2 + noise\n", + "regY[regX[:, 0] > 0.3] = noise[regX[:, 0] > 0.3]\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.scatter(regX[:, 0], regY)\n", + "ax.set_title(\"Nuage de points linéaire par morceaux\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On cale une régression avec *scikit-learn*." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "lr = LinearRegression()\n", + "lr.fit(regX, regY)\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.scatter(regX[:, 0], regY)\n", + "ax.scatter(regX[:, 0], lr.predict(regX))\n", + "ax.set_title(\"Régression scikit-learn\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et maintenant un neurone avec une fonction d'activation \"identity\"." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NeuralTreeNode(weights=array([-0.2630011]), bias=np.float64(0.009885644406795709), activation='identity')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.ml.neural_tree import NeuralTreeNode\n", + "\n", + "neu = NeuralTreeNode(1, activation=\"identity\")\n", + "neu" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0/20: loss: 84.07 lr=0.002 max(coef): 0.26 l1=0/0.27 l2=0/0.069\n", + "1/20: loss: 4.075 lr=0.000163 max(coef): 0.092 l1=1.4/0.17 l2=1.2/0.014\n", + "2/20: loss: 2.51 lr=0.000115 max(coef): 0.12 l1=0.6/0.17 l2=0.22/0.017\n", + "3/20: loss: 2.661 lr=9.42e-05 max(coef): 0.13 l1=0.34/0.19 l2=0.072/0.021\n", + "4/20: loss: 2.493 lr=8.16e-05 max(coef): 0.13 l1=0.59/0.18 l2=0.23/0.019\n", + "5/20: loss: 2.477 lr=7.3e-05 max(coef): 0.13 l1=3.1/0.18 l2=6.8/0.019\n", + "6/20: loss: 2.48 lr=6.66e-05 max(coef): 0.13 l1=0.32/0.18 l2=0.069/0.019\n", + "7/20: loss: 2.48 lr=6.17e-05 max(coef): 0.14 l1=0.46/0.19 l2=0.13/0.021\n", + "8/20: loss: 2.567 lr=5.77e-05 max(coef): 0.14 l1=1.3/0.19 l2=1.1/0.022\n", + "9/20: loss: 2.46 lr=5.44e-05 max(coef): 0.14 l1=0.27/0.19 l2=0.048/0.022\n", + "10/20: loss: 2.476 lr=5.16e-05 max(coef): 0.14 l1=2.7/0.19 l2=5.5/0.022\n", + "11/20: loss: 2.478 lr=4.92e-05 max(coef): 0.14 l1=1.8/0.19 l2=2.2/0.021\n", + "12/20: loss: 2.465 lr=4.71e-05 max(coef): 0.14 l1=2.5/0.19 l2=4.5/0.022\n", + "13/20: loss: 2.48 lr=4.53e-05 max(coef): 0.14 l1=0.19/0.19 l2=0.024/0.022\n", + "14/20: loss: 2.464 lr=4.36e-05 max(coef): 0.14 l1=0.12/0.19 l2=0.0072/0.023\n", + "15/20: loss: 2.467 lr=4.22e-05 max(coef): 0.14 l1=0.85/0.19 l2=0.49/0.023\n", + "16/20: loss: 2.472 lr=4.08e-05 max(coef): 0.14 l1=0.61/0.19 l2=0.34/0.023\n", + "17/20: loss: 2.463 lr=3.96e-05 max(coef): 0.14 l1=0.42/0.19 l2=0.11/0.023\n", + "18/20: loss: 2.46 lr=3.85e-05 max(coef): 0.14 l1=0.6/0.19 l2=0.18/0.022\n", + "19/20: loss: 2.483 lr=3.75e-05 max(coef): 0.14 l1=0.1/0.19 l2=0.0064/0.022\n", + "20/20: loss: 2.46 lr=3.65e-05 max(coef): 0.14 l1=1.5/0.19 l2=1.6/0.022\n" + ] + }, + { + "data": { + "text/plain": [ + "NeuralTreeNode(weights=array([-0.05022479]), bias=np.float64(0.1388943013680868), activation='identity')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neu.fit(regX, regY, verbose=True, max_iter=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.scatter(regX[:, 0], regY)\n", + "ax.scatter(regX[:, 0], lr.predict(regX), label=\"sklearn\")\n", + "ax.scatter(regX[:, 0], neu.predict(regX), label=\"NeuralTreeNode\")\n", + "ax.legend()\n", + "ax.set_title(\"Régression et neurones\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ca marche. Et avec d'autres fonctions d'activation..." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:00<00:00, 3.22it/s]\n" + ] + } + ], + "source": [ + "neus = {\"identity\": neu}\n", + "for act in tqdm([\"relu\", \"leakyrelu\", \"sigmoid\"]):\n", + " nact = NeuralTreeNode(1, activation=act)\n", + " nact.fit(regX, regY)\n", + " neus[act] = nact" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(NeuralTreeNode(weights=array([-0.17040281]), bias=np.float64(-0.286333299470671), activation='relu'),\n", + " NeuralTreeNode(weights=array([-0.21481952]), bias=np.float64(-0.48562866410120015), activation='leakyrelu'))" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neus[\"relu\"], neus[\"leakyrelu\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.scatter(regX[:, 0], regY)\n", + "ax.scatter(regX[:, 0], lr.predict(regX), label=\"sklearn\")\n", + "for k, v in neus.items():\n", + " ax.scatter(regX[:, 0], v.predict(regX), label=k)\n", + "ax.legend()\n", + "ax.set_title(\"Régression, neurone\\nactivation\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rien de surprenant. La fonction sigmoïde prend ses valeurs entre 0 et 1. La fonction *relu* est parfois nulle sur une demi-droite, dès que la fonction est nulle sur l'ensemble du nuage de points, le gradient est nul partout (voir [Rectifier (neural networks)](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))). La fonction leaky relu est définie comme suit :\n", + "\n", + "$$f(x) = \\left\\{\\begin{array}{l} x \\, si \\, x > 0 \\\\ \\frac{x}{100} \\, sinon \\end{array}\\right.$$\n", + "\n", + "Le gradient n'est pas nul sur la partie la plus plate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intermède de simples neurones de classification\n", + "\n", + "Avant d'apprendre ou plutôt de continuer l'apprentissage des coefficients du réseaux de neurones, voyons comment un neurone se débrouille sur un problème de classification. Le neurone n'est pas converti mais appris." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "\n", + "clsX = numpy.empty((100, 2), dtype=numpy.float64)\n", + "clsX[:50] = numpy.random.randn(50, 2)\n", + "clsX[50:] = numpy.random.randn(50, 2) + 2\n", + "clsy = numpy.zeros(100, dtype=numpy.int64)\n", + "clsy[50:] = 1\n", + "\n", + "logr = LogisticRegression()\n", + "logr.fit(clsX, clsy)\n", + "pred1 = logr.predict(clsX)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def line_cls(x0, x1, coef, bias):\n", + " y0 = -(coef[0, 0] * x0 + bias) / coef[0, 1]\n", + " y1 = -(coef[0, 0] * x1 + bias) / coef[0, 1]\n", + " return x0, y0, x1, y1\n", + "\n", + "\n", + "x0, y0, x1, y1 = line_cls(-5, 5, logr.coef_, logr.intercept_)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "h = 0.1\n", + "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n", + "ax.scatter(clsX[clsy == 0, 0], clsX[clsy == 0, 1], label=\"cl0\")\n", + "ax.scatter(clsX[clsy == 1, 0], clsX[clsy == 1, 1], label=\"cl1\")\n", + "ax.scatter(clsX[pred1 == 0, 0] + h, clsX[pred1 == 0, 1] + h, label=\"LR0\")\n", + "ax.scatter(clsX[pred1 == 1, 0] + h, clsX[pred1 == 1, 1] + h, label=\"LR1\")\n", + "ax.plot([x0, x1], [y0, y1], \"y--\", lw=4, label=\"frontière LR\")\n", + "ax.set_ylim([-3, 3])\n", + "ax.legend()\n", + "ax.set_title(\"Classification et neurones\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un neurone de classification binaire produit deux sorties, une pour chaque classe, et sont normalisées à 1. La fonction d'activation est la fonction [softmax](https://en.wikipedia.org/wiki/Softmax_function)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "clsY = numpy.empty((clsy.shape[0], 2), dtype=numpy.float64)\n", + "clsY[:, 1] = clsy\n", + "clsY[:, 0] = 1 - clsy" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NeuralTreeNode(weights=array([[1.30014733, 1.48516886],\n", + " [0.12365284, 0.50958825]]), bias=array([-1.00277994, -0.24843673]), activation='softmax')" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "softneu = NeuralTreeNode(2, activation=\"softmax\")\n", + "softneu" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0/20: loss: 1225 lr=0.001 max(coef): 1.5 l1=0/4.7 l2=0/5.2\n", + "1/20: loss: 285.3 lr=9.95e-05 max(coef): 6.1 l1=41/26 l2=3.4e+02/1.2e+02\n", + "2/20: loss: 262.8 lr=7.05e-05 max(coef): 6.9 l1=30/30 l2=2e+02/1.6e+02\n", + "3/20: loss: 255.5 lr=5.76e-05 max(coef): 7.1 l1=36/31 l2=2.5e+02/1.7e+02\n", + "4/20: loss: 253 lr=4.99e-05 max(coef): 7.3 l1=32/32 l2=2e+02/1.9e+02\n", + "5/20: loss: 245.7 lr=4.47e-05 max(coef): 7.5 l1=26/33 l2=1.5e+02/2e+02\n", + "6/20: loss: 243.7 lr=4.08e-05 max(coef): 7.7 l1=34/34 l2=2.2e+02/2.1e+02\n", + "7/20: loss: 240.7 lr=3.78e-05 max(coef): 7.8 l1=32/35 l2=2.7e+02/2.2e+02\n", + "8/20: loss: 238.3 lr=3.53e-05 max(coef): 7.9 l1=27/36 l2=1.5e+02/2.2e+02\n", + "9/20: loss: 232.6 lr=3.33e-05 max(coef): 8 l1=11/36 l2=27/2.3e+02\n", + "10/20: loss: 232.2 lr=3.16e-05 max(coef): 8.1 l1=12/37 l2=32/2.4e+02\n", + "11/20: loss: 228.4 lr=3.01e-05 max(coef): 8.2 l1=27/37 l2=1.5e+02/2.4e+02\n", + "12/20: loss: 223.3 lr=2.89e-05 max(coef): 8.3 l1=32/38 l2=2e+02/2.5e+02\n", + "13/20: loss: 221.1 lr=2.77e-05 max(coef): 8.4 l1=27/38 l2=1.5e+02/2.6e+02\n", + "14/20: loss: 219.5 lr=2.67e-05 max(coef): 8.5 l1=15/39 l2=58/2.6e+02\n", + "15/20: loss: 216.1 lr=2.58e-05 max(coef): 8.5 l1=37/39 l2=2.7e+02/2.7e+02\n", + "16/20: loss: 214.6 lr=2.5e-05 max(coef): 8.6 l1=19/40 l2=98/2.7e+02\n", + "17/20: loss: 212.4 lr=2.42e-05 max(coef): 8.7 l1=18/40 l2=83/2.8e+02\n", + "18/20: loss: 210.7 lr=2.36e-05 max(coef): 8.7 l1=20/40 l2=85/2.9e+02\n", + "19/20: loss: 208.3 lr=2.29e-05 max(coef): 8.8 l1=19/41 l2=1.1e+02/2.9e+02\n", + "20/20: loss: 206.4 lr=2.24e-05 max(coef): 8.8 l1=35/41 l2=2.7e+02/3e+02\n" + ] + }, + { + "data": { + "text/plain": [ + "NeuralTreeNode(weights=array([[5.54615581, 5.83619756],\n", + " [8.82929618, 8.48175986]]), bias=array([7.52191022, 4.91851196]), activation='softmax')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "softneu.fit(clsX, clsY, verbose=True, max_iter=20, lr=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3.69523065e-01, 6.30476935e-01],\n", + " [8.29284938e-01, 1.70715062e-01],\n", + " [3.48656758e-01, 6.51343242e-01],\n", + " [9.38509501e-01, 6.14904995e-02],\n", + " [9.99116470e-01, 8.83529803e-04]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = softneu.predict(clsX)\n", + "pred[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "pred2 = (pred[:, 1] > 0.5).astype(numpy.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "x00, y00, x01, y01 = line_cls(-4, 4, softneu.coef[:1, 1:], softneu.bias[0])\n", + "x10, y10, x11, y11 = line_cls(-4, 4, softneu.coef[1:, 1:], softneu.bias[1])\n", + "xa, ya, xb, yb = line_cls(\n", + " -5,\n", + " 5,\n", + " softneu.coef[1:, 1:] - softneu.coef[:1, 1:],\n", + " softneu.bias[1] - softneu.bias[0],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n", + "for i in [0, 1]:\n", + " ax[i].scatter(clsX[clsy == 0, 0], clsX[clsy == 0, 1], label=\"cl0\")\n", + " ax[i].scatter(clsX[clsy == 1, 0], clsX[clsy == 1, 1], label=\"cl1\")\n", + " ax[i].scatter(clsX[pred1 == 0, 0] + h, clsX[pred1 == 0, 1] + h, label=\"LR0\")\n", + " ax[i].scatter(clsX[pred1 == 1, 0] + h, clsX[pred1 == 1, 1] + h, label=\"LR1\")\n", + " ax[i].scatter(clsX[pred2 == 0, 0] + h, clsX[pred2 == 0, 1] - h, label=\"NN0\")\n", + " ax[i].scatter(clsX[pred2 == 1, 0] + h, clsX[pred2 == 1, 1] - h, label=\"NN1\")\n", + "ax[0].plot([x0, x1], [y0, y1], \"y--\", lw=4, label=\"frontière LR\")\n", + "ax[1].plot([x00, x01], [y00, y01], \"r--\", lw=4, label=\"droite neurone 0\")\n", + "ax[1].plot([x10, x11], [y10, y11], \"b--\", lw=4, label=\"droite neurone 1\")\n", + "ax[0].plot([xa, xb], [ya, yb], \"c--\", lw=4, label=\"frontière neurone\")\n", + "ax[0].set_ylim(\n", + " [max(-6, min([-3, y10, y11, y11, y01])), min(6, max([3, y10, y11, y11, y01]))]\n", + ")\n", + "ax[1].set_ylim(\n", + " [max(-6, min([-3, y10, y11, y11, y01])), min(6, max([3, y10, y11, y11, y01]))]\n", + ")\n", + "ax[0].legend()\n", + "ax[1].legend()\n", + "ax[0].set_title(\"Frontière de classification\")\n", + "ax[1].set_title(\"Neurones\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ca marche. On vérifie en calculant le score. Le neurone a deux sorties. La frontière est définie par l'ensemble des points pour lesquels les deux sorties sont égales. Par conséquent, la distance entre les deux droites définies par les coefficients du neurone doivent être égales. Il existe une infinité de solutions menant à la même frontière. On pourrait pénaliser les coefficients pour converger toujours vers la même solution." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.9896)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import roc_auc_score\n", + "\n", + "roc_auc_score(clsy, logr.predict_proba(clsX)[:, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.9871999999999999)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roc_auc_score(clsy, softneu.predict(clsX)[:, 1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La performance est quasiment identique. Que ce soit la régression ou la classification, l'apprentissage d'un neurone fonctionne. En sera-t-il de même pour un assemblage de neurones ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apprentissage du réseau de neurones\n", + "\n", + "Maintenant qu'on a vu les différentes fonctions d'activations et leur application sur des problèmes simples, on revient aux arbres convertis sous la forme d'un réseau de neurones. La prochaine étape est de pouvoir améliorer les performances du modèle issu de la conversion d'un arbre de classification avec un algorithme du gradient. On construit pour cela un nuage de points un peu traficoté." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6666666666666666" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clsX = numpy.empty((150, 2), dtype=numpy.float64)\n", + "clsX[:100] = numpy.random.randn(100, 2)\n", + "clsX[:20, 0] -= 1\n", + "clsX[20:40, 0] -= 0.8\n", + "clsX[:100, 1] /= 2\n", + "clsX[:100, 1] += clsX[:100, 0] ** 2\n", + "clsX[100:] = numpy.random.randn(50, 2)\n", + "clsX[100:, 0] /= 2\n", + "clsX[100:, 1] += 2.5\n", + "clsy = numpy.zeros(X.shape[0], dtype=numpy.int64)\n", + "clsy[100:] = 1\n", + "\n", + "logr = LogisticRegression()\n", + "logr.fit(clsX, clsy)\n", + "pred1 = logr.predict(clsX)\n", + "logr.score(clsX, clsy)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "x0, y0, x1, y1 = line_cls(-3, 3, logr.coef_, logr.intercept_)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n", + "plot_grid(clsX, clsy, logr.predict, logr.__class__.__name__, ax=ax)\n", + "ax.plot([x0, x1], [y0, y1], \"y--\", lw=4, label=\"frontière LR\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Même chose avec un arbre de décision et le réseau de neurones converti." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.88" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dec = DecisionTreeClassifier(max_depth=2)\n", + "dec.fit(clsX, clsy)\n", + "pred2 = dec.predict(clsX)\n", + "dec.score(clsX, clsy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On convertit de réseau de neurones. Le second argument définit la pente dans la fonction d'activation." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "net = NeuralTreeNet.create_from_tree(dec, 0.5)\n", + "net15 = NeuralTreeNet.create_from_tree(dec, 15)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.8886), 0.88)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " roc_auc_score(clsy, dec.predict_proba(clsX)[:, 1]),\n", + " accuracy_score(clsy, dec.predict(clsX)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.8550000000000001), 0.6933333333333334)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " roc_auc_score(clsy, net.predict(clsX)[:, -1]),\n", + " accuracy_score(clsy, numpy.argmax(net.predict(clsX)[:, -2:], axis=1)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.8956000000000001), 0.88)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " roc_auc_score(clsy, net15.predict(clsX)[:, -1]),\n", + " accuracy_score(clsy, numpy.argmax(net15.predict(clsX)[:, -2:], axis=1)),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le réseau de neurones est plus ou moins performant selon la pente dans la fonction d'activation." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n", + "plot_grid(clsX, clsy, dec.predict, dec.__class__.__name__, ax=ax[0])\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(net.predict(x)[:, -2:], axis=1),\n", + " net.__class__.__name__,\n", + " ax=ax[1],\n", + ")\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(net15.predict(x)[:, -2:], axis=1),\n", + " net15.__class__.__name__ + \" 15\",\n", + " ax=ax[2],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et on apprend le réseau de neurones en partant de l'arbre de départ. On choisit celui qui a la pente d'activation la plus faible." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0.],\n", + " [1., 0.],\n", + " [1., 0.]])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.ml.neural_tree import label_class_to_softmax_output\n", + "\n", + "clsY = label_class_to_softmax_output(clsy)\n", + "clsY[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0/25: loss: 733.9 lr=3e-06 max(coef): 1 l1=0/15 l2=0/10\n", + "1/25: loss: 718.5 lr=2.44e-07 max(coef): 1 l1=1.8e+02/15 l2=3.2e+03/9.8\n", + "2/25: loss: 717.2 lr=1.73e-07 max(coef): 1 l1=2.6e+02/15 l2=3.9e+03/9.8\n", + "3/25: loss: 716.7 lr=1.41e-07 max(coef): 1 l1=1.9e+02/15 l2=3.3e+03/9.8\n", + "4/25: loss: 716.2 lr=1.22e-07 max(coef): 1 l1=2.4e+02/15 l2=4.1e+03/9.8\n", + "5/25: loss: 716 lr=1.09e-07 max(coef): 1.1 l1=1.9e+02/15 l2=3.2e+03/9.8\n", + "6/25: loss: 715.7 lr=9.99e-08 max(coef): 1.1 l1=2e+02/15 l2=3.1e+03/9.8\n", + "7/25: loss: 715.4 lr=9.25e-08 max(coef): 1.1 l1=2.8e+02/15 l2=4.3e+03/9.8\n", + "8/25: loss: 715.2 lr=8.66e-08 max(coef): 1.1 l1=3.3e+02/15 l2=9.2e+03/9.8\n", + "9/25: loss: 715 lr=8.16e-08 max(coef): 1.1 l1=2.8e+02/15 l2=4.1e+03/9.8\n", + "10/25: loss: 714.8 lr=7.74e-08 max(coef): 1.1 l1=1.9e+02/15 l2=3.4e+03/9.8\n", + "11/25: loss: 714.7 lr=7.38e-08 max(coef): 1.1 l1=3e+02/15 l2=4.9e+03/9.8\n", + "12/25: loss: 714.5 lr=7.07e-08 max(coef): 1.1 l1=2.8e+02/15 l2=4.2e+03/9.8\n", + "13/25: loss: 714.3 lr=6.79e-08 max(coef): 1.1 l1=2.2e+02/15 l2=4.2e+03/9.8\n", + "14/25: loss: 714.3 lr=6.54e-08 max(coef): 1.1 l1=2.9e+02/15 l2=3.9e+03/9.8\n", + "15/25: loss: 714.2 lr=6.32e-08 max(coef): 1.1 l1=3e+02/15 l2=7.1e+03/9.8\n", + "16/25: loss: 714 lr=6.12e-08 max(coef): 1.1 l1=2.7e+02/15 l2=4.1e+03/9.8\n", + "17/25: loss: 713.9 lr=5.94e-08 max(coef): 1.1 l1=2.9e+02/15 l2=4.5e+03/9.8\n", + "18/25: loss: 713.9 lr=5.77e-08 max(coef): 1.1 l1=2.3e+02/15 l2=3.4e+03/9.8\n", + "19/25: loss: 713.8 lr=5.62e-08 max(coef): 1.1 l1=2e+02/15 l2=3.2e+03/9.8\n", + "20/25: loss: 713.8 lr=5.48e-08 max(coef): 1.1 l1=2.8e+02/15 l2=4.2e+03/9.8\n", + "21/25: loss: 713.8 lr=5.34e-08 max(coef): 1.1 l1=2.8e+02/15 l2=4.2e+03/9.8\n", + "22/25: loss: 713.8 lr=5.22e-08 max(coef): 1.1 l1=2e+02/15 l2=3.4e+03/9.8\n", + "23/25: loss: 713.7 lr=5.11e-08 max(coef): 1.1 l1=2.9e+02/15 l2=4e+03/9.8\n", + "24/25: loss: 713.7 lr=5e-08 max(coef): 1.1 l1=2.8e+02/15 l2=3.9e+03/9.8\n", + "25/25: loss: 713.6 lr=4.9e-08 max(coef): 1.1 l1=2e+02/15 l2=3.5e+03/9.8\n" + ] + }, + { + "data": { + "text/plain": [ + "NeuralTreeNet(2)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net2 = net.copy()\n", + "net2.fit(clsX, clsY, verbose=True, max_iter=25, lr=3e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(net.predict(x)[:, -2:], axis=1),\n", + " \"Avant apprentissage\",\n", + " ax=ax[0],\n", + ")\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(net2.predict(x)[:, -2:], axis=1),\n", + " \"Après apprentissage\",\n", + " ax=ax[1],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ca ne marche pas ou pas très bien. Il faudrait vérifier que la configuration actuelle ne se trouve pas dans un minimum local auquel cas l'apprentissage par gradient ne donnera quasiment rien." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.8514), 0.6666666666666666)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " roc_auc_score(clsy, net2.predict(clsX)[:, -1]),\n", + " accuracy_score(clsy, numpy.argmax(net2.predict(clsX)[:, -2:], axis=1)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.60888966, 0.39111034],\n", + " [0.64307934, 0.35692066],\n", + " [0.55569147, 0.44430853],\n", + " [0.67979624, 0.32020376],\n", + " [0.93106273, 0.06893727]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net2.predict(clsX)[-5:, -2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.54506228, 0.45493772],\n", + " [0.58240467, 0.41759533],\n", + " [0.48917761, 0.51082239],\n", + " [0.62370674, 0.37629326],\n", + " [0.88382805, 0.11617195]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net.predict(clsX)[-5:, -2:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut essayer de repartir à zéro. Des fois ça peut marcher mais il faudrait beaucoup plus d'essai." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0/25: loss: 881.1 lr=3e-06 max(coef): 2.2 l1=0/32 l2=0/44\n", + "1/25: loss: 855.3 lr=2.44e-07 max(coef): 2.2 l1=1.3e+03/31 l2=1.8e+05/43\n", + "2/25: loss: 852.3 lr=1.73e-07 max(coef): 2.2 l1=4.7e+02/31 l2=2.1e+04/43\n", + "3/25: loss: 849.6 lr=1.41e-07 max(coef): 2.2 l1=4.4e+02/31 l2=1.8e+04/43\n", + "4/25: loss: 847.5 lr=1.22e-07 max(coef): 2.2 l1=3.4e+02/31 l2=6.4e+03/43\n", + "5/25: loss: 845.5 lr=1.09e-07 max(coef): 2.2 l1=6e+02/31 l2=2.8e+04/43\n", + "6/25: loss: 843.4 lr=9.99e-08 max(coef): 2.2 l1=2.9e+02/31 l2=5.9e+03/43\n", + "7/25: loss: 842 lr=9.25e-08 max(coef): 2.2 l1=7.2e+02/31 l2=5.5e+04/43\n", + "8/25: loss: 840.9 lr=8.66e-08 max(coef): 2.2 l1=6.8e+02/31 l2=3.6e+04/43\n", + "9/25: loss: 839.7 lr=8.16e-08 max(coef): 2.2 l1=3.7e+02/31 l2=8.7e+03/43\n", + "10/25: loss: 838 lr=7.74e-08 max(coef): 2.2 l1=1.3e+03/31 l2=1.8e+05/43\n", + "11/25: loss: 836.8 lr=7.38e-08 max(coef): 2.2 l1=4.2e+02/31 l2=1.7e+04/43\n", + "12/25: loss: 835.9 lr=7.07e-08 max(coef): 2.2 l1=5.7e+02/31 l2=4.1e+04/43\n", + "13/25: loss: 835.2 lr=6.79e-08 max(coef): 2.2 l1=9.1e+02/31 l2=7.7e+04/43\n", + "14/25: loss: 834 lr=6.54e-08 max(coef): 2.2 l1=9.2e+02/31 l2=7.5e+04/43\n", + "15/25: loss: 833.7 lr=6.32e-08 max(coef): 2.2 l1=3.6e+02/31 l2=8.4e+03/43\n", + "16/25: loss: 833.1 lr=6.12e-08 max(coef): 2.2 l1=4.9e+02/31 l2=2.2e+04/43\n", + "17/25: loss: 832.2 lr=5.94e-08 max(coef): 2.2 l1=3.3e+02/31 l2=6.4e+03/43\n", + "18/25: loss: 831.3 lr=5.77e-08 max(coef): 2.2 l1=4.5e+02/31 l2=1.5e+04/43\n", + "19/25: loss: 830.3 lr=5.62e-08 max(coef): 2.2 l1=5.2e+02/31 l2=3.2e+04/43\n", + "20/25: loss: 829.4 lr=5.48e-08 max(coef): 2.2 l1=9.5e+02/31 l2=8.5e+04/43\n", + "21/25: loss: 828.9 lr=5.34e-08 max(coef): 2.2 l1=7.4e+02/31 l2=4.1e+04/43\n", + "22/25: loss: 828.3 lr=5.22e-08 max(coef): 2.2 l1=5.4e+02/31 l2=2.2e+04/43\n", + "23/25: loss: 827.5 lr=5.11e-08 max(coef): 2.2 l1=1e+03/31 l2=1e+05/43\n", + "24/25: loss: 826.8 lr=5e-08 max(coef): 2.2 l1=5.1e+02/31 l2=2.4e+04/43\n", + "25/25: loss: 826 lr=4.9e-08 max(coef): 2.2 l1=3.1e+02/31 l2=6.8e+03/43\n" + ] + }, + { + "data": { + "text/plain": [ + "NeuralTreeNet(2)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net3 = net.copy()\n", + "dim = net3.training_weights.shape\n", + "net3.update_training_weights(numpy.random.randn(dim[0]))\n", + "net3.fit(clsX, clsY, verbose=True, max_iter=25, lr=3e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.7857999999999999), 0.6666666666666666)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " roc_auc_score(clsy, net3.predict(clsX)[:, -1]),\n", + " accuracy_score(clsy, numpy.argmax(net3.predict(clsX)[:, -2:], axis=1)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(net.predict(x)[:, -2:], axis=1),\n", + " \"Avant apprentissage\",\n", + " ax=ax[0],\n", + ")\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(net3.predict(x)[:, -2:], axis=1),\n", + " \"Après apprentissage\",\n", + " ax=ax[1],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autre architecture\n", + "\n", + "Cette fois-ci, on réduit le nombre de neurones. Au lieu d'avoir deux neurones par noeud du graphe, on assemble tous les neurones en deux : un pour les entrées, un autre pour le calcul des sorties. Les deux représentations ne sont pas implémentées de façon rigoureusement identique dans le module *mlstatpy*. Le code précise les différences." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" ], - "source": [ - "net.predict(clsX)[-5:, -2:]" + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" + ], + "source": [ + "netc = NeuralTreeNet.create_from_tree(dec, 1, arch=\"compact\")\n", + "RenderJsDot(netc.to_dot())" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.8584), 0.32666666666666666)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "(\n", + " roc_auc_score(clsy, netc.predict(clsX)[:, -1]),\n", + " accuracy_score(clsy, numpy.argmax(netc.predict(clsX)[:, -2:], axis=1)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(dec.predict_proba(x), axis=1),\n", + " \"Avant conversion\",\n", + " ax=ax[0],\n", + ")\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(netc.predict(x)[:, -2:], axis=1),\n", + " \"Après comversion\",\n", + " ax=ax[1],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On réapprend." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0/25: loss: 1224 lr=1e-06 max(coef): 1.8 l1=0/27 l2=0/28\n", + "1/25: loss: 853.2 lr=8.14e-08 max(coef): 1.8 l1=8.1e+02/27 l2=4.2e+04/28\n", + "2/25: loss: 812.8 lr=5.76e-08 max(coef): 1.8 l1=2.8e+02/27 l2=6.1e+03/28\n", + "3/25: loss: 794.9 lr=4.71e-08 max(coef): 1.8 l1=6.6e+02/27 l2=2.9e+04/28\n", + "4/25: loss: 783.6 lr=4.08e-08 max(coef): 1.8 l1=5.6e+02/27 l2=1.5e+04/28\n", + "5/25: loss: 771.7 lr=3.65e-08 max(coef): 1.8 l1=6e+02/27 l2=1.9e+04/28\n", + "6/25: loss: 763 lr=3.33e-08 max(coef): 1.8 l1=1.3e+02/27 l2=1.8e+03/28\n", + "7/25: loss: 755.1 lr=3.08e-08 max(coef): 1.8 l1=5.3e+02/27 l2=1.5e+04/28\n", + "8/25: loss: 748.3 lr=2.89e-08 max(coef): 1.8 l1=6.1e+02/27 l2=1.9e+04/28\n", + "9/25: loss: 741.3 lr=2.72e-08 max(coef): 1.8 l1=1.3e+03/27 l2=2.5e+05/28\n", + "10/25: loss: 736 lr=2.58e-08 max(coef): 1.8 l1=5.9e+02/27 l2=1.8e+04/28\n", + "11/25: loss: 729.2 lr=2.46e-08 max(coef): 1.8 l1=6.1e+02/27 l2=1.9e+04/28\n", + "12/25: loss: 723 lr=2.36e-08 max(coef): 1.8 l1=4.8e+02/27 l2=1e+04/28\n", + "13/25: loss: 718.7 lr=2.26e-08 max(coef): 1.8 l1=1e+03/27 l2=7.8e+04/28\n", + "14/25: loss: 713.8 lr=2.18e-08 max(coef): 1.8 l1=2.5e+02/27 l2=4.8e+03/28\n", + "15/25: loss: 709 lr=2.11e-08 max(coef): 1.8 l1=1.3e+03/27 l2=2.4e+05/28\n", + "16/25: loss: 705.2 lr=2.04e-08 max(coef): 1.8 l1=6.1e+02/27 l2=1.9e+04/28\n", + "17/25: loss: 701 lr=1.98e-08 max(coef): 1.8 l1=6.9e+02/27 l2=2.8e+04/28\n", + "18/25: loss: 696.8 lr=1.92e-08 max(coef): 1.8 l1=6.2e+02/27 l2=2e+04/28\n", + "19/25: loss: 693.1 lr=1.87e-08 max(coef): 1.8 l1=4.9e+02/27 l2=1.2e+04/28\n", + "20/25: loss: 689.5 lr=1.83e-08 max(coef): 1.8 l1=4.6e+02/27 l2=1.3e+04/28\n", + "21/25: loss: 686.3 lr=1.78e-08 max(coef): 1.8 l1=1.2e+03/27 l2=2e+05/28\n", + "22/25: loss: 683.7 lr=1.74e-08 max(coef): 1.8 l1=1.3e+02/27 l2=2.2e+03/28\n", + "23/25: loss: 680.5 lr=1.7e-08 max(coef): 1.8 l1=6e+02/27 l2=1.8e+04/28\n", + "24/25: loss: 677.4 lr=1.67e-08 max(coef): 1.8 l1=1.2e+03/27 l2=2e+05/28\n", + "25/25: loss: 674.6 lr=1.63e-08 max(coef): 1.8 l1=5.1e+02/27 l2=1.3e+04/28\n" + ] + }, + { + "data": { + "text/plain": [ + "NeuralTreeNet(2)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "netc4 = netc.copy()\n", + "netc4.fit(clsX, clsY, verbose=True, max_iter=25, lr=1e-6)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.858), 0.8)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + " roc_auc_score(clsy, netc4.predict(clsX)[:, -1]),\n", + " accuracy_score(clsy, numpy.argmax(netc4.predict(clsX)[:, -2:], axis=1)),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(netc.predict(x)[:, -2:], axis=1),\n", + " \"Avant apprentissage\",\n", + " ax=ax[0],\n", + ")\n", + "plot_grid(\n", + " clsX,\n", + " clsy,\n", + " lambda x: numpy.argmax(netc4.predict(x)[:, -2:], axis=1),\n", + " \"Après apprentissage\",\n", + " ax=ax[1],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est mieux..." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 4 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } \ No newline at end of file diff --git a/_doc/notebooks/ml/neural_tree_cost.ipynb b/_doc/notebooks/ml/neural_tree_cost.ipynb new file mode 100644 index 00000000..2411696f --- /dev/null +++ b/_doc/notebooks/ml/neural_tree_cost.ipynb @@ -0,0 +1,873 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "59a065e8", + "metadata": {}, + "source": [ + "# NeuralTreeNet et coût\n", + "\n", + "La classe *NeuralTreeNet* convertit un arbre de décision en réseau de neurones. Si la conversion n'est pas exacte mais elle permet d'obtenir un modèle différentiable et apprenable avec un algorithme d'optimisation à base de gradient. Ce notebook compare le temps d'éxécution entre un arbre et le réseau de neurones." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e6ad71f6", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "52d5e7f8", + "metadata": {}, + "source": [ + "## Jeux de données\n", + "\n", + "On construit un jeu de données aléatoire." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0abef0bf", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "X = numpy.random.randn(10000, 10)\n", + "y = X.sum(axis=1) / X.shape[1]\n", + "X = X.astype(numpy.float64)\n", + "y = y.astype(numpy.float64)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8850b4e7", + "metadata": {}, + "outputs": [], + "source": [ + "middle = X.shape[0] // 2\n", + "X_train, X_test = X[:middle], X[middle:]\n", + "y_train, y_test = y[:middle], y[middle:]" + ] + }, + { + "cell_type": "markdown", + "id": "12c4a84c", + "metadata": {}, + "source": [ + "## Caler un arbre de décision" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1c0b0169", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6225001966466359, 0.37938295559354807)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "tree = DecisionTreeRegressor(max_depth=7)\n", + "tree.fit(X_train, y_train)\n", + "tree.score(X_train, y_train), tree.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6b158b44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.37938295559354807" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "\n", + "r2_score(y_test, tree.predict(X_test))" + ] + }, + { + "cell_type": "markdown", + "id": "36db83ef", + "metadata": {}, + "source": [ + "Covnersion de l'arbre en réseau de neurones" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "60e3e6ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
average absolute error0.208776
max absolute error1.427806
\n", + "
" + ], + "text/plain": [ + " 0\n", + "average absolute error 0.208776\n", + "max absolute error 1.427806" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas import DataFrame\n", + "from mlstatpy.ml.neural_tree import NeuralTreeNet, NeuralTreeNetRegressor\n", + "\n", + "xe = X_test.astype(numpy.float32)\n", + "expected = tree.predict(xe)\n", + "\n", + "nn = NeuralTreeNetRegressor(NeuralTreeNet.create_from_tree(tree, arch=\"compact\"))\n", + "got = nn.predict(xe)\n", + "me = numpy.abs(got - expected).mean()\n", + "mx = numpy.abs(got - expected).max()\n", + "DataFrame([{\"average absolute error\": me, \"max absolute error\": mx}]).T" + ] + }, + { + "cell_type": "markdown", + "id": "559f0a25", + "metadata": {}, + "source": [ + "La conversion est loin d'être parfaite. La raison vient du fait que les fonctions de seuil sont approchées par des fonctions sigmoïdes. Il suffit d'une erreur minime pour que la décision prenne un chemin différent dans l'arbre et soit complètement différente." + ] + }, + { + "cell_type": "markdown", + "id": "c1ad28cf", + "metadata": {}, + "source": [ + "## Conversion au format ONNX" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b01518ec", + "metadata": {}, + "outputs": [], + "source": [ + "from skl2onnx import to_onnx\n", + "\n", + "onx_tree = to_onnx(tree, X[:1].astype(numpy.float32))\n", + "onx_nn = to_onnx(nn, X[:1].astype(numpy.float32))" + ] + }, + { + "cell_type": "markdown", + "id": "f59994a5", + "metadata": {}, + "source": [ + "Le réseau de neurones peut être représenté comme suit." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a33fedcb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "opset: domain='' version=21\n", + "input: name='X' type=dtype('float32') shape=['', 10]\n", + "init: name='Ma_MatMulcst' type=dtype('float32') shape=(10, 127)\n", + "init: name='Ad_Addcst' type=dtype('float32') shape=(127,)\n", + "init: name='Mu_Mulcst' type=dtype('float32') shape=(1,) -- array([4.], dtype=float32)\n", + "init: name='Ma_MatMulcst1' type=dtype('float32') shape=(127, 128)\n", + "init: name='Ad_Addcst1' type=dtype('float32') shape=(128,)\n", + "init: name='Ma_MatMulcst2' type=dtype('float32') shape=(128, 1)\n", + "init: name='Ad_Addcst2' type=dtype('float32') shape=(1,) -- array([0.], dtype=float32)\n", + "MatMul(X, Ma_MatMulcst) -> Ma_Y02\n", + " Add(Ma_Y02, Ad_Addcst) -> Ad_C02\n", + " Mul(Ad_C02, Mu_Mulcst) -> Mu_C01\n", + " Sigmoid(Mu_C01) -> Si_Y01\n", + " MatMul(Si_Y01, Ma_MatMulcst1) -> Ma_Y01\n", + " Add(Ma_Y01, Ad_Addcst1) -> Ad_C01\n", + " Mul(Ad_C01, Mu_Mulcst) -> Mu_C0\n", + " Sigmoid(Mu_C0) -> Si_Y0\n", + " MatMul(Si_Y0, Ma_MatMulcst2) -> Ma_Y0\n", + " Add(Ma_Y0, Ad_Addcst2) -> Ad_C0\n", + " Identity(Ad_C0) -> variable\n", + "output: name='variable' type=dtype('float32') shape=['', 1]\n" + ] + } + ], + "source": [ + "from onnx_array_api.plotting.text_plot import onnx_simple_text_plot\n", + "\n", + "print(onnx_simple_text_plot(onx_nn))" + ] + }, + { + "cell_type": "markdown", + "id": "857f2e42", + "metadata": {}, + "source": [ + "## Temps de calcul des prédictions" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7c810819", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "584 μs ± 16.3 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "from onnxruntime import InferenceSession\n", + "\n", + "oinf_tree = InferenceSession(onx_tree.SerializeToString())\n", + "oinf_nn = InferenceSession(onx_nn.SerializeToString())\n", + "\n", + "%timeit tree.predict(xe)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "51f6c958", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "48.4 μs ± 1.16 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit oinf_tree.run(None, {'X': xe})" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ab1ff3a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.28 ms ± 97.7 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit oinf_nn.run(None, {'X': xe})" + ] + }, + { + "cell_type": "markdown", + "id": "5d8ecaa5", + "metadata": {}, + "source": [ + "Le temps de calcul est nettement plus long pour le réseau de neurones. Si l'arbre de décision a une profondeur de *d*, l'arbre de décision va faire exactement *d* comparaisons. Le réseau de neurones quant à lui évalue tous les seuils pour chaque prédiction, soit $2^d$. Vérifions cela en faisant variable la profondeur." + ] + }, + { + "cell_type": "markdown", + "id": "b27675e4", + "metadata": {}, + "source": [ + "## Temps de calcul en fonction de la profondeur" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecef383a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 8/8 [00:04<00:00, 1.63it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
averagedeviationmin_execmax_execrepeatnumberttimecontext_sizedexp
00.0002070.0000450.0001530.00031720200.004147642skl
10.0001510.0002460.0000310.00082510100.001515642onx_tree
20.0001780.0000930.0001190.00037110100.001781642onx_nn
30.0002490.0000360.0002200.00036020200.004980643skl
40.0003120.0001560.0001130.00066110100.003117643onx_tree
50.0003520.0002040.0001820.00083110100.003523643onx_nn
60.0003390.0000730.0002570.00048720200.006775644skl
70.0003370.0004230.0000590.00153710100.003368644onx_tree
80.0006190.0003540.0002210.00132010100.006194644onx_nn
90.0003590.0000380.0003090.00045320200.007171645skl
100.0004730.0005650.0000640.00192310100.004729645onx_tree
110.0011970.0009440.0003090.00352910100.011973645onx_nn
120.0003860.0000220.0003590.00043920200.007715646skl
130.0007930.0007700.0000970.00244510100.007926646onx_tree
140.0015210.0009190.0006520.00382010100.015207646onx_nn
150.0004290.0000240.0004040.00049420200.008579647skl
160.0006580.0006620.0002070.00248410100.006575647onx_tree
170.0029250.0027700.0014890.01104810100.029251647onx_nn
180.0005080.0000590.0004520.00073320200.010157648skl
190.0012350.0012080.0001210.00384210100.012347648onx_tree
200.0046270.0042390.0029620.01730010100.046271648onx_nn
210.0005580.0000450.0004980.00070020200.011152649skl
220.0007450.0005400.0001380.00216610100.007449649onx_tree
230.0111270.0048560.0090140.02566710100.111265649onx_nn
\n", + "
" + ], + "text/plain": [ + " average deviation min_exec max_exec repeat number ttime \\\n", + "0 0.000207 0.000045 0.000153 0.000317 20 20 0.004147 \n", + "1 0.000151 0.000246 0.000031 0.000825 10 10 0.001515 \n", + "2 0.000178 0.000093 0.000119 0.000371 10 10 0.001781 \n", + "3 0.000249 0.000036 0.000220 0.000360 20 20 0.004980 \n", + "4 0.000312 0.000156 0.000113 0.000661 10 10 0.003117 \n", + "5 0.000352 0.000204 0.000182 0.000831 10 10 0.003523 \n", + "6 0.000339 0.000073 0.000257 0.000487 20 20 0.006775 \n", + "7 0.000337 0.000423 0.000059 0.001537 10 10 0.003368 \n", + "8 0.000619 0.000354 0.000221 0.001320 10 10 0.006194 \n", + "9 0.000359 0.000038 0.000309 0.000453 20 20 0.007171 \n", + "10 0.000473 0.000565 0.000064 0.001923 10 10 0.004729 \n", + "11 0.001197 0.000944 0.000309 0.003529 10 10 0.011973 \n", + "12 0.000386 0.000022 0.000359 0.000439 20 20 0.007715 \n", + "13 0.000793 0.000770 0.000097 0.002445 10 10 0.007926 \n", + "14 0.001521 0.000919 0.000652 0.003820 10 10 0.015207 \n", + "15 0.000429 0.000024 0.000404 0.000494 20 20 0.008579 \n", + "16 0.000658 0.000662 0.000207 0.002484 10 10 0.006575 \n", + "17 0.002925 0.002770 0.001489 0.011048 10 10 0.029251 \n", + "18 0.000508 0.000059 0.000452 0.000733 20 20 0.010157 \n", + "19 0.001235 0.001208 0.000121 0.003842 10 10 0.012347 \n", + "20 0.004627 0.004239 0.002962 0.017300 10 10 0.046271 \n", + "21 0.000558 0.000045 0.000498 0.000700 20 20 0.011152 \n", + "22 0.000745 0.000540 0.000138 0.002166 10 10 0.007449 \n", + "23 0.011127 0.004856 0.009014 0.025667 10 10 0.111265 \n", + "\n", + " context_size d exp \n", + "0 64 2 skl \n", + "1 64 2 onx_tree \n", + "2 64 2 onx_nn \n", + "3 64 3 skl \n", + "4 64 3 onx_tree \n", + "5 64 3 onx_nn \n", + "6 64 4 skl \n", + "7 64 4 onx_tree \n", + "8 64 4 onx_nn \n", + "9 64 5 skl \n", + "10 64 5 onx_tree \n", + "11 64 5 onx_nn \n", + "12 64 6 skl \n", + "13 64 6 onx_tree \n", + "14 64 6 onx_nn \n", + "15 64 7 skl \n", + "16 64 7 onx_tree \n", + "17 64 7 onx_nn \n", + "18 64 8 skl \n", + "19 64 8 onx_tree \n", + "20 64 8 onx_nn \n", + "21 64 9 skl \n", + "22 64 9 onx_tree \n", + "23 64 9 onx_nn " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tqdm import tqdm\n", + "from mlstatpy.ext_test_case import measure_time\n", + "\n", + "data = []\n", + "for d in tqdm(range(2, 10)):\n", + " tree = DecisionTreeRegressor(max_depth=d)\n", + " tree.fit(X_train, y_train)\n", + " obs = measure_time(lambda tree=tree: tree.predict(xe), number=20, repeat=20)\n", + " obs.update(dict(d=d, exp=\"skl\"))\n", + " data.append(obs)\n", + "\n", + " nn = NeuralTreeNetRegressor(NeuralTreeNet.create_from_tree(tree, arch=\"compact\"))\n", + "\n", + " onx_tree = to_onnx(tree, X[:1].astype(numpy.float32))\n", + " onx_nn = to_onnx(nn, X[:1].astype(numpy.float32))\n", + " oinf_tree = InferenceSession(\n", + " onx_tree.SerializePartialToString(), providers=[\"CPUExecutionProvider\"]\n", + " )\n", + " oinf_nn = InferenceSession(\n", + " onx_nn.SerializePartialToString(), providers=[\"CPUExecutionProvider\"]\n", + " )\n", + "\n", + " obs = measure_time(\n", + " lambda oinf_tree=oinf_tree: oinf_tree.run(None, {\"X\": xe}), number=10, repeat=10\n", + " )\n", + " obs.update(dict(d=d, exp=\"onx_tree\"))\n", + " data.append(obs)\n", + "\n", + " obs = measure_time(\n", + " lambda oinf_nn=oinf_nn: oinf_nn.run(None, {\"X\": xe}), number=10, repeat=10\n", + " )\n", + " obs.update(dict(d=d, exp=\"onx_nn\"))\n", + " data.append(obs)\n", + "\n", + "df = DataFrame(data)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "871130aa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "piv = df.pivot(index=\"d\", columns=\"exp\", values=\"average\")\n", + "piv.plot(logy=True, title=\"Temps de calcul en fonction de la profondeur\");" + ] + }, + { + "cell_type": "markdown", + "id": "b30bbefe", + "metadata": {}, + "source": [ + "L'hypothèse est vérifiée." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8b163d83", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/_doc/notebooks/ml/neural_tree_onnx.ipynb b/_doc/notebooks/ml/neural_tree_onnx.ipynb new file mode 100644 index 00000000..1afcce43 --- /dev/null +++ b/_doc/notebooks/ml/neural_tree_onnx.ipynb @@ -0,0 +1,4075 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4b855db5", + "metadata": {}, + "source": [ + "# NeuralTreeNet et ONNX\n", + "\n", + "La conversion d'un arbre de décision au format ONNX peut créer des différences entre le modèle original et le modèle converti (voir [Issues when switching to float](https://onnx.ai/sklearn-onnx/auto_tutorial/plot_ebegin_float_double.html). Le problème vient d'un changement de type, les seuils de décisions sont arrondis au float32 le plus proche de leur valeur en float64 (double). Qu'advient-il si l'arbre de décision est converti en réseau de neurones d'abord.\n", + "\n", + "L'approximation des seuils de décision ne change pas grand chose dans la majorité des cas. Cependant, il est possible que la comparaison d'une variable à un seuil de décision arrondi soit l'opposé de celle avec le seuil non arrondi. Dans ce cas, la décision suit un chemin différent dans l'arbre." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2f698cc0", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "c7b2fb41", + "metadata": {}, + "source": [ + "## Jeu de données\n", + "\n", + "On construit un jeu de donnée aléatoire." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a8feffa5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "X = numpy.random.randn(10000, 10)\n", + "y = X.sum(axis=1) / X.shape[1]\n", + "X = X.astype(numpy.float64)\n", + "y = y.astype(numpy.float64)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3c854905", + "metadata": {}, + "outputs": [], + "source": [ + "middle = X.shape[0] // 2\n", + "X_train, X_test = X[:middle], X[middle:]\n", + "y_train, y_test = y[:middle], y[middle:]" + ] + }, + { + "cell_type": "markdown", + "id": "2972ef7f", + "metadata": {}, + "source": [ + "## Partie scikit-learn" + ] + }, + { + "cell_type": "markdown", + "id": "2a19a0c1", + "metadata": {}, + "source": [ + "### Caler un arbre de décision" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bfc49123", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6168207374163092, 0.35236821090506987)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "tree = DecisionTreeRegressor(max_depth=7)\n", + "tree.fit(X_train, y_train)\n", + "tree.score(X_train, y_train), tree.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a38b0426", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.35236821090506987" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "\n", + "r2_score(y_test, tree.predict(X_test))" + ] + }, + { + "cell_type": "markdown", + "id": "86a0f0a3", + "metadata": {}, + "source": [ + "La profondeur de l'arbre est insuffisante mais ce n'est pas ce qui nous intéresse ici." + ] + }, + { + "cell_type": "markdown", + "id": "8e6038ff", + "metadata": {}, + "source": [ + "### Conversion au format ONNX" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f6849a2d", + "metadata": {}, + "outputs": [], + "source": [ + "from skl2onnx import to_onnx\n", + "\n", + "onx = to_onnx(tree, X[:1].astype(numpy.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3daf9db1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(1.7091389654766018)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from onnxruntime import InferenceSession\n", + "\n", + "x_exp = X_test\n", + "\n", + "oinf = InferenceSession(onx.SerializeToString())\n", + "expected = tree.predict(x_exp)\n", + "\n", + "got = oinf.run(None, {\"X\": x_exp.astype(numpy.float32)})[0]\n", + "numpy.abs(got - expected).max()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7ce247da", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "opset: domain='ai.onnx.ml' version=1\n", + "opset: domain='' version=21\n", + "opset: domain='' version=21\n", + "input: name='X' type=dtype('float32') shape=['', 10]\n", + "TreeEnsembleRegressor(X, n_targets=1, nodes_falsenodeids=255:[128,65,34...254,0,0], nodes_featureids=255:[3,4,0...4,0,0], nodes_hitrates=255:[1.0,1.0...1.0,1.0], nodes_missing_value_tracks_true=255:[0,0,0...0,0,0], nodes_modes=255:[b'BRANCH_LEQ',b'BRANCH_LEQ'...b'LEAF',b'LEAF'], nodes_nodeids=255:[0,1,2...252,253,254], nodes_treeids=255:[0,0,0...0,0,0], nodes_truenodeids=255:[1,2,3...253,0,0], nodes_values=255:[0.12306099385023117,-0.19721701741218567...0.0,0.0], post_transform=b'NONE', target_ids=128:[0,0,0...0,0,0], target_nodeids=128:[7,8,10...251,253,254], target_treeids=128:[0,0,0...0,0,0], target_weights=128:[-0.9612963795661926,-0.5883080959320068...0.49337825179100037,0.7387731075286865]) -> variable\n", + "output: name='variable' type=dtype('float32') shape=['', 1]\n" + ] + } + ], + "source": [ + "from onnx_array_api.plotting.text_plot import onnx_simple_text_plot\n", + "\n", + "print(onnx_simple_text_plot(onx))" + ] + }, + { + "cell_type": "markdown", + "id": "1ada8e37", + "metadata": {}, + "source": [ + "## Après la conversion en un réseau de neurones" + ] + }, + { + "cell_type": "markdown", + "id": "7238d09b", + "metadata": {}, + "source": [ + "### Conversion en un réseau de neurones\n", + "\n", + "Un paramètre permet de faire varier la pente des fonctions sigmoïdes utilisées." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7729c242", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 18/18 [00:00<00:00, 53.66it/s]\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "from pandas import DataFrame\n", + "from mlstatpy.ml.neural_tree import NeuralTreeNet\n", + "\n", + "xe = x_exp[:500]\n", + "expected = tree.predict(xe)\n", + "\n", + "data = []\n", + "trees = {}\n", + "for i in tqdm([0.3, 0.4, 0.5, 0.7, 0.9, 1] + list(range(5, 61, 5))):\n", + " root = NeuralTreeNet.create_from_tree(tree, k=i, arch=\"compact\")\n", + " got = root.predict(xe)[:, -1]\n", + " me = numpy.abs(got - expected).mean()\n", + " mx = numpy.abs(got - expected).max()\n", + " obs = dict(k=i, max=mx, mean=me)\n", + " data.append(obs)\n", + " trees[i] = root" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9d35377e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
kmaxmean
00.30.8906660.212437
10.40.5869970.141997
20.50.5209520.129502
30.70.5882610.127598
40.90.5795150.123064
51.00.5997040.119385
65.00.4863860.021135
710.00.4851850.005929
815.00.3253950.002471
920.00.3093160.001763
1025.00.2146920.000968
1130.00.2146290.000846
1235.00.1634060.000659
1340.00.0691120.000268
1445.00.0644030.000214
1550.00.0593070.000172
1655.00.0539150.000140
1760.00.0483360.000114
\n", + "
" + ], + "text/plain": [ + " k max mean\n", + "0 0.3 0.890666 0.212437\n", + "1 0.4 0.586997 0.141997\n", + "2 0.5 0.520952 0.129502\n", + "3 0.7 0.588261 0.127598\n", + "4 0.9 0.579515 0.123064\n", + "5 1.0 0.599704 0.119385\n", + "6 5.0 0.486386 0.021135\n", + "7 10.0 0.485185 0.005929\n", + "8 15.0 0.325395 0.002471\n", + "9 20.0 0.309316 0.001763\n", + "10 25.0 0.214692 0.000968\n", + "11 30.0 0.214629 0.000846\n", + "12 35.0 0.163406 0.000659\n", + "13 40.0 0.069112 0.000268\n", + "14 45.0 0.064403 0.000214\n", + "15 50.0 0.059307 0.000172\n", + "16 55.0 0.053915 0.000140\n", + "17 60.0 0.048336 0.000114" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = DataFrame(data)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0fcb9789", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.set_index(\"k\").plot(title=\"Précision de la conversion\\nen réseau de neurones\");" + ] + }, + { + "cell_type": "markdown", + "id": "1f4bb3d9", + "metadata": {}, + "source": [ + "L'erreur est meilleure mais il faudrait recommencer l'expérience plusieurs fois avant de pouvoir conclure afin d'obtenir un interval de confiance pour le même type de jeu de données. Ce sera pour une autre fois. Le résultat dépend du jeu de données et surtout de la proximité des seuils de décisions. Néanmoins, on calcule l'erreur sur l'ensemble de la base de test. Celle-ci a été tronquée pour aller plus vite." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2f3eb6d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(0.14867156347163313), np.float64(0.00014171388788628532))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected = tree.predict(x_exp)\n", + "got = trees[50].predict(x_exp)[:, -1]\n", + "numpy.abs(got - expected).max(), numpy.abs(got - expected).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "77163512", + "metadata": {}, + "source": [ + "On voit que l'erreur peut-être très grande. Elle reste néanmoins plus petite que l'erreur de conversion introduite par ONNX." + ] + }, + { + "cell_type": "markdown", + "id": "738c8547", + "metadata": {}, + "source": [ + "### Conversion au format ONNX\n", + "\n", + "On crée tout d'abord une classe qui suit l'API de scikit-learn et qui englobe l'arbre qui vient d'être créé qui sera ensuite convertit en ONNX." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2439e4fa", + "metadata": {}, + "outputs": [], + "source": [ + "from mlstatpy.ml.neural_tree import NeuralTreeNetRegressor\n", + "\n", + "reg = NeuralTreeNetRegressor(trees[50])\n", + "onx2 = to_onnx(reg, X[:1].astype(numpy.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "eae47e6a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "opset: domain='' version=21\n", + "input: name='X' type=dtype('float32') shape=['', 10]\n", + "init: name='Ma_MatMulcst' type=float32 shape=(10, 127)\n", + "init: name='Ad_Addcst' type=float32 shape=(127,)\n", + "init: name='Mu_Mulcst' type=float32 shape=(1,) -- array([4.], dtype=float32)\n", + "init: name='Ma_MatMulcst1' type=float32 shape=(127, 128)\n", + "init: name='Ad_Addcst1' type=float32 shape=(128,)\n", + "init: name='Ma_MatMulcst2' type=float32 shape=(128, 1)\n", + "init: name='Ad_Addcst2' type=float32 shape=(1,) -- array([0.], dtype=float32)\n", + "MatMul(X, Ma_MatMulcst) -> Ma_Y02\n", + " Add(Ma_Y02, Ad_Addcst) -> Ad_C02\n", + " Mul(Ad_C02, Mu_Mulcst) -> Mu_C01\n", + " Sigmoid(Mu_C01) -> Si_Y01\n", + " MatMul(Si_Y01, Ma_MatMulcst1) -> Ma_Y01\n", + " Add(Ma_Y01, Ad_Addcst1) -> Ad_C01\n", + " Mul(Ad_C01, Mu_Mulcst) -> Mu_C0\n", + " Sigmoid(Mu_C0) -> Si_Y0\n", + " MatMul(Si_Y0, Ma_MatMulcst2) -> Ma_Y0\n", + " Add(Ma_Y0, Ad_Addcst2) -> Ad_C0\n", + " Identity(Ad_C0) -> variable\n", + "output: name='variable' type=dtype('float32') shape=['', 1]\n" + ] + } + ], + "source": [ + "print(onnx_simple_text_plot(onx2))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1d4e272f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(1.7091389654766018)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oinf2 = InferenceSession(\n", + " onx2.SerializePartialToString(), providers=[\"CPUExecutionProvider\"]\n", + ")\n", + "expected = tree.predict(x_exp)\n", + "\n", + "got = oinf2.run([\"variable\"], {\"X\": x_exp.astype(numpy.float32)})[0]\n", + "numpy.abs(got - expected).max()" + ] + }, + { + "cell_type": "markdown", + "id": "f4e64f63", + "metadata": {}, + "source": [ + "L'erreur est la même." + ] + }, + { + "cell_type": "markdown", + "id": "c9207392", + "metadata": {}, + "source": [ + "## Temps de calcul" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a6febd37", + "metadata": {}, + "outputs": [], + "source": [ + "x_exp32 = x_exp.astype(numpy.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "1bf0109e", + "metadata": {}, + "source": [ + "Tout d'abord le temps de calcul pour scikit-learn." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "07caad53", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "312 μs ± 9.06 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit tree.predict(x_exp32)" + ] + }, + { + "cell_type": "markdown", + "id": "0cea5139", + "metadata": {}, + "source": [ + "Le temps de calcul pour l'arbre de décision au format ONNX." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "984413fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "35 μs ± 595 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit oinf.run(None, {'X': x_exp32})[0]" + ] + }, + { + "cell_type": "markdown", + "id": "afb4f6bb", + "metadata": {}, + "source": [ + "Et le temps de calcul pour le réseau de neurones au format ONNX.m" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e3268dcd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.18 ms ± 7.98 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit oinf2.run(None, {'X': x_exp32})[0]" + ] + }, + { + "cell_type": "markdown", + "id": "b3eafba0", + "metadata": {}, + "source": [ + "Ce temps de calcul très long est attendu car le modèle contient une multiplication de matrice très grande et surtout que tous les seuils de l'arbre sont calculés pour chaque observation. Là où l'implémentation de l'arbre de décision calcule *d* seuils, la profondeur de l'arbre, la nouvelle implémentation calcule tous les seuils soit $2^d$ pour chaque feuille. Il y a $2^d$ feuilles. Même en étant sparse, on peut réduire les calculs à $d * 2^d$ ce qui fait encore beaucoup de calculs inutiles." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d9911fff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(127, 11) (127,)\n", + "(128, 128) (128,)\n", + "(129,) ()\n" + ] + } + ], + "source": [ + "for node in trees[50].nodes:\n", + " print(node.coef.shape, node.bias.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "27e187ac", + "metadata": {}, + "source": [ + "Cela dit, la plus grande matrice est creuse, elle peut être réduite considérablement." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e97479fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "coef.shape=(127, 11), size dense=1397, size sparse=254, ratio=0.18181818181818182\n", + "coef.shape=(128, 128), size dense=16384, size sparse=1024, ratio=0.0625\n", + "coef.shape=(129,), size dense=129, size sparse=128, ratio=0.9922480620155039\n" + ] + } + ], + "source": [ + "from scipy.sparse import csr_matrix\n", + "\n", + "for node in trees[50].nodes:\n", + " csr = csr_matrix(node.coef)\n", + " print(\n", + " f\"coef.shape={node.coef.shape}, size dense={node.coef.size}, \"\n", + " f\"size sparse={csr.size}, ratio={csr.size / node.coef.size}\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "125547d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.87 μs ± 177 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" + ] + } + ], + "source": [ + "r = numpy.random.randn(trees[50].nodes[1].coef.shape[0])\n", + "mat = trees[50].nodes[1].coef\n", + "%timeit mat @ r" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ad7173e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.53 μs ± 88.3 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" + ] + } + ], + "source": [ + "csr = csr_matrix(mat)\n", + "%timeit csr @ r" + ] + }, + { + "cell_type": "markdown", + "id": "7599d94e", + "metadata": {}, + "source": [ + "Ce serait beaucoup plus rapide avec une matrice sparse et d'autant plus rapide que l'arbre est profond. Le modèle ONNX se décompose comme suit." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "0c1839fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "opset: domain='' version=21\n", + "input: name='X' type=dtype('float32') shape=['', 10]\n", + "init: name='Ma_MatMulcst' type=float32 shape=(10, 127)\n", + "init: name='Ad_Addcst' type=float32 shape=(127,)\n", + "init: name='Mu_Mulcst' type=float32 shape=(1,) -- array([4.], dtype=float32)\n", + "init: name='Ma_MatMulcst1' type=float32 shape=(127, 128)\n", + "init: name='Ad_Addcst1' type=float32 shape=(128,)\n", + "init: name='Ma_MatMulcst2' type=float32 shape=(128, 1)\n", + "init: name='Ad_Addcst2' type=float32 shape=(1,) -- array([0.], dtype=float32)\n", + "MatMul(X, Ma_MatMulcst) -> Ma_Y02\n", + " Add(Ma_Y02, Ad_Addcst) -> Ad_C02\n", + " Mul(Ad_C02, Mu_Mulcst) -> Mu_C01\n", + " Sigmoid(Mu_C01) -> Si_Y01\n", + " MatMul(Si_Y01, Ma_MatMulcst1) -> Ma_Y01\n", + " Add(Ma_Y01, Ad_Addcst1) -> Ad_C01\n", + " Mul(Ad_C01, Mu_Mulcst) -> Mu_C0\n", + " Sigmoid(Mu_C0) -> Si_Y0\n", + " MatMul(Si_Y0, Ma_MatMulcst2) -> Ma_Y0\n", + " Add(Ma_Y0, Ad_Addcst2) -> Ad_C0\n", + " Identity(Ad_C0) -> variable\n", + "output: name='variable' type=dtype('float32') shape=['', 1]\n" + ] + } + ], + "source": [ + "print(onnx_simple_text_plot(onx2))" + ] + }, + { + "cell_type": "markdown", + "id": "318b95d7", + "metadata": {}, + "source": [ + "Voyons comment le temps de calcul se répartit." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "11bccd22", + "metadata": {}, + "outputs": [], + "source": [ + "from onnxruntime import InferenceSession, SessionOptions\n", + "from onnx_diagnostic.helpers.rt_helper import js_profile_to_dataframe\n", + "\n", + "sess_options = SessionOptions()\n", + "sess_options.enable_profiling = True\n", + "\n", + "sess = InferenceSession(\n", + " onx2.SerializeToString(), sess_options, providers=[\"CPUExecutionProvider\"]\n", + ")\n", + "for i in range(43):\n", + " sess.run(None, {\"X\": x_exp32})\n", + "\n", + "prof = sess.end_profiling()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5485970b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
catpidtiddurtsphnameargs_thread_scheduling_statsargs_output_sizeargs_parameter_sizeargs_activation_sizeargs_node_indexargs_providerargs_op_nameop_nameevent_nameiterationit==0
0Session50840508404589Xmodel_loading_arrayNaNNaNNaNNaNNaNNaNNaNNaNmodel_loading_array-11
1Session50840508401365529Xsession_initializationNaNNaNNaNNaNNaNNaNNaNNaNsession_initialization-11
2Node508405084034372343XMa_MatMul/MatMulAddFusion_kernel_time{'main_thread': {'thread_pool_name': 'session-...254000050820000011CPUExecutionProviderGemmMa_MatMul/MatMulAddFusionkernel_time-11
3Node50840508407765808XMu_Mul_kernel_time{'main_thread': {'thread_pool_name': 'session-...2540000425400002CPUExecutionProviderMulMu_Mulkernel_time-11
4Node50840508401306604XSi_Sigmoid_kernel_time{'main_thread': {'thread_pool_name': 'session-...2540000025400003CPUExecutionProviderSigmoidSi_Sigmoidkernel_time-11
.........................................................
384Node508405084052134871XMu_Mul1_kernel_time{'main_thread': {'thread_pool_name': 'session-...2560000425600006CPUExecutionProviderMulMu_Mul1kernel_time410
385Node508405084072134943XSi_Sigmoid1_kernel_time{'main_thread': {'thread_pool_name': 'session-...2560000025600007CPUExecutionProviderSigmoidSi_Sigmoid1kernel_time410
386Node508405084079135022XMa_MatMul2_kernel_time{'main_thread': {'thread_pool_name': 'session-...20000025600008CPUExecutionProviderMatMulMa_MatMul2kernel_time410
387Session50840508401508133600XSequentialExecutor::ExecuteNaNNaNNaNNaNNaNNaNNaNNaNSequentialExecutor::Execute420
388Session50840508401523133591Xmodel_runNaNNaNNaNNaNNaNNaNNaNNaNmodel_run420
\n", + "

389 rows × 18 columns

\n", + "
" + ], + "text/plain": [ + " cat pid tid dur ts ph \\\n", + "0 Session 50840 50840 458 9 X \n", + "1 Session 50840 50840 1365 529 X \n", + "2 Node 50840 50840 3437 2343 X \n", + "3 Node 50840 50840 776 5808 X \n", + "4 Node 50840 50840 130 6604 X \n", + ".. ... ... ... ... ... .. \n", + "384 Node 50840 50840 52 134871 X \n", + "385 Node 50840 50840 72 134943 X \n", + "386 Node 50840 50840 79 135022 X \n", + "387 Session 50840 50840 1508 133600 X \n", + "388 Session 50840 50840 1523 133591 X \n", + "\n", + " name \\\n", + "0 model_loading_array \n", + "1 session_initialization \n", + "2 Ma_MatMul/MatMulAddFusion_kernel_time \n", + "3 Mu_Mul_kernel_time \n", + "4 Si_Sigmoid_kernel_time \n", + ".. ... \n", + "384 Mu_Mul1_kernel_time \n", + "385 Si_Sigmoid1_kernel_time \n", + "386 Ma_MatMul2_kernel_time \n", + "387 SequentialExecutor::Execute \n", + "388 model_run \n", + "\n", + " args_thread_scheduling_stats args_output_size \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 {'main_thread': {'thread_pool_name': 'session-... 2540000 \n", + "3 {'main_thread': {'thread_pool_name': 'session-... 2540000 \n", + "4 {'main_thread': {'thread_pool_name': 'session-... 2540000 \n", + ".. ... ... \n", + "384 {'main_thread': {'thread_pool_name': 'session-... 2560000 \n", + "385 {'main_thread': {'thread_pool_name': 'session-... 2560000 \n", + "386 {'main_thread': {'thread_pool_name': 'session-... 20000 \n", + "387 NaN NaN \n", + "388 NaN NaN \n", + "\n", + " args_parameter_size args_activation_size args_node_index \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 508 200000 11 \n", + "3 4 2540000 2 \n", + "4 0 2540000 3 \n", + ".. ... ... ... \n", + "384 4 2560000 6 \n", + "385 0 2560000 7 \n", + "386 0 2560000 8 \n", + "387 NaN NaN NaN \n", + "388 NaN NaN NaN \n", + "\n", + " args_provider args_op_name op_name \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 CPUExecutionProvider Gemm Ma_MatMul/MatMulAddFusion \n", + "3 CPUExecutionProvider Mul Mu_Mul \n", + "4 CPUExecutionProvider Sigmoid Si_Sigmoid \n", + ".. ... ... ... \n", + "384 CPUExecutionProvider Mul Mu_Mul1 \n", + "385 CPUExecutionProvider Sigmoid Si_Sigmoid1 \n", + "386 CPUExecutionProvider MatMul Ma_MatMul2 \n", + "387 NaN NaN NaN \n", + "388 NaN NaN NaN \n", + "\n", + " event_name iteration it==0 \n", + "0 model_loading_array -1 1 \n", + "1 session_initialization -1 1 \n", + "2 kernel_time -1 1 \n", + "3 kernel_time -1 1 \n", + "4 kernel_time -1 1 \n", + ".. ... ... ... \n", + "384 kernel_time 41 0 \n", + "385 kernel_time 41 0 \n", + "386 kernel_time 41 0 \n", + "387 SequentialExecutor::Execute 42 0 \n", + "388 model_run 42 0 \n", + "\n", + "[389 rows x 18 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = js_profile_to_dataframe(prof, first_it_out=True)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "19bb5d0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CPUExecutionProvider', nan}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(df[\"args_provider\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e42d5644", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dur
args_op_namename
MulMu_Mul16486
SigmoidSi_Sigmoid17064
MulMu_Mul7401
SigmoidSi_Sigmoid7594
MatMulMa_MatMul28032
GemmMa_MatMul/MatMulAddFusion28069
Ma_MatMul1/MatMulAddFusion55140
\n", + "
" + ], + "text/plain": [ + " dur\n", + "args_op_name name \n", + "Mul Mu_Mul1 6486\n", + "Sigmoid Si_Sigmoid1 7064\n", + "Mul Mu_Mul 7401\n", + "Sigmoid Si_Sigmoid 7594\n", + "MatMul Ma_MatMul2 8032\n", + "Gemm Ma_MatMul/MatMulAddFusion 28069\n", + " Ma_MatMul1/MatMulAddFusion 55140" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dfp = df[df.args_provider == \"CPUExecutionProvider\"].copy()\n", + "dfp[\"name\"] = dfp[\"name\"].apply(lambda s: s.replace(\"_kernel_time\", \"\"))\n", + "gr_dur = (\n", + " dfp[[\"dur\", \"args_op_name\", \"name\"]]\n", + " .groupby([\"args_op_name\", \"name\"])\n", + " .sum()\n", + " .sort_values(\"dur\")\n", + ")\n", + "gr_dur" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "34b33616", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dur
args_op_namename
MulMu_Mul143
SigmoidSi_Sigmoid143
MulMu_Mul43
SigmoidSi_Sigmoid43
MatMulMa_MatMul243
GemmMa_MatMul/MatMulAddFusion43
Ma_MatMul1/MatMulAddFusion43
\n", + "
" + ], + "text/plain": [ + " dur\n", + "args_op_name name \n", + "Mul Mu_Mul1 43\n", + "Sigmoid Si_Sigmoid1 43\n", + "Mul Mu_Mul 43\n", + "Sigmoid Si_Sigmoid 43\n", + "MatMul Ma_MatMul2 43\n", + "Gemm Ma_MatMul/MatMulAddFusion 43\n", + " Ma_MatMul1/MatMulAddFusion 43" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gr_n = (\n", + " dfp[[\"dur\", \"args_op_name\", \"name\"]]\n", + " .groupby([\"args_op_name\", \"name\"])\n", + " .count()\n", + " .sort_values(\"dur\")\n", + ")\n", + "gr_n = gr_n.loc[gr_dur.index, :]\n", + "gr_n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f34b2908", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "gr_dur.plot.barh(ax=ax[0])\n", + "gr_n.plot.barh(ax=ax[1])\n", + "ax[0].set_title(\"duration\")\n", + "ax[1].set_title(\"n occurences\");" + ] + }, + { + "cell_type": "markdown", + "id": "7b10ca8a", + "metadata": {}, + "source": [ + "onnxruntime passe principalement son temps dans un produit matriciel. On vérifie plus précisément." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4cbc2fa0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
112
catNodeNode
pid5084050840
tid5084050840
dur35493437
ts104392343
phXX
nameMa_MatMul/MatMulAddFusion_kernel_timeMa_MatMul/MatMulAddFusion_kernel_time
args_thread_scheduling_stats{'main_thread': {'thread_pool_name': 'session-...{'main_thread': {'thread_pool_name': 'session-...
args_output_size25400002540000
args_parameter_size508508
args_activation_size200000200000
args_node_index1111
args_providerCPUExecutionProviderCPUExecutionProvider
args_op_nameGemmGemm
op_nameMa_MatMul/MatMulAddFusionMa_MatMul/MatMulAddFusion
event_namekernel_timekernel_time
iteration0-1
it==011
\n", + "
" + ], + "text/plain": [ + " 11 \\\n", + "cat Node \n", + "pid 50840 \n", + "tid 50840 \n", + "dur 3549 \n", + "ts 10439 \n", + "ph X \n", + "name Ma_MatMul/MatMulAddFusion_kernel_time \n", + "args_thread_scheduling_stats {'main_thread': {'thread_pool_name': 'session-... \n", + "args_output_size 2540000 \n", + "args_parameter_size 508 \n", + "args_activation_size 200000 \n", + "args_node_index 11 \n", + "args_provider CPUExecutionProvider \n", + "args_op_name Gemm \n", + "op_name Ma_MatMul/MatMulAddFusion \n", + "event_name kernel_time \n", + "iteration 0 \n", + "it==0 1 \n", + "\n", + " 2 \n", + "cat Node \n", + "pid 50840 \n", + "tid 50840 \n", + "dur 3437 \n", + "ts 2343 \n", + "ph X \n", + "name Ma_MatMul/MatMulAddFusion_kernel_time \n", + "args_thread_scheduling_stats {'main_thread': {'thread_pool_name': 'session-... \n", + "args_output_size 2540000 \n", + "args_parameter_size 508 \n", + "args_activation_size 200000 \n", + "args_node_index 11 \n", + "args_provider CPUExecutionProvider \n", + "args_op_name Gemm \n", + "op_name Ma_MatMul/MatMulAddFusion \n", + "event_name kernel_time \n", + "iteration -1 \n", + "it==0 1 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[(df.args_op_name == \"Gemm\") & (df.dur > 0)].sort_values(\"dur\", ascending=False).head(\n", + " n=2\n", + ").T" + ] + }, + { + "cell_type": "markdown", + "id": "58320942", + "metadata": {}, + "source": [ + "C'est un produit matriciel d'environ *5000x800* par *800x800*." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "de43df2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dur
args_op_namename
MulMu_Mul10.054147
SigmoidSi_Sigmoid10.058972
MulMu_Mul0.061785
SigmoidSi_Sigmoid0.063396
MatMulMa_MatMul20.067053
GemmMa_MatMul/MatMulAddFusion0.234326
Ma_MatMul1/MatMulAddFusion0.460321
\n", + "
" + ], + "text/plain": [ + " dur\n", + "args_op_name name \n", + "Mul Mu_Mul1 0.054147\n", + "Sigmoid Si_Sigmoid1 0.058972\n", + "Mul Mu_Mul 0.061785\n", + "Sigmoid Si_Sigmoid 0.063396\n", + "MatMul Ma_MatMul2 0.067053\n", + "Gemm Ma_MatMul/MatMulAddFusion 0.234326\n", + " Ma_MatMul1/MatMulAddFusion 0.460321" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gr_dur / gr_dur.dur.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "0e5c02ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.46032090561501343)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r = (gr_dur / gr_dur.dur.sum()).dur.max()\n", + "r" + ] + }, + { + "cell_type": "markdown", + "id": "113a480a", + "metadata": {}, + "source": [ + "Il occupe 82% du temps. et d'après l'expérience précédente, son temps d'éxecution peut-être réduit par 10 en le remplaçant par une matrice sparse. Cela ne suffira pas pour accélerer le temps de calcul de ce réseau de neurones. Il est 84 ms comparé à 247 µs pour l'arbre de décision. Avec cette optimisation, il pourrait passer de :" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "fa7950bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(2.167646886948391)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t = 3.75 # ms\n", + "t * (1 - r) + r * t / 12" + ] + }, + { + "cell_type": "markdown", + "id": "7c641d19", + "metadata": {}, + "source": [ + "Soit une réduction du temps de calcul. Ce n'est pas mal mais pas assez." + ] + }, + { + "cell_type": "markdown", + "id": "535b7e56", + "metadata": {}, + "source": [ + "## Hummingbird\n", + "\n", + "[hummingbird](https://github.com/microsoft/hummingbird) est une librairie qui convertit un arbre de décision en réseau de neurones. Voyons ses performances." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3b3aa43b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(2.7422816128996885e-08), np.float64(3.844877509922521e-09))" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from hummingbird.ml import convert\n", + "\n", + "model = convert(tree, \"torch\")\n", + "\n", + "expected = tree.predict(x_exp)\n", + "got = model.predict(x_exp)\n", + "numpy.abs(got - expected).max(), numpy.abs(got - expected).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "92365d70", + "metadata": {}, + "source": [ + "Le résultat est beaucoup plus fidèle au modèle." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "605df039", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "526 μs ± 41.2 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + ] + } + ], + "source": [ + "%timeit model.predict(x_exp)" + ] + }, + { + "cell_type": "markdown", + "id": "c2f80290", + "metadata": {}, + "source": [ + "Il reste plus lent mais beaucoup plus rapide que la solution manuelle proposée dans les précédents paragraphes. Il contient un attribut `model`." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "e77ff4f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "\n", + "isinstance(model.model, torch.nn.Module)" + ] + }, + { + "cell_type": "markdown", + "id": "871277df", + "metadata": {}, + "source": [ + "On convertit ce modèle au format ONNX." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "3c875b35", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_50840/2369393440.py:4: DeprecationWarning: You are using the legacy TorchScript-based ONNX export. Starting in PyTorch 2.9, the new torch.export-based ONNX exporter has become the default. Learn more about the new export logic: https://docs.pytorch.org/docs/stable/onnx_export.html. For exporting control flow: https://pytorch.org/tutorials/beginner/onnx/export_control_flow_model_to_onnx_tutorial.html\n", + " torch.onnx.export(\n" + ] + } + ], + "source": [ + "import torch.onnx\n", + "\n", + "x = torch.randn(x_exp.shape[0], x_exp.shape[1], requires_grad=True)\n", + "torch.onnx.export(\n", + " model.model,\n", + " x,\n", + " \"tree_torch.onnx\",\n", + " opset_version=15,\n", + " input_names=[\"X\"],\n", + " output_names=[\"variable\"],\n", + " dynamic_axes={\"X\": {0: \"batch_size\"}, \"variable\": {0: \"batch_size\"}},\n", + " dynamo=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "b8c41c5e", + "metadata": {}, + "outputs": [], + "source": [ + "import onnx\n", + "\n", + "onxh = onnx.load(\"tree_torch.onnx\")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "861a94d0", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "opset: domain='' version=15\n", + "input: name='X' type=dtype('float32') shape=['batch_size', 10]\n", + "init: name='_operators.0.root_nodes' type=int64 shape=(1,) -- array([3])\n", + "init: name='_operators.0.root_biases' type=float32 shape=(1,) -- array([0.123061], dtype=float32)\n", + "init: name='_operators.0.tree_indices' type=int64 shape=(1,) -- array([0])\n", + "init: name='_operators.0.leaf_nodes' type=float32 shape=(128, 1)\n", + "init: name='_operators.0.nodes.0' type=int64 shape=(2,) -- array([2, 4])\n", + "init: name='_operators.0.nodes.1' type=int64 shape=(4,) -- array([5, 8, 1, 0])\n", + "init: name='_operators.0.nodes.2' type=int64 shape=(8,)\n", + "init: name='_operators.0.nodes.3' type=int64 shape=(16,)\n", + "init: name='_operators.0.nodes.4' type=int64 shape=(32,)\n", + "init: name='_operators.0.nodes.5' type=int64 shape=(64,)\n", + "init: name='_operators.0.biases.0' type=float32 shape=(2,) -- array([-0.00307798, -0.19721702], dtype=float32)\n", + "init: name='_operators.0.biases.1' type=float32 shape=(4,) -- array([ 0.04036466, -0.18311241, 0.2513926 , -0.7457566 ], dtype=float32)\n", + "init: name='_operators.0.biases.2' type=float32 shape=(8,)\n", + "init: name='_operators.0.biases.3' type=float32 shape=(16,)\n", + "init: name='_operators.0.biases.4' type=float32 shape=(32,)\n", + "init: name='_operators.0.biases.5' type=float32 shape=(64,)\n", + "Constant(value=[-1]) -> /_operators.0/Constant_output_0\n", + "Gather(X, _operators.0.root_nodes, axis=1) -> /_operators.0/Gather_output_0\n", + " LessOrEqual(/_operators.0/Gather_output_0, _operators.0.root_biases) -> /_operators.0/LessOrEqual_output_0\n", + " Cast(/_operators.0/LessOrEqual_output_0, to=7) -> /_operators.0/Cast_output_0\n", + " Add(/_operators.0/Cast_output_0, _operators.0.tree_indices) -> /_operators.0/Add_output_0\n", + " Reshape(/_operators.0/Add_output_0, /_operators.0/Constant_output_0, allowzero=0) -> /_operators.0/Reshape_output_0\n", + " Gather(_operators.0.nodes.0, /_operators.0/Reshape_output_0, axis=0) -> /_operators.0/Gather_1_output_0\n", + "Constant(value=[-1, 1]) -> /_operators.0/Constant_1_output_0\n", + " Reshape(/_operators.0/Gather_1_output_0, /_operators.0/Constant_1_output_0, allowzero=0) -> /_operators.0/Reshape_1_output_0\n", + " GatherElements(X, /_operators.0/Reshape_1_output_0, axis=1) -> /_operators.0/GatherElements_output_0\n", + "Constant(value=[-1]) -> /_operators.0/Constant_2_output_0\n", + " Reshape(/_operators.0/GatherElements_output_0, /_operators.0/Constant_2_output_0, allowzero=0) -> /_operators.0/Reshape_2_output_0\n", + "Constant(value=2) -> /_operators.0/Constant_3_output_0\n", + " Mul(/_operators.0/Reshape_output_0, /_operators.0/Constant_3_output_0) -> /_operators.0/Mul_output_0\n", + "Gather(_operators.0.biases.0, /_operators.0/Reshape_output_0, axis=0) -> /_operators.0/Gather_2_output_0\n", + " LessOrEqual(/_operators.0/Reshape_2_output_0, /_operators.0/Gather_2_output_0) -> /_operators.0/LessOrEqual_1_output_0\n", + " Cast(/_operators.0/LessOrEqual_1_output_0, to=7) -> /_operators.0/Cast_1_output_0\n", + " Add(/_operators.0/Mul_output_0, /_operators.0/Cast_1_output_0) -> /_operators.0/Add_1_output_0\n", + " Gather(_operators.0.nodes.1, /_operators.0/Add_1_output_0, axis=0) -> /_operators.0/Gather_3_output_0\n", + "Constant(value=[-1, 1]) -> /_operators.0/Constant_4_output_0\n", + " Reshape(/_operators.0/Gather_3_output_0, /_operators.0/Constant_4_output_0, allowzero=0) -> /_operators.0/Reshape_3_output_0\n", + " GatherElements(X, /_operators.0/Reshape_3_output_0, axis=1) -> /_operators.0/GatherElements_1_output_0\n", + "Constant(value=[-1]) -> /_operators.0/Constant_5_output_0\n", + " Reshape(/_operators.0/GatherElements_1_output_0, /_operators.0/Constant_5_output_0, allowzero=0) -> /_operators.0/Reshape_4_output_0\n", + "Constant(value=2) -> /_operators.0/Constant_6_output_0\n", + " Mul(/_operators.0/Add_1_output_0, /_operators.0/Constant_6_output_0) -> /_operators.0/Mul_1_output_0\n", + "Gather(_operators.0.biases.1, /_operators.0/Add_1_output_0, axis=0) -> /_operators.0/Gather_4_output_0\n", + " LessOrEqual(/_operators.0/Reshape_4_output_0, /_operators.0/Gather_4_output_0) -> /_operators.0/LessOrEqual_2_output_0\n", + " Cast(/_operators.0/LessOrEqual_2_output_0, to=7) -> /_operators.0/Cast_2_output_0\n", + " Add(/_operators.0/Mul_1_output_0, /_operators.0/Cast_2_output_0) -> /_operators.0/Add_2_output_0\n", + " Gather(_operators.0.nodes.2, /_operators.0/Add_2_output_0, axis=0) -> /_operators.0/Gather_5_output_0\n", + "Constant(value=[-1, 1]) -> /_operators.0/Constant_7_output_0\n", + " Reshape(/_operators.0/Gather_5_output_0, /_operators.0/Constant_7_output_0, allowzero=0) -> /_operators.0/Reshape_5_output_0\n", + " GatherElements(X, /_operators.0/Reshape_5_output_0, axis=1) -> /_operators.0/GatherElements_2_output_0\n", + "Constant(value=[-1]) -> /_operators.0/Constant_8_output_0\n", + " Reshape(/_operators.0/GatherElements_2_output_0, /_operators.0/Constant_8_output_0, allowzero=0) -> /_operators.0/Reshape_6_output_0\n", + "Constant(value=2) -> /_operators.0/Constant_9_output_0\n", + " Mul(/_operators.0/Add_2_output_0, /_operators.0/Constant_9_output_0) -> /_operators.0/Mul_2_output_0\n", + "Gather(_operators.0.biases.2, /_operators.0/Add_2_output_0, axis=0) -> /_operators.0/Gather_6_output_0\n", + " LessOrEqual(/_operators.0/Reshape_6_output_0, /_operators.0/Gather_6_output_0) -> /_operators.0/LessOrEqual_3_output_0\n", + " Cast(/_operators.0/LessOrEqual_3_output_0, to=7) -> /_operators.0/Cast_3_output_0\n", + " Add(/_operators.0/Mul_2_output_0, /_operators.0/Cast_3_output_0) -> /_operators.0/Add_3_output_0\n", + " Gather(_operators.0.nodes.3, /_operators.0/Add_3_output_0, axis=0) -> /_operators.0/Gather_7_output_0\n", + "Constant(value=[-1, 1]) -> /_operators.0/Constant_10_output_0\n", + " Reshape(/_operators.0/Gather_7_output_0, /_operators.0/Constant_10_output_0, allowzero=0) -> /_operators.0/Reshape_7_output_0\n", + " GatherElements(X, /_operators.0/Reshape_7_output_0, axis=1) -> /_operators.0/GatherElements_3_output_0\n", + "Constant(value=[-1]) -> /_operators.0/Constant_11_output_0\n", + " Reshape(/_operators.0/GatherElements_3_output_0, /_operators.0/Constant_11_output_0, allowzero=0) -> /_operators.0/Reshape_8_output_0\n", + "Constant(value=2) -> /_operators.0/Constant_12_output_0\n", + " Mul(/_operators.0/Add_3_output_0, /_operators.0/Constant_12_output_0) -> /_operators.0/Mul_3_output_0\n", + "Gather(_operators.0.biases.3, /_operators.0/Add_3_output_0, axis=0) -> /_operators.0/Gather_8_output_0\n", + " LessOrEqual(/_operators.0/Reshape_8_output_0, /_operators.0/Gather_8_output_0) -> /_operators.0/LessOrEqual_4_output_0\n", + " Cast(/_operators.0/LessOrEqual_4_output_0, to=7) -> /_operators.0/Cast_4_output_0\n", + " Add(/_operators.0/Mul_3_output_0, /_operators.0/Cast_4_output_0) -> /_operators.0/Add_4_output_0\n", + " Gather(_operators.0.nodes.4, /_operators.0/Add_4_output_0, axis=0) -> /_operators.0/Gather_9_output_0\n", + "Constant(value=[-1, 1]) -> /_operators.0/Constant_13_output_0\n", + " Reshape(/_operators.0/Gather_9_output_0, /_operators.0/Constant_13_output_0, allowzero=0) -> /_operators.0/Reshape_9_output_0\n", + " GatherElements(X, /_operators.0/Reshape_9_output_0, axis=1) -> /_operators.0/GatherElements_4_output_0\n", + "Constant(value=[-1]) -> /_operators.0/Constant_14_output_0\n", + " Reshape(/_operators.0/GatherElements_4_output_0, /_operators.0/Constant_14_output_0, allowzero=0) -> /_operators.0/Reshape_10_output_0\n", + "Constant(value=2) -> /_operators.0/Constant_15_output_0\n", + " Mul(/_operators.0/Add_4_output_0, /_operators.0/Constant_15_output_0) -> /_operators.0/Mul_4_output_0\n", + "Gather(_operators.0.biases.4, /_operators.0/Add_4_output_0, axis=0) -> /_operators.0/Gather_10_output_0\n", + " LessOrEqual(/_operators.0/Reshape_10_output_0, /_operators.0/Gather_10_output_0) -> /_operators.0/LessOrEqual_5_output_0\n", + " Cast(/_operators.0/LessOrEqual_5_output_0, to=7) -> /_operators.0/Cast_5_output_0\n", + " Add(/_operators.0/Mul_4_output_0, /_operators.0/Cast_5_output_0) -> /_operators.0/Add_5_output_0\n", + " Gather(_operators.0.nodes.5, /_operators.0/Add_5_output_0, axis=0) -> /_operators.0/Gather_11_output_0\n", + "Constant(value=[-1, 1]) -> /_operators.0/Constant_16_output_0\n", + " Reshape(/_operators.0/Gather_11_output_0, /_operators.0/Constant_16_output_0, allowzero=0) -> /_operators.0/Reshape_11_output_0\n", + " GatherElements(X, /_operators.0/Reshape_11_output_0, axis=1) -> /_operators.0/GatherElements_5_output_0\n", + "Constant(value=[-1]) -> /_operators.0/Constant_17_output_0\n", + " Reshape(/_operators.0/GatherElements_5_output_0, /_operators.0/Constant_17_output_0, allowzero=0) -> /_operators.0/Reshape_12_output_0\n", + "Constant(value=2) -> /_operators.0/Constant_18_output_0\n", + " Mul(/_operators.0/Add_5_output_0, /_operators.0/Constant_18_output_0) -> /_operators.0/Mul_5_output_0\n", + "Gather(_operators.0.biases.5, /_operators.0/Add_5_output_0, axis=0) -> /_operators.0/Gather_12_output_0\n", + " LessOrEqual(/_operators.0/Reshape_12_output_0, /_operators.0/Gather_12_output_0) -> /_operators.0/LessOrEqual_6_output_0\n", + " Cast(/_operators.0/LessOrEqual_6_output_0, to=7) -> /_operators.0/Cast_6_output_0\n", + " Add(/_operators.0/Mul_5_output_0, /_operators.0/Cast_6_output_0) -> /_operators.0/Add_6_output_0\n", + " Gather(_operators.0.leaf_nodes, /_operators.0/Add_6_output_0, axis=0) -> /_operators.0/Gather_13_output_0\n", + "Constant(value=[-1, 1, 1]) -> /_operators.0/Constant_19_output_0\n", + " Reshape(/_operators.0/Gather_13_output_0, /_operators.0/Constant_19_output_0, allowzero=0) -> /_operators.0/Reshape_13_output_0\n", + "Constant(value=[1]) -> onnx::ReduceSum_98\n", + " ReduceSum(/_operators.0/Reshape_13_output_0, onnx::ReduceSum_98, keepdims=0) -> variable\n", + "output: name='variable' type=dtype('float32') shape=['batch_size', 'ReduceSumvariable_dim_1']\n" + ] + } + ], + "source": [ + "print(onnx_simple_text_plot(onxh, raise_exc=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "6ecbffca", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "I_0\n", + "\n", + "X\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "Gather_8\n", + "\n", + "Gather(., [3], axis=1)\n", + "\n", + "\n", + "\n", + "I_0->Gather_8\n", + "\n", + "\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "GatherElements_15\n", + "\n", + "GatherElements(., ., axis=1)\n", + "\n", + "\n", + "\n", + "I_0->GatherElements_15\n", + "\n", + "\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "GatherElements_24\n", + "\n", + "GatherElements(., ., axis=1)\n", + "\n", + "\n", + "\n", + "I_0->GatherElements_24\n", + "\n", + "\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "GatherElements_33\n", + "\n", + "GatherElements(., ., axis=1)\n", + "\n", + "\n", + "\n", + "I_0->GatherElements_33\n", + "\n", + "\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "GatherElements_42\n", + "\n", + "GatherElements(., ., axis=1)\n", + "\n", + "\n", + "\n", + "I_0->GatherElements_42\n", + "\n", + "\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "GatherElements_51\n", + "\n", + "GatherElements(., ., axis=1)\n", + "\n", + "\n", + "\n", + "I_0->GatherElements_51\n", + "\n", + "\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "GatherElements_60\n", + "\n", + "GatherElements(., ., axis=1)\n", + "\n", + "\n", + "\n", + "I_0->GatherElements_60\n", + "\n", + "\n", + "FLOAT(batch_size,10)\n", + "\n", + "\n", + "\n", + "i_1\n", + "\n", + "_operators.0.leaf_nodes\n", + "FLOAT(128, 1)\n", + "\n", + "\n", + "\n", + "Gather_67\n", + "\n", + "Gather(., ., axis=0)\n", + "\n", + "\n", + "\n", + "i_1->Gather_67\n", + "\n", + "\n", + "FLOAT(128, 1)\n", + "\n", + "\n", + "\n", + "i_2\n", + "\n", + "_operators.0.nodes.3\n", + "INT64(16)\n", + "\n", + "\n", + "\n", + "Gather_40\n", + "\n", + "Gather(., ., axis=0)\n", + "\n", + "\n", + "\n", + "i_2->Gather_40\n", + "\n", + "\n", + "INT64(16)\n", + "\n", + "\n", + "\n", + "i_3\n", + "\n", + "_operators.0.nodes.4\n", + "INT64(32)\n", + "\n", + "\n", + "\n", + "Gather_49\n", + "\n", + "Gather(., ., axis=0)\n", + "\n", + "\n", + "\n", + "i_3->Gather_49\n", + "\n", + "\n", + "INT64(32)\n", + "\n", + "\n", + "\n", + "i_4\n", + "\n", + "_operators.0.nodes.5\n", + "INT64(64)\n", + "\n", + "\n", + "\n", + "Gather_58\n", + "\n", + "Gather(., ., axis=0)\n", + "\n", + "\n", + "\n", + "i_4->Gather_58\n", + "\n", + "\n", + "INT64(64)\n", + "\n", + "\n", + "\n", + "i_5\n", + "\n", + "_operators.0.biases.3\n", + "FLOAT(16)\n", + "\n", + "\n", + "\n", + "Gather_45\n", + "\n", + "Gather(., ., axis=0)\n", + "\n", + "\n", + "\n", + "i_5->Gather_45\n", + "\n", + "\n", + "FLOAT(16)\n", + "\n", + "\n", + "\n", + "i_6\n", + "\n", + "_operators.0.biases.4\n", + "FLOAT(32)\n", + "\n", + "\n", + "\n", + "Gather_54\n", + "\n", + "Gather(., ., axis=0)\n", + "\n", + "\n", + "\n", + "i_6->Gather_54\n", + "\n", + "\n", + "FLOAT(32)\n", + "\n", + "\n", + "\n", + "i_7\n", + "\n", + "_operators.0.biases.5\n", + "FLOAT(64)\n", + "\n", + "\n", + "\n", + "Gather_63\n", + "\n", + "Gather(., ., axis=0)\n", + "\n", + "\n", + "\n", + "i_7->Gather_63\n", + "\n", + "\n", + "FLOAT(64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_9\n", + "\n", + "LessOrEqual(., [0.123061])\n", + "\n", + "\n", + "\n", + "Gather_8->LessOrEqual_9\n", + "\n", + "\n", + "FLOAT(batch_size,1)\n", + "\n", + "\n", + "\n", + "Cast_10\n", + "\n", + "Cast(., to=INT64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_9->Cast_10\n", + "\n", + "\n", + "BOOL(batch_size,1)\n", + "\n", + "\n", + "\n", + "Add_11\n", + "\n", + "Add(., [0])\n", + "\n", + "\n", + "\n", + "Cast_10->Add_11\n", + "\n", + "\n", + "INT64(batch_size,1)\n", + "\n", + "\n", + "\n", + "Reshape_12\n", + "\n", + "Reshape(., [-1])\n", + "\n", + "\n", + "\n", + "Add_11->Reshape_12\n", + "\n", + "\n", + "INT64(batch_size,1)\n", + "\n", + "\n", + "\n", + "Gather_13\n", + "\n", + "Gather([2, 4], ., axis=0)\n", + "\n", + "\n", + "\n", + "Reshape_12->Gather_13\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Mul_17\n", + "\n", + "Mul(., 2)\n", + "\n", + "\n", + "\n", + "Reshape_12->Mul_17\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_18\n", + "\n", + "Gather\n", + "([-0.0030779822, -0.19721702], ., axis=0)\n", + "\n", + "\n", + "\n", + "Reshape_12->Gather_18\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_14\n", + "\n", + "Reshape(., [-1, 1])\n", + "\n", + "\n", + "\n", + "Gather_13->Reshape_14\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_14->GatherElements_15\n", + "\n", + "\n", + "INT64(?,1)\n", + "\n", + "\n", + "\n", + "Reshape_16\n", + "\n", + "Reshape(., [-1])\n", + "\n", + "\n", + "\n", + "GatherElements_15->Reshape_16\n", + "\n", + "\n", + "FLOAT(?,1)\n", + "\n", + "\n", + "\n", + "LessOrEqual_19\n", + "\n", + "LessOrEqual(., .)\n", + "\n", + "\n", + "\n", + "Reshape_16->LessOrEqual_19\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Add_21\n", + "\n", + "Add(., .)\n", + "\n", + "\n", + "\n", + "Mul_17->Add_21\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_18->LessOrEqual_19\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Cast_20\n", + "\n", + "Cast(., to=INT64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_19->Cast_20\n", + "\n", + "\n", + "BOOL(?)\n", + "\n", + "\n", + "\n", + "Cast_20->Add_21\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_22\n", + "\n", + "Gather([5, 8, 1, 0], ., axis=0)\n", + "\n", + "\n", + "\n", + "Add_21->Gather_22\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Mul_26\n", + "\n", + "Mul(., 2)\n", + "\n", + "\n", + "\n", + "Add_21->Mul_26\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_27\n", + "\n", + "Gather\n", + "([0.040364657, -0.18311241, 0.2513926, -0.7457566], ., axis=0)\n", + "\n", + "\n", + "\n", + "Add_21->Gather_27\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_23\n", + "\n", + "Reshape(., [-1, 1])\n", + "\n", + "\n", + "\n", + "Gather_22->Reshape_23\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_23->GatherElements_24\n", + "\n", + "\n", + "INT64(?,1)\n", + "\n", + "\n", + "\n", + "Reshape_25\n", + "\n", + "Reshape(., [-1])\n", + "\n", + "\n", + "\n", + "GatherElements_24->Reshape_25\n", + "\n", + "\n", + "FLOAT(?,1)\n", + "\n", + "\n", + "\n", + "LessOrEqual_28\n", + "\n", + "LessOrEqual(., .)\n", + "\n", + "\n", + "\n", + "Reshape_25->LessOrEqual_28\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Add_30\n", + "\n", + "Add(., .)\n", + "\n", + "\n", + "\n", + "Mul_26->Add_30\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_27->LessOrEqual_28\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Cast_29\n", + "\n", + "Cast(., to=INT64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_28->Cast_29\n", + "\n", + "\n", + "BOOL(?)\n", + "\n", + "\n", + "\n", + "Cast_29->Add_30\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_31\n", + "\n", + "Gather\n", + "([6, 1, 1, 5, 0, 7, 9, 8], ., axis=0)\n", + "\n", + "\n", + "\n", + "Add_30->Gather_31\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Mul_35\n", + "\n", + "Mul(., 2)\n", + "\n", + "\n", + "\n", + "Add_30->Mul_35\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_36\n", + "\n", + "Gather\n", + "([-0.38214105, 0.028844688, 0.30779052, -0.5173236, -0.4752456, -0.3372159, -0.43787128, -0.31271878], ., axis=0)\n", + "\n", + "\n", + "\n", + "Add_30->Gather_36\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_32\n", + "\n", + "Reshape(., [-1, 1])\n", + "\n", + "\n", + "\n", + "Gather_31->Reshape_32\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_32->GatherElements_33\n", + "\n", + "\n", + "INT64(?,1)\n", + "\n", + "\n", + "\n", + "Reshape_34\n", + "\n", + "Reshape(., [-1])\n", + "\n", + "\n", + "\n", + "GatherElements_33->Reshape_34\n", + "\n", + "\n", + "FLOAT(?,1)\n", + "\n", + "\n", + "\n", + "LessOrEqual_37\n", + "\n", + "LessOrEqual(., .)\n", + "\n", + "\n", + "\n", + "Reshape_34->LessOrEqual_37\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Add_39\n", + "\n", + "Add(., .)\n", + "\n", + "\n", + "\n", + "Mul_35->Add_39\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_36->LessOrEqual_37\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Cast_38\n", + "\n", + "Cast(., to=INT64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_37->Cast_38\n", + "\n", + "\n", + "BOOL(?)\n", + "\n", + "\n", + "\n", + "Cast_38->Add_39\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Add_39->Gather_40\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Mul_44\n", + "\n", + "Mul(., 2)\n", + "\n", + "\n", + "\n", + "Add_39->Mul_44\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Add_39->Gather_45\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_41\n", + "\n", + "Reshape(., [-1, 1])\n", + "\n", + "\n", + "\n", + "Gather_40->Reshape_41\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_41->GatherElements_42\n", + "\n", + "\n", + "INT64(?,1)\n", + "\n", + "\n", + "\n", + "Reshape_43\n", + "\n", + "Reshape(., [-1])\n", + "\n", + "\n", + "\n", + "GatherElements_42->Reshape_43\n", + "\n", + "\n", + "FLOAT(?,1)\n", + "\n", + "\n", + "\n", + "LessOrEqual_46\n", + "\n", + "LessOrEqual(., .)\n", + "\n", + "\n", + "\n", + "Reshape_43->LessOrEqual_46\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Add_48\n", + "\n", + "Add(., .)\n", + "\n", + "\n", + "\n", + "Mul_44->Add_48\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_45->LessOrEqual_46\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Cast_47\n", + "\n", + "Cast(., to=INT64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_46->Cast_47\n", + "\n", + "\n", + "BOOL(?)\n", + "\n", + "\n", + "\n", + "Cast_47->Add_48\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Add_48->Gather_49\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Mul_53\n", + "\n", + "Mul(., 2)\n", + "\n", + "\n", + "\n", + "Add_48->Mul_53\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Add_48->Gather_54\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_50\n", + "\n", + "Reshape(., [-1, 1])\n", + "\n", + "\n", + "\n", + "Gather_49->Reshape_50\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_50->GatherElements_51\n", + "\n", + "\n", + "INT64(?,1)\n", + "\n", + "\n", + "\n", + "Reshape_52\n", + "\n", + "Reshape(., [-1])\n", + "\n", + "\n", + "\n", + "GatherElements_51->Reshape_52\n", + "\n", + "\n", + "FLOAT(?,1)\n", + "\n", + "\n", + "\n", + "LessOrEqual_55\n", + "\n", + "LessOrEqual(., .)\n", + "\n", + "\n", + "\n", + "Reshape_52->LessOrEqual_55\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Add_57\n", + "\n", + "Add(., .)\n", + "\n", + "\n", + "\n", + "Mul_53->Add_57\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_54->LessOrEqual_55\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Cast_56\n", + "\n", + "Cast(., to=INT64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_55->Cast_56\n", + "\n", + "\n", + "BOOL(?)\n", + "\n", + "\n", + "\n", + "Cast_56->Add_57\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Add_57->Gather_58\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Mul_62\n", + "\n", + "Mul(., 2)\n", + "\n", + "\n", + "\n", + "Add_57->Mul_62\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Add_57->Gather_63\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_59\n", + "\n", + "Reshape(., [-1, 1])\n", + "\n", + "\n", + "\n", + "Gather_58->Reshape_59\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_59->GatherElements_60\n", + "\n", + "\n", + "INT64(?,1)\n", + "\n", + "\n", + "\n", + "Reshape_61\n", + "\n", + "Reshape(., [-1])\n", + "\n", + "\n", + "\n", + "GatherElements_60->Reshape_61\n", + "\n", + "\n", + "FLOAT(?,1)\n", + "\n", + "\n", + "\n", + "LessOrEqual_64\n", + "\n", + "LessOrEqual(., .)\n", + "\n", + "\n", + "\n", + "Reshape_61->LessOrEqual_64\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Add_66\n", + "\n", + "Add(., .)\n", + "\n", + "\n", + "\n", + "Mul_62->Add_66\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Gather_63->LessOrEqual_64\n", + "\n", + "\n", + "FLOAT(?)\n", + "\n", + "\n", + "\n", + "Cast_65\n", + "\n", + "Cast(., to=INT64)\n", + "\n", + "\n", + "\n", + "LessOrEqual_64->Cast_65\n", + "\n", + "\n", + "BOOL(?)\n", + "\n", + "\n", + "\n", + "Cast_65->Add_66\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Add_66->Gather_67\n", + "\n", + "\n", + "INT64(?)\n", + "\n", + "\n", + "\n", + "Reshape_68\n", + "\n", + "Reshape(., [-1, 1, 1])\n", + "\n", + "\n", + "\n", + "Gather_67->Reshape_68\n", + "\n", + "\n", + "FLOAT(?,1)\n", + "\n", + "\n", + "\n", + "ReduceSum_69\n", + "\n", + "ReduceSum(., [1])\n", + "\n", + "\n", + "\n", + "Reshape_68->ReduceSum_69\n", + "\n", + "\n", + "FLOAT(?,1,1)\n", + "\n", + "\n", + "\n", + "O_70\n", + "\n", + "variable\n", + "FLOAT(batch_size,1)\n", + "\n", + "\n", + "\n", + "ReduceSum_69->O_70\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from onnx_diagnostic.helpers.dot_helper import to_dot\n", + "import graphviz\n", + "\n", + "dot = to_dot(onxh)\n", + "\n", + "with open(\"dump_model.dot\", \"w\") as f:\n", + " f.write(dot)\n", + "graph = graphviz.Source.from_file(\"dump_model.dot\")\n", + "graph" + ] + }, + { + "cell_type": "markdown", + "id": "1edb6177", + "metadata": {}, + "source": [ + "La librairie réimplémente la décision d'un arbre décision à partir d'un produit matriciel pour chaque niveau de l'arbre. Tous les seuils sont évalués. Les matrices n'ont pas besoin d'être sparses car les features nécessaires sont récupérées. Le seuil de décision est implémenté avec un test et non une sigmoïde. Ce modèle est donc identique en terme de prédiction au modèle initial." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "2220ca2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(1.7091389654766018)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oinfh = InferenceSession(onxh.SerializeToString(), providers=[\"CPUExecutionProvider\"])\n", + "expected = tree.predict(x_exp)\n", + "\n", + "got = oinfh.run(None, {\"X\": x_exp.astype(numpy.float32)})[0]\n", + "numpy.abs(got - expected).max()" + ] + }, + { + "cell_type": "markdown", + "id": "10de2a80", + "metadata": {}, + "source": [ + "La conversion reste imparfaite également." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "fd13b28b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.02 ms ± 34.1 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%timeit oinfh.run(None, {'X': x_exp32})[0]" + ] + }, + { + "cell_type": "markdown", + "id": "11a36a32", + "metadata": {}, + "source": [ + "Et le temps de calcul est aussi plus long." + ] + }, + { + "cell_type": "markdown", + "id": "20afcc41", + "metadata": {}, + "source": [ + "## Apprentissage\n", + "\n", + "L'idée derrière tout cela est aussi de pouvoir réestimer les coefficients du réseau de neurones une fois converti." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "96abfddb", + "metadata": {}, + "outputs": [], + "source": [ + "x_train = X_train[:100]\n", + "expected = tree.predict(x_train)\n", + "reg = NeuralTreeNetRegressor(trees[1], verbose=1, max_iter=10, lr=1e-4)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "94dc4d66", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(1.1582154970123497), np.float64(0.21548286223135504))" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "got = reg.predict(x_train)\n", + "numpy.abs(got - expected).max(), numpy.abs(got - expected).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "111970a1", + "metadata": {}, + "source": [ + "La différence est grande." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "a50b3384", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0/10: loss: 2.025 lr=0.0001 max(coef): 6.5 l1=0/1.5e+03 l2=0/2.5e+03\n", + "1/10: loss: 2.03 lr=9.95e-06 max(coef): 6.5 l1=4e+02/1.5e+03 l2=67/2.5e+03\n", + "2/10: loss: 2.019 lr=7.05e-06 max(coef): 6.5 l1=7.6e+02/1.5e+03 l2=2.8e+02/2.5e+03\n", + "3/10: loss: 2.014 lr=5.76e-06 max(coef): 6.5 l1=2.3e+02/1.5e+03 l2=39/2.5e+03\n", + "4/10: loss: 2.013 lr=4.99e-06 max(coef): 6.5 l1=2.3e+03/1.5e+03 l2=4.5e+03/2.5e+03\n", + "5/10: loss: 2.01 lr=4.47e-06 max(coef): 6.5 l1=7.1e+02/1.5e+03 l2=1.6e+02/2.5e+03\n", + "6/10: loss: 2.007 lr=4.08e-06 max(coef): 6.5 l1=7.1e+02/1.5e+03 l2=2e+02/2.5e+03\n", + "7/10: loss: 2.005 lr=3.78e-06 max(coef): 6.5 l1=1.1e+03/1.5e+03 l2=5.9e+02/2.5e+03\n", + "8/10: loss: 2 lr=3.53e-06 max(coef): 6.5 l1=7.1e+02/1.5e+03 l2=2e+02/2.5e+03\n", + "9/10: loss: 1.997 lr=3.33e-06 max(coef): 6.5 l1=9.3e+02/1.5e+03 l2=8.5e+02/2.5e+03\n", + "10/10: loss: 1.994 lr=3.16e-06 max(coef): 6.5 l1=2e+03/1.5e+03 l2=5.1e+03/2.5e+03\n" + ] + }, + { + "data": { + "text/html": [ + "
NeuralTreeNetRegressor(estimator=None, lr=0.0001, max_iter=10, verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "NeuralTreeNetRegressor(estimator=None, lr=0.0001, max_iter=10, verbose=1)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reg.fit(x_train, expected)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "c3ae49b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(np.float64(1.2809916184057408), np.float64(0.22175907540246548))" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "got = reg.predict(x_train)\n", + "numpy.abs(got - expected).max(), numpy.abs(got - expected).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "831e538f", + "metadata": {}, + "source": [ + "Ca ne marche pas aussi bien que prévu. Il faudrait sans doute plusieurs itérations et jouer avec les paramètres d'apprentissage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cfe39bd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22587d4f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "this312", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/_doc/notebooks/ml/piecewise_linear_regression.ipynb b/_doc/notebooks/ml/piecewise_linear_regression.ipynb index 1671ac1b..90f81aaf 100644 --- a/_doc/notebooks/ml/piecewise_linear_regression.ipynb +++ b/_doc/notebooks/ml/piecewise_linear_regression.ipynb @@ -1,449 +1,315 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# R\u00e9gression lin\u00e9aire par morceaux\n", - "\n", - "La r\u00e9gression lin\u00e9aire par morceaux a l'avantage de produire un mod\u00e8le localement interpr\u00e9table. Mais ce n'est pas \u00e9vident d'estimer un tel mod\u00e8le quand on ne conna\u00eet pas les morceaux par avance." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Des donn\u00e9es artificielles" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from numpy.random import normal\n", - "import numpy\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def nuage(n, alpha, noise=0.2):\n", - " eps = normal(0, 2, (n, 2))\n", - " X = eps[:, 0] + 2\n", - " X1 = eps[:, 0].copy()\n", - " X2 = eps[:, 0].copy()\n", - " th = 1.\n", - " X1[X1 <= th] = 0\n", - " X2[X2 > th] = 0\n", - " sel = numpy.zeros((n,))\n", - " sel[X1 > th] = 1\n", - " Y = X1 * alpha - X2 * alpha + eps[:, 1] * noise - sel * alpha * th * 2\n", - " return X, Y\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", - "alpha, noise = 0.5, 0.2\n", - "X, Y = nuage(200, alpha)\n", - "ax[0].plot(X, Y, '.')\n", - "ax[0].set_title(\"alpha=%1.2f noise=%1.2f\" % (alpha, noise));\n", - "alpha, noise = 2., 0.4\n", - "X, Y = nuage(200, alpha, noise=0.4)\n", - "ax[1].plot(X, Y, '.')\n", - "ax[1].set_title(\"alpha=%1.2f noise=%1.2f\" % (alpha, noise));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quelques exemples avec un arbre de d\u00e9cision\n", - "\n", - "La segmentation est r\u00e9alis\u00e9e d'abord avec un arbre de d\u00e9cision dont on fixe la profondeur. Chaque segment est choisi de telle sorte \u00e0 minimiser l'approximation de la fonction par une constante sur chaque segment." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from mlinsights.mlmodel import PiecewiseRegressor\n", - "from sklearn.tree import DecisionTreeRegressor\n", - "\n", - "\n", - "def nuage_piecewise(n, alpha, noise=0.2, max_depth=1):\n", - " X, Y = nuage(n, alpha, noise=noise)\n", - " clr = PiecewiseRegressor(binner=DecisionTreeRegressor(max_depth=max_depth))\n", - " Xm = X.reshape((len(X), 1))\n", - " clr.fit(Xm, Y)\n", - " mi, ma = X.min(), X.max()\n", - " Xm = numpy.arange(0, 200) * (ma - mi) / 200 + mi\n", - " Xm = Xm.reshape((len(Xm), 1))\n", - " return X, Y, Xm, clr.predict(Xm)\n", - "\n", - "def plot(i, j, alpha, noise, max_depth, ax):\n", - " X, Y, XX, Z = nuage_piecewise(200, alpha, max_depth=max_depth)\n", - " ax[i, j].plot(X, Y, '.')\n", - " ax[i, j].plot(XX, Z, '.')\n", - " ax[i, j].set_title(\"alpha=%1.2f noise=%1.2f max_depth=%d\" % (\n", - " alpha, noise, max_depth))\n", - "\n", - "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n", - "\n", - "alpha, noise, max_depth = 0.5, 0.2, 1\n", - "plot(0, 0, alpha, noise, max_depth, ax)\n", - "\n", - "alpha, noise, max_depth = 2., 0.4, 1\n", - "plot(0, 1, alpha, noise, max_depth, ax)\n", - "\n", - "alpha, noise, max_depth = 0.5, 0.2, 2\n", - "plot(1, 0, alpha, noise, max_depth, ax)\n", - "\n", - "alpha, noise, max_depth = 2., 0.4, 2\n", - "plot(1, 1, alpha, noise, max_depth, ax)\n", - "\n", - "plt.suptitle(\"R\u00e9gression lin\u00e9aire avec DecisionTreeRegressor\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quelques exemples avec un KBinsDiscretizer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from mlinsights.mlmodel import PiecewiseRegressor\n", - "from sklearn.preprocessing import KBinsDiscretizer\n", - "\n", - "\n", - "def nuage_piecewise2(n, alpha, noise=0.2, n_bins=2):\n", - " X, Y = nuage(n, alpha, noise=noise)\n", - " clr = PiecewiseRegressor(binner=KBinsDiscretizer(n_bins=n_bins))\n", - " Xm = X.reshape((len(X), 1))\n", - " clr.fit(Xm, Y)\n", - " mi, ma = X.min(), X.max()\n", - " Xm = numpy.arange(0, 200) * (ma - mi) / 200 + mi\n", - " Xm = Xm.reshape((len(Xm), 1))\n", - " return X, Y, Xm, clr.predict(Xm)\n", - "\n", - "def plot2(i, j, alpha, noise, n_bins, ax):\n", - " X, Y, XX, Z = nuage_piecewise2(200, alpha, n_bins=n_bins)\n", - " ax[i, j].plot(X, Y, '.')\n", - " ax[i, j].plot(XX, Z, '.')\n", - " ax[i, j].set_title(\"alpha=%1.2f noise=%1.2f n_bins=%d\" % (\n", - " alpha, noise, n_bins))\n", - "\n", - "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n", - "\n", - "alpha, noise, n_bins = 0.5, 0.2, 2\n", - "plot2(0, 0, alpha, noise, n_bins, ax)\n", - "\n", - "alpha, noise, n_bins = 2., 0.4, 2\n", - "plot2(0, 1, alpha, noise, n_bins, ax)\n", - "\n", - "alpha, noise, n_bins = 0.5, 0.2, 4\n", - "plot2(1, 0, alpha, noise, n_bins, ax)\n", - "\n", - "alpha, noise, n_bins = 2., 0.4, 4\n", - "plot2(1, 1, alpha, noise, n_bins, ax)\n", - "\n", - "plt.suptitle(\"R\u00e9gression lin\u00e9aire avec KBinsDiscretizer\");" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Régression linéaire par morceaux\n", + "\n", + "La régression linéaire par morceaux a l'avantage de produire un modèle localement interprétable. Mais ce n'est pas évident d'estimer un tel modèle quand on ne connaît pas les morceaux par avance." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Des données artificielles" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "C'est mieux mais ce n'est pas parfait. La classe [KBinsDiscretizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html) fonctionne simplement en segmentant les donn\u00e9es mais elle ne tient pas compte de la cible." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from numpy.random import normal\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def nuage(n, alpha, noise=0.2):\n", + " eps = normal(0, 2, (n, 2))\n", + " X = eps[:, 0] + 2\n", + " X1 = eps[:, 0].copy()\n", + " X2 = eps[:, 0].copy()\n", + " th = 1.0\n", + " X1[th >= X1] = 0\n", + " X2[th < X2] = 0\n", + " sel = numpy.zeros((n,))\n", + " sel[th < X1] = 1\n", + " Y = X1 * alpha - X2 * alpha + eps[:, 1] * noise - sel * alpha * th * 2\n", + " return X, Y\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "alpha, noise = 0.5, 0.2\n", + "X, Y = nuage(200, alpha)\n", + "ax[0].plot(X, Y, \".\")\n", + "ax[0].set_title(\"alpha=%1.2f noise=%1.2f\" % (alpha, noise))\n", + "alpha, noise = 2.0, 0.4\n", + "X, Y = nuage(200, alpha, noise=0.4)\n", + "ax[1].plot(X, Y, \".\")\n", + "ax[1].set_title(\"alpha=%1.2f noise=%1.2f\" % (alpha, noise));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quelques exemples avec un arbre de décision\n", + "\n", + "La segmentation est réalisée d'abord avec un arbre de décision dont on fixe la profondeur. Chaque segment est choisi de telle sorte à minimiser l'approximation de la fonction par une constante sur chaque segment." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Arbre de d\u00e9cision optimis\u00e9 pour la r\u00e9gression lin\u00e9aire\n", - "\n", - "L'arbre suivant reprend l'algorithme de l'arbre de d\u00e9cision \u00e0 ceci pr\u00e8s qu'il optimise un crit\u00e8re [MSE](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html) en approximant le nuage de points $(X_i, y_i)$ par une fonction lin\u00e9aire $y_i = X_i \\beta + \\epsilon_i$. Il faut n\u00e9anmoins augmenter le nombre de points par feuille pour \u00e9viter quelques artefacts." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mlinsights.mlmodel import PiecewiseRegressor\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "\n", + "\n", + "def nuage_piecewise(n, alpha, noise=0.2, max_depth=1):\n", + " X, Y = nuage(n, alpha, noise=noise)\n", + " clr = PiecewiseRegressor(binner=DecisionTreeRegressor(max_depth=max_depth))\n", + " Xm = X.reshape((len(X), 1))\n", + " clr.fit(Xm, Y)\n", + " mi, ma = X.min(), X.max()\n", + " Xm = numpy.arange(0, 200) * (ma - mi) / 200 + mi\n", + " Xm = Xm.reshape((len(Xm), 1))\n", + " return X, Y, Xm, clr.predict(Xm)\n", + "\n", + "\n", + "def plot(i, j, alpha, noise, max_depth, ax):\n", + " X, Y, XX, Z = nuage_piecewise(200, alpha, max_depth=max_depth)\n", + " ax[i, j].plot(X, Y, \".\")\n", + " ax[i, j].plot(XX, Z, \".\")\n", + " ax[i, j].set_title(\n", + " \"alpha=%1.2f noise=%1.2f max_depth=%d\" % (alpha, noise, max_depth)\n", + " )\n", + "\n", + "\n", + "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n", + "\n", + "alpha, noise, max_depth = 0.5, 0.2, 1\n", + "plot(0, 0, alpha, noise, max_depth, ax)\n", + "\n", + "alpha, noise, max_depth = 2.0, 0.4, 1\n", + "plot(0, 1, alpha, noise, max_depth, ax)\n", + "\n", + "alpha, noise, max_depth = 0.5, 0.2, 2\n", + "plot(1, 0, alpha, noise, max_depth, ax)\n", + "\n", + "alpha, noise, max_depth = 2.0, 0.4, 2\n", + "plot(1, 1, alpha, noise, max_depth, ax)\n", + "\n", + "plt.suptitle(\"Régression linéaire avec DecisionTreeRegressor\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quelques exemples avec un KBinsDiscretizer" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from mlinsights.mlmodel.piecewise_tree_regression import PiecewiseTreeRegressor\n", - "from sklearn.preprocessing import KBinsDiscretizer\n", - "\n", - "\n", - "def nuage_piecewise2(n, alpha, noise=0.2, min_samples_leaf=30):\n", - " X, Y = nuage(n, alpha, noise=noise)\n", - " clr = PiecewiseTreeRegressor(criterion='mselin', \n", - " min_samples_leaf=min_samples_leaf)\n", - " Xm = X.reshape((len(X), 1))\n", - " clr.fit(Xm, Y)\n", - " mi, ma = X.min(), X.max()\n", - " Xm = numpy.arange(0, 200) * (ma - mi) / 200 + mi\n", - " Xm = Xm.reshape((len(Xm), 1))\n", - " return X, Y, Xm, clr.predict(Xm)\n", - "\n", - "def plot2(i, j, alpha, noise, min_samples_leaf, ax):\n", - " X, Y, XX, Z = nuage_piecewise2(200, alpha,\n", - " min_samples_leaf=min_samples_leaf)\n", - " ax[i, j].plot(X, Y, '.')\n", - " ax[i, j].plot(XX, Z, '.')\n", - " ax[i, j].set_title(\"alpha=%1.2f noise=%1.2f min_samples_leaf=%d\" %(\n", - " alpha, noise, min_samples_leaf))\n", - "\n", - "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n", - "\n", - "alpha, noise, min_samples_leaf = 0.5, 0.2, 40\n", - "plot2(0, 0, alpha, noise, min_samples_leaf, ax)\n", - "\n", - "alpha, noise, min_samples_leaf = 2., 0.4, 40\n", - "plot2(0, 1, alpha, noise, min_samples_leaf, ax)\n", - "\n", - "alpha, noise, min_samples_leaf = 0.5, 0.2, 30\n", - "plot2(1, 0, alpha, noise, min_samples_leaf, ax)\n", - "\n", - "alpha, noise, min_samples_leaf = 2., 0.4, 30\n", - "plot2(1, 1, alpha, noise, min_samples_leaf, ax)\n", - "\n", - "plt.suptitle(\"Arbre de d\u00e9cision optimis\u00e9\\npour la r\u00e9gression lin\u00e9aire par morceaux\");" + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import KBinsDiscretizer\n", + "\n", + "\n", + "def nuage_piecewise2(n, alpha, noise=0.2, n_bins=2):\n", + " X, Y = nuage(n, alpha, noise=noise)\n", + " clr = PiecewiseRegressor(binner=KBinsDiscretizer(n_bins=n_bins))\n", + " Xm = X.reshape((len(X), 1))\n", + " clr.fit(Xm, Y)\n", + " mi, ma = X.min(), X.max()\n", + " Xm = numpy.arange(0, 200) * (ma - mi) / 200 + mi\n", + " Xm = Xm.reshape((len(Xm), 1))\n", + " return X, Y, Xm, clr.predict(Xm)\n", + "\n", + "\n", + "def plot2(i, j, alpha, noise, n_bins, ax):\n", + " X, Y, XX, Z = nuage_piecewise2(200, alpha, n_bins=n_bins)\n", + " ax[i, j].plot(X, Y, \".\")\n", + " ax[i, j].plot(XX, Z, \".\")\n", + " ax[i, j].set_title(\"alpha=%1.2f noise=%1.2f n_bins=%d\" % (alpha, noise, n_bins))\n", + "\n", + "\n", + "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n", + "\n", + "alpha, noise, n_bins = 0.5, 0.2, 2\n", + "plot2(0, 0, alpha, noise, n_bins, ax)\n", + "\n", + "alpha, noise, n_bins = 2.0, 0.4, 2\n", + "plot2(0, 1, alpha, noise, n_bins, ax)\n", + "\n", + "alpha, noise, n_bins = 0.5, 0.2, 4\n", + "plot2(1, 0, alpha, noise, n_bins, ax)\n", + "\n", + "alpha, noise, n_bins = 2.0, 0.4, 4\n", + "plot2(1, 1, alpha, noise, n_bins, ax)\n", + "\n", + "plt.suptitle(\"Régression linéaire avec KBinsDiscretizer\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est mieux mais ce n'est pas parfait. La classe [KBinsDiscretizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html) fonctionne simplement en segmentant les données mais elle ne tient pas compte de la cible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Arbre de décision optimisé pour la régression linéaire\n", + "\n", + "L'arbre suivant reprend l'algorithme de l'arbre de décision à ceci près qu'il optimise un critère [MSE](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html) en approximant le nuage de points $(X_i, y_i)$ par une fonction linéaire $y_i = X_i \\beta + \\epsilon_i$. Il faut néanmoins augmenter le nombre de points par feuille pour éviter quelques artefacts." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Il faudrait ajouter des contraintes de continuit\u00e9." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "from mlinsights.mlmodel.piecewise_tree_regression import PiecewiseTreeRegressor\n", + "\n", + "\n", + "def nuage_piecewise3(n, alpha, noise=0.2, min_samples_leaf=30):\n", + " X, Y = nuage(n, alpha, noise=noise)\n", + " clr = PiecewiseTreeRegressor(criterion=\"mselin\", min_samples_leaf=min_samples_leaf)\n", + " Xm = X.reshape((len(X), 1))\n", + " clr.fit(Xm, Y)\n", + " mi, ma = X.min(), X.max()\n", + " Xm = numpy.arange(0, 200) * (ma - mi) / 200 + mi\n", + " Xm = Xm.reshape((len(Xm), 1))\n", + " return X, Y, Xm, clr.predict(Xm)\n", + "\n", + "\n", + "def plot3(i, j, alpha, noise, min_samples_leaf, ax):\n", + " X, Y, XX, Z = nuage_piecewise3(200, alpha, min_samples_leaf=min_samples_leaf)\n", + " ax[i, j].plot(X, Y, \".\")\n", + " ax[i, j].plot(XX, Z, \".\")\n", + " ax[i, j].set_title(\n", + " \"alpha=%1.2f noise=%1.2f min_samples_leaf=%d\" % (alpha, noise, min_samples_leaf)\n", + " )\n", + "\n", + "\n", + "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n", + "\n", + "alpha, noise, min_samples_leaf = 0.5, 0.2, 40\n", + "plot3(0, 0, alpha, noise, min_samples_leaf, ax)\n", + "\n", + "alpha, noise, min_samples_leaf = 2.0, 0.4, 40\n", + "plot3(0, 1, alpha, noise, min_samples_leaf, ax)\n", + "\n", + "alpha, noise, min_samples_leaf = 0.5, 0.2, 30\n", + "plot3(1, 0, alpha, noise, min_samples_leaf, ax)\n", + "\n", + "alpha, noise, min_samples_leaf = 2.0, 0.4, 30\n", + "plot3(1, 1, alpha, noise, min_samples_leaf, ax)\n", + "\n", + "plt.suptitle(\"Arbre de décision optimisé\\npour la régression linéaire par morceaux\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il faudrait ajouter des contraintes de continuité." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/ml/regression_no_inversion.ipynb b/_doc/notebooks/ml/regression_no_inversion.ipynb index 6d7bf8ee..161883d1 100644 --- a/_doc/notebooks/ml/regression_no_inversion.ipynb +++ b/_doc/notebooks/ml/regression_no_inversion.ipynb @@ -1,949 +1,984 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# R\u00e9gression sans inversion\n", - "\n", - "Ce notebook mesure le temps de calcul dans deux algorithmes pour r\u00e9soudre une r\u00e9gression lin\u00e9aire, le premier inverse un matrice, le second le fait sans inverser une matrice, le troisi\u00e8me reprend l'id\u00e9e du second mais utilise une d\u00e9composition [QR](https://fr.wikipedia.org/wiki/D%C3%A9composition_QR) puis inverse la matrice *R*." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((1000, 7), (1000,), (1000, 1))" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy.random as rnd\n", - "X = rnd.randn(1000, 7)\n", - "eps = rnd.randn(1000, 1) / 3\n", - "y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps\n", - "y = y.ravel()\n", - "X.shape, y.shape, eps.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.97915374, 1.00078055, 1.00537618, 1.01021414, 1.0003261 ,\n", - " 0.9944518 , 0.98742625]),\n", - " array([0.97915374, 1.00078055, 1.00537618, 1.01021414, 1.0003261 ,\n", - " 0.9944518 , 0.98742625]))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.ml.matrices import linear_regression, gram_schmidt\n", - "beta1 = linear_regression(X, y, algo=None)\n", - "beta2 = linear_regression(X, y, algo=\"gram\")\n", - "beta1, beta2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "38.4 \u00b5s \u00b1 2.07 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit linear_regression(X, y, algo=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "310 \u00b5s \u00b1 13.6 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n" - ] - } - ], - "source": [ - "%timeit linear_regression(X, y, algo=\"gram\")" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Régression sans inversion\n", + "\n", + "Ce notebook mesure le temps de calcul dans deux algorithmes pour résoudre une régression linéaire, le premier inverse un matrice, le second le fait sans inverser une matrice, le troisième reprend l'idée du second mais utilise une décomposition [QR](https://fr.wikipedia.org/wiki/D%C3%A9composition_QR) puis inverse la matrice *R*." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "139 \u00b5s \u00b1 8.29 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit linear_regression(X, y, algo=\"qr\")" + "data": { + "text/plain": [ + "((1000, 7), (1000,), (1000, 1))" ] - }, + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy.random as rnd\n", + "\n", + "X = rnd.randn(1000, 7)\n", + "eps = rnd.randn(1000, 1) / 3\n", + "y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps\n", + "y = y.ravel()\n", + "X.shape, y.shape, eps.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "210 \u00b5s \u00b1 5.91 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n" - ] - } - ], - "source": [ - "Xt = X.T\n", - "%timeit gram_schmidt(Xt)" + "data": { + "text/plain": [ + "(array([0.97915374, 1.00078055, 1.00537618, 1.01021414, 1.0003261 ,\n", + " 0.9944518 , 0.98742625]),\n", + " array([0.97915374, 1.00078055, 1.00537618, 1.01021414, 1.0003261 ,\n", + " 0.9944518 , 0.98742625]))" ] - }, + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.ml.matrices import linear_regression, gram_schmidt\n", + "\n", + "beta1 = linear_regression(X, y, algo=None)\n", + "beta2 = linear_regression(X, y, algo=\"gram\")\n", + "beta1, beta2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Un exemple avec [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "38.4 µs ± 2.07 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" + ] + } + ], + "source": [ + "%timeit linear_regression(X, y, algo=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "443 \u00b5s \u00b1 48.3 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n" - ] - } - ], - "source": [ - "from sklearn.linear_model import LinearRegression\n", - "clr = LinearRegression()\n", - "%timeit clr.fit(X, y)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "310 µs ± 13.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" + ] + } + ], + "source": [ + "%timeit linear_regression(X, y, algo=\"gram\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Qui utilise la fonction [lstsq](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html?highlight=lstsq):" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "139 µs ± 8.29 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" + ] + } + ], + "source": [ + "%timeit linear_regression(X, y, algo=\"qr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "75.5 \u00b5s \u00b1 2.57 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "from numpy.linalg import lstsq\n", - "%timeit lstsq(X, y, rcond=None)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "210 µs ± 5.91 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" + ] + } + ], + "source": [ + "Xt = X.T\n", + "%timeit gram_schmidt(Xt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Un exemple avec [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Il serait sans doute possible d'optimiser les calculs en r\u00e9duisant le nombre de copie et de transpos\u00e9es. La version utilisant une d\u00e9composition [QR](https://fr.wikipedia.org/wiki/D%C3%A9composition_QR) est assez rapide. Le code est l\u00e0 [matrices.py](https://github.com/sdpython/mlstatpy/blob/master/src/mlstatpy/ml/matrices.py). Pour d\u00e9passer [numpy](https://www.numpy.org/), il faut passer au C++. *scikit-learn* ajoute des \u00e9tapes interm\u00e9diaires pour v\u00e9rifier les donn\u00e9es ce qui explique la longueur. On r\u00e9sume le tout par un graphique." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "443 µs ± 48.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "clr = LinearRegression()\n", + "%timeit clr.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Qui utilise la fonction [lstsq](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html?highlight=lstsq):" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from cpyquickhelper.numbers import measure_time" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "75.5 µs ± 2.57 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" + ] + } + ], + "source": [ + "from numpy.linalg import lstsq\n", + "\n", + "%timeit lstsq(X, y, rcond=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il serait sans doute possible d'optimiser les calculs en réduisant le nombre de copie et de transposées. La version utilisant une décomposition [QR](https://fr.wikipedia.org/wiki/D%C3%A9composition_QR) est assez rapide. Le code est là [matrices.py](https://github.com/sdpython/mlstatpy/blob/main/mlstatpy/ml/matrices.py). Pour dépasser [numpy](https://www.numpy.org/), il faut passer au C++. *scikit-learn* ajoute des étapes intermédiaires pour vérifier les données ce qui explique la longueur. On résume le tout par un graphique." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlstatpy.ext_test_case import measure_time" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100 10\n", - "1000 10\n", - "10000 10\n", - "100 20\n", - "1000 20\n", - "10000 20\n", - "100 50\n", - "1000 50\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
averagecontext_sizedeviationdimfctmax_execmin_execnamenumberrepeatsize
00.0000393680.00001910linear_regression(X, y, algo=None)0.0000910.000018lr_matrix2020100
10.0003653680.00004510linear_regression(X, y, algo='gram')0.0004850.000312lr_gram2020100
20.0001143680.00003110linear_regression(X, y, algo='qr')0.0002230.000093lr_qr2020100
30.0002293680.00002010gram_schmidt(Xt)0.0002560.000197gram2020100
40.0004033680.00003110clr.fit(X, y)0.0004640.000346sklearn2020100
\n", - "
" - ], - "text/plain": [ - " average context_size deviation dim \\\n", - "0 0.000039 368 0.000019 10 \n", - "1 0.000365 368 0.000045 10 \n", - "2 0.000114 368 0.000031 10 \n", - "3 0.000229 368 0.000020 10 \n", - "4 0.000403 368 0.000031 10 \n", - "\n", - " fct max_exec min_exec name \\\n", - "0 linear_regression(X, y, algo=None) 0.000091 0.000018 lr_matrix \n", - "1 linear_regression(X, y, algo='gram') 0.000485 0.000312 lr_gram \n", - "2 linear_regression(X, y, algo='qr') 0.000223 0.000093 lr_qr \n", - "3 gram_schmidt(Xt) 0.000256 0.000197 gram \n", - "4 clr.fit(X, y) 0.000464 0.000346 sklearn \n", - "\n", - " number repeat size \n", - "0 20 20 100 \n", - "1 20 20 100 \n", - "2 20 20 100 \n", - "3 20 20 100 \n", - "4 20 20 100 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stmts = [dict(name='lr_matrix', fct=\"linear_regression(X, y, algo=None)\"),\n", - " dict(name='lr_gram', fct=\"linear_regression(X, y, algo='gram')\"),\n", - " dict(name='lr_qr', fct=\"linear_regression(X, y, algo='qr')\"),\n", - " dict(name='gram', fct=\"gram_schmidt(Xt)\"),\n", - " dict(name='sklearn', fct=\"clr.fit(X, y)\"),\n", - " dict(name='lstsq', fct=\"lstsq(X, y)\")]\n", - "\n", - "memo = []\n", - "for size, dim in [(100, 10), (1000, 10), (10000, 10),\n", - " (100, 20), (1000, 20), (10000, 20),\n", - " (100, 50), (1000, 50)]:\n", - " print(size, dim)\n", - " X = rnd.randn(size, dim)\n", - " eps = rnd.randn(size, 1) / 3\n", - " y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps\n", - " y = y.ravel()\n", - " context = dict(linear_regression=linear_regression, Xt=X.T,\n", - " X=X, y=y, gram_schmidt=gram_schmidt, clr=clr,\n", - " lstsq=lambda X, y: lstsq(X, y, rcond=None))\n", - " \n", - " for stmt in stmts:\n", - " res = measure_time(stmt['fct'], number=20, repeat=20, div_by_number=True, context=context)\n", - " res.update(stmt)\n", - " res['size'] = size\n", - " res['dim'] = dim\n", - " memo.append(res)\n", - "\n", - "import pandas\n", - "df = pandas.DataFrame(memo)\n", - "df.head()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "100 10\n", + "1000 10\n", + "10000 10\n", + "100 20\n", + "1000 20\n", + "10000 20\n", + "100 50\n", + "1000 50\n" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
namegramlr_gramlr_matrixlr_qrlstsqsklearn
sizedim
100100.0002290.0003650.0000390.0001140.0000810.000403
200.0004420.0007720.0000570.0001420.0001430.000433
500.0013840.0023030.0001150.0002980.0006190.000935
1000100.0003350.0004980.0000520.0001680.0001400.000633
200.0008670.0011970.0000930.0003350.0002460.000641
500.0032420.0044820.0002630.0012200.0009450.001545
10000100.0014340.0013090.0002340.0027600.0005510.001828
200.0102120.0109440.0002930.0059260.0021280.005581
\n", - "
" - ], - "text/plain": [ - "name gram lr_gram lr_matrix lr_qr lstsq sklearn\n", - "size dim \n", - "100 10 0.000229 0.000365 0.000039 0.000114 0.000081 0.000403\n", - " 20 0.000442 0.000772 0.000057 0.000142 0.000143 0.000433\n", - " 50 0.001384 0.002303 0.000115 0.000298 0.000619 0.000935\n", - "1000 10 0.000335 0.000498 0.000052 0.000168 0.000140 0.000633\n", - " 20 0.000867 0.001197 0.000093 0.000335 0.000246 0.000641\n", - " 50 0.003242 0.004482 0.000263 0.001220 0.000945 0.001545\n", - "10000 10 0.001434 0.001309 0.000234 0.002760 0.000551 0.001828\n", - " 20 0.010212 0.010944 0.000293 0.005926 0.002128 0.005581" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
averagecontext_sizedeviationdimfctmax_execmin_execnamenumberrepeatsize
00.0000393680.00001910linear_regression(X, y, algo=None)0.0000910.000018lr_matrix2020100
10.0003653680.00004510linear_regression(X, y, algo='gram')0.0004850.000312lr_gram2020100
20.0001143680.00003110linear_regression(X, y, algo='qr')0.0002230.000093lr_qr2020100
30.0002293680.00002010gram_schmidt(Xt)0.0002560.000197gram2020100
40.0004033680.00003110clr.fit(X, y)0.0004640.000346sklearn2020100
\n", + "
" ], - "source": [ - "piv = pandas.pivot_table(df, index=['size', 'dim'], columns='name', values='average')\n", - "piv" + "text/plain": [ + " average context_size deviation dim \\\n", + "0 0.000039 368 0.000019 10 \n", + "1 0.000365 368 0.000045 10 \n", + "2 0.000114 368 0.000031 10 \n", + "3 0.000229 368 0.000020 10 \n", + "4 0.000403 368 0.000031 10 \n", + "\n", + " fct max_exec min_exec name \\\n", + "0 linear_regression(X, y, algo=None) 0.000091 0.000018 lr_matrix \n", + "1 linear_regression(X, y, algo='gram') 0.000485 0.000312 lr_gram \n", + "2 linear_regression(X, y, algo='qr') 0.000223 0.000093 lr_qr \n", + "3 gram_schmidt(Xt) 0.000256 0.000197 gram \n", + "4 clr.fit(X, y) 0.000464 0.000346 sklearn \n", + "\n", + " number repeat size \n", + "0 20 20 100 \n", + "1 20 20 100 \n", + "2 20 20 100 \n", + "3 20 20 100 \n", + "4 20 20 100 " ] - }, + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stmts = [\n", + " dict(name=\"lr_matrix\", fct=\"linear_regression(X, y, algo=None)\"),\n", + " dict(name=\"lr_gram\", fct=\"linear_regression(X, y, algo='gram')\"),\n", + " dict(name=\"lr_qr\", fct=\"linear_regression(X, y, algo='qr')\"),\n", + " dict(name=\"gram\", fct=\"gram_schmidt(Xt)\"),\n", + " dict(name=\"sklearn\", fct=\"clr.fit(X, y)\"),\n", + " dict(name=\"lstsq\", fct=\"lstsq(X, y)\"),\n", + "]\n", + "\n", + "memo = []\n", + "for size, dim in [\n", + " (100, 10),\n", + " (1000, 10),\n", + " (10000, 10),\n", + " (100, 20),\n", + " (1000, 20),\n", + " (10000, 20),\n", + " (100, 50),\n", + " (1000, 50),\n", + "]:\n", + " print(size, dim)\n", + " X = rnd.randn(size, dim)\n", + " eps = rnd.randn(size, 1) / 3\n", + " y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps\n", + " y = y.ravel()\n", + " context = dict(\n", + " linear_regression=linear_regression,\n", + " Xt=X.T,\n", + " X=X,\n", + " y=y,\n", + " gram_schmidt=gram_schmidt,\n", + " clr=clr,\n", + " lstsq=lambda X, y: lstsq(X, y, rcond=None),\n", + " )\n", + "\n", + " for stmt in stmts:\n", + " res = measure_time(\n", + " stmt[\"fct\"], number=20, repeat=20, div_by_number=True, context=context\n", + " )\n", + " res.update(stmt)\n", + " res[\"size\"] = size\n", + " res[\"dim\"] = dim\n", + " memo.append(res)\n", + "\n", + "import pandas\n", + "\n", + "df = pandas.DataFrame(memo)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namegramlr_gramlr_matrixlr_qrlstsqsklearn
sizedim
100100.0002290.0003650.0000390.0001140.0000810.000403
200.0004420.0007720.0000570.0001420.0001430.000433
500.0013840.0023030.0001150.0002980.0006190.000935
1000100.0003350.0004980.0000520.0001680.0001400.000633
200.0008670.0011970.0000930.0003350.0002460.000641
500.0032420.0044820.0002630.0012200.0009450.001545
10000100.0014340.0013090.0002340.0027600.0005510.001828
200.0102120.0109440.0002930.0059260.0021280.005581
\n", + "
" ], - "source": [ - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 2, figsize=(14,4))\n", - "piv[:6].plot(kind=\"bar\", ax=ax[0])\n", - "piv[6:].plot(kind=\"bar\", ax=ax[1])\n", - "ax[0].set_title(\"R\u00e9gression Lin\u00e9aire, size < 10000\")\n", - "ax[1].set_title(\"R\u00e9gression Lin\u00e9aire, size >= 10000\");" + "text/plain": [ + "name gram lr_gram lr_matrix lr_qr lstsq sklearn\n", + "size dim \n", + "100 10 0.000229 0.000365 0.000039 0.000114 0.000081 0.000403\n", + " 20 0.000442 0.000772 0.000057 0.000142 0.000143 0.000433\n", + " 50 0.001384 0.002303 0.000115 0.000298 0.000619 0.000935\n", + "1000 10 0.000335 0.000498 0.000052 0.000168 0.000140 0.000633\n", + " 20 0.000867 0.001197 0.000093 0.000335 0.000246 0.000641\n", + " 50 0.003242 0.004482 0.000263 0.001220 0.000945 0.001545\n", + "10000 10 0.001434 0.001309 0.000234 0.002760 0.000551 0.001828\n", + " 20 0.010212 0.010944 0.000293 0.005926 0.002128 0.005581" ] - }, + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "piv = pandas.pivot_table(df, index=[\"size\", \"dim\"], columns=\"name\", values=\"average\")\n", + "piv" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Streaming versions\n", - "\n", - "L'id\u00e9e est diff\u00e9rente ici puisqu'il s'agit de calculer toutes les r\u00e9gressions lin\u00e9aires interm\u00e9diaires. Les algorithmes sont d\u00e9crits par l'expos\u00e9 [R\u00e9gression lin\u00e9aire par morceaux](find://l-reglin-piecewise-streaming)." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n", + "piv[:6].plot(kind=\"bar\", ax=ax[0])\n", + "piv[6:].plot(kind=\"bar\", ax=ax[1])\n", + "ax[0].set_title(\"Régression Linéaire, size < 10000\")\n", + "ax[1].set_title(\"Régression Linéaire, size >= 10000\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming versions\n", + "\n", + "L'idée est différente ici puisqu'il s'agit de calculer toutes les régressions linéaires intermédiaires. Les algorithmes sont décrits par l'exposé [Régression linéaire par morceaux](find://l-reglin-piecewise-streaming)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100 10\n", - "600 10\n", - "1100 10\n", - "1600 10\n", - "2100 10\n", - "2600 10\n", - "3100 10\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
averagecontext_sizedeviationdimfctmax_execmin_execnamenumberrepeatsize
00.0025893680.00126310list(all_linear_regression(X, y))0.0050700.001753lr_matrix55100
10.0026213680.00003810list(streaming_linear_regression(X, y))0.0026880.002572lr_st_mat55100
20.0310223680.00000010list(streaming_linear_regression_gram_schmidt(...0.0310220.031022lr_st_gram11100
30.0185943680.00074910list(all_linear_regression(X, y))0.0195320.017664lr_matrix55600
40.0220983680.00180510list(streaming_linear_regression(X, y))0.0248960.020070lr_st_mat55600
\n", - "
" - ], - "text/plain": [ - " average context_size deviation dim \\\n", - "0 0.002589 368 0.001263 10 \n", - "1 0.002621 368 0.000038 10 \n", - "2 0.031022 368 0.000000 10 \n", - "3 0.018594 368 0.000749 10 \n", - "4 0.022098 368 0.001805 10 \n", - "\n", - " fct max_exec min_exec \\\n", - "0 list(all_linear_regression(X, y)) 0.005070 0.001753 \n", - "1 list(streaming_linear_regression(X, y)) 0.002688 0.002572 \n", - "2 list(streaming_linear_regression_gram_schmidt(... 0.031022 0.031022 \n", - "3 list(all_linear_regression(X, y)) 0.019532 0.017664 \n", - "4 list(streaming_linear_regression(X, y)) 0.024896 0.020070 \n", - "\n", - " name number repeat size \n", - "0 lr_matrix 5 5 100 \n", - "1 lr_st_mat 5 5 100 \n", - "2 lr_st_gram 1 1 100 \n", - "3 lr_matrix 5 5 600 \n", - "4 lr_st_mat 5 5 600 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlstatpy.ml.matrices import streaming_linear_regression, streaming_linear_regression_gram_schmidt\n", - "\n", - "def all_linear_regression(X, y):\n", - " for i in range(X.shape[1], X.shape[0]):\n", - " yield linear_regression(X[:i], y[:i])\n", - "\n", - "stmts = [dict(name='lr_matrix', fct=\"list(all_linear_regression(X, y))\"),\n", - " dict(name='lr_st_mat', fct=\"list(streaming_linear_regression(X, y))\"),\n", - " dict(name='lr_st_gram', fct=\"list(streaming_linear_regression_gram_schmidt(X, y))\"),\n", - " ]\n", - "\n", - "memo = []\n", - "for dim in (10, ):\n", - " for size in range(100, 3500, 500):\n", - " print(size, dim)\n", - " X = rnd.randn(size, dim)\n", - " eps = rnd.randn(size, 1) / 3\n", - " y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps\n", - " y = y.ravel()\n", - " context = dict(X=X, y=y,\n", - " all_linear_regression=all_linear_regression,\n", - " streaming_linear_regression=streaming_linear_regression,\n", - " streaming_linear_regression_gram_schmidt=streaming_linear_regression_gram_schmidt)\n", - "\n", - " for stmt in stmts:\n", - " if \"gram\" in stmt['name']:\n", - " nn = 1\n", - " if size >= 1000:\n", - " continue\n", - " else:\n", - " nn = 5\n", - " res = measure_time(stmt['fct'], number=nn, repeat=nn, div_by_number=True, context=context)\n", - " res.update(stmt)\n", - " res['size'] = size\n", - " res['dim'] = dim\n", - " memo.append(res)\n", - "\n", - "import pandas\n", - "df = pandas.DataFrame(memo)\n", - "df.head()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "100 10\n", + "600 10\n", + "1100 10\n", + "1600 10\n", + "2100 10\n", + "2600 10\n", + "3100 10\n" + ] }, { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
namelr_matrixlr_st_gramlr_st_mat
size
1000.0025890.0310220.002621
6000.0185940.2047680.022098
11000.040404NaN0.034072
16000.062186NaN0.052658
21000.097438NaN0.060824
26000.128451NaN0.079594
31000.161074NaN0.090113
\n", - "
" - ], - "text/plain": [ - "name lr_matrix lr_st_gram lr_st_mat\n", - "size \n", - "100 0.002589 0.031022 0.002621\n", - "600 0.018594 0.204768 0.022098\n", - "1100 0.040404 NaN 0.034072\n", - "1600 0.062186 NaN 0.052658\n", - "2100 0.097438 NaN 0.060824\n", - "2600 0.128451 NaN 0.079594\n", - "3100 0.161074 NaN 0.090113" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
averagecontext_sizedeviationdimfctmax_execmin_execnamenumberrepeatsize
00.0025893680.00126310list(all_linear_regression(X, y))0.0050700.001753lr_matrix55100
10.0026213680.00003810list(streaming_linear_regression(X, y))0.0026880.002572lr_st_mat55100
20.0310223680.00000010list(streaming_linear_regression_gram_schmidt(...0.0310220.031022lr_st_gram11100
30.0185943680.00074910list(all_linear_regression(X, y))0.0195320.017664lr_matrix55600
40.0220983680.00180510list(streaming_linear_regression(X, y))0.0248960.020070lr_st_mat55600
\n", + "
" ], - "source": [ - "piv = pandas.pivot_table(df, index=['size'], columns='name', values='average')\n", - "piv" + "text/plain": [ + " average context_size deviation dim \\\n", + "0 0.002589 368 0.001263 10 \n", + "1 0.002621 368 0.000038 10 \n", + "2 0.031022 368 0.000000 10 \n", + "3 0.018594 368 0.000749 10 \n", + "4 0.022098 368 0.001805 10 \n", + "\n", + " fct max_exec min_exec \\\n", + "0 list(all_linear_regression(X, y)) 0.005070 0.001753 \n", + "1 list(streaming_linear_regression(X, y)) 0.002688 0.002572 \n", + "2 list(streaming_linear_regression_gram_schmidt(... 0.031022 0.031022 \n", + "3 list(all_linear_regression(X, y)) 0.019532 0.017664 \n", + "4 list(streaming_linear_regression(X, y)) 0.024896 0.020070 \n", + "\n", + " name number repeat size \n", + "0 lr_matrix 5 5 100 \n", + "1 lr_st_mat 5 5 100 \n", + "2 lr_st_gram 1 1 100 \n", + "3 lr_matrix 5 5 600 \n", + "4 lr_st_mat 5 5 600 " ] - }, + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlstatpy.ml.matrices import (\n", + " streaming_linear_regression,\n", + " streaming_linear_regression_gram_schmidt,\n", + ")\n", + "\n", + "\n", + "def all_linear_regression(X, y):\n", + " for i in range(X.shape[1], X.shape[0]):\n", + " yield linear_regression(X[:i], y[:i])\n", + "\n", + "\n", + "stmts = [\n", + " dict(name=\"lr_matrix\", fct=\"list(all_linear_regression(X, y))\"),\n", + " dict(name=\"lr_st_mat\", fct=\"list(streaming_linear_regression(X, y))\"),\n", + " dict(name=\"lr_st_gram\", fct=\"list(streaming_linear_regression_gram_schmidt(X, y))\"),\n", + "]\n", + "\n", + "memo = []\n", + "for dim in (10,):\n", + " for size in range(100, 3500, 500):\n", + " print(size, dim)\n", + " X = rnd.randn(size, dim)\n", + " eps = rnd.randn(size, 1) / 3\n", + " y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps\n", + " y = y.ravel()\n", + " context = dict(\n", + " X=X,\n", + " y=y,\n", + " all_linear_regression=all_linear_regression,\n", + " streaming_linear_regression=streaming_linear_regression,\n", + " streaming_linear_regression_gram_schmidt=streaming_linear_regression_gram_schmidt,\n", + " )\n", + "\n", + " for stmt in stmts:\n", + " if \"gram\" in stmt[\"name\"]:\n", + " nn = 1\n", + " if size >= 1000:\n", + " continue\n", + " else:\n", + " nn = 5\n", + " res = measure_time(\n", + " stmt[\"fct\"], number=nn, repeat=nn, div_by_number=True, context=context\n", + " )\n", + " res.update(stmt)\n", + " res[\"size\"] = size\n", + " res[\"dim\"] = dim\n", + " memo.append(res)\n", + "\n", + "import pandas\n", + "\n", + "df = pandas.DataFrame(memo)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namelr_matrixlr_st_gramlr_st_mat
size
1000.0025890.0310220.002621
6000.0185940.2047680.022098
11000.040404NaN0.034072
16000.062186NaN0.052658
21000.097438NaN0.060824
26000.128451NaN0.079594
31000.161074NaN0.090113
\n", + "
" ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(14,4))\n", - "piv[[\"lr_matrix\", \"lr_st_mat\"]].plot(ax=ax[0])\n", - "piv.plot(ax=ax[1])\n", - "ax[0].set_title(\"R\u00e9gression Lin\u00e9aire streaming (all)\\n10 features\")\n", - "ax[1].set_title(\"R\u00e9gression Lin\u00e9aire no Gram-Schmidt\\n10 features\");" + "text/plain": [ + "name lr_matrix lr_st_gram lr_st_mat\n", + "size \n", + "100 0.002589 0.031022 0.002621\n", + "600 0.018594 0.204768 0.022098\n", + "1100 0.040404 NaN 0.034072\n", + "1600 0.062186 NaN 0.052658\n", + "2100 0.097438 NaN 0.060824\n", + "2600 0.128451 NaN 0.079594\n", + "3100 0.161074 NaN 0.090113" ] - }, + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "piv = pandas.pivot_table(df, index=[\"size\"], columns=\"name\", values=\"average\")\n", + "piv" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La version streaming devient plus int\u00e9ressante \u00e0 partir de 1000 observations, le co\u00fbt en lin\u00e9aire en *N* contrairement \u00e0 la version classique qui est en $N^2$. La version Gram-Schmidt devrait \u00eatre r\u00e9\u00e9crite en C++ pour proposer des temps comparables." + "data": { + "image/png": "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", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n", + "piv[[\"lr_matrix\", \"lr_st_mat\"]].plot(ax=ax[0])\n", + "piv.plot(ax=ax[1])\n", + "ax[0].set_title(\"Régression Linéaire streaming (all)\\n10 features\")\n", + "ax[1].set_title(\"Régression Linéaire no Gram-Schmidt\\n10 features\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La version streaming devient plus intéressante à partir de 1000 observations, le coût en linéaire en *N* contrairement à la version classique qui est en $N^2$. La version Gram-Schmidt devrait être réécrite en C++ pour proposer des temps comparables." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/ml/reseau_neurones.ipynb b/_doc/notebooks/ml/reseau_neurones.ipynb index dc6a187e..e9752d04 100644 --- a/_doc/notebooks/ml/reseau_neurones.ipynb +++ b/_doc/notebooks/ml/reseau_neurones.ipynb @@ -1,212 +1,219 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# R\u00e9seaux de neurones\n", - "\n", - "R\u00e9seaux de neurones avec scikit-learn." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Réseaux de neurones\n", + "\n", + "Réseaux de neurones avec scikit-learn." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" + "data": { + "text/plain": [ + "Perceptron(alpha=0.0001, class_weight=None, eta0=1.0, fit_intercept=True,\n", + " n_iter=5, n_jobs=1, penalty=None, random_state=0, shuffle=True,\n", + " verbose=0, warm_start=False)" ] - }, + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import Perceptron\n", + "\n", + "X = [[0.0, 0.0], [1.0, 1.0]]\n", + "y = [0, 1]\n", + "clf = Perceptron()\n", + "clf.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Perceptron(alpha=0.0001, class_weight=None, eta0=1.0, fit_intercept=True,\n", - " n_iter=5, n_jobs=1, penalty=None, random_state=0, shuffle=True,\n", - " verbose=0, warm_start=False)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import Perceptron\n", - "X = [[0., 0.], [1., 1.]]\n", - "y = [0, 1]\n", - "clf = Perceptron()\n", - "clf.fit(X, y) " + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD7CAYAAACRxdTpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n330mRclatOcAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy\n", - "def softmax(x):\n", - " return 1.0 / (1 + numpy.exp(-x))\n", - "def dsoftmax(x):\n", - " t = numpy.exp(-x)\n", - " return t / (1 + t)**2\n", - "x = numpy.arange(-10,10, 0.1)\n", - "y = softmax(x)\n", - "dy = dsoftmax(x)\n", - "fig, ax = plt.subplots(1,1)\n", - "ax.plot(x,y, label=\"softmax\")\n", - "ax.plot(x,dy, label=\"d\u00e9riv\u00e9e\")\n", - "ax.set_ylim([-0.1, 1.1])\n", - "ax.plot([-5, -5], [-0.1, 1.1], \"r\")\n", - "ax.plot([5, 5], [-0.1, 1.1], \"r\")\n", - "ax.legend(loc=2)" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD7CAYAAACRxdTpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n330mRclatOcAAAAASUVORK5CYII=\n", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy\n", + "\n", + "\n", + "def softmax(x):\n", + " return 1.0 / (1 + numpy.exp(-x))\n", + "\n", + "\n", + "def dsoftmax(x):\n", + " t = numpy.exp(-x)\n", + " return t / (1 + t) ** 2\n", + "\n", + "\n", + "x = numpy.arange(-10, 10, 0.1)\n", + "y = softmax(x)\n", + "dy = dsoftmax(x)\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.plot(x, y, label=\"softmax\")\n", + "ax.plot(x, dy, label=\"dérivée\")\n", + "ax.set_ylim([-0.1, 1.1])\n", + "ax.plot([-5, -5], [-0.1, 1.1], \"r\")\n", + "ax.plot([5, 5], [-0.1, 1.1], \"r\")\n", + "ax.legend(loc=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ -1.00000000e+01, -9.90000000e+00, -9.80000000e+00,\n", - " -9.70000000e+00, -9.60000000e+00, -9.50000000e+00,\n", - " -9.40000000e+00, -9.30000000e+00, -9.20000000e+00,\n", - " -9.10000000e+00, -9.00000000e+00, -8.90000000e+00,\n", - " -8.80000000e+00, -8.70000000e+00, -8.60000000e+00,\n", - " -8.50000000e+00, -8.40000000e+00, -8.30000000e+00,\n", - " -8.20000000e+00, -8.10000000e+00, -8.00000000e+00,\n", - " -7.90000000e+00, -7.80000000e+00, -7.70000000e+00,\n", - " -7.60000000e+00, -7.50000000e+00, -7.40000000e+00,\n", - " -7.30000000e+00, -7.20000000e+00, -7.10000000e+00,\n", - " -7.00000000e+00, -6.90000000e+00, -6.80000000e+00,\n", - " -6.70000000e+00, -6.60000000e+00, -6.50000000e+00,\n", - " -6.40000000e+00, -6.30000000e+00, -6.20000000e+00,\n", - " -6.10000000e+00, -6.00000000e+00, -5.90000000e+00,\n", - " -5.80000000e+00, -5.70000000e+00, -5.60000000e+00,\n", - " -5.50000000e+00, -5.40000000e+00, -5.30000000e+00,\n", - " -5.20000000e+00, -5.10000000e+00, -5.00000000e+00,\n", - " -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n", - " -4.60000000e+00, -4.50000000e+00, -4.40000000e+00,\n", - " -4.30000000e+00, -4.20000000e+00, -4.10000000e+00,\n", - " -4.00000000e+00, -3.90000000e+00, -3.80000000e+00,\n", - " -3.70000000e+00, -3.60000000e+00, -3.50000000e+00,\n", - " -3.40000000e+00, -3.30000000e+00, -3.20000000e+00,\n", - " -3.10000000e+00, -3.00000000e+00, -2.90000000e+00,\n", - " -2.80000000e+00, -2.70000000e+00, -2.60000000e+00,\n", - " -2.50000000e+00, -2.40000000e+00, -2.30000000e+00,\n", - " -2.20000000e+00, -2.10000000e+00, -2.00000000e+00,\n", - " -1.90000000e+00, -1.80000000e+00, -1.70000000e+00,\n", - " -1.60000000e+00, -1.50000000e+00, -1.40000000e+00,\n", - " -1.30000000e+00, -1.20000000e+00, -1.10000000e+00,\n", - " -1.00000000e+00, -9.00000000e-01, -8.00000000e-01,\n", - " -7.00000000e-01, -6.00000000e-01, -5.00000000e-01,\n", - " -4.00000000e-01, -3.00000000e-01, -2.00000000e-01,\n", - " -1.00000000e-01, -3.55271368e-14, 1.00000000e-01,\n", - " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01,\n", - " 5.00000000e-01, 6.00000000e-01, 7.00000000e-01,\n", - " 8.00000000e-01, 9.00000000e-01, 1.00000000e+00,\n", - " 1.10000000e+00, 1.20000000e+00, 1.30000000e+00,\n", - " 1.40000000e+00, 1.50000000e+00, 1.60000000e+00,\n", - " 1.70000000e+00, 1.80000000e+00, 1.90000000e+00,\n", - " 2.00000000e+00, 2.10000000e+00, 2.20000000e+00,\n", - " 2.30000000e+00, 2.40000000e+00, 2.50000000e+00,\n", - " 2.60000000e+00, 2.70000000e+00, 2.80000000e+00,\n", - " 2.90000000e+00, 3.00000000e+00, 3.10000000e+00,\n", - " 3.20000000e+00, 3.30000000e+00, 3.40000000e+00,\n", - " 3.50000000e+00, 3.60000000e+00, 3.70000000e+00,\n", - " 3.80000000e+00, 3.90000000e+00, 4.00000000e+00,\n", - " 4.10000000e+00, 4.20000000e+00, 4.30000000e+00,\n", - " 4.40000000e+00, 4.50000000e+00, 4.60000000e+00,\n", - " 4.70000000e+00, 4.80000000e+00, 4.90000000e+00,\n", - " 5.00000000e+00, 5.10000000e+00, 5.20000000e+00,\n", - " 5.30000000e+00, 5.40000000e+00, 5.50000000e+00,\n", - " 5.60000000e+00, 5.70000000e+00, 5.80000000e+00,\n", - " 5.90000000e+00, 6.00000000e+00, 6.10000000e+00,\n", - " 6.20000000e+00, 6.30000000e+00, 6.40000000e+00,\n", - " 6.50000000e+00, 6.60000000e+00, 6.70000000e+00,\n", - " 6.80000000e+00, 6.90000000e+00, 7.00000000e+00,\n", - " 7.10000000e+00, 7.20000000e+00, 7.30000000e+00,\n", - " 7.40000000e+00, 7.50000000e+00, 7.60000000e+00,\n", - " 7.70000000e+00, 7.80000000e+00, 7.90000000e+00,\n", - " 8.00000000e+00, 8.10000000e+00, 8.20000000e+00,\n", - " 8.30000000e+00, 8.40000000e+00, 8.50000000e+00,\n", - " 8.60000000e+00, 8.70000000e+00, 8.80000000e+00,\n", - " 8.90000000e+00, 9.00000000e+00, 9.10000000e+00,\n", - " 9.20000000e+00, 9.30000000e+00, 9.40000000e+00,\n", - " 9.50000000e+00, 9.60000000e+00, 9.70000000e+00,\n", - " 9.80000000e+00, 9.90000000e+00])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x\n" + "data": { + "text/plain": [ + "array([ -1.00000000e+01, -9.90000000e+00, -9.80000000e+00,\n", + " -9.70000000e+00, -9.60000000e+00, -9.50000000e+00,\n", + " -9.40000000e+00, -9.30000000e+00, -9.20000000e+00,\n", + " -9.10000000e+00, -9.00000000e+00, -8.90000000e+00,\n", + " -8.80000000e+00, -8.70000000e+00, -8.60000000e+00,\n", + " -8.50000000e+00, -8.40000000e+00, -8.30000000e+00,\n", + " -8.20000000e+00, -8.10000000e+00, -8.00000000e+00,\n", + " -7.90000000e+00, -7.80000000e+00, -7.70000000e+00,\n", + " -7.60000000e+00, -7.50000000e+00, -7.40000000e+00,\n", + " -7.30000000e+00, -7.20000000e+00, -7.10000000e+00,\n", + " -7.00000000e+00, -6.90000000e+00, -6.80000000e+00,\n", + " -6.70000000e+00, -6.60000000e+00, -6.50000000e+00,\n", + " -6.40000000e+00, -6.30000000e+00, -6.20000000e+00,\n", + " -6.10000000e+00, -6.00000000e+00, -5.90000000e+00,\n", + " -5.80000000e+00, -5.70000000e+00, -5.60000000e+00,\n", + " -5.50000000e+00, -5.40000000e+00, -5.30000000e+00,\n", + " -5.20000000e+00, -5.10000000e+00, -5.00000000e+00,\n", + " -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n", + " -4.60000000e+00, -4.50000000e+00, -4.40000000e+00,\n", + " -4.30000000e+00, -4.20000000e+00, -4.10000000e+00,\n", + " -4.00000000e+00, -3.90000000e+00, -3.80000000e+00,\n", + " -3.70000000e+00, -3.60000000e+00, -3.50000000e+00,\n", + " -3.40000000e+00, -3.30000000e+00, -3.20000000e+00,\n", + " -3.10000000e+00, -3.00000000e+00, -2.90000000e+00,\n", + " -2.80000000e+00, -2.70000000e+00, -2.60000000e+00,\n", + " -2.50000000e+00, -2.40000000e+00, -2.30000000e+00,\n", + " -2.20000000e+00, -2.10000000e+00, -2.00000000e+00,\n", + " -1.90000000e+00, -1.80000000e+00, -1.70000000e+00,\n", + " -1.60000000e+00, -1.50000000e+00, -1.40000000e+00,\n", + " -1.30000000e+00, -1.20000000e+00, -1.10000000e+00,\n", + " -1.00000000e+00, -9.00000000e-01, -8.00000000e-01,\n", + " -7.00000000e-01, -6.00000000e-01, -5.00000000e-01,\n", + " -4.00000000e-01, -3.00000000e-01, -2.00000000e-01,\n", + " -1.00000000e-01, -3.55271368e-14, 1.00000000e-01,\n", + " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01,\n", + " 5.00000000e-01, 6.00000000e-01, 7.00000000e-01,\n", + " 8.00000000e-01, 9.00000000e-01, 1.00000000e+00,\n", + " 1.10000000e+00, 1.20000000e+00, 1.30000000e+00,\n", + " 1.40000000e+00, 1.50000000e+00, 1.60000000e+00,\n", + " 1.70000000e+00, 1.80000000e+00, 1.90000000e+00,\n", + " 2.00000000e+00, 2.10000000e+00, 2.20000000e+00,\n", + " 2.30000000e+00, 2.40000000e+00, 2.50000000e+00,\n", + " 2.60000000e+00, 2.70000000e+00, 2.80000000e+00,\n", + " 2.90000000e+00, 3.00000000e+00, 3.10000000e+00,\n", + " 3.20000000e+00, 3.30000000e+00, 3.40000000e+00,\n", + " 3.50000000e+00, 3.60000000e+00, 3.70000000e+00,\n", + " 3.80000000e+00, 3.90000000e+00, 4.00000000e+00,\n", + " 4.10000000e+00, 4.20000000e+00, 4.30000000e+00,\n", + " 4.40000000e+00, 4.50000000e+00, 4.60000000e+00,\n", + " 4.70000000e+00, 4.80000000e+00, 4.90000000e+00,\n", + " 5.00000000e+00, 5.10000000e+00, 5.20000000e+00,\n", + " 5.30000000e+00, 5.40000000e+00, 5.50000000e+00,\n", + " 5.60000000e+00, 5.70000000e+00, 5.80000000e+00,\n", + " 5.90000000e+00, 6.00000000e+00, 6.10000000e+00,\n", + " 6.20000000e+00, 6.30000000e+00, 6.40000000e+00,\n", + " 6.50000000e+00, 6.60000000e+00, 6.70000000e+00,\n", + " 6.80000000e+00, 6.90000000e+00, 7.00000000e+00,\n", + " 7.10000000e+00, 7.20000000e+00, 7.30000000e+00,\n", + " 7.40000000e+00, 7.50000000e+00, 7.60000000e+00,\n", + " 7.70000000e+00, 7.80000000e+00, 7.90000000e+00,\n", + " 8.00000000e+00, 8.10000000e+00, 8.20000000e+00,\n", + " 8.30000000e+00, 8.40000000e+00, 8.50000000e+00,\n", + " 8.60000000e+00, 8.70000000e+00, 8.80000000e+00,\n", + " 8.90000000e+00, 9.00000000e+00, 9.10000000e+00,\n", + " 9.20000000e+00, 9.30000000e+00, 9.40000000e+00,\n", + " 9.50000000e+00, 9.60000000e+00, 9.70000000e+00,\n", + " 9.80000000e+00, 9.90000000e+00])" ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 1 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } \ No newline at end of file diff --git a/_doc/notebooks/ml/survival.ipynb b/_doc/notebooks/ml/survival.ipynb new file mode 100644 index 00000000..92723968 --- /dev/null +++ b/_doc/notebooks/ml/survival.ipynb @@ -0,0 +1,1234 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse de survie en pratique" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quelques données\n", + "\n", + "On récupère les données disponibles sur *open.data.gouv.fr* [Données hospitalières relatives à l'épidémie de COVID-19](https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/). Ces données ne permettent pas de construire la courbe de [Kaplan-Meier](https://fr.wikipedia.org/wiki/Estimateur_de_Kaplan-Meier). On sait combien de personnes rentrent et sortent chaque jour mais on ne sait pas quand une personne qui sort un 1er avril est entrée." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
jourraddc
02020-03-18NaNNaN
12020-03-19695.0207.0
22020-03-20806.0248.0
32020-03-21452.0151.0
42020-03-22608.0210.0
\n", + "
" + ], + "text/plain": [ + " jour rad dc\n", + "0 2020-03-18 NaN NaN\n", + "1 2020-03-19 695.0 207.0\n", + "2 2020-03-20 806.0 248.0\n", + "3 2020-03-21 452.0 151.0\n", + "4 2020-03-22 608.0 210.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy.random as rnd\n", + "\n", + "import pandas\n", + "\n", + "df = pandas.read_csv(\n", + " \"https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7\",\n", + " sep=\";\",\n", + ")\n", + "gr = df[[\"jour\", \"rad\", \"dc\"]].groupby([\"jour\"]).sum()\n", + "diff = gr.diff().reset_index(drop=False)\n", + "diff.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
entreesortieissue
1905678-14831
577877-147401
1126578-14061
1140232-140111
1205621-131261
\n", + "
" + ], + "text/plain": [ + " entree sortie issue\n", + "1905678 -148 3 1\n", + "577877 -147 40 1\n", + "1126578 -140 6 1\n", + "1140232 -140 11 1\n", + "1205621 -131 26 1" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def donnees_artificielles(hosp, mu=14, nu=21):\n", + " dt = pandas.to_datetime(hosp[\"jour\"])\n", + " res = []\n", + " for i in range(hosp.shape[0]):\n", + " date = dt[i].dayofyear\n", + " h1 = hosp.iloc[i, 1]\n", + " h2 = hosp.iloc[i, 2]\n", + " if h1 < 0 or h2 < 0:\n", + " continue\n", + " delay1 = rnd.exponential(mu, int(h1))\n", + " for j in range(delay1.shape[0]):\n", + " res.append([date - int(delay1[j]), date, 1])\n", + " delay2 = rnd.exponential(mu, int(h2))\n", + " for j in range(delay2.shape[0]):\n", + " res.append([date - int(delay2[j]), date, 0])\n", + " return pandas.DataFrame(res, columns=[\"entree\", \"sortie\", \"issue\"])\n", + "\n", + "\n", + "data = donnees_artificielles(diff[1:].reset_index(drop=True)).sort_values(\"entree\")\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chaque ligne est une personne, `entree` est le jour d'entrée à l'hôpital, `sortie` celui de la sortie, `issue`, 0 pour décès, 1 pour en vie." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
entreesortieissue
count1.993886e+061.993886e+061.993886e+06
mean1.481621e+021.616597e+028.642781e-01
std1.152239e+021.143726e+023.424931e-01
min-1.480000e+021.000000e+000.000000e+00
25%5.100000e+016.400000e+011.000000e+00
50%1.130000e+021.250000e+021.000000e+00
75%2.600000e+022.750000e+021.000000e+00
max3.660000e+023.660000e+021.000000e+00
\n", + "
" + ], + "text/plain": [ + " entree sortie issue\n", + "count 1.993886e+06 1.993886e+06 1.993886e+06\n", + "mean 1.481621e+02 1.616597e+02 8.642781e-01\n", + "std 1.152239e+02 1.143726e+02 3.424931e-01\n", + "min -1.480000e+02 1.000000e+00 0.000000e+00\n", + "25% 5.100000e+01 6.400000e+01 1.000000e+00\n", + "50% 1.130000e+02 1.250000e+02 1.000000e+00\n", + "75% 2.600000e+02 2.750000e+02 1.000000e+00\n", + "max 3.660000e+02 3.660000e+02 1.000000e+00" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il y a environ 80% de survie dans ces données." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "\n", + "duree = data.sortie - data.entree\n", + "deces = (data.issue == 0).astype(numpy.int32)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "from lifelines import KaplanMeierFitter\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 4))\n", + "kmf = KaplanMeierFitter()\n", + "kmf.fit(duree, deces)\n", + "kmf.plot(ax=ax)\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Régression de Cox\n", + "\n", + "On reprend les données artificiellement générées et on ajoute une variable identique à la durée plus un bruit mais quasi nul " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
dureedecesX1X2
190567815100.650961-1.128843
5778771870-1.9565250.108041
112657814600.026987-0.130392
114023215101.1493850.280224
12056211570-0.0323980.400499
\n", + "
" + ], + "text/plain": [ + " duree deces X1 X2\n", + "1905678 151 0 0.650961 -1.128843\n", + "577877 187 0 -1.956525 0.108041\n", + "1126578 146 0 0.026987 -0.130392\n", + "1140232 151 0 1.149385 0.280224\n", + "1205621 157 0 -0.032398 0.400499" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas\n", + "\n", + "data_simple = pandas.DataFrame(\n", + " {\n", + " \"duree\": duree,\n", + " \"deces\": deces,\n", + " \"X1\": duree * 0.57 * deces + numpy.random.randn(duree.shape[0]),\n", + " \"X2\": duree * (-0.57) * deces + numpy.random.randn(duree.shape[0]),\n", + " }\n", + ")\n", + "data_simple.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "data_train, data_test = train_test_split(data_simple, test_size=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: norm_delta = 5.01e-01, step_size = 0.9500, log_lik = -647954.57157, newton_decrement = 1.86e+04, seconds_since_start = 2.0\n", + "Iteration 2: norm_delta = 1.29e-01, step_size = 0.9500, log_lik = -668348.17665, newton_decrement = 2.19e+04, seconds_since_start = 4.2\n", + "Iteration 3: norm_delta = 8.30e-02, step_size = 0.9500, log_lik = -642917.75741, newton_decrement = 4.33e+03, seconds_since_start = 6.2\n", + "Iteration 4: norm_delta = 3.36e-02, step_size = 1.0000, log_lik = -637879.13496, newton_decrement = 4.09e+02, seconds_since_start = 8.5\n", + "Iteration 5: norm_delta = 3.94e-03, step_size = 1.0000, log_lik = -637443.76633, newton_decrement = 4.61e+00, seconds_since_start = 10.7\n", + "Iteration 6: norm_delta = 4.65e-05, step_size = 1.0000, log_lik = -637439.12353, newton_decrement = 6.25e-04, seconds_since_start = 13.0\n", + "Iteration 7: norm_delta = 6.33e-09, step_size = 1.0000, log_lik = -637439.12291, newton_decrement = 1.16e-11, seconds_since_start = 15.1\n", + "Convergence success after 7 iterations.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from lifelines.fitters.coxph_fitter import CoxPHFitter\n", + "\n", + "cox = CoxPHFitter()\n", + "cox.fit(\n", + " data_train[[\"duree\", \"deces\", \"X1\"]],\n", + " duration_col=\"duree\",\n", + " event_col=\"deces\",\n", + " show_progress=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modellifelines.CoxPHFitter
duration col'duree'
event col'deces'
baseline estimationbreslow
number of observations398777
number of events observed54338
partial log-likelihood-637439.12
time fit was run2024-10-07 10:42:15 UTC
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
X10.061.060.000.060.061.061.060.00176.66<0.005inf

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Concordance0.75
Partial AIC1274880.25
log-likelihood ratio test21030.90 on 1 df
-log2(p) of ll-ratio testinf
\n", + "
" + ], + "text/latex": [ + "\\begin{tabular}{lrrrrrrrrrrr}\n", + " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "covariate & & & & & & & & & & & \\\\\n", + "X1 & 0.06 & 1.06 & 0.00 & 0.06 & 0.06 & 1.06 & 1.06 & 0.00 & 176.66 & 0.00 & inf \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'duree'\n", + " event col = 'deces'\n", + " baseline estimation = breslow\n", + " number of observations = 398777\n", + "number of events observed = 54338\n", + " partial log-likelihood = -637439.12\n", + " time fit was run = 2024-10-07 10:42:15 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "covariate \n", + "X1 0.06 1.06 0.00 0.06 0.06 1.06 1.06\n", + "\n", + " cmp to z p -log2(p)\n", + "covariate \n", + "X1 0.00 176.66 <0.005 inf\n", + "---\n", + "Concordance = 0.75\n", + "Partial AIC = 1274880.25\n", + "log-likelihood ratio test = 21030.90 on 1 df\n", + "-log2(p) of ll-ratio test = inf" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cox.print_summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: norm_delta = 5.01e-01, step_size = 0.9500, log_lik = -647954.57157, newton_decrement = 1.86e+04, seconds_since_start = 2.4\n", + "Iteration 2: norm_delta = 1.31e-01, step_size = 0.9500, log_lik = -668036.09368, newton_decrement = 2.18e+04, seconds_since_start = 5.0\n", + "Iteration 3: norm_delta = 8.29e-02, step_size = 0.9500, log_lik = -642745.26291, newton_decrement = 4.23e+03, seconds_since_start = 7.2\n", + "Iteration 4: norm_delta = 3.27e-02, step_size = 1.0000, log_lik = -637838.96866, newton_decrement = 3.84e+02, seconds_since_start = 9.4\n", + "Iteration 5: norm_delta = 3.70e-03, step_size = 1.0000, log_lik = -637430.64477, newton_decrement = 4.03e+00, seconds_since_start = 11.5\n", + "Iteration 6: norm_delta = 4.05e-05, step_size = 1.0000, log_lik = -637426.59011, newton_decrement = 4.72e-04, seconds_since_start = 13.6\n", + "Iteration 7: norm_delta = 4.77e-09, step_size = 1.0000, log_lik = -637426.58963, newton_decrement = 6.55e-12, seconds_since_start = 15.7\n", + "Convergence success after 7 iterations.\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modellifelines.CoxPHFitter
duration col'duree'
event col'deces'
baseline estimationbreslow
number of observations398777
number of events observed54338
partial log-likelihood-637426.59
time fit was run2024-10-07 10:42:35 UTC
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
X2-0.060.940.00-0.06-0.060.940.950.00-176.68<0.005inf

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Concordance0.75
Partial AIC1274855.18
log-likelihood ratio test21055.96 on 1 df
-log2(p) of ll-ratio testinf
\n", + "
" + ], + "text/latex": [ + "\\begin{tabular}{lrrrrrrrrrrr}\n", + " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "covariate & & & & & & & & & & & \\\\\n", + "X2 & -0.06 & 0.94 & 0.00 & -0.06 & -0.06 & 0.94 & 0.95 & 0.00 & -176.68 & 0.00 & inf \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'duree'\n", + " event col = 'deces'\n", + " baseline estimation = breslow\n", + " number of observations = 398777\n", + "number of events observed = 54338\n", + " partial log-likelihood = -637426.59\n", + " time fit was run = 2024-10-07 10:42:35 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "covariate \n", + "X2 -0.06 0.94 0.00 -0.06 -0.06 0.94 0.95\n", + "\n", + " cmp to z p -log2(p)\n", + "covariate \n", + "X2 0.00 -176.68 <0.005 inf\n", + "---\n", + "Concordance = 0.75\n", + "Partial AIC = 1274855.18\n", + "log-likelihood ratio test = 21055.96 on 1 df\n", + "-log2(p) of ll-ratio test = inf" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cox2 = CoxPHFitter()\n", + "cox2.fit(\n", + " data_train[[\"duree\", \"deces\", \"X2\"]],\n", + " duration_col=\"duree\",\n", + " event_col=\"deces\",\n", + " show_progress=True,\n", + ")\n", + "cox2.print_summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1369970834048121705511197061444869
0.00.0089090.0081110.0201180.0086010.008080
1.00.0175920.0160170.0397270.0169850.015956
2.00.0262730.0239210.0593300.0253660.023830
3.00.0351190.0319750.0793080.0339080.031854
4.00.0437950.0398750.0989010.0422840.039724
..................
156.00.5324020.4847421.2022930.5140340.482903
158.00.5383750.4901811.2157830.5198010.488322
163.00.5383750.4901811.2157830.5198010.488322
170.00.5383750.4901811.2157830.5198010.488322
186.00.5383750.4901811.2157830.5198010.488322
\n", + "

151 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " 1369970 834048 1217055 1119706 1444869\n", + "0.0 0.008909 0.008111 0.020118 0.008601 0.008080\n", + "1.0 0.017592 0.016017 0.039727 0.016985 0.015956\n", + "2.0 0.026273 0.023921 0.059330 0.025366 0.023830\n", + "3.0 0.035119 0.031975 0.079308 0.033908 0.031854\n", + "4.0 0.043795 0.039875 0.098901 0.042284 0.039724\n", + "... ... ... ... ... ...\n", + "156.0 0.532402 0.484742 1.202293 0.514034 0.482903\n", + "158.0 0.538375 0.490181 1.215783 0.519801 0.488322\n", + "163.0 0.538375 0.490181 1.215783 0.519801 0.488322\n", + "170.0 0.538375 0.490181 1.215783 0.519801 0.488322\n", + "186.0 0.538375 0.490181 1.215783 0.519801 0.488322\n", + "\n", + "[151 rows x 5 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cox.predict_cumulative_hazard(data_test[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1369970834048121705511197061444869
0.00.9911310.9919220.9800830.9914350.991952
1.00.9825620.9841110.9610520.9831590.984170
2.00.9740690.9763630.9423960.9749530.976452
3.00.9654900.9685300.9237550.9666610.968648
4.00.9571500.9609100.9058330.9585970.961055
..................
156.00.5871930.6158560.3005040.5980780.616989
158.00.5836960.6125160.2964780.5946390.613656
163.00.5836960.6125160.2964780.5946390.613656
170.00.5836960.6125160.2964780.5946390.613656
186.00.5836960.6125160.2964780.5946390.613656
\n", + "

151 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " 1369970 834048 1217055 1119706 1444869\n", + "0.0 0.991131 0.991922 0.980083 0.991435 0.991952\n", + "1.0 0.982562 0.984111 0.961052 0.983159 0.984170\n", + "2.0 0.974069 0.976363 0.942396 0.974953 0.976452\n", + "3.0 0.965490 0.968530 0.923755 0.966661 0.968648\n", + "4.0 0.957150 0.960910 0.905833 0.958597 0.961055\n", + "... ... ... ... ... ...\n", + "156.0 0.587193 0.615856 0.300504 0.598078 0.616989\n", + "158.0 0.583696 0.612516 0.296478 0.594639 0.613656\n", + "163.0 0.583696 0.612516 0.296478 0.594639 0.613656\n", + "170.0 0.583696 0.612516 0.296478 0.594639 0.613656\n", + "186.0 0.583696 0.612516 0.296478 0.594639 0.613656\n", + "\n", + "[151 rows x 5 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cox.predict_survival_function(data_test[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_doc/notebooks/ml/valeurs_manquantes_mf.ipynb b/_doc/notebooks/ml/valeurs_manquantes_mf.ipynb index 41404dcd..66943120 100644 --- a/_doc/notebooks/ml/valeurs_manquantes_mf.ipynb +++ b/_doc/notebooks/ml/valeurs_manquantes_mf.ipynb @@ -1,1056 +1,922 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Valeurs manquantes et factorisation de matrices\n", - "\n", - "R\u00e9flexion autour des valeur manquantes et de la factorisation de matrice positive." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Matrice \u00e0 coefficients al\u00e9atoires\n", - "\n", - "On \u00e9tudie la factorisation d'une matrice \u00e0 coefficients tout \u00e0 fait al\u00e9atoires qui suivent une loi uniforme sur l'intervalle $[0,1]$. Essayons sur une petite matrice :" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.05119593, 0.43722929, 0.9290821 ],\n", - " [ 0.4588466 , 0.14187813, 0.23762633],\n", - " [ 0.9768084 , 0.47674026, 0.79044526]])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numpy.random import rand\n", - "M = rand(3, 3)\n", - "M" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.67825803],\n", - " [ 0.38030919],\n", - " [ 1.02295362]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import NMF\n", - "mf = NMF(1)\n", - "mf.fit_transform(M)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La matrice pr\u00e9c\u00e9dente est la matrice $W$ dans le produit $WH$, la matrice qui suit est $H$." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.73190904, 0.50765757, 0.92611883]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.components_" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.07236890712696428" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.reconstruction_err_ / (M.shape[0] * M.shape[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On recalcule l'erreur :" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.072368907126964283" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d = M - mf.fit_transform(M) @ mf.components_\n", - "a = d.ravel()\n", - "e = a @ a.T\n", - "e ** 0.5 / (M.shape[0] * M.shape[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.42421796])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "e.ravel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et maintenant sur une grande et plus n\u00e9cessairement carr\u00e9e :" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.004996164872801101" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = rand(300, 10)\n", - "mf = NMF(1)\n", - "mf.fit_transform(M)\n", - "mf.reconstruction_err_ / (M.shape[0] * M.shape[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "L'erreur est la m\u00eame :" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "errs = []\n", - "rangs = list(range(1, 11))\n", - "for k in rangs:\n", - " mf = NMF(k)\n", - " mf.fit_transform(M)\n", - " e = mf.reconstruction_err_ / (M.shape[0] * M.shape[1])\n", - " errs.append(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas\n", - "df = pandas.DataFrame(dict(rang=rangs, erreur=errs))\n", - "df.plot(x=\"rang\", y=\"erreur\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Matrice avec des vecteurs colonnes corr\u00e9l\u00e9s\n", - "\n", - "Supposons maintenant que la matrice pr\u00e9c\u00e9dente $M$ est de rang 3. Pour s'en assurer, on tire une matrice al\u00e9alatoire avec 3 vecteurs colonnes et on r\u00e9plique des colonnes jusqu'\u00e0 la dimension souhait\u00e9e." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(300, 10)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numpy import hstack\n", - "M = rand(300, 3)\n", - "M = hstack([M, M, M, M[:,:1]])\n", - "M.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "errs = []\n", - "rangs = list(range(1, 11))\n", - "for k in rangs:\n", - " mf = NMF(k)\n", - " mf.fit_transform(M)\n", - " e = mf.reconstruction_err_ / (M.shape[0] * M.shape[1])\n", - " errs.append(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npqDkZEoAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas\n", - "df = pandas.DataFrame(dict(rang=rangs, erreur=errs))\n", - "df.plot(x=\"rang\", y=\"erreur\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On essaye \u00e0 nouveausur une matrice un peu plus petite." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.27190312, 0.6497563 , 0.27190312],\n", - " [ 0.44853292, 0.87097224, 0.44853292],\n", - " [ 0.29424835, 0.65106952, 0.29424835]])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "M = rand(3, 2)\n", - "M = hstack([M, M[:,:1]])\n", - "M" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.61835197, 0. ],\n", - " [ 0.82887888, 0.29866219],\n", - " [ 0.61960446, 0.07743224]])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf = NMF(2)\n", - "mf.fit_transform(M)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.43972536, 1.05078419, 0.43972536],\n", - " [ 0.28143493, 0. , 0.28143493]])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.components_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La derni\u00e8re colonne est identique \u00e0 la premi\u00e8re." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Matrice identit\u00e9\n", - "\n", - "Et maintenant si la matrice $M$ est la matrice identit\u00e9, que se passe-t-il ?" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 0., 0.],\n", - " [ 0., 1., 0.],\n", - " [ 0., 0., 1.]])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numpy import identity\n", - "M = identity(3)\n", - "M" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.],\n", - " [ 1.],\n", - " [ 0.]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf = NMF(1)\n", - "mf.fit_transform(M)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 1., 0.]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.components_" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.0000000000000004" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.reconstruction_err_ ** 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On essaye avec $k=2$." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 0. ],\n", - " [ 0. , 1.03940448],\n", - " [ 0.95521772, 0. ]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf = NMF(2)\n", - "mf.fit_transform(M)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 0. , 1.04688175],\n", - " [ 0. , 0.96208937, 0. ]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.components_" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.reconstruction_err_ ** 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Avec des vecteurs norm\u00e9s et ind\u00e9pendants (formant donc une base de l'espace vectoriel), l'algorithme aboutit \u00e0 une matrice $W$ \u00e9gale au $k$ premiers vecteurs et oublie les autres." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## Matrice identit\u00e9 et repr\u00e9sentation spatiale\n", - "\n", - "Pour comprendre un peu mieux ce dernier exemple, il est utile de chercher d'autres solutions dont l'erreur est \u00e9quivalente." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def erreur_mf(M, W, H):\n", - " d = M - W @ H\n", - " a = d.ravel()\n", - " e = a @ a.T\n", - " e ** 0.5 / (M.shape[0] * M.shape[1])\n", - " return e\n", - "\n", - "M = identity(3)\n", - "mf = NMF(2)\n", - "W = mf.fit_transform(M)\n", - "H = mf.components_\n", - "erreur_mf(M, W, H)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 0. ],\n", - " [ 0.9703523 , 0. ],\n", - " [ 0. , 1.02721047]])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 1.03055354, 0. ],\n", - " [ 0. , 0. , 0.97351032]])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "H" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 0., 0.],\n", - " [ 0., 1., 0.],\n", - " [ 0., 0., 1.]])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W @ H" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "wh = W @ H\n", - "ax.scatter(M[:,0], M[:,1], M[:,2], c='b', marker='o', s=20)\n", - "ax.scatter(wh[:,0], wh[:,1], wh[:,2], c='r', marker='^')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Et si on pose maintenant :" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.5, 0. ],\n", - " [ 0.5, 0. ],\n", - " [ 0. , 1. ]])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy\n", - "W = numpy.array([[0.5, 0.5, 0], [0, 0, 1]]).T\n", - "H = numpy.array([[1, 1, 0], [0.0, 0.0, 1.0]])\n", - "W" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1., 1., 0.],\n", - " [ 0., 0., 1.]])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "H" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.5, 0.5, 0. ],\n", - " [ 0.5, 0.5, 0. ],\n", - " [ 0. , 0. , 1. ]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "W @ H" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "erreur_mf(M, W, H)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEg/h/wDaPK2y8PWC8AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "wh = W @ H\n", - "ax.scatter(M[:,0], M[:,1], M[:,2], c='b', marker='o', s=20)\n", - "ax.scatter(wh[:,0], wh[:,1], wh[:,2], c='r', marker='^')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On peut voir la matrice $M$ comme un ensemble de $n$ points dans un espace vectoriel. La matrice $W$ est un ensemble de $k < n$ points dans le m\u00eame espace. La matrice $WH$, de rang $k$ est une approximation de cet ensemble dans le m\u00eame espace, c'est aussi $n$ combinaisons lin\u00e9aires de $k$ points de fa\u00e7on \u00e0 former $n$ points les plus proches proches de $n$ points de la matrice $M$." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Valeurs manquantes et factorisation de matrices\n", + "\n", + "Réflexion autour des valeur manquantes et de la factorisation de matrice positive." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matrice à coefficients aléatoires\n", + "\n", + "On étudie la factorisation d'une matrice à coefficients tout à fait aléatoires qui suivent une loi uniforme sur l'intervalle $[0,1]$. Essayons sur une petite matrice :" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.05119593, 0.43722929, 0.9290821 ],\n", + " [ 0.4588466 , 0.14187813, 0.23762633],\n", + " [ 0.9768084 , 0.47674026, 0.79044526]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" + ], + "source": [ + "from numpy.random import rand\n", + "\n", + "M = rand(3, 3)\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.67825803],\n", + " [ 0.38030919],\n", + " [ 1.02295362]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import NMF\n", + "\n", + "mf = NMF(1)\n", + "mf.fit_transform(M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La matrice précédente est la matrice $W$ dans le produit $WH$, la matrice qui suit est $H$." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.73190904, 0.50765757, 0.92611883]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf.components_" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.07236890712696428" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf.reconstruction_err_ / (M.shape[0] * M.shape[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On recalcule l'erreur :" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.072368907126964283" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = M - mf.fit_transform(M) @ mf.components_\n", + "a = d.ravel()\n", + "e = a @ a.T\n", + "e**0.5 / (M.shape[0] * M.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.42421796])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "e.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et maintenant sur une grande et plus nécessairement carrée :" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.004996164872801101" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = rand(300, 10)\n", + "mf = NMF(1)\n", + "mf.fit_transform(M)\n", + "mf.reconstruction_err_ / (M.shape[0] * M.shape[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'erreur est la même :" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "errs = []\n", + "rangs = list(range(1, 11))\n", + "for k in rangs:\n", + " mf = NMF(k)\n", + " mf.fit_transform(M)\n", + " e = mf.reconstruction_err_ / (M.shape[0] * M.shape[1])\n", + " errs.append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas\n", + "\n", + "df = pandas.DataFrame(dict(rang=rangs, erreur=errs))\n", + "df.plot(x=\"rang\", y=\"erreur\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matrice avec des vecteurs colonnes corrélés\n", + "\n", + "Supposons maintenant que la matrice précédente $M$ est de rang 3. Pour s'en assurer, on tire une matrice aléalatoire avec 3 vecteurs colonnes et on réplique des colonnes jusqu'à la dimension souhaitée." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(300, 10)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import hstack\n", + "\n", + "M = rand(300, 3)\n", + "M = hstack([M, M, M, M[:, :1]])\n", + "M.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "errs = []\n", + "rangs = list(range(1, 11))\n", + "for k in rangs:\n", + " mf = NMF(k)\n", + " mf.fit_transform(M)\n", + " e = mf.reconstruction_err_ / (M.shape[0] * M.shape[1])\n", + " errs.append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas\n", + "\n", + "df = pandas.DataFrame(dict(rang=rangs, erreur=errs))\n", + "df.plot(x=\"rang\", y=\"erreur\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On essaye à nouveausur une matrice un peu plus petite." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.27190312, 0.6497563 , 0.27190312],\n", + " [ 0.44853292, 0.87097224, 0.44853292],\n", + " [ 0.29424835, 0.65106952, 0.29424835]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = rand(3, 2)\n", + "M = hstack([M, M[:, :1]])\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.61835197, 0. ],\n", + " [ 0.82887888, 0.29866219],\n", + " [ 0.61960446, 0.07743224]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf = NMF(2)\n", + "mf.fit_transform(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.43972536, 1.05078419, 0.43972536],\n", + " [ 0.28143493, 0. , 0.28143493]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf.components_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La dernière colonne est identique à la première." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Matrice identité\n", + "\n", + "Et maintenant si la matrice $M$ est la matrice identité, que se passe-t-il ?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numpy import identity\n", + "\n", + "M = identity(3)\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.],\n", + " [ 1.],\n", + " [ 0.]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf = NMF(1)\n", + "mf.fit_transform(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 1., 0.]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf.components_" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0000000000000004" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf.reconstruction_err_**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On essaye avec $k=2$." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. ],\n", + " [ 0. , 1.03940448],\n", + " [ 0.95521772, 0. ]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf = NMF(2)\n", + "mf.fit_transform(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. , 1.04688175],\n", + " [ 0. , 0.96208937, 0. ]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf.components_" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mf.reconstruction_err_**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Avec des vecteurs normés et indépendants (formant donc une base de l'espace vectoriel), l'algorithme aboutit à une matrice $W$ égale au $k$ premiers vecteurs et oublie les autres." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Matrice identité et représentation spatiale\n", + "\n", + "Pour comprendre un peu mieux ce dernier exemple, il est utile de chercher d'autres solutions dont l'erreur est équivalente." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def erreur_mf(M, W, H):\n", + " d = M - W @ H\n", + " a = d.ravel()\n", + " e = a @ a.T\n", + " e**0.5 / (M.shape[0] * M.shape[1])\n", + " return e\n", + "\n", + "\n", + "M = identity(3)\n", + "mf = NMF(2)\n", + "W = mf.fit_transform(M)\n", + "H = mf.components_\n", + "erreur_mf(M, W, H)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. ],\n", + " [ 0.9703523 , 0. ],\n", + " [ 0. , 1.02721047]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 1.03055354, 0. ],\n", + " [ 0. , 0. , 0.97351032]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W @ H" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "wh = W @ H\n", + "ax.scatter(M[:, 0], M[:, 1], M[:, 2], c=\"b\", marker=\"o\", s=20)\n", + "ax.scatter(wh[:, 0], wh[:, 1], wh[:, 2], c=\"r\", marker=\"^\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Et si on pose maintenant :" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.5, 0. ],\n", + " [ 0.5, 0. ],\n", + " [ 0. , 1. ]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "\n", + "W = numpy.array([[0.5, 0.5, 0], [0, 0, 1]]).T\n", + "H = numpy.array([[1, 1, 0], [0.0, 0.0, 1.0]])\n", + "W" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 1., 0.],\n", + " [ 0., 0., 1.]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.5, 0.5, 0. ],\n", + " [ 0.5, 0.5, 0. ],\n", + " [ 0. , 0. , 1. ]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "W @ H" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "erreur_mf(M, W, H)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmQJGd9LXpq66W6qvdluqenl+qu6mUWzdI9mrEeChnCyIhAD66XJ8IXIQTGEAgJGxvjcNiW4GKwJSF4GsBhBBKrBJawxeUK2cgRko2QZqTRaBnNTHet3VXVXb3Xvuby/pj35WRVZVZlVmUtXZ0nYkLQ3ZVZW578fb/vnPPTsCwLFSpUqFBRe2hr/QRUqFChQsVVqISsQoUKFXUClZBVqFChok6gErIKFSpU1AlUQlahQoWKOoFKyCpUqFBRJ1AJWYUKFSrqBCohq1ChQkWdQCVkFSpUqKgT6GX+vWrrU6FChQr50Ej5I7VCVqFChYo6gUrIKlSoUFEnUAlZhQoVKuoEKiGrUKFCRZ1AJWQVKlSoqBOohKxChQoVdQKVkFWoUKGiTqASsgoVKlTUCVRCVqFChYo6gUrIKlSoUFEnUAlZhQoVKuoEKiGrUKFCRZ1AbriQChUFwbIsaJoGAOh0Omg0kjJVVKhQAZWQVSgEhmFA0zQoikIqleJ+rtFooNPpuH9arRZarRYajUYlaxUqcqASsoqywDAMKIriqmKNRsMRLsteTWslRJ37uEQiga6uLuj1epWoVaiASsgqSgDLsmAYBvF4HHr91a8QIVJCwuRn/P/ykU6n4fF40NbWhnQ6nfUYrVYLnU6nErWKPQeVkFVIBiFi0pZ44403MD8/XxJRksfodDrBc9A0nUXU5G9z2x8qUatoJKiErKIo+ETMMAxXxbIsWzIZ5lbT/J8LHZNP1Lnn5RN0bp9ahYrdBJWQVYiCKCYoiuJIkPzT6XSChCoVYoRc6O/lEjUhaKENRRUq6hEqIavIgxARa7XZknWNRgOGYfIeG41GsbKygpaWFrS1tcFoNHJ95tzHl0Po/OOIETXLsshkMkin0wgEAjAajejo6FCJWkXdQiVkFRxYluUUE2JETJBLXuFwGE6nExRFYWBgAOl0Gn6/H/F4HDRNo7m5GW1tbdy/pqamir6WXKJOp9NoaWnhXg9FUchkMlmPUYlaRa2hErIKjoiJNK0QEediZ2cHLpcLGo0GFosFHR0deUTHsizS6TRisRhisRhWVlYQjUYRjUbx+uuvZxF1W1ubYEWtFMSUH6RaFyJq0qLhqz5U04uKSkAl5D0MvpkDEF/+54JlWWxtbSEWi2FpaQk2mw1ms5n7XS40Gg2am5vR3NyM7u5uAFe1yRcuXMDMzAxH1Kurq4jFYqBpGk1NTXVF1LktHJZlC1bUKlmrKAUqIe9BMAyDUCiE5ubmrI26YmBZFuvr63C73Whra0NLSwuOHj0q+HdSCSmXqMnjSUUdj8cRCAQQi8VAURQMBgNMJhOMRiNH1AaDQfqLlwmpRJ37GPJcSZtEJWoVUqAS8h4Bf5OLYRi8+eabOHHihCQyYxgGgUAAHo8HnZ2duO6669Da2orf/OY3JT+fQsQkVFET8Fsfa2trWUSdW1HXkqhXVlbQ3NyM/v7+rMeophcVhaAScoMjV0MMSO8RMwwDv9+P5eVl9Pb24sSJE2hubpZ83mKkW4rKoqmpCU1NTejq6sr6eTGijsfj0Gq1aG9vr+iGopA+mkA1vagoBpWQGxRCZg7+Ba7VagVla8DVjS2fzwe/34+BgQGcPHlScrUpZKGuBsSIOpPJIBaLwePxIBwOY2dnB5lMBnq9Pq+iVpKohW5I5Zhe+D1qVfnRuFAJucFQyMzBhxAhZzIZLC8vIxAIYGhoCNdff33FNtKqRSgGgwGdnZ0wm83o7OxET08PgGtEHYvFsLGxAY/HI0rUBoNB9vOV00dXTS8qCFRCbhBIMXPwwSfkVCoFj8eDzc1NHDhwAKdPn5Yse9utIETd2dmZ9fNMJoN4PI5YLIbNzU0sLS0hnU5Dp9MJVtRiBFiOrZxAqumF/I3f78eBAwdUot7FUAl5l0OOmYMPrVaLRCKBpaUlBINBjI2NwWq1NjwRF4PBYEBHRwc6Ojqyfk5RFFdRb21tYXl5uSBRK0HIYhAj6kAggAMHDqiml10MlZB3Kcoxc8RiMQSDQYTDYUxOTmJmZqbkC7OSxFNP0Ov1RYl6e3sbXq8XqVSK22SMRqMcUROZYSUgtE9AUMj0QohZr9erRF0HUAl5l4FhmLyJHFIvnkgkAqfTiXQ6jba2NoyPj+fJyuSAbN7t5YtXjKivXLkCs9kMrVaLnZ0d+Hw+pFIpaLXavIpaCaImRhUhqKaX3QOVkHcJ+JM5Xn75ZZw+fVryhREMBuFyucAwDCwWC7q7u3HlypWylRC1UFPsFmg0GphMpjyipmmaq6h3dnbg9/uRTCaziJqYXlpaWiR/xgzDyG43lWp6EauoVaIuHyoh1zFyzRxA4SkcuY/d3t6Gy+WCXq/HxMREFjkUkr1JhUrI4hBbOeh0OrS3t6O9vT3r5zRNc5uJoVAIKysrHFHzXYliRF0KIYuhGFGTVRrDMHA4HLDZbKrpRSGohFyHEDNzSLU3b2xswO12o7W1FTMzMzCZTHl/pwSZipF6PRJ1tVsrcs+n0+lgNpu5TBACqURdjbCjXKJmGAbJZJLLxlZNL+VDJeQ6QjEzR+7f8n/Osixnb25vb8fhw4dhNBpFz6VWyJWFUjeAQkSdSCQQjUYRDocRCoUQiUTwyiuv5FXUra2tFSE/mqY5J6IULTX/ZyRBTzW9ZEMl5DqAVDMHAX8zjWEYrKysYHl5Gd3d3Th27BhaWlqKnlMJQiZjnFTko9IVuU6ng8lk4lY/kUgEPp8PU1NTXEUdiUQQCASQTCYBQHGi5hOyGFTTizyohFxDyDVzEOh0OmQyGQQCAfh8PvT392Nubk6W9VepClnoGERaVcm4zHpHtVskpIes1WqziJr/+3g8jng8jkgkgrW1NSQSCQBAa2trHlFL+R7SNF1y31qu6QXYG0S9d6+YGqJUMwdwVUuaTCZx7tw57N+/v2R7cyVaFvF4HC6XC6FQCMDV18m/2E0mk+SLfbejVoQsBj5R8xPoGIZBIpHglB/r6+uSiVpKhSwXhYgauPr9f+uttzAyMsK15BqJqFVCriLKMXOk02ksLS1hfX0dOp0OR44cyesryoFWq80zCZRyDJZlEY/H4XQ6EYvFYLFYYLPZuAuIf7Fvbm4iHo8DuHqxp1IpbGxsVLTPWSvUgpBLOR9fbpd7vEJEbTQauRWekgoPMfA3FCmKQlNTU9agXTHTy9e//nV8/vOfr2gUq5JQCbkKIL0yj8eDjo4OtLe3S754kskkPB4Ptre3MTIygtOnT+Ott94q+2JXokKmaRoLCwugKAoWiwW9vb3QaDTIZDIcIRmNRhiNRvT19XGPIxf7G2+8wfU5E4lEnnLAZDJV1N1WSdRC1aEkKRYi6mQyycWcxuNxnD9/Pm81RPTUlSBqiqK4VWExid5TTz2Fv/7rv1b8OVQKKiFXEHwzB3CVXKVWgvF4HG63G+FwGGNjY5iamuIep9PpsnatS0E5hByNRuFyubCzs4OJiQmMjIzIIh9ysRsMBlgsFu7nuRIvv9+PVCoFnU4Ho9EIk8kkKdhHDPUseysX1ahSAXA3TaPRCIqi0NHRgQMHDoBlWcHVkBBRt7a2ltXqkPJa+e203XRDVwlZYYiZOYjMpxiRErJLJBIYHx/H7OyspOhMuSjlGNFoFE6nE6lUChMTE9DpdOjo6Mh7fqVeAGISL4qiEI/HEY1Gs4J9+FGZhKzFlqbVVoM0KiHzwd/UE1sNEaImN9qtrS3E43EwDIOWlpa8iloqUct5b1VC3oOQYubQ6XR5VlSCUCgEl8vFLf+7u7tFv0jVrpBJBkYmk8HExASXf7G+vl72jUEK9Hq9oLuNn2m8vr6OaDSaNXePf7FXG3uBkBmGKars4RN1b28v93OWZbnWBwlmUoKo+aAoSvFNx0pDJeQyIcfMIUSkxN6s1WphsVjy8nmFUK0KORwOw+l0gqKoLCImUMIYUg5xiWUap9NpRKPRrEnW8XgcoVAInZ2dHFmXeqFLwV4g5HJUFhqNBq2trWhtbRUl6ng8jp2dHcRiMY6ojUYjMpkMwuEw51AUQygUyssSqXeohFwi5Jo5gKuVXiKRAMuy2NzchMvlQktLC6ampmQpJipdIYdCITidTjAMg4mJibyxSASFCFkqIVWCuJqamtDd3Z11A1lcXERHRwf0en1eRVYJaV4tCLnauu9Kyd4IUfPBsixSqRQikQjW19fh9/sRj8dB0zSam5vzKmq9Xo9wOCypwKknqIQsE6WaOYCrJBgKhfDyyy/DbDbj0KFDJS2nK1Uhh0IhOBwOAMDExETRL3O5z6OahKXRaLiZe2SMEwDRzSjgmg6XVNRypHlqhawsNBoNF6rU1taGmZkZANeImnx+hKh//OMf47XXXgNFUXj00UcxOztbNE6AjzvvvBO/+MUv0N/fj4sXL+b9nmVZ3HPPPXjmmWdgNBrx2GOP4fjx42W/TpWQJaIcMwfDMFhdXYXT6YROp8Px48fzKgA5UKJC5le3wWAQDocDWq0Wk5OTkpd55bYs6iELo5g0j29BTiQSHCFIkeaphKw8+JI34BpRt7S0ZN1ojx07hp/97Gf4xS9+gXA4jO9+97u48847cf3110s6zx133IG77roLt99+u+Dvf/nLX8Jut8Nut+Ps2bP45Cc/ibNnz5b34qASclGwLItYLMYRsBwipmkafr8fXq8XfX19mJmZwfr6ellkDChn6kgmk3j11Veh0+lgs9nyNs2KQakecj2Cr8PlO9vE0tdypXkMw1S1Sq61yqKa55TSmiGZzYcPH8Y999wj+zw33ngjPB6P6O+ffvpp3H777dBoNDh16hSCwSBWV1cxODgo+1x8qIQsAmLmoCgKFy9ehNVqFYyxFAJFUfB6vVhZWcG+fftw8uRJGAwGRCKRsitb4GqFTAJjSsH29jbsdjtisRjm5uZkEzHBbmpZKHU+qdK8ZDKJV155RZY0rxyU6tQr95y1qJClnpNs4lYCZKAswfDwMPx+v0rISiPXzKHRaGAwGCQRaTqdxvLyMtbW1gRzJpRoNQClESEJrHc6nWhqaoLVaoXT6SyZjAHxClkqMVSzZVHp8+RK84LBIObn52VJ88rZlFPaqScF9dCyKIRgMJhFmrsBKiGjsJkDKE6kqVQKHo8Hm5ubnL1Z6OJQipDlHIdlWWxtbcHlcqG5uRmzs7MwmUxZeulSUW78Zj30kCsNOdK8XMWAyWSSLM3bqz3kQqikymL//v3wer3c//f5fNi/f3/Zx93ThCx1Moderxc0dCQSCbjdbgSDQYyNjcFqtRa8KKpZIfOlda2trRwRyzlGMYjFb8p5/F6FkDQvVzHg9XoFpXlCORG1IGSg+p+h1B4ycJWQK6VDvvXWW3HmzBncdtttOHv2LDo6OspuVwB7lJCJdI0kVRXTEOcScjQahdvtRiwWw/j4OGZmZiR9MatRIRMidjqdaGtrE5XWKVGdlkvIQP1u6tUCYooBYpYgFbWQNC+RSCCVSsFoNDb0jY6iKEkDGIDyesgf/OAH8fzzz2NzcxPDw8O47777uI30T3ziE7jlllvwzDPPYHJyEkajEY8++mhJ58nFniLkUswcwDXLczgchsvlQjqdhsViQU9PT0089ULVLZml53K5YDKZcOTIkYKaSyWei1arFbWCS8FeaFkoAb5ZQkyaFwgE4PV6OfmilMGouxFyWhahUEjU1FQMjz/+eMHfazQafOMb3yjp2IWwJwi5HDMHcLVHHAgEsLGxAYvFUvKHrBR0Oh1HyCzLYn19HS6XC+3t7UWJWEmIVcikFSQnkasaaARC4oMvzfP7/ZidneU2oMUGo/LVHqWm5tUS1SLkWqGhCbmcQHj+ZhjDMOjq6sKhQ4cq+XQlg1SmgUAAbrcb7e3tOHr0aNn6ZrnIJVSWZbGysgKPx8Pd+PgbVCaTadcRwG4B/wZYTmoe+ayKSfNqtbKRs5EYi8VqEixVDhqSkFmWRTQaRSqVQltbm6S2BP+x6+vrcLvdMBqNmJ2dRTqdxtraWoWftTSQHnEwGERbW5vkoaaVAFFZsCyL1dVVeDwe9PT04MSJE9zfEALY2dmBz+dDKpXiCCCZTCIcDsNgMFQ8h6HRWyNSViRSUvM2NjbgdruLSvNqobAApFfI5PPebePCGoqQ+WaOYDCInZ0dTE1NSX5sIBCAx+NBZ2dn1tI/FAopshkHXFvmy/2i8Emvs7OTu1nUGuFwGC+99BK6urpw4sQJNDc3g2EYpNNp0YGbhADC4TA2Njbg8/k4yRchACL52m0XVC1RidS8WCyGaDSaJ81raWkBRVGIRCIVTc3LhZyWhZxCrF7QEIRcjpmDYRjO3tzT04Pjx4/nVZxisrdSQBQScnMwlpaW0N3dzZHeb37zG0WeTykgq4jFxUUum0NOlU4IwGg0YnR0FCaTiZN8ESUBCTIHro2vJ+S+W8c67TY0NTVxgUwE5HPa3t5GMBiUJc1TAlINMLVwLiqBXU3ILMsinU4LaogNBkPBvAeapuH1euH3+zEwMIC5uTnRsO1KEHKxHh3DMFhZWcHS0hJ6e3s5IlYacvIW+EoOs9mMiYkJhMPhklsm/PPyJV/8fFwyvl4oO4JfTVfKklwKGrk9Qj6n9vZ2mM1mbpUmRZpXSmpeqQiHw2UNAa4VdjUh8wccSjVzZDIZLC8vY3V1VdDeLASlCbnQsfhE3NfXh/n5+aJTGUoF2ZQrdnHwtc18Sd3Ozg6CwWDZ5y8EfttjYGCA+zlFUdxymm9JznW6tbW1Vb3tUe3ozVogt4csRZoXjUZFU/OUluYFg8Fdl4UM7HJCBsQdZ7kVcjqdhsfjwcbGBg4cOIDTp09L7nsp4Woj0Ov1gq0U0jpZXl5Gf39/RYmYgLwuMcIi+RcOhwNGozFPUlfL+E29Xo+Ojo4sJxZZMZEqjSynidqG/N5kMlVUl7sXCVkMhaZXkw1fqdI8Od+VSgYLVRK7npDFQMgmmUzC7XZjZ2cHo6OjmJyclF0xKXlx5brsGIaBz+eD1+tFf38/lwxXDRS60RAibm5uFnX7KZFloSQ0Gg2am5vR3Nyc5XRjGAaXLl1CS0sLIpEIVldX8y5+QgBK3AT3CiGXs/IQ2/ClaZpTfORK81pbW0FRFHZ2dopK83bj+CaggQk5kUggkUjgwoULGB8fx/T0dN5FkskAn/60Hu/6/p14W38YrX/9GXzuczQqeS0RQqZpGj6fDz6fLyuiUw7KvfCFCHlnZwcOhwMGgyEv/yIXhazTcvrSlYZWq4XBYEB3d3eW5Itc/NFoFBsbG/B4PMhkMpzci1+lyVERVJuQa9GzrpTsTafTiUrzdnZ2EIlEJEnzduP4JqABCDn3ix+JROByuZBMJqHX63Hq1CnRi+Pv/k6H1x6341vMT/B/p3+Gya98HCMjrfjgBys3SVmj0SAQCGBhYQGDg4OSethCINWpUoRMpobodDpMT09L2hDZTRNDhM4jdvGTtkc0GoXf7+eGbLa2tmZtJIptTtWCkBs9C9lgMHADAGw2G/dzIWnegw8+CL/fj46ODnz/+9/HoUOHcOTIEcnX2bPPPot77rkHNE3jYx/7GD7/+c9n/X55eRkf/vCHEQwGQdM0vvKVr+CWW25R5HXuekIm4A/mJPbml156qeCX9f/8Hy2+lPpr6ECBhhZ/nPh/8b//918JEnKp+mECElrv8/nQ1dWFU6dOlWWG0Gq1ZS8bNRoNwuEwLl++DI1GI3tqSKPGb4olsSUSCa4/vba2hmQyKehG3Cvz9Ko9VFVIgywkzfvhD3+I+++/H/F4HBsbGzhz5gweeOCBvKnpQqBpGp/61Kfwq1/9CsPDw5ifn8ett96apfn/X//rf+EP//AP8clPfhKXLl3CLbfcUnC6iBzsekIOh8O4dOkSdDodJiYmsvpGBoMBFEWJ9gWPGRdwC56BATQMSOAv8Q/4u+5PAcjPgiBKC7k9RkLEfr8f+/fvh8VigUajKfvLLFU+J4ZIJIJgMIhUKoXp6emS+m2FWhaN1kflz97jg+RG8O3IqVQK6XQai4uLWa2PSlWUtSLkSsgwC0HqtBBSrNxwww34vd/7PVnnOHfuHCYnJ2GxWAAAt912G55++uksQiaFDHC1EBwaGpJ1jkLY9YSs1WpFl9jFSPRMz9+iCWnQuEocrUjgb7rOAPic7GPlgqIoLC8vY2VlBcPDw5yqY2VlBalUSvoLFEGpyo9IJAKn04lMJgOz2SxrqGkuCk0MkULG9Vohy4FQbkQikcDi4iL6+voQjUaxsrLCudxaWlqySLq1tbVsMt0r4fRyqvJSg4WERjPlDi+999578e53vxsPP/wwYrEYnnvuOdnnEcOuJ+T29nZRA0gxc0j7n/w/2D54CAuLWuh0LA7Osmj73f8LQhQhVYtMdM6BQCCLiAlqMTUEuJrh7HQ6kU6nMTExge7ubly6dKksOV+jtizKBcuy0Ol06OrqynO58c0T6+vrnCaXPyBVbghTLVxptRhwKjfprVKbeo8//jjuuOMOfPazn8VLL72ED33oQ7h48aIi78euJ+RCKEaizPveh/b3vQ/zvJ+J0UMxAsxkMlhaWsLa2hoOHDiAU6dOCVYQ1Z6rF4vF4HQ6kUwmMTExkSUHU2JIqRpQnw+xdk0h8wSRegmFMPEraiFC2kvz9OSE05dSIUsZzfSd73wHzz77LADg9OnTSCaT2NzczJpOXioampBJD1kJiJF7Op3G0tIS1tfXOcNJNcY4FTtOPB6Hy+VCLBbjiDiXJJQgZDFCldJDblQlgtxzabVawbhMEsJEHG7RaFQwhKkW1Wq9T5wudXzT/Pw87HY73G439u/fjyeeeAI//vGPs/5mZGQE//mf/4k77rgDly9fRjKZzLrBloNdT8iFvvh6vb5gy0IOcgmZ7/wrNNi02HFKhRiZJhIJuFwuRCIRTExMoLe3V/Q9KpeQC7Us9koPWQhKkb9QChs/hIlsJEYiEWQyGbz99ttVC2Gq9x5yJBIpaaK6Xq/HmTNncPPNN4Omadx55504ePAg/vZv/xZzc3O49dZb8eCDD+KP//iP8dBDD0Gj0eCxxx5T7H3e9YRcCJUg5HQ6Dbfbjc3NTYyOjkomYgIlK2Q+mSaTSbhcLoRCIUxMTGB2drbol6RSLQsSf1rM+aYSsnwIhTDt7OxgY2MDQ0NDnBXZ7/cjlUpVLISp3idOkz5+KbjlllvydMVf+MIXuP89OzuLF198saRjF8OuJ+RCX3yDwcAlTpULkkfs9XoxOjpadMK0GJTsIdM0jVQqBZfLhWAwKGvgKjmGklOjGYaB1+uF1+tFe3s7EokEMpkMmpqauKptL+Qc10KHTIg311nJD2FaW1tDLBbLCmHiu9zkfCb1vKm3m2/yu56QC0GJ9gDJwlhfX+dGJZVzsSlFyCzLwu/3w+12i1rDi0Gp0CR+Qt2+fftw/fXXczv//MAfssQmN0mWZTl9biONd6onY0ixEKZoNIrt7W3EYjEAyHMjioUw1WIjUWoPmT9JfrehIQhZbOlbTPZWCPxQovHxcfT19WFzc7PsD7lcQia965WVFW5cUqnPiVTZpYJlWWQyGbz88svo7e3l8jjIBQ8UDvxxOp2gaRo7Ozvwer1Ip9NZORK1is8sF/VEyEIo9JkQNyI/hEmn0+W5EclxqgmpPeRoNLors5CBBiFkMZRSIScSCbjdboRCoazKMxQKKbIZV+qXOJPJwOPxYH19HWNjY7BarUin02VnWZTymkg+ssPhAE3TOHXqlGzXllarRUtLC/R6PQYHB7mf8ys3r9fLVW5kaojZbK77arreCVkMYlGZpO3Bn7kXi8Xw+uuv57U9Kt1XlvK+hkKhkjb06gENQchiFbIcQibqhHA4DIvFkteLLRYsXylQFAWPx4O1tbUsNUcgECi73VBKy2J7ext2ux1GoxFHjx7FhQsXFLXQCuVI8LNzg8Fglk43t5quxeDNXOxWQhaDUNvj3LlzmJmZ4frTPp8va5STlBCmSmG3Rm8CDULIYpBCyESvG41GYbFYRNUJYsHylQJFUVhaWkIgEBDUNyvRi5ZDyKFQCHa7HXq9HgcPHiwYyykVUlUWhYalCqWyEdcbX/5VzzrkclFtTTB5faTtIRbCRDYSE4lEVvVNPpdKDWDYrdNCgAYhZLEvf6GLIh6Pw+l0IhaLwWKx4ODBg0U1zdWokGmazsrAEHP8KbEhJ8VpF41GYbfbwTCM7DQ4KecvZ0fcYDAI2pOFJlGkUikwDIOuri6OECpFYrWI36xmn72QVZsfwsR3ruUGzy8tLXHZ07luRKHPRY49fLdOCwEahJDlgFiJE4kELBZLQeMEH0qpIwDhKE/+0NX9+/eLErGSz6cQqcfjcTgcDiSTSVit1pJsqFKgtESJH4fJn8F38eJF9PT0gKbprJH2ZHlN/ikx2qnRWhZC55N7MyuUPU3aHvxVTm4Ik16vr4sci0pjTxAyy7KCmQ5yLppKjHEihEhykoeGhiQH1itRIQsdg28wmZyclHzDKgXVNIYQezK/7cFfXueqCggRmM1m0QwJMTQ6IStpChHKM+aHMJEhtrFYDKlUCpcuXSoawhQKhbLUI7sJDUHIxb78r7/+OjKZDJdyVuvdeZ1Oh0wmwxlNiHZXzkWvdIVMHIhbW1uCm5qVQK2demLLa76Zgp8hwa/a6mliyG4mZCEIhTBFIhF4vV4cOHAA0Wg0SypJNnfb2trg9/uxvb3N5RnvNjQEIQuB5P4mEgmMj49nSatqCYZhkEqlcP78eQwODpY81FSpCpmiKDgcDqytrWFsbAw2m002mQgR0G7OshAzU/CrNv5mFX9pvRcmhtTKpWcwGERDmEik6WOPPYaXXnoJTz75JH7wgx/g+PHjuPfeeyWfp9j4JgD46U9/invvvRcajQbXXXddXvhQOWgIQuZ/+cPhMJxOJyiK4iZMK6EIIOcp9cvPMAxWV1extLQElmVx8OBBSSNlxFC6hzoHAAAgAElEQVRuhUx6qZubm+ju7padyUFASLXWq45KQyw6M3dQqtvtRiKRgF6vRzqdzqqmK0VijVYhC6GQbZq/ufvNb34Tn/rUp3DXXXdhYGAALpdL8jmkjG+y2+348pe/jBdffBFdXV1YX18v+7Xx0RCEDGTP1JuYmOB6UoFAQPGAITlyHZKB4fF40NPTg7m5OTgcjrIJrNQKmWEY+P1+LC8vo7e3F93d3RgZGSn5eYhVuVLjN2s55FQJCG1W+Xw+UBQFk8nEETUJoucrCoqNspeK3bCpVy7khtN3d3djdHQUo6Ojks8hZXzTt7/9bXzqU5/i+EWJDGQ+GoKQE4kEHA4HJiYm8nZXlZSryalKWZZFIBCA2+1Gd3c3Tpw4wRkolOj/yj0GuTG43W709/fj5MmTYBgGFy9eLOt5lEOq1W5ZVLOKb2pqQm9vL5fIBmRX05ubm9woe37QTynhS3uhQpZzzlJVFlLGNy0uLgIAbrjhBtA0jXvvvRe/+7u/K/tcYmgIQjYajThx4oTg75QkZCnHYlkW6+vrcLlc6OzsxPHjx/OmHChByFLJhWVZbGxswOl0orOzE3Nzc9yNIZPJ1MTtR1CvPeRyIbY6EKqmC4Uv5VbTYiuzao9wqlXLInfArBgikUjFnHoURcFut+P555+Hz+fDjTfeiLfeeksxmV1DEHIhlBMwlItChMwnvo6ODhw7dkx03Ey1TCZbW1twOBxoa2vD0aNH0dramvV7JUYwlTs1pBEh53UXCvoh1TTfSEGiTPkZEuVk/5aCeush54JhmJKmuksZ3zQ8PIzrr78eBoMB4+PjsNlssNvtmJ+fzz1cSWgIQi7msFNiyjMgnGdBgnacTidMJpMg8Qkdp5I27GAwCLvdjqamJhw6dCgvLIZAKbdfLiGT6MNi1e9eq5DlQGysk1D4Ujwex8LCAtrb27Oq6UpOCymF8KpxTpZlS/5OSRnf9P73vx+PP/44PvKRj2BzcxOLi4uKSuwagpCBykRw5oKfZ8GyLLa2tuB0OmE0GnHkyBHJSyqdTqfYTYKPSCQCu90OAJieni4aQVju1GhyDD6psyyLtbU1uN1ubvabyWSC2WzOIwmVkOVDKHzp1VdfxejoKOLxeMEoU6PRqEhlW6sKWco5yfeplPdfyvimm2++Gf/xH/+B2dlZ6HQ63H///YqaUBqGkMVQiR7y9vY2HA4HWlpaClagYlC6Qo7FYnA4HEin07BarVW1jfJJdWtrC3a7HWazGYcOHeKGdIZCIfh8viySMJvNoChKkYD8ekMtsizMZrOgLZnoc30+H2KxGFiWzbOLy529V88qi3g8Lvt65KPY+CaNRoOvfvWr+OpXv1ryOQqhYQi5UIWsFCGnUikEAgF0dHRgdna2ZH2zUoRMVBLRaBRWq7UmdlGtVotIJILLly9Dr9fj8OHDaGtrQzqd5vqd/EwJQhKRSISbVBEMBrMIQik5WK1Qi9650PnEokyJXZw/e09OlGk995CDweCujd4EGoiQxaDEoNNgMAiHwwGKotDb25ulSywFSkwNcblcSCQSmJiYKJpUVymQJXIsFsPs7KykC4FPEu3t7djc3ITFYskzV1AUlWVVNpvNZQX/VLM1Us+bmfwYTP6NslCUKV/t0dLSUjPZmxRp324OFgL2CCGXWiGHQiHOxDE1NYVUKoWtra2yn1OphMyfGjI+Po6Ojg7ZIUlKIJ1Ow+l0cpOlrVZrSVUJWdWIycH4VuVAIJAV/EOCguSE0jdqHrISEIsyFQpfSqVSYFkWnZ2dWWlslYbUaSFqhVwHEPuwStm4CofDcDgcYFkWk5OT3AdM07Qi7Q+5hMzPSOaH1a+urla1B8ufXmKxWDA9PY3Lly+XZQwp9DshqzJFURxBkEqOZVnBUPpaYTcSshDEwpcuXLiA/v5+pFIp0SjTtra2qk8KAVRCbihEIhFuTtzk5GTFXH9SCZlhGPh8Pni9XgwNDeVlJFdaPif0PIaHh7NyL8rVMsslc71ej87OzqzPhj/iia8yID1sk8kEiqKqatNuBEIWA8uy6OrqyqqKhVY0iUSCG5DKv1nKrablvJ9qy6JOUM4FEI1G4XA4kMlkMDk5KRrGrhQhFzsOy7JYWVmBx+PBwMCAaDSnEjpicj6xGEli/+7v7xd8HmKbqdXUIYuNeEqlUhxBxGIxXLx4kSMI0vIohSCKodEJWaiHXGhFQ/YH1tbWuEnjUqNMyfmkWsNVQt4FIOPuc79E/ND6ycnJoulrSlWkYschGl6Xy4Wenh7Mz88XDDJSyoItRCBkqnR7e3tWDofY48s5d6XAd8BFIhFYLBY0NzcjFoshEolkEYSSk0OqPb+vFpD6+gpFmZLPYX19HfF4nNtwzFXbyDGihEKhrDyK3YaGIWQp8/AIIZN5evF4nCNiKV8wpSrk3HMRk4nD4YDZbBbMvxCCklNDSAUSCoWwuLiIpqYmSWYXpar0akFsA1Fo84ovBTObzZKNFY08UFUJ8KtpsfAlvtqGRJmura0VjTJVK+RdAOLWYxgGTqcT0WgUExMTsscTVeKLv7OzA7vdjpaWFlluP0DZqSHxeByLi4ugKApTU1OSh5nWc4Us53kIbV7xpWDEpgxcDbPitzxyVzHVJMlqJ71VEmI3y83NTaysrCCRSGRFmZLPgT/OKRwOq4S8G2C325FKpSRNmK4GaJrG+fPnodVqMTMzU9TmLASlqtMrV64gHo+XZC7ZLYRcynmEpGDk5hWJRLJCf/g2caWs+lLQSIQsBI1Gw7UyxsbGuJ/TNM1t5G5tbcHlcuETn/gEJ6O02+04ceIEZmZmJJ1HyqQQAHjqqafw+7//+3jllVcwNzenxEvMQsMQshDBplIpuFwubG9vY3h4GEePHq05EcdiMdjtdiSTSRw5cqQsiU45FTJFUXC73QiFQrBarTh8+HBJ7025N4XdlocstIFIIjQjkQhXUV+8eDGv5VEJvW61CbkWKxqhHrJOp8sLXzp37hze//7343d+53fg9Xrx9NNPSyJkKZNCgKsqrK9//eu4/vrrlXlhAmgYQuYjlUrB7XZzww6bmppgNBoVI+NSlqSJRAJOpxOxWAyTk5OIx+OS2wJiKIUMGYbB8vIyF8bd29tblrmk3Aq5EcCP0Ozt7UUkEsHk5CSampqyZGBkWCrZQCRtD7lZEnzshXB6qbZpYuP/wAc+IEuHLmVSCAD8zd/8Df7yL/8S999/v7wXIAMNQ8gajSZrcvLY2Bimpqag0WiwvLyseMCQ1KwFUqUHg0FMTEygr68PGo2Gq27LqZjkTjAhE0P4U65DoVBZFW4h2Vupj93tYFkWWq0WOp1OUGEgliXB70u3tbVJItq9QshSrze5I9YAaZNCXnvtNXi9Xrz3ve9VCVkKWJbFG2+8gaGhIVit1qwvqcFgQCKRUOQ8JIKz2Bckk8nA7XZjc3MT4+PjmJ6eziIpQuzlELJWqy3arySbIg6HA52dnXlSunJbDurEkHwUWkEV2kAkLQ/+BmKxqSF7ZeJ0sYxxoHLtFIZh8Gd/9md47LHHKnJ8PhqGkDUaDU6ePCn4oSg9V6/QsSiKwtLSEgKBAEZHR3Hq1CnBL3A15uoRCVtzc7NocH65hFyOU69RWha5KHUDUSiZjT81xOPxcDP4SMuj2uObahG9KXclKff9KDYpJBKJ4OLFi7jpppsAXB2cfOutt+LnP/+54ht7DUPIhaBkBKcYuTMMA6/Xy32YuTbnXCgpWctFLBbD4uIiGIYpGlRfbki9VqvNez8ymQxcLhdomuY2XsScWHutQpYDoakhLMtyDkQSYUrUBrkRppUgznruISeTSUmVdC6KTQrp6OjA5uYm9/9vuukmPPDAA6rKohjElsBKRHDyj8UnIIZhsLKygqWlpazebDFUokJOJpOcztpqtRZ1HgLKVsj8DcPh4WHo9XrEYjGsr69zuQakT2o2m2EwGOpa9lbOuSpVtWo0GrS0tKClpQW9vb0wm80IhUIYHR3lnG+rq6uIRqNcfGahqS1yUc/TQoLBYEkb5VImhVQLDUXIYqjE1BC+zbm3txcnT56UFaquZIXM71dPTExgdnZW8kWnFCGvrq7C5XJh3759OHXqFFiWBUVRebkG/D5pNBpFIpHApUuXsoi6UlGOjeieIxuIYhZlUj3zNxD5o52IA1FqX7ieK+RyXHrFJoXw8fzzz5d0DiloKEIWuwiUnKun1WoRDAbhdrvR0dFRMOehEJTKoQiFQjh37hxGRkZE+9XFjlEOIcfjcfh8PvT392Nubo57L8gNMJkEXnxRi6UlLTo69LjxRgMOHLhqtMhkMnjrrbdw4MABRCIRbGxscK2O1tbWLJKu5NBOpVEvTj2NRiMYRs8flLq0tIR4PA4AeaFLQgVGrcLppRLybo7eBBqMkMWglKNte3sbXq8XBoNB0nTpQiiHkEkanMvlgkajwenTp0u+SEp9b0ifOpVKFZyi8sILOvj9GgwMsIjFgGee0eH3f59CW9u1G6hQnzSRSCASiWTN4yNxmsX60rVGvRCyGIRGOxHnG7kxCk1tITGm1VZZSH2NwWBwV9umgT1CyOVeHKFQCHa7HXq9HiMjI1wFVw5KIWSWZbGxsQGn04nu7m4cPXoUi4uLZVUscgk5nU7D4XAgHA7DZrOBYRjBKSoajQYUBfh8WgwNXT2+2QxEIkAopEFbG1tQw0ykYfzKjr+Zxe9L800WJpOpLqzE9UzIQhByvuVmHK+trSEUCkGn0yEWi2U5EKtdNQthtwcLAQ1GyEpfBNFoFHa7HQzDwGazob29HRsbG9je3i772DqdTlYbhYQQtba2ctV5Op1WJO1Nyo2Bpml4PB4EAgFYLBbMzMxAo9Fga2srj1RZlgVN02BZCnq9AfE4C6NRA4YBaBrgS2nlbLbx4zQJcieIRKNRAPnL70YFwzAV7bnnZhx7PB40NzejtbVVcP4e/+ZY7TaTSsi7CKRXKqWaiMfjcDgcSCaTsFqtWeEyxBhSLnQ6HZLJZNG/i0ajWFxcBIC8ECKlNgYL3RhYloXf78fS0hL279+fNS2EPJ7cFAi5XiVjFnq9DjfemMavfmXA1hYLmgZmZ9Po7AQYRhlTiNgEEaI4IH3paDSKK1euoKOjg6sEd1NfWgy1MIY0NTUJvuekzcSf2mIwGLJujHI2EAF57Z9wOIx9+/bJfk31hIYiZCmZyIVslclkEi6XC+FwWDSeU8kxToWOk0wm4XA4EIvFYLPZBKeYKJmHLITNzU3Y7XZ0dXWJqkhI24FlWTAMwxkVdDod9Ho9JieB7m4GwaAGBgONq+Y0FjTNIBKJcME8Wq2Wu1DLJRgh/e4bb7yB0dFRpNPpLMUBvy9NCGM3kXS9WKf506z54E9t2draQjwe5zYb+UoPsSpfjptVrZB3EQoRMj8Dg78cL3QcJZ6PUHVLTBXb29tZ2RdCUMp8kEvIkUgECwsLMBgMuO666wpmNGs0GiSTSSQSCRgMBmg0mrzn1d2txdX9o6tft2QyCbvdjnQ6jenpaej1ejAMw70f5L/kWCSCsVy0trais7Mzy7LM70tvbGwgHo9zfel665EKoV4IWQxCbSZ+EP36+jqnrCEbiOTm2NLSIlmDDKiEXHcoRFBC0jf+FOWxsTHYbLaiJKdky4J/HP5k6dHRUUnPRQnwCZkQZSKRwNTUVEEJEamIm5ub0dLSgjfffBMURcFoNKK9vZ2rUPmSQPJ+kxuf0AqEYRju2OS/wLU2iFarVZSkpfSlyWRrUtURwpCjO68Uqk3ISpxPLIg+mUwiEolkTW3RaDSgaRorKytc6JIYQauEvIvAr2xpmobX6+VSnnL7ooVQrNUgFYSQ+U4/ocnSlQaxPi8uLmJzcxOTk5MFq3I+WV7tE+sxPT3N/Y5Ip3Z2drC0tIR0Os3FS0ajUQwNDWFubk70NZLPgf/7QiTNf5xSJF2oL507XkgoSrOaIDepaqFSOmT+BiJ/BbO5uYnV1VXQNJ11cxRyIIZCIdEBxbsFe4aQSYXs9XqxvLyMwcHBksiv3OwH/nHi8Thefvll9PT0yHb6KQGGYbC+vo5AIACr1VrQWJK7YSdEfnwjAtlcIUlzra2tGBgYQDgcxrlz59DU1MRV0aQtIHYTkELS5H8TkiaTI5RaZfD70oODg9w5hKI0E4kEHA5HlhOuUqsdmqar2vOuhTGkra0tKx6TTG2JRqPY2dnBm2++ic985jNgGAYPPvgg5ubmcP3112N8fFzS8YtNC/nqV7+KRx55BHq9Hn19ffjud7+L0dFRRV8jQUMRcqGqLhaLwev1Yv/+/TUhPz52dnawsLCAVCqF06dPSxpoqiRYlsX6+jqcTifMZjP6+vowMjJS8O/5G3ZCfeJcEOOITqfDddddl6fbJhM2wuFwXu+WtDwKaYrFSJr8l0/YFEUhmUwik8lAp9MptnkoFqV59uxZdHV1IRKJYHNzs6J96UapkMUg1EPOndoyOTmJCxcu4B3veAfe+c534o033kAsFsNHP/rRoseXMi3k2LFjePXVV2E0GvGtb30Ln/vc5/CTn/xE2Rf6/6OhCBnIDhjiGyn0ej2Gh4cxOTlZs+cWiUSwuLgIrVaLgwcP4uLFi4qQsRxpUDAYxOLiIoxGI44fP87lYIgdl5AbAElEnE6n4XK5EIlEYLVaRXt6TU1N6Onpyevdkh7i8vIylwlMSIwQdbF2B/kvcTQuLy/jwIEDMBqNnEYaqNzmoVarFXxtRG2Qu/QmlXcpfelajHCqdkUuRWVBPrv3vOc9eZkUhSBlWshv//Zvc//71KlT+OEPfyjjFchDwxEywdbWFhwOB9ra2nD06FHOhqsU5HwxyRI2kUjAZrNxJKVE60Pq5BEyVZqm6Sw9M+lj81EKEZOkt9XV1axpLXKg1+vzhorSNM1tsK2urnKxosT4QYg6l8jIjaezsxNzc3N5v6/25qFYX1rMrszP8Sg04qkWQ06rScjk/ZAKuc9NyrQQPr7zne/gPe95j6xzyEHDEXIoFMLCwgKamppw6NAhTheZSCQUDamXQoKkWtzZ2cHk5KSgqqBcFNMi85+DzWbLmyqda+zg92SlEDFpf7jdbgwMDODkyZOKLmmFxiARIguHw9jc3ITb7UYmk0FrayuMRiNCoRA0Gg1mZ2dFXXr1sHnIX3rz+9J8tcHKygqX0MYnadKXbvSp01J1yERTXkn88Ic/xKuvvooXXnihYudoOEIOBoOCoexKJr4VG7/EtxmPj4+XVC1KhZhbj2EYLC0tYWVlpWDFSgj5mt1ZeMNOCCTjw2g04tixY1VTGAhNfqYoCk6nE2tra+jo6ABFUXjrrbfQ3Nyc1e4oFEikxOZhuZ+zmNqAP9Wa35dOJBIIBALo7Oysa710qZDasy416a3YtBCC5557Dl/60pfwwgsvVPR73nCEPDY2JlgxVmOME8Mw8Pv9WF5eFrQZVwK5FTJ/mKkUJYlGo0EikcD6+rpkOzFxERJjRy2zIkiF7nK5MDQ0hBtuuCGrh5xKpbK0rcTAwm93FLLzytk8BK4SJ8mCVrLlIdZzf/XVV6HRaAT70vxBAEqgFtNdKp2FXGxaCABcuHABf/Inf4Jnn3026yZZCTQcIRfKRFYypJ5flfLD6vv6+iRPDSGPLaeq0ul0HBlsb29jcXER7e3tecNMhc5LHjc+Ps4t/SmK4vqzpKokx+EbO4i1vJYgm6Stra04ceJE3uvlT9fgB+WTajMSicDtdiMWi2XJ2giZSd08BK6+n0TbPj4+npUzXanNQ71eD51Oh+HhYe47xO9L8+fw8V1wxfrSYqhFe0QqIYfD4YpNC/mLv/gLRKNR/MEf/AEAYGRkBD//+c9ln0vS86nIUesQShk6gOxqm2wemkwm2WH1UnvRhaDVarlUOo1Gg8OHD+flCfCRu2Gn1WoxODiY1cMk/dmtrS2uP6vRaJBKpdDf348jR46UHT9aDkgEKNkkLTQzUAhi1WY0GkU4HIbP58tKjeM7D4U+K7KBSDI/xFoe/Pddyc1DPrEKtXMK9aVzczwKPYd6JuRyspCLTQt57rnnSjpuKWg4Qq5k7gOBXq9HOByGx+OBTqfL2jyUg3IJOZVKYWdnBzs7Ozh48GBBl5LUDTu+uWNwcBBbW1uw2+1ob29HR0cH4vE4Ll26hHQ6jZaWFo6s2tvbS6q65IAMkl1ZWYHFYkF/f79i5xNTQRCFx9raGhwOB2ia5loCra2tWF9fB0VROHjwoOB3oB42Dwv1pcnr4wf/5OZLk+deq2khUnvIu902DTQgIVca8Xgc6+vrYBgGhw8fLmtkTKnxmfwMDqPRiOHh4aJkLHfDjlTdYsYOftVFqspUKoXm5uYskm5paVGENInjr7+/X3Elhxi0Wq1g5gIZfbS0tMStiOx2e1ZfutDNSYnNQ6X60kKTQ/gyw2g0yvWlm5ubQVEUMplM1YxVUlt6KiHXKaTItEohiHQ6DafTiWAwiK6uLhiNxrLnd8klZKFsYrfbLSp7q6SxQ6jq4m+ihcNhbhOtqakpi6TljF4ijj+9Xo+jR49W3dWYC9Ke6O3txTve8Q7odLq8m5Pf70cymcx63cUs1HI3D8kNlpC2UtV0IZnhxsYGNwcxty9N0tlqFV0aCoU4c8duRsMRciGU0iLgV6MWiwXT09MIBAJIJBKKPZ9iYFmWqxC7u7uzrN9COuRyjR2lSvXENtH4Soe1tTXE43FO6UAIKzfLgjgIg8FglpmmVkilUlhcXARFUTh8+HBWJKlYS0Dodev1+rzXXUzhkft70kpZXFxEd3d3xTcPSV86k8mAoihYrdas8U78dDby+qT2pZWCWiHvQhAtshRCZhgGPp8PXq8Xw8PDWRI2JUPqixFyOBzGwsICmpubBQer8o9RChFX2tgBXIu45KsyMpkMwuFwXg6x2WwGTdPY2dnB2NgYrFZrTQPjSd96dXWVy6eWCrHXTUh6aWkJ0WiUIzxC1IUUHjRNw+12Y3t7OysitdDmIaAMSdM0zT2WfxPKVbCI9aX51bSU75icTcRwOKwScj1CytSQQmBZFoFAAG63G/39/YISNqUykQsdJ5FIwG63I5VKwWazibZHSHwmWcJKddgB14wdbW1tOH78eMWdTnwYDIY8pcPGxgbsdju31Pf5fFhZWcnqzVZziOn29jbsdjv6+vowPz+vyI3KYDCI9m1JuyMajYJhmCyZmtlsRigUgsPh4CJMc0dpAdI2D4lGmjxOal+aYZii70GhvnQ0GuX60sT+njuDjw+500LKbSHWAxqOkAuhmBaZrygoJGFTMhM59zhkYsjW1hasVmtRu7VWq0U4HEY0GpW8PCTZGplMpubGDuCq0YTkbFx33XVZagW+HM3r9XJytFySVrKqJ8+HZdmqSPzE+rZkLuDq6irefPNNsCyLzs5O0DSN7e3tvAEAuSjUl+bfwIWkeELtklJVFoX60mS009LSEjKZTFZfWqfTyZoWstuzkIEGJORiFbKQfZpUigaDAUeOHCk4sogcR+mWBVkae71ejI6Owmq1FiRXciF1dnYiGo1yQ1kNBoPoBlq9GTuIxXxjY4PL+siFkBwtt6KMRCIApKfCiYHYzdfW1mC1WvNyP6oJMqNue3sbkUgEhw8fRnd3t+AAABJGRF53oc01IaIV2jzkq3KAq9cViS9V6vWRNgbJzs7tSweDQUSjUbz22mt5OR6514ZKyHUMfgQnH7kVMklAoygKNptNstNHSUJOp9MIBAJwuVzo7+/HqVOnCi7TcvvETU1NmJiY4H6fTqcRDocRDoe5jSRih45GoxgeHi44saMaIM5GYlc9efKkrDaEWMVFSDo3FY6vdBCTa5HVEemj1zqwh6g5enp6stoluQMAiLKF9OP5Co9ce7gckgayWx7RaJQbdUaKGqU3D3P70js7O9jc3MTY2BiX47G0tJQ1KFWn08Hn8yGdTpe0kikWTp9KpXD77bfj/Pnz6OnpwU9+8hOMjY2V/VrFoJHpT6++mb0EkDyBXPh8PtA0jX379sHpdCIcDpdUCTEMg7Nnz+L06dNlPU+n0wmfz4eenh5YrdaCy89SNuyAq0RD8o/b2toQi8U4ki5VilYOwuEwFhcX0dbWhomJiYr2rfnLfkJYxNhBXrvBYIDb7YZGo4HNZqu5rC6TycButyOZTGJqaqokwxGQPQAgEolkbZryVRDFiJS/iUjaW/zvYu51piRJb2xsIBqNCk7+IINSnU4nvva1r+HXv/41hoeHMTU1hTvuuAM333xz0ePTNA2bzZYVTv/4449nZSF/85vfxJtvvol/+qd/whNPPIF//dd/LTWcXtLFtacI2e/3w+/3g6IoWCwWDAwMlExCv/nNb/Bbv/VbJT2WaGuTySTMZjMOHTok+relRGIC14wder0ek5OTghM7SCVNLthKknS5dmelQKbHkJFL0WiUqyb5r72aG5zkea2urmJpaQnj4+NlfTfFwB8AEA6HswYA8FcRpBonk20GBwdx4MABUYIV2zzkv7ZSTC2rq6ugKCorr1gILMvixhtvxKuvvorFxUUYDAZYrdaix3/ppZdw77334t///d8BAF/+8pcBAH/1V3/F/c3NN9+Me++9F6dPnwZFUdi3bx82NjZK+WwkPWBPtCxIf9bj8aC1tbXg7LhKgphLQqEQJ+daW1sT/ftSHHbkHNFotOjEjt7e3qy+rVi7oxySrqTduRSQdDuv14vBwUHMzc1Bo9EI9mZbW1uziLpS1vBoNIqFhQW0tbUJhukrBakDAGia5swmZDp4oe+e0puHBELjm4RA1Bh6vT6rui0GKeH0/L/R6/Xo6OjA1tZWxfZfGpKQCfhRlPv27cPhw4exsrJSdTKmaRpLS0uc6WJ6ehoajQahUEhQ9lZKe4JM0s49hxwoTdK1sDsXQjwex8LCAgwGQ15+s1BvNplMIhwOIxQKwev1IpVKydpAKwaaprnhAXxNcTXB78fzU0DmX6oAACAASURBVAuHh4fR1NTEbZxmMpmscVPFblDFNg9zreFCemmKoiSFdYVCoZKS3uoRDUvIGxsbcDgc6Ozs5KIo4/G4YolvQHEbNv+GMDQ0lJdNnCt7K9XYQTbIBgcHFSe+UkjaYDBgaWkJBoOhLuzORM2xubkJm80maTeev8E0MDAAoPAGGsm8KBaCT0BmPe7fvx/z8/M1XTUAV6WQV65cQVNTE+bn5/OqdDJhm6gfyA2Kn10idQBAoc1D/jUQDofR1tZWNFu6VJeelHB68jfDw8OgKAqhUKii6puGJGSXy4VwOJznbBOTvZUCYlkWIz+ymca/IeSCGENK3bALhUJYXFzkoj+r1fcUI+nt7W1uOKnBYEBrayu8Xm/VNw4JyJBbEl4/Pz9f1uqIbw3PtUgTkg4EApw1nL+KICqHZDKJhYUFaLXaqk5ZEQPJcV5ZWYHNZssydPCh0VybsJ17gypnAACQ3/KIRCK4dOkSent70d3dLZgtTR5HVpqlrC6khNPfeuut+N73vofTp0/jySefxDvf+c6KfocbkpAnJiYEWwFKTg0hx8olZBKaTlLSCmmac112UomYGDsoisLMzEzNjR3Efk001IODg9BoNBXpSUtFLBbjLOeVJr7m5mb09fUJhuCHw2FsbGwgFotxn/XQ0BCGhoaqlpgmhkgkgitXrqCrq6skJ6LSAwAYhuFaOAcPHsz7Xosl4j3zzDPw+/2yX7+UcPqPfvSj+NCHPoTJyUl0d3fjiSeekH0eOWhIlQVN06LEW446go/XX38dVquVkyWRsUaxWAxTU1NFl1BkzM+bb76JRCKBlpYWdHR0cEtfoWqXoihOgjQ5OVlT4wLBzs4O7HY7urq6MD4+LmnwayXVHfy+bD2EEgFXNcULCwvo6upCd3c3t4nGJyryuRcKG1IK5D0Smz9ZCfAdl0RTDFwbAKDVauHz+TA4OIiRkRFJn//6+jo++9nPQqvV4gtf+AJmZmYq/TLKwd6VvVWDkC9evIiRkREYjUa43W5sbGxgYmKiqIpAqD0BgNtAIv/S6TSnlzWZTIjH41hZWcGBAwewf//+uug52u12MAwDm81W1N1YCEqQNH+23vDwcNZYo1qBSP2SySSmp6cF36NcKRo/bIi8fiWt4cQAMzQ0hAMHDtQ8uCkcDsPpdCIWi3FFCNk8zB0hRsCyLJ566incf//9uO+++/CBD3yg5p+1BOxdQmYYRrRXrBQhX7p0CVqtFltbWxgeHi6o0wTkb9iRTRS/388pQ/R6PVdRkH/ljH8qBXy7cyXtxXJImsjGWltbMTk5iSa9HrrvfQ/0+94H1MAeXq6mmKbpPJIGxPXCUpBOpzlX6vT0dM03WoFrOuf9+/dzN1CiEyevPxKJcBLE//7v/0ZzczN++ctfore3F1//+tdrbv+XAZWQhfDyyy+XldxFNorefvttdHZ24vDhw7KsznKMHUTkTowd/Hl35B9N03kkXQl5Wa7deXh4uOryQSGSpigKLMtieHgY+/btQ2trK7SvvQbDl74E6n/8DzD/839W9TlGo1FcuXIFJpMJk5OTit0w+dZwQtL8RDhC1LnnI+mFHo+nLnTgwNVVgcPhQDwex8zMTFHLM/neP/DAA3juuee4XI2xsTH827/9W81fj0TsXUJmWRbpdFrwd+fPn8ehQ4dK2uQhqobm5mYulWpoaEj0OfA3IKQSMd/YUSh2k4CkZoVCIY6oGIbJ6kuWu+Stpt1ZCvgkMzQ0BKPRyFVT8WgUtkcegTGTQRNNI/WP/4jWKrQv+H3ZqampquhiiTWcfO7EGk6mhjc3N8Pv98NoNMJqtdZ8ExG4pj4aGRnB0NCQpM8lEAjgT//0T9He3o6vfe1r3Kpse3tbVBVSh1AJWQhvvPEGJiYmZCkTEokEFhcXkU6nuYtteXkZwNWR4ELnl6ucoGkay8vLWFtbw/j4eFmVjFA1xbJsHkkXq3Drxe7MRyQSwcLCAkwmEyYmJvJIRnP+PLQPPYTk8DAYlwubR49i+bd/u6LqDqJ5r4feNcuyiEQi8Hg82N7e5goPsh8h1petNDKZDBYXF7nIVyktE4Zh8NOf/hQPPfQQvvSlL+F973vfbqmGhbB3rdOFIEf6RrKJiaqBL+3R6/VIpVJZf6+UsaPcVoDQcE5+ZCXJFdZoNII7/GScUyAQgMViQV9fX80vBPJZhMNh8QqUYaD76U+hYRgYt7cBsxmmy5ex/+MfR7q9XXEJXiKRwMLCAnQ6HY4fP15zTTFwTcrW09ODQ4cOQavVckt+MsXD4/FwfdncqeGVALlhjY2NYd++fZLe49XVVXzmM59Bd3c3Xnjhhd1UCZeFhqyQAeSRJcHi4iK6uroKjuIhhOT3+zE6OiqoalhfX+cyKUrtEweDQW5ascViqXrVQjaPcvuS6XSak7GZzeaaV3xkg4yvcRYERUH71FMAf96hXg/m3e8GeEYOglLVHfwbViEzRTVB0zSXYChl6ADfeUdef641vNj07GJIp9NYWFgAy7KYnp6W9P1mGAaPP/44Hn74Yfz93/893vve99a8GFAIe7dlAYgnvrlcLrS2tmJwcDDvd6RadTqd2LdvH8bGxkR7r9vb21hbW4PNZpNNxEQyRtM0rFZrzY0dwDUjhV6vR39/PyfDyx3MWSxbV0mQeYLt7e2wWCxV6YEWI2kA8Hg86O/vx9jYWM1zk4FrmSHDw8NlSSL51nDy+pPJJJqbm7M+/2L5HfxVH5GCSsHKygruueceDAwM4MEHH2yIwHkeVEIWem3Ly8vQaDR5kX47OzucDXlycrJoNnE4HMbbb7+NkZERdHR0SFru1qOxg98KEEuHIwNJyT8+SUm9SOU+J7ILXw+9a2IL93g8SKVS0Ov1aGlpqUmedO7zWlhYAMMwmJqaqpiULZekE4lEXmQpuUmnUilcuXIFOp0OU1NTkm6iDMPgRz/6Eb7xjW/gK1/5Ct7znvc0SlXMx94m5Ewmk5fLCoDz21ssFgDXsolZloXNZpO01KNpGgzDYGdnh1M3JJNJ7iJtb29HR0cHt0RjGAZ+vx8+n69ujB0sy8Lv9+fZnaWCX0mGw2EkEgkuaKbU5S7/OcnpN1YSLMtiZWUFy8vLWbKxaudJ5z4n0saRU4EqCfL6ScsjHo9zclNiDW9rayv6+v1+P+6++27s378fDzzwQF04KysElZCFCHljY4MbMe9wOBAOhyX1AYv1iclyjxA0cdvp9XokEgl0dXXBarXWhSB/e3sbDodDst1ZKviVlNBNqtDGEZEUdnR0wGKxVN3wIgSi6DCbzZiYmCjZFs5f7pdL0vF4HJcvX0ZbW5uiOudykEwmcfnyZRgMBgwMDHBSvNwpJbkbxz/4wQ/wrW99C//4j/+Im2++ueY33wpDJWQhQiY6SIZhYLFYilZh5Rg7yA48fzglTdOc46rUYZylQkm7sxTk9iTD4TBSqRS3u08IamlpCYlEAlNTU3XRTycbZKFQqGxNsVIkTQawrq+vS8pKqQbIisbn84kWNcQaTl7/q6++ijNnzoBlWfT19eGLX/wiTp06VRcKlQpjbxMySdYiIEtPp9MJnU6H06dPK2p1JkilUnA6nYjH47BarXnGDiLm5xs5gKtj7Um4kNIBM/w84Fr3rknwOxmhFAqFuKhK/r9amRjW19fhdDor2lqSS9KhUAhXrlxBf38/RkdH62IjkVTqZM9FSlHBMAy+973v4ZFHHsGHP/xh6HQ6XLhwAe9617vwR3/0R1V41jWFSsiEkDc3N7lEsuHhYSwsLODEiROCjyvVYVeOsUNIfqbT6bIIqhRlA9/RRnbg6+FiJhOVu7q6YLFYoNVqi1rChWzBSoJoivV6PWw2W9UliEIkTfKyyaYdyQauJfj5ydPT05Ir9eXlZXz605/GxMQE7r///ppv1NYAe5uQaZrmwkvIRWY0GkHTNF555RWcOnUq7zGlOOz4pEeiA5UgvULKBlJJF9o0I3Znk8lUE42zENLpNOx2O9LpNGw2W8GJyiRkhv8ekOwGJds9pBVAJIz1oCkGru512O129PT0wGAwVKwnLQexWAyXLl1CZ2cnLBaL5Kr40UcfxSOPPIIHH3wQ73rXuyr2fO+880784he/QH9/Py5evJj3e5Zlcc899+CZZ56B0WjEY489huPHj1fkuQhgbxMyaU/kzipjWRYvvfRSVuLbbjF28PuxoVCIE/Lzc5RZluUiH+ulJ8swDHw+HzfotFTnHz+7gVSSLMvmkbTUGyKROtZTKyCVSmFhYQEajQY2my2vt1qNjcNc8PvXMzMzknvqS0tLuOuuuzA9PY1/+Id/qPh38b/+679gMplw++23CxLyM888g4cffhjPPPMMzp49i3vuuSdvqGkFsbet0/39/YJLvFxlRClEzDd2zM7OFqz0lETuZAr+IM6trS1cuXIFqVQK7e3t6OvrQyaT4Sby1gqE9Hp7e8tK2QOQNXWCzD5jGIZr9/h8Pi6qkhBUR0dHXk+eX6kfOXKkaNpYNcCX/OXa9PmQM+NQCZKORCK4fPkyenp6JI/AYhgG3/nOd/Doo4/ioYcewk033VSVKv7GG2+Ex+MR/f3TTz+N22+/HRqNBqdOnUIwGMTq6qqgSaxWaFhCJvO2xEB6c4B0Is5kMlxoS603x4BrI3Si0Sh2dnawf/9+HDhwgCPptbU1TlXBvzjlVJGlIpVKcSPlDx8+XDFFh1ar5aYmE/B78ktLS1zou9lsBk3TCAaDmJyclJ1TXCnEYjFcvnwZZrMZ8/Pzsm+glSBphmHgdruxtbWFmZkZyT1ft9uNT3/60zh48CBefPHFqhUrUuD3+7MMYcPDw/D7/Soh1wpkw06v1+Ptt9/mLuRiAna+sWNkZASTk5N1cSGTzOSmpqas6c4mkykrGpSkv4VCIa6K5AcLSXkPpIKf8zAxMVEwM6RS0Ol06OzszNpwCgaDuHz5MvR6Pdrb2+HxeOD1esveOC0HfNLLba2Vi3JIOhwO48qVKxgYGMDc3JykmzdN03jkkUfw/e9/H1/72tdw44031sU1stvQsISc+2Xgb9gdPXqUIyhSQRFVAyFpQm6bm5twOp3ckrsehPh8u7OUzGSx9DdycZJBlPz3oJRl7vb2Nux2O/r6+spuTygFiqLgcrkQCoVw6NChrEovk8lwlTQZI5QrwavUphmZsyeH9MpFMZIOBAIIBoNgWRb9/f1oaWlBMpks+h64XC58+tOfxnXXXYdf//rXdVUV87F//354vV7u//t8Pq71VS+oPbtUGEJ9YqFlLl/VEAgEEIvFkMlk0NzcjNHRUfT29tacjFmWhc/ng8/nw+joKGw2W8lkodPp0NXVlRXgkvse5NqhOzo6BAX8yWSSs5/XU092Y2OD0xRbrda898pgMKC7uztLWcGfFp0b06lEbgfJ6UgkEhVt5UgFIWm9Xo/19XVYLBYMDAwIvgf87Aoyweaf//mf8aMf/YiriusZt956K86cOYPbbrsNZ8+eRUdHR121K4AGVlkQJUJnZyfXI5Zj7IjFYhgbG+MGMYZCIWQyGbS1tXFkXk2XHak+e3p6MDY2VrWbQ+7wVeK06+jogMlkQigUwubmZkXn68lFIpHAlStX0NTUBKvVWrb6RcgSnpvbIcUSv7a2BpfLVTc5HcC1cUqxWAyzs7OiN1N+Jb24uIg///M/RyaTwcDAAO6++27cdNNNgsMaqokPfvCDeP7557G5uYmBgQHcd9993Ci3T3ziE2BZFnfddReeffZZGI1GPProo5ibm6vW09vbsrdz587hs5/9LEKhEKanp3HixAnMz8/juuuuE/zSSTF28LWxoVCIk10Rl52SvVgCMq0EAKxWa80rKv7wVb/fD51OB71enyU9q9Rcv2Lgy7NsNlvF4hvFLOG5EkRyI0gmk7hy5UrNTCdiIBOo5cR20jSNb33rW3jiiSdw3333wWAw4Pz58xgaGsJHPvKRKjzrXYu9TcgEmUwGb7/9Nl5++WW88soreP3116HVanHs2DEcP34cx48fx69//WsMDAzg+PHjRadH54K/ox8KhRCLxbiNI3JxlrLEpWma2/CxWq11Y1ggNwiik21paeFuVHw7ONEHk/dAysiocrC9vY3FxUUMDAzURFPMlyDyw6WAq9XlyMgIhoeH62KuXSaTgd1uRyqVwszMjOTAq8XFRdx99904efIkvvjFL1asNfXss8/innvuAU3T+NjHPobPf/7zWb9fXl7Ghz/8YQSDQdA0ja985Su45ZZbKvJcFIRKyEJgWRbRaBTnz5/HE088gSeffBLDw8Po6enB8ePHceLECZw8ebIsSVQmk+HIKRQKcalnpIoulNVQr3ZnmqaxtLSEjY0NSTcIvj44FApx0jN+Fa3EaoKMtyez2uqhfw1cVcBcunQJJpMJnZ2d3PgsiqKy5tu1t7dXdW+ilHFKFEXhm9/8Jv7lX/4FDz/8cJapSmnQNA2bzYZf/epXGB4exvz8PB5//HHMzs5yf/Pxj38cx44dwyc/+UlcunQJt9xyS0H9cZ1gbxtDxEDkXqdPn8Zjjz2GF198ETabDaurqzh37hxefvllfPvb38b6+jomJydx4sQJzM3N4dixYzCZTJK+wAaDIWs3O9fA4Xa7QVEU148mGyVExmY2mzE3N1cX1RQAbnNscHBQsjmAv3FKtJ8URXHVo8vlylI1yF1N8I0URF5XDz1ZsrLZ3t4W1O/y214kyCg3t6MSLR9y46JpWtb8vytXruDuu+/GDTfcgBdffLHi8bHnzp3D5OQkl1d+22234emnn84iZI1Gg3A4DOBq8JLY5PfdiD1XIUsFTdNYWFjA2bNncfbsWVy4cAGZTAZHjhzhSHp2drZk0uTbgLe3t7G1tQWWZdHT04Pe3l7FKshyEI/HsbCwAIPBAKvVWpGIRP5mEVlNNDc3Z/Vic89LBnnWU3YycK1tQjJNpH52QpbwXDOPyWQqmaTJZiJRUEgBRVE4c+YMfvazn+Eb3/gGrr/++pLOLRdPPvkknn32WTzyyCMAgB/84Ac4e/Yszpw5w/3N6uoq3v3ud2NnZwexWAzPPfecaFhYHUGtkMuBTqfD7OwsZmdnuc2KeDz+/7V3rkFRndkafjY2iIAaMBovKCB3NSgCpRmrEp1UwkimyNQZ4q008Vhj5lia6MSYm6WHMReT0bKSmKiJ0Zo55YhakzgxZ5REnOgYwh2MEq6RMQRjomhowYZuuvnOD7P32S0gG6HpRr+nKlXpZlP7a6tZvXqtd72LkpIS8vPzefvttykrK2Pw4MFagE5KSiI4ONhwBunv78/ly5e1DndQUJCmj66pqdEcv9TgpNdHuxJ9/dqVzTFor43VG/03NDRQW1uLzWbDz8+PgIAAmpqasNlsxMTEeIxjmLri3mazddo0vhmdjYSrZY7z58872bTqg/TN3mv6dUoJCQmGm4nl5eU89dRTPPDAA3zxxRcesVRBT0ZGBosXL2b16tXk5OSwaNEiSktLPaK011NkhtwDhBBcvnyZ/Px88vLyyM/P57vvvmPcuHEkJSWRkJBAQkKCJr3T/96lS5eoqanpsgmlZpD6VVGq7Ky3vYP15xo9erThDxdXo5oTnTt3Dn9/f9ra2txq9K+iX+YZFhbm8lFsh8OhBWk1k1aD+Y11ebUPcTNfjBux2+289dZbHDp0iG3btpGUlOSy19IZOTk5pKen8+mnnwKwceNGAF588UXtmokTJ5KZmamVwsaPH09ubq5bVll1A9nUcwdtbW3U1NRopY7CwkItA05MTCQwMJBjx46xatWqLpepdoTe4F0N0mpw0tejuxtI1a3TAwcO7BXtbm+hlk1u1BTfzOi/M1Oh3kSvdY6KinJbvf9GL239puixY8cSGBhoqPRVVlbGU089xS9/+UvWr1/vtg0edrudqKgojh07xpgxY0hKSmLv3r1MnDhRu2b27NnMnTuXxYsXU15ezoMPPsj58+c9oodwE2RA9hRsNhtffPEFL7/8MlVVVYSGhmK324mPjycxMZHExEQiIiJuOXjog5OqaFAUxWnCrjOfBofDQU1NDT/99BNRUVEesRoIrr+mc+fOcenSJcNlk86M/vXbWHrqVyGEoLa2lgsXLniUf/KNTU6TyeTkpd3ZwoPW1lbefPNN/vGPf7Bt27a+HJTolMOHD7Nq1SocDgdLlixh7dq1rF+/nsTERFJTUykrK2Pp0qXa+/xPf/oTDz/8sLuP3RUyIHsSJ0+epLa2lvnz52td4oKCAq3UoaoY1Hp0YmJij5QD6i4zNYO8du0aPj4+TqUOtVYdHBxMcHCwx2QYanNs5MiRPTb8V/0q1H+HG0eh1XFwI69dtaIMCgoiLCzMI7w64Hq2Xl5ejp+fX6eLT29ceHDw4EE+++wzmpqamDJlCq+88grR0dEe8x64DZEBuT+h+lTk5uaSn59Pfn4+V65cISoqSgvQU6ZM6VGGp06XXbp0iYsXLyKE0FzR1EDtTsWC1Wqluroau91OdHS0yzTFNpvNaTu4fju2+u+gL9mo3yIaGhqIjY31CNN/cF6nFB0dbbj52traypYtW8jKymLOnDlcvXqVwsJCli1bxq9+9SsXn/qORQbk/o7dbqe8vFybMiwpKUEIweTJk7UgHR0dbTiI6p3PoqKiGDJkCM3NzU7ByeFwONVhXT1hB///YXT+/Hm3WHbeOGWn9y0xmUxcuXKF4OBgQkJCPCaDVD2UVemf0Wz9zJkzPP3008yePZuXXnrJZb2CrqbtAA4cOEB6ejqKojB58mT27t3rkrN4CDIg326oQwVFRUVaFl1ZWUlgYKCT9G706NHtVB1q133cuHHtfq5H752sNon09cehQ4f2qiXl1atXqays7Naetr7AarVSVlaG1WolICAAi8Wi7fTrSfO0p6h+0z/++CMxMTGGPZRtNhubN28mKyuLHTt2MGXKFJed0ci0XXV1NXPmzOGf//wngYGBXLx40dNVEj1FBuQ7ASEEFy9e1FQdBQUFXLhwgbCwMBISEhg2bBhHjx5l3bp1REZG3pIaQJ2w09dhVbczdRqvu5mW6jLW1NRETEyMR5UB1A+vGwcp9NpgffP0Rm2wq7LopqYmpxq20Q+Dr776ipUrV/LrX/+aF154weUKGiPSteeee46oqCh+97vfufQsHoQcDLkTUBSFe+65h9TUVFJTU4HrgaOoqIi1a9dSXl5OeHg4S5cuZdKkSZrr3cSJEw3/YZpMpna+wepXfLPZrA1vqOO/qjVpR6UUvXY3JCTEoxpJFouFiooKBg0a1OHout6LIzg4GPh/ZYfZbObcuXNORv+99Y1CVZzU19d3a52S1Wpl06ZNfP7553zwwQfExcXd8hm6Q0erkm5cJqo6GM6YMQOHw0F6erqsXyMD8m2Jl5cXPj4+LFy4kEWLFqEoClarlVOnTpGbm8v27dspLS3Fz8+PqVOnavXo0NBQw1mXr68vvr6+2tdMIQQWiwWz2azt8tM7vg0dOhQvLy8qKyvx9fXt1uSYq9GXAbo7mdjRuii9ouHHH3+kubm5ncLF6PSbuk5p+PDh3doscurUKVauXMlvfvMb/vWvf3mML4qK3W6nurqa48ePU1dXx/3338+ZM2c8RnbpLm6rgNxVI8FqtfL4449TVFTEsGHD2L9/P6Ghoe45rIuZPHkykydP1h4PHDiQadOmaZ4EQgh++uknCgoKyM3N5W9/+5vmMKcG6ISEhA43d3eEoij4+/vj7+/vtMuvsbGRhoYGSktLsVgs+Pn54e/vz5UrV7RRcHdmyGazmcrKym5tVe4Kb29vhg0b5mTYr46Dq9uxVaN/vbJDHzT1yo4JEyYYLulYrVbeeOMNTp48ye7du7n33nt7/Hq6i5FVScHBwUybNg1vb2/CwsKIioqiurraLdOBnsRtU0M20kjYtm0bp0+fZseOHezbt4+DBw+yf/9+N57as1AN3vX16MbGRieD/+6uaFJN0FVNsX6Xn9lsprm5+aaSM1dht9s5e/as9vr6uoatGv3rtcGqNaePjw/19fWMGTOG0NBQwx9YxcXFrFq1it/+9rc8++yzbsuKjUzbZWZmkpGRwV/+8hfq6+uJj4/n1KlTHrN1xgXcWU09I42E5ORk0tPTue+++7Db7YwcOZJLly55TA3TE2ltbaW0tFTTR58+fZoBAwZoBv9JSUlERka2U0dYrVaqqqpoa2sjOjq606/oejOhvlqVVV9fT3V1NWPHjjW8KaMvUGWOjY2NDB48mObm5naubx0pO1paWti4cSM5OTm89957ToHPXXQ1bSeEYPXq1WRmZjJgwADWrl3LvHnz3H1sV3JnBWQjtn2TJk0iMzNTa8iEh4eTl5fntIVXcnOEEDQ2NlJUVKTpo9VN0wkJCUydOpWSkhLuuece0tLSbklT3NWqrFtVM1itViorKwGIjo7udb8GIWD37gFkZQ1gzBjBmjWtGHS71KYTb1ynpDf61xsKORwOioqKGD58OFu3bmXevHk888wzHmNFKmmHVFlIeh/VI2PWrFnMmjULuB5Av//+ezIyMlizZg0jRoygra2N7OxszfFu6tSphv2dFUUhICCAgIAArR6t96lQ1QxGV2Wp56utre2W+1l3WbfOm127TFit4OUFhw8PICenhZtJhdXmVnNzc4fWnR1tSFd/Jzs7mzNnzjBw4ECOHDlCUFDQnSQjuy25bQKykUaCek1wcDB2ux2z2Xw716z6DEVRGDNmDE1NTRw9epTY2FgcDgcVFRXk5eXx97//nfXr1+NwONoZ/BvN6G6mZjCbzXz//fcdrsqy2WxUVFTg7+9PUlKSyzLItjZ4/30TQoB6i4YGhczMAcyd6+jwd9TSSUhICDExMd2qFa9evZr58+fz4YcfYjKZuHz5MleuXOmtlyNxE7dNycJII+Hdd9/lzJkzWlPvo48+4sCBA7d8z65UHVu2bOGDDz7AZDIxfPhwdu/eTUhIX2B8pAAACrBJREFUyC3fr79jsVgoLi7WpgzLy8sZMmSI05RhT3YI6kegGxoauHjxIjabjcDAQG0Li6t8k9vaYMSIQSgKqHHVZILNm20sWOAckFVD+9bWVmJjYw2XTpqbm3nllVcoLi7mvffeIyYmprdfhhNGxp8BPvzwQ9LS0igoKPAItzgP5c6qIUPXjYSWlhYWLVpESUkJQUFB7Nu3T9vd1V2MqDo+//xzpk2bhp+fH9u3b+f48eNS1aFDCEF9fb2TwX9dXR0hISFO0ruhQ4d2q17c0NBAZWUlI0aMYOzYsZqaQa1Hq9N1ahbdW6uyli3z5qOPTNjt1x8PGQK5uc1OdWR1j153De1zc3N59tlnWbhwIStXrnT5iLmR9zdcd8B75JFHsNlsvPPOOzIgd86dF5D7EiOqDj0lJSWsWLGC7OzsPjtjf6StrY2zZ89qAbqwsBCLxaIZ/CcmJnLvvfd2mFWq49jXrl0jJiYGf3//Du+hn64zm80drsoyasmpp7UVXn/dRFbWAEaNErz6aivh4df/ZNTSiaIoREdHG5b2WSwWXn75ZU6dOsXOnTuJiorq1pluFaPv71WrVvHQQw+xadMmNm/eLANy58imnisxMh6qZ9euXcyePbsvjtav8fLyIjIyksjISBYuXAhcD2ZfffUVeXl57Ny5k9LSUgYOHOhk8J+fn4+Pjw8zZszochy7o3q0flWWWo/u7qosb29Yt87OunV27Tn9qHh4eHi3DHS+/PJL1qxZwxNPPMGWLVv61HjJyPu7uLiY7777jkceeYRNmzb12dluZ2RA7gP27NlDYWEhJ06ccPdR+iU+Pj4kJSWRlJTEihUrEEJgNpspKCggKyuLdevWMWTIEEJDQykvLycpKYnExETuvvtuw1luR8tW1VVZ9fX11NTUdHtVVktLCxUVFXh7e3fojdEZ165dY8OGDZSWlnLgwAEiIyMN/V5f0tbWxjPPPMOf//xndx/ltkIG5FvEiKoDICsri1dffZUTJ064bU/Z7YaiKNx111089NBD7Nmzh927d5OcnExtbS15eXnk5OTw9ttva2up9Ab/Ro1+FEVh0KBBDBo0iJEjRwLOq7Lq6uo6XZUFaDK7qKgow0oeIQTZ2dk8//zzLFmyhDfffNNtdqRdvb8bGxspLS1l5syZAPzwww+kpqZy6NAhWbboAbKGfIsYUXWUlJSQlpZGZmZmr2Q5suvdPex2O19//bU2Bl5SUqKZoesN/nsS9G5cldXU1ITNZsPX15eQkBCCgoIMfRBfu3aN9PR0KioqeP/99wkPD7/lM/UGRt7fembOnClryDdH1pBdiclk4p133iE5OVlTdUycONFJ1bFmzRqampp47LHHABg3bhyHDh26pfs5HA6WL1/u1PVOTU3tsOv91ltvaSZCdzImk0kzWXryySe1CcDCwkLy8/N54403qKysJCgoyEl6N2rUKMOlDpPJRGBgIHfddRd1dXVYLBZiY2Px8vLCbDZz4cIFrFYrfn5+TkMsqh5aCMHJkyd54YUXWLp0KVu3bu1z0/uOMPL+lvQ+MkPuJ8iut2tQm256Q6UffviB8ePHa4ZK8fHxDB48uNMgbbFYKC8vZ/DgwYSHh7fLuFUjIf2qrLy8PE6cOEFraysNDQ3s2bOnzxQUErcgM+TbCdn1dg2KojBy5EgeffRRHn30UeB6rbiqqorc3Fw++eQT/vjHP2Kz2doZ/CuKwokTJwgICCA6OrpTL19FUfDz88PPz49Ro0YhhKChoYGDBw8yfvx4Ro8ezYIFC1i8eDErVqzoy5cv8TBkQL5NkF3v3sPLy4uYmBhiYmJYvHgxcF0xoRr8v/vuuxQVFXH16lUSEhJIS0tjxIgRDBkypMtyQ2NjI+vWrePcuXNkZGQ4+XF389tql8hJ0v6HDMj9BNn1di++vr5Mnz6d6dOnc/ToUWpqati+fTtWq5Xc3FwOHDjAt99+y9ixY52mDAMDA1EUBSEEx48f56WXXmL58uXs2LGjXfDuTRtQIz2H+Ph4CgsLtUnS5557Tk6SuhshRHf+k7iJ1tZWERYWJmpqaoTVahVxcXGitLS00+sfeOABUVBQ0KN7HjlyRERFRYnw8HCxcePGDq/Zv3+/iI2NFRMmTBDz58/v0f36CxaLRdhstnbPOxwOcfbsWfHXv/5VrFy5UsyYMUPExcWJtLQ0cf/994vk5GTx7bff9skZv/zyS/Hwww9rj1977TXx2muvdXp9cXGx+MUvftEXR7tTMRRjZYbcT+jrrreRDKu6upqNGzeSnZ2trXK/E+hsY4qXlxfjx49n/PjxLFiwALhuJHT69Gk++eQT1q9f32cKCjlJ2j+RAbkfkZKSQkpKitNzGzZs6PDa48eP9+he+fn5REREaOZL8+bN4+OPP3YKyDt37mT58uXaUtDujAXfKXh7e2ue0J6KnCT1HNwveJR4JB1lWOfPn3e6pqqqiqqqKmbMmMH06dPJzMzs62NKOqG7k6SHDh2Sk6QegMyQJbeMXOXuuSQlJVFdXc2///1vxowZw759+9i7d6/TNSUlJfz+978nMzNTfrvxEGSGLOkQo6vcU1NT261yl7gffc8hNjaWOXPmaD0HdVpUP0k6ZcoUOX3nAchJPUmHyFXuEkmvYkjTKDNkSYcYybCSk5MZNmwYEyZMYNasWWzatKnHwTgzM5Po6GgiIiJ4/fXX2/28traWWbNmER8fT1xcHIcPH+7R/SQST0JmyBKPwcjaoCeffJL4+HiWLVtGWVkZKSkpnDt3zn2HlkiMITNkSf9CL7Xz8fHRpHZ6FEXh6tWrAJjNZkaPHu2Oo/YZXX1jsFqtzJ07l4iICKZNmyY/nPo5MiBLPAYjUrv09HT27NlDcHAwKSkpbN26ta+P2WeowzlHjhyhrKyMjIwMysrKnK7ZtWsXgYGBfPPNN/zhD3/g+eefd9NpJb2BDMiSfkVGRgaLFy+mrq6Ow4cPs2jRItra2tx9LJdg5BvDxx9/zBNPPAFAWloax44d63WTIknfIQOyxGMwIrXbtWsXc+bMAeC+++6jpaWF+vr6Pj1nX2HkG4P+GnVz9uXLl/v0nJLeQwZkicegH2aw2Wzs27evnTZ23LhxHDt2DIDy8nJaWloYPnx4j+67ZMkSRowYwaRJkzr8uRCCp59+moiICOLi4iguLu7R/SSSzuiuykIicSmKoqQAbwIDgN1CiFcVRdkAFAohDimKMgHYCQRwXfXznBDisx7e836gCfgfIUS7qPzzmZ4CUoBpwFtCCJfvyFIU5T4gXQiR/PPjFwGEEBt113z68zU5iqKYgB+A4UL+YfdLZECWSABFUUKB/+0kIL8HHBdCZPz8uBKYKYS44OIzmYAq4EHgPFAALBBCfK27ZjlwrxDivxRFmQf8hxBijivPJXEdsmQhkXTNGOA73eO6n59zKUIIO7AC+BQoBw4IIb5WFGWDoihqLWcXMExRlG+AZ4COV5FL+gXSXEgi8WCEEIeBwzc8t173/y3AY319LolrkBmyRNI154GxusfBPz8nkfQqMiBLJF1zCHhcuc50wOzq+rHkzkSWLCR3PIqiZAAzgbsVRakD/hvwBhBC7OB6ySAF+AawAP/pnpNKbnekykIikUg8BFmykEgkEg9BBmSJRCLxEGRAlkgkEg/h/wDaPK2y8PWC8AAAAABJRU5ErkJggg==", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "wh = W @ H\n", + "ax.scatter(M[:, 0], M[:, 1], M[:, 2], c=\"b\", marker=\"o\", s=20)\n", + "ax.scatter(wh[:, 0], wh[:, 1], wh[:, 2], c=\"r\", marker=\"^\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut voir la matrice $M$ comme un ensemble de $n$ points dans un espace vectoriel. La matrice $W$ est un ensemble de $k < n$ points dans le même espace. La matrice $WH$, de rang $k$ est une approximation de cet ensemble dans le même espace, c'est aussi $n$ combinaisons linéaires de $k$ points de façon à former $n$ points les plus proches proches de $n$ points de la matrice $M$." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/_doc/notebooks/nlp/README.txt b/_doc/notebooks/nlp/README.txt deleted file mode 100644 index 30e371c4..00000000 --- a/_doc/notebooks/nlp/README.txt +++ /dev/null @@ -1,11 +0,0 @@ - -NLP - Natural Language Processing ---------------------------------- - -.. contents:: - :local: - - - - - diff --git a/_doc/notebooks/nlp/completion_profiling.ipynb b/_doc/notebooks/nlp/completion_profiling.ipynb index 5673888a..e0eb912c 100644 --- a/_doc/notebooks/nlp/completion_profiling.ipynb +++ b/_doc/notebooks/nlp/completion_profiling.ipynb @@ -1,761 +1,648 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Completion profiling\n", - "\n", - "Profiling avec [cProfile](https://docs.python.org/3.7/library/profile.html), [memory_profiler](https://pypi.org/project/memory-profiler/), [pyinstrument](https://github.com/joerick/pyinstrument), [snakeviz](https://jiffyclub.github.io/snakeviz/).\n", - "\n", - "[line_profiler](https://github.com/rkern/line_profiler) ne semble pas plus \u00eatre maintenu." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Function to profile" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from mlstatpy.nlp.completion import CompletionTrieNode\n", - "\n", - "def gain_dynamique_moyen_par_mot(queries, weights):\n", - " per = list(zip(weights, queries))\n", - " total = sum(weights) * 1.0\n", - " res = []\n", - " trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", - " trie.precompute_stat()\n", - " trie.update_stat_dynamic()\n", - " wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n", - " wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0])\n", - " for p, w in per]\n", - " wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0])\n", - " for p, w in per]\n", - " gain = sum(g * p / total for w, p, g in wks)\n", - " gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n", - " gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n", - " ave_length = sum(len(w) * p / total for p, w in per)\n", - " return gain, gain_dyn, gain_dyn2, ave_length" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from mlstatpy.data.wikipedia import download_titles\n", - "file_titles = download_titles(country='fr')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "33" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(file_titles)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "from mlstatpy.data.wikipedia import enumerate_titles\n", - "list_titles = list(sorted(set(_ for _ in enumerate_titles(file_titles) if 'A' <= _[0] <= 'Z')))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "sample1000 = random.sample(list_titles, 1000)\n", - "with open(\"sample1000.txt\", \"w\", encoding=\"utf-8\") as f:\n", - " f.write(\"\\n\".join(sample1000))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Standard modules" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### cProfile" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import cProfile, io, pstats, os\n", - "\n", - "def toprofile0(lines):\n", - " gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", - "\n", - "def doprofile(lines, filename):\n", - " pr = cProfile.Profile()\n", - " pr.enable()\n", - " toprofile0(lines)\n", - " pr.disable()\n", - " s = io.StringIO()\n", - " ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')\n", - " ps.print_stats()\n", - " rem = os.path.normpath(os.path.join(os.getcwd(), \"..\", \"..\", \"..\"))\n", - " res = s.getvalue().replace(rem, \"\")\n", - " ps.dump_stats(filename)\n", - " return res" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1311797 function calls in 1.865 seconds\n", - "\n", - " Ordered by: cumulative time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 1.865 1.865 :3(toprofile0)\n", - " 1 0.000 0.000 1.865 1.865 :3(gain_dynamique_moyen_par_mot)\n", - " 1 0.241 0.241 1.232 1.232 \\src\\mlstatpy\\nlp\\completion.py:415(precompute_stat)\n", - " 15982 0.244 0.000 0.770 0.000 \\src\\mlstatpy\\nlp\\completion.py:503(merge_completions)\n", - " 1 0.088 0.088 0.366 0.366 \\src\\mlstatpy\\nlp\\completion.py:450(update_stat_dynamic)\n", - " 15982 0.307 0.000 0.314 0.000 {built-in method builtins.__build_class__}\n", - " 1 0.194 0.194 0.220 0.220 \\src\\mlstatpy\\nlp\\completion.py:203(build)\n", - " 16982 0.094 0.000 0.165 0.000 \\src\\mlstatpy\\nlp\\completion.py:555(update_dynamic_minimum_keystroke)\n", - " 36051 0.114 0.000 0.130 0.000 \\src\\mlstatpy\\nlp\\completion.py:523()\n", - " 37609 0.035 0.000 0.071 0.000 {built-in method builtins.all}\n", - " 16982 0.051 0.000 0.058 0.000 \\src\\mlstatpy\\nlp\\completion.py:588(second_step)\n", - " 314299 0.053 0.000 0.053 0.000 {built-in method builtins.len}\n", - " 15983 0.006 0.000 0.049 0.000 {method 'extend' of 'collections.deque' objects}\n", - " 16983 0.031 0.000 0.047 0.000 \\src\\mlstatpy\\nlp\\completion.py:97(unsorted_iter)\n", - " 15982 0.039 0.000 0.046 0.000 \\src\\mlstatpy\\nlp\\completion.py:542(update_minimum_keystroke)\n", - " 16982 0.041 0.000 0.044 0.000 \\src\\mlstatpy\\nlp\\completion.py:624(init_dynamic_minimum_keystroke)\n", - " 1001 0.028 0.000 0.043 0.000 \\src\\mlstatpy\\nlp\\completion.py:132(leaves)\n", - " 115015 0.041 0.000 0.041 0.000 \\src\\mlstatpy\\nlp\\completion.py:435()\n", - " 15982 0.024 0.000 0.032 0.000 {built-in method builtins.sorted}\n", - " 3000 0.031 0.000 0.031 0.000 \\src\\mlstatpy\\nlp\\completion.py:257(find)\n", - " 110110 0.027 0.000 0.027 0.000 {built-in method builtins.hasattr}\n", - " 117519 0.023 0.000 0.023 0.000 {method 'values' of 'dict' objects}\n", - " 1 0.001 0.001 0.017 0.017 :10()\n", - " 16982 0.015 0.000 0.017 0.000 \\src\\mlstatpy\\nlp\\completion.py:20(__init__)\n", - " 47946 0.016 0.000 0.016 0.000 {method 'extend' of 'list' objects}\n", - " 23287 0.015 0.000 0.015 0.000 {built-in method builtins.min}\n", - " 1000 0.002 0.000 0.015 0.000 \\src\\mlstatpy\\nlp\\completion.py:321(min_keystroke0)\n", - " 1 0.001 0.001 0.013 0.013 :13()\n", - " 50946 0.013 0.000 0.013 0.000 {method 'pop' of 'list' objects}\n", - " 1 0.001 0.001 0.013 0.013 :11()\n", - " 20069 0.012 0.000 0.012 0.000 {built-in method builtins.max}\n", - " 1000 0.002 0.000 0.012 0.000 \\src\\mlstatpy\\nlp\\completion.py:382(min_dynamic_keystroke2)\n", - " 1000 0.002 0.000 0.012 0.000 \\src\\mlstatpy\\nlp\\completion.py:352(min_dynamic_keystroke)\n", - " 56589 0.011 0.000 0.011 0.000 {method 'popleft' of 'collections.deque' objects}\n", - " 52034 0.011 0.000 0.011 0.000 {method 'append' of 'list' objects}\n", - " 38608 0.009 0.000 0.009 0.000 {method 'append' of 'collections.deque' objects}\n", - " 16982 0.008 0.000 0.008 0.000 \\src\\mlstatpy\\nlp\\completion.py:517()\n", - " 16981 0.007 0.000 0.007 0.000 \\src\\mlstatpy\\nlp\\completion.py:54(_add)\n", - " 15982 0.007 0.000 0.007 0.000 \\src\\mlstatpy\\nlp\\completion.py:511()\n", - " 15982 0.007 0.000 0.007 0.000 \\src\\mlstatpy\\nlp\\completion.py:508(Fake)\n", - " 31964 0.006 0.000 0.006 0.000 {method 'items' of 'dict' objects}\n", - " 5 0.001 0.000 0.002 0.000 {built-in method builtins.sum}\n", - " 17982 0.002 0.000 0.002 0.000 {built-in method builtins.isinstance}\n", - " 1001 0.000 0.000 0.001 0.000 :18()\n", - " 1001 0.001 0.000 0.001 0.000 :15()\n", - " 1 0.000 0.000 0.000 0.000 :7()\n", - " 1001 0.000 0.000 0.000 0.000 :16()\n", - " 1001 0.000 0.000 0.000 0.000 :17()\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "r = doprofile(sample1000, \"completion.prof\")\n", - "print(r)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Others informations when profiling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### memory_profiler\n", - "\n", - "See [memory_profiler](https://pypi.python.org/pypi/memory_profiler/0.41). Version 0.56 is bugged (see [#258](https://github.com/pythonprofilers/memory_profiler/issues/258))." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from memory_profiler import profile, __version__\n", - "%load_ext memory_profiler" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "peak memory: 411.20 MiB, increment: 18.40 MiB\n" - ] - } - ], - "source": [ - "%memit toprofile0(sample1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR: Could not find file \n", - "NOTE: %mprun can only be used on functions defined in physical files, and not in the IPython environment.\n" - ] - } - ], - "source": [ - "from io import StringIO\n", - "st = StringIO()\n", - "@profile(stream=st)\n", - "def toprofile(lines):\n", - " gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", - "toprofile(sample1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting temp_mem_profile.py\n" - ] - } - ], - "source": [ - "%%file temp_mem_profile.py\n", - "\n", - "from mlstatpy.nlp.completion import CompletionTrieNode\n", - "from memory_profiler import profile\n", - "\n", - "@profile(precision=4)\n", - "def gain_dynamique_moyen_par_mot(queries, weights):\n", - " per = list(zip(weights, queries))\n", - " total = sum(weights) * 1.0\n", - " res = []\n", - " trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", - " trie.precompute_stat()\n", - " trie.update_stat_dynamic()\n", - " wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n", - " wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0])\n", - " for p, w in per]\n", - " wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0])\n", - " for p, w in per]\n", - " gain = sum(g * p / total for w, p, g in wks)\n", - " gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n", - " gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n", - " ave_length = sum(len(w) * p / total for p, w in per)\n", - " return gain, gain_dyn, gain_dyn2, ave_length\n", - "\n", - "@profile(precision=4)\n", - "def toprofile():\n", - " with open(\"sample1000.txt\", \"r\", encoding=\"utf-8\") as f:\n", - " lines = [_.strip(\"\\n\\r \") for _ in f.readlines()]\n", - " gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", - "toprofile()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: temp_mem_profile.py\n", - "\n", - "Line # Mem usage Increment Line Contents\n", - "================================================\n", - " 5 56.7930 MiB 56.7930 MiB @profile(precision=4)\n", - " 6 def gain_dynamique_moyen_par_mot(queries, weights):\n", - " 7 56.7930 MiB 0.0000 MiB per = list(zip(weights, queries))\n", - " 8 56.7930 MiB 0.0000 MiB total = sum(weights) * 1.0\n", - " 9 56.7930 MiB 0.0000 MiB res = []\n", - " 10 63.3047 MiB 6.4492 MiB trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", - " 11 71.0742 MiB 7.7695 MiB trie.precompute_stat()\n", - " 12 80.6211 MiB 9.5469 MiB trie.update_stat_dynamic()\n", - " 13 80.7305 MiB 0.1094 MiB wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n", - " 14 80.7930 MiB 0.0469 MiB wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0])\n", - " 15 80.7930 MiB 0.0000 MiB for p, w in per]\n", - " 16 80.8398 MiB 0.0430 MiB wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0])\n", - " 17 80.8398 MiB 0.0000 MiB for p, w in per]\n", - " 18 80.8398 MiB 0.0000 MiB gain = sum(g * p / total for w, p, g in wks)\n", - " 19 80.8398 MiB 0.0000 MiB gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n", - " 20 80.8398 MiB 0.0000 MiB gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n", - " 21 80.8398 MiB 0.0000 MiB ave_length = sum(len(w) * p / total for p, w in per)\n", - " 22 80.8398 MiB 0.0000 MiB return gain, gain_dyn, gain_dyn2, ave_length\n", - "\n", - "\n", - "Filename: temp_mem_profile.py\n", - "\n", - "Line # Mem usage Increment Line Contents\n", - "================================================\n", - " 24 56.5820 MiB 56.5820 MiB @profile(precision=4)\n", - " 25 def toprofile():\n", - " 26 56.5820 MiB 0.0000 MiB with open(\"sample1000.txt\", \"r\", encoding=\"utf-8\") as f:\n", - " 27 56.7930 MiB 0.0742 MiB lines = [_.strip(\"\\n\\r \") for _ in f.readlines()]\n", - " 28 80.8398 MiB 24.0469 MiB gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "import sys\n", - "cmd = sys.executable\n", - "from pyquickhelper.loghelper import run_cmd\n", - "cmd += \" -m memory_profiler temp_mem_profile.py\"\n", - "out, err = run_cmd(cmd, wait=True)\n", - "print(out)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Static Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### pyinstrument\n", - "\n", - "See [pyinstrument](https://github.com/joerick/pyinstrument)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " _ ._ __/__ _ _ _ _ _/_ Recorded: 18:17:34 Samples: 1048\n", - " /_//_/// /_/ / //_// / //_'/ // Duration: 1.802 CPU time: 1.703\n", - "/ _/ v3.0.1\n", - "\n", - "Program: -f pstats completion.prof -o completion.dot\n", - "\n", - "1.799 run_code IPython/core/interactiveshell.py:3288\n", - "`- 1.799 :6\n", - " `- 1.799 toprofile0 :3\n", - " `- 1.799 gain_dynamique_moyen_par_mot :3\n", - " |- 1.251 precompute_stat mlstatpy/nlp/completion.py:415\n", - " | |- 0.917 merge_completions mlstatpy/nlp/completion.py:503\n", - " | | |- 0.771 [self] \n", - " | | `- 0.136 mlstatpy/nlp/completion.py:523\n", - " | |- 0.224 [self] \n", - " | |- 0.051 update_minimum_keystroke mlstatpy/nlp/completion.py:542\n", - " | |- 0.037 mlstatpy/nlp/completion.py:435\n", - " | `- 0.021 leaves mlstatpy/nlp/completion.py:132\n", - " |- 0.289 update_stat_dynamic mlstatpy/nlp/completion.py:450\n", - " | |- 0.147 update_dynamic_minimum_keystroke mlstatpy/nlp/completion.py:555\n", - " | | |- 0.100 [self] \n", - " | | `- 0.046 second_step mlstatpy/nlp/completion.py:588\n", - " | |- 0.084 [self] \n", - " | |- 0.040 init_dynamic_minimum_keystroke mlstatpy/nlp/completion.py:624\n", - " | `- 0.018 unsorted_iter mlstatpy/nlp/completion.py:97\n", - " |- 0.204 build mlstatpy/nlp/completion.py:203\n", - " | `- 0.190 [self] \n", - " |- 0.020 :10\n", - " | `- 0.019 min_keystroke0 mlstatpy/nlp/completion.py:321\n", - " `- 0.018 :13\n", - "\n", - "\n" - ] - } - ], - "source": [ - "from pyinstrument import Profiler\n", - "\n", - "profiler = Profiler(use_signal=False)\n", - "profiler.start()\n", - "\n", - "toprofile0(sample1000)\n", - "\n", - "profiler.stop()\n", - "out = profiler.output_text(unicode=False, color=False)\n", - "print(out.replace(\"\\\\\", \"/\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Javascript Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SnakeViz" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext snakeviz" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "L'instruction qui suit lance l'explorateur par d\u00e9faut avec les donn\u00e9es du profilage." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# %snakeviz toprofile0(sample1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": { - "image/jpeg": { - "width": 400 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from pyquickhelper.helpgen import NbImage\n", - "NbImage(\"images/func_info.jpg\", width=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### vprof, py-spy\n", - "\n", - "See [vprof](https://github.com/nvdv/vprof) or [py-spy](https://github.com/benfred/py-spy). The second one outputs a SVG file easy to handle." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# from vprof import profiler\n", - "\n", - "# needs to be run from a file not from a notebook\n", - "# profiler.run(toprofile0, 'cmh', args=(sample1000,), host='localhost', port=8000)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": { - "image/jpeg": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from pyquickhelper.helpgen import NbImage\n", - "NbImage(\"images/vprof.jpg\", width=800)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Completion profiling\n", + "\n", + "Profiling avec [cProfile](https://docs.python.org/3.7/library/profile.html), [memory_profiler](https://pypi.org/project/memory-profiler/), [pyinstrument](https://github.com/joerick/pyinstrument), [snakeviz](https://jiffyclub.github.io/snakeviz/).\n", + "\n", + "[line_profiler](https://github.com/rkern/line_profiler) ne semble pas plus être maintenu." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to profile" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from mlstatpy.nlp.completion import CompletionTrieNode\n", + "\n", + "\n", + "def gain_dynamique_moyen_par_mot(queries, weights):\n", + " per = list(zip(weights, queries))\n", + " total = sum(weights) * 1.0\n", + " trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", + " trie.precompute_stat()\n", + " trie.update_stat_dynamic()\n", + " wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n", + " wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for p, w in per]\n", + " wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) for p, w in per]\n", + " gain = sum(g * p / total for w, p, g in wks)\n", + " gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n", + " gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n", + " ave_length = sum(len(w) * p / total for p, w in per)\n", + " return gain, gain_dyn, gain_dyn2, ave_length" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from mlstatpy.data.wikipedia import download_titles\n", + "\n", + "file_titles = download_titles(country=\"fr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" + ], + "source": [ + "len(file_titles)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "from mlstatpy.data.wikipedia import enumerate_titles\n", + "\n", + "list_titles = list(\n", + " sorted(set(_ for _ in enumerate_titles(file_titles) if \"A\" <= _[0] <= \"Z\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "sample1000 = random.sample(list_titles, 1000)\n", + "with open(\"sample1000.txt\", \"w\", encoding=\"utf-8\") as f:\n", + " f.write(\"\\n\".join(sample1000))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Standard modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### cProfile" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import cProfile, io, pstats, os\n", + "\n", + "\n", + "def toprofile0(lines):\n", + " gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", + "\n", + "\n", + "def doprofile(lines, filename):\n", + " pr = cProfile.Profile()\n", + " pr.enable()\n", + " toprofile0(lines)\n", + " pr.disable()\n", + " s = io.StringIO()\n", + " ps = pstats.Stats(pr, stream=s).sort_stats(\"cumulative\")\n", + " ps.print_stats()\n", + " rem = os.path.normpath(os.path.join(os.getcwd(), \"..\", \"..\", \"..\"))\n", + " res = s.getvalue().replace(rem, \"\")\n", + " ps.dump_stats(filename)\n", + " return res" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1289418 function calls in 1.487 seconds\n", + "\n", + " Ordered by: cumulative time\n", + "\n", + " ncalls tottime percall cumtime percall filename:lineno(function)\n", + " 1 0.000 0.000 1.487 1.487 /tmp/ipykernel_54937/4045418276.py:4(toprofile0)\n", + " 1 0.075 0.075 1.487 1.487 /tmp/ipykernel_54937/1707536480.py:4(gain_dynamique_moyen_par_mot)\n", + " 1 0.314 0.314 1.107 1.107 /mlstatpy/nlp/completion.py:442(precompute_stat)\n", + " 16034 0.167 0.000 0.652 0.000 /mlstatpy/nlp/completion.py:531(merge_completions)\n", + " 16034 0.308 0.000 0.319 0.000 {built-in method builtins.__build_class__}\n", + " 1 0.055 0.055 0.204 0.204 /mlstatpy/nlp/completion.py:477(update_stat_dynamic)\n", + " 36051 0.081 0.000 0.094 0.000 /mlstatpy/nlp/completion.py:554()\n", + " 17034 0.046 0.000 0.088 0.000 /mlstatpy/nlp/completion.py:594(update_dynamic_minimum_keystroke)\n", + " 1 0.045 0.045 0.075 0.075 /mlstatpy/nlp/completion.py:205(build)\n", + " 35841 0.021 0.000 0.044 0.000 {built-in method builtins.all}\n", + " 309964 0.041 0.000 0.041 0.000 {built-in method builtins.len}\n", + " 17034 0.030 0.000 0.034 0.000 /mlstatpy/nlp/completion.py:627(second_step)\n", + " 16034 0.023 0.000 0.029 0.000 /mlstatpy/nlp/completion.py:581(update_minimum_keystroke)\n", + " 16035 0.006 0.000 0.028 0.000 {method 'extend' of 'collections.deque' objects}\n", + " 17035 0.018 0.000 0.028 0.000 /mlstatpy/nlp/completion.py:90(unsorted_iter)\n", + " 97520 0.027 0.000 0.027 0.000 /mlstatpy/nlp/completion.py:462()\n", + " 16034 0.017 0.000 0.023 0.000 {built-in method builtins.sorted}\n", + " 1001 0.014 0.000 0.021 0.000 /mlstatpy/nlp/completion.py:126(leaves)\n", + " 110289 0.019 0.000 0.019 0.000 {built-in method builtins.hasattr}\n", + " 17034 0.017 0.000 0.019 0.000 /mlstatpy/nlp/completion.py:664(init_dynamic_minimum_keystroke)\n", + " 17034 0.015 0.000 0.018 0.000 /mlstatpy/nlp/completion.py:15(__init__)\n", + " 116511 0.016 0.000 0.016 0.000 {method 'values' of 'dict' objects}\n", + " 52086 0.015 0.000 0.015 0.000 {method 'append' of 'list' objects}\n", + " 500 0.005 0.000 0.014 0.000 /home/xadupre/vv/this/lib/python3.10/site-packages/ipykernel/ipkernel.py:775(_clean_thread_parent_frames)\n", + " 3000 0.013 0.000 0.013 0.000 /mlstatpy/nlp/completion.py:263(find)\n", + " 23123 0.010 0.000 0.010 0.000 {built-in method builtins.min}\n", + " 48102 0.009 0.000 0.009 0.000 {method 'extend' of 'list' objects}\n", + " 54873 0.008 0.000 0.008 0.000 {method 'popleft' of 'collections.deque' objects}\n", + " 1 0.001 0.001 0.008 0.008 /tmp/ipykernel_54937/1707536480.py:10()\n", + " 1 0.001 0.001 0.008 0.008 /tmp/ipykernel_54937/1707536480.py:11()\n", + " 20017 0.008 0.000 0.008 0.000 {built-in method builtins.max}\n", + " 17033 0.007 0.000 0.007 0.000 /mlstatpy/nlp/completion.py:48(_add)\n", + " 1 0.001 0.001 0.007 0.007 /tmp/ipykernel_54937/1707536480.py:12()\n", + " 1000 0.001 0.000 0.007 0.000 /mlstatpy/nlp/completion.py:328(min_keystroke0)\n", + " 1000 0.002 0.000 0.007 0.000 /mlstatpy/nlp/completion.py:364(min_dynamic_keystroke)\n", + " 250 0.002 0.000 0.007 0.000 /home/xadupre/vv/this/lib/python3.10/site-packages/ipykernel/ipkernel.py:790()\n", + " 16034 0.007 0.000 0.007 0.000 /mlstatpy/nlp/completion.py:541()\n", + " 36840 0.007 0.000 0.007 0.000 {method 'append' of 'collections.deque' objects}\n", + " 1000 0.001 0.000 0.006 0.000 /mlstatpy/nlp/completion.py:400(min_dynamic_keystroke2)\n", + " 16034 0.006 0.000 0.006 0.000 /mlstatpy/nlp/completion.py:537(Fake)\n", + " 51102 0.006 0.000 0.006 0.000 {method 'pop' of 'list' objects}\n", + " 17034 0.006 0.000 0.006 0.000 /mlstatpy/nlp/completion.py:547()\n", + " 1750 0.004 0.000 0.004 0.000 /usr/lib/python3.10/threading.py:1145(ident)\n", + " 32068 0.003 0.000 0.003 0.000 {method 'items' of 'dict' objects}\n", + " 18534 0.003 0.000 0.003 0.000 {built-in method builtins.isinstance}\n", + " 250 0.002 0.000 0.003 0.000 /usr/lib/python3.10/threading.py:1478(enumerate)\n", + " 5 0.000 0.000 0.002 0.000 {built-in method builtins.sum}\n", + " 1 0.000 0.000 0.000 0.000 /tmp/ipykernel_54937/1707536480.py:7()\n", + " 1001 0.000 0.000 0.000 0.000 /tmp/ipykernel_54937/1707536480.py:16()\n", + " 1001 0.000 0.000 0.000 0.000 /tmp/ipykernel_54937/1707536480.py:13()\n", + " 250 0.000 0.000 0.000 0.000 {method '__exit__' of '_thread.RLock' objects}\n", + " 1000 0.000 0.000 0.000 0.000 {method 'keys' of 'dict' objects}\n", + " 1001 0.000 0.000 0.000 0.000 /tmp/ipykernel_54937/1707536480.py:14()\n", + " 1001 0.000 0.000 0.000 0.000 /tmp/ipykernel_54937/1707536480.py:15()\n", + " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", + "\n", + "\n", + "\n" + ] } + ], + "source": [ + "r = doprofile(sample1000, \"completion.prof\")\n", + "print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Others informations when profiling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### memory_profiler\n", + "\n", + "See [memory_profiler](https://pypi.python.org/pypi/memory_profiler/0.41). Version 0.56 is bugged (see [#258](https://github.com/pythonprofilers/memory_profiler/issues/258))." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from memory_profiler import profile\n", + "\n", + "%load_ext memory_profiler" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "peak memory: 547.55 MiB, increment: 0.00 MiB\n" + ] + } + ], + "source": [ + "%memit toprofile0(sample1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR: Could not find file /tmp/ipykernel_54937/1913397401.py\n" + ] + } + ], + "source": [ + "from io import StringIO\n", + "\n", + "st = StringIO()\n", + "\n", + "\n", + "@profile(stream=st)\n", + "def toprofile(lines):\n", + " gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", + "\n", + "\n", + "toprofile(sample1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing temp_mem_profile.py\n" + ] + } + ], + "source": [ + "%%file temp_mem_profile.py\n", + "\n", + "from mlstatpy.nlp.completion import CompletionTrieNode\n", + "from memory_profiler import profile\n", + "\n", + "\n", + "@profile(precision=4)\n", + "def gain_dynamique_moyen_par_mot(queries, weights):\n", + " per = list(zip(weights, queries))\n", + " total = sum(weights) * 1.0\n", + " res = []\n", + " trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", + " trie.precompute_stat()\n", + " trie.update_stat_dynamic()\n", + " wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n", + " wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for p, w in per]\n", + " wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) for p, w in per]\n", + " gain = sum(g * p / total for w, p, g in wks)\n", + " gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n", + " gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n", + " ave_length = sum(len(w) * p / total for p, w in per)\n", + " return gain, gain_dyn, gain_dyn2, ave_length\n", + "\n", + "\n", + "@profile(precision=4)\n", + "def toprofile():\n", + " with open(\"sample1000.txt\", \"r\", encoding=\"utf-8\") as f:\n", + " lines = [_.strip(\"\\n\\r \") for _ in f.readlines()]\n", + " gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", + "\n", + "\n", + "toprofile()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: temp_mem_profile.py\n", + "\n", + "Line # Mem usage Increment Occurrences Line Contents\n", + "=============================================================\n", + " 6 45.8438 MiB 45.8438 MiB 1 @profile(precision=4)\n", + " 7 def gain_dynamique_moyen_par_mot(queries, weights):\n", + " 8 45.8438 MiB 0.0000 MiB 1 per = list(zip(weights, queries))\n", + " 9 45.8438 MiB 0.0000 MiB 1 total = sum(weights) * 1.0\n", + " 10 45.8438 MiB 0.0000 MiB 1 res = []\n", + " 11 52.5469 MiB 6.7031 MiB 1003 trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", + " 12 60.0234 MiB 7.4766 MiB 1 trie.precompute_stat()\n", + " 13 69.5625 MiB 9.5391 MiB 1 trie.update_stat_dynamic()\n", + " 14 69.5625 MiB 0.0000 MiB 1003 wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n", + " 15 69.5625 MiB 0.0000 MiB 1003 wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for p, w in per]\n", + " 16 69.5625 MiB 0.0000 MiB 1003 wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) for p, w in per]\n", + " 17 69.5625 MiB 0.0000 MiB 2003 gain = sum(g * p / total for w, p, g in wks)\n", + " 18 69.5625 MiB 0.0000 MiB 2003 gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n", + " 19 69.5625 MiB 0.0000 MiB 2003 gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n", + " 20 69.5625 MiB 0.0000 MiB 2003 ave_length = sum(len(w) * p / total for p, w in per)\n", + " 21 69.5625 MiB 0.0000 MiB 1 return gain, gain_dyn, gain_dyn2, ave_length\n", + "\n", + "\n", + "Filename: temp_mem_profile.py\n", + "\n", + "Line # Mem usage Increment Occurrences Line Contents\n", + "=============================================================\n", + " 24 45.5859 MiB 45.5859 MiB 1 @profile(precision=4)\n", + " 25 def toprofile():\n", + " 26 45.8438 MiB 0.0000 MiB 2 with open(\"sample1000.txt\", \"r\", encoding=\"utf-8\") as f:\n", + " 27 45.8438 MiB 0.2578 MiB 1003 lines = [_.strip(\"\\n\\r \") for _ in f.readlines()]\n", + " 28 69.5625 MiB 23.7188 MiB 1 gain_dynamique_moyen_par_mot(lines, [1.0] * len(lines))\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "import sys\n", + "\n", + "cmd = sys.executable\n", + "from sphinx_runpython.runpython import run_cmd\n", + "\n", + "cmd += \" -m memory_profiler temp_mem_profile.py\"\n", + "out, err = run_cmd(cmd, wait=True)\n", + "print(out)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Static Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### pyinstrument\n", + "\n", + "See [pyinstrument](https://github.com/joerick/pyinstrument)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " _ ._ __/__ _ _ _ _ _/_ Recorded: 12:44:24 Samples: 862\n", + " /_//_/// /_/ / //_// / //_'/ // Duration: 1.340 CPU time: 1.345\n", + "/ _/ v4.7.3\n", + "\n", + "Profile at /tmp/ipykernel_54937/259320473.py:4\n", + "\n", + "1.338 ZMQInteractiveShell.run_ast_nodes IPython/core/interactiveshell.py:3418\n", + "`- 1.337 ../../../tmp/ipykernel_54937/259320473.py:1\n", + " `- 1.337 toprofile0 ../../../tmp/ipykernel_54937/4045418276.py:4\n", + " `- 1.337 gain_dynamique_moyen_par_mot ../../../tmp/ipykernel_54937/1707536480.py:4\n", + " |- 0.683 CompletionTrieNode.precompute_stat mlstatpy/nlp/completion.py:442\n", + " | |- 0.467 _Stat.merge_completions mlstatpy/nlp/completion.py:531\n", + " | | |- 0.236 [self] mlstatpy/nlp/completion.py\n", + " | | |- 0.110 __build_class__ \n", + " | | `- 0.076 mlstatpy/nlp/completion.py:554\n", + " | | `- 0.068 [self] mlstatpy/nlp/completion.py\n", + " | |- 0.126 [self] mlstatpy/nlp/completion.py\n", + " | |- 0.025 _Stat.update_minimum_keystroke mlstatpy/nlp/completion.py:581\n", + " | | `- 0.020 [self] mlstatpy/nlp/completion.py\n", + " | |- 0.022 mlstatpy/nlp/completion.py:462\n", + " | `- 0.016 CompletionTrieNode.leaves mlstatpy/nlp/completion.py:126\n", + " |- 0.408 build mlstatpy/nlp/completion.py:205\n", + " | |- 0.382 [self] mlstatpy/nlp/completion.py\n", + " | `- 0.014 CompletionTrieNode.__init__ mlstatpy/nlp/completion.py:15\n", + " `- 0.220 CompletionTrieNode.update_stat_dynamic mlstatpy/nlp/completion.py:477\n", + " |- 0.104 int.update_dynamic_minimum_keystroke mlstatpy/nlp/completion.py:594\n", + " | |- 0.057 [self] mlstatpy/nlp/completion.py\n", + " | `- 0.041 second_step mlstatpy/nlp/completion.py:627\n", + " | `- 0.037 [self] mlstatpy/nlp/completion.py\n", + " |- 0.055 [self] mlstatpy/nlp/completion.py\n", + " |- 0.024 CompletionTrieNode.unsorted_iter mlstatpy/nlp/completion.py:90\n", + " | `- 0.017 [self] mlstatpy/nlp/completion.py\n", + " `- 0.023 _Stat.init_dynamic_minimum_keystroke mlstatpy/nlp/completion.py:664\n", + " `- 0.022 [self] mlstatpy/nlp/completion.py\n", + "\n", + "\n" + ] + } + ], + "source": [ + "from pyinstrument import Profiler\n", + "\n", + "profiler = Profiler()\n", + "profiler.start()\n", + "\n", + "toprofile0(sample1000)\n", + "\n", + "profiler.stop()\n", + "out = profiler.output_text(unicode=False, color=False)\n", + "print(out.replace(\"\\\\\", \"/\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Javascript Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SnakeViz" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext snakeviz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'instruction qui suit lance l'explorateur par défaut avec les données du profilage." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# %snakeviz toprofile0(sample1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": { + "image/jpeg": { + "width": 400 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "\n", + "Image(\"images/func_info.jpg\", width=400)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### vprof, py-spy\n", + "\n", + "See [vprof](https://github.com/nvdv/vprof) or [py-spy](https://github.com/benfred/py-spy). The second one outputs a SVG file easy to handle." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# from vprof import profiler\n", + "\n", + "# needs to be run from a file not from a notebook\n", + "# profiler.run(toprofile0, 'cmh', args=(sample1000,), host='localhost', port=8000)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": { + "image/jpeg": { + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "\n", + "Image(\"images/vprof.jpg\", width=800)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 1 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } \ No newline at end of file diff --git a/_doc/notebooks/nlp/completion_simple.ipynb b/_doc/notebooks/nlp/completion_simple.ipynb index 1899847c..3bf545be 100644 --- a/_doc/notebooks/nlp/completion_simple.ipynb +++ b/_doc/notebooks/nlp/completion_simple.ipynb @@ -1,411 +1,288 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compl\u00e9tion Simple\n", - "\n", - "Evaluation d'une m\u00e9trique pour un syst\u00e8me de compl\u00e9tion sur quelques cas simples." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## M\u00e9trique M'" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=0 : M'=1 | po\n", - "n=1 : M'=2 | po rouge\n", - "n=2 : M'=3 | po vert\n", - "n=3 : M'=4 | po orange\n", - "n=4 : M'=3 | port\n", - "n=5 : M'=4 | port blanc\n", - "n=6 : M'=5 | port bleu\n", - "n=7 : M'=6 | port rouge\n" - ] - } - ], - "source": [ - "from mlstatpy.nlp import CompletionSystem\n", - "mots = [\"po\", \"po rouge\", \"po vert\", \"po orange\", \"port\", \"port blanc\", \"port bleu\", \"port rouge\"]\n", - "ens = CompletionSystem(mots)\n", - "ens.compute_metrics()\n", - "for el in ens:\n", - " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=0 : M'=1 | po\n", - "n=1 : M'=2 | po rouge\n", - "n=2 : M'=3 | po vert\n", - "n=3 : M'=4 | po orange\n", - "n=4 : M'=3 | port rouge\n", - "n=5 : M'=4 | port blanc\n", - "n=6 : M'=5 | port bleu\n", - "n=7 : M'=3 | port\n" - ] - } - ], - "source": [ - "mots_rev = mots.copy()\n", - "mots_rev[4], mots_rev[-1] = mots_rev[-1], mots_rev[4]\n", - "ens = CompletionSystem(mots_rev)\n", - "ens.compute_metrics()\n", - "for el in ens:\n", - " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Complétion Simple\n", + "\n", + "Evaluation d'une métrique pour un système de complétion sur quelques cas simples." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Métrique M'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=0 : M'=1 | blanc\n", - "n=1 : M'=2 | bleu\n", - "n=2 : M'=3 | rouge\n" - ] - } - ], - "source": [ - "mots_court = [m[4:] for m in mots if m.startswith(\"port\") and len(m) > 4]\n", - "ens = CompletionSystem(mots_court)\n", - "ens.compute_metrics()\n", - "for el in ens:\n", - " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "n=0 : M'=1 | po\n", + "n=1 : M'=2 | po rouge\n", + "n=2 : M'=3 | po vert\n", + "n=3 : M'=4 | po orange\n", + "n=4 : M'=3 | port\n", + "n=5 : M'=4 | port blanc\n", + "n=6 : M'=5 | port bleu\n", + "n=7 : M'=6 | port rouge\n" + ] + } + ], + "source": [ + "from mlstatpy.nlp import CompletionSystem\n", + "\n", + "mots = [\n", + " \"po\",\n", + " \"po rouge\",\n", + " \"po vert\",\n", + " \"po orange\",\n", + " \"port\",\n", + " \"port blanc\",\n", + " \"port bleu\",\n", + " \"port rouge\",\n", + "]\n", + "ens = CompletionSystem(mots)\n", + "ens.compute_metrics()\n", + "for el in ens:\n", + " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=0 : M'=1 | po\n", - "n=1 : M'=2 | po rouge\n", - "n=2 : M'=3 | po vert\n", - "n=3 : M'=4 | po orange\n", - "n=4 : M'=3 | port blanc\n", - "n=5 : M'=4 | port bleu\n", - "n=6 : M'=5 | port rouge\n" - ] - } - ], - "source": [ - "mots_court = [m for m in mots if m != \"port\"]\n", - "ens = CompletionSystem(mots_court)\n", - "ens.compute_metrics()\n", - "for el in ens:\n", - " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "n=0 : M'=1 | po\n", + "n=1 : M'=2 | po rouge\n", + "n=2 : M'=3 | po vert\n", + "n=3 : M'=4 | po orange\n", + "n=4 : M'=3 | port rouge\n", + "n=5 : M'=4 | port blanc\n", + "n=6 : M'=5 | port bleu\n", + "n=7 : M'=3 | port\n" + ] + } + ], + "source": [ + "mots_rev = mots.copy()\n", + "mots_rev[4], mots_rev[-1] = mots_rev[-1], mots_rev[4]\n", + "ens = CompletionSystem(mots_rev)\n", + "ens.compute_metrics()\n", + "for el in ens:\n", + " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=0 : M'=1 | port\n", - "n=1 : M'=2 | port rouge\n", - "n=2 : M'=3 | port vert\n", - "n=3 : M'=4 | port orange\n", - "n=4 : M'=4 | pore\n", - "n=5 : M'=4 | pour\n", - "n=6 : M'=3 | portes\n", - "n=7 : M'=4 | portes blanc\n", - "n=8 : M'=5 | portes vert\n", - "n=9 : M'=6 | portes orange\n", - "n=10 : M'=6 | portes rouge\n", - "n=11 : M'=6 | portes noir\n", - "n=12 : M'=7 | portes noire\n", - "n=13 : M'=5 | portes blanche\n" - ] - } - ], - "source": [ - "couleur = [\"blanc\", \"vert\", \"orange\", \"rouge\", \"noir\", \"noire\", \"blanche\"]\n", - "key = \"portes\"\n", - "mots = [\"port\", \"port rouge\", \"port vert\", \"port orange\", \"pore\", \"pour\"]\n", - "mots.append(key)\n", - "mots += [key + \" \" + c for c in couleur]\n", - "ens = CompletionSystem(mots)\n", - "ens.compute_metrics()\n", - "for el in ens:\n", - " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "n=0 : M'=1 | blanc\n", + "n=1 : M'=2 | bleu\n", + "n=2 : M'=3 | rouge\n" + ] + } + ], + "source": [ + "mots_court = [m[4:] for m in mots if m.startswith(\"port\") and len(m) > 4]\n", + "ens = CompletionSystem(mots_court)\n", + "ens.compute_metrics()\n", + "for el in ens:\n", + " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=0 : M'=1 | port\n", - "n=1 : M'=2 | port rouge\n", - "n=2 : M'=3 | port vert\n", - "n=3 : M'=4 | port orange\n", - "n=4 : M'=4 | pore\n", - "n=5 : M'=4 | pour\n", - "n=6 : M'=3 | portes blanc\n", - "n=7 : M'=4 | portes vert\n", - "n=8 : M'=5 | portes orange\n", - "n=9 : M'=6 | portes rouge\n", - "n=10 : M'=6 | portes noir\n", - "n=11 : M'=7 | portes noire\n", - "n=12 : M'=4 | portes blanche\n" - ] - } - ], - "source": [ - "mots2 = [m for m in mots if m != \"portes\"]\n", - "ens = CompletionSystem(mots2)\n", - "ens.compute_metrics()\n", - "for el in ens:\n", - " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "n=0 : M'=1 | po\n", + "n=1 : M'=2 | po rouge\n", + "n=2 : M'=3 | po vert\n", + "n=3 : M'=4 | po orange\n", + "n=4 : M'=3 | port blanc\n", + "n=5 : M'=4 | port bleu\n", + "n=6 : M'=5 | port rouge\n" + ] + } + ], + "source": [ + "mots_court = [m for m in mots if m != \"port\"]\n", + "ens = CompletionSystem(mots_court)\n", + "ens.compute_metrics()\n", + "for el in ens:\n", + " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n=0 : M'=1 | port\n", - "n=1 : M'=2 | portes\n", - "n=2 : M'=3 | port rouge\n", - "n=3 : M'=4 | port vert\n", - "n=4 : M'=4 | port orange\n", - "n=5 : M'=4 | pore\n", - "n=6 : M'=4 | pour\n", - "n=7 : M'=3 | portes blanc\n", - "n=8 : M'=4 | portes vert\n", - "n=9 : M'=5 | portes orange\n", - "n=10 : M'=5 | portes rouge\n", - "n=11 : M'=5 | portes noir\n", - "n=12 : M'=6 | portes noire\n", - "n=13 : M'=4 | portes blanche\n" - ] - } - ], - "source": [ - "mots3 = mots2.copy()\n", - "mots3.insert(1, \"portes\")\n", - "ens = CompletionSystem(mots3)\n", - "ens.compute_metrics()\n", - "for el in ens:\n", - " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "n=0 : M'=1 | port\n", + "n=1 : M'=2 | port rouge\n", + "n=2 : M'=3 | port vert\n", + "n=3 : M'=4 | port orange\n", + "n=4 : M'=4 | pore\n", + "n=5 : M'=4 | pour\n", + "n=6 : M'=3 | portes\n", + "n=7 : M'=4 | portes blanc\n", + "n=8 : M'=5 | portes vert\n", + "n=9 : M'=6 | portes orange\n", + "n=10 : M'=6 | portes rouge\n", + "n=11 : M'=6 | portes noir\n", + "n=12 : M'=7 | portes noire\n", + "n=13 : M'=5 | portes blanche\n" + ] + } + ], + "source": [ + "couleur = [\"blanc\", \"vert\", \"orange\", \"rouge\", \"noir\", \"noire\", \"blanche\"]\n", + "key = \"portes\"\n", + "mots = [\"port\", \"port rouge\", \"port vert\", \"port orange\", \"pore\", \"pour\"]\n", + "mots.append(key)\n", + "mots += [key + \" \" + c for c in couleur]\n", + "ens = CompletionSystem(mots)\n", + "ens.compute_metrics()\n", + "for el in ens:\n", + " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "n=0 : M'=1 | port\n", + "n=1 : M'=2 | port rouge\n", + "n=2 : M'=3 | port vert\n", + "n=3 : M'=4 | port orange\n", + "n=4 : M'=4 | pore\n", + "n=5 : M'=4 | pour\n", + "n=6 : M'=3 | portes blanc\n", + "n=7 : M'=4 | portes vert\n", + "n=8 : M'=5 | portes orange\n", + "n=9 : M'=6 | portes rouge\n", + "n=10 : M'=6 | portes noir\n", + "n=11 : M'=7 | portes noire\n", + "n=12 : M'=4 | portes blanche\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" + ], + "source": [ + "mots2 = [m for m in mots if m != \"portes\"]\n", + "ens = CompletionSystem(mots2)\n", + "ens.compute_metrics()\n", + "for el in ens:\n", + " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n=0 : M'=1 | port\n", + "n=1 : M'=2 | portes\n", + "n=2 : M'=3 | port rouge\n", + "n=3 : M'=4 | port vert\n", + "n=4 : M'=4 | port orange\n", + "n=5 : M'=4 | pore\n", + "n=6 : M'=4 | pour\n", + "n=7 : M'=3 | portes blanc\n", + "n=8 : M'=4 | portes vert\n", + "n=9 : M'=5 | portes orange\n", + "n=10 : M'=5 | portes rouge\n", + "n=11 : M'=5 | portes noir\n", + "n=12 : M'=6 | portes noire\n", + "n=13 : M'=4 | portes blanche\n" + ] } + ], + "source": [ + "mots3 = mots2.copy()\n", + "mots3.insert(1, \"portes\")\n", + "ens = CompletionSystem(mots3)\n", + "ens.compute_metrics()\n", + "for el in ens:\n", + " print(\"n={1} : M'={0} | {2}\".format(el.mks1, el.weight, el.value))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 1 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } \ No newline at end of file diff --git a/_doc/notebooks/nlp/completion_trie.ipynb b/_doc/notebooks/nlp/completion_trie.ipynb index 1acdc74c..4ca9cf6d 100644 --- a/_doc/notebooks/nlp/completion_trie.ipynb +++ b/_doc/notebooks/nlp/completion_trie.ipynb @@ -1,665 +1,552 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compl\u00e9tion\n", - "\n", - "Comparaion de plusieurs algorithmes pour impl\u00e9menter un syst\u00e8me de compl\u00e9tion." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tester des id\u00e9es" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Meilleur ordre pour a, ab, abc, abcd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 ordre ('a', 'ab', 'abc', 'abcd')\n", - "1 ordre ('a', 'ab', 'abcd', 'abc')\n", - "1 ordre ('a', 'abc', 'ab', 'abcd')\n", - "2 ordre ('a', 'abc', 'abcd', 'ab')\n", - "2 ordre ('a', 'abcd', 'ab', 'abc')\n", - "2 ordre ('a', 'abcd', 'abc', 'ab')\n", - "1 ordre ('ab', 'a', 'abc', 'abcd')\n", - "2 ordre ('ab', 'a', 'abcd', 'abc')\n", - "2 ordre ('ab', 'abc', 'a', 'abcd')\n", - "3 ordre ('ab', 'abc', 'abcd', 'a')\n", - "3 ordre ('ab', 'abcd', 'a', 'abc')\n", - "3 ordre ('ab', 'abcd', 'abc', 'a')\n", - "2 ordre ('abc', 'a', 'ab', 'abcd')\n", - "3 ordre ('abc', 'a', 'abcd', 'ab')\n", - "2 ordre ('abc', 'ab', 'a', 'abcd')\n", - "3 ordre ('abc', 'ab', 'abcd', 'a')\n", - "4 ordre ('abc', 'abcd', 'a', 'ab')\n", - "4 ordre ('abc', 'abcd', 'ab', 'a')\n", - "3 ordre ('abcd', 'a', 'ab', 'abc')\n", - "3 ordre ('abcd', 'a', 'abc', 'ab')\n", - "3 ordre ('abcd', 'ab', 'a', 'abc')\n", - "3 ordre ('abcd', 'ab', 'abc', 'a')\n", - "4 ordre ('abcd', 'abc', 'a', 'ab')\n", - "4 ordre ('abcd', 'abc', 'ab', 'a')\n" - ] - } - ], - "source": [ - "from mlstatpy.nlp.completion import CompletionTrieNode\n", - "import itertools\n", - "queries = ['a', 'ab', 'abc', 'abcd']\n", - "for per in itertools.permutations(queries):\n", - " trie = CompletionTrieNode.build([(None, w) for w in per])\n", - " gain = sum( len(w) - trie.min_keystroke(w)[0] for w in per)\n", - " print(gain, \"ordre\", per)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Meilleur ordre pour a, ab, abc, abcd, edf, edfh" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(6, 'ordre', ('edfh', 'edf', 'abcd', 'abc', 'ab', 'a'))\n", - "(6, 'ordre', ('edfh', 'edf', 'abcd', 'abc', 'a', 'ab'))\n", - "(6, 'ordre', ('edfh', 'edf', 'abcd', 'ab', 'abc', 'a'))\n", - "(6, 'ordre', ('edfh', 'edf', 'abcd', 'ab', 'a', 'abc'))\n", - "(6, 'ordre', ('edfh', 'edf', 'abcd', 'a', 'abc', 'ab'))\n", - "(6, 'ordre', ('edfh', 'edf', 'abcd', 'a', 'ab', 'abc'))\n", - "(6, 'ordre', ('edfh', 'edf', 'abc', 'abcd', 'ab', 'a'))\n", - "(6, 'ordre', ('edfh', 'edf', 'abc', 'abcd', 'a', 'ab'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abcd', 'abc', 'ab', 'a'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abcd', 'abc', 'a', 'ab'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abcd', 'ab', 'abc', 'a'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abcd', 'ab', 'a', 'abc'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abcd', 'a', 'abc', 'ab'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abcd', 'a', 'ab', 'abc'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abc', 'abcd', 'ab', 'a'))\n", - "(6, 'ordre', ('edf', 'edfh', 'abc', 'abcd', 'a', 'ab'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edfh', 'edf', 'ab', 'a'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edfh', 'edf', 'a', 'ab'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edfh', 'ab', 'edf', 'a'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edfh', 'ab', 'a', 'edf'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edfh', 'a', 'edf', 'ab'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edfh', 'a', 'ab', 'edf'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edf', 'edfh', 'ab', 'a'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edf', 'edfh', 'a', 'ab'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edf', 'ab', 'edfh', 'a'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edf', 'ab', 'a', 'edfh'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edf', 'a', 'edfh', 'ab'))\n", - "(6, 'ordre', ('abcd', 'abc', 'edf', 'a', 'ab', 'edfh'))\n", - "(6, 'ordre', ('abcd', 'abc', 'ab', 'edfh', 'edf', 'a'))\n", - "(6, 'ordre', ('abcd', 'abc', 'ab', 'edfh', 'a', 'edf'))\n" - ] - } - ], - "source": [ - "queries = ['a', 'ab', 'abc', 'abcd', 'edf', 'edfh']\n", - "res = []\n", - "for per in itertools.permutations(queries):\n", - " trie = CompletionTrieNode.build([(None, w) for w in per])\n", - " gain = sum( len(w) - trie.min_keystroke(w)[0] for w in per)\n", - " res.append((gain, \"ordre\", per))\n", - "res.sort(reverse=True)\n", - "for r in res[:30]:\n", - " print(r)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Influence du poids" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "19.2 - actes p=2.0 g=4.0 | actuellement p=1.0 g=10.0 | acte p=1.0 g=1.0 | actualit\u00e9 p=1.0 g=5.0\n", - "19.2 - actes p=2.0 g=4.0 | actualit\u00e9 p=1.0 g=7.0 | acte p=1.0 g=1.0 | actuellement p=1.0 g=8.0\n", - "19.2 - actes p=2.0 g=4.0 | acte p=1.0 g=2.0 | actualit\u00e9 p=1.0 g=6.0 | actuellement p=1.0 g=8.0\n", - "19.2 - actes p=2.0 g=4.0 | actuellement p=1.0 g=10.0 | actualit\u00e9 p=1.0 g=6.0 | acte p=1.0 g=0.0\n", - "19.2 - actes p=2.0 g=4.0 | actualit\u00e9 p=1.0 g=7.0 | actuellement p=1.0 g=9.0 | acte p=1.0 g=0.0\n", - "19.2 - actes p=2.0 g=4.0 | acte p=1.0 g=2.0 | actuellement p=1.0 g=9.0 | actualit\u00e9 p=1.0 g=5.0\n", - "18.4 - actuellement p=1.0 g=11.0 | actes p=2.0 g=3.0 | actualit\u00e9 p=1.0 g=6.0 | acte p=1.0 g=0.0\n", - "18.4 - actuellement p=1.0 g=11.0 | actes p=2.0 g=3.0 | acte p=1.0 g=1.0 | actualit\u00e9 p=1.0 g=5.0\n", - "18.4 - actualit\u00e9 p=1.0 g=8.0 | actes p=2.0 g=3.0 | actuellement p=1.0 g=9.0 | acte p=1.0 g=0.0\n", - "18.4 - actualit\u00e9 p=1.0 g=8.0 | actes p=2.0 g=3.0 | acte p=1.0 g=1.0 | actuellement p=1.0 g=8.0\n", - "18.4 - acte p=1.0 g=3.0 | actes p=2.0 g=3.0 | actuellement p=1.0 g=9.0 | actualit\u00e9 p=1.0 g=5.0\n", - "18.4 - acte p=1.0 g=3.0 | actes p=2.0 g=3.0 | actualit\u00e9 p=1.0 g=6.0 | actuellement p=1.0 g=8.0\n", - "17.6 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0 | actes p=2.0 g=2.0 | acte p=1.0 g=0.0\n", - "17.6 - actuellement p=1.0 g=11.0 | acte p=1.0 g=2.0 | actes p=2.0 g=2.0 | actualit\u00e9 p=1.0 g=5.0\n", - "17.6 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actes p=2.0 g=2.0 | acte p=1.0 g=0.0\n", - "17.6 - actualit\u00e9 p=1.0 g=8.0 | acte p=1.0 g=2.0 | actes p=2.0 g=2.0 | actuellement p=1.0 g=8.0\n", - "17.6 - acte p=1.0 g=3.0 | actuellement p=1.0 g=10.0 | actes p=2.0 g=2.0 | actualit\u00e9 p=1.0 g=5.0\n", - "17.6 - acte p=1.0 g=3.0 | actualit\u00e9 p=1.0 g=7.0 | actes p=2.0 g=2.0 | actuellement p=1.0 g=8.0\n", - "16.8 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0 | acte p=1.0 g=1.0 | actes p=2.0 g=1.0\n", - "16.8 - actuellement p=1.0 g=11.0 | acte p=1.0 g=2.0 | actualit\u00e9 p=1.0 g=6.0 | actes p=2.0 g=1.0\n", - "16.8 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | acte p=1.0 g=1.0 | actes p=2.0 g=1.0\n", - "16.8 - actualit\u00e9 p=1.0 g=8.0 | acte p=1.0 g=2.0 | actuellement p=1.0 g=9.0 | actes p=2.0 g=1.0\n", - "16.8 - acte p=1.0 g=3.0 | actuellement p=1.0 g=10.0 | actualit\u00e9 p=1.0 g=6.0 | actes p=2.0 g=1.0\n", - "16.8 - acte p=1.0 g=3.0 | actualit\u00e9 p=1.0 g=7.0 | actuellement p=1.0 g=9.0 | actes p=2.0 g=1.0\n" - ] - } - ], - "source": [ - "queries = ['actuellement', 'actualit\u00e9', 'acte', 'actes']\n", - "weights = [1, 1, 1, 2]\n", - "total = sum(weights) * 1.0 / len(queries)\n", - "res = []\n", - "for per in itertools.permutations(zip(queries, weights)):\n", - " trie = CompletionTrieNode.build([(None, w) for w, p in per])\n", - " wks = [(w, p, len(w)-trie.min_keystroke(w)[0]) for w, p in per]\n", - " gain = sum( g*p/total for w, p, g in wks)\n", - " res.append((gain, wks))\n", - "res.sort(reverse=True)\n", - "for r in res:\n", - " print(\"{0:3.4} - {1}\".format(r[0], \" | \".join(\"%s p=%1.1f g=%1.1f\" % _ for _ in r[1])))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nouvelle m\u00e9trique" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Intuition" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def gain_moyen_par_mot(queries, weights):\n", - " total = sum(weights) * 1.0 \n", - " res = []\n", - " for per in itertools.permutations(zip(queries, weights)):\n", - " trie = CompletionTrieNode.build([(None, w) for w, p in per])\n", - " wks = [(w, p, len(w)-trie.min_keystroke(w)[0]) for w, p in per]\n", - " gain = sum( g*p/total for w, p, g in wks)\n", - " res.append((gain, wks))\n", - " res.sort(reverse=True)\n", - " for i, r in enumerate(res):\n", - " print(\"{0:3.4} - {1}\".format(r[0], \" | \".join(\"%s p=%1.1f g=%1.1f\" % _ for _ in r[1])))\n", - " if i > 10:\n", - " print(\"...\")\n", - " break " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7.0 - actuellement p=1.0 g=11.0 | actuel p=1.0 g=4.0 | actualit\u00e9 p=1.0 g=6.0\n", - "7.0 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0 | actuel p=1.0 g=3.0\n", - "7.0 - actuel p=1.0 g=5.0 | actuellement p=1.0 g=10.0 | actualit\u00e9 p=1.0 g=6.0\n", - "7.0 - actuel p=1.0 g=5.0 | actualit\u00e9 p=1.0 g=7.0 | actuellement p=1.0 g=9.0\n", - "7.0 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=1.0 g=3.0\n", - "7.0 - actualit\u00e9 p=1.0 g=8.0 | actuel p=1.0 g=4.0 | actuellement p=1.0 g=9.0\n" - ] - } - ], - "source": [ - "queries = ['actuellement', 'actualit\u00e9', 'actuel']\n", - "weights = [1, 1, 1]\n", - "gain_moyen_par_mot(queries, weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9.0 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0 | actuel p=0.0 g=3.0\n", - "9.0 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=0.0 g=3.0\n", - "8.5 - actuellement p=1.0 g=11.0 | actuel p=0.0 g=4.0 | actualit\u00e9 p=1.0 g=6.0\n", - "8.5 - actualit\u00e9 p=1.0 g=8.0 | actuel p=0.0 g=4.0 | actuellement p=1.0 g=9.0\n", - "8.0 - actuel p=0.0 g=5.0 | actuellement p=1.0 g=10.0 | actualit\u00e9 p=1.0 g=6.0\n", - "8.0 - actuel p=0.0 g=5.0 | actualit\u00e9 p=1.0 g=7.0 | actuellement p=1.0 g=9.0\n" - ] - } - ], - "source": [ - "queries = ['actuellement', 'actualit\u00e9', 'actuel']\n", - "weights = [1, 1, 0]\n", - "gain_moyen_par_mot(queries, weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9.0 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0\n", - "9.0 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0\n" - ] - } - ], - "source": [ - "queries = ['actuellement', 'actualit\u00e9']\n", - "weights = [1, 1]\n", - "gain_moyen_par_mot(queries, weights)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "## V\u00e9rification" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def gain_dynamique_moyen_par_mot(queries, weights, permutation=True):\n", - " total = sum(weights) * 1.0 \n", - " res = []\n", - " for per in itertools.permutations(zip(queries, weights)):\n", - " trie = CompletionTrieNode.build([(None, w) for w, p in per])\n", - " trie.precompute_stat()\n", - " trie.update_stat_dynamic()\n", - " wks = [(w, p, len(w)-trie.min_dynamic_keystroke(w)[0]) for w, p in per]\n", - " gain = sum( g*p/total for w, p, g in wks)\n", - " res.append((gain, wks))\n", - " if not permutation:\n", - " break\n", - " res.sort(reverse=True)\n", - " for i, r in enumerate(res):\n", - " print(\"{0:3.4} - {1}\".format(r[0], \" | \".join(\"%s p=%1.1f g=%1.1f\" % _ for _ in r[1])))\n", - " if i > 10:\n", - " print(\"...\")\n", - " break" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Complétion\n", + "\n", + "Comparaion de plusieurs algorithmes pour implémenter un système de complétion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tester des idées" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Meilleur ordre pour a, ab, abc, abcd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pas de changement : " - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 ordre ('a', 'ab', 'abc', 'abcd')\n", + "1 ordre ('a', 'ab', 'abcd', 'abc')\n", + "1 ordre ('a', 'abc', 'ab', 'abcd')\n", + "2 ordre ('a', 'abc', 'abcd', 'ab')\n", + "2 ordre ('a', 'abcd', 'ab', 'abc')\n", + "2 ordre ('a', 'abcd', 'abc', 'ab')\n", + "1 ordre ('ab', 'a', 'abc', 'abcd')\n", + "2 ordre ('ab', 'a', 'abcd', 'abc')\n", + "2 ordre ('ab', 'abc', 'a', 'abcd')\n", + "3 ordre ('ab', 'abc', 'abcd', 'a')\n", + "3 ordre ('ab', 'abcd', 'a', 'abc')\n", + "3 ordre ('ab', 'abcd', 'abc', 'a')\n", + "2 ordre ('abc', 'a', 'ab', 'abcd')\n", + "3 ordre ('abc', 'a', 'abcd', 'ab')\n", + "2 ordre ('abc', 'ab', 'a', 'abcd')\n", + "3 ordre ('abc', 'ab', 'abcd', 'a')\n", + "4 ordre ('abc', 'abcd', 'a', 'ab')\n", + "4 ordre ('abc', 'abcd', 'ab', 'a')\n", + "3 ordre ('abcd', 'a', 'ab', 'abc')\n", + "3 ordre ('abcd', 'a', 'abc', 'ab')\n", + "3 ordre ('abcd', 'ab', 'a', 'abc')\n", + "3 ordre ('abcd', 'ab', 'abc', 'a')\n", + "4 ordre ('abcd', 'abc', 'a', 'ab')\n", + "4 ordre ('abcd', 'abc', 'ab', 'a')\n" + ] + } + ], + "source": [ + "from mlstatpy.nlp.completion import CompletionTrieNode\n", + "import itertools\n", + "\n", + "queries = [\"a\", \"ab\", \"abc\", \"abcd\"]\n", + "for per in itertools.permutations(queries):\n", + " trie = CompletionTrieNode.build([(None, w) for w in per])\n", + " gain = sum(len(w) - trie.min_keystroke(w)[0] for w in per)\n", + " print(gain, \"ordre\", per)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Meilleur ordre pour a, ab, abc, abcd, edf, edfh" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9.0 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0 | actuel p=0.0 g=3.0\n", - "9.0 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=0.0 g=3.0\n", - "8.5 - actuellement p=1.0 g=11.0 | actuel p=0.0 g=4.0 | actualit\u00e9 p=1.0 g=6.0\n", - "8.5 - actuel p=0.0 g=5.0 | actualit\u00e9 p=1.0 g=7.0 | actuellement p=1.0 g=10.0\n", - "8.5 - actualit\u00e9 p=1.0 g=8.0 | actuel p=0.0 g=4.0 | actuellement p=1.0 g=9.0\n", - "8.0 - actuel p=0.0 g=5.0 | actuellement p=1.0 g=10.0 | actualit\u00e9 p=1.0 g=6.0\n" - ] - } - ], - "source": [ - "queries = ['actuellement', 'actualit\u00e9', 'actuel']\n", - "weights = [1, 1, 0]\n", - "gain_dynamique_moyen_par_mot(queries, weights)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "(6, 'ordre', ('edfh', 'edf', 'abcd', 'abc', 'ab', 'a'))\n", + "(6, 'ordre', ('edfh', 'edf', 'abcd', 'abc', 'a', 'ab'))\n", + "(6, 'ordre', ('edfh', 'edf', 'abcd', 'ab', 'abc', 'a'))\n", + "(6, 'ordre', ('edfh', 'edf', 'abcd', 'ab', 'a', 'abc'))\n", + "(6, 'ordre', ('edfh', 'edf', 'abcd', 'a', 'abc', 'ab'))\n", + "(6, 'ordre', ('edfh', 'edf', 'abcd', 'a', 'ab', 'abc'))\n", + "(6, 'ordre', ('edfh', 'edf', 'abc', 'abcd', 'ab', 'a'))\n", + "(6, 'ordre', ('edfh', 'edf', 'abc', 'abcd', 'a', 'ab'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abcd', 'abc', 'ab', 'a'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abcd', 'abc', 'a', 'ab'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abcd', 'ab', 'abc', 'a'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abcd', 'ab', 'a', 'abc'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abcd', 'a', 'abc', 'ab'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abcd', 'a', 'ab', 'abc'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abc', 'abcd', 'ab', 'a'))\n", + "(6, 'ordre', ('edf', 'edfh', 'abc', 'abcd', 'a', 'ab'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edfh', 'edf', 'ab', 'a'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edfh', 'edf', 'a', 'ab'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edfh', 'ab', 'edf', 'a'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edfh', 'ab', 'a', 'edf'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edfh', 'a', 'edf', 'ab'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edfh', 'a', 'ab', 'edf'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edf', 'edfh', 'ab', 'a'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edf', 'edfh', 'a', 'ab'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edf', 'ab', 'edfh', 'a'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edf', 'ab', 'a', 'edfh'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edf', 'a', 'edfh', 'ab'))\n", + "(6, 'ordre', ('abcd', 'abc', 'edf', 'a', 'ab', 'edfh'))\n", + "(6, 'ordre', ('abcd', 'abc', 'ab', 'edfh', 'edf', 'a'))\n", + "(6, 'ordre', ('abcd', 'abc', 'ab', 'edfh', 'a', 'edf'))\n" + ] + } + ], + "source": [ + "queries = [\"a\", \"ab\", \"abc\", \"abcd\", \"edf\", \"edfh\"]\n", + "res = []\n", + "for per in itertools.permutations(queries):\n", + " trie = CompletionTrieNode.build([(None, w) for w in per])\n", + " gain = sum(len(w) - trie.min_keystroke(w)[0] for w in per)\n", + " res.append((gain, \"ordre\", per))\n", + "res.sort(reverse=True)\n", + "for r in res[:30]:\n", + " print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Influence du poids" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Changements :" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "19.2 - actes p=2.0 g=4.0 | actuellement p=1.0 g=10.0 | acte p=1.0 g=1.0 | actualité p=1.0 g=5.0\n", + "19.2 - actes p=2.0 g=4.0 | actualité p=1.0 g=7.0 | acte p=1.0 g=1.0 | actuellement p=1.0 g=8.0\n", + "19.2 - actes p=2.0 g=4.0 | acte p=1.0 g=2.0 | actualité p=1.0 g=6.0 | actuellement p=1.0 g=8.0\n", + "19.2 - actes p=2.0 g=4.0 | actuellement p=1.0 g=10.0 | actualité p=1.0 g=6.0 | acte p=1.0 g=0.0\n", + "19.2 - actes p=2.0 g=4.0 | actualité p=1.0 g=7.0 | actuellement p=1.0 g=9.0 | acte p=1.0 g=0.0\n", + "19.2 - actes p=2.0 g=4.0 | acte p=1.0 g=2.0 | actuellement p=1.0 g=9.0 | actualité p=1.0 g=5.0\n", + "18.4 - actuellement p=1.0 g=11.0 | actes p=2.0 g=3.0 | actualité p=1.0 g=6.0 | acte p=1.0 g=0.0\n", + "18.4 - actuellement p=1.0 g=11.0 | actes p=2.0 g=3.0 | acte p=1.0 g=1.0 | actualité p=1.0 g=5.0\n", + "18.4 - actualité p=1.0 g=8.0 | actes p=2.0 g=3.0 | actuellement p=1.0 g=9.0 | acte p=1.0 g=0.0\n", + "18.4 - actualité p=1.0 g=8.0 | actes p=2.0 g=3.0 | acte p=1.0 g=1.0 | actuellement p=1.0 g=8.0\n", + "18.4 - acte p=1.0 g=3.0 | actes p=2.0 g=3.0 | actuellement p=1.0 g=9.0 | actualité p=1.0 g=5.0\n", + "18.4 - acte p=1.0 g=3.0 | actes p=2.0 g=3.0 | actualité p=1.0 g=6.0 | actuellement p=1.0 g=8.0\n", + "17.6 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0 | actes p=2.0 g=2.0 | acte p=1.0 g=0.0\n", + "17.6 - actuellement p=1.0 g=11.0 | acte p=1.0 g=2.0 | actes p=2.0 g=2.0 | actualité p=1.0 g=5.0\n", + "17.6 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actes p=2.0 g=2.0 | acte p=1.0 g=0.0\n", + "17.6 - actualité p=1.0 g=8.0 | acte p=1.0 g=2.0 | actes p=2.0 g=2.0 | actuellement p=1.0 g=8.0\n", + "17.6 - acte p=1.0 g=3.0 | actuellement p=1.0 g=10.0 | actes p=2.0 g=2.0 | actualité p=1.0 g=5.0\n", + "17.6 - acte p=1.0 g=3.0 | actualité p=1.0 g=7.0 | actes p=2.0 g=2.0 | actuellement p=1.0 g=8.0\n", + "16.8 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0 | acte p=1.0 g=1.0 | actes p=2.0 g=1.0\n", + "16.8 - actuellement p=1.0 g=11.0 | acte p=1.0 g=2.0 | actualité p=1.0 g=6.0 | actes p=2.0 g=1.0\n", + "16.8 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | acte p=1.0 g=1.0 | actes p=2.0 g=1.0\n", + "16.8 - actualité p=1.0 g=8.0 | acte p=1.0 g=2.0 | actuellement p=1.0 g=9.0 | actes p=2.0 g=1.0\n", + "16.8 - acte p=1.0 g=3.0 | actuellement p=1.0 g=10.0 | actualité p=1.0 g=6.0 | actes p=2.0 g=1.0\n", + "16.8 - acte p=1.0 g=3.0 | actualité p=1.0 g=7.0 | actuellement p=1.0 g=9.0 | actes p=2.0 g=1.0\n" + ] + } + ], + "source": [ + "queries = [\"actuellement\", \"actualité\", \"acte\", \"actes\"]\n", + "weights = [1, 1, 1, 2]\n", + "total = sum(weights) * 1.0 / len(queries)\n", + "res = []\n", + "for per in itertools.permutations(zip(queries, weights)):\n", + " trie = CompletionTrieNode.build([(None, w) for w, p in per])\n", + " wks = [(w, p, len(w) - trie.min_keystroke(w)[0]) for w, p in per]\n", + " gain = sum(g * p / total for w, p, g in wks)\n", + " res.append((gain, wks))\n", + "res.sort(reverse=True)\n", + "for r in res:\n", + " print(\n", + " \"{0:3.4} - {1}\".format(r[0], \" | \".join(\"%s p=%1.1f g=%1.1f\" % _ for _ in r[1]))\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nouvelle métrique" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intuition" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def gain_moyen_par_mot(queries, weights):\n", + " total = sum(weights) * 1.0\n", + " res = []\n", + " for per in itertools.permutations(zip(queries, weights)):\n", + " trie = CompletionTrieNode.build([(None, w) for w, p in per])\n", + " wks = [(w, p, len(w) - trie.min_keystroke(w)[0]) for w, p in per]\n", + " gain = sum(g * p / total for w, p, g in wks)\n", + " res.append((gain, wks))\n", + " res.sort(reverse=True)\n", + " for i, r in enumerate(res):\n", + " print(\n", + " \"{0:3.4} - {1}\".format(\n", + " r[0], \" | \".join(\"%s p=%1.1f g=%1.1f\" % _ for _ in r[1])\n", + " )\n", + " )\n", + " if i > 10:\n", + " print(\"...\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7.333 - actuel p=1.0 g=5.0 | actualit\u00e9 p=1.0 g=7.0 | actuellement p=1.0 g=10.0\n", - "7.0 - actuellement p=1.0 g=11.0 | actuel p=1.0 g=4.0 | actualit\u00e9 p=1.0 g=6.0\n", - "7.0 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0 | actuel p=1.0 g=3.0\n", - "7.0 - actuel p=1.0 g=5.0 | actuellement p=1.0 g=10.0 | actualit\u00e9 p=1.0 g=6.0\n", - "7.0 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=1.0 g=3.0\n", - "7.0 - actualit\u00e9 p=1.0 g=8.0 | actuel p=1.0 g=4.0 | actuellement p=1.0 g=9.0\n" - ] - } - ], - "source": [ - "queries = ['actuellement', 'actualit\u00e9', 'actuel']\n", - "weights = [1, 1, 1]\n", - "gain_dynamique_moyen_par_mot(queries, weights)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "7.0 - actuellement p=1.0 g=11.0 | actuel p=1.0 g=4.0 | actualité p=1.0 g=6.0\n", + "7.0 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0 | actuel p=1.0 g=3.0\n", + "7.0 - actuel p=1.0 g=5.0 | actuellement p=1.0 g=10.0 | actualité p=1.0 g=6.0\n", + "7.0 - actuel p=1.0 g=5.0 | actualité p=1.0 g=7.0 | actuellement p=1.0 g=9.0\n", + "7.0 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=1.0 g=3.0\n", + "7.0 - actualité p=1.0 g=8.0 | actuel p=1.0 g=4.0 | actuellement p=1.0 g=9.0\n" + ] + } + ], + "source": [ + "queries = [\"actuellement\", \"actualité\", \"actuel\"]\n", + "weights = [1, 1, 1]\n", + "gain_moyen_par_mot(queries, weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7.0 - actuellement p=1.0 g=11.0 | actuel p=1.0 g=4.0 | actualit\u00e9 p=1.0 g=6.0\n", - "7.0 - actuellement p=1.0 g=11.0 | actualit\u00e9 p=1.0 g=7.0 | actuel p=1.0 g=3.0\n", - "7.0 - actuel p=1.0 g=5.0 | actuellement p=1.0 g=10.0 | actualit\u00e9 p=1.0 g=6.0\n", - "7.0 - actuel p=1.0 g=5.0 | actualit\u00e9 p=1.0 g=7.0 | actuellement p=1.0 g=9.0\n", - "7.0 - actualit\u00e9 p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=1.0 g=3.0\n", - "7.0 - actualit\u00e9 p=1.0 g=8.0 | actuel p=1.0 g=4.0 | actuellement p=1.0 g=9.0\n" - ] - } - ], - "source": [ - "gain_moyen_par_mot(queries, weights)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "9.0 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0 | actuel p=0.0 g=3.0\n", + "9.0 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=0.0 g=3.0\n", + "8.5 - actuellement p=1.0 g=11.0 | actuel p=0.0 g=4.0 | actualité p=1.0 g=6.0\n", + "8.5 - actualité p=1.0 g=8.0 | actuel p=0.0 g=4.0 | actuellement p=1.0 g=9.0\n", + "8.0 - actuel p=0.0 g=5.0 | actuellement p=1.0 g=10.0 | actualité p=1.0 g=6.0\n", + "8.0 - actuel p=0.0 g=5.0 | actualité p=1.0 g=7.0 | actuellement p=1.0 g=9.0\n" + ] + } + ], + "source": [ + "queries = [\"actuellement\", \"actualité\", \"actuel\"]\n", + "weights = [1, 1, 0]\n", + "gain_moyen_par_mot(queries, weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Ajouter une compl\u00e9tion" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "9.0 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0\n", + "9.0 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0\n" + ] + } + ], + "source": [ + "queries = [\"actuellement\", \"actualité\"]\n", + "weights = [1, 1]\n", + "gain_moyen_par_mot(queries, weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Vérification" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def gain_dynamique_moyen_par_mot(queries, weights, permutation=True):\n", + " total = sum(weights) * 1.0\n", + " res = []\n", + " for per in itertools.permutations(zip(queries, weights)):\n", + " trie = CompletionTrieNode.build([(None, w) for w, p in per])\n", + " trie.precompute_stat()\n", + " trie.update_stat_dynamic()\n", + " wks = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for w, p in per]\n", + " gain = sum(g * p / total for w, p, g in wks)\n", + " res.append((gain, wks))\n", + " if not permutation:\n", + " break\n", + " res.sort(reverse=True)\n", + " for i, r in enumerate(res):\n", + " print(\n", + " \"{0:3.4} - {1}\".format(\n", + " r[0], \" | \".join(\"%s p=%1.1f g=%1.1f\" % _ for _ in r[1])\n", + " )\n", + " )\n", + " if i > 10:\n", + " print(\"...\")\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pas de changement : " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10.1 - mac\u00e9rer p=1.0 g=6.0 | maline p=1.0 g=4.0 | machinerie p=1.0 g=7.0 | machinerie infernale p=1.0 g=16.0 | machinerie infernalissime p=1.0 g=20.0 | machine artistique p=1.0 g=12.0 | machine automatique p=1.0 g=12.0 | machine chaplin p=1.0 g=7.0 | machine intelligente p=1.0 g=11.0 | machine learning p=1.0 g=6.0\n" - ] - } - ], - "source": [ - "queries = ['mac\u00e9rer', 'maline', 'machinerie', 'machinerie infernale', 'machinerie infernalissime', \n", - " 'machine artistique', 'machine automatique',\n", - " 'machine chaplin', 'machine intelligente', 'machine learning']\n", - "weights = [1] * len(queries)\n", - "gain_dynamique_moyen_par_mot(queries, weights, permutation=False)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "9.0 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0 | actuel p=0.0 g=3.0\n", + "9.0 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=0.0 g=3.0\n", + "8.5 - actuellement p=1.0 g=11.0 | actuel p=0.0 g=4.0 | actualité p=1.0 g=6.0\n", + "8.5 - actuel p=0.0 g=5.0 | actualité p=1.0 g=7.0 | actuellement p=1.0 g=10.0\n", + "8.5 - actualité p=1.0 g=8.0 | actuel p=0.0 g=4.0 | actuellement p=1.0 g=9.0\n", + "8.0 - actuel p=0.0 g=5.0 | actuellement p=1.0 g=10.0 | actualité p=1.0 g=6.0\n" + ] + } + ], + "source": [ + "queries = [\"actuellement\", \"actualité\", \"actuel\"]\n", + "weights = [1, 1, 0]\n", + "gain_dynamique_moyen_par_mot(queries, weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Changements :" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12.3 - machine p=0.0 g=6.0 | mac\u00e9rer p=1.0 g=5.0 | maline p=1.0 g=3.0 | machinerie p=1.0 g=8.0 | machinerie infernale p=1.0 g=17.0 | machinerie infernalissime p=1.0 g=21.0 | machine artistique p=1.0 g=15.0 | machine automatique p=1.0 g=15.0 | machine chaplin p=1.0 g=11.0 | machine intelligente p=1.0 g=16.0 | machine learning p=1.0 g=12.0\n" - ] - } - ], - "source": [ - "queries = ['machine'] + queries\n", - "weights = [1] * len(queries)\n", - "weights[queries.index('machine')] = 0.0\n", - "gain_dynamique_moyen_par_mot(queries, weights, permutation=False)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "7.333 - actuel p=1.0 g=5.0 | actualité p=1.0 g=7.0 | actuellement p=1.0 g=10.0\n", + "7.0 - actuellement p=1.0 g=11.0 | actuel p=1.0 g=4.0 | actualité p=1.0 g=6.0\n", + "7.0 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0 | actuel p=1.0 g=3.0\n", + "7.0 - actuel p=1.0 g=5.0 | actuellement p=1.0 g=10.0 | actualité p=1.0 g=6.0\n", + "7.0 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=1.0 g=3.0\n", + "7.0 - actualité p=1.0 g=8.0 | actuel p=1.0 g=4.0 | actuellement p=1.0 g=9.0\n" + ] + } + ], + "source": [ + "queries = [\"actuellement\", \"actualité\", \"actuel\"]\n", + "weights = [1, 1, 1]\n", + "gain_dynamique_moyen_par_mot(queries, weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Wikipedia\n", - "\n", - "* [PageCount](https://dumps.wikimedia.org/other/pagecounts-raw/)\n", - "* [dump](https://dumps.wikimedia.org/backup-index.html)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "7.0 - actuellement p=1.0 g=11.0 | actuel p=1.0 g=4.0 | actualité p=1.0 g=6.0\n", + "7.0 - actuellement p=1.0 g=11.0 | actualité p=1.0 g=7.0 | actuel p=1.0 g=3.0\n", + "7.0 - actuel p=1.0 g=5.0 | actuellement p=1.0 g=10.0 | actualité p=1.0 g=6.0\n", + "7.0 - actuel p=1.0 g=5.0 | actualité p=1.0 g=7.0 | actuellement p=1.0 g=9.0\n", + "7.0 - actualité p=1.0 g=8.0 | actuellement p=1.0 g=10.0 | actuel p=1.0 g=3.0\n", + "7.0 - actualité p=1.0 g=8.0 | actuel p=1.0 g=4.0 | actuellement p=1.0 g=9.0\n" + ] + } + ], + "source": [ + "gain_moyen_par_mot(queries, weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ajouter une complétion" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "10.1 - macérer p=1.0 g=6.0 | maline p=1.0 g=4.0 | machinerie p=1.0 g=7.0 | machinerie infernale p=1.0 g=16.0 | machinerie infernalissime p=1.0 g=20.0 | machine artistique p=1.0 g=12.0 | machine automatique p=1.0 g=12.0 | machine chaplin p=1.0 g=7.0 | machine intelligente p=1.0 g=11.0 | machine learning p=1.0 g=6.0\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" + ], + "source": [ + "queries = [\n", + " \"macérer\",\n", + " \"maline\",\n", + " \"machinerie\",\n", + " \"machinerie infernale\",\n", + " \"machinerie infernalissime\",\n", + " \"machine artistique\",\n", + " \"machine automatique\",\n", + " \"machine chaplin\",\n", + " \"machine intelligente\",\n", + " \"machine learning\",\n", + "]\n", + "weights = [1] * len(queries)\n", + "gain_dynamique_moyen_par_mot(queries, weights, permutation=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12.3 - machine p=0.0 g=6.0 | macérer p=1.0 g=5.0 | maline p=1.0 g=3.0 | machinerie p=1.0 g=8.0 | machinerie infernale p=1.0 g=17.0 | machinerie infernalissime p=1.0 g=21.0 | machine artistique p=1.0 g=15.0 | machine automatique p=1.0 g=15.0 | machine chaplin p=1.0 g=11.0 | machine intelligente p=1.0 g=16.0 | machine learning p=1.0 g=12.0\n" + ] } + ], + "source": [ + "queries = [\"machine\"] + queries\n", + "weights = [1] * len(queries)\n", + "weights[queries.index(\"machine\")] = 0.0\n", + "gain_dynamique_moyen_par_mot(queries, weights, permutation=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wikipedia\n", + "\n", + "* [PageCount](https://dumps.wikimedia.org/other/pagecounts-raw/)\n", + "* [dump](https://dumps.wikimedia.org/backup-index.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/_doc/notebooks/nlp/completion_trie_long.ipynb b/_doc/notebooks/nlp/completion_trie_long.ipynb index be8ba5a4..a9b0621e 100644 --- a/_doc/notebooks/nlp/completion_trie_long.ipynb +++ b/_doc/notebooks/nlp/completion_trie_long.ipynb @@ -1,898 +1,920 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Completion Trie and metrics\n", - "\n", - "Evaluation of a completion system on wikpedia pages." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Completion Trie and metrics\n", + "\n", + "Evaluation of a completion system on wikpedia pages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
run previous cell, wait for 2 seconds
\n", + "" ], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "plt.style.use('ggplot')\n", - "from jyquickhelper import add_notebook_menu\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Wikipedia titles, uniform" + "text/plain": [ + "" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wikipedia titles, uniform" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from mlstatpy.data.wikipedia import download_titles\n", + "\n", + "file_titles = download_titles(country=\"fr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from mlstatpy.data.wikipedia import enumerate_titles\n", + "\n", + "list_titles = list(\n", + " sorted(set(_ for _ in enumerate_titles(file_titles) if \"A\" <= _[0] <= \"Z\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from mlstatpy.data.wikipedia import download_titles\n", - "file_titles = download_titles(country='fr')" + "data": { + "text/plain": [ + "(3108490,\n", + " ['A',\n", + " 'A & A',\n", + " 'A (Airport Express)',\n", + " 'A (Ayumi Hamasaki)',\n", + " \"A (Disque d'Ayumi Hamasaki)\"],\n", + " ['Fantasy in the sky',\n", + " 'Fantasy mythique',\n", + " 'Fantasy of manners',\n", + " 'Fantasy tennis',\n", + " 'Fantasy urbaine'])" ] - }, + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(list_titles), list_titles[:5], list_titles[1000000:1000005]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from mlstatpy.nlp.completion import CompletionTrieNode\n", + "\n", + "\n", + "def gain_dynamique_moyen_par_mot(queries, weights):\n", + " per = list(zip(weights, queries))\n", + " total = sum(w * len(q) for q, w in zip(queries, weights))\n", + " trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", + " trie.precompute_stat()\n", + " trie.update_stat_dynamic()\n", + " wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per]\n", + " wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for p, w in per]\n", + " wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) for p, w in per]\n", + " gain = sum(g * p / total for w, p, g in wks)\n", + " gain_dyn = sum(g * p / total for w, p, g in wks_dyn)\n", + " gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2)\n", + " ave_length = sum(len(w) * p / total for p, w in per)\n", + " return gain, gain_dyn, gain_dyn2, ave_length" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from mlstatpy.data.wikipedia import enumerate_titles\n", - "list_titles = list(sorted(set(_ for _ in enumerate_titles(file_titles) if 'A' <= _[0] <= 'Z')))" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "time 0\n", + "time: 0.21504800644533353s - nb=200 gain (0.820872274143302, 0.820872274143302, 0.820872274143302, 1.0)\n", + "time: 0.6058446756721159s - nb=500 gain (0.7976588628762532, 0.7976588628762532, 0.7976588628762532, 1.0)\n", + "time: 1.009366944402156s - nb=800 gain (0.779308535065277, 0.779308535065277, 0.779308535065277, 1.0)\n", + "time: 1.2731077523609795s - nb=1000 gain (0.7819106501794998, 0.7819106501794998, 0.7819106501794998, 1.0)\n", + "time: 3.0382918326608044s - nb=2000 gain (0.7491075326810025, 0.7491075326810025, 0.7491075326810025, 1.0)\n", + "time: 6.941259884811901s - nb=5000 gain (0.7193327903836085, 0.7193534087277493, 0.7193534087277493, 1.0)\n", + "time: 12.096078319013222s - nb=8000 gain (0.6971821041145199, 0.6971821041145199, 0.6971821041145199, 1.0)\n", + "time: 17.030497306746902s - nb=10000 gain (0.6881011563817098, 0.6881371807341721, 0.6881371807341721, 1.0)\n", + "time: 30.55692095058407s - nb=20000 gain (0.6579791591697565, 0.6582343738435791, 0.6582343738435791, 1.0)\n" + ] }, { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(3108490,\n", - " ['A',\n", - " 'A & A',\n", - " 'A (Airport Express)',\n", - " 'A (Ayumi Hamasaki)',\n", - " \"A (Disque d'Ayumi Hamasaki)\"],\n", - " ['Fantasy in the sky',\n", - " 'Fantasy mythique',\n", - " 'Fantasy of manners',\n", - " 'Fantasy tennis',\n", - " 'Fantasy urbaine'])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
71000017.0304970.6881010.6881370.6881371.00.6881011.00.6881371.0000520.6881371.000052
82000030.5569210.6579790.6582340.6582341.00.6579791.00.6582341.0003880.6582341.000388
\n", + "
" ], - "source": [ - "len(list_titles), list_titles[:5], list_titles[1000000:1000005]" + "text/plain": [ + " size time mks mks' mks\" ave_len %mks mks/mks \\\n", + "7 10000 17.030497 0.688101 0.688137 0.688137 1.0 0.688101 1.0 \n", + "8 20000 30.556921 0.657979 0.658234 0.658234 1.0 0.657979 1.0 \n", + "\n", + " %mks' mks'/mks %mks\" mks\"/mks \n", + "7 0.688137 1.000052 0.688137 1.000052 \n", + "8 0.658234 1.000388 0.658234 1.000388 " ] - }, + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import time, random, pandas\n", + "\n", + "\n", + "def benchmark(sizes):\n", + " print(\"time\", 0)\n", + " allres = []\n", + " for size in sizes:\n", + " begin = time.perf_counter()\n", + " if size is None:\n", + " size = len(list_titles)\n", + " spl = list_titles\n", + " else:\n", + " spl = random.sample(list_titles, size)\n", + " spl.sort()\n", + " res = gain_dynamique_moyen_par_mot(spl, [1.0] * len(spl))\n", + " dt = time.perf_counter() - begin\n", + " print(\n", + " \"time: {0}s - nb={1}\".format(dt, len(spl)),\n", + " \"gain\",\n", + " tuple(_ / res[-1] for _ in res),\n", + " )\n", + " allres.append((size, dt) + res)\n", + " # with open(\"sample%d.txt\" % len(spl), \"w\", encoding=\"utf-8\") as f:\n", + " # f.write(\"\\n\".join(spl))\n", + " df = pandas.DataFrame(allres, columns=\"size time mks mks' mks\\\" ave_len\".split())\n", + " for c in \"mks mks' mks\\\"\".split():\n", + " df[\"%\" + c] = df[c] / df[\"ave_len\"]\n", + " df[c + \"/mks\"] = df[c] / df[\"mks\"]\n", + " return df\n", + "\n", + "\n", + "df = benchmark([200, 500, 800, 1000, 2000, 5000, 8000, 10000, 20000])\n", + "df.tail(n=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from mlstatpy.nlp.completion import CompletionTrieNode\n", - "\n", - "def gain_dynamique_moyen_par_mot(queries, weights):\n", - " per = list(zip(weights, queries))\n", - " total = sum(w * len(q) for q, w in zip(queries, weights))\n", - " res = []\n", - " trie = CompletionTrieNode.build([(None, q) for _, q in per])\n", - " trie.precompute_stat()\n", - " trie.update_stat_dynamic()\n", - " wks = [(w, p, len(w)-trie.min_keystroke0(w)[0]) for p, w in per]\n", - " wks_dyn = [(w, p, len(w)-trie.min_dynamic_keystroke(w)[0]) for p, w in per]\n", - " wks_dyn2 = [(w, p, len(w)-trie.min_dynamic_keystroke2(w)[0]) for p, w in per]\n", - " gain = sum( g*p/total for w, p, g in wks)\n", - " gain_dyn = sum( g*p/total for w, p, g in wks_dyn)\n", - " gain_dyn2 = sum( g*p/total for w, p, g in wks_dyn2)\n", - " ave_length = sum( len(w) * p / total for p, w in per)\n", - " return gain, gain_dyn, gain_dyn2, ave_length" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time 0\n", - "time: 0.21504800644533353s - nb=200 gain (0.820872274143302, 0.820872274143302, 0.820872274143302, 1.0)\n", - "time: 0.6058446756721159s - nb=500 gain (0.7976588628762532, 0.7976588628762532, 0.7976588628762532, 1.0)\n", - "time: 1.009366944402156s - nb=800 gain (0.779308535065277, 0.779308535065277, 0.779308535065277, 1.0)\n", - "time: 1.2731077523609795s - nb=1000 gain (0.7819106501794998, 0.7819106501794998, 0.7819106501794998, 1.0)\n", - "time: 3.0382918326608044s - nb=2000 gain (0.7491075326810025, 0.7491075326810025, 0.7491075326810025, 1.0)\n", - "time: 6.941259884811901s - nb=5000 gain (0.7193327903836085, 0.7193534087277493, 0.7193534087277493, 1.0)\n", - "time: 12.096078319013222s - nb=8000 gain (0.6971821041145199, 0.6971821041145199, 0.6971821041145199, 1.0)\n", - "time: 17.030497306746902s - nb=10000 gain (0.6881011563817098, 0.6881371807341721, 0.6881371807341721, 1.0)\n", - "time: 30.55692095058407s - nb=20000 gain (0.6579791591697565, 0.6582343738435791, 0.6582343738435791, 1.0)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
71000017.0304970.6881010.6881370.6881371.00.6881011.00.6881371.0000520.6881371.000052
82000030.5569210.6579790.6582340.6582341.00.6579791.00.6582341.0003880.6582341.000388
\n", - "
" - ], - "text/plain": [ - " size time mks mks' mks\" ave_len %mks mks/mks \\\n", - "7 10000 17.030497 0.688101 0.688137 0.688137 1.0 0.688101 1.0 \n", - "8 20000 30.556921 0.657979 0.658234 0.658234 1.0 0.657979 1.0 \n", - "\n", - " %mks' mks'/mks %mks\" mks\"/mks \n", - "7 0.688137 1.000052 0.688137 1.000052 \n", - "8 0.658234 1.000388 0.658234 1.000388 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import time, random, pandas\n", - "\n", - "def benchmark(sizes):\n", - " print(\"time\", 0)\n", - " allres = []\n", - " for size in sizes:\n", - " begin = time.perf_counter()\n", - " if size is None:\n", - " size = len(list_titles)\n", - " spl = list_titles\n", - " else:\n", - " spl = random.sample(list_titles, size)\n", - " spl.sort()\n", - " res = gain_dynamique_moyen_par_mot(spl, [1.0] * len(spl))\n", - " dt = time.perf_counter() - begin\n", - " print(\"time: {0}s - nb={1}\".format(dt, len(spl)), \"gain\", tuple(_/res[-1] for _ in res))\n", - " allres.append((size, dt) + res)\n", - " # with open(\"sample%d.txt\" % len(spl), \"w\", encoding=\"utf-8\") as f:\n", - " # f.write(\"\\n\".join(spl))\n", - " df = pandas.DataFrame(allres, columns=\"size time mks mks' mks\\\" ave_len\".split()) \n", - " for c in \"mks mks' mks\\\"\".split():\n", - " df[\"%\" + c] = df[c] / df[\"ave_len\"]\n", - " df[c + \"/mks\"] = df[c] / df[\"mks\"] \n", - " return df\n", - " \n", - "df = benchmark([200, 500, 800, 1000, 2000, 5000, 8000, 10000, 20000])\n", - "df.tail(n=2)" + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "f, ax = plt.subplots(2, 2, figsize=(14, 10))\n", + "df.plot(x=\"size\", y=\"time\", ax=ax[1, 0])\n", + "df.plot(x=\"size\", y=[\"mks\", \"mks'\", 'mks\"', \"ave_len\"], ax=ax[0, 0])\n", + "df.plot(x=\"size\", y=[\"%mks\", \"%mks'\", '%mks\"'], ax=ax[0, 1])\n", + "df.plot(x=\"size\", y=[\"mks'/mks\", 'mks\"/mks'], ax=ax[1, 1])\n", + "ax[0, 0].legend()\n", + "ax[0, 1].legend()\n", + "ax[1, 0].legend()\n", + "ax[1, 1].legend()\n", + "ax[1, 1].set_ylim([0.9, 1.1])\n", + "ax[0, 0].set_title(\"Raw Gain\")\n", + "ax[0, 1].set_title(\"Relative Gain\")\n", + "ax[1, 0].set_title(\"Time\")\n", + "ax[1, 1].set_title(\"Comparison between MKS\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reduce the alphabet size" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from mlstatpy.data.wikipedia import enumerate_titles\n", + "\n", + "list_titles = list(\n", + " sorted(set(_ for _ in enumerate_titles(file_titles) if \"A\" <= _[0] <= \"Z\"))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJeCAYAAACOHyXpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRESaqUASERERERFppgJJRERERESk2f8HTk4z8XM/5aIAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "f, ax = plt.subplots(2, 2, figsize=(14,10))\n", - "df.plot(x=\"size\", y=\"time\", ax=ax[1,0])\n", - "df.plot(x=\"size\", y=[\"mks\", \"mks'\", \"mks\\\"\", \"ave_len\"], ax=ax[0,0])\n", - "df.plot(x=\"size\", y=[\"%mks\", \"%mks'\", \"%mks\\\"\"], ax=ax[0,1])\n", - "df.plot(x=\"size\", y=[\"mks'/mks\", \"mks\\\"/mks\"], ax=ax[1,1])\n", - "ax[0,0].legend()\n", - "ax[0,1].legend()\n", - "ax[1,0].legend()\n", - "ax[1,1].legend()\n", - "ax[1,1].set_ylim([0.9, 1.1])\n", - "ax[0,0].set_title(\"Raw Gain\")\n", - "ax[0,1].set_title(\"Relative Gain\")\n", - "ax[1,0].set_title(\"Time\")\n", - "ax[1,1].set_title(\"Comparison between MKS\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "time 0\n", + "time: 7.59344921135289s - nb=5000 gain (0.716585290640898, 0.716585290640898, 0.716585290640898, 1.0)\n", + "time: 3.8923985946166795s - nb=4581 gain (0.41594360086768417, 0.4448874994683378, 0.4448874994683378, 1.0)\n", + "time: 5.085379287694195s - nb=4942 gain (0.5571683533987387, 0.5620376961406324, 0.5620376961406324, 1.0)\n", + "time: 5.121866923020207s - nb=4974 gain (0.5983975448244626, 0.6052151883090817, 0.6052151883090817, 1.0)\n", + "time: 5.501076360438674s - nb=4991 gain (0.6380275314306908, 0.6382847383691052, 0.6382847383691052, 1.0)\n", + "time: 5.524899975880544s - nb=4988 gain (0.6475382003395598, 0.6479497864896859, 0.6479497864896859, 1.0)\n", + "time: 6.245833967660474s - nb=4997 gain (0.6639308855291576, 0.6639308855291576, 0.6639308855291576, 1.0)\n", + "time: 6.012760238038936s - nb=4997 gain (0.6712028636672216, 0.6712028636672216, 0.6712028636672216, 1.0)\n", + "time: 6.076252674864918s - nb=4997 gain (0.6838256469329845, 0.6839490681696653, 0.6839490681696653, 1.0)\n", + "time: 6.111897439143831s - nb=4999 gain (0.6822851853756178, 0.6823160384634976, 0.6823160384634976, 1.0)\n", + "time: 5.873518026578495s - nb=4997 gain (0.6900718921309502, 0.6900718921309502, 0.6900718921309502, 1.0)\n", + "time: 6.684070891827105s - nb=4999 gain (0.6925798323648767, 0.6925798323648767, 0.6925798323648767, 1.0)\n", + "time: 6.735858496876062s - nb=4997 gain (0.6969017445687994, 0.6969017445687994, 0.6969017445687994, 1.0)\n", + "time: 6.131690155300021s - nb=4999 gain (0.6960868000205542, 0.6960868000205542, 0.6960868000205542, 1.0)\n", + "time: 6.2186773552921295s - nb=4999 gain (0.7022574175965309, 0.7022574175965309, 0.7022574175965309, 1.0)\n", + "time: 5.907541621836572s - nb=4998 gain (0.6991010265166325, 0.6991010265166325, 0.6991010265166325, 1.0)\n", + "time: 6.31432889332882s - nb=4999 gain (0.7022368488712789, 0.7022471332339055, 0.7022471332339055, 1.0)\n", + "time: 5.892940837380593s - nb=4998 gain (0.7070717459272685, 0.7070717459272685, 0.7070717459272685, 1.0)\n", + "time: 6.061792582734597s - nb=4999 gain (0.7097547179513399, 0.7097547179513399, 0.7097547179513399, 1.0)\n", + "time: 6.094942944771901s - nb=4999 gain (0.7079858075795616, 0.7080166606674415, 0.7080166606674415, 1.0)\n", + "time: 6.141645954818159s - nb=4999 gain (0.7118732966524257, 0.7118732966524257, 0.7118732966524257, 1.0)\n", + "time: 5.9873731844709255s - nb=4999 gain (0.7094359027099135, 0.7094359027099135, 0.7094359027099135, 1.0)\n", + "time: 6.0718454556808865s - nb=4999 gain (0.7120892682675833, 0.7120892682675833, 0.7120892682675833, 1.0)\n", + "time: 6.133951068150054s - nb=4999 gain (0.7124903584100222, 0.7124903584100222, 0.7124903584100222, 1.0)\n", + "time: 6.292655432947868s - nb=4999 gain (0.713611353936324, 0.713611353936324, 0.713611353936324, 1.0)\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reduce the alphabet size" + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
23246.1339510.7124900.7124900.7124901.00.7124901.00.7124901.00.7124901.0
24256.2926550.7136110.7136110.7136111.00.7136111.00.7136111.00.7136111.0
\n", + "
" + ], + "text/plain": [ + " size time mks mks' mks\" ave_len %mks mks/mks \\\n", + "23 24 6.133951 0.712490 0.712490 0.712490 1.0 0.712490 1.0 \n", + "24 25 6.292655 0.713611 0.713611 0.713611 1.0 0.713611 1.0 \n", + "\n", + " %mks' mks'/mks %mks\" mks\"/mks \n", + "23 0.712490 1.0 0.712490 1.0 \n", + "24 0.713611 1.0 0.713611 1.0 " ] - }, + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import time, random, pandas\n", + "\n", + "\n", + "def char_modulo(c, size):\n", + " if len(c) != 1:\n", + " raise Exception(\"unexpected size '%s'\" % c)\n", + " # if len(c) != len(c.lower()):\n", + " # raise Exception(\"unexpected lower size '%s' != '%s' (%d != %d)\" % (c, c.lower(), len(c), len(c.lower())))\n", + " if size is None:\n", + " return c\n", + " else:\n", + " cl = c.lower()\n", + " if len(cl) > len(c):\n", + " cl = c\n", + " o = ord(cl)\n", + " a = 97\n", + " d = (o - a) + size * 10\n", + " return chr(97 + (d % size))\n", + "\n", + "\n", + "def reduce_alphabet(sample, size):\n", + " return [\"\".join(char_modulo(c, size) for c in word) for word in sample]\n", + "\n", + "\n", + "def benchmark_size(size, alphabet_sizes):\n", + " if size is None:\n", + " size = len(list_titles)\n", + " sample = list_titles\n", + " else:\n", + " sample = random.sample(list_titles, size)\n", + " print(\"time\", 0)\n", + " allres = []\n", + " for size in alphabet_sizes:\n", + " begin = time.perf_counter()\n", + " spl = reduce_alphabet(sample, size)\n", + " spl = list(sorted(set(spl)))\n", + " res = gain_dynamique_moyen_par_mot(spl, [1.0] * len(spl))\n", + " dt = time.perf_counter() - begin\n", + " print(\n", + " \"time: {0}s - nb={1}\".format(dt, len(spl)),\n", + " \"gain\",\n", + " tuple(_ / res[-1] for _ in res),\n", + " )\n", + " if size is None:\n", + " size = max(_ for _ in alphabet_sizes if _ is not None) + 5\n", + " allres.append((size, dt) + res)\n", + " # with open(\"sample%d.txt\" % len(spl), \"w\", encoding=\"utf-8\") as f:\n", + " # f.write(\"\\n\".join(spl))\n", + " df = pandas.DataFrame(allres, columns=\"size time mks mks' mks\\\" ave_len\".split())\n", + " for c in \"mks mks' mks\\\"\".split():\n", + " df[\"%\" + c] = df[c] / df[\"ave_len\"]\n", + " df[c + \"/mks\"] = df[c] / df[\"mks\"]\n", + " return df\n", + "\n", + "\n", + "df = benchmark_size(5000, [None] + list(range(2, 26)))\n", + "df.tail(n=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df = df.sort_values(\"size\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from mlstatpy.data.wikipedia import enumerate_titles\n", - "list_titles = list(sorted(set(_ for _ in enumerate_titles(file_titles) if 'A' <= _[0] <= 'Z')))" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time 0\n", - "time: 7.59344921135289s - nb=5000 gain (0.716585290640898, 0.716585290640898, 0.716585290640898, 1.0)\n", - "time: 3.8923985946166795s - nb=4581 gain (0.41594360086768417, 0.4448874994683378, 0.4448874994683378, 1.0)\n", - "time: 5.085379287694195s - nb=4942 gain (0.5571683533987387, 0.5620376961406324, 0.5620376961406324, 1.0)\n", - "time: 5.121866923020207s - nb=4974 gain (0.5983975448244626, 0.6052151883090817, 0.6052151883090817, 1.0)\n", - "time: 5.501076360438674s - nb=4991 gain (0.6380275314306908, 0.6382847383691052, 0.6382847383691052, 1.0)\n", - "time: 5.524899975880544s - nb=4988 gain (0.6475382003395598, 0.6479497864896859, 0.6479497864896859, 1.0)\n", - "time: 6.245833967660474s - nb=4997 gain (0.6639308855291576, 0.6639308855291576, 0.6639308855291576, 1.0)\n", - "time: 6.012760238038936s - nb=4997 gain (0.6712028636672216, 0.6712028636672216, 0.6712028636672216, 1.0)\n", - "time: 6.076252674864918s - nb=4997 gain (0.6838256469329845, 0.6839490681696653, 0.6839490681696653, 1.0)\n", - "time: 6.111897439143831s - nb=4999 gain (0.6822851853756178, 0.6823160384634976, 0.6823160384634976, 1.0)\n", - "time: 5.873518026578495s - nb=4997 gain (0.6900718921309502, 0.6900718921309502, 0.6900718921309502, 1.0)\n", - "time: 6.684070891827105s - nb=4999 gain (0.6925798323648767, 0.6925798323648767, 0.6925798323648767, 1.0)\n", - "time: 6.735858496876062s - nb=4997 gain (0.6969017445687994, 0.6969017445687994, 0.6969017445687994, 1.0)\n", - "time: 6.131690155300021s - nb=4999 gain (0.6960868000205542, 0.6960868000205542, 0.6960868000205542, 1.0)\n", - "time: 6.2186773552921295s - nb=4999 gain (0.7022574175965309, 0.7022574175965309, 0.7022574175965309, 1.0)\n", - "time: 5.907541621836572s - nb=4998 gain (0.6991010265166325, 0.6991010265166325, 0.6991010265166325, 1.0)\n", - "time: 6.31432889332882s - nb=4999 gain (0.7022368488712789, 0.7022471332339055, 0.7022471332339055, 1.0)\n", - "time: 5.892940837380593s - nb=4998 gain (0.7070717459272685, 0.7070717459272685, 0.7070717459272685, 1.0)\n", - "time: 6.061792582734597s - nb=4999 gain (0.7097547179513399, 0.7097547179513399, 0.7097547179513399, 1.0)\n", - "time: 6.094942944771901s - nb=4999 gain (0.7079858075795616, 0.7080166606674415, 0.7080166606674415, 1.0)\n", - "time: 6.141645954818159s - nb=4999 gain (0.7118732966524257, 0.7118732966524257, 0.7118732966524257, 1.0)\n", - "time: 5.9873731844709255s - nb=4999 gain (0.7094359027099135, 0.7094359027099135, 0.7094359027099135, 1.0)\n", - "time: 6.0718454556808865s - nb=4999 gain (0.7120892682675833, 0.7120892682675833, 0.7120892682675833, 1.0)\n", - "time: 6.133951068150054s - nb=4999 gain (0.7124903584100222, 0.7124903584100222, 0.7124903584100222, 1.0)\n", - "time: 6.292655432947868s - nb=4999 gain (0.713611353936324, 0.713611353936324, 0.713611353936324, 1.0)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
23246.1339510.7124900.7124900.7124901.00.7124901.00.7124901.00.7124901.0
24256.2926550.7136110.7136110.7136111.00.7136111.00.7136111.00.7136111.0
\n", - "
" - ], - "text/plain": [ - " size time mks mks' mks\" ave_len %mks mks/mks \\\n", - "23 24 6.133951 0.712490 0.712490 0.712490 1.0 0.712490 1.0 \n", - "24 25 6.292655 0.713611 0.713611 0.713611 1.0 0.713611 1.0 \n", - "\n", - " %mks' mks'/mks %mks\" mks\"/mks \n", - "23 0.712490 1.0 0.712490 1.0 \n", - "24 0.713611 1.0 0.713611 1.0 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import time, random, pandas\n", - "\n", - "def char_modulo(c, size):\n", - " if len(c) != 1:\n", - " raise Exception(\"unexpected size '%s'\" % c)\n", - " # if len(c) != len(c.lower()):\n", - " # raise Exception(\"unexpected lower size '%s' != '%s' (%d != %d)\" % (c, c.lower(), len(c), len(c.lower())))\n", - " if size is None:\n", - " return c\n", - " else:\n", - " cl = c.lower()\n", - " if len(cl) > len(c):\n", - " cl = c\n", - " o = ord(cl)\n", - " a = 97\n", - " d = (o - a) + size * 10\n", - " return chr(97 + (d % size))\n", - "\n", - "def reduce_alphabet(sample, size):\n", - " return [\"\".join(char_modulo(c, size) for c in word) for word in sample]\n", - "\n", - "def benchmark_size(size, alphabet_sizes):\n", - " if size is None:\n", - " size = len(list_titles)\n", - " sample = list_titles\n", - " else:\n", - " sample = random.sample(list_titles, size)\n", - " print(\"time\", 0)\n", - " allres = []\n", - " for size in alphabet_sizes:\n", - " begin = time.perf_counter()\n", - " spl = reduce_alphabet(sample, size)\n", - " spl = list(sorted(set(spl)))\n", - " res = gain_dynamique_moyen_par_mot(spl, [1.0] * len(spl))\n", - " dt = time.perf_counter() - begin\n", - " print(\"time: {0}s - nb={1}\".format(dt, len(spl)), \"gain\", tuple(_/res[-1] for _ in res))\n", - " if size is None:\n", - " size = max(_ for _ in alphabet_sizes if _ is not None) + 5\n", - " allres.append((size, dt) + res)\n", - " # with open(\"sample%d.txt\" % len(spl), \"w\", encoding=\"utf-8\") as f:\n", - " # f.write(\"\\n\".join(spl))\n", - " df = pandas.DataFrame(allres, columns=\"size time mks mks' mks\\\" ave_len\".split()) \n", - " for c in \"mks mks' mks\\\"\".split():\n", - " df[\"%\" + c] = df[c] / df[\"ave_len\"]\n", - " df[c + \"/mks\"] = df[c] / df[\"mks\"] \n", - " return df\n", - " \n", - "df = benchmark_size(5000, [None] + list(range(2, 26)))\n", - "df.tail(n=2)" + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAJeCAYAAAB4VqBxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt0VfWd///nZ+9zyUlObuccSAh3gyhoFTBVQWu5ZERtrUxby9T7wpmuWX5/av2231WZwdrvOLZ8V3W16+vYmXZKcapjS73UaW3tqlGp34oiDAQrUOQmtwRCLuR6rnvv3x+B1MglARJOkvN6sM7i7HP23uf1jpidd/Znf7bxPM9DRERERERkBLCyHUBERERERGSgqMEREREREZERQw2OiIiIiIiMGGpwRERERERkxFCDIyIiIiIiI4YaHBERERERGTHU4IgMQZMmTeKf//mfsx1DREQG2EB9f3/qqafw+XwDkOjc+ta3vsWUKVOyHUNGODU4MmzdddddGGMwxmDbNuPGjeOOO+7gwIEDWcnT3NzM0qVLmT59Ovn5+ZSWljJjxgz+8R//kX379p3WvtatW8cDDzwwSElFROR0ZPN4s3//fowxrF69utfrixcvPqfHu5/97GfMmzeP0tJSQqEQ559/PosXL+b1118/rf18/etf55133hmklCLd1ODIsPapT32K+vp69u7dy7PPPsvGjRu5+eabz3mOffv2MXPmTH7xi1+wdOlS3nnnHWpra/n+979PU1MTjz322Gntb9SoURQUFAxSWhEROV1D5XhzTCgUoqys7Jx81t13381dd93F7Nmz+fWvf822bdt4/vnnmT17Nv/jf/yP09pXOBwmFosNUlKRbmpwZFgLBAKUl5czduxYrrnmGr7yla/w9ttv09bW1rPOs88+yxVXXEFxcTGxWIzPfOYzfPDBBz3v33777dx66609yytXrsQYw49//OOe12699Va+/OUvnzTHPffcQyqVYuPGjdx+++1ccsklTJw4kblz5/Jv//ZvfP/73+9Z99VXX2Xu3LlEIhGKi4v59Kc/zbvvvttrfx8fwjBp0iS++c1vcv/99xOJRCgrK+OBBx4gk8mc2RdOREROS3+ON+l0mm9961tMnjyZvLw8LrroIn74wx+ecr99HaPGjx8PwLx58zDGMGnSJKD3ELW2tjby8/N59tlne+27rq4On89HTU3NGed74YUX+MlPfsIzzzzDt7/9ba6++momTJjApZdeyle/+lW2bNnSs25LSwu33XYbEyZMIBQKccEFF/D444/jeV7POh8fonZs+b/+67+48MILKSgoYO7cuWzfvv2UuURORQ2OjBh1dXU8//zz2LaNbds9ryeTSZYtW8aGDRt49dVXsW2bz3zmM6RSKaD7oPHGG2/0rP/6668zatSoXqfd33jjDebPn3/Cz21ubua3v/0t9957L0VFRSdcxxjT87yjo4N77rmHt99+mzVr1nD++edz3XXX0dTUdMr6nnjiCcaMGcPatWt54okn+Jd/+Rf+4z/+o+8vjIiIDKiTHW/+7u/+jhdffJEf/vCHbN26lW9+85t84xvfYMWKFSfdV1/HqA0bNgDdjUZ9fT3r1q07bh9FRUUsWrSIp59+utfrzzzzDGPGjOk5fp1Jvqeffprzzz//pGerPnp8SyaTXHzxxbz00kts2bKFhx56iIcffpinnnrqpPsHqK+v51//9V/5z//8T9asWUN7eztLliw55TYip+SJDFN33nmnZ9u2V1BQ4IVCIQ/wAO9rX/vaKbdramryAO+Pf/yj53met3v3bg/wNm/e7Hme540dO9Z77LHHvPLycs/zPG/Lli0e4O3YseOE+1u7dq0HeC+++GKv12fPnu0VFBR4BQUF3vTp00+ax3Ecr6SkxHvmmWd6Xps4caL3yCOP9Fq+8cYbe2133XXXeX/zN39zylpFROTs9ed4s2vXLs8Y423durXXtv/7f/9v79JLL+1Z/vj394/7+DFq3759HuC98cYbvdZbuXKlZ9t2z/Irr7zi2bbt1dfX97x28cUXew8++OBp5fu4adOmeZ/73Od6vfbkk0/2HN8KCgq8N99886Tb33fffV51dXXP8sMPP+xVVlb2WrZt22toaOh57ec//7lnjPHi8fhJ9ytyKjqDI8PaFVdcQW1tLe+++y4PPfQQs2fPPm52mtraWv76r/+ayZMnU1hYyIQJEwDYs2cP0D38a9KkSbz++uts27aNI0eOcM8999DV1cWWLVt4/fXXmTBhApWVlafM4n3kFDzAqlWrqK2t5Stf+QqdnZ09r+/evZvbb7+dKVOmUFRURFFREa2trT15TmbGjBm9lisqKjh06NCpv0AiIjIg+jrerF+/Hs/zqKqqIhwO9zy+/e1vn3K4VV/HqP76q7/6K0aPHt0zTG3Dhg28//773HHHHWeVD44/vt16663U1tbyu9/9js7OThzHAcB1XZYvX86MGTOIxWKEw2H+7d/+rc9aKioqGDVqVK9lz/NoaGg4ra+ByDHDb35BkY8IhUI9Y3kvvvhidu7cyb333su///u/A9DV1cW1117L1VdfzcqVK3suyLzooot6Tv8DzJ8/n9deew3btrn66qsJhUJcc801vP7666ccngYwZcoULMti69atvV4/Nm46Eon0ev2zn/0ssViMJ598kvHjxxMIBLj66qt75TmRQCDQa9kYg+u6p9xGREQGRl/Hm2Pfj9esWUN+fn6vbT86jOuj+nuM6g/btrn11lv56U9/yv/8n/+Tn/70p3zyk59k2rRpZ5wPYOrUqWzevLnXa8XFxRQXF5OXl9fr9ccff5zvfOc7fO9732PmzJkUFhbyve99j9/85jenzH6i49tHM4ucLp3BkRHlW9/6FitXrmT9+vUAbN26lcOHD/Poo48yd+5cpk2bRktLy3G/jZo3bx5/+MMfqKmpYcGCBcBfmp7Vq1efssGJRCJcf/31PPHEE7S2tp4yX1NTE1u2bOHBBx9k4cKFTJ8+nby8PP2WSkRkmPn48eayyy4DYO/evUyZMqXX42QjAPpzjDr2w/+xsySncuedd7Jp0yY2btzIz372s56zN2eaD+C2225jx44d/PznP+/z8998802uu+46lixZwsyZM5kyZYomC5CsUIMjI8r555/PjTfeyD/+4z8CMHHiRILBIE888QQ7d+7ktdde4/777z/ut1Xz58+npaWFX/3qVz3NzPz583n55Zdpbm4+ZYMD8IMf/AC/38/MmTP56U9/ynvvvceuXbt45ZVXePnll3suQi0tLWXUqFH8+7//Ox988AFvv/02X/7ylwmFQoPw1RARkcHy8ePNlClTWLJkCX/3d3/H008/zY4dO9i0aRM/+clP+D//5/+ccB/9OUYdG+r1+9//noMHD9LS0nLSTBdffDEzZ85kyZIlHDlypNfsn2eSD+CLX/wid955J3feeScPPvggf/zjH9mzZw/r16/ne9/7HkDPMe6CCy5g9erVvPHGG3zwwQcsW7aMtWvX9v+LKjJA1ODIiPO//tf/4ve//z2rV68mFovxzDPP8Oqrr3LRRRfx9a9/ncceewzL6v1Pv6KigqlTp1JYWMjMmTMBuOSSSygpKWHq1KmMHTv2lJ85YcKEnnsifOc73+GKK67goosu4mtf+xqzZ8/mtddeA8CyLJ577jl27tzJJZdcwl133cVXv/pVxowZMzhfDBERGTQfPd4A/OhHP+KBBx7g0UcfZfr06SxYsID/+I//4Lzzzjvh9v05RlmWxZNPPskvfvELxo0b13OMOpk777yT2tpabrjhBqLRaK/3TjffMU899RQrVqzgnXfe4bOf/SxTpkzhxhtvZPfu3bz88st86lOfAuChhx7i05/+NDfddBOzZ8+mpaWF++67r68vo8iAM97Hx+qIiIiIiIgMUzqDIyIiIiIiI4YaHBERERERGTHU4IiIiIiIyIiR1fvg1NXVZfPjTygWi9HY2JjtGFmTy/Wr9tysHXK7/oqKimxHGNJ0nBp6crn+XK4dcrv+XK79TI5TOoMjIiIiIiIjhhocEREREREZMdTgiIiIiIjIiKEGR0RERERERgw1OCIiIiIiMmKowRERERERkRGjz2mif/CDH7BhwwaKi4t5/PHHj3vf8zxWrlzJxo0bCQaD3HPPPZx33nmDElZERERERORU+jyDM3fuXP7hH/7hpO9v3LiRgwcP8n//7//lK1/5Cj/+8Y8HNKCIiIiIiEh/9XkGZ/r06TQ0NJz0/fXr13PNNddgjGHq1Kl0dnbS0tJCaWlpnx/+wgsvnF7ac8Dv95NOp7MdI2tyuX7Vnpu1Q27Xf++992Y7goiI5Kh0Ok28M0miK0UyniKZyJBMpEmmXNKpDOm0x5fuOP0bffbZ4PSlubmZWCzWsxyNRmlubj5hg1NTU0NNTQ0Ay5cvx+/3n+3HDzhjzJDMda7kcv2qPTdrB9UvIiLiOA7JRIpEZ4pkPEkinu5uOJIOqXSGdNIllfHIZDzSDjgOZBxwXHA8C9c1eJ7BxQLPwsMCYwE2BgvLWBgMtrGwsLAx2MYc/XQLyBuwWs66wTkd1dXVVFdX9yx/7nOfO5cf3y+xWIzGxsZsx8iaXK5ftedm7aD6RURkaEqn07S1dHKkoZPDDS0kk2lSSYdUyiGd9khnXNIZyDgeGcfguOC6BsczuJ6F51l4mO5mg780G+Zok2FhsMzRZgOD6Wk4Akcfp2Yffbieh2O6H67n4hoXz3PxOPbI4HguBhfXc7GMi2N5WMbDtjx8FvhsD5/P4LcNfr9FIGDwB+wz+rqddYMTiUR6/WDQ1NREJBI5292KiIiIiAwLmUyGVDJNOpEhncyQTqePNiEOmZRDOuOSSbtkHId00qUr6ZJIeqQyhrRj47g2Ljbgw+DDNjZ+4yNgjl0unwbCp8xwrIUByOB2Nxt4uEf//KXhyOC5Lp5xcXEwdDcmlnGxLQ/bAtv28NkGv013s+Ez+IM2gYBNMOgjmOcjL99PXl6QvIIgwby+m6Fz6awbnKqqKn73u99x1VVXsX37dvLz8/t1/Y2IiIiIyEBJpdJ88Kf97N4bpy2eh0cAQ4I8X4riMJSNDjFhcoziaOFp7zvRmWTPjkPU1XXS3AGJdAAIkWfl4cNg9Zz5ADD05+wHgB+w8EhZDo7n4HgZPFI4XgZw8IyD3+dREPLheZnjzm4EAz6CIR/BPD95+QHy8gME8wL4fOd0kNaQ02f13//+99myZQvt7e38/d//PV/60pfIZDIAXHvttcycOZMNGzZw3333EQgEuOeeewY9tIiIiIgMP4l4kr3bD4ExjK4ooai0AMs6s9syNje2s+W9OuoPeySdAvKtAvymFCjFbzKkvAQ+U0zA8xNvhw/b4cOdDgmviaSbwBAnz5+muMCjbHSIiZUxUkmHPbsaaTicpC3uI+3m4TMhQsaPZYqAIgJAxqRJu3FSXiOO5WIZ7+gDbAssy8OywLYMPhts22BbFj4f+AM2heEAhcUhimNh8gtCfdaqodSnp88G56tf/eop3zfG8Ld/+7cDFkhEREREhrfmw63s2dHIocNJ2rosUk4ethUi3wSONgqwbYtD2jtC0kvjeGkMKXxWmryAQzhkUVzkJzo6zOiKEvLyfGzZ9CEbNuyjucMPhCm08oAyAp5HigRJ7zBFhQ5Tp5Yy9rzR2Hb39RudbV3s3XmYg4e6ONLhkU75gRB+U0zA7d38dOuePCuIh2OSZNwOUr4kJWGPMeUhJk0ZTbi45Jx/TaX/cvv8lYiIiIicsSONbWz9Uz2NLQ4dSR+OFyJghQgZHxAFIIBHxkrieF2krGaK87vPbnTEPdJpH67rAxPENmEC+LDSFl1p6GqD+v3AhjgZz8VnLGAMQeMQdzpI2c2MG20z7dIKikrHAGNOmLGgKJ9pMycy7QTvdbR2sm/XYQ4ejHOkw8MYiJVYjBtfxNjzRuH367KL4UgNjoiIiIj0i+M4/Ll2L9t2xelKF1FoFWCZMgACxiXhxcm4R8j4U0SLDWPHFTK+soxAsH+NguM4tDa2cbi+lebmOK3tDl1JQ8q1KSgwTBwfpPLisfh80QGpJ1xcwLSZBSdsfmT4UoMjIiIiIifVfLiVjesOcOhIAJ9VQsiU4qcUQ5ykOcSE0YZJlVFGjS3Fts9uJl3btomUlRIpO74h0nUo0l9qcERERESkR8Zx2LpxLx/sThBPF1Fo5WOZCoKWQ8JppagwzqWXjmbMxJMPCxPJJjU4IiIybNTW1rJy5Upc12XBggUsWrSo1/tPPfUUmzdvBiCVStHa2spTTz0FwOrVq3nxxRcB+PznP8/cuXPPZXSRM9ZY18Lba+uIpwIUhtKMqwhROa28X7Nv9Ufz4TZ2bjtEfUOa9kSQgFVMniklACToImUOct44P5+omkggODBDw0QGkxocEREZFlzXZcWKFSxbtoxoNMrSpUupqqpi3LhxPevcddddPc9feeUVdu/eDUBHRwfPP/88y5cvB+DBBx+kqqqKcPjUN84TyZauzjhr3/yQA80hCu1iLDOWgOeRiRs+3Am7diTo9FrxvE4KAknKozbnXTCK0RUnHyKWcRz27zjE7t2tNLVCwgnhNwXkW35gVPfdWyyHlHOEoqIEM2aWUTauAqg4V2WLDAg1OCIiMizs2LGD8vJyysq6L2ieM2cO69at69XgfNRbb73Fl770JaD7zM8ll1zS09Bccskl1NbWcvXVV5+b8CL9kMlkqH1nNx/shaAVJWDGELAyJGjgkqlBZs+bwX+//T57P2ynsQ1cNx+/KcJ2/BxugMMNEHcbSXmdBOw4kUKXVNqjtcuP4+WTb+XjN/lAPn7PI21SZNx20r4Eo0sNlVOijB4fxbZ1lkaGNzU4IiIyLDQ3NxON/uUHr2g0yvbt20+47uHDh2loaODiiy8+4baRSITm5ubjtqupqaGmpgaA5cuXE4vFBrKEAeHz+YZkrnNlJNa/ecMO3lrbQCpTQoE1ijzLJeEdYfIEm7kLLyEQvBDorv2KT83gik/13v7QvsO8/95e9tUnyMT9WCafoFdEvN0AEDQucS9OiibCYZfJ44u4eOZkCkuH1xnMkfjfvr9yufYzoQZHRERGnLfeeosrr7zytO+QXl1dTXV1dc/yUJyxKddnkhpK9TcfbuVPG+s42GxwPQvbuNiWi8+GgM/D7zcEA4a8oE1eyE8o309BQYCCohDxrhTvrj1EW6KEIjsf2xtFxm0nWNjIvE9NJFx8HgBt7a3Q3v15J6vdDhkuvWIil37ktURnkl3b6snL9zNhShk+X++ha0knQbIxMVhfmkExlP7bn2u5XHtFxekPkVSDIyIiw0IkEqGpqalnuampiUjkxNcbrFmzhrvvvrvXtlu2bOlZbm5uZvr06YMXVkaktpZ23vvvAxxoNGTcQsJWCMuMIYhHxrj4sLA9AxnIZCCTgPgJ9+QANlCBIY5j13HFJ2OMmThhwLLmFQSZPmvSgO1PZDhRgyMiIsNCZWUl9fX1NDQ0EIlEWLNmDffdd99x6x04cIDOzk6mTp3a89qMGTP42c9+RkdHBwCbNm3illtuOWfZZXjqbOvivf/ex74Gj7RTSNjKxzLlBPFI00naHGRihc1Fs8b3zGiWiCfpak/Q2REn3pmmqytFMpEhmXRJpj1SaY90xuB5cOGUfKbNnISmWhYZWGpwRERkWLBtmyVLlvDoo4/iui7z5s1j/PjxrFq1isrKSqqqqoDu4Wlz5szBGNOzbTgc5gtf+AJLly4F4Itf/KJmUJNeEvEk+3cdpr6ug0PNHgmnkLBVgG3KCHoeabp6bmr5icvGU1B0/I0oAfJCQfJCQSKji89xBSJyjBocEREZNmbNmsWsWbN6vbZ48eJey8dmTvu4+fPnM3/+/EHLJkNfJpOhfk8jB/a20njEoSNhk/Hy8JkQIePHMmEgjN/zSBInxSHGxgyfuGwsRSVjsx1fRPpJDY6IiIiMSB/8aR+1WzpJOUEsEyJkAvhMHpAHdM8u5npJHLeDlC9JUb7H6FiQyeePoiSm+7+IDFdqcERERGTEcByHDW/vYtveAMV2MUEvjGNSOG6clH2EYNAhFgkwfmIxo8dFsW0725FFZICpwREREZFhL51O88fXdlDXXEyRPYo8yyFJPddcU8boirJsxxORc0gNjoiIiAxbnW1dvPH6h3TEYxRYY7BNChM4wPULJlNQNC3b8UQkC9TgiIiIyLDTcPAIb755EM8dRZ6pwPE6CRc1ct2C8/EHRmc7nohkkRocEREROaeaWzp55XeH8ZswaS+N56WxrDR+2yEv6BHOtyguDhKN5RMrL6IgFOjZdut7e6j5Qx15JkrQlNPqHGHyhBQ3zKnEtjXTmYiowREREZFzqHbDPrZtyyNsiunINOHhw1gBfF4+AceGuKEjDh1NcGAXQBcpr4OUl8b1HAqtECETo8ttpOoiP1MvnZTlikRkqFGDIyIiIoPOdV1+/avteInRQIaJFfXMuGZ6z/ue55Hp7KC5/ghNTV20tKVo6/SIpyzSrg/HC4Dxk7IauOryEsZMOj97xYjIkKYGR0RERAZVc3MHv/1dE8V2Ga2ZFq6fFyY6dnqvdYwx+MOFlJ1fSNkpepdYLEZjY+MgJxaR4UwNjoiIiAyaTev3sHV7iEKrCNfs48uLL8T2+7MdS0RGMDU4IiIiMuAcx+E3v9qOmyzDkGHiuEPMuPoT2Y4lIjlADY6IiIgMqObGdn77+yaK7XI6nBaun19EdMyF2Y4lIjlCDY6IiIgMmPfW72bL9gIKrWIca3/3kDSfftwQkXNH33FERETkrDmOw2//axtOagyGDBPGH2bmVRdnO5aI5CA1OCIiInJW9u86yOp34hTbFXQ4R7h+QQnR8qnZjiUiOUoNjoiIiJw2x3GofXsXW/fYFNqlFFpBHLuOL39hqoakiUhW6TuQiIiI9FtXRxd/eH03LR0RCu1R5NkOCQ5x5WXFTDx/et87EBEZZP1qcGpra1m5ciWu67JgwQIWLVrU6/3Dhw/zr//6r7S1tREOh7n33nuJRqODElhERETOvX27DvL2uiMYbxR5ZiyYBHawjuvnT6KgSDOkicjQ0WeD47ouK1asYNmyZUSjUZYuXUpVVRXjxo3rWefpp5/mmmuuYe7cubz//vs8++yz3HvvvYMaXERERAaX4zhsfHsXf97ro9AqIUgZ7e4RJo7LcMNV52Hb5dmOKCJynD4bnB07dlBeXk5ZWRkAc+bMYd26db0anP3793PHHXcAcNFFF/Hd7353kOKKiIjIYOvs6OLN1z+kpaO0exia5ZCg4egwtMnZjicickp9NjjNzc29hptFo1G2b9/ea52JEyfy7rvvcsMNN/Duu+8Sj8dpb2+nsLCw13o1NTXU1NQAsHz5cmKx2EDUMKB8Pt+QzHWu5HL9qj03awfVL/JRWzbu4f0/FxCyKj42DO2CbEcTEemXAZlk4Pbbb+cnP/kJq1evZtq0aUQiESzLOm696upqqqure5YbGxsH4uMHVCwWG5K5zpVcrl+152btkNv1V1RUZDuCDCEb1u5m9+4iPFxGlx/ihqunaBiaiAw7fTY4kUiEpqamnuWmpiYikchx63z9618HIJFIsHbtWgoKCgY4qoiIiAyWd/64i7r9JaS9DHOqHCacrzM2IjI8HX+a5WMqKyupr6+noaGBTCbDmjVrqKqq6rVOW1sbrusC8Mtf/pJ58+YNTloREREZcH94YycH95eScFPMne0x4fwx2Y4kInLG+jyDY9s2S5Ys4dFHH8V1XebNm8f48eNZtWoVlZWVVFVVsWXLFp599lmMMUybNo277777XGQXERGRs/T73+8g3hyl0+3iuk8HiI4dle1IIiJnpV/X4MyaNYtZs2b1em3x4sU9z6+88kquvPLKgU0mIiIig+rl3+zEbY/S7rTzub8KUzQ60vdGIiJD3IBMMiAiIiLDyy9/tRNfPEpbpoXPfyZCfmlxtiOJiAwINTgiIiI5xHVdnn9pD6F0lLbUYW6+qZxgUWHfG4qIDBNqcEREZNiora1l5cqVuK7LggULWLRo0XHrrFmzhueeew5jDBMnTuT+++8HuodWT5gwAeieGvwb3/jGOc0+FDiuyy9e3EvYKaU9WceXvjgBf34427FERAaUGhwRERkWXNdlxYoVLFu2jGg0ytKlS6mqqmLcuHE969TX1/PSSy/xyCOPEA6HaW1t7XkvEAjw3e9+NxvRh4R0xuUXL+6nyCuhK7mHxTdPxQ6Fsh1LRGTA9TlNtIiIyFCwY8cOysvLKSsrw+fzMWfOHNatW9drnddee42FCxcSDneflSgu1nUlAPGUw6oX6ijyikgld/DFxReouRGREUtncEREZFhobm4mGo32LEejUbZv395rnbq6OgAeeughXNfl5ptvZsaMGQCk02kefPBBbNvmpptu4vLLLz/uM2pqaqipqQFg+fLlxGKxwSrnjPl8vtPK1dqZ5j9XbaWYMCa1ja/cey3G7x/EhIPrdOsfSXK5dsjt+nO59jOhBkdEREYM13Wpr6/n4Ycfprm5mYcffpjHHnuMgoICfvCDHxCJRDh06BD/9E//xIQJEygvL++1fXV1NdXV1T3LjY2N57qEPsVisX7nemtjE3u3OYQJ4U+9x8Jbr6LpI8P2hqPTqX+kyeXaIbfrz+XaKyoqTnsbDVETEZFhIRKJ0NTU1LPc1NREJBI5bp2qqip8Ph+jR49mzJgx1NfX97wHUFZWxvTp0/nwww/PWfZz7YO9nTyz6gDNH9gY1yPmbWThbVdhbDvb0UREBp0aHBERGRYqKyupr6+noaGBTCbDmjVrqKqq6rXO5ZdfzubNmwFoa2ujvr6esrIyOjo6SKfTPa9v27at1+QEI0VjW5qf/3IvW9ekCHl5OF2bWXRlB1d9eT7GUnMjIrlBQ9RERGRYsG2bJUuW8Oijj+K6LvPmzWP8+PGsWrWKyspKqqqquPTSS9m0aRMPPPAAlmVx2223UVhYyLZt2/jRj36EZVm4rsuiRYtGVIOTyrj87rUDpFryCVFIV2I/C6anGfXJORhjsh1PROScMp7nedn68GMXgw4luTzGEXK7ftWem7VDbtd/JmObc8lQP05sAukYAAAgAElEQVS5rstb6xvYv8sibAK0plq4LLqfC6+djfGNzN9h5vL/r7lcO+R2/blc+5kcp0bmdz8REZERbtvuVtat7aDYFICbpMR+jxv+egZ2eHK2o4mIZJUaHBERkWGkvrGT55/fQ36miBB5uMk/89d/NZG8MddkO5qIyJCgBkdERGSY2LCxjt3b8siniK7kfv5qlp/YJ67MdiwRkSFFDY6IiMgw4LouW/9s8OFwcdkeps67TBMIiIicgBocERGRYeCtDY0UWSEC7OCC+VV9byAikqN0HxwREZEhznVd9u3w6HRT/PUNU7MdR0RkSFODIyIiMsT9obaNQhOkpOs98iefl+04IiJDmhocERGRISzjuNRvT9PhJFgwqzjbcUREhjw1OCIiIkPY6o3tFOJnVNMafJdelu04IiJDniYZEBERGaLSGZfDO9N4TprrptoYS7+XFBHpi75TioiIDFGv/3cbYXxU7P89vk8tyHYcEZFhQWdwREREhqBk2qXlQwcnk+D6sXFMfjjbkUREhgWdwRERERmCata1UoDNxA9/jT3/M9mOIyIybKjBERERGWLiKYeOfS5tmQ6uyK/DjBmf7UgiIsOGhqiJiIgMMTVrW8nHZtTOF/DddGO244iIDCs6gyMiIjKEdCUcEnXQmmnnk4nN8IlZ2Y4kIjKs6AyOiIjIEPL7ta3kYTF5+yrMvM9gLDvbkUREhhWdwRERERki2royZA5Cq9vOzMZ3MVdpamgRkdPVrzM4tbW1rFy5Etd1WbBgAYsWLer1fmNjI08++SSdnZ24rsstt9zCrFk6pS4iInI6ata2EsRmyrafYWbP09TQIiJnoM8Gx3VdVqxYwbJly4hGoyxdupSqqirGjRvXs84LL7zA7Nmzufbaa9m/fz/f+c531OCIiIichpaODF6DoZV2bjjwR8xX/iXbkUREhqU+h6jt2LGD8vJyysrK8Pl8zJkzh3Xr1vVaxxhDV1cXAF1dXZSWlg5OWhERkRHqtXdaCWAxY8cqmD4DUzEh25FERIalPs/gNDc3E41Ge5aj0Sjbt2/vtc7NN9/MP//zP/O73/2OZDLJQw89dMJ91dTUUFNTA8Dy5cuJxWJnk31Q+Hy+IZnrXMnl+lV7btYOql+yr6k1jdVkaLU6mfbhm1j/37JsRxIRGbYGZBa1t956i7lz53LjjTfywQcf8MQTT/D4449jWb1PEFVXV1NdXd2z3NjYOBAfP6BisdiQzHWu5HL9qj03a4fcrr+ioiLbEQR4bW0beZ5FVd2vYVQ5fOKybEcSERm2+hyiFolEaGpq6lluamoiEon0Wuf1119n9uzZAEydOpV0Ok17e/sARxURERl5DrWk8DdbtAcTnP/+bzU1tIjIWeqzwamsrKS+vp6GhgYymQxr1qyhqqqq1zqxWIz3338fgP3795NOpykqKhqcxCIiIiPIG2vbsIAr2lZDIKipoUVEzlKfQ9Rs22bJkiU8+uijuK7LvHnzGD9+PKtWraKyspKqqiruuOMOfvjDH/Kb3/wGgHvuuQdjzKCHFxERGc4ONCXJO2LTWZBm8h+ex1xdramhRUTOUr+uwZk1a9Zx0z4vXry45/m4ceN45JFHBjaZiIjICPfm2nZC2Fzl/Ddk0ph5n8l2JBGRYa/PIWoiIiIy8A62pAi12cQLM4xb8xxMu1RTQ4uIDAA1OCIiIlnwdm0HtjFcHt4DLY1YC27MdiQRkRFhQKaJFhERORdqa2tZuXIlruuyYMECFi1adNw6a9as4bnnnsMYw8SJE7n//vsBWL16NS+++CIAn//855k7d+65jN6L47okD3t0+jJMeud5TQ0tIjKA1OCIiMiw4LouK1asYNmyZUSjUZYuXUpVVRXjxo3rWae+vp6XXnqJRx55hHA4TGtrKwAdHR08//zzLF++HIAHH3yQqqoqwuHsXNC/flsnBZ5NXqQNdmzB3LxEU0OLiAwQDVETEZFhYceOHZSXl1NWVobP52POnDmsW7eu1zqvvfYaCxcu7GlciouLge4zP5dccgnhcJhwOMwll1xCbW3tOa/hmB3bkyRxmbPn191TQ19d3fdGIiLSLzqDIyIiw0JzczPRaLRnORqNsn379l7r1NXVAfDQQw/hui4333wzM2bMOG7bSCRCc3PzcZ9RU1NDTU0NAMuXLycWiw14HQ0tCQq6WsiUuuT94Q1CCz5D0YRJ/d7e5/MNSq7hIpfrz+XaIbfrz+Xaz4QaHBERGTFc16W+vp6HH36Y5uZmHn74YR577LF+b19dXU119V/OpjQ2Ng54xpf/XzO2sbgg+R6kUySvnH9anxOLxQYl13CRy/Xncu2Q2/Xncu0VFRWnvY2GqImIyLAQiURoamrqWW5qaiISiRy3TlVVFT6fj9GjRzNmzBjq6+uP27a5ufm4bc8F13XprHdpszNMfedncP50zNiJ5zyHiMhIpgZHRESGhcrKSurr62loaCCTybBmzRqqqqp6rXP55ZezefNmANra2qivr6esrIwZM2awadMmOjo66OjoYNOmTcyYMeOc17BpV5yw5yNS0AqHD2Lm3nDOM4iIjHQaoiYiIsOCbdssWbKERx99FNd1mTdvHuPHj2fVqlVUVlZSVVXFpZdeyqZNm3jggQewLIvbbruNwsJCAL7whS+wdOlSAL74xS9mZQa1rX+Ok4fFp3a+CIXFmFmzz3kGEZGRTg2OiIgMG7NmzWLWrFm9Xlu8eHHPc2MMd955J3feeedx286fP5/58+cPesaTaevKEOqwieenKHzvLcz1X8D4/FnLIyIyUmmImoiIyDnw/2rb8RnDRcn3AA9zzcJsRxIRGZF0BkdEROQcOHLAwVgw/Z2n4RNVmOjobEcSERmR1OCIiIgMss0fdlHk+iDUgNV2BEuTC4iIDBoNURMRERlk723pIuN5zN7+AsTK4KKZ2Y4kIjJiqcEREREZRF0Jh0C7RVcwSemf38Zccx3G0uFXRGSwaIiaiIjIIPp/77UTwGJC1ybw+TBXV2c7kojIiKYGR0REZBA17stgGZixdiXmsqswhcXZjiQiMqLpHLmIiMgg2X4gTlHGR56vCSvehdHkAiIig04NjoiIyCDZ8H4Xrudx5QerYNwkqLww25FEREY8NTgiIiKDIJlysI4Y2v1JRu9cj/n09Rhjsh1LRGTE0zU4IiIig+Ct9zvIw6KioxbyQpgrP53tSCIiOUFncERERAZB3YdpunD45DsrMFfOw+TlZzuSiEhOUIMjIiIywPY2JChO+7DtRuxMEjP3+mxHEhHJGWpwREREBtjaTZ24nscVW56F86djxk7MdiQRkZyha3BERIYhx3HIpB0ymQyZpEPGccikM6STDo7jksk4OGm3+7nj4WRcHMfDcY797eG6Hn9zV0W2Sxlx0hkXrxna7SQV+zZi/vZr2Y4kIpJT1OCISM5wHId0IkNXV4JUPE0yniKZzLA70EB7W0d3I+A4uI6Hk/FwXHDc7kbAdcFx6X7ugeua7r898DxwPYPngecZPI79bfA8A3Q/7/nbM2C6l7sfFt1za1kYY+j9h57XrKPLFgbrhLNx+dC39ex7Z0sHIWxGt78LhcWYWXOyHUlEJKfoSCgifXIch8ZDrRw60EpTU4K2To+MA0E/5AUhHLIJFwUoLglRGiskFM7Dtu1+7TvRmaTx0BFaW7poa0vR2eXQlfRIpi0yrn20QaCnQYDuRqJ3g8BHnh9tBYxF9x+DZSxsDDbmI9P0frwZiJz218Wi9zhfz/NwARcPzxz728PzPD76h+P+do/+ncFzu5e7Y7oYPPDAGK+7PTIexoD10ecGLMvDMubo8788bGOwbbAsC9sG27awfQbbMtg+jVIeDHt2pghgqH73J5hrb8T4/dmOJCKSU9TgiAwziXiSloZWWpq6aGtN0vHRhsCxcfAfPU/gYFsutuXisz0CPo+g3xAM2oRCNvn5AfILghQUBvFZQXZsOUBjQwctbQ6dcYuU48MliG0C5Bk/PmMBpUD3D/UBwEtBPAXxdjjccCxhGsdLkfIcMmRwvTSQwSLTfUYDPwY/tvERMD78xgKCRx/dDBDwPIxxcY82CMBfGgTT/Ryvuy2gp1k4tlYaz3PxcHGPNgmO8bCMi2152BbYlofPNvh8kB8K4mTSWJY52gSY7kbgaDPgsy18fhvbNvj8Nj6fhe33dT/32wT8fnxBG59P31JzXV1TksKkTdIcIuCmMNcszHYkEZGco6OxyBCSSqbZv7uBuv0dNLVl6Er6cdwAxvixjR+/8REwx9qLQM92Bgh6HlgueBkAbGPjw8LvWZABJwNdCehqh5bjPvkQUHD00d1cuMYh7SVx3U5Sdgqf36EobBGJ5DGmooRwcT4tjW20tnTR3p6kozNDPAHJNKQcC8ezcfEDPmyTj8/YuJ5Hxsvgkcbx4qTIgO2Q5/fIz7MoLPRTVJxHaSxMSazwnDUMsViMxsbGc/JZMrK9814HfmNTteVn8IkqTKws25FERHJOv356qK2tZeXKlbiuy4IFC1i0aFGv95966ik2b94MQCqVorW1laeeemrAw4qMBJlMhvo9jRzY10pji0NHwibj5WGbPPJNAMt0NxoWEDAuSZPEJY3jJYE0WA7BgEdB0CJcaFNcHCISDVM8Koz/BENh0qk0nW1ddLTF6WpP0dWVJh5Pk0i5JFMe6QyATdDvUFrsZ3RZmLLxUfLyg8ft6+PGFIxijCaHEgHAcV1Sh6HLJJhYtxHri9/MdiQRkZzUZ4Pjui4rVqxg2bJlRKNRli5dSlVVFePGjetZ56677up5/sorr7B79+5BCSsyUNLpNHs+OMSHe9pobrdIOSEMB/BIYZs0ATtDKOhRWGBRUpJHbFQB0fJSAsGTj6V3HIeO1i4OH2ylpbmL1rYMnQlIpi3Sjg+PAJYJEDIBfCYPyAMgaFxcL4njdZG0WigKuYyKBRg/KUK0rLjf17KcjD/gpyRWTEms+KTr6AyGyNlb9+dOCjybvLZ1ECuDi2ZmO5KISE7qs8HZsWMH5eXllJV1n2afM2cO69at69XgfNRbb73Fl770pYFNKXIWOlo72b7lIAcOJWjrCuCRT74VwmfCQJiA55EhgUcG24TwmyICno2TgCMJONIEH+4Ez+sgiUPaTeOSxpDCwwYC+IyfoPEdvU4lfPRx9FoVz8MzGTJeGtdNkLKPEAw6xEp9jJ1QQvn4KLau3RAZ9nbuSBLEYt6Gn2A+txhjnd0vJ0RE5Mz0+VNVc3Mz0Wi0ZzkajbJ9+/YTrnv48GEaGhq4+OKLT/h+TU0NNTU1ACxfvpxYLHYmmQeVz+cbkrnOlWzW7zgOjXXN7N5xiLpD3Re7dyVtHC9wdHJcl2MXkxvj9swoZRmwjNf9sMC2uqfz7UwGsEw+BSaAMd3/hoOWQ8KNkzZNFBfDeZNLuGjGZEIFefh8PjKZ7utXujrj1O1poOFgG43NXbR1OMQT4GQsPHwYAvhMqPsSdi+FSwdpk8EX8AjnW5QUBRk1Kp8xY6PExpQO+QZG/+5zu34ZGHbCkHDbCJHBXF2d7TgiIjlrQH/qeuutt7jyyiuxrBNPPVpdXU119V++6Q/FITG5PlRnMOt3HIfOti4OHWjl0MEOWtpculI2aSeIZYLkWcGjM2p1D9+yAJ9xcL0kHl73JL/G6rk7SM8UwF73wzamuwcCbMA2KTJeJym7mVElMPm8UsZOHoVtR3vl6ox30BnvOK72ktEFlIwuYOpZ1t1y5MhZ7mHw6d997tZfUaEbfQ6EVMYl5Fqk2uswl12FKTz5kFARERlcfTY4kUiEpqamnuWmpiYikRPfL2LNmjXcfffdA5dOhrR0Os2BXYc50hKnoyNNZ9wlkYJkpnu6YvfofUYs48dnfASwu5uQjwzhCuDiWikcL0nKa8f2ZygpNIwelc/YSRGKSkv6nefYnd3TyTQA4eL+bysicjb2H05hGUNh237MouuzHUdEJKf12eBUVlZSX19PQ0MDkUiENWvWcN999x233oEDB+js7GTq1LP9fbcMVZ0dcbbU7mdffZqudAEhq5CAyQfye9ax6J6u2FguGS+Dc/S6k4zpnv0rYLvk50EsEqBiXAmjxpae9UX0x9i2jW3bBPMCfa8sIjKA6hpTAIyy2qByWpbTiIjktj4bHNu2WbJkCY8++iiu6zJv3jzGjx/PqlWrqKyspKqqCugenjZnzpyP3CVchrumhlY2b6rnYBOk3ELCVj62GYUfsE2CpNtEqCBFYYFFuCBAcWkeJZEwRZGwbngoIjml5UgGsBhfgo6DIiJZ1q+fQmfNmsWsWbN6vbZ48eJey5o5bWjIOA4b1u5h+x4PzwsCDgYHY9yP3dUeAn5DMGDIC9rkhXz4/Tb1B3dQ3+QBhRRaeUA5ATzSdJHkEGMiLtMvriA2phwoz26xIiJDREe7S8DziJTkZTuKiEjO06/ZR4gDe5t4593DpNMRCqwIecYh7saxTADL2NjGwudZ+FwLXHDSEI9D/Lg9FRI0LnGnnbTdxPgxfqbPGEdBYWkWqhIR6a2vG0+vXr2ap59+uuda0euuu44FCxYA3b+YmzBhAtA9scQ3vvGNAcvlxF0SThpr1OgB26eIiJwZNTjDWCKR5u0/7OJAUx6FVhEBU07cbSdYcIj510ykoKT3bGGe55HuStDVEaejPUlXZ5J4V4ZEwiGRckinPaZMGcXY80rxB048kYSISLb058bT0H2/thNNeBMIBPjud787KNl8GQsn1YqJjhqU/YuISP+pwRmGPnh/Pxvf78QiRp4pI2DSpKin6uIwky+aAIw/4XbGGAIFIQIFIUrKTrzvXJ4uV0SGttO98fS5kkw5hDybdOIwRKZkNYuIiKjBGTbaWjp46829NHcWU2SHyaOAducIY0bHue6aKfiD+q2hiIxs/b3x9Nq1a9m6dStjxozhzjvv7LmJazqd5sEHH8S2bW666SYuv/zy47Y9kxtSb97dimXaKeqoI3b+ZzHB4JmW2C+5fmPaXK4/l2uH3K4/l2s/E2pwhqjWpna2vlfHgcMO8UwB+VYhflOBZZI49gFmX1lO2bjJ2Y4pIjKkXHbZZVx11VX4/X5effVVnnzySR5++GEAfvCDHxCJRDh06BD/9E//xIQJEygv7z1ZypnckHrbrjYARqcO0dTeDu3tA1jR8XL9THsu15/LtUNu15/LtZ/JDanV4AwBmUyGXVvq2Lm7nZbOAJhCwiaIMWUEgJRJkHQbmTTZ5tLLJ2HbJxlfJiIygvXnxtOFhYU9zxcsWMAzzzzTa3uAsrIypk+fzocffnhcg3MmWlozWFiM8x05632JiMjZU4OTBc0NrWz900HqG10SmQJCdpiAKQKKCFgOCbeDlN3C2FEWF1w8hsgoTcksItKfG0+3tLRQWto96+P69et7rs/p6OggGAzi9/tpa2tj27Zt3HTTTQOSq6vdxe95lBZrimgRkaFADc45tPPPdazd0EWBFcUyZfg97+jZmSYKCtNMOa+ISReOwWdH+96ZiEiO6c+Np1955RXWr1+PbduEw2HuueceAA4cOMCPfvQjLMvCdV0WLVo0YJMTOAlwnTgmqimiRUSGAjU458C2zXWs35QgbJUSsvLo8hqorDBc+IkKikp1fxkRkf7q68bTt9xyC7fccstx211wwQU8/vjjg5LJnzFkkkegXJO9iIgMBWpwBtGf3jvAps1piq0SQlYeSe8gn746Rtn4C7IdTUREBkBX0iEfm2TisO6BIyIyRKjBGQQbNhxgy58diu0iQsYh4R5g3jWjiY2dlu1oIiIygPYeSgJQ0nEAInOynEZEREANzoBxXZf16+v4YCcUW2HyLIe0u48Fc8dQMuaibMcTEZFBcLApA8Dott0QXZTlNCIiAmpwzprrurz9bh27d1sUW2ECJoPjfsh1CyYQHv2JbMcTEZFB1NKawYfF+K59kF+Q7TgiIoIanDOSzrjUvn+Y3Ts7cVIFhK0wftIYbxefrZ5EfmxGtiOKiMg5EO/sniK6qDCAMSbbcUREBDU4/dbQnGDjhnoaGy3yCBMwQYJegHanDZ+1l5sWTiEvMqvvHYmIyIjhJSDpxEFTRIuIDBlqcE7CdV22bGti259bSSTyKTQhjCklQIZk+jCRgnZmXRKjqHISxkzMdlwREckCf8aQSR3RDGoiIkOIGpyPSKQcXn75T+zaE8fnFREyfgLESLidpLy9TK3wmH75efjCF2Y7qoiIZFl73CGETbrzoO6BIyIyhKjBOaoznuGXLx2k2AoT9ILE080UhVqZOb2Y0Reeh7HGZjuiiIgMIXsbuqeILu48AJFJ2Q0jIiI91OAAXQmHX/6qgSJTQMDs4tprzyNYOiXbsUREZAhraEoDUNa6GxO9PMtpRETkGCvbAbKtK+nw4q8PU+SFCCY2ccvfVxMsLcl2LBERGeJaWrvvgTOhZZsmGRARGUJyusHpbm6aKHKCBI68w7VfvhJj5fSXRERE+inR6RH3MhR4CSjWL8ZERIaKnP1pvivp8OLLzRRl/AQa/sDCL1VhgnnZjiUiIsOEl4CUl4TSGMaysx1HRESOyskGpyvp8OJvmilK+/DXvcbCz07DRDQDjoiI9F/AsbDSbRqeJiIyxOTcJAPdzU0LRSkf/gOvsvDqMsx5F2Q7loiIDCOtnRnysPB1HdIvyEREhpicOoPzl+bGxn+ghoXnJbGu+HS2Y4mIyDCz5+gU0SVHPtQZHBGRISZnGpx46iPNTd3rXBvajrnp1mzHEhGRYaihqXsGtbLWHRCJZTmNiIh8VE40OPGUwwsvt1Cc9uE/9AeubX8Ta8kDmjFNRETOSGtbBs/zGH9kO0ZncEREhpQR/xP+R5sbu2kN1+59Eev/W4bJC2U7moiIDFOJTo84DvmZuIaoiYgMMSO6wenV3LRvYOGffox1zz9gorogVEREzkIS0qS6n2uImojIkNKvWdRqa2tZuXIlruuyYMECFi1adNw6a9as4bnnnsMYw8SJE7n//vsHPOzpSKZdXvjN0eYm82cWrv0+5u4HMJUXZjWXiIgMf0HHIu12QFEJxh/IdhwREfmIPhsc13VZsWIFy5YtIxqNsnTpUqqqqhg3blzPOvX19bz00ks88sgjhMNhWltbBzV0f/zytWaKUz7swH4W1nwbc/0XsK6cl+1YIiIyzDW1Zwhi4Us2aniaiMgQ1OcQtR07dlBeXk5ZWRk+n485c+awbt26Xuu89tprLFy4kHA4DEBxcfHgpO2nNza2UtDqozPQzsJXlsGMKzCLbs9qpv+fvTuPi6rcHzj+OTPDLiKIgBp6XTARcyG0AlNcKjPLJatr5YaZ5dZyr5Zbu+USZSbdNBHXbnbVyG5RilZqZmq2KqW4UqBsorIznOf3hz/mhoCAAjMw3/fr5as55zznnO93huaZ55znPI8QQoiGIen/h4j2vHBa5sARQggbVOkdnMzMTJo2bWpZbtq0KUePHi1VJjk5GYC5c+ei6zr33Xcf3bp1K3Os+Ph44uPjAZg/fz7e3jXfb/m3Uxc4d6SYfKPOsJ1zMLVui+eMeRhcXKu0v8lkqpW46gt7zl9yt8/cQfIX1ZOaUQSAX+oh6NbWytEIIYS4XJWewamMruukpKTw/PPPk5mZyfPPP8/rr7+Om5tbqXIDBgxgwIABluX09PSaOL1Fbn4xWz87hyMGwo4sw5Fi9MeeJTMnF3Jyq3QMb2/vGo+rPrHn/CV3+8wd7Dv/Fi1aWDuEeufChWIclAH/9ATwusna4QghhLhMpV3UvLy8yMjIsCxnZGTg5eVVpkxISAgmkwkfHx+aN29OSkpKzUdbidj4czQqNuKXvYfrTu3H8NgzMj+BEEKIGlWQq8jTinHSC2VUTiGEsEGVNnDatWtHSkoKqampmM1m9uzZQ0hISKkyPXv25NChQwBcuHCBlJQUfH19ayfiCnz+XRbuOSYKDGe5Ze9ytPsi0K6/oU5jEEIIYQcKwKxd6qYmgwwIIYTtqbSLmtFoJCIignnz5qHrOn379sXf358NGzbQrl07QkJC6Nq1Kz/99BNPPfUUBoOBhx9+GHd397qIH4DDp3PJO6HIMRRwX/xMtJvD0frfXWfnF0IIYR90XcdZN1Co5V1aIXdwhBDC5lTpGZzg4GCCg4NLrXvggQcsrzVNY8yYMYwZM6Zmo6uC8zlmft6bh0GD/gdew+T/N7RRk9E0rc5jEUIIUbsqm5ftq6++Yu3atZau1AMHDqR///6WbZs3bwZg+PDhhIeHV/v86RfNOGLAVJwFLq5oro2uLSEhhBA1rkYGGbAWXdf5JD4Ld91Ii5SP8CnKwDDpDTRHJ2uHJoQQooZVZV42gNDQUMaPH19qXXZ2Nhs3bmT+/PkAPPvss4SEhFimN6iqP84WAuCVkwwyRLQQQtikSp/BsWWffpOFR74JveAowb9twTBxhgwqIIQQDVRV5mWryI8//kiXLl1o1KgRjRo1okuXLvz444/VjiHtnBmAFpm/SwNHCCFsVL29g/NDYg76nxoXtYs8sPsVtAfGo3XsYu2whBBC1JKqzMsG8N1335GQkEDz5s0ZM2YM3t7eZfb18vIiMzOzzL6VzdeWl3ceTelcl/ITLr360dgK8yfZ+7xN9py/PecO9p2/Ped+NeplAyfjfBFHvs9HaYpBu+divDkcrf891g5LCCGEld14442EhYXh4ODAtm3biIqK4vnnn6/y/pXN15ZzvgijQcOUnUW+mzuFVpg/yZ7nbQL7zt+ecwf7zt+ec7+a+drqXRe1Yl0nbvt5nJSBTr9F4+HTRAYVEEIIO1CVednc3d1xcHAAoH///hw/frzcfTMzM8vsWxVaIZiN5v8PSLqoCSGELap3DZzYr87hUWTClLWfoKxfMEyaJYMKCCGEHajKvGznzp2zvD5w4IBlAIJu3brx008/kZ2dTXZ2Nj/99BPdunWr1vlLhog2GQoA5JlPIYSwUfWqi9rewwEk6gEAACAASURBVBdxSDVwoTid+394B8NTL0kFI4QQdqIq87LFxcVx4MABjEYjjRo1YtKkSQA0atSIe++9l5kzZwIwYsSIao+gdjarCAcMOJB9aYXMgSOEEDap3jRwkjMKSPqlCDNm7t49F+N942RQASEEAEop8vPz0XX9qrqrnj17loKCglqIzDYopTAYDDg7O9f77ryVzcv24IMP8uCDD5a7b79+/ejXr99Vn/uPtCIAmhamgdEEjT2v+lhCCCFqT71p4Oz4+iKuykCXn96iUUgPGVRACGGRn5+Pg4MDJtPVfaWZTCaMRmMNR2VbzGYz+fn5uLi4WDuUeiu9ZIjoi8fByxvNUO96eQshhF2oF9/OqecL8SgyoZ8/THvXHLSHZVABIcT/6Lp+1Y0be2EymdB13dph1GsXLxRTrBTN02UOHCGEsGX1ooHz05FcAALS9l0aVMBJBhUQQvyPXPCoGnmfrk1hriLPoGPKOCvPfwohhA2rF5c8U5MLcVRGOnW9TioVIYQQVmEo1Ch20OF8pgwwIIQQNszm7+AU6zqOeUYK885gur6TtcMRQoirFhkZybvvvmvtMMRVKNZ1XHQDDo7FoJR0URNCCBtm8w2cQ6fycNaMNMv4Ff4WYO1whBBC2KGUzCJMmkYjYx4gc+AIIYQts/kGTuKJfABu4LhM6CmEsFlJSUn07t2bJ598kl69ejFlyhR27tzJkCFDCAsL44cffihVfv369Tz88MPk5eURHR1NeHg4AwYM4PHHH7dSBuJK/kwrBKCp+v+JRKWLmhBC2CybfwYnL1ORZ87Bt7WftUMRQtQD+gfvoZJOVG8fTUMpVeF2zb8Nhr9PqPQ4J0+eZNmyZbzxxhsMGjSI2NhYYmNj2bp1K2+//TZBQUEAxMTEsHPnTqKjo3FyciIqKopvv/0WJycnzp8/X63YRd24NES0RsuC5EsrPKWBI4QQtsqm7+Ccu2imkdmI44XjaAGB1g5HCCGuyN/fn8DAQAwGAx06dKBXr15omkbHjh1JSkoCYOPGjezYsYPly5fj9P8jQgYGBjJlyhQ2bdokw13bqOyLxZiVwi/rOHh4oTk4WDskIYQQFbDpmvTHxBwMmkabM3uh3aPWDkcIUQ9U5U7L5UwmE2az+ZrP7fSXIewNBgOOjo6W18XFxQB07NiRQ4cOkZKSQqtWrQBYs2YNe/fuZdu2bSxZsoTt27dLQ8fGFOVCsUHHkJkGXt7WDkcIIcQV2PQdnJTkIgqVTmf9NJq7h7XDEUKIa9a5c2cWLFjAuHHjOHPmDLquk5ycTFhYGLNnz+bixYvk5ORYO0xxGWORhu6oICNVBhgQQggbZ7OXCHVdx5StkVeQjmPA9dYORwghakzPnj2ZO3cuo0eP5t///jdTp07l4sWLKKWIiIjAw0Mu6NgSc/GlIaILXYshMx2632ztkIQQQlyBzTZwjv5ZgAtGGmX8AjfJ8zdCCNvm7+/Pjh07LMuLFy+ucBtAeHg44eHhAMTGxtZJjOLqJGcUYtQ03J2LwVwEcgdHCCFsms12UUs4fmmugRv++EoGGBBCCGE1f6YVAeBtzAZAk0k+hRDCptnsHZzsDB2DXkQL7Tw0a27tcIQQQtipjJIhovWMSytkDhwhhLBpNtnAuZhXTKNCIwW5J6F9JzRNs3ZIQggh7FR2djGOGPDJ/vPSCi/poiaEuDZKKfLz89F1vUq/c8+ePUtBQUEdRGYdSikMBgPOzs418rvfJhs4Px7NwahptPrzW7RbpHuaEEII6zHngW7QMZxLRbm4obm6WTskIUQ9l5+fj4ODQ5WnBDCZTBiNxlqOyrrMZjP5+fm4uLhc87Fs8hmcP/8sxKx0uv65E619J2uHI4QQwo6ZijSUIyiZA0cIUUN0XZf5zi5jMpnQdb1GjmWTDRwuauSoHJxMgH8ba0cjhBDCThWaLw0R7eiqQUaqjKAmhKgR8vhF+WrqfbG5Bs6JM/m4KSMe2YnQ9no0ad0KIRqIyMhI3n333SqV3bBhA5GRkbUckajMH2mFGDSNxo2NkJmGJgMMCCGEzbO5Bs6vibkABB39DK29PH8jhBDCepLTCwHwdleQmyN3cIQQDUJGRgZDhw6lX79+fP7555b148aN48yZM1U+TlJSEv369auNEK+JzTVwzqfp5GCm9bnf5fkbIUS9kZSURO/evXnyySfp1asXU6ZMYefOnQwZMoSwsDB++OGHUuXXr1/Pww8/TF5eHtHR0YSHhzNgwAAef/xxAJydnXFzk4fZre1clhmA64znL62QOXCEEA1AbGwso0aN4tNPP2XFihUAbN26lc6dO+Pn52fl6K5dlfp//fjjj8TExKDrOv3792fo0KGltn/11VesXbsWLy8vAAYOHEj//v2rHUxeYTFuBUbytQzQDND2+mofQwhh31YcOMuJc/nV2kfTNJRSFW5v4+nMIyG+lR7n5MmTLFu2jDfeeINBgwYRGxtLbGwsW7du5e233yYoKAiAmJgYdu7cSXR0NE5OTkRFRfHtt9/i5OTE+fOXfkgPGTKkWjmI2pGTreMAeOemATLJpxCiYTCZTOTl5VFQUIDBYMBsNrNixQpWr15tKTNixAiCgoLYt28fubm5vPXWWyxdupSEhATuuecennnmmVLHPHXqFBMmTGDhwoW4uLjw9NNPU1hYiFKK5cuX07Zt27rLr7ICuq4THR3NnDlzaNq0KTNnziQkJITrrruuVLnQ0FDGjx9/TcH8lJiLSdO4LutX8P8bmovrNR1PCCHqkr+/P4GBl7rWdujQgV69eqFpGh07diQpKYmgoCA2btxI8+bNWblyJQ4ODgAEBgYyZcoUBg4cyMCBA62ZgriMOQ+KDTpaVioKpIuaEKLG6R+8h0o6ceUylVyIu5zm3wbD3ydUuH3YsGFMnjyZ9evXM2vWLFavXs29995bZohmR0dH4uLiWLFiBREREcTFxdGkSRNCQ0OZMOF/x09MTGTSpEm8+eabBAUFMWfOHMaPH8/w4cMpLCykuLi4yrHXhEobOImJifj5+eHre+nqZWhoKPv37y/TwKkJp5IKcFJGuiZ8jNbz5ho/vhCi4avKnZbLmUwmzGbzNZ/bycnJ8tpgMODo6Gh5XfLl3rFjRw4dOkRKSgqtWrUCYM2aNezdu5dt27axZMkStm/fLsOH2giTWaPYWUFGGphM0LiJtUMSQohr1rhxY9auXQtAVlYWUVFRREdHM336dLKyspg4cSIAt99+O3Cp7urQoYOlPdC6dWuSk5Px8PAgIyODiIgIVqxYQYcOHQC48cYbWbJkCSkpKdx55511evcGqtDAyczMpGnTppblpk2bcvTo0TLlvvvuOxISEmjevDljxozB27vsXAHx8fHEx8cDMH/+/DJl9AsZ5DgU4ZaTQePgm3Au5xi1zWQylRu7vbDn/CX3+pv72bNnr7lBcK37l0zAVnIcg8GA0WgsNTmbwWCgS5cujBs3jnHjxrFhwwZ8fHxITk6mT58+hIaGsmXLFgoKCnB2dr6meMrj5ORUrz9na3DRDRS56nAmDbyaoRls7tFVIUQ9d6U7LSVq6kJceRYvXsy0adOIjY2lR48eDB48mEceeQSg1IW6ktclyyUX7tzd3WnZsiX79u2zNHCGDRtG9+7d2b59O6NGjWLBggX06tWrVuIvT41cIrzxxhsJCwvDwcGBbdu2ERUVxfPPP1+m3IABAxgwYIBlOT093fL6j/QCGulGig1nAbjocx3Zf9leV7y9vUvFZW/sOX/Jvf7mXlBQcE0zPNdExVHyRV9yHF3XKS4uxmw2W7bpuo6u69x4443MnTuXBx98kH//+99MmjSJixcvopQiIiICNze3WqnICgoKynzOLVq0qPHzNCQlQ0SrjFQZYEAI0eAcP36clJQUQkNDOXz4ME5OTmiaRn5+PoYqXtBxdHQkOjqaBx98EDc3N4YNG8apU6do3bo148eP588//yQhIcG2GjheXl5kZGRYljMyMiyDCZRwd3e3vO7fvz/r1q2rdiC/HM0DoGPm99DUB01mixZC1CP+/v7s2LHDsrx48eIKtwGEh4cTHh4OXBrNRtgun6YOkJGG1rm7tUMRQogatWDBAstgAUOHDiUiIoKoqCj++c9/EhMTU+XjuLq6snr1akaOHImbmxtHjhxh06ZNmEwmfHx8mDp1am2lUK5KGzjt2rUjJSWF1NRUvLy82LNnD9OmTStV5ty5c3h6egJw4MCBq3o+JzPVjAmNdr/HowV2rfb+QgghRG24ztMA5zPBSwYYEEI0LMuWLbO89vb2ZsuWLZblu+66y/I6NDSU0NBQy/LGjRstr0su4Hl4ePDZZ58Bl57dmTJlSq3FXZlKGzhGo5GIiAjmzZuHruv07dsXf39/NmzYQLt27QgJCSEuLo4DBw5gNBpp1KgRkyZNqlYQhWYd53wDBS6FGM6fg3YywacQQoiyKpu2oMTevXt54403eO2112jXrh2pqak89dRTli55AQEBPProo5WerwCdpuYsdJAR1IQQop6o0jM4wcHBBAcHl1r3wAMPWF4/+OCDPPjgg1cdxM/Hc3HEgJcxFQAtQCb4FEIIUVpVpy3Iy8sjLi6OgICAUuv9/PxYtGhRtc5ZYNQh4//rJuk6LYQQ9YJNDAdz4lQBulJ0y9gHrm7Q3N/aIQkhhLAxf522wGQyWaYtuNyGDRsYMmSIZZ6ha+IEKvPSJJ9yB0cIIeoHm5hooShLUeBQTONDB6FdoAzDKYQQooyqTFtw/Phx0tPTCQ4OLtWXHCA1NZUZM2bg4uLC3//+d8ukrH91+XQG7k2ccM3PIUfT8A7oiFYTjaZrVN+Hdb9W9py/PecODSv/q5newB7mR6up6Qys/k6lnivEXTdR1KQQzvyBdktfa4ckhBCiHtJ1nTVr1pT7HKinpyfvvPMO7u7uHD9+nEWLFhEZGYmrq2upcpdPZ+DqopN77CR4eJJx/nxtp1Al9X1Y92tlz/nbc+7QsPKv7vQGtTkPji2pqekMrN7A+fFoLgABjmcA0NrL8zdCiIYpMjISNzc3HnvssUrLbtiwgT/++AOA6667rtRzj/aqsmkL8vPzSUpK4sUXXwQuzc69cOFCZsyYQbt27Sxd1tq2bYuvry8pKSm0a9fuiuf0aWpCZaTJHDhCCFGPWL0vWNoZM/nodDz7PZhM0Cag8p2EEELYnb9OW2A2m9mzZw8hISGW7a6urkRHRxMVFUVUVBQBAQGWxs2FCxfQdR241DUkJSUFX1/fSs/Z2scJMlLR5PkbIUQDkpGRwdChQ+nXrx+ff/65Zf24ceM4c+ZMlY+TlJREv379qlz+pptuqlacV8uqd3DMxTpOeQYKXHUMhxKgdXs0B0drhiSEEFclKSmJhx56iODgYA4cOEC3bt24//77iYyMJD09naVLl5Yqv379euLi4njvvfd4//33Wbt2LSaTiYCAAP71r3/h7OyMm5sbAM7OztZIyeZUZdqCihw+fJgPP/wQo9GIwWBgwoQJNGrUqNJzNnYxoJ9Lh+DQSssKIUR9ERsby6hRoxg0aBCjRo1i4MCBbN26lc6dO+Pn52ft8K6ZVRs4h0/l4YSBJr7Ap4loA+62ZjhCiAbg14O5XMgqrtY+mqahlKpwe+MmRjoHu1a4vcTJkydZtmwZb7zxBoMGDSI2NpbY2Fi2bt3K22+/TVBQEAAxMTHs3LmT6OhonJyciIqK4ttvv8XJyYnz//+cx5AhQ6qVg72obNqCv3rhhRcsr2+++WZuvvnm6p/wQhaYzTKCmhCiQTGZTOTl5VFQUIDBYMBsNrNixQpWr15tKTNixAiCgoLYt28fubm5vPXWWyxdupSEhATuuecennnmmVLHPHXqFBMmTGDhwoW4uLjw9NNPU1hYiFKK5cuX07Zt21IDxdRqfnVylgocPZmPgzLQzeksFJvl+RshRL3m7+9vGZmrQ4cO9OrVC03T6NixI0lJSQQFBbFx40aaN2/OypUrLc+EBAYGMmXKFAYOHMjAgQOtmYK4nGUOHHkGRwhRO1YcOMuJc/lXLFPZhbjLtfF05pGQirvhDhs2jMmTJ7N+/XpmzZrF6tWruffee3FxcSlVztHRkbi4OFasWEFERARxcXE0adKE0NBQJkyYYCmXmJjIpEmTePPNNwkKCmLOnDmMHz+e4cOHU1hYSHHxpQuPn332WZVzuBZWbeDkZyryTcV4/fkrCqBd2SE7hRCiOqpyp+VyNTU6jZOTk+W1wWDA0dHR8rrky71jx44cOnSIlJQUWrVqBcCaNWvYu3cv27ZtY8mSJWzfvt0uhgOtD/43B440cIQQDUfjxo1Zu3YtcGlAlqioKKKjo5k+fTpZWVlMnDgRgNtvvx24VHd16NDB8uxi69atSU5OxsPDg4yMDCIiIlixYgUdOnQA4MYbb2TJkiWkpKRw55130rZt2zrNz6o1aCOzkaJmOupQAjT3R3NvbM1whBCi1nXu3JnRo0czbtw41q9fj4+PD8nJyYSFhdGzZ0+2bNlCTk4OHh4e1g5VgOUOjnRRE0LUlivdaSlRm8NEL168mGnTphEbG0uPHj0YPHgwjzzyCECpC3Ulr0uWSy7cubu707JlS/bt22dp4AwbNozu3buzfft2Ro0axYIFC+jVq1etxF8eq46iZtA02rZyhGMJaO3l7o0Qwj707NmTuXPnMnr0aM6dO8fUqVPp378/d9xxBxEREdK4sSWZaeDqhuZS/TuDQghh644fP05KSgqhoaHk5eVhMBjQNI38/Ct3mfsrR0dHoqOj2bhxIx999BFw6Xmc1q1bM378eO644w4SEhJqK4VyWfUOTgE6nZ2yIDcHpIEjhKjH/P392bFjh2V58eLFFW4DCA8PJzw8HLg0mo2wTZfmwJG7N0KIhmnBggWWwQKGDh1KREQEUVFR/POf/yQmJqbKx3F1dWX16tWMHDkSNzc3jhw5wqZNmzCZTPj4+DB16tTaSqFc1m3gOOuYjiegkAk+hRBC2KCMVPCuvPuIEELUR8uWLbO89vb2ZsuWLZblu+66y/I6NDSU0ND/DZe/ceNGy+uSC3geHh6WQQRuv/12pkyZUmtxV8aqXdSa+pkg8TB4eEKz+j/mthBCiAYmM01GUBNCiHrGqg2crgEuqMQEaB+IpmnWDEUIIYQoKy9XBhgQQoh6xqoNHD8uQkaqDDAghBDCZmkyRLQQQtQrVm3gqGOXRlSQ52+EEELYLOmiJoQQ9YpVGzgcPQxOzuBft5P/CCGEEFUmXdSEEKJese4dnMTD0PZ6NKPRmmEIIYQQ5TM5gLvMSySEEPWJde/g/HEKrZ08fyOEEBW56aabyMzMtHYY9surGZrBulWlEELUtIyMDIYOHUq/fv34/PPPLevHjRvHmTNnqnycpKQk+vXrV+XyN910E0lJSYwYMaJa8VaXdb+1lY4WIA0cIYQQNkoGGBBCNECxsbGMGjWKTz/9lBUrVgCwdetWOnfujJ9f/Z+6xaoTfaIZoO31Vg1BCNGw7Ny5k7S0tGrto2kaSqkKtzdr1ozevXtf8RgREREkJydTUFDA+PHj0XWdU6dOMXfuXAA2bNjAzz//zLx589i0aRMrV66ksLCQ7t2789prr2GsQlfdivYLCAhg/PjxxMfH4+zsTExMDM2ayQ/zmiBz4AghGiKTyUReXh4FBQUYDAbMZjMrVqxg9erVljIjRowgKCiIffv2kZuby1tvvcXSpUtJSEjgnnvu4Zlnnil1zFOnTjFhwgQWLlyIi4sLTz/9NIWFhSilWL58OW3btqVp06YYDAaaNGlSu/nV6tEr498GzdnVqiEIIURNiIyMxNPTk7y8PO666y42bNjA0KFDLQ2cTz75hGnTpnH06FG2bNlCbGwsDg4OzJw5k82bN3Pfffdd8fhX2i83N5fg4GCeffZZXnnlFdavX8+TTz5ZF2k3fDLAgBCilv16MJcLWcVXLFPZhbjLNW5ipHNwxb+xhw0bxuTJk1m/fj2zZs1i9erV3Hvvvbi4uJQq5+joSFxcHCtWrCAiIoK4uDiaNGlCaGgoEyZMsJRLTExk0qRJvPnmmwQFBTFnzhzGjx/P8OHDKSwspLj4Un6fffYZgOWuUW2xagNHC5DhoYUQNauyOy3lMZlMmM3mazrvypUriYuLAyA5OZnTp0/TqlUrvv/+e9q0aUNiYiI9evRg1apV/PLLLwwaNAiA/Px8vL29Kz3+7t27K9zP0dGR2267DYAbbriBXbt2XVMu4i+ki5oQogFq3Lgxa9euBSArK4uoqCiio6OZPn06WVlZTJw4EYDbb78dgI4dO9KhQwd8fX0BaN26NcnJyXh4eJCRkUFERAQrVqygQ4cOANx4440sWbKElJQU7rzzTtq2rdsRk617B0cGGBBCNAB79uxh165dfPLJJ7i4uDBixAgKCgoYMmQIn3zyCe3bt2fgwIGWK3D33XcfM2fOrNY5rrSfyWRC0zQAjEbjNTfWxP9IFzUhRG270p2WEjVxIa4iixcvZtq0acTGxtKjRw8GDx7MI488Aly6gAZgMBgsr0uWS+7KuLu707JlS/bt22dp4AwbNozu3buzfft2Ro0axYIFC+jVq1etxF8eqw4yoLWXBo4Qov67ePEiHh4euLi4kJiYyMGDBwEYOHAgW7duJTY2liFDhgDQq1cv/vvf/5Keng7AuXPn+OOPPyo9x9XuJ66RdFETQjRgx48fJyUlhdDQUPLy8jAYDGiaRn5+fpWP4ejoSHR0NBs3buSjjz4CLj2P07p1a8aPH88dd9xBQkJCbaVQLut2UfNsas3TCyFEjQgPD2ft2rX06dOHdu3aERwcDECTJk1o3749R48epXv37gB06NCBGTNmMHLkSJRSmEwm5s2bx3XXXXfFc1ztfuIaST0lhGjAFixYYBksYOjQoURERBAVFcU///lPYmJiqnwcV1dXVq9ezciRI3Fzc+PIkSNs2rQJk8mEj48PU6dOra0UyqWp6jyxVMOSk5OtdeoKeXt7W66Q2iN7zl9yr7+55+bm4up69QOW1Oatf1tS3vvUokULK0VTP0g9ZXvsOX97zh0aVv7Vrbeknqoemb1MCCGEEEII0WBUqYvajz/+SExMDLqu079/f4YOHVpuub179/LGG2/w2muv0a5duxoNVAghGrLBgwdTUFBQat2SJUsIDJRnFYUQQojqqLSBo+s60dHRzJkzh6ZNmzJz5kxCQkLK9PvOy8sjLi6OgICAWgtWCCHKY8WetjXmv//9b62foyG8T0II0RDI93H5aup9qbSLWmJiIn5+fvj6+mIymQgNDWX//v1lym3YsIEhQ4bg4OBQI4EJIURVlczCLCpmNpsxGKRXshBC2AKpt8qqyXqq0js4mZmZNG36v1FkmjZtytGjR0uVOX78OOnp6QQHB7Nly5YKjxUfH098fDwA8+fPr9LkdnXNZDLZZFx1xZ7zl9zrb+5KKTIzM6+6stB1vcFfTXNwcMDX19cyX44QQgjrcXZ2Jj8/n4KCgip9Lzs5OZXpxtyQKKUwGAw4OzvXyPGueZhoXddZs2YNkyZNqrTsgAEDGDBggGXZFkfCaEgjdFwNe85fcq//uRuNxqvar6HkfyVKKTIyMsqsl1HUhBCi7mmahouLS5XL20M9VZMqbeB4eXmVqhQzMjLw8vKyLOfn55OUlMSLL74IQFZWFgsXLmTGjBky0IAQQgghhBCiTlXawGnXrh0pKSmkpqbi5eXFnj17mDZtmmW7q6sr0dHRluUXXniBUaNGSeNGCCGEEEIIUecqbeAYjUYiIiKYN28euq7Tt29f/P392bBhA+3atSMkJKQu4hRCCCGEEEKISmmqoT9ZK4QQQgghhLAbMmboZZ599llrh2BV9py/5G6/7Dl/e869vrL3z8ye87fn3MG+85fcq0caOEIIIYQQQogGQxo4QgghhBBCiAbD+MILL7xg7SBsTdu2ba0dglXZc/6Su/2y5/ztOff6yt4/M3vO355zB/vOX3KvOhlkQAghhBBCCNFgSBc1IYQQQgghRIMhDRwhhBBCCCFEg1HpRJ/2ZPLkyTg7O2MwGDAajcyfP9/aIdWad955h4MHD+Lh4UFkZCQA2dnZvPnmm6SlpdGsWTOeeuopGjVqZOVIa0d5+X/44Yds376dxo0bAzBy5EiCg4OtGWatSE9PJyoqiqysLDRNY8CAAQwaNMguPv+KcreXz76wsJDnn38es9lMcXExN998M/fffz+pqaksXryYixcv0rZtW6ZOnYrJJNWDLbKnegrsu66SekrqKamnrqGeUsJi0qRJ6vz589YOo04cOnRIHTt2TD399NOWdWvXrlUfffSRUkqpjz76SK1du9Za4dW68vLfsGGD+vjjj60YVd3IzMxUx44dU0oplZubq6ZNm6aSkpLs4vOvKHd7+ex1XVd5eXlKKaWKiorUzJkz1e+//64iIyPV7t27lVJKLVu2TH3xxRfWDFNcgT3VU0rZd10l9ZTUU1JPXX09JV3U7FSnTp3KXPXYv38/ffr0AaBPnz7s37/fGqHVifLytxeenp6W0UhcXFxo2bIlmZmZdvH5V5S7vdA0DWdnZwCKi4spLi5G0zQOHTrEzTffDEB4eHiD/OxF/WTPdZXUU1JPST119fWU9EG4zLx58wC47bbbGDBggJWjqVvnz5/H09MTgCZNmnD+/HkrR1T3vvjiC3bu3Enbtm0ZPXp0g69cUlNTOXHiBO3bt7e7z/+vuf/2229289nrus4zzzzDmTNnuOOOO/D19cXV1RWj0QiAl5eXXVWm9ZE911MgdZW9fFeVkHpK6qmrqaekgfMXL7/8Ml5eXpw/f55XXnmFFi1a0KlTJ2uHZRWapqFpmrXDqFO33347I0aMAGDDhg2sWbOGSZMmWTmq2pOfn09kZCRjx47F1dW11LaG/vlfnrs9ffYGg4FFixaRk5PD66+/TnJysrVDEtUg9VRpDf27RECQYQAAIABJREFU6nL29F0FUk9JPXX19ZR0UfsLLy8vADw8POjRoweJiYlWjqhueXh4cO7cOQDOnTtneZDNXjRp0gSDwYDBYKB///4cO3bM2iHVGrPZTGRkJLfeeis33XQTYD+ff3m529NnX8LNzY2goCCOHDlCbm4uxcXFAGRmZlq+C4Xtsfd6Cuznu6o89vRdJfWU1FPXUk9JA+f/5efnk5eXZ3n9888/06pVKytHVbdCQkL4+uuvAfj666/p0aOHlSOqWyVfmgD79u3D39/fitHUHqUU7777Li1btmTw4MGW9fbw+VeUu7189hcuXCAnJwe4NFLNzz//TMuWLQkKCmLv3r0AfPXVV4SEhFgzTFEBqacusYfvqorYy3eV1FNST8G11VOaUkrVerT1wNmzZ3n99deBSw819erVi+HDh1s5qtqzePFiDh8+zMWLF/Hw8OD++++nR48evPnmm6SnpzfY4RdLlJf/oUOHOHnyJJqm0axZMx599FFLX9+G5LfffuO5556jVatWltv7I0eOJCAgoMF//hXl/s0339jFZ3/q1CmioqLQdR2lFLfccgsjRozg7NmzLF68mOzsbNq0acPUqVNxcHCwdrjiMvZWT4F911VST0k9JfXU1ddT0sARQgghhBBCNBjSRU0IIYQQQgjRYEgDRwghhBBCCNFgSANHCCGEEEII0WBIA0cIIYQQQgjRYEgDRwghhBBCCNFgSANHiCravHkz7777rrXDEEIIIcol9ZQQl8gw0UIIIYQQQogGQ+7gCCGEEEIIIRoMk7UDEMIWxcbGEhcXR15eHp6enjzyyCMkJCRw5swZpk2bRnR0NF999ZWlfFFREcOHD+f+++8nMzOTlStXkpCQgLOzM3fddReDBg2yXjJCCCEaHKmnhKiYNHCEuExycjJffPEFr732Gl5eXqSmpqLrOgkJCZYy48ePZ/z48QCcPHmSl19+mR49eqDrOgsWLKBHjx48+eSTZGRk8PLLL9OiRQu6detmrZSEEEI0IFJPCXFl0kVNiMsYDAaKior4448/MJvN+Pj44OfnV27ZCxcusGjRIiIiImjTpg3Hjh3jwoULjBgxApPJhK+vL/3792fPnj11nIUQQoiGSuopIa5M7uAIcRk/Pz/Gjh3Lf/7zH/744w+6du3K6NGjy5Qzm81ERkYSFhZGWFgYAGlpaZw7d46xY8dayum6TmBgYF2FL4QQooGTekqIK5MGjhDl6NWrF7169SI3N5fly5ezfv16fH19S5VZuXIlLi4u/P3vf7es8/b2xsfHhyVLltR1yEIIIeyI1FNCVEy6qAlxmeTkZH799VeKiopwdHTE0dERTdNKldm2bRsJCQlMmzYNg+F//xu1b98eFxcXYmNjKSwsRNd1Tp8+TWJiYl2nIYQQooGSekqIK5M7OEJcpqioiPXr1/Pnn39iNBq5/vrrefTRR4mPj7eU+eabbzh79iwTJ060rBs2bBjDhw/nmWeeYc2aNUyePBmz2UyLFi144IEHrJGKEEKIBkjqKSGuTCb6FEIIIYQQQjQY0kVNCCGEEEII0WBIA0cIIYQQQgjRYEgDRwghhBBCCNFgSANHiCsIDw/nkUcesXYYQggh6oFVq1ZhMll//KavvvoKTdP4448/rB2KEFYhDRxhlzRNu+K/v/3tbwBs3ryZN954w7rBCiFEA5KRkcGMGTO4/vrrcXZ2xsfHh969e7NmzRrMZrO1w7smDzzwAH/++ae1w6gx69atKzP8dH2zatUqNE3Dz8+PoqKiUtvS0tJwcnJC0zR2795tWa9pGuvWrStV9qWXXsLJyYn3338fgLy8PObOnUtAQAAuLi54eXnRo0cPmV/IRlj/MoMQVpCSkmJ5vWfPHu69914OHjxI8+bNATAajQB4eXlZJT4hhGiIkpKS6NWrFyaTiZdeeonu3bvj4ODAnj17eP311+nSpQvdunWzdpjVppTCbDbj4uKCi4uLtcMRlzEajZhMJj755BOGDx9uWR8TE0Pz5s05depUhfsWFxczefJk3n//fT799FMGDBgAwOOPP86XX37JW2+9RdeuXblw4QI//PADp0+frvV8ROXkDo6wS35+fpZ/JY2YZs2aWdY1a9YMKNtFLTw8nPHjxzNnzhx8fHxo0qQJs2fPRtd1XnrpJXx9fWnWrBmzZ88udb6ioiJeeOEF2rRpg7OzM0FBQSxbtqzuEhZCCBswadIkCgoKOHjwIA899BCdOnUiICCAMWPG8P333xMQEABc+s589tlnadmyJY6OjnTq1Mly5byEpmm8/fbbPPDAA7i5udGqVSs2btzI+fPneeihh3B3d6dt27Zs2rTJss/JkyctV+f79++Pi4sLbdu25YMPPih17NmzZxMYGIirqyv+/v489thjnD9/3rK9pCval19+Sffu3XFyciI+Pr5MF7ULFy4wbtw4/Pz8cHJywt/fn6efftqyvap5vvPOO4waNQp3d3euu+46XnvttSq93z/88AM9e/bE2dmZzp07s2PHjlLbExMTuffee2nSpAmenp7cfvvt/PLLL8Clbm6jRo2yxKBpGmPHjmX79u04OjqSm5sLQH5+Ps7OzvTq1cty3G3btuHo6Eh2djYA2dnZPPHEE7Rs2RJXV1e6d+/O5s2bS8Vy9uxZxo4dS7NmzXB3dycsLIydO3datpd0u9u2bRu9e/fG1dWVTp06ERcXV6X3IiIigvfee8+yrJRixYoVjB8/vsJ98vLyuPfee/n444/ZuXOnpXEDEBsby/Tp0xk6dCht2rSha9eujB07lueee65K8YhapoSwc19++aUCVFJSUpltffr0UePHjy+13LhxYzVjxgz1+++/q+joaAWogQMHqunTp6vff/9drVq1SgHqs88+s+w3ZswYdcMNN6gvvvhCHT9+XH3wwQfKw8NDrVixok5yFEIIa8vIyFAGg0G9/PLLlZb95z//qby8vNSHH36ofv/9dzVv3jylaZqKj4+3lAGUr6+vWrVqlTp69Kh6/PHHlbOzsxo4cKCKiYlRR48eVVOmTFGurq4qPT1dKaXUiRMnFKCaN2+u1q1bp3777Tc1e/ZsZTAY1MGDBy3Hfvnll9XOnTvViRMnVHx8vLr++uvV6NGjLdtjYmKUpmmqR48easeOHerYsWMqNTVVxcTEKKPRaCk3depU1aVLF7V371516tQp9c0336jly5dXO08fHx+1fPlylZiYqJYuXaqAUmUuV1KvtW/fXn3yySfq8OHDKiIiQrm6uqrk5GSllFJnzpxRvr6+6rHHHlM///yz+u2339SUKVOUl5eXSk1NVQUFBZZzpaSkqJSUFJWVlaVyc3OVk5OT+vzzz5VSSsXHxytvb2/l6OiosrOzlVJKPfvssyo0NFQppZSu6yo8PFz16dNH7dq1Sx07dkwtW7ZMOTg4WHLIzc1VgYGBavjw4Wr//v3q6NGj6pVXXlGOjo7q8OHDpXLq0qWLiouLU0eOHFFjx45V7u7uKjMzs8L3ouQzOXXqlDKZTOrUqVNKKaW2b9+uPD091eHDhxWgdu3aVeo9f+utt1RoaKjq0KGDOnHiRJnjduzYUd11110qIyOjwnML65EGjrB71W3gdO3atVSZTp06qc6dO5da16VLF/WPf/xDKaXU8ePHlaZpKiEhoVSZF198scyxhBCiofruu+8UoDZt2nTFcjk5OcrR0VFFRUWVWj906FDVt29fyzKgnnjiCctyamqqAtSUKVMs6zIzMxWgPvnkE6XU/xo4c+bMKXXsW265RT388MMVxrR582bl6OioiouLlVKXfjQDaufOnaXKXd7Aueeee9SYMWOuOc+pU6eWKtOxY0f17LPPVhhvSb3214toRUVFqlWrVpbcn3/+eXXTTTeV2k/XddW2bVv15ptvKqWUWrt2rSrvWnifPn3U9OnTlVJKzZo1S0VERKjAwEAVFxenlFKqZ8+elvN8+eWXysnJSWVlZZU6xrhx49SQIUOUUpfet5YtW6qioqJSZfr27Wv5jEty+uvfz5kzZxRgaWyV56+fyZ133qmee+45pZRSDzzwgJo6darlb+LyBo6jo6Py9fVVaWlp5R539+7dqlWrVspgMKgbbrhBTZgwQX300UdK1/UKYxF1R7qoCVFNXbt2LbXs5+dHly5dyqxLTU0F4MCBAyilCAkJoVGjRpZ/r776KkePHq2zuIUQwpqUUlUql5iYSGFhIb179y61vk+fPhw6dKjUur9+Hzdr1gyj0Vjq+9jT0xNHR0fL93GJW265pdRyWFhYqWNv3ryZ3r1706JFCxo1asRDDz1EYWEhZ86cKbVfjx49rpjLpEmT2LhxI507d+aJJ54gLi4OXderneflzyW1aNGCs2fPXvHcl+dpMpno2bOn5dj79+/n+++/L1Uvubu7c/LkyUrrpr59+1q6u+3YsYP+/ftb1l24cIHvv/+efv36Wc5TWFhIy5YtS51r3bp1lvPs37+fM2fO0KRJk1Jldu3aVSaWv74Xvr6+GI3GKr0XAI8++igrV67k7NmzfPTRR0yYMKHCsoMHDyYzM5N58+aVuz0sLIxjx46xa9cuxowZw9mzZxkxYgT33HNPlf/WRe2RQQaEqCYHB4dSy5qmlbuupBIr+e+ePXtwdXUtU04IIexBQEAABoOBw4cPl3rQ+1pc/t1b3rq/fh9XxXfffcd9993HzJkzWbRoEZ6enuzdu5cxY8ZQWFhoKWc0GnF2dr7ise644w5Onz7NF198wVdffcXDDz/MDTfcwPbt26scD4Cjo+M15VQeXdfp378/S5cuLbPNw8Pjivv269ePl156idOnT1saM05OTrz22mvceuutODg4EBoaajmPh4cH+/fvL3Ockrx0XScwMJCPPvqoTJnL683L34uS/ati8ODBTJ48mYceeojg4GBuuOEGTp48WW7ZYcOGMW7cOEaMGEFOTg7vvvsuBkPp+wImk4nQ0FBCQ0P5xz/+wbp16xg1ahQ7d+6kT58+VYpJ1A5p4AhRy2688UYATp8+zeDBg60cjRBCWIeXlxd33nknS5cuZerUqWV+RBcVFVFYWEj79u1xcnJi586ddO7c2bL966+/LrV8Lfbu3cugQYMsy3v27KFTp04A7N69G29vb1555RXL9o0bN171uby8vBg5ciQjR45k3Lhx3HLLLRw+fLjO8izJy2w2s2/fPsvAASEhIaxatYrrrruuwoZaSWOiuLjYMroowE033YSzszMvvfQSAQEB+Pn50bdvX/7+97+zefNmQkNDcXJyspwnKyuL/Pz8CvMKCQlhzZo1NG7cGB8fnxrJvTwmk4mIiAheeeUVoqOjKy0/ePBg/vvf/zJkyBDy8vJYtWpVqffhcoGBgQBl7hiKuidd1ISoZe3btyciIoIJEyawdu1aEhMT+emnn1i5ciULFiywdnhCCFFn3nnnHRwcHLjxxht5//33OXz4MImJiaxbt46QkBCOHj2Kq6sr06ZNY+7cufznP//hyJEjvPrqq3z88cfMmjWrRuKIjo7m/fff58iRIzz33HN8++23ltHNrr/+etLS0oiOjub48eOsWbOGd95556rOM3v2bDZv3szvv//O0aNHWb9+PY0aNaJVq1Z1kuf8+fP57LPPSEhI4PHHHyctLY1JkyYBMGXKFIqLixkyZAi7du3i5MmT7N69m9mzZ7Nnzx4A2rRpA8CWLVtIS0uzjIrm6OhIWFgYq1evtnRF8/LyonPnzqxbt86yDi7d7RkwYADDhw8nNjaW48eP8/333/P2229bRjV76KGHaNOmDXfddRdbt27l5MmTfPfdd7z22mvExsbWyHtR4rnnniMtLY0xY8ZUqfyAAQP44osv2LJlCw888IBlLp0+ffrw7rvvcuDAAU6dOsX27duZNGkSTZo0oW/fvjUas6g+uYMjRB1Yvnw5kZGRzJs3j+PHj9O4cWOCgoKYMmWKtUMTQog606pVKw4ePMiCBQt44YUXOH36NI0bNyYwMJDp06dbrvDPmzcPg8HAk08+SVpaGu3bt7cM7VwT5s+fz/Lly4mIiKB58+asW7eO4OBg4NJV+9mzZzNr1iyys7Pp06cPixYt4sEHH6z2eZydnXnuuec4efIkRqORbt26ERcXZ7l7Vdt5vv7668ydO5dff/2Vdu3a8fHHH9OiRQvg0vMr3377LbNmzWL48OFcuHABPz8/br31VsuccD169OCJJ55g4sSJlkbBqlWrgEvP4Wzbtq1MY+bHH38stU7TNLZs2cKLL77IU089xZ9//omXlxfdunVjxowZlvfp66+/Zs6cOYwbN460tDSaNWtGz549GThwYI28FyUcHBzw9vau1j69evVi+/bt3HHHHQwdOpRNmzZx5513sn79ep577jkuXLhgmbA2Jiam2scXNU9T8iSUEEIIIezAyZMnadOmDbt27So1b4sQomGRLmpCCCGEEEKIBkMaOEIIIYQQQogGQ7qoCSGEEEIIIRoMqw4ykJycbM3Tl8vb25v09HRrh2E19py/5G6fuYN951/ywLEon9RTtsee87fn3MG+87fn3K+mnpIuakIIIYQQQogGQxo4QgghhBBCiAZDGjhCCCGEEEKIBsOmJvpUSpGfn4+u62iaZpUYzp49S0FBQa0dXymFwWDA2dnZajkKIYQQQohrU5e/W2v796m11fTvY5tq4OTn5+Pg4IDJZL2wTCYTRqOxVs9hNpvJz8/HxcWlVs8jhBBCCCFqR13+bq2L36fWVpO/j22qgaPrulUbN3XFZDI16Fa4EELUhnfeeYeDBw/i4eFBZGRkme1KKWJiYvjhhx9wcnJi0qRJtG3bll9//ZXVq1dbyiUnJ/PEE0/Qs2fPugxfCNHA2Mvv1rpSk7+PbepTsacuW/aUqxBC1ITw8HAGDhxIVFRUudt/+OEHzpw5w5IlSzh69CgrVqzg1VdfpXPnzixatAiA7Oxspk6dSteuXesydCFEAyS/5WpeTb2nMsiAEEKIeqFTp040atSowu0HDhygd+/eaJpGhw4dyMnJ4dy5c6XK7N27l+7du+Pk5FTb4QohhLASm7qDY23nz59ny5YtjBo1ijNnzjB37lzee+89a4clhBC1Qn2/B1qMsHYYNSYzMxNvb2/LctOmTcnMzMTT09Oy7ptvvmHw4MEVHiM+Pp74+HgA5s+fTyNXN5xdbet5SZPJVCpPe2PP+dtz7mB7+Z89e7ZOu6hd7bkWLVqEm5sbkyZNqrTsBx98QFJSEtOnTwcu5Th16lQ+/PDDKp1r2rRp3Hbbbdx9991XFauTk1ONfMbSwPmLCxcuEBMTw6hRo/Dz85PGjRCiwVJ/nkKPWQx3N5wGTmXOnTvH6dOnr9g9bcCAAQwYMMCyfPrEKbx8becHFdj3jOZg3/nbc+5ge/kXFBTU2YP/JpMJs9l8Vfvquo6u61Xav7i4uFTZ+Ph4evfuXeVz67pOcXHxVcdaUFBQ5jNu0aJFtY9jsw0c/YP3UEknavSYmn8bDH+fUOH2V199lVOnTnHbbbfRpk0bEhMT2bFjBxs2bOCLL74gNzeXEydO8Nhjj1FYWMimTZtwdHRk7dq1eHp6cvLkSWbPnk1GRgYuLi4sWrSI9u3b12gOQghxrVTORfSoeeBsW3cmrpWXl1epijEjIwMvLy/L8rfffkvPnj2rdRU0+2KOzTVwhBC2xxq/WwGSkpJ46KGHCA4O5sCBA3Tr1o3777+fyMhI0tPTWbp0aany69evJy4ujvfee4/333+ftWvXYjKZCAgI4F//+hfOzs64ublZyn/55Zc8/fTT7Nmzh8jISBo3bsxvv/3G3XffTceOHYmOjiY/P5/o6Gj+9re/lTrXwoULSU5OJjIykgULFrB161ZMJhO9e/fmueeeq7H3qTw228CxhlmzZvH777+zbds2kpKSGDNmjGXb77//zhdffEFBQQFhYWHMmjWLrVu38vzzz7Nx40YmTJjAjBkzmD9/Pm3btuXgwYPMnDmT//znP1bMSAghSlN6Mfry1yEzHcP0V60dTo0KCQnh888/JywsjKNHj+Lq6lqme9rIkSOrdcyc7LyaDlMIIWrUyZMnWbZsGW+88QaDBg0iNjaW2NhYtm7dyttvv01QUBAAMTEx7Ny5k+joaJycnIiKiuLbb7/FycmJ8+fPAzBkyBDLcYuLizl27BgdOnQgPT2dw4cP89VXX9GkSRNCQ0MZOXIkn376KStWrGDlypW89NJLln1ffvllsrOzefPNNzl37hxxcXHs3LkTTdMs56pNlTZwkpOTefPNNy3Lqamp3H///dx1112WdYcOHWLhwoX4+PgAcNNNNzFixLV1e6isxVrXQkNDadSoEY0aNcLd3Z3bbrsNgMDAQA4fPkxOTg7ff/89EydOtOxTWFhorXCFEKJc6qN1cPgHtNFT0Np1tHY41bJ48WIOHz7MxYsXeeyxx7j//vst3SBuv/12unfvzsGDB5k2bRqOjo6l+punpqaSnp5Op06dqnXO7Bxp4AghKmfN363+/v4EBgYC0KFDB3r16oWmaXTs2JGkpCSCgoLYuHEjzZs3Z+XKlTg4OACXfsNOmTKFgQMHMnDgwDLHPXjwIN27d7csd+3aFV9fXwBat25Nnz59AOjYsSN79uyxlFu8eDHBwcEsXLgQgMaNG+Pk5MQ//vGPMt2Aa0ulDZwWLVpYhtfUdZ2JEyeWO3dAYGAgzz77bM1HaCMcHR0trw0Gg2UEHk3TLP0VGzduzLZt26wVohBCXJG+fxfq801ofQZiuPV2a4dTbU8++eQVt2uaxiOPPFLuNh8fH5YtW1btc+bkyYUqIYRt++uokAaDwfKb1WAwUFxcDFxqhBw6dIiUlBRatWoFwJo1a9i7dy/btm1jyZIlbN++vVQX3i+//JK+fftali//LfzX8/z1mZtu3brx888/c+7cOTw9PTGZTHz66afs3r2bTz/9lJiYmFrv4VStYaJ/+eUX/Pz8aNasWW3FY1Vubm7k5ORc1b7u7u74+/vzySefAJcmnDt06FBNhieEEFdNJZ1ArVoC7QPRbOwOuS3LzpcGjhCi/uvcuTMLFixg3LhxnDlzBl3XSU5OJiwsjNmzZ3Px4sUyv4F3797NrbfeWu1zhYeHM3nyZEaPHk12djY5OTlcvHiR/v3788ILL3D48OGaSqtC1XoG55tvviEsLKzcbUeOHGH69Ol4enoyatQo/P39y5S5fPjNy4eBq+vh9i7n4+NDjx496NevHx06dAAujVphNBoxGAyW2DRNw2g0ltn2r3/9i2eeeYYlS5ZgNpsZOnRohaP11NQweDXN1oZgrEuSu33mDg0/f/3CeTLenY/BvTFesxZi9Gxq7ZDqjZyCYmuHIIQQNaJnz57MnTuX0aNH8+9//5upU6dy8eJFlFJERETg4eFhKZuRkYGTk9MV5x67krvvvpucnBzGjh1LVFQUERERFBQUoJTi+eefr6mUKqQppVRVCprNZiZOnEhkZCRNmjQptS03NxeDwYCzszMHDx5k1apVLFmypNJjJicnlzmOq6trNcKvedcyDF912EKu5bG1IRjrkuRun7lDw85fFRejv/UCHD2EYcZ8tDYdSm2/muE37ckrkWuJGNnf2mGU0pD/XqvCnvO359zB9vKvy99ydfX7tMSmTZtISUlhypQpdXZOKP89rdVhon/44QfatGlTpnEDlAokODiY6OhoLly4QOPGjasdkBBCiJqjNq+GhJ/Qxk4r07gRlcupu98TQghhM+69915rh3BNqvwMzpW6p2VlZVFyIygxMRFd13F3d6+ZCIUQQlwVfe9XqK2xaH3vwhBW+6PWNETZ0kNNCCHqnSrdwcnPz+fnn3/m0UcftazbunUrcGlozr1797J161aMRiOOjo48+eSTaJpW7WCq2FuuQbCnXIUQdU+dOoZasxQ6BKHdP97a4dRbOXq1xuIRQghhA6rUwHF2dmblypWl1t1++/+GGK1o/OzqKhlmzpoDDdQFs9mMwSCVphCidqiL59HfefX/2LvT+KjKu43jv/tkJRMSssgSFgFlFRAU0IoiKlKqdaml2Lq0LlQxWKSuaNvHVquiQEupUBeUVmtbW9vaWhUprlVrFUJNkF2RJYiQBEgyWWfO/bwYTYkhZJJMciaZ6/vGZM6Zc66b+Enmf+4NuqbhXHsbppP/Tm1L5doPW0Skw4mq39zJyclUVVVRXV3doh6gSEhKSqK6urrNrm+trVuQQUQk0mwggPvwA1B2EOe2eZi0hvMmJXx+k9j0SSIiElWiqsAxxtClSxdPM0TbCh0iIs1hn1kOmwowV30fc/SxXsfp8PyOChwRkY5G46RERDoJ9+2XsS8/h5l8Ps6Xzmj6DdKkirikup3ARUQ6ooULF/LQQw+Fde7TTz/NwoULWbhwIU8//XTd62vWrOGWW24J+57Tpk3j/fffb3bWSFGBIyLSCdhtW7BPLoWhozDTrvQ6TqdhjUNFmb/pE0VEOrFXX32VSZMmeR0jbFE1RE1ERJrPlu7H/dV9kJ6Bc82tmLg4ryN1Kv6DZXTtpn3dRKRxy1Z/yrb9VRG95oCMZGaM7XHEc3bu3Mmll17KCSecwOrVqxk9ejTTp09n4cKFFBUV8eCDD9Y7/6mnnuLFF1/k0Ucf5Xe/+x1PPvkk8fHxDBo0iF/96lckJyfj8/kA6s0Xf/PNN7nmmmt4+umneemll6ioqGDbtm3MnDmTmpoa/vznP5OYmMiTTz5JRkZG3ftc1+XGG2+kV69e3Hzzzdx0003k5+djjOHiiy+ut0JzJKnAERHpwGygFveh+8FfinPbA5iu+iAeaX5/pdcRREQa9fHHH/Pwww/zs5/9jHPOOYdnn32WZ599lpUrV/LLX/6S4447DoDly5fzxhtv8Nhjj5GUlMSSJUv497//TVJSEgcPHgTgggsuaHD9kpIS4uPjSUsL/X3ZtGkTL730EtXV1UyYMIE77riDlStXcuc9H7udAAAgAElEQVSdd/LMM8/w3e9+FwitGnz99dczZMgQbrjhBvLz89mzZw+vvPIKQN0924IKHBGRDsz+8THYsh4z4yZMv4Fex+mUyssrvI4gIlGuqZ6WttS3b1+GDRsGwODBgzn11FMxxjB06FB27tzJcccdxzPPPEOvXr14/PHHSUhIAGDYsGFcf/31TW738vrrr3P66afXfX/KKaeQmppKamoqXbt25eyzz6673vr16+vOu+222zjvvPO44YYbAOjXrx87duzghz/8IWeddVa9a0aa5uCIiHRQ7r9WYl99ATPlazgntd0filjnr2i7rQNERForKSmp7mvHcUhMTKz7+vNFUoYOHcquXbv45JNP6s594oknuOKKKygoKOCcc84hEAgc9vqvvPIKZ5zxv4VrPr/+5/f4/P7GmHqLsowdO5a3336bqqrQ0L1u3brxz3/+ky996Us8+eST3Hzzza1teqNU4IiIdED2w43Y3z0Ew0djLvq213E6tfLKGq8jiIi0yogRI7j//vu58sor2bNnD67rsnv3biZMmMAPfvADysrK8PsbLqhirWXDhg11w9ya41vf+hZnnnkmM2fOJBAIUFJSguu6nHvuudx6660UFBREommHpSFqIiIdjD1QgvvQPMjIxrnmFi0q0Mb81Yd/qiki0pGMHz+eH/3oR3z729/m97//Pd/73vcoKyvDWstVV11Fenp6g/fk5+czYsQIjDEtuue1115LWVkZs2fPZtasWdx44424rgvA7bff3qr2HImx1to2u3oTdu/e7dWtGxXrG33GcvvV9thsO3Ss9ttALe6CH8DObTi3z8f06d+q6+Xk5EQmWCd10gOruChhD5dffKbXUep0pP9f20Istz+W2w7R1/6KigpSUlLa5V7x8fGNDiFrS4sWLWLAgAGHXXygLRzu37Qlf6fUgyMi0oHY3z8KH27EufbWVhc30rSUYBV+49lzQBERT82ZM8frCC2iAkdEpINwX1+BfWMF5itfx4w91es4MSE1WIO2+RQR6Vi0yICISAdgt67H/v4RGHEC5sLLvI4TM3zUUu62bOy5iHRuHs7y6LQi9W+qAkdEJMrZ/cWhzTyzjsKZcTPG0aIC7cVHAL/Vv7eINOQ4jifzYjqrQCCA40SmNNEQNRGRKGZra3B/dR9UVeF8/26ML9XrSDHF57gUu0lNnygiMSc5OZmqqiqqq6tbvMpYuJKSkqiu7rx7cllrcRyH5OTkiFxPBY6ISJSy1mKfegi2bca5bi6mdz+vI3lq6dKl5OXlkZ6ezsKFCxsct9ayfPly1q5dS1JSErm5uQwcOBCAoqIiHnroIYqLi4HQ8qTdu3dv8p6pcRY/iU2eJyKxxxhDly5d2uVe0baCXLRTgSMiEqXsay9i31qF+erFmBNO8TqO5yZNmsTUqVNZsmTJYY+vXbuWPXv2sHjxYrZs2cKyZcu49957AXjwwQe56KKLGDVqFFVVVWE/bU1NcPCjHhwRkY5EBY6ISBSym9dhn34URo3DnPctr+NEheHDh7N3795Gj69evZqJEydijGHw4MH4/X7279+P3+8nGAwyatQogGYNgfAlONQGE6iuqiIpQkMnRESkbanAERGJMrZkX2hRgaN64lx9IyZCky47u5KSErKzs+u+z8rKoqSkhOLiYnw+HwsWLGDv3r2MHDmSSy+99LCTWVetWsWqVasAmDdvHpnpPqiCeOLrXdtL8fHRk8ULsdz+WG47xHb7Y7ntLaECR0Qkitiaatyl90FtDU7uDzApPq8jdXiu67JhwwYeeOABsrOz+fnPf85rr73GmWee2eDcyZMnM3ny5Lrv440LQOGuQuKSo+NPZqyPxY/l9sdy2yG22x/Lbc/JyWn2e5r8bb17925+/vOf132/d+9epk+fzrnnnlv32pEmdoqISHistdjfLoXtW3Fm/QDTq4/XkTqUzMzMeh8AiouLyczMJBgM0r9/f3r06AHA+PHj2bx582ELnC/q2iU0/8ZfVtE2oUVEJOKaLHBycnKYP38+EHoKdu211zJ+/Ph65xxpYqeIiITHvvIP7L9fxZx/CWb0SV7H6XDGjh3LihUrmDBhAlu2bCElJYWMjAzS09OpqKigtLSUtLQ01q1bF/ZDOJ8vNO+mvKKqLaOLiEgENau/vaCggJ49e3LUUUfVe72xiZ0ZGRkRDSsi0lnZjfnYPz4Go0/GnDvd6zhRadGiRaxfv56ysjJmzpzJ9OnT6zbZmzJlCmPGjCEvL4/Zs2eTmJhIbm4uENqM7/LLL+euu+7CWsvAgQPrDUM7El9XH+CnvKLz7j8hItLZNKvAeeutt5gwYUKD1xub2PnFAueLkzejcbJUrE/iiuX2q+2x2Xbwvv3BvZ9Q/OgC4nL6kXnL3Tiad3NYc+bMOeJxYwwzZsw47LFRo0axYMGCZt/T1zUF8OOvrm32e0VExBthFziBQIA1a9ZwySWXtPhmX5y8GY2TpWJ5EhfEdvvV9thsO3jbfltdjfvAbVBbi732NkoqKqGist3u35LJm7HEl9YV2Ie/KuB1FBERCVPYa4+uXbuWAQMG0K1btwbHGpvYKSIijbPWYp94EHZuw5lxI6Znb68jyRckJiWSFKzBX+t6HUVERMIUdoHT2PA0CE3sfOONN7DWsnnz5rqJnSIi0jj7z79h330dc8GlmFHjvI4jjfAFqykPWK9jiIhImMIaolZVVUV+fj7XXHNN3WsrV64EjjyxU0REDs+u/y/2mV/DCadgzvmG13HkCHy2Bn/QeB1DRETCFFaBk5yczOOPP17vtSlTptR9faSJnSIiUp/dtwf3kfmQ0xfnyhswRh+eo1kqAcrdOK9jiIhImMIeoiYiIq1nq6twl94L1sXJvQOT3MXrSNIEnwniRwWOiEhHoQJHRKSdWGuxv14MhTtwvnsLpnsvryNJGFLjLH6T6HUMEREJkwocEZF2Yl/6C3b1m5iLLseMOMHrOBImXxyUO0lexxARkTCpwBERaQd2XR72L09gxp2G+fJFXseRZvAlOFTGJRIMBr2OIiIiYVCBIyLSxuze3biPzofeR2O+8z0tKtDBpCbFYY1DRanf6ygiIhIGFTgiIm3IVlXiLrkXjBNaVCAp2etI0ky+pAQA/KVlHicREZFwqMAREWkj1lrc5b+AT3bhXHML5qieXkeSFkhNCRU45eWVHicREZFwqMAREWkj9oU/Qd7bmGlXYIaP9jqOtJAvJdTr5vdXeJxERETCoQJHRKQN2Pz3sH97CnPS6ZizL/A6jrRCampor6Jyf7XHSUREJBzxXgcQORxbWwvbNmE3r8N+vBXnnG9gBg7xOpZIWOyeQtxlC6HvAMzl12tRgQ7Ol+oDDuCvqvE6ioiIhEEFjkQFW1sDH23CblqH3bwOPtoEtTVgDDgObnw8cTPneh1TpEm2sgJ36b0QF//ZogLaP6WjS01PBQ5QXhXwOoqIiIRBBY54wtZUw4cbsZs/wG4ugI82Q6A2VND0HYg5/SuYISNg0HHYvz2FffOf2MoKTJcUr6OLNMq6Lu7jP4dPC3FuvBuT1d3rSBIByb4UHBvEX6N9cEREOgIVONKu7Lo1uM//CT7eDIEAGAf6DcSceS5m8EgYNAyTklr/TeNPw776PPa//8F86QxvgouEwT7/R/jvfzDf/C5myEiv40iEOI6DL1BNubFeRxERkTCowJF25f7+UaitwZx1XugD4DHDMCm+I79p4FDIPAr73r9ABY5EKfvf/2D//jvMl87AnPlVr+NIhPncGio0Qk1EpENQgSPtxpbsg727MRdfjTM5/FWljONgxp2KXfV3bHkpJjWtDVOKNJ/9ZBfuYz+Do4/FXJarRQU6IR81lLv6uYqIdARaJlrajd1YAIAZOqrZ7zXjJ0IwiM17O9KxRFrFVvhxl9wDCYk4ubdjErWoQGfkI0i51TNBEZGOQAWOtJ+N+ZCaBjlHN/+9fQdCz97Yd/8V+VwiLWRdN9RzU7QHZ+ZcTOZRXkeSNpLquPg16EFEpENQgSPtwlqL3ZSPGTIS4zT/fztjDGbcabB5HfZAcRskFGk++9zvIf89zMXfxQw+zus40oZ8cRa/k+h1DBERCYMeR0n72PcJlBTBV77R4kuYcROxz/0Bu/pNTDPm8Ii0BZv3NvYfT2NOPRsz6Stex4kJS5cuJS8vj/T0dBYuXNjguLWW5cuXs3btWpKSksjNzWXgwIEAXHzxxfTr1w+A7OxsbrvttmbdOzXBwY+GH4qIdAQqcKRd2I35QMvm33zO9OoDfQeEhqmpwBEP2cIduI8vggGDMZfM1KIC7WTSpElMnTqVJUuWHPb42rVr2bNnD4sXL2bLli0sW7aMe++9F4DExETmz5/f4nv7EhxqgwlUVVaS3KVLi68jIiJtT0PUpH1sLIBuWdAjp1WXMeMnwrbN2H17IhRMpHmsvxx36T2Q3AXnutsxCQleR4oZw4cPJzU1tdHjq1evZuLEiRhjGDx4MH6/n/3790fk3r7k0PNA/8HyiFxPRETaTlg9OH6/n4ceeoidO3dijOG6665j8ODBdcc/+OADHnjgAbp3D+3afdJJJzFt2rS2SSwdjrUWuzEfc9wJrX7Sbcadhv3zb7Dv/QtzTsuHu4m0hHWDuMsWQPE+nJvvwWRkeR1JDlFSUkJ2dnbd91lZWZSUlJCRkUFtbS1z584lLi6OCy64gPHjxx/2GqtWrWLVqlUAzJs3r+56R2WkwQGIM3H17uGF+Ph4zzN4KZbbH8tth9hufyy3vSXCKnCWL1/O6NGjuemmmwgEAlRXVzc4Z9iwYcydOzfiAaUT2L0Dyg5CK4anfc5kdYdjhmLffQNU4Eg7s88+BevyMJfnYo4d5nUcaYalS5eSmZnJp59+yl133UW/fv3o2bNng/MmT57M5MmT674vKioCIA4XgE92f0q3HpntE7oR2dnZdbliUSy3P5bbDrHd/lhue05O80f/NDlEraKigg0bNnDmmWcCoQrS52ti53mRQ/xv/s3IiFzPjJsIhduxhTsicj2RcLjvvYl98RnMxKk4E6d6HUcOIzMzs94HgOLiYjIzM+uOAfTo0YPhw4fz8ccfN+vavtTQvJtyf2VkwoqISJtpsgdn7969pKWlsXTpUrZv387AgQO54oorSE5Ornfe5s2bueWWW8jIyODyyy+nb9++Da7VWNd/NIn1LsC2aP+BjzYS6Nmb7CHDI3K94JTzKPrjMrp8sJrU40+IyDUhtn/2sdx2aLr9tR9vpeQ3i0kYOpKM6zXvJlqNHTuWFStWMGHCBLZs2UJKSgoZGRmUl5eTlJREQkICpaWlbNq0iQsuaN5CJb7UFMBPeWVN24QXEZGIabLACQaDbNu2jauuuopBgwaxfPlynn32Wb75zW/WnTNgwACWLl1KcnIyeXl5zJ8/n8WLFze4VmNd/9EklrsAIfLtt24QtyAPM3ZCZP9dh4zE//pLVJ79tYitYBXLP/tYbjscuf3WX4Z7zy3QJYXg1TdRfPBgO6drWy3p+vfKokWLWL9+PWVlZcycOZPp06cTCAQAmDJlCmPGjCEvL4/Zs2eTmJhIbm4uAIWFhTzyyCM4joPrulx44YX06dOnWfdOTfMBfvxVtZFuloiIRFiTBU5WVhZZWVkMGjQIgJNPPplnn3223jkpKSl1X59wwgk89thjlJaWkpaWFuG40uHs+Agq/RGZf3MoM+407BMPwvat0H9QRK8t8jkbDOI+Mh8OFOPcch+mm7dzL2LdnDlzjnjcGMOMGTMavD5kyJDD7pvTHL60rsBe/NWBVl1HRETaXpNzcLp160ZWVha7d+8GoKCgoMGTrwMHDmCtBWDr1q24rkvXrl3bIK50NJGef/M5c8IpEBePfe9fEb2uyKHsX56A9f/FXHodZuAQr+OIhxISE0gOVlNe63odRUREmhDWKmpXXXUVixcvJhAI0L17d3Jzc1m5ciUQGhbwzjvvsHLlSuLi4khMTGTOnDna+E6AzwqcnH6YtIyIXtf4UmHECdj33sR+/QqMoy2dJLLc/7yOXflXzBnn4Jx6ttdxJAqkBGvwG+t1DBERaUJYBU7//v2ZN29evdemTJlS9/XUqVOZOlWrCkl9NlALW9Zj2ujDoRl3Gvb9d2HrBhh8XJvcQ2KT3fER9olfwqDhmOkNhzxJbEq1NZQH9fBORCTa6bG3tJ1tW6CmGhPh+TefM8ePh8RE7HtvtMn1JTbZslLcpfeCLw1n5m2Y+LCeA0kM8BHA78Z5HUNERJqgAkfajN2YD8bA4BFtcn2T3AVz/EnY1W9hg8E2uYfEFhsM4j58Pxzcj5N7e8SHVkrH5jNB/KjAERGJdipwpM3YjfnQ75jQfJk2YsadBuWlsOH9NruHxA77zHLYVIC5fBZGq/PJF6TGWcpNotcxRESkCSpwpE3Y6mr4aGPEV09rYMSJ0MWn1dSk1dx/v4pd9XfMWefhnHKm13EkCvniwe8keR1DRESaoAJH2saHGyAQaLP5N58zCQmYMSdj1/4bWxv9O4zb0v1eR5DDqP1wI/bJJTBkJGbalV7HkSjli3eoiE+u21xURESikwocaRN2Yz7ExcGxw9v8Xmb8RKisgHV5bX6v1nD//SruzVdiP9zodRQ5hC09wIF5t0PXdJxrb9WiAtKo1KTQ/JvKsgqPk4iIyJGowJE2YTfmw4DBmOQubX+zoaOgazr23ehdTc0GarF/ewqsi/v333sdRz5jD5TgPvhT3NIDOLPuwHRN9zqSRDFfUgIA5aVlHicREZEjUYEjEWcr/PDx1jYfnvY5ExeHOXECNv9dbFVlu9yzueybq6B4L4wcC+vXqhcnCtgN7+PedQMUbif9+z/G9DvG60gS5VJTQgWOvzw6f8+IiEiIChyJvC3rwbrtVuDAZ6up1dSENv6MMra2Bvv8H+HYYTjX3AKpabj/+IPXsWKWdV3cf/wB9+d3Qmoazh0LST75dK9jSQfgS0kGoLxcQ9RERKKZCpx2Zg/uJ3jHNbhvrPA6SpuxG/MhIREGDmm/mx47DDKyo3I1Nfv6CjhQjHPhZaG9e6ZcCOvysNs2ex0t5tiyg7iLf4L92+8w40/DuWMBpnc/r2NJB5GaGhpy66+o9jiJiIgciQqcdmbffxf27cH+9lfYvLe9jtMm7MZ8OHYYJqH99oswjoMZd2qocPA3f3y83fEhge0fRjyXra7CvvAnGDoKMyS0ZLY54xzwdcV9Tr047clu3YB71xzYtA5zeS7m6hvbZ46YdBq+rj4Ayquif8VGEZFYpgKnndmCNZCZDQMG4z66ELv5A68jRZQtK4Vd2+o+zLcnM34iBAPYvH+Hdb6trcF9+2WC996Me/f3KbljJrakKKKZ7KvPQ9lBnAsu/V/O5BTM2RdAwWrsti0RvZ80ZK3FXflX3AV3QEICztwHcCZOxRjjdTTpYFLTQpsW+6u0TLSISDRTgdOObG0tbHgfM2oczvU/guzuuEt+ii3c4XW0yNlcANCu82/q9DsGuvdqcpia3bcH95nluLdeiV3+C6iswHztcmwwiPvEL7HWRiSOrazArvgLjDgRc+ywesfMmV+FlFTNxWljtqIcd+m92D8th1HjcH74c8zRWkxAWibZl4Jjg5TXBL2OIiIiR6ANH9rTlg+guhIzYiymaxrODT/GnXcr7i9+jDP3fkzmUV4nbDW7MR+Su0D/Qe1+b2MMZvxE7PN/wh7cj0nP+F8u14UP8nBffQHWrQFjYPRJOJPOCQ0fM4aU7O6UPboQ++Y/MadNaXUe+/LfwV+Gc8ElDbN2CfXi2L89hd2+FXP0sa2+n9Rnt2/Ffeh+2F+EufhqzFnnq9dGWsVxHHyBavwmMg9BRESkbagHpx3ZgjUQnwBDP5uLkd0D54YfQ1UF7i9+gvWXexswAuzGfBh0HCYuzpP7m3GngXWxq98K5SkvxX3pL7g/uBZ38V2wfSvm3Ok49y0j7rrbMcOOr/vQ22Xq12DISOwfH8MW721VDusvx678G4w+GdNIsRfqxfFpLk6EWWtxX3sBd96t4AZxbrkPZ/IFKm4kIlLdavwaoSYiEtVU4LQju241DB2JSUque830HYBz3e3w6e7QcLXajjt51e4vhj2F3gxP+4zJ6Qd9+mP/9RLu44twb7kS+8yvISMLc80tOPc/hnPBpZjM7IbvdRycK2aDBfc3rRuqZlc+C5V+nAu+1XjWFB9m8gXw/rvYHZFf4CDSrLXYok9x//M67u8fwf3Lb6Ju3yFbXopdthD71EMw9HicHy3CHDPU61jSiaRQi99VsSwiEs00RK2d2L2fhD78Tzq3wTEz7HjM1d/HPjIfd9lCnGtvxTje9IC0ht2UD3g0/+YQZvxE7F+ewBZ9iplwFmbSOZg+/cN7b3YPzDeuxP52Kfb1FZhJX2n2/W3ZQezLf8eMOw3TZ8CR73fWV7H//Bvuc08TN+uOZt+rLdnaGti+FfvhJuxHG+HDTXCwJHQwMQlqa7Br3wn9/9pEO9sso7WwZxc2/73QCoVbQxuomq9djpn6dYyjZzgSWakmSLnVn04RkWim39LtxK5bA4AZecJhjzvjTsM9WIJ9+jHs7x+FS65t0yE1tqYaPtmF3b0DCreH/ru/iKpLvguDWrgC2sZ88HWFMIuJtmLOOg/TPQeGHY9J8TX//RO/jF3zFvaZ5djjxmCO6tms99sVf4GaGsx5jffe1N0rJRUz+Tzsc3/A7tyG6etNoQBgS/ZhP9wEH23EfrgRdnwEwc/G4hzVEzN0JBwzFDNwaOhnvHkd7mM/w73n5tAcl9O/0i7DwGxtLWxZh81fjc1/D/btCR3oOwBzzjTM2AmeFVzS+fmMS5FN8jqGiIgcgQqcdmILVkOP3qEP3o1wJl+Ae6AE+9JfoVsm5tzprb9voBY+3f2/QqZwB+zeEfpQaN3QSfHx0LMPBGo5+LMf43z/J5jBI5p3H2uxG/JhyEjPn5qbxCQ48ZSWv98YnO/Mxv3x9bi/+SXOjXeH3SZ7oAT76vOYk0/H9OoT3v3OOh+76u+4//gDcdfd3uLcYeWzFsoOhno9Ptn12X93sm/PLtzifaGTEhPh6GMxk8/HHDsUBg7BpGU0vNiw43H+7xe4j/8c+9RD2A35ON+5HpOSGvncpfuxBWtCBc0H/4XqytBmssOOx3z5IszIEzvFIh0S/VLjwR9ovz2+RESk+VTgtANbXQ0bC8Ia7mQu+g4cKME++1vcbpk4EyY3715VlbD+v9j8d0N7rHxaCMHPljR1HOieA337Y06aiOl9NOT0g6N6YeLjsf4yzPw7CC65B2fuA5hefcO/cdGnULIPM/XrzcobrUzWUZjpV2OfeBD72guhBQHCYF98BoIBzFe/Gf69fKmYs87D/uNp7K5tEel9sG4QivfBJzuxe3aFeus+2Ql7CuHQjVATk6BnHxJHnEB1r6MxxwyBPgMw8eH9ajBp3XBm34ld+dfQ/7N3bcW55hbMwCGtb0PxPuw7r4aGnm3bHHoxIztUPI4aB0NGYZL0JF3aly/eUG6TcV0XR0MgRUSikgqc9rApHwK1mJFjmzzVOA5cMTs0j+OJB7Fp3Zp8n91fjH3/3dDT7Q3vQ6AWUnyh1cyOHw+9j8b07gc9+mASEhq/t68rGT9aSNGtM3B/8ROc2+fXW2r5iBk2Rsf8m0gyp56NzXsb++ffYEeciOne64jn2+J92DdWYCZMbvLcBvea/HkvztPEzZzb4sy2ZB/urxfD1g1w6IIVXdOhVx/MiRNC/+3ZB3r1DS2+4DikZ2dTVNSyTU6N42Cmfh076DjcRxfgPjA3NAfm7Aub3Ztn3SAUrMF9fQWsywMs9B+EueDS0P/LffprNbQYtnTpUvLy8khPT2fhwoUNjltrWb58OWvXriUpKYnc3FwGDhxYd7yiooIbb7yRcePGcfXVV7cogy/RIRCMp6a6muQuXVrcFhERaTsqcNqBLVgDSckw6LiwzjfxCTjXzcWd/wPch+7HufkezIDB/7uetbDzI+z7n02s3r41dOConqEJ9aPHwzHDwn4Kf6i4Hjk43/sR7vw7cBffhXPLvZjkMP6Ib8yH9Ezo2bvZ94xWxhicy6/H/fH3cH/9C5yb7z3iB3b7wh9D7zv34ubfy9cVc+Z52Bf+iC3cHupdaya7+QPch+ZBbU2ot7BX39AwuV59Mb6uzb5ec5ljhuL8aFFos9Rnfo3dWIBz1RxM1/Sms+8vxr75T+ybK6GkCNIzQ/NpTpuCyere5tmlY5g0aRJTp05lyZIlhz2+du1a9uzZw+LFi9myZQvLli3j3nvvrTv+9NNPM2zYsMO+N1y+pHioBP/BMhU4IiJRKqxPwH6/n4ceeoidO3dijOG6665j8OD6H7iP9NQslllrQ/Nvhh1/xN6TLzLJKTg3/B/uvNtChcbN90BdT827oQ+BxoTmR1z07dDT7V59I/J02/QfhHPNrbhL7sF9ZD7OrB8ccV8bay12Y369PWU6C5OZjbl4BvbXv8C+8g/M5PMPe57dtwf71irMxKmYrJbNBTFnn499+TnsP57GXHtr2O+z1mJffxH7h0chu2fo5xXm/J9IM75UnJlzsa+9iP3jY7g/uQHnuzdhhjRcuMK6Lqz/b6i3Jv9dcF0YPgbn4u/CqHEtKtClcxs+fDh79za+R9Xq1auZOHEixhgGDx6M3+9n//79ZGRk8NFHH3Hw4EFGjx7Nhx+2fFn21C6JcAD8ZRVkNW/9ERERaSdhfYJYvnw5o0eP5qabbiIQCFBdXV3veFNPzWLaJzuheC/mnGnNfqtJy8C54ce499+G++PvhV5MTILhYzDnX4IZORaT1i3CgT+79/HjMJfODC2X/LuH4LLcxouXT3ZC6QHoRMPTDmVOOTO0qtpfnwgNVTtML5V97g/gxGHO+UbL75OahjnzHOyKv2B37wjt6dMEG6jF/u5h7L9WwsixODNubJNJ/s1hjMGccQ72mKG4j12ZwGAAACAASURBVMzHXfhDzLkXY867GOPEhRYMeOtl7BsvheZudU3HTPlaqLemmUP7RA5VUlJCdvb/9rjKysqipKSE9PR0nnjiCb73ve9RUFBwxGusWrWKVatWATBv3rx61wPontUNPnGx1mlwrL3Ex8d7du9oEMvtj+W2Q2y3P5bb3hJNFjgVFRVs2LCBWbNmhd4QH0/8F56sHumpWayzBZ8tDz3ixBa93/TIwZnzE+y/X8EMHx1apSyxfSZWO6dPxS3+FPvinyGre6Mf3jvj/JtDGWNwvj0L987rQ0PVbr2v3j5F9pNd2Hdew5x9PqZbZuvudfbXsK88H+rFueaWI55rD+7H/dV98OFGzFemYS68NKr2TzL9BuL88GfY3z2E/ccfsJsLMGkZ2LXvhJafHjIy1Ps4+uRm9W6KNNfKlSsZM2YMWVlZTZ47efJkJk/+3+IuX5ybZkxoA+C9e4taPG+ttbJbMWeuM4jl9sdy2yG22x/Lbc/JaXwF4sY0WeDs3buXtLQ0li5dyvbt2xk4cCBXXHEFycnJdec09tTsiwVOU0/GokGkK+SSje9j+x9L1uBWjPvOzoYTxkcs05F8sf12xvcp9ZdR9dcn8R09kC6nf7nBew58tIlAjxyyh4Y3xyhaHfFnn51N5XdvovQXd5Hy75fxXXBJ3aEDv1lMTVIS2Zd8FyfMRRkalZ1N2Tlfp+LZ39Ht8uuI79v/sKfVblnPgftvh/Iy0m++m+QJZ7Xqtm36ZOjWe6h89UXKHlkA8QmknDuNLlMuIL4F84zaip6MdQ6ZmZn1PgAUFxeTmZnJ5s2b2bBhAytXrqSqqopAIEBycjKXXnpps+/hS00B/JRV1jR5roiIeKPJAicYDLJt2zauuuoqBg0axPLly3n22Wf55jfDXwb3c009GYsGkayQbWUF7ob3MVMujMq2Hs7h2m+/eS3s2U3pL++hPC6hXk+NdYO4BWswY07uMG1sTFM/e3vciTD6JMp/+zAVA4djevXB7voY981VmHO+QUltECLwb2BPmwrPP0PJbx/G+e5NDY67b7+MfXIppGfg3HY/5X0HUN7K+7b5k6GR4zALngDHUJ2QSDVE5N8qUvRkrHMYO3YsK1asYMKECWzZsoWUlBQyMjKYPXt23TmvvfYaH374YYuKG4DUNB/gp6KqNkKpRUQk0ppcwzUrK4usrCwGDRoEwMknn8y2bdvqndPYU7OYt/6/EAxiRjS9PHQ0MwkJOLl3QPdeuEvvC20W+rmdH0NFeaedf3MoYwzOZbmQlIy7fBE2GMT9+++gSwpmyoWRu0/XdMykc7Dv/Su0h81nbDCI+/Qy7PJfwDFDcX7wM0zf1u+Z015MUhImQRskSsstWrSIH/7wh+zevZuZM2fyyiuvsHLlSlauXAnAmDFj6N69O7Nnz+bhhx9mxowZEc/gSwutSFheHYj4tUVEJDKa7MHp1q0bWVlZ7N69m5ycHAoKCujTp/4KTY09NYt1tmA1dPHBMUO9jtJqxpeKc8OduPfdgrv4x6E9crplHTL/puEqWZ2RSc/AXHIt9tEF2F//Ata+gznvWxFfhtl8+ULsa89jn/8T5urvY8tLcR9+ADbmY846DzPtSq0yJjFnzpw5RzxujGmyqJk0aRKTJk1qcYaExASSg9X4a90WX0NERNpWWJ+QrrrqKhYvXkwgEKB79+7k5ubWPTGbMmUKY8aMIS8vj9mzZ5OYmEhubm6bhu4IrLXYdWswx4054hLLHYnJ6o7zvf/DnX97aOnqW+8LFTg9+2C6NT15t7Mw407Drnkb+85r4Ova6NLRrbpHWgbm9K9gVz2HHX0S7p8eh4MlmCtuwGnlfBsRaR1fsJryzxYbEBGR6BNWgdO/f3/mzZtX77UpU6bUfR3OU7OYs/MjOLgfRrZs9bRoZY4+Bufa23AfvBv3ofth6wbMl870Ola7MsbgXDoT95Odod6UFF/b3OfLF2FfezG0eWe3TJxb7sMMHNIm9xKR8PlsLf5g59rzS0SkM9EYlzbS2uWho5kZeSLmslzsEw+Gvo+B+TdfZNK64fzkwTbd2NSkZ2AuuBS7qQDn29e3eglqEYmMVGrxu52jZ15EpDNSgdNGbMFq6D+ozTbi9Jpz2hTc/UXYVc9BjMy/+aK2LG4+53z5a/Dlr7X5fUQkfCnGpchqwQwRkWjV5Cpq0ny2rBQ+2oTpZMPTvsg5/xKcn/824hPsRUSiWWqci99oc1oRkWilHpw2YNevBWsxIzv28tDh6CwLKIiIhMsXb/DbJK9jiIhII9SD0xYKVkPXdDj6WK+TiIhIhKUmOFTEJxMIaC8cEZFopAInwqwbxH6QhxlxAsbRP6+ISGfjSwz1XFeU+T1OIiIih6NP4JG2bQuUl0EMDE8TEYlFvuTQ/Bt/abnHSURE5HBU4ESYLVgNxsEMH+N1FBERaQOpXUIFTnl5hcdJRETkcFTgRJgtWAPHDMX4Ur2OIiIibSDVlwyAv7zS4yQiInI4KnAiyB4ogR0fdvrloUVEYpnP1wUAf0W1x0lERORwVOBEkP0gDwAzSvNvREQ6K19XHwDlVTUeJxERkcNRgRNBNn81dMuC3v29jiIiIm0kNS00BLm8SstEi4hEIxU4EWIDAVi/FjPyRIwxXscREZE2kuxLwbFB/DVBr6OIiMhhxHsdoNP4cANUVWK0PLSISKfmOA6pgSr8xnodRUREDkM9OBFiC1ZDXDwMG+V1FBERaWM+twa/RqiJiEQlFTgRYgvWwODjMMkpXkcREZE25qOWclfDkUVEopEKnAiwxXth9w4NTxMRiRE+E6TcapS3iEg0UoETAbZgNYD2vxERiRGpxqVC01hFRKKSCpwIsAVr4Kie0KO311FERKQd+OKh3En0OoaIiByGCpxWsrU1sPF9zAgtDy0iEit88QZ/XDKu63odRUREvkAFTmttWgc1NZhRmn8jIhIrfIkOASeemspqr6OIiMgXhDWAeNasWSQnJ+M4DnFxccybN6/e8Q8++IAHHniA7t27A3DSSScxbdq0yKeNItYNwoH92HffgMREGDzC60giItJOUpPioRL8ZWUk+7p4HUdERA4R9gzJO++8k7S0tEaPDxs2jLlz50YklNestVBeCiX7oKQIW1IE+z//eh/sL4IDJfD50ITRJ2ESk7wNLSLSyS1dupS8vDzS09NZuHBhg+PWWpYvX87atWtJSkoiNzeXgQMHsm/fPhYsWIDrugSDQaZOncqUKVNalcXXJREOQHlpBVk9W3UpERGJMC0BcwhbsIaiZ5bj7tsDtTX1D8YnQEYWZB6FGTISMo6CzGxMZjYcM9SbwCIiMWTSpElMnTqVJUuWHPb42rVr2bNnD4sXL2bLli0sW7aMe++9l4yMDH7605+SkJBAVVUVN910E2PHjiUzM7PFWVK7hB5q+f0VLb6GiIi0jbALnHvuuQeAs88+m8mTJzc4vnnzZm655RYyMjK4/PLL6du3b4NzVq1axapVqwCYN28e2dnZLc0dcba6iqKnlkJiMinnTCMuuztOdg/ijupBXFZ3THpGTCwiEB8fH1U/l/aktsdm20Ht7yiGDx/O3r17Gz2+evVqJk6ciDGGwYMH4/f72b9/PxkZGXXn1NbWRmRhAJ8vGbCUl1e1+loiIhJZYRU4d999N5mZmRw8eJCf/vSn5OTkMHz48LrjAwYMYOnSpSQnJ5OXl8f8+fNZvHhxg+tMnjy5XnFUVFQUgSZEhvv8H7HF+8i4Zyml3fvUPxhwobjYm2DtLDs7O6p+Lu1JbY/NtkNstz8nJ8frCBFTUlJSr1DNysqipKSEjIwMioqKmDdvHnv27OGyyy5rtPcm3Adx5b1LoWA3QZx2L45jvSCP5fbHctshttsfy21vibAKnM//EKSnpzNu3Di2bt1ar8BJSUmp+/qEE07gscceo7S09IhzdqKJLT2AXfFnGH0SicNHQ4x+0BER6ayys7NZsGABJSUlzJ8/n5NPPplu3bo1OC/cB3EBGwgd31/W7sVxLBfkENvtj+W2Q2y3P5bb3pIHcU0uE11VVUVlZWXd1/n5+fTr16/eOQcOHAhNzAe2bt2K67p07dq12WG8Yv/xB6ipxvn6d7yOIiIiLZSZmVnvA0BxcXGDnprMzEz69u3Lxo0bW3UvX1rob5y/JtCq64iISOQ12YNz8OBBFixYAEAwGOTUU09l9OjRrFy5EoApU6bwzjvvsHLlSuLi4khMTGTOnDkdZr6K3VOIfeMlzMSpmJ59mn6DiIhEpbFjx7JixQomTJjAli1bSElJISMjg+LiYrp27UpiYiLl5eVs2rSJr371q626V0JiAsnBasprtNGniEi0abLA6dGjB/Pnz2/w+qFLbE6dOpWpU6dGNlk7cf/yG0hIxJz3Ta+jiIjIESxatIj169dTVlbGzJkzmT59OoFAqAdlypQpjBkzhry8PGbPnk1iYiK5ubkAFBYW8sQTT2CMwVrLeeed12AkQkv4gtX4jW31dUREJLJieplou2U9rH0Hc+FlmLSGY7FFRCR6zJkz54jHjTHMmDGjweujRo2qG4kQST5bS3mgyZHeIiLSzmL2N7O1FvdPj0O3LMzkC7yOIyIiHUwqtVTYmP0zKiIStWL2N7Nd/RZs24y58FJMUpLXcUREpIPxGZfy2B4IISISlWKywLG1tdi/PgG9j8Z86Qyv44iISAfki3MpNwlexxARkS+IzQLn9Rdg3x6caVdinDiv44iISAeUGm+ocDQCQEQk2sRcgWMryrH/+CMMH40ZcYLXcUREpIPyJThUxCfXreQmIiLRIfYKnBf+BBXlONOu9DqKiIh0YL6k0AiAijK/x0lERORQMVXg2OK92Jf/gTn5DEzfAV7HERGRDsyXFJp/U36wzOMkIiJyqNgqcP76JBiDufAyr6OIiEgH17VLIgB+f6XHSURE5FAxU+DY7Vux/3kdM/l8TGa213FERKSD8/lCCwz4y1TgiIhEk5gocEKbei6HrumYr0zzOo6IiHQCPl8KAP7Kao+TiIjIoWKiwKFgNWwqwJz3TUyXFK/TiIhIJ+DrGvp7UlZZ43ESERE5VKcvcGwwiPvMr6FHb8xpX/Y6joiIdBKp6V0B8FdrmWgRkWjS+Quct1bBJztxLvo2Jj7e6zgiItJJJKd0wbFB/DVBr6OIiMghOvUnfltVif377+DYYTDmZK/jiIhIJ+I4DqmBKvzGeh1FREQO0al7cOzKZ+HgfpxpV2KM8TqOiIh0Mj63hnKNUBMRiSqdpgfHWgv7i2H3dmzhdti1HZv3FubECZhjhnodT0REOiEftfhdPUATEYkmHbLAsRXlULgjVMgUbscWfgyFO6Ci/H8ndcuEYaMx06/yLKeIiHRuPhOk3HbIP6UiIp1Wh/mt7L71MnbNW1D4MZQU/e9AlxTI6YcZeyr0ORqTczT07odJTfMsq4iIxIZU47LPJnsdQ0REDtEhChxrLfbpZZCUjBk8IlTI9D4aeveHzGzNrxEREU/44sEfSPA6hoiIHKJDFDiUFEGlH3PR5TiTzvE6jYiICAC+eIPfJuO6Lo7TqdftERHpMMIqcGbNmkVycjKO4xAXF8e8efPqHbfWsnz5ctauXUtSUhK5ubkMHDgwcil3bwfA9O4fuWuKiIi0UmpiHIFgPDWV1ST7ungdR0REaEYPzp133kla2uHntaxdu5Y9e/awePFitmzZwrJly7j33nsjFtLuChU49O4XsWuKiIi0li8pDiqhvLRMBY6ISJSISH/66tWrmThxIsYYBg8ejN/vZ//+/ZG4dEjhx5CRjUlJjdw1RUREWim1SyIA/rIKj5OIiMjnwu7BueeeewA4++yzmTx5cr1jJSUlZGdn132flZVFSUkJGRkZ9c5btWoVq1atAmDevHn13nMkxZ8W4gwYREaY57dGfHx82Lk6o1huv9oem20Htb+jWLp0KXl5eaSnp7Nw4cIGxxsbLv3xxx/z6KOPUllZieM4XHTRRZxyyikRyeTrkgRAebkKHBGRaBFWgXP33XeTmZnJwYMH+elPf0pOTg7Dhw9v9s0mT55crzgqKio6wtkhNhDA3fkxZsiosM5vrezs7Ha5T7SK5far7bHZdojt9ufk5HgdIWyTJk1i6tSpLFmy5LDHGxsunZiYyPXXX0+vXr0oKSlh7ty5HH/88fh8vlZnSvV1AVz8/qpWX0tERCIjrCFqmZmZAKSnpzNu3Di2bt3a4PihHw6Ki4vr3tNqn+6GYAD6HB2Z64mISIc0fPhwUlMbH6rc2HDpnJwcevXqBYT+XqWnp1NaWhqRTL6uoXk35ZU1EbmeiIi0XpM9OFVVVVhr6dKlC1VVVeTn5zNt2rR654wdO5YVK1YwYcIEtmzZQkpKSoPhaS1lCz8GtIKaiIgcWTjDpbdu3UogEKBHjx6HvUZzh1I7LvD2RgLWtNswx1gfUhnL7Y/ltkNstz+W294STRY4Bw8eZMGCBQAEg0FOPfVURo8ezcqVKwGYMmUKY8aMIS8vj9mzZ5OYmEhubm7kEhbuAMeBnn0id00REYk5+/fv55e//CWzZs1qdM+a5g6lrg3UAlBcVtFuwxxjeUglxHb7Y7ntENvtj+W2t2QodZMFTo8ePZg/f36D16dMmVL3tTGGGTNmNPvm4bCFH0OP3pgE7RQtIiKNO9Jw6YqKCubNm8e3vvUtBg8eHLF7JiQmkBysxl/jRuyaIiLSOtG/7XLhdkxvzb8REZEjGzt2LG+88QbWWjZv3lw3XDoQCLBgwQImTpzIySefHPH7+oLV+AMRv6yIiLRQ2MtEe8FWVUDRpzBhctMni4hIp7Zo0SLWr19PWVkZM2fOZPr06QQCocriSMOl3377bTZs2EBZWRmvvfYaALNmzaJ///4RyZVqaygPmohcS0REWi+qCxwKdwBgtIKaiEjMmzNnzhGPNzZceuLEiUycOLGtYuEjgN9G/4AIEZFYEdW/kW3h9tAXWkFNRESilM+4lEf580IRkVgS1QUOu3dAUjJkdfc6iYiIyGH54lz8RgvhiIhEi6h+5GR3fQw5/TCNLOcpIiLitdR4g98meR1DREQ+E7WVg7VWK6iJiEjUS01wqIxPJlCrpdRERKJB1BY4lB6A8lJQgSMiIlEsJSkOAH9ZucdJREQEornAKfwYQD04IiIS1VKTQvNv/KUqcEREokHUFjh212crqPXp72kOERGRI0ntkgiAv6zC4yQiIgJRXOBQuB3SumG6pnudREREpFE+XzIAfn+Vx0lERASiuMCxhds1/0ZERKKez9cFgPJKFTgiItEgKgsc6wbhkx0YbfApIiJRLrWrD4DyylqPk4iICERpgcO+T6GmBnr38zqJiIjIEfnSUwHwV6vAERGJBtFZ4NStoNbf0xgiIiJNSU7pQpwbxF/jeh1FRESI0gLH7toOxkCOenBERCS6OY6DL1hFea0KHBGRaBCdBU7hdjiqJyYpyesoIiIiTfK5NfiDxusYIiJClBY47NYKaiIi0nH4qKXcjc4/qSIisSbqfhvbmmr49BPNvxERkQ4jlSB+G+d1DBERIQoLHD7ZBdbFaAU1ERHpIHyOi58Er2OIiAhRWODYz1ZQQz04IiLSQfjiodxJ9DqGiIgQhQUOhdshPgG69/I6iYiISFh88YaKuCRcVyupiYh4LT7cE13XZe7cuWRmZjJ37tx6x1577TWefPJJMjMzAZg6dSpnnXVWiwLZXdshpy8mTmOZRUSkY0hNjCMQjKe6soouvhSv44iIxLSwC5wXXniB3r17U1lZedjjp5xyCldffXXrE+3ejhl2fOuvIyIi0k5Sk+OgEvyl5SpwREQ8FtYQteLiYvLy8lrcKxMu6y+DAyWafyMiIh2KLzk0/6a81B/2e9569T2ue/zfrH7rv20VS0QkJoXVg/PrX/+ayy67rNHeG4D//Oc/bNiwgV69evGd73yH7OzsBuesWrWKVatWATBv3rwG59Ts2cl+IH34SJIO8/72EB8ff9jssSKW26+2x2bbQe2X1ktNCW1M7fc3/nfycwdLDvDIc2t4M743JMFzmz5h7IS2TigiEjuaLHDWrFlDeno6AwcO5IMPPjjsOSeeeCITJkwgISGBf/7znyxZsoQ777yzwXmTJ09m8uTJdd8XFRXVO+6ufx+A0tQMzBeOtZfs7OwGuWJJLLdfbY/NtkNstz8nJ8frCGFbunQpeXl5pKens3DhwgbHrbUsX76ctWvXkpSURG5uLgMHDgTgnnvuYcuWLQwdOrTBPNJI8KV0AVzK/VVHPO8//8pj6YcuZXE9+VbSJ1QFLH9L7EnJp0Vk9lCRLSISCU0OUdu0aROrV69m1qxZLFq0iHXr1rF48eJ653Tt2pWEhND6/2eddRYfffRRy9IUboeUVOiW2bL3i4hIpzVp0iTuuOOORo+vXbuWPXv2sHjxYq655hqWLVtWd+z888/n+uuvb7NsqWmheTf+yprDHi87UMqiJ17m3h0ppLtVzD8xiW9OO4Mzxx6Daxz+9c76NssmIhJrmuzBueSSS7jkkksA+OCDD3juueeYPXt2vXP2799PRkYGAKtXr6ZPnz4tCmMLt0OfozHGtOj9IiLSeQ0fPpy9e/c2enz16tVMnDgRYwyDBw/G7/fX/X0aOXJko6MQIsGX6gPKKa9qWODkvf1fHtxUw/6EnkyL383F004lMSk0Z6ffsUcz8I1/8Xo1XNBm6UREYkvYq6h90dNPP80xxxzD2LFjefHFF1m9ejVxcXGkpqaSm5vb7OtZa6FwO+ZLZ7Q0koiIxLCSkpJ6c6mysrIoKSmpewAXjqbmijYmPT0d+JQa69S9p/xgKYueWMmLbk/6UME9EzIYOe70Bu89u1cCD5d0o7ToAAOHHtvkvWJ9zlgstz+W2w6x3f5YbntLNKvAOe644zjuuOMAuPjii+teP7SXp8VK9kFVpVZQExERzzQ1V/RIkoPVHPBXU1RURP57BfxyXQX7ErpzYXwhl3xrAknJyYe93kljjuHRVfv4+yt5XJbdrcn7xPKcMYjt9sdy2yG22x/LbW/JXNEW9+BE3K7tAJjeR3scREREOqLMzMx6HwCKi4vrNqBuD6nBaopdeOSpl3me3vSyLvcdZxg25shbLGT1PIqRtet4ozaFS1wXxwlrBwcREWlE1PwWtYUfh77I6edpDhER6ZjGjh3LG2+8gbWWzZs3k5KS0qzhaa3lszW8ndCb5+nNuaaQRRePZtiYYWG9d2JOMp8mdmNT/uY2Tiki0vlFTw9O4XbIPAqT4vM6iYiIRKFFixaxfv16ysrKmDlzJtOnTycQCAAwZcoUxowZQ15eHrNnzyYxMbHefND/+7//o7CwkKqqKmbOnMnMmTMZPXp0RPP1i6umquYA149IZdS45m2M/aUvjeThZz/itQ/2MWz00IjmEhGJNVFT4NjC7aDhaSIi0og5c+Yc8bgxhhkzZhz22F133dUWker5/mWTMMa0aIiZLy2V8XYfb9VmMKOmloTEhDZIKCISG6JiiJoN1MKeXZg+KnBERKRjiouLa9X8mdMHZlCWkMLa/xREMJWISOyJigKHT3dDMKgV1EREJGaNOWkkXWsreP2j/V5HERHp0KKiwLG7Pga0gpqIiMSuhMQEJiTs511zFBVl5V7HERHpsKKiwKFwO8TFQc/eXicRERHxzKThOdTEJfLvtzVMTUSkpaKiwLGF26FHb0y8JlWKiEjsGnL8EHrUHOD13VVeRxER6bCiosChcLuGp4mISMxzHIeJvgoKEnpQvGef13FERDokzwscW1kBxXu1RLSIiAhw+gnH4hqHf/1ng9dRREQ6JM8LHAq3A2D69Pc2h4iISBToe2w/jqnexxvFxusoIiIdkucFjt0dKnDUgyMiIhJyepblw6Sj2Ll1h9dRREQ6HM8LHHZth6QukNXd6yQiIiJR4bSTh+NYl9fztnodRUSkw/G8wLGF26F3P4xRV7yIiAhAZo9sRtV8yhv+FFzX9TqOiEiH4mmBY63VCmoiIiKHMbF3Mp8mdmPT+5u8jiIi0qF424NzsAT8ZdC7v6cxREREos2XThlJYrCG19bv9jqKiEiH4m2Bs+vzFdT+v727j42ibPc4/p3ZdttuC6XLS7EFciiIkT4HUdtHDCgEEA34j5WAEEOIeNBAaNQ/fPtDTfD1SANBMfpE0UA4CWoAY6IhogEiHiIIgpQi0iMK8kChW/u+3Ze5zx8tBWqLy0s77c7vk0zYzgy719Ub9uq19/QezeCIiIhcLNAvi3+as3wbyyHSEnE7HBGRPsPdS9S0gpqIiEiXpozKoSElwP7vD7kdiohIn+H+DE52ECurv6thiIiI9Ebj//mf9I82sqOyxu1QRET6DHdncP74TbM3IiIiXUj1pzIx9U/22ENorGtwOxwRkT4h4QbHcRyefvppXn/99b8ci0ajrFy5kmXLlvH8889TVVWV2JP++wRW/oiEgxUREfGayYX5RHyp/O93P7kdiohIn5Bwg/PFF1+Qn5/f6bFvvvmGzMxM3nrrLWbNmsWGDRsSe9JoRCuoiYiIXMZN48aQG/mTHafCbociItInJNTgVFdXs2/fPqZNm9bp8b179zJlyhQAJkyYwKFDh1rvcZMAraAmIiLSNdu2uTuziZ/8uVSfTvAKCRERD0tJ5KSPPvqIhx9+mObm5k6Ph0IhBg4cCIDP5yMQCFBfX0///pcuHrBt2za2bdsG0Hqpm20z6B+3YqWlXUsO11VKSgqDBg1yOwzXeDl/5e7N3EH5S+835fbRfLK7iZ27K7jpH2PdDkdEpFf72wbnhx9+IDs7m4KCAsrLy6/pxaZPn8706dMv7Bh8A9X19VBff03Pez0NGjSIc+fOuR2Ga7ycv3L3Zu7g7fzz8vLcDkESMGzUCEbt2MmOFpv/cjsYEZFe7m8bnJ9//pm9e/eyf/9+IpEIzc3NrF69mtLS0vZzgsEg1dXVDBw4kHg8GuPuwgAADW9JREFUTlNTE/369fv7V9cKaiIiIgmZPAjW1g/mWPlRBuQG3Q5HRKTX+tsGZ/78+cyfPx+A8vJyPv/880uaG4Dbb7+d7du3M2bMGHbv3k1hYSGWZf3ti2sFNRERkcTcdcdYPvqqii93HWJeyd2XPTcSjtBYX09TQxON9c2kpfu5YUQe/nR/D0UrIuKehH4HpzMbN25k1KhRFBUVMXXqVN5++22WLVtGVlYWTzzxRELPYWkFNRERSdA777zDvn37yM7Opqys7C/HjTF8+OGH7N+/n7S0NJYsWUJBQQEA27dvZ9OmTQCUlJS0L4zTlwRzBzEu+hNb6/rR+D9f0xg3NMUtmhybRnw0kUqTnUqTL42ondrhb4exv/+FoZE6hlnNDA/AsGCA4XkDGfYf+WRkBlzJSUSkO1xRg1NYWEhhYSEAc+fObd/v9/t56qmnrvzVdYmaiIgkaMqUKdx3332sWbOm0+P79+/n9OnTrF69ml9++YX333+fV199lYaGBj799NP2+7g9++yzFBUVkZWV1ZPhXxf3FfTnv0+k81UslUA8QqaJECBGfyvGDXaUgA8yUywCqRaBtBQCaalkpvtpbolyMtTIiZjhpJPBD5Fs4lU+qIrDj78zOFLLMBoZnm4YNiCd4UNzyMi4sgWAHMcQDkdat5YozZEY4WiMcMQhHIsTjhnCcUPYsWh2LMLGxgH8lrmw2Qa/bZFmg99nkeqz8fss0lJs/Ckp+P0+sjIDtIRbsG0L27bwWRa2bWP7rLZ9NrZlt+2zsW2LBC4q6RPO9TtLfX1dwucbQ/uqtsaAwYBjLjw25x+3nmC4sAKuhQWWhQWt3z/LwqLte2lf9BiwrCv7Hp+PyzEGJ+7gOAbHcdo347TefzFuTNsxgzEOgfNjb7WuLnj+30DrYxvbuuhrn33FcfVmVzr2yeRqflf0qmdwroshQ119eRER6TvGjh172RtJ7927l7vvvhvLshgzZgyNjY3U1NRQXl7OuHHj2huacePG8eOPPzJp0qSeCv26uXPy7ewIBgmFQtf0PNFIlH//foqTJ6s4ca6BkzGHE3E/5dEBREKpEAKIXsUz20B623aBZRzSnCjpTpQMJ0o6MdJwsDE0Gh81xiaCjxYnhajlI2KnEImnYKyu7maRyI8vTtuWTKq78bmttq0rpm3rLjaJ3b0kkdlGjX0y2TP1yv+Oqw2OZfvcfHkREUkioVDokuW+Bw4cSCgUuuRWBtC6ME5XDULH2xn0xuXDr9ey5jfk3cBtHfbF43FOHT/Jr//3By2RK2twLBsy0tIIBNIIBDLICKSTmRkg0C+TtEAGtp3wvcWB1k/wo5EILc0thJvDtDSHaQm3YAxEoxHi8dZP/ePxC5/8x+MOccfBOX/MaZ0dSBa2beM4V/aDu9XWt7TOuLRt/HVW5uKvL5nd6WS2xxjTutG6I8FbH14alw0+23fRbIyNz2dh2z58toVl2/h8F/Zblo1l2cRiMZy4Q9yJt/1pcJw4jgOOE2/72hB34pgk6nGuZuy9zN0ZHBERkV6k4+0MeuPy4d29rHlGdiZjbx1zXZ7LAI3hZhrDnd9HL1G230eGP5OM7ExPL+vu5dzB2/l7OfercWUfp4iIiPRSwWDwkh8AqqurCQaD7bcyOC8UChEMapllEZFkpQZHRESSQlFRETt37sQYw9GjRwkEAuTk5DB+/HgOHDhAQ0MDDQ0NHDhwgPHjx7sdroiIdBNdoiYiIn3CqlWrOHz4MPX19Tz++OPMmTOHWCwGwIwZM7j11lvZt28fpaWl+P1+lixZAkBWVhYPPvggzz33HACzZ8/ukyuoiYhIYtTgiIhIn/B391izLItHH32002NTp05l6tSrWIpHRET6HF2iJiIiIiIiSUMNjoiIiIiIJA01OCIiIiIikjTU4IiIiIiISNJQgyMiIiIiIknDMsYYt4MQERERERG5HjSD08Gzzz7rdgiu8nL+yt27vJy/l3Pvq7w+Zl7O38u5g7fzV+5XRg2OiIiIiIgkDTU4IiIiIiKSNHwvvfTSS24H0dsUFBS4HYKrvJy/cvcuL+fv5dz7Kq+PmZfz93Lu4O38lXvitMiAiIiIiIgkDV2iJiIiIiIiSUMNjoiIiIiIJI0UtwPoTZYuXUp6ejq2bePz+Xj99dfdDqnbvPPOO+zbt4/s7GzKysoAaGhoYOXKlZw9e5bBgwfz5JNPkpWV5XKk3aOz/D/++GO+/vpr+vfvD8C8efO47bbb3AyzW5w7d441a9bw559/YlkW06dPZ+bMmZ4Y/65y98rYRyIRXnzxRWKxGPF4nAkTJjBnzhyqqqpYtWoV9fX1FBQUsGzZMlJSVB56Iy/VKfB2rVKdUp1SnbqGOmWk3ZIlS0xtba3bYfSI8vJyU1lZaZ566qn2fevXrzebN282xhizefNms379erfC63ad5b9x40bz2WefuRhVzwiFQqaystIYY0xTU5MpLS01J06c8MT4d5W7V8becRzT3NxsjDEmGo2a5557zvz888+mrKzMfPvtt8YYY9577z2zdetWN8OUy/BSnTLG27VKdUp1SnXq6uuULlHzqLFjx/7lU489e/YwefJkACZPnsyePXvcCK1HdJa/V+Tk5LSvRpKRkUF+fj6hUMgT499V7l5hWRbp6ekAxONx4vE4lmVRXl7OhAkTAJgyZUpSjr30TV6uVapTqlOqU1dfp3QNQgevvPIKAPfccw/Tp093OZqeVVtbS05ODgADBgygtrbW5Yh63tatW9m5cycFBQUsWLAg6YtLVVUVv/76K6NHj/bc+F+c+5EjRzwz9o7j8Mwzz3D69GnuvfdecnNzCQQC+Hw+AILBoKeKaV/k5ToFqlVeea86T3VKdepq6pQanIssX76cYDBIbW0tL7/8Mnl5eYwdO9btsFxhWRaWZbkdRo+aMWMGs2fPBmDjxo2sW7eOJUuWuBxV9wmHw5SVlbFw4UICgcAlx5J9/Dvm7qWxt22bN998k8bGRlasWMGpU6fcDkmugOrUpZL9vaojL71XgeqU6tTV1yldonaRYDAIQHZ2NsXFxRw7dszliHpWdnY2NTU1ANTU1LT/IptXDBgwANu2sW2badOmUVlZ6XZI3SYWi1FWVsZdd93FHXfcAXhn/DvL3Utjf15mZiaFhYUcPXqUpqYm4vE4AKFQqP29UHofr9cp8M57VWe89F6lOqU6dS11Sg1Om3A4THNzc/vjgwcPMmLECJej6llFRUXs2LEDgB07dlBcXOxyRD3r/JsmwPfff8/w4cNdjKb7GGN49913yc/P5/7772/f74Xx7yp3r4x9XV0djY2NQOtKNQcPHiQ/P5/CwkJ2794NwPbt2ykqKnIzTOmC6lQrL7xXdcUr71WqU6pTcG11yjLGmG6Ptg84c+YMK1asAFp/qWnSpEmUlJS4HFX3WbVqFYcPH6a+vp7s7GzmzJlDcXExK1eu5Ny5c0m7/OJ5neVfXl7O8ePHsSyLwYMHs3jx4vZrfZPJkSNHeOGFFxgxYkT79P68efO48cYbk378u8p9165dnhj73377jTVr1uA4DsYY7rzzTmbPns2ZM2dYtWoVDQ0NjBw5kmXLlpGamup2uNKB1+oUeLtWqU6pTqlOXX2dUoMjIiIiIiJJQ5eoiYiIiIhI0lCDIyIiIiIiSUMNjoiIiIiIJA01OCIiIiIikjTU4IiIiIiISNJQgyOSoE2bNvHuu++6HYaIiEinVKdEWmmZaBERERERSRqawRERERERkaSR4nYAIr3Rli1b+PLLL2lubiYnJ4dHH32UiooKTp8+TWlpKR988AHbt29vPz8ajVJSUsKcOXMIhUKsXbuWiooK0tPTmTVrFjNnznQvGRERSTqqUyJdU4Mj0sGpU6fYunUrr732GsFgkKqqKhzHoaKiov2cRYsWsWjRIgCOHz/O8uXLKS4uxnEc3njjDYqLi3niiSeorq5m+fLl5OXlMX78eLdSEhGRJKI6JXJ5ukRNpAPbtolGo5w8eZJYLMaQIUMYOnRop+fW1dXx5ptv8sgjjzBy5EgqKyupq6tj9uzZpKSkkJuby7Rp0/juu+96OAsREUlWqlMil6cZHJEOhg4dysKFC/nkk084efIkt9xyCwsWLPjLebFYjLKyMiZOnMjEiRMBOHv2LDU1NSxcuLD9PMdxuPnmm3sqfBERSXKqUyKXpwZHpBOTJk1i0qRJNDU18a9//YsNGzaQm5t7yTlr164lIyODhx56qH3foEGDGDJkCKtXr+7pkEVExENUp0S6pkvURDo4deoUhw4dIhqN4vf78fv9WJZ1yTlfffUVFRUVlJaWYtsX/huNHj2ajIwMtmzZQiQSwXEcfv/9d44dO9bTaYiISJJSnRK5PM3giHQQjUbZsGEDf/zxBz6fj5tuuonFixezbdu29nN27drFmTNneOyxx9r3PfDAA5SUlPDMM8+wbt06li5dSiwWIy8vj7lz57qRioiIJCHVKZHL040+RUREREQkaegSNRERERERSRpqcEREREREJGmowRERERERkaShBkdERERERJKGGhwREREREUkaanBERERERCRpqMEREREREZGkoQZHRERERESSxv8D5j1u1zmfra0AAAAASUVORK5CYII=", + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "f, ax = plt.subplots(2, 2, figsize=(14, 10))\n", + "df.plot(x=\"size\", y=\"time\", ax=ax[1, 0])\n", + "df.plot(x=\"size\", y=[\"mks\", \"mks'\", 'mks\"', \"ave_len\"], ax=ax[0, 0])\n", + "df.plot(x=\"size\", y=[\"%mks\", \"%mks'\", '%mks\"'], ax=ax[0, 1])\n", + "df.plot(x=\"size\", y=[\"mks'/mks\", 'mks\"/mks'], ax=ax[1, 1])\n", + "ax[0, 0].legend()\n", + "ax[0, 1].legend()\n", + "ax[1, 0].legend()\n", + "ax[1, 1].legend()\n", + "# ax[1,1].set_ylim([0.9, 1.1])\n", + "ax[0, 0].set_title(\"Raw Gain\")\n", + "ax[0, 1].set_title(\"Relative Gain\")\n", + "ax[1, 0].set_title(\"Time\")\n", + "ax[1, 1].set_title(\"Comparison between MKS\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wikipedia titles, uniform, longer test" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "df = df.sort_values(\"size\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "time 0\n", + "time: 52.057980205573585s - nb=50000 gain (0.6162242515637921, 0.616305075104518, 0.616305075104518, 1.0)\n" + ] }, { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAJeCAYAAAB4VqBxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQZHRERERESSxv8D5j1u1zmfra0AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
05000052.057980.6162240.6163050.6163051.00.6162241.00.6163051.0001310.6163051.000131
\n", + "
" ], - "source": [ - "import matplotlib.pyplot as plt\n", - "f, ax = plt.subplots(2, 2, figsize=(14,10))\n", - "df.plot(x=\"size\", y=\"time\", ax=ax[1,0])\n", - "df.plot(x=\"size\", y=[\"mks\", \"mks'\", \"mks\\\"\", \"ave_len\"], ax=ax[0,0])\n", - "df.plot(x=\"size\", y=[\"%mks\", \"%mks'\", \"%mks\\\"\"], ax=ax[0,1])\n", - "df.plot(x=\"size\", y=[\"mks'/mks\", \"mks\\\"/mks\"], ax=ax[1,1])\n", - "ax[0,0].legend()\n", - "ax[0,1].legend()\n", - "ax[1,0].legend()\n", - "ax[1,1].legend()\n", - "#ax[1,1].set_ylim([0.9, 1.1])\n", - "ax[0,0].set_title(\"Raw Gain\")\n", - "ax[0,1].set_title(\"Relative Gain\")\n", - "ax[1,0].set_title(\"Time\")\n", - "ax[1,1].set_title(\"Comparison between MKS\")" + "text/plain": [ + " size time mks mks' mks\" ave_len %mks mks/mks \\\n", + "0 50000 52.05798 0.616224 0.616305 0.616305 1.0 0.616224 1.0 \n", + "\n", + " %mks' mks'/mks %mks\" mks\"/mks \n", + "0 0.616305 1.000131 0.616305 1.000131 " ] - }, + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = benchmark([50000])\n", + "df2.tail(n=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Wikipedia titles, uniform, longer test" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "time 0\n", + "time: 52.51158252780897s - nb=50000 gain (0.615225173328998, 0.6153599275825006, 0.6153599275825006, 1.0)\n", + "time: 105.0721302614229s - nb=100000 gain (0.5836043296652512, 0.5841384772496148, 0.5841384772496148, 1.0)\n", + "time: 187.86111486480695s - nb=200000 gain (0.5507786166438062, 0.5518801462043321, 0.5518801462043321, 1.0)\n" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time 0\n", - "time: 52.057980205573585s - nb=50000 gain (0.6162242515637921, 0.616305075104518, 0.616305075104518, 1.0)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
05000052.057980.6162240.6163050.6163051.00.6162241.00.6163051.0001310.6163051.000131
\n", - "
" - ], - "text/plain": [ - " size time mks mks' mks\" ave_len %mks mks/mks \\\n", - "0 50000 52.05798 0.616224 0.616305 0.616305 1.0 0.616224 1.0 \n", - "\n", - " %mks' mks'/mks %mks\" mks\"/mks \n", - "0 0.616305 1.000131 0.616305 1.000131 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
1100000105.0721300.5836040.5841380.5841381.00.5836041.00.5841381.0009150.5841381.000915
2200000187.8611150.5507790.5518800.5518801.00.5507791.00.5518801.0020000.5518801.002000
\n", + "
" ], - "source": [ - "df2 = benchmark([50000])\n", - "df2.tail(n=2)" + "text/plain": [ + " size time mks mks' mks\" ave_len %mks \\\n", + "1 100000 105.072130 0.583604 0.584138 0.584138 1.0 0.583604 \n", + "2 200000 187.861115 0.550779 0.551880 0.551880 1.0 0.550779 \n", + "\n", + " mks/mks %mks' mks'/mks %mks\" mks\"/mks \n", + "1 1.0 0.584138 1.000915 0.584138 1.000915 \n", + "2 1.0 0.551880 1.002000 0.551880 1.002000 " ] - }, + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = benchmark(\n", + " [50000, 100000, 200000]\n", + ") # , 500000, 500000, 1000000, 2000000, None]) too long in python\n", + "df2.tail(n=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time 0\n", - "time: 52.51158252780897s - nb=50000 gain (0.615225173328998, 0.6153599275825006, 0.6153599275825006, 1.0)\n", - "time: 105.0721302614229s - nb=100000 gain (0.5836043296652512, 0.5841384772496148, 0.5841384772496148, 1.0)\n", - "time: 187.86111486480695s - nb=200000 gain (0.5507786166438062, 0.5518801462043321, 0.5518801462043321, 1.0)\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sizetimemksmks'mks\"ave_len%mksmks/mks%mks'mks'/mks%mks\"mks\"/mks
1100000105.0721300.5836040.5841380.5841381.00.5836041.00.5841381.0009150.5841381.000915
2200000187.8611150.5507790.5518800.5518801.00.5507791.00.5518801.0020000.5518801.002000
\n", - "
" - ], - "text/plain": [ - " size time mks mks' mks\" ave_len %mks \\\n", - "1 100000 105.072130 0.583604 0.584138 0.584138 1.0 0.583604 \n", - "2 200000 187.861115 0.550779 0.551880 0.551880 1.0 0.550779 \n", - "\n", - " mks/mks %mks' mks'/mks %mks\" mks\"/mks \n", - "1 1.0 0.584138 1.000915 0.584138 1.000915 \n", - "2 1.0 0.551880 1.002000 0.551880 1.002000 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df2 = benchmark([50000, 100000, 200000]) # , 500000, 500000, 1000000, 2000000, None]) too long in python\n", - "df2.tail(n=2)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAAJeCAYAAACOHyXpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dfall = pandas.concat([df, df2])\n", - "f, ax = plt.subplots(2, 2, figsize=(14,10))\n", - "dfall.plot(x=\"size\", y=\"time\", ax=ax[1,0])\n", - "dfall.plot(x=\"size\", y=[\"mks\", \"mks'\", \"mks\\\"\", \"ave_len\"], ax=ax[0,0])\n", - "dfall.plot(x=\"size\", y=[\"%mks\", \"%mks'\", \"%mks\\\"\"], ax=ax[0,1])\n", - "dfall.plot(x=\"size\", y=[\"mks'/mks\", \"mks\\\"/mks\"], ax=ax[1,1])\n", - "ax[0,0].legend()\n", - "ax[0,1].legend()\n", - "ax[1,0].legend()\n", - "ax[1,1].legend()\n", - "ax[1,1].set_ylim([0.9, 1.1])\n", - "ax[0,0].set_title(\"Raw Gain\")\n", - "ax[0,1].set_title(\"Relative Gain\")\n", - "ax[1,0].set_title(\"Time\")\n", - "ax[1,1].set_title(\"Comparison between MKS\")" + "data": { + "image/png": "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", + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "dfall = pandas.concat([df, df2])\n", + "f, ax = plt.subplots(2, 2, figsize=(14, 10))\n", + "dfall.plot(x=\"size\", y=\"time\", ax=ax[1, 0])\n", + "dfall.plot(x=\"size\", y=[\"mks\", \"mks'\", 'mks\"', \"ave_len\"], ax=ax[0, 0])\n", + "dfall.plot(x=\"size\", y=[\"%mks\", \"%mks'\", '%mks\"'], ax=ax[0, 1])\n", + "dfall.plot(x=\"size\", y=[\"mks'/mks\", 'mks\"/mks'], ax=ax[1, 1])\n", + "ax[0, 0].legend()\n", + "ax[0, 1].legend()\n", + "ax[1, 0].legend()\n", + "ax[1, 1].legend()\n", + "ax[1, 1].set_ylim([0.9, 1.1])\n", + "ax[0, 0].set_title(\"Raw Gain\")\n", + "ax[0, 1].set_title(\"Relative Gain\")\n", + "ax[1, 0].set_title(\"Time\")\n", + "ax[1, 1].set_title(\"Comparison between MKS\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] }, - "nbformat": 4, - "nbformat_minor": 0 + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/_doc/notebooks/nlp/index.rst b/_doc/notebooks/nlp/index.rst new file mode 100644 index 00000000..d1ca6753 --- /dev/null +++ b/_doc/notebooks/nlp/index.rst @@ -0,0 +1,9 @@ +NLP - Natural Language Processing +================================= + +.. nbgallery:: + :caption: Notebooks Gallery + :name: rst-nb-gallery-nlp + :glob: + + * diff --git a/_doc/notebooks/nlp/notebook.tex b/_doc/notebooks/nlp/notebook.tex deleted file mode 100644 index 073b7ce0..00000000 --- a/_doc/notebooks/nlp/notebook.tex +++ /dev/null @@ -1,831 +0,0 @@ - -% Default to the notebook output style - - - - -% Inherit from the specified cell style. - - - - - -\documentclass[11pt]{article} - - - - \usepackage[T1]{fontenc} - % Nicer default font (+ math font) than Computer Modern for most use cases - \usepackage{mathpazo} - - % Basic figure setup, for now with no caption control since it's done - % automatically by Pandoc (which extracts ![](path) syntax from Markdown). - \usepackage{graphicx} - % We will generate all images so they have a width \maxwidth. This means - % that they will get their normal width if they fit onto the page, but - % are scaled down if they would overflow the margins. - \makeatletter - \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth - \else\Gin@nat@width\fi} - \makeatother - \let\Oldincludegraphics\includegraphics - % Set max figure width to be 80% of text width, for now hardcoded. - \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}} - % Ensure that by default, figures have no caption (until we provide a - % proper Figure object with a Caption API and a way to capture that - % in the conversion process - todo). - \usepackage{caption} - \DeclareCaptionLabelFormat{nolabel}{} - \captionsetup{labelformat=nolabel} - - \usepackage{adjustbox} % Used to constrain images to a maximum size - \usepackage{xcolor} % Allow colors to be defined - \usepackage{enumerate} % Needed for markdown enumerations to work - \usepackage{geometry} % Used to adjust the document margins - \usepackage{amsmath} % Equations - \usepackage{amssymb} % Equations - \usepackage{textcomp} % defines textquotesingle - % Hack from http://tex.stackexchange.com/a/47451/13684: - \AtBeginDocument{% - \def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code - } - \usepackage{upquote} % Upright quotes for verbatim code - \usepackage{eurosym} % defines \euro - \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support - \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document - \usepackage{fancyvrb} % verbatim replacement that allows latex - \usepackage{grffile} % extends the file name processing of package graphics - % to support a larger range - % The hyperref package gives us a pdf with properly built - % internal navigation ('pdf bookmarks' for the table of contents, - % internal cross-reference links, web links for URLs, etc.) - \usepackage{hyperref} - \usepackage{longtable} % longtable support required by pandoc >1.10 - \usepackage{booktabs} % table support for pandoc > 1.12.2 - \usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment) - \usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout) - % normalem makes italics be italics, not underlines - - - - - % Colors for the hyperref package - \definecolor{urlcolor}{rgb}{0,.145,.698} - \definecolor{linkcolor}{rgb}{.71,0.21,0.01} - \definecolor{citecolor}{rgb}{.12,.54,.11} - - % ANSI colors - \definecolor{ansi-black}{HTML}{3E424D} - \definecolor{ansi-black-intense}{HTML}{282C36} - \definecolor{ansi-red}{HTML}{E75C58} - \definecolor{ansi-red-intense}{HTML}{B22B31} - \definecolor{ansi-green}{HTML}{00A250} - \definecolor{ansi-green-intense}{HTML}{007427} - \definecolor{ansi-yellow}{HTML}{DDB62B} - \definecolor{ansi-yellow-intense}{HTML}{B27D12} - \definecolor{ansi-blue}{HTML}{208FFB} - \definecolor{ansi-blue-intense}{HTML}{0065CA} - \definecolor{ansi-magenta}{HTML}{D160C4} - \definecolor{ansi-magenta-intense}{HTML}{A03196} - \definecolor{ansi-cyan}{HTML}{60C6C8} - \definecolor{ansi-cyan-intense}{HTML}{258F8F} - \definecolor{ansi-white}{HTML}{C5C1B4} - \definecolor{ansi-white-intense}{HTML}{A1A6B2} - - % commands and environments needed by pandoc snippets - % extracted from the output of `pandoc -s` - \providecommand{\tightlist}{% - \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} - \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}} - % Add ',fontsize=\small' for more characters per line - \newenvironment{Shaded}{}{} - \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}} - \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}} - \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}} - \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}} - \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}} - \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} - \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} - \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}} - \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}} - \newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}} - \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}} - \newcommand{\RegionMarkerTok}[1]{{#1}} - \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}} - \newcommand{\NormalTok}[1]{{#1}} - - % Additional commands for more recent versions of Pandoc - \newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}} - \newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} - \newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} - \newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}} - \newcommand{\ImportTok}[1]{{#1}} - \newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}} - \newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} - \newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} - \newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}} - \newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}} - \newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}} - \newcommand{\BuiltInTok}[1]{{#1}} - \newcommand{\ExtensionTok}[1]{{#1}} - \newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}} - \newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}} - \newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} - \newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} - - - % Define a nice break command that doesn't care if a line doesn't already - % exist. - \def\br{\hspace*{\fill} \\* } - % Math Jax compatability definitions - \def\gt{>} - \def\lt{<} - % Document parameters - \title{completion\_profiling} - - - - - % Pygments definitions - -\makeatletter -\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax% - \let\PY@ul=\relax \let\PY@tc=\relax% - \let\PY@bc=\relax \let\PY@ff=\relax} -\def\PY@tok#1{\csname PY@tok@#1\endcsname} -\def\PY@toks#1+{\ifx\relax#1\empty\else% - \PY@tok{#1}\expandafter\PY@toks\fi} -\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{% - \PY@it{\PY@bf{\PY@ff{#1}}}}}}} -\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}} - -\expandafter\def\csname PY@tok@w\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.73,0.73}{##1}}} -\expandafter\def\csname PY@tok@c\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}} -\expandafter\def\csname PY@tok@cp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.74,0.48,0.00}{##1}}} -\expandafter\def\csname PY@tok@k\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@kp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@kt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.69,0.00,0.25}{##1}}} -\expandafter\def\csname PY@tok@o\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@ow\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}} -\expandafter\def\csname PY@tok@nb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@nf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}} -\expandafter\def\csname PY@tok@nc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}} -\expandafter\def\csname PY@tok@nn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}} -\expandafter\def\csname PY@tok@ne\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.82,0.25,0.23}{##1}}} -\expandafter\def\csname PY@tok@nv\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} -\expandafter\def\csname PY@tok@no\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.00,0.00}{##1}}} -\expandafter\def\csname PY@tok@nl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.63,0.00}{##1}}} -\expandafter\def\csname PY@tok@ni\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.60,0.60,0.60}{##1}}} -\expandafter\def\csname PY@tok@na\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.49,0.56,0.16}{##1}}} -\expandafter\def\csname PY@tok@nt\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@nd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}} -\expandafter\def\csname PY@tok@s\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@sd\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@si\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}} -\expandafter\def\csname PY@tok@se\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.13}{##1}}} -\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}} -\expandafter\def\csname PY@tok@ss\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} -\expandafter\def\csname PY@tok@sx\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@m\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@gh\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}} -\expandafter\def\csname PY@tok@gu\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.50,0.00,0.50}{##1}}} -\expandafter\def\csname PY@tok@gd\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.63,0.00,0.00}{##1}}} -\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}} -\expandafter\def\csname PY@tok@gr\endcsname{\def\PY@tc##1{\textcolor[rgb]{1.00,0.00,0.00}{##1}}} -\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit} -\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf} -\expandafter\def\csname PY@tok@gp\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,0.50}{##1}}} -\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}} -\expandafter\def\csname PY@tok@gt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.27,0.87}{##1}}} -\expandafter\def\csname PY@tok@err\endcsname{\def\PY@bc##1{\setlength{\fboxsep}{0pt}\fcolorbox[rgb]{1.00,0.00,0.00}{1,1,1}{\strut ##1}}} -\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@kd\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@kn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@bp\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} -\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}} -\expandafter\def\csname PY@tok@vc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} -\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} -\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} -\expandafter\def\csname PY@tok@vm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} -\expandafter\def\csname PY@tok@sa\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@sb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@sc\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@sh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@s1\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} -\expandafter\def\csname PY@tok@mb\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@mf\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@mh\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@mi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@il\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@mo\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} -\expandafter\def\csname PY@tok@ch\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}} -\expandafter\def\csname PY@tok@cm\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}} -\expandafter\def\csname PY@tok@cpf\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}} -\expandafter\def\csname PY@tok@c1\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}} -\expandafter\def\csname PY@tok@cs\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}} - -\def\PYZbs{\char`\\} -\def\PYZus{\char`\_} -\def\PYZob{\char`\{} -\def\PYZcb{\char`\}} -\def\PYZca{\char`\^} -\def\PYZam{\char`\&} -\def\PYZlt{\char`\<} -\def\PYZgt{\char`\>} -\def\PYZsh{\char`\#} -\def\PYZpc{\char`\%} -\def\PYZdl{\char`\$} -\def\PYZhy{\char`\-} -\def\PYZsq{\char`\'} -\def\PYZdq{\char`\"} -\def\PYZti{\char`\~} -% for compatibility with earlier versions -\def\PYZat{@} -\def\PYZlb{[} -\def\PYZrb{]} -\makeatother - - - % Exact colors from NB - \definecolor{incolor}{rgb}{0.0, 0.0, 0.5} - \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0} - - - - - % Prevent overflowing lines due to hard-to-break entities - \sloppy - % Setup hyperref package - \hypersetup{ - breaklinks=true, % so long urls are correctly broken across lines - colorlinks=true, - urlcolor=urlcolor, - linkcolor=linkcolor, - citecolor=citecolor, - } - % Slightly bigger margins than the latex defaults - - \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in} - - - - \begin{document} - - - \maketitle - - - - - \section{Completion profiling}\label{completion-profiling} - -Profiling avec cProfile, memory\_profiler, line\_profiler, pyinstrument, -snakeviz. - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}2}]:} \PY{o}{\PYZpc{}}\PY{k}{matplotlib} inline - \PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt} - \PY{n}{plt}\PY{o}{.}\PY{n}{style}\PY{o}{.}\PY{n}{use}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{ggplot}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} - \PY{k+kn}{from} \PY{n+nn}{jyquickhelper} \PY{k}{import} \PY{n}{add\PYZus{}notebook\PYZus{}menu} - \PY{n}{add\PYZus{}notebook\PYZus{}menu}\PY{p}{(}\PY{p}{)} -\end{Verbatim} - - -\begin{Verbatim}[commandchars=\\\{\}] -{\color{outcolor}Out[{\color{outcolor}2}]:} -\end{Verbatim} - - \subsection{Setup}\label{setup} - - \subsubsection{Function to profile}\label{function-to-profile} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}3}]:} \PY{k+kn}{from} \PY{n+nn}{mlstatpy}\PY{n+nn}{.}\PY{n+nn}{nlp}\PY{n+nn}{.}\PY{n+nn}{completion} \PY{k}{import} \PY{n}{CompletionTrieNode} - - \PY{k}{def} \PY{n+nf}{gain\PYZus{}dynamique\PYZus{}moyen\PYZus{}par\PYZus{}mot}\PY{p}{(}\PY{n}{queries}\PY{p}{,} \PY{n}{weights}\PY{p}{)}\PY{p}{:} - \PY{n}{per} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n+nb}{zip}\PY{p}{(}\PY{n}{weights}\PY{p}{,} \PY{n}{queries}\PY{p}{)}\PY{p}{)} - \PY{n}{total} \PY{o}{=} \PY{n+nb}{sum}\PY{p}{(}\PY{n}{weights}\PY{p}{)} \PY{o}{*} \PY{l+m+mf}{1.0} - \PY{n}{res} \PY{o}{=} \PY{p}{[}\PY{p}{]} - \PY{n}{trie} \PY{o}{=} \PY{n}{CompletionTrieNode}\PY{o}{.}\PY{n}{build}\PY{p}{(}\PY{p}{[}\PY{p}{(}\PY{k+kc}{None}\PY{p}{,} \PY{n}{q}\PY{p}{)} \PY{k}{for} \PY{n}{\PYZus{}}\PY{p}{,} \PY{n}{q} \PY{o+ow}{in} \PY{n}{per}\PY{p}{]}\PY{p}{)} - \PY{n}{trie}\PY{o}{.}\PY{n}{precompute\PYZus{}stat}\PY{p}{(}\PY{p}{)} - \PY{n}{trie}\PY{o}{.}\PY{n}{update\PYZus{}stat\PYZus{}dynamic}\PY{p}{(}\PY{p}{)} - \PY{n}{wks} \PY{o}{=} \PY{p}{[}\PY{p}{(}\PY{n}{w}\PY{p}{,} \PY{n}{p}\PY{p}{,} \PY{n+nb}{len}\PY{p}{(}\PY{n}{w}\PY{p}{)} \PY{o}{\PYZhy{}} \PY{n}{trie}\PY{o}{.}\PY{n}{min\PYZus{}keystroke0}\PY{p}{(}\PY{n}{w}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)} \PY{k}{for} \PY{n}{p}\PY{p}{,} \PY{n}{w} \PY{o+ow}{in} \PY{n}{per}\PY{p}{]} - \PY{n}{wks\PYZus{}dyn} \PY{o}{=} \PY{p}{[}\PY{p}{(}\PY{n}{w}\PY{p}{,} \PY{n}{p}\PY{p}{,} \PY{n+nb}{len}\PY{p}{(}\PY{n}{w}\PY{p}{)} \PY{o}{\PYZhy{}} \PY{n}{trie}\PY{o}{.}\PY{n}{min\PYZus{}dynamic\PYZus{}keystroke}\PY{p}{(}\PY{n}{w}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)} - \PY{k}{for} \PY{n}{p}\PY{p}{,} \PY{n}{w} \PY{o+ow}{in} \PY{n}{per}\PY{p}{]} - \PY{n}{wks\PYZus{}dyn2} \PY{o}{=} \PY{p}{[}\PY{p}{(}\PY{n}{w}\PY{p}{,} \PY{n}{p}\PY{p}{,} \PY{n+nb}{len}\PY{p}{(}\PY{n}{w}\PY{p}{)} \PY{o}{\PYZhy{}} \PY{n}{trie}\PY{o}{.}\PY{n}{min\PYZus{}dynamic\PYZus{}keystroke2}\PY{p}{(}\PY{n}{w}\PY{p}{)}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]}\PY{p}{)} - \PY{k}{for} \PY{n}{p}\PY{p}{,} \PY{n}{w} \PY{o+ow}{in} \PY{n}{per}\PY{p}{]} - \PY{n}{gain} \PY{o}{=} \PY{n+nb}{sum}\PY{p}{(}\PY{n}{g} \PY{o}{*} \PY{n}{p} \PY{o}{/} \PY{n}{total} \PY{k}{for} \PY{n}{w}\PY{p}{,} \PY{n}{p}\PY{p}{,} \PY{n}{g} \PY{o+ow}{in} \PY{n}{wks}\PY{p}{)} - \PY{n}{gain\PYZus{}dyn} \PY{o}{=} \PY{n+nb}{sum}\PY{p}{(}\PY{n}{g} \PY{o}{*} \PY{n}{p} \PY{o}{/} \PY{n}{total} \PY{k}{for} \PY{n}{w}\PY{p}{,} \PY{n}{p}\PY{p}{,} \PY{n}{g} \PY{o+ow}{in} \PY{n}{wks\PYZus{}dyn}\PY{p}{)} - \PY{n}{gain\PYZus{}dyn2} \PY{o}{=} \PY{n+nb}{sum}\PY{p}{(}\PY{n}{g} \PY{o}{*} \PY{n}{p} \PY{o}{/} \PY{n}{total} \PY{k}{for} \PY{n}{w}\PY{p}{,} \PY{n}{p}\PY{p}{,} \PY{n}{g} \PY{o+ow}{in} \PY{n}{wks\PYZus{}dyn2}\PY{p}{)} - \PY{n}{ave\PYZus{}length} \PY{o}{=} \PY{n+nb}{sum}\PY{p}{(}\PY{n+nb}{len}\PY{p}{(}\PY{n}{w}\PY{p}{)} \PY{o}{*} \PY{n}{p} \PY{o}{/} \PY{n}{total} \PY{k}{for} \PY{n}{p}\PY{p}{,} \PY{n}{w} \PY{o+ow}{in} \PY{n}{per}\PY{p}{)} - \PY{k}{return} \PY{n}{gain}\PY{p}{,} \PY{n}{gain\PYZus{}dyn}\PY{p}{,} \PY{n}{gain\PYZus{}dyn2}\PY{p}{,} \PY{n}{ave\PYZus{}length} -\end{Verbatim} - - - \subsubsection{Data}\label{data} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}4}]:} \PY{k+kn}{from} \PY{n+nn}{mlstatpy}\PY{n+nn}{.}\PY{n+nn}{data}\PY{n+nn}{.}\PY{n+nn}{wikipedia} \PY{k}{import} \PY{n}{download\PYZus{}titles} - \PY{n}{file\PYZus{}titles} \PY{o}{=} \PY{n}{download\PYZus{}titles}\PY{p}{(}\PY{n}{country}\PY{o}{=}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{fr}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}5}]:} \PY{n+nb}{len}\PY{p}{(}\PY{n}{file\PYZus{}titles}\PY{p}{)} -\end{Verbatim} - - -\begin{Verbatim}[commandchars=\\\{\}] -{\color{outcolor}Out[{\color{outcolor}5}]:} 33 -\end{Verbatim} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}6}]:} \PY{k+kn}{from} \PY{n+nn}{mlstatpy}\PY{n+nn}{.}\PY{n+nn}{data}\PY{n+nn}{.}\PY{n+nn}{wikipedia} \PY{k}{import} \PY{n}{enumerate\PYZus{}titles} - \PY{n}{list\PYZus{}titles} \PY{o}{=} \PY{n+nb}{list}\PY{p}{(}\PY{n+nb}{sorted}\PY{p}{(}\PY{n+nb}{set}\PY{p}{(}\PY{n}{\PYZus{}} \PY{k}{for} \PY{n}{\PYZus{}} \PY{o+ow}{in} \PY{n}{enumerate\PYZus{}titles}\PY{p}{(}\PY{n}{file\PYZus{}titles}\PY{p}{)} \PY{k}{if} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{A}\PY{l+s+s1}{\PYZsq{}} \PY{o}{\PYZlt{}}\PY{o}{=} \PY{n}{\PYZus{}}\PY{p}{[}\PY{l+m+mi}{0}\PY{p}{]} \PY{o}{\PYZlt{}}\PY{o}{=} \PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{Z}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)}\PY{p}{)}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}7}]:} \PY{k+kn}{import} \PY{n+nn}{random} - \PY{n}{sample1000} \PY{o}{=} \PY{n}{random}\PY{o}{.}\PY{n}{sample}\PY{p}{(}\PY{n}{list\PYZus{}titles}\PY{p}{,} \PY{l+m+mi}{1000}\PY{p}{)} - \PY{k}{with} \PY{n+nb}{open}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{sample1000.txt}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{w}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{encoding}\PY{o}{=}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{utf\PYZhy{}8}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} \PY{k}{as} \PY{n}{f}\PY{p}{:} - \PY{n}{f}\PY{o}{.}\PY{n}{write}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+se}{\PYZbs{}n}\PY{l+s+s2}{\PYZdq{}}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{n}{sample1000}\PY{p}{)}\PY{p}{)} -\end{Verbatim} - - - \subsection{Standard modules}\label{standard-modules} - - \subsubsection{cProfile}\label{cprofile} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}8}]:} \PY{k+kn}{import} \PY{n+nn}{cProfile}\PY{o}{,} \PY{n+nn}{io}\PY{o}{,} \PY{n+nn}{pstats}\PY{o}{,} \PY{n+nn}{os} - - \PY{k}{def} \PY{n+nf}{toprofile0}\PY{p}{(}\PY{n}{lines}\PY{p}{)}\PY{p}{:} - \PY{n}{gain\PYZus{}dynamique\PYZus{}moyen\PYZus{}par\PYZus{}mot}\PY{p}{(}\PY{n}{lines}\PY{p}{,} \PY{p}{[}\PY{l+m+mf}{1.0}\PY{p}{]} \PY{o}{*} \PY{n+nb}{len}\PY{p}{(}\PY{n}{lines}\PY{p}{)}\PY{p}{)} - - \PY{k}{def} \PY{n+nf}{doprofile}\PY{p}{(}\PY{n}{lines}\PY{p}{,} \PY{n}{filename}\PY{p}{)}\PY{p}{:} - \PY{n}{pr} \PY{o}{=} \PY{n}{cProfile}\PY{o}{.}\PY{n}{Profile}\PY{p}{(}\PY{p}{)} - \PY{n}{pr}\PY{o}{.}\PY{n}{enable}\PY{p}{(}\PY{p}{)} - \PY{n}{toprofile0}\PY{p}{(}\PY{n}{lines}\PY{p}{)} - \PY{n}{pr}\PY{o}{.}\PY{n}{disable}\PY{p}{(}\PY{p}{)} - \PY{n}{s} \PY{o}{=} \PY{n}{io}\PY{o}{.}\PY{n}{StringIO}\PY{p}{(}\PY{p}{)} - \PY{n}{ps} \PY{o}{=} \PY{n}{pstats}\PY{o}{.}\PY{n}{Stats}\PY{p}{(}\PY{n}{pr}\PY{p}{,} \PY{n}{stream}\PY{o}{=}\PY{n}{s}\PY{p}{)}\PY{o}{.}\PY{n}{sort\PYZus{}stats}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{cumulative}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} - \PY{n}{ps}\PY{o}{.}\PY{n}{print\PYZus{}stats}\PY{p}{(}\PY{p}{)} - \PY{n}{rem} \PY{o}{=} \PY{n}{os}\PY{o}{.}\PY{n}{path}\PY{o}{.}\PY{n}{normpath}\PY{p}{(}\PY{n}{os}\PY{o}{.}\PY{n}{path}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{n}{os}\PY{o}{.}\PY{n}{getcwd}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{..}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{..}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{..}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)} - \PY{n}{res} \PY{o}{=} \PY{n}{s}\PY{o}{.}\PY{n}{getvalue}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{n}{rem}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} - \PY{n}{ps}\PY{o}{.}\PY{n}{dump\PYZus{}stats}\PY{p}{(}\PY{n}{filename}\PY{p}{)} - \PY{k}{return} \PY{n}{res} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}9}]:} \PY{n}{r} \PY{o}{=} \PY{n}{doprofile}\PY{p}{(}\PY{n}{sample1000}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{completion.prof}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} - \PY{n+nb}{print}\PY{p}{(}\PY{n}{r}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] - 1225828 function calls in 1.261 seconds - - Ordered by: cumulative time - - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.000 0.000 1.261 1.261 :3(toprofile0) - 1 0.000 0.000 1.261 1.261 :3(gain\_dynamique\_moyen\_par\_mot) - 1 0.129 0.129 0.916 0.916 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:416(precompute\_stat) - 15148 0.260 0.000 0.662 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:504(merge\_completions) - 15148 0.277 0.000 0.281 0.000 \{built-in method builtins.\_\_build\_class\_\_\} - 1 0.051 0.051 0.216 0.216 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:451(update\_stat\_dynamic) - 1 0.087 0.087 0.107 0.107 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:203(build) - 16147 0.048 0.000 0.090 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:556(update\_dynamic\_minimum\_keystroke) - 34403 0.061 0.000 0.070 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:524() - 34127 0.019 0.000 0.037 0.000 \{built-in method builtins.all\} - 16147 0.026 0.000 0.037 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:625(init\_dynamic\_minimum\_keystroke) - 16147 0.029 0.000 0.034 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:589(second\_step) - 298714 0.030 0.000 0.030 0.000 \{built-in method builtins.len\} - 15148 0.023 0.000 0.029 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:543(update\_minimum\_keystroke) - 105060 0.028 0.000 0.028 0.000 \{built-in method builtins.hasattr\} - 15149 0.003 0.000 0.027 0.000 \{method 'extend' of 'collections.deque' objects\} - 16148 0.018 0.000 0.027 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:97(unsorted\_iter) - 1001 0.016 0.000 0.023 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:132(leaves) - 15148 0.018 0.000 0.023 0.000 \{built-in method builtins.sorted\} - 93541 0.022 0.000 0.022 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:436() - 3000 0.013 0.000 0.014 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:258(find) - 16147 0.011 0.000 0.013 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:20(\_\_init\_\_) - 109867 0.013 0.000 0.013 0.000 \{method 'values' of 'dict' objects\} - 22498 0.009 0.000 0.009 0.000 \{built-in method builtins.min\} - 45444 0.009 0.000 0.009 0.000 \{method 'extend' of 'list' objects\} - 1 0.001 0.001 0.009 0.009 :13() - 1000 0.001 0.000 0.008 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:383(min\_dynamic\_keystroke2) - 48441 0.007 0.000 0.007 0.000 \{method 'pop' of 'list' objects\} - 49552 0.007 0.000 0.007 0.000 \{method 'append' of 'list' objects\} - 19255 0.007 0.000 0.007 0.000 \{built-in method builtins.max\} - 52271 0.006 0.000 0.006 0.000 \{method 'popleft' of 'collections.deque' objects\} - 1 0.001 0.001 0.006 0.006 :10() - 35125 0.005 0.000 0.005 0.000 \{method 'append' of 'collections.deque' objects\} - 1 0.001 0.001 0.005 0.005 :11() - 16146 0.005 0.000 0.005 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:54(\_add) - 1000 0.001 0.000 0.005 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:322(min\_keystroke0) - 1000 0.001 0.000 0.005 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:353(min\_dynamic\_keystroke) - 16148 0.005 0.000 0.005 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:518() - 15148 0.004 0.000 0.004 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:509(Fake) - 15148 0.004 0.000 0.004 0.000 \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py:512() - 30296 0.003 0.000 0.003 0.000 \{method 'items' of 'dict' objects\} - 17147 0.002 0.000 0.002 0.000 \{built-in method builtins.isinstance\} - 5 0.000 0.000 0.001 0.000 \{built-in method builtins.sum\} - 1 0.000 0.000 0.000 0.000 :7() - 1001 0.000 0.000 0.000 0.000 :18() - 1001 0.000 0.000 0.000 0.000 :15() - 1001 0.000 0.000 0.000 0.000 :16() - 1001 0.000 0.000 0.000 0.000 :17() - 1 0.000 0.000 0.000 0.000 \{method 'disable' of '\_lsprof.Profiler' objects\} - - - - - \end{Verbatim} - - \subsection{Others informations when -profiling}\label{others-informations-when-profiling} - - \subsubsection{memory\_profiler}\label{memory_profiler} - -See -\href{https://pypi.python.org/pypi/memory_profiler/0.41}{memory\_profiler}. - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}10}]:} \PY{k+kn}{from} \PY{n+nn}{memory\PYZus{}profiler} \PY{k}{import} \PY{n}{profile} - \PY{o}{\PYZpc{}}\PY{k}{load\PYZus{}ext} memory\PYZus{}profiler -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}11}]:} \PY{o}{\PYZpc{}}\PY{k}{memit} toprofile0(sample1000) -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -peak memory: 412.05 MiB, increment: 15.60 MiB - - \end{Verbatim} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}12}]:} \PY{k+kn}{from} \PY{n+nn}{io} \PY{k}{import} \PY{n}{StringIO} - \PY{n}{st} \PY{o}{=} \PY{n}{StringIO}\PY{p}{(}\PY{p}{)} - \PY{n+nd}{@profile}\PY{p}{(}\PY{n}{stream}\PY{o}{=}\PY{n}{st}\PY{p}{)} - \PY{k}{def} \PY{n+nf}{toprofile}\PY{p}{(}\PY{n}{lines}\PY{p}{)}\PY{p}{:} - \PY{n}{gain\PYZus{}dynamique\PYZus{}moyen\PYZus{}par\PYZus{}mot}\PY{p}{(}\PY{n}{lines}\PY{p}{,} \PY{p}{[}\PY{l+m+mf}{1.0}\PY{p}{]} \PY{o}{*} \PY{n+nb}{len}\PY{p}{(}\PY{n}{lines}\PY{p}{)}\PY{p}{)} - \PY{n}{toprofile}\PY{p}{(}\PY{n}{sample1000}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -ERROR: Could not find file -NOTE: \%mprun can only be used on functions defined in physical files, and not in the IPython environment. - - \end{Verbatim} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}13}]:} \PY{o}{\PYZpc{}\PYZpc{}}\PY{k}{file} temp\PYZus{}mem\PYZus{}profile.py - - from mlstatpy.nlp.completion import CompletionTrieNode - from memory\PYZus{}profiler import profile - - @profile(precision=4) - def gain\PYZus{}dynamique\PYZus{}moyen\PYZus{}par\PYZus{}mot(queries, weights): - per = list(zip(weights, queries)) - total = sum(weights) * 1.0 - res = [] - trie = CompletionTrieNode.build([(None, q) for \PYZus{}, q in per]) - trie.precompute\PYZus{}stat() - trie.update\PYZus{}stat\PYZus{}dynamic() - wks = [(w, p, len(w) \PYZhy{} trie.min\PYZus{}keystroke0(w)[0]) for p, w in per] - wks\PYZus{}dyn = [(w, p, len(w) \PYZhy{} trie.min\PYZus{}dynamic\PYZus{}keystroke(w)[0]) - for p, w in per] - wks\PYZus{}dyn2 = [(w, p, len(w) \PYZhy{} trie.min\PYZus{}dynamic\PYZus{}keystroke2(w)[0]) - for p, w in per] - gain = sum(g * p / total for w, p, g in wks) - gain\PYZus{}dyn = sum(g * p / total for w, p, g in wks\PYZus{}dyn) - gain\PYZus{}dyn2 = sum(g * p / total for w, p, g in wks\PYZus{}dyn2) - ave\PYZus{}length = sum(len(w) * p / total for p, w in per) - return gain, gain\PYZus{}dyn, gain\PYZus{}dyn2, ave\PYZus{}length - - @profile(precision=4) - def toprofile(): - with open(\PYZdq{}sample1000.txt\PYZdq{}, \PYZdq{}r\PYZdq{}, encoding=\PYZdq{}utf\PYZhy{}8\PYZdq{}) as f: - lines = [\PYZus{}.strip(\PYZdq{}\PYZbs{}n\PYZbs{}r \PYZdq{}) for \PYZus{} in f.readlines()] - gain\PYZus{}dynamique\PYZus{}moyen\PYZus{}par\PYZus{}mot(lines, [1.0] * len(lines)) - toprofile() -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -Overwriting temp\_mem\_profile.py - - \end{Verbatim} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}14}]:} \PY{k+kn}{import} \PY{n+nn}{sys} - \PY{n}{cmd} \PY{o}{=} \PY{n}{sys}\PY{o}{.}\PY{n}{executable} - \PY{k+kn}{from} \PY{n+nn}{pyquickhelper}\PY{n+nn}{.}\PY{n+nn}{loghelper} \PY{k}{import} \PY{n}{run\PYZus{}cmd} - \PY{n}{cmd} \PY{o}{+}\PY{o}{=} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{ \PYZhy{}m memory\PYZus{}profiler temp\PYZus{}mem\PYZus{}profile.py}\PY{l+s+s2}{\PYZdq{}} - \PY{n}{out}\PY{p}{,} \PY{n}{err} \PY{o}{=} \PY{n}{run\PYZus{}cmd}\PY{p}{(}\PY{n}{cmd}\PY{p}{,} \PY{n}{wait}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)} - \PY{n+nb}{print}\PY{p}{(}\PY{n}{out}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -Filename: temp\_mem\_profile.py - -Line \# Mem usage Increment Line Contents -================================================ - 5 56.0625 MiB 56.0625 MiB @profile(precision=4) - 6 def gain\_dynamique\_moyen\_par\_mot(queries, weights): - 7 56.0625 MiB 0.0000 MiB per = list(zip(weights, queries)) - 8 56.0625 MiB 0.0000 MiB total = sum(weights) * 1.0 - 9 56.0625 MiB 0.0000 MiB res = [] - 10 62.2500 MiB 6.1875 MiB trie = CompletionTrieNode.build([(None, q) for \_, q in per]) - 11 69.5625 MiB 7.3125 MiB trie.precompute\_stat() - 12 78.7695 MiB 9.2070 MiB trie.update\_stat\_dynamic() - 13 78.7734 MiB 0.0039 MiB wks = [(w, p, len(w) - trie.min\_keystroke0(w)[0]) for p, w in per] - 14 78.7969 MiB 0.0234 MiB wks\_dyn = [(w, p, len(w) - trie.min\_dynamic\_keystroke(w)[0]) - 15 78.7969 MiB 0.0000 MiB for p, w in per] - 16 78.7969 MiB 0.0000 MiB wks\_dyn2 = [(w, p, len(w) - trie.min\_dynamic\_keystroke2(w)[0]) - 17 78.7969 MiB 0.0000 MiB for p, w in per] - 18 78.7969 MiB 0.0000 MiB gain = sum(g * p / total for w, p, g in wks) - 19 78.7969 MiB 0.0000 MiB gain\_dyn = sum(g * p / total for w, p, g in wks\_dyn) - 20 78.7969 MiB 0.0000 MiB gain\_dyn2 = sum(g * p / total for w, p, g in wks\_dyn2) - 21 78.7969 MiB 0.0000 MiB ave\_length = sum(len(w) * p / total for p, w in per) - 22 78.7969 MiB 0.0000 MiB return gain, gain\_dyn, gain\_dyn2, ave\_length - - -Filename: temp\_mem\_profile.py - -Line \# Mem usage Increment Line Contents -================================================ - 24 55.9570 MiB 55.9570 MiB @profile(precision=4) - 25 def toprofile(): - 26 55.9570 MiB 0.0000 MiB with open("sample1000.txt", "r", encoding="utf-8") as f: - 27 56.0625 MiB 0.1055 MiB lines = [\_.strip("\textbackslash{}n\textbackslash{}r ") for \_ in f.readlines()] - 28 78.7969 MiB 22.7344 MiB gain\_dynamique\_moyen\_par\_mot(lines, [1.0] * len(lines)) - - - - - \end{Verbatim} - - \subsubsection{line\_profiler}\label{line_profiler} - -See \href{https://github.com/rkern/line_profiler}{line\_profiler}. - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}15}]:} \PY{k}{def} \PY{n+nf}{lineprofile}\PY{p}{(}\PY{n}{lines}\PY{p}{)}\PY{p}{:} - \PY{n}{gain\PYZus{}dynamique\PYZus{}moyen\PYZus{}par\PYZus{}mot}\PY{p}{(}\PY{n}{lines}\PY{p}{,} \PY{p}{[}\PY{l+m+mf}{1.0}\PY{p}{]} \PY{o}{*} \PY{n+nb}{len}\PY{p}{(}\PY{n}{lines}\PY{p}{)}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}16}]:} \PY{k+kn}{from} \PY{n+nn}{mlstatpy}\PY{n+nn}{.}\PY{n+nn}{nlp}\PY{n+nn}{.}\PY{n+nn}{completion} \PY{k}{import} \PY{n}{CompletionTrieNode} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}17}]:} \PY{k+kn}{from} \PY{n+nn}{line\PYZus{}profiler} \PY{k}{import} \PY{n}{LineProfiler} - \PY{n}{prof} \PY{o}{=} \PY{n}{LineProfiler}\PY{p}{(}\PY{p}{)} - \PY{n}{prof}\PY{o}{.}\PY{n}{add\PYZus{}function}\PY{p}{(}\PY{n}{gain\PYZus{}dynamique\PYZus{}moyen\PYZus{}par\PYZus{}mot}\PY{p}{)} - \PY{n}{prof}\PY{o}{.}\PY{n}{add\PYZus{}function}\PY{p}{(}\PY{n}{CompletionTrieNode}\PY{o}{.}\PY{n}{precompute\PYZus{}stat}\PY{p}{)} - \PY{n}{prof}\PY{o}{.}\PY{n}{run}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{lineprofile(sample1000)}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} - \PY{n}{st} \PY{o}{=} \PY{n}{io}\PY{o}{.}\PY{n}{StringIO}\PY{p}{(}\PY{p}{)} - \PY{n}{prof}\PY{o}{.}\PY{n}{print\PYZus{}stats}\PY{p}{(}\PY{n}{stream}\PY{o}{=}\PY{n}{st}\PY{p}{)} - \PY{n}{rem} \PY{o}{=} \PY{n}{os}\PY{o}{.}\PY{n}{path}\PY{o}{.}\PY{n}{normpath}\PY{p}{(}\PY{n}{os}\PY{o}{.}\PY{n}{path}\PY{o}{.}\PY{n}{join}\PY{p}{(}\PY{n}{os}\PY{o}{.}\PY{n}{getcwd}\PY{p}{(}\PY{p}{)}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{..}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{..}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{..}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)} - \PY{n}{res} \PY{o}{=} \PY{n}{st}\PY{o}{.}\PY{n}{getvalue}\PY{p}{(}\PY{p}{)}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{n}{rem}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} - \PY{n+nb}{print}\PY{p}{(}\PY{n}{res}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -Timer unit: 3.95062e-07 s - -Total time: 3.3536 s -File: -Function: gain\_dynamique\_moyen\_par\_mot at line 3 - -Line \# Hits Time Per Hit \% Time Line Contents -============================================================== - 3 def gain\_dynamique\_moyen\_par\_mot(queries, weights): - 4 1 393.0 393.0 0.0 per = list(zip(weights, queries)) - 5 1 23.0 23.0 0.0 total = sum(weights) * 1.0 - 6 1 4.0 4.0 0.0 res = [] - 7 1 379526.0 379526.0 4.5 trie = CompletionTrieNode.build([(None, q) for \_, q in per]) - 8 1 6658020.0 6658020.0 78.4 trie.precompute\_stat() - 9 1 1241489.0 1241489.0 14.6 trie.update\_stat\_dynamic() - 10 1 64879.0 64879.0 0.8 wks = [(w, p, len(w) - trie.min\_keystroke0(w)[0]) for p, w in per] - 11 1 16.0 16.0 0.0 wks\_dyn = [(w, p, len(w) - trie.min\_dynamic\_keystroke(w)[0]) - 12 1 58635.0 58635.0 0.7 for p, w in per] - 13 1 10.0 10.0 0.0 wks\_dyn2 = [(w, p, len(w) - trie.min\_dynamic\_keystroke2(w)[0]) - 14 1 80850.0 80850.0 1.0 for p, w in per] - 15 1 1318.0 1318.0 0.0 gain = sum(g * p / total for w, p, g in wks) - 16 1 1221.0 1221.0 0.0 gain\_dyn = sum(g * p / total for w, p, g in wks\_dyn) - 17 1 1073.0 1073.0 0.0 gain\_dyn2 = sum(g * p / total for w, p, g in wks\_dyn2) - 18 1 1349.0 1349.0 0.0 ave\_length = sum(len(w) * p / total for p, w in per) - 19 1 4.0 4.0 0.0 return gain, gain\_dyn, gain\_dyn2, ave\_length - -Total time: 2.19801 s -File: \textbackslash{}src\textbackslash{}mlstatpy\textbackslash{}nlp\textbackslash{}completion.py -Function: precompute\_stat at line 416 - -Line \# Hits Time Per Hit \% Time Line Contents -============================================================== - 416 def precompute\_stat(self): - 417 """ - 418 computes and stores list of completions for each node, - 419 computes mks - 420 - 421 @param clean clean stat - 422 """ - 423 1 5.0 5.0 0.0 stack = deque() - 424 1 78759.0 78759.0 1.4 stack.extend(self.leaves()) - 425 52272 168548.0 3.2 3.0 while len(stack) > 0: - 426 52271 169512.0 3.2 3.0 pop = stack.popleft() - 427 52271 148123.0 2.8 2.7 if pop.stat is not None: - 428 17145 42365.0 2.5 0.8 continue - 429 35126 105270.0 3.0 1.9 if not pop.children: - 430 999 2545.0 2.5 0.0 pop.stat = CompletionTrieNode.\_Stat() - 431 999 5598.0 5.6 0.1 pop.stat.completions = [] - 432 999 2812.0 2.8 0.1 pop.stat.mks0 = len(pop.value) - 433 999 2370.0 2.4 0.0 pop.stat.mks0\_ = len(pop.value) - 434 999 2233.0 2.2 0.0 if pop.parent is not None: - 435 999 2339.0 2.3 0.0 stack.append(pop.parent) - 436 34127 397661.0 11.7 7.1 elif all(v.stat is not None for v in pop.children.values()): - 437 15148 55784.0 3.7 1.0 pop.stat = CompletionTrieNode.\_Stat() - 438 15148 48358.0 3.2 0.9 if pop.leave: - 439 1 5.0 5.0 0.0 pop.stat.mks0 = len(pop.value) - 440 1 5.0 5.0 0.0 pop.stat.mks0\_ = len(pop.value) - 441 15148 76740.0 5.1 1.4 stack.extend(pop.children.values()) - 442 15148 3717484.0 245.4 66.8 pop.stat.merge\_completions(pop.value, pop.children.values()) - 443 15148 62928.0 4.2 1.1 pop.stat.next\_nodes = pop.children - 444 15148 292243.0 19.3 5.3 pop.stat.update\_minimum\_keystroke(len(pop.value)) - 445 15148 55688.0 3.7 1.0 if pop.parent is not None: - 446 15147 64826.0 4.3 1.2 stack.append(pop.parent) - 447 else: - 448 \# we'll do it again later - 449 18979 61515.0 3.2 1.1 stack.append(pop) - - - - \end{Verbatim} - - \subsection{Static Visualization}\label{static-visualization} - - \subsubsection{gprof2dot}\label{gprof2dot} - -See \href{https://github.com/jrfonseca/gprof2dot}{gprof2dot}. - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}18}]:} \PY{k+kn}{import} \PY{n+nn}{gprof2dot} - \PY{k+kn}{import} \PY{n+nn}{sys} - \PY{n}{sys}\PY{o}{.}\PY{n}{argv}\PY{o}{=}\PY{p}{[}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZhy{}f}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{pstats}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{completion.prof}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{\PYZhy{}o}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{completion.dot}\PY{l+s+s2}{\PYZdq{}}\PY{p}{]} - \PY{n}{gprof2dot}\PY{o}{.}\PY{n}{main}\PY{p}{(}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}19}]:} \PY{k+kn}{from} \PY{n+nn}{pyquickhelper}\PY{n+nn}{.}\PY{n+nn}{helpgen}\PY{n+nn}{.}\PY{n+nn}{conf\PYZus{}path\PYZus{}tools} \PY{k}{import} \PY{n}{find\PYZus{}graphviz\PYZus{}dot} - \PY{n}{dot} \PY{o}{=} \PY{n}{find\PYZus{}graphviz\PYZus{}dot}\PY{p}{(}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}20}]:} \PY{k+kn}{from} \PY{n+nn}{pyquickhelper}\PY{n+nn}{.}\PY{n+nn}{loghelper} \PY{k}{import} \PY{n}{run\PYZus{}cmd} - \PY{n}{out}\PY{p}{,} \PY{n}{err} \PY{o}{=} \PY{n}{run\PYZus{}cmd}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{\PYZdq{}}\PY{l+s+si}{\PYZob{}0\PYZcb{}}\PY{l+s+s1}{\PYZdq{}}\PY{l+s+s1}{ completion.dot \PYZhy{}Tpng \PYZhy{}ocompletion.png}\PY{l+s+s1}{\PYZsq{}}\PY{o}{.}\PY{n}{format}\PY{p}{(}\PY{n}{dot}\PY{p}{)}\PY{p}{,} \PY{n}{wait}\PY{o}{=}\PY{k+kc}{True}\PY{p}{)} - \PY{n+nb}{print}\PY{p}{(}\PY{n}{out}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] - - - \end{Verbatim} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}21}]:} \PY{k+kn}{from} \PY{n+nn}{pyquickhelper}\PY{n+nn}{.}\PY{n+nn}{helpgen} \PY{k}{import} \PY{n}{NbImage} - \PY{n}{name} \PY{o}{=} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{completion.png}\PY{l+s+s2}{\PYZdq{}} - \PY{k}{if} \PY{n}{os}\PY{o}{.}\PY{n}{path}\PY{o}{.}\PY{n}{exists}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{images/completion.jpg}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{:} - \PY{n}{name} \PY{o}{=} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{images/completion.jpg}\PY{l+s+s2}{\PYZdq{}} - \PY{n}{NbImage}\PY{p}{(}\PY{n}{name}\PY{p}{,} \PY{n}{width}\PY{o}{=}\PY{l+m+mi}{800}\PY{p}{)} -\end{Verbatim} - -\texttt{\color{outcolor}Out[{\color{outcolor}21}]:} - - \begin{center} - \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_30_0.jpeg} - \end{center} - { \hspace*{\fill} \\} - - - \subsubsection{pyinstrument}\label{pyinstrument} - -See \href{https://github.com/joerick/pyinstrument}{pyinstrument}. - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}22}]:} \PY{k+kn}{from} \PY{n+nn}{pyinstrument} \PY{k}{import} \PY{n}{Profiler} - - \PY{n}{profiler} \PY{o}{=} \PY{n}{Profiler}\PY{p}{(}\PY{n}{use\PYZus{}signal}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)} - \PY{n}{profiler}\PY{o}{.}\PY{n}{start}\PY{p}{(}\PY{p}{)} - - \PY{n}{toprofile0}\PY{p}{(}\PY{n}{sample1000}\PY{p}{)} - - \PY{n}{profiler}\PY{o}{.}\PY{n}{stop}\PY{p}{(}\PY{p}{)} - \PY{n}{out} \PY{o}{=} \PY{n}{profiler}\PY{o}{.}\PY{n}{output\PYZus{}text}\PY{p}{(}\PY{n}{unicode}\PY{o}{=}\PY{k+kc}{False}\PY{p}{,} \PY{n}{color}\PY{o}{=}\PY{k+kc}{False}\PY{p}{)} - \PY{n+nb}{print}\PY{p}{(}\PY{n}{out}\PY{o}{.}\PY{n}{replace}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+se}{\PYZbs{}\PYZbs{}}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{/}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)}\PY{p}{)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -\_\_main\_\_:3: DeprecationWarning: use\_signal is deprecated and should no longer be used. - - \end{Verbatim} - - \begin{Verbatim}[commandchars=\\\{\}] -1.414 gain\_dynamique\_moyen\_par\_mot :3 -|- 1.050 precompute\_stat mlstatpy/nlp/completion.py:416 -| |- 0.846 merge\_completions mlstatpy/nlp/completion.py:504 -| | `- 0.071 mlstatpy/nlp/completion.py:524 -| |- 0.027 mlstatpy/nlp/completion.py:436 -| |- 0.026 leaves mlstatpy/nlp/completion.py:132 -| `- 0.015 update\_minimum\_keystroke mlstatpy/nlp/completion.py:543 -|- 0.235 update\_stat\_dynamic mlstatpy/nlp/completion.py:451 -| |- 0.119 update\_dynamic\_minimum\_keystroke mlstatpy/nlp/completion.py:556 -| | `- 0.037 second\_step mlstatpy/nlp/completion.py:589 -| |- 0.041 init\_dynamic\_minimum\_keystroke mlstatpy/nlp/completion.py:625 -| `- 0.020 unsorted\_iter mlstatpy/nlp/completion.py:97 -`- 0.096 build mlstatpy/nlp/completion.py:203 - `- 0.019 \_\_init\_\_ mlstatpy/nlp/completion.py:20 - - - \end{Verbatim} - - \subsection{Javascript Visualization}\label{javascript-visualization} - - \subsubsection{SnakeViz}\label{snakeviz} - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}23}]:} \PY{o}{\PYZpc{}}\PY{k}{load\PYZus{}ext} snakeviz -\end{Verbatim} - - - L'instruction qui suit lance l'explorateur par défaut avec les données -du profilage. - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}24}]:} \PY{c+c1}{\PYZsh{} \PYZpc{}snakeviz toprofile0(sample1000)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}25}]:} \PY{k+kn}{from} \PY{n+nn}{pyquickhelper}\PY{n+nn}{.}\PY{n+nn}{helpgen} \PY{k}{import} \PY{n}{NbImage} - \PY{n}{NbImage}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{images/func\PYZus{}info.jpg}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{width}\PY{o}{=}\PY{l+m+mi}{400}\PY{p}{)} -\end{Verbatim} - -\texttt{\color{outcolor}Out[{\color{outcolor}25}]:} - - \begin{center} - \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_38_0.jpeg} - \end{center} - { \hspace*{\fill} \\} - - - \subsubsection{vprof}\label{vprof} - -See \href{https://github.com/nvdv/vprof}{vprof}. - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}26}]:} \PY{k+kn}{from} \PY{n+nn}{vprof} \PY{k}{import} \PY{n}{profiler} - - \PY{c+c1}{\PYZsh{} needs to be run from a file not from a notebook} - \PY{c+c1}{\PYZsh{} profiler.run(toprofile0, \PYZsq{}cmh\PYZsq{}, args=(sample1000,), host=\PYZsq{}localhost\PYZsq{}, port=8000)} -\end{Verbatim} - - - \begin{Verbatim}[commandchars=\\\{\}] -{\color{incolor}In [{\color{incolor}27}]:} \PY{k+kn}{from} \PY{n+nn}{pyquickhelper}\PY{n+nn}{.}\PY{n+nn}{helpgen} \PY{k}{import} \PY{n}{NbImage} - \PY{n}{NbImage}\PY{p}{(}\PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{images/vprof.jpg}\PY{l+s+s2}{\PYZdq{}}\PY{p}{,} \PY{n}{width}\PY{o}{=}\PY{l+m+mi}{800}\PY{p}{)} -\end{Verbatim} - -\texttt{\color{outcolor}Out[{\color{outcolor}27}]:} - - \begin{center} - \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{output_41_0.jpeg} - \end{center} - { \hspace*{\fill} \\} - - - - % Add a bibliography block to the postdoc - - - - \end{document} diff --git a/_doc/sphinxdoc/source/api/data.rst b/_doc/sphinxdoc/source/api/data.rst deleted file mode 100644 index 8a97804f..00000000 --- a/_doc/sphinxdoc/source/api/data.rst +++ /dev/null @@ -1,20 +0,0 @@ - -Source de données -================= - -.. contents:: - :local: - :depth: 2 - -Wikipédia -+++++++++ - -.. autosignature:: mlstatpy.data.wikipedia.download_dump - -.. autosignature:: mlstatpy.data.wikipedia.download_pageviews - -.. autosignature:: mlstatpy.data.wikipedia.download_titles - -.. autosignature:: mlstatpy.data.wikipedia.enumerate_titles - -.. autosignature:: mlstatpy.data.wikipedia.download_dump diff --git a/_doc/sphinxdoc/source/api/graph.rst b/_doc/sphinxdoc/source/api/graph.rst deleted file mode 100644 index 1e0f58a7..00000000 --- a/_doc/sphinxdoc/source/api/graph.rst +++ /dev/null @@ -1,13 +0,0 @@ - -Graphes -======= - -.. contents:: - :local: - :depth: 2 - -Distance -++++++++ - -.. autosignature:: mlstatpy.graph.graph_distance.GraphDistance - :members: distance_matching_graphs_paths diff --git a/_doc/sphinxdoc/source/api/image.rst b/_doc/sphinxdoc/source/api/image.rst deleted file mode 100644 index 4b7983cd..00000000 --- a/_doc/sphinxdoc/source/api/image.rst +++ /dev/null @@ -1,28 +0,0 @@ - -Image -===== - -.. contents:: - :local: - :depth: 2 - -Conversion -++++++++++ - -.. autosignature:: mlstatpy.image.detection_segment.detection_segment.convert_array2PIL - -.. autosignature:: mlstatpy.image.detection_segment.detection_segment.convert_PIL2array - -Images aléatoires -+++++++++++++++++ - -.. autosignature:: mlstatpy.image.detection_segment.random_image.random_noise_image - -.. autosignature:: mlstatpy.image.detection_segment.random_image.random_segment_image - -Segments -++++++++ - -.. autosignature:: mlstatpy.image.detection_segment.detection_segment.detect_segments - -.. autosignature:: mlstatpy.image.detection_segment.detection_segment.plot_segments diff --git a/_doc/sphinxdoc/source/api/ml.rst b/_doc/sphinxdoc/source/api/ml.rst deleted file mode 100644 index b00c1b59..00000000 --- a/_doc/sphinxdoc/source/api/ml.rst +++ /dev/null @@ -1,23 +0,0 @@ - -Machine Learning -================ - -.. contents:: - :local: - :depth: 2 - -Métriques -+++++++++ - -.. autosignature:: mlstatpy.ml.ml_grid_benchmark.MlGridBenchMark - -.. autosignature:: mlstatpy.ml.roc.ROC - -.. autosignature:: mlstatpy.ml.voronoi.voronoi_estimation_from_lr - -Tree and neural networks -++++++++++++++++++++++++ - -.. autosignature:: mlstatpy.ml.neural_tree.NeuralTreeNode - -.. autosignature:: mlstatpy.ml.neural_tree.NeuralTreeNet diff --git a/_doc/sphinxdoc/source/api/optim.rst b/_doc/sphinxdoc/source/api/optim.rst deleted file mode 100644 index b00d2d35..00000000 --- a/_doc/sphinxdoc/source/api/optim.rst +++ /dev/null @@ -1,12 +0,0 @@ - -Optimisation -================ - -.. contents:: - :local: - :depth: 2 - -Gradient -++++++++ - -.. autosignature:: mlstatpy.optim.sgd.SGDOptimizer diff --git a/_doc/sphinxdoc/source/api/text.rst b/_doc/sphinxdoc/source/api/text.rst deleted file mode 100644 index f4945e1e..00000000 --- a/_doc/sphinxdoc/source/api/text.rst +++ /dev/null @@ -1,23 +0,0 @@ - -Traitement du langage naturel -============================= - -.. contents:: - :local: - :depth: 2 - -Complétion -++++++++++ - -.. autosignature:: mlstatpy.nlp.completion_simple.CompletionElement - :members: - -.. autosignature:: mlstatpy.nlp.completion_simple.CompletionSystem - :members: - -Normalisation -+++++++++++++ - -.. autosignature:: mlstatpy.data.wikipedia.normalize_wiki_text - -.. autosignature:: mlstatpy.nlp.normalize.remove_diacritics diff --git a/_doc/sphinxdoc/source/blog/2016/2016-06-19_first_blog.rst b/_doc/sphinxdoc/source/blog/2016/2016-06-19_first_blog.rst deleted file mode 100644 index 16e90a6b..00000000 --- a/_doc/sphinxdoc/source/blog/2016/2016-06-19_first_blog.rst +++ /dev/null @@ -1,8 +0,0 @@ - -.. blogpost:: - :title: Premier blog, juste un essai - :keywords: - :date: 2016-06-19 - :categories: blog - - Premier blog. diff --git a/_doc/sphinxdoc/source/blog/2016/2016-08-09_cnn.rst b/_doc/sphinxdoc/source/blog/2016/2016-08-09_cnn.rst deleted file mode 100644 index e3c58983..00000000 --- a/_doc/sphinxdoc/source/blog/2016/2016-08-09_cnn.rst +++ /dev/null @@ -1,9 +0,0 @@ - -.. blogpost:: - :title: Lectures - :keywords: CNN, deep learning - :date: 2016-06-19 - :categories: article - - Un article intéressant plus pratique que théorique : - `Recent Advances in Convolutional Neural Networks `_. diff --git a/_doc/sphinxdoc/source/blog/2016/2016-08-17_gradient.rst b/_doc/sphinxdoc/source/blog/2016/2016-08-17_gradient.rst deleted file mode 100644 index a16600d3..00000000 --- a/_doc/sphinxdoc/source/blog/2016/2016-08-17_gradient.rst +++ /dev/null @@ -1,9 +0,0 @@ - -.. blogpost:: - :title: Articles autour du gradient - :keywords: gradient - :date: 2016-08-17 - :categories: article - - * `DSA: Decentralized Double Stochastic Averaging Gradient Algorithm `_ - * `Distributed Coordinate Descent Method for Learning with Big Data `_ diff --git a/_doc/sphinxdoc/source/blog/2017/2017-02-16_gradient.rst b/_doc/sphinxdoc/source/blog/2017/2017-02-16_gradient.rst deleted file mode 100644 index fbdef585..00000000 --- a/_doc/sphinxdoc/source/blog/2017/2017-02-16_gradient.rst +++ /dev/null @@ -1,17 +0,0 @@ - -.. blogpost:: - :title: Adam - :keywords: gradient - :date: 2017-02-16 - :categories: machine learning - - `Adam `_ - n'est pas le personnage de la saison 4 - de `Buffy contre les vampires `_ - mais un algorithme de descente de gradient : - `Adam: A Method for Stochastic Optimization `_. - Si vous ne me croyez pas, vous devriez lire cette petite revue - `An overview of gradient descent optimization algorithms `_. - Un autre algorithme intéressant est - `Hogwild `_, - asynchrone et distribué. Bref, a unicorn comme disent les anglais. diff --git a/_doc/sphinxdoc/source/blog/2018/2018-09-10_stat.rst b/_doc/sphinxdoc/source/blog/2018/2018-09-10_stat.rst deleted file mode 100644 index 3d9e06e6..00000000 --- a/_doc/sphinxdoc/source/blog/2018/2018-09-10_stat.rst +++ /dev/null @@ -1,9 +0,0 @@ - -.. blogpost:: - :title: One Hundred Probability/Statistics Inequalities - :keywords: inégalités - :date: 2018-08-10 - :categories: statistiques - - Découvert dans un tweet : - `One Hundred Probability/Statistics Inequalities `_. diff --git a/_doc/sphinxdoc/source/blog/2019/2019-05-05_maths.rst b/_doc/sphinxdoc/source/blog/2019/2019-05-05_maths.rst deleted file mode 100644 index 633dba6a..00000000 --- a/_doc/sphinxdoc/source/blog/2019/2019-05-05_maths.rst +++ /dev/null @@ -1,47 +0,0 @@ - -.. blogpost:: - :title: Les maths, ça bugge moins quand même - :keywords: inégalités - :date: 2019-05-05 - :categories: machine learning - - Trouver un bug dans un millier de lignes de codes, - c'est rarement le jeu qui apporte le plus de joie - excepté peut-être le moment l'erreur surgit sur l'autel - comme la mariée apparaît dans l'église. Les bugs font - souvent de mauvais mariages et de très bons divorces. - Le pire survient après avoir découvert qu'ils se sont - de nouveau invités dans le pâté et le fromage. - Je me suis amusé avec les régressions linéaires - :ref:`l-reglin-variations`, quantiles et par morceaux. - Et je me suis retrouvé un jour avec une question - existencielle à propos d'une régression logistique - qui ressemblait visuellement beaucoup à un diagramme - de Voronoï tant est si bien que je me suis demandé - s'ils étaient jumeaux ou simplement parent - (:ref:`l-lrvor-connection`). Je recycle quelques vieilles - idées qui m'ont ramené au temps que j'ai passé chez Yahoo - :ref:`l-graph_distance`. Et celui-là aussi - :ref:`l-k-algo-gest` dont je trouve l'idée toujours - aussi séduisante. J'ai dû fixer quelques erreurs - dans :ref:`l-roc-theoritically` car, j'ai beau faire, - je n'arrive toujours pas à retenir la définition de - *False Positive Rate*... C'est quand le prédicteur - dit blanc alors que c'est noir ou l'inverse. - Bref, je n'insiste plus, je suis un dyslexique - du classifieur. Je me suis amusé dans - d'autres domaines : :epkg:`Predictable t-SNE`, - :epkg:`Visualize a scikit-learn pipeline`, - :epkg:`Regression with confidence interval`. - - Il est 1h30 du matin et je viens de trouver mon bug - de ce soir... Un bug mathématique pour changer, un - oubli dont je ne suis pas fier qui m'a fait relire mon - code encore et encore jusqu'à trouver le petit détail - qui a fait dérailler mon intuition, mais pas complètement - dérailler. Bref j'ai fini par écrire un algorithme - de streaming pour une orth-normalisation de Gram-Schmidt : - :func:`streaming_gram_schmidt_update - `. - Il ne reste plus qu'à écrire un algorithme de streaming - pour la régression linéaire. diff --git a/_doc/sphinxdoc/source/blog/2020/2020-08-22_nn.rst b/_doc/sphinxdoc/source/blog/2020/2020-08-22_nn.rst deleted file mode 100644 index cdef4945..00000000 --- a/_doc/sphinxdoc/source/blog/2020/2020-08-22_nn.rst +++ /dev/null @@ -1,36 +0,0 @@ - -.. blogpost:: - :title: Réseaux de neurones et arbres de décision - :keywords: inégalités - :date: 2020-08-22 - :categories: machine learning - - Je ne peux m'empêcher parfois de m'embarquer dans - l'implémentation d'une idée que j'ai eue, simplement - parce que je pense que c'est possible, que je la voie - devant moi sans pouvoir la toucher. J'ai imaginé - une façon de convertir un arbre de décision en un arbre - de décision, parce qu'une fonction sigmoïde est une - approximation d'une fonction en escalier. Je me suis - toujours que c'était possible sans vraiment aller jusqu'au - bout car je n'avais aucun doute là-dessus. Et puis - j'ai commencé à faire un lien entre ce mécanisme et la - possibilité de créer une arbre de décision où chaque noeud - n'est plus un seuil sur une variable mais sur une droite : - :ref:`l-decnntrees`. Cela permettrait de construire des arbres - avec n'importe quelle séparation linéaire au lieu de seulement - des horizontales et des verticales, donc tout autant - interprétable et probablement plus petit. - - J'ai pensé à plusieurs façons de constuire de tels arbres. - L'une d'elles est la conversion d'un arbre de décision - en un réseau de neurones puis de s'en servir comme - initialisation lors de l'apprentissage des coefficients. - La suite est lisible dans le notebook :ref:`neuraltreerst`. - - Ca doit être la cinquième fois que j'implémente des réseaux - de neurones. La première... c'était il y a plus de 20 ans. - On peut faire des choses très jolies en terme de design - mais le plus efficace est souvent de générer du code C++ pour - une architecture précise et de recompiler le tout, - ce que je n'ai pas fait cette fois-ci ce que fait *tensorflow*. diff --git a/_doc/sphinxdoc/source/c_dist/index.rst b/_doc/sphinxdoc/source/c_dist/index.rst deleted file mode 100644 index d959c184..00000000 --- a/_doc/sphinxdoc/source/c_dist/index.rst +++ /dev/null @@ -1,9 +0,0 @@ - -################ -Distances -################ - -.. toctree:: - :maxdepth: 1 - - edit_distance diff --git a/_doc/sphinxdoc/source/c_graph/index.rst b/_doc/sphinxdoc/source/c_graph/index.rst deleted file mode 100644 index 0896a281..00000000 --- a/_doc/sphinxdoc/source/c_graph/index.rst +++ /dev/null @@ -1,15 +0,0 @@ - -####### -Graphes -####### - -Les graphes sont très utilisés en informatique. -Arbre de décision en machine learning, réseaux sociaux, -recommandations, agencement de tâche, optimisation de flux, -la structure de graphe apparaît naturellement dans de nombreux -domaines. - -.. toctree:: - :maxdepth: 1 - - graph_distance diff --git a/_doc/sphinxdoc/source/c_metric/rocimg/rocwi.png b/_doc/sphinxdoc/source/c_metric/rocimg/rocwi.png deleted file mode 100644 index 965d4d5f..00000000 Binary files a/_doc/sphinxdoc/source/c_metric/rocimg/rocwi.png and /dev/null differ diff --git a/_doc/sphinxdoc/source/c_nlp/index.rst b/_doc/sphinxdoc/source/c_nlp/index.rst deleted file mode 100644 index 9eaa07e8..00000000 --- a/_doc/sphinxdoc/source/c_nlp/index.rst +++ /dev/null @@ -1,11 +0,0 @@ - -########################### -Natural Language Processing -########################### - -Ou `traitement du langage naturel `_. - -.. toctree:: - :maxdepth: 1 - - completion diff --git a/_doc/sphinxdoc/source/completed_todoextlist.rst b/_doc/sphinxdoc/source/completed_todoextlist.rst deleted file mode 100644 index 07a46d3e..00000000 --- a/_doc/sphinxdoc/source/completed_todoextlist.rst +++ /dev/null @@ -1,9 +0,0 @@ - -.. _l-completed-todolist: - -Terminé -======= - -.. todoextlist:: - :tag: done - :sort: date diff --git a/_doc/sphinxdoc/source/conf.py b/_doc/sphinxdoc/source/conf.py deleted file mode 100644 index c62639a6..00000000 --- a/_doc/sphinxdoc/source/conf.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -import sys -import os -from pyquickhelper.helpgen.default_conf import set_sphinx_variables, get_default_stylesheet - -choice = "bootstrap" - -if choice == "sphtheme": - import sphinx_theme_pd as sphtheme - html_theme = sphtheme.__name__ - html_theme_path = [sphtheme.get_html_theme_path()] -elif choice == "bootstrap": - import sphinx_bootstrap_theme - html_theme = 'bootstrap' - html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() -else: - raise NotImplementedError() - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.split(__file__)[0]))) - -local_template = os.path.join(os.path.abspath( - os.path.dirname(__file__)), "phdoc_templates") - -set_sphinx_variables(__file__, "mlstatpy", "Xavier Dupré", 2019, - html_theme, html_theme_path, locals(), - extlinks=dict( - issue=('https://github.com/sdpython/mlstatpy/issues/%s', 'issue')), - title="Machine Learning, Statistiques et Programmation", book=True, nblayout='table') - -# next - -blog_root = "http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/" - -html_context = { - 'css_files': get_default_stylesheet() + ['_static/my-styles.css'], -} - -html_logo = "phdoc_static/project_ico_small.png" - -if choice == "bootstrap": - html_theme_options = { - 'navbar_title': "BASE", - 'navbar_site_name': "Site", - 'navbar_links': [ - ("XD", "http://www.xavierdupre.fr", True), - ("blog", "blog/main_0000.html", True), - ("index", "genindex"), - ], - 'navbar_sidebarrel': True, - 'navbar_pagenav': True, - 'navbar_pagenav_name': "Page", - 'bootswatch_theme': "readable", - # united = weird colors, sandstone=green, simplex=red, paper=trop bleu - # lumen: OK - # to try, yeti, flatly, paper - 'bootstrap_version': "3", - 'source_link_position': "footer", - } - -language = "fr" - -preamble = ''' -\\usepackage{etex} -\\usepackage{fixltx2e} % LaTeX patches, \\textsubscript -\\usepackage{cmap} % fix search and cut-and-paste in Acrobat -\\usepackage[raccourcis]{fast-diagram} -\\usepackage{titlesec} -\\usepackage{amsmath} -\\usepackage{amssymb} -\\usepackage{amsfonts} -\\usepackage{graphics} -\\usepackage{epic} -\\usepackage{eepic} -%\\usepackage{pict2e} -%%% Redefined titleformat -\\setlength{\\parindent}{0cm} -\\setlength{\\parskip}{1ex plus 0.5ex minus 0.2ex} -\\newcommand{\\hsp}{\\hspace{20pt}} -\\newcommand{\\acc}[1]{\\left\\{#1\\right\\}} -\\newcommand{\\cro}[1]{\\left[#1\\right]} -\\newcommand{\\pa}[1]{\\left(#1\\right)} -\\newcommand{\\R}{\\mathbb{R}} -\\newcommand{\\HRule}{\\rule{\\linewidth}{0.5mm}} -%\\titleformat{\\chapter}[hang]{\\Huge\\bfseries\\sffamily}{\\thechapter\\hsp}{0pt}{\\Huge\\bfseries\\sffamily} -''' - -custom_preamble = """\n -\\usepackage[all]{xy} -\\newcommand{\\vecteur}[2]{\\pa{#1,\\dots,#2}} -\\newcommand{\\N}[0]{\\mathbb{N}} -\\newcommand{\\indicatrice}[1]{\\mathbf{1\\!\\!1}_{\\acc{#1}}} -\\newcommand{\\infegal}[0]{\\leqslant} -\\newcommand{\\supegal}[0]{\\geqslant} -\\newcommand{\\ensemble}[2]{\\acc{#1,\\dots,#2}} -\\newcommand{\\fleche}[1]{\\overrightarrow{ #1 }} -\\newcommand{\\intervalle}[2]{\\left\\{#1,\\cdots,#2\\right\\}} -\\newcommand{\\independant}[0]{\\;\\makebox[3ex]{\\makebox[0ex]{\\rule[-0.2ex]{3ex}{.1ex}}\\!\\!\\!\\!\\makebox[.5ex][l]{\\rule[-.2ex]{.1ex}{2ex}}\\makebox[.5ex][l]{\\rule[-.2ex]{.1ex}{2ex}}} \\,\\,} -\\newcommand{\\esp}{\\mathbb{E}} -\\newcommand{\\espf}[2]{\\mathbb{E}_{#1}\\pa{#2}} -\\newcommand{\\var}{\\mathbb{V}} -\\newcommand{\\pr}[1]{\\mathbb{P}\\pa{#1}} -\\newcommand{\\loi}[0]{{\\cal L}} -\\newcommand{\\vecteurno}[2]{#1,\\dots,#2} -\\newcommand{\\norm}[1]{\\left\\Vert#1\\right\\Vert} -\\newcommand{\\norme}[1]{\\left\\Vert#1\\right\\Vert} -\\newcommand{\\scal}[2]{\\left<#1,#2\\right>} -\\newcommand{\\dans}[0]{\\rightarrow} -\\newcommand{\\partialfrac}[2]{\\frac{\\partial #1}{\\partial #2}} -\\newcommand{\\partialdfrac}[2]{\\dfrac{\\partial #1}{\\partial #2}} -\\newcommand{\\trace}[1]{tr\\pa{#1}} -\\newcommand{\\sac}[0]{|} -\\newcommand{\\abs}[1]{\\left|#1\\right|} -\\newcommand{\\loinormale}[2]{{\\cal N} \\pa{#1,#2}} -\\newcommand{\\loibinomialea}[1]{{\\cal B} \\pa{#1}} -\\newcommand{\\loibinomiale}[2]{{\\cal B} \\pa{#1,#2}} -\\newcommand{\\loimultinomiale}[1]{{\\cal M} \\pa{#1}} -\\newcommand{\\variance}[1]{\\mathbb{V}\\pa{#1}} -""" -# \\usepackage{eepic} - -imgmath_latex_preamble = preamble + custom_preamble -latex_elements['preamble'] = preamble + custom_preamble -mathdef_link_only = True - -epkg_dictionary.update({ - 'ACP': 'https://fr.wikipedia.org/wiki/Analyse_en_composantes_principales', - "AESA": "https://tavianator.com/aesa/", - 'ApproximateNMFPredictor': - 'http://www.xavierdupre.fr/app/mlinsights/helpsphinx/mlinsights/mlmodel/anmf_predictor.html', - "B+ tree": "https://en.wikipedia.org/wiki/B%2B_tree", - "Branch and Bound": "https://en.wikipedia.org/wiki/Branch_and_bound", - "Custom Criterion for DecisionTreeRegressor": - "http://www.xavierdupre.fr/app/mlinsights/helpsphinx/notebooks/piecewise_linear_regression_criterion.html", - 'cython': 'https://cython.org/', - 'DecisionTreeClassifier': - 'https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html', - 'DecisionTreeRegressor optimized for Linear Regression': - "http://www.xavierdupre.fr/app/mlinsights/helpsphinx/notebooks/piecewise_linear_regression_criterion.html", - 'dot': 'https://fr.wikipedia.org/wiki/DOT_(langage)', - 'ICML 2016': 'https://icml.cc/2016/index.html', - 'KMeans': 'https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html', - "LAESA": "https://tavianator.com/aesa/", - 'LAPACK': 'http://www.netlib.org/lapack/', - 'mlinsights': 'http://www.xavierdupre.fr/app/mlinsights/helpsphinx/index.html', - 'PiecewiseTreeRegressor': - 'http://www.xavierdupre.fr/app/mlinsights/helpsphinx/mlinsights/mlmodel/' - 'piecewise_tree_regression.html#mlinsights.mlmodel.piecewise_tree_regression.PiecewiseTreeRegressor', - 'Predictable t-SNE': 'http://www.xavierdupre.fr/app/mlinsights/helpsphinx/notebooks/predictable_tsne.html', - "R-tree": "https://en.wikipedia.org/wiki/R-tree", - "R* tree": "https://en.wikipedia.org/wiki/R*_tree", - 'Regression with confidence interval': - 'http://www.xavierdupre.fr/app/mlinsights/helpsphinx/notebooks/regression_confidence_interval.html', - 'ROC': 'https://fr.wikipedia.org/wiki/Courbe_ROC', - 'statsmodels': 'http://www.statsmodels.org/stable/index.html', - 'SVD': 'https://fr.wikipedia.org/wiki/D%C3%A9composition_en_valeurs_singuli%C3%A8res', - 'Visualize a scikit-learn pipeline': - 'http://www.xavierdupre.fr/app/mlinsights/helpsphinx/notebooks/visualize_pipeline.html', - "X-tree": "https://en.wikipedia.org/wiki/X-tree", -}) - -nblinks = { - 'l-reglin-piecewise-streaming': - 'http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/c_ml/piecewise.html#streaming-linear-regression', -} diff --git a/_doc/sphinxdoc/source/end_index.rst b/_doc/sphinxdoc/source/end_index.rst deleted file mode 100644 index 1b5da79f..00000000 --- a/_doc/sphinxdoc/source/end_index.rst +++ /dev/null @@ -1,8 +0,0 @@ - -##### -Index -##### - -.. toctree:: - - end_index2 diff --git a/_doc/sphinxdoc/source/end_index2.rst b/_doc/sphinxdoc/source/end_index2.rst deleted file mode 100644 index 7919e612..00000000 --- a/_doc/sphinxdoc/source/end_index2.rst +++ /dev/null @@ -1,12 +0,0 @@ - -############ -Autres index -############ - -.. toctree:: - - end_index_changes - end_index_glossaire - gyexamples/index - gynotebooks/index - blog/blogindex diff --git a/_doc/sphinxdoc/source/end_index_changes.rst b/_doc/sphinxdoc/source/end_index_changes.rst deleted file mode 100644 index 11a9e014..00000000 --- a/_doc/sphinxdoc/source/end_index_changes.rst +++ /dev/null @@ -1,12 +0,0 @@ - -============= -Modifications -============= - -.. toctree:: - - defthe_index - issues_todoextlist - completed_todoextlist - filechanges - all_report diff --git a/_doc/sphinxdoc/source/end_index_glossaire.rst b/_doc/sphinxdoc/source/end_index_glossaire.rst deleted file mode 100644 index 0340fe97..00000000 --- a/_doc/sphinxdoc/source/end_index_glossaire.rst +++ /dev/null @@ -1,19 +0,0 @@ - -====== -README -====== - -.. only:: html - - .. toctree:: - - glossary - README - license - -.. only:: not html - - .. toctree:: - - glossary - license diff --git a/_doc/sphinxdoc/source/generatedoc.rst b/_doc/sphinxdoc/source/generatedoc.rst deleted file mode 100644 index 90cb408d..00000000 --- a/_doc/sphinxdoc/source/generatedoc.rst +++ /dev/null @@ -1,60 +0,0 @@ -Generate this documentation -=========================== - -.. generatedoc: - -See `Generating the documention with pyquickhelper `_. - -Configuration: - -.. literalinclude:: conf.py - -Extensions to install -+++++++++++++++++++++ - -* `pyquickhelper `_ -* `wild_sphinx_theme `_ - -Tips -++++ - -Module `pyquickhelper `_ -defines sphinx command -`runpython `_ -which generates from a python script included in the documentation itself. -The following snippet produces a table. - -.. runpython:: - :rst: - :showcode: - - from pyquickhelper.pandashelper import df2rst - import pandas - df = pandas.DataFrame([{"x": 3, "y":4}, {"x": 3.5, "y":5}]) - print(df2rst(df)) - -The next one is more complex. The code produces titles, -label and references. It requires Sphinx engine to be processed. - -.. runpython:: - :rst: - :showcode: - :showout: - - rows = [] - list_title = ["T1", "T2", "T3"] - back = None - for t in list_title: - rows.append("") - rows.append(".. _l-fake_title-" + t + ":") - rows.append("") - rows.append(t*3) - rows.append("^" * len(t*3)) - rows.append("") - if back: - rows.append("link :ref:`l-fake_title-" + back + "`") - else: - rows.append("no link") - rows.append("") - back = t - print("\n".join(rows)) diff --git a/_doc/sphinxdoc/source/generatesetup.rst b/_doc/sphinxdoc/source/generatesetup.rst deleted file mode 100644 index 9a987316..00000000 --- a/_doc/sphinxdoc/source/generatesetup.rst +++ /dev/null @@ -1,9 +0,0 @@ -Generate the setup -================== - -See `Generating the setup with pyquickhelper `_. - -Extensions to install -+++++++++++++++++++++ - -* `pyquickhelper `_ diff --git a/_doc/sphinxdoc/source/i_faq.rst b/_doc/sphinxdoc/source/i_faq.rst deleted file mode 100644 index 26ded95c..00000000 --- a/_doc/sphinxdoc/source/i_faq.rst +++ /dev/null @@ -1,11 +0,0 @@ - -.. _l-FAQ2: - -FAQ -=== - -.. contents:: - :local: - -.. faqreflist:: - :contents: diff --git a/_doc/sphinxdoc/source/i_idee.rst b/_doc/sphinxdoc/source/i_idee.rst deleted file mode 100644 index 461abc4b..00000000 --- a/_doc/sphinxdoc/source/i_idee.rst +++ /dev/null @@ -1,12 +0,0 @@ - -.. _l-IDEE2: - -Idées à explorer -================ - -.. contents:: - :local: - -.. todoextlist:: - :contents: - :tag: idee diff --git a/_doc/sphinxdoc/source/index.rst b/_doc/sphinxdoc/source/index.rst deleted file mode 100644 index 82f88138..00000000 --- a/_doc/sphinxdoc/source/index.rst +++ /dev/null @@ -1,100 +0,0 @@ - -*en construction permanente* - -.. |gitlogo| image:: _static/git_logo.png - :height: 20 - -Les maths d'abord, la programmation ensuite -=========================================== - -Le livre `The Elements of Statistical Learning `_ -est considéré comme la bible en matière de machine learning. Ce site aborde des sujets connexes. -Le site est aussi disponible en `PDF `_ -(format brut de fonderie) et sur -`GitHub/mlstatpy `_ |gitlogo|. - -.. toctree:: - :maxdepth: 2 - - introduction - c_clus/index - c_ml/index - c_ml/index_reglin - c_ml/index_reglog - c_nlp/index - c_metric/index - c_dist/index - c_graph/index - c_algo/index - c_garden/index - api/index - i_idee - end_index - -Exemples -======== - -.. toctree:: - :maxdepth: 1 - - gyexamples/index - all_notebooks - blog/blogindex - -On fait beaucoup de choses avec l'informatique mais en pratique -on doit maintenir, on doit réécrire sans cesse. -Faire un peu de théorie ça repose. - -Xavier Dupré - -.. only:: html - - .. image:: https://travis-ci.org/sdpython/mlstatpy.svg?branch=master - :target: https://travis-ci.org/sdpython/mlstatpy - :alt: Build status - - .. image:: https://ci.appveyor.com/api/projects/status/5env33qptorgshaq?svg=true - :target: https://ci.appveyor.com/project/sdpython/mlstatpy - :alt: Build Status Windows - - .. image:: https://circleci.com/gh/sdpython/mlstatpy/tree/master.svg?style=svg - :target: https://circleci.com/gh/sdpython/mlstatpy/tree/master - - .. image:: https://badge.fury.io/py/mlstatpy.svg - :target: https://pypi.org/project/mlstatpy/ - - .. image:: https://img.shields.io/badge/license-MIT-blue.svg - :alt: MIT License - :target: http://opensource.org/licenses/MIT - - .. image:: https://requires.io/github/sdpython/mlstatpy/requirements.svg?branch=master - :target: https://requires.io/github/sdpython/mlstatpy/requirements/?branch=master - :alt: Requirements Status - - .. image:: https://codecov.io/github/sdpython/mlstatpy/coverage.svg?branch=master - :target: https://codecov.io/github/sdpython/mlstatpy?branch=master - - .. image:: http://img.shields.io/github/issues/sdpython/mlstatpy.png - :alt: GitHub Issues - :target: https://github.com/sdpython/mlstatpy/issues - - .. image:: https://api.codacy.com/project/badge/Grade/677db5dda93b40d4ba1ec2f870cfd934 - :target: https://www.codacy.com/app/sdpython/mlstatpy?utm_source=github.com&utm_medium=referral&utm_content=sdpython/mlstatpy&utm_campaign=Badge_Grade - :alt: Codacy - - .. image:: nbcov.png - :target: http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/all_notebooks_coverage.html - :alt: Notebook Coverage - -**Links:** `github `_, -`documentation `_, -:ref:`l-README`, -:ref:`blog ` - -+----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+ -| :ref:`l-modules` | :ref:`l-functions` | :ref:`l-classes` | :ref:`l-methods` | :ref:`l-staticmethods` | :ref:`l-properties` | -+----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+ -| :ref:`modindex` | :ref:`l-EX2` | :ref:`search` | :ref:`l-license` | :ref:`l-changes` | :ref:`l-README` | -+----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+ -| :ref:`genindex` | :ref:`l-FAQ2` | :ref:`l-notebooks` | | :ref:`l-statcode` | `Unit Test Coverage `_ | -+----------------------+---------------------+---------------------+--------------------+------------------------+------------------------------------------------+ diff --git a/_doc/sphinxdoc/source/issues_todoextlist.rst b/_doc/sphinxdoc/source/issues_todoextlist.rst deleted file mode 100644 index 74a66423..00000000 --- a/_doc/sphinxdoc/source/issues_todoextlist.rst +++ /dev/null @@ -1,21 +0,0 @@ - -.. _l-issues-todolist: - -Bugs et améliorations -===================== - -.. index:: issues, todo - -.. contents:: - -Bugs -++++ - -.. todoextlist:: - :tag: bug - -Amélioration -++++++++++++ - -.. todoextlist:: - :tag: plus diff --git a/_doc/sphinxdoc/source/license.rst b/_doc/sphinxdoc/source/license.rst deleted file mode 100644 index d1a0e751..00000000 --- a/_doc/sphinxdoc/source/license.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _l-license: - -License -======= - -.. include:: LICENSE.txt - :literal: diff --git a/_doc/sphinxdoc/source/phdoc_static/layout.html b/_doc/sphinxdoc/source/phdoc_static/layout.html deleted file mode 100644 index 08baa3ec..00000000 --- a/_doc/sphinxdoc/source/phdoc_static/layout.html +++ /dev/null @@ -1,5 +0,0 @@ -{# Import the theme's layout. #} -{% extends "!layout.html" %} - -{# Custom CSS overrides #} -{% set bootswatch_css_custom = ['_static/my-styles.css'] %} \ No newline at end of file diff --git a/_doc/sphinxdoc/source/phdoc_static/my-styles.css b/_doc/sphinxdoc/source/phdoc_static/my-styles.css deleted file mode 100644 index b8d8d4ab..00000000 --- a/_doc/sphinxdoc/source/phdoc_static/my-styles.css +++ /dev/null @@ -1,53 +0,0 @@ -.admonition-mathdef { - color: #424242; - background-color: #F9F9F9; - font-size: 14px; -} -.admonition-todoext { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-faqref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-exref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-nbref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-blocref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} - -table { - border: 1px solid #dddddd; - border-spacing: 2x; - padding: 4px; -} - -th, td { - border: 1px solid #dddddd; - border-spacing: 1px; - padding: 2px; -} - -pre.highlight-ipython3 { - background-color: #b5b5b5; -} - -.line-block { - margin-left: 30px; - margin-bottom: 5px; - margin-top: 5px; - font-size: 14; -} \ No newline at end of file diff --git a/_doc/sphinxdoc/source/phdoc_static/project_ico.ico b/_doc/sphinxdoc/source/phdoc_static/project_ico.ico deleted file mode 100644 index c9fa7fbc..00000000 Binary files a/_doc/sphinxdoc/source/phdoc_static/project_ico.ico and /dev/null differ diff --git a/_doc/sphinxdoc/source/phdoc_static/project_ico.png b/_doc/sphinxdoc/source/phdoc_static/project_ico.png deleted file mode 100644 index fe73c06c..00000000 Binary files a/_doc/sphinxdoc/source/phdoc_static/project_ico.png and /dev/null differ diff --git a/_doc/sphinxdoc/source/phdoc_static/project_ico_small.png b/_doc/sphinxdoc/source/phdoc_static/project_ico_small.png deleted file mode 100644 index 97e78226..00000000 Binary files a/_doc/sphinxdoc/source/phdoc_static/project_ico_small.png and /dev/null differ diff --git a/_doc/sphinxdoc/source/phdoc_static/style_notebook_snippet.css b/_doc/sphinxdoc/source/phdoc_static/style_notebook_snippet.css deleted file mode 100644 index 1f12a1b5..00000000 --- a/_doc/sphinxdoc/source/phdoc_static/style_notebook_snippet.css +++ /dev/null @@ -1,81 +0,0 @@ - -div.sphx-pyq-thumb { - box-shadow: none; - background: #FFF; - margin: 5px; - padding-top: 5px; - min-height: 230px; - border: solid white 1px; - -webkit-border-radius: 5px; - -moz-border-radius: 5px; - border-radius: 5px; - float: left; - position: relative; -} - -div.sphx-pyq-thumb:hover { - box-shadow: 0 0 15px rgba(142, 176, 202, 0.5); - border: solid #B4DDFC 1px; } - div.sphx-pyq-thumb a.internal { - display: block; - position: absolute; - padding: 150px 10px 0px 10px; - top: 0px; - right: 0px; - bottom: 0px; - left: 0px; -} - -div.sphx-pyq-thumb p { - margin: 0 0 .1em 0; -} - -div.sphx-pyq-thumb .figure { - margin: 10px; - width: 160px; -} - -div.sphx-pyq-thumb img { - max-width: 100%; - max-height: 160px; - display: inline; -} - -div.sphx-pyq-thumb[tooltip]:hover:after { - background: rgba(0, 0, 0, 0.8); - -webkit-border-radius: 5px; - -moz-border-radius: 5px; - border-radius: 5px; - color: white; - content: attr(tooltip); - left: 95%; - padding: 5px 15px; - position: absolute; - z-index: 98; - width: 220px; - bottom: 52%; -} - -div.sphx-pyq-thumb[tooltip]:hover:before { - content: ""; - position: absolute; - z-index: 99; - border: solid; - border-color: #333 transparent; - border-width: 18px 0px 0px 20px; - left: 85%; - bottom: 58%; -} - -.sphx-pyq-download { - background-color: #ffc; - border: 1px solid #c2c22d; - border-radius: 4px; - margin: 1em auto 1ex auto; - max-width: 45ex; - padding: 1ex; -} - -.sphx-pyq-download a { - color: #4b4600; -} diff --git a/_doc/sphinxdoc/source/phdoc_templates/layout.html b/_doc/sphinxdoc/source/phdoc_templates/layout.html deleted file mode 100644 index 08baa3ec..00000000 --- a/_doc/sphinxdoc/source/phdoc_templates/layout.html +++ /dev/null @@ -1,5 +0,0 @@ -{# Import the theme's layout. #} -{% extends "!layout.html" %} - -{# Custom CSS overrides #} -{% set bootswatch_css_custom = ['_static/my-styles.css'] %} \ No newline at end of file diff --git a/_doc/sphinxdoc/source/phdoc_templates/my-styles.css b/_doc/sphinxdoc/source/phdoc_templates/my-styles.css deleted file mode 100644 index 40e73d29..00000000 --- a/_doc/sphinxdoc/source/phdoc_templates/my-styles.css +++ /dev/null @@ -1,37 +0,0 @@ -.admonition-mathdef { - color: #424242; - background-color: #F9F9F9; - font-size: 14px; -} -.admonition-todoext { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-faqref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-exref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-nbref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} -.admonition-blocref { - color: #424242; - background-color: #F9F9B9; - font-size: 14px; -} - -.line-block { - margin-left: 30px; - margin-bottom: 5px; - margin-top: 5px; - font-size: 14; -} \ No newline at end of file diff --git a/_doc/sphinxdoc/source/phdoc_templates/page.html b/_doc/sphinxdoc/source/phdoc_templates/page.html deleted file mode 100644 index 1be6020a..00000000 --- a/_doc/sphinxdoc/source/phdoc_templates/page.html +++ /dev/null @@ -1,4 +0,0 @@ -{% extends "layout.html" %} -{% block body %} -{{ body }} -{% endblock body %} diff --git a/_todo/clas_supervise/clas_super_biblio.tex b/_todo/clas_supervise/clas_super_biblio.tex index 0651a925..e2191cc5 100644 --- a/_todo/clas_supervise/clas_super_biblio.tex +++ b/_todo/clas_supervise/clas_super_biblio.tex @@ -1,12 +1,12 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin \bibitemstyle{Chang1974} {1974} {C. L. Chang} {Finding prototypes for nearest neighbor classifiers} diff --git a/_todo/clas_supervise/clas_supervise.tex b/_todo/clas_supervise/clas_supervise.tex index 3ad47bda..280835e9 100644 --- a/_todo/clas_supervise/clas_supervise.tex +++ b/_todo/clas_supervise/clas_supervise.tex @@ -4,7 +4,7 @@ \firstpassagedo{\input{clas_super_chapter.tex}} -Cette annexe recense diffrents moyens d'effectuer une classification supervise. Cette tche consiste tiqueter un lment $x$ sachant qu'on connat dj cet tiquetage pour un certain nombre d'lments $\vecteur{x_1}{x_N}$ dont les labels sont $\vecteur{c\pa{x_1}}{c\pa{x_N}}$. +Cette annexe recense diff�rents moyens d'effectuer une classification supervis�e. Cette t�che consiste � �tiqueter un �l�ment $x$ sachant qu'on conna�t d�j� cet �tiquetage pour un certain nombre d'�l�ments $\vecteur{x_1}{x_N}$ dont les labels sont $\vecteur{c\pa{x_1}}{c\pa{x_N}}$. \label{classification_supervisee} @@ -22,68 +22,68 @@ \section{Plus proches voisins} \indexfr{plus proches voisins} \label{clas_super_ppv_par} -Cette mthode est la plus simple puisqu'elle consiste associer $x$, l'lment classer, le label $c\pa{x_{i^*}}$ de l'lment le plus proche $x_{i^*}$ dans l'ensemble $\vecteur{x_1}{x_N}$. Ceci mne l'algorithme de classification suivant~: +Cette m�thode est la plus simple puisqu'elle consiste � associer � $x$, l'�l�ment � classer, le label $c\pa{x_{i^*}}$ de l'�l�ment le plus proche $x_{i^*}$ dans l'ensemble $\vecteur{x_1}{x_N}$. Ceci m�ne � l'algorithme de classification suivant~: - \begin{xalgorithm}{1-PPV ou plus proche voisin} - \label{clas_super_1ppv_algo} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. Soit $x$ - un lment classer, on cherche dterminer la classe $\hat{c}(x)$ associe $x$. On dfinit $x_{i^*}$ - comme tant~: - \begin{eqnarray*} - x_{i^*} &=& \underset{i \in \intervalle{1}{N}}{\arg \min} \; d\pa{x_i,x} - \end{eqnarray*} - Alors~: $\hat{c}(x) = c\pa{x_i^*}$ - \end{xalgorithm} + \begin{xalgorithm}{1-PPV ou plus proche voisin} + \label{clas_super_1ppv_algo} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. Soit $x$ + un �l�ment � classer, on cherche � d�terminer la classe $\hat{c}(x)$ associ�e � $x$. On d�finit $x_{i^*}$ + comme �tant~: + \begin{eqnarray*} + x_{i^*} &=& \underset{i \in \intervalle{1}{N}}{\arg \min} \; d\pa{x_i,x} + \end{eqnarray*} + Alors~: $\hat{c}(x) = c\pa{x_i^*}$ + \end{xalgorithm} \indexfrr{PPV}{1-PPV} \indexfrr{PPV}{k-PPV} \indexfr{nearest neighbors} -Cet algorithme est souvent appel \emph{1-PPV} (ou \emph{1-NN} pour Nearest Neighbors). Il existe une version amliore \emph{k-PPV} qui consiste attribuer $x$ la classe la plus reprsente parmi ses $k$ plus proches voisins. +Cet algorithme est souvent appel� \emph{1-PPV} (ou \emph{1-NN} pour Nearest Neighbors). Il existe une version am�lior�e \emph{k-PPV} qui consiste � attribuer � $x$ la classe la plus repr�sent�e parmi ses $k$ plus proches voisins. - \begin{xalgorithm}{k-PPV ou k plus proches voisins} - \label{clas_super_kppv_simple} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. Soit $x$ - un lment classer, on cherche dterminer la classe $c(x)$ associe $x$. On dfinit l'ensemble $S^*_k$ - incluant les $k$-plus proches voisins de $x$, cet ensemble vrifie~: - \begin{eqnarray*} - \card{S^*_k} = 0 \text{ et } - \underset{y \in S^*_k}{\max} \; d\pa{y,x} \infegal - \underset{y \in X - S^*_k}{\min} \; d\pa{y,x} - \end{eqnarray*} - On calcule les occurrences $f(i)$ de chaque classe $i$ dans l'ensemble $S^*_k$~: - \begin{eqnarray} - f(i) = \summyone{y \in S^*_k} \, \omega\pa{x,y} \, \indicatrice{c(y) = i} - \label{class_super_kppv_contribution_eq} - \end{eqnarray} - On assigne alors $x$ la classe $c(x)$ choisie dans l'ensemble~: - \begin{eqnarray*} - \hat{c}(x) \in \underset{i \in \N}{\arg \max} \; f(i) - \end{eqnarray*} - \end{xalgorithm} + \begin{xalgorithm}{k-PPV ou k plus proches voisins} + \label{clas_super_kppv_simple} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. Soit $x$ + un �l�ment � classer, on cherche � d�terminer la classe $c(x)$ associ�e � $x$. On d�finit l'ensemble $S^*_k$ + incluant les $k$-plus proches voisins de $x$, cet ensemble v�rifie~: + \begin{eqnarray*} + \card{S^*_k} = 0 \text{ et } + \underset{y \in S^*_k}{\max} \; d\pa{y,x} \leqslant + \underset{y \in X - S^*_k}{\min} \; d\pa{y,x} + \end{eqnarray*} + On calcule les occurrences $f(i)$ de chaque classe $i$ dans l'ensemble $S^*_k$~: + \begin{eqnarray} + f(i) = \summyone{y \in S^*_k} \, \omega\pa{x,y} \, \indicatrice{c(y) = i} + \label{class_super_kppv_contribution_eq} + \end{eqnarray} + On assigne alors � $x$ la classe $c(x)$ choisie dans l'ensemble~: + \begin{eqnarray*} + \hat{c}(x) \in \underset{i \in \N}{\arg \max} \; f(i) + \end{eqnarray*} + \end{xalgorithm} -Dans sa version la plus simple, la fonction $\omega\pa{x,y}$ utilise lors du calcul de la contribution $f$ (\ref{class_super_kppv_contribution_eq}) est constante. Mais il est possible de lui affecter une valeur tenant compte de la proximit entre $x$ et $y$. La table~\ref{clas_super_omega_contribution} donne quelques exemples de contributions possibles. +Dans sa version la plus simple, la fonction $\omega\pa{x,y}$ utilis�e lors du calcul de la contribution $f$ (\ref{class_super_kppv_contribution_eq}) est constante. Mais il est possible de lui affecter une valeur tenant compte de la proximit� entre $x$ et $y$. La table~\ref{clas_super_omega_contribution} donne quelques exemples de contributions possibles. - \begin{table}[ht] - $$\begin{tabular}{|ll|} \hline - fonction constante & $\omega\pa{x,y} = 1$ \\ \hline - distance inverse & $\omega\pa{x,y} = \frac{1}{1 + d\pa{x,y}}$ \\ \hline - noyau & $\omega\pa{x,y} = \exp\pa{ - d^2 \pa{x,y}}$ \\ \hline - \end{tabular}$$ - \caption{Exemple de contribution $w\pa{x,y}$ pour l'algorithme~\ref{clas_super_kppv_simple} des k-PPV. - Ces fonctions sont toutes dcroissantes (strictement ou non) par rapport la distance $d$.} - \label{clas_super_omega_contribution} - \end{table} + \begin{table}[ht] + $$\begin{tabular}{|ll|} \hline + fonction constante & $\omega\pa{x,y} = 1$ \\ \hline + distance inverse & $\omega\pa{x,y} = \frac{1}{1 + d\pa{x,y}}$ \\ \hline + noyau & $\omega\pa{x,y} = \exp\pa{ - d^2 \pa{x,y}}$ \\ \hline + \end{tabular}$$ + \caption{Exemple de contribution $w\pa{x,y}$ pour l'algorithme~\ref{clas_super_kppv_simple} des k-PPV. + Ces fonctions sont toutes d�croissantes (strictement ou non) par rapport � la distance $d$.} + \label{clas_super_omega_contribution} + \end{table} -L'inconvnient majeur de la mthode des plus proches voisins est sa longueur puisqu'elle implique le calcul des distances entre $x$ et chacun des lments de l'ensemble $\vecteur{x_1}{x_N}$. C'est pourquoi de nombreuses mthodes d'optimisation ont t dveloppes afin d'acclrer ce processus. Il est possible d'optimiser le calcul de la distance ou bien d'viter un trop nombre de calculs en utilisant des lments pivots\seeannex{space_metric_introduction}{recherche dans un espace mtrique}. L'optimisation de la vitesse est souvent prconise lorsque l'espace mtrique $E$ n'est pas vectoriel, comme un espace de suites finies. En revanche, l'utilisation de pivots de manire viter l'exploration de la totalit de l'ensemble $X$ est valable pour tout espace mtrique. Ces mthodes sont l'objet de l'annexe~\ref{space_metric_introduction}. +L'inconv�nient majeur de la m�thode des plus proches voisins est sa longueur puisqu'elle implique le calcul des distances entre $x$ et chacun des �l�ments de l'ensemble $\vecteur{x_1}{x_N}$. C'est pourquoi de nombreuses m�thodes d'optimisation ont �t� d�velopp�es afin d'acc�l�rer ce processus. Il est possible d'optimiser le calcul de la distance ou bien d'�viter un trop nombre de calculs en utilisant des �l�ments pivots\seeannex{space_metric_introduction}{recherche dans un espace m�trique}. L'optimisation de la vitesse est souvent pr�conis�e lorsque l'espace m�trique $E$ n'est pas vectoriel, comme un espace de suites finies. En revanche, l'utilisation de pivots de mani�re � �viter l'exploration de la totalit� de l'ensemble $X$ est valable pour tout espace m�trique. Ces m�thodes sont l'objet de l'annexe~\ref{space_metric_introduction}. @@ -96,18 +96,18 @@ \section{Support Vector Machines (SVM)} \indexfrr{hyperplan}{SVM} \label{clas_super_svm_par} -L'algorithme~\ref{clas_super_kppv_simple} utilise une contribution note $\omega$ lors du calcul de $f$ (\ref{class_super_kppv_contribution_eq}). Si celle-ci est dfinie de manire explicite, on reste dans le cadre des plus proches voisins. En revanche, si celle-ci est estime partir d'un chantillon suppos reprsentatif du problme de classification rsoudre, on se place dans le cadre des \emph{Support Vector Machines}. Ce formalisme introduit par Vapnik (\citeindex{Vapnik1998}) n'est pas simplement un prolongement de la mthode des plus proches voisins mais peut aussi tre interprt comme la recherche du meilleur hyperplan de sparation entre deux classes. Cette mthode est prsente plus en dtail par l'annexe~\ref{annexe_svm}\seeannex{annexe_svm}{SVM}. +L'algorithme~\ref{clas_super_kppv_simple} utilise une contribution not�e $\omega$ lors du calcul de $f$ (\ref{class_super_kppv_contribution_eq}). Si celle-ci est d�finie de mani�re explicite, on reste dans le cadre des plus proches voisins. En revanche, si celle-ci est estim�e � partir d'un �chantillon suppos� repr�sentatif du probl�me de classification � r�soudre, on se place dans le cadre des \emph{Support Vector Machines}. Ce formalisme introduit par Vapnik (\citeindex{Vapnik1998}) n'est pas simplement un prolongement de la m�thode des plus proches voisins mais peut aussi �tre interpr�t� comme la recherche du meilleur hyperplan de s�paration entre deux classes. Cette m�thode est pr�sent�e plus en d�tail par l'annexe~\ref{annexe_svm}\seeannex{annexe_svm}{SVM}. %-------------------------------------------------------------------------------------------------------------------- -\section{Rseaux de neurones} +\section{R�seaux de neurones} %-------------------------------------------------------------------------------------------------------------------- -\indexfrr{rseau de neurones}{classification} +\indexfrr{r�seau de neurones}{classification} \label{clas_super_nn_par} -Cette mthode est prsente plus en dtail aux paragraphes~\ref{subsection_classifieur} (page~\pageref{subsection_classifieur}) et~\ref{classification} (page~\pageref{classification})\seeannex{annexe_reseau_neurone}{rseau de neurones}. Elle permet de construire une fonction $f\pa{x} = y = \vecteur{y_1}{y_C} \in \R^C$ o $C$ est le nombre de classes, $y_i \in \cro{0,1}$ et $\sum^C_1 y_i = 1$. Chaque sortie $y_i$ du rseau de neurones correspond la probabilit que le vecteur $x$ appartient la classe~$i$. Contrairement aux deux mthodes prcdentes, les rseaux de neurones permettent de construire une fonction $f$ indpendante du nombre d'exemples permettant de l'estimer. Nanmoins, les paramtres de cette fonction ne sont plus aussi interprtables que les contributions voques aux paragraphes~\ref{clas_super_ppv_par} et~\ref{clas_super_svm_par}. Ceci explique que le fort intrt de ces modles depuis les annes 1980 au milieu des annes 1990 ait dcru au profit d'autres solutions comme les SVM. +Cette m�thode est pr�sent�e plus en d�tail aux paragraphes~\ref{subsection_classifieur} (page~\pageref{subsection_classifieur}) et~\ref{classification} (page~\pageref{classification})\seeannex{annexe_reseau_neurone}{r�seau de neurones}. Elle permet de construire une fonction $f\pa{x} = y = \vecteur{y_1}{y_C} \in \mathbb{R}^C$ o� $C$ est le nombre de classes, $y_i \in \cro{0,1}$ et $\sum^C_1 y_i = 1$. Chaque sortie $y_i$ du r�seau de neurones correspond � la probabilit� que le vecteur $x$ appartient � la classe~$i$. Contrairement aux deux m�thodes pr�c�dentes, les r�seaux de neurones permettent de construire une fonction $f$ ind�pendante du nombre d'exemples permettant de l'estimer. N�anmoins, les param�tres de cette fonction ne sont plus aussi interpr�tables que les contributions �voqu�es aux paragraphes~\ref{clas_super_ppv_par} et~\ref{clas_super_svm_par}. Ceci explique que le fort int�r�t de ces mod�les depuis les ann�es 1980 au milieu des ann�es 1990 ait d�cru au profit d'autres solutions comme les SVM. @@ -123,46 +123,46 @@ \section{Learning Vector Quantization (LVQ)} \indexfr{plus proches voisins} \indexfrr{prototype}{LVQ} -Cette mthode est souvent associ des mthodes de classification par plus proches voisins voques dans l'annexe~\ref{space_metric_introduction}. Lors de la classification d'un lment, on recherche dans un ensemble le plus proche lment et on attribue l'lment classer la classe de l'lment trouv. Alors que l'annexe~\ref{space_metric_introduction} cherche acclrer la recherche de l'lment le plus proche, la mthode LVQ essaye de rsumer l'information au travers de prototypes. Plus simplement, les mthodes abordes ici permettent tente rduire au minimum l'ensemble dans lequel seront cherchs les voisins sans changer ou sans trop changer le rsultat de la classification. +Cette m�thode est souvent associ� � des m�thodes de classification par plus proches voisins �voqu�es dans l'annexe~\ref{space_metric_introduction}. Lors de la classification d'un �l�ment, on recherche dans un ensemble le plus proche �l�ment et on attribue � l'�l�ment � classer la classe de l'�l�ment trouv�. Alors que l'annexe~\ref{space_metric_introduction} cherche � acc�l�rer la recherche de l'�l�ment le plus proche, la m�thode LVQ essaye de r�sumer l'information au travers de prototypes. Plus simplement, les m�thodes abord�es ici permettent tente r�duire au minimum l'ensemble dans lequel seront cherch�s les voisins sans changer ou sans trop changer le r�sultat de la classification. -En ce qui concerne les nues dynamiques, les prototypes sont les centres des classes dtermines par l'algorithme des centres mobiles. Pour les diffrentes versions LVQ qui suivent, les prototypes doivent reprsenter au mieux une classification impose. L'article~\citeindex{Bezdek2001} propose une revue rcente de ces mthodes que reprend en partie seulement un article~\citeindex{Kim2003} sur lequel s'appuie les paragraphes qui suivent. +En ce qui concerne les nu�es dynamiques, les prototypes sont les centres des classes d�termin�es par l'algorithme des centres mobiles. Pour les diff�rentes versions LVQ qui suivent, les prototypes doivent repr�senter au mieux une classification impos�e. L'article~\citeindex{Bezdek2001} propose une revue r�cente de ces m�thodes que reprend en partie seulement un article~\citeindex{Kim2003} sur lequel s'appuie les paragraphes qui suivent. \subsection{Principe} \label{clas_super_principe_lvq} -Lors de l'algorithme~\ref{clas_super_1ppv_algo} qui permet de classer un lment $x$ en l'associant la mme classe que son plus proche voisin, il faut calculer toutes les distances de $x$ aux voisins possibles $X$. Les mthodes LVQ ont pour objectif de rduire l'ensemble $X$ une taille raisonnable en utilisant l'information de la classe. Aprs rduction, l'algorithme de classification doit retourner les mmes rponses. Par consquent, l'algorithme suivant~\ref{clas_super_1ppv_lvq_algo} doit retourner les mmes rponses que la mthode 1-ppv~\ref{clas_super_1ppv_algo}. - - - - \begin{xalgorithm}{1-PPV avec LVQ} - \label{clas_super_1ppv_lvq_algo} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. Soit $x$ - un lment classer, on cherche dterminer la classe $c(x)$ associe $x$. - - \begin{xalgostep}{LVQ}\label{clas_super_lvq_step_identity} - On rduit l'ensemble $X$ un ensemble de prototypes $X'$ qui n'est pas forcment - inclus dans $X$. On note $X' = \vecteur{x'_1}{x'_n}$ avec de prfrence $n << N$. - La classe de l'lment $x_i'$ est toujours note $c\pa{x'_i}$. Les algorithmes effectuant - cette rduction sont prsentes dans les paragraphes qui suivent comme~\ref{clas_super_lvq_cnn} - ou~\ref{clas_super_lvq_pnn}. - \end{xalgostep} - - \begin{xalgostep}{classification}\label{clas_super_lvq_step_clas_b} - On dfinit $x'_{i^*}$ comme tant~: - \begin{eqnarray*} - x'_{i^*} &=& \underset{i \in \intervalle{1}{n}}{\arg \min} \; d\pa{x'_i,x} - \end{eqnarray*} - Alors~: $\hat{c}(x) = c\pa{x'_{i^*}}$ - \end{xalgostep} - \end{xalgorithm} +Lors de l'algorithme~\ref{clas_super_1ppv_algo} qui permet de classer un �l�ment $x$ en l'associant � la m�me classe que son plus proche voisin, il faut calculer toutes les distances de $x$ aux voisins possibles $X$. Les m�thodes LVQ ont pour objectif de r�duire l'ensemble $X$ � une taille raisonnable en utilisant l'information de la classe. Apr�s r�duction, l'algorithme de classification doit retourner les m�mes r�ponses. Par cons�quent, l'algorithme suivant~\ref{clas_super_1ppv_lvq_algo} doit retourner les m�mes r�ponses que la m�thode 1-ppv~\ref{clas_super_1ppv_algo}. + + + + \begin{xalgorithm}{1-PPV avec LVQ} + \label{clas_super_1ppv_lvq_algo} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. Soit $x$ + un �l�ment � classer, on cherche � d�terminer la classe $c(x)$ associ�e � $x$. + + \begin{xalgostep}{LVQ}\label{clas_super_lvq_step_identity} + On r�duit l'ensemble $X$ � un ensemble de prototypes $X'$ qui n'est pas forc�ment + inclus dans $X$. On note $X' = \vecteur{x'_1}{x'_n}$ avec de pr�f�rence $n << N$. + La classe de l'�l�ment $x_i'$ est toujours not�e $c\pa{x'_i}$. Les algorithmes effectuant + cette r�duction sont pr�sent�es dans les paragraphes qui suivent comme~\ref{clas_super_lvq_cnn} + ou~\ref{clas_super_lvq_pnn}. + \end{xalgostep} + + \begin{xalgostep}{classification}\label{clas_super_lvq_step_clas_b} + On d�finit $x'_{i^*}$ comme �tant~: + \begin{eqnarray*} + x'_{i^*} &=& \underset{i \in \intervalle{1}{n}}{\arg \min} \; d\pa{x'_i,x} + \end{eqnarray*} + Alors~: $\hat{c}(x) = c\pa{x'_{i^*}}$ + \end{xalgostep} + \end{xalgorithm} \indexfrr{prototype}{LVQ} -Les paragraphes qui suivent prsentent des algorithmes permettant de calculer un ensemble $X'$ satisfaisant l'tape~\ref{clas_super_lvq_step_identity} et le plus rduit possible. Cette tape~\ref{clas_super_lvq_step_identity} est un fait un prtraitement, elle n'est effectue qu'une seule fois tandis que l'tape~\ref{clas_super_lvq_step_clas_b} intervient pour chaque nouvel lment classer. L'ensemble $X'$ est appel l'ensemble des \emph{prototypes}. Les chapitres qui suivent concernent essentiellement les espaces vectoriels except pour le paragraphe~\ref{clas_super_lvq_cnn}. Pour des espaces mtriques non vectoriels, l'annexe~\ref{space_metric_introduction} prsente d'autres mthodes de slection de prototypes\seeannex{space_metric_suppression_voisins_inutile}{suppression des voisins inutiles}. +Les paragraphes qui suivent pr�sentent des algorithmes permettant de calculer un ensemble $X'$ satisfaisant � l'�tape~\ref{clas_super_lvq_step_identity} et le plus r�duit possible. Cette �tape~\ref{clas_super_lvq_step_identity} est un fait un pr�traitement, elle n'est effectu�e qu'une seule fois tandis que l'�tape~\ref{clas_super_lvq_step_clas_b} intervient pour chaque nouvel �l�ment � classer. L'ensemble $X'$ est appel� l'ensemble des \emph{prototypes}. Les chapitres qui suivent concernent essentiellement les espaces vectoriels except� pour le paragraphe~\ref{clas_super_lvq_cnn}. Pour des espaces m�triques non vectoriels, l'annexe~\ref{space_metric_introduction} pr�sente d'autres m�thodes de s�lection de prototypes\seeannex{space_metric_suppression_voisins_inutile}{suppression des voisins inutiles}. @@ -175,34 +175,34 @@ \subsection{Condensed nearest neighbors rule (CNN)} \indexfr{CNN} \indexsee{Condensed nearest neighbors}{CNN} -Cette mthode est dveloppe dans \citeindex{Hart1968}. Elle consiste construire un ensemble $X'$ partir des lments de $X$. Un premier lment est choisi alatoirement puis plac dans $X'$. On parcourt ensuite l'ensemble $X$, pour chaque lment $x$, on applique l'algorithme~\ref{clas_super_1ppv_lvq_algo}. Si le rsultat ne correspond pas la classe $c(x)$, cet lment est ajout l'ensemble $X'$. Ceci mne l'algorithme suivant~: - - - \begin{xalgorithm}{CNN}\label{clas_super_algorithme_cnn_choice} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. - - \begin{xalgostep}{initialisation} - Soit $x$ un lment de $X$, $X' \longleftarrow \acc{ x}$ et $Y \longleftarrow \acc{x}$. - \end{xalgostep} - - \begin{xalgostep}{construction de $X'$}\label{clas_super_cnn_step_b} - \begin{xwhile}{$Y \neq X$} - Soit $x \in X - Y$, on applique l'tape~\ref{clas_super_lvq_step_clas_b} de - l'algorithme~\ref{clas_super_1ppv_lvq_algo} l'lment $x$.\\ - \begin{xif}{$\hat{c}(x) \neq c(x)$} - $X' \longleftarrow X' \cup \acc{x}$ - \end{xif}\\ - $Y \longleftarrow Y \cup \acc{x}$ - \end{xwhile} - \end{xalgostep} - - \end{xalgorithm} +Cette m�thode est d�velopp�e dans \citeindex{Hart1968}. Elle consiste � construire un ensemble $X'$ � partir des �l�ments de $X$. Un premier �l�ment est choisi al�atoirement puis plac� dans $X'$. On parcourt ensuite l'ensemble $X$, pour chaque �l�ment $x$, on applique l'algorithme~\ref{clas_super_1ppv_lvq_algo}. Si le r�sultat ne correspond pas � la classe $c(x)$, cet �l�ment est ajout� � l'ensemble $X'$. Ceci m�ne � l'algorithme suivant~: + + + \begin{xalgorithm}{CNN}\label{clas_super_algorithme_cnn_choice} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. + + \begin{xalgostep}{initialisation} + Soit $x$ un �l�ment de $X$, $X' \longleftarrow \acc{ x}$ et $Y \longleftarrow \acc{x}$. + \end{xalgostep} + + \begin{xalgostep}{construction de $X'$}\label{clas_super_cnn_step_b} + \begin{xwhile}{$Y \neq X$} + Soit $x \in X - Y$, on applique l'�tape~\ref{clas_super_lvq_step_clas_b} de + l'algorithme~\ref{clas_super_1ppv_lvq_algo} � l'�l�ment $x$.\\ + \begin{xif}{$\hat{c}(x) \neq c(x)$} + $X' \longleftarrow X' \cup \acc{x}$ + \end{xif}\\ + $Y \longleftarrow Y \cup \acc{x}$ + \end{xwhile} + \end{xalgostep} + + \end{xalgorithm} \indexfrr{ordre d'insertion}{LVQ} -Cet algorithme n'impose pas un nombre prcis de prototypes. De plus, puisque $X' \subset X$, cette mthode est applicable tout espace mtrique, il ne ncessite pas qu'il soit vectoriel. Toutefois, l'algorithme est sensible l'ordre dans lequel sont traits les lments de $X$. +Cet algorithme n'impose pas un nombre pr�cis de prototypes. De plus, puisque $X' \subset X$, cette m�thode est applicable � tout espace m�trique, il ne n�cessite pas qu'il soit vectoriel. Toutefois, l'algorithme est sensible � l'ordre dans lequel sont trait�s les �l�ments de $X$. @@ -211,54 +211,54 @@ \subsection{Prototype for nearest neighbors (PNN)} \indexsee{Prototype nearest neighbors}{PNN} \label{clas_super_lvq_pnn} -Cette mthode est dveloppe dans \citeindex{Chang1974}. Contrairement l'algorithme prcdent~\ref{clas_super_algorithme_cnn_choice}, l'ensemble $X'$ n'est plus inclus dans $X$ et est construit de manire obtenir autant que faire ce peu des barycentres des classes. Il ne s'applique donc qu' des espaces vectoriels. Au dpart, tous les lments de $X$ sont considrs comme des prototypes. Puis les plus proches d'entre eux appartenant la mme classe vont tre agrgs si aucune erreur de classification n'est constate. - - - - \begin{xalgorithm}{PNN}\label{clas_super_algorithme_pnn_choice} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. - - \begin{xalgostep}{initialisation} - On dfinit les ensembles $A \longleftarrow \emptyset$ et $B \longleftarrow X$ ainsi que la suite - $\vecteur{p\pa{x_1}}{p\pa{x_N}}$ telle que $\forall i, \; p\pa{x_i} = 1$. - $t\longleftarrow 0$ et $\epsilon_0 \longleftarrow \infty$. - \end{xalgostep} - - \begin{xalgostep}{construction de $B$}\label{clas_super_pnn_step_b} - \begin{xwhile}{$B \neq \emptyset$} - $m \longleftarrow 0$. On dfinit $x_A \in A$ et $x_B \in B$ tels que~: - $$d\pa{x_A,x_B} = \min \acc{d\pa{x,y} \sac x \in A, y \in B}$$ - \begin{xif}{$c\pa{x_A} \neq c\pa{x_B}$} - $B \longleftarrow B - \acc{x_B}$ et $A \longleftarrow A \cup \acc{x_B}$ - \xelse - $x \longleftarrow \dfrac{ p\pa{x_A} \,x_A + p\pa{x_B} \,x_B }{ p\pa{x_A} + p\pa{x_B}}$ \\ - $p\pa{x} \longleftarrow p\pa{x_A} + p\pa{x_B}$ \\ - On note $\epsilon_t$ le taux de classification obtenu avec l'ensemble de prototypes - $X' = A \cup \acc{ x }$. \\ - \begin{xif}{$\epsilon_t > \epsilon_{t-1}$} - $B \longleftarrow B - \acc{x_B}$ et $A \longleftarrow A \cup \acc{x_B}$ - \xelse - $B \longleftarrow B - \acc{x_B}$ et $A \longleftarrow \cro{ A - \acc{x_A}} \cup \acc{ x }$ \\ - $m \longleftarrow m + 1$ - \end{xif} - \end{xif} - \end{xwhile} - \end{xalgostep} - - \begin{xalgostep}{terminaison} - \begin{xif}{$ m > 0 $} - $B \longleftarrow A$ et $A \longleftarrow \emptyset$. \\ - On retourne l'tape~\ref{clas_super_pnn_step_b}. - \xelse - L'algorithme s'arrte et l'ensemble cherch $X' \longleftarrow A$. - \end{xif} - \end{xalgostep} - - \end{xalgorithm} - -L'article \citeindex{Bezdek2001} suggre de ne considrer lors de l'tape~\ref{clas_super_pnn_step_b} que des paires $\pa{x_A,x_B}$ appartenant une mme classe de manire ce que le rsultat obtenu soit plus consistent. Ce second algorithme est plus lent que l'algorithme~\ref{clas_super_algorithme_cnn_choice} mais la remarque propos l'ordre d'insertion ne le concerne plus. +Cette m�thode est d�velopp�e dans \citeindex{Chang1974}. Contrairement � l'algorithme pr�c�dent~\ref{clas_super_algorithme_cnn_choice}, l'ensemble $X'$ n'est plus inclus dans $X$ et est construit de mani�re � obtenir autant que faire ce peu des barycentres des classes. Il ne s'applique donc qu'� des espaces vectoriels. Au d�part, tous les �l�ments de $X$ sont consid�r�s comme des prototypes. Puis les plus proches d'entre eux appartenant � la m�me classe vont �tre agr�g�s si aucune erreur de classification n'est constat�e. + + + + \begin{xalgorithm}{PNN}\label{clas_super_algorithme_pnn_choice} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. + + \begin{xalgostep}{initialisation} + On d�finit les ensembles $A \longleftarrow \emptyset$ et $B \longleftarrow X$ ainsi que la suite + $\vecteur{p\pa{x_1}}{p\pa{x_N}}$ telle que $\forall i, \; p\pa{x_i} = 1$. + $t\longleftarrow 0$ et $\epsilon_0 \longleftarrow \infty$. + \end{xalgostep} + + \begin{xalgostep}{construction de $B$}\label{clas_super_pnn_step_b} + \begin{xwhile}{$B \neq \emptyset$} + $m \longleftarrow 0$. On d�finit $x_A \in A$ et $x_B \in B$ tels que~: + $$d\pa{x_A,x_B} = \min \acc{d\pa{x,y} \sac x \in A, y \in B}$$ + \begin{xif}{$c\pa{x_A} \neq c\pa{x_B}$} + $B \longleftarrow B - \acc{x_B}$ et $A \longleftarrow A \cup \acc{x_B}$ + \xelse + $x \longleftarrow \dfrac{ p\pa{x_A} \,x_A + p\pa{x_B} \,x_B }{ p\pa{x_A} + p\pa{x_B}}$ \\ + $p\pa{x} \longleftarrow p\pa{x_A} + p\pa{x_B}$ \\ + On note $\epsilon_t$ le taux de classification obtenu avec l'ensemble de prototypes + $X' = A \cup \acc{ x }$. \\ + \begin{xif}{$\epsilon_t > \epsilon_{t-1}$} + $B \longleftarrow B - \acc{x_B}$ et $A \longleftarrow A \cup \acc{x_B}$ + \xelse + $B \longleftarrow B - \acc{x_B}$ et $A \longleftarrow \cro{ A - \acc{x_A}} \cup \acc{ x }$ \\ + $m \longleftarrow m + 1$ + \end{xif} + \end{xif} + \end{xwhile} + \end{xalgostep} + + \begin{xalgostep}{terminaison} + \begin{xif}{$ m > 0 $} + $B \longleftarrow A$ et $A \longleftarrow \emptyset$. \\ + On retourne � l'�tape~\ref{clas_super_pnn_step_b}. + \xelse + L'algorithme s'arr�te et l'ensemble cherch� $X' \longleftarrow A$. + \end{xif} + \end{xalgostep} + + \end{xalgorithm} + +L'article \citeindex{Bezdek2001} sugg�re de ne consid�rer lors de l'�tape~\ref{clas_super_pnn_step_b} que des paires $\pa{x_A,x_B}$ appartenant � une m�me classe de mani�re � ce que le r�sultat obtenu soit plus consistent. Ce second algorithme est plus lent que l'algorithme~\ref{clas_super_algorithme_cnn_choice} mais la remarque � propos l'ordre d'insertion ne le concerne plus. @@ -266,146 +266,146 @@ \subsection{Prototype for nearest neighbors (PNN)} \subsection{LVQ1, ..., LVQ4} -Les LVQ ont t introduits dans \citeindex{Linde1980}, adapts par la suite par Kohonen la reconnaissance des formes (\citeindex{Kohonen1982}, \citeindex{Kohonen1995}). Historiquement, le premier algorithme LVQ1 est d Kohonen et permet de dterminer un nombre fix de prototypes, contrairement aux deux algorithmes~\ref{clas_super_algorithme_cnn_choice} et~\ref{clas_super_algorithme_pnn_choice} des paragraphes prcdents ne ncessitant aucun a priori sur leur nombre. +Les LVQ ont �t� introduits dans \citeindex{Linde1980}, adapt�s par la suite par Kohonen � la reconnaissance des formes (\citeindex{Kohonen1982}, \citeindex{Kohonen1995}). Historiquement, le premier algorithme LVQ1 est d� � Kohonen et permet de d�terminer un nombre fix� de prototypes, contrairement aux deux algorithmes~\ref{clas_super_algorithme_cnn_choice} et~\ref{clas_super_algorithme_pnn_choice} des paragraphes pr�c�dents ne n�cessitant aucun a priori sur leur nombre. \indexfrr{LVQ}{LVQ1} - \begin{xalgorithm}{LVQ1}\label{clas_super_lvq1_algo} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. - Soit $P = \vecteur{p_1}{p_k}$ $k$ prototypes tirs alatoirement. - On associe une classe $\overline{c}\pa{p_k}$ chaque - prototype. La suite $\pa{\alpha_t}$ est une suite positive vrifiant~: - $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. Enfin, $t \longleftarrow 0$. - - \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq1_step1} - $t \longleftarrow t +1$ \\ - On choisit alatoirement un lment $x \in X$. - On dtermine $p^* = \underset{p \in P}{\arg \min} \, d\pa{x,p}$. - \end{xalgostep} - - \begin{xalgostep}{mise jour} - $p^* \longleftarrow p^* + \left\{ \begin{array}{rl} - \alpha_t \pa{ x - p^*} & \text{si } \overline{c}\pa{p^*} = c\pa{x}\\ - - \alpha_t \pa{ x - p^*} & \text{si } \overline{c}\pa{p^*} \neq c\pa{x} - \end{array} \right.$ \\ - On retourne l'tape~\ref{clas_super_lvq1_step1} tant que les prototypes continuent d'voluer. - \end{xalgostep} - \end{xalgorithm} - -Le nombre de prototypes est fix au dpart ainsi que la classe qui est associe chacun d'eux. Cet algorithme est souvent utilis avec autant de prototypes qu'il y a de classes. La suite $\pa{\alpha_t}$ est en principe une suite dcroissante mais qui peut tre choisie de telle manire que~: - - \begin{eqnarray} - \alpha_{t+1} = \left\{ \begin{array}{ll} - \frac{\alpha_t}{1 + \alpha_t} & \text{si } \overline{c}\pa{p^*} = c\pa{x}\\ - \frac{\alpha_t}{1 - \alpha_t} & \text{si } \overline{c}\pa{p^*} \neq c\pa{x} - \end{array} \right. - \end{eqnarray} - -\indexfrr{Algorithme}{OLVQ1} - -Le pas d'apprentissage $\alpha_t$ crot si le prototype le plus proche est d'une classe diffrente de celle de l'lment $x$. Cette version de l'algorithme LVQ1 est appel \emph{Optimized LVQ1}. Cette optimisation est valable pour tous les algorithmes de la famille LVQ qui suivent. La seconde version de cet algorithme propose la mise jour simultane de deux prototypes qui permet d'amliorer les frontires de dcision. + \begin{xalgorithm}{LVQ1}\label{clas_super_lvq1_algo} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. + Soit $P = \vecteur{p_1}{p_k}$ $k$ prototypes tir�s al�atoirement. + On associe une classe $\overline{c}\pa{p_k}$ � chaque + prototype. La suite $\pa{\alpha_t}$ est une suite positive v�rifiant~: + $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. Enfin, $t \longleftarrow 0$. + + \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq1_step1} + $t \longleftarrow t +1$ \\ + On choisit al�atoirement un �l�ment $x \in X$. + On d�termine $p^* = \underset{p \in P}{\arg \min} \, d\pa{x,p}$. + \end{xalgostep} + + \begin{xalgostep}{mise � jour} + $p^* \longleftarrow p^* + \left\{ \begin{array}{rl} + \alpha_t \pa{ x - p^*} & \text{si } \overline{c}\pa{p^*} = c\pa{x}\\ + - \alpha_t \pa{ x - p^*} & \text{si } \overline{c}\pa{p^*} \neq c\pa{x} + \end{array} \right.$ \\ + On retourne � l'�tape~\ref{clas_super_lvq1_step1} tant que les prototypes continuent d'�voluer. + \end{xalgostep} + \end{xalgorithm} + +Le nombre de prototypes est fix� au d�part ainsi que la classe qui est associ�e � chacun d'eux. Cet algorithme est souvent utilis� avec autant de prototypes qu'il y a de classes. La suite $\pa{\alpha_t}$ est en principe une suite d�croissante mais qui peut �tre choisie de telle mani�re que~: + + \begin{eqnarray} + \alpha_{t+1} = \left\{ \begin{array}{ll} + \frac{\alpha_t}{1 + \alpha_t} & \text{si } \overline{c}\pa{p^*} = c\pa{x}\\ + \frac{\alpha_t}{1 - \alpha_t} & \text{si } \overline{c}\pa{p^*} \neq c\pa{x} + \end{array} \right. + \end{eqnarray} + +\indexfrr{Algorithme}{OLVQ1} + +Le pas d'apprentissage $\alpha_t$ cro�t si le prototype le plus proche est d'une classe diff�rente de celle de l'�l�ment $x$. Cette version de l'algorithme LVQ1 est appel� \emph{Optimized LVQ1}. Cette optimisation est valable pour tous les algorithmes de la famille LVQ qui suivent. La seconde version de cet algorithme propose la mise � jour simultan�e de deux prototypes qui permet d'am�liorer les fronti�res de d�cision. \indexfrr{LVQ}{LVQ2} - \begin{xalgorithm}{LVQ2} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. - Soit $P = \vecteur{p_1}{p_k}$ $k$ prototypes tirs alatoirement. - On associe une classe $\overline{c}\pa{p_k}$ chaque - prototype. La suite $\pa{\alpha_t}$ est une suite positive vrifiant~: - $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. On pose galement $t \longleftarrow 0$. - Soit $w \in \left]0,1\right[$. - - \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq2_step1} - $t \longleftarrow t +1$ \\ - On choisit alatoirement un lment $x \in X$. On dtermine~: \\ - $p^*_1 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, \overline{p}\pa{p} = c\pa{x} }$ et \\ - $p^*_2 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, \overline{p}\pa{p} \neq c\pa{x} }$. - \end{xalgostep} - - \begin{xalgostep}{mise jour} - \begin{xif}{$\frac{1-w}{1+w} < \frac{d\pa{x,p^*_1}}{d\pa{x,p^*_2}} < \frac{1+w}{1-w}$} - $p^*_1 \longleftarrow p^*_1 + \alpha_t \pa{ x - p^*_1}$ \\ - $p^*_2 \longleftarrow p^*_2 - \alpha_t \pa{ x - p^*_2}$ - \end{xif} \\ - On retourne l'tape~\ref{clas_super_lvq2_step1} tant que les prototypes continuent d'voluer. - \end{xalgostep} - \end{xalgorithm} - - -\indexfrr{LVQ}{LVQ3} - -Le livre \citeindex{Kohonen1995} suggre de choisir $w \in \cro{0,2 \,;\, 0,3 }$. L'algorithme LVQ3 qui suit propose une extension de l'algorithme LVQ2 pour des prototypes $p^*_1$ et $p^*_2$ appartenant la mme classe. - - \begin{xalgorithm}{LVQ3} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. - Soit $P = \vecteur{p_1}{p_k}$ $k$ prototypes tirs alatoirement. - On associe une classe $\overline{c}\pa{p_k}$ chaque - prototype. La suite $\pa{\alpha_t}$ est une suite positive vrifiant~: - $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. On pose galement $t \longleftarrow 0$. - Soit $w \in \left]0,1\right[$ et $\epsilon \in \cro{0,1 \,;\, 0,5}$. - - \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq2_step1} - $t \longleftarrow t +1$ \\ - On choisit alatoirement un lment $x \in X$. On dtermine~: \\ - $p^*_1 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, \overline{p}\pa{p} = c\pa{x} }$ et \\ - $p^*_2 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, p^*_2 \neq p^*_1}$. - \end{xalgostep} - - \begin{xalgostep}{mise jour} - \begin{xif}{$\overline{c}\pa{p^*_1} \neq \overline{c}\pa{p^*_2}$} - \begin{xif}{$\frac{1-w}{1+w} < \frac{d\pa{x,p^*_1}}{d\pa{x,p^*_2}} < \frac{1+w}{1-w}$} - $p^*_1 \longleftarrow p^*_1 + \alpha_t \pa{ x - p^*_1}$ \\ - $p^*_2 \longleftarrow p^*_2 - \alpha_t \pa{ x - p^*_2}$ - \end{xif} - \xelse - $p^*_1 \longleftarrow p^*_1 + \epsilon \alpha_t \pa{ x - p^*_1}$ \\ - $p^*_2 \longleftarrow p^*_2 + \epsilon \alpha_t \pa{ x - p^*_2}$ - \end{xif} - On retourne l'tape~\ref{clas_super_lvq2_step1} tant que les prototypes continuent d'voluer. - \end{xalgostep} - \end{xalgorithm} - -L'algorithme LVQ4 est la version la plus rcente. Il s'inspire de l'algorithme LVQ1 mais modifie le poids de l'apprentissage $\alpha_t$ de manire plus pertinente. - - - \begin{xalgorithm}{LVQ4} - Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'lments d'un espace mtrique quelconque, - soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associes chacun des lments de $X$. On note - $d$ la distance dfinie sur l'espace mtrique $E$. - Soit $P_t = \vecteur{p_{t,1}}{p_{t,k}}$ $k$ prototypes tirs alatoirement. - On associe une classe $\overline{c}\pa{p_{t,k}}$ chaque - prototype. La suite $\pa{\alpha_t}$ est une suite positive vrifiant~: - $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. On pose galement $t \longleftarrow 0$. - On suppose que $\forall t, \, \alpha_t \in ]0,1[$. Soit $\lambda > 1$. - - \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq4_step1} - $t \longleftarrow t +1$ \\ - On choisit alatoirement un lment $x \in X$. - On dtermine $p^*_t = \underset{p \in P_t}{\arg \min} \, d\pa{x,p}$. - \end{xalgostep} - - \begin{xalgostep}{mise jour} - $p^*_t \longleftarrow p^*_t + s\pa{p^*_t} \, \alpha_t \pa{ x - p^*_t}$ o - $$ s\pa{p^*_t} = \left\{ \begin{array}{ll} - \lambda & \text{si } \overline{c}\pa{p^*_t} = c\pa{x} \text{ et } M\pa{p^*_t} = 0\\ - \frac{B\pa{p^*_t}}{M\pa{p^*_t}} & \text{si } \overline{c}\pa{p^*_t} = c\pa{x} \text{ et } M\pa{p^*_t} > 0\\ - -1 & \text{si } \overline{c}\pa{p^*_t} \neq c\pa{x} - \end{array} \right.$$ - - $B\pa{p^*_t}$ reprsente le nombre d'exemples bien classs avec le prototype $p^*_t$ - tandis que $M\pa{p^*_t}$ est le nombre d'exemples mal classs. - On retourne l'tape~\ref{clas_super_lvq4_step1} tant que les prototypes continuent d'voluer. - \end{xalgostep} - \end{xalgorithm} - -L'inconvnient de cet algorithme est le calcul coteux de $B\pa{p^*_t}$ et $M\pa{p^*_t}$. L'valuation des nombres $B\pa{p}$ et $M\pa{t}$ pour $p \in P_t$ devrait tre effectue chaque itration $t$, c'est--dire chaque fois qu'un prototype est actualis. Afin d'acclrer l'algorithme, cette valuation n'est pas effectue chaque itration mais toutes les $T$ itrations o $T$ serait la priode de mise jour. Durant une priode, ces nombres peuvent tre considrs comme constants o voluer en tenant de compte leur pass. Diffrentes variantes de l'algorithme $LVQ4$ sont proposes et discutes dans l'article \citeindex{Vakil2003}. + \begin{xalgorithm}{LVQ2} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. + Soit $P = \vecteur{p_1}{p_k}$ $k$ prototypes tir�s al�atoirement. + On associe une classe $\overline{c}\pa{p_k}$ � chaque + prototype. La suite $\pa{\alpha_t}$ est une suite positive v�rifiant~: + $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. On pose �galement $t \longleftarrow 0$. + Soit $w \in \left]0,1\right[$. + + \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq2_step1} + $t \longleftarrow t +1$ \\ + On choisit al�atoirement un �l�ment $x \in X$. On d�termine~: \\ + $p^*_1 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, \overline{p}\pa{p} = c\pa{x} }$ et \\ + $p^*_2 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, \overline{p}\pa{p} \neq c\pa{x} }$. + \end{xalgostep} + + \begin{xalgostep}{mise � jour} + \begin{xif}{$\frac{1-w}{1+w} < \frac{d\pa{x,p^*_1}}{d\pa{x,p^*_2}} < \frac{1+w}{1-w}$} + $p^*_1 \longleftarrow p^*_1 + \alpha_t \pa{ x - p^*_1}$ \\ + $p^*_2 \longleftarrow p^*_2 - \alpha_t \pa{ x - p^*_2}$ + \end{xif} \\ + On retourne � l'�tape~\ref{clas_super_lvq2_step1} tant que les prototypes continuent d'�voluer. + \end{xalgostep} + \end{xalgorithm} + + +\indexfrr{LVQ}{LVQ3} + +Le livre \citeindex{Kohonen1995} sugg�re de choisir $w \in \cro{0,2 \,;\, 0,3 }$. L'algorithme LVQ3 qui suit propose une extension de l'algorithme LVQ2 pour des prototypes $p^*_1$ et $p^*_2$ appartenant � la m�me classe. + + \begin{xalgorithm}{LVQ3} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. + Soit $P = \vecteur{p_1}{p_k}$ $k$ prototypes tir�s al�atoirement. + On associe une classe $\overline{c}\pa{p_k}$ � chaque + prototype. La suite $\pa{\alpha_t}$ est une suite positive v�rifiant~: + $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. On pose �galement $t \longleftarrow 0$. + Soit $w \in \left]0,1\right[$ et $\epsilon \in \cro{0,1 \,;\, 0,5}$. + + \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq2_step1} + $t \longleftarrow t +1$ \\ + On choisit al�atoirement un �l�ment $x \in X$. On d�termine~: \\ + $p^*_1 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, \overline{p}\pa{p} = c\pa{x} }$ et \\ + $p^*_2 = \arg \min \acc{ d\pa{x,p} \sac p \in P, \, p^*_2 \neq p^*_1}$. + \end{xalgostep} + + \begin{xalgostep}{mise � jour} + \begin{xif}{$\overline{c}\pa{p^*_1} \neq \overline{c}\pa{p^*_2}$} + \begin{xif}{$\frac{1-w}{1+w} < \frac{d\pa{x,p^*_1}}{d\pa{x,p^*_2}} < \frac{1+w}{1-w}$} + $p^*_1 \longleftarrow p^*_1 + \alpha_t \pa{ x - p^*_1}$ \\ + $p^*_2 \longleftarrow p^*_2 - \alpha_t \pa{ x - p^*_2}$ + \end{xif} + \xelse + $p^*_1 \longleftarrow p^*_1 + \epsilon \alpha_t \pa{ x - p^*_1}$ \\ + $p^*_2 \longleftarrow p^*_2 + \epsilon \alpha_t \pa{ x - p^*_2}$ + \end{xif} + On retourne � l'�tape~\ref{clas_super_lvq2_step1} tant que les prototypes continuent d'�voluer. + \end{xalgostep} + \end{xalgorithm} + +L'algorithme LVQ4 est la version la plus r�cente. Il s'inspire de l'algorithme LVQ1 mais modifie le poids de l'apprentissage $\alpha_t$ de mani�re plus pertinente. + + + \begin{xalgorithm}{LVQ4} + Soit $X = \vecteur{x_1}{x_N} \subset E$ un ensemble d'�l�ments d'un espace m�trique quelconque, + soit $\vecteur{c\pa{x_1}}{c\pa{x_N}}$ les classes associ�es � chacun des �l�ments de $X$. On note + $d$ la distance d�finie sur l'espace m�trique $E$. + Soit $P_t = \vecteur{p_{t,1}}{p_{t,k}}$ $k$ prototypes tir�s al�atoirement. + On associe une classe $\overline{c}\pa{p_{t,k}}$ � chaque + prototype. La suite $\pa{\alpha_t}$ est une suite positive v�rifiant~: + $\sum_t \alpha_t = \infty$ et $\sum_t \alpha_t^2 < \infty$. On pose �galement $t \longleftarrow 0$. + On suppose que $\forall t, \, \alpha_t \in ]0,1[$. Soit $\lambda > 1$. + + \begin{xalgostep}{meilleur prototype}\label{clas_super_lvq4_step1} + $t \longleftarrow t +1$ \\ + On choisit al�atoirement un �l�ment $x \in X$. + On d�termine $p^*_t = \underset{p \in P_t}{\arg \min} \, d\pa{x,p}$. + \end{xalgostep} + + \begin{xalgostep}{mise � jour} + $p^*_t \longleftarrow p^*_t + s\pa{p^*_t} \, \alpha_t \pa{ x - p^*_t}$ o� + $$ s\pa{p^*_t} = \left\{ \begin{array}{ll} + \lambda & \text{si } \overline{c}\pa{p^*_t} = c\pa{x} \text{ et } M\pa{p^*_t} = 0\\ + \frac{B\pa{p^*_t}}{M\pa{p^*_t}} & \text{si } \overline{c}\pa{p^*_t} = c\pa{x} \text{ et } M\pa{p^*_t} > 0\\ + -1 & \text{si } \overline{c}\pa{p^*_t} \neq c\pa{x} + \end{array} \right.$$ + + $B\pa{p^*_t}$ repr�sente le nombre d'exemples bien class�s avec le prototype $p^*_t$ + tandis que $M\pa{p^*_t}$ est le nombre d'exemples mal class�s. + On retourne � l'�tape~\ref{clas_super_lvq4_step1} tant que les prototypes continuent d'�voluer. + \end{xalgostep} + \end{xalgorithm} + +L'inconv�nient de cet algorithme est le calcul co�teux de $B\pa{p^*_t}$ et $M\pa{p^*_t}$. L'�valuation des nombres $B\pa{p}$ et $M\pa{t}$ pour $p \in P_t$ devrait �tre effectu�e � chaque it�ration $t$, c'est-�-dire � chaque fois qu'un prototype est actualis�. Afin d'acc�l�rer l'algorithme, cette �valuation n'est pas effectu�e � chaque it�ration mais toutes les $T$ it�rations o� $T$ serait la p�riode de mise � jour. Durant une p�riode, ces nombres peuvent �tre consid�r�s comme constants o� �voluer en tenant de compte leur pass�. Diff�rentes variantes de l'algorithme $LVQ4$ sont propos�es et discut�es dans l'article \citeindex{Vakil2003}. @@ -415,34 +415,34 @@ \subsection{LVQ1, ..., LVQ4} \section{Prolongations} %-------------------------------------------------------------------------------------------------------------------- -\subsection{Liens entre LVQ et la rtropropagation} +\subsection{Liens entre LVQ et la r�tropropagation} -\indexfrr{LVQ}{rtropropagation} -\indexfrr{rtropropagation}{LVQ} +\indexfrr{LVQ}{r�tropropagation} +\indexfrr{r�tropropagation}{LVQ} \indexfr{RBF} \indexsee{Radial basis function}{RBF} -\indexsee{fonction base radiale}{RBF} +\indexsee{fonction � base radiale}{RBF} -L'article \citeindex{Frasconi1997} met en rapport l'algorithme LVQ1~(\ref{clas_super_lvq1_algo}) et l'algorithme de rtropropagation~\ref{algo_retropropagation_class} dans un rseau de neurones sont les fonctions de transfert sont des fonctions base radiale ou RBF\seeannex{rnn_fonction_base_radiale_rbf}{fonction base radiale}. Ce rseau de neurones contient autant de neurones sur la couche cache qu'il y a de prototypes dans l'algorithme LVQ1. La sortie des neurones cachs est donne par~: +L'article \citeindex{Frasconi1997} met en rapport l'algorithme LVQ1~(\ref{clas_super_lvq1_algo}) et l'algorithme de r�tropropagation~\ref{algo_retropropagation_class} dans un r�seau de neurones sont les fonctions de transfert sont des fonctions � base radiale ou RBF\seeannex{rnn_fonction_base_radiale_rbf}{fonction � base radiale}. Ce r�seau de neurones contient autant de neurones sur la couche cach�e qu'il y a de prototypes dans l'algorithme LVQ1. La sortie des neurones cach�s est donn�e par~: - $$ - z_i = \exp\cro{ - \frac{\norme{p - x}^2}{\sigma^2}} - $$ + $$ + z_i = \exp\cro{ - \frac{\norme{p - x}^2}{\sigma^2}} + $$ -$p$ est un prototype, $x$ est un lment, l'lment pour lequel on value les sorties du rseau de neurones. L'article~\citeindex{Frasconi1997} que lorsque $\sigma \longrightarrow 0$, on construit ensuite le nombre~: +$p$ est un prototype, $x$ est un �l�ment, l'�l�ment pour lequel on �value les sorties du r�seau de neurones. L'article~\citeindex{Frasconi1997} que lorsque $\sigma \longrightarrow 0$, on construit ensuite le nombre~: - $$ - z'_i = \frac{z_i}{\summyone{i}z_i} - $$ + $$ + z'_i = \frac{z_i}{\summyone{i}z_i} + $$ -Lorsque $\sigma\longrightarrow 0$, le vecteur $\vecteur{z'_1}{z'_k}$ converge vers un vecteur presque nul sauf pour le prototype $i$ le plus proche. De mme, lorsque $\sigma\longrightarrow 0$, une itration d'un apprentissage par rtropropagation d'un tel rseau de neurones est quivalente une itration de l'algorithme LVQ1. +Lorsque $\sigma\longrightarrow 0$, le vecteur $\vecteur{z'_1}{z'_k}$ converge vers un vecteur presque nul sauf pour le prototype $i$ le plus proche. De m�me, lorsque $\sigma\longrightarrow 0$, une it�ration d'un apprentissage par r�tropropagation d'un tel r�seau de neurones est �quivalente � une it�ration de l'algorithme LVQ1. \firstpassagedo{ - \begin{thebibliography}{99} - \input{clas_super_biblio.tex} - \end{thebibliography} + \begin{thebibliography}{99} + \input{clas_super_biblio.tex} + \end{thebibliography} } diff --git a/_todo/classification/classification.tex b/_todo/classification/classification.tex index 080e9832..0e2858d1 100644 --- a/_todo/classification/classification.tex +++ b/_todo/classification/classification.tex @@ -4,7 +4,7 @@ \firstpassagedo{\input{classification_chapter.tex}} -Cette annexe recense diffrents moyens d'effectuer une classification non supervise et de dterminer le nombre de classes appropri. +Cette annexe recense diff�rents moyens d'effectuer une classification non supervis�e et de d�terminer le nombre de classes appropri�. \label{classification_non_supervisee} @@ -28,47 +28,47 @@ \subsection{Neural gas} \indexfr{LVQ} \indexsee{learning vector quantization}{LVQ} -Cette mthode propose dans~\citeindex{Martinetz1993} constitue une mthode non supervise de quantification vectorielle (learning vector quantization, LVQ). Toutefois, elle peut aussi tre considre comme une extension de la mthode RPCL vue au paragraphe~\ref{class_rpcl}. L'article \citeindex{Camastra2003} l'applique dans le cadre de reconnaissance caractre et le compare aux diffrents algorithmes LVQ~(1,2,3) et aux cartes de Kohonen (voir paragraphe~\ref{classification_carte_kohonen}). +Cette m�thode propos�e dans~\citeindex{Martinetz1993} constitue une m�thode non supervis�e de quantification vectorielle (learning vector quantization, LVQ). Toutefois, elle peut aussi �tre consid�r�e comme une extension de la m�thode RPCL vue au paragraphe~\ref{class_rpcl}. L'article \citeindex{Camastra2003} l'applique dans le cadre de reconnaissance caract�re et le compare aux diff�rents algorithmes LVQ~(1,2,3) et aux cartes de Kohonen (voir paragraphe~\ref{classification_carte_kohonen}). - \begin{xalgorithm}{Neural Gas} - \label{classif_algo_neural_gas} - Soient $\vecteur{X_1}{X_N}$, $N$ vecteurs classer et $T$ classes de centres $\vecteur{C_1}{C_T}$. - Soient quatre rels $\epsilon_i$, $\epsilon_f$, $\lambda_i$, $\lambda_f$ et un nombre - d'itrations maximum $t_f$ (des valeurs pratiques pour ces paramtres sont donnes - dans~\citeindex{Martinetz1993}). + \begin{xalgorithm}{Neural Gas} + \label{classif_algo_neural_gas} + Soient $\vecteur{X_1}{X_N}$, $N$ vecteurs � classer et $T$ classes de centres $\vecteur{C_1}{C_T}$. + Soient quatre r�els $\epsilon_i$, $\epsilon_f$, $\lambda_i$, $\lambda_f$ et un nombre + d'it�rations maximum $t_f$ (des valeurs pratiques pour ces param�tres sont donn�es + dans~\citeindex{Martinetz1993}). - \begin{xalgostep}{initialisation} - Tirer alatoirement les centres $\vecteur{C_1}{C_T}$. \\ - \end{xalgostep} + \begin{xalgostep}{initialisation} + Tirer al�atoirement les centres $\vecteur{C_1}{C_T}$. \\ + \end{xalgostep} - \begin{xalgostep}{mise jour} \label{class_neural_gas_step_2} - Choisir alatoirement un point $X_i$. \\ - Classer les centres $C_k$ par proximit croissante de $X_i$ de sorte que~: - $d\pa{X_i,C_{\sigma\pa{1}}} \infegal ... \infegal d\pa{X_i,C_{\sigma\pa{T}}}$ \\ - \begin{xfor}{j}{1}{C} - $ - \begin{array}{lcl} - C_j^{t+1} &\longleftarrow& C_j^t + \epsilon_j \pa{\dfrac{\epsilon_f}{\epsilon_j}}^{\frac{t}{t_f}} \; - exp\pa{ - - \biggcro{ \sigma\pa{j} - 1 } - \cro{ \lambda_j \pa{\dfrac{\lambda_f}{\lambda_j}}^{\frac{t}{t_f}} } ^{-1} - } - \; \pa{ X_i - C_j^t } - \end{array} - $ - \end{xfor} \\ - $ t \longleftarrow t+1$ - \end{xalgostep} + \begin{xalgostep}{mise � jour} \label{class_neural_gas_step_2} + Choisir al�atoirement un point $X_i$. \\ + Classer les centres $C_k$ par proximit� croissante de $X_i$ de sorte que~: + $d\pa{X_i,C_{\sigma\pa{1}}} \leqslant ... \leqslant d\pa{X_i,C_{\sigma\pa{T}}}$ \\ + \begin{xfor}{j}{1}{C} + $ + \begin{array}{lcl} + C_j^{t+1} &\longleftarrow& C_j^t + \epsilon_j \pa{\dfrac{\epsilon_f}{\epsilon_j}}^{\frac{t}{t_f}} \; + exp\pa{ + - \biggcro{ \sigma\pa{j} - 1 } + \cro{ \lambda_j \pa{\dfrac{\lambda_f}{\lambda_j}}^{\frac{t}{t_f}} } ^{-1} + } + \; \pa{ X_i - C_j^t } + \end{array} + $ + \end{xfor} \\ + $ t \longleftarrow t+1$ + \end{xalgostep} - \begin{xalgostep}{terminaison} \label{class_rpcl_step_3} - si $t < t_f$ alors retour l'tape~\ref{class_neural_gas_step_2} - \end{xalgostep} + \begin{xalgostep}{terminaison} \label{class_rpcl_step_3} + si $t < t_f$ alors retour � l'�tape~\ref{class_neural_gas_step_2} + \end{xalgostep} - \end{xalgorithm} + \end{xalgorithm} -Cet algorithme ressemble celui des cartes de Kohonen (paragraphe~\ref{classification_carte_kohonen}) sans toutefois imposer de topologie entre les diffrentes classes. Il ressemble galement l'algorithme RPCL~(\ref{classif_algo_rpcl}) ceci prs que lorsqu'un point $X_i$ est choisi alatoirement, tous les centres des classes sont rapprochs des degrs diffrents alors que l'algorithme RPCL rapproche le centre le plus proche et repousse le second centre le plus proche. +Cet algorithme ressemble � celui des cartes de Kohonen (paragraphe~\ref{classification_carte_kohonen}) sans toutefois imposer de topologie entre les diff�rentes classes. Il ressemble �galement � l'algorithme RPCL~(\ref{classif_algo_rpcl}) � ceci pr�s que lorsqu'un point $X_i$ est choisi al�atoirement, tous les centres des classes sont rapproch�s � des degr�s diff�rents alors que l'algorithme RPCL rapproche le centre le plus proche et repousse le second centre le plus proche. @@ -88,158 +88,158 @@ \subsection{Neural gas} -\subsection{Classification ascendante hirarchique} +\subsection{Classification ascendante hi�rarchique} \label{classification_ascendante_hierarchique_CAH} -\indexfrr{classification}{ascendante hirarchique (CAH)} +\indexfrr{classification}{ascendante hi�rarchique (CAH)} \indexfr{CAH} -Comme l'algorithme des centres mobiles (\ref{algo_centre_mobile}), cet algorithme permet galement d'effectuer une classification non supervise des donnes. Soit un ensemble $E = \vecteur{x_1}{x_N}$ classer, on suppose galement qu'il existe une distance entre ces lments note $d\pa{x,y}$. De cette distance, on en dduit un critre ou une inertie entre deux parties ne possdant pas d'intersection commune. Par exemple, soient deux parties non vide $A$ et $B$ de $E$ telles que $A \cap B = \emptyset$, on note $\abs{A}$ le nombre d'lments de $A$. Voici divers critres possibles~: +Comme l'algorithme des centres mobiles (\ref{algo_centre_mobile}), cet algorithme permet �galement d'effectuer une classification non supervis�e des donn�es. Soit un ensemble $E = \vecteur{x_1}{x_N}$ � classer, on suppose �galement qu'il existe une distance entre ces �l�ments not�e $d\pa{x,y}$. De cette distance, on en d�duit un crit�re ou une inertie entre deux parties ne poss�dant pas d'intersection commune. Par exemple, soient deux parties non vide $A$ et $B$ de $E$ telles que $A \cap B = \emptyset$, on note $\abs{A}$ le nombre d'�l�ments de $A$. Voici divers crit�res possibles~: - \begin{eqnarray*} - \text{le diamtre } D\pa{A,B} &=& \max \acc{ d\pa{x,y} \sac x,y \in A \cup B } \\ - \text{l'inertie } I\pa{A,B} &=& \frac{1}{\abs{A \cup B}} \; \summyone{x \in A \cup B} \; d\pa{x,G_{A \cup B}} \\ - && \text{o } G_{A \cup B} \text{ est le barycentre de la partie } A \cup B - \end{eqnarray*} + \begin{eqnarray*} + \text{le diam�tre } D\pa{A,B} &=& \max \acc{ d\pa{x,y} \sac x,y \in A \cup B } \\ + \text{l'inertie } I\pa{A,B} &=& \frac{1}{\abs{A \cup B}} \; \summyone{x \in A \cup B} \; d\pa{x,G_{A \cup B}} \\ + && \text{o� } G_{A \cup B} \text{ est le barycentre de la partie } A \cup B + \end{eqnarray*} -On note $C\pa{A,B}$ le critre de proximit entre deux parties, la classification ascendante hirarchique consiste regrouper d'abord les deux parties minimisant le critre $C\pa{A,B}$. +On note $C\pa{A,B}$ le crit�re de proximit� entre deux parties, la classification ascendante hi�rarchique consiste � regrouper d'abord les deux parties minimisant le crit�re $C\pa{A,B}$. - \begin{xalgorithm}{CAH} - Les notations sont celles utilises dans les paragraphes prcdents. - Soit l'ensemble des singletons $P = \vecteur{\acc{x_1}}{\acc{x_N}}$. + \begin{xalgorithm}{CAH} + Les notations sont celles utilis�es dans les paragraphes pr�c�dents. + Soit l'ensemble des singletons $P = \vecteur{\acc{x_1}}{\acc{x_N}}$. - \begin{xalgostep}{initialisation} - $t \longrightarrow 0$ - \end{xalgostep} + \begin{xalgostep}{initialisation} + $t \longrightarrow 0$ + \end{xalgostep} - \begin{xalgostep}{choix des deux meilleures parties}\label{classif_cah_step_a} - Soit le couple de parties $\pa{A,B}$ dfini par~: - $$\begin{array}{l} - C\pa{A,B} = \min \acc{ C\pa{M,N} \sac M,N \in P, \text{ et } M \neq N } - \end{array}$$ - \end{xalgostep} + \begin{xalgostep}{choix des deux meilleures parties}\label{classif_cah_step_a} + Soit le couple de parties $\pa{A,B}$ d�fini par~: + $$\begin{array}{l} + C\pa{A,B} = \min \acc{ C\pa{M,N} \sac M,N \in P, \text{ et } M \neq N } + \end{array}$$ + \end{xalgostep} - \begin{xalgostep}{mise jour} - $\begin{array}{lll} - c_t &\longleftarrow& C\pa{A,B} \\ - P &\longleftarrow& P - \acc{A} - \acc{B} \\ - P &\longleftarrow& P \cup \acc{ A \cup B} - \end{array}$ - Tant que $P \neq \acc{E}$, $t \longleftarrow t+1$ et retour l'tape~\ref{classif_cah_step_a}. - \end{xalgostep} - - \end{xalgorithm} + \begin{xalgostep}{mise � jour} + $\begin{array}{lll} + c_t &\longleftarrow& C\pa{A,B} \\ + P &\longleftarrow& P - \acc{A} - \acc{B} \\ + P &\longleftarrow& P \cup \acc{ A \cup B} + \end{array}$ + Tant que $P \neq \acc{E}$, $t \longleftarrow t+1$ et retour � l'�tape~\ref{classif_cah_step_a}. + \end{xalgostep} + + \end{xalgorithm} -L'volution de l'ensemble des parties $P$ est souvent reprsente par un graphe comme celui de la figure~\ref{classification_fig_cah}. C'est ce graphe qui permet de dterminer le nombre de classes appropri l'ensemble $E$ par l'intermdiaire de la courbe $\pa{t,c_t}$. Le bon nombre de classe est souvent situ au niveau d'un changement de pente ou d'un point d'inflexion de cette courbe. Cette mthode est dcrite de manire plus complte dans \citeindex{Saporta1990}. +L'�volution de l'ensemble des parties $P$ est souvent repr�sent�e par un graphe comme celui de la figure~\ref{classification_fig_cah}. C'est ce graphe qui permet de d�terminer le nombre de classes appropri� � l'ensemble $E$ par l'interm�diaire de la courbe $\pa{t,c_t}$. Le bon nombre de classe est souvent situ� au niveau d'un changement de pente ou d'un point d'inflexion de cette courbe. Cette m�thode est d�crite de mani�re plus compl�te dans \citeindex{Saporta1990}. - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \includegraphics[height=5cm, width=7cm]{\filext{../classification/image/cah_ex}} - %\filefig{../classification/fig_cah} - \\ \hline \end{tabular}$$ - \caption{ Reprsentation classique de l'arbre obtenu par une CAH. Chaque palier indique un regroupement - de deux parties et la valeur du critre de proximit correspondant.} - \indexfr{CAH} - \label{classification_fig_cah} - \end{figure} - + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \includegraphics[height=5cm, width=7cm]{\filext{../classification/image/cah_ex}} + %\filefig{../classification/fig_cah} + \\ \hline \end{tabular}$$ + \caption{ Repr�sentation classique de l'arbre obtenu par une CAH. Chaque palier indique un regroupement + de deux parties et la valeur du crit�re de proximit� correspondant.} + \indexfr{CAH} + \label{classification_fig_cah} + \end{figure} + -\subsection{Classification partir de graphes} +\subsection{Classification � partir de graphes} \label{classification_graphe_voisinage} \indexfr{graphe} \indexfr{Kruskal} \indexfrr{arbre}{poids minimal} -L'article \citeindex{Bandyopadhyay2004} propose une mthode qui s'appuie sur les graphes et permettant de classer automatiquement un nuage de points organis sous forme de graphe. Chaque lment est d'abord reli ses plus proches voisins, les arcs du graphe obtenus sont pondrs par la distance reliant les lments associs chacun un n\oe ud. Les artes sont ensuite classes par ordre croissant afin de dterminer un seuil au del duquel ces arcs relient deux lments appartenant deux classes diffrentes. Ceci mne l'algorithme~\ref{classification_graphe_band}. La figure~\ref{classification_fig_Bandyopadhyay2004} illustre quelques rsultats obtenus sur des nuages de points difficiles segmenter par des mthodes apparentes aux nues dynamiques. - - \begin{xalgorithm}{classification par graphe de voisinage} - \label{classification_graphe_band} - On dsigne par $e_{ij}$ les arcs du graphe $G(S,A)$ - reliant les lments $i$ et $j$ et pondrs par $d_{ij} = d\pa{x_i,x_j}$ la distance - entre les lments $x_i$ et $x_j$ de l'ensemble $\vecteur{x_1}{x_N}$. $S$ dsigne l'ensemble - des sommets et $A$ l'ensemble des arcs $A = \pa{e_{ij}}_{ij}$. - On numrote les artes de $1$ - $N^2$ de telle sorte qu'elles soient tries~: $w_{\sigma(1)} \infegal w_{\sigma(2)} \infegal ... \infegal - w_{\sigma(N^2)}$. On limine dans cette liste les arcs de mme poids, on construit donc la fonction $\sigma'$ - de telle sorte que~: $w_{\sigma'(1)} < w_{\sigma'(2)} < ... < w_{\sigma'(n)}$ avec $n \infegal N^2$. On pose - $\lambda = 2$. - - \begin{xalgostep}{dtermination de l'ensemble des arcs conserver} - On dsigne par $X$ l'ensemble des arcs conserver. $X = A$. Si $w_{\sigma'(n)} < \lambda w_{\sigma'(1)}$ alors $X$ - est inchang et on passe l'tape suivante. Sinon, on construit la suite - $\delta_i = w_{\sigma'(i+1)} - w_{\sigma'(i)}$ pour $i \in \ensemble{1}{n-1}$. La suite $\delta_{\phi(i)}$ - correspond la mme suite trie~: $\delta_{\phi(1)} \infegal ... \infegal \delta_{\phi(n-1)}$. On dfinit - $t = \frac{\delta_{\phi(1)} + \delta_{\phi(n-1)}} {2}$. On dfinit alors le seuil $\alpha$ tel que~: - $$ - \alpha = \min \acc{ w_{\sigma(i)} \sac - 1 \infegal i \infegal n-1 \text{ et } - w_{\sigma'(i+1)} - w_{\sigma'(i)} \supegal t \text{ et } - w_{\sigma'(i)} \supegal \lambda w_{\sigma'(1)}} - $$ - Si $\alpha$ n'est pas dfini, $X$ est inchang et on passe l'tape suivante, sinon~: - $$ - X = \acc{ e_{ij} \in A \sac d_{ij} \infegal \alpha} - $$ - \end{xalgostep} - - \begin{xalgostep}{dtermination des classes} - Si $X = A$ alors l'algorithme ne retourne qu'une seule classe. Dans le cas contraire, - on extrait du graphe $G(S,X)$ l'ensemble des composantes connexes $\ensemble{C_1}{C_p}$ o - $p$ dsigne le nombre de composantes connexes du graphe. - Si $p > \sqrt{ \card{X}}$, l'algorithme mne une sur-segmentation, on ne retourne nouveau qu'une seule - classe. Dans le cas contraire, on applique ce mme algorithme chacune des composantes connexes $(C_k)$ - extraites du graphe. - \end{xalgostep} - - L'algorithme est donc appliqu de manire rcursive tant qu'un sous-ensemble - peut tre segment. - \end{xalgorithm} - - - - \begin{figure}[p] - $$\begin{tabular}{|cc|cc|}\hline - $(a)$ & \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band21}} & - \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band22}} & $(d)$ \\ \hline - $(b)$ & \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band23}} & - \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band24}} & $(e)$ \\ \hline - $(c)$ & \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band25}} & - \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band26}} & $(f)$ - \\ \hline \end{tabular}$$ - \caption{ Figures extraites de \citeindexfig{Bandyopadhyay2004}, - diffrents nuages de points bien segments par l'algorithme~\ref{classification_graphe_band} - et de manire vidente impossible traiter avec des mthodes apparentes aux nues dynamiques - puisque les classes obtenues ne sont pas convexes. L'image $(a)$ permet de vrifier - qu'un nuage compact distribu selon une loi normale n'est pas segment. L'image $(b)$ - reprsente un nuage compose de deux classes bien segmentes. Les autres images montrent - des problmes o les classes ne sont plus circulaires $(d)$ ou non convexes $(c)$, $(e)$, $(f)$. - } - \indexfrr{classification}{voisinage} - \indexfrr{classification}{graphe} - \label{classification_fig_Bandyopadhyay2004} - \end{figure} - -L'algorithme~\ref{classification_graphe_band}, puisqu'il est appliqu rcursivement, permet de construire une hirarchie de classes comme celle obtenue par une classification ascendante hirarchique\seeannex{classification_ascendante_hierarchique_CAH}{classification ascendante hirarchique} mais cette fois-ci, l'arbre final est obtenu depuis la racine jusqu'aux feuilles. Le seuil caractrisant les cas de sur-segmentation (ici $\sqrt{X}$) est celui choisi dans l'article \citeindex{Bandyopadhyay2004} permettant de traiter les cas de la figure~\ref{classification_fig_Bandyopadhyay2004}. Celui-ci peut tre modifi en fonction du problme rsoudre. - -Cet article prcise aussi que l'algorithme peut former des classes de trs petites tailles qui devront tre agrges avec leurs voisines moins que celles-ci ne soient trop loignes, la distance entre classes tant ici la distance minimum entre leurs lments. La rgle choisie dans l'article \citeindex{Bandyopadhyay2004} est que une classe sera unie sa voisine si le diamtre de la premire est infrieur $\mu$ fois la distance qui les spare, avec $\mu = 3 \supegal 2$. Ce paramtre peut diffrer selon les problmes. +L'article \citeindex{Bandyopadhyay2004} propose une m�thode qui s'appuie sur les graphes et permettant de classer automatiquement un nuage de points organis� sous forme de graphe. Chaque �l�ment est d'abord reli� � ses plus proches voisins, les arcs du graphe obtenus sont pond�r�s par la distance reliant les �l�ments associ�s chacun � un n\oe ud. Les ar�tes sont ensuite class�es par ordre croissant afin de d�terminer un seuil au del� duquel ces arcs relient deux �l�ments appartenant � deux classes diff�rentes. Ceci m�ne � l'algorithme~\ref{classification_graphe_band}. La figure~\ref{classification_fig_Bandyopadhyay2004} illustre quelques r�sultats obtenus sur des nuages de points difficiles � segmenter par des m�thodes apparent�es aux nu�es dynamiques. + + \begin{xalgorithm}{classification par graphe de voisinage} + \label{classification_graphe_band} + On d�signe par $e_{ij}$ les arcs du graphe $G(S,A)$ + reliant les �l�ments $i$ et $j$ et pond�r�s par $d_{ij} = d\pa{x_i,x_j}$ la distance + entre les �l�ments $x_i$ et $x_j$ de l'ensemble $\vecteur{x_1}{x_N}$. $S$ d�signe l'ensemble + des sommets et $A$ l'ensemble des arcs $A = \pa{e_{ij}}_{ij}$. + On num�rote les ar�tes de $1$ + � $N^2$ de telle sorte qu'elles soient tri�es~: $w_{\sigma(1)} \leqslant w_{\sigma(2)} \leqslant ... \leqslant + w_{\sigma(N^2)}$. On �limine dans cette liste les arcs de m�me poids, on construit donc la fonction $\sigma'$ + de telle sorte que~: $w_{\sigma'(1)} < w_{\sigma'(2)} < ... < w_{\sigma'(n)}$ avec $n \leqslant N^2$. On pose + $\lambda = 2$. + + \begin{xalgostep}{d�termination de l'ensemble des arcs � conserver} + On d�signe par $X$ l'ensemble des arcs � conserver. $X = A$. Si $w_{\sigma'(n)} < \lambda w_{\sigma'(1)}$ alors $X$ + est inchang� et on passe � l'�tape suivante. Sinon, on construit la suite + $\delta_i = w_{\sigma'(i+1)} - w_{\sigma'(i)}$ pour $i \in \ensemble{1}{n-1}$. La suite $\delta_{\phi(i)}$ + correspond � la m�me suite tri�e~: $\delta_{\phi(1)} \leqslant ... \leqslant \delta_{\phi(n-1)}$. On d�finit + $t = \frac{\delta_{\phi(1)} + \delta_{\phi(n-1)}} {2}$. On d�finit alors le seuil $\alpha$ tel que~: + $$ + \alpha = \min \acc{ w_{\sigma(i)} \sac + 1 \leqslant i \leqslant n-1 \text{ et } + w_{\sigma'(i+1)} - w_{\sigma'(i)} \supegal t \text{ et } + w_{\sigma'(i)} \supegal \lambda w_{\sigma'(1)}} + $$ + Si $\alpha$ n'est pas d�fini, $X$ est inchang� et on passe � l'�tape suivante, sinon~: + $$ + X = \acc{ e_{ij} \in A \sac d_{ij} \leqslant \alpha} + $$ + \end{xalgostep} + + \begin{xalgostep}{d�termination des classes} + Si $X = A$ alors l'algorithme ne retourne qu'une seule classe. Dans le cas contraire, + on extrait du graphe $G(S,X)$ l'ensemble des composantes connexes $\ensemble{C_1}{C_p}$ o� + $p$ d�signe le nombre de composantes connexes du graphe. + Si $p > \sqrt{ \card{X}}$, l'algorithme m�ne � une sur-segmentation, on ne retourne � nouveau qu'une seule + classe. Dans le cas contraire, on applique ce m�me algorithme � chacune des composantes connexes $(C_k)$ + extraites du graphe. + \end{xalgostep} + + L'algorithme est donc appliqu� de mani�re r�cursive tant qu'un sous-ensemble + peut �tre segment�. + \end{xalgorithm} + + + + \begin{figure}[p] + $$\begin{tabular}{|cc|cc|}\hline + $(a)$ & \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band21}} & + \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band22}} & $(d)$ \\ \hline + $(b)$ & \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band23}} & + \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band24}} & $(e)$ \\ \hline + $(c)$ & \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band25}} & + \includegraphics[height=7cm, width=7cm]{\filext{../classification/image/band26}} & $(f)$ + \\ \hline \end{tabular}$$ + \caption{ Figures extraites de \citeindexfig{Bandyopadhyay2004}, + diff�rents nuages de points bien segment�s par l'algorithme~\ref{classification_graphe_band} + et de mani�re �vidente impossible � traiter avec des m�thodes apparent�es aux nu�es dynamiques + puisque les classes obtenues ne sont pas convexes. L'image $(a)$ permet de v�rifier + qu'un nuage compact distribu� selon une loi normale n'est pas segment�. L'image $(b)$ + repr�sente un nuage compos�e de deux classes bien segment�es. Les autres images montrent + des probl�mes o� les classes ne sont plus circulaires $(d)$ ou non convexes $(c)$, $(e)$, $(f)$. + } + \indexfrr{classification}{voisinage} + \indexfrr{classification}{graphe} + \label{classification_fig_Bandyopadhyay2004} + \end{figure} + +L'algorithme~\ref{classification_graphe_band}, puisqu'il est appliqu� r�cursivement, permet de construire une hi�rarchie de classes comme celle obtenue par une classification ascendante hi�rarchique\seeannex{classification_ascendante_hierarchique_CAH}{classification ascendante hi�rarchique} mais cette fois-ci, l'arbre final est obtenu depuis la racine jusqu'aux feuilles. Le seuil caract�risant les cas de sur-segmentation (ici $\sqrt{X}$) est celui choisi dans l'article \citeindex{Bandyopadhyay2004} permettant de traiter les cas de la figure~\ref{classification_fig_Bandyopadhyay2004}. Celui-ci peut �tre modifi� en fonction du probl�me � r�soudre. + +Cet article pr�cise aussi que l'algorithme peut former des classes de tr�s petites tailles qui devront �tre agr�g�es avec leurs voisines � moins que celles-ci ne soient trop �loign�es, la distance entre classes �tant ici la distance minimum entre leurs �l�ments. La r�gle choisie dans l'article \citeindex{Bandyopadhyay2004} est que une classe sera unie � sa voisine si le diam�tre de la premi�re est inf�rieur � $\mu$ fois la distance qui les s�pare, avec $\mu = 3 \supegal 2$. Ce param�tre peut diff�rer selon les probl�mes. %----------------------------------------------------------------------------------------------------------------------- \section{Prolongations} %----------------------------------------------------------------------------------------------------------------------- -\subsection{Classe sous-reprsente} +\subsection{Classe sous-repr�sent�e} -\indexfrr{classification}{classe sous-reprsente} +\indexfrr{classification}{classe sous-repr�sent�e} -Ce paragraphe regroupe quelques pistes de lecture. Les remarques qui suivent s'appliquent de prfrence une classification supervise mais peuvent tre tendues au cas non supervis. Le premier article \citeindex{Barandela2003} rsume les ides concernant le cas d'un problme de classification incluant une classe sous-reprsente. Par exemple, pour un problme deux classes~A et~B lorsque~A regroupe 98\% des exemples, rpondre~A quelque soit l'exemple correspond une erreur de 2\%. Avec plus de 2\% d'erreur, une mthode de classification serait moins performante et pourtant les classes sous-reprsentes favorise cette configuration. Diverses mthodes sont utilises pour contrecarrer cet inconvnient comme la pondration des exemples sous-reprsents, la multiplication de ces mmes exemples, bruites ou non bruites ou encore la rduction des classes sur-reprsentes un chantillon reprsentatif. Cette dernire option est celle discute par l'article \citeindex{Barandela2003} qui envisage diffrentes mthodes de slection de cet chantillon. +Ce paragraphe regroupe quelques pistes de lecture. Les remarques qui suivent s'appliquent de pr�f�rence � une classification supervis�e mais peuvent �tre �tendues au cas non supervis�. Le premier article \citeindex{Barandela2003} r�sume les id�es concernant le cas d'un probl�me de classification incluant une classe sous-repr�sent�e. Par exemple, pour un probl�me � deux classes~A et~B lorsque~A regroupe 98\% des exemples, r�pondre~A quelque soit l'exemple correspond � une erreur de 2\%. Avec plus de 2\% d'erreur, une m�thode de classification serait moins performante et pourtant les classes sous-repr�sent�es favorise cette configuration. Diverses m�thodes sont utilis�es pour contrecarrer cet inconv�nient comme la pond�ration des exemples sous-repr�sent�s, la multiplication de ces m�mes exemples, bruit�es ou non bruit�es ou encore la r�duction des classes sur-repr�sent�es � un �chantillon repr�sentatif. Cette derni�re option est celle discut�e par l'article \citeindex{Barandela2003} qui envisage diff�rentes m�thodes de s�lection de cet �chantillon. @@ -252,36 +252,36 @@ \subsection{Apprentissage d'une distance} \label{classification_graphem_carac_dist} -\indexfrr{caractristiques}{distance} +\indexfrr{caract�ristiques}{distance} \indexfrr{distance}{apprentissage} \indexfrr{apprentissage}{distance} -Jusqu' prsent, seule la classification a t traite mais on peut se demander quelle est la distance la mieux adapte une classification. La distance euclidienne accorde un poids gal toutes les dimensions d'un vecteur. On peut se demander quelle est la pondration optimale pour un problme de classification donn. On dfinit une distance $d_W$ avec $W = \vecteur{W_1}{W_d}$ pondrant les dimensions de manire non uniforme~: +Jusqu'� pr�sent, seule la classification a �t� trait�e mais on peut se demander quelle est la distance la mieux adapt�e � une classification. La distance euclidienne accorde un poids �gal � toutes les dimensions d'un vecteur. On peut se demander quelle est la pond�ration optimale pour un probl�me de classification donn�. On d�finit une distance $d_W$ avec $W = \vecteur{W_1}{W_d}$ pond�rant les dimensions de mani�re non uniforme~: - \begin{eqnarray} - d_W\pa{X^1,X^2} = \summy{k=1}{d} \, W_k^2 \, \pa{X^1_k - X^2_k}^2 - \end{eqnarray} - -\indexfr{prototype} + \begin{eqnarray} + d_W\pa{X^1,X^2} = \summy{k=1}{d} \, W_k^2 \, \pa{X^1_k - X^2_k}^2 + \end{eqnarray} + +\indexfr{prototype} -Il reste dterminer le vecteurs de poids $W = \vecteur{W_1}{W_d}$ en s'inspirant par exemple de la mthode dveloppe par \citeindex{Waard1995}. On considre $P$ vecteurs aussi appels prototypes et nots $\vecteur{X^1}{X^p}$ extrait du nuage $\vecteur{X^1}{X^N}$. On note ensuite pour tout $p \in \ensemble{1}{P}$~: +Il reste � d�terminer le vecteurs de poids $W = \vecteur{W_1}{W_d}$ en s'inspirant par exemple de la m�thode d�velopp�e par \citeindex{Waard1995}. On consid�re $P$ vecteurs aussi appel�s prototypes et not�s $\vecteur{X^1}{X^p}$ extrait du nuage $\vecteur{X^1}{X^N}$. On note ensuite pour tout $p \in \ensemble{1}{P}$~: - \begin{eqnarray} - y_p\pa{X} = \frac{1}{1 + \exp\pa{d_{W}\pa{X,X^p} + b}} - \end{eqnarray} - -On cherche minimiser le critre~: + \begin{eqnarray} + y_p\pa{X} = \frac{1}{1 + \exp\pa{d_{W}\pa{X,X^p} + b}} + \end{eqnarray} + +On cherche � minimiser le crit�re~: - \begin{eqnarray} - E = \summyone{\pa{p,l} \in A} \pa{y_p\pa{X_l} - d_{pl}}^2 \text{ o } - A = \ensemble{1}{P} \times \ensemble{1}{N} - \end{eqnarray} - -\indexfr{rseau de neurones} + \begin{eqnarray} + E = \summyone{\pa{p,l} \in A} \pa{y_p\pa{X_l} - d_{pl}}^2 \text{ o� } + A = \ensemble{1}{P} \times \ensemble{1}{N} + \end{eqnarray} + +\indexfr{r�seau de neurones} -Cette minimisation peut tre effectue par une descente de gradient ou dans un algorithme similaire ceux utiliss pour l'apprentissage des rseaux de neurones (voir paragraphe~\ref{rn_section_train_rn}). Chaque prototype $X_p$ appartient une classe $C_p$, les coefficients $d_{pl} \in \cro{0,1}$ sont choisis de manire dcrire l'appartenance du vecteur $X_l$ la classe $C_p$. +Cette minimisation peut �tre effectu�e par une descente de gradient ou dans un algorithme similaire � ceux utilis�s pour l'apprentissage des r�seaux de neurones (voir paragraphe~\ref{rn_section_train_rn}). Chaque prototype $X_p$ appartient � une classe $C_p$, les coefficients $d_{pl} \in \cro{0,1}$ sont choisis de mani�re � d�crire l'appartenance du vecteur $X_l$ � la classe $C_p$. -Cette classification pourrait tre obtenue partir d'une classification non supervise (centres mobiles, classification ascendante hirarchique) mais cela suppose de disposer dj d'une distance (comme celle par exemple dcrite au paragraphe~\ref{reco_graphem_contour}). Il est possible de rpter le processus jusqu' convergence, la premire classification est effectue l'aide d'une distance euclidienne puis une seconde distance est ensuite apprise grce la mthode dveloppe dans ce paragraphe. Cette seconde distance induit une nouvelle classification qui pourra son tour dfinir une troisime distance. Ce processus peut tre rpt jusqu' la classification n'volue plus. +Cette classification pourrait �tre obtenue � partir d'une classification non supervis�e (centres mobiles, classification ascendante hi�rarchique) mais cela suppose de disposer d�j� d'une distance (comme celle par exemple d�crite au paragraphe~\ref{reco_graphem_contour}). Il est possible de r�p�ter le processus jusqu'� convergence, la premi�re classification est effectu�e � l'aide d'une distance euclidienne puis une seconde distance est ensuite apprise gr�ce � la m�thode d�velopp�e dans ce paragraphe. Cette seconde distance induit une nouvelle classification qui pourra � son tour d�finir une troisi�me distance. Ce processus peut �tre r�p�t� jusqu'� la classification n'�volue plus. @@ -295,127 +295,127 @@ \subsection{Apprentissage d'une distance} -\subsection{Classification partir de voisinages} +\subsection{Classification � partir de voisinages} \label{classification_distance_voisinage} \indexfrr{classification}{voisinage} \indexfrr{voisinage}{classification} -L'ide de cette classification est dveloppe dans l'article \citeindex{ZhangYG2004}. Elle repose sur la construction d'un voisinage pour chaque lment d'un ensemble $E = \ensemble{x_1}{x_n}$ classer. La classification est ensuite obtenue en regroupant ensemble les voisinages ayant une intersection commune. L'objectif tant de proposer une rponse au problme dcrit par la figure~\ref{classification_fig_zhang1}. +L'id�e de cette classification est d�velopp�e dans l'article \citeindex{ZhangYG2004}. Elle repose sur la construction d'un voisinage pour chaque �l�ment d'un ensemble $E = \ensemble{x_1}{x_n}$ � classer. La classification est ensuite obtenue en regroupant ensemble les voisinages ayant une intersection commune. L'objectif �tant de proposer une r�ponse au probl�me d�crit par la figure~\ref{classification_fig_zhang1}. - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \includegraphics[height=5cm, width=6cm]{\filext{../classification/image/zhang1}} - \\ \hline \end{tabular}$$ - \caption{ Figure extraite de \citeindexfig{ZhangYG2004}, problme classique de classification consistant - sparer deux spirales imbriques l'une dans l'autre.} - \indexfrr{classification}{voisinage} - \label{classification_fig_zhang1} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \includegraphics[height=5cm, width=6cm]{\filext{../classification/image/zhang1}} + \\ \hline \end{tabular}$$ + \caption{ Figure extraite de \citeindexfig{ZhangYG2004}, probl�me classique de classification consistant + � s�parer deux spirales imbriqu�es l'une dans l'autre.} + \indexfrr{classification}{voisinage} + \label{classification_fig_zhang1} + \end{figure} -Pour chaque $x_i \in E$, on dfinit son voisinage local $\omega_i = \ensemble{x_{i_1}}{x_{i_K}}$. $K$ est le nombre de voisins et ceux-ci sont classs par ordre de proximit croissante. Par la suite, $x_i$ sera galement not $x_{i_0}$. On dfinit ensuite la matrice de covariance $S_i$ locale associe $\omega_i$~: +Pour chaque $x_i \in E$, on d�finit son voisinage local $\omega_i = \ensemble{x_{i_1}}{x_{i_K}}$. $K$ est le nombre de voisins et ceux-ci sont class�s par ordre de proximit� croissante. Par la suite, $x_i$ sera �galement not� $x_{i_0}$. On d�finit ensuite la matrice de covariance $S_i$ locale associ�e � $\omega_i$~: - \begin{eqnarray} - m_i = \frac{1}{K+1} \; \summy{k=0}{K} x_{i_k} \text{ et } - S_i = \frac{1}{K+1} \; \summy{k=0}{K} \pa{x_{i_k} - m_i } \pa{x_{i_k} - m_i }' - \label{classif_zhang_eq1} - \end{eqnarray} + \begin{eqnarray} + m_i = \frac{1}{K+1} \; \summy{k=0}{K} x_{i_k} \text{ et } + S_i = \frac{1}{K+1} \; \summy{k=0}{K} \pa{x_{i_k} - m_i } \pa{x_{i_k} - m_i }' + \label{classif_zhang_eq1} + \end{eqnarray} -Le vecteur $\lambda_i = \vecteur{\lambda_{i,1}}{\lambda_{i,d}}'$ vrifiant $\lambda_{i,1} \supegal ... \supegal \lambda_{i,d}$, $d$ est la dimension de l'espace vectoriel. L'\emph{adaptibilit} $a_i$ de l'ensemble $\omega_i$ est dfinie par~: \indexfr{adaptabilit} +Le vecteur $\lambda_i = \vecteur{\lambda_{i,1}}{\lambda_{i,d}}'$ v�rifiant $\lambda_{i,1} \supegal ... \supegal \lambda_{i,d}$, $d$ est la dimension de l'espace vectoriel. L'\emph{adaptibilit�} $a_i$ de l'ensemble $\omega_i$ est d�finie par~: \indexfr{adaptabilit�} - \begin{eqnarray} - \overline{\lambda_{i,j}} = \frac{1}{K} \; \summyone{t \in \ensemble{i_1}{i_K} } \lambda_{t,j} \text{ et } - a_i = \frac{1}{d} \; \summy{j=1}{d} \frac{ \lambda_{i,j} } { \overline { \lambda_{i,j}} } - \label{classif_zhang_eq2} - \end{eqnarray} - -On note galement~: + \begin{eqnarray} + \overline{\lambda_{i,j}} = \frac{1}{K} \; \summyone{t \in \ensemble{i_1}{i_K} } \lambda_{t,j} \text{ et } + a_i = \frac{1}{d} \; \summy{j=1}{d} \frac{ \lambda_{i,j} } { \overline { \lambda_{i,j}} } + \label{classif_zhang_eq2} + \end{eqnarray} + +On note �galement~: - \begin{eqnarray} - E\pa{a_i} = \frac{1}{N} \; \summy{i=1}{N} a_i \text{ et } - D\pa{a_i} = \sqrt{ \frac{1}{N} \; \summy{i=1}{N} \pa{ a_i - E\pa{a_i} }^2 } - \label{classif_zhang_eq3} - \end{eqnarray} - - -Dans un premier temps, les voisinages dtermins par cette mthode vont tre nettoys des voisins indsirables. Ce systme est souvent reprsent sous forme de graphe, chaque n\oe ud reprsente un lment, chaque arc dtermine l'appartenance d'un lment au voisinage d'un autre. Ces graphes sont appels "\emph{mutual neighborhood graph}" ou \emph{graphe des voisinages mutuels}. + \begin{eqnarray} + E\pa{a_i} = \frac{1}{N} \; \summy{i=1}{N} a_i \text{ et } + D\pa{a_i} = \sqrt{ \frac{1}{N} \; \summy{i=1}{N} \pa{ a_i - E\pa{a_i} }^2 } + \label{classif_zhang_eq3} + \end{eqnarray} + + +Dans un premier temps, les voisinages d�termin�s par cette m�thode vont �tre nettoy�s des voisins ind�sirables. Ce syst�me est souvent repr�sent� sous forme de graphe, chaque n\oe ud repr�sente un �l�ment, chaque arc d�termine l'appartenance d'un �l�ment au voisinage d'un autre. Ces graphes sont appel�s "\emph{mutual neighborhood graph}" ou \emph{graphe des voisinages mutuels}. \indexfr{mutual neighborhood graph} \indexfr{graphe des voisinages mutuels} - \begin{xalgorithm}{nettoyage des voisinages} - Les notations utilises sont celles des expressions (\ref{classif_zhang_eq1}), - (\ref{classif_zhang_eq2}), (\ref{classif_zhang_eq3}). - - \begin{xalgostep}{estimation}\label{classif_algo_zhang_1} - Les valeurs $a_i^l$ sont calcules pour chaque ensemble $\omega_i$ priv de $x_{i_l}$ pour lment - de l'ensemble $\omega_i$. - \end{xalgostep} + \begin{xalgorithm}{nettoyage des voisinages} + Les notations utilis�es sont celles des expressions (\ref{classif_zhang_eq1}), + (\ref{classif_zhang_eq2}), (\ref{classif_zhang_eq3}). + + \begin{xalgostep}{estimation}\label{classif_algo_zhang_1} + Les valeurs $a_i^l$ sont calcul�es pour chaque ensemble $\omega_i$ priv� de $x_{i_l}$ pour �l�ment + de l'ensemble $\omega_i$. + \end{xalgostep} - \begin{xalgostep}{suppression} - Si $a_i^l > E\pa{a_i} + D\pa{a_i}$, alors l'algorithme s'arrte. Sinon, l'lment $x_{i_s}$ correspondant - la plus petite valeur $x_{i_l}$ est supprime de l'ensemble $\omega_i$. - On retourne ensuite l'tape~\ref{classif_algo_zhang_1}. - \end{xalgostep} - - \end{xalgorithm} + \begin{xalgostep}{suppression} + Si $a_i^l > E\pa{a_i} + D\pa{a_i}$, alors l'algorithme s'arr�te. Sinon, l'�l�ment $x_{i_s}$ correspondant + � la plus petite valeur $x_{i_l}$ est supprim�e de l'ensemble $\omega_i$. + On retourne ensuite � l'�tape~\ref{classif_algo_zhang_1}. + \end{xalgostep} + + \end{xalgorithm} \indexfr{composante connexe}\indexfrr{distance}{euclidienne} -La classification correspond aux composantes connexes du graphe nettoy qui dtermine par ce biais le nombre de classes. L'article suggre galement d'associer chaque lment $x_i$ le vecteur $\pa{x_i, \beta \lambda_i}'$ o $\beta$ est un paramtre de normalisation. Le vecteur $\beta \lambda_i$ caractrise le voisinage. Ainsi, la distance entre deux points dpend la fois de leur position et de leur voisinage. Les auteurs proposent galement d'autres distances que la distance euclidienne. Il reste toutefois dterminer les paramtres $K$ et $\beta$. +La classification correspond aux composantes connexes du graphe nettoy� qui d�termine par ce biais le nombre de classes. L'article sugg�re �galement d'associer � chaque �l�ment $x_i$ le vecteur $\pa{x_i, \beta \lambda_i}'$ o� $\beta$ est un param�tre de normalisation. Le vecteur $\beta \lambda_i$ caract�rise le voisinage. Ainsi, la distance entre deux points d�pend � la fois de leur position et de leur voisinage. Les auteurs proposent �galement d'autres distances que la distance euclidienne. Il reste toutefois � d�terminer les param�tres $K$ et $\beta$. -\subsection{Modlisation de la densit des observations} +\subsection{Mod�lisation de la densit� des observations} \label{classification_modelisation_densite} -\indexfrr{densit}{semi-paramtrique} +\indexfrr{densit�}{semi-param�trique} -L'article \citeindex{Hoti2004} prsente une modlisation semi-paramtrique. Soit $Z = \pa{X,Y}$ une variable alatoire compose du couple $\pa{X,Y}$. La densit de $z$ est exprime comme suit~: +L'article \citeindex{Hoti2004} pr�sente une mod�lisation semi-param�trique. Soit $Z = \pa{X,Y}$ une variable al�atoire compos�e du couple $\pa{X,Y}$. La densit� de $z$ est exprim�e comme suit~: - \begin{eqnarray} - f_{X,Y}(x,y) = f_{Y | X=x}(y) \, f_X(x) - \end{eqnarray} + \begin{eqnarray} + f_{X,Y}(x,y) = f_{Y | X=x}(y) \, f_X(x) + \end{eqnarray} -Dans cet article, la densit $f_X\pa{x}$ est estime de faon non paramtrique tandis que $f_{Y|X}\pa{y}$ est modlise par une loi gaussienne. On note $p$ la dimension de $X$ et $q$ celle de $Y$. On note $K_H \pa{x} = \frac{1}{\det H} K \pa{H^{-1} X}$ o $H \in M_p\pa{\R}$ est une matrice carre dfinie strictement positive et $K$ un noyau vrifiant $\int_{\R^p} K\pa{x} dx = 1$. $K$ peut par exemple tre une fonction gaussienne. Les notations reprennent celles du paragraphe~\ref{modification_janvier_2004_new} (page~\pageref{modification_janvier_2004_new}). On suppose galement que la variable $Y | X=x \sim \loinormale{\mu(x)}{\sigma(x)}$. Par consquent, la densit de la variable $Z =\pa{X,Y}$ s'exprime de la faon suivante~: +Dans cet article, la densit� $f_X\pa{x}$ est estim�e de fa�on non param�trique tandis que $f_{Y|X}\pa{y}$ est mod�lis�e par une loi gaussienne. On note $p$ la dimension de $X$ et $q$ celle de $Y$. On note $K_H \pa{x} = \frac{1}{\det H} K \pa{H^{-1} X}$ o� $H \in M_p\pa{\mathbb{R}}$ est une matrice carr�e d�finie strictement positive et $K$ un noyau v�rifiant $\int_{\mathbb{R}^p} K\pa{x} dx = 1$. $K$ peut par exemple �tre une fonction gaussienne. Les notations reprennent celles du paragraphe~\ref{modification_janvier_2004_new} (page~\pageref{modification_janvier_2004_new}). On suppose �galement que la variable $Y | X=x \sim \loinormale{\mu(x)}{\sigma(x)}$. Par cons�quent, la densit� de la variable $Z =\pa{X,Y}$ s'exprime de la fa�on suivante~: - \begin{eqnarray} - f_{X,Y}(x,y) = \frac{ f_X(x) } { \sqrt{ \pa{2 \pi}^q \, \det \sigma^2(x) } } \; - \exp \pa{ - \frac{1}{2} \cro{y - \mu(x)} \, \sigma^{-1}(x) \, \cro{y - \mu(x)}' } - \end{eqnarray} + \begin{eqnarray} + f_{X,Y}(x,y) = \frac{ f_X(x) } { \sqrt{ \pa{2 \pi}^q \, \det \sigma^2(x) } } \; + \exp \pa{ - \frac{1}{2} \cro{y - \mu(x)} \, \sigma^{-1}(x) \, \cro{y - \mu(x)}' } + \end{eqnarray} -La densit $f_X$ est estime avec un estimateur noyau l'aide de l'chantillon $\pa{X_i,Y_i}_{1 \infegal i \infegal N}$~: +La densit� $f_X$ est estim�e avec un estimateur � noyau � l'aide de l'�chantillon $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N}$~: - - \begin{eqnarray} - \widehat{f_X} (x) = \frac{1}{N} \; \summy{i=1}{N} K_{H} \pa{ x - X_i} - \end{eqnarray} + + \begin{eqnarray} + \widehat{f_X} (x) = \frac{1}{N} \; \summy{i=1}{N} K_{H} \pa{ x - X_i} + \end{eqnarray} On note~: - \begin{eqnarray} - W_H\pa{x - X_i} = \frac{K_H\pa{x - X_i}} {\summy{i=1}{N} K_H\pa{x - X_i} } - \end{eqnarray} - -Les fonctions $\mu(x)$ et $\sigma(x)$ sont estimes l'aide d'un estimateur du maximum de vraisemblance~: - - \begin{eqnarray} - \widehat{\mu}(x) &=& \summy{i=1}{n} W_H\pa{x - X_i} \, Y_i \\ - \widehat{\sigma}(x) &=& \summy{i=1}{n} W_H\pa{x - X_i} \, - \cro{Y_i - \widehat{\mu}(x)}' \cro{Y_i - \widehat{\mu}(x)} - \end{eqnarray} + \begin{eqnarray} + W_H\pa{x - X_i} = \frac{K_H\pa{x - X_i}} {\summy{i=1}{N} K_H\pa{x - X_i} } + \end{eqnarray} + +Les fonctions $\mu(x)$ et $\sigma(x)$ sont estim�es � l'aide d'un estimateur du maximum de vraisemblance~: + + \begin{eqnarray} + \widehat{\mu}(x) &=& \summy{i=1}{n} W_H\pa{x - X_i} \, Y_i \\ + \widehat{\sigma}(x) &=& \summy{i=1}{n} W_H\pa{x - X_i} \, + \cro{Y_i - \widehat{\mu}(x)}' \cro{Y_i - \widehat{\mu}(x)} + \end{eqnarray} -Le paragraphe~\ref{modification_janvier_2004_new} (page~\pageref{modification_janvier_2004_new}) discute du choix d'une matrice $H$ approprie (voir galement \citeindex{Silverman1986}). En ce qui concerne le problme de classification tudie ici, la variable $X$ est simplement discrte et dsigne la classe de la variable $Y$. Cette mthode est proche de celle dveloppe au paragraphe~\ref{classification_melange_loi_normale} la seule diffrence que l'information $X_i$ est ici connue. L'intrt de cette mthode est sa gnralisation au cas o $X$ est une variable continue comme par exemple un vecteur form des distances du point $X_i$ aux centres des classes dtermines par un algorithme de classification non supervise. L'article \citeindex{Hoti2004} discute galement d'un choix d'une densit $f_X$ paramtrique. +Le paragraphe~\ref{modification_janvier_2004_new} (page~\pageref{modification_janvier_2004_new}) discute du choix d'une matrice $H$ appropri�e (voir �galement \citeindex{Silverman1986}). En ce qui concerne le probl�me de classification �tudi�e ici, la variable $X$ est simplement discr�te et d�signe la classe de la variable $Y$. Cette m�thode est proche de celle d�velopp�e au paragraphe~\ref{classification_melange_loi_normale} � la seule diff�rence que l'information $X_i$ est ici connue. L'int�r�t de cette m�thode est sa g�n�ralisation au cas o� $X$ est une variable continue comme par exemple un vecteur form� des distances du point $X_i$ aux centres des classes d�termin�es par un algorithme de classification non supervis�e. L'article \citeindex{Hoti2004} discute �galement d'un choix d'une densit� $f_X$ param�trique. @@ -426,9 +426,9 @@ \subsection{Mod \firstpassagedo{ - \begin{thebibliography}{99} - \input{classification_bibliographie.tex} - \end{thebibliography} + \begin{thebibliography}{99} + \input{classification_bibliographie.tex} + \end{thebibliography} } diff --git a/_todo/classification/classification_bibliographie.tex b/_todo/classification/classification_bibliographie.tex index 85d1fc60..e9858565 100644 --- a/_todo/classification/classification_bibliographie.tex +++ b/_todo/classification/classification_bibliographie.tex @@ -1,12 +1,12 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin \bibitemstyle{Balakrishnan1996} {1996} {P. V. Sundar Balakrishnan, Martha Cooper, Varghese S. Jacob, Phillip A. Lewis} {Comparative performance of the FSCL neural net and K-means algorithm for market segmentation} @@ -38,7 +38,7 @@ \bibitemstyle{Celeux1995}{1995} {Gilles Celeux, Didier Chauveau, Jean Diebolt} {On stochastic version of the EM algorithm} -{Rapport de recherche de l'INRIA}{n2514}{0}{} +{Rapport de recherche de l'INRIA}{n�2514}{0}{} \bibitemstyle{Davies1979} {1979} {D. L. Davies, D. W. Bouldin} {A cluster Separation Measure} @@ -60,7 +60,7 @@ {Estimation of the number of clusters and influence zones} {Pattern Recognition Letters}{22}{1557}{1568} -\bibitemstyle{Hoti2004} {2004} {Fabian Hoti, Lasse Holmstrm} +\bibitemstyle{Hoti2004} {2004} {Fabian Hoti, Lasse Holmstr�m} {A semiparametric density estimation approach to pattern classification} {Pattern Recognition}{77}{409}{419} @@ -93,7 +93,7 @@ {IEEE Trans. Neural Networks}{4(4)}{558}{569} \bibitemstyle{Saporta1990} {1990} {Gilbert Saporta} -{Probabilits, analyse des donnes et statistique} +{Probabilit�s, analyse des donn�es et statistique} {Editions Technip}{}{0}{} \bibitemstyle{Silverman1986} {1986} {B. W. Silverman} diff --git a/_todo/edit_distance/edit_bibliographie.tex b/_todo/edit_distance/edit_bibliographie.tex index 2a6a0c82..1212f329 100644 --- a/_todo/edit_distance/edit_bibliographie.tex +++ b/_todo/edit_distance/edit_bibliographie.tex @@ -1,12 +1,12 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin \bibitemstyle{Damerau1964}{1964} {F. J. Damerau} {A technique for computer detection and correction of spelling errors} diff --git a/_todo/edit_distance/edit_distance.tex b/_todo/edit_distance/edit_distance.tex index f465c00e..54d7bd19 100644 --- a/_todo/edit_distance/edit_distance.tex +++ b/_todo/edit_distance/edit_distance.tex @@ -6,26 +6,26 @@ \label{edit_distance_annexe} -\indexfrr{distance}{dition} -\indexsee{dition}{distance d'dition} +\indexfrr{distance}{�dition} +\indexsee{�dition}{distance d'�dition} -Les distances d'dition permettent de comparer deux mots entre eux ou plus gnralement deux squences de symboles entre elles. L'usage le plus simple est de trouver, pour un mot mal orthographi, le mot le plus proche dans un dictionnaire, c'est une option propose dans la plupart des traitements de texte. La distance prsente est la distance de Levenstein (voir \citeindex{Levenstein1966}).\indexfr{Levenstein} Elle est parfois appele Damerau Levenstein Matching (DLM) (voir galement \citeindex{Damerau1964}). Cette distance fait intervenir trois oprations lmentaires~: +Les distances d'�dition permettent de comparer deux mots entre eux ou plus g�n�ralement deux s�quences de symboles entre elles. L'usage le plus simple est de trouver, pour un mot mal orthographi�, le mot le plus proche dans un dictionnaire, c'est une option propos�e dans la plupart des traitements de texte. La distance pr�sent�e est la distance de Levenstein (voir \citeindex{Levenstein1966}).\indexfr{Levenstein} Elle est parfois appel�e Damerau Levenstein Matching (DLM) (voir �galement \citeindex{Damerau1964}). Cette distance fait intervenir trois op�rations �l�mentaires~: \begin{enumerate} - \item comparaison entre deux caractres - \item insertion d'un caractre - \item suppression d'un caractre + \item comparaison entre deux caract�res + \item insertion d'un caract�re + \item suppression d'un caract�re \end{enumerate} -Pour comparer deux mots, il faut construire une mthode associant ces trois oprations afin que le premier mot se transforme en le second mot. L'exemple~\ref{figure_distance_edition_exemple_un} utilise les mots "idstzance" et "distances", il montre une mthode permettant de passer du premier au second. La distance sera la somme des cots associs chacune des oprations choisies. La comparaison entre deux lettres identiques est en gnral de cot nul, toute autre opration tant de cot strictement positif. +Pour comparer deux mots, il faut construire une m�thode associant ces trois op�rations afin que le premier mot se transforme en le second mot. L'exemple~\ref{figure_distance_edition_exemple_un} utilise les mots "idstzance" et "distances", il montre une m�thode permettant de passer du premier au second. La distance sera la somme des co�ts associ�s � chacune des op�rations choisies. La comparaison entre deux lettres identiques est en g�n�ral de co�t nul, toute autre op�ration �tant de co�t strictement positif. \begin{figure}[ht] $$ \frame{$% \begin{array}[c]{cc|c|c}% - \text{\textbf{mot 1}} & \text{\textbf{mot 2}} & \text{\textbf{opration}}% - & \text{\textbf{cot}}\\ \hline + \text{\textbf{mot 1}} & \text{\textbf{mot 2}} & \text{\textbf{op�ration}}% + & \text{\textbf{co�t}}\\ \hline i & d & \text{comparaison entre "i" et "d"} & 1\\ d & i & \text{comparaison entre "d" et "i"} & 1\\ s & s & \text{comparaison entre "s" et "s"} & 0\\ @@ -40,9 +40,9 @@ \end{array} $} $$ - \caption{ Distance d'dition entre les mots "idstzance" et "distances". - La succession d'oprations propose n'est pas la seule qui permettent - de construire le second mot partir du premier mais c'est la moins coteuse. } + \caption{ Distance d'�dition entre les mots "idstzance" et "distances". + La succession d'op�rations propos�e n'est pas la seule qui permettent + de construire le second mot � partir du premier mais c'est la moins co�teuse. } \label{figure_distance_edition_exemple_un} \end{figure} @@ -53,30 +53,30 @@ %-------------------------------------------------------------------------------------------------------------------- -\section{Dfinition et proprits}\indexfrr{distance}{dition} +\section{D�finition et propri�t�s}\indexfrr{distance}{�dition} %-------------------------------------------------------------------------------------------------------------------- -\subsection{Dfinition} +\subsection{D�finition} \indexfr{mot} -\indexfr{squence} +\indexfr{s�quence} -Tout d'abord, il faut dfinir ce qu'est un mot ou une squence~: +Tout d'abord, il faut d�finir ce qu'est un mot ou une s�quence~: \begin{xdefinition}{mot}\label{definition_edit_mot} - On note $\mathcal{C}$ l'espace des caractres ou des symboles. Un mot ou une squence est + On note $\mathcal{C}$ l'espace des caract�res ou des symboles. Un mot ou une s�quence est une suite finie de $\mathcal{C}$. On note - $\mathcal{S}_\mathcal{C} = \union{k=1}{\infty} C^k$ l'espace des mots forms de caractres appartenant $\mathcal{C}$. + $\mathcal{S}_\mathcal{C} = \union{k=1}{\infty} C^k$ l'espace des mots form�s de caract�res appartenant � $\mathcal{C}$. \end{xdefinition} -On peut dfinir la distance d'dition~: +On peut d�finir la distance d'�dition~: - \begin{xdefinition}{distance d'dition}\label{defition_distance_edition_1}% - La distance d'dition $d$ sur $\mathcal{S}_\mathcal{C}$ est dfinie par~: + \begin{xdefinition}{distance d'�dition}\label{defition_distance_edition_1}% + La distance d'�dition $d$ sur $\mathcal{S}_\mathcal{C}$ est d�finie par~: $$ \begin{array}{crcl} - d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \R^+\\ - & \pa{m_1,m_2} & \longrightarrow & \underset{ \begin{subarray} OO \text{ squence} \\ \text{d'oprations} + d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \mathbb{R}^+\\ + & \pa{m_1,m_2} & \longrightarrow & \underset{ \begin{subarray} OO \text{ s�quence} \\ \text{d'op�rations} \end{subarray}}{ \min} \, d\pa{m_1,m_2,O} \end{array} @@ -84,8 +84,8 @@ \subsection{D \end{xdefinition} -La distance~\ref{defition_distance_edition_1} est le cot de la transformation du mot $m_1$ en $m_2$ la moins coteuse. Il reste dmontrer que cette distance en est bien une (paragraphe~\ref{edit_demonstration}) puis proposer une mthode de -calcul plus rapide que celle suggre par cette dfinition. +La distance~\ref{defition_distance_edition_1} est le co�t de la transformation du mot $m_1$ en $m_2$ la moins co�teuse. Il reste � d�montrer que cette distance en est bien une (paragraphe~\ref{edit_demonstration}) puis � proposer une m�thode de +calcul plus rapide que celle sugg�r�e par cette d�finition. @@ -97,18 +97,18 @@ \subsection{D -\subsection{Proprits}\label{edit_demonstration}\indexfrr{distance}{dition} +\subsection{Propri�t�s}\label{edit_demonstration}\indexfrr{distance}{�dition} -Ce paragraphe a pour objectif de dmontrer que la distance dfinie en~\ref{defition_distance_edition_1} en est bien une. +Ce paragraphe a pour objectif de d�montrer que la distance d�finie en~\ref{defition_distance_edition_1} en est bien une. - \begin{xdefinition}{distance entre caractres} + \begin{xdefinition}{distance entre caract�res} \label{edition_distance_definition_1} - Soit $\mathcal{C}' = \mathcal{C} \bigcup \accolade{.}$ l'ensemble des caractres ajout au caractre vide "$.$".\newline% - On note $c : \pa{\mathcal{C}'}^2 \longrightarrow \R^+$ la fonction cot dfinie comme suit : + Soit $\mathcal{C}' = \mathcal{C} \bigcup \accolade{.}$ l'ensemble des caract�res ajout� au caract�re vide "$.$".\newline% + On note $c : \pa{\mathcal{C}'}^2 \longrightarrow \mathbb{R}^+$ la fonction co�t d�finie comme suit : \begin{eqnarray} - \forall \pa{x,y} \in \pa{\mathcal{C}'}^2, \; c\pa{x,y} \text{ est le cot } \left\{ + \forall \pa{x,y} \in \pa{\mathcal{C}'}^2, \; c\pa{x,y} \text{ est le co�t } \left\{ \begin{array}{ll} \text { d'une comparaison} & \text{si } \pa{x,y} \in \pa{\mathcal{C}}^2\\ \text { d'une insertion} & \text{si } \pa{x,y} \in \pa{\mathcal{C}} \times \accolade{.}\\ @@ -123,28 +123,28 @@ \subsection{Propri \end{xdefinition} -Pour modliser les transformations d'un mot vers un autre, on dfinit pour un mot $m$ un \emph{mot +Pour mod�liser les transformations d'un mot vers un autre, on d�finit pour un mot $m$ un \emph{mot acceptable}\indexfrr{mot}{acceptable}~: \begin{xdefinition}{mot acceptable} \label{edition_distance_mot_acceptable_1}% \indexfrr{mot}{acceptable}% - Soit $m = \vecteur{m_1}{m_n}$ un mot tel qu'il est dfini en~\ref{definition_edit_mot}. Soit $M=\pa{M_i}_{i \supegal 1}$ - une suite infinie de caractres, on dit que $M$ est un mot acceptable pour $m$ si et seulement si la sous-suite - extraite de $M$ contenant tous les caractres diffrents de $\acc{.}$ est gal au mot $m$. On note $acc\pa{m}$ + Soit $m = \vecteur{m_1}{m_n}$ un mot tel qu'il est d�fini en~\ref{definition_edit_mot}. Soit $M=\pa{M_i}_{i \supegal 1}$ + une suite infinie de caract�res, on dit que $M$ est un mot acceptable pour $m$ si et seulement si la sous-suite + extraite de $M$ contenant tous les caract�res diff�rents de $\acc{.}$ est �gal au mot $m$. On note $acc\pa{m}$ l'ensemble des mots acceptables pour le mot $m$. \end{xdefinition} -Par consquent, tout mot acceptable $m'$ pour le mot $m$ est gal $m$ si on supprime les caractres $\acc{.}$ du mot $m'$. En particulier, partir d'un certain indice, $m'$ est une suite infinie de caractres $\acc{.}$. Il reste alors exprimer la dfinition~\ref{defition_distance_edition_1} en utilisant les mots acceptables~: +Par cons�quent, tout mot acceptable $m'$ pour le mot $m$ est �gal � $m$ si on supprime les caract�res $\acc{.}$ du mot $m'$. En particulier, � partir d'un certain indice, $m'$ est une suite infinie de caract�res $\acc{.}$. Il reste alors � exprimer la d�finition~\ref{defition_distance_edition_1} en utilisant les mots acceptables~: - \begin{xdefinition}{distance d'dition}\label{defition_distance_edition_2}% - Soit $c$ la distance dfinie en~\ref{edition_distance_definition_1}, la distance d'dition $d$ sur - $\mathcal{S}_\mathcal{C}$ est dfinie par~: + \begin{xdefinition}{distance d'�dition}\label{defition_distance_edition_2}% + Soit $c$ la distance d�finie en~\ref{edition_distance_definition_1}, la distance d'�dition $d$ sur + $\mathcal{S}_\mathcal{C}$ est d�finie par~: \begin{eqnarray} \begin{array}{crcl} - d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \R^+\\ + d : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \mathbb{R}^+\\ & \pa{m_1,m_2} & \longrightarrow & \min \acc{ \summy{i=1}{+\infty} c\pa{M_1^i, M_2^i} \sachant \pa{M_1,M_2} \in acc\pa{m_1} \times acc\pa{m_2}} @@ -154,15 +154,15 @@ \subsection{Propri \end{xdefinition} -Il est vident que la srie $\summy{i=1}{+\infty} c\pa{M_1^i, M_2^i}$ est convergente. La distance de caractres dfinie en~\ref{edition_distance_definition_1} implique que les distance d'dition dfinies en~\ref{defition_distance_edition_1} et~\ref{defition_distance_edition_2} sont identiques. +Il est �vident que la s�rie $\summy{i=1}{+\infty} c\pa{M_1^i, M_2^i}$ est convergente. La distance de caract�res d�finie en~\ref{edition_distance_definition_1} implique que les distance d'�dition d�finies en~\ref{defition_distance_edition_1} et~\ref{defition_distance_edition_2} sont identiques. - \begin{xtheoremmine}{distance d'dition} + \begin{xtheoremmine}{distance d'�dition} \label{edition_distance_theoreme001} - Soit $c$ et $d$ les fonctions dfinies respectivement par (\ref{equation_edit_car}) et (\ref{equation_edit_mot}), + Soit $c$ et $d$ les fonctions d�finies respectivement par (\ref{equation_edit_car}) et (\ref{equation_edit_mot}), alors~: $$ c \text{ est une distance sur } \mathcal{C}' \Longleftrightarrow d \text { est une distance sur } @@ -176,24 +176,24 @@ \subsection{Propri -\begin{xdemomine}{thorme}{\ref{edition_distance_theoreme001}} +\begin{xdemomine}{th�or�me}{\ref{edition_distance_theoreme001}} \itemdemo -On cherche d'abord dmontrer que~: +On cherche d'abord � d�montrer que~: \[ \begin{tabular}{c} $c$ est une distance sur $\mathcal{C}'$ $\Longleftarrow$ $d$ est une distance sur $\mathcal{S}_\mathcal{C}$ \end{tabular} \] -Cette assertion est vidente car, si $\pa{m_1,m_2}$ sont deux mots de un caractre, la distance $d$ sur -$\mathcal{S}_\mathcal{C}$ dfinit alors la distance $c$ sur $\mathcal{C}'$. +Cette assertion est �vidente car, si $\pa{m_1,m_2}$ sont deux mots de un caract�re, la distance $d$ sur +$\mathcal{S}_\mathcal{C}$ d�finit alors la distance $c$ sur $\mathcal{C}'$. \itemdemo -On cherche dmontrer que~: +On cherche d�montrer que~: \[ \begin{tabular}{c} $c$ est une distance sur $\mathcal{C}'$ $\Longrightarrow$ $d$ est une distance sur $\mathcal{S}_\mathcal{C}$ @@ -206,7 +206,7 @@ \subsection{Propri d\pa{M_1,M_2} = d\pa{M_2,M_1} $$ -D'o, d'aprs la dfinition~\ref{defition_distance_edition_2}~: +D'o�, d'apr�s la d�finition~\ref{defition_distance_edition_2}~: \begin{eqnarray} d\pa{m_1,m_2} = d\pa{m_2,m_1} \label{edit_demo_eq_1} \end{eqnarray} @@ -225,12 +225,12 @@ \subsection{Propri d\pa{m_1,m_2} = 0 & \Longrightarrow & m_1 = m_2 \label{edit_demo_eq_2} \end{eqnarray} -Il reste dmontrer l'ingalit triangulaire. Soient trois mots $\pa{m_1,m_2,m_3}$, on veut dmontrer que~: +Il reste � d�montrer l'in�galit� triangulaire. Soient trois mots $\pa{m_1,m_2,m_3}$, on veut d�montrer que~: $$ - d\pa{m_1,m_3} \infegal d\pa{m_1,m_2} + d \pa{m_2,m_3} + d\pa{m_1,m_3} \leqslant d\pa{m_1,m_2} + d \pa{m_2,m_3} $$ -On dfinit~: +On d�finit~: \begin{eqnarray*} \pa{N_1,N_2} \in acc\pa{m_1} \times acc\pa{m_2} & \text{ tels que } & d\pa{m_1,m_2} = d\pa{N_1,N_2} \\ @@ -238,7 +238,7 @@ \subsection{Propri \pa{O_1,O_3} \in acc\pa{m_1} \times acc\pa{m_3} & \text{ tels que } & d\pa{m_1,m_3} = d\pa{O_1,O_3} \end{eqnarray*} -Mais il est possible, d'aprs la dfinition~\ref{edition_distance_mot_acceptable_1} d'insrer des caractres $\acc{.}$ +Mais il est possible, d'apr�s la d�finition~\ref{edition_distance_mot_acceptable_1} d'ins�rer des caract�res $\acc{.}$ dans les mots $N_1,N_2,P_2,P_3,O_1,O_3$ de telle sorte qu'il existe $\pa{M_1,M_2,M_3} \in acc\pa{m_1} \times \in acc\pa{m_2} \times \in acc\pa{m_3}$ tels que~: (voir figure~\ref{edition_distance_demonstration}) @@ -250,12 +250,12 @@ \subsection{Propri Or comme la fonction $c$ est une distance sur $\mathcal{C}'$, on peut affirmer que~: $$ - d\pa{M_1,M_3} \infegal d\pa{M_1,M_2} + d \pa{M_2,M_3} + d\pa{M_1,M_3} \leqslant d\pa{M_1,M_2} + d \pa{M_2,M_3} $$ -D'o~: +D'o�~: \begin{eqnarray} - d\pa{m_1,m_3} \infegal d\pa{m_1,m_2} + d \pa{m_2,m_3} \label{edit_demo_eq_3} + d\pa{m_1,m_3} \leqslant d\pa{m_1,m_2} + d \pa{m_2,m_3} \label{edit_demo_eq_3} \end{eqnarray} Les assertions (\ref{edit_demo_eq_1}), (\ref{edit_demo_eq_2}), (\ref{edit_demo_eq_3}) montrent que $d$ est bien une @@ -271,7 +271,7 @@ \subsection{Propri $M_3$ & d & i & s & t & & a & n & c & e & s \\ \hline \end{tabular} \] - \caption{Dmonstration du thorme~\ref{edition_distance_theoreme001}, illustration des suites $M_1,M_2,M_3$ pour les mots + \caption{D�monstration du th�or�me~\ref{edition_distance_theoreme001}, illustration des suites $M_1,M_2,M_3$ pour les mots \textit{idtzance}, \textit{tonce}, \textit{distances}} \label{edition_distance_demonstration} \end{figure} @@ -288,23 +288,23 @@ \subsection{Propri \begin{xremark}{longueur des mots} -La distance d'dition~\ref{defition_distance_edition_2} ne tient pas compte de la longueur des mots qu'elle compare. On -serait tent de dfinir une nouvelle distance d'dition inspire de la prcdente~: +La distance d'�dition~\ref{defition_distance_edition_2} ne tient pas compte de la longueur des mots qu'elle compare. On +serait tent� de d�finir une nouvelle distance d'�dition inspir�e de la pr�c�dente~: -Soit $d^*$ la distance d'dition dfinie en~\ref{defition_distance_edition_2} pour laquelle les cots -de comparaison, d'insertion et de suppression sont tous gaux 1.\newline% -La distance d'dition $d'$ sur $\mathcal{S}_\mathcal{C}$ est dfinie par : +Soit $d^*$ la distance d'�dition d�finie en~\ref{defition_distance_edition_2} pour laquelle les co�ts +de comparaison, d'insertion et de suppression sont tous �gaux � 1.\newline% +La distance d'�dition $d'$ sur $\mathcal{S}_\mathcal{C}$ est d�finie par : \begin{eqnarray} \begin{array}{crcl} - d' : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \R^+\\ + d' : & \mathcal{S}_\mathcal{C} \times \mathcal{S}_\mathcal{C} & \longrightarrow & \mathbb{R}^+\\ & \pa{m_1,m_2} & \longrightarrow & d'\pa{m_1,m_2} = \dfrac{d^*\pa{m_1,m_2}}{ \max \acc {l\pa{m_1}, l\pa{m_2}}} \\ \\ - & & & \text{o } l\pa{m} \text{ est la longueur du mot } m + & & & \text{o� } l\pa{m} \text{ est la longueur du mot } m \end{array} \label{edit_equ_pseudo_dist} \end{eqnarray} -Le tableau~\ref{edition_distance_tableau_longueur_un} donne un exemple pour lequel l'ingalit triangulaire n'est pas -vrifie. La fonction $d^*$ n'est donc pas une distance. +Le tableau~\ref{edition_distance_tableau_longueur_un} donne un exemple pour lequel l'in�galit� triangulaire n'est pas +v�rifi�e. La fonction $d^*$ n'est donc pas une distance. \end{xremark} @@ -321,16 +321,16 @@ \subsection{Propri APOLLINE & APPOLINE & 2 & 2 / 8 \end{array} \\ \\ \begin{array}{l} - \text{Par consquent : } \\ + \text{Par cons�quent : } \\ d\pa{APOLLINE,APPOLINE} > \\ \quad d\pa{APOLLINE,APPOLLINE} + d\pa{APPOLLINE,APPOLINE} \end{array} \end{array} $} $$ - \caption{Distance d'dition et longueur de mots, cas particulier o la fonction $d^*$ dfinie par + \caption{Distance d'�dition et longueur de mots, cas particulier o� la fonction $d^*$ d�finie par (\ref{edit_equ_pseudo_dist}) - ne vrifie pas l'ingalit triangulaire.} + ne v�rifie pas l'in�galit� triangulaire.} \label{edition_distance_tableau_longueur_un} \end{table} @@ -346,13 +346,13 @@ \subsection{Propri \section{Factorisation des calculs} %--------------------------------------------------------------------------------------------------------------------- -La dfinition de la distance d'dition ne permet pas d'envisager le calcul de la distance dans un temps raisonnable. Il -est possible nanmoins d'exprimer cette distance d'une autre manire afin de rsoudre ce problme -(\citeindex{Wagner1974}). On dfinit la suite suivante~: +La d�finition de la distance d'�dition ne permet pas d'envisager le calcul de la distance dans un temps raisonnable. Il +est possible n�anmoins d'exprimer cette distance d'une autre mani�re afin de r�soudre ce probl�me +(\citeindex{Wagner1974}). On d�finit la suite suivante~: - \begin{xdefinition}{distance d'dition tronque} \label{definition_edit_dist_tronc} + \begin{xdefinition}{distance d'�dition tronqu�e} \label{definition_edit_dist_tronc} - Soient deux mots $\pa{m_1,m_2}$, on dfinit la suite~: + Soient deux mots $\pa{m_1,m_2}$, on d�finit la suite~: $$ \left( d_{i,j}^{m_{1},m_{2}}\right) _{\substack{0\leqslant i\leqslant n_{1}\\0\leqslant j\leqslant n_{2}}}\left( =\left(d_{i,j}\right) _{\substack{0\leqslant i\leqslant @@ -382,28 +382,28 @@ \section{Factorisation des calculs} \end{xdefinition} -Cette suite tronque permet d'obtenir le rsultat de la proprit suivante~: +Cette suite tronqu�e permet d'obtenir le r�sultat de la propri�t� suivante~: - \begin{xproperty}{calcul rapide de la distance d'dition} + \begin{xproperty}{calcul rapide de la distance d'�dition} \label{edition_distance_propriete_001}% - La suite dfinie en~\ref{definition_edit_dist_tronc} vrifie~: + La suite d�finie en~\ref{definition_edit_dist_tronc} v�rifie~: $$ d\left( m_{1},m_{2}\right) =d_{n_{1},n_{2}} $$ - o $d$ est la distance d'dition dfinie en~\ref{defition_distance_edition_1} ou ~\ref{defition_distance_edition_2}. + o� $d$ est la distance d'�dition d�finie en~\ref{defition_distance_edition_1} ou ~\ref{defition_distance_edition_2}. \end{xproperty} -Cette factorisation des calculs est illustre par les tableaux de la figure~\ref{figure_distance_edition_exemple_deux} +Cette factorisation des calculs est illustr�e par les tableaux de la figure~\ref{figure_distance_edition_exemple_deux} (page~\pageref{figure_distance_edition_exemple_deux}). -\begin{xdemo}{proprit}{\ref{edition_distance_propriete_001}} +\begin{xdemo}{propri�t�}{\ref{edition_distance_propriete_001}} -La dmonstration s'effectue par rcurrence, la dfinition~\ref{definition_edit_dist_tronc} est bien sr quivalente ~\ref{defition_distance_edition_1} pour des mots de longueur un. On suppose donc que ce rsultat est vrai pour un couple de mots $\pa{m_1,m_2}$ de longueur $\pa{l_1,l_2}$ vrifiant $l_1 \infegal i$ et $l_2 \infegal j$ avec au plus une galit. Soit $m$ un mot, on note $n$ le nombre de lettres qu'il contient. On note $m\left( l\right) $ le mot form des $l$ premires lettres de $m$. Alors~: +La d�monstration s'effectue par r�currence, la d�finition~\ref{definition_edit_dist_tronc} est bien s�r �quivalente �~\ref{defition_distance_edition_1} pour des mots de longueur un. On suppose donc que ce r�sultat est vrai pour un couple de mots $\pa{m_1,m_2}$ de longueur $\pa{l_1,l_2}$ v�rifiant $l_1 \leqslant i$ et $l_2 \leqslant j$ avec au plus une �galit�. Soit $m$ un mot, on note $n$ le nombre de lettres qu'il contient. On note $m\left( l\right) $ le mot form� des $l$ premi�res lettres de $m$. Alors~: \begin{eqnarray*} d_{i,j}^{m_{1},m_{2}} &=& d\left( m_{1}\left( i\right) ,m_{2}\left( j\right) \right)\\ @@ -424,7 +424,7 @@ \section{Factorisation des calculs} \end{xdemo} -Le calcul factoris de la distance d'dition entre deux mots de longueur $l_1$ et $l_2$ a un cot de l'ordre $O\pa{l_1 l_2}$. Il est souvent illustr par un tableau comme celui de la figure~\ref{figure_distance_edition_exemple_deux} qui permet galement de retrouver la meilleure squence d'oprations permettant de passer du premier mot au second. +Le calcul factoris� de la distance d'�dition entre deux mots de longueur $l_1$ et $l_2$ a un co�t de l'ordre $O\pa{l_1 l_2}$. Il est souvent illustr� par un tableau comme celui de la figure~\ref{figure_distance_edition_exemple_deux} qui permet �galement de retrouver la meilleure s�quence d'op�rations permettant de passer du premier mot au second. @@ -496,15 +496,15 @@ \section{Factorisation des calculs} \\ \begin{tabular}{c}% \begin{minipage}{15cm} - Chaque case $\pa{i,j}$ contient la distance qui spare les $i$ premires lettres du mot $1$ - des $j$ premires lettres du mot $2$ selon le chemin ou la mthode choisie. - La dernire case indique la distance qui spare les deux mots quel que soit le chemin choisi. + Chaque case $\pa{i,j}$ contient la distance qui s�pare les $i$ premi�res lettres du mot $1$ + des $j$ premi�res lettres du mot $2$ selon le chemin ou la m�thode choisie. + La derni�re case indique la distance qui s�pare les deux mots quel que soit le chemin choisi. \end{minipage} \end{tabular} \end{array} $}% $$ - \caption{Chemins possibles afin de comparer les mots "idstzance" et "distances" avec une distance d'dition} + \caption{Chemins possibles afin de comparer les mots "idstzance" et "distances" avec une distance d'�dition} \label{figure_distance_edition_exemple_deux} \end{figure} @@ -516,17 +516,17 @@ \section{Factorisation des calculs} %---------------------------------------------------------------------------------------------------------------------- -\section{Extension de la distance d'dition} +\section{Extension de la distance d'�dition} %---------------------------------------------------------------------------------------------------------------------- -Jusqu' prsent, seuls trois types d'oprations ont t envisags pour constuire la distance d'dition, tous trois portent sur des caractres et aucunement sur des paires de caractres. L'article~\citeindex{Kripasundar1996} (voir aussi~\citeindex{Seni1996}) suggre d'tendre la dfinition~\ref{definition_edit_dist_tronc} aux permutations de lettres~: +Jusqu'� pr�sent, seuls trois types d'op�rations ont �t� envisag�s pour constuire la distance d'�dition, tous trois portent sur des caract�res et aucunement sur des paires de caract�res. L'article~\citeindex{Kripasundar1996} (voir aussi~\citeindex{Seni1996}) sugg�re d'�tendre la d�finition~\ref{definition_edit_dist_tronc} aux permutations de lettres~: - \begin{xdefinition}{distance d'dition tronque tendue} \label{definition_edit_dist_tronc_2} + \begin{xdefinition}{distance d'�dition tronqu�e �tendue} \label{definition_edit_dist_tronc_2} - Soit deux mots $\pa{m_1,m_2}$, on dfinit la suite~: + Soit deux mots $\pa{m_1,m_2}$, on d�finit la suite~: $$ \left( d_{i,j}^{m_{1},m_{2}}\right) _{\substack{0\leqslant i\leqslant n_{1}\\0\leqslant j\leqslant n_{2}}}\left( =\left(d_{i,j}\right) _{\substack{0\leqslant i\leqslant @@ -557,8 +557,8 @@ \section{Extension de la distance d' \indexfr{permutation} \end{xdefinition} -La distance d'dition cherche est toujours $d\pa{m_1,m_2} = d_{n_1,n_2}$ mais la dmonstration du -fait que $d$ est bien une distance ne peut pas tre copie sur celle du thorme~\ref{edition_distance_theoreme001} mais sur les travaux prsents dans l'article~\citeindex{Wagner1974}. +La distance d'�dition cherch�e est toujours $d\pa{m_1,m_2} = d_{n_1,n_2}$ mais la d�monstration du +fait que $d$ est bien une distance ne peut pas �tre copi�e sur celle du th�or�me~\ref{edition_distance_theoreme001} mais sur les travaux pr�sent�s dans l'article~\citeindex{Wagner1974}. @@ -568,18 +568,18 @@ \section{Extension de la distance d' %--------------------------------------------------------------------------------------------------------------------- -\section{Apprentissage d'une distance d'dition}\indexfrr{apprentissage}{distance d'dition} +\section{Apprentissage d'une distance d'�dition}\indexfrr{apprentissage}{distance d'�dition} %--------------------------------------------------------------------------------------------------------------------- \label{distance_edition_apprentissage_coef_par} -L'article \citeindex{Waard1995} suggre l'apprentissage des cots des oprations lmentaires associes une distance d'dition (comparaison, insertion, suppression, permutation,~...). On note l'ensemble de ces cots ou paramtres $\Theta = \vecteur{\theta_1}{\theta_n}$. On considre deux mots $X$ et $Y$, la distance d'dition $d\pa{X,Y}$ est une fonction linaire des cots. Soit $D = \vecteur{\pa{X_1,Y_1}}{\pa{X_N,Y_N}}$ une liste de couple de mots pour lesquels le rsultat de la distance d'dition est connu et not $\vecteur{c_1}{c_N}$, il est alors possible de calculer une erreur s'exprimant sous la forme~: +L'article \citeindex{Waard1995} sugg�re l'apprentissage des co�ts des op�rations �l�mentaires associ�es � une distance d'�dition (comparaison, insertion, suppression, permutation,~...). On note l'ensemble de ces co�ts ou param�tres $\Theta = \vecteur{\theta_1}{\theta_n}$. On consid�re deux mots $X$ et $Y$, la distance d'�dition $d\pa{X,Y}$ est une fonction lin�aire des co�ts. Soit $D = \vecteur{\pa{X_1,Y_1}}{\pa{X_N,Y_N}}$ une liste de couple de mots pour lesquels le r�sultat de la distance d'�dition est connu et not� $\vecteur{c_1}{c_N}$, il est alors possible de calculer une erreur s'exprimant sous la forme~: \begin{eqnarray} E = \summy{i=1}{N} \; \pa{d\pa{X_i,Y_i} - c_i}^2 =\summy{i=1}{N} \; \pa{ \summy{k=1}{n} \alpha_{ik}\pa{\Theta} \, \theta_k - c_i}^2 \\ \end{eqnarray} -Les coefficients $\alpha_{ik}\pa{\Theta}$ dpendent des paramtres $\Theta$ car la distance d'dition correspond au cot de la transformation de moindre cot d'aprs la dfinition~\ref{defition_distance_edition_2}, $\alpha_{ik}\pa{\Theta}$ correspond au nombre de fois que le paramtre $\theta_k$ intervient dans la transformation de moindre cot entre $X_i$ et $Y_i$. Cette expression doit tre minimale afin d'optenir les cots $\Theta$ optimaux. Toutefois, les cots $\theta_k$ sont tous strictement positifs et plutt que d'effectuer une optimisation sous contrainte, ces cots sont modliss de la faon suivante~: +Les coefficients $\alpha_{ik}\pa{\Theta}$ d�pendent des param�tres $\Theta$ car la distance d'�dition correspond au co�t de la transformation de moindre co�t d'apr�s la d�finition~\ref{defition_distance_edition_2}, $\alpha_{ik}\pa{\Theta}$ correspond au nombre de fois que le param�tre $\theta_k$ intervient dans la transformation de moindre co�t entre $X_i$ et $Y_i$. Cette expression doit �tre minimale afin d'optenir les co�ts $\Theta$ optimaux. Toutefois, les co�ts $\theta_k$ sont tous strictement positifs et plut�t que d'effectuer une optimisation sous contrainte, ces co�ts sont mod�lis�s de la fa�on suivante~: \begin{eqnarray} E = \summy{i=1}{N} \; \pa{ \summy{k=1}{n} \, \alpha_{ik}\pa{\Omega} \, \frac{1}{1 + e^{-\omega_k}} - c_i}^2 @@ -590,32 +590,32 @@ \section{Apprentissage d'une distance d' \indexfrr{optimisation}{sans contrainte} \indexfr{descente de gradient} -Les fonctions $\alpha_{ik}\pa{\Omega}$ ne sont pas drivable par rapport $\Omega$ mais il est possible d'effectuer une optimisation sans contrainte par descente de gradient. Les cots sont donc appris en deux tapes~: +Les fonctions $\alpha_{ik}\pa{\Omega}$ ne sont pas d�rivable par rapport $\Omega$ mais il est possible d'effectuer une optimisation sans contrainte par descente de gradient. Les co�ts sont donc appris en deux �tapes~: - \begin{xalgorithm}{apprentissage d'une distance d'dition}\label{edit_distance_app_optom} - Les notations sont celles utiliss pour l'quation (\ref{edit_distance_eq_2_app}). Les cots $\Omega$ sont tirs - alatoirement. + \begin{xalgorithm}{apprentissage d'une distance d'�dition}\label{edit_distance_app_optom} + Les notations sont celles utilis�s pour l'�quation (\ref{edit_distance_eq_2_app}). Les co�ts $\Omega$ sont tir�s + al�atoirement. \begin{xalgostep}{estimation}\label{edit_distance_step_b_app} - Les coefficients $\alpha_{ik}\pa{\Omega}$ sont calcules. + Les coefficients $\alpha_{ik}\pa{\Omega}$ sont calcul�es. \end{xalgostep} \begin{xalgostep}{calcul du gradient}\label{edit_distance_step_a_app} - Dans cette tape, les coefficients $\alpha_{ik}\pa{\Omega}$ restent constants. Il suffit alors de minimiser la fonction - drivable $E\pa{\Omega}$ sur $\R^n$, ceci peut tre effectu au moyen d'un algorithme de descente de - gradient\seeannex{optimisation_newton}{descente de gradient} similaire ceux utiliss pour les rseaux de neurones. + Dans cette �tape, les coefficients $\alpha_{ik}\pa{\Omega}$ restent constants. Il suffit alors de minimiser la fonction + d�rivable $E\pa{\Omega}$ sur $\mathbb{R}^n$, ceci peut �tre effectu� au moyen d'un algorithme de descente de + gradient\seeannex{optimisation_newton}{descente de gradient} similaire � ceux utilis�s pour les r�seaux de neurones. \end{xalgostep} \begin{xalgostep}{calcul du gradient}\label{edit_distance_step_c_app} - Tant que l'erreur $E\pa{\Omega}$ ne converge pas, retour l'tape~\ref{edit_distance_step_b_app}. + Tant que l'erreur $E\pa{\Omega}$ ne converge pas, retour � l'�tape~\ref{edit_distance_step_b_app}. \end{xalgostep} \end{xalgorithm} -\begin{xremark}{dcroissance de l'erreur} -A partir du moment o l'tape~\ref{edit_distance_step_a_app} de l'algorithme~\ref{edit_distance_app_optom} fait dcrotre l'erreur $E$, l'erreur $E$ diminue jusqu' converger puisque l'tape~\ref{edit_distance_step_b_app}, qui restime les coefficients $\alpha_{ik}\pa{\Omega}$, les minimise $\Omega = \vecteur{\omega_1}{\omega_n}$ constant. +\begin{xremark}{d�croissance de l'erreur} +A partir du moment o� l'�tape~\ref{edit_distance_step_a_app} de l'algorithme~\ref{edit_distance_app_optom} fait d�cro�tre l'erreur $E$, l'erreur $E$ diminue jusqu'� converger puisque l'�tape~\ref{edit_distance_step_b_app}, qui r�estime les coefficients $\alpha_{ik}\pa{\Omega}$, les minimise � $\Omega = \vecteur{\omega_1}{\omega_n}$ constant. \end{xremark} diff --git a/_todo/hmm/hmm.tex b/_todo/hmm/hmm.tex index 18fd8397..7cec20b4 100644 --- a/_todo/hmm/hmm.tex +++ b/_todo/hmm/hmm.tex @@ -6,9 +6,9 @@ -\indexsee{modle de Markov cach}{MMC} +\indexsee{mod�le de Markov cach�}{MMC} \indexfr{MMC} -\indexsee{chane de Markov cache}{MMC} +\indexsee{cha�ne de Markov cach�e}{MMC} \indexsee{Hidden Markov Model}{MMC} \indexfr{HMM} @@ -19,52 +19,52 @@ -Cette annexe dtaille les concepts et les proprits des modles de Markov cachs en vitant aussi souvent que possible les rfrences la reconnaissance de l'criture manuscrite. +Cette annexe d�taille les concepts et les propri�t�s des mod�les de Markov cach�s en �vitant aussi souvent que possible les r�f�rences � la reconnaissance de l'�criture manuscrite. %------------------------------------------------------------------------------------------------------------------ -\section{Chane de Markov} +\section{Cha�ne de Markov} %------------------------------------------------------------------------------------------------------------------ -\indexfr{chane de Markov} - -\subsection{Dfinition} - -\indexfr{squence} -\indexfr{tat} - -Une chane de Markov est un modle probabiliste modlisant des squences de symboles appartenant un ensemble fini. Ces squences peuvent tre considres galement comme des suites entires finies. - - \begin{xdefinition}{chane de Markov} - \label{markov_chaine_definition}% - Soit $M$ une chane de Markov.\newline - Soit $Q = \intervalle{1}{N}$ l'ensemble des tats.\newline - Soit $S=\underset{T=1} {\overset{+\infty}{\cup}} Q^T$ l'espace des squences.\newline - On note $s = \pa{q_1,\dots,q_{T_s}} \in S$ une squence de longueur $T_s$.\newline - \indexfrr{modle}{probabiliste} - Une chane de Markov est un modle probabiliste sur $S$ vrifiant les deux hypothses suivantes~: - \begin{enumerate} - \item L'tat l'instant $t$ ne dpend que de l'tat l'instant $t-1$~: - $$ - \forall s \in S, \; \forall t \in \intervalle{2}{T_s}, \; - \pr{q_t \sachant \vecteurno{q_1}{q_{t-1}},M} = \pr{q_t \sachant q_{t-1},M} - $$ - On appelle $\pr{q_t \sachant q_{t-1},M}$ la \emph{probabilit de transition} - \indexfrr{probabilit}{transition} - de l'tat $q_{t-1}$ l'tat $q_t$ l'instant $t$. - \item Les probabilits de transition ne dpendent pas du temps~: - $$ - \forall s \in S, \; \forall \pa{t,t'} \in \intervalle{2}{T_s}, \; - \pr{q_t \sachant q_{t-1},M} = \pr{q_{t'} \sachant q_{t'-1},M} - $$ - \end{enumerate} - \end{xdefinition} - - - -Afin de simplifier les notations ultrieurement, on dfinit pour la chane de Markov $M$, la matrice des probabilits de transition $A_M \in M_N\pa{\R}$~: \indexfrr{notation}{probabilit de transition} +\indexfr{cha�ne de Markov} + +\subsection{D�finition} + +\indexfr{s�quence} +\indexfr{�tat} + +Une cha�ne de Markov est un mod�le probabiliste mod�lisant des s�quences de symboles appartenant � un ensemble fini. Ces s�quences peuvent �tre consid�r�es �galement comme des suites enti�res finies. + + \begin{xdefinition}{cha�ne de Markov} + \label{markov_chaine_definition}% + Soit $M$ une cha�ne de Markov.\newline + Soit $Q = \intervalle{1}{N}$ l'ensemble des �tats.\newline + Soit $S=\underset{T=1} {\overset{+\infty}{\cup}} Q^T$ l'espace des s�quences.\newline + On note $s = \pa{q_1,\dots,q_{T_s}} \in S$ une s�quence de longueur $T_s$.\newline + \indexfrr{mod�le}{probabiliste} + Une cha�ne de Markov est un mod�le probabiliste sur $S$ v�rifiant les deux hypoth�ses suivantes~: + \begin{enumerate} + \item L'�tat � l'instant $t$ ne d�pend que de l'�tat � l'instant $t-1$~: + $$ + \forall s \in S, \; \forall t \in \intervalle{2}{T_s}, \; + \pr{q_t \sachant \vecteurno{q_1}{q_{t-1}},M} = \pr{q_t \sachant q_{t-1},M} + $$ + On appelle $\pr{q_t \sachant q_{t-1},M}$ la \emph{probabilit� de transition} + \indexfrr{probabilit�}{transition} + de l'�tat $q_{t-1}$ � l'�tat $q_t$ � l'instant $t$. + \item Les probabilit�s de transition ne d�pendent pas du temps~: + $$ + \forall s \in S, \; \forall \pa{t,t'} \in \intervalle{2}{T_s}, \; + \pr{q_t \sachant q_{t-1},M} = \pr{q_{t'} \sachant q_{t'-1},M} + $$ + \end{enumerate} + \end{xdefinition} + + + +Afin de simplifier les notations ult�rieurement, on d�finit pour la cha�ne de Markov $M$, la matrice des probabilit�s de transition $A_M \in M_N\pa{\mathbb{R}}$~: \indexfrr{notation}{probabilit� de transition} \begin{eqnarray} A_{M}= \pa { a_{M,ij} } _{\substack{1\leqslant i\leqslant N\\1\leqslant j\leqslant N}} = @@ -72,35 +72,35 @@ \subsection{D \label{hmm_eq_1} \end{eqnarray} -On dfinit galement le vecteur des probabilits d'entres $\pi_M \in \R^N$ : +On d�finit �galement le vecteur des probabilit�s d'entr�es $\pi_M \in \mathbb{R}^N$ : \begin{eqnarray} \forall i\in \ensemble{1}{N} ,\, \pi_{M,i}=\pa { q_{1}=i \sachant M } \label{hmm_eq_2} \end{eqnarray} - \begin{xproperty}{contrainte} - \label{propriete_mmc_contrainte_1}% - La dfinition~\ref{markov_chaine_definition} d'une chane de Markov et ses paramtres dfinis en - (\ref{hmm_eq_1}) et (\ref{hmm_eq_2}) implique que~: - \begin{eqnarray*} - \forall i \in \ensemble{1}{N} , \,\summy{j=1}{N}a_{M,ij}&=&1 \text{ et } \summy{j=1}{N} \,\pi_{M,j} = 1 - \end{eqnarray*} - Par abus de notation, on crira $a_{ij}=a_{M,ij}$, et $\pi_{i}=\pi_{M,i}$ - \end{xproperty} + \begin{xproperty}{contrainte} + \label{propriete_mmc_contrainte_1}% + La d�finition~\ref{markov_chaine_definition} d'une cha�ne de Markov et ses param�tres d�finis en + (\ref{hmm_eq_1}) et (\ref{hmm_eq_2}) implique que~: + \begin{eqnarray*} + \forall i \in \ensemble{1}{N} , \,\summy{j=1}{N}a_{M,ij}&=&1 \text{ et } \summy{j=1}{N} \,\pi_{M,j} = 1 + \end{eqnarray*} + Par abus de notation, on �crira $a_{ij}=a_{M,ij}$, et $\pi_{i}=\pi_{M,i}$ + \end{xproperty} -La dfinition d'une chane de Markov simplifie l'criture de la probabilit d'une squence~: +La d�finition d'une cha�ne de Markov simplifie l'�criture de la probabilit� d'une s�quence~: - \begin{xproperty}{probabilit d'une squence} - La dfinition~\ref{markov_chaine_definition} d'une chane de Markov et ses paramtres dfinis en (\ref{hmm_eq_1}) et - (\ref{hmm_eq_2}) implique que~: - \begin{eqnarray*} - \pr{s|M} &=& \pr{q_1,\dots,q_T \sachant M} = \pr{q_1 \sachant M}\prody{t=2}{T} - \pr{q_t \sachant \overline{q_{t-1}},M} - = \pi_{q_1} \prody{t=2}{T_s} a_{q_{t-1},q_t} - \end{eqnarray*} - \end{xproperty} + \begin{xproperty}{probabilit� d'une s�quence} + La d�finition~\ref{markov_chaine_definition} d'une cha�ne de Markov et ses param�tres d�finis en (\ref{hmm_eq_1}) et + (\ref{hmm_eq_2}) implique que~: + \begin{eqnarray*} + \pr{s|M} &=& \pr{q_1,\dots,q_T \sachant M} = \pr{q_1 \sachant M}\prody{t=2}{T} + \pr{q_t \sachant \overline{q_{t-1}},M} + = \pi_{q_1} \prody{t=2}{T_s} a_{q_{t-1},q_t} + \end{eqnarray*} + \end{xproperty} @@ -110,14 +110,14 @@ \subsection{D -\subsection{Exemple : pice de monnaie truque} \label{chaine_markov_exemple} +\subsection{Exemple : pi�ce de monnaie truqu�e} \label{chaine_markov_exemple} -\indexfrr{chane de Markov}{exemple}% -\indexfrr{exemple}{pice de monnaie} +\indexfrr{cha�ne de Markov}{exemple}% +\indexfrr{exemple}{pi�ce de monnaie} -\para{Enonc} +\para{Enonc�} -On considre une pice truque qui a $7$ chances sur $10$ de retomber sur pile (tat $1$), et $3$ chances sur $10$ de retomber sur face (tat $2$), et une vraie pice. Si la face pile tombe, c'est la vraie pice qui sera joue, sinon, ce sera la pice truque. La premire joue est tire au hasard. La chane de Markov correspondant ce problme est dfinie par les probabilits suivantes~: +On consid�re une pi�ce truqu�e qui a $7$ chances sur $10$ de retomber sur pile (�tat $1$), et $3$ chances sur $10$ de retomber sur face (�tat $2$), et une vraie pi�ce. Si la face pile tombe, c'est la vraie pi�ce qui sera jou�e, sinon, ce sera la pi�ce truqu�e. La premi�re jou�e est tir�e au hasard. La cha�ne de Markov correspondant � ce probl�me est d�finie par les probabilit�s suivantes~: $$ \begin{array} @@ -127,7 +127,7 @@ \subsection{Exemple : pi \end{array} $$ -Par consquent : +Par cons�quent : $$ \begin{array}{ccc} @@ -149,7 +149,7 @@ \subsection{Exemple : pi \end{array} $$ -Ce modle peut tre reprsent graphiquement par un schma utilisant des graphes o chaque n\oe ud est un tat du modle et chaque probabilit de transition positive un arc du graphe (voir figure~\ref{figure_chaine_markov_exemple-fig}). Plus de dtails sont donns dans le paragraphe~\ref{hmm_representation_graphe}. +Ce mod�le peut �tre repr�sent� graphiquement par un sch�ma utilisant des graphes o� chaque n\oe ud est un �tat du mod�le et chaque probabilit� de transition positive un arc du graphe (voir figure~\ref{figure_chaine_markov_exemple-fig}). Plus de d�tails sont donn�s dans le paragraphe~\ref{hmm_representation_graphe}. \begin{figure}[ht] @@ -158,41 +158,41 @@ \subsection{Exemple : pi \filefig{../hmm/fig_markov} } $$ - \caption{Reprsentation d'une chane de Markov sous forme de graphe.} + \caption{Repr�sentation d'une cha�ne de Markov sous forme de graphe.} \label{figure_chaine_markov_exemple-fig} - \indexfrr{chane de Markov}{graphe}% + \indexfrr{cha�ne de Markov}{graphe}% \end{figure} -\para{Rsultats intressants} +\para{R�sultats int�ressants} -Cette modlisation simple permet d'obtenir la probabilit que la face pile apparaisse un instant donn, et de dfinir le gain que peut esprer un tricheur en utilisant sa pice truque. On s'intresse d'abord ~: +Cette mod�lisation simple permet d'obtenir la probabilit� que la face pile apparaisse � un instant donn�, et de d�finir le gain que peut esp�rer un tricheur en utilisant sa pi�ce truqu�e. On s'int�resse d'abord �~: $$ \pr{ q_{t}=j \sac q_{t-2}=i,M } = \summy{k=1}{N} \pr{ q_{t}=j \sac q_{t-1}=k,M } \pr{ q_{t-1}=k \sac q_{t-2}=i,M } = \pa { A_{M} ^{2} } _{i,j} $$ -Par rcurrence, on en dduit que : +Par r�currence, on en d�duit que : $$ \pr{ q_{t}=j \sac q_{t-d}=i,M } = \pa { A_{M}^{d} } _{i,j} $$ -On note $\pi'_M$ la transpose de la matrice $\pi_M$, on obtient alors que : +On note $\pi'_M$ la transpos�e de la matrice $\pi_M$, on obtient alors que : $$ \pi'_M \pa { A_{M}^{t} } = \pa { \pr { q_{t}=i \sac M } } _{1\leqslant i\leqslant N} $$ -Finalement, cette formule applique l'exemple prcdent donne : +Finalement, cette formule appliqu�e � l'exemple pr�c�dent donne : $$ \underset{t\rightarrow+\infty}{\lim} \pr{ q_{t}=1 } =\frac{7}{12} \text{ et } \underset{t\rightarrow+\infty}{\lim} \pr{ q_{t}=2 } =\frac{5}{12}% $$ -Les lois limites des tats sont utiles pour calculer une esprence de gain. Si le joueur gagne un franc lorsque la face pile sort et perd un franc dans l'autre cas, sur une suite de douze coups, il gagne en moyenne deux francs. On peut galement calculer la probabilit d'une squence de quatre gain conscutifs~: +Les lois limites des �tats sont utiles pour calculer une esp�rence de gain. Si le joueur gagne un franc lorsque la face pile sort et perd un franc dans l'autre cas, sur une suite de douze coups, il gagne en moyenne deux francs. On peut �galement calculer la probabilit� d'une s�quence de quatre gain cons�cutifs~: $$ \pr { 1111 \sac M } =0,5\ast0,3\ast0,3\ast0,3=0,0135 @@ -210,18 +210,18 @@ \subsection{Exemple : pi %---------------------------------------------------------------------------------------------------------------------- -\section{Chane de Markov cache} +\section{Cha�ne de Markov cach�e} %---------------------------------------------------------------------------------------------------------------------- \label{interdoc_mmc} -Il n'est pas vident qu'un processus (ou squence de variables alatoires) suive la loi d'une chane de Markov, nanmoins, ce processus peut parfois tre expliqu par un autre cach qui suit la loi d'une chane de Markov. Ce second processus est dit cach car le premier, celui qu'il explique, est le seul observ. Afin de comprendre ce concept, l'exemple prcdent (paragraphe~\ref{chaine_markov_exemple}) sera lgrement modifi de manire prsenter les chanes de Markov caches. +Il n'est pas �vident qu'un processus (ou s�quence de variables al�atoires) suive la loi d'une cha�ne de Markov, n�anmoins, ce processus peut parfois �tre expliqu� par un autre cach� qui suit la loi d'une cha�ne de Markov. Ce second processus est dit cach� car le premier, celui qu'il explique, est le seul observ�. Afin de comprendre ce concept, l'exemple pr�c�dent (paragraphe~\ref{chaine_markov_exemple}) sera l�g�rement modifi� de mani�re � pr�senter les cha�nes de Markov cach�es. -\subsection{Exemple : pice de monnaie truque} +\subsection{Exemple : pi�ce de monnaie truqu�e} -\indexfrr{exemple}{pice de monnaie} +\indexfrr{exemple}{pi�ce de monnaie} \label{chaine_markov_cachee_exemple} -Dans l'exemple des deux pices truque et non truque (paragraphe~\ref{chaine_markov_exemple}), les tats de la chane de Markov et faces des pices taient identiques. Les tats correspondent maintenant aux pices (information cache) et les observations correspondent aux faces (information observe). L'tat $1$ sera la pice non truque et l'tat $2$ sera la pice truque. On suppose que la dcision du joueur concernant le choix de la pice ne dpend plus de la face qui apparat aprs le lancer mais dpend du choix de la pice au lancer prcdent. Les probabilits de transition sont dfinies ainsi~: +Dans l'exemple des deux pi�ces truqu�e et non truqu�e (paragraphe~\ref{chaine_markov_exemple}), les �tats de la cha�ne de Markov et faces des pi�ces �taient identiques. Les �tats correspondent maintenant aux pi�ces (information cach�e) et les observations correspondent aux faces (information observ�e). L'�tat $1$ sera la pi�ce non truqu�e et l'�tat $2$ sera la pi�ce truqu�e. On suppose que la d�cision du joueur concernant le choix de la pi�ce ne d�pend plus de la face qui appara�t apr�s le lancer mais d�pend du choix de la pi�ce au lancer pr�c�dent. Les probabilit�s de transition sont d�finies ainsi~: $$ \begin{array}[c]{ccccc}% @@ -230,8 +230,8 @@ \subsection{Exemple : pi \end{array} $$ -De chaque tat dpendent les probabilits de voir apparatre pile ou face. On note $O_t$ la face qui apparat l'instant $t$, -les probabilits qui suivent sont appeles \emph{probabilits d'mission}.\indexfrr{probabilit}{mission} +De chaque �tat d�pendent les probabilit�s de voir appara�tre pile ou face. On note $O_t$ la face qui appara�t � l'instant $t$, +les probabilit�s qui suivent sont appel�es \emph{probabilit�s d'�mission}.\indexfrr{probabilit�}{�mission} $$ \begin{array}[c]{ccc}% @@ -242,7 +242,7 @@ \subsection{Exemple : pi \indexfrr{MMC}{graphe} -Ce modle peut toujours tre reprsent graphiquement par un schma utilisant des graphes (figure~\ref{figure_chaine_markov_cachee_exemple-fig}). Plus de dtails seront donns au paragraphe~\ref{hmm_representation_graphe}. +Ce mod�le peut toujours �tre repr�sent� graphiquement par un sch�ma utilisant des graphes (figure~\ref{figure_chaine_markov_cachee_exemple-fig}). Plus de d�tails seront donn�s au paragraphe~\ref{hmm_representation_graphe}. \begin{figure}[ht] @@ -251,13 +251,13 @@ \subsection{Exemple : pi \filefig{../hmm/fig_hmarkov} } $$ - \caption{Reprsentation d'une chane de Markov sous forme de graphe.} + \caption{Repr�sentation d'une cha�ne de Markov sous forme de graphe.} \label{figure_chaine_markov_cachee_exemple-fig} - \indexfrr{chane de Markov}{graphe}% + \indexfrr{cha�ne de Markov}{graphe}% \end{figure} -On cherche alors calculer probabilit de la squence $1111$ qui correspond une srie de quatre "face". Si la squence d'observations est connue, il n'en est pas de mme pour la squence d'tats (ou pices) : il faut les envisager toutes et connatre la probabilit d'mettre une srie de quatre "face" pour chacune d'elles. La rponse ncessite d'abord de dfinir ce qu'est mathmatiquement une chane de Markov cache car le calcul n'est plus aussi vident que pour celui des chanes de Markov non caches. +On cherche alors � calculer probabilit� de la s�quence $1111$ qui correspond � une s�rie de quatre "face". Si la s�quence d'observations est connue, il n'en est pas de m�me pour la s�quence d'�tats (ou pi�ces) : il faut les envisager toutes et conna�tre la probabilit� d'�mettre une s�rie de quatre "face" pour chacune d'elles. La r�ponse n�cessite d'abord de d�finir ce qu'est math�matiquement une cha�ne de Markov cach�e car le calcul n'est plus aussi �vident que pour celui des cha�nes de Markov non cach�es. @@ -266,62 +266,62 @@ \subsection{Exemple : pi -\subsection{Dfinition d'une chane de Markov cache} +\subsection{D�finition d'une cha�ne de Markov cach�e} -Une chane de Markov cache part de l'ide que le processus stochatisque observ (la squence d'observations) est expliqu par un autre processus (la squence d'tats) qui est inconnu. +Une cha�ne de Markov cach�e part de l'id�e que le processus stochatisque observ� (la s�quence d'observations) est expliqu� par un autre processus (la s�quence d'�tats) qui est inconnu. - \begin{xdefinition} {chane de Markov cache} - \label{markov_chaine_cachee_definition}% - Soit $M$ une chane de Markov cache,\newline% - Soit $Q = \intervalle{1}{N}$ l'ensemble des tats,\newline% - Soit $S=\underset{T=1} {\overset{+\infty}{\cup}} Q^T$ l'espace des squences d'tats,\newline% - Soit $\mathcal{O} = \intervalle{1}{D}$ l'ensemble des observations,\newline% - Soit $\mathbf{O}=\underset{T=1} {\overset{+\infty}{\cup}} \mathcal{O}^T$ l'espace des squences d'observations,\newline% - On note $s = \pa{q_1,\dots,q_{T_s}} \in S$ une squence de longueur $T_s$,\newline% - Soit $O = \pa{O_1,\dots,O_{T_O}} \in \mathbf{O}$ une squence de longueur $T_O$,\newline% - Alors une chane de Markov cache est un modle probabiliste vrifiant les quatre conditions suivantes~: - - \begin{enumerate} - \item L'observation l'instant $t$ ne dpend que de l'tat l'instant $t$~: - - \indexfrr{probabilit}{transition} - \indexfrr{probabilit}{mission} - \indexfrr{probabilit}{entre} - - $$ - \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{1}{T_O}, \; - \pr{O_t|\overline{q_t},\overline{O_{t-1}},M} = \pr{O_t|q_t,M} - $$ - On appelle $\pr{O_t|q_t,M}$ la \emph{probabilit d'mission} de l'observation $O_t$ - sachant l'tat $q_t$ l'instant $t$. - \item Les probabilits d'missions ne dpendent pas du temps : - $$ - \forall s \in S \text{ telle que } T_s = T_O, \; - \forall \pa{t,t'} \in \intervalle{2}{T_s}, \; \pr{O_t|q_t,M} = \pr{O_{t'}|q_{t'},M} - $$ - \item L'tat l'instant $t$ ne dpend que de l'tat l'instant $t-1$~: - $$ - \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{2}{T_s}, \; - \pr{q_t| \overline{q_{t-1}},\overline{O_{t-1}},M} = \pr{q_t|q_{t-1},M} - $$ - On appelle $\pr{q_t|q_{t-1},M}$ la \emph{probabilit de transition} de l'tat $q_{t-1}$ l'tat $q_t$ l'instant $t$. - \item Les probabilits de transition ne dpendent pas du temps~: - $$ - \forall s \in S \text{ telle que } T_s = T_O, \; \forall \pa{t,t'} - \in \intervalle{2}{T_s}, \; \pr{q_t|q_{t-1},M} = \pr{q_{t'}|q_{t'-1},M} - $$ - \end{enumerate} - \end{xdefinition} + \begin{xdefinition} {cha�ne de Markov cach�e} + \label{markov_chaine_cachee_definition}% + Soit $M$ une cha�ne de Markov cach�e,\newline% + Soit $Q = \intervalle{1}{N}$ l'ensemble des �tats,\newline% + Soit $S=\underset{T=1} {\overset{+\infty}{\cup}} Q^T$ l'espace des s�quences d'�tats,\newline% + Soit $\mathcal{O} = \intervalle{1}{D}$ l'ensemble des observations,\newline% + Soit $\mathbf{O}=\underset{T=1} {\overset{+\infty}{\cup}} \mathcal{O}^T$ l'espace des s�quences d'observations,\newline% + On note $s = \pa{q_1,\dots,q_{T_s}} \in S$ une s�quence de longueur $T_s$,\newline% + Soit $O = \pa{O_1,\dots,O_{T_O}} \in \mathbf{O}$ une s�quence de longueur $T_O$,\newline% + Alors une cha�ne de Markov cach�e est un mod�le probabiliste v�rifiant les quatre conditions suivantes~: + + \begin{enumerate} + \item L'observation � l'instant $t$ ne d�pend que de l'�tat � l'instant $t$~: + + \indexfrr{probabilit�}{transition} + \indexfrr{probabilit�}{�mission} + \indexfrr{probabilit�}{entr�e} + + $$ + \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{1}{T_O}, \; + \pr{O_t|\overline{q_t},\overline{O_{t-1}},M} = \pr{O_t|q_t,M} + $$ + On appelle $\pr{O_t|q_t,M}$ la \emph{probabilit� d'�mission} de l'observation $O_t$ + sachant l'�tat $q_t$ � l'instant $t$. + \item Les probabilit�s d'�missions ne d�pendent pas du temps : + $$ + \forall s \in S \text{ telle que } T_s = T_O, \; + \forall \pa{t,t'} \in \intervalle{2}{T_s}, \; \pr{O_t|q_t,M} = \pr{O_{t'}|q_{t'},M} + $$ + \item L'�tat � l'instant $t$ ne d�pend que de l'�tat � l'instant $t-1$~: + $$ + \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{2}{T_s}, \; + \pr{q_t| \overline{q_{t-1}},\overline{O_{t-1}},M} = \pr{q_t|q_{t-1},M} + $$ + On appelle $\pr{q_t|q_{t-1},M}$ la \emph{probabilit� de transition} de l'�tat $q_{t-1}$ � l'�tat $q_t$ � l'instant $t$. + \item Les probabilit�s de transition ne d�pendent pas du temps~: + $$ + \forall s \in S \text{ telle que } T_s = T_O, \; \forall \pa{t,t'} + \in \intervalle{2}{T_s}, \; \pr{q_t|q_{t-1},M} = \pr{q_{t'}|q_{t'-1},M} + $$ + \end{enumerate} + \end{xdefinition} -Etant donn que ni les probabilits de transitions ni les probabilits d'missions ne dpendent du temps, on dfinit pour le modle $M$ $N$ tats, les matrices $A_M$, $B_M$ et le vecteur $\Pi_M$ par ~: +Etant donn� que ni les probabilit�s de transitions ni les probabilit�s d'�missions ne d�pendent du temps, on d�finit pour le mod�le $M$ � $N$ �tats, les matrices $A_M$, $B_M$ et le vecteur $\Pi_M$ par ~: -\indexfrr{probabilit}{transition} -\indexfrr{probabilit}{mission} -\indexfrr{probabilit}{entre} +\indexfrr{probabilit�}{transition} +\indexfrr{probabilit�}{�mission} +\indexfrr{probabilit�}{entr�e} \begin{eqnarray*} A_M &=& \pa { a_{M,ij} } _ {\substack{ 1\leqslant i\leqslant N\\1\leqslant j\leqslant N}} = @@ -330,32 +330,32 @@ \subsection{D B_M &=& \pa{ b_{M,ij} } _ {\substack{1\leqslant i\leqslant N\\1\leqslant j\leqslant D}}= \pa { \pr{ O_{t}=j \sac q_{t}=i,M } } _ {\substack{1\leqslant i\leqslant N\\1\leqslant j\leqslant D}} \label{hmm_contrainte_2}\\ - \Pi_M &=& \pa { \pi_{M,i} } _ { 1 \infegal i \infegal N } = \pa { \pr { q_1 = i \sac M} } _ { 1 \infegal i \infegal N } - \label{hmm_contrainte_3} + \Pi_M &=& \pa { \pi_{M,i} } _ { 1 \leqslant i \leqslant N } = \pa { \pr { q_1 = i \sac M} } _ { 1 \leqslant i \leqslant N } + \label{hmm_contrainte_3} \end{eqnarray*} -La dfinition d'une chane de Markov cache implique les contraintes suivantes sur les paramtres $A_M$, $B_M$, $\Pi_M$ -rsumes par la proprit suivante~: +La d�finition d'une cha�ne de Markov cach�e implique les contraintes suivantes sur les param�tres $A_M$, $B_M$, $\Pi_M$ +r�sum�es par la propri�t� suivante~: - \begin{xproperty}{contrainte} - \label{propriete_mmc_contrainte}% - La dfinition~\ref{markov_chaine_cachee_definition} et les notations dfinies en (\ref{hmm_contrainte_1}), - (\ref{hmm_contrainte_2}) et (\ref{hmm_contrainte_3}) impliquent que~: - - \begin{eqnarray*} - \forall i\in \ensemble{1}{N}, \; &&\summy{j=1}{N} \; a_{M,ij}=1 \text{ et } - \summy{j=1}{N} \; b_{M,ij}=1 \\ - &&\summy{i=1}{N} \; \Pi_{M,i} = 1 - \end{eqnarray*} - \end{xproperty} + \begin{xproperty}{contrainte} + \label{propriete_mmc_contrainte}% + La d�finition~\ref{markov_chaine_cachee_definition} et les notations d�finies en (\ref{hmm_contrainte_1}), + (\ref{hmm_contrainte_2}) et (\ref{hmm_contrainte_3}) impliquent que~: + + \begin{eqnarray*} + \forall i\in \ensemble{1}{N}, \; &&\summy{j=1}{N} \; a_{M,ij}=1 \text{ et } + \summy{j=1}{N} \; b_{M,ij}=1 \\ + &&\summy{i=1}{N} \; \Pi_{M,i} = 1 + \end{eqnarray*} + \end{xproperty} -Par abus de notation, lorsqu'il n'y a aucune ambigut, on note $A=A_M$, $B=B_M$, $\Pi=\Pi_M$. On cherche maintenant exprimer la probabilit d'une squence d'observations, soit $O \in \mathbf{O}$, on peut dornavant dfinir~: +Par abus de notation, lorsqu'il n'y a aucune ambigu�t�, on note $A=A_M$, $B=B_M$, $\Pi=\Pi_M$. On cherche maintenant � exprimer la probabilit� d'une s�quence d'observations, soit $O \in \mathbf{O}$, on peut dor�navant d�finir~: -\indexfrr{probabilit}{squence} -\indexfrr{squence}{observation} +\indexfrr{probabilit�}{s�quence} +\indexfrr{s�quence}{observation} \begin{eqnarray*} \pr{O \sac M} &=& \summyone{\begin{subarray}{c}s \in S \\ T_s = T_O \end{subarray}} @@ -364,8 +364,8 @@ \subsection{D \pr{\vecteurno{O_1}{O_{T_O}},\vecteurno{s_1}{s_{T_s}}|M} \end{eqnarray*} -En utilisant les hypothses de la dfinition~\ref{markov_chaine_cachee_definition}, on cherche exprimer cette probabilit - l'aide des paramtres $A$, $B$, $\Pi$ du modle $M$~: +En utilisant les hypoth�ses de la d�finition~\ref{markov_chaine_cachee_definition}, on cherche � exprimer cette probabilit� +� l'aide des param�tres $A$, $B$, $\Pi$ du mod�le $M$~: \begin{eqnarray} \pr{O|M} &=& \summyone{\begin{subarray}{c}s \in S \\ T_s = T_O \end{subarray}} @@ -381,8 +381,8 @@ \subsection{D \label{mmc_expression_proba_seq} \end{eqnarray} -Nanmoins, cette expression (\ref{mmc_expression_proba_seq}) suppose un calcul coteux en temps, -il est ncessaire de factoriser certaines oprations. +N�anmoins, cette expression (\ref{mmc_expression_proba_seq}) suppose un calcul co�teux en temps, +il est n�cessaire de factoriser certaines op�rations. \indexfr{factoriser} @@ -392,11 +392,11 @@ \subsection{D -\subsection{Calcul factoris de la probabilit d'une squence} +\subsection{Calcul factoris� de la probabilit� d'une s�quence} \label{hmm_alpha_definition_forward} -Soit $O=\vecteur{O_1}{O_T}$ une squence d'observations, les squences d'tats seront notes $s=\vecteur{q_1}{q_T}$. On pose pour $1\leqslant i\leqslant N$ et $1\leqslant t\leqslant T$ : +Soit $O=\vecteur{O_1}{O_T}$ une s�quence d'observations, les s�quences d'�tats seront not�es $s=\vecteur{q_1}{q_T}$. On pose pour $1\leqslant i\leqslant N$ et $1\leqslant t\leqslant T$ : \begin{eqnarray} \alpha_{t} \pa{i} = \alpha_{M,t} \pa{i} = \pr{ q_{t}=i,O_{1},...,O_{t} \sac M } \label{hmm_eq_alpha_1} @@ -413,7 +413,7 @@ \subsection{Calcul factoris \indexfr{forward} \indexfr{$\alpha_t\pa{.}$} -On tablit la rcurrence suivante sur $t$ et pour tout $j \in \ensemble{1}{N}$~: +On �tablit la r�currence suivante sur $t$ et pour tout $j \in \ensemble{1}{N}$~: \begin{eqnarray} \alpha_{t+1}\pa{j} &=& \pr{ q_{t+1}=j,O_{1},...,O_{t+1} \sac M } \nonumber\\ @@ -424,10 +424,10 @@ \subsection{Calcul factoris \alpha_{t+1}\pa{j} &=& b_{j}\pa{O_{t+1}} \; \summy{i=1}{N} \; \pr { q_{t+1}=j \sac q_{t}=i,O_{1},...,O_{t},M } \pr { q_{t}=i,O_{1},...,O_{t} \sac M } \nonumber\\ \alpha_{t+1}\pa{j} &=& b_{j}\pa{O_{t+1}} \; \summy{i=1}{N} \; a_{ij} \, \alpha_{t} \pa{i} - \label{mmc_alpha_forward_2}\label{hmm_eq_alpha_3} + \label{mmc_alpha_forward_2}\label{hmm_eq_alpha_3} \end{eqnarray} -Finalement, la probabilit de la squence est obtenue grce la suite $\alpha_t\pa{.}$ par un calcul appel \emph{forward}~: +Finalement, la probabilit� de la s�quence est obtenue gr�ce � la suite $\alpha_t\pa{.}$ par un calcul appel� \emph{forward}~: \indexfr{forward} @@ -435,49 +435,49 @@ \subsection{Calcul factoris \pr {O_{1},...,O_{T} \sac M} = \summy{i=1}{N} \alpha_{T}\pa{i}\label{hmm_eq_alpha_4} \end{eqnarray} -De ces formules, on tire l'algorithme suivant permettant de calculer la probabilit d'une squence d'observations. - - \begin{xalgorithm}{forward} \label{hmm_algo_forward} - Les notations utilises sont celles des formules (\ref{hmm_eq_alpha_1}), (\ref{hmm_eq_alpha_2}), - (\ref{hmm_eq_alpha_3}), (\ref{hmm_eq_alpha_4}). - - \begin{xalgostep}{initialisation} - \begin{xfor}{i}{1}{N} - $\alpha_1\pa{i} \longleftarrow \pi_{i}b_{i,O_{1}}$ - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{rcurrence} - \begin{xfor}{t}{2}{T} - \begin{xfor}{j}{1}{N} - $\alpha_{t}\pa{j} \longleftarrow 0$ \\ - \begin{xfor}{i}{1}{N} - $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} + a_{ij} \, \alpha_{t-1} \pa{i}$ - \end{xfor} \\ - $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} \; b_{j}\pa{O_{t+1}}$ - \end{xfor} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{terminaison} - $p \longleftarrow 0$ \\ - \begin{xfor}{i}{1}{N} - $p \longleftarrow p + \alpha_{T}\pa{i}$ - \end{xfor} - \end{xalgostep} - - La probabilit de la squence $\vecteur{O_1}{O_T}$ est $p$ obtenue la dernire tape. - - \end{xalgorithm} - - - - - - - - -De la mme manire, on dfinit pour $1\leqslant i\leqslant N$ et $1\leqslant t\leqslant T$ la suite~: +De ces formules, on tire l'algorithme suivant permettant de calculer la probabilit� d'une s�quence d'observations. + + \begin{xalgorithm}{forward} \label{hmm_algo_forward} + Les notations utilis�es sont celles des formules (\ref{hmm_eq_alpha_1}), (\ref{hmm_eq_alpha_2}), + (\ref{hmm_eq_alpha_3}), (\ref{hmm_eq_alpha_4}). + + \begin{xalgostep}{initialisation} + \begin{xfor}{i}{1}{N} + $\alpha_1\pa{i} \longleftarrow \pi_{i}b_{i,O_{1}}$ + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{r�currence} + \begin{xfor}{t}{2}{T} + \begin{xfor}{j}{1}{N} + $\alpha_{t}\pa{j} \longleftarrow 0$ \\ + \begin{xfor}{i}{1}{N} + $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} + a_{ij} \, \alpha_{t-1} \pa{i}$ + \end{xfor} \\ + $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} \; b_{j}\pa{O_{t+1}}$ + \end{xfor} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{terminaison} + $p \longleftarrow 0$ \\ + \begin{xfor}{i}{1}{N} + $p \longleftarrow p + \alpha_{T}\pa{i}$ + \end{xfor} + \end{xalgostep} + + La probabilit� de la s�quence $\vecteur{O_1}{O_T}$ est $p$ obtenue � la derni�re �tape. + + \end{xalgorithm} + + + + + + + + +De la m�me mani�re, on d�finit pour $1\leqslant i\leqslant N$ et $1\leqslant t\leqslant T$ la suite~: \indexfr{$\beta_t\pa{.}$} \indexfr{backward} @@ -486,14 +486,14 @@ \subsection{Calcul factoris \beta_{t}\pa{i} = \beta_{M,t}\pa{i} = \pr{ O_{t+1} ,...,O_{T} \sac q_{t}=i,M} \label{hmm_eq_beta_1} \end{eqnarray} -Par un calcul analogue (\ref{mmc_alpha_forward_1}) et (\ref{mmc_alpha_forward_2}), on obtient pour tout $i \in \ensemble{1}{N}$~: +Par un calcul analogue � (\ref{mmc_alpha_forward_1}) et (\ref{mmc_alpha_forward_2}), on obtient pour tout $i \in \ensemble{1}{N}$~: \begin{eqnarray} \beta_{T}\pa{i} &=& \pr{ \emptyset \sac q_{T}=i,M } = 1 \label{hmm_eq_beta_2}\\ \beta_{t}\pa{i} &=& \summy{j=1}{N} b_{j} \pa{O_{t+1}}\,a_{ij}\,\beta_{t+1}\pa{j} \label{hmm_eq_beta_3} \end{eqnarray} -Finalement, la probabilit de la squence est galement obtenue grce la suite $\beta_t\pa{.}$ par un calcul appel \emph{backward}~: +Finalement, la probabilit� de la s�quence est �galement obtenue gr�ce � la suite $\beta_t\pa{.}$ par un calcul appel� \emph{backward}~: \indexfr{backward} @@ -505,45 +505,45 @@ \subsection{Calcul factoris \indexfr{forward} \indexfr{$\beta_t\pa{.}$} \indexfr{$\alpha_t\pa{.}$} -\indexfr{cot} +\indexfr{co�t} -Les fonctions $\alpha_t\pa{.}$ et $\beta_t\pa{.}$ permettent de calculer la probabilit d'une squence avec un cot en $O\pa{TN^2}$ oprations. Ces calculs sont semblables des algorithmes de programmation dynamique et parfois appels algorithmes \emph{forward} ($\alpha_t\pa{.}$) et \emph{backward} ($\beta_t\pa{.}$) (\citeindex{Rabiner1986}). De ces formules, on tire l'algorithme suivant permettant de calculer la probabilit d'une squence d'observations. +Les fonctions $\alpha_t\pa{.}$ et $\beta_t\pa{.}$ permettent de calculer la probabilit� d'une s�quence avec un co�t en $O\pa{TN^2}$ op�rations. Ces calculs sont semblables � des algorithmes de programmation dynamique et parfois appel�s algorithmes \emph{forward} ($\alpha_t\pa{.}$) et \emph{backward} ($\beta_t\pa{.}$) (\citeindex{Rabiner1986}). De ces formules, on tire l'algorithme suivant permettant de calculer la probabilit� d'une s�quence d'observations. - \begin{xalgorithm}{backward} \label{hmm_algo_backward} - Les notations utilises sont celles des formules (\ref{hmm_eq_beta_1}), (\ref{hmm_eq_beta_2}), (\ref{hmm_eq_beta_3}), - (\ref{hmm_eq_beta_4}). - - \begin{xalgostep}{initialisation} - \begin{xfor}{i}{1}{N} - $\beta_T\pa{i} \longleftarrow 1$ - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{rcurrence} - \begin{xfor}{t}{T-1}{1} - \begin{xfor}{i}{1}{N} - $\beta_{t}\pa{j} \longleftarrow 0$ \\ - \begin{xfor}{j}{1}{N} - $\beta_{t}\pa{i} \longleftarrow \beta_{t}\pa{i} + a_{ij} - \, b_{j}\pa{O_{t+1}} \, \beta_{t+1} \pa{j}$ - \end{xfor} \\ - \end{xfor} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{terminaison} - $p \longleftarrow 0$ \\ - \begin{xfor}{i}{1}{N} - $p \longleftarrow p + \beta_{1}\pa{i} \, b_i\pa{O_1} \, \pi_i$ - \end{xfor} - \end{xalgostep} - - La probabilit de la squence $\vecteur{O_1}{O_T}$ est $p$ obtenue la dernire tape. - - \end{xalgorithm} - - + \begin{xalgorithm}{backward} \label{hmm_algo_backward} + Les notations utilis�es sont celles des formules (\ref{hmm_eq_beta_1}), (\ref{hmm_eq_beta_2}), (\ref{hmm_eq_beta_3}), + (\ref{hmm_eq_beta_4}). + + \begin{xalgostep}{initialisation} + \begin{xfor}{i}{1}{N} + $\beta_T\pa{i} \longleftarrow 1$ + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{r�currence} + \begin{xfor}{t}{T-1}{1} + \begin{xfor}{i}{1}{N} + $\beta_{t}\pa{j} \longleftarrow 0$ \\ + \begin{xfor}{j}{1}{N} + $\beta_{t}\pa{i} \longleftarrow \beta_{t}\pa{i} + a_{ij} + \, b_{j}\pa{O_{t+1}} \, \beta_{t+1} \pa{j}$ + \end{xfor} \\ + \end{xfor} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{terminaison} + $p \longleftarrow 0$ \\ + \begin{xfor}{i}{1}{N} + $p \longleftarrow p + \beta_{1}\pa{i} \, b_i\pa{O_1} \, \pi_i$ + \end{xfor} + \end{xalgostep} + + La probabilit� de la s�quence $\vecteur{O_1}{O_T}$ est $p$ obtenue � la derni�re �tape. + + \end{xalgorithm} + + @@ -554,11 +554,11 @@ \subsection{Calcul factoris -\subsection{Autres rsultats intressants} +\subsection{Autres r�sultats int�ressants} -Trois autres rsultats intressants utiliss lors de l'apprentissage (paragraphe~\ref{par_apprentissage_hmm}) -peuvent tre obtenus de manire similaire~: +Trois autres r�sultats int�ressants utilis�s lors de l'apprentissage (paragraphe~\ref{par_apprentissage_hmm}) +peuvent �tre obtenus de mani�re similaire~: \label{hmm_probabilite_etat_transition_posteriori}% @@ -577,10 +577,10 @@ \subsection{Autres r -\subsection{Retour l'exemple} -\indexfrr{exemple}{pice de monnaie} +\subsection{Retour � l'exemple} +\indexfrr{exemple}{pi�ce de monnaie} -Rappel des probabilits de transitions et d'mission~:% +Rappel des probabilit�s de transitions et d'�mission~:% $$ \begin{array}[c]{ccccc}% @@ -596,15 +596,15 @@ \subsection{Retour \end{array} $$ -On cherche calculer la probabilite de la squence 1111 :% +On cherche � calculer la probabilit�e de la s�quence 1111 :% $$ \begin{array}[c]{c}% \frame{$ \begin{array}[c]{ccccc}% & \alpha_{1}\left( .\right) & \alpha_{2}\left( .\right) & \alpha_{3}\left( .\right) & \alpha_{4}\left( .\right) \\ - \text{tat}\,1 & 0,15 & 0,094 & 0,04099 & 0,0174454\\ - \text{tat}\,2 & 0,25 & 0,085 & 0,03535 & 0,014986 + \text{�tat}\,1 & 0,15 & 0,094 & 0,04099 & 0,0174454\\ + \text{�tat}\,2 & 0,25 & 0,085 & 0,03535 & 0,014986 \end{array} $} \\ @@ -615,7 +615,7 @@ \subsection{Retour \end{array} $$ -Les lois limites des observations peuvent galement tre obtenus : +Les lois limites des observations peuvent �galement �tre obtenus : \begin{eqnarray*} \pr{O=k|t} &=& \pr{O=k|q=1,t}\pr{q=1|t} + \pr{O=k|q=2,t}\pr{q=2|t} \\ @@ -634,7 +634,7 @@ \subsection{Retour \end{array} $$ -On en dduit que : +On en d�duit que : $$ \begin{array}[c]{c}% @@ -649,152 +649,152 @@ \subsection{Retour -\subsection{Introduction d'un tat d'entre et d'un tat de sortie} +\subsection{Introduction d'un �tat d'entr�e et d'un �tat de sortie} \label{hmm_intro_entree_sortie} -En reconnaissance de l'criture, les squences d'observations ne dpassent pas quelques graphmes par lettres~: toutes les squences d'observations sont finies, or cette information supplmentaire n'est pas prise en compte dans les chanes de Markov caches prsentes jusqu' prsent. L'introduction d'un tat d'entre et d'un tat de sortie va y remdier afin de signifier la fin de la squence (voir \citeindex{Chen1994}). La figure~\ref{figure_model_optimaux_M-fig} montre les deux modles optimaux (avec ou sans tat de sortie) pour la lettre "M". Le dessin de cette lettre fait intervenir trois graphmes identiques. Le premier modle (1) sans tat de sortie ne peut prendre en compte la "dure" de la lettre "M", des squences de deux, trois, cent graphmes auront toutes la mme probabilit. Le second modle (2) ne permet qu'une seule criture de la lettre "M" en trois graphmes. Tous les tats de la chane de Markov sont des tats \emph{metteurs} (voir dfinition~\ref{definition_etat_emetteur}) car chaque observation est associ un tat, les tats d'entres et de sortie sont \emph{non metteurs} (voir dfinition~\ref{definition_etat_non_emetteur}). +En reconnaissance de l'�criture, les s�quences d'observations ne d�passent pas quelques graph�mes par lettres~: toutes les s�quences d'observations sont finies, or cette information suppl�mentaire n'est pas prise en compte dans les cha�nes de Markov cach�es pr�sent�es jusqu'� pr�sent. L'introduction d'un �tat d'entr�e et d'un �tat de sortie va y rem�dier afin de signifier la fin de la s�quence (voir \citeindex{Chen1994}). La figure~\ref{figure_model_optimaux_M-fig} montre les deux mod�les optimaux (avec ou sans �tat de sortie) pour la lettre "M". Le dessin de cette lettre fait intervenir trois graph�mes identiques. Le premier mod�le (1) sans �tat de sortie ne peut prendre en compte la "dur�e" de la lettre "M", des s�quences de deux, trois, cent graph�mes auront toutes la m�me probabilit�. Le second mod�le (2) ne permet qu'une seule �criture de la lettre "M" en trois graph�mes. Tous les �tats de la cha�ne de Markov sont des �tats \emph{�metteurs} (voir d�finition~\ref{definition_etat_emetteur}) car chaque observation est associ� un �tat, les �tats d'entr�es et de sortie sont \emph{non �metteurs} (voir d�finition~\ref{definition_etat_non_emetteur}). - \begin{figure}[t] + \begin{figure}[t] $$\frame{$\begin{array}[c|c]{c}\includegraphics[height=9cm, width=15cm] {\filext{../dessin2/chaine_markov_etat_sortie}}\end{array}$}$$ - \caption{Modles optimaux pour la lettre "M" avec et sans tat de sortie} + \caption{Mod�les optimaux pour la lettre "M" avec et sans �tat de sortie} \label{figure_model_optimaux_M-fig} - \end{figure} - -\indexfrr{tat}{metteur} -\indexfrr{tat}{non metteur} -\indexfrr{tat}{muet} -\indexfrr{tat}{entre} -\indexfrr{tat}{sortie} - - - - \begin{xdefinition}{tat metteur} - \label{definition_etat_emetteur}% - \indexfrr{tat}{metteur}% - Un tat d'une chane de Markov cache est dit \emph{metteur} si le passage par cet tat implique - l'mission d'une observation. Par dfinition, pour une squence d'observations $O$ de longueur $T$, - toutes les squences d'tats cachs permises pour cette squence $O$ contiennent exactement $T$ tats metteurs. - \end{xdefinition} - - - \begin{xdefinition}{tat non metteur} - \label{definition_etat_non_emetteur}% - \indexfrr{tat}{non metteur} - \indexsee{tat}{muet} - Un tat d'une chane de Markov cache est dit \emph{non metteur} (ou \emph{muet}) - si le passage par cet tat n'implique - aucune mission d'observation. Par dfinition, pour toute squence d'observations, - une squence d'tats cachs peut - contenir une infinit d'tats non metteurs. - \end{xdefinition} - - - - \begin{xdefinition}{chane de Markov cache, entre et sortie (ES)} - \label{markov_chaine_cachee_definition_es}% - Soit $M$ une chane de Markov cache (ES),\newline% - Soit $Q = \intervalle{1}{N}$ l'ensemble des tats,\newline% - Soit $S=\underset{T=1} {\overset{+\infty}{\cup}} Q^T$ l'espace des squences d'tats,\newline% - Soit $\mathcal{O} = \intervalle{1}{D}$ l'ensemble des observations,\newline% - Soit $\mathbf{O}=\underset{T=1} {\overset{+\infty}{\cup}} \mathcal{O}^T$ l'espace des squences d'observations,\newline% - On note $s = \pa{q_1,\dots,q_{T_s}} \in S$ une squence de longueur $T_s$,\newline% - Soit $O = \pa{O_1,\dots,O_{T_O}} \in \mathbf{O}$ une squence de longueur $T_O$,\newline% - Alors une chane de Markov cache est un modle probabiliste vrifiant les quatre conditions suivantes~: - \begin{enumerate} - \indexfrr{probabilit}{transition} - \indexfrr{probabilit}{mission} - \indexfrr{probabilit}{entre} - \indexfrr{probabilit}{sortie} - \item L'observation l'instant $t$ ne dpend que de l'tat l'instant $t$~: - $$ - \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{1}{T_O}, \; - \pr{O_t|\overline{q_t},\overline{O_{t-1}},M} = \pr{O_t|q_t,M} - $$ - On appelle $\pr{O_t|q_t,M}$ la \emph{probabilit d'mission} de l'observation $O_t$ - sachant l'tat $q_t$ l'instant $t$. - - \item Les probabilits d'missions ne dpendent pas du temps : - $$ - \forall s \in S \text{ telle que } T_s = T_O, \; - \forall \pa{t,t'} \in \intervalle{2}{T_s}, \; \pr{O_t|q_t,M} = \pr{O_{t'}|q_{t'},M} - $$ - - \item L'tat l'instant $t$ ou la sortie ne dpend que de l'tat l'instant $t-1$~: - \begin{eqnarray*} - \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{2}{T_s}, \; && - \pr{q_t| \overline{q_{t-1}},\overline{O_{t-1}},M} = \pr{q_t|q_{t-1},M} \\ - \forall s \in S \text{ telle que } T_s = T_O, \forall t \in \intervalle{2}{T_s}, \; && - \pr{ sortie | \overline{q_{t-1}},\overline{O_{t-1}},M} = \pr{sortie |q_{t-1},M} - \end{eqnarray*} - - On appelle $\pr{q_t|q_{t-1},M}$ la probabilit de transition de l'tat $q_{t-1}$ l'tat $q_t$ l'instant $t$ et - $\pr{sortie|q_{t-1},M} = \pr{s|q_{t-1},M}$ la \emph{probabilit de sortie} l'instant $t-1$. - - \item Les probabilits de transition et de sortie ne dpendent pas du temps~: - \begin{eqnarray*} - \forall s \in S \text{ telle que } T_s = T_O, \; \forall \pa{t,t'} \in \intervalle{2}{T_s}, && - \pr{q_t|q_{t-1},M} = \pr{q_{t'}|q_{t'-1},M} \\ - \forall s \in S \text{ telle que } T_s = T_O, \; \forall \pa{t,t'} \in \intervalle{2}{T_s}, && - \pr{sortie|q_{t-1},M} = \pr{sortie|q_{t'-1},M} - \end{eqnarray*} - - \end{enumerate} - - \end{xdefinition} - - - -La chane de Markov cache (ES) $M$ $N$ tats est dfinie par les paramtres $A_M$, $B_M$, $\Pi_M$, $\Theta_M$~: + \end{figure} + +\indexfrr{�tat}{�metteur} +\indexfrr{�tat}{non �metteur} +\indexfrr{�tat}{muet} +\indexfrr{�tat}{entr�e} +\indexfrr{�tat}{sortie} + + + + \begin{xdefinition}{�tat �metteur} + \label{definition_etat_emetteur}% + \indexfrr{�tat}{�metteur}% + Un �tat d'une cha�ne de Markov cach�e est dit \emph{�metteur} si le passage par cet �tat implique + l'�mission d'une observation. Par d�finition, pour une s�quence d'observations $O$ de longueur $T$, + toutes les s�quences d'�tats cach�s permises pour cette s�quence $O$ contiennent exactement $T$ �tats �metteurs. + \end{xdefinition} + + + \begin{xdefinition}{�tat non �metteur} + \label{definition_etat_non_emetteur}% + \indexfrr{�tat}{non �metteur} + \indexsee{�tat}{muet} + Un �tat d'une cha�ne de Markov cach�e est dit \emph{non �metteur} (ou \emph{muet}) + si le passage par cet �tat n'implique + aucune �mission d'observation. Par d�finition, pour toute s�quence d'observations, + une s�quence d'�tats cach�s peut + contenir une infinit� d'�tats non �metteurs. + \end{xdefinition} + + + + \begin{xdefinition}{cha�ne de Markov cach�e, entr�e et sortie (ES)} + \label{markov_chaine_cachee_definition_es}% + Soit $M$ une cha�ne de Markov cach�e (ES),\newline% + Soit $Q = \intervalle{1}{N}$ l'ensemble des �tats,\newline% + Soit $S=\underset{T=1} {\overset{+\infty}{\cup}} Q^T$ l'espace des s�quences d'�tats,\newline% + Soit $\mathcal{O} = \intervalle{1}{D}$ l'ensemble des observations,\newline% + Soit $\mathbf{O}=\underset{T=1} {\overset{+\infty}{\cup}} \mathcal{O}^T$ l'espace des s�quences d'observations,\newline% + On note $s = \pa{q_1,\dots,q_{T_s}} \in S$ une s�quence de longueur $T_s$,\newline% + Soit $O = \pa{O_1,\dots,O_{T_O}} \in \mathbf{O}$ une s�quence de longueur $T_O$,\newline% + Alors une cha�ne de Markov cach�e est un mod�le probabiliste v�rifiant les quatre conditions suivantes~: + \begin{enumerate} + \indexfrr{probabilit�}{transition} + \indexfrr{probabilit�}{�mission} + \indexfrr{probabilit�}{entr�e} + \indexfrr{probabilit�}{sortie} + \item L'observation � l'instant $t$ ne d�pend que de l'�tat � l'instant $t$~: + $$ + \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{1}{T_O}, \; + \pr{O_t|\overline{q_t},\overline{O_{t-1}},M} = \pr{O_t|q_t,M} + $$ + On appelle $\pr{O_t|q_t,M}$ la \emph{probabilit� d'�mission} de l'observation $O_t$ + sachant l'�tat $q_t$ � l'instant $t$. + + \item Les probabilit�s d'�missions ne d�pendent pas du temps : + $$ + \forall s \in S \text{ telle que } T_s = T_O, \; + \forall \pa{t,t'} \in \intervalle{2}{T_s}, \; \pr{O_t|q_t,M} = \pr{O_{t'}|q_{t'},M} + $$ + + \item L'�tat � l'instant $t$ ou la sortie ne d�pend que de l'�tat � l'instant $t-1$~: + \begin{eqnarray*} + \forall s \in S \text{ telle que } T_s = T_O, \; \forall t \in \intervalle{2}{T_s}, \; && + \pr{q_t| \overline{q_{t-1}},\overline{O_{t-1}},M} = \pr{q_t|q_{t-1},M} \\ + \forall s \in S \text{ telle que } T_s = T_O, \forall t \in \intervalle{2}{T_s}, \; && + \pr{ sortie | \overline{q_{t-1}},\overline{O_{t-1}},M} = \pr{sortie |q_{t-1},M} + \end{eqnarray*} + + On appelle $\pr{q_t|q_{t-1},M}$ la probabilit� de transition de l'�tat $q_{t-1}$ � l'�tat $q_t$ � l'instant $t$ et + $\pr{sortie|q_{t-1},M} = \pr{s|q_{t-1},M}$ la \emph{probabilit� de sortie} � l'instant $t-1$. + + \item Les probabilit�s de transition et de sortie ne d�pendent pas du temps~: + \begin{eqnarray*} + \forall s \in S \text{ telle que } T_s = T_O, \; \forall \pa{t,t'} \in \intervalle{2}{T_s}, && + \pr{q_t|q_{t-1},M} = \pr{q_{t'}|q_{t'-1},M} \\ + \forall s \in S \text{ telle que } T_s = T_O, \; \forall \pa{t,t'} \in \intervalle{2}{T_s}, && + \pr{sortie|q_{t-1},M} = \pr{sortie|q_{t'-1},M} + \end{eqnarray*} + + \end{enumerate} + + \end{xdefinition} + + + +La cha�ne de Markov cach�e (ES) $M$ � $N$ �tats est d�finie par les param�tres $A_M$, $B_M$, $\Pi_M$, $\Theta_M$~: \begin{eqnarray} A_M &=& \pa { a_{M,ij} } _ {\substack{ 1\leqslant i\leqslant N\\1\leqslant j\leqslant N}} = \pa{ \pr { q_{t}=j \sac q_{t-1} =i,M } } _{\substack{1\leqslant i\leqslant N\\ - 1\leqslant j\leqslant N}} \label{hmm_contrainte_es_1}\\ + 1\leqslant j\leqslant N}} \label{hmm_contrainte_es_1}\\ B_M &=& \pa{ b_{M,ij} } _ {\substack{1\leqslant i\leqslant N\\1\leqslant j\leqslant D}}= \pa { \pr{ O_{t}=j \sac q_{t}=i,M } } _ {\substack{1\leqslant i\leqslant N - \\1\leqslant j\leqslant D}} \label{hmm_contrainte_es_2}\\ - \Pi_M &=& \pa { \pi_{M,i} } _ { 1 \infegal i \infegal N } = \pa { \pr { q_1 = i \sac M} } _ - { 1 \infegal i \infegal N } \label{hmm_contrainte_es_3} \\ - \Theta_M &=& \pa { \theta_{M,i} } _ { 1 \infegal i \infegal N } = \pa { \pr { s \sac q_{t}, M} } _ - { 1 \infegal i \infegal N } \label{hmm_contrainte_es_4} + \\1\leqslant j\leqslant D}} \label{hmm_contrainte_es_2}\\ + \Pi_M &=& \pa { \pi_{M,i} } _ { 1 \leqslant i \leqslant N } = \pa { \pr { q_1 = i \sac M} } _ + { 1 \leqslant i \leqslant N } \label{hmm_contrainte_es_3} \\ + \Theta_M &=& \pa { \theta_{M,i} } _ { 1 \leqslant i \leqslant N } = \pa { \pr { s \sac q_{t}, M} } _ + { 1 \leqslant i \leqslant N } \label{hmm_contrainte_es_4} \end{eqnarray} -La dfinition d'une chane de Markov cache (ES) implique les contraintes suivantes sur les paramtres $A_M$, $B_M$, $\Pi_M$, $\Theta_M$ -rsumes par la proprit suivante~: +La d�finition d'une cha�ne de Markov cach�e (ES) implique les contraintes suivantes sur les param�tres $A_M$, $B_M$, $\Pi_M$, $\Theta_M$ +r�sum�es par la propri�t� suivante~: - \begin{xproperty}{contrainte} - \label{propriete_mmc_contrainte_es}% - La dfintion~\ref{markov_chaine_cachee_definition_es} et les notations dfinies en (\ref{hmm_contrainte_es_1}), - (\ref{hmm_contrainte_es_2}), (\ref{hmm_contrainte_es_3}) et (\ref{hmm_contrainte_es_4}) impliquent que~: - - \begin{eqnarray} - \forall i\in \ensemble{1}{N}, \; && \summy{j=1}{N} \; a_{M,ij} + \theta_{M,i} =1 \\ - \forall i\in \ensemble{1}{N}, \; &&\summy{j=1}{N} \; b_{M,ij}=1 \\ - &&\summy{i=1}{N} \; \Pi_{M,i} = 1 - \end{eqnarray} - \end{xproperty} - - + \begin{xproperty}{contrainte} + \label{propriete_mmc_contrainte_es}% + La d�fintion~\ref{markov_chaine_cachee_definition_es} et les notations d�finies en (\ref{hmm_contrainte_es_1}), + (\ref{hmm_contrainte_es_2}), (\ref{hmm_contrainte_es_3}) et (\ref{hmm_contrainte_es_4}) impliquent que~: + + \begin{eqnarray} + \forall i\in \ensemble{1}{N}, \; && \summy{j=1}{N} \; a_{M,ij} + \theta_{M,i} =1 \\ + \forall i\in \ensemble{1}{N}, \; &&\summy{j=1}{N} \; b_{M,ij}=1 \\ + &&\summy{i=1}{N} \; \Pi_{M,i} = 1 + \end{eqnarray} + \end{xproperty} + + -En utilisant les hypothses de la dfinition~\ref{markov_chaine_cachee_definition}, on cherche exprimer la probabilit d'une squence -d'observations l'aide des paramtres $A=A_M$, $B=B_M$, $\Pi=\pi_M$, $\Theta=\Theta_M$ du modle $M$~: +En utilisant les hypoth�ses de la d�finition~\ref{markov_chaine_cachee_definition}, on cherche � exprimer la probabilit� d'une s�quence +d'observations � l'aide des param�tres $A=A_M$, $B=B_M$, $\Pi=\pi_M$, $\Theta=\Theta_M$ du mod�le $M$~: \begin{eqnarray} \pr{O|M} &=& \summyone{\begin{subarray}{c}s \in S \\ T_s = T_O \end{subarray}} \pr{O,s|M} \nonumber\\ \pr{O|M} &=& \summyone{\begin{subarray}{c}s \in S \\ T_s = T_O \end{subarray}} \crochet{ \pi_{s_1} \theta_{s_T} \prody{t=2}{T_O}a_{s_{t-1},s_t} - \prody{t=1}{T_O} b_{q_t}\pa{O_t} } \label{mmc_expression_proba_seq_es} + \prody{t=1}{T_O} b_{q_t}\pa{O_t} } \label{mmc_expression_proba_seq_es} \end{eqnarray} -Le calcul factoris de cette probabilit est aussi modifi, les suites $\alpha_t\pa{.}$ et $\beta_t\pa{.}$ deviennent~: +Le calcul factoris� de cette probabilit� est aussi modifi�, les suites $\alpha_t\pa{.}$ et $\beta_t\pa{.}$ deviennent~: \indexfr{factoriser} \indexfr{$\alpha_t\pa{.}$} @@ -823,13 +823,13 @@ \subsection{Introduction d'un \label{hmm_eq_alpha_es_2} \end{eqnarray} -Par rcurrence : +Par r�currence : \begin{eqnarray} \alpha_{t+1} \pa{j} = b_{j,O_{t+1}} \summy{i=1}{N} a_{ij}\alpha_{t}\pa{i} \label{hmm_eq_alpha_es_3} \end{eqnarray} -Seule change la probabilit de la squence~: +Seule change la probabilit� de la s�quence~: \begin{eqnarray} \pr{ O_{1},...,O_{T} \sac M} = \summy{i=1}{N} \; \alpha_{T} \pa{i} \,\theta_{i} \label{hmm_eq_alpha_es_4} @@ -838,41 +838,41 @@ \subsection{Introduction d'un L'algorithme~\ref{hmm_algo_forward} devient le suivant~: - \begin{xalgorithm}{forward} \label{hmm_algo_forward_es} - Les notations utilises sont celles des formules (\ref{hmm_eq_alpha_es_1}), (\ref{hmm_eq_alpha_es_2}), - (\ref{hmm_eq_alpha_es_3}), (\ref{hmm_eq_alpha_es_4}). - - \begin{xalgostep}{initialisation} - \begin{xfor}{i}{1}{N} - $\alpha_1\pa{i} \longleftarrow \pi_{i}b_{i,O_{1}}$ - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{rcurrence} - \begin{xfor}{t}{2}{T} - \begin{xfor}{j}{1}{N} - $\alpha_{t}\pa{j} \longleftarrow 0$ \\ - \begin{xfor}{i}{1}{N} - $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} + a_{ij} \, \alpha_{t-1} \pa{i}$ - \end{xfor} \\ - $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} \; b_{j}\pa{O_{t+1}}$ - \end{xfor} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{terminaison} - $p \longleftarrow 0$ \\ - \begin{xfor}{i}{1}{N} - $p \longleftarrow p + \alpha_{T}\pa{i} \; \theta_i$ - \end{xfor} - \end{xalgostep} - - La probabilit de la squence $\vecteur{O_1}{O_T}$ est $p$ obtenue la dernire tape. - - \end{xalgorithm} - - - + \begin{xalgorithm}{forward} \label{hmm_algo_forward_es} + Les notations utilis�es sont celles des formules (\ref{hmm_eq_alpha_es_1}), (\ref{hmm_eq_alpha_es_2}), + (\ref{hmm_eq_alpha_es_3}), (\ref{hmm_eq_alpha_es_4}). + + \begin{xalgostep}{initialisation} + \begin{xfor}{i}{1}{N} + $\alpha_1\pa{i} \longleftarrow \pi_{i}b_{i,O_{1}}$ + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{r�currence} + \begin{xfor}{t}{2}{T} + \begin{xfor}{j}{1}{N} + $\alpha_{t}\pa{j} \longleftarrow 0$ \\ + \begin{xfor}{i}{1}{N} + $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} + a_{ij} \, \alpha_{t-1} \pa{i}$ + \end{xfor} \\ + $\alpha_{t}\pa{j} \longleftarrow \alpha_{t}\pa{j} \; b_{j}\pa{O_{t+1}}$ + \end{xfor} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{terminaison} + $p \longleftarrow 0$ \\ + \begin{xfor}{i}{1}{N} + $p \longleftarrow p + \alpha_{T}\pa{i} \; \theta_i$ + \end{xfor} + \end{xalgostep} + + La probabilit� de la s�quence $\vecteur{O_1}{O_T}$ est $p$ obtenue � la derni�re �tape. + + \end{xalgorithm} + + + @@ -896,11 +896,11 @@ \subsection{Introduction d'un \begin{eqnarray} \begin{array}{l} \beta_{T}\pa{i} = \pr{ \emptyset \sac q_{T}=i,M} = \theta_{i} \\ - \qquad(=\text{probabilit que la squence soit finie sachant que }q_{T}=i) + \qquad(=\text{probabilit� que la s�quence soit finie sachant que }q_{T}=i) \end{array} \label{hmm_eq_beta_es_2} \end{eqnarray} -Par rcurrence : +Par r�currence : \begin{eqnarray} \beta_{t} \pa{i} = \summy{j=1}{N} \, b_{j}\pa{O_{t+1}} \, a_{ij} \, \beta_{t+1}\pa{j} \label{hmm_eq_beta_es_3} @@ -915,41 +915,41 @@ \subsection{Introduction d'un L'algorithme~\ref{hmm_algo_backward} devient le suivant~: - \begin{xalgorithm}{backward} \label{hmm_algo_backward_es} - Les notations utilises sont celles des formules (\ref{hmm_eq_beta_es_1}), (\ref{hmm_eq_beta_es_2}), (\ref{hmm_eq_beta_es_3}), - (\ref{hmm_eq_beta_es_4}). - - \begin{xalgostep}{initialisation} - \begin{xfor}{i}{1}{N} - $\beta_T\pa{i} \longleftarrow \theta_i$ - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{rcurrence} - \begin{xfor}{t}{T-1}{1} - \begin{xfor}{i}{1}{N} - $\beta_{t}\pa{j} \longleftarrow 0$ \\ - \begin{xfor}{j}{1}{N} - $\beta_{t}\pa{i} \longleftarrow \beta_{t}\pa{i} + a_{ij} \, b_{j}\pa{O_{t+1}} \, \beta_{t+1} \pa{j}$ - \end{xfor} \\ - \end{xfor} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{terminaison} - $p \longleftarrow 0$ \\ - \begin{xfor}{i}{1}{N} - $p \longleftarrow p + \beta_{1}\pa{i} \, b_i\pa{O_1} \, \pi_i $ - \end{xfor} - \end{xalgostep} - - La probabilit de la squence $\vecteur{O_1}{O_T}$ est $p$ obtenue la dernire tape. - - \end{xalgorithm} - + \begin{xalgorithm}{backward} \label{hmm_algo_backward_es} + Les notations utilis�es sont celles des formules (\ref{hmm_eq_beta_es_1}), (\ref{hmm_eq_beta_es_2}), (\ref{hmm_eq_beta_es_3}), + (\ref{hmm_eq_beta_es_4}). + + \begin{xalgostep}{initialisation} + \begin{xfor}{i}{1}{N} + $\beta_T\pa{i} \longleftarrow \theta_i$ + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{r�currence} + \begin{xfor}{t}{T-1}{1} + \begin{xfor}{i}{1}{N} + $\beta_{t}\pa{j} \longleftarrow 0$ \\ + \begin{xfor}{j}{1}{N} + $\beta_{t}\pa{i} \longleftarrow \beta_{t}\pa{i} + a_{ij} \, b_{j}\pa{O_{t+1}} \, \beta_{t+1} \pa{j}$ + \end{xfor} \\ + \end{xfor} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{terminaison} + $p \longleftarrow 0$ \\ + \begin{xfor}{i}{1}{N} + $p \longleftarrow p + \beta_{1}\pa{i} \, b_i\pa{O_1} \, \pi_i $ + \end{xfor} + \end{xalgostep} + + La probabilit� de la s�quence $\vecteur{O_1}{O_T}$ est $p$ obtenue � la derni�re �tape. + + \end{xalgorithm} + - -Par la suite, toutes les chanes de Markov seront supposes possder un tat d'entre et un tat de sortie. + +Par la suite, toutes les cha�nes de Markov seront suppos�es poss�der un �tat d'entr�e et un �tat de sortie. @@ -961,14 +961,14 @@ \subsection{Introduction d'un -\subsection{Reprsentation d'une chane de Markov sous forme de graphe} +\subsection{Repr�sentation d'une cha�ne de Markov sous forme de graphe} \indexfr{graphe}% \indexfrr{transition}{nulles}% \indexfrr{connexion}{transition} \label{hmm_representation_graphe}% -Les paragraphes prcdents ont dj montr qu'il tait possible de modliser une chane de Markov sous forme de graphe o les noeuds sont les tats et les transitions les arcs. Une probabilit non nulle de passer d'un tat $i$ un autre tat $j$ peut tre envisage comme un lien unidirectionnel entre ces deux tats dont le poids est la probabilit de transition de l'tat $i$ vers l'tat $j$. L'ensemble des probabilits non nulles d'un modle dfinit un ensemble de liens entre les tats qui peut tre dcrit par un graphe. Un modle entirement connect de $N$ tats contient $N^{2}+2N$ connexions. Une structure de graphe permet de diminuer ce nombre de connexions en ne tenant compte que des connexions non nulles (les modles utiliss pour la reconnaissance de l'criture contiennent en gnral une grande part de connexions nulles.), voir figures~\ref{figure_rn_graphe_trans_un-fig}, \ref{figure_rn_graphe_trans_deux-fig}. +Les paragraphes pr�c�dents ont d�j� montr� qu'il �tait possible de mod�liser une cha�ne de Markov sous forme de graphe o� les noeuds sont les �tats et les transitions les arcs. Une probabilit� non nulle de passer d'un �tat $i$ � un autre �tat $j$ peut �tre envisag�e comme un lien unidirectionnel entre ces deux �tats dont le poids est la probabilit� de transition de l'�tat $i$ vers l'�tat $j$. L'ensemble des probabilit�s non nulles d'un mod�le d�finit un ensemble de liens entre les �tats qui peut �tre d�crit par un graphe. Un mod�le enti�rement connect� de $N$ �tats contient $N^{2}+2N$ connexions. Une structure de graphe permet de diminuer ce nombre de connexions en ne tenant compte que des connexions non nulles (les mod�les utilis�s pour la reconnaissance de l'�criture contiennent en g�n�ral une grande part de connexions nulles.), voir figures~\ref{figure_rn_graphe_trans_un-fig}, \ref{figure_rn_graphe_trans_deux-fig}. \begin{figure}[ht] @@ -989,7 +989,7 @@ \subsection{Repr \small Matrice de transition & $\longleftrightarrow$ & Graphe de transition \\ \hline \end{tabular} $$ - \caption{Equivalence entre matrice de transition et graphe de transition pour une chane de Markov.} + \caption{Equivalence entre matrice de transition et graphe de transition pour une cha�ne de Markov.} \label{figure_rn_graphe_trans_un-fig} \end{figure} @@ -1015,7 +1015,7 @@ \subsection{Repr \end{tabular} $$ \caption{Equivalence entre matrice de transition et graphe de transition - pour une chane de Markov ES.} + pour une cha�ne de Markov ES.} \indexfrr{graphe}{transition} \indexfrr{matrice}{transition} \label{figure_rn_graphe_trans_deux-fig} @@ -1024,12 +1024,12 @@ \subsection{Repr -La reconnaissance de l'criture utilise peu de modles ergodiques (ou entirement connects) car le sens de la lecture interdit de revenir un tat dj visit. Par consquent, les matrices de connexions sont triangulaires suprieures avec des zros sur la diagonale. En dfinitive, il existe peu de connexions non nulles par rapport toutes celles qui sont possibles. Le tableau~\ref{table_connexion_nulle-tab} (page~\pageref{table_connexion_nulle-tab}) montre que, en gnral, seules 10\% des connexions possibles sont non nulles~: la description sous forme de graphe de ces chanes de Markov cache est plus avantagueuse qu'une description matricielle. Ce rsultat est bien sr propre la reconnaissance de l'criture manuscrite. +La reconnaissance de l'�criture utilise peu de mod�les ergodiques (ou enti�rement connect�s) car le sens de la lecture interdit de revenir � un �tat d�j� visit�. Par cons�quent, les matrices de connexions sont triangulaires sup�rieures avec des z�ros sur la diagonale. En d�finitive, il existe peu de connexions non nulles par rapport � toutes celles qui sont possibles. Le tableau~\ref{table_connexion_nulle-tab} (page~\pageref{table_connexion_nulle-tab}) montre que, en g�n�ral, seules 10\% des connexions possibles sont non nulles~: la description sous forme de graphe de ces cha�nes de Markov cach�e est plus avantagueuse qu'une description matricielle. Ce r�sultat est bien s�r propre � la reconnaissance de l'�criture manuscrite. - \begin{table}[t] + \begin{table}[t] $$\fbox{$\small \begin{array}{ccccc} - \textbf{lettre} & \begin{array}{c} \textbf{nombre} \\ \textbf{d'tats} \end{array} + \textbf{lettre} & \begin{array}{c} \textbf{nombre} \\ \textbf{d'�tats} \end{array} & \begin{array}{c} \textbf{connexions} \\ \textbf{possibles} \end{array} & \begin{array}{c} \textbf{connexions} \\ \textbf{non nulles} \end{array} & \textbf{rapport}\\ @@ -1061,9 +1061,9 @@ \subsection{Repr Z & 38 & 1520 & 90 &5,9\% \end{array} $}$$ - \caption{Connexions non nulles dans les modles de reconnaissance de lettres.} + \caption{Connexions non nulles dans les mod�les de reconnaissance de lettres.} \label{table_connexion_nulle-tab} - \end{table} + \end{table} @@ -1085,10 +1085,10 @@ \section{Algorithme du meilleur chemin : algorithme de Viterbi} \indexfr{Viterbi}% \indexfrr{meilleur(e)}{chemin}% -\indexfrr{squence}{tat} -\indexfrr{squence}{observation} +\indexfrr{s�quence}{�tat} +\indexfrr{s�quence}{observation} -Nous avons vu que le calcul des suites $\alpha_{t}\pa{.}$ et $\beta _{t}\pa{.}$ permet de calculer la probabilit d'une squence d'observations, qui est une somme de probabilits sur l'ensemble des squences d'tats possibles. L'algorithme de Viterbi permet de trouver parmi toutes ces squences d'tats, celle dont la probabilit d'mettre la squence d'observations est la plus forte. On appelle aussi cette squence d'tats ou meilleur chemin la squence d'tats la plus probable ayant mis la squence d'observations. Soit une squence d'observations $O=\left( O_{1},...,O_{T}\right)$, et le modle $M$, cet algorithme permet de trouver la squence $s^{\ast }\left( O_{1},...,O_{T},M\right)$~:% +Nous avons vu que le calcul des suites $\alpha_{t}\pa{.}$ et $\beta _{t}\pa{.}$ permet de calculer la probabilit� d'une s�quence d'observations, qui est une somme de probabilit�s sur l'ensemble des s�quences d'�tats possibles. L'algorithme de Viterbi permet de trouver parmi toutes ces s�quences d'�tats, celle dont la probabilit� d'�mettre la s�quence d'observations est la plus forte. On appelle aussi cette s�quence d'�tats ou meilleur chemin la s�quence d'�tats la plus probable ayant �mis la s�quence d'observations. Soit une s�quence d'observations $O=\left( O_{1},...,O_{T}\right)$, et le mod�le $M$, cet algorithme permet de trouver la s�quence $s^{\ast }\left( O_{1},...,O_{T},M\right)$~:% \begin{eqnarray} s^* \pa{ O_{1},...,O_{T},M } =\underset{s}{\arg\max} \, \pr{ s \sac O_{1},...,O_{T},M } @@ -1096,14 +1096,14 @@ \section{Algorithme du meilleur chemin : algorithme de Viterbi} \label{hmm_viterbi_eq_1} \end{eqnarray} -On note $\pa{ \delta_{t} \pa{i}} _{\substack{1\leqslant t\leqslant T\\1\leqslant i\leqslant N}}$ la probabilit de la squence d'tats $\left( q_{1},...,q_{t}\right) $ la plus probable telle que $q_{t}=i$ ayant mis la squence $\left( O_{1},...,O_{t}\right)$~: +On note $\pa{ \delta_{t} \pa{i}} _{\substack{1\leqslant t\leqslant T\\1\leqslant i\leqslant N}}$ la probabilit� de la s�quence d'�tats $\left( q_{1},...,q_{t}\right) $ la plus probable telle que $q_{t}=i$ ayant �mis la s�quence $\left( O_{1},...,O_{t}\right)$~: \begin{eqnarray} \delta_t\pa{i} = \underset{\vecteur{q_1}{q_{t-1}}}{\arg \max} \, \pr{ \vecteurno{O_1}{O_t}, \vecteurno{q_1}{q_{t-1}},q_t=i | M} \label{hmm_viterbi_eq_2} \end{eqnarray} -alors $\delta_t\pa{i}$ vrifie : +alors $\delta_t\pa{i}$ v�rifie : \begin{eqnarray} \begin{array}{rl} @@ -1115,7 +1115,7 @@ \section{Algorithme du meilleur chemin : algorithme de Viterbi} \label{hmm_viterbi_eq_3} \end{eqnarray} -On dfinit galement la suite $\pa{ \lambda_{t}}_{1\leqslant t\leqslant T}$ par : +On d�finit �galement la suite $\pa{ \lambda_{t}}_{1\leqslant t\leqslant T}$ par : \begin{eqnarray} \begin{array}{rl} @@ -1126,108 +1126,108 @@ \section{Algorithme du meilleur chemin : algorithme de Viterbi} \label{hmm_viterbi_eq_4} \end{eqnarray} -Par consquent, le meilleur chemin est la squence d'tats $\left( \lambda_{1},...,\lambda_{T}\right) $ et a pour probabilit +Par cons�quent, le meilleur chemin est la s�quence d'�tats $\left( \lambda_{1},...,\lambda_{T}\right) $ et a pour probabilit� $\delta_T\pa{\lambda_T}\theta_{\lambda_T}$. -On en dduit l'algorithme suivant~: - - \begin{xalgorithm}{Viterbi}\label{hmm_algo_viterbi_etat} - \indexfr{Viterbi} - Les notations utilises sont celles des quations (\ref{hmm_viterbi_eq_1}), (\ref{hmm_viterbi_eq_2}), - (\ref{hmm_viterbi_eq_3}), (\ref{hmm_viterbi_eq_4}). - - \begin{xalgostep}{initialisation}\label{hmm_viterbi_step_a} - \begin{xfor}{i}{1}{N} - $ - \begin{array}{lll} - \delta_1\pa{i} &\longleftarrow& \pi_i \, b_i\pa{O_1} \\ - \lambda_1\pa{i} &\longleftarrow& -1 - \end{array} - $ - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{rcurrence}\label{hmm_viterbi_step_b} - \begin{xfor}{t}{2}{T} - \begin{xfor}{j}{1}{N} - $ - \begin{array}{lll} - \delta_t\pa{j} &\longleftarrow& \delta_{t-1}\pa{1} \; a_{1j} \, b_j\pa{O_t} \\ - \lambda_t\pa{j} &\longleftarrow& 1 - \end{array} - $ \\ - \begin{xfor}{i}{2}{N} - $x \longleftarrow \delta_{t-1}\pa{i} \, a_{ij} \, b_j\pa{O_t}$ \\ - \begin{xif}{$x < \delta_t\pa{j}$} - $ - \begin{array}{lll} - \delta_t\pa{j} &\longleftarrow& x \\ - \lambda_t\pa{j} &\longleftarrow& i - \end{array} - $ - \end{xif} - \end{xfor} - \end{xfor} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{terminaison}\label{hmm_viterbi_step_c} - $ - \begin{array}{lll} - \delta_{T+1} &\longleftarrow& \delta_{T}\pa{1} \, \theta_{1} \\ - \lambda_{T+1} &\longleftarrow& 1 - \end{array} - $ \\ - \begin{xfor}{i}{2}{N} - $x \longleftarrow \delta_{T}\pa{i} \, \theta_i$ \\ - \begin{xif}{$x < \delta_{T+1}$} - $ - \begin{array}{lll} - \delta_{T+1} &\longleftarrow& x \\ - \lambda_{T+1} &\longleftarrow& i - \end{array} - $ - \end{xif} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{squence d'tats la plus probable}\label{hmm_viterbi_step_d} - $q^*_T \longleftarrow \lambda_{T+1}$ \\ - \begin{xfor}{t}{T-1}{1} - $q^*_t \longleftarrow \lambda_{t+1}\pa{ q^*_{t+1}}$ - \end{xfor} - \end{xalgostep} - - La squence d'tats la plus probable est $\vecteur{q^*_1}{q^*_T}$ et a pour probabilit $\delta_{T+1}$. - - \end{xalgorithm} - +On en d�duit l'algorithme suivant~: + + \begin{xalgorithm}{Viterbi}\label{hmm_algo_viterbi_etat} + \indexfr{Viterbi} + Les notations utilis�es sont celles des �quations (\ref{hmm_viterbi_eq_1}), (\ref{hmm_viterbi_eq_2}), + (\ref{hmm_viterbi_eq_3}), (\ref{hmm_viterbi_eq_4}). + + \begin{xalgostep}{initialisation}\label{hmm_viterbi_step_a} + \begin{xfor}{i}{1}{N} + $ + \begin{array}{lll} + \delta_1\pa{i} &\longleftarrow& \pi_i \, b_i\pa{O_1} \\ + \lambda_1\pa{i} &\longleftarrow& -1 + \end{array} + $ + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{r�currence}\label{hmm_viterbi_step_b} + \begin{xfor}{t}{2}{T} + \begin{xfor}{j}{1}{N} + $ + \begin{array}{lll} + \delta_t\pa{j} &\longleftarrow& \delta_{t-1}\pa{1} \; a_{1j} \, b_j\pa{O_t} \\ + \lambda_t\pa{j} &\longleftarrow& 1 + \end{array} + $ \\ + \begin{xfor}{i}{2}{N} + $x \longleftarrow \delta_{t-1}\pa{i} \, a_{ij} \, b_j\pa{O_t}$ \\ + \begin{xif}{$x < \delta_t\pa{j}$} + $ + \begin{array}{lll} + \delta_t\pa{j} &\longleftarrow& x \\ + \lambda_t\pa{j} &\longleftarrow& i + \end{array} + $ + \end{xif} + \end{xfor} + \end{xfor} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{terminaison}\label{hmm_viterbi_step_c} + $ + \begin{array}{lll} + \delta_{T+1} &\longleftarrow& \delta_{T}\pa{1} \, \theta_{1} \\ + \lambda_{T+1} &\longleftarrow& 1 + \end{array} + $ \\ + \begin{xfor}{i}{2}{N} + $x \longleftarrow \delta_{T}\pa{i} \, \theta_i$ \\ + \begin{xif}{$x < \delta_{T+1}$} + $ + \begin{array}{lll} + \delta_{T+1} &\longleftarrow& x \\ + \lambda_{T+1} &\longleftarrow& i + \end{array} + $ + \end{xif} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{s�quence d'�tats la plus probable}\label{hmm_viterbi_step_d} + $q^*_T \longleftarrow \lambda_{T+1}$ \\ + \begin{xfor}{t}{T-1}{1} + $q^*_t \longleftarrow \lambda_{t+1}\pa{ q^*_{t+1}}$ + \end{xfor} + \end{xalgostep} + + La s�quence d'�tats la plus probable est $\vecteur{q^*_1}{q^*_T}$ et a pour probabilit� $\delta_{T+1}$. + + \end{xalgorithm} + \begin{xremark}{forward et backward} -L'obtention du meilleur chemin ncessite deux passages, le premier pour le calcul des matrices $\delta_t\pa{i}$ et $\lambda_t\pa{i}$ \indexfr{forward} \indexfr{backward} lors des tapes~\ref{hmm_viterbi_step_a}, \ref{hmm_viterbi_step_b}, \ref{hmm_viterbi_step_c}. Ce calcul est semblable celui de l'algorithme forward~\ref{hmm_algo_forward_es}. Le second passage de l'tape~\ref{hmm_viterbi_step_d} dans l'autre sens (indice dcroissant) permet de retrouver le meilleur chemin. Cette tape n'est pas ncessaire pour obtenir seulement la probabilit de la meilleur squence d'tats. +L'obtention du meilleur chemin n�cessite deux passages, le premier pour le calcul des matrices $\delta_t\pa{i}$ et $\lambda_t\pa{i}$ \indexfr{forward} \indexfr{backward} lors des �tapes~\ref{hmm_viterbi_step_a}, \ref{hmm_viterbi_step_b}, \ref{hmm_viterbi_step_c}. Ce calcul est semblable � celui de l'algorithme forward~\ref{hmm_algo_forward_es}. Le second passage de l'�tape~\ref{hmm_viterbi_step_d} dans l'autre sens (indice d�croissant) permet de retrouver le meilleur chemin. Cette �tape n'est pas n�cessaire pour obtenir seulement la probabilit� de la meilleur s�quence d'�tats. \end{xremark} -\begin{xremark}{meilleure squence, plus court chemin} -Cet algorithme est rapprocher d'un algorithme de recherche du plus court chemin dans un graphe. En effet, la probabilit d'un chemin s'exprime comme un produit de probabilits :% +\begin{xremark}{meilleure s�quence, plus court chemin} +Cet algorithme est � rapprocher d'un algorithme de recherche du plus court chemin dans un graphe. En effet, la probabilit� d'un chemin s'exprime comme un produit de probabilit�s :% -\indexfrr{meilleur(e)}{squence} +\indexfrr{meilleur(e)}{s�quence} \indexfrr{meilleur(e)}{chemin} \indexfr{graphe} $$ \pr { q_{1},...,q_{T},O_{1},...,O_{T} \sac M} =\pi_{q_{1}}b_{q_{1}} - \left( O_{1}\right) \underset{t=2}{\overset{T}{\prod} + \left( O_{1}\right) \underset{t=2}{\overset{T}{\prod} }a_{q_{t-1},q_{t}}b_{q_{t}}\left( O_{t}\right) $$ -En passant au logarithme, on obtient une somme de termes qui peuvent tre considrs comme des distances entre deux tats. L'algorithme de Viterbi n'est autre qu'un algorithme de recherche du meilleur chemin de type Dijkstra (\citeindex{Dijkstra1971}). +En passant au logarithme, on obtient une somme de termes qui peuvent �tre consid�r�s comme des distances entre deux �tats. L'algorithme de Viterbi n'est autre qu'un algorithme de recherche du meilleur chemin de type Dijkstra (\citeindex{Dijkstra1971}). \end{xremark} @@ -1245,7 +1245,7 @@ \section{Algorithme du meilleur chemin : algorithme de Viterbi} %---------------------------------------------------------------------------------------------------------------------- -\section{Apprentissage d'une chane de Markov cache} +\section{Apprentissage d'une cha�ne de Markov cach�e} %---------------------------------------------------------------------------------------------------------------------- \label{hmm_apprentissage_chapter} @@ -1254,135 +1254,135 @@ \subsection{Principe} \indexfr{apprentissage}% \label{par_apprentissage_hmm} -Dans l'exemple paragraphe~\ref{chaine_markov_cachee_exemple}, la chane de Markov cache adapte au problme se dduisait de l'nonc : les probabilits de transitions et d'missions taient fixes par la dfinition des deux pices truque et non truque. Les questions que l'on cherche rsoudre dans ces problmes sont en gnral des esprances de gain, des dures, des informations sur le comportement du jeu sur une longue priode, sur de longues squences d'observations. A partir du modle, on cherche donc dduire des proprits sur les observations. +Dans l'exemple paragraphe~\ref{chaine_markov_cachee_exemple}, la cha�ne de Markov cach�e adapt�e au probl�me se d�duisait de l'�nonc� : les probabilit�s de transitions et d'�missions �taient fix�es par la d�finition des deux pi�ces truqu�e et non truqu�e. Les questions que l'on cherche � r�soudre dans ces probl�mes sont en g�n�ral des esp�rances de gain, des dur�es, des informations sur le comportement du jeu sur une longue p�riode, sur de longues s�quences d'observations. A partir du mod�le, on cherche donc � d�duire des propri�t�s sur les observations. -L'apprentissage d'une chane de Markov cache est exactement la tche inverse. On dispose de squences d'observations dont il faut dduire le modle qui les a gnres. Une fois la topologie du modle choisie, l'apprentissage revient donc estimer les probabilits d'entres, de transitions, d'missions qui modlisent au mieux la base d'chantillons. +L'apprentissage d'une cha�ne de Markov cach�e est exactement la t�che inverse. On dispose de s�quences d'observations dont il faut d�duire le mod�le qui les a g�n�r�es. Une fois la topologie du mod�le choisie, l'apprentissage revient donc � estimer les probabilit�s d'entr�es, de transitions, d'�missions qui mod�lisent au mieux la base d'�chantillons. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=3cm] - {\filext{../dessin2/imagemg}}\end{array}$}$$ - \caption{Un mot segment en graphmes.} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=3cm] + {\filext{../dessin2/imagemg}}\end{array}$}$$ + \caption{Un mot segment� en graph�mes.} \label{figure_exemple_grapheme} - \end{figure} + \end{figure} -La figure~\ref{figure_exemple_grapheme} est un exemple de mot reconnatre, la reconnaissance avec dictionnaire (deux mots pour cet exemple) consiste reconnatre que c'est le mot "CHARLES" plutt que "JEROME" qui est crit sur cette image. +La figure~\ref{figure_exemple_grapheme} est un exemple de mot � reconna�tre, la reconnaissance avec dictionnaire (deux mots pour cet exemple) consiste � reconna�tre que c'est le mot "CHARLES" plut�t que "JEROME" qui est �crit sur cette image. \indexfrr{reconnaissance}{dictionnaire} -Pour rpondre cette question, deux modles de Markov cachs sont construits, l'un pour le mot "CHARLES", $M_{CHARLES}$, et l'autre pour le mot "JEROME", $M_{JEROME}$. Le prtraitement de l'image aboutit la squence d'observations $O = \vecteur{O_1}{O_T}$. On dit que~: +Pour r�pondre � cette question, deux mod�les de Markov cach�s sont construits, l'un pour le mot "CHARLES", $M_{CHARLES}$, et l'autre pour le mot "JEROME", $M_{JEROME}$. Le pr�traitement de l'image aboutit � la s�quence d'observations $O = \vecteur{O_1}{O_T}$. On dit que~: $$ \text{si } \pr{O|M_{CHARLES}} > \pr{O|M_{JEROME}} \text{ alors l'image contient le mot "CHARLES"} $$ -Il reste construire les modles $M_{CHARLES}$ et $M_{JEROME}$ et pour cela on dispose d'une base d'images annotes\indexfr{annotation} qui contiennent des images des mots "CHARLES" et "JEROME". Le mot $M_{CHARLES}$ va apprendre toutes les squences issues des images annotes "CHARLES", il en sera de mme pour le mot "JEROME". Si on note $\vecteur{O_1^C}{O_K^C}$ les squences d'observations annotes "CHARLES" et $\vecteur{O_1^J}{O_L^J}$ celles annotes "JEROME", l'apprentissage consiste maximiser la vraisemblance~: +Il reste � construire les mod�les $M_{CHARLES}$ et $M_{JEROME}$ et pour cela on dispose d'une base d'images annot�es\indexfr{annotation} qui contiennent des images des mots "CHARLES" et "JEROME". Le mot $M_{CHARLES}$ va apprendre toutes les s�quences issues des images annot�es "CHARLES", il en sera de m�me pour le mot "JEROME". Si on note $\vecteur{O_1^C}{O_K^C}$ les s�quences d'observations annot�es "CHARLES" et $\vecteur{O_1^J}{O_L^J}$ celles annot�es "JEROME", l'apprentissage consiste � maximiser la vraisemblance~: \indexfr{vraisemblance} \begin{eqnarray} L\pa{\theta_C, \theta_J, \vecteurno{O_1^C}{O_K^C},\vecteurno{O_1^J}{O_K^J}} &=& \prody{n=1}{K} \pr{O_n^C | M_{CHARLES}} \; \prody{n=1}{L} \pr{O_n^L | M_{JEROME}} - \nonumber \\ + \nonumber \\ &=& \prody{n=1}{K} \pr{O_n^C | \theta_C} \; \prody{n=1}{L} \pr{O_n^L | \theta_J} \nonumber \\ &=& L\pa{\theta_C, \vecteurno{O_1^C}{O_K^C}} L\pa{\theta_J, \vecteurno{O_1^J}{O_K^J}} - \label{hmm_eq_vraisemblance} \\ - && \text{o } \theta_C \text{ et } \theta_J \text{ sont les paramtres} \nonumber \\ - && \text{des modles } M_{CHARLES} \text{ et } M_{JEROME} \nonumber + \label{hmm_eq_vraisemblance} \\ + && \text{o� } \theta_C \text{ et } \theta_J \text{ sont les param�tres} \nonumber \\ + && \text{des mod�les } M_{CHARLES} \text{ et } M_{JEROME} \nonumber \end{eqnarray} -L'apprentissage soulve deux questions : +L'apprentissage soul�ve deux questions : \begin{enumerate} -\item Le choix des modles pour les mots "CHARLES" et "JEROME", ce point sera tudi dans la partie~\ref{selection_architecture_chaine_MMC}. \item L'apprentissage de ces modles, ce point est dtaill dans les paragraphes qui suivent (algorithme, convergence, dmonstration). +\item Le choix des mod�les pour les mots "CHARLES" et "JEROME", ce point sera �tudi� dans la partie~\ref{selection_architecture_chaine_MMC}. \item L'apprentissage de ces mod�les, ce point est d�taill� dans les paragraphes qui suivent (algorithme, convergence, d�monstration). \end{enumerate} -L'quation (\ref{hmm_eq_vraisemblance}) suggre que l'apprentissage des modles "CHARLES" et "JEROME" peut s'effectuer de manire indpendante condition que les vraisemblances associes ces deux modles dpendent de paramtres diffrents. Dans le cas contraire, la rsolution du problme se dduit du cas o on suppose qu'un seul modle $M$ doit apprendre les squences d'observations~: +L'�quation (\ref{hmm_eq_vraisemblance}) sugg�re que l'apprentissage des mod�les "CHARLES" et "JEROME" peut s'effectuer de mani�re ind�pendante � condition que les vraisemblances associ�es � ces deux mod�les d�pendent de param�tres diff�rents. Dans le cas contraire, la r�solution du probl�me se d�duit du cas o� on suppose qu'un seul mod�le $M$ doit apprendre les s�quences d'observations~: $$ \left( O^{k}=\left( O_{1}^{k},..., O_{T_{k}}^{k} \right) \right) _{1\leqslant k\leqslant K} $$ - \begin{xproblem}{apprentissage d'une chane de Markov cache} - \label{hmm_problem_apprentissage_hmm} - \indexfr{apprentissage} - Les notations utilises sont celles de la dfinition~\ref{markov_chaine_cachee_definition}, l'quation - (\ref{hmm_eq_vraisemblance}) - permet de dfinir l'apprentissage d'une chane de Markov cache comme tant la solution du problme - d'optimisation suivant~: - - \indexfr{vraisemblance} - \indexfr{optimisation}% - - $$ - \begin{array}{l} - \left( A_{M},\pi_{M},\theta_{M},B_{M}\right) =\underset{A,\pi,\theta,B } {\arg\max} \; - \underset{\text{vraisemblance du modle}} {\underbrace - {\prody{k=1}{K} \pr{ O_{1}^{k},...,O_{T_{k}}^{k}\left| M\right. }}} \\ \\ - \begin{array}{rcl} - \text{ avec les contraintes } & & - \left\{ - \begin{subarray}{l} - \summyone{i}\pi_i=1 \\ - \forall j, \; \summyone{i}a_{ij} = 1 \\ - \forall j, \; \summyone{o}b_j\pa{o} = 1 \\ - \forall i, \; \pi_i \supegal 0 \\ - \forall i, \; \theta_i \supegal 0 \\ - \forall \pa{i,j} \; a_{ij} \supegal 0 \\ - \forall \pa{i,o} \; b_j\pa{o} \supegal 0 - \end{subarray} - \right. - \end{array} - \end{array} - $$ - \end{xproblem} - - + \begin{xproblem}{apprentissage d'une cha�ne de Markov cach�e} + \label{hmm_problem_apprentissage_hmm} + \indexfr{apprentissage} + Les notations utilis�es sont celles de la d�finition~\ref{markov_chaine_cachee_definition}, l'�quation + (\ref{hmm_eq_vraisemblance}) + permet de d�finir l'apprentissage d'une cha�ne de Markov cach�e comme �tant la solution du probl�me + d'optimisation suivant~: + + \indexfr{vraisemblance} + \indexfr{optimisation}% + + $$ + \begin{array}{l} + \left( A_{M},\pi_{M},\theta_{M},B_{M}\right) =\underset{A,\pi,\theta,B } {\arg\max} \; + \underset{\text{vraisemblance du mod�le}} {\underbrace + {\prody{k=1}{K} \pr{ O_{1}^{k},...,O_{T_{k}}^{k}\left| M\right. }}} \\ \\ + \begin{array}{rcl} + \text{ avec les contraintes } & & + \left\{ + \begin{subarray}{l} + \summyone{i}\pi_i=1 \\ + \forall j, \; \summyone{i}a_{ij} = 1 \\ + \forall j, \; \summyone{o}b_j\pa{o} = 1 \\ + \forall i, \; \pi_i \supegal 0 \\ + \forall i, \; \theta_i \supegal 0 \\ + \forall \pa{i,j} \; a_{ij} \supegal 0 \\ + \forall \pa{i,o} \; b_j\pa{o} \supegal 0 + \end{subarray} + \right. + \end{array} + \end{array} + $$ + \end{xproblem} + + \indexfr{Baum-Welch} -L'algorithme d'optimisation ou apprentissage des modles de Markov cachs est bas sur les formules de Baum-Welch qui prennent en compte les contraintes du problme (voir \citeindex{Baum1972} ou \citeindex{Rabiner1986}), utilises comme un cas particulier de l'algorithme EM -(Expectation-Maximisation, voir \citeindex{Dempster1977}). Trois tapes composent cet algorithme itratif~: - - - - - \begin{xalgorithm} {apprentissage d'une chane de Markov cache} - \indexfr{restimation} - \indexfr{optimisation}\label{hmm_algorithme_baumwelch} - Cet algorithme permet d'obtenir une solution au problme~\ref{hmm_problem_apprentissage_hmm} - correspondant un minimum local - \indexfr{minimum local} de la vraisemblance\indexfr{vraisemblance} (\ref{hmm_eq_vraisemblance}) comme le - montre le thorme~\ref{theoreme_hmm_baum_welch_1})~: - - \begin{xalgostep}{initialisation} - Les paramtres $A_0, \Pi_0, \Theta_0, B_0$ reoivent des valeurs alatoires.\\ - $t \longleftarrow 0$ \\ - calcul de la vraisemblance du modle $L_0$ - \end{xalgostep} - - \begin{xalgostep}{rcurrence} \label{hmm_algo_apprentissage_step_recurrence} - \begin{xwhile}{$L_{t} > L_{t-1}$} - $t \longleftarrow t+1$ \\ - Les paramtres $\overline{A_{t}}, \overline{\Pi_{t}}, \overline{\Theta_{t}}, - \overline{B_{t}}$ sont estims en fonction des formules de Baum-Welch - (voir table~\ref{figure_formule_baumwelch-fig}).\\ - $ - \begin{array}{lll} - A_t & \longleftarrow & \overline{A_{t}} \\ - \Pi_t & \longleftarrow & \overline{\Pi_{t}} \\ - \Theta_t & \longleftarrow & \overline{\Theta_{t}} \\ - B_t & \longleftarrow & \overline{B_{t}} - \end{array} - $ \\ - calcul de la vraisemblance du modle $L_t$ - \end{xwhile} - \end{xalgostep} - - \end{xalgorithm} - +L'algorithme d'optimisation ou apprentissage des mod�les de Markov cach�s est bas� sur les formules de Baum-Welch qui prennent en compte les contraintes du probl�me (voir \citeindex{Baum1972} ou \citeindex{Rabiner1986}), utilis�es comme un cas particulier de l'algorithme EM +(Expectation-Maximisation, voir \citeindex{Dempster1977}). Trois �tapes composent cet algorithme it�ratif~: + + + + + \begin{xalgorithm} {apprentissage d'une cha�ne de Markov cach�e} + \indexfr{r�estimation} + \indexfr{optimisation}\label{hmm_algorithme_baumwelch} + Cet algorithme permet d'obtenir une solution au probl�me~\ref{hmm_problem_apprentissage_hmm} + correspondant � un minimum local + \indexfr{minimum local} de la vraisemblance\indexfr{vraisemblance} (\ref{hmm_eq_vraisemblance}) comme le + montre le th�or�me~\ref{theoreme_hmm_baum_welch_1})~: + + \begin{xalgostep}{initialisation} + Les param�tres $A_0, \Pi_0, \Theta_0, B_0$ re�oivent des valeurs al�atoires.\\ + $t \longleftarrow 0$ \\ + calcul de la vraisemblance du mod�le $L_0$ + \end{xalgostep} + + \begin{xalgostep}{r�currence} \label{hmm_algo_apprentissage_step_recurrence} + \begin{xwhile}{$L_{t} > L_{t-1}$} + $t \longleftarrow t+1$ \\ + Les param�tres $\overline{A_{t}}, \overline{\Pi_{t}}, \overline{\Theta_{t}}, + \overline{B_{t}}$ sont estim�s en fonction des formules de Baum-Welch + (voir table~\ref{figure_formule_baumwelch-fig}).\\ + $ + \begin{array}{lll} + A_t & \longleftarrow & \overline{A_{t}} \\ + \Pi_t & \longleftarrow & \overline{\Pi_{t}} \\ + \Theta_t & \longleftarrow & \overline{\Theta_{t}} \\ + B_t & \longleftarrow & \overline{B_{t}} + \end{array} + $ \\ + calcul de la vraisemblance du mod�le $L_t$ + \end{xwhile} + \end{xalgostep} + + \end{xalgorithm} + \begin{table}[t] \[ @@ -1404,7 +1404,7 @@ \subsection{Principe} \dfrac{\underset{k=1}{\overset{K}{{\displaystyle\sum}}}\dfrac{1}{P_{k}}\left[ \underset{t=1}{\overset{T_{k}}{{\displaystyle\sum} }}\alpha_{t}^{k}\left( i\right) \beta_{t}^{k}\left( i\right) - \,\indicatrice{O_{t}^{k}=o}\right] } + \,\indicatrice{O_{t}^{k}=o}\right] } {\underset {k=1}{\overset{K}{ {\displaystyle\sum}}}\dfrac{1}{P_{k}}\left[ \underset{t=1}{\overset{T_{k}}{{\displaystyle\sum} }}\alpha_{t}^{k}\left( i\right) \beta_{t}^{k}\left( i\right) \right] } @@ -1433,39 +1433,39 @@ \subsection{Principe} \end{array} $} \] - \caption{Formules de restimation de Baum-Welch.} + \caption{Formules de r�estimation de Baum-Welch.} \label{figure_formule_baumwelch-fig} \indexfr{Baum-Welch} - \indexfr{restimation} - \label{formule_baumwelch} + \indexfr{r�estimation} + \label{formule_baumwelch} \end{table} - \begin{xtheorem} {convergence de l'algorithme~\ref{hmm_algorithme_baumwelch}} - \label{theoreme_hmm_baum_welch_1}% - \indexfr{Baum-Welch} - Soit $M =\pa{A,B,\Theta,\Pi}$ une chane de Markov et $\left( O^{k}=\left( O_{1}^{k},..., O_{T_{k}}^{k} \right) - \right) _{1\leqslant k\leqslant K}$ - une suite de squences d'observations, l'algorithme~\ref{hmm_algorithme_baumwelch} - implique la convergence croissante de la vraisemblance~: - \indexfrr{squence}{observation} - \begin{eqnarray} - \underset{k=1}{\overset{K}{\prod}} \pr{ O_{1}^{k},...,O_{T_{k}}^{k}\left| M\right. } - \label{hmm_eq_vraisemblance_theo} - \end{eqnarray} - \end{xtheorem} + \begin{xtheorem} {convergence de l'algorithme~\ref{hmm_algorithme_baumwelch}} + \label{theoreme_hmm_baum_welch_1}% + \indexfr{Baum-Welch} + Soit $M =\pa{A,B,\Theta,\Pi}$ une cha�ne de Markov et $\left( O^{k}=\left( O_{1}^{k},..., O_{T_{k}}^{k} \right) + \right) _{1\leqslant k\leqslant K}$ + une suite de s�quences d'observations, l'algorithme~\ref{hmm_algorithme_baumwelch} + implique la convergence croissante de la vraisemblance~: + \indexfrr{s�quence}{observation} + \begin{eqnarray} + \underset{k=1}{\overset{K}{\prod}} \pr{ O_{1}^{k},...,O_{T_{k}}^{k}\left| M\right. } + \label{hmm_eq_vraisemblance_theo} + \end{eqnarray} + \end{xtheorem} \begin{xremark}{convergence vers un minimum local} -Le thorme~\ref{theoreme_hmm_baum_welch_1} dmontre de la suite $\pa{L_t}_{t \infegal 0}$ construite par l'algorithme~\ref{hmm_algorithme_baumwelch}, la valeur atteinte correspond un minimum local et non global de la vraisemblance (\ref{hmm_eq_vraisemblance_theo}). +Le th�or�me~\ref{theoreme_hmm_baum_welch_1} d�montre de la suite $\pa{L_t}_{t \leqslant 0}$ construite par l'algorithme~\ref{hmm_algorithme_baumwelch}, la valeur atteinte correspond � un minimum local et non global de la vraisemblance (\ref{hmm_eq_vraisemblance_theo}). \indexfr{minimum local} \end{xremark} -Les paragraphes qui suivent (\ref{hmm_apprentissage_chapter}...) donnent diffrentes dmonstrations de ce thorme. +Les paragraphes qui suivent (\ref{hmm_apprentissage_chapter}...) donnent diff�rentes d�monstrations de ce th�or�me. @@ -1474,85 +1474,85 @@ \subsection{Principe} -\subsection{Dmonstration intuitive} +\subsection{D�monstration intuitive} \label{baumwelch_sens} -\begin{xdemo}{thorme}{\ref{theoreme_hmm_baum_welch_1}} +\begin{xdemo}{th�or�me}{\ref{theoreme_hmm_baum_welch_1}} -L'tape~\ref{hmm_algo_apprentissage_step_recurrence} de l'algorithme d'apprentissage~\ref{hmm_algorithme_baumwelch} consiste restimer les paramtres $A,\pi,\theta,B$ de manire accrotre la vraisemblance $L\pa{A,\pi,\theta,B}$ :% +L'�tape~\ref{hmm_algo_apprentissage_step_recurrence} de l'algorithme d'apprentissage~\ref{hmm_algorithme_baumwelch} consiste � r�estimer les param�tres $A,\pi,\theta,B$ de mani�re � accro�tre la vraisemblance $L\pa{A,\pi,\theta,B}$ :% - \begin{eqnarray*} - L\pa{A,\pi,\theta,B} &=& \underset{k=1}{\overset{K}{\prod}} - \pr{ O_{1}^{k},...,O_{T_{k}}^{k}\left| M\right. }\\ - L\pa{A,\pi,\theta,B} &=& \underset{k=1}{\overset{K}{\prod}} \crochet - {\summyone{s \in S} _pr{ O_{1}^{k},...,O_{T_{k}}^{k},s\left| M\right. } }\\ - && \text{o } s \text { est l'ensemble des squences d'tats} \\ - && \text{de la chane de Markov cache } M - \end{eqnarray*} + \begin{eqnarray*} + L\pa{A,\pi,\theta,B} &=& \underset{k=1}{\overset{K}{\prod}} + \pr{ O_{1}^{k},...,O_{T_{k}}^{k}\left| M\right. }\\ + L\pa{A,\pi,\theta,B} &=& \underset{k=1}{\overset{K}{\prod}} \crochet + {\summyone{s \in S} _pr{ O_{1}^{k},...,O_{T_{k}}^{k},s\left| M\right. } }\\ + && \text{o� } s \text { est l'ensemble des s�quences d'�tats} \\ + && \text{de la cha�ne de Markov cach�e } M + \end{eqnarray*} -Le principe des formules de Baum-Welch consiste augmenter la valeur des paramtres trs probables et diminuer celle de ceux peu probables. On note~: +Le principe des formules de Baum-Welch consiste � augmenter la valeur des param�tres tr�s probables et � diminuer celle de ceux peu probables. On note~: \begin{itemize} -\item $l_{i,t}$ le nombre de chemins (ou squences) partant de l'tat $i$ l'instant $t$ (voir figure ~\ref{figure_baumwelch_idee-fig}) -\item $l_{i,j,t}$ le nombre de chemins partant de l'tat $i$ l'instant $t$ et passant l'tat $j$ l'instant $t+1$ +\item $l_{i,t}$ le nombre de chemins (ou s�quences) partant de l'�tat $i$ � l'instant $t$ (voir figure ~\ref{figure_baumwelch_idee-fig}) +\item $l_{i,j,t}$ le nombre de chemins partant de l'�tat $i$ � l'instant $t$ et passant � l'�tat $j$ � l'instant $t+1$ (voir figure~\ref{figure_baumwelch_idee-fig}) \end{itemize} \begin{figure}[t] $$\frame{$\begin{array}[c|c]{c}\includegraphics[height=6cm, width=15cm] {\filext{../dessin2/hmm_baumwelch_idee}}\end{array}$}$$ - \caption{ Ide des formules de Baum-Welch~: donner une nouvelle valeur - un coefficient tenant compte du nombre de chemins - qui l'empruntent.} + \caption{ Id�e des formules de Baum-Welch~: donner une nouvelle valeur + � un coefficient tenant compte du nombre de chemins + qui l'empruntent.} \label{figure_baumwelch_idee-fig} \end{figure} -La nouvelle valeur $\overline{a_{ij}}$ sera : $\overline{a_{ij}} = \dfrac{\summyone{t}l_{i,j,t}}{\summyone{t}l_{i,t}}$. Il reste exprimer cette expression en termes de probabilits~: +La nouvelle valeur $\overline{a_{ij}}$ sera : $\overline{a_{ij}} = \dfrac{\summyone{t}l_{i,j,t}}{\summyone{t}l_{i,t}}$. Il reste � exprimer cette expression en termes de probabilit�s~: $$ \begin{array}{l} \overline{a_{i,j}} = \dfrac{\overset{K}{\underset{k=1}{\sum}}\dfrac{1}{P_{k} } - \overset{T_{k}-1}{\underset{t=1}{\sum}} - \pr{ q_{t+1}=j,q_{t} =i,O^{k}} } - {\overset{K}{\underset{k=1}{\sum}}\dfrac{1}{P_{k}} - \overset{T_{k}}{\underset{t=1}{\sum}} + \overset{T_{k}-1}{\underset{t=1}{\sum}} + \pr{ q_{t+1}=j,q_{t} =i,O^{k}} } + {\overset{K}{\underset{k=1}{\sum}}\dfrac{1}{P_{k}} + \overset{T_{k}}{\underset{t=1}{\sum}} \pr{ q_{t}=i,O^{k}} }% =\dfrac{\underset{k=1}{\overset{K}{\sum}}\dfrac{1}{P_{k}}\left[ \underset{t=1} - {\overset{T_{k}-1}{\sum}}\alpha_{t}^{k}\left( i\right) + {\overset{T_{k}-1}{\sum}}\alpha_{t}^{k}\left( i\right) \,a_{i,j}\,b_{j,O_{t+1}\,}\beta_{t+1}^{k}\left( j\right) \right] } - {\underset{k=1}{\overset{K}{\sum}}\dfrac{1}{P_{k}}\left[ \underset + {\underset{k=1}{\overset{K}{\sum}}\dfrac{1}{P_{k}}\left[ \underset {t=1}{\overset{T_{k}}{\sum}}\alpha_{t}^{k}\left( i\right) \beta_{t}^{k}\left( i\right) \right] } \\ - \text{d'aprs l'expression (\ref{hmm_proba_transition}), page~\pageref{hmm_proba_transition}} + \text{d'apr�s l'expression (\ref{hmm_proba_transition}), page~\pageref{hmm_proba_transition}} \end{array} $$ -La restimation des probabilits d'mission suit le mme raisonnement, pour allger les notations, on note $O=O^{k}$ et $C_{o}^{t}=\indicatrice{O_{t}=o}$ : +La r�estimation des probabilit�s d'�mission suit le m�me raisonnement, pour all�ger les notations, on note $O=O^{k}$ et $C_{o}^{t}=\indicatrice{O_{t}=o}$ : \begin{eqnarray*} \pr{ O_{t}=o,q_{t},O} &=& \pr{ C_{o}^{t},q_{t},O} = \pr{ O_{t+1},..,O_{T}\left| - C_{o}^{t},q_{t},O_{1},...,O_{t}\right. + C_{o}^{t},q_{t},O_{1},...,O_{t}\right. } \pr{ C_{o}^{t},q_{t},O_{1},...,O_{t}}\\ \pr{C_o^t,q_t,O} &=& \pr{\vecteurno{O_1}{O_T} |q_t} \pr{C_o^t | q_t, \vecteurno{O_1}{O_t}} - \pr{q_t, \vecteurno{O_1}{O_t}} \\ + \pr{q_t, \vecteurno{O_1}{O_t}} \\ \pr{C_o^t,q_t,O} &=& \beta_t \pa{q_t} \indicatrice{O_t=o} \alpha_t \pa{q_t} \end{eqnarray*} -D'o :% +D'o� :% $$ \overline{b_i\pa{o}}=\dfrac{\underset{k=1}{\overset{K}{\sum}}\dfrac{1}{P_{k} } - \left[ P\left( O_{t}=o,q_{t}=i,O\right) \right] }{\underset{k=1} + \left[ P\left( O_{t}=o,q_{t}=i,O\right) \right] }{\underset{k=1} {\overset{K}{\sum}}\dfrac{1}{P_{k}}\left[ \underset{t=1} - {\overset{T_{k}}{\sum}}P\left( q_{t}=i,O\right) \right]}=\dfrac{\underset{k=1}{\overset + {\overset{T_{k}}{\sum}}P\left( q_{t}=i,O\right) \right]}=\dfrac{\underset{k=1}{\overset {K}{\sum}}\dfrac{1}{P_{k}}\underset{t=1}{\overset{T_{k}}{\sum}} - \alpha_{t} ^{k}\left( i\right) \beta_{t}^{k}\left( i\right) \,\indicatrice{ + \alpha_{t} ^{k}\left( i\right) \beta_{t}^{k}\left( i\right) \,\indicatrice{ O_{t}^{k}=o}}{\underset{k=1}{\overset{K}{\sum}}\dfrac{1}{P_{k} } - \underset{t=1}{\overset{T_{k}}{\sum}}\alpha_{t}^{k}\left( i\right) + \underset{t=1}{\overset{T_{k}}{\sum}}\alpha_{t}^{k}\left( i\right) \beta_{t}^{k}\left( i\right) } $$ -On peut calculer de mme~: +On peut calculer de m�me~: $$ \overline{\theta_{i}}=\dfrac{\underset {k=1}{\overset{K}{\sum}}\dfrac{1}{P_{k}} \pr{ q_{T_{k}}=i,O} @@ -1574,30 +1574,30 @@ \subsection{D -\subsection{Lemmes et thormes intermdiaires} +\subsection{Lemmes et th�or�mes interm�diaires} -Ces lemmes servent des dmonstrations plus rigoureuses exposes au paragraphe suivant (\ref{hmm_demo_gradient_par}). +Ces lemmes servent des d�monstrations plus rigoureuses expos�es au paragraphe suivant (\ref{hmm_demo_gradient_par}). - \begin{xlemma}{Levinson1983 (1)}\label{hmm_lemme_baumwelch_1_un} (voir \citeindex{Levinson1983}) - - Soit $\left( u_{1},...,u_{N}\right) \in\left( \R_{+}^{\ast}\right) ^{N}$ et - $\left( v_{1},...,v_{N}\right) \in\left( \R_{+}\right) ^{N}$ tels que $\overset{N}{\underset {i=1}{\sum}}v_{i}>0$ alors : - - $$ - \ln\left[ \frac{\overset{N}{\underset{i=1}{\sum}}v_{i}}{\overset{N}{\underset{i=1}{\sum}} - u_{i}}\right] \geqslant\dfrac{\overset - {N}{\underset{i=1}{\sum}}u_{i}\ln v_{i}-u_{i}\ln u_{i}}{\overset{N} {\underset{i=1}{\sum}}u_{i}} - $$ - - \end{xlemma} + \begin{xlemma}{Levinson1983 (1)}\label{hmm_lemme_baumwelch_1_un} (voir \citeindex{Levinson1983}) + + Soit $\left( u_{1},...,u_{N}\right) \in\left( \mathbb{R}_{+}^{\ast}\right) ^{N}$ et + $\left( v_{1},...,v_{N}\right) \in\left( \mathbb{R}_{+}\right) ^{N}$ tels que $\overset{N}{\underset {i=1}{\sum}}v_{i}>0$ alors : + + $$ + \ln\left[ \frac{\overset{N}{\underset{i=1}{\sum}}v_{i}}{\overset{N}{\underset{i=1}{\sum}} + u_{i}}\right] \geqslant\dfrac{\overset + {N}{\underset{i=1}{\sum}}u_{i}\ln v_{i}-u_{i}\ln u_{i}}{\overset{N} {\underset{i=1}{\sum}}u_{i}} + $$ + + \end{xlemma} \begin{xdemo}{lemme}{\ref{hmm_lemme_baumwelch_1_un}} -On utilise la concavit de la fonction logarithme : +On utilise la concavit� de la fonction logarithme : $$ \ln\left[ \frac{\overset{N}{\underset{i=1}{\sum}}v_{i}}{\overset {N}{\underset{i=1}{\sum}}u_{i}}\right] =\ln\left[ \overset{\text{moyenne - pondre}}{\overbrace{\overset{N}{\underset{i=1}{\sum}}\frac{u_{i}% + pond�r�e}}{\overbrace{\overset{N}{\underset{i=1}{\sum}}\frac{u_{i}% }{\overset{N}{\underset{k=1}{\sum}}u_{k}}\dfrac{v_{i}}{u_{i}}}}\right] \geqslant\overset{N}{\underset{i=1}{\sum}}\frac{u_{i}}{\overset{N}% {\underset{k=1}{\sum}}u_{k}}\ln\dfrac{v_{i}}{u_{i}}=\frac{1}{\overset @@ -1614,20 +1614,20 @@ \subsection{Lemmes et th - \begin{xlemma}{Levinson1983 (2)}\label{hmm_lemme_baumwelch_2_deux} (voir \citeindex{Levinson1983}) - Soit $\left( u_{1},...,u_{N}\right) \in\left( \R_{+}^{\ast}\right) ^{N}$, - la solution unique de la maximisation sous contrainte suivante~: - $$ - \left| - \begin{array}{l}% - \underset{\left( x_{1},...,x_{N}\right) }{\max}\,\overset{N}{\underset - {i=1}{\sum}}u_{i}\ln x_{i}\\ - \overset{N}{\underset{i=1}{\sum}}x_{i}=1 - \end{array} - \right. - $$ - est obtenue pour $\forall i\in\left\{ 1,...,N\right\} ,\; x_{i}=\dfrac{u_{i}}{\overset{N}{\underset{i=1}{\sum}}u_{i}}$ - \end{xlemma} + \begin{xlemma}{Levinson1983 (2)}\label{hmm_lemme_baumwelch_2_deux} (voir \citeindex{Levinson1983}) + Soit $\left( u_{1},...,u_{N}\right) \in\left( \mathbb{R}_{+}^{\ast}\right) ^{N}$, + la solution unique de la maximisation sous contrainte suivante~: + $$ + \left| + \begin{array}{l}% + \underset{\left( x_{1},...,x_{N}\right) }{\max}\,\overset{N}{\underset + {i=1}{\sum}}u_{i}\ln x_{i}\\ + \overset{N}{\underset{i=1}{\sum}}x_{i}=1 + \end{array} + \right. + $$ + est obtenue pour $\forall i\in\left\{ 1,...,N\right\} ,\; x_{i}=\dfrac{u_{i}}{\overset{N}{\underset{i=1}{\sum}}u_{i}}$ + \end{xlemma} @@ -1635,7 +1635,7 @@ \subsection{Lemmes et th On utilise les multiplicateurs de Lagrange, on pose : $F\left( x_{1} ,...,x_{N},\lambda\right) =\overset{N}{\underset{i=1}{\sum}}u_{i}\ln x_{i}+\lambda\left( \overset{N}{\underset{i=1}{\sum}}x_{i}-1\right) $ -Lorsque $F$ est maximum, ses drives partielles vrifient : +Lorsque $F$ est maximum, ses d�riv�es partielles v�rifient : \begin{eqnarray*} \dfrac{\partial F\left( x_{1},...,x_{N},\lambda\right) }{\partial x_{k} }=\dfrac{u_{k}}{x_{k}}+\lambda=0 &\Longleftrightarrow& x_{k}=-\dfrac{u_{k} }{\lambda}\\ @@ -1654,31 +1654,31 @@ \subsection{Lemmes et th - \begin{xlemma}{multiplicateurs de Lagrange}\label{hmm_lemme_baumwelch_3_trois} - La solution du problme de maximisation suivant : - $$ - \left|\begin{array}{l}% - \underset{\left( x_{1},...,x_{N}\right) }{\max}f\left( x_{1},...,x_{N}% - \right) \\ - \overset{N}{\underset{i=1}{\sum}}x_{i}=1 - \end{array} - \right. - $$ - $$ - \text{ vrifie }\quad \underset{k=1}{\overset{N}{% - {\displaystyle\sum} }}x_{k}\dfrac{\partial f}{\partial x_{k}} - \left( x_{1},...,x_{N}\right) \neq0\Longrightarrow\forall i \in \intervalle{1}{N}, - \; x_{i}=\dfrac{x_{i}\dfrac{\partial f}{\partial x_{i}}\left( x_{1},...,x_{N}\right) }{\underset{k=1}{\overset - {N}{% - {\displaystyle\sum} }}x_{k}\dfrac{\partial f}{\partial x_{k}}\left( x_{1},...,x_{N}\right) } - $$ - \end{xlemma} + \begin{xlemma}{multiplicateurs de Lagrange}\label{hmm_lemme_baumwelch_3_trois} + La solution du probl�me de maximisation suivant : + $$ + \left|\begin{array}{l}% + \underset{\left( x_{1},...,x_{N}\right) }{\max}f\left( x_{1},...,x_{N}% + \right) \\ + \overset{N}{\underset{i=1}{\sum}}x_{i}=1 + \end{array} + \right. + $$ + $$ + \text{ v�rifie }\quad \underset{k=1}{\overset{N}{% + {\displaystyle\sum} }}x_{k}\dfrac{\partial f}{\partial x_{k}} + \left( x_{1},...,x_{N}\right) \neq0\Longrightarrow\forall i \in \intervalle{1}{N}, + \; x_{i}=\dfrac{x_{i}\dfrac{\partial f}{\partial x_{i}}\left( x_{1},...,x_{N}\right) }{\underset{k=1}{\overset + {N}{% + {\displaystyle\sum} }}x_{k}\dfrac{\partial f}{\partial x_{k}}\left( x_{1},...,x_{N}\right) } + $$ + \end{xlemma} \begin{xdemo}{lemme}{\ref{hmm_lemme_baumwelch_3_trois}} On utilise les multiplicateurs de Lagrange, on pose : $F\left( x_{1},...,x_{N},\lambda\right) =f\left( x_{1},...,x_{N}\right) +\lambda\left( \overset{N}{\underset{i=1}{\sum}}x_{i}-1\right) $ Lorsque $F$ est -maximum, ses drives partielles vrifient (on pose $X=\vecteur{x_1}{x_N}$~: +maximum, ses d�riv�es partielles v�rifient (on pose $X=\vecteur{x_1}{x_N}$~: \begin{eqnarray*} \dfrac{\partial F\left( X,\lambda\right) }{\partial x_{k} }=\dfrac{\partial f}{\partial x_{k}}\left(X\right) @@ -1686,14 +1686,14 @@ \subsection{Lemmes et th &\Longrightarrow& \underset{k=1}{\overset{N}{{\displaystyle\sum} }}\left[ x_{k}\dfrac{\partial f}{\partial x_{k}}\left(X\right) +\lambda x_{k}\right] =0\\ &\Longrightarrow& \underset{k=1}{\overset{N}{{\displaystyle\sum}}}x_{k} - \dfrac{\partial f}{\partial x_{k}}\left(X\right) =-\lambda + \dfrac{\partial f}{\partial x_{k}}\left(X\right) =-\lambda \end{eqnarray*} -En remplaant $\lambda$ par $-\underset{k=1}{\overset{N}{ {\displaystyle\sum} }}x_{k}\dfrac{\partial f}{\partial x_{k}}\left( ...\right) $ dans chacune des quations aux drives partielles, on dmontre le lemme ~\ref{hmm_lemme_baumwelch_3_trois}. Cette optimisation revient chercher le maximum de la fonction $f$ sur l'hyperplan d'quation $\overset{N}{\underset{i=1}{\sum}}x_{i}=1$. +En rempla�ant $\lambda$ par $-\underset{k=1}{\overset{N}{ {\displaystyle\sum} }}x_{k}\dfrac{\partial f}{\partial x_{k}}\left( ...\right) $ dans chacune des �quations aux d�riv�es partielles, on d�montre le lemme ~\ref{hmm_lemme_baumwelch_3_trois}. Cette optimisation revient � chercher le maximum de la fonction $f$ sur l'hyperplan d'�quation $\overset{N}{\underset{i=1}{\sum}}x_{i}=1$. \end{xdemo} -C'est ce lemme qui est la source du thorme~\ref{theorem_loglogconvexe} mais au pralable suivent une dfinition et un lemme. +C'est ce lemme qui est � la source du th�or�me~\ref{theorem_loglogconvexe} mais au pr�alable suivent une d�finition et un lemme. @@ -1702,25 +1702,25 @@ \subsection{Lemmes et th - \begin{xdefinition}{fonction log-log-convexe} - \label{definition_log_log_convexe} - \indexfr{log-log-convexe}% - Soit une fonction : - $$ - \begin{array}{rccl} - f : & \pa{\R^*_+}^n &\longrightarrow& \R+^* \\ - & \vecteur{x_1}{x_n} &\longrightarrow& f \vecteur{x_1}{x_n} - \end{array} - $$ - $f$ est une fonction \emph{log-log-convexe} si et seulement si la fonction : - $$ - \begin{array}{rccl} - g : & \pa{\R}^n &\longrightarrow& \R+^* \\ - & \vecteur{u_1}{u_n} &\longrightarrow& g\vecteur{u_1}{u_n} = \ln \crochet{ f \vecteur{e^{u_1}}{e^{u_n}}} - \end{array} - $$ - est une fonction convexe. - \end{xdefinition} + \begin{xdefinition}{fonction log-log-convexe} + \label{definition_log_log_convexe} + \indexfr{log-log-convexe}% + Soit une fonction : + $$ + \begin{array}{rccl} + f : & \pa{\mathbb{R}^*_+}^n &\longrightarrow& \mathbb{R}+^* \\ + & \vecteur{x_1}{x_n} &\longrightarrow& f \vecteur{x_1}{x_n} + \end{array} + $$ + $f$ est une fonction \emph{log-log-convexe} si et seulement si la fonction : + $$ + \begin{array}{rccl} + g : & \pa{\mathbb{R}}^n &\longrightarrow& \mathbb{R}+^* \\ + & \vecteur{u_1}{u_n} &\longrightarrow& g\vecteur{u_1}{u_n} = \ln \crochet{ f \vecteur{e^{u_1}}{e^{u_n}}} + \end{array} + $$ + est une fonction convexe. + \end{xdefinition} @@ -1728,27 +1728,27 @@ \subsection{Lemmes et th - \begin{xlemma}{distance de Kullback-Leiber} - \label{lemme_loglogconvexe}% - \indexfrr{distance}{Kullback-Leiber}% - On pose $D = \accolade{x=\vecteur{x_1}{x_n} \in \R^n \left| \summy{i=1}{n} x_i = 1 \text{ et } - \forall i \in \intervalle{1}{n}, x_i > 0 \right.} $.\newline% - Soit $\pa{x,y} \in D^2$ alors : - $$ - \summy{i=1}{n} y_i \ln \crochet{\dfrac{x_i}{y_i}} \infegal 0 - $$ - \end{xlemma} + \begin{xlemma}{distance de Kullback-Leiber} + \label{lemme_loglogconvexe}% + \indexfrr{distance}{Kullback-Leiber}% + On pose $D = \accolade{x=\vecteur{x_1}{x_n} \in \mathbb{R}^n \left| \summy{i=1}{n} x_i = 1 \text{ et } + \forall i \in \intervalle{1}{n}, x_i > 0 \right.} $.\newline% + Soit $\pa{x,y} \in D^2$ alors : + $$ + \summy{i=1}{n} y_i \ln \crochet{\dfrac{x_i}{y_i}} \leqslant 0 + $$ + \end{xlemma} \begin{xdemo}{lemme}{\ref{lemme_loglogconvexe}} La fonction $x \longrightarrow \ln x$ est concave, donc : \begin{eqnarray*} \summy{i=1}{n} y_i \ln \crochet{\dfrac{x_i}{y_i}} &=& \summy{i=1}{n} \; \dfrac{y_i}{\summy{k=0}{n}y_k} \; - \ln \crochet{\dfrac{x_i}{y_i}} \text{ car } y \in D\\ - &\infegal& \ln \crochet { \summy{i=1}{n} \dfrac{y_i}{\summy{k=0}{n}y_k} \dfrac{x_i}{y_i} } \\ - &\infegal& \ln \crochet { \dfrac{\summy{i=1}{n}x_i}{\summy{i=0}{n}y_i} } = + \ln \crochet{\dfrac{x_i}{y_i}} \text{ car } y \in D\\ + &\leqslant& \ln \crochet { \summy{i=1}{n} \dfrac{y_i}{\summy{k=0}{n}y_k} \dfrac{x_i}{y_i} } \\ + &\leqslant& \ln \crochet { \dfrac{\summy{i=1}{n}x_i}{\summy{i=0}{n}y_i} } = \ln \dfrac{1}{1} \text{ car } \pa{x,y} \in D^2 \\ - &\infegal& 0 + &\leqslant& 0 \end{eqnarray*} \end{xdemo} @@ -1762,35 +1762,35 @@ \subsection{Lemmes et th - \begin{xtheorem}{fonction log-log-convexe} - \label{theorem_loglogconvexe}% - \indexfr{log-log-convexe}% - On pose $D = \accolade{x=\vecteur{x_1}{x_n} \in \R^n \left| \summy{i=1}{n} x_i = 1 \text{ et } \forall i - \in \intervalle{1}{n}, x_i > 0 \right.} $.\newline% - Soit $f : \pa{\R^*_+}^n \longrightarrow \R+^*$ une fonction log-log-convexe drivable.\newline% - Soit $x \in D$, on dfinit $y \pa{x} = \vecteur{y_1\pa{x}}{y_n\pa{x}} \in D$ tel que : - $$ - \forall i \in \intervalle{1}{n}, \; y_i \pa{x} = \frac{x_i \partialfrac{f}{x_i}\pa{x}} - {\summy{k=1}{n}x_k \partialfrac{f}{x_k}\pa{x}} - $$ - alors $f$ vrifie : - $$ - \forall x \in D, \; f\pa{ y \pa{x}} \supegal f\pa{x} - $$ - \end{xtheorem} - - - - -\begin{xdemo}{thorme}{\ref{theorem_loglogconvexe}} + \begin{xtheorem}{fonction log-log-convexe} + \label{theorem_loglogconvexe}% + \indexfr{log-log-convexe}% + On pose $D = \accolade{x=\vecteur{x_1}{x_n} \in \mathbb{R}^n \left| \summy{i=1}{n} x_i = 1 \text{ et } \forall i + \in \intervalle{1}{n}, x_i > 0 \right.} $.\newline% + Soit $f : \pa{\mathbb{R}^*_+}^n \longrightarrow \mathbb{R}+^*$ une fonction log-log-convexe d�rivable.\newline% + Soit $x \in D$, on d�finit $y \pa{x} = \vecteur{y_1\pa{x}}{y_n\pa{x}} \in D$ tel que : + $$ + \forall i \in \intervalle{1}{n}, \; y_i \pa{x} = \frac{x_i \partialfrac{f}{x_i}\pa{x}} + {\summy{k=1}{n}x_k \partialfrac{f}{x_k}\pa{x}} + $$ + alors $f$ v�rifie : + $$ + \forall x \in D, \; f\pa{ y \pa{x}} \supegal f\pa{x} + $$ + \end{xtheorem} + + + + +\begin{xdemo}{th�or�me}{\ref{theorem_loglogconvexe}} -Soit $\pa{x,x'} \in D^2$, on dfinit $\pa{u,u'} \in \pa{\R^n}^2$ tel que : +Soit $\pa{x,x'} \in D^2$, on d�finit $\pa{u,u'} \in \pa{\mathbb{R}^n}^2$ tel que : $$ \forall i \in \intervalle{1}{n}, \; u_i = \ln x_i \text{ et } u'_i = \ln x'_i $$ -$u$ et $u'$ vrifient : +$u$ et $u'$ v�rifient : $$ \summy{i=1}{n} e^{u_i} = 1 \text{ et } \summy{i=1}{n} e^{u'_i} = 1 @@ -1801,21 +1801,21 @@ \subsection{Lemmes et th \begin{eqnarray*} h \pa{u'} - h \pa{u} &\supegal& \summy{i=1}{n} \partialfrac{h}{u_i}\pa{u} \pa{u'_i - u_i} \\ \ln f \pa{x'} - \ln f \pa{x} &\supegal& \summy{i=1}{n} \dfrac{e^{u_i}}{f\pa{x}} \partialfrac{f}{x_i} - \pa{x}\pa{\ln x'_i - \ln x_i} \\ + \pa{x}\pa{\ln x'_i - \ln x_i} \\ \ln \crochet{\dfrac{f \pa{x'}}{f \pa{x}}} &\supegal& \summy{i=1}{n} \dfrac{x_i}{f\pa{x}} \partialfrac{f}{x_i} - \pa{x}\ln \crochet { + \pa{x}\ln \crochet { \dfrac{x'_i}{x_i}} \end{eqnarray*} -Si $x' = y\pa{x} = \pa{y_i \pa{x} = \frac{x_i \partialfrac{f}{x_i}\pa{x}}{\summy{k=1}{n}x_k \partialfrac{f}{x_k}\pa{x}}}_{1 \infegal i \infegal n}$, alors~: +Si $x' = y\pa{x} = \pa{y_i \pa{x} = \frac{x_i \partialfrac{f}{x_i}\pa{x}}{\summy{k=1}{n}x_k \partialfrac{f}{x_k}\pa{x}}}_{1 \leqslant i \leqslant n}$, alors~: $$ \begin{array}{rrcl} - & - \ln \crochet{\dfrac{f \pa{y\pa{x}}}{f \pa{x}}} &\infegal& + & - \ln \crochet{\dfrac{f \pa{y\pa{x}}}{f \pa{x}}} &\leqslant& \summy{i=1}{n} \dfrac{x_i}{f\pa{x}} \partialfrac{f}{x_i} \pa{x}\ln \crochet {\dfrac{x_i}{y_i\pa{x}}} \\ - \Longrightarrow & - \ln \crochet{\dfrac{f \pa{y\pa{x}}}{f \pa{x}}} &\infegal& + \Longrightarrow & - \ln \crochet{\dfrac{f \pa{y\pa{x}}}{f \pa{x}}} &\leqslant& \crochet{\summy{i=1}{n} \dfrac{ x_i\partialfrac{f}{x_i} \pa{x} } {f\pa{x}}} - \underset{\infegal 0 \text{ d'aprs le lemme ~\ref{lemme_loglogconvexe}}}{\underbrace{\crochet{\summy{i=1}{n} + \underset{\leqslant 0 \text{ d'apr�s le lemme ~\ref{lemme_loglogconvexe}}}{\underbrace{\crochet{\summy{i=1}{n} y_i\pa{x} \ln \crochet {\dfrac{x_i}{y_i\pa{x}}}}}} \\ \Longrightarrow & \ln \crochet{\dfrac{f \pa{y\pa{x}}}{f \pa{x}}} &\supegal& 0 \\ \\ \Longrightarrow & f \pa{y\pa{x}} &\supegal& f \pa{x} @@ -1831,62 +1831,62 @@ \subsection{Lemmes et th - \begin{xcorollary}{polynme coefficients positifs (Baum1968)} \label{hmm_theorem_baumwelch_un} (voir \citeindex{Baum1968}) - \indexfr{log-log-convexe}% - \indexfr{polynme}% - - Soit $P : \R^N \dans \R$ un polynme dont les coefficients sont tous positifs, - soit $x = \vecteur{x_1}{x_N} \in \R^N$ tels que $\summy{i=1}{N}x_i = 1$ et $\forall i \in \intervalle{1}{N}, - \; x_i \supegal 0$, - soit $y = \vecteur{y_1}{y_N} \in \R^N$ dfini par~: - $$ - \forall i \in \intervalle{1}{N}, \; y_i = \frac{x_i \partialfrac{P}{x_i}\pa{x}}{\summy{k=1}{N}x_k \partialfrac{P}{x_k}\pa{x}} - $$ - alors~: - $$ - P\pa{y} \supegal P\pa{x} - $$ - \end{xcorollary} + \begin{xcorollary}{polyn�me � coefficients positifs (Baum1968)} \label{hmm_theorem_baumwelch_un} (voir \citeindex{Baum1968}) + \indexfr{log-log-convexe}% + \indexfr{polyn�me}% + + Soit $P : \mathbb{R}^N \dans \mathbb{R}$ un polyn�me dont les coefficients sont tous positifs, + soit $x = \vecteur{x_1}{x_N} \in \mathbb{R}^N$ tels que $\summy{i=1}{N}x_i = 1$ et $\forall i \in \intervalle{1}{N}, + \; x_i \supegal 0$, + soit $y = \vecteur{y_1}{y_N} \in \mathbb{R}^N$ d�fini par~: + $$ + \forall i \in \intervalle{1}{N}, \; y_i = \frac{x_i \partialfrac{P}{x_i}\pa{x}}{\summy{k=1}{N}x_k \partialfrac{P}{x_k}\pa{x}} + $$ + alors~: + $$ + P\pa{y} \supegal P\pa{x} + $$ + \end{xcorollary} \begin{xdemomine}{corollaire}{\ref{hmm_theorem_baumwelch_un}} -Soit $D$ l'ensemble dfini dans le thorme~\ref{theorem_loglogconvexe}, si $f$ est une fonction log-log-convexe dfini sur $\overline{D}$, si $f$ est continue alors les galits dmontres sur $D$ le sont aussi sur $\overline{D}$. Il ne reste plus qu' dmontrer qu'un polynme coefficients positifs est log-log-convexe. +Soit $D$ l'ensemble d�fini dans le th�or�me~\ref{theorem_loglogconvexe}, si $f$ est une fonction log-log-convexe d�fini sur $\overline{D}$, si $f$ est continue alors les �galit�s d�montr�es sur $D$ le sont aussi sur $\overline{D}$. Il ne reste plus qu'� d�montrer qu'un polyn�me � coefficients positifs est log-log-convexe. -Soit $x=\vecteur{x_1}{x_n} \in D$, on note $e^u = \vecteur{e_{u_1}}{e_{u_n}}$, on peut crire le polynme $P$ sous la forme : +Soit $x=\vecteur{x_1}{x_n} \in D$, on note $e^u = \vecteur{e_{u_1}}{e_{u_n}}$, on peut �crire le polyn�me $P$ sous la forme : \begin{eqnarray*} - P\pa{x} &=& \summyone{ 0 \infegal \vecteurno{i_1}{i_n} \infegal \deg P} \; - a_{\vecteurno{i_1}{i_n}} \prody{k=1}{n} x_k^{i_k} \\ - P\pa{e^u} &=& \summyone{ 0 \infegal \vecteurno{i_1}{i_n} \infegal \deg P} \; - a_{\vecteurno{i_1}{i_n}} \prody{k=1}{n} \pa{e^{u_k}}^{i_k} \\ - P\pa{e^u} &=& \summyone{ 0 \infegal \vecteurno{i_1}{i_n} \infegal \deg P} \; - a_{\vecteurno{i_1}{i_n}} e^{ \summy{k=1}{n} i_k u_k} \\ - \partialfrac{P\pa{e^u}}{u_l} &=& \summyone{ 0 \infegal \vecteurno{i_1}{i_n} - \infegal \deg P} \; a_{\vecteurno{i_1}{i_n}} \, i_l \, e^{ \summy{k=1}{n} i_k + P\pa{x} &=& \summyone{ 0 \leqslant \vecteurno{i_1}{i_n} \leqslant \deg P} \; + a_{\vecteurno{i_1}{i_n}} \prody{k=1}{n} x_k^{i_k} \\ + P\pa{e^u} &=& \summyone{ 0 \leqslant \vecteurno{i_1}{i_n} \leqslant \deg P} \; + a_{\vecteurno{i_1}{i_n}} \prody{k=1}{n} \pa{e^{u_k}}^{i_k} \\ + P\pa{e^u} &=& \summyone{ 0 \leqslant \vecteurno{i_1}{i_n} \leqslant \deg P} \; + a_{\vecteurno{i_1}{i_n}} e^{ \summy{k=1}{n} i_k u_k} \\ + \partialfrac{P\pa{e^u}}{u_l} &=& \summyone{ 0 \leqslant \vecteurno{i_1}{i_n} + \leqslant \deg P} \; a_{\vecteurno{i_1}{i_n}} \, i_l \, e^{ \summy{k=1}{n} i_k u_k} \end{eqnarray*} -On pose $i = \vecteur{i_1}{i_n}$. On en dduit pour $m \in \intervalle{1}{n}$ que : +On pose $i = \vecteur{i_1}{i_n}$. On en d�duit pour $m \in \intervalle{1}{n}$ que : \begin{eqnarray*} \dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} &=& \dfrac{1}{P^2\pa{e_u}} \crochet {% \begin{array}{cl} & \pa{\summyone{i} \; a_i \, i_l i_m \, e^{\scal{i,u}}} \pa{\summyone{i} \; a_i \, e^{\scal{i,u}}} \\% - & \pa{\summyone{i} \; a_i \, i_l \, e^{\scal{i,u}}} \pa{\summyone{i} \; a_i \, - i_m \, e^{\scal{i,u}}}% + i_m \, e^{\scal{i,u}}}% \end{array}} \\ \dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} &=& \dfrac{1}{P^2\pa{e_u}} \crochet {% \summyone{i} \summyone{j} \; a_i \, a_j \, i_l \pa{i_m -j_m} \, e^{\scal{i,u}} \, e^{\scal{j,u}}} \end{eqnarray*} -Il faut montrer que la matrice $\pa{\dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m}}_{u_l u_m}$ est dfinie positive. On cherche donc montrer que pour tout $y \in \R^n$, $\summyone{l,m} y_l y_m \dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} \supegal 0$. On note $b_{\vecteurno{i_1}{i_n}} = a_{\vecteurno{i_1}{i_n}} e^{ \summy{k=1}{n} i_k u_k} \supegal 0 $. +Il faut montrer que la matrice $\pa{\dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m}}_{u_l u_m}$ est d�finie positive. On cherche donc � montrer que pour tout $y \in \mathbb{R}^n$, $\summyone{l,m} y_l y_m \dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} \supegal 0$. On note $b_{\vecteurno{i_1}{i_n}} = a_{\vecteurno{i_1}{i_n}} e^{ \summy{k=1}{n} i_k u_k} \supegal 0 $. \begin{eqnarray*} \summyone{l,m} y_l y_m \dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} & = & \summyone{l,m} y_l y_m \summyone{i} \summyone{j} \; a_i \, a_j \, i_l \pa{i_m -j_m} \, - e^{\scal{i,u}} \, e^{\scal{j,u}} \\ \\ + e^{\scal{i,u}} \, e^{\scal{j,u}} \\ \\ & = & \summyone{i} \summyone{j} \; \summyone{l,m} \; y_l y_m \; b_i b_j i_l \pa{i_m -j_m} \\ & = & \summyone{i} \summyone{j} \; b_i b_j \pa{ \summyone{l,m} \; y_l y_m i_l \pa{i_m -j_m}} \\ & = & \summyone{i} \summyone{j} \; b_i b_j \pa{ @@ -1894,7 +1894,7 @@ \subsection{Lemmes et th - \crochet { \summyone{l} y_l i_l } \crochet{ \summyone{m} y_m j_m } } \end{eqnarray*} -On pose $I_i = \summyone{l} y_l i_l $, comme $\dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} =\dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_m \partial u_l}$, on peut crire que~: +On pose $I_i = \summyone{l} y_l i_l $, comme $\dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} =\dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_m \partial u_l}$, on peut �crire que~: \begin{eqnarray*} \summyone{l,m} y_l y_m \dfrac{\partial \pa{\ln P\pa{e^u}}}{\partial u_l \partial u_m} & = & @@ -1905,7 +1905,7 @@ \subsection{Lemmes et th & \supegal & 0 \end{eqnarray*} -Un polynme coefficients positifs est donc log-log-convexe. +Un polyn�me � coefficients positifs est donc log-log-convexe. \end{xdemomine} @@ -1916,17 +1916,17 @@ \subsection{Lemmes et th -\subsection{Dmonstration base sur le gradient} +\subsection{D�monstration bas�e sur le gradient} \label{hmm_demo_gradient_par} \indexfr{gradient} -Cette dmonstration est prsente dans \citeindex{Levinson1983}. +Cette d�monstration est pr�sent�e dans \citeindex{Levinson1983}. -\begin{xdemo}{thorme}{\ref{theoreme_hmm_baum_welch_1}} +\begin{xdemo}{th�or�me}{\ref{theoreme_hmm_baum_welch_1}} -L'expression de la probabilit d'une squence $O$ connaissant le modle $M$ est :% +L'expression de la probabilit� d'une s�quence $O$ connaissant le mod�le $M$ est :% $$ \pr{ O\left| M\right. } =\underset{\left( q_{1},...,q_{T}% @@ -1934,35 +1934,35 @@ \subsection{D {t=1}{\overset{T-1}{\prod}}a_{q_{t},q_{t+1}}b_{q_{t+1}}\left( O_{t}\right) \right) \theta_{q_{T}}\right] $$ -Chaque coefficient intervient plusieurs fois dans l'expression de la probabilit, cependant il ne peut intervenir qu'une seule fois chaque temps $u\in\left\{ 1,...T\right\} $. C'est pourquoi on dcompose $P\left( O\left| M\right. \right) $ en~: +Chaque coefficient intervient plusieurs fois dans l'expression de la probabilit�, cependant il ne peut intervenir qu'une seule fois � chaque temps $u\in\left\{ 1,...T\right\} $. C'est pourquoi on d�compose $P\left( O\left| M\right. \right) $ en~: \begin{eqnarray} \pr{ O\left| M\right. } &=& \underset{u=1}{\overset{T-1}{\sum} } - \left[ \underset{\left( i,j\right) }{\overset{}{ + \left[ \underset{\left( i,j\right) }{\overset{}{ {\displaystyle\sum}}}a_{ij}b_{j}\left( O_{t}\right) \underset{= - \pr{ q_{u}=i,\,q_{u+1} =j,O\left| M\right. } + \pr{ q_{u}=i,\,q_{u+1} =j,O\left| M\right. } }{\underbrace{\underset{\left( q_{1},...,q_{T}\right) } {\overset{q_{u}=i,\,q_{u+1}=j}{ {\displaystyle\sum} }}\pi_{q_{1}}b_{q_{1}}\left( O_{1}\right) \theta_{q_{T}}\left(\underset{t=1,t\neq u}{\overset{T-1}{\prod}}a_{q_{t},q_{t+1}}b_{q_{t+1} }\left( O_{t}\right) \right) }}\right] \nonumber\\ \Longleftrightarrow \pr{ O\left| M\right. } &=& \underset{u=1} - {\overset{T-1}{\sum}}\left[ \underset{\left( i,j\right) + {\overset{T-1}{\sum}}\left[ \underset{\left( i,j\right) }{\overset{}{{\displaystyle\sum} }}a_{ij}b_{j}\left( O_{t}\right) \alpha_{u} - \left( i\right) \beta _{u+1}\left( j\right) \right] + \left( i\right) \beta _{u+1}\left( j\right) \right] \label{hmm_gradient_equation_un}\\ \Longleftrightarrow \pr{ O\left| M\right. } &=& \underset - {u=1}{\overset{T}{\sum}}\,\underset{i,o}{\overset{}{{\displaystyle\sum} + {u=1}{\overset{T}{\sum}}\,\underset{i,o}{\overset{}{{\displaystyle\sum} }}b_{i}\left( o\right) \underset{=\pr{ q_{u}=i,\,O_{u}=o,O\left| - M\right. } = \pr{ q_{u}=i,\,O\left| M\right. } + M\right. } = \pr{ q_{u}=i,\,O\left| M\right. } \,\indicatrice{O_{u}=o}}{\underbrace{\underset{\left(q_{1},...,q_{T}\right) } {\overset{q_{u}=i,\,O_{u}=o}{ {\displaystyle\sum} }}\left[ \pi_{q_{1}}b_{q_{1}}\left( O_{1}\right) \left( \underset - {t=1}{\overset{T-1}{\prod}}a_{q_{t},q_{t+1}}b_{q_{t+1}}\left( O_{t}\right) + {t=1}{\overset{T-1}{\prod}}a_{q_{t},q_{t+1}}b_{q_{t+1}}\left( O_{t}\right) \right) \theta_{q_{T}}\right] \label{hmm_gradient_equation_deux} }} \end{eqnarray} -On en dduit que :% +On en d�duit que :% $$ \begin{array} @@ -1971,7 +1971,7 @@ \subsection{D \dfrac{\partial \pr{ O\left| M\right. } }{\partial a_{ij} }% & = &% \underset{u=1}{\overset{T-1}{\sum}}b_{j}\left(O_{t}\right) - \alpha _{u}\left( i\right) \beta_{u+1}\left( j\right) + \alpha _{u}\left( i\right) \beta_{u+1}\left( j\right) \\ (\ref{hmm_gradient_equation_un})\Longrightarrow & % \dfrac{\partial \pr{ O\left| M\right. } }{\partial\pi_{i}}% @@ -1987,11 +1987,11 @@ \subsection{D \dfrac{\partial \pr{ O\left| M\right. } }{\partial b_{i}\left( o\right) }% & = &% \underset{u=1}{\overset{T}{\sum}}\alpha_{u}\left( i\right)\beta_{u} - \left( i\right) \,\mathbf{1}_{\left\{ O_{u}=o\right\} }% + \left( i\right) \,\mathbf{1}_{\left\{ O_{u}=o\right\} }% \end{array} $$ -De plus, dans le cas de plusieurs squences, si $x$ est un coefficient du modle, on utilise le fait que : +De plus, dans le cas de plusieurs s�quences, si $x$ est un coefficient du mod�le, on utilise le fait que : $$ \dfrac{\partial L}{\partial x}=\dfrac{\partial\left( \underset{k=1}{\overset{K}{\prod}} \pr{ @@ -2001,34 +2001,34 @@ \subsection{D }\dfrac{\partial \pr{ O^{k}\left| M\right. } }{\partial x} $$ -Comme la fonction $\pa{A,B,\theta,\pi} \longrightarrow \pr{O|M}$ est un polynme coefficients positifs, le thorme~\ref{hmm_theorem_baumwelch_un} nous assure de la croissance de $\pr{O|M}$ au cours des itrations de l'algorithme d'apprentissage. Comme cette suite est majore par $1$, elle est convergente. +Comme la fonction $\pa{A,B,\theta,\pi} \longrightarrow \pr{O|M}$ est un polyn�me � coefficients positifs, le th�or�me~\ref{hmm_theorem_baumwelch_un} nous assure de la croissance de $\pr{O|M}$ au cours des it�rations de l'algorithme d'apprentissage. Comme cette suite est major�e par $1$, elle est convergente. \end{xdemo} -\subsection{Dmonstration antrieure la dcouverte de l'algorithme EM} +\subsection{D�monstration ant�rieure � la d�couverte de l'algorithme EM} \label{hmm_demo_em_em} -Cette dmonstration est prsente dans \citeindex{Levinson1983}. +Cette d�monstration est pr�sent�e dans \citeindex{Levinson1983}. -\begin{xdemo}{thorme}{\ref{theoreme_hmm_baum_welch_1}} +\begin{xdemo}{th�or�me}{\ref{theoreme_hmm_baum_welch_1}} -Soient deux modles $M$ et $M^{\prime}$ possdant le mme nombre d'tats. On dfinit $P_{k}\left( M\right) = \pr{ O^{k}\left| M\right. } $ et $P_{k}\left( M^{\prime}\right) = \pr{ O^{k}\left| M^{\prime}\right. }$. Pour cette squence d'observations, il existe $N^{T_{k}}$ squences d'tats possibles. On note $s^{i}=\left( s_{1}^{i},...,s_{T_{k}}% -^{i}\right) $ la squence d'tat d'indice $i$. +Soient deux mod�les $M$ et $M^{\prime}$ poss�dant le m�me nombre d'�tats. On d�finit $P_{k}\left( M\right) = \pr{ O^{k}\left| M\right. } $ et $P_{k}\left( M^{\prime}\right) = \pr{ O^{k}\left| M^{\prime}\right. }$. Pour cette s�quence d'observations, il existe $N^{T_{k}}$ s�quences d'�tats possibles. On note $s^{i}=\left( s_{1}^{i},...,s_{T_{k}}% +^{i}\right) $ la s�quence d'�tat d'indice $i$. $$ P_{k}\left( M\right) =\underset{\left( q_{1},...,q_{T_{k}}\right)}{\overset{}{{\displaystyle\sum}}}\left[ \pi_{q_{1}}b_{q_{1}}\left( O_{1}^{k}\right) \left( \underset {t=1}{\overset{T_{k}-1} - {\prod}}a_{q_{t},q_{t+1}}b_{q_{t+1}}\left( O_{t} ^{k}\right) \right) + {\prod}}a_{q_{t},q_{t+1}}b_{q_{t+1}}\left( O_{t} ^{k}\right) \right) \theta_{q_{T_{k}}}\right] =\underset{i=1} {\overset{N^{T_{k}}}{ {\displaystyle\sum}}}u_{i}^{k} $$ -De manire analogue : +De mani�re analogue : $$ P_{k}\left( M^{\prime}\right) =\underset{\left(q_{1},...,q_{T}\right) } @@ -2036,7 +2036,7 @@ \subsection{D \pi_{q_{1}}^{\prime}b_{q_{1}}^{\prime}\left( O_{1}^{k}\right) \left( \underset{t=1}{\overset{T_{k}-1}{\prod}}a_{q_{t},q_{t+1}}^{\prime }b_{q_{t+1}}^{\prime}\left( O_{t}^{k}\right) \right) - \theta_{q_{T_{k}} }^{\prime}\right] =\underset{i=1}{\overset{N^{T_{k}}}{ + \theta_{q_{T_{k}} }^{\prime}\right] =\underset{i=1}{\overset{N^{T_{k}}}{ {\displaystyle\sum} }}v_{i}^{k} $$ @@ -2056,7 +2056,7 @@ \subsection{D }{P_{k}\left( M\right) }-\frac{Q_{k}\left( M,M\right) }{P_{k}\left( M\right) }\right] $$ -On cherche maximiser : +On cherche � maximiser : $$ \underset{\left( v_{1},...,v_{N^{T}}\right) }{\max}\,\underset{k=1}{\overset{K}{\sum}} @@ -2076,7 +2076,7 @@ \subsection{D _{s_{T}^{i}}^{\prime}\right] $$ -On cherche crire diffremment la somme $\overset{N^{T_{k}}}{\underset{i=1}{\sum}}u_{i}^{k}\ln v_{i}^{k}$ sous la forme$\,$ : +On cherche � �crire diff�remment la somme $\overset{N^{T_{k}}}{\underset{i=1}{\sum}}u_{i}^{k}\ln v_{i}^{k}$ sous la forme$\,$ : \begin{eqnarray} \underset{k=1}{\overset{K}{\sum}}\left[\dfrac{1}{P_{k}\left( M\right) } @@ -2090,9 +2090,9 @@ \subsection{D Pour cela, on note : \begin{itemize} -\item $p\left( s,i,j\right) $ avec $\left( i,j\right) \in\left\{ E,S,1,...,N\right\} ^{2}$ le nombre de fois o la connexion $i\rightarrow j$ est utilise dans la squence d'tats $s$ +\item $p\left( s,i,j\right) $ avec $\left( i,j\right) \in\left\{ E,S,1,...,N\right\} ^{2}$ le nombre de fois o� la connexion $i\rightarrow j$ est utilis�e dans la s�quence d'�tats $s$ -\item $p^{\prime}\left( s,i,o\right) $ avec $\left( i,o\right) \in\left\{ E,S,1,...,N\right\} \times\mathbf{O}$ le nombre de fois o l'tat $i$ met l'observation $o$ dans la squence d'tats $s$ +\item $p^{\prime}\left( s,i,o\right) $ avec $\left( i,o\right) \in\left\{ E,S,1,...,N\right\} \times\mathbf{O}$ le nombre de fois o� l'�tat $i$ �met l'observation $o$ dans la s�quence d'�tats $s$ \end{itemize}\bigskip @@ -2133,7 +2133,7 @@ \subsection{D \pr{ q_{T_{k}}=n,O^{k}\left| M\right. }\label{hmm_em_demo_eq_quatre}% \end{eqnarray} -Les quations (\ref{hmm_proba_state}) et (\ref{hmm_proba_transition}) (page~\pageref{hmm_proba_transition}) permettent de dduire les valeurs de $A_{nm}, B_{n}, C_{n}, D_{n}$. En appliquant le lemme~\ref{hmm_lemme_baumwelch_2_deux} l'quation (\ref{hmm_em_equation_trois}), on dmontre que les valeurs qui maximisent $Q\left(M,M^{\prime}\right) $ sont~:% +Les �quations (\ref{hmm_proba_state}) et (\ref{hmm_proba_transition}) (page~\pageref{hmm_proba_transition}) permettent de d�duire les valeurs de $A_{nm}, B_{n}, C_{n}, D_{n}$. En appliquant le lemme~\ref{hmm_lemme_baumwelch_2_deux} � l'�quation (\ref{hmm_em_equation_trois}), on d�montre que les valeurs qui maximisent $Q\left(M,M^{\prime}\right) $ sont~:% $$ \begin{array} @@ -2146,7 +2146,7 @@ \subsection{D \end{array} $$ -On retrouve les formules de Baum-Welch. Si on note $\left( M_{t}\right) $ la suite de modles obtenus aprs chaque itration, la dmonstration prcdente (paragraphe~\ref{hmm_demo_gradient_par}) nous assure que la suite $\pr{ O\left| M_{t}\right. } $ est croissante, comme elle est majore par un, elle est convergente. Cependant, la convergence s'effectue vers un maximum local. +On retrouve les formules de Baum-Welch. Si on note $\left( M_{t}\right) $ la suite de mod�les obtenus apr�s chaque it�ration, la d�monstration pr�c�dente (paragraphe~\ref{hmm_demo_gradient_par}) nous assure que la suite $\pr{ O\left| M_{t}\right. } $ est croissante, comme elle est major�e par un, elle est convergente. Cependant, la convergence s'effectue vers un maximum local. \end{xdemo} @@ -2175,36 +2175,36 @@ \subsection{Algorithme EM (Expectation-Maximisation)} \indexfr{EM} -\subsubsection{Dfinition} +\subsubsection{D�finition} -Dans le cas gnral, l'algorithme permet d'estimer les paramtres d'une loi lorsque certaines donnes sont manquantes ou caches. On considre deux variables alatoires~: +Dans le cas g�n�ral, l'algorithme permet d'estimer les param�tres d'une loi lorsque certaines donn�es sont manquantes ou cach�es. On consid�re deux variables al�atoires~: \begin{itemize} -\item $X\in\mathcal{X}\subset \R^p$ de densit $f\left( x\left| \theta\right. \right) $ avec $\theta\in\Theta$ ($\Theta$ est l'ensemble des paramtres) -\item $Z\in\mathcal{Z}\subset\R^{q}$ de densit $g\left( z\left| \theta\right. \right) $ +\item $X\in\mathcal{X}\subset \mathbb{R}^p$ de densit� $f\left( x\left| \theta\right. \right) $ avec $\theta\in\Theta$ ($\Theta$ est l'ensemble des param�tres) +\item $Z\in\mathcal{Z}\subset\mathbb{R}^{q}$ de densit� $g\left( z\left| \theta\right. \right) $ \end{itemize} -Les variables $X$, $Z$, $\pa{X,Z}$ sont appeles : +Les variables $X$, $Z$, $\pa{X,Z}$ sont appel�es : \begin{itemize} -\item $X$ est la variable observe ou incomplte -\item $Z$ est la variable cache ou manquante -\item $\left( X,Z\right) $ est la variable complte +\item $X$ est la variable observ�e ou incompl�te +\item $Z$ est la variable cach�e ou manquante +\item $\left( X,Z\right) $ est la variable compl�te \end{itemize} -On note $h\left( x,z\left| \theta\right. \right) $ la densit de $\left( X,Z\right)$ et $k\left( z\left| x,\theta\right. \right) $ la densit de la variable $E\left( Z\left| X\right. \right)$. D'aprs la rgle de Bayes~: +On note $h\left( x,z\left| \theta\right. \right) $ la densit� de $\left( X,Z\right)$ et $k\left( z\left| x,\theta\right. \right) $ la densit� de la variable $E\left( Z\left| X\right. \right)$. D'apr�s la r�gle de Bayes~: \begin{eqnarray*} && h\left( x,z\left| \theta\right. \right) = k\left( z\left| x,\theta\right. \right) f\left( x\left| \theta\right. \right)\\ &\Longrightarrow & f\left( x\left| \theta\right. \right) =\dfrac{h\left( x,z\left| - \theta\right. \right) } + \theta\right. \right) } {k\left( z\left| x,\theta\right. \right) }\\ & \Longrightarrow & \ln f\left( x\left| \theta\right. \right) =\ln h\left( x,z\left| - \theta\right. \right) + \theta\right. \right) -\ln k\left( z\left| x,\theta\right. \right) \\ @@ -2214,79 +2214,79 @@ \subsubsection{D -\left[ \ln k\left( z\left| x,\theta\right. \right) \right]k\left( z\left| x,\theta\right. \right) \\ & \Longrightarrow & \int_{\mathcal{Z}}\left[ \ln f\left( x\left| \theta\right. \right) - \right] k\left( z\left| x,\theta\right. + \right] k\left( z\left| x,\theta\right. \right) dz=\int_{\mathcal{Z}}\left[ \ln h\left( x,z\left| \theta\right. \right) \right] \,k\left( z\left| x,\theta\right. \right) dz - \int _{\mathcal{Z}}\left[ \ln k\left( z\left| x,\theta\right. \right) \right] \,k\left( z\left| x,\theta\right. \right) dz \end{eqnarray*} -On dfinit : +On d�finit : \begin{eqnarray*} Q_{\mathcal{Z}}\left( \varphi,\theta\right) &=& E_{\mathcal{Z}}\left[ \ln h\left( - x,z\left| \varphi\right. \right) \,\left| + x,z\left| \varphi\right. \right) \,\left| \,x,\theta\right. \right] =\int_{\mathcal{Z}}\left[ \ln h\left( x,z\left| \varphi\right. \right) \right] \,k\left( z\left| x,\theta\right.\right) dz\\ H_{\mathcal{Z}}\left( \varphi,\theta\right) &=& E_{\mathcal{Z}}\left[ - \ln h\left( z\left| x,\varphi\right. \right) \,\left| + \ln h\left( z\left| x,\varphi\right. \right) \,\left| \,x,\theta\right. \right] =\int_{\mathcal{Z}}\left[ \ln k\left( z\left| x,\varphi \right. \right) \right] \,k\left( z\left| x,\theta\right. \right) dz \end{eqnarray*} -D'o :% +D'o� :% \begin{eqnarray*} & \Longrightarrow & \ln f\left( x\left| \theta\right. \right) = - \underset {=Q_{\mathcal{Z}}\left( \theta,\theta\right) + \underset {=Q_{\mathcal{Z}}\left( \theta,\theta\right) }{\underbrace{E_{\mathcal{Z} }\left[ \ln h\left( x,z\left| \theta\right. \right) \,\left| - \,x,\theta\right. \right] + \,x,\theta\right. \right] }}-\underset{=H_{\mathcal{Z}}\left( \theta ,\theta\right) }{\underbrace{E_{\mathcal{Z}} - \left[ \ln h\left( z\left| + \left[ \ln h\left( z\left| x,\theta\right. \right) \,\left| \,x,\theta\right. \right] }}\\ & \Longrightarrow & \ln f\left( x\left| \theta\right. \right) =Q_{\mathcal{Z}}\left( - \theta,\theta\right) -H_{\mathcal{Z}}\left( + \theta,\theta\right) -H_{\mathcal{Z}}\left( \theta,\theta\right) \end{eqnarray*} Or : \begin{eqnarray*} H_{\mathcal{Z}}\left( \varphi,\theta\right) -H_{\mathcal{Z}}\left( \theta,\theta\right) - =\int_{\mathcal{Z}}\left[ \ln\dfrac{k\left( + =\int_{\mathcal{Z}}\left[ \ln\dfrac{k\left( z\left| x,\varphi\right. \right) }{k\left( z\left| x,\theta\right. \right) }\right] - k\left( z\left| x,\theta\right. \right) dz + k\left( z\left| x,\theta\right. \right) dz \end{eqnarray*} - \begin{xtheorem}{Ingalit de Jensen} - \label{theoreme_inegalite_jensen_1}% - \indexfr{Jensen}% - Soit $X\in\mathcal{X}$ une variable alatoire de densit $f$, soit $g:\mathcal{X}\longrightarrow\R$ - une fonction convexe alors~: - - $$ - E\left( g\left( X\right) \right) =\int _{\mathcal{X}}g\left( x\right) - f\left( x\right) dx\leqslant g\left( - \int_{\mathcal{X}}f\left( x\right) dx\right) =g\left( E\left( X\right) \right) - $$ - - \end{xtheorem} + \begin{xtheorem}{In�galit� de Jensen} + \label{theoreme_inegalite_jensen_1}% + \indexfr{Jensen}% + Soit $X\in\mathcal{X}$ une variable al�atoire de densit� $f$, soit $g:\mathcal{X}\longrightarrow\mathbb{R}$ + une fonction convexe alors~: + + $$ + E\left( g\left( X\right) \right) =\int _{\mathcal{X}}g\left( x\right) + f\left( x\right) dx\leqslant g\left( + \int_{\mathcal{X}}f\left( x\right) dx\right) =g\left( E\left( X\right) \right) + $$ + + \end{xtheorem} -D'aprs l'ingalit de Jensen, on dduit que, puisque la fonction $t\rightarrow-\ln t$ est convexe~: +D'apr�s l'in�galit� de Jensen, on d�duit que, puisque la fonction $t\rightarrow-\ln t$ est convexe~: $$ H_{\mathcal{Z}}\left( \varphi,\theta\right) -H_{\mathcal{Z}} - \left( \theta,\theta\right) \leqslant\ln\int_{\mathcal{Z}}\left[ + \left( \theta,\theta\right) \leqslant\ln\int_{\mathcal{Z}}\left[ \dfrac{k\left( z\left| x,\varphi\right. \right) }{k\left( z\left| x,\theta\right. \right) }\right] k\left( z\left| x,\theta\right. \right) dz=\ln\int_{\mathcal{Z}}k\left( z\left| x,\varphi\right. \right) dz=\ln1=0 $$ -Par consquent~: +Par cons�quent~: \begin{eqnarray} \forall\varphi\in\Theta,\; H_{\mathcal{Z}}\left( \theta,\theta\right) \geqslant H_{\mathcal{Z}}\left( @@ -2297,68 +2297,68 @@ \subsubsection{D \begin{eqnarray} && \text{soit } \varphi\in\Theta \text { tel que } Q_{\mathcal{Z} }\left( \varphi,\theta\right) - \geqslant Q_{\mathcal{Z}}\left( \theta ,\theta\right) + \geqslant Q_{\mathcal{Z}}\left( \theta ,\theta\right) \nonumber\\ & \Longrightarrow & E_{\mathcal{Z}}\left[ \ln h\left( x,z\left| \varphi \right. \right) \,\left| - \,x,\theta\right. \right] -E_{\mathcal{Z} + \,x,\theta\right. \right] -E_{\mathcal{Z} }\left[ \ln h\left( z\left| x,\varphi\right. \right) \,\left| \,x,\theta\right. \right] \geqslant - E_{\mathcal{Z}}\left[ \ln h\left( + E_{\mathcal{Z}}\left[ \ln h\left( x,z\left| \theta\right. \right) \,\left| \,x,\theta\right. \right] -E_{\mathcal{Z}}\left[ \ln h\left( - z\left| x,\theta\right. \right) + z\left| x,\theta\right. \right) \,\left| \,x,\theta\right. \right] \nonumber \\ & \Longrightarrow & \ln f\left( x\left| \varphi\right. \right) \geqslant\ln f\left( x\left| \theta\right. - \right) \label{hmm_eq_em_jensen_2} + \right) \label{hmm_eq_em_jensen_2} \end{eqnarray} -L'algorithme~EM a pour objectif de trouver $\theta^{\ast}\in\Theta$ qui maximise $\ln f\left( x\left| \theta\right. \right) $, il est inspir de ce qui prcde~: - - \begin{xalgorithm}{EM} - \label{algorithme_EM} - \indexfr{EM} - Les notations adoptes sont celles des paragraphes qui prcdent. L'objectif de cet algorithme est de trouver - les paramtres $\theta$ qui maximisent la densit $f\pa{x \sac \theta}$~: - - \begin{xalgostep}{initialisation} - On choisit $\theta_{0}\in\Theta$ de manire alatoire. - \end{xalgostep} - - \begin{xalgostep}{expectation "E"}\label{hmm_em_step_e} - Pour $\theta_{t}$, on calcule $Q_{\mathcal{Z}}\left( \varphi,\theta _{t}\right) = - \displaystyle\int_{\mathcal{Z}}\left[ \ln h\left( x,z\left| - \varphi\right. \right) \right] \,k\left( z\left| x,\theta_t\right. \right) dz$. - \end{xalgostep} - - \begin{xalgostep}{maximisation "M"} - On obtient $\theta_{t+1}=\underset{\varphi\in\Theta}{\arg\max }\; - Q_{\mathcal{Z}}\left( \varphi,\theta_{t}\right)$. - \end{xalgostep} - - \begin{xalgostep}{terminaison} - On retourne l'tape~\ref{hmm_em_step_e} jusqu' ce que la suite - $\left( \ln f\left( x\left| \theta_{t}\right. \right)\right) _{t}$ converge. - \end{xalgostep} - - \end{xalgorithm} - - - \begin{xtheorem}{convergence de l'algorithme EM}\label{hmm_theorem_em} - \indexfr{EM} - - La suite $\left( \ln f\left( x\left| \theta_{t}\right. \right)\right) _{t}$ construite par l'algorthime - EM (\ref{algorithme_EM}) - converge vers un minimum local de la fonction $\theta \longrightarrow f\pa{x \sac \theta}$. - \end{xtheorem} - - -\begin{xdemo}{thorme}{\ref{hmm_theorem_em}} -Le thorme est dmontre par l'quation (\ref{hmm_eq_em_jensen_2}), la suite $\left( \ln f\left( x\left| \theta_{t}\right. \right)\right) _{t}$ est croissante et borne, donc elle converge. +L'algorithme~EM a pour objectif de trouver $\theta^{\ast}\in\Theta$ qui maximise $\ln f\left( x\left| \theta\right. \right) $, il est inspir� de ce qui pr�c�de~: + + \begin{xalgorithm}{EM} + \label{algorithme_EM} + \indexfr{EM} + Les notations adopt�es sont celles des paragraphes qui pr�c�dent. L'objectif de cet algorithme est de trouver + les param�tres $\theta$ qui maximisent la densit� $f\pa{x \sac \theta}$~: + + \begin{xalgostep}{initialisation} + On choisit $\theta_{0}\in\Theta$ de mani�re al�atoire. + \end{xalgostep} + + \begin{xalgostep}{expectation "E"}\label{hmm_em_step_e} + Pour $\theta_{t}$, on calcule $Q_{\mathcal{Z}}\left( \varphi,\theta _{t}\right) = + \displaystyle\int_{\mathcal{Z}}\left[ \ln h\left( x,z\left| + \varphi\right. \right) \right] \,k\left( z\left| x,\theta_t\right. \right) dz$. + \end{xalgostep} + + \begin{xalgostep}{maximisation "M"} + On obtient $\theta_{t+1}=\underset{\varphi\in\Theta}{\arg\max }\; + Q_{\mathcal{Z}}\left( \varphi,\theta_{t}\right)$. + \end{xalgostep} + + \begin{xalgostep}{terminaison} + On retourne � l'�tape~\ref{hmm_em_step_e} jusqu'� ce que la suite + $\left( \ln f\left( x\left| \theta_{t}\right. \right)\right) _{t}$ converge. + \end{xalgostep} + + \end{xalgorithm} + + + \begin{xtheorem}{convergence de l'algorithme EM}\label{hmm_theorem_em} + \indexfr{EM} + + La suite $\left( \ln f\left( x\left| \theta_{t}\right. \right)\right) _{t}$ construite par l'algorthime + EM (\ref{algorithme_EM}) + converge vers un minimum local de la fonction $\theta \longrightarrow f\pa{x \sac \theta}$. + \end{xtheorem} + + +\begin{xdemo}{th�or�me}{\ref{hmm_theorem_em}} +Le th�or�me est d�montr�e par l'�quation (\ref{hmm_eq_em_jensen_2}), la suite $\left( \ln f\left( x\left| \theta_{t}\right. \right)\right) _{t}$ est croissante et born�e, donc elle converge. \end{xdemo} - - - - - + + + + + @@ -2367,60 +2367,60 @@ \subsubsection{Exemple : la taille d'un poisson selon le sexe} \indexfr{poisson} -Soit $X$ une variable alatoire reprsentant la taille d'un poisson d'une espce donne l'ge adulte. La taille dpend fortement du sexe du poisson. Soit $Z\in\left\{ 0,1\right\} $ le sexe du poisson, $X$ est la variable observe, $Z$ la variable manquante. On suppose que, connaissant le sexe du poisson, sa taille suit une loi normale de paramtre $\left( \mu_{i},\sigma_{i}\right) _{i\in\left\{ 0,1\right\} }$. Le sexe du poisson suit une loi binomiale de paramtre $p$. +Soit $X$ une variable al�atoire repr�sentant la taille d'un poisson d'une esp�ce donn�e � l'�ge adulte. La taille d�pend fortement du sexe du poisson. Soit $Z\in\left\{ 0,1\right\} $ le sexe du poisson, $X$ est la variable observ�e, $Z$ la variable manquante. On suppose que, connaissant le sexe du poisson, sa taille suit une loi normale de param�tre $\left( \mu_{i},\sigma_{i}\right) _{i\in\left\{ 0,1\right\} }$. Le sexe du poisson suit une loi binomiale de param�tre $p$. -Dans ce cas, $\theta=\left( p,\mu_{0},\sigma_{0},\mu_{1},\sigma_{1}\right)$, la densit de $X|Z$ est :% +Dans ce cas, $\theta=\left( p,\mu_{0},\sigma_{0},\mu_{1},\sigma_{1}\right)$, la densit� de $X|Z$ est :% $$ l\left( x\left| z,\theta\right. \right) = \dfrac{1}{\sigma_z\sqrt{2\pi} } e^{-\dfrac{\left( x-\mu_{z}\right) ^{2}}{2\sigma_{z}^{2}}} $$ -et la densit de $Z|\theta$ est~: +et la densit� de $Z|\theta$ est~: $$ \pr{Z=z\left| \theta\right. } = p\,\mathbf{1}_{\left\{z=0\right\} }+\left( 1-p\right) \,\mathbf{1}_{\left\{ z=1\right\} } $$ -D'aprs les hypothses, on en dduit que :% +D'apr�s les hypoth�ses, on en d�duit que :% \begin{eqnarray*} h\left( x,z\left| \theta\right. \right) &=& \indicatrice{z=0} \left[ \dfrac{p}{\sigma_0 - \sqrt{2\pi}}e^{-\dfrac{\left( x-\mu + \sqrt{2\pi}}e^{-\dfrac{\left( x-\mu _{0}\right) ^{2}}{2\sigma_{0}^{2}}}\right] + \indicatrice{z=1} \left[ - \dfrac{\left( 1-p\right) }{\sigma_1 + \dfrac{\left( 1-p\right) }{\sigma_1 \sqrt{2\pi}}e^{-\dfrac {\left( x-\mu_{1}\right) ^{2}}{2\sigma_{1}^{2}}}\right] \\ \ln h\left( x,z\left| \theta\right. \right) &=& \indicatrice{z=0}\left[ - \ln\dfrac{p}{\sigma_0 \sqrt{2\pi}}-\dfrac{\left( + \ln\dfrac{p}{\sigma_0 \sqrt{2\pi}}-\dfrac{\left( x-\mu _{0}\right) ^{2}}{2\sigma_{0}^{2}}\right] +\indicatrice{z=1}\left[ - \ln\dfrac{\left( 1-p\right) }{\sigma_1 + \ln\dfrac{\left( 1-p\right) }{\sigma_1 \sqrt{2\pi}}-\dfrac{\left( x-\mu _{1}\right) ^{2}}{2\sigma_{1}^{2}}\right] \\ f\left( x\left| \theta\right. \right) &=& \dfrac{p}{\sigma_0 \sqrt{2\pi} } - e^{-\dfrac{\left( x-\mu_{0}\right) + e^{-\dfrac{\left( x-\mu_{0}\right) ^{2}}{2\sigma_{0}^{2}}}+\dfrac{\left(1-p\right) }{\sigma_1 \sqrt{2\pi}} - e^{-\dfrac{\left( x-\mu_{1}\right) ^{2}} + e^{-\dfrac{\left( x-\mu_{1}\right) ^{2}} {2\sigma_{1}^{2}}}\\ \pr{ Z=z\left| x,\theta\right. } &=& k\left( z\left| x,\theta\right. - \right) =\dfrac{h\left( x,z\left| + \right) =\dfrac{h\left( x,z\left| \theta\right. \right) }{f\left( x\left| \theta\right. \right) } \end{eqnarray*} -L'objectif est de trouver les vritables valeurs de $\left( p,\mu _{0},\sigma_{0},\mu_{1},\sigma_{1}\right) $ partir d'une liste d'observation $\left( x_{1},...,x_{n}\right) $ donc de trouver : +L'objectif est de trouver les v�ritables valeurs de $\left( p,\mu _{0},\sigma_{0},\mu_{1},\sigma_{1}\right) $ � partir d'une liste d'observation $\left( x_{1},...,x_{n}\right) $ donc de trouver : $$ \theta^{\ast}=\underset{\theta}{\arg\max}\underset{i=1}{\overset{n}{\prod} }f\left( x_{i}\left| \theta\right. \right) =\underset{\theta}{\arg\max - }\underset{i=1}{\overset{n}{\sum}}\ln f\left( + }\underset{i=1}{\overset{n}{\sum}}\ln f\left( x_{i}\left| \theta\right. \right) $$ -Ce problme n'est pas soluble par maximum de vraisemblance. En effet, maximiser la vraisemblance du modle aboutit la rsolution d'un systme d'quations cinq inconnues insoluble. L'algorithme EM est une alternative, dans ce cas~: +Ce probl�me n'est pas soluble par maximum de vraisemblance. En effet, maximiser la vraisemblance du mod�le aboutit � la r�solution d'un syst�me d'�quations � cinq inconnues insoluble. L'algorithme EM est une alternative, dans ce cas~: \begin{eqnarray*} Q_{\mathcal{Z}}\left( \theta,\theta_{t}\right) &=& \overset {n}{\underset{i=1}{\sum}}\overset{1} @@ -2430,15 +2430,15 @@ \subsubsection{Exemple : la taille d'un poisson selon le sexe} \left[ \ln\dfrac{p}{\sigma_0 \sqrt{2\pi}} - \dfrac{\left( x_{i}-\mu_{0}\right) ^{2}} {2\sigma_{0}^{2}}\right] k\pa{0 |x_i,\theta_t} + \left[ \ln\dfrac{\left( 1-p\right) }{\sigma_1 \sqrt{2\pi}}-\dfrac{\left( x_i-\mu_{1}\right) ^{2}} - {2\sigma_{1}^{2} }\right] k\pa{1 |x_i,\theta_t} + {2\sigma_{1}^{2} }\right] k\pa{1 |x_i,\theta_t} \end{eqnarray*} -Trouver $\theta_{t+1}=\underset{\theta}{\arg\max}Q_{\mathcal{Z}}\left( \theta,\theta_{t}\right) $ est un problme plus simple que le prcdent et soluble car cette fois, le systme d'quation cinq inconnues obtenu est soluble. Cet exemple est un cas particulier des mlanges de lois normales. +Trouver $\theta_{t+1}=\underset{\theta}{\arg\max}Q_{\mathcal{Z}}\left( \theta,\theta_{t}\right) $ est un probl�me plus simple que le pr�c�dent et soluble car cette fois, le syst�me d'�quation � cinq inconnues obtenu est soluble. Cet exemple est un cas particulier des m�langes de lois normales. \indexfr{EM}\indexfr{SEM}\indexfr{SAEM} \indexfr{stochastique} -Les formules de Baum-Welch sont un cas particulier de cet algorithme dont la dcourverte est postrieure. L'algorithme EM est aussi dclin dans plusieurs variantes SEM, SAEM, CEM, ... (voir \citeindex{Celeux1985}, \citeindex{Celeux1995}). En particulier, comme pour les rseaux de neurones\seeannex{rn_section_train_rn}{rseau de neurones}, il existe une version stochastique de l'algorithme EM note SEM. +Les formules de Baum-Welch sont un cas particulier de cet algorithme dont la d�courverte est post�rieure. L'algorithme EM est aussi d�clin� dans plusieurs variantes SEM, SAEM, CEM, ... (voir \citeindex{Celeux1985}, \citeindex{Celeux1995}). En particulier, comme pour les r�seaux de neurones\seeannex{rn_section_train_rn}{r�seau de neurones}, il existe une version stochastique de l'algorithme EM not�e SEM. @@ -2449,27 +2449,27 @@ \subsubsection{Exemple : la taille d'un poisson selon le sexe} -\subsection{Dmonstration des formules de Baum-Welch avec l'algorithme EM} +\subsection{D�monstration des formules de Baum-Welch avec l'algorithme EM} -\begin{xdemo}{thorme}{\ref{theoreme_hmm_baum_welch_1}} +\begin{xdemo}{th�or�me}{\ref{theoreme_hmm_baum_welch_1}} -Les formules de Baum-Welch (voir table~\ref{figure_formule_baumwelch-fig}, page~\pageref{figure_formule_baumwelch-fig}) se dduisent de l'algorithme EM (algorithme ~\ref{algorithme_EM}). Soit $M\pa{\phi}$ un modle de Markov cach dont les paramtres sont le vecteur $\phi = \pa{A_\phi, B_\phi, \pi_\phi, \theta_\phi}$. Soit $O=\vecteur{O_1}{O_K}$ $K$ squences d'observations avec $\forall k \in \intervalle{1}{K}, \; O^k = \vecteur{O_1^k}{O_{T_k}^k}$, $s = \vecteur{q_1}{q_T}$ est une squence d'tats cachs. Les densits $h$, $k$, $f$ de l'algorithme EM correspondent ~: +Les formules de Baum-Welch (voir table~\ref{figure_formule_baumwelch-fig}, page~\pageref{figure_formule_baumwelch-fig}) se d�duisent de l'algorithme EM (algorithme ~\ref{algorithme_EM}). Soit $M\pa{\phi}$ un mod�le de Markov cach� dont les param�tres sont le vecteur $\phi = \pa{A_\phi, B_\phi, \pi_\phi, \theta_\phi}$. Soit $O=\vecteur{O_1}{O_K}$ $K$ s�quences d'observations avec $\forall k \in \intervalle{1}{K}, \; O^k = \vecteur{O_1^k}{O_{T_k}^k}$, $s = \vecteur{q_1}{q_T}$ est une s�quence d'�tats cach�s. Les densit�s $h$, $k$, $f$ de l'algorithme EM correspondent �~: \begin{eqnarray*} h\pa{O,s | M\pa{\phi}} &=& \prody{k=1}{K} \pr{\vecteurno{O_1^k}{O_{T_k}^k}, - \vecteurno{q_1}{q_{T_k}} | M\pa{\phi}} \\ + \vecteurno{q_1}{q_{T_k}} | M\pa{\phi}} \\ &=& \prody{k=1}{K} \pi_{\phi,q_1} b_{\phi,q_1}\pa{O_1} \crochet { \prody{t=2}{T_k} - a_{\phi,q_{t-1} q_t} b_{\phi,q_t} \pa{O_t} + a_{\phi,q_{t-1} q_t} b_{\phi,q_t} \pa{O_t} } \theta_{\phi,q_{T_k}} \\ f\pa{O | M\pa{\phi}} &=& \summyone{s} h\pa{O,s | M\pa{\phi}} \\ k\pa{s | O,M\pa{\phi}} &=& \dfrac{ h\pa{O,s | M\pa{\phi}} } {f\pa{O | M\pa{\phi}}} \end{eqnarray*} -On note $\mathcal{S}$ l'ensemble des squences d'tats cachs, alors : +On note $\mathcal{S}$ l'ensemble des s�quences d'�tats cach�s, alors : \begin{eqnarray} Q_{\mathcal{S}} \pa{\psi, \phi} &=& \summyone{s \in \mathcal{S}} \ln h\pa{O,s | M\pa{\psi}} - k\pa{s | O,M\pa{\phi}} \\ + k\pa{s | O,M\pa{\phi}} \\ &=& \summyone{s \in \mathcal{S}} k\pa{s | O,M\pa{\phi}} \summy{k=1}{K} \crochet{ \begin{array}{cl} @@ -2479,13 +2479,13 @@ \subsection{D \end{array} } \label{hmm_em_demo_un} \end{eqnarray} -$\Theta$ est l'ensemble des paramtres $\phi = \pa{A_\phi, B_\phi, \pi_\phi, \theta_\phi}$ vrifiant les contraintes inhrentes aux chanes de Markov caches. L'objectif est de trouver : +$\Theta$ est l'ensemble des param�tres $\phi = \pa{A_\phi, B_\phi, \pi_\phi, \theta_\phi}$ v�rifiant les contraintes inh�rentes aux cha�nes de Markov cach�es. L'objectif est de trouver : $$ \psi^* = \underset{\psi \in \Theta} {\arg \max} \; Q_{\mathcal{S}} \pa{\psi, \phi} $$ -Pour cela, on crit diffremment l'quation (\ref{hmm_em_demo_un}) : +Pour cela, on �crit diff�remment l'�quation (\ref{hmm_em_demo_un}) : \begin{eqnarray} Q_{\mathcal{S}} \pa{\psi, \phi} = \summy{n=1}{N} C_{n}\ln \pi_{\psi,n} + \summy{n=1}{N}\summy{m=1}{N} A_{nm} @@ -2493,7 +2493,7 @@ \subsection{D \summyone{o} B_{n,o} \ln b_{\psi,n} \pa{o} + \summy{n=1}{N} D_{n} \ln\theta_{\psi,n} \end{eqnarray} -Les matrices $C_{n}, A_{nm}, B_{n,o}, D_{n}$ ont les valeurs donnes par les quations (\ref{hmm_em_demo_eq_un}) (\ref{hmm_em_demo_eq_quatre}) (page~\pageref{hmm_em_demo_eq_un}). +Les matrices $C_{n}, A_{nm}, B_{n,o}, D_{n}$ ont les valeurs donn�es par les �quations (\ref{hmm_em_demo_eq_un}) � (\ref{hmm_em_demo_eq_quatre}) (page~\pageref{hmm_em_demo_eq_un}). \end{xdemo} @@ -2509,22 +2509,22 @@ \subsection{D -\subsection{Amlioration de l'apprentissage} +\subsection{Am�lioration de l'apprentissage} \indexfrr{coefficients}{nuls}% -\indexfr{recuit simul}% +\indexfr{recuit simul�}% \indexfr{apprentissage}% \label{hmm_apprentissage_ameliore}% -Les formules de Baum-Welch (voir table~\ref{figure_formule_baumwelch-fig}) impliquent que si un coefficient devient nul aprs un certain nombre d'itrations, il le restera aux itrations suivantes. Cela ne signifie pas que la valeur optimale pour ce coefficient soit diffrente de zro, cependant si ce n'est pas le cas, l'apprentissage est biais. C'est pourquoi on prfre que tous les coefficients soient non nuls. +Les formules de Baum-Welch (voir table~\ref{figure_formule_baumwelch-fig}) impliquent que si un coefficient devient nul apr�s un certain nombre d'it�rations, il le restera aux it�rations suivantes. Cela ne signifie pas que la valeur optimale pour ce coefficient soit diff�rente de z�ro, cependant si ce n'est pas le cas, l'apprentissage est biais�. C'est pourquoi on pr�f�re que tous les coefficients soient non nuls. -Afin d'viter cet cueil, les coefficients infrieurs un certain seuil sont modifis alatoirement de sorte qu'ils soient suprieurs ce seuil, ce seuil $s_t$ dcrot avec le nombre d'itrations~$t$~: +Afin d'�viter cet �cueil, les coefficients inf�rieurs � un certain seuil sont modifi�s al�atoirement de sorte qu'ils soient sup�rieurs � ce seuil, ce seuil $s_t$ d�cro�t avec le nombre d'it�rations~$t$~: - $$ - s_t = \frac{s_0}{ 1 + \gamma t } \text{ avec } \gamma \in \R^+ - $$ + $$ + s_t = \frac{s_0}{ 1 + \gamma t } \text{ avec } \gamma \in \mathbb{R}^+ + $$ -La vraisemblance des modles obtenue lors des itrations successives dcrot globalement mais l'alatoire introduit entre chaque itration fait osciller la valeur de cette vraisemblance lorsque les coefficients sont proches d'un optimum local. Cette oscillation dcrot au fur et mesure que $s_t$ dcrot. +La vraisemblance des mod�les obtenue lors des it�rations successives d�cro�t globalement mais l'al�atoire introduit entre chaque it�ration fait osciller la valeur de cette vraisemblance lorsque les coefficients sont proches d'un optimum local. Cette oscillation d�cro�t au fur et � mesure que $s_t$ d�cro�t. @@ -2541,20 +2541,20 @@ \subsection{Am %---------------------------------------------------------------------------------------------------------------------- -\section{Observations continues et rseau de neurones} \label{hmm_sec_rn_obs_cont} +\section{Observations continues et r�seau de neurones} \label{hmm_sec_rn_obs_cont} %---------------------------------------------------------------------------------------------------------------------- \indexfrr{observations}{continues}% -\indexfrr{missions}{continues}% +\indexfrr{�missions}{continues}% -Jusqu' prsent, les observations taient discrtes~: les squences d'observations taient des squences d'entiers pris dans un ensemble fini. Lorsque les donnes observes sont continues, il est possible de se ramener ce cas-l en construisant une partition de l'espace (avec l'algorithme des centres mobiles par exemple, voir paragraphe~\ref{emission_continue_centre_mobile}). Nanmoins, une classification traite de manire insatisfaisante les ambiguts : les observations situes sur une frontire entre deux classes (voir figure~\ref{figure_partitionnement_ambigu-fig}). Il est alors prfrable de conserver des probabilits d'appartenir telle ou telle classe (voir paragraphe~\ref{hmm_classification_obs_trois}). +Jusqu'� pr�sent, les observations �taient discr�tes~: les s�quences d'observations �taient des s�quences d'entiers pris dans un ensemble fini. Lorsque les donn�es observ�es sont continues, il est possible de se ramener � ce cas-l� en construisant une partition de l'espace (avec l'algorithme des centres mobiles par exemple, voir paragraphe~\ref{emission_continue_centre_mobile}). N�anmoins, une classification traite de mani�re insatisfaisante les ambigu�t�s : les observations situ�es sur une fronti�re entre deux classes (voir figure~\ref{figure_partitionnement_ambigu-fig}). Il est alors pr�f�rable de conserver des probabilit�s d'appartenir � telle ou telle classe (voir paragraphe~\ref{hmm_classification_obs_trois}). - \begin{figure}[ht] + \begin{figure}[ht] $$\frame{$\begin{array}[c|c]{c}\includegraphics[height=4cm, width=5cm] {\filext{../dessin2/hmm_rn_ambigu}}\end{array}$}$$ - \caption{Ambiguts d'un partitionnenement d'un espace continu.} + \caption{Ambigu�t�s d'un partitionnenement d'un espace continu.} \label{figure_partitionnement_ambigu-fig} - \end{figure} + \end{figure} @@ -2562,187 +2562,187 @@ \section{Observations continues et r -\subsection{Chane de Markov cache incluant un rseau de neurones} +\subsection{Cha�ne de Markov cach�e incluant un r�seau de neurones} \label{hmm_reseau_neurone} \indexfr{MMC} -\indexfr{rseau de neurones} +\indexfr{r�seau de neurones} -Les chanes de Markov caches incluant un rseau de neurones sont qualifies de \emph{hybrides},\indexfrr{MMC}{hybride} les missions sont en quelque sorte sous-traites par la chane de Markov un rseau de neurones. +Les cha�nes de Markov cach�es incluant un r�seau de neurones sont qualifi�es de \emph{hybrides},\indexfrr{MMC}{hybride} les �missions sont en quelque sorte sous-trait�es par la cha�ne de Markov � un r�seau de neurones. -\indexfrr{MMC}{rseau de neurones}% +\indexfrr{MMC}{r�seau de neurones}% \subsubsection{Initialisation} -On suppose que l'espace des observations continues a t partitionn l'aide des mthodes prsentes dans les paragraphes~\ref{hmm_classification_obs_un}, \ref{hmm_classification_obs_deux}, \ref{hmm_classification_obs_trois} (pages~\pageref{hmm_classification_obs_un} et suivantes), on dispose donc pour chaque observation $x$ des probabilits $\pr{ c\left| x\right.}$, elles sont retournes par un rseau de neurones classifieur (voir paragraphe~\ref{classification}, page~\pageref{classification}) dont l'apprentissage est voqu au paragraphe~\ref{hmm_classification_obs_trois}. +On suppose que l'espace des observations continues a �t� partitionn� � l'aide des m�thodes pr�sent�es dans les paragraphes~\ref{hmm_classification_obs_un}, \ref{hmm_classification_obs_deux}, \ref{hmm_classification_obs_trois} (pages~\pageref{hmm_classification_obs_un} et suivantes), on dispose donc pour chaque observation $x$ des probabilit�s $\pr{ c\left| x\right.}$, elles sont retourn�es par un r�seau de neurones classifieur (voir paragraphe~\ref{classification}, page~\pageref{classification}) dont l'apprentissage est �voqu� au paragraphe~\ref{hmm_classification_obs_trois}. -\subsubsection{Des observations discrtes aux observations continues} +\subsubsection{Des observations discr�tes aux observations continues} \label{hmm_definition_observation_continue} -Jusqu' prsent, les modles d'missions ont toujours t discrets, les modles de Markov retournaient la probabilits de squences discrtes. Les modles d'missions discretes sont entirement dcrits par une matrice : +Jusqu'� pr�sent, les mod�les d'�missions ont toujours �t� discrets, les mod�les de Markov retournaient la probabilit�s de s�quences discr�tes. Les mod�les d'�missions discretes sont enti�rement d�crits par une matrice : $$ \begin{array}{l} \left( b_{i,o}\right)_{\substack{1\leqslant i\leqslant N\\1\leqslant o\leqslant O}}= - \left( \pr{ o\left| i\right. } \right) + \left( \pr{ o\left| i\right. } \right) _{\substack{1\leqslant i\leqslant N\\1\leqslant o\leqslant O}}\\ \\ \text{avec } \left | \begin{array}{l} - N \text{ est le nombre d'tats de la chane de Markov} \\ + N \text{ est le nombre d'�tats de la cha�ne de Markov} \\ O \text{ le nombre d'observations possibles} \\ - \pr{ o\left| i\right. } \text{ est la probabilit d'mettre l'observation } o - \text{ connaissant l'tat } i + \pr{ o\left| i\right. } \text{ est la probabilit� d'�mettre l'observation } o + \text{ connaissant l'�tat } i \end{array} \right. \end{array} $$ -Comme les observations sont continues, il faut construire un modle d'mission estimant la densit d'une observation continue $x$ sachant l'tat~$i$~: $f\left( x\left| i\right. \right)$. On note $x\longrightarrow Rn\left( x\right) =\left( \pr{ c\left| x\right. } \right) _{1\leqslant c\leqslant C}$ la fonction dfinie par le rseau de neurones appris grce la classification (voir paragraphe~\ref{hmm_classification_obs_trois}). +Comme les observations sont continues, il faut construire un mod�le d'�mission estimant la densit� d'une observation continue $x$ sachant l'�tat~$i$~: $f\left( x\left| i\right. \right)$. On note $x\longrightarrow Rn\left( x\right) =\left( \pr{ c\left| x\right. } \right) _{1\leqslant c\leqslant C}$ la fonction d�finie par le r�seau de neurones appris gr�ce � la classification (voir paragraphe~\ref{hmm_classification_obs_trois}). - \begin{xdefinition}{Chane de Markov cache hybride} - \label{definition_mmc_1} - \indexfr{hybride} - - Une chane de Markov cache hybride dont les observations sont continues vrifie les conditions 2 4 vrifies - par une chane de Markov cache dont les observations sont discrtes - (voir dfinition~\ref{markov_chaine_cachee_definition}, - page~\pageref{markov_chaine_cachee_definition}), et les conditions 1 3 qui suivent~: - - \begin{enumerate} - \item La densit d'une observation ne dpend que de sa classe : $f\left( x\left| c,i\right. \right) = - f\left( x\left| c\right. \right)$ - \item La probabilit que l'observation l'instant $t$ soit dans la classe $c$ ne dpend que - de l'tat l'instant $t$ : - - $$ - \pr{ O_{t}\in classe\left( c\right) \,\left| \,q_{1},...,q_{t},O_{1},...,O_{t-1}\right. } - = \pr{ O_{t}\in classe\left( c\right) - \left| q_{t}\right. } = \pr{ c\left| q_{t}\right. } - $$ - - \item La probabilit que l'observation l'instant $t$ soit dans la classe $c$ ne dpend pas du temps : - - $$ - \forall t_{1},t_{2},\,\forall i,c,\; \pr{ O_{t_1}\in classe\left( c\right) \left| q_{t_{1}}=i\right. - } = \pr{ O_{t_2}\in - classe\left( c\right) \left| q_{t_{2}}=i\right. } - $$ - \end{enumerate}% - \end{xdefinition} - + \begin{xdefinition}{Cha�ne de Markov cach�e hybride} + \label{definition_mmc_1} + \indexfr{hybride} + + Une cha�ne de Markov cach�e hybride dont les observations sont continues v�rifie les conditions 2 � 4 v�rifi�es + par une cha�ne de Markov cach�e dont les observations sont discr�tes + (voir d�finition~\ref{markov_chaine_cachee_definition}, + page~\pageref{markov_chaine_cachee_definition}), et les conditions 1 � 3 qui suivent~: + + \begin{enumerate} + \item La densit� d'une observation ne d�pend que de sa classe : $f\left( x\left| c,i\right. \right) = + f\left( x\left| c\right. \right)$ + \item La probabilit� que l'observation � l'instant $t$ soit dans la classe $c$ ne d�pend que + de l'�tat � l'instant $t$ : + + $$ + \pr{ O_{t}\in classe\left( c\right) \,\left| \,q_{1},...,q_{t},O_{1},...,O_{t-1}\right. } + = \pr{ O_{t}\in classe\left( c\right) + \left| q_{t}\right. } = \pr{ c\left| q_{t}\right. } + $$ + + \item La probabilit� que l'observation � l'instant $t$ soit dans la classe $c$ ne d�pend pas du temps : + + $$ + \forall t_{1},t_{2},\,\forall i,c,\; \pr{ O_{t_1}\in classe\left( c\right) \left| q_{t_{1}}=i\right. + } = \pr{ O_{t_2}\in + classe\left( c\right) \left| q_{t_{2}}=i\right. } + $$ + \end{enumerate}% + \end{xdefinition} + -\indexfr{densit}% +\indexfr{densit�}% - \begin{xproperty}{probabilit d'mission} - On en dduit que la densit $f\pa{x|i}$ d'une observation $x$ sachant l'tat $i$ est~: - - $$ - f\left( x\left| i\right. \right) =\underset{c=1}{\overset{C}{\sum} }f\left( x,c\left| i\right. - \right) =\underset{c=1}{\overset{C}{\sum} - }f\left( x\left| c,i\right. \right) \pr{ c\left| i\right. } =\underset{c=1}{\overset{C} - {\sum}}\dfrac{ \pr{ c\left| x\right. - } \pr{ c\left| i\right. } f\left( x\right) }{ \pr{ c} } - $$ - - o~: - - \begin{eqnarray*} - \pr{ c\left| x\right. } && \text{est estim par le rseau de neurones : } - \pr{ c\left| x\right. } =Rn\left( x\right)\\ - \pr{ c\left| i\right. } && \text{est un coefficient qui sera estim de la mme manire que - les probabilits de transition}\\ - f\left( x\right) && \text{est la densit des observations, elle est inconnue mais peut tre estime par - (\ref{hmm_rn_densite_x})}\\ - \pr{ c } && \text{est la probabilit de la classe $c$, elle est incoonue mais peut tre estime par - (\ref{hmm_rn_densite_p})} - \end{eqnarray*} - \end{xproperty} + \begin{xproperty}{probabilit� d'�mission} + On en d�duit que la densit� $f\pa{x|i}$ d'une observation $x$ sachant l'�tat $i$ est~: + + $$ + f\left( x\left| i\right. \right) =\underset{c=1}{\overset{C}{\sum} }f\left( x,c\left| i\right. + \right) =\underset{c=1}{\overset{C}{\sum} + }f\left( x\left| c,i\right. \right) \pr{ c\left| i\right. } =\underset{c=1}{\overset{C} + {\sum}}\dfrac{ \pr{ c\left| x\right. + } \pr{ c\left| i\right. } f\left( x\right) }{ \pr{ c} } + $$ + + o�~: + + \begin{eqnarray*} + \pr{ c\left| x\right. } && \text{est estim� par le r�seau de neurones : } + \pr{ c\left| x\right. } =Rn\left( x\right)\\ + \pr{ c\left| i\right. } && \text{est un coefficient qui sera estim� de la m�me mani�re que + les probabilit�s de transition}\\ + f\left( x\right) && \text{est la densit� des observations, elle est inconnue mais peut �tre estim�e par + (\ref{hmm_rn_densite_x})}\\ + \pr{ c } && \text{est la probabilit� de la classe $c$, elle est incoonue mais peut �tre estim�e par + (\ref{hmm_rn_densite_p})} + \end{eqnarray*} + \end{xproperty} -On note $\pr{c|i}_{i,c} = \pa{c_{i,c}}_{i,c}$ la matrice des probabilits missions dans le cas d'observations continues. +On note $\pr{c|i}_{i,c} = \pa{c_{i,c}}_{i,c}$ la matrice des probabilit�s �missions dans le cas d'observations continues. -\subsection{Restimation de $\pa{c_{i,c}}_{i,c}$} +\subsection{R�estimation de $\pa{c_{i,c}}_{i,c}$} -On dfinit de nouveaux coefficients pour le modle de Markov cach : +On d�finit de nouveaux coefficients pour le mod�le de Markov cach� : $$ - \pa{c_{ci}}_{\begin{subarray}{c} 1 \infegal i \infegal N \\ 1 \infegal c \infegal C \end{subarray}} =% - \pa{\pr{c|i}}_{\begin{subarray}{c} 1 \infegal i \infegal N \\ 1 \infegal c \infegal C \end{subarray}} + \pa{c_{ci}}_{\begin{subarray}{c} 1 \leqslant i \leqslant N \\ 1 \leqslant c \leqslant C \end{subarray}} =% + \pa{\pr{c|i}}_{\begin{subarray}{c} 1 \leqslant i \leqslant N \\ 1 \leqslant c \leqslant C \end{subarray}} $$ \label{hmm_reestimation_emission_rn}% \indexfr{Baum-Welch}% -De la mme manire que pour les formules de Baum-Welch, on cherche estimer $\pr{ c,i,O} $ o $O$ est une squence -d'observations. $C_{t}$ dsigne la classe de l'observation $O_{t}$.% +De la m�me mani�re que pour les formules de Baum-Welch, on cherche � estimer $\pr{ c,i,O} $ o� $O$ est une s�quence +d'observations. $C_{t}$ d�signe la classe de l'observation $O_{t}$.% $$ \pr{ c\left| i\right. } =\overline{c_{i,c}}=\dfrac{\underset {k=1}{\overset{K} - { {\displaystyle\sum} }}\dfrac{1}{P_{k}} + { {\displaystyle\sum} }}\dfrac{1}{P_{k}} \underset{t=1}{\overset{T_{k}}{ {\displaystyle\sum}}} \pr{ C_{t}=c, \, q_{t}=i,O^{k}} } - {\underset{k=1}{\overset{K}{ {\displaystyle\sum} + {\underset{k=1}{\overset{K}{ {\displaystyle\sum} }}\dfrac{1}{P_{k}}\underset{t=1}{\overset{T_{k}}{ {\displaystyle\sum}}} \pr{ q_{t}=i, \, O^{k}} } $$ -La dmonstration de la formule de restimation de $c_{i,c}$ pour un modle apprenant la squence d'observations $\vecteur{O_1}{O_T}$~: +La d�monstration de la formule de r�estimation de $c_{i,c}$ pour un mod�le apprenant la s�quence d'observations $\vecteur{O_1}{O_T}$~: \begin{eqnarray} \pr{ C_{t},q_{t},O } &=& \pr{ O_{t+1},..,O_{T}\left| - C_{t},q_{t},O_{1},...,O_{t}\right. } \pr{ C_{t},q_{t},O_{1} + C_{t},q_{t},O_{1},...,O_{t}\right. } \pr{ C_{t},q_{t},O_{1} ,...,O_{t}} \nonumber\\ \pr{ C_{t},q_{t},O} &=& \pr{ O_{t+1},..,O_{T}\left|q_{t}\right. } - \pr{ O_{t}\left| C_{t},q_{t},O_{1},...,O_{t-1} + \pr{ O_{t}\left| C_{t},q_{t},O_{1},...,O_{t-1} \right. } \pr{ C_{t},q_{t},O_{1},...,O_{t-1}} \nonumber\\ \pr{ C_{t},q_{t},O} &=& \beta_{q_{t}}\left( t\right) \pr{O_{t}\left| C_{t}\right. } - \pr{ C_{t}\left| q_{t},O_{1} + \pr{ C_{t}\left| q_{t},O_{1} ,...,O_{t-1}\right. } \pr{ q_{t},O_{1},...,O_{t-1}} \nonumber\\ \pr{ C_{t},q_{t},O} &=& \beta_{q_{t}}\left( t\right) \pr{ O_{t}\left| C_{t}\right. } - \pr{ C_{t}\left| q_{t}\right. + \pr{ C_{t}\left| q_{t}\right. } \underset{q_{t-1}}{\overset{}{\sum}} \pr{ q_{t},q_{t-1} ,O_{1},...,O_{t-1}} \nonumber\\ \pr{ C_{t},q_{t},O} &=& \beta_{q_{t}}\left( t\right) \pr{ O_{t}\left| C_{t}\right. } - \,c_{q_{t},C_{t}}\underset{q_{t-1} + \,c_{q_{t},C_{t}}\underset{q_{t-1} }{\overset{}{\sum}}a_{q_{t-1},q_{t}}\,\alpha_{q_{t-1}}\left( t\right) =\dfrac{\beta_{q_{t}}\left( t\right) - \pr{ O_{t}\left| C_{t}\right. + \pr{ O_{t}\left| C_{t}\right. } \,c_{q_{t},C_{t}}\,\alpha_{q_{t}}\left( t\right) }{b_{q_{t}}\left( O_{t}\right) } \nonumber\\ \pr{ C_{t},q_{t},O} &=& \dfrac{\beta_{q_{t}}\left( t\right) \pr{ O_{t}\left| C_{t}\right. - }\,c_{q_{t},C_{t}} \, + }\,c_{q_{t},C_{t}} \, \alpha_{q_{t}} \left( t\right) }{\underset{d=1}{\overset{N}{{\displaystyle\sum}}} - \pr{ O_{t}\left| C_{t}=d\right.} + \pr{ O_{t}\left| C_{t}=d\right.} \,c_{q_{t},d}} \label{hmm_equation_reestimation}\\ \text{Rappel :} \pr{ q_{t},O} &=& \alpha_{q_{t}}\left( t\right) \beta_{q_{t} }\left( t\right) \nonumber \end{eqnarray} -Si le modle apprend plusieurs squences $\vecteur{O^1}{O^K}$ de longueurs respectives $\vecteur{T_1}{T_K}$, alors la formule de la table~\ref{figure_formule_baumwelch-fig_2} vient s'ajouter celles de la table~\ref{figure_formule_baumwelch-fig}.% +Si le mod�le apprend plusieurs s�quences $\vecteur{O^1}{O^K}$ de longueurs respectives $\vecteur{T_1}{T_K}$, alors la formule de la table~\ref{figure_formule_baumwelch-fig_2} vient s'ajouter � celles de la table~\ref{figure_formule_baumwelch-fig}.% \begin{table}[t] \[ \fbox{$\begin{array}[c]{c}% \overline{c_{i,c}}= \pr{ c\left| i,O\right. } =\dfrac{\underset{k=1} - {\overset{K}{ {\displaystyle\sum} }}\dfrac{1}{P_{k}} + {\overset{K}{ {\displaystyle\sum} }}\dfrac{1}{P_{k}} \underset{t=1}{\overset{T_{k}}{ {\displaystyle\sum} }}\dfrac{\beta_{i}^{k} - \left( t\right) \pr{ O_{t}^k\left| c\right. } + \left( t\right) \pr{ O_{t}^k\left| c\right. } c_{i,c}\alpha_{i}^{k}\left( t\right) }{\underset{d=1}{\overset {N}{\sum}} - \pr{ O_{t}^k\left| c\right. } c_{i,d}}}{\underset + \pr{ O_{t}^k\left| c\right. } c_{i,d}}}{\underset {k=1}{\overset{K}{ {\displaystyle\sum} }}\dfrac{1}{P_{k}}\underset{t=1} - {\overset{T_{k}}{ {\displaystyle\sum} }}\alpha_{i}^{k} \left( t\right) + {\overset{T_{k}}{ {\displaystyle\sum} }}\alpha_{i}^{k} \left( t\right) \beta_{i}^{k}\left( t\right) }\end{array}$} \] - \caption{Formules de restimation de Baum-Welch, modle hybride} + \caption{Formules de r�estimation de Baum-Welch, mod�le hybride} \label{figure_formule_baumwelch-fig_2} \indexfr{Baum-Welch} - \indexfr{restimation} + \indexfr{r�estimation} \indexfr{hybride} \end{table} @@ -2751,26 +2751,26 @@ \subsection{R -\subsection{Restimation des $\pr{c |o }$} +\subsection{R�estimation des $\pr{c |o }$} \indexfrr{MMC}{annotation RN par MMC}% \label{hmm_reestimation_rn_classification}% -\indexfr{restimation} +\indexfr{r�estimation} -Ces probabilits sont fournies par le rseau de neurones dont l'apprentissage peut tre mis en parallle avec celui du modle de Markov cach ou tre diffr. Dans cette seconde solution, il faut estimer les probabilits $\left( \pr{ c\left| x\right. } \right) _{1\leqslant c\leqslant C}$ qui diminueront la vraisemblance des observations. De la mme manire que prcdemment, on estime la probabilit $\pr{C_{t}\left| O_{t}^{k}\right. } $ pour la squence $O^{k}$. +Ces probabilit�s sont fournies par le r�seau de neurones dont l'apprentissage peut �tre mis en parall�le avec celui du mod�le de Markov cach� ou �tre diff�r�. Dans cette seconde solution, il faut estimer les probabilit�s $\left( \pr{ c\left| x\right. } \right) _{1\leqslant c\leqslant C}$ qui diminueront la vraisemblance des observations. De la m�me mani�re que pr�c�demment, on estime la probabilit� $\pr{C_{t}\left| O_{t}^{k}\right. } $ pour la s�quence $O^{k}$. -$C_{t}$ dsigne toujours la classe de l'observation $O_{t}$, la dmonstration des formules sera faite pour une squence, pour abrger les notations, $O=O^{k}$~: +$C_{t}$ d�signe toujours la classe de l'observation $O_{t}$, la d�monstration des formules sera faite pour une s�quence, pour abr�ger les notations, $O=O^{k}$~: \begin{eqnarray*} \pr{C_t|O_t} &=& \pr{C_t|O} = \frac{\pr{C_t,O}}{\pr{O}} = \frac{1}{\pr{O}} \summyone{q_t} \pr{C_t,q_t,O}\\ &=& \frac{1}{\pr{O}} \summyone{q_t} \dfrac{\beta_{q_{t}}\left( t\right) \pr{ O_{t}\left| - C_{t}\right. }\,c_{q_{t},C_{t}} \, + C_{t}\right. }\,c_{q_{t},C_{t}} \, \alpha_{q_{t}} \left( t\right) }{\underset{d=1}{\overset{N}{{\displaystyle\sum}}}\pr{ O_{t}\left| - C_{t}=d\right. } - \,c_{q_{t},d}} \quad \text{ d'aprs (\ref{hmm_equation_reestimation})} + C_{t}=d\right. } + \,c_{q_{t},d}} \quad \text{ d'apr�s (\ref{hmm_equation_reestimation})} \end{eqnarray*} -Par consquent, dans un premier temps, la base d'apprentissage du rseau de neurones est la base $\left( X,Y\right) $ dfinies par la table~\ref{figure_formule_baumwelch-fig_3}.% +Par cons�quent, dans un premier temps, la base d'apprentissage du r�seau de neurones est la base $\left( X,Y\right) $ d�finies par la table~\ref{figure_formule_baumwelch-fig_3}.% \begin{table}[t] \[ @@ -2778,92 +2778,92 @@ \subsection{R \begin{array}{l}% i {{}^\circ} \text{ ligne de }X\text{ : }X_{i}=O_{t}^k\\ k {{}^\circ} \text{ ligne de }Y\text{ : }Y_{k}=\left( \dfrac{1}{\pr{O^k} - } \bigsummyone{q_t} \dfrac{\beta_{q_{t}^k}\left( t\right) \pr{ + } \bigsummyone{q_t} \dfrac{\beta_{q_{t}^k}\left( t\right) \pr{ O_{t}^k\left| c\right. }\,c_{q_{t},c} \, \alpha_{q_{t}^k} \left( t\right) - }{\underset{d=1}{\overset{C}{{\displaystyle\sum}}} \pr{ + }{\underset{d=1}{\overset{C}{{\displaystyle\sum}}} \pr{ O_{t}^k\left| d\right. } \,c_{q_{t},d}} \right) _{1\leqslant c\leqslant C} % \end{array} $}% \] - \caption{Formules de restimation de Baum-Welch, modle hybride, partie rseau de neurones. - On passe d'une ligne la suivante en incrmentant~$t$ ou lorsque~$t$ correspond - la dernire observations de la squence~$O^k$, en incrmentant~$k$. Les - matrices $X$ et $Y$ constituent la base d'apprentissage du rseau de neurones, $X$ - contient les entres, $Y$ les sorties dsires. } + \caption{Formules de r�estimation de Baum-Welch, mod�le hybride, partie r�seau de neurones. + On passe d'une ligne � la suivante en incr�mentant~$t$ ou lorsque~$t$ correspond + � la derni�re observations de la s�quence~$O^k$, en incr�mentant~$k$. Les + matrices $X$ et $Y$ constituent la base d'apprentissage du r�seau de neurones, $X$ + contient les entr�es, $Y$ les sorties d�sir�es. } \label{figure_formule_baumwelch-fig_3} \indexfr{Baum-Welch} - \indexfr{restimation} + \indexfr{r�estimation} \indexfr{hybride} - \indexfr{rseau de neurones} + \indexfr{r�seau de neurones} \end{table} -Toutefois la formule dcrite dans cette table~\ref{figure_formule_baumwelch-fig_3} ne donne pas le vecteur de probabilit $Y$ qui maximise la vraisemblance des observations mais celui-ci peut tre obtenu en adaptant l'algorithme EM\indexfr{EM} ce cas-l. On note $Y_{ct} = \pr{ O_j \sac C_c }$. $O_t$ dsigne la $t^\text{me}$ observations de la squence $O = \vecteur{O_1}{O_T}$ et $C_i$ la $c^\text{me}$ classe. Par consquent $Y_{tc}$ est la sortie $c^\text{me}$ dsire du rseau de neurones lorsqu'il a pour entre le vecteur $O_t$. Comme les coefficients $c_{i,c}$ (voir table~\ref{figure_formule_baumwelch-fig_2}), les nombres $Y_{tc}$ sont mis jour selon la formules de la table~\ref{figure_formule_baumwelch-fig_3prime}. +Toutefois la formule d�crite dans cette table~\ref{figure_formule_baumwelch-fig_3} ne donne pas le vecteur de probabilit� $Y$ qui maximise la vraisemblance des observations mais celui-ci peut �tre obtenu en adaptant l'algorithme EM\indexfr{EM} � ce cas-l�. On note $Y_{ct} = \pr{ O_j \sac C_c }$. $O_t$ d�signe la $t^\text{�me}$ observations de la s�quence $O = \vecteur{O_1}{O_T}$ et $C_i$ la $c^\text{�me}$ classe. Par cons�quent $Y_{tc}$ est la sortie $c^\text{�me}$ d�sir�e du r�seau de neurones lorsqu'il a pour entr�e le vecteur $O_t$. Comme les coefficients $c_{i,c}$ (voir table~\ref{figure_formule_baumwelch-fig_2}), les nombres $Y_{tc}$ sont mis � jour selon la formules de la table~\ref{figure_formule_baumwelch-fig_3prime}. \begin{table}[t] $$\begin{array}{|l|}\hline \overline{Y_{tc}^k} = \dfrac{1}{ \pr{O^k} } \pa { - \summyone{q_t} \; - \dfrac{ \beta_{q_t}^k\pa{t} \, Y_{tc}^k \, c_{q_t,c} \, \alpha_{q_t}^k \pa{t} } - { \summy{d=1}{C} \, Y_{td}^k \, c_{q_t,d} } - } + \summyone{q_t} \; + \dfrac{ \beta_{q_t}^k\pa{t} \, Y_{tc}^k \, c_{q_t,c} \, \alpha_{q_t}^k \pa{t} } + { \summy{d=1}{C} \, Y_{td}^k \, c_{q_t,d} } + } \\ \hline \end{array}$$ - \caption{ Formules de restimation de Baum-Welch, modle hybride, partie rseau de neurones. - Cette formule de restimation vient en complment de la - table~\ref{figure_formule_baumwelch-fig_3} o le terme $Y_i$ est remplac par le - vecteur $\vecteur{ \overline{Y_{t1}^k} }{ \overline{Y_{tC}^k} } $ obtenu aprs convergence - de la probabilit $\pr{ O_t \sac M}$ et en utilisant la formule - de restimation ci-dessus. La premire valeur pour $Y_{tC}^k$ correspond la sortie - du rseau de neurones avant rapprentissage. } + \caption{ Formules de r�estimation de Baum-Welch, mod�le hybride, partie r�seau de neurones. + Cette formule de r�estimation vient en compl�ment de la + table~\ref{figure_formule_baumwelch-fig_3} o� le terme $Y_i$ est remplac� par le + vecteur $\vecteur{ \overline{Y_{t1}^k} }{ \overline{Y_{tC}^k} } $ obtenu apr�s convergence + de la probabilit� $\pr{ O_t \sac M}$ et en utilisant la formule + de r�estimation ci-dessus. La premi�re valeur pour $Y_{tC}^k$ correspond � la sortie + du r�seau de neurones avant r�apprentissage. } \label{figure_formule_baumwelch-fig_3prime} \indexfr{Baum-Welch} - \indexfr{restimation} + \indexfr{r�estimation} \indexfr{hybride} - \indexfr{rseau de neurones} + \indexfr{r�seau de neurones} \end{table} -Il faut d'ajouter que l'utilisation de telles formules de convergence mnent souvent des sorties dsires pour le rseau de neurones qui sont soient nulles, soient gales un. La remarque~\ref{nn_remark_classification_output_alpha}\seeannex{nn_remark_classification_output_alpha}{rseau de neurones} suggre de ne pas utiliser ces sorties telles quelles afin de faciliter l'apprentissage. - - - \begin{xalgorithm}{apprentissage altern du modle hybride complet} - \label{algorithme_apprentissge_modelel_complet_1}% - \indexfrr{apprentissage}{MMC + RN}% - \indexfr{rseau de neurones} - L'apprentissage propos alterne l'apprentissage de la chane de Markov cache de celui du rseau de neurones, - il est constitu de trois tapes~: - - \begin{xalgostep}{initialisation} - Initialisation du rseau de neurones l'aide des mthodes proposes dans les paragraphes : - \ref{hmm_classification_obs_un} (page~\pageref{hmm_classification_obs_un}), - \ref{hmm_classification_obs_deux} (page~\pageref{hmm_classification_obs_deux}), - \ref{hmm_classification_obs_trois} (page~\pageref{hmm_classification_obs_trois}) - \end{xalgostep} - - \begin{xalgostep}{apprentissage MMC}\label{hmm_rn_step_algo_mmc} - Apprentissage Baum-Welch des probabilits de transitions et d'missions, voir les paragraphes~: - \ref{formule_baumwelch} (page~\pageref{formule_baumwelch}), - \ref{hmm_reestimation_emission_rn} (page~\pageref{hmm_reestimation_emission_rn}), - \end{xalgostep} - - \begin{xalgostep}{apprentissage RN} - Rapprentissage du rseau de neurones, voir ce paragraphe - \ref{hmm_reestimation_rn_classification} (page~\pageref{hmm_reestimation_rn_classification}) - ainsi que les tables~\ref{figure_formule_baumwelch-fig_3} - et~\ref{figure_formule_baumwelch-fig_3prime}. \\ - Retour l'tape~\ref{hmm_rn_step_algo_mmc} jusqu' convergence. - \end{xalgostep} - - \end{xalgorithm} - +Il faut d'ajouter que l'utilisation de telles formules de convergence m�nent souvent � des sorties d�sir�es pour le r�seau de neurones qui sont soient nulles, soient �gales � un. La remarque~\ref{nn_remark_classification_output_alpha}\seeannex{nn_remark_classification_output_alpha}{r�seau de neurones} sugg�re de ne pas utiliser ces sorties telles quelles afin de faciliter l'apprentissage. + + + \begin{xalgorithm}{apprentissage altern� du mod�le hybride complet} + \label{algorithme_apprentissge_modelel_complet_1}% + \indexfrr{apprentissage}{MMC + RN}% + \indexfr{r�seau de neurones} + L'apprentissage propos� alterne l'apprentissage de la cha�ne de Markov cach�e de celui du r�seau de neurones, + il est constitu� de trois �tapes~: + + \begin{xalgostep}{initialisation} + Initialisation du r�seau de neurones � l'aide des m�thodes propos�es dans les paragraphes : + \ref{hmm_classification_obs_un} (page~\pageref{hmm_classification_obs_un}), + \ref{hmm_classification_obs_deux} (page~\pageref{hmm_classification_obs_deux}), + \ref{hmm_classification_obs_trois} (page~\pageref{hmm_classification_obs_trois}) + \end{xalgostep} + + \begin{xalgostep}{apprentissage MMC}\label{hmm_rn_step_algo_mmc} + Apprentissage Baum-Welch des probabilit�s de transitions et d'�missions, voir les paragraphes~: + \ref{formule_baumwelch} (page~\pageref{formule_baumwelch}), + \ref{hmm_reestimation_emission_rn} (page~\pageref{hmm_reestimation_emission_rn}), + \end{xalgostep} + + \begin{xalgostep}{apprentissage RN} + R�apprentissage du r�seau de neurones, voir ce paragraphe + \ref{hmm_reestimation_rn_classification} (page~\pageref{hmm_reestimation_rn_classification}) + ainsi que les tables~\ref{figure_formule_baumwelch-fig_3} + et~\ref{figure_formule_baumwelch-fig_3prime}. \\ + Retour � l'�tape~\ref{hmm_rn_step_algo_mmc} jusqu'� convergence. + \end{xalgostep} + + \end{xalgorithm} + \begin{xremark}{convergence non monotone} -Il est conseill de conserver les versions des modles chaque itration car la convergence est rarement monotone. +Il est conseill� de conserver les versions des mod�les � chaque it�ration car la convergence est rarement monotone. \indexfr{monotone} \end{xremark} @@ -2876,46 +2876,46 @@ \subsection{R -\subsection{Emissions continues modlises par une loi normale multidimensionnelle} +\subsection{Emissions continues mod�lis�es par une loi normale multidimensionnelle} \label{hmm_loi_normale_emission_section} -\indexfrr{missions}{continues}% +\indexfrr{�missions}{continues}% \indexfrr{loi}{normale multidimensionnelle}% -Les probabilits d'mission peuvent tre modlises par des lois normales, soit $\vecteur{O_1}{O_T}$ une squence d'observations et $\vecteur{q_1}{q_T}$ une squence d'tats du modle $M$. On suppose que la variable~: +Les probabilit�s d'�mission peuvent �tre mod�lis�es par des lois normales, soit $\vecteur{O_1}{O_T}$ une s�quence d'observations et $\vecteur{q_1}{q_T}$ une s�quence d'�tats du mod�le $M$. On suppose que la variable~: $$ O_t | q_t = i \sim \loinormale{\mu_i}{V_i} $$ -Par consquent~: +Par cons�quent~: $$ b_i\pa{O_t} = f\pa{O_t | q_t = i,M} = \dfrac{1}{\pa{2\pi}^{\frac{n}{2}} \sqrt{ \det \pa{ V_i}} } \; e ^{ - \frac{1}{2} \pa{O_t - \mu_i}' \, V_i^{-1} \pa{O_t - \mu_i} } $$ -Les formules de restimation (voir \citeindex{Bottou1991}) utiliser lors de l'algorithme de Baum-Welch sont les suivantes pour les squences d'observations $\vecteur{O^k_1}{O^k_{T_k}}_{1 \infegal k \infegal K}$, on note $P_k = f\pa{O^k|M}$ o $f$ est la densit des squences d'observations~:% +Les formules de r�estimation (voir \citeindex{Bottou1991}) � utiliser lors de l'algorithme de Baum-Welch sont les suivantes pour les s�quences d'observations $\vecteur{O^k_1}{O^k_{T_k}}_{1 \leqslant k \leqslant K}$, on note $P_k = f\pa{O^k|M}$ o� $f$ est la densit� des s�quences d'observations~:% \indexfrr{MMC}{Baum-Welch}% \begin{eqnarray} \overline {\mu_{i}} &=& \dfrac { \summy{k=1}{K} \; \dfrac{1}{P_k} \, - \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} O_t^k } + \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} O_t^k } { \summy{k=1}{K} \; \dfrac{1}{P_k} \, - \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} } \\ + \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} } \\ && \nonumber\\ \overline {V_{i}} &=& \dfrac { \summy{k=1}{K} \; \dfrac{1}{P_k} \, - \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} O_t^k {O_t^k}' } + \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} O_t^k {O_t^k}' } { \summy{k=1}{K} \; \dfrac{1}{P_k} \, - \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} } + \summy{t=1}{T_k} \alpha_t^k\pa{i} \beta_t^k\pa{i} } - \mu_i \mu'_i \end{eqnarray} -L'inconvnient de ces modles est le calcul de la densit qui implique un produit matrice en $O\pa{d^3}$ o $d$ est la dimension de l'espace des observations. Etant donn la taille considrable de cette matrice, elles sont soit rduites leur diagonale, soit factorises entre les tats. Pour cette dernire solution, le modle hybride rsultant est agenc de manire semblable celui regroupant un modle de Markov cach et un rseau de neurones. Le rseau de neurones agit comme un classifieur commun tous les tats, dans l'autre cas, c'est un mlange de lois normales qui modlisent la distribution des observations. \indexfrr{loi}{mlange} +L'inconv�nient de ces mod�les est le calcul de la densit� qui implique un produit matrice en $O\pa{d^3}$ o� $d$ est la dimension de l'espace des observations. Etant donn� la taille consid�rable de cette matrice, elles sont soit r�duites � leur diagonale, soit factoris�es entre les �tats. Pour cette derni�re solution, le mod�le hybride r�sultant est agenc� de mani�re semblable � celui regroupant un mod�le de Markov cach� et un r�seau de neurones. Le r�seau de neurones agit comme un classifieur commun � tous les �tats, dans l'autre cas, c'est un m�lange de lois normales qui mod�lisent la distribution des observations. \indexfrr{loi}{m�lange} -\begin{xremark}{calcul pratique de probabilits~: utlisation de cots} -L'utilisation de lois normales \index{cot} peut poser des problmes informatiques de mise en \oe uvre. En effet, les probabilits sont alors souvent trs faibles pour des espaces de grandes dimensions, quelques dizaines comme pour la reconnaissance de l'criture manuscrite. Il arrive frquemment que l'estimation de tels modles ncessite le calcul de probabilits parfois infrieures $10^{-300}$ qui est la limite d'un rel cod informatique sur huit octets. Il est alors prfrable d'utiliser des cots ou logarithme de probabilits lors des calculs. +\begin{xremark}{calcul pratique de probabilit�s~: utlisation de co�ts} +L'utilisation de lois normales \index{co�t} peut poser des probl�mes informatiques de mise en \oe uvre. En effet, les probabilit�s sont alors souvent tr�s faibles pour des espaces de grandes dimensions, quelques dizaines comme pour la reconnaissance de l'�criture manuscrite. Il arrive fr�quemment que l'estimation de tels mod�les n�cessite le calcul de probabilit�s parfois inf�rieures � $10^{-300}$ qui est la limite d'un r�el cod� informatique sur huit octets. Il est alors pr�f�rable d'utiliser des co�ts ou logarithme de probabilit�s lors des calculs. \end{xremark} @@ -2928,47 +2928,47 @@ \subsection{Emissions continues mod %---------------------------------------------------------------------------------------------------------------------- -\section{Chanes de Markov d'ordres suprieurs} +\section{Cha�nes de Markov d'ordres sup�rieurs} %---------------------------------------------------------------------------------------------------------------------- \label{par_chaine_ordre_superieur} -Jusqu' prsent, seules les chanes de Markov caches d'ordre un ont t utilises, ceci signifie que l'tat l'instant $t$ ne dpend que de l'tat l'instant $t-1$, un ordre suprieur signifie que l'tat l'instat $t$ dpend de plusieurs des tats prcdents. - -\subsection{Dfinition d'une chane de Markov d'ordre $n$} - -La dfinition suivante concerne une chane de Markov et non une chane de Markov cache : les missions ne sont pas prises en compte. - - \begin{xdefinition}{Chane de Markov d'ordre $n$} - \label{definition_mmc_ordre_n} - \indexfr{ordre} - - Soit $N\in\N^{\ast}$, on appelle une chane de Markov $N$ tats d'ordre $n$ une chane de Markov - qui vrifie les conditions suivantes~: - - \begin{enumerate} - \item l'tat l'instant $t$ ne dpend que des tats aux instants $\left( t-1,...,t-n\right)$. - Par consquent~: - $$ - \forall t>n, \; \pr{ q_{t}\left| q_{t-1},...,q_{1},M\right. } = \pr{ q_{t}\left| - q_{t-1},...,q_{t-n},M\right. } - $$ - - \item les probabilits de transition ne dpendent pas du temps, par consquent : - - $$ - \begin{array}{l} \forall t_{1},t_{2}>1,\,\forall\left(i_{0},i_{1},...,i_{d}\right),\\ - \pr{ q_{t_{1}}=i_{0}\left|q_{t_{1}-1}=i_{1},..., - q_{t_{1}-d}=i_{d},M\right. } = \pr{ q_{t_{2}}=i_{0}\left| - q_{t_{2}-1}=i_{1},...,q_{t_{2}-d}=i_{d},M\right. } - \end{array} - $$ - - \end{enumerate} - \end{xdefinition} - - -On note $\mathcal{M}_{n}$ l'ensemble des chane de Markov d'ordre $n$. +Jusqu'� pr�sent, seules les cha�nes de Markov cach�es d'ordre un ont �t� utilis�es, ceci signifie que l'�tat � l'instant $t$ ne d�pend que de l'�tat � l'instant $t-1$, un ordre sup�rieur signifie que l'�tat � l'instat $t$ d�pend de plusieurs des �tats pr�c�dents. + +\subsection{D�finition d'une cha�ne de Markov d'ordre $n$} + +La d�finition suivante concerne une cha�ne de Markov et non une cha�ne de Markov cach�e : les �missions ne sont pas prises en compte. + + \begin{xdefinition}{Cha�ne de Markov d'ordre $n$} + \label{definition_mmc_ordre_n} + \indexfr{ordre} + + Soit $N\in\N^{\ast}$, on appelle une cha�ne de Markov � $N$ �tats d'ordre $n$ une cha�ne de Markov + qui v�rifie les conditions suivantes~: + + \begin{enumerate} + \item l'�tat � l'instant $t$ ne d�pend que des �tats aux instants $\left( t-1,...,t-n\right)$. + Par cons�quent~: + $$ + \forall t>n, \; \pr{ q_{t}\left| q_{t-1},...,q_{1},M\right. } = \pr{ q_{t}\left| + q_{t-1},...,q_{t-n},M\right. } + $$ + + \item les probabilit�s de transition ne d�pendent pas du temps, par cons�quent : + + $$ + \begin{array}{l} \forall t_{1},t_{2}>1,\,\forall\left(i_{0},i_{1},...,i_{d}\right),\\ + \pr{ q_{t_{1}}=i_{0}\left|q_{t_{1}-1}=i_{1},..., + q_{t_{1}-d}=i_{d},M\right. } = \pr{ q_{t_{2}}=i_{0}\left| + q_{t_{2}-1}=i_{1},...,q_{t_{2}-d}=i_{d},M\right. } + \end{array} + $$ + + \end{enumerate} + \end{xdefinition} + + +On note $\mathcal{M}_{n}$ l'ensemble des cha�ne de Markov d'ordre $n$. @@ -2977,7 +2977,7 @@ \subsection{Descente d'ordre} \indexfr{descente d'ordre}% \indexfrr{ordre}{descente} -Tout d'abord, on dfinit la fonction suivante $f$ : +Tout d'abord, on d�finit la fonction suivante $f$ : \begin{eqnarray} \begin{array}{l} @@ -2987,60 +2987,60 @@ \subsection{Descente d'ordre} \label{markov_ordre_homomorphisme_un} \end{eqnarray} - \begin{xproperty}{homomorphisme} - \label{propriete_chaine_ordre_n_1}% - Si $g$ est la fonction dfinie en (\ref{markov_ordre_homomorphisme_un}), alors $g$ est bijective. - \end{xproperty} + \begin{xproperty}{homomorphisme} + \label{propriete_chaine_ordre_n_1}% + Si $g$ est la fonction d�finie en (\ref{markov_ordre_homomorphisme_un}), alors $g$ est bijective. + \end{xproperty} -Par consquent, $g^{-1}$ existe et, on notera $\left[g^{-1}\left(l\right)\right] _{k}$ la $k{{}^\circ}$ coordonnes de $g^{-1}\left(l\right)$. Cette fonction est tout simplement l'criture des entiers en base $N$. +Par cons�quent, $g^{-1}$ existe et, on notera $\left[g^{-1}\left(l\right)\right] _{k}$ la $k{{}^\circ}$ coordonn�es de $g^{-1}\left(l\right)$. Cette fonction est tout simplement l'�criture des entiers en base $N$. -Dans toute la suite, les tat de sorties et d'entres ne seront plus distincts des autres tats, ceci permettra de ne pas traiter les probabilits de transition, les probabilits d'entre et les probabilits de sortie de manire spare. +Dans toute la suite, les �tat de sorties et d'entr�es ne seront plus distincts des autres �tats, ceci permettra de ne pas traiter les probabilit�s de transition, les probabilit�s d'entr�e et les probabilit�s de sortie de mani�re s�par�e. -Soit une chane de Markov $M$ d'ordre $n$ contenant $N$ tats reprsents par l'ensemble $\vecteur{1}{N}$. Cette chane est entirement dfinie par une hyper-matrice carre $A_{M}\in\mathcal{M}_{N}^{n+1}\left(\R\right) $ contenant les $\left( N\right)^{n+1}$ probabilits de transition de la chane de Markov $M\in\mathcal{M}_{n}$~: +Soit une cha�ne de Markov $M$ d'ordre $n$ contenant $N$ �tats repr�sent�s par l'ensemble $\vecteur{1}{N}$. Cette cha�ne est enti�rement d�finie par une hyper-matrice carr�e $A_{M}\in\mathcal{M}_{N}^{n+1}\left(\mathbb{R}\right) $ contenant les $\left( N\right)^{n+1}$ probabilit�s de transition de la cha�ne de Markov $M\in\mathcal{M}_{n}$~: $$ A_{M}\left(i_{1},...,i_{n},i_{n+1}\right) =\pr{ q_{t}=i_{n+1}\left| q_{t-1}=i_{n},...,q_{t-n}=i_{1},M\right. } $$ -Si $e$ est l'tat d'entre du modle, on pose comme convention : +Si $e$ est l'�tat d'entr�e du mod�le, on pose comme convention : \begin{eqnarray} A_M \vecteur{i_1}{i_{n+1}} &=& \left \{ \begin{array}{l} 0 \text{ si } i_{n+1} = e \\ - 0 \text{ si } \exists k \infegal n \text{ tel que } i_k \neq e \text{ et } i_{k+1} = e \\ - 1 \text{ si } \exists k \text{ tel que } 3 \infegal k \infegal n \text{ et } \forall k' < k, \; i_{k'} = - e \text { et } \forall k' \supegal k, \; i_{k'} \neq e \\ + 0 \text{ si } \exists k \leqslant n \text{ tel que } i_k \neq e \text{ et } i_{k+1} = e \\ + 1 \text{ si } \exists k \text{ tel que } 3 \leqslant k \leqslant n \text{ et } \forall k' < k, \; i_{k'} = + e \text { et } \forall k' \supegal k, \; i_{k'} \neq e \\ \pr{q_t = i_{n+1} \, | \, \vecteurno{q_{t-1} = i_n}{q_{t-n} = i_1}} \text{ sinon} \end{array} \right. \label{hmm_equation_convention_ordre} \end{eqnarray} -On construit la chane de Markov $M^{\prime}$ d'ordre un contenant $N^{n}$ tats reprsents par l'ensemble $\vecteur{1}{N^n}$. Cette chane est entirement dfinie par sa matrice carre $A_{M^{\prime}}^{\prime }\in \mathcal{M}_{N^{n}}^{2}\left( \R\right) $ contenant les $\pa{N^n}^2$ probabilits de transition de la chane $M^{\prime}\in\mathcal{M}_1$~: +On construit la cha�ne de Markov $M^{\prime}$ d'ordre un contenant $N^{n}$ �tats repr�sent�s par l'ensemble $\vecteur{1}{N^n}$. Cette cha�ne est enti�rement d�finie par sa matrice carr�e $A_{M^{\prime}}^{\prime }\in \mathcal{M}_{N^{n}}^{2}\left( \mathbb{R}\right) $ contenant les $\pa{N^n}^2$ probabilit�s de transition de la cha�ne $M^{\prime}\in\mathcal{M}_1$~: $$ A_{M^{\prime}}^{\prime}\left( k,l\right)=\pr{ q_{t}=l\left| q_{t-1}=k,M^{\prime}\right. } $$ -La matrice des transitions $A_{M^{\prime}}^{\prime }$ est dfinie partir de l'hyper-matrice $A_M$~: +La matrice des transitions $A_{M^{\prime}}^{\prime }$ est d�finie � partir de l'hyper-matrice $A_M$~: \begin{eqnarray} A_{M^{\prime}}^{\prime}\left( k,l\right) &=&\left\{ \begin{array}[c]{l}% A_{M}\left( \left[ g^{-1}\left( k\right) \right] _{1},g^{-1}\left( l\right) \right) - \text{ si }\forall i\in\left\{ 1,...,n-1\right\} + \text{ si }\forall i\in\left\{ 1,...,n-1\right\} ,\,\left[ g^{-1}\left( l\right) \right] _{i}=\left[ g^{-1}\left( k\right) \right] _{i+1}\\ 0\text{ sinon}% \end{array} \right. \label{markov_ordre_homomorphisme_deux}\\ - && \text{avec $g$ la fonction dfinie en (\ref{markov_ordre_homomorphisme_un})} \nonumber + && \text{avec $g$ la fonction d�finie en (\ref{markov_ordre_homomorphisme_un})} \nonumber \end{eqnarray} -Enfin on dfinit la fonction $h$~:% +Enfin on d�finit la fonction $h$~:% \begin{eqnarray} \begin{array}[c]{l} @@ -3050,28 +3050,28 @@ \subsection{Descente d'ordre} \label{markov_ordre_homomorphisme_trois} \end{eqnarray} -On doit d'abord dfinir l'quivalence entre deux chanes de Markov~: +On doit d'abord d�finir l'�quivalence entre deux cha�nes de Markov~: - \begin{xdefinition}{quivalence entre deux chanes de Markov} - \label{definition_mm_equivalence}% - \indexfr{quivalence} - Soient $M_1$ et $M_2$ deux chanes de Markov, on note $S$ l'ensemble des squences d'tats, alors~: - $$ - \pa{M_1 \Longleftrightarrow M_2} \Longleftrightarrow \pa{\forall s \in S, \; \pr{s|M_1} = \pr{s|M_2}} - $$ - \end{xdefinition} - + \begin{xdefinition}{�quivalence entre deux cha�nes de Markov} + \label{definition_mm_equivalence}% + \indexfr{�quivalence} + Soient $M_1$ et $M_2$ deux cha�nes de Markov, on note $S$ l'ensemble des s�quences d'�tats, alors~: + $$ + \pa{M_1 \Longleftrightarrow M_2} \Longleftrightarrow \pa{\forall s \in S, \; \pr{s|M_1} = \pr{s|M_2}} + $$ + \end{xdefinition} + -On peut maintenant noncer le thorme suivant~: +On peut maintenant �noncer le th�or�me suivant~: - \begin{xtheorem}{homomorphisme} - \label{markov_ordre_homomorphisme_trois_th}% - Avec ces notations, la fonction $h$ (\ref{markov_ordre_homomorphisme_trois}) dfinit un homomorphisme de $\left(\mathcal{M}_{n} , - \Longleftrightarrow \right)$ dans $\left( \mathcal{M}_{1},\Longleftrightarrow\right) $ o $\Longleftrightarrow$ - est la relation d'quivalence entre deux chanes de Markov. - \end{xtheorem} + \begin{xtheorem}{homomorphisme} + \label{markov_ordre_homomorphisme_trois_th}% + Avec ces notations, la fonction $h$ (\ref{markov_ordre_homomorphisme_trois}) d�finit un homomorphisme de $\left(\mathcal{M}_{n} , + \Longleftrightarrow \right)$ dans $\left( \mathcal{M}_{1},\Longleftrightarrow\right) $ o� $\Longleftrightarrow$ + est la relation d'�quivalence entre deux cha�nes de Markov. + \end{xtheorem} @@ -3079,26 +3079,26 @@ \subsection{Descente d'ordre} \para{Rappel :} - \begin{xdefinition}{homomorphisme} - \label{definition_mmc_homomorphisme}% - Soit $\left( E,\perp\right) $ et $\left( F,\perp\right) $ deux espaces munis chacun - d'une relation d'quivalence.\newline% - Soit $h:\left( E,\perp\right) \longrightarrow\left( F,\perp\right) $ une fonction, $h$ est un homomorphisme - si et seulement si : - $$ - \forall\left( x,y\right) \in E^{2},\; x\perp y\Longrightarrow h\left( x\right) \perp h\left(y\right) - $$ - \end{xdefinition} - + \begin{xdefinition}{homomorphisme} + \label{definition_mmc_homomorphisme}% + Soit $\left( E,\perp\right) $ et $\left( F,\perp\right) $ deux espaces munis chacun + d'une relation d'�quivalence.\newline% + Soit $h:\left( E,\perp\right) \longrightarrow\left( F,\perp\right) $ une fonction, $h$ est un homomorphisme + si et seulement si : + $$ + \forall\left( x,y\right) \in E^{2},\; x\perp y\Longrightarrow h\left( x\right) \perp h\left(y\right) + $$ + \end{xdefinition} + \begin{xdemo}{theoreme}{\ref{markov_ordre_homomorphisme_trois_th}} -\textbf{Rappel :} L'tat 0 correspond l'tat d'entre.\newline% +\textbf{Rappel :} L'�tat 0 correspond � l'�tat d'entr�e.\newline% -Soit $s=\vecteur{s_1}{s_T}$ une squence d'tats du modle $M$.\newline% -On dfinit la squence d'tats $u=k\left( s\right) $ comme suit~: +Soit $s=\vecteur{s_1}{s_T}$ une s�quence d'�tats du mod�le $M$.\newline% +On d�finit la s�quence d'�tats $u=k\left( s\right) $ comme suit~: $$ u=k\pa{s}=\left( u_{1},...,u_{T}\right) =\left( \left( @@ -3121,12 +3121,12 @@ \subsection{Descente d'ordre} \left\{ \begin{array}[c]{l}% s_{t+l-n} \text { si } t+l-n > 0\\ - e \text { si } l+t-n\leqslant0 \text{ o } e \text{ est l'tat d'entre de } M + e \text { si } l+t-n\leqslant0 \text{ o� } e \text{ est l'�tat d'entr�e de } M \end{array} \right. $$ -Cette squence vrifie : +Cette s�quence v�rifie : $$ \begin{array}{rrcl} @@ -3146,14 +3146,14 @@ \subsection{Descente d'ordre} $$ \biggcro{M \Longleftrightarrow N } \Longrightarrow \biggcro{ \forall s \in S ,\; - _pr{ s\left| M\right. } + _pr{ s\left| M\right. } = \pr{ s\left| N\right. } \Longrightarrow \forall s,\, \pr{ k\left( s\right) \left| h\left( M\right) \right. } = \pr{ k\left( s\right) \left| g\left( N\right) \right. }} $$ -De plus, d'aprs (\ref{markov_ordre_homomorphisme_deux}), on dduit que : +De plus, d'apr�s (\ref{markov_ordre_homomorphisme_deux}), on d�duit que : $$ \forall u,\; \biggcro{ \nexists s \in S \text{ tel que }k\left( s\right) =u } \Longrightarrow \pr{ @@ -3178,90 +3178,90 @@ \subsection{Descente d'ordre} -\subsection{Dfinition d'une chane de Markov cache d'ordre $n$} - -Le paragraphe prcdent a montr comment transformer une chane de Markov d'ordre $n$ en une chane de Markov d'ordre un. Ce rsultat peut tre tendu aux chanes de Markov caches~: - - \begin{xdefinition}{chane de Markov cache d'ordre $n$} - \label{hmm_markov_ordre_n_def}% - \indexfr{ordre} - - Soit $N\in\N^{\ast}$, soit une chane de Markov cache $N$ tats d'ordre $n$ dont les missions sont discrtes - et appartiennent l'ensemble $\vecteur{1}{D}$, cette chane vrifie les hypothses suivantes~: - - \begin{enumerate} - \item L'tat l'instant $t$ ne dpend que des tats aux instants $\left( t-1,...,t-n\right)$. Par consquent : - $$ - \forall t>n,\quad \pr{q_{t}\left| q_{t-1},...,q_{1},M\right. } = \pr{ q_{t}\left| - q_{t-1},...,q_{t-n},M\right. } - $$ - - \item Les probabilits de transition ne dpendent pas du temps, par consquent : - $$ - \begin{array}{l} - \forall t_{1},t_{2}>1,\,\forall\left(i_{0},i_{1},...,i_{n}\right) ,\\ - \pr{ q_{t_{1}}=i_{0}\left|q_{t_{1}-1}=i_{1},...,q_{t_{1}-n}=i_{n},M\right. } - = \pr{ q_{t_{2}}=i_{0}\left| q_{t_{2}-1}=i_{1}, - ...,q_{t_{2}-n}=i_{n},M\right. } - \end{array} - $$ - - \item Les probabilits d'mission ne dpendent que des tats aux instants $\left( t,...,t-n+1\right)$ : - - $$ - \forall t\geqslant 1,\, \pr{ O_{t}\left|q_{1},...,q_{t},O_{1},...,O_{t-1},M\right. } = - \pr{ O_{t}\left| - \vecteur{q_{t}}{q_{t-n+1}},M\right.} - $$ - - \item Les probabilits d'mission ne dpendent pas du temps : - - $$ - \begin{array}{l} - \forall t_{1},t_{2}>1,\;\forall\left(i_{1},...,i_{n}\right), \; \forall o, \\ - \pr{ O_{t_{1}}=o\left|q_{t_{1}}=i_{1},...,q_{t_{1}-n+1}=i_{n},M\right. } = - \pr{ O_{t_{2}}=o\left| - q_{t_{2}}=i_{1}, - ...,q_{t_{2}-n+1}=i_{n},M\right. } - \end{array} - $$ - - \end{enumerate} - - \end{xdefinition} - -\begin{xremark}{autres types d'mission} -Cette dfinition peut tre dcline pour des observations d'un type diffrent (rseau de neurones, normales...). +\subsection{D�finition d'une cha�ne de Markov cach�e d'ordre $n$} + +Le paragraphe pr�c�dent a montr� comment transformer une cha�ne de Markov d'ordre $n$ en une cha�ne de Markov d'ordre un. Ce r�sultat peut �tre �tendu aux cha�nes de Markov cach�es~: + + \begin{xdefinition}{cha�ne de Markov cach�e d'ordre $n$} + \label{hmm_markov_ordre_n_def}% + \indexfr{ordre} + + Soit $N\in\N^{\ast}$, soit une cha�ne de Markov cach�e � $N$ �tats d'ordre $n$ dont les �missions sont discr�tes + et appartiennent � l'ensemble $\vecteur{1}{D}$, cette cha�ne v�rifie les hypoth�ses suivantes~: + + \begin{enumerate} + \item L'�tat � l'instant $t$ ne d�pend que des �tats aux instants $\left( t-1,...,t-n\right)$. Par cons�quent : + $$ + \forall t>n,\quad \pr{q_{t}\left| q_{t-1},...,q_{1},M\right. } = \pr{ q_{t}\left| + q_{t-1},...,q_{t-n},M\right. } + $$ + + \item Les probabilit�s de transition ne d�pendent pas du temps, par cons�quent : + $$ + \begin{array}{l} + \forall t_{1},t_{2}>1,\,\forall\left(i_{0},i_{1},...,i_{n}\right) ,\\ + \pr{ q_{t_{1}}=i_{0}\left|q_{t_{1}-1}=i_{1},...,q_{t_{1}-n}=i_{n},M\right. } + = \pr{ q_{t_{2}}=i_{0}\left| q_{t_{2}-1}=i_{1}, + ...,q_{t_{2}-n}=i_{n},M\right. } + \end{array} + $$ + + \item Les probabilit�s d'�mission ne d�pendent que des �tats aux instants $\left( t,...,t-n+1\right)$ : + + $$ + \forall t\geqslant 1,\, \pr{ O_{t}\left|q_{1},...,q_{t},O_{1},...,O_{t-1},M\right. } = + \pr{ O_{t}\left| + \vecteur{q_{t}}{q_{t-n+1}},M\right.} + $$ + + \item Les probabilit�s d'�mission ne d�pendent pas du temps : + + $$ + \begin{array}{l} + \forall t_{1},t_{2}>1,\;\forall\left(i_{1},...,i_{n}\right), \; \forall o, \\ + \pr{ O_{t_{1}}=o\left|q_{t_{1}}=i_{1},...,q_{t_{1}-n+1}=i_{n},M\right. } = + \pr{ O_{t_{2}}=o\left| + q_{t_{2}}=i_{1}, + ...,q_{t_{2}-n+1}=i_{n},M\right. } + \end{array} + $$ + + \end{enumerate} + + \end{xdefinition} + +\begin{xremark}{autres types d'�mission} +Cette d�finition peut �tre d�clin�e pour des observations d'un type diff�rent (r�seau de neurones, normales...). \end{xremark} -On note $\mathcal{C}_{n}$ l'ensemble des chanes de Markov caches d'ordre $n$, en appliquant les rsultats du paragraphe prcdent, il est possible de construire une fonction $h^{\prime}$ :% +On note $\mathcal{C}_{n}$ l'ensemble des cha�nes de Markov cach�es d'ordre $n$, en appliquant les r�sultats du paragraphe pr�c�dent, il est possible de construire une fonction $h^{\prime}$ :% \begin{eqnarray}% \begin{array}{l} h^{\prime}:\mathcal{C}_{n}\longrightarrow\mathcal{C}_{1}\\ - h'\left( C\right) =C^{\prime} \text{ avec } M_{C'} = h\pa{M_C} \text{ o } \\ - \quad\quad\quad\quad\quad\quad M_C \text { est la chane de Markov cache dans } C \\ - \quad\quad\quad\quad\quad\quad M_{C'} \text { est la chane de Markov cache dans } C'% + h'\left( C\right) =C^{\prime} \text{ avec } M_{C'} = h\pa{M_C} \text{ o� } \\ + \quad\quad\quad\quad\quad\quad M_C \text { est la cha�ne de Markov cach�e dans } C \\ + \quad\quad\quad\quad\quad\quad M_{C'} \text { est la cha�ne de Markov cach�e dans } C'% \end{array} \label{markov_ordre_homomorphisme_quatre} \end{eqnarray} -$h\pa{C}$ vrifie~: +$h\pa{C}$ v�rifie~: \begin{eqnarray*} \pr{ q_{t}=l\left| q_{t-1}=k,M^{\prime}\right. } &=& \left\{% \begin{array}[c]{l}% \pr{ q_{t}=\left[ g^{-1}\left( l\right) \right] _{n}\left| - \vecteurno{q_{t-1}=\crochet{g^{-1}\pa{k}}_{n}} {q_{t-n}= + \vecteurno{q_{t-1}=\crochet{g^{-1}\pa{k}}_{n}} {q_{t-n}= \crochet{g^{-1}\pa{k}}_{1}} \right. ,M} \medskip\\ \quad\quad\quad \text{ si }\forall i\in\left\{ 1,...,n-1\right\} ,\,\left[ g^{-1}\left( l\right) \right] _{i}=\left[ g^{-1}\left( k\right) \right] _{i+1}\\ - e\text{ sinon (} e \text{ est l'tat d'entre de la chane de Markov)}% + e\text{ sinon (} e \text{ est l'�tat d'entr�e de la cha�ne de Markov)}% \end{array} \right. \\ \pr{ O_{t}=o\left| q_{t}=l,M^{\prime}\right. } &=& \pr{ O_{t}=o\left| @@ -3269,39 +3269,39 @@ \subsection{D \crochet{g^{-1}\pa{l}}_{1}} ,M\right. } \end{eqnarray*} -On doit d'abord dfinir l'quivalence entre deux chanes de Markov caches : +On doit d'abord d�finir l'�quivalence entre deux cha�nes de Markov cach�es : - \begin{xdefinition}{quivalence entre deux chanes de Markov caches} - \label{definition_hmm_equivalence}% - Soient $C_1$ et $C_2$ deux chanes de Markov cache, on note $\mathcal{O}$ l'ensemble des squences - d'observations, alors : - $$ - \biggcro{C_1 \Longleftrightarrow C_2} \Longleftrightarrow \biggcro{\forall O - \in \mathcal{O}, \; \pr{O|C_1} = \pr{O|C_2}} - $$ - \end{xdefinition} + \begin{xdefinition}{�quivalence entre deux cha�nes de Markov cach�es} + \label{definition_hmm_equivalence}% + Soient $C_1$ et $C_2$ deux cha�nes de Markov cach�e, on note $\mathcal{O}$ l'ensemble des s�quences + d'observations, alors : + $$ + \biggcro{C_1 \Longleftrightarrow C_2} \Longleftrightarrow \biggcro{\forall O + \in \mathcal{O}, \; \pr{O|C_1} = \pr{O|C_2}} + $$ + \end{xdefinition} -On peut maintenant noncer le thorme suivant : +On peut maintenant �noncer le th�or�me suivant : - \begin{xtheorem}{homomorphisme} - \label{theoreme_equivalence_cachee}% - \indexfr{homomorphisme} - Avec ces notations, la fonction $h'$ (\ref{markov_ordre_homomorphisme_quatre}) - dfinit un homomorphisme de $\left(\mathcal{C}_{n} , - \Longleftrightarrow \right)$ dans $\left( \mathcal{C}_{1},\Longleftrightarrow\right) $ o - $\Longleftrightarrow$ est la relation - d'quivalence entre deux chanes de Markov. De plus, $\forall C\in \mathcal{C}_n, \; h\pa{C} - \Longleftrightarrow C$. - \end{xtheorem} + \begin{xtheorem}{homomorphisme} + \label{theoreme_equivalence_cachee}% + \indexfr{homomorphisme} + Avec ces notations, la fonction $h'$ (\ref{markov_ordre_homomorphisme_quatre}) + d�finit un homomorphisme de $\left(\mathcal{C}_{n} , + \Longleftrightarrow \right)$ dans $\left( \mathcal{C}_{1},\Longleftrightarrow\right) $ o� + $\Longleftrightarrow$ est la relation + d'�quivalence entre deux cha�nes de Markov. De plus, $\forall C\in \mathcal{C}_n, \; h\pa{C} + \Longleftrightarrow C$. + \end{xtheorem} -\begin{xdemo}{thorme}{\ref{theoreme_equivalence_cachee}} -Ce thorme est un corollaire du thorme~\ref{markov_ordre_homomorphisme_trois_th} (page~\pageref{markov_ordre_homomorphisme_trois_th}). +\begin{xdemo}{th�or�me}{\ref{theoreme_equivalence_cachee}} +Ce th�or�me est un corollaire du th�or�me~\ref{markov_ordre_homomorphisme_trois_th} (page~\pageref{markov_ordre_homomorphisme_trois_th}). \end{xdemo} @@ -3314,76 +3314,76 @@ \subsection{D -\subsection{Dfinition d'une chane de Markov cache d'ordre $\pa{p,q}$} - - -Ces modles sont ceux dont les hypothses sont les moins contraignantes. - - - \begin{xdefinition}{chane de Markov cache d'ordre $\pa{p,q}$}% - \label{hmm_markov_ordre_pq_def}% - \indexfr{ordre} - Soit $N\in\N^{\ast}$, soit une chane de Markov cache $N$ tats d'ordre $p>0$ dont les missions - sont discrtes et appartiennent l'ensemble $\vecteur{1}{D}$, cette chane vrifie les hypothses suivantes~: - - \begin{enumerate} - \item L'tat l'instant $t$ ne dpend que des tats aux instants $\left( t-1,...,t-p\right)$ et - des observations aux instants - $\vecteur{O_{t-1}}{O_{t-q}}$ : - $$ - \forall t>p,\; \pr{ q_{t}\left| \overline{q_{t-1}}, \overline{O_{t-1}},M\right. } - = \pr{ q_{t}\left| q_{t-1},...,q_{t-p},O_{t-1},...,O_{t-q},M\right. } - $$ - - \item Les probabilits de transition ne dpendent pas du temps, Par consquent : - $$ - \begin{array}{l} - \forall t_{1},t_{2}>1,\;\forall\left(i_{0},...,i_{p}\right), \; \forall\left(x_{1},...,x_{q}\right), \; \\ - \pr{ q_{t_{0}}=i_0\left|q_{t_{1}}=i_{1},...,q_{t_{1}-p+1}=i_{n}, - O_{t_{1}}=x_{1},...,O_{t_{1}-q}=x_{n},M\right. } \\ - \quad\quad= \pr{ q_{t_{2}}=i_0\left| - q_{t_{2}}=i_{1},...,q_{t_{2}-n+1}=i_{n},O_{t_{2}}=x_{1},...,O_{t_{2}-q}=x_{n},M\right. } - \end{array} - $$ - - \item Les probabilits d'mission ne dpendent que des tats aux instants $\left( t,...,t-p+1\right)$ - et des observations aux instants $\vecteur{O_{t-1}}{O_{t-Q}}$~: - - $$ - \forall t\geqslant 1,\, \pr{ O_{t}\left|q_{1},...,q_{t},O_{1},...,O_{t-1},M\right. } = - \pr{ O_{t}\left| \vecteur{q_{t}}{q_{t-p+1}},\vecteur{O_{t-1}}{O_{t-q}},M\right. } - $$ - - \item Les probabilits d'mission ne dpendent pas du temps~: - - $$ - \begin{array}{l} - \forall t_{1},t_{2}>1,\;\forall\left(i_{1},...,i_{p}\right), \; - \forall\left(x_{1},...,x_{q}\right), \; \forall o, \\ - \pr{ O_{t_{1}}=o\left|q_{t_{1}}=i_{1},...,q_{t_{1}-p+1}=i_{n},O_{t_{1}} = - x_{1},...,O_{t_{1}-q}=x_{n},M\right. } \\ - \quad\quad= \pr{ O_{t_{2}}=o\left| - q_{t_{2}}=i_{1},...,q_{t_{2}-n+1}=i_{n},O_{t_{2}}=x_{1},...,O_{t_{2}-q}=x_{n},M\right. } - \end{array} - $$ - - \end{enumerate} - \end{xdefinition} - - - -Grce une dmonstration similaire, les paragraphes prcdents nous permettent d'noncer le thorme suivant~: - - \begin{xtheorem}{homomorphisme}% - \label{theoreme_hmm_homomorphisme_1}% - Soit $\mathcal{C}_{p,q} \pa{\mathcal{X}}$ l'ensemble des chanes de Markov caches d'ordre $\pa{p,q}$ - dont les observations - appartiennent l'ensemble $\mathcal{X}$. Alors il existe un homomorphisme de $\pa{\mathcal{C}_{p,q} - \pa{\mathcal{X}},\Longleftrightarrow}$ dans $\pa{\mathcal{C}_{1,1} \pa{\mathcal{X}^q},\Longleftrightarrow}$ - \end{xtheorem} - -\begin{xdemo}{thorme}{\ref{theoreme_hmm_homomorphisme_1}} -La dmonstration est similaire celle du thorme~\ref{markov_ordre_homomorphisme_trois_th} (page~\pageref{markov_ordre_homomorphisme_trois_th}). +\subsection{D�finition d'une cha�ne de Markov cach�e d'ordre $\pa{p,q}$} + + +Ces mod�les sont ceux dont les hypoth�ses sont les moins contraignantes. + + + \begin{xdefinition}{cha�ne de Markov cach�e d'ordre $\pa{p,q}$}% + \label{hmm_markov_ordre_pq_def}% + \indexfr{ordre} + Soit $N\in\N^{\ast}$, soit une cha�ne de Markov cach�e � $N$ �tats d'ordre $p>0$ dont les �missions + sont discr�tes et appartiennent � l'ensemble $\vecteur{1}{D}$, cette cha�ne v�rifie les hypoth�ses suivantes~: + + \begin{enumerate} + \item L'�tat � l'instant $t$ ne d�pend que des �tats aux instants $\left( t-1,...,t-p\right)$ et + des observations aux instants + $\vecteur{O_{t-1}}{O_{t-q}}$ : + $$ + \forall t>p,\; \pr{ q_{t}\left| \overline{q_{t-1}}, \overline{O_{t-1}},M\right. } + = \pr{ q_{t}\left| q_{t-1},...,q_{t-p},O_{t-1},...,O_{t-q},M\right. } + $$ + + \item Les probabilit�s de transition ne d�pendent pas du temps, Par cons�quent : + $$ + \begin{array}{l} + \forall t_{1},t_{2}>1,\;\forall\left(i_{0},...,i_{p}\right), \; \forall\left(x_{1},...,x_{q}\right), \; \\ + \pr{ q_{t_{0}}=i_0\left|q_{t_{1}}=i_{1},...,q_{t_{1}-p+1}=i_{n}, + O_{t_{1}}=x_{1},...,O_{t_{1}-q}=x_{n},M\right. } \\ + \quad\quad= \pr{ q_{t_{2}}=i_0\left| + q_{t_{2}}=i_{1},...,q_{t_{2}-n+1}=i_{n},O_{t_{2}}=x_{1},...,O_{t_{2}-q}=x_{n},M\right. } + \end{array} + $$ + + \item Les probabilit�s d'�mission ne d�pendent que des �tats aux instants $\left( t,...,t-p+1\right)$ + et des observations aux instants $\vecteur{O_{t-1}}{O_{t-Q}}$~: + + $$ + \forall t\geqslant 1,\, \pr{ O_{t}\left|q_{1},...,q_{t},O_{1},...,O_{t-1},M\right. } = + \pr{ O_{t}\left| \vecteur{q_{t}}{q_{t-p+1}},\vecteur{O_{t-1}}{O_{t-q}},M\right. } + $$ + + \item Les probabilit�s d'�mission ne d�pendent pas du temps~: + + $$ + \begin{array}{l} + \forall t_{1},t_{2}>1,\;\forall\left(i_{1},...,i_{p}\right), \; + \forall\left(x_{1},...,x_{q}\right), \; \forall o, \\ + \pr{ O_{t_{1}}=o\left|q_{t_{1}}=i_{1},...,q_{t_{1}-p+1}=i_{n},O_{t_{1}} = + x_{1},...,O_{t_{1}-q}=x_{n},M\right. } \\ + \quad\quad= \pr{ O_{t_{2}}=o\left| + q_{t_{2}}=i_{1},...,q_{t_{2}-n+1}=i_{n},O_{t_{2}}=x_{1},...,O_{t_{2}-q}=x_{n},M\right. } + \end{array} + $$ + + \end{enumerate} + \end{xdefinition} + + + +Gr�ce � une d�monstration similaire, les paragraphes pr�c�dents nous permettent d'�noncer le th�or�me suivant~: + + \begin{xtheorem}{homomorphisme}% + \label{theoreme_hmm_homomorphisme_1}% + Soit $\mathcal{C}_{p,q} \pa{\mathcal{X}}$ l'ensemble des cha�nes de Markov cach�es d'ordre $\pa{p,q}$ + dont les observations + appartiennent � l'ensemble $\mathcal{X}$. Alors il existe un homomorphisme de $\pa{\mathcal{C}_{p,q} + \pa{\mathcal{X}},\Longleftrightarrow}$ dans $\pa{\mathcal{C}_{1,1} \pa{\mathcal{X}^q},\Longleftrightarrow}$ + \end{xtheorem} + +\begin{xdemo}{th�or�me}{\ref{theoreme_hmm_homomorphisme_1}} +La d�monstration est similaire � celle du th�or�me~\ref{markov_ordre_homomorphisme_trois_th} (page~\pageref{markov_ordre_homomorphisme_trois_th}). \end{xdemo} @@ -3391,12 +3391,12 @@ \subsection{D - \begin{xcorollary}{homomorphisme} - \label{corollaire_chaine_markov_cachee_1}% - Si $\mathcal{X}$ est un ensemble fini, il existe un homomorphisme de - $\pa{\mathcal{C}_{p,q} \pa{\mathcal{X}},\Longleftrightarrow}$ dans - $\pa{\mathcal{C}_{1,0} \pa{\mathcal{X}^q},\Longleftrightarrow}$ - \end{xcorollary} + \begin{xcorollary}{homomorphisme} + \label{corollaire_chaine_markov_cachee_1}% + Si $\mathcal{X}$ est un ensemble fini, il existe un homomorphisme de + $\pa{\mathcal{C}_{p,q} \pa{\mathcal{X}},\Longleftrightarrow}$ dans + $\pa{\mathcal{C}_{1,0} \pa{\mathcal{X}^q},\Longleftrightarrow}$ + \end{xcorollary} @@ -3409,7 +3409,7 @@ \subsection{D \subsection{Conclusion} -Cette annexe a prsent les modles de Markov cachs, leur utilisation pour la modlisation de squences discrtes ou continues et leur estimation. Pour des raisons calculatoires, les chanes de Markov caches d'ordre $\pa{p,q}$ avec $q>0$ aux missions continues sont peu utilises. Il est alors possible de n'envisager que des modles de chanes de Markov caches d'ordre $\pa{1,0}$ puisque toute chane d'ordre $\pa{p>1,0}$ a son quivalent d'ordre $\pa{1,0}$. Les calculs avec ces modles sont simples et leur reprsentation peut tre rduite un simple graphe, ce dernier point facilite leur ralisation informatique. +Cette annexe a pr�sent� les mod�les de Markov cach�s, leur utilisation pour la mod�lisation de s�quences discr�tes ou continues et leur estimation. Pour des raisons calculatoires, les cha�nes de Markov cach�es d'ordre $\pa{p,q}$ avec $q>0$ aux �missions continues sont peu utilis�es. Il est alors possible de n'envisager que des mod�les de cha�nes de Markov cach�es d'ordre $\pa{1,0}$ puisque toute cha�ne d'ordre $\pa{p>1,0}$ a son �quivalent d'ordre $\pa{1,0}$. Les calculs avec ces mod�les sont simples et leur repr�sentation peut �tre r�duite � un simple graphe, ce dernier point facilite leur r�alisation informatique. @@ -3417,15 +3417,15 @@ \subsection{Extension} \indexfrr{loi}{normale} -L'article \citeindex{Bicego2003} dmontre aussi l'quivalence entre un modle de Markov cach dont les missions associs aux tats sont des mlanges de lois normales avec un modle de Markov cach dont les missions sont des lois gaussiennes. Le second modle comporte bien videmment plus d'tats. +L'article \citeindex{Bicego2003} d�montre aussi l'�quivalence entre un mod�le de Markov cach� dont les �missions associ�s aux �tats sont des m�langes de lois normales avec un mod�le de Markov cach� dont les �missions sont des lois gaussiennes. Le second mod�le comporte bien �videmment plus d'�tats. \firstpassagedo{ - \begin{thebibliography}{99} - \input{hmm_bibliographie.tex} - \end{thebibliography} + \begin{thebibliography}{99} + \input{hmm_bibliographie.tex} + \end{thebibliography} } diff --git a/_todo/hmm/hmm_bibliographie.tex b/_todo/hmm/hmm_bibliographie.tex index 7f263207..1801656d 100644 --- a/_todo/hmm/hmm_bibliographie.tex +++ b/_todo/hmm/hmm_bibliographie.tex @@ -1,12 +1,12 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin \bibitemstyle{Baum1968} {1968} { L. E. Baum, G. R. Sell} {Growth transformation for functions on manifolds} @@ -21,8 +21,8 @@ {Pattern Recognition Letters}{24}{1395}{1407} \bibitemstyle{Bottou1991} {1991} {L. Bottou} -{Une approche thorique de l'apprentissage connexionniste, Application la reconnaissance de la parole} -{Thse de l'Universit de Paris Sud, Centre d'Orsay}{}{0}{} +{Une approche th�orique de l'apprentissage connexionniste, Application � la reconnaissance de la parole} +{Th�se de l'Universit� de Paris Sud, Centre d'Orsay}{}{0}{} \bibitemstyle{Celeux1985}{1985}{G. Celeux, J. Diebolt} {The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem} @@ -30,7 +30,7 @@ \bibitemstyle{Celeux1995}{1995}{Gilles Celeux, Didier Chauveau, Jean Diebolt} {On stochastic version of the EM algorithm} -{Rapport de recherche de l'INRIA}{n2514}{0}{} +{Rapport de recherche de l'INRIA}{n�2514}{0}{} \bibitemstyle{Chen1994}{1994}{Mou-Yen Chen, Amlan Kundu, Jian Zhou} {Off-line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network} diff --git a/_todo/image/image.tex b/_todo/image/image.tex index cd7aa865..052bf0c2 100644 --- a/_todo/image/image.tex +++ b/_todo/image/image.tex @@ -13,56 +13,56 @@ \chapter{Traitement d'images} \label{image_chapitre_label} -Ce chapitre regroupe tous les traitements d'images pralables l'utilisation de modles probabilistes qu'on peut scinder en deux ensembles. Le premier corrige les imperfections de l'image comme un bruit importun, un soulignement non dsir, une mauvaise inclinaison. Le second groupe concerne essentiellement la segmentation en graphmes qui consiste dcouper l'image d'un mot cursif en petites images, ceci afin de rduire la complexit des modles probabilistes utiliss par la suite. La premire ide explore fut d'un apprentissage de cette segmentation. Les rsultats insuffisants orientrent ensuite ces travaux vers la ralisation d'une segmentation l'aide d'algorithmes plus classiques incluant notamment un traitement dissoci des accents dont la pertinence a t value. +Ce chapitre regroupe tous les traitements d'images pr�alables � l'utilisation de mod�les probabilistes qu'on peut scinder en deux ensembles. Le premier corrige les imperfections de l'image comme un bruit importun, un soulignement non d�sir�, une mauvaise inclinaison. Le second groupe concerne essentiellement la segmentation en graph�mes qui consiste � d�couper l'image d'un mot cursif en petites images, ceci afin de r�duire la complexit� des mod�les probabilistes utilis�s par la suite. La premi�re id�e explor�e fut d'un apprentissage de cette segmentation. Les r�sultats insuffisants orient�rent ensuite ces travaux vers la r�alisation d'une segmentation � l'aide d'algorithmes plus classiques incluant notamment un traitement dissoci� des accents dont la pertinence a �t� �valu�e. %-------------------------------------------------------------------------------------------------------------- -\section{Prambule} +\section{Pr�ambule} %-------------------------------------------------------------------------------------------------------------- -\indexfr{prtraitement} - -\indexfrr{squence}{observations} -\indexfrr{mot}{mathmatique} - -Avant de se lancer dans la reconnaissance proprement parler, l'image doit tre prtraite de manire passer d'une information souvent bruite, toujours de taille variable une information standardise. Une srie de traitements parfois simples, parfois complexes est d'abord applique l'image avant de la convertir en une squence d'observations ou mot mathmatique, matriau utilis par les modles de reconnaissance statistique. L'image d'un mot affronte des traitements tels que l'extraction de la zone reconnatre, la binarisation, le nettoyage, le redressement de l'inclinaison, la squelettisation, la segmentation en graphmes, en mots (voir figure~\ref{image_global}). - - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=6cm, width=10cm] - {\filext{../image/image/global}}\end{array}$}$$ - \caption{Schma global des prtraitements d'image.} - \label{image_global} - \end{figure} - -\indexfr{heuristique} +\indexfr{pr�traitement} + +\indexfrr{s�quence}{observations} +\indexfrr{mot}{math�matique} + +Avant de se lancer dans la reconnaissance � proprement parler, l'image doit �tre pr�trait�e de mani�re � passer d'une information souvent bruit�e, toujours de taille variable � une information standardis�e. Une s�rie de traitements parfois simples, parfois complexes est d'abord appliqu�e � l'image avant de la convertir en une s�quence d'observations ou mot math�matique, mat�riau utilis� par les mod�les de reconnaissance statistique. L'image d'un mot affronte des traitements tels que l'extraction de la zone � reconna�tre, la binarisation, le nettoyage, le redressement de l'inclinaison, la squelettisation, la segmentation en graph�mes, en mots (voir figure~\ref{image_global}). + + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=6cm, width=10cm] + {\filext{../image/image/global}}\end{array}$}$$ + \caption{Sch�ma global des pr�traitements d'image.} + \label{image_global} + \end{figure} + +\indexfr{heuristique} \indexfr{nettoyage} -\indexfr{graphme} -Chacune de ces tapes est souvent trs rapide et est frquemment base sur des heuristiques. L'extraction, la binarisation, la squelettisation\seeannex{annexe_squelettisation}{squelettisation} sont des traitements communs qui ne seront pas plus dtaills. Le nettoyage est en pratique adapt pour chaque type de problme. Le nettoyage d'un peigne est diffrent du nettoyage d'une ligne et il n'existe pas encore de mthode gnrale pour ce type de tche. Le redressement se rduit l'estimation de l'inclinaison du texte, une mthode base sur des histogrammes convient comme celle explique au paragraphe~\ref{image_seg_line} ou celle du paragraphe~\ref{image_redressement_sobel}. La plupart de ces prtraitements sont dcrits sommairement dans~\citeindex{Yanikoglu1998}. - -L'objectif avou de cette partie est la conception d'une segmentation en graphmes, c'est--dire le dcoupage de l'image d'un mot en une succession d'images correspondant ses lettres ou des morceaux de ses lettres qui seront utiliss plus tard par des modles de reconnaissance de l'criture. C'est un traitement souvent complexe et rarement parfait. Segmentation et reconnaissance sont encore deux tapes distinctes et ceci explique pourquoi ce traitement inclut gnralement une multitude de cas particuliers (voir~\citeindex{Lecolinet1991}, \citeindex{Simon1992}). La figure~\ref{image_grapheme_erreur} rsume les faiblesses d'un algorithme de segmentation en graphmes. Ce traitement produit des erreurs quelle que soit la mthode choisie car il est des configurations qui ncessitent la reconnaissance des lettres segmenter afin d'tre tranches comme la lettre "m" qui se confond avec le couple "rn". - - - \begin{figure}[t] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=8cm, width=16cm] - {\filext{../image/image/failure}}\end{array}$}$$ - \caption{ Erreurs de segmentation en graphmes pour un algorithme (voir~\citeindexfig{Baret1991}) - qui s'appuie - essentiellement sur le squelette. Cette opration est erronne dans environ 10\% des cas. - Comment segmenter les deux premires lettres du mot "souris" ou "chat" alors que, dans ces - deux cas, ce sont presque deux boucles qui possdent une paroi commune~? Ces configurations - sont difficiles segmenter car les lettres sont souvent crites de manire enchevtre - comme les deux "t" conscutifs, les lettres liaisons hautes (b,o,v,w).} - \label{image_grapheme_erreur} - \end{figure} - -\indexfrr{liaison}{haute} - -L'algorithme utilis pour dcouper les mots de la figure~\ref{image_grapheme_erreur} segmente mal les couples de lettres liaison haute comme "oi" contrairement au couple "da" pour lequel, il y a trs peu d'erreurs. Il n'est pas vident de juger de l'efficacit d'un algorithme de segmentation en graphmes, le rsultat peut tre dcevant pour l'\oe il humain et nanmoins tre performant s'il est appari des modles de reconnaissance qui peuvent par exemple modliser ses erreurs (voir paragraphe~\ref{hmm_bi_lettre}, page~\pageref{hmm_bi_lettre}). +\indexfr{graph�me} +Chacune de ces �tapes est souvent tr�s rapide et est fr�quemment bas�e sur des heuristiques. L'extraction, la binarisation, la squelettisation\seeannex{annexe_squelettisation}{squelettisation} sont des traitements communs qui ne seront pas plus d�taill�s. Le nettoyage est en pratique adapt� pour chaque type de probl�me. Le nettoyage d'un peigne est diff�rent du nettoyage d'une ligne et il n'existe pas encore de m�thode g�n�rale pour ce type de t�che. Le redressement se r�duit � l'estimation de l'inclinaison du texte, une m�thode bas�e sur des histogrammes convient comme celle expliqu�e au paragraphe~\ref{image_seg_line} ou celle du paragraphe~\ref{image_redressement_sobel}. La plupart de ces pr�traitements sont d�crits sommairement dans~\citeindex{Yanikoglu1998}. + +L'objectif avou� de cette partie est la conception d'une segmentation en graph�mes, c'est-�-dire le d�coupage de l'image d'un mot en une succession d'images correspondant � ses lettres ou � des morceaux de ses lettres qui seront utilis�s plus tard par des mod�les de reconnaissance de l'�criture. C'est un traitement souvent complexe et rarement parfait. Segmentation et reconnaissance sont encore deux �tapes distinctes et ceci explique pourquoi ce traitement inclut g�n�ralement une multitude de cas particuliers (voir~\citeindex{Lecolinet1991}, \citeindex{Simon1992}). La figure~\ref{image_grapheme_erreur} r�sume les faiblesses d'un algorithme de segmentation en graph�mes. Ce traitement produit des erreurs quelle que soit la m�thode choisie car il est des configurations qui n�cessitent la reconnaissance des lettres � segmenter afin d'�tre tranch�es comme la lettre "m" qui se confond avec le couple "rn". + + + \begin{figure}[t] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=8cm, width=16cm] + {\filext{../image/image/failure}}\end{array}$}$$ + \caption{ Erreurs de segmentation en graph�mes pour un algorithme (voir~\citeindexfig{Baret1991}) + qui s'appuie + essentiellement sur le squelette. Cette op�ration est erronn�e dans environ 10\% des cas. + Comment segmenter les deux premi�res lettres du mot "souris" ou "chat" alors que, dans ces + deux cas, ce sont presque deux boucles qui poss�dent une paroi commune~? Ces configurations + sont difficiles � segmenter car les lettres sont souvent �crites de mani�re enchev�tr�e + comme les deux "t" cons�cutifs, les lettres � liaisons hautes (b,o,v,w).} + \label{image_grapheme_erreur} + \end{figure} + +\indexfrr{liaison}{haute} + +L'algorithme utilis� pour d�couper les mots de la figure~\ref{image_grapheme_erreur} segmente mal les couples de lettres � liaison haute comme "oi" contrairement au couple "da" pour lequel, il y a tr�s peu d'erreurs. Il n'est pas �vident de juger de l'efficacit� d'un algorithme de segmentation en graph�mes, le r�sultat peut �tre d�cevant pour l'\oe il humain et n�anmoins �tre performant s'il est appari� � des mod�les de reconnaissance qui peuvent par exemple mod�liser ses erreurs (voir paragraphe~\ref{hmm_bi_lettre}, page~\pageref{hmm_bi_lettre}). -Les paragraphes qui suivent se proposent de dcrire diffrentes mthodes de segmentations (lignes, mots, graphmes) qui permettront de rsoudre le problme de reconnaissance de mots-cl dans un paragraphe manuscrit. Il y aura peu d'valuation de performances car il est difficile de juger la qualit d'un traitement d'image autrement qu'en observant. La seule sanction est le taux de reconnaissance~: combien d'images ont-elles t bien dcryptes~? Et dans le cas d'une amlioration des performances, on peut se demander si celles-ci sont dues une amlioration de la segmentation en graphmes ou une meilleure modlisation de cette dernire par des modles probabilistes. +Les paragraphes qui suivent se proposent de d�crire diff�rentes m�thodes de segmentations (lignes, mots, graph�mes) qui permettront de r�soudre le probl�me de reconnaissance de mots-cl� dans un paragraphe manuscrit. Il y aura peu d'�valuation de performances car il est difficile de juger la qualit� d'un traitement d'image autrement qu'en observant. La seule sanction est le taux de reconnaissance~: combien d'images ont-elles �t� bien d�crypt�es~? Et dans le cas d'une am�lioration des performances, on peut se demander si celles-ci sont dues � une am�lioration de la segmentation en graph�mes ou � une meilleure mod�lisation de cette derni�re par des mod�les probabilistes. -L'objectif de cette partie n'est donc pas d'amliorer une segmentation graphme existante (celle developpe dans~\citeindex{Baret1991}) mais d'en proposer une autre afin d'obtenir deux chanes de reconnaissance suffisamment diffrentes afin que leurs rsultats soient si possible corrls pour des images de bonne qualit mais divergents pour des images de qualit moyenne. +L'objectif de cette partie n'est donc pas d'am�liorer une segmentation graph�me existante (celle developp�e dans~\citeindex{Baret1991}) mais d'en proposer une autre afin d'obtenir deux cha�nes de reconnaissance suffisamment diff�rentes afin que leurs r�sultats soient si possible corr�l�s pour des images de bonne qualit� mais divergents pour des images de qualit� moyenne. @@ -75,13 +75,13 @@ \section{Apprentissage d'une segmentation} %------------------------------------------------------------------------------------------------------------- \label{image_apprentissage_segmentation} \indexfrr{apprentissage}{segmentation} -\indexfr{Vorono} -\indexfr{diagramme de Vorono} +\indexfr{Vorono�} +\indexfr{diagramme de Vorono�} \indexfr{composante connexe} -\indexfr{connexit} +\indexfr{connexit�} \indexfr{squelette} -La segmentation en graphmes prsente par la suite (paragraphe~\ref{image_choix_segmentation}) s'appuie sur de nombreux seuils fixs "manuellement", ajusts lors de la visualisation du rsultat sur quelques images. Ces heuristiques interviennent lors de la segmentation d'une manire qui rend impossible une estimation autre qu'un ttonnement progressif. Une segmentation pouvant tre apprise a l'avantage de pouvoir tre modifie en utilisant les rsultats de la reconnaissance. Le second objectif vis est une adaptation plus facile lorsque les documents traiter changent. De plus, il serait possible d'envisager une boucle alternant les apprentissages de la reconnaissance et de la segmentation automatique. +La segmentation en graph�mes pr�sent�e par la suite (paragraphe~\ref{image_choix_segmentation}) s'appuie sur de nombreux seuils fix�s "manuellement", ajust�s lors de la visualisation du r�sultat sur quelques images. Ces heuristiques interviennent lors de la segmentation d'une mani�re qui rend impossible une estimation autre qu'un t�tonnement progressif. Une segmentation pouvant �tre apprise a l'avantage de pouvoir �tre modifi�e en utilisant les r�sultats de la reconnaissance. Le second objectif vis� est une adaptation plus facile lorsque les documents � traiter changent. De plus, il serait possible d'envisager une boucle alternant les apprentissages de la reconnaissance et de la segmentation automatique. @@ -89,81 +89,81 @@ \section{Apprentissage d'une segmentation} \subsection{Principe} -Cette ide s'appuie sur les diagrammes de Vorono qui proposent un maillage d'une image (figure~\ref{image_voronoi1}). L'image est d'abord dcrite par ses composantes connexes puis rduite l'tat de squelette\seeannex{annexe_squelettisation}{squelettisation}. Ce squelette est ensuite dcoup de manire ce que les morceaux ainsi forms soient cohrents avec la segmentation dsire. En rsum, aucun des morceaux obtenus ne doit appartenir deux zones diffrentes (voir~\ref{image_voronoi1}). +Cette id�e s'appuie sur les diagrammes de Vorono� qui proposent un maillage d'une image (figure~\ref{image_voronoi1}). L'image est d'abord d�crite par ses composantes connexes puis r�duite � l'�tat de squelette\seeannex{annexe_squelettisation}{squelettisation}. Ce squelette est ensuite d�coup� de mani�re � ce que les morceaux ainsi form�s soient coh�rents avec la segmentation d�sir�e. En r�sum�, aucun des morceaux obtenus ne doit appartenir � deux zones diff�rentes (voir~\ref{image_voronoi1}). - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{ccc} - \includegraphics[height=3.5cm, width=5cm] {\filext{../image/image/voronoi0}} & & - \includegraphics[height=3.5cm, width=5cm] {\filext{../image/image/voronoi1}} - \end{array}$}$$ - \caption{ Diagramme de Vorono utilis pour une segmentation en lignes~: l'image de gauche reprsente la - segmentation apprendre, les lignes fonces de l'image de droite indiquent les frontires du - diagramme de Vorono correspondant le mieux aux frontires entre les zones - de la segmentation dsire.} - \label{image_voronoi1} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{ccc} + \includegraphics[height=3.5cm, width=5cm] {\filext{../image/image/voronoi0}} & & + \includegraphics[height=3.5cm, width=5cm] {\filext{../image/image/voronoi1}} + \end{array}$}$$ + \caption{ Diagramme de Vorono� utilis� pour une segmentation en lignes~: l'image de gauche repr�sente la + segmentation � apprendre, les lignes fonc�es de l'image de droite indiquent les fronti�res du + diagramme de Vorono� correspondant le mieux aux fronti�res entre les zones + de la segmentation d�sir�e.} + \label{image_voronoi1} + \end{figure} -La segmentation en lignes d'une image telle que celle de la figure~\ref{image_voronoi1} devient un problme de classification en deux classes~: chaque segment du diagramme de Vorono est une frontire entre deux zones partager ou ne l'est pas. Comme le montre la figure~\ref{image_voronoi_local}, la classification d'un segment peut intgrer des informations relatives aux segments connects aux deux extrmits ainsi que des caractristiques sur la forme du texte dans le voisinage de ce segment. L'objectif est la recherche d'une fonction du type~: +La segmentation en lignes d'une image telle que celle de la figure~\ref{image_voronoi1} devient un probl�me de classification en deux classes~: chaque segment du diagramme de Vorono� est une fronti�re entre deux zones � partager ou ne l'est pas. Comme le montre la figure~\ref{image_voronoi_local}, la classification d'un segment peut int�grer des informations relatives aux segments connect�s aux deux extr�mit�s ainsi que des caract�ristiques sur la forme du texte dans le voisinage de ce segment. L'objectif est la recherche d'une fonction du type~: - \begin{eqnarray} - f : S \times S^S_1 \times S^S_2 \times F^S_1 \times F^S_2 \longrightarrow \cro{0,1} - \label{image_voronoi_f} - \end{eqnarray} - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{ccc} - \includegraphics[height=3cm, width=3cm] {\filext{../image/image/voronoi10}} && - \includegraphics[height=3cm, width=3cm] {\filext{../image/image/voronoi11}} - \end{array}$}$$ - \caption{ Voisinage d'un segment du diagramme de Vorono~: tout segment - est connect d'autres segments ses - deux extrmits et il spare deux zones contenant chacune une petite partie - du texte que contient l'image.} - \label{image_voronoi_local} - \end{figure} - -$S$ est vecteur caractrisant le segment classer, $S^S_1$ et $S^S_2$ sont deux vecteurs de mme dimension caractrisant les vecteurs connects $S$ chacune de ses deux extrmits, $F^S_1$ et $F^S_2$ sont deux vecteurs caractrisant la forme du contenu des deux zones de textes que $S$ spare (figure~\ref{image_voronoi_local}). - + \begin{eqnarray} + f : S \times S^S_1 \times S^S_2 \times F^S_1 \times F^S_2 \longrightarrow \cro{0,1} + \label{image_voronoi_f} + \end{eqnarray} + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{ccc} + \includegraphics[height=3cm, width=3cm] {\filext{../image/image/voronoi10}} && + \includegraphics[height=3cm, width=3cm] {\filext{../image/image/voronoi11}} + \end{array}$}$$ + \caption{ Voisinage d'un segment du diagramme de Vorono�~: tout segment + est connect� � d'autres segments � ses + deux extr�mit�s et il s�pare deux zones contenant chacune une petite partie + du texte que contient l'image.} + \label{image_voronoi_local} + \end{figure} + +$S$ est vecteur caract�risant le segment � classer, $S^S_1$ et $S^S_2$ sont deux vecteurs de m�me dimension caract�risant les vecteurs connect�s � $S$ � chacune de ses deux extr�mit�s, $F^S_1$ et $F^S_2$ sont deux vecteurs caract�risant la forme du contenu des deux zones de textes que $S$ s�pare (figure~\ref{image_voronoi_local}). + -\subsection{Exprimentations} +\subsection{Exp�rimentations} -Dans un premier temps, la fonction $f$ (\ref{image_voronoi_f}) a t estime l'aide d'un rseau de neurones classifieur\seeannex{subsection_classifieur}{classifieur}. Les vecteurs $S$, $F^S_1$, $F^S_2$ contenaient des informations relatives la longueur du segment, sa courbure, son inclinaison, la distance du segment au texte. Les vecteurs $S^S_1$ et $S^S_2$ contenaient des moyennes des mmes informations. L'estimation de la fonction $f$ a conduit au rsultat figure~\ref{image_voronoi2} avec un pourcentage de bonne classification proche de 95\%. +Dans un premier temps, la fonction $f$ (\ref{image_voronoi_f}) a �t� estim�e � l'aide d'un r�seau de neurones classifieur\seeannex{subsection_classifieur}{classifieur}. Les vecteurs $S$, $F^S_1$, $F^S_2$ contenaient des informations relatives � la longueur du segment, sa courbure, son inclinaison, la distance du segment au texte. Les vecteurs $S^S_1$ et $S^S_2$ contenaient des moyennes des m�mes informations. L'estimation de la fonction $f$ a conduit au r�sultat figure~\ref{image_voronoi2} avec un pourcentage de bonne classification proche de 95\%. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=7cm] - {\filext{../image/image/voronoi2}}\end{array}$}$$ - \caption{ Diagramme de Vorono utilis pour une segmentation en lignes~: - le rsultat laisse apparatre des lignes - en pointill. Dans 95\% des cas, les segments de Vorono sont bien classs.} - \label{image_voronoi2} - \end{figure} - -Mme si le pourcentage d'erreur est faible, il mne l'apparition de lignes "troues" qui suggre soit l'abandon de la mthode, soit son perfectionnement selon deux directions qui sont la cration d'un processus itratif permettant de faire voluer la probabilit d'un segment en fonction de ses voisins et un post-traitement dont l'objectif est l'limination des "trous". La premire direction passe par la construction d'une suite $\pa{p_t}$ pour chaque segment de telle sorte que~: + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=7cm] + {\filext{../image/image/voronoi2}}\end{array}$}$$ + \caption{ Diagramme de Vorono� utilis� pour une segmentation en lignes~: + le r�sultat laisse appara�tre des lignes + en pointill�. Dans 95\% des cas, les segments de Vorono� sont bien class�s.} + \label{image_voronoi2} + \end{figure} + +M�me si le pourcentage d'erreur est faible, il m�ne � l'apparition de lignes "trou�es" qui sugg�re soit l'abandon de la m�thode, soit son perfectionnement selon deux directions qui sont la cr�ation d'un processus it�ratif permettant de faire �voluer la probabilit� d'un segment en fonction de ses voisins et un post-traitement dont l'objectif est l'�limination des "trous". La premi�re direction passe par la construction d'une suite $\pa{p_t}$ pour chaque segment de telle sorte que~: - \begin{eqnarray*} - p^S_0 &=& f\pa{S, S^S_1, S^S_2, F^S_1, F^S_2} \\ - \forall t \supegal 0, \; p^S_{t+1} &=& g\pa{p^S_t, S, p^{S_1}_t, S^S_1, p^{S_2}_t, S^S_2, F^S_1, F^S_2} - \end{eqnarray*} - -Le processus s'arrte lorsque la suite $\pa{p^S_t}_{t\supegal 0}$ converge pour chaque segment $S$. Il reste estimer la fonction $g$. Le nombre d'itrations ncessaires la convergence d'un tel systme demeure inconnu. La seconde direction correspond en quelque sorte au nettoyage des rsultats retourns par la fonction $f$ (ou son prolongement $g$), les lignes presque acheves sont compltes, les bouts de lignes ne menant rien sont effaces. + \begin{eqnarray*} + p^S_0 &=& f\pa{S, S^S_1, S^S_2, F^S_1, F^S_2} \\ + \forall t \supegal 0, \; p^S_{t+1} &=& g\pa{p^S_t, S, p^{S_1}_t, S^S_1, p^{S_2}_t, S^S_2, F^S_1, F^S_2} + \end{eqnarray*} + +Le processus s'arr�te lorsque la suite $\pa{p^S_t}_{t\supegal 0}$ converge pour chaque segment $S$. Il reste � estimer la fonction $g$. Le nombre d'it�rations n�cessaires � la convergence d'un tel syst�me demeure inconnu. La seconde direction correspond en quelque sorte au nettoyage des r�sultats retourn�s par la fonction $f$ (ou son prolongement $g$), les lignes presque achev�es sont compl�t�es, les bouts de lignes ne menant � rien sont effac�es. -Cette mthode s'appuie sur un diagramme de Vorono qui peut s'avrer instable lorsque l'image est de mauvaise qualit, lorsque quelques pixels gars crent des rgions artificielles. Les diagrammes de Vorono flous (\citeindex{Zhao2000}) seraient peut-tre une alternative ce problme. De plus, la convergence de l'ensemble n'est pas assure et peut dboucher sur des temps de traitements longs inconvenants pour des applications telles que la reconnaissance de l'criture. Aucun des deux prolongements n'a t tudi. - - +Cette m�thode s'appuie sur un diagramme de Vorono� qui peut s'av�rer instable lorsque l'image est de mauvaise qualit�, lorsque quelques pixels �gar�s cr�ent des r�gions artificielles. Les diagrammes de Vorono� flous (\citeindex{Zhao2000}) seraient peut-�tre une alternative � ce probl�me. De plus, la convergence de l'ensemble n'est pas assur�e et peut d�boucher sur des temps de traitements longs inconvenants pour des applications telles que la reconnaissance de l'�criture. Aucun des deux prolongements n'a �t� �tudi�. + + -\subsection{Extension au problme de nettoyage} +\subsection{Extension au probl�me de nettoyage} \indexfr{nettoyage} -\indexfr{Vorono} +\indexfr{Vorono�} -Le nettoyage est un problme dual du prcdent puisqu'au lieu de classer les segments du diagramme de Vorono, il suffit de classer les zones dlimites par ce diagramme en deux classes~: zone nettoyer ou non. L'avantage du diagramme de Vorono est de proposer un voisinage (figure~\ref{image_voronoi_local}) pour chaque petite rgion. Une application pratique est la suppression d'une ligne qui sert de guide pour l'criture comme celle montre figure~\ref{image_global}. L'intrt de la mthode rside toujours dans son apprentissage et son inconvnient dans la forte sensibilit du diagramme de Vorono aux ruptures de connexit. +Le nettoyage est un probl�me dual du pr�c�dent puisqu'au lieu de classer les segments du diagramme de Vorono�, il suffit de classer les zones d�limit�es par ce diagramme en deux classes~: zone � nettoyer ou non. L'avantage du diagramme de Vorono� est de proposer un voisinage (figure~\ref{image_voronoi_local}) pour chaque petite r�gion. Une application pratique est la suppression d'une ligne qui sert de guide pour l'�criture comme celle montr�e figure~\ref{image_global}. L'int�r�t de la m�thode r�side toujours dans son apprentissage et son inconv�nient dans la forte sensibilit� du diagramme de Vorono� aux ruptures de connexit�. @@ -179,28 +179,28 @@ \subsection{Diagramme de Kohonen} \indexfrr{segmentation}{ligne} \indexfrr{segmentation}{mot} -Outre le fait que le diagramme de Vorono est trs sensible au bruit, pour une rgion donne, le nombre de voisins est trs variable, il est alors ncessaire de rsumer l'information contenue par ce voisinage. On utilise une carte de Kohonen dont la structure est celle d'un quadrillage. Les pixels noirs attirent les neurones qui tirent les artes qui les relient comme le montre la figure~\ref{image_koho_lines}a. Les artes les plus grandes forment des ponts entre deux rgions, un simple seuillage (figure~\ref{image_koho_lines}b) permet presque d'isoler les mots. L'avantage de cette nouvelle structure est son voisinage de taille fixe, quelle que soit la dformation du treillis de Kohonen, chaque neurone conservera quatre voisins, il est alors possible d'utiliser des algorithmes (relaxation probabiliste, champs de Markov) permettant de classer les artes en deux catgories~: arte l'intrieur d'une rgion, arte reliant deux rgions segmenter. +Outre le fait que le diagramme de Vorono� est tr�s sensible au bruit, pour une r�gion donn�e, le nombre de voisins est tr�s variable, il est alors n�cessaire de r�sumer l'information contenue par ce voisinage. On utilise une carte de Kohonen dont la structure est celle d'un quadrillage. Les pixels noirs attirent les neurones qui �tirent les ar�tes qui les relient comme le montre la figure~\ref{image_koho_lines}a. Les ar�tes les plus grandes forment des ponts entre deux r�gions, un simple seuillage (figure~\ref{image_koho_lines}b) permet presque d'isoler les mots. L'avantage de cette nouvelle structure est son voisinage de taille fixe, quelle que soit la d�formation du treillis de Kohonen, chaque neurone conservera quatre voisins, il est alors possible d'utiliser des algorithmes (relaxation probabiliste, champs de Markov) permettant de classer les ar�tes en deux cat�gories~: ar�te � l'int�rieur d'une r�gion, ar�te reliant deux r�gions � segmenter. - \begin{figure}[ht] - $$\begin{tabular}{|c|c|}\hline - \includegraphics[height=4cm, width=6cm]{\filext{../image/image/koholine1}} & - \includegraphics[height=4cm, width=6cm]{\filext{../image/image/koholine2}} \\ - $(a)$ & $(b)$ - \\ \hline \end{tabular}$$ - \caption{ Treillis de Kohonen appliqu la segmentation en ligne, l'image $(a)$ - prsente le rsultat aprs convergence - des neurones, l'image $(b)$ reprsente le mme treillis dont les artes les - plus grandes ont t tes. Il reste dans le meilleur des cas des assemblages connexes - recouvrant l'image d'un des mots.} - \label{image_koho_lines} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|}\hline + \includegraphics[height=4cm, width=6cm]{\filext{../image/image/koholine1}} & + \includegraphics[height=4cm, width=6cm]{\filext{../image/image/koholine2}} \\ + $(a)$ & $(b)$ + \\ \hline \end{tabular}$$ + \caption{ Treillis de Kohonen appliqu� � la segmentation en ligne, l'image $(a)$ + pr�sente le r�sultat apr�s convergence + des neurones, l'image $(b)$ repr�sente le m�me treillis dont les ar�tes les + plus grandes ont �t� �t�es. Il reste dans le meilleur des cas des assemblages connexes + recouvrant l'image d'un des mots.} + \label{image_koho_lines} + \end{figure} -L'inconvnient de cette mthode rside dans l'obtention du treillis final de Kohonen, la convergence est gourmande en temps de calcul pour de grandes images. C'est pour cela que cette ide n'a pas t poursuivie. En revanche, ce temps de calcul devient acceptable si la dimension de l'image est celle d'un mot, cette mthode pourrait donc tre utilise pour apprendre une segmentation en graphmes. +L'inconv�nient de cette m�thode r�side dans l'obtention du treillis final de Kohonen, la convergence est gourmande en temps de calcul pour de grandes images. C'est pour cela que cette id�e n'a pas �t� poursuivie. En revanche, ce temps de calcul devient acceptable si la dimension de l'image est celle d'un mot, cette m�thode pourrait donc �tre utilis�e pour apprendre une segmentation en graph�mes. -Cet apprentissage ncessite malgr tout de nombreuses images pour lesquelles la segmentation en graphmes doit tre connue. L'obtention d'une telle base de donnes peut tre manuelle mais ce travail est long ou effectu partir d'un systme de reconnaissance dj existant mais contenant des erreurs. Les mots les mieux reconnus sont alors dcoups en graphmes ou caractres selon l'usage dsir puis serviront d'apprentissage. Cette direction n'a pour le moment pas t envisage, une autre permettant de modliser des erreurs de segmentation en graphmes lui a t prfre dans un premier temps (voir paragraphe~\ref{hmm_bi_lettre}, page~\pageref{hmm_bi_lettre}). \indexfrr{segmentation}{graphme} \indexfr{graphme} Cette modlisation permet d'ailleurs une meilleure apprciation de la segmentation en graphmes. +Cet apprentissage n�cessite malgr� tout de nombreuses images pour lesquelles la segmentation en graph�mes doit �tre connue. L'obtention d'une telle base de donn�es peut �tre manuelle mais ce travail est long ou effectu� � partir d'un syst�me de reconnaissance d�j� existant mais contenant des erreurs. Les mots les mieux reconnus sont alors d�coup�s en graph�mes ou caract�res selon l'usage d�sir� puis serviront d'apprentissage. Cette direction n'a pour le moment pas �t� envisag�e, une autre permettant de mod�liser des erreurs de segmentation en graph�mes lui a �t� pr�f�r�e dans un premier temps (voir paragraphe~\ref{hmm_bi_lettre}, page~\pageref{hmm_bi_lettre}). \indexfrr{segmentation}{graph�me} \indexfr{graph�me} Cette mod�lisation permet d'ailleurs une meilleure appr�ciation de la segmentation en graph�mes. @@ -221,7 +221,7 @@ \section{Segmentation en lignes} \indexfrr{segmentation}{ligne} \indexfr{histogramme} -Lors de la scannerisation d'un document, il peut arriver que celui-ci soit inclin (figure~\ref{image_segline_direction}). La premire tape consiste donc redresser une image de telle sorte que les lignes qui la composent soient horizontales. Ce paragraphe aborde diverses solutions existantes et rsume les rsultats noncs dans~\citeindex{Dupr2000}. +Lors de la scannerisation d'un document, il peut arriver que celui-ci soit inclin� (figure~\ref{image_segline_direction}). La premi�re �tape consiste donc � redresser une image de telle sorte que les lignes qui la composent soient horizontales. Ce paragraphe aborde diverses solutions existantes et r�sume les r�sultats �nonc�s dans~\citeindex{Dupr�2000}. @@ -232,63 +232,63 @@ \subsection{Redressement de l'inclinaison de l'image} \indexfr{inclinaison} -De nombreuses mthodes sont utilises pour dtecter l'inclinaison des lignes, leurs robustesses variant avec la difficult du problme. L'article~\citeindex{Cao2003} par exemple propose une mthode plus adapte aux textes imprims. Les composantes connexes (des lettres principalement) sont toutes dcrites par un point situ au milieu du bord infrieur de leurs botes englobantes. Par la suite, ces points sont regroups et classs en lignes. Une rgression linaire sur chacune des lignes termine l'estimation de l'inclinaison de l'image. \indexfr{chane de plus proches voisins} Une autre mthode prsente dans~\citeindex{Lu2003} utilise des chanes de plus proches voisins (ou nearest neighbors chains), celles-ci sont constitues par l'appariement de voisins. L'inclinaison est mesure sur chacune des chanes qui doivent tre suffisamment longues pour une mesure prcise mais pas trop pour viter le regroupement de voisins trop loigns n'appartenant pas la mme ligne de texte. \indexfrr{Hough}{transforme de ...} La transforme de Hough est aussi une mthode trs utilise (voir~\citeindex{Pal1996}), chaque petit segment de l'image permet d'estimer les coefficients du vecteur directeur de la droite qui le soutient. La direction de l'inclinaison du document correspond aux coefficients les plus reprsents. Les histogrammes permettent galement d'estimer cette inclinaison (voir~\citeindex{Bloomberg1995}) comme de segmenter l'image redresse en lignes (voir~\citeindex{Gatos1997}, \citeindex{Pal2001}). C'est cette approche qui est prsente ici. +De nombreuses m�thodes sont utilis�es pour d�tecter l'inclinaison des lignes, leurs robustesses variant avec la difficult� du probl�me. L'article~\citeindex{Cao2003} par exemple propose une m�thode plus adapt�e aux textes imprim�s. Les composantes connexes (des lettres principalement) sont toutes d�crites par un point situ� au milieu du bord inf�rieur de leurs bo�tes englobantes. Par la suite, ces points sont regroup�s et class�s en lignes. Une r�gression lin�aire sur chacune des lignes termine l'estimation de l'inclinaison de l'image. \indexfr{cha�ne de plus proches voisins} Une autre m�thode pr�sent�e dans~\citeindex{Lu2003} utilise des cha�nes de plus proches voisins (ou nearest neighbors chains), celles-ci sont constitu�es par l'appariement de voisins. L'inclinaison est mesur�e sur chacune des cha�nes qui doivent �tre suffisamment longues pour une mesure pr�cise mais pas trop pour �viter le regroupement de voisins trop �loign�s n'appartenant pas � la m�me ligne de texte. \indexfrr{Hough}{transform�e de ...} La transform�e de Hough est aussi une m�thode tr�s utilis�e (voir~\citeindex{Pal1996}), chaque petit segment de l'image permet d'estimer les coefficients du vecteur directeur de la droite qui le soutient. La direction de l'inclinaison du document correspond aux coefficients les plus repr�sent�s. Les histogrammes permettent �galement d'estimer cette inclinaison (voir~\citeindex{Bloomberg1995}) comme de segmenter l'image redress�e en lignes (voir~\citeindex{Gatos1997}, \citeindex{Pal2001}). C'est cette approche qui est pr�sent�e ici. - \begin{xdefinition}{histogramme} - \indexfr{histogramme} - - L'histogramme d'une image selon une direction $\alpha$ est une projection de cette image - sur une droite paralllement une droite de vecteur directeur $d=\pa{\begin{subarray}{c} 1 - \\ tan \alpha \end{subarray}}$. Concrtement, si $I$ est une image de dimension $\pa{X,Y}$, un - histogramme est un vecteur dont chaque lment contient le nombre de pixels noirs sur une ligne de - direction~$d$ trace avec un algorithme comme celui de~\citeindex{Bresenham1965} (voir - galement~\citeindex{Bresenham1977}). - - \end{xdefinition} + \begin{xdefinition}{histogramme} + \indexfr{histogramme} + + L'histogramme d'une image selon une direction $\alpha$ est une projection de cette image + sur une droite parall�lement � une droite de vecteur directeur $d=\pa{\begin{subarray}{c} 1 + \\ tan \alpha \end{subarray}}$. Concr�tement, si $I$ est une image de dimension $\pa{X,Y}$, un + histogramme est un vecteur dont chaque �l�ment contient le nombre de pixels noirs sur une ligne de + direction~$d$ trac�e avec un algorithme comme celui de~\citeindex{Bresenham1965} (voir + �galement~\citeindex{Bresenham1977}). + + \end{xdefinition} -La qualit de l'histogramme ou sa pertinence est estime par son entropie. +La qualit� de l'histogramme ou sa pertinence est estim�e par son entropie. - \begin{xdefinition}{entropie d'un histogramme} - \indexfr{entropie d'un histogramme} - - Soit $H = \vecteur{h_1}{h_n}$, on dfinit le vecteur dfini par $H' = \vecteur{p_1}{p_n}$~: - - $$ - \forall i \in \intervalle{1}{n}, \; p_i = \frac{h_i} { \summy{k=1}{n} \, h_k } - $$ - - L'entropie de l'histogramme $H$ est le nombre suivant calcul sur l'histogramme $H'$~: - - \begin{eqnarray} - E\pa{H} &=& E\pa{H'} = \summy{i=1}{n} \; p_i \, \ln p_i - \end{eqnarray} - - \end{xdefinition} + \begin{xdefinition}{entropie d'un histogramme} + \indexfr{entropie d'un histogramme} + + Soit $H = \vecteur{h_1}{h_n}$, on d�finit le vecteur d�fini par $H' = \vecteur{p_1}{p_n}$~: + + $$ + \forall i \in \intervalle{1}{n}, \; p_i = \frac{h_i} { \summy{k=1}{n} \, h_k } + $$ + + L'entropie de l'histogramme $H$ est le nombre suivant calcul� sur l'histogramme $H'$~: + + \begin{eqnarray} + E\pa{H} &=& E\pa{H'} = \summy{i=1}{n} \; p_i \, \ln p_i + \end{eqnarray} + + \end{xdefinition} \indexfr{redressement}\indexfr{glissement de pixels} -La direction la plus probable est celle qui maximise l'entropie (voir~\citeindex{Ct1997}). Graphiquement, l'histogramme d'entropie maximale est celui dont les extrema sont les plus marqus (voir figure~\ref{image_segline_direction}). -L'image est finalement redresse de faon ce que l'image ne contienne plus des lignes horizontales. Ce redressement peut tout simplement tre effectu par un glissement des colonnes de pixels de l'image les unes par rapport aux autres. +La direction la plus probable est celle qui maximise l'entropie (voir~\citeindex{C�t�1997}). Graphiquement, l'histogramme d'entropie maximale est celui dont les extrema sont les plus marqu�s (voir figure~\ref{image_segline_direction}). +L'image est finalement redress�e de fa�on � ce que l'image ne contienne plus des lignes horizontales. Ce redressement peut tout simplement �tre effectu� par un glissement des colonnes de pixels de l'image les unes par rapport aux autres. - \begin{figure}[t] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=14cm] - {\filext{../image/image/segline1}}\end{array}$}$$ - \caption{ Segmentation en lignes~: recherche de la meilleure orientation, celle-ci accentue - le plus possible les extrema.} - \label{image_segline_direction} - \end{figure} + \begin{figure}[t] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=14cm] + {\filext{../image/image/segline1}}\end{array}$}$$ + \caption{ Segmentation en lignes~: recherche de la meilleure orientation, celle-ci accentue + le plus possible les extrema.} + \label{image_segline_direction} + \end{figure} -\indexfr{Radon}\indexfrr{transforme}{Radon}\indexfr{Hough}\indexfrr{transforme}{Hough} -Il existe des mthodes plus rcentes comme par exemple celle dcrite dans \citeindex{Kapoor2004}. A partir d'une transforme de Radon de l'image et d'une transforme de Hough. Cette mthode est plus souple que la prcdente. La mthode des histogrammes dtermine l'orientation la plus probable dans un ensemble discret de solutions possibles. L'article \citeindex{Kapoor2004} dtermine directement cette meilleure orientation. +\indexfr{Radon}\indexfrr{transform�e}{Radon}\indexfr{Hough}\indexfrr{transform�e}{Hough} +Il existe des m�thodes plus r�centes comme par exemple celle d�crite dans \citeindex{Kapoor2004}. A partir d'une transform�e de Radon de l'image et d'une transform�e de Hough. Cette m�thode est plus souple que la pr�c�dente. La m�thode des histogrammes d�termine l'orientation la plus probable dans un ensemble discret de solutions possibles. L'article \citeindex{Kapoor2004} d�termine directement cette meilleure orientation. @@ -297,133 +297,133 @@ \subsection{Segmentation en lignes}\label{section_segmentation_ligne} \indexfrr{segmentation}{ligne} -L'histogramme obtenu figure~\ref{image_segline_direction} est bruit. Afin de diminuer l'importance de ce bruit, l'histogramme est liss par la mthode des moyennes mobiles. Selon les problmes, la taille de cette moyenne est plus ou moins grande. Soit $H_l = \vecteur{l_1}{l_n}$ l'histogramme liss, il est donc obtenu partir de $H$ comme suit~: +L'histogramme obtenu figure~\ref{image_segline_direction} est bruit�. Afin de diminuer l'importance de ce bruit, l'histogramme est liss� par la m�thode des moyennes mobiles. Selon les probl�mes, la taille de cette moyenne est plus ou moins grande. Soit $H_l = \vecteur{l_1}{l_n}$ l'histogramme liss�, il est donc obtenu � partir de $H$ comme suit~: - \begin{eqnarray} - \begin{array}{rrcl} - \forall i \in \intervalle{w+1}{n-w-1}, \; & l_i &=& \dfrac{1}{2w+1} \, \summy{k=-w}{+w} \, h_{i+k} \\ - \forall i \in \intervalle{1}{w}, \; & l_i &=& \dfrac{1}{i+w} \, \summy{k=1}{i+w} \, h_k \\ - \forall i \in \intervalle{n-w}{n}, \; & l_i &=& \dfrac{1}{n-i+ w + 1} \, \summy{k=i-w}{n} \, h_k - \end{array} - \label{image_lissage_equation} - \end{eqnarray} + \begin{eqnarray} + \begin{array}{rrcl} + \forall i \in \intervalle{w+1}{n-w-1}, \; & l_i &=& \dfrac{1}{2w+1} \, \summy{k=-w}{+w} \, h_{i+k} \\ + \forall i \in \intervalle{1}{w}, \; & l_i &=& \dfrac{1}{i+w} \, \summy{k=1}{i+w} \, h_k \\ + \forall i \in \intervalle{n-w}{n}, \; & l_i &=& \dfrac{1}{n-i+ w + 1} \, \summy{k=i-w}{n} \, h_k + \end{array} + \label{image_lissage_equation} + \end{eqnarray} -Les maxima locaux indiquent la position des lignes, les minima locaux la position des frontires entre lignes. On dfinit pour chaque ligne les minima $\pa{m_i^x}_i$ et les maxima $\pa{M_i^x}_i$~: +Les maxima locaux indiquent la position des lignes, les minima locaux la position des fronti�res entre lignes. On d�finit pour chaque ligne les minima $\pa{m_i^x}_i$ et les maxima $\pa{M_i^x}_i$~: - $$ - \begin{array}{rcl} - \forall i, \; m_i^x = \left\{ \begin{array}{l} - 1 \text{ si } l_i = \min \acc { l_k \sac l-x \infegal k \infegal l+x } \\ - 0 \text{ sinon} - \end{array} \right. \\ - \forall i, \; M_i^x = \left\{ \begin{array}{l} - 1 \text{ si } l_i = \max \acc { l_k \sac l-x \infegal k \infegal l+x } \\ - 0 \text{ sinon} - \end{array} \right. - \end{array} - $$ + $$ + \begin{array}{rcl} + \forall i, \; m_i^x = \left\{ \begin{array}{l} + 1 \text{ si } l_i = \min \acc { l_k \sac l-x \leqslant k \leqslant l+x } \\ + 0 \text{ sinon} + \end{array} \right. \\ + \forall i, \; M_i^x = \left\{ \begin{array}{l} + 1 \text{ si } l_i = \max \acc { l_k \sac l-x \leqslant k \leqslant l+x } \\ + 0 \text{ sinon} + \end{array} \right. + \end{array} + $$ -La figure~\ref{image_segline_extrema} montre que bien souvent le nombre d'extrema dtects est suprieur au nombre rel d'extrema. Une tude sur quelques dizaines d'images a permis d'liminer les cas de mauvaises dtections les plus courants~: +La figure~\ref{image_segline_extrema} montre que bien souvent le nombre d'extrema d�tect�s est sup�rieur au nombre r�el d'extrema. Une �tude sur quelques dizaines d'images a permis d'�liminer les cas de mauvaises d�tections les plus courants~: \begin{enumerate} \indexfr{petit palier} -\item \textit{Le petit palier}~: ce cas se prsente le plus souvent lorsqu'une ou plusieurs majuscules font partie de la ligne de texte. Le dessin de ces lettres contient des traits horizontaux tracs au-dessus de la ligne des minuscules. Une barre de "F" bien marque peut entraner de mauvaises segmentations. +\item \textit{Le petit palier}~: ce cas se pr�sente le plus souvent lorsqu'une ou plusieurs majuscules font partie de la ligne de texte. Le dessin de ces lettres contient des traits horizontaux trac�s au-dessus de la ligne des minuscules. Une barre de "F" bien marqu�e peut entra�ner de mauvaises segmentations. \indexfr{petit extremum} -\item \textit{Le petit extremum}~: lorsque les mots ne sont pas tout--fait bien aligns sur une mme horizontale, les extrema sont plus diffus, il faut alors regrouper plusieurs maxima ensemble. +\item \textit{Le petit extremum}~: lorsque les mots ne sont pas tout-�-fait bien align�s sur une m�me horizontale, les extrema sont plus diffus, il faut alors regrouper plusieurs maxima ensemble. \end{enumerate} -Deux rgles permettent l'limination de ces mauvaises dtections~: +Deux r�gles permettent l'�limination de ces mauvaises d�tections~: - \begin{enumerate} - \item Soit $\acc{e_i \sac 1 \infegal i \infegal 4}$ quatre extrema conscutifs, alors~: - \begin{eqnarray} - \abs{e_2 - e_3} \infegal \beta \abs{e_1 - e_4} \Longrightarrow - \acc{e_2, \, e_3} \text{ doivent tre limins.} - \label{image_ligne_critere_palier_1} - \end{eqnarray} - \item Soit $e_2$ un minimum et $e_1$ et $e_3$ les extrema qui l'entourent, alors~: - \begin{eqnarray} - e_2 \infegal \gamma \min\acc{e_1,e_3} \Longrightarrow - \acc{e_2, \, e_1 \text{ ou } e_3} \text{ doivent tre limins.} - \label{image_ligne_critere_palier_2} - \end{eqnarray} - \end{enumerate} - -Ce processus est illustr par la figure~\ref{image_segline_extrema}. Les valeurs intressantes pour les quatre paramtres $w$, $x$, $\beta$, $\gamma$ sont~: + \begin{enumerate} + \item Soit $\acc{e_i \sac 1 \leqslant i \leqslant 4}$ quatre extrema cons�cutifs, alors~: + \begin{eqnarray} + \abs{e_2 - e_3} \leqslant \beta \abs{e_1 - e_4} \Longrightarrow + \acc{e_2, \, e_3} \text{ doivent �tre �limin�s.} + \label{image_ligne_critere_palier_1} + \end{eqnarray} + \item Soit $e_2$ un minimum et $e_1$ et $e_3$ les extrema qui l'entourent, alors~: + \begin{eqnarray} + e_2 \leqslant \gamma \min\acc{e_1,e_3} \Longrightarrow + \acc{e_2, \, e_1 \text{ ou } e_3} \text{ doivent �tre �limin�s.} + \label{image_ligne_critere_palier_2} + \end{eqnarray} + \end{enumerate} + +Ce processus est illustr� par la figure~\ref{image_segline_extrema}. Les valeurs int�ressantes pour les quatre param�tres $w$, $x$, $\beta$, $\gamma$ sont~: - $$ - \begin{array}{ccccccc} - w &=& 4 \text{ pixels} && \beta &=& 0,2 \\ - x &=& 4 \text{ pixels} && \gamma &=& 0,5 - \end{array} - $$ + $$ + \begin{array}{ccccccc} + w &=& 4 \text{ pixels} && \beta &=& 0,2 \\ + x &=& 4 \text{ pixels} && \gamma &=& 0,5 + \end{array} + $$ - \begin{figure}[t] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=6cm, width=12cm] - {\filext{../image/image/segline2}}\end{array}$}$$ - \caption{ Segmentation en lignes~: recherche des bons extrema. Les extrema trop proches vrifiant les - critres~(\ref{image_ligne_critere_palier_1}) et~(\ref{image_ligne_critere_palier_2}) - ne sont pas pris en compte.} - \label{image_segline_extrema} - \end{figure} + \begin{figure}[t] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=6cm, width=12cm] + {\filext{../image/image/segline2}}\end{array}$}$$ + \caption{ Segmentation en lignes~: recherche des bons extrema. Les extrema trop proches v�rifiant les + crit�res~(\ref{image_ligne_critere_palier_1}) et~(\ref{image_ligne_critere_palier_2}) + ne sont pas pris en compte.} + \label{image_segline_extrema} + \end{figure} -Le processus de suppression des "faux" extrema ncessite plusieurs itrations, chacune d'elle, le plus petit palier est isol et supprim, ensuite, l'opration est rpte pour les petits extrema. Le processus s'arrte lorsqu'il ne peut plus rien supprimer, autrement dit, lorsqu'aucun petit palier et aucun petit extremum n'a pu tre trouv. +Le processus de suppression des "faux" extrema n�cessite plusieurs it�rations, � chacune d'elle, le plus petit palier est isol� et supprim�, ensuite, l'op�ration est r�p�t�e pour les petits extrema. Le processus s'arr�te lorsqu'il ne peut plus rien supprimer, autrement dit, lorsqu'aucun petit palier et aucun petit extremum n'a pu �tre trouv�. -\subsection{Traitements des lignes enchevtres} +\subsection{Traitements des lignes enchev�tr�es} -\indexfrr{ligne}{enchevtre} +\indexfrr{ligne}{enchev�tr�e} -C'est la dernire tape avant la reconnaissance du contenu des lignes. L'tude de l'histogramme a permis d'encadrer chaque ligne par un rectangle dont dpassent certaines grandes lettres ascendants et/ou descendants comme les "j" ou les "p". Le module de reconnaissance des mots est bas sur une extraction de graphmes utilisant la connexit du dessin des lettres. +C'est la derni�re �tape avant la reconnaissance du contenu des lignes. L'�tude de l'histogramme a permis d'encadrer chaque ligne par un rectangle dont d�passent certaines grandes lettres ascendants et/ou descendants comme les "j" ou les "p". Le module de reconnaissance des mots est bas� sur une extraction de graph�mes utilisant la connexit� du dessin des lettres. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] - {\filext{../image/image/segline3}}\end{array}$}$$ - \caption{Segmentation en lignes~: connexit} - \label{image_segline_connex} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] + {\filext{../image/image/segline3}}\end{array}$}$$ + \caption{Segmentation en lignes~: connexit�} + \label{image_segline_connex} + \end{figure} -En partant de la mme ide, on va supposer que le "j" de "Lajoie" (figure~\ref{image_segline_connex}) est form d'une seule composante connexe. La segmentation en lignes s'achve donc par le recollement des morceaux d'une mme lettre gars des deux cts d'une frontire sparant deux lignes. Le principe est le suivant~: +En partant de la m�me id�e, on va supposer que le "j" de "Lajoie" (figure~\ref{image_segline_connex}) est form� d'une seule composante connexe. La segmentation en lignes s'ach�ve donc par le recollement des morceaux d'une m�me lettre �gar�s des deux c�t�s d'une fronti�re s�parant deux lignes. Le principe est le suivant~: - \begin{enumerate} - \item On parcourt la frontire entre deux lignes jusqu' ce qu'on intercepte une lettre. - \item On parcourt le contour extrieur du morceau situ au-dessus, si lors de ce parcours, - on revient la mme frontire sans en rencontrer aucune autre, alors ce morceau de - lettre est considr comme tant du mauvais ct. - \item On parcourt le contour extrieur du morceau situ au-dessous, si lors de ce parcours, - on revient la mme frontire sans en rencontrer aucune autre, alors ce morceau de - lettre est considr comme tant du mauvais ct. - \item Si un seul des deux morceaux est du mauvais ct alors ce morceau est remis dans la bonne ligne, - sinon on ne fait rien. - \item On continue le parcours de la frontire au cas o d'autres lettres intercepteraient celle-ci. - \end{enumerate} + \begin{enumerate} + \item On parcourt la fronti�re entre deux lignes jusqu'� ce qu'on intercepte une lettre. + \item On parcourt le contour ext�rieur du morceau situ� au-dessus, si lors de ce parcours, + on revient � la m�me fronti�re sans en rencontrer aucune autre, alors ce morceau de + lettre est consid�r� comme �tant du mauvais c�t�. + \item On parcourt le contour ext�rieur du morceau situ� au-dessous, si lors de ce parcours, + on revient � la m�me fronti�re sans en rencontrer aucune autre, alors ce morceau de + lettre est consid�r� comme �tant du mauvais c�t�. + \item Si un seul des deux morceaux est du mauvais c�t� alors ce morceau est remis dans la bonne ligne, + sinon on ne fait rien. + \item On continue le parcours de la fronti�re au cas o� d'autres lettres intercepteraient celle-ci. + \end{enumerate} -Dornavant, l'extraction des lignes est termine. Cette mthode fonctionne efficacement sur des adresses mais possde quelques cueils rcurrents (figure~\ref{image_segline_bad}). +Dor�navant, l'extraction des lignes est termin�e. Cette m�thode fonctionne efficacement sur des adresses mais poss�de quelques �cueils r�currents (figure~\ref{image_segline_bad}). - \begin{figure}[t] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=12cm, width=8cm] - {\filext{../image/image/segline_bad}}\end{array}$}$$ - \caption{Segmentation en lignes~: exemples qui ne marchent pas.} - \label{image_segline_bad} - \end{figure} + \begin{figure}[t] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=12cm, width=8cm] + {\filext{../image/image/segline_bad}}\end{array}$}$$ + \caption{Segmentation en lignes~: exemples qui ne marchent pas.} + \label{image_segline_bad} + \end{figure} @@ -432,36 +432,36 @@ \subsection{Traitements des lignes enchev -\subsection{Segmentation partir d'un graphe} +\subsection{Segmentation � partir d'un graphe} \indexfr{graphe} \indexfrr{segmentation}{ligne} -L'article \citeindex{Abuhaiba1996} propose une autre alternative, une mthode de segmentation en lignes base sur un graphe $k$-connexe (voir figure~\ref{image_graphe_distance_segment_fig}). L'image d'un paragraphe est d'abord squelettise puis vectorise\seeannex{squelette_vectorisation_Abuhaiba1996}{squelettisation}. Chaque arc ainsi obtenu est ensuite reli $k$-plus proches voisins ordonns selon la distance (\ref{image_graphe_distance_segment}). Soient deux segments $S_1$ et $S_2$, la distance $d\pa{S_1,S_2}$ est dfinie par~: +L'article \citeindex{Abuhaiba1996} propose une autre alternative, une m�thode de segmentation en lignes bas�e sur un graphe $k$-connexe (voir figure~\ref{image_graphe_distance_segment_fig}). L'image d'un paragraphe est d'abord squelettis�e puis vectoris�e\seeannex{squelette_vectorisation_Abuhaiba1996}{squelettisation}. Chaque arc ainsi obtenu est ensuite reli� � $k$-plus proches voisins ordonn�s selon la distance (\ref{image_graphe_distance_segment}). Soient deux segments $S_1$ et $S_2$, la distance $d\pa{S_1,S_2}$ est d�finie par~: - \begin{eqnarray} - d_x\pa{S_1,S_2} &=& \underset{\pa{u,v} \in S_1 \times S_2} {\min } \; \abs{ u_x - v_x } \nonumber \\ - d_y\pa{S_1,S_2} &=& \underset{\pa{u,v} \in S_1 \times S_2} {\min } \; \abs{ u_y - v_y } \nonumber \\ - d\pa{S_1,S_2} &=& \cro { 1 + \gamma \, \pa{ \frac{\pi}{2} }^{-1} \arctan \frac{ d_y\pa{S_1,S_2} } {d_x\pa{S_1,S_2} } } - \; \sqrt { d_x\pa{S_1,S_2} ^2 + d_y\pa{S_1,S_2} ^ 2} - \label{image_graphe_distance_segment} - \end{eqnarray} + \begin{eqnarray} + d_x\pa{S_1,S_2} &=& \underset{\pa{u,v} \in S_1 \times S_2} {\min } \; \abs{ u_x - v_x } \nonumber \\ + d_y\pa{S_1,S_2} &=& \underset{\pa{u,v} \in S_1 \times S_2} {\min } \; \abs{ u_y - v_y } \nonumber \\ + d\pa{S_1,S_2} &=& \cro { 1 + \gamma \, \pa{ \frac{\pi}{2} }^{-1} \arctan \frac{ d_y\pa{S_1,S_2} } {d_x\pa{S_1,S_2} } } + \; \sqrt { d_x\pa{S_1,S_2} ^2 + d_y\pa{S_1,S_2} ^ 2} + \label{image_graphe_distance_segment} + \end{eqnarray} - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=6cm] - {\filext{../image/image/abusl}}\end{array}$}$$ - \caption{Segmentation en lignes en recherchant l'arbre de poids minimal. L'axe vertical - sur la droite de l'image est ajout de faon relier toutes les lignes entre elles, figure - extraite de \citeindexfig{Abuhaiba1996}.} - \label{image_graphe_distance_segment_fig} - \end{figure} - + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=6cm] + {\filext{../image/image/abusl}}\end{array}$}$$ + \caption{Segmentation en lignes en recherchant l'arbre de poids minimal. L'axe vertical + sur la droite de l'image est ajout� de fa�on � relier toutes les lignes entre elles, figure + extraite de \citeindexfig{Abuhaiba1996}.} + \label{image_graphe_distance_segment_fig} + \end{figure} + \indexfr{Kruskal} \indexfrr{arbre}{poids minimal} - -Le paramtre $\gamma$ est choisi de telle sorte que deux segments appartenant la mme ligne soient plus proches que deux segments situs sur deux lignes conscutives. Chaque segment est donc reli ses $k$ plus proches voisins par un arc dont le poids est la distance (\ref{image_graphe_distance_segment}). A ce graphe est ajout un axe constitu de petits segments trs peu loigns de sorte qu'une connexion cet axe est beaucoup moins coteuse que tout autre connexion. L'arbre est ensuite rduit un arbre de poids minimal en appliquant l'algorithme de Kruskal (voir \citeindex{Kruskal1956}). La segmentation en lignes s'achve par la dtection de toutes les liaisons l'axe virtuel non dtruit par l'algortihme de Kruskal. + +Le param�tre $\gamma$ est choisi de telle sorte que deux segments appartenant � la m�me ligne soient plus proches que deux segments situ�s sur deux lignes cons�cutives. Chaque segment est donc reli� � ses $k$ plus proches voisins par un arc dont le poids est la distance (\ref{image_graphe_distance_segment}). A ce graphe est ajout� un axe constitu� de petits segments tr�s peu �loign�s de sorte qu'une connexion � cet axe est beaucoup moins co�teuse que tout autre connexion. L'arbre est ensuite r�duit � un arbre de poids minimal en appliquant l'algorithme de Kruskal (voir \citeindex{Kruskal1956}). La segmentation en lignes s'ach�ve par la d�tection de toutes les liaisons � l'axe virtuel non d�truit par l'algortihme de Kruskal. @@ -472,11 +472,11 @@ \subsection{Segmentation %------------------------------------------------------------------------------------------------------------- -\section{Prtraitements de l'image} +\section{Pr�traitements de l'image} %------------------------------------------------------------------------------------------------------------- -\indexfr{prtraitement de l'image} +\indexfr{pr�traitement de l'image} -Ce paragraphe regroupe ensemble diffrents prtraitements prcdant une segmentation en graphmes, il regroupe le redressement d'image ou l'estimation de diffrents paramtres comme la largeur moyenne d'une lettre, son paisseur moyenne. Contrairement la segmentation en lignes, ces mthodes sont particulires l'criture romaine. Les caractres chinois par exemple prsentent des "imperfections" qui leur sont propres et qui ncessitent des traitements diffrents. +Ce paragraphe regroupe ensemble diff�rents pr�traitements pr�c�dant une segmentation en graph�mes, il regroupe le redressement d'image ou l'estimation de diff�rents param�tres comme la largeur moyenne d'une lettre, son �paisseur moyenne. Contrairement � la segmentation en lignes, ces m�thodes sont particuli�res � l'�criture romaine. Les caract�res chinois par exemple pr�sentent des "imperfections" qui leur sont propres et qui n�cessitent des traitements diff�rents. @@ -489,111 +489,111 @@ \subsection{Redressement de l'image} \indexfrr{gradient}{image} \indexfr{inclinaison} -Une fois les lignes d'un paragraphe extraites, il est parfois utile de redresser son image lorsque le scripteur a crit "pench". Le rapport~\citeindex{Slavik2000} compare les performances en reconnaissance sur des images redresses ou non et montre l'apport substantiel des mthodes de prtraitement d'image. Les mthodes de redressement diffrent bien sr par leurs mthodes d'estimation de l'inclinaison mais aussi par les rgions de l'image utilises pour effectuer cette estimation. +Une fois les lignes d'un paragraphe extraites, il est parfois utile de redresser son image lorsque le scripteur a �crit "pench�". Le rapport~\citeindex{Slavik2000} compare les performances en reconnaissance sur des images redress�es ou non et montre l'apport substantiel des m�thodes de pr�traitement d'image. Les m�thodes de redressement diff�rent bien s�r par leurs m�thodes d'estimation de l'inclinaison mais aussi par les r�gions de l'image utilis�es pour effectuer cette estimation. \indexfr{ascendant} \indexfr{descendant} -L'inclinaison d'un mot est surtout visible pour les lettres possdant des ascendants et des descendants et c'est a priori cette partie de l'image qui doit tre utilise pour l'estimation de l'inclinaison comme le suggre la mthode de~\citeindex{Bozinovic1989} (voir figure~\ref{image_slant_correction_bozinovic}) qui slectionne les ascendants et descendants situs dans les parties suprieure et infrieure de l'image. +L'inclinaison d'un mot est surtout visible pour les lettres poss�dant des ascendants et des descendants et c'est a priori cette partie de l'image qui doit �tre utilis�e pour l'estimation de l'inclinaison comme le sugg�re la m�thode de~\citeindex{Bozinovic1989} (voir figure~\ref{image_slant_correction_bozinovic}) qui s�lectionne les ascendants et descendants situ�s dans les parties sup�rieure et inf�rieure de l'image. - \begin{figure}[ht] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=3cm, width=6cm]{\filext{../image/image/slantst}} \\ \hline - \end{tabular} - $$ - \caption{ Mthode de Bozinovic et Srihari (voir~\citeindexfig{Bozinovic1989}), figure exraite - de~\citeindexfig{Vinciarelli2000}. Le principe de cette mthode consiste estimer - l'orientation du texte en ne considrant que les ascendants ou descendants suffisamment - grands compris entre deux lignes verticales relies aux bords suprieur et infrieur de l'image.} - \label{image_slant_correction_bozinovic} - \indexfr{ascendant} - \indexfr{descendant} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=3cm, width=6cm]{\filext{../image/image/slantst}} \\ \hline + \end{tabular} + $$ + \caption{ M�thode de Bozinovic et Srihari (voir~\citeindexfig{Bozinovic1989}), figure exraite + de~\citeindexfig{Vinciarelli2000}. Le principe de cette m�thode consiste � estimer + l'orientation du texte en ne consid�rant que les ascendants ou descendants suffisamment + grands compris entre deux lignes verticales reli�es aux bords sup�rieur et inf�rieur de l'image.} + \label{image_slant_correction_bozinovic} + \indexfr{ascendant} + \indexfr{descendant} + \end{figure} -La mthode dcrite dans~\citeindex{Slavik2000} quant elle mesure l'inclinaison de chaque bord latral des diffrentes composantes connexes puis en fait la moyenne pondre par la longueur des segments obtenus. \citeindex{Kim1997} et \citeindex{Knerr1997} propose une estimation fonde sur les contours. Si $n_+$ et $n_-$ dsignent le nombre de dplacements positifs et ngatifs selon l'axe des abscisses, et $n_v$ le nombre de dplacements verticaux, l'angle~$\theta$ de l'inclinaison est obtenu partir de l'expression de sa tangente~: $\tan \theta = \frac{n_+ - n_-}{n_v+n_++n_-}$. La mthode de \citeindex{Vinciarelli2000} utilise des histogrammes. Pour diffrentes valeurs d'angles $\alpha$, on calcule le nombre $H_{\alpha}\pa{x} = \frac{h_{\alpha}\pa{x}}{\Delta y_{\alpha}\pa{x}}$, o $h_{\alpha}\pa{x}$ est le nombre de pixels sur la colonne $x$ et $\Delta y_{\alpha}\pa{x}$ la distance qui spare les pixels noirs situs le plus haut et le plus bas. $H_{\alpha}\pa{x}$ vaut $1$ uniquement si la colonne est constitue d'un seul segment. Ensuite, pour chaque valeur d'angle, on calcule $S\pa{\alpha} = \sum_{x, H_{\alpha}\pa{x} = 1} h_{\alpha}^2\pa{x}$. L'inclinaison du mot est alors l'angle $\alpha$ qui maximise $S\pa{\alpha}$. Toutes ces mthodes sont mieux adaptes la dtection de l'inclinaison de l'criture romaine, d'autres types d'criture, comme le montre l'article \citeindex{You2003} dans le cas de l'criture corenne, ncessitent des dveloppements plus spcifiques. \indexfrr{criture}{corenne} +La m�thode d�crite dans~\citeindex{Slavik2000} quant � elle mesure l'inclinaison de chaque bord lat�ral des diff�rentes composantes connexes puis en fait la moyenne pond�r�e par la longueur des segments obtenus. \citeindex{Kim1997} et \citeindex{Knerr1997} propose une estimation fond�e sur les contours. Si $n_+$ et $n_-$ d�signent le nombre de d�placements positifs et n�gatifs selon l'axe des abscisses, et $n_v$ le nombre de d�placements verticaux, l'angle~$\theta$ de l'inclinaison est obtenu � partir de l'expression de sa tangente~: $\tan \theta = \frac{n_+ - n_-}{n_v+n_++n_-}$. La m�thode de \citeindex{Vinciarelli2000} utilise des histogrammes. Pour diff�rentes valeurs d'angles $\alpha$, on calcule le nombre $H_{\alpha}\pa{x} = \frac{h_{\alpha}\pa{x}}{\Delta y_{\alpha}\pa{x}}$, o� $h_{\alpha}\pa{x}$ est le nombre de pixels sur la colonne $x$ et $\Delta y_{\alpha}\pa{x}$ la distance qui s�pare les pixels noirs situ�s le plus haut et le plus bas. $H_{\alpha}\pa{x}$ vaut $1$ uniquement si la colonne est constitu�e d'un seul segment. Ensuite, pour chaque valeur d'angle, on calcule $S\pa{\alpha} = \sum_{x, H_{\alpha}\pa{x} = 1} h_{\alpha}^2\pa{x}$. L'inclinaison du mot est alors l'angle $\alpha$ qui maximise $S\pa{\alpha}$. Toutes ces m�thodes sont mieux adapt�es � la d�tection de l'inclinaison de l'�criture romaine, d'autres types d'�criture, comme le montre l'article \citeindex{You2003} dans le cas de l'�criture cor�enne, n�cessitent des d�veloppements plus sp�cifiques. \indexfrr{�criture}{cor�enne} -La mthode propose ici s'inspire de celle dcrite dans~\citeindex{Yanikoglu1998} et s'avre plus simple que l'estimation d'histogrammes. Elle donne les mmes rsultats qu'une mthode estimant l'inclinaison partir du contour sans pour autant chercher les obtenir. Les deux filtres de Sobel (\ref{image_eq_sobel_filtre}) (voir~\citeindex{Prewitt1970}) permettent de retrouver en chaque point de l'image la direction du gradient. +La m�thode propos�e ici s'inspire de celle d�crite dans~\citeindex{Yanikoglu1998} et s'av�re plus simple que l'estimation d'histogrammes. Elle donne les m�mes r�sultats qu'une m�thode estimant l'inclinaison � partir du contour sans pour autant chercher � les obtenir. Les deux filtres de Sobel (\ref{image_eq_sobel_filtre}) (voir~\citeindex{Prewitt1970}) permettent de retrouver en chaque point de l'image la direction du gradient. - \begin{eqnarray} - F_x = - \begin{array}{|c|c|c|} \hline - -1 & 0 & 1 \\ \hline - -2 & 0 & 2 \\ \hline - -1 & 0 & 1 \\ \hline - \end{array} - & - \text{ et } - & - F_y = - \begin{array}{|c|c|c|} \hline - 1 & 2 & 1 \\ \hline - 0 & 0 & 0 \\ \hline - -1 & -2 & -1 \\ \hline - \end{array} - \label{image_eq_sobel_filtre} - \end{eqnarray} + \begin{eqnarray} + F_x = + \begin{array}{|c|c|c|} \hline + -1 & 0 & 1 \\ \hline + -2 & 0 & 2 \\ \hline + -1 & 0 & 1 \\ \hline + \end{array} + & + \text{ et } + & + F_y = + \begin{array}{|c|c|c|} \hline + 1 & 2 & 1 \\ \hline + 0 & 0 & 0 \\ \hline + -1 & -2 & -1 \\ \hline + \end{array} + \label{image_eq_sobel_filtre} + \end{eqnarray} -Soit $I$ l'image d'un mot, On note $G_x = I * F_x$ et $G_y = I * F_y$ les produits de convolution de l'image par les deux filtres dcrits en (\ref{image_eq_sobel_filtre}). Il est alors possible de dterminer en un point $\pa{x,y}$ la direction du gradient de la faon suivante en s'arrangeant pour que celle-ci appartienne l'intervalle $\left[0,\pi \right[$~: +Soit $I$ l'image d'un mot, On note $G_x = I * F_x$ et $G_y = I * F_y$ les produits de convolution de l'image par les deux filtres d�crits en (\ref{image_eq_sobel_filtre}). Il est alors possible de d�terminer en un point $\pa{x,y}$ la direction du gradient de la fa�on suivante en s'arrangeant pour que celle-ci appartienne � l'intervalle $\left[0,\pi \right[$~: - \begin{eqnarray} - \theta\pa{x,y} &=& \arctan\cro{\dfrac{G_y\pa{x,y}}{G_x\pa{x,y}}} - \in \left[0,\pi \right[ - \end{eqnarray} + \begin{eqnarray} + \theta\pa{x,y} &=& \arctan\cro{\dfrac{G_y\pa{x,y}}{G_x\pa{x,y}}} + \in \left[0,\pi \right[ + \end{eqnarray} -Cet intervalle est ensuite divis en $n$ sous-intervalles de longueur identique afin de construire l'histogramme $\pa{\alpha_i}_{1 \infegal i \infegal n}$ suivant~: +Cet intervalle est ensuite divis� en $n$ sous-intervalles de longueur identique afin de construire l'histogramme $\pa{\alpha_i}_{1 \leqslant i \leqslant n}$ suivant~: - \begin{eqnarray} - \forall i \in \ensemble{1}{n}, \; \alpha_i &=& card - \acc{ \pa{x,y} \in I \sac \theta\pa{x,y} \in \left[0,\pi \right[ } \nonumber - \end{eqnarray} + \begin{eqnarray} + \forall i \in \ensemble{1}{n}, \; \alpha_i &=& card + \acc{ \pa{x,y} \in I \sac \theta\pa{x,y} \in \left[0,\pi \right[ } \nonumber + \end{eqnarray} - \begin{figure}[ht] - $$\begin{array}{|c|c|} \hline - \includegraphics[height=1.1cm, width=2.5cm]{\filext{../image/image/histo_word}} & - \includegraphics[height=2cm, width=5cm]{\filext{../image/image/histo_incl}} \\ \hline - \end{array} - $$ - \caption{ Rpartition de la direction du gradient pour une image de mot en neuf intervalles - d'angle (les angles sont en radians). - Le pic de l'histogramme est dcal par rapport $\pi/2$. } - \label{image_gradient_direction_histogramme} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|c|} \hline + \includegraphics[height=1.1cm, width=2.5cm]{\filext{../image/image/histo_word}} & + \includegraphics[height=2cm, width=5cm]{\filext{../image/image/histo_incl}} \\ \hline + \end{array} + $$ + \caption{ R�partition de la direction du gradient pour une image de mot en neuf intervalles + d'angle (les angles sont en radians). + Le pic de l'histogramme est d�cal� par rapport � $\pi/2$. } + \label{image_gradient_direction_histogramme} + \end{figure} -Un exemple d'un tel histogramme est donn par la figure~\ref{image_gradient_direction_histogramme}. Il montre un pic principal qui correspond l'orientation des lettres hautes (t,l,...). Toutefois, partir de quelques images, on a constat que cet histogramme mne une estimation moins prcise de la direction d'inclinaison qu'une moyenne sur l'ensemble des directions calcules en vitant les directions proches de l'horizontale. On note $\hat{\theta}$ cette estimation~: +Un exemple d'un tel histogramme est donn� par la figure~\ref{image_gradient_direction_histogramme}. Il montre un pic principal qui correspond � l'orientation des lettres hautes (t,l,...). Toutefois, � partir de quelques images, on a constat� que cet histogramme m�ne � une estimation moins pr�cise de la direction d'inclinaison qu'une moyenne sur l'ensemble des directions calcul�es en �vitant les directions proches de l'horizontale. On note $\hat{\theta}$ cette estimation~: - \begin{eqnarray} - \hat{\theta} &=& \dfrac{1}{ - \summyone{\pa{x,y}} \; - \indicatrice{ \theta\pa{x,y} \in \cro{\frac{\pi}{8},\frac{7\pi}{8}}} } \; - \summyone{\pa{x,y}} \; \theta\pa{x,y} - \indicatrice{ \theta\pa{x,y} \in \cro{\frac{\pi}{8},\frac{7\pi}{8}}} - \label{image_direction_estimation} - \end{eqnarray} - + \begin{eqnarray} + \hat{\theta} &=& \dfrac{1}{ + \summyone{\pa{x,y}} \; + \indicatrice{ \theta\pa{x,y} \in \cro{\frac{\pi}{8},\frac{7\pi}{8}}} } \; + \summyone{\pa{x,y}} \; \theta\pa{x,y} + \indicatrice{ \theta\pa{x,y} \in \cro{\frac{\pi}{8},\frac{7\pi}{8}}} + \label{image_direction_estimation} + \end{eqnarray} + -L'image est ensuite redresse en faisant glisser les lignes de pixels les unes sur les autres comme le montre la figure~\ref{image_gradient_direction_histogramme_correct}. +L'image est ensuite redress�e en faisant glisser les lignes de pixels les unes sur les autres comme le montre la figure~\ref{image_gradient_direction_histogramme_correct}. - \begin{figure}[ht] - $$\begin{array}{|c|c|} \hline - \includegraphics[height=1.1cm, width=2.5cm]{\filext{../image/image/histo_word}} & - \includegraphics[height=1.1cm, width=3cm]{\filext{../image/image/histo_wori}} \\ \hline - \end{array}$$ - \caption{ Correction de l'inclinaison de l'image effectue par un glissement des lignes - les unes sur les autres. L'estimation de la direction par - (\ref{image_direction_estimation}) donne 58 degrs, 90 tant la valeur pour - criture non incline.} - \label{image_gradient_direction_histogramme_correct} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|c|} \hline + \includegraphics[height=1.1cm, width=2.5cm]{\filext{../image/image/histo_word}} & + \includegraphics[height=1.1cm, width=3cm]{\filext{../image/image/histo_wori}} \\ \hline + \end{array}$$ + \caption{ Correction de l'inclinaison de l'image effectu�e par un glissement des lignes + les unes sur les autres. L'estimation de la direction par + (\ref{image_direction_estimation}) donne 58 degr�s, 90 �tant la valeur pour + �criture non inclin�e.} + \label{image_gradient_direction_histogramme_correct} + \end{figure} @@ -611,55 +611,55 @@ \subsection{Lissage du contour} \indexfrr{contour}{lissage} \label{image_lissage_contour__} -La correction de l'inclinaison se termine par la construction de l'image corrige qui est le rsultat d'un glissement des lignes de pixels les unes par rapport aux autres. Cette mthode simple a pourtant l'inconvnient de produire des irrgularits tout le long du contour des lettres (voir figure~\ref{image_lissage_contour_said}). Ces petits bruits peuvent altrer les rsultats de la reconnaissance (voir~\citeindex{Slavik2000}). +La correction de l'inclinaison se termine par la construction de l'image corrig�e qui est le r�sultat d'un glissement des lignes de pixels les unes par rapport aux autres. Cette m�thode simple a pourtant l'inconv�nient de produire des irr�gularit�s tout le long du contour des lettres (voir figure~\ref{image_lissage_contour_said}). Ces petits bruits peuvent alt�rer les r�sultats de la reconnaissance (voir~\citeindex{Slavik2000}). - \begin{figure}[ht] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=3cm, width=3cm]{\filext{../image/image/smoothsaid}} \\ \hline - \end{tabular}$$ - \caption{ Ces deux images proviennent de la barre du "d" - de la figure~\ref{image_gradient_direction_histogramme_correct}. Le rsultat obtenu - d aux glissements des lignes les unes par rapport aux autres - prsente de nombreuses irrgularits qu'il serait prfrable de gommer. - } - \label{image_lissage_contour_said} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=3cm, width=3cm]{\filext{../image/image/smoothsaid}} \\ \hline + \end{tabular}$$ + \caption{ Ces deux images proviennent de la barre du "d" + de la figure~\ref{image_gradient_direction_histogramme_correct}. Le r�sultat obtenu + d� aux glissements des lignes les unes par rapport aux autres + pr�sente de nombreuses irr�gularit�s qu'il serait pr�f�rable de gommer. + } + \label{image_lissage_contour_said} + \end{figure} \indexfrr{point}{inflexion} -La mthode propose dans~\citeindex{Slavik2000} s'appuie sur la mme mthode que celle qui permet d'obtenir le squelette d'une image\seeannex{annexe_squelettisation}{squelettisation}. Par l'application des masques de la figure~\ref{image_lissage_contour}, les lettres dont l'inclinaison a t corrige perdent peu peu leurs petites rides. Le processus continue tant que l'image volue. Cet algorithme a t utilis pour lisser l'image de la figure~\ref{image_smooth_deslant} dont l'inclinaison a t corrige. -La figure~\ref{image_smooth_deslant}c montre le rsultat obtenu grce cet algorithme de lissage utilisant les masques cits par la figure~\ref{image_lissage_contour}. Le rsultat est satisfaisant, les lignes droites incluent nanmoins de larges crneaux qu'il serait possible d'laguer en tudiant la convexit du contour, en minimisant le nombre de ses points d'inflexion. +La m�thode propos�e dans~\citeindex{Slavik2000} s'appuie sur la m�me m�thode que celle qui permet d'obtenir le squelette d'une image\seeannex{annexe_squelettisation}{squelettisation}. Par l'application des masques de la figure~\ref{image_lissage_contour}, les lettres dont l'inclinaison a �t� corrig�e perdent peu � peu leurs petites rides. Le processus continue tant que l'image �volue. Cet algorithme a �t� utilis� pour lisser l'image de la figure~\ref{image_smooth_deslant} dont l'inclinaison a �t� corrig�e. +La figure~\ref{image_smooth_deslant}c montre le r�sultat obtenu gr�ce � cet algorithme de lissage utilisant les masques cit�s par la figure~\ref{image_lissage_contour}. Le r�sultat est satisfaisant, les lignes droites incluent n�anmoins de larges cr�neaux qu'il serait possible d'�laguer en �tudiant la convexit� du contour, en minimisant le nombre de ses points d'inflexion. - \begin{figure}[t] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=6cm, width=5cm]{\filext{../image/image/smoothbo}} \\ \hline - \end{tabular}$$ - \caption{ Les configurations pixelliques ci-dessus permettent de lisser le contour aprs que - l'inclinaison d'un mot a t corrige. Cette figure est extraite de \citeindexfig{Slavik2000} - laquelle il faut ajouter les configurations obtenues par rotation de celles prsentes - ci-dessus.} - \label{image_lissage_contour} - \end{figure} + \begin{figure}[t] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=6cm, width=5cm]{\filext{../image/image/smoothbo}} \\ \hline + \end{tabular}$$ + \caption{ Les configurations pixelliques ci-dessus permettent de lisser le contour apr�s que + l'inclinaison d'un mot a �t� corrig�e. Cette figure est extraite de \citeindexfig{Slavik2000} + � laquelle il faut ajouter les configurations obtenues par rotation de celles pr�sent�es + ci-dessus.} + \label{image_lissage_contour} + \end{figure} - \begin{figure}[t] - $$\begin{tabular}{|c|c|c|} \hline - \includegraphics[height=3cm, width=4cm]{\filext{../image/image/imslant}} & - \includegraphics[height=4cm, width=6cm]{\filext{../image/image/imslant_}} & - \includegraphics[height=4cm, width=6cm]{\filext{../image/image/imslants}} \\ - (a) & (b) & (c) \\ \hline - \end{tabular}$$ - \caption{ L'inclinaison de l'image~(a) est corrige par la mthode expose - au paragraphe~\ref{image_redressement_sobel} et donne l'image~(b). Les irrgularits - du contour sont ensuite corriges grce l'algorithme prsent au - paragraphe~\ref{image_lissage_contour__} et qui aboutit l'image~(c).} - \label{image_smooth_deslant} - \end{figure} - + \begin{figure}[t] + $$\begin{tabular}{|c|c|c|} \hline + \includegraphics[height=3cm, width=4cm]{\filext{../image/image/imslant}} & + \includegraphics[height=4cm, width=6cm]{\filext{../image/image/imslant_}} & + \includegraphics[height=4cm, width=6cm]{\filext{../image/image/imslants}} \\ + (a) & (b) & (c) \\ \hline + \end{tabular}$$ + \caption{ L'inclinaison de l'image~(a) est corrig�e par la m�thode expos�e + au paragraphe~\ref{image_redressement_sobel} et donne l'image~(b). Les irr�gularit�s + du contour sont ensuite corrig�es gr�ce � l'algorithme pr�sent� au + paragraphe~\ref{image_lissage_contour__} et qui aboutit � l'image~(c).} + \label{image_smooth_deslant} + \end{figure} + @@ -675,54 +675,54 @@ \subsection{Lignes d'appui} \indexfrr{ligne}{base} \indexfr{ascendant}\indexfr{descendant} -Les lignes d'appui encadrent la bande des minuscules et dlimitent les zones contenant les ascendants et descendants (voir figure~\ref{image_ligne_appui_fig}). Plusieurs mthodes permettent de dtecter ces lignes virtuelles mais toutes ncessitent quelques lettres afin de retourner un rsultat fiable. Il est par exemple impossible de distinguer un "o"~minuscule d'un "O"~majuscule si aucune autre lettre qui serait juxtapose ne vient aider la lecture. +Les lignes d'appui encadrent la bande des minuscules et d�limitent les zones contenant les ascendants et descendants (voir figure~\ref{image_ligne_appui_fig}). Plusieurs m�thodes permettent de d�tecter ces lignes virtuelles mais toutes n�cessitent quelques lettres afin de retourner un r�sultat fiable. Il est par exemple impossible de distinguer un "o"~minuscule d'un "O"~majuscule si aucune autre lettre qui serait juxtapos�e ne vient aider la lecture. -\indexfrr{Hough}{transforme de ...} -L'article \citeindex{Wang1997} propose une mthode utilisant ces mmes extrema locaux du contour extrieur de l'image d'un mot mais les lignes d'appui sont estimes globalement sur toute l'image partir d'une transforme de Hough. Comme les images sont supposes contenir deux lignes d'appui parallles et proches, les rsultats sont affins afin d'obtenir cette configuration. +\indexfrr{Hough}{transform�e de ...} +L'article \citeindex{Wang1997} propose une m�thode utilisant ces m�mes extrema locaux du contour ext�rieur de l'image d'un mot mais les lignes d'appui sont estim�es globalement sur toute l'image � partir d'une transform�e de Hough. Comme les images sont suppos�es contenir deux lignes d'appui parall�les et proches, les r�sultats sont affin�s afin d'obtenir cette configuration. -Un autre article (\citeindex{Madhvanath1999}) suggre que l'estimation globale de la position de ces lignes mne frquemment un rsultat de mauvaise qualit surtout si les lettres ne sont pas disposes sur une droite ou si les minuscules prsentent des tailles diffrentes. La mthode propose dans cet article s'appuie sur les extrema du contour de l'image d'un mot puis regroupe localement ces points en petits segments regroupant des points proches et presque aligns. L'ensemble de ces petits segments dfinit des lignes d'appui variables tout au long du mot. +Un autre article (\citeindex{Madhvanath1999}) sugg�re que l'estimation globale de la position de ces lignes m�ne fr�quemment � un r�sultat de mauvaise qualit� surtout si les lettres ne sont pas dispos�es sur une droite ou si les minuscules pr�sentent des tailles diff�rentes. La m�thode propos�e dans cet article s'appuie sur les extrema du contour de l'image d'un mot puis regroupe localement ces points en petits segments regroupant des points proches et presque align�s. L'ensemble de ces petits segments d�finit des lignes d'appui variables tout au long du mot. -Plusieurs histogrammes peuvent tre utiliss, paisseurs des traits, nombre de transitions blanc-noir, projection des points du contour, celui-ci est souvent liss par une moyenne mobile. En rgle gnrale, la ligne d'appui basse est la plus fiable. Soit un histogramme $h=\vecteur{h_1}{h_N}$, o $h_1$ correspond au bas de l'image et $h_i$ est la moyenne des longueurs des segments blancs de la ligne $i$. L'histogramme est liss avec une moyenne mobile analogue (\ref{image_lissage_equation}). On dfinit $l_b$ comme la ligne d'appui basse, $l_h$ la ligne d'appui haute, $l$ la ligne correspond au maximum de l'histogramme~: +Plusieurs histogrammes peuvent �tre utilis�s, �paisseurs des traits, nombre de transitions blanc-noir, projection des points du contour, celui-ci est souvent liss� par une moyenne mobile. En r�gle g�n�rale, la ligne d'appui basse est la plus fiable. Soit un histogramme $h=\vecteur{h_1}{h_N}$, o� $h_1$ correspond au bas de l'image et $h_i$ est la moyenne des longueurs des segments blancs de la ligne $i$. L'histogramme est liss� avec une moyenne mobile analogue � (\ref{image_lissage_equation}). On d�finit $l_b$ comme la ligne d'appui basse, $l_h$ la ligne d'appui haute, $l$ la ligne correspond au maximum de l'histogramme~: - \begin{eqnarray} - l \in \underset{i \in \ensemble{1}{N}} { \arg \min } \; h_i - \end{eqnarray} + \begin{eqnarray} + l \in \underset{i \in \ensemble{1}{N}} { \arg \min } \; h_i + \end{eqnarray} -On dfinit ensuite l'intervalle $\cro{l_b,l_h}$ autour de $l$ vrifiant~: +On d�finit ensuite l'intervalle $\cro{l_b,l_h}$ autour de $l$ v�rifiant~: - \begin{eqnarray} - \forall i \in \cro{l_b,l_h}, \; h_i \infegal \alpha \, h_l - \end{eqnarray} + \begin{eqnarray} + \forall i \in \cro{l_b,l_h}, \; h_i \leqslant \alpha \, h_l + \end{eqnarray} -Le rsultat de la figure~\ref{image_ligne_appui_fig} est obtenu pour $\alpha = 3$ ainsi que ceux de la figure~\ref{image_ligne_appui_fig_bad}. Ces formules peuvent tre ajustes manuellement sur quelques images. Puisqu'elles sont bases sur des histogrammes, elles sont en gnral robustes. De plus, un cart de quelques pixels n'altre pas les rsultats de la reconnaissance, l'essentiel est de dfinir un repre qui permette de positionner les lettres les unes par rapport aux autres partir d'une origine dfinie par les lettres. La seconde ligne d'appui reprsente en quelque sorte un facteur d'chelle. +Le r�sultat de la figure~\ref{image_ligne_appui_fig} est obtenu pour $\alpha = 3$ ainsi que ceux de la figure~\ref{image_ligne_appui_fig_bad}. Ces formules peuvent �tre ajust�es manuellement sur quelques images. Puisqu'elles sont bas�es sur des histogrammes, elles sont en g�n�ral robustes. De plus, un �cart de quelques pixels n'alt�re pas les r�sultats de la reconnaissance, l'essentiel est de d�finir un rep�re qui permette de positionner les lettres les unes par rapport aux autres � partir d'une origine d�finie par les lettres. La seconde ligne d'appui repr�sente en quelque sorte un facteur d'�chelle. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=4cm] - {\filext{../image/image/segline_appui}}\end{array}$}$$ - \caption{ Lignes d'appui encadrant la bande des minuscules} - \indexfrr{ligne}{appui} - \label{image_ligne_appui_fig} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=4cm] + {\filext{../image/image/segline_appui}}\end{array}$}$$ + \caption{ Lignes d'appui encadrant la bande des minuscules} + \indexfrr{ligne}{appui} + \label{image_ligne_appui_fig} + \end{figure} - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=8cm] - {\filext{../image/image/seg_appui_bad}}\end{array}$}$$ - \caption{ Mauvais positionnement des lignes d'appui~: les mots sont trop courts ou incluent - des minuscules dcores comme la lettre "i". Ces cas sont minoritaires.} - \indexfrr{ligne}{appui} - \label{image_ligne_appui_fig_bad} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=8cm] + {\filext{../image/image/seg_appui_bad}}\end{array}$}$$ + \caption{ Mauvais positionnement des lignes d'appui~: les mots sont trop courts ou incluent + des minuscules d�cor�es comme la lettre "i". Ces cas sont minoritaires.} + \indexfrr{ligne}{appui} + \label{image_ligne_appui_fig_bad} + \end{figure} -Afin de limiter les erreurs comme celles prsentes figure~\ref{image_ligne_appui_fig_bad}, les algorithmes incluent parfois une prclassification des histogrammes en quatre classes qui dterminent s'il faut chercher les lignes d'appui, une seule ou aucune~: (voir~\citeindex{Hennig2002}) +Afin de limiter les erreurs comme celles pr�sent�es figure~\ref{image_ligne_appui_fig_bad}, les algorithmes incluent parfois une pr�classification des histogrammes en quatre classes qui d�terminent s'il faut chercher les lignes d'appui, une seule ou aucune~: (voir~\citeindex{Hennig2002}) - \begin{enumerate} - \item mot sans ascendant, sans descendant - \item mot avec ascendant(s), sans descendant - \item mot sans ascendant, avec descendant(s) - \item mot avec ascendant(s), avec descendant(s) - \end{enumerate} + \begin{enumerate} + \item mot sans ascendant, sans descendant + \item mot avec ascendant(s), sans descendant + \item mot sans ascendant, avec descendant(s) + \item mot avec ascendant(s), avec descendant(s) + \end{enumerate} -Le principe expos ci-dessus est valable essentiellement pour des images de mots. Pour une ligne entire compose de plusieurs mots, mme si la mthode ci-dessus peut servir de premire approximation, elle doit tre affine pour chacun des mots en utilisant les botes englobantes des graphmes par exemple (paragraphe~\ref{image_segmentation_grapheme}). L'article~\citeindex{Hennig2002} prsente une autre mthode base sur des splines rsolvant ce problme. +Le principe expos� ci-dessus est valable essentiellement pour des images de mots. Pour une ligne enti�re compos�e de plusieurs mots, m�me si la m�thode ci-dessus peut servir de premi�re approximation, elle doit �tre affin�e pour chacun des mots en utilisant les bo�tes englobantes des graph�mes par exemple (paragraphe~\ref{image_segmentation_grapheme}). L'article~\citeindex{Hennig2002} pr�sente une autre m�thode bas�e sur des splines r�solvant ce probl�me. @@ -744,88 +744,88 @@ \subsection{Lignes d'appui} -\subsection{Estimation de l'paisseur du trac} +\subsection{Estimation de l'�paisseur du trac�} \label{image_epaisseur_trace} -\indexfr{paisseur du trac} +\indexfr{�paisseur du trac�} -Selon les scripteurs, l'criture peut-tre plus ou moins paisse (voir figure~\ref{image_trace_epaisseur}). Cette diffrence n'est pas toujours intressante prendre en compte (redressement de l'image) comme elle peut parfois tre une donne non ngligeable. Par exemple, on considre un histogramme de projection verticale utilis pour la segmentation graphme (paragraphe~\ref{image_segmentation_histogramme_direction}), ses minima locaux sont a priori suprieurs l'paisseur du trait qui peut servir de seuil de coupure. +Selon les scripteurs, l'�criture peut-�tre plus ou moins �paisse (voir figure~\ref{image_trace_epaisseur}). Cette diff�rence n'est pas toujours int�ressante � prendre en compte (redressement de l'image) comme elle peut parfois �tre une donn�e non n�gligeable. Par exemple, on consid�re un histogramme de projection verticale utilis� pour la segmentation graph�me (paragraphe~\ref{image_segmentation_histogramme_direction}), ses minima locaux sont a priori sup�rieurs � l'�paisseur du trait qui peut servir de seuil de coupure. - \begin{figure}[ht] - $$\begin{array}{|c|c|}\hline - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/attitude1}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/attitude2}} \\ \hline - \end{array}$$ - \caption{ Diffrentes paisseurs de trac pour deux images dont les dimensions sont 206x69 pixels - pour la premire et 211x97 pixels pour la seconde.} - \label{image_trace_epaisseur} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|c|}\hline + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/attitude1}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/attitude2}} \\ \hline + \end{array}$$ + \caption{ Diff�rentes �paisseurs de trac� pour deux images dont les dimensions sont 206x69 pixels + pour la premi�re et 211x97 pixels pour la seconde.} + \label{image_trace_epaisseur} + \end{figure} -L'article \citeindex{Abuhaiba1996} propose une estimation partir d'une carte de distance\seeannex{ske_carte_distance_sec}{carte de distance} et d'un masque de distance dfini comme suit~: +L'article \citeindex{Abuhaiba1996} propose une estimation � partir d'une carte de distance\seeannex{ske_carte_distance_sec}{carte de distance} et d'un masque de distance d�fini comme suit~: - $$ - \begin{array}{c|c|c} - 4 & 3 & 4 \\ \hline - 3 & 0 & 3 \\ \hline - 4 & 3 & 4 \\ - \end{array} - $$ + $$ + \begin{array}{c|c|c} + 4 & 3 & 4 \\ \hline + 3 & 0 & 3 \\ \hline + 4 & 3 & 4 \\ + \end{array} + $$ -On note $d_*$ la valeur de la distance la plus frquente, l'paisseur du trait $\widehat{e_0}$ est alors dfinie comme~: +On note $d_*$ la valeur de la distance la plus fr�quente, l'�paisseur du trait $\widehat{e_0}$ est alors d�finie comme~: - \begin{eqnarray} - \widehat{e_0} &=& \frac{2}{3} \; d^* - 1 - \end{eqnarray} + \begin{eqnarray} + \widehat{e_0} &=& \frac{2}{3} \; d^* - 1 + \end{eqnarray} \indexfrr{histogramme}{segment} -Cette estimation peut tre aussi effectue au moyen d'histogrammes de projection. Une ligne ou une colonne de pixels extraite de l'image est constitue d'une suite de segments de la couleur de l'criture. A priori, la longueur minimale de ces segments est gale l'paisseur du trait. Par consquent, on construit l'histogramme $\pa{h_i}_{1 \infegal i }$ suivant~: +Cette estimation peut �tre aussi effectu�e au moyen d'histogrammes de projection. Une ligne ou une colonne de pixels extraite de l'image est constitu�e d'une suite de segments de la couleur de l'�criture. A priori, la longueur minimale de ces segments est �gale � l'�paisseur du trait. Par cons�quent, on construit l'histogramme $\pa{h_i}_{1 \leqslant i }$ suivant~: - \begin{eqnarray} - \forall i \supegal 1, \; h_i &=& card \acc{ \text{ segments de longueur $i$ } } - \end{eqnarray} + \begin{eqnarray} + \forall i \supegal 1, \; h_i &=& card \acc{ \text{ segments de longueur $i$ } } + \end{eqnarray} -La figure~\ref{image_trace_epaisseur_histo} illustre les histogrammes obtenus pour les deux images de la figure~\ref{image_trace_epaisseur}. La longueur (\ref{image_epaisseur_estimateur_1}) correspondant au maximum est une premire estimation de l'paisseur du trait. Un second estimateur (\ref{image_epaisseur_estimateur_2}) est construit partir de celui-ci dans le cas o on considre que la distribution de la longueur des segments suit grossirement une loi normale autour de cet extremum~: +La figure~\ref{image_trace_epaisseur_histo} illustre les histogrammes obtenus pour les deux images de la figure~\ref{image_trace_epaisseur}. La longueur (\ref{image_epaisseur_estimateur_1}) correspondant au maximum est une premi�re estimation de l'�paisseur du trait. Un second estimateur (\ref{image_epaisseur_estimateur_2}) est construit � partir de celui-ci dans le cas o� on consid�re que la distribution de la longueur des segments suit grossi�rement une loi normale autour de cet extremum~: - \begin{eqnarray} - \widehat{e_1} &=& \underset{i \supegal 1}{\arg \max} \; h_i \label{image_epaisseur_estimateur_1} \\ - \widehat{e_2} &=& \frac{1}{h} \; \summy{i = 1}{2\widehat{e_1}} \; i \; h_i - \label{image_epaisseur_estimateur_2} - \end{eqnarray} + \begin{eqnarray} + \widehat{e_1} &=& \underset{i \supegal 1}{\arg \max} \; h_i \label{image_epaisseur_estimateur_1} \\ + \widehat{e_2} &=& \frac{1}{h} \; \summy{i = 1}{2\widehat{e_1}} \; i \; h_i + \label{image_epaisseur_estimateur_2} + \end{eqnarray} - \begin{figure}[ht] - $$\begin{array}{|c|}\hline - \includegraphics[height=3cm, width=6cm]{\filext{../image/image/epais}} \\ \hline - \end{array}$$ - \caption{ Histogramme de rpartition des longueurs des traits pour les deux images - de la figure~\ref{image_trace_epaisseur}.} - \label{image_trace_epaisseur_histo} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|}\hline + \includegraphics[height=3cm, width=6cm]{\filext{../image/image/epais}} \\ \hline + \end{array}$$ + \caption{ Histogramme de r�partition des longueurs des traits pour les deux images + de la figure~\ref{image_trace_epaisseur}.} + \label{image_trace_epaisseur_histo} + \end{figure} - \begin{table}[ht] + \begin{table}[ht] $$\begin{array}{|c|c|c|}\hline - & \text{premire image} & \text{seconde image} \\ \hline - \widehat{e_1} & 4 & 10 \\ \hline - \widehat{\sigma\pa{\widehat{e_2}}} - & 0,15 & 0,20 \\ \hline + & \text{premi�re image} & \text{seconde image} \\ \hline + \widehat{e_1} & 4 & 10 \\ \hline + \widehat{\sigma\pa{\widehat{e_2}}} + & 0,15 & 0,20 \\ \hline \end{array}$$ - \caption{ Valeurs obtenues par les deux estimateurs dfinis en (\ref{image_epaisseur_estimateur_1}) - et (\ref{image_epaisseur_estimateur_2}) - pour les deux images de la figure~\ref{image_trace_epaisseur}.} + \caption{ Valeurs obtenues par les deux estimateurs d�finis en (\ref{image_epaisseur_estimateur_1}) + et (\ref{image_epaisseur_estimateur_2}) + pour les deux images de la figure~\ref{image_trace_epaisseur}.} \label{image_trace_epaisseur_estimateur_valeur} - \end{table} + \end{table} \indexfr{largeur moyenne d'une lettre} -Cette paisseur est calcule pour les deux images de la figure~\ref{image_trace_epaisseur} dans la table~\ref{image_trace_epaisseur_estimateur_valeur}. Ncessairement, la largeur d'une lettre dpasse l'paisseur du trait et on peut vraisemblablement penser que la largeur des lettres est au moins suprieure deux fois cette paisseur (voir~\citeindex{Yanikoglu1998}). +Cette �paisseur est calcul�e pour les deux images de la figure~\ref{image_trace_epaisseur} dans la table~\ref{image_trace_epaisseur_estimateur_valeur}. N�cessairement, la largeur d'une lettre d�passe l'�paisseur du trait et on peut vraisemblablement penser que la largeur des lettres est au moins sup�rieure � deux fois cette �paisseur (voir~\citeindex{Yanikoglu1998}). @@ -841,7 +841,7 @@ \subsection{Estimation de la largeur moyenne d'une lettre} \label{image_largeur_lettre} \indexfr{largeur moyenne d'une lettre} -La largeur d'une lettre peut tre une information intressante prendre en compte lors de la segmentation en graphmes. Cette grandeur est d'abord estime par la longueur moyenne entre deux transitions pixel noir - pixel blanc dans la bande des minuscules dlimites par les deux lignes d'appui estimes au paragraphe~\ref{image_ligne_appui}. Cette estimation est note $e_l$ par la suite. +La largeur d'une lettre peut �tre une information int�ressante � prendre en compte lors de la segmentation en graph�mes. Cette grandeur est d'abord estim�e par la longueur moyenne entre deux transitions pixel noir - pixel blanc dans la bande des minuscules d�limit�es par les deux lignes d'appui estim�es au paragraphe~\ref{image_ligne_appui}. Cette estimation est not�e $e_l$ par la suite. @@ -853,83 +853,83 @@ \subsection{Nettoyage de l'image} \indexfr{nettoyage} -Les illustrations reprsentent souvent des images binaires o seul le mot reconnatre apparat. Toutefois, ces images "propres" sont rarement celles immdiatement obtenues aprs scannerisation du document. Il n'existe pas de mthodes gnrales associes ces nettoyages car ils dpendent fortement du type de documents traiter et des informations qui doivent y tre reconnues. Les algorithmes dvelopps sont donc spcifiques un type prcis de document (chque, ordonnance, feuille de maladie, ...). Nanmoins, il se dgage trois catgories de prtraitements~: la binarisation (voir \citeindex{Kwon2004}) ou tout traitement d'image global, la localisation ou la recherche de l'information reconnatre et extraire, le nettoyage proprement dit qui consiste enlever tout ce qui peut gner la reconnaissance de la partie extraite, c'est un traitement local. +Les illustrations repr�sentent souvent des images binaires o� seul le mot � reconna�tre appara�t. Toutefois, ces images "propres" sont rarement celles imm�diatement obtenues apr�s scannerisation du document. Il n'existe pas de m�thodes g�n�rales associ�es � ces nettoyages car ils d�pendent fortement du type de documents � traiter et des informations qui doivent y �tre reconnues. Les algorithmes d�velopp�s sont donc sp�cifiques � un type pr�cis de document (ch�que, ordonnance, feuille de maladie, ...). N�anmoins, il se d�gage trois cat�gories de pr�traitements~: la binarisation (voir \citeindex{Kwon2004}) ou tout traitement d'image global, la localisation ou la recherche de l'information � reconna�tre et � extraire, le nettoyage proprement dit qui consiste � enlever tout ce qui peut g�ner la reconnaissance de la partie extraite, c'est un traitement local. \indexfr{soulignement} \indexfr{binarisation} -La figure~\ref{image_nettoyage_texte} est un exemple emprunt une image en niveaux de gris dont le fond est fonc. La premire tape consiste gnralement binariser l'image. Ce premier traitement n'est pas incontournable mais il permet de rduire fortement la taille des images lors de la constitution de grandes bases de donnes et d'utiliser des algorithmes fonds sur la connexit. L'image de la figure~\ref{image_nettoyage_texte} est dj le rsultat d'une extraction dont il faut ensuite enlever le trait de soulignement et les formes situes au-dessous du mot. Le rsultat de ces prtraitements correspond la seconde image de la figure~\ref{image_nettoyage_texte}. +La figure~\ref{image_nettoyage_texte} est un exemple emprunt� � une image en niveaux de gris dont le fond est fonc�. La premi�re �tape consiste g�n�ralement � binariser l'image. Ce premier traitement n'est pas incontournable mais il permet de r�duire fortement la taille des images lors de la constitution de grandes bases de donn�es et d'utiliser des algorithmes fond�s sur la connexit�. L'image de la figure~\ref{image_nettoyage_texte} est d�j� le r�sultat d'une extraction dont il faut ensuite enlever le trait de soulignement et les formes situ�es au-dessous du mot. Le r�sultat de ces pr�traitements correspond � la seconde image de la figure~\ref{image_nettoyage_texte}. - \begin{figure}[ht] - $$\begin{array}{|c|c|}\hline - \includegraphics[height=1.5cm, width=4cm]{\filext{../image/image/nettoy}} & - \includegraphics[height=1.5cm, width=4cm]{\filext{../image/image/nettoy2}} \\ \hline - \end{array}$$ - \caption{ Un mot extrait d'une page en niveaux de gris, avant de pouvoir reconnatre le mot, - il faut binariser l'image et extraire le mot reconnatre ce qui revient - enlever le soulignement et les divers lettres ou trait situs en-dessous. - La seconde image est issue de la premire aprs avoir t nettoye.} - \label{image_nettoyage_texte} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|c|}\hline + \includegraphics[height=1.5cm, width=4cm]{\filext{../image/image/nettoy}} & + \includegraphics[height=1.5cm, width=4cm]{\filext{../image/image/nettoy2}} \\ \hline + \end{array}$$ + \caption{ Un mot extrait d'une page en niveaux de gris, avant de pouvoir reconna�tre le mot, + il faut binariser l'image et extraire le mot � reconna�tre ce qui revient + � enlever le soulignement et les divers lettres ou trait situ�s en-dessous. + La seconde image est issue de la premi�re apr�s avoir �t� nettoy�e.} + \label{image_nettoyage_texte} + \end{figure} -Les procdures de nettoyage du trait de soulignement consiste d'abord estimer son paisseur puis enlever les pixels qui le composent en prenant soin de laisser les pixels communs aux lettres et au trait de soulignement. Ces derniers sont frquemment reprs par une zone de sur-paisseur due au chevauchement des traits. +Les proc�dures de nettoyage du trait de soulignement consiste d'abord � estimer son �paisseur puis � enlever les pixels qui le composent en prenant soin de laisser les pixels communs aux lettres et au trait de soulignement. Ces derniers sont fr�quemment rep�r�s par une zone de sur-�paisseur due au chevauchement des traits. \indexfr{histogramme} \label{image_nettoyage_desolneux} -La dtection des traits n'est pas non plus un problme simple mme si, pour certains documents, le trait de soulignement est toujours prsent (criture d'un nombre de famille sur une ligne horizontale par exemple). Il est possible d'utiliser des mthodes base d'histogrammes comme ceux prsents au paragraphe~\ref{image_seg_line}. Une autre mthode intressante est prsente dans les articles \citeindex{Desolneux2000}, \citeindex{Desolneux2002}, \citeindex{Desolneux2003} qui propose un formalisme adapt la dtection de toute figure gomtrique simple comme un segment, un carr, un cercle. Par exemple, un alignement de segments comme celui de la figure~\ref{image_nettoyage_texte_morel} n'est dtect que s'il est suffisamment isol pour que sa prsence ne puisse pas tre considre comme une concidence. En rsum, partir des segments prsents dans l'image, on quantifie d'abord la probabilit d'obtenir un alignement quelconque de petits segments n'importe o dans l'image ou plutt le nombre moyen de segments faisant partie d'un alignement. S'il existe un ensemble de segments aligns suprieur au seuil dtermin juste avant, alors, on considre que cet alignement est plus que probable. +La d�tection des traits n'est pas non plus un probl�me simple m�me si, pour certains documents, le trait de soulignement est toujours pr�sent (�criture d'un nombre de famille sur une ligne horizontale par exemple). Il est possible d'utiliser des m�thodes � base d'histogrammes comme ceux pr�sent�s au paragraphe~\ref{image_seg_line}. Une autre m�thode int�ressante est pr�sent�e dans les articles \citeindex{Desolneux2000}, \citeindex{Desolneux2002}, \citeindex{Desolneux2003} qui propose un formalisme adapt� � la d�tection de toute figure g�om�trique simple comme un segment, un carr�, un cercle. Par exemple, un alignement de segments comme celui de la figure~\ref{image_nettoyage_texte_morel} n'est d�tect� que s'il est suffisamment isol� pour que sa pr�sence ne puisse pas �tre consid�r�e comme une co�ncidence. En r�sum�, � partir des segments pr�sents dans l'image, on quantifie d'abord la probabilit� d'obtenir un alignement quelconque de petits segments n'importe o� dans l'image ou plut�t le nombre moyen de segments faisant partie d'un alignement. S'il existe un ensemble de segments align�s sup�rieur au seuil d�termin� juste avant, alors, on consid�re que cet alignement est plus que probable. - \begin{figure}[ht] - $$\begin{array}{|c|}\hline - \includegraphics[height=3cm, width=6cm]{\filext{../image/image/desol}} \\ \hline - \end{array}$$ - \caption{ Figure extraite de \citeindexfig{Desolneux2002}, les traits isols prsents - au bas de la figure paraissent aligns mais noys dans le nuage au-dessus, ils - deviennent "invisibles".} - \label{image_nettoyage_texte_morel} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|}\hline + \includegraphics[height=3cm, width=6cm]{\filext{../image/image/desol}} \\ \hline + \end{array}$$ + \caption{ Figure extraite de \citeindexfig{Desolneux2002}, les traits isol�s pr�sents + au bas de la figure paraissent align�s mais noy�s dans le nuage au-dessus, ils + deviennent "invisibles".} + \label{image_nettoyage_texte_morel} + \end{figure} \indexfr{squelettisation} -Une mthode plus labore dcrite dans \citeindex{Cheng2004} permet de dbarrasser l'image de mots manuscrits d'une ligne ou d'une courbe sur laquelle les lettres s'appuient, ou une courbe qui traverse l'image comme celle de l'exemple de la figure~\ref{image_nettoyage_ligne_courbe}. Cette mthode s'appuie sur la construction d'un graphe qui rsulte d'une squelettisation. La courbe principale dcoule d'une ou plusieurs recherche d'un plus court chemin. +Une m�thode plus �labor�e d�crite dans \citeindex{Cheng2004} permet de d�barrasser l'image de mots manuscrits d'une ligne ou d'une courbe sur laquelle les lettres s'appuient, ou une courbe qui traverse l'image comme celle de l'exemple de la figure~\ref{image_nettoyage_ligne_courbe}. Cette m�thode s'appuie sur la construction d'un graphe qui r�sulte d'une squelettisation. La courbe principale d�coule d'une ou plusieurs recherche d'un plus court chemin. - \begin{figure}[ht] - $$\begin{array}{|c|}\hline - \includegraphics[height=5cm, width=8cm]{\filext{../image/image/cheng}} \\ \hline - \end{array}$$ - \caption{ Figure extraite de \citeindexfig{Cheng2004}, la premire image $(a)$ est l'image originale, - les deux images suivantes rsultent du nettoyage de cette premire image, la courbe principale - et les mots ont t spares.} - \label{image_nettoyage_ligne_courbe} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|}\hline + \includegraphics[height=5cm, width=8cm]{\filext{../image/image/cheng}} \\ \hline + \end{array}$$ + \caption{ Figure extraite de \citeindexfig{Cheng2004}, la premi�re image $(a)$ est l'image originale, + les deux images suivantes r�sultent du nettoyage de cette premi�re image, la courbe principale + et les mots ont �t� s�par�es.} + \label{image_nettoyage_ligne_courbe} + \end{figure} %------------------------------------------------------------------------------------------------------------- -\section{Diverses segmentations en graphmes} +\section{Diverses segmentations en graph�mes} %------------------------------------------------------------------------------------------------------------- -\indexfrr{segmentation}{graphme} -\indexfr{graphme} +\indexfrr{segmentation}{graph�me} +\indexfr{graph�me} \label{image_segmentation_grapheme} -La segmentation en graphmes permet de dlocaliser le problme de reconnaissance du niveau des mots au niveau des lettres. Reconnatre l'image d'un mot sans la dcouper au pralable est une mthode limite des problmes restreints o le nombre de mots est faible comme l'criture d'un nombre en lettres. Comme la reconnaissance se rsume un problme de classification. Plus la liste des mots possibles est longue, plus le classifieur construire est complexe. C'est pourquoi il est prfrable de scinder ce problme de reconnaissance d'un mot en une somme de problmes plus simples qui sont la reconaissance des lettres prsentes dans l'image. +La segmentation en graph�mes permet de d�localiser le probl�me de reconnaissance du niveau des mots au niveau des lettres. Reconna�tre l'image d'un mot sans la d�couper au pr�alable est une m�thode limit�e � des probl�mes restreints o� le nombre de mots est faible comme l'�criture d'un nombre en lettres. Comme la reconnaissance se r�sume � un probl�me de classification. Plus la liste des mots possibles est longue, plus le classifieur � construire est complexe. C'est pourquoi il est pr�f�rable de scinder ce probl�me de reconnaissance d'un mot en une somme de probl�mes plus simples qui sont la reconaissance des lettres pr�sentes dans l'image. -\indexfr{MMC}\indexfrr{squence}{observation} -Dcouper l'image soulve plusieurs questions dont la premire concerne le rsultat obtenir~: est-il dpendant des modles de reconnaissance utiliss par la suite~? Dans le cas de modles de Markov cachs, le rsultat souhait est une squence d'observations, ce qui signifie que la seule dimension variable du dcoupage est le nombre d'objets ainsi forms. Une extension de ces modles statistiques permet d'tendre le concept de squence un graphe d'observations incluant plusieurs options de segmentations. Toutefois, quelle que soit l'option choisie, elle rsulte des compromis suivants~: +\indexfr{MMC}\indexfrr{s�quence}{observation} +D�couper l'image soul�ve plusieurs questions dont la premi�re concerne le r�sultat � obtenir~: est-il d�pendant des mod�les de reconnaissance utilis�s par la suite~? Dans le cas de mod�les de Markov cach�s, le r�sultat souhait� est une s�quence d'observations, ce qui signifie que la seule dimension variable du d�coupage est le nombre d'objets ainsi form�s. Une extension de ces mod�les statistiques permet d'�tendre le concept de s�quence � un graphe d'observations incluant plusieurs options de segmentations. Toutefois, quelle que soit l'option choisie, elle r�sulte des compromis suivants~: - \begin{enumerate} - \item Plus la segmentation possde de degrs de libert (plus elle propose d'alternatives), - plus les modles de reconnaissance seront complexes, plus les modles de reconnaissance - sont complexes, plus ils sont difficiles apprendre. - \item Moins la segmentation possde de degrs de libert, plus elle est susceptible de faire des erreurs. - \end{enumerate} + \begin{enumerate} + \item Plus la segmentation poss�de de degr�s de libert� (plus elle propose d'alternatives), + plus les mod�les de reconnaissance seront complexes, plus les mod�les de reconnaissance + sont complexes, plus ils sont difficiles � apprendre. + \item Moins la segmentation poss�de de degr�s de libert�, plus elle est susceptible de faire des erreurs. + \end{enumerate} -Nous allons aborder une segmentation sous forme de squences de graphmes. Cette tape de segmentation est indispensable pour la construction d'un systme de reconnaissance de l'criture, diverses mthodes sont passes en revue dans les articles \citeindex{Lecolinet1991} ou plus rcemment \citeindex{Lu1996}. Les paragraphes qui suivent reprennent quelques-unes des mthodes prsentes dans ces articles puis concluent sur la conception de la segmentation qui a t labore dans le cadre de ces travaux de recherche. \indexfrr{accent}{graphme}\indexfrr{graphme}{accent} Cette segmentation propose galement une solution au problme des accents dont la lettre d'attache est parfois situe assez loin, ce qui n'a pas t pris en compte jusqu' prsent. +Nous allons aborder une segmentation sous forme de s�quences de graph�mes. Cette �tape de segmentation est indispensable pour la construction d'un syst�me de reconnaissance de l'�criture, diverses m�thodes sont pass�es en revue dans les articles \citeindex{Lecolinet1991} ou plus r�cemment \citeindex{Lu1996}. Les paragraphes qui suivent reprennent quelques-unes des m�thodes pr�sent�es dans ces articles puis concluent sur la conception de la segmentation qui a �t� �labor�e dans le cadre de ces travaux de recherche. \indexfrr{accent}{graph�me}\indexfrr{graph�me}{accent} Cette segmentation propose �galement une solution au probl�me des accents dont la lettre d'attache est parfois situ�e assez loin, ce qui n'a pas �t� pris en compte jusqu'� pr�sent. @@ -946,40 +946,40 @@ \section{Diverses segmentations en graph -\subsection{Segmentation partir du squelette} -\indexfrr{segmentation}{graphme} +\subsection{Segmentation � partir du squelette} +\indexfrr{segmentation}{graph�me} \label{image_sequence_graphem} \indexfr{ordonnancement} -\indexfrr{graphme}{taille} -\indexfrr{graphme}{ordonnancement} +\indexfrr{graph�me}{taille} +\indexfrr{graph�me}{ordonnancement} -Les graphmes sont des images extraites de l'image segmenter. Passer d'une seule image une squence de graphmes soulve deux problmes (voir~\citeindex{Baret1991}) qui sont la taille que doivent avoir les graphmes et l'ordonnancement des morceaux segments. Ils ne doivent pas tre trop petits afin d'tre diffrents les uns des autres, diffrents d'un simple trait. Ils ne doivent pas tre trop gros pour ne pas dpasser la taille d'une lettre. Chaque lettre reprsentera entre un et trois graphmes. Ce choix facilite l'ordonnancement des graphmes qui doivent respecter le sens gauche-droite de la lecture. +Les graph�mes sont des images extraites de l'image � segmenter. Passer d'une seule image � une s�quence de graph�mes soul�ve deux probl�mes (voir~\citeindex{Baret1991}) qui sont la taille que doivent avoir les graph�mes et l'ordonnancement des morceaux segment�s. Ils ne doivent pas �tre trop petits afin d'�tre diff�rents les uns des autres, diff�rents d'un simple trait. Ils ne doivent pas �tre trop gros pour ne pas d�passer la taille d'une lettre. Chaque lettre repr�sentera entre un et trois graph�mes. Ce choix facilite l'ordonnancement des graph�mes qui doivent respecter le sens gauche-droite de la lecture. \indexfr{squelette}\indexfr{motifs} -L'image peut tre rendue l'tat de squelette\seeannex{annexe_squelettisation}{squelettisation}. Ce dernier est ensuite parcouru de manire reprer certains motifs synonymes de csure entre lettres (figure~\ref{image_graphe_cut}). La dtection de ces motifs introduit des calculs de courbure, d'angle qui sont compars des seuils ajusts de manire obtenir le rsultat dsir. Ces algorithmes sont mieux dtaills dans~\citeindex{Lecolinet1991}. +L'image peut �tre rendue � l'�tat de squelette\seeannex{annexe_squelettisation}{squelettisation}. Ce dernier est ensuite parcouru de mani�re � rep�rer certains motifs synonymes de c�sure entre lettres (figure~\ref{image_graphe_cut}). La d�tection de ces motifs introduit des calculs de courbure, d'angle qui sont compar�s � des seuils ajust�s de mani�re � obtenir le r�sultat d�sir�. Ces algorithmes sont mieux d�taill�s dans~\citeindex{Lecolinet1991}. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=10cm] - {\filext{../image/image/grm_cut}}\end{array}$}$$ - \caption{Segmentation partir du squelette~: segmentation base sur des motifs.} - \label{image_graphe_cut} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=10cm] + {\filext{../image/image/grm_cut}}\end{array}$}$$ + \caption{Segmentation � partir du squelette~: segmentation bas�e sur des motifs.} + \label{image_graphe_cut} + \end{figure} -La figure~\ref{image_graphe_noel} est un exemple de ce qui est obtenu et des problmes qui accompagnent l'utilisation de seuils. Les lettres en fin de mots, plus petites, sont parfois agrges. La figure~\ref{image_grapheme_erreur} recense la liste de ces problmes. +La figure~\ref{image_graphe_noel} est un exemple de ce qui est obtenu et des probl�mes qui accompagnent l'utilisation de seuils. Les lettres en fin de mots, plus petites, sont parfois agr�g�es. La figure~\ref{image_grapheme_erreur} recense la liste de ces probl�mes. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1.5cm, width=5cm] - {\filext{../image/image/grm_noel}}\end{array}$}$$ - \caption{ Segmentation partir du squelette~: - chaque graphme est entour de sa bote englobante, les deux lignes horizontales - modlisent les lignes d'appui (ou lignes de bases) qui encadrent la bande o - sont crites les lettres - minuscules (paragraphe~\ref{image_ligne_appui}).} - \label{image_graphe_noel} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1.5cm, width=5cm] + {\filext{../image/image/grm_noel}}\end{array}$}$$ + \caption{ Segmentation � partir du squelette~: + chaque graph�me est entour� de sa bo�te englobante, les deux lignes horizontales + mod�lisent les lignes d'appui (ou lignes de bases) qui encadrent la bande o� + sont �crites les lettres + minuscules (paragraphe~\ref{image_ligne_appui}).} + \label{image_graphe_noel} + \end{figure} @@ -987,39 +987,39 @@ \subsection{Segmentation -\subsection{Segmentation partir du contour} -\indexfrr{segmentation}{graphme} +\subsection{Segmentation � partir du contour} +\indexfrr{segmentation}{graph�me} \label{image_sequence_graphem_contour} \indexfr{contour} -Cette mthode esquisse dans~\citeindex{Madhvanath2001} ne s'intresse pas au squelette mais uniquement au contour dont elle dtermine les meilleurs points candidats une coupure entre graphmes (voir figure~\ref{image_segmentation_contour}). Lors du parcours du contour, les extrema locaux sont marqus (point culminant et selle) puis les paires les plus proches sont regroupes de part et d'autre du trait. +Cette m�thode esquiss�e dans~\citeindex{Madhvanath2001} ne s'int�resse pas au squelette mais uniquement au contour dont elle d�termine les meilleurs points candidats � une coupure entre graph�mes (voir figure~\ref{image_segmentation_contour}). Lors du parcours du contour, les extrema locaux sont marqu�s (point culminant et selle) puis les paires les plus proches sont regroup�es de part et d'autre du trait. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] - {\filext{../image/image/grm_seg_bound}}\end{array}$}$$ - \caption{ Segmentation partir du contour~: les points reprsentent les minima et les maxima - locaux (ordonne des points) le long du contour. Les paires des points des plus - proches disposs de part et d'autre du trait - forment les candidats les plus probables pour une csure. - } - \indexfrr{ligne}{appui} - \label{image_segmentation_contour} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] + {\filext{../image/image/grm_seg_bound}}\end{array}$}$$ + \caption{ Segmentation � partir du contour~: les points repr�sentent les minima et les maxima + locaux (ordonn�e des points) le long du contour. Les paires des points des plus + proches dispos�s de part et d'autre du trait + forment les candidats les plus probables pour une c�sure. + } + \indexfrr{ligne}{appui} + \label{image_segmentation_contour} + \end{figure} -\indexfr{ttonnement} -La direction de coupure n'est pas toujours horizontale comme le montre la figure~\ref{image_segmentation_contour2}. A l'instar de la mthode prcdente, la segmentation en graphmes partir du contour ncessite de nombreux ajustements avant de trouver les critres de dcision. Cette mise au point par ttonnements est le point commun de nombreux traitements d'images lies l'criture manuscrite. Faciles ajuster lorsque la qualit de l'criture est bonne (figure~\ref{image_segmentation_contour}), ces prtraitements peuvent avoir des comportements tout--fait erratiques lorsque l'criture est de mauvaise qualit (voir figure~\ref{image_graphe_grapheme}). +\indexfr{t�tonnement} +La direction de coupure n'est pas toujours horizontale comme le montre la figure~\ref{image_segmentation_contour2}. A l'instar de la m�thode pr�c�dente, la segmentation en graph�mes � partir du contour n�cessite de nombreux ajustements avant de trouver les crit�res de d�cision. Cette mise au point par t�tonnements est le point commun de nombreux traitements d'images li�es � l'�criture manuscrite. Faciles � ajuster lorsque la qualit� de l'�criture est bonne (figure~\ref{image_segmentation_contour}), ces pr�traitements peuvent avoir des comportements tout-�-fait erratiques lorsque l'�criture est de mauvaise qualit� (voir figure~\ref{image_graphe_grapheme}). - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] - {\filext{../image/image/grm_seg_bound2}}\end{array}$}$$ - \caption{ Segmentation partir du contour~: si la csure du couple "Je" s'appuie sur les extrema - environnants, sa direction est plus horizontale que verticale.} - \label{image_segmentation_contour2} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] + {\filext{../image/image/grm_seg_bound2}}\end{array}$}$$ + \caption{ Segmentation � partir du contour~: si la c�sure du couple "Je" s'appuie sur les extrema + environnants, sa direction est plus horizontale que verticale.} + \label{image_segmentation_contour2} + \end{figure} @@ -1027,17 +1027,17 @@ \subsection{Segmentation - - + + \subsection{Ordonnancement} -\indexfrr{graphme}{ordonnancement} +\indexfrr{graph�me}{ordonnancement} \indexfr{ordonnancement} -\indexfrr{graphme}{squence} -\indexfrr{squence}{graphme} +\indexfrr{graph�me}{s�quence} +\indexfrr{s�quence}{graph�me} \label{image_ordonnancement} \indexfr{voyageur de commerce} -Une fois la segmentation effectue, il ne reste plus qu' ordonner les morceaux afin de former une squence d'observations. Ce problme n'est pas simple et doit inclure des tapes de regroupement afin de traiter des problmes tels que des accents qui doivent tre associs une lettre. Dans un premier temps, les accents, les points, c'est--dire tout morceau isol au-dessus de la ligne d'appui haute, ne sont pas intgrs par l'algorithme d'ordonnancement, ils sont affects aux graphmes une fois ce dernier termin. Ce problme d'ordonnancement est similaire au problme du voyageur de commerce. Une fois que les graphmes de dbut et de fin sont dtermins, le plus court chemin reliant les graphmes de dbut et de fin et incluant tous les autres graphmes peut tre assimil la squence la plus probable. +Une fois la segmentation effectu�e, il ne reste plus qu'� ordonner les morceaux afin de former une s�quence d'observations. Ce probl�me n'est pas simple et doit inclure des �tapes de regroupement afin de traiter des probl�mes tels que des accents qui doivent �tre associ�s � une lettre. Dans un premier temps, les accents, les points, c'est-�-dire tout morceau isol� au-dessus de la ligne d'appui haute, ne sont pas int�gr�s par l'algorithme d'ordonnancement, ils sont affect�s aux graph�mes une fois ce dernier termin�. Ce probl�me d'ordonnancement est similaire au probl�me du voyageur de commerce. Une fois que les graph�mes de d�but et de fin sont d�termin�s, le plus court chemin reliant les graph�mes de d�but et de fin et incluant tous les autres graph�mes peut �tre assimil� � la s�quence la plus probable. @@ -1046,23 +1046,23 @@ \subsection{Ordonnancement} -\subsection{Fentres glissantes} -\indexfrr{fentre}{glissante} +\subsection{Fen�tres glissantes} +\indexfrr{fen�tre}{glissante} \label{image_fenetre_glissante} -Dcouper l'image en bandelettes verticales est la segmentation la plus simple comme le montre la figure~\ref{image_window_slide}. Ce dcoupage peut tre rgulier ou dpendre des minima d'un histogramme de projection par exemple, il peut galement se recouvrir. L'inconvnient de cette mthode est qu'elle gnre trop de graphmes regroupant les morceaux de plusieurs lettres, c'est d'ailleurs pourquoi les petites images obtenues se recouvrent en partie. Le paragraphe suivant~\ref{image_segmentation_histogramme_direction} tend cette mthode plusieurs directions de projection pour les histogrammes. Cette reprsentation est utilise par le systme de reconnaissance dcrit dans \citeindex{Knerr2001}. +D�couper l'image en bandelettes verticales est la segmentation la plus simple comme le montre la figure~\ref{image_window_slide}. Ce d�coupage peut �tre r�gulier ou d�pendre des minima d'un histogramme de projection par exemple, il peut �galement se recouvrir. L'inconv�nient de cette m�thode est qu'elle g�n�re trop de graph�mes regroupant les morceaux de plusieurs lettres, c'est d'ailleurs pourquoi les petites images obtenues se recouvrent en partie. Le paragraphe suivant~\ref{image_segmentation_histogramme_direction} �tend cette m�thode � plusieurs directions de projection pour les histogrammes. Cette repr�sentation est utilis�e par le syst�me de reconnaissance d�crit dans \citeindex{Knerr2001}. \begin{figure}[t] $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=16cm] {\filext{../bibliographie/image/biblio_window_slide}}\end{array}$}$$ - \caption{ Image extraite de~\citeindexfig{Knerr2001} illustrant le dcoupage de l'image d'un mot - par des fentres glissantes.} + \caption{ Image extraite de~\citeindexfig{Knerr2001} illustrant le d�coupage de l'image d'un mot + par des fen�tres glissantes.} \label{image_window_slide} \end{figure} -\indexfr{connexit}\indexfr{ordonnancement} -Cette mthode comme la suivante d'ailleurs possde nanmoins l'avantage par rapport celle prsente dans les paragraphes~\ref{image_sequence_graphem} et~\ref{image_sequence_graphem_contour} de n'tre pas dpendante de la connexit et d'tre moins sensible au bruit. L'ordonnancement (voir paragraphe~\ref{image_ordonnancement}) est lui aussi vident puisque la segmentation base sur le squelette ou le contour produit des morceaux rpartis dans un espace en deux dimensions alors que cette mthode segmente l'image selon l'axe des abscisses qui est aussi l'axe de lecture. +\indexfr{connexit�}\indexfr{ordonnancement} +Cette m�thode comme la suivante d'ailleurs poss�de n�anmoins l'avantage par rapport � celle pr�sent�e dans les paragraphes~\ref{image_sequence_graphem} et~\ref{image_sequence_graphem_contour} de n'�tre pas d�pendante de la connexit� et d'�tre moins sensible au bruit. L'ordonnancement (voir paragraphe~\ref{image_ordonnancement}) est lui aussi �vident puisque la segmentation bas�e sur le squelette ou le contour produit des morceaux r�partis dans un espace en deux dimensions alors que cette m�thode segmente l'image selon l'axe des abscisses qui est aussi l'axe de lecture. @@ -1070,42 +1070,42 @@ \subsection{Fen -\subsection{Segmentation base sur des histogrammes} +\subsection{Segmentation bas�e sur des histogrammes} \indexfrr{segmentation}{histogramme} \label{image_segmentation_histogramme_direction} -\indexfr{connexit} +\indexfr{connexit�} \indexfr{ordonnancement} -Cette mthode est dcrite dans l'article~\citeindex{Yanikoglu1998} et produit une segmentation semblable celle illustre figure~\ref{image_grapheme_segmentation_histogramme}. Elle consiste dterminer des droites de segmentation de l'image partir d'histogrammes de projection effectus selon diffrentes directions proches de la verticale. Ces droites sont choisies de telle sorte qu'elles interceptent le moins de pixels noirs et sont rgulirement espaces dans l'image. Cette mthode simple et peu dpendante de la connexit ne peut malgr tout pas tout rsoudre comme en tmoignent les exemples de la figure~\ref{image_grapheme_segmentation_histogramme_bad}. La mthode possde galement l'avantage de ne pas tre assujettie au problme d'ordonnancement (voir paragraphe~\ref{image_ordonnancement}). +Cette m�thode est d�crite dans l'article~\citeindex{Yanikoglu1998} et produit une segmentation semblable � celle illustr�e figure~\ref{image_grapheme_segmentation_histogramme}. Elle consiste � d�terminer des droites de segmentation de l'image � partir d'histogrammes de projection effectu�s selon diff�rentes directions proches de la verticale. Ces droites sont choisies de telle sorte qu'elles interceptent le moins de pixels noirs et sont r�guli�rement espac�es dans l'image. Cette m�thode simple et peu d�pendante de la connexit� ne peut malgr� tout pas tout r�soudre comme en t�moignent les exemples de la figure~\ref{image_grapheme_segmentation_histogramme_bad}. La m�thode poss�de �galement l'avantage de ne pas �tre assujettie au probl�me d'ordonnancement (voir paragraphe~\ref{image_ordonnancement}). - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=4cm] - {\filext{../image/image/seg_dir_gra}}\end{array}$}$$ - \caption{ Segmentation en graphmes partir d'histogrammes de projection selon - plusieurs directions.} - \label{image_grapheme_segmentation_histogramme} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=4cm] + {\filext{../image/image/seg_dir_gra}}\end{array}$}$$ + \caption{ Segmentation en graph�mes � partir d'histogrammes de projection selon + plusieurs directions.} + \label{image_grapheme_segmentation_histogramme} + \end{figure} - \begin{figure}[ht] - $$\begin{array}{|c|c|c|} \hline - \includegraphics[height=1cm, width=4cm] {\filext{../image/image/histo_seg1}} & - \includegraphics[height=1cm, width=2.5cm] {\filext{../image/image/histo_seg2}} & - \includegraphics[height=1cm, width=2.5cm] {\filext{../image/image/lahs_black}} \\ \hline - \end{array}$$ - \caption{ Deux exemples o la segmentation par histogramme est difficilement applicable~: - le premier cas contient une barre de "t" qui sera ncessairement coupe puisqu'elle - touche la lettre "a". Le second exemple contient le couple "op" fortement - li du fait de l'paisseur du trait, la liaison entre les - deux lettres est plus paisse qu'ailleurs. Le dernier mot prsente un couple - "La" qu'aucune droite ne saurait sparer.} - \label{image_grapheme_segmentation_histogramme_bad} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|c|c|} \hline + \includegraphics[height=1cm, width=4cm] {\filext{../image/image/histo_seg1}} & + \includegraphics[height=1cm, width=2.5cm] {\filext{../image/image/histo_seg2}} & + \includegraphics[height=1cm, width=2.5cm] {\filext{../image/image/lahs_black}} \\ \hline + \end{array}$$ + \caption{ Deux exemples o� la segmentation par histogramme est difficilement applicable~: + le premier cas contient une barre de "t" qui sera n�cessairement coup�e puisqu'elle + touche la lettre "a". Le second exemple contient le couple "op" fortement + li� du fait de l'�paisseur du trait, la liaison entre les + deux lettres est plus �paisse qu'ailleurs. Le dernier mot pr�sente un couple + "La" qu'aucune droite ne saurait s�parer.} + \label{image_grapheme_segmentation_histogramme_bad} + \end{figure} @@ -1114,42 +1114,42 @@ \subsection{Segmentation bas -\subsection{Segmentation base sur des rservoirs} -\indexfrr{segmentation}{rservoir} +\subsection{Segmentation bas�e sur des r�servoirs} +\indexfrr{segmentation}{r�servoir} \label{image_segmentation_reservoir} -\indexfr{rservoir} +\indexfr{r�servoir} -Cette ide est dvelope dans l'article~\citeindex{Pal2003} et est applique dans le cadre d'une segmentation de chiffres cursifs. Elle consiste dtecter tout d'abord les valles et les collines sparant deux chiffres appartenant la mme composante connexe, ces formes sont illustres pour un mot dans la figure~\ref{image_grapheme_reservoir}. Deux chiffres lis dans une mme composante seront spars si une valle ou une colline reprsente un espace suffisamment grand par rapport la taille des chiffres. En ce qui concerne les lettres, les rgles de dcision sont plus difficiles mettre en place car les lettres ont des hauteurs variables. +Cette id�e est d�velop�e dans l'article~\citeindex{Pal2003} et est appliqu�e dans le cadre d'une segmentation de chiffres cursifs. Elle consiste � d�tecter tout d'abord les vall�es et les collines s�parant deux chiffres appartenant � la m�me composante connexe, ces formes sont illustr�es pour un mot dans la figure~\ref{image_grapheme_reservoir}. Deux chiffres li�s dans une m�me composante seront s�par�s si une vall�e ou une colline repr�sente un espace suffisamment grand par rapport � la taille des chiffres. En ce qui concerne les lettres, les r�gles de d�cision sont plus difficiles � mettre en place car les lettres ont des hauteurs variables. - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \includegraphics[height=2cm, width=4cm]{\filext{../image/image/reser}} - \\ \hline \end{tabular}$$ - \caption{ Segmentation partir de rservoirs d'eau (\citeindexfig{Pal2003})~: - les valles et les collines sont en quelque sorte remplies d'eau, si elles - sont suffisamment profondes ou hautes, elles marquent la sparation entre deux - caractres.} - \label{image_grapheme_reservoir} - \indexfrr{rservoir}{eau} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \includegraphics[height=2cm, width=4cm]{\filext{../image/image/reser}} + \\ \hline \end{tabular}$$ + \caption{ Segmentation � partir de r�servoirs d'eau (\citeindexfig{Pal2003})~: + les vall�es et les collines sont en quelque sorte remplies d'eau, si elles + sont suffisamment profondes ou hautes, elles marquent la s�paration entre deux + caract�res.} + \label{image_grapheme_reservoir} + \indexfrr{r�servoir}{eau} + \end{figure} -Une fois que les zones de coupures sont dtectes, il reste dterminer quelle catgorie elle appartient (voir figure~\ref{image_grapheme_reservoir_cut}) afin de placer la csure l'endroit le plus appropri. Cette ide est reprise dans~\citeindex{Elnagar2003} ceci prs que la mthode s'applique au squelette des chiffres et pas l'image initiale. +Une fois que les zones de coupures sont d�tect�es, il reste � d�terminer � quelle cat�gorie elle appartient (voir figure~\ref{image_grapheme_reservoir_cut}) afin de placer la c�sure � l'endroit le plus appropri�. Cette id�e est reprise dans~\citeindex{Elnagar2003} � ceci pr�s que la m�thode s'applique au squelette des chiffres et pas � l'image initiale. - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \includegraphics[height=2cm, width=4cm]{\filext{../image/image/grmcut1}} - \\ \hline \end{tabular}$$ - \caption{ Segmentation partir de rservoirs d'eau (\citeindexfig{Pal2003})~: deux points de coupures - diffrents, le premier est situ au fond d'une valle droite sur un embranchement, - le second est situ dans un creux, au milieu d'une courbe en "u". La coupure ne doit donc pas - toujours intervenir l'endroit du minimum local.} - \label{image_grapheme_reservoir_cut} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \includegraphics[height=2cm, width=4cm]{\filext{../image/image/grmcut1}} + \\ \hline \end{tabular}$$ + \caption{ Segmentation � partir de r�servoirs d'eau (\citeindexfig{Pal2003})~: deux points de coupures + diff�rents, le premier est situ� au fond d'une vall�e � droite sur un embranchement, + le second est situ� dans un creux, au milieu d'une courbe en "u". La coupure ne doit donc pas + toujours intervenir � l'endroit du minimum local.} + \label{image_grapheme_reservoir_cut} + \end{figure} @@ -1161,23 +1161,23 @@ \subsection{Segmentation bas -\subsection{Graphes de graphmes} -\indexfrr{graphe}{graphme} -\indexfrr{graphme}{graphe} +\subsection{Graphes de graph�mes} +\indexfrr{graphe}{graph�me} +\indexfrr{graph�me}{graphe} -La segmentation en graphmes donne parfois des rsultats errons. La figure~\ref{image_graphe_grapheme} propose une manire d'viter ces erreurs en rsumant au travers d'un graphe plusieurs options de segmentation. La squence de graphmes est un cas particulier de ce graphe, elle est la segmentation la plus probable pour la partie qui concerne le traitement d'image mais pas forcment la meilleure pour la partie reconnaissance qui suit. Il peut donc tre intressant de proposer plusieurs segmentations afin d'augmenter la probabilit que la bonne segmentation soit trouve. Ce procd revient souvent comme un leitmotiv dans la reconnaissance de l'criture, il s'agit de retarder la prise de dcision afin de conserver chaque tape le plus de solutions possibles. +La segmentation en graph�mes donne parfois des r�sultats erron�s. La figure~\ref{image_graphe_grapheme} propose une mani�re d'�viter ces erreurs en r�sumant au travers d'un graphe plusieurs options de segmentation. La s�quence de graph�mes est un cas particulier de ce graphe, elle est la segmentation la plus probable pour la partie qui concerne le traitement d'image mais pas forc�ment la meilleure pour la partie reconnaissance qui suit. Il peut donc �tre int�ressant de proposer plusieurs segmentations afin d'augmenter la probabilit� que la bonne segmentation soit trouv�e. Ce proc�d� revient souvent comme un leitmotiv dans la reconnaissance de l'�criture, il s'agit de retarder la prise de d�cision afin de conserver � chaque �tape le plus de solutions possibles. - \begin{figure}[t] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=7.5cm, width=12cm] - {\filext{../hmm_seq/image/graphe_obs}}\end{array}$}$$ - \caption{ Segmentation en graphmes~: graphes. Aucune dcision n'est prise ce niveau, - le choix de la bonne segmentation sera effectu par le module de reconnaissance.} - \label{image_graphe_grapheme} - \end{figure} + \begin{figure}[t] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=7.5cm, width=12cm] + {\filext{../hmm_seq/image/graphe_obs}}\end{array}$}$$ + \caption{ Segmentation en graph�mes~: graphes. Aucune d�cision n'est prise � ce niveau, + le choix de la bonne segmentation sera effectu� par le module de reconnaissance.} + \label{image_graphe_grapheme} + \end{figure} @@ -1185,391 +1185,391 @@ \subsection{Graphes de graph %------------------------------------------------------------------------------------------------------------- -\section{Choix d'une segmentation en graphmes} +\section{Choix d'une segmentation en graph�mes} %------------------------------------------------------------------------------------------------------------- -\indexfrr{segmentation}{graphme} +\indexfrr{segmentation}{graph�me} \label{image_choix_segmentation} -La segmentation dcrite dans les paragraphes qui suivent fonctionne bien lorsque l'criture est de bonne qualit. Comme tous les algorithmes de segmentation fonds sur des heuristiques, celui-ci ne peut traiter correctement la totalit des images. Cependant sa construction met en lumire les difficults rencontres lorsque la qualit de l'criture dcrot et les ajustements rendus ncessaires par des problmes rcurrents. +La segmentation d�crite dans les paragraphes qui suivent fonctionne bien lorsque l'�criture est de bonne qualit�. Comme tous les algorithmes de segmentation fond�s sur des heuristiques, celui-ci ne peut traiter correctement la totalit� des images. Cependant sa construction met en lumi�re les difficult�s rencontr�es lorsque la qualit� de l'�criture d�cro�t et les ajustements rendus n�cessaires par des probl�mes r�currents. -\subsection{Segmentation partir d'histogrammes} +\subsection{Segmentation � partir d'histogrammes} -Le choix d'une segmentation dpend des modles de reconnaissance qui devront l'utiliser. Segmenter en lettres ou morceaux de lettres les mots illustrs par la figure~\ref{image_grapheme_segmentation_histogramme_bad} ou~\ref{image_graphe_grapheme_ing} peut paratre une gageure. Toutefois le paragraphe~\ref{hmm_bi_lettre} permet d'assouplir cette contrainte. La segmentation propose ici vise seulement le dcoupage d'un mot en morceaux pouvant aller d'une simple partie de lettre des groupes de deux ou trois lettres, pourvu que ceux-ci soient aisment identifiables. L'objectif est aussi d'viter le plus possible les traitements bass sur la connexit car ils sont trs sensibles au bruit. +Le choix d'une segmentation d�pend des mod�les de reconnaissance qui devront l'utiliser. Segmenter en lettres ou morceaux de lettres les mots illustr�s par la figure~\ref{image_grapheme_segmentation_histogramme_bad} ou~\ref{image_graphe_grapheme_ing} peut para�tre une gageure. Toutefois le paragraphe~\ref{hmm_bi_lettre} permet d'assouplir cette contrainte. La segmentation propos�e ici vise seulement le d�coupage d'un mot en morceaux pouvant aller d'une simple partie de lettre � des groupes de deux ou trois lettres, pourvu que ceux-ci soient ais�ment identifiables. L'objectif est aussi d'�viter le plus possible les traitements bas�s sur la connexit� car ils sont tr�s sensibles au bruit. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=2cm] - {\filext{../image/image/being}}\end{array}$}$$ - \caption{ Segmentation en graphme d'un mot couramment employ en langue anglaise~: \textit{being}. - Les trois dernires lettres "ing" sont simplement esquisses.} - \label{image_graphe_grapheme_ing} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=2cm] + {\filext{../image/image/being}}\end{array}$}$$ + \caption{ Segmentation en graph�me d'un mot couramment employ� en langue anglaise~: \textit{being}. + Les trois derni�res lettres "ing" sont simplement esquiss�es.} + \label{image_graphe_grapheme_ing} + \end{figure} -L'impossibilit de qualifier la pertinence d'une segmentation demeure un inconvnient majeur, il est en effet difficile d'apprcier directement un prtraitement de l'image dont on attend les bnfices seulement en fin de chane, c'est--dire en terme de taux de reconnaissance. L'apprciation n'est donc que visuelle. +L'impossibilit� de qualifier la pertinence d'une segmentation demeure un inconv�nient majeur, il est en effet difficile d'appr�cier directement un pr�traitement de l'image dont on attend les b�n�fices seulement en fin de cha�ne, c'est-�-dire en terme de taux de reconnaissance. L'appr�ciation n'est donc que visuelle. \indexfrr{segmentation}{contour} \indexfrr{segmentation}{squelette} \indexfrr{segmentation}{histogramme} -L'approche propose ici est un compromis. La premire tape consiste segmenter grce la mthode des histogrammes (paragraphe~\ref{image_segmentation_histogramme_direction}) en ne conservant que des coupures videntes. Un premier ensemble de points de coupures est ainsi obtenu parmi lesquels seront slectionns ceux dfinissant la segmentation en graphmes finale. Ce dernier rsultat n'est pourtant pas encore parfait, ce que tenteront de corriger les mthodes bases sur les contours ou les rservoirs, celles-ci permettront ensuite de couper les morceaux litigieux. +L'approche propos�e ici est un compromis. La premi�re �tape consiste � segmenter gr�ce � la m�thode des histogrammes (paragraphe~\ref{image_segmentation_histogramme_direction}) en ne conservant que des coupures �videntes. Un premier ensemble de points de coupures est ainsi obtenu parmi lesquels seront s�lectionn�s ceux d�finissant la segmentation en graph�mes finale. Ce dernier r�sultat n'est pourtant pas encore parfait, ce que tenteront de corriger les m�thodes bas�es sur les contours ou les r�servoirs, celles-ci permettront ensuite de couper les morceaux litigieux. -Tout d'abord, les pixels sont projets selon sept directions entourant la direction verticale $-30^o$, $-20^o$, $-10^o$, $0^o$, $10^o$, $20^o$, $30^o$. On note $\pa{h_{ij}} _ { \begin{subarray}{c} -3 \infegal i \infegal 3 \\ 1 \infegal j \infegal X \end{subarray} }$ les sept histogrammes obtenus o $X$ est la longueur de l'image. $h_{ij}$ est donc le nombre de pixels noirs (crits) selon une droite formant un angle $i \times 10^o$ avec la verticale et interceptant la ligne d'appui basse au point d'abscisse $j$. Ces histogrammes sont ensuite lisss par une moyenne mobile analogue aux formules~(\ref{image_lissage_equation}). On dfinit $e_t$ comme tant l'paisseur du trac (paragraphe~\ref{image_epaisseur_trace}), $e_l$ correspond la largeur moyenne d'une lettre estime au paragraphe~\ref{image_largeur_lettre}. Enfin $C$ est l'ensemble de coupures et dfini par~: +Tout d'abord, les pixels sont projet�s selon sept directions entourant la direction verticale $-30^o$, $-20^o$, $-10^o$, $0^o$, $10^o$, $20^o$, $30^o$. On note $\pa{h_{ij}} _ { \begin{subarray}{c} -3 \leqslant i \leqslant 3 \\ 1 \leqslant j \leqslant X \end{subarray} }$ les sept histogrammes obtenus o� $X$ est la longueur de l'image. $h_{ij}$ est donc le nombre de pixels noirs (�crits) selon une droite formant un angle $i \times 10^o$ avec la verticale et interceptant la ligne d'appui basse au point d'abscisse $j$. Ces histogrammes sont ensuite liss�s par une moyenne mobile analogue aux formules~(\ref{image_lissage_equation}). On d�finit $e_t$ comme �tant l'�paisseur du trac� (paragraphe~\ref{image_epaisseur_trace}), $e_l$ correspond � la largeur moyenne d'une lettre estim�e au paragraphe~\ref{image_largeur_lettre}. Enfin $C$ est l'ensemble de coupures et d�fini par~: - \begin{eqnarray} - C &=& \acc{ h_{ij} \sac h_{ij} \infegal \beta \, e_t } \text{ o } \beta \supegal 1 - \label{image_graphem_seg_eq_1} - \end{eqnarray} + \begin{eqnarray} + C &=& \acc{ h_{ij} \sac h_{ij} \leqslant \beta \, e_t } \text{ o� } \beta \supegal 1 + \label{image_graphem_seg_eq_1} + \end{eqnarray} -Le paramtre $\beta$ est gnralement compris entre $1$ et $2$ de manire ne pas couper un mot selon une droite interceptant deux fois le trac. Il est possible d'crire l'ensemble $C$ comme une runion d'intervalles. +Le param�tre $\beta$ est g�n�ralement compris entre $1$ et $2$ de mani�re � ne pas couper un mot selon une droite interceptant deux fois le trac�. Il est possible d'�crire l'ensemble $C$ comme une r�union d'intervalles. - \begin{eqnarray} - C &=& \union{k=-3}{3} \union{i=1}{I} \; \cro{a_i^k, b_i^k} - \text{ avec } \left\{ \begin{array}{l} - a_i^k \infegal b_i^k < a^k_{i+1} \; \forall i,k \\ - h_x^k \infegal \beta \, e_t \; \forall x \in \cro{a_i^k, b_i^k} - \end{array} \right. - \label{image_graphem_seg_eq_2} - \end{eqnarray} + \begin{eqnarray} + C &=& \union{k=-3}{3} \union{i=1}{I} \; \cro{a_i^k, b_i^k} + \text{ avec } \left\{ \begin{array}{l} + a_i^k \leqslant b_i^k < a^k_{i+1} \; \forall i,k \\ + h_x^k \leqslant \beta \, e_t \; \forall x \in \cro{a_i^k, b_i^k} + \end{array} \right. + \label{image_graphem_seg_eq_2} + \end{eqnarray} -Pour chaque intervalle de la forme $\cro{a_i^k, b_i^k}$, on vrifie que $b_i^k - a_i^k \infegal e_l$. Dans le cas contraire, on scinde cet intervalle jusqu' ce que chacun des morceaux soit infrieur $e_l$. La figure~\ref{image_graphem_zone_coupure_soulignement} soulve le problme de soulignement. Etant donn la condition exprime en~(\ref{image_graphem_seg_eq_2}), il est impossible de slectionner une seule zone de coupure probable entre graphmes. Par consquent, la solution adopte est l'introduction de points de coupure entre les zones de non-coupure correspondant des minima locaux. +Pour chaque intervalle de la forme $\cro{a_i^k, b_i^k}$, on v�rifie que $b_i^k - a_i^k \leqslant e_l$. Dans le cas contraire, on scinde cet intervalle jusqu'� ce que chacun des morceaux soit inf�rieur � $e_l$. La figure~\ref{image_graphem_zone_coupure_soulignement} soul�ve le probl�me de soulignement. Etant donn� la condition exprim�e en~(\ref{image_graphem_seg_eq_2}), il est impossible de s�lectionner une seule zone de coupure probable entre graph�mes. Par cons�quent, la solution adopt�e est l'introduction de points de coupure entre les zones de non-coupure correspondant � des minima locaux. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1.6cm, width=3cm] - {\filext{../image/image/souligne}}\end{array}$}$$ - \caption{ Slection des zones de coupures entre graphmes~: problme des mots souligns, - l'histogramme reprsent correspond une projection verticale lisse par une - moyenne mobile uniforme d'ordre trois.} - \label{image_graphem_zone_coupure_soulignement} - \indexfr{soulignement} - \end{figure} - -Dans ce cas, pour une direction donne $k$, l'ensemble des points de coupures correspond aux minima locaux de l'histogramme $\pa{h_i^k}_i$. Un minimum $m^k$ local vrifie la condition suivante~: + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1.6cm, width=3cm] + {\filext{../image/image/souligne}}\end{array}$}$$ + \caption{ S�lection des zones de coupures entre graph�mes~: probl�me des mots soulign�s, + l'histogramme repr�sent� correspond � une projection verticale liss�e par une + moyenne mobile uniforme d'ordre trois.} + \label{image_graphem_zone_coupure_soulignement} + \indexfr{soulignement} + \end{figure} + +Dans ce cas, pour une direction donn�e $k$, l'ensemble des points de coupures correspond aux minima locaux de l'histogramme $\pa{h_i^k}_i$. Un minimum $m^k$ local v�rifie la condition suivante~: - \begin{eqnarray} - h^k_{m^k} &=& \min \acc{ h_x^k \sac m^k - e_t \infegal x \infegal m^k + e_t } - \label{image_graphem_seg_eq_2_prime} - \end{eqnarray} + \begin{eqnarray} + h^k_{m^k} &=& \min \acc{ h_x^k \sac m^k - e_t \leqslant x \leqslant m^k + e_t } + \label{image_graphem_seg_eq_2_prime} + \end{eqnarray} -Ces minima locaux n'existent pas toujours, dans ces cas, on cherche dterminer le point $c_i^k \in \cro{a_i^k, b_i^k}$, l'unique point de coupure de la zone de coupure $\cro{a_i^k, b_i^k}$. La figure~\ref{image_grapheme_reservoir_cut} suggre que ce point se situe droite du milieu de cet intervalle, par consquent, $\tau_2 > \tau_4 > \tau_3$ dans la dfinition suivante~: +Ces minima locaux n'existent pas toujours, dans ces cas, on cherche � d�terminer le point $c_i^k \in \cro{a_i^k, b_i^k}$, l'unique point de coupure de la zone de coupure $\cro{a_i^k, b_i^k}$. La figure~\ref{image_grapheme_reservoir_cut} sugg�re que ce point se situe � droite du milieu de cet intervalle, par cons�quent, $\tau_2 > \tau_4 > \tau_3$ dans la d�finition suivante~: - \begin{eqnarray} - m_i^k &=& \frac{a_i^k + b_i^k}{2} \nonumber \\ - c^k_i &=& \underset{x \in \pa{a_i^k, b_i^k}}{\arg \min} \; \cro { - \tau_1 \, \frac{h_x^k}{e_t} + - \frac{4}{e_l^2} \cro{ \indicatrice{x < m_i^k} - \pa{\tau_2 - \tau_3} + \tau_3} \; - \cro{x - m_i^k}^2 + - \frac{2 \tau_4 \; e_t^2}{e_t^2 + \abs{s_{x^-}^k - s_{x^+}^k} } - } - \label{image_graphem_seg_eq_3} - \end{eqnarray} - + \begin{eqnarray} + m_i^k &=& \frac{a_i^k + b_i^k}{2} \nonumber \\ + c^k_i &=& \underset{x \in \pa{a_i^k, b_i^k}}{\arg \min} \; \cro { + \tau_1 \, \frac{h_x^k}{e_t} + + \frac{4}{e_l^2} \cro{ \indicatrice{x < m_i^k} + \pa{\tau_2 - \tau_3} + \tau_3} \; + \cro{x - m_i^k}^2 + + \frac{2 \tau_4 \; e_t^2}{e_t^2 + \abs{s_{x^-}^k - s_{x^+}^k} } + } + \label{image_graphem_seg_eq_3} + \end{eqnarray} + \indexfrr{droite}{coupure} \indexfrr{ligne}{appui} - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=2cm] - {\filext{../image/image/cutcut}}\end{array}$}$$ - \caption{ Dfinition des nombres $s_{x^-}^k$ et $s_{x^+}^k$ de la - formule~(\ref{image_graphem_seg_eq_3}). $s_{x^-}^k$ correspond au nombre de pixels contenus - dans la premire colonne du rectangle quadrill (couleur gris fonc), $s_{x^+}^k$ correspond - la colonne de droite (couleur noire). Le ct des petits carrs est gal $e_t$ soit - l'paisseur moyenne de l'criture. - } - \label{image_graphem_aire_cut} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=2cm] + {\filext{../image/image/cutcut}}\end{array}$}$$ + \caption{ D�finition des nombres $s_{x^-}^k$ et $s_{x^+}^k$ de la + formule~(\ref{image_graphem_seg_eq_3}). $s_{x^-}^k$ correspond au nombre de pixels contenus + dans la premi�re colonne du rectangle quadrill� (couleur gris fonc�), $s_{x^+}^k$ correspond + � la colonne de droite (couleur noire). Le c�t� des petits carr�s est �gal � $e_t$ soit + l'�paisseur moyenne de l'�criture. + } + \label{image_graphem_aire_cut} + \end{figure} -Les nombres $s_{x^-}^k$ et $s_{x^+}^k$ sont dfinis par la figure~\ref{image_graphem_aire_cut}. L'ensemble $\acc{c^k_i}_{ik}$ est l'ensemble des droites de segmentation possibles slectionnes par l'algorithme, cet ensemble est tri par $i$ et $k$ croissant ($i$ d'abord, l'indice $k$ dpartageant les points de mme indice $i$) et aboutit la suite $\pa{d_n}_{ 1 \infegal n \infegal N }$. Il reste dterminer quelles sont parmi les points de cette suite les droites de segmentation les plus pertinentes. +Les nombres $s_{x^-}^k$ et $s_{x^+}^k$ sont d�finis par la figure~\ref{image_graphem_aire_cut}. L'ensemble $\acc{c^k_i}_{ik}$ est l'ensemble des droites de segmentation possibles s�lectionn�es par l'algorithme, cet ensemble est tri� par $i$ et $k$ croissant ($i$ d'abord, l'indice $k$ d�partageant les points de m�me indice $i$) et aboutit � la suite $\pa{d_n}_{ 1 \leqslant n \leqslant N }$. Il reste � d�terminer quelles sont parmi les points de cette suite les droites de segmentation les plus pertinentes. -A cette suite, sont ajouts les lments slectionns par l'quation~(\ref{image_graphem_seg_eq_2_prime}) et deux lments $d_0$ et $d_{N+1}$ qui correspondent aux deux sparations verticales de dbut et de fin, c'est--dire les limites de l'image. On suppose qu'il existe une distance entre deux droites de coupures $i$ et $j$ note $\pa{D_{ij}}_{ 0 \infegal i,j \infegal N+1 }$, trouver la meilleure segmentation revient alors trouver le plus court chemin entre les n\oe uds $d_0$ et $d_{N+1}$ en passant ou non par $n$ autres n\oe uds $\pa{d_n}_{ 1 \infegal n \infegal N }$. Ce problme est usuel et rsolu par un algorithme du plus court chemin de type Djikstra (voir \citeindex{Dijkstra1971}). Il reste dterminer la distance $D_{ij}$ entre deux droites de coupures qui doit prendre en compte trois lments~: +A cette suite, sont ajout�s les �l�ments s�lectionn�s par l'�quation~(\ref{image_graphem_seg_eq_2_prime}) et deux �l�ments $d_0$ et $d_{N+1}$ qui correspondent aux deux s�parations verticales de d�but et de fin, c'est-�-dire les limites de l'image. On suppose qu'il existe une distance entre deux droites de coupures $i$ et $j$ not�e $\pa{D_{ij}}_{ 0 \leqslant i,j \leqslant N+1 }$, trouver la meilleure segmentation revient alors � trouver le plus court chemin entre les n\oe uds $d_0$ et $d_{N+1}$ en passant ou non par $n$ autres n\oe uds $\pa{d_n}_{ 1 \leqslant n \leqslant N }$. Ce probl�me est usuel et r�solu par un algorithme du plus court chemin de type Djikstra (voir \citeindex{Dijkstra1971}). Il reste � d�terminer la distance $D_{ij}$ entre deux droites de coupures qui doit prendre en compte trois �l�ments~: - \indexfr{Djikstra} - \indexfr{plus court chemin} - \indexfr{droite de coupure!distance@distance} + \indexfr{Djikstra} + \indexfr{plus court chemin} + \indexfr{droite de coupure!distance@distance} - \begin{enumerate} - \item Le nombre de pixels noirs intercepts par les droites de coupures, not $p_i$ et $p_j$. - \item Le fait que les droites s'interceptent ou non, not $t_{ij} \in \acc{0,1}$ - (voir figure~\ref{image_droite_coupure_croisees}). - \item La distance entre les deux points d'intersection des deux droites avec la ligne d'appui basse, - note $d_j - d_i$ cette distance devrait tre proche de - $\lambda_4$ fois la largeur suppose d'une lettre, note $e_l$ et calcule au - paragraphe~\ref{image_largeur_lettre}. - \end{enumerate} - + \begin{enumerate} + \item Le nombre de pixels noirs intercept�s par les droites de coupures, not� $p_i$ et $p_j$. + \item Le fait que les droites s'interceptent ou non, not� $t_{ij} \in \acc{0,1}$ + (voir figure~\ref{image_droite_coupure_croisees}). + \item La distance entre les deux points d'intersection des deux droites avec la ligne d'appui basse, + not�e $d_j - d_i$ cette distance devrait �tre proche de + $\lambda_4$ fois la largeur suppos�e d'une lettre, not�e $e_l$ et calcul�e au + paragraphe~\ref{image_largeur_lettre}. + \end{enumerate} + - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=2cm] - {\filext{../image/image/droit_sec}}\end{array}$}$$ - \caption{ Droites de coupures scantes issues d'une segmentation non pertinente, - deux droites de coupures peuvent se croiser, auquel cas, - il n'est pas trs pertinent de les associer ensemble pour former la segmentation en graphmes. } - \label{image_droite_coupure_croisees} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1cm, width=2cm] + {\filext{../image/image/droit_sec}}\end{array}$}$$ + \caption{ Droites de coupures s�cantes issues d'une segmentation non pertinente, + deux droites de coupures peuvent se croiser, auquel cas, + il n'est pas tr�s pertinent de les associer ensemble pour former la segmentation en graph�mes. } + \label{image_droite_coupure_croisees} + \end{figure} -A chaque abcisse $d_i$ est associ un angle $u_i$ qui correspond la direction de l'histogramme qui a permis d'obtenir $d_i$. A l'aide de ces notations, la distance $D_{ij}$ est dfinie par~: +A chaque abcisse $d_i$ est associ� un angle $u_i$ qui correspond � la direction de l'histogramme qui a permis d'obtenir $d_i$. A l'aide de ces notations, la distance $D_{ij}$ est d�finie par~: - \begin{eqnarray} - D_{ij} &=& \lambda_1 \frac{p_i + p_j} {e_t} + - \lambda_2 \, t_{ij} + - \lambda_3 \frac{\pa{ d_j - d_i - \lambda_4 e_l}^2} {e_l^2} + - \lambda_5 \pa{u_j - u_i}^2 - \label{image_distance_droite_coupure} - \label{image_graphem_seg_eq_4} - \end{eqnarray} + \begin{eqnarray} + D_{ij} &=& \lambda_1 \frac{p_i + p_j} {e_t} + + \lambda_2 \, t_{ij} + + \lambda_3 \frac{\pa{ d_j - d_i - \lambda_4 e_l}^2} {e_l^2} + + \lambda_5 \pa{u_j - u_i}^2 + \label{image_distance_droite_coupure} + \label{image_graphem_seg_eq_4} + \end{eqnarray} - \begin{figure}[ht] - $$\begin{tabular}{|c|c|c|c|c|} \hline - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/lahsene}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp1}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp2}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp3}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp4}} \\ - \textit{(a)} & \textit{(b)} & \textit{(c)} & \textit{(d)} & \textit{(e)} \\ \hline - \end{tabular}$$ - \caption{ Rsultat intermdiaire de la segmentation graphme aprs obtention du meilleur - chemin dans le graphe dfini par la matrice d'adjacence - (\ref{image_distance_droite_coupure}). Les valeurs des paramtres utilises - pour cet exemple sont donnes par le tableau~\ref{image_graphem_segmentation_parametre}, - page~\pageref{image_graphem_segmentation_parametre}.} - \label{image_segmentation_grapheme_1} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|c|c|c|} \hline + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/lahsene}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp1}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp2}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp3}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/grtemp4}} \\ + \textit{(a)} & \textit{(b)} & \textit{(c)} & \textit{(d)} & \textit{(e)} \\ \hline + \end{tabular}$$ + \caption{ R�sultat interm�diaire de la segmentation graph�me apr�s obtention du meilleur + chemin dans le graphe d�fini par la matrice d'adjacence + (\ref{image_distance_droite_coupure}). Les valeurs des param�tres utilis�es + pour cet exemple sont donn�es par le tableau~\ref{image_graphem_segmentation_parametre}, + page~\pageref{image_graphem_segmentation_parametre}.} + \label{image_segmentation_grapheme_1} + \end{figure} -En pratique, $\lambda_4$ est choisi proche de $1$, $\lambda_2$ est grand, $\lambda_3$ et $\lambda_1$ sont choisis proches de $1$. Divers problmes subsistent aprs ce traitement illustr par la figure~\ref{image_segmentation_grapheme_1}, il reste des bouts de lettres mal apparis, des accents mal placs, des couples de lettres insparables par une droite et pourtant forms de deux composantes connexes ou presque disjointes, des petits morceaux qu'on pourrait associer un graphme voisin plus gros. Ce dcoupage plus fin s'effectue en deux tapes~: +En pratique, $\lambda_4$ est choisi proche de $1$, $\lambda_2$ est grand, $\lambda_3$ et $\lambda_1$ sont choisis proches de $1$. Divers probl�mes subsistent apr�s ce traitement illustr� par la figure~\ref{image_segmentation_grapheme_1}, il reste des bouts de lettres mal appari�s, des accents mal plac�s, des couples de lettres ins�parables par une droite et pourtant form�s de deux composantes connexes ou presque disjointes, des petits morceaux qu'on pourrait associer � un graph�me voisin plus gros. Ce d�coupage plus fin s'effectue en deux �tapes~: - \begin{enumerate} - \item Dcoupage d'un graphme contenant deux formes relies par un pont de pixels - comme le couple "lf" de l'image~\textit{(c)} de la figure~\ref{image_segmentation_grapheme_1}. - \item Dcoupage d'un graphme contenant plusieurs composantes connexes de tailles suffisantes pour tre scindes - en plusieurs graphmes, image \textit{(e)} de la figure~\ref{image_segmentation_grapheme_1}. - \end{enumerate} + \begin{enumerate} + \item D�coupage d'un graph�me contenant deux formes reli�es par un pont de pixels + comme le couple "lf" de l'image~\textit{(c)} de la figure~\ref{image_segmentation_grapheme_1}. + \item D�coupage d'un graph�me contenant plusieurs composantes connexes de tailles suffisantes pour �tre scind�es + en plusieurs graph�mes, image \textit{(e)} de la figure~\ref{image_segmentation_grapheme_1}. + \end{enumerate} -\subsection{Segmentation partir de "rservoirs"} +\subsection{Segmentation � partir de "r�servoirs"} \label{image_segmentation_reservoir_graphem} -\indexfr{rservoir} -\indexfrr{graphme}{rservoir} - -\indexfr{valle}\indexfr{colline} - -Comme le montre la figure~\ref{image_segmentation_grapheme_1}, le traitement prcdent laisse quelques imperfections qu'il serait possible de rsorber en utilisant la mthode des rservoirs illustre par la figure~\ref{image_grapheme_reservoir} et dveloppe dans~\citeindex{Pal2003}. On considre les valles et les collines dont la profondeur est suprieure $\eta_1 e_t$ et la surface est suprieure $\eta_2 \, e_t \pa{e_l-e_t}$. Il s'agit ensuite d'isoler les parties du trac qui constituent le fond des valles et des collines et susceptibles d'tre coupes. Ce trac correspond la frontire d'une valle ou d'une colline dont la largeur dcrot, cette frontire inclut le fond de la valle ou le sommet d'une colline not $\pa{x^v, y^v}$. Si $e_t\pa{x}$ est l'paisseur du trac l'abscisse $x$, il est possible de choisir le point de coupure $c_v$ en s'inspirant de l'quation~(\ref{image_graphem_seg_eq_3})~: - - - \begin{eqnarray} - c^v &=& \underset{\pa{x,y} \in \textit{valle}}{\arg \min} \; \cro { - \tau_1 \, \frac{e_t\pa{x}}{e_t} + - \frac{4}{e_l^2} \cro{ \indicatrice{x < x^v} - \pa{\tau_2 - \tau_3} + \tau_3} \; - \cro{x - x_v}^2 + - \frac{\tau_5}{e_t} \, \abs{y - y^v} - } - \label{image_graphem_reservoir_1} - \end{eqnarray} - -\indexfrr{valle}{sans fond} -\label{image_valley_eta} - -L'algorithme dcrit dans~\citeindex{Pal2003} s'applique l'ensemble du mot, il coupe en deux l'image du mot, puis ritre le procd pour chaque morceau obtenu jusqu' qu'il ne puisse plus couper. Ce processus sera galement appliqu chaque graphme. Il reste traiter le cas des valles sans fond comme celle de la figure~\ref{image_graphem_reservoir_vallee_sans_fond}. Comme prcdemment, si la largeur de cette valle correspondant la partie grise est suprieure $\eta_2 e_t \, \pa{e_l - e_t}$, alors le graphme sera scind. - - - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=1.5cm, width=1cm] - {\filext{../image/image/grmvalno}}\end{array}$}$$ - \caption{ Segmentation partir de rservoirs~: valle sans fond, la partie grise correspond la - largeur de la valle, si celle-ci est suprieure $e_l e_t$, alors le graphme sera - scind en deux morceaux. } - \label{image_graphem_reservoir_vallee_sans_fond} - \indexfrr{valle}{sans fond} - \end{figure} - -\indexfrr{graphme}{accent} -\indexfrr{accent}{graphme} - - -Vient ensuite le problme des accents qui se prsente sous deux formes~\textit{image~(a)} et~\textit{images~(c)-(d)} de la figure~\ref{image_graphem_reservoir_decouper_accent}. Dans le premier cas, il suffit de sparer deux composantes connexes en utilisant une valle sans fond. Le second cas parat impossible, le point de la lettre "i" vient toucher la lettre "d" (image~\textit{(c)}) ou la lettre "a" (image~\textit{(d)}). Le problme pos par l'image~\textit{(e)} ou~\textit{(f)} apparat frquemment, il s'agit de lettres dont les tracs parallles se chevauchent, comme les couples "oc" ou "cl" des images~\textit{(d)} et~\textit{(e)} de la figure~\ref{image_graphem_reservoir_decouper_accent}. - - \begin{figure}[ht] - $$\begin{tabular}{|c|c|c|c|c|c|} \hline - \includegraphics[height=1.5cm, width=1cm]{\filext{../image/image/valaccent}} & - \includegraphics[height=1.5cm, width=1cm]{\filext{../image/image/valaccent2}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent3}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent4}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent5}} & - \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent6}} - \\ \textit{(a)} & \textit{(b)} & \textit{(c)} & \textit{(d)} & \textit{(e)} & \textit{(f)} - \\ \hline - \end{tabular}$$ - \caption{ Segmentation partir de rservoirs~: cas des accents, il serait prfrable que - ceux-ci soient dissocis de la squence de graphmes car leur position est variable. - Toutefois, aucune valle ne permet de sparer l'accent (image~\textit{(a)}) - moins de considrer la transpose de l'image, mais dans ce cas, certaines lettres comme celle - de l'image~\textit{(b)} seraient coupes par la mthode des rservoirs. - De plus, comment dtecter l'accent de l'image~\textit{(c)} - ou celui de l'image~\textit{(d)}~? Comment traiter le problme des traits - parallles se chevauchant (images~\textit{(e)} ou~\textit{(f)} et des couples "oc" et "cl")~? - } - \indexfrr{graphme}{accent} - \label{image_graphem_reservoir_decouper_accent} - \end{figure} +\indexfr{r�servoir} +\indexfrr{graph�me}{r�servoir} + +\indexfr{vall�e}\indexfr{colline} + +Comme le montre la figure~\ref{image_segmentation_grapheme_1}, le traitement pr�c�dent laisse quelques imperfections qu'il serait possible de r�sorber en utilisant la m�thode des r�servoirs illustr�e par la figure~\ref{image_grapheme_reservoir} et d�velopp�e dans~\citeindex{Pal2003}. On consid�re les vall�es et les collines dont la profondeur est sup�rieure � $\eta_1 e_t$ et la surface est sup�rieure � $\eta_2 \, e_t \pa{e_l-e_t}$. Il s'agit ensuite d'isoler les parties du trac� qui constituent le fond des vall�es et des collines et susceptibles d'�tre coup�es. Ce trac� correspond � la fronti�re d'une vall�e ou d'une colline dont la largeur d�cro�t, cette fronti�re inclut le fond de la vall�e ou le sommet d'une colline not� $\pa{x^v, y^v}$. Si $e_t\pa{x}$ est l'�paisseur du trac� � l'abscisse $x$, il est possible de choisir le point de coupure $c_v$ en s'inspirant de l'�quation~(\ref{image_graphem_seg_eq_3})~: + + + \begin{eqnarray} + c^v &=& \underset{\pa{x,y} \in \textit{vall�e}}{\arg \min} \; \cro { + \tau_1 \, \frac{e_t\pa{x}}{e_t} + + \frac{4}{e_l^2} \cro{ \indicatrice{x < x^v} + \pa{\tau_2 - \tau_3} + \tau_3} \; + \cro{x - x_v}^2 + + \frac{\tau_5}{e_t} \, \abs{y - y^v} + } + \label{image_graphem_reservoir_1} + \end{eqnarray} + +\indexfrr{vall�e}{sans fond} +\label{image_valley_eta} + +L'algorithme d�crit dans~\citeindex{Pal2003} s'applique � l'ensemble du mot, il coupe en deux l'image du mot, puis r�it�re le proc�d� pour chaque morceau obtenu jusqu'� qu'il ne puisse plus couper. Ce processus sera �galement appliqu� � chaque graph�me. Il reste � traiter le cas des vall�es sans fond comme celle de la figure~\ref{image_graphem_reservoir_vallee_sans_fond}. Comme pr�c�demment, si la largeur de cette vall�e correspondant � la partie gris�e est sup�rieure � $\eta_2 e_t \, \pa{e_l - e_t}$, alors le graph�me sera scind�. + + + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=1.5cm, width=1cm] + {\filext{../image/image/grmvalno}}\end{array}$}$$ + \caption{ Segmentation � partir de r�servoirs~: vall�e sans fond, la partie gris�e correspond � la + largeur de la vall�e, si celle-ci est sup�rieure � $e_l e_t$, alors le graph�me sera + scind� en deux morceaux. } + \label{image_graphem_reservoir_vallee_sans_fond} + \indexfrr{vall�e}{sans fond} + \end{figure} + +\indexfrr{graph�me}{accent} +\indexfrr{accent}{graph�me} + + +Vient ensuite le probl�me des accents qui se pr�sente sous deux formes~\textit{image~(a)} et~\textit{images~(c)-(d)} de la figure~\ref{image_graphem_reservoir_decouper_accent}. Dans le premier cas, il suffit de s�parer deux composantes connexes en utilisant une vall�e sans fond. Le second cas para�t impossible, le point de la lettre "i" vient toucher la lettre "d" (image~\textit{(c)}) ou la lettre "a" (image~\textit{(d)}). Le probl�me pos� par l'image~\textit{(e)} ou~\textit{(f)} appara�t fr�quemment, il s'agit de lettres dont les trac�s parall�les se chevauchent, comme les couples "oc" ou "cl" des images~\textit{(d)} et~\textit{(e)} de la figure~\ref{image_graphem_reservoir_decouper_accent}. + + \begin{figure}[ht] + $$\begin{tabular}{|c|c|c|c|c|c|} \hline + \includegraphics[height=1.5cm, width=1cm]{\filext{../image/image/valaccent}} & + \includegraphics[height=1.5cm, width=1cm]{\filext{../image/image/valaccent2}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent3}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent4}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent5}} & + \includegraphics[height=1cm, width=3cm]{\filext{../image/image/valaccent6}} + \\ \textit{(a)} & \textit{(b)} & \textit{(c)} & \textit{(d)} & \textit{(e)} & \textit{(f)} + \\ \hline + \end{tabular}$$ + \caption{ Segmentation � partir de r�servoirs~: cas des accents, il serait pr�f�rable que + ceux-ci soient dissoci�s de la s�quence de graph�mes car leur position est variable. + Toutefois, aucune vall�e ne permet de s�parer l'accent (image~\textit{(a)}) + � moins de consid�rer la transpos�e de l'image, mais dans ce cas, certaines lettres comme celle + de l'image~\textit{(b)} seraient coup�es par la m�thode des r�servoirs. + De plus, comment d�tecter l'accent de l'image~\textit{(c)} + ou celui de l'image~\textit{(d)}~? Comment traiter le probl�me des traits + parall�les se chevauchant (images~\textit{(e)} ou~\textit{(f)} et des couples "oc" et "cl")~? + } + \indexfrr{graph�me}{accent} + \label{image_graphem_reservoir_decouper_accent} + \end{figure} \indexfr{composante connexe} -Parmi ces diffrents problmes, seul le cas des accents appartenant des composantes connexes diffrentes sera trait. Les autres n'apparaissent que pour des couples ou des groupes de lettres prcis et seront modliss ultrieurement notamment (voir paragraphe~\ref{hmm_bi_lettre}, page~\pageref{hmm_bi_lettre}). Il n'est pas non plus ncessaire de traiter des problmes qui ne surviennent que peu frquemment, des dveloppements spcifiques risquent d'introduire de mauvais cas parmi ceux dj bien traits. Un motif trop peu frquent ne peut tre appris par des modles probabilistes tels que les chanes de Markov caches et les rseaux de neurones, et ce, qu'il soit bien ou mal segment. - - -L'algorithme qui suit permet de dterminer la profondeur des valles, celle des collines s'en dduit facilement. On cherche la valle la plus profonde et pour ce faire, on construit la matrice $v\pa{x,y}_{ \begin{subarray}{c} 1 \infegal x \infegal X \\ 1 \infegal y \infegal Y \end{subarray}}$ o $\pa{x,y}$ est un pixel de l'image. Cette matrice est dfinie par l'algorithme suivant~\ref{image_algorithm_vpaxy_profondeur}. - - - \begin{xalgorithm}{profondeur des valles, calcul de - $v\pa{x,y}$ - %$v\pa{x,y}_{ \begin{subarray} - % 1 \infegal x \infegal X \\ 1 \infegal y \infegal Y \end{subarray}}$ - } - \label{image_algorithm_vpaxy_profondeur} - - On considre une image $I\pa{x,y}$ de dimension $\pa{X,Y}$, on note la proprit qu'un pixel - soit noir par $N\pa{x,y}$. Le premier pixel est le coin suprieur gauche. - $v\pa{x,y}$ dsigne la profondeur de la valle. - - \begin{xalgostep}{initialisation} - \begin{xfor}{x}{1}{X} - $v\pa{x,Y} \longleftarrow 0$ - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{mise jour} - \begin{xfor}{y}{Y-1}{1} - \begin{xfor}{x}{1}{X} - $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} - 0 & \text{si } N\pa{x,y} \\ - v\pa{x,y+1}+1 & \text{sinon} - \end{array} \right.$ - \end{xfor} \\ - \begin{xfor}{x}{2}{X} - $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} - 0 & - \text{si } \forall i \infegal x, \; N\pa{i,y} \text{ est faux}\\ - max \acc{\; v\pa{x,y}, \; v\pa{x,y+1}+1 \; } & \text{sinon} - \end{array} \right.$ - \end{xfor} \\ - \begin{xfor}{x}{X-1}{1} - $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} - 0 & - \text{si } \forall i \supegal x, \; N\pa{i,y} \text{ est faux}\\ - max \acc{\; v\pa{x,y}, \; v\pa{x,y+1}+1 \; } & \text{sinon} - \end{array} \right.$ - \end{xfor} - \begin{xfor}{x}{2}{X} - $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} - 0 & \text{si } N\pa{x,y} \\ - max \acc{ \; v\pa{x-1,y}, \; v\pa{x,y} \; } & \text{sinon} - \end{array} \right.$ - \end{xfor} \\ - \begin{xfor}{x}{X-1}{1} - $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} - 0 & \text{si } N\pa{x,y} \\ - max \acc{ \; v\pa{x,y}, \; v\pa{x+1,y} \; } & \text{sinon} - \end{array} \right.$ - \end{xfor} \\ - \end{xfor} - \end{xalgostep} - - Le maximum atteint sur la premire ligne de l'image correspond la valle la plus profonde. - - \end{xalgorithm} - - -En conservant en chaque point de l'image le pixel qui a permis d'atteindre le maximum, il est possible d'en dduire la surface de la valle la plus profonde. L'algorithme peut tre adapt de manire estimer la surface de la colline la plus profonde. Il reste dterminer l'paisseur locale de l'criture depuis une position particulire sur le contour, ce qui n'est pas toujours vident comme le montre la figure~\ref{image_graphem_valley_where_to_cut}. - - - - \begin{figure}[ht] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=3cm, width=2cm]{\filext{../image/image/valleyt}} \\ \hline - \end{tabular}$$ - \caption{ Cette figure illustre une valle qui propose trois directions diffrentes de coupure - afin de segmenter ce graphme en deux composantes connexes. La premire direction - (vers la gauche n'est pas adapte, la direction verticale est souvent la meilleure, - la dernire (la plus droite) mne vers une boucle. Ces trois directions correspondent - trois mesures de l'paisseur du trac. - } - \label{image_graphem_valley_where_to_cut} - \end{figure} - - -Sur les trois directions proposes par la figure~\ref{image_graphem_valley_where_to_cut}, seules deux seront conserves, celles qui permettent de relier un point de la valle un point n'y appartenant pas mais pour lequel il existe un chemin le reliant l'un des bords de l'image, autrement dit, un point non inclus dans une boucle. Le segment de coupure doit tre le plus vertical possible, la distance entre un point $\pa{x^v,y^v}$ de la valle et un point de l'extrieur $\pa{x^e,y^e}$ est donne par~: - - - \begin{eqnarray} - d\pa{ \pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}}, - \pa{\begin{subarray}{c} x^e \\ y^e \end{subarray}}} &=& - \abs{ x^v - x^e } + \mu \, \abs{ y^v - y^e } - \label{image_graphem_reservoir_2} - \end{eqnarray} - -Le nombre $e_t\pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}}$ dfini dans l'quation~\ref{image_graphem_reservoir_1} est alors gal ~: - - - \begin{eqnarray} - e_t\pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}} &=& - \underset{\pa{x^e,y^e}}{\min} \; - d\pa{ \pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}}, - \pa{\begin{subarray}{c} x^e \\ y^e \end{subarray}}} - \label{image_graphem_reservoir_3} - \end{eqnarray} - - - - -\subsection{Dtection des accents} +Parmi ces diff�rents probl�mes, seul le cas des accents appartenant � des composantes connexes diff�rentes sera trait�. Les autres n'apparaissent que pour des couples ou des groupes de lettres pr�cis et seront mod�lis�s ult�rieurement notamment (voir paragraphe~\ref{hmm_bi_lettre}, page~\pageref{hmm_bi_lettre}). Il n'est pas non plus n�cessaire de traiter des probl�mes qui ne surviennent que peu fr�quemment, des d�veloppements sp�cifiques risquent d'introduire de mauvais cas parmi ceux d�j� bien trait�s. Un motif trop peu fr�quent ne peut �tre appris par des mod�les probabilistes tels que les cha�nes de Markov cach�es et les r�seaux de neurones, et ce, qu'il soit bien ou mal segment�. + + +L'algorithme qui suit permet de d�terminer la profondeur des vall�es, celle des collines s'en d�duit facilement. On cherche la vall�e la plus profonde et pour ce faire, on construit la matrice $v\pa{x,y}_{ \begin{subarray}{c} 1 \leqslant x \leqslant X \\ 1 \leqslant y \leqslant Y \end{subarray}}$ o� $\pa{x,y}$ est un pixel de l'image. Cette matrice est d�finie par l'algorithme suivant~\ref{image_algorithm_vpaxy_profondeur}. + + + \begin{xalgorithm}{profondeur des vall�es, calcul de + $v\pa{x,y}$ + %$v\pa{x,y}_{ \begin{subarray} + % 1 \leqslant x \leqslant X \\ 1 \leqslant y \leqslant Y \end{subarray}}$ + } + \label{image_algorithm_vpaxy_profondeur} + + On consid�re une image $I\pa{x,y}$ de dimension $\pa{X,Y}$, on note la propri�t� qu'un pixel + soit noir par $N\pa{x,y}$. Le premier pixel est le coin sup�rieur gauche. + $v\pa{x,y}$ d�signe la profondeur de la vall�e. + + \begin{xalgostep}{initialisation} + \begin{xfor}{x}{1}{X} + $v\pa{x,Y} \longleftarrow 0$ + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{mise � jour} + \begin{xfor}{y}{Y-1}{1} + \begin{xfor}{x}{1}{X} + $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} + 0 & \text{si } N\pa{x,y} \\ + v\pa{x,y+1}+1 & \text{sinon} + \end{array} \right.$ + \end{xfor} \\ + \begin{xfor}{x}{2}{X} + $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} + 0 & + \text{si } \forall i \leqslant x, \; N\pa{i,y} \text{ est faux}\\ + max \acc{\; v\pa{x,y}, \; v\pa{x,y+1}+1 \; } & \text{sinon} + \end{array} \right.$ + \end{xfor} \\ + \begin{xfor}{x}{X-1}{1} + $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} + 0 & + \text{si } \forall i \supegal x, \; N\pa{i,y} \text{ est faux}\\ + max \acc{\; v\pa{x,y}, \; v\pa{x,y+1}+1 \; } & \text{sinon} + \end{array} \right.$ + \end{xfor} + \begin{xfor}{x}{2}{X} + $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} + 0 & \text{si } N\pa{x,y} \\ + max \acc{ \; v\pa{x-1,y}, \; v\pa{x,y} \; } & \text{sinon} + \end{array} \right.$ + \end{xfor} \\ + \begin{xfor}{x}{X-1}{1} + $v\pa{x,y} \longleftarrow \left\{ \begin{array} {ll} + 0 & \text{si } N\pa{x,y} \\ + max \acc{ \; v\pa{x,y}, \; v\pa{x+1,y} \; } & \text{sinon} + \end{array} \right.$ + \end{xfor} \\ + \end{xfor} + \end{xalgostep} + + Le maximum atteint sur la premi�re ligne de l'image correspond � la vall�e la plus profonde. + + \end{xalgorithm} + + +En conservant en chaque point de l'image le pixel qui a permis d'atteindre le maximum, il est possible d'en d�duire la surface de la vall�e la plus profonde. L'algorithme peut �tre adapt� de mani�re � estimer la surface de la colline la plus profonde. Il reste � d�terminer l'�paisseur locale de l'�criture depuis une position particuli�re sur le contour, ce qui n'est pas toujours �vident comme le montre la figure~\ref{image_graphem_valley_where_to_cut}. + + + + \begin{figure}[ht] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=3cm, width=2cm]{\filext{../image/image/valleyt}} \\ \hline + \end{tabular}$$ + \caption{ Cette figure illustre une vall�e qui propose trois directions diff�rentes de coupure + afin de segmenter ce graph�me en deux composantes connexes. La premi�re direction + (vers la gauche n'est pas adapt�e, la direction verticale est souvent la meilleure, + la derni�re (la plus � droite) m�ne vers une boucle. Ces trois directions correspondent + � trois mesures de l'�paisseur du trac�. + } + \label{image_graphem_valley_where_to_cut} + \end{figure} + + +Sur les trois directions propos�es par la figure~\ref{image_graphem_valley_where_to_cut}, seules deux seront conserv�es, celles qui permettent de relier un point de la vall�e � un point n'y appartenant pas mais pour lequel il existe un chemin le reliant � l'un des bords de l'image, autrement dit, � un point non inclus dans une boucle. Le segment de coupure doit �tre le plus vertical possible, la distance entre un point $\pa{x^v,y^v}$ de la vall�e et un point de l'ext�rieur $\pa{x^e,y^e}$ est donn�e par~: + + + \begin{eqnarray} + d\pa{ \pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}}, + \pa{\begin{subarray}{c} x^e \\ y^e \end{subarray}}} &=& + \abs{ x^v - x^e } + \mu \, \abs{ y^v - y^e } + \label{image_graphem_reservoir_2} + \end{eqnarray} + +Le nombre $e_t\pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}}$ d�fini dans l'�quation~\ref{image_graphem_reservoir_1} est alors �gal �~: + + + \begin{eqnarray} + e_t\pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}} &=& + \underset{\pa{x^e,y^e}}{\min} \; + d\pa{ \pa{\begin{subarray}{c} x^y \\ y^v \end{subarray}}, + \pa{\begin{subarray}{c} x^e \\ y^e \end{subarray}}} + \label{image_graphem_reservoir_3} + \end{eqnarray} + + + + +\subsection{D�tection des accents} \indexfr{composante connexe} \label{image_segmentation_connexe_graphem} -L'tape suivante consiste extraire les accents et les points de la squence de graphmes dj obtenue afin de les placer dans une autre squence. L'ide la plus simple utilise une sparation horizontale de l'image reprsente par la figure~\ref{image_graphem_valley_accent_d}. Si cette division est possible, la distance entre les deux objets est alors suprieure $\pa{\zeta_1 e_t}$ et la surface de l'accent est suprieure $\pa{\zeta_2 \,\frac{\pi}{4} \, e_t^2}$. Dans ce cas, l'objet suprieur sera nettoy de la squence de graphmes et insr dans la squence des accents. +L'�tape suivante consiste � extraire les accents et les points de la s�quence de graph�mes d�j� obtenue afin de les placer dans une autre s�quence. L'id�e la plus simple utilise une s�paration horizontale de l'image repr�sent�e par la figure~\ref{image_graphem_valley_accent_d}. Si cette division est possible, la distance entre les deux objets est alors sup�rieure � $\pa{\zeta_1 e_t}$ et la surface de l'accent est sup�rieure � $\pa{\zeta_2 \,\frac{\pi}{4} \, e_t^2}$. Dans ce cas, l'objet sup�rieur sera nettoy� de la s�quence de graph�mes et ins�r� dans la s�quence des accents. - \begin{figure}[ht] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=2cm, width=2cm]{\filext{../image/image/accentd}} \\ \hline - \end{tabular}$$ - \caption{ Deux objets spars par une ligne horizontale. - La double flche reprsente la distance qui les spare. } - \label{image_graphem_valley_accent_d} - \end{figure} - + \begin{figure}[ht] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=2cm, width=2cm]{\filext{../image/image/accentd}} \\ \hline + \end{tabular}$$ + \caption{ Deux objets s�par�s par une ligne horizontale. + La double fl�che repr�sente la distance qui les s�pare. } + \label{image_graphem_valley_accent_d} + \end{figure} + \subsection{Recollement de petits segments} -\indexfrr{graphme}{recollement} +\indexfrr{graph�me}{recollement} -Il arrive parfois que les mthodes dcrites dans les paragraphes~\ref{image_segmentation_reservoir_graphem} et~\ref{image_segmentation_connexe_graphem} divisent les graphmes de manire trop fine, en particulier en ce qui concerne les traits quasiment horizontaux. Il est parfois utile de recoller de trop petits segments aux lettres voisines afin d'viter qu'ils ne soient considrs comme des points. +Il arrive parfois que les m�thodes d�crites dans les paragraphes~\ref{image_segmentation_reservoir_graphem} et~\ref{image_segmentation_connexe_graphem} divisent les graph�mes de mani�re trop fine, en particulier en ce qui concerne les traits quasiment horizontaux. Il est parfois utile de recoller de trop petits segments aux lettres voisines afin d'�viter qu'ils ne soient consid�r�s comme des points. @@ -1578,127 +1578,127 @@ \subsection{Recollement de petits segments} -\subsection{Illustration et rsultats} +\subsection{Illustration et r�sultats} \label{image_illustration_resultat} - \begin{table}[t] - $$ - \begin{array}{|c|c|c|c|} \hline - \text{paramtre} & \text{valeur} & \text{quation} & \text{page} \\ \hline - \beta & 1,5 & (\ref{image_graphem_seg_eq_2}) - & \pageref{image_graphem_seg_eq_2} \\ \hline - \tau_1 & 1 & (\ref{image_graphem_seg_eq_3}) - & \pageref{image_graphem_seg_eq_3} \\ \hline - \tau_2 & 1 & (\ref{image_graphem_seg_eq_3}) - & \pageref{image_graphem_seg_eq_3} \\ \hline - \tau_3 & 0,2 & (\ref{image_graphem_seg_eq_3}) - & \pageref{image_graphem_seg_eq_3} \\ \hline - \tau_4 & 0,3 & (\ref{image_graphem_seg_eq_3}) - & \pageref{image_graphem_seg_eq_3} \\ \hline - \tau_5 & 1 & (\ref{image_graphem_reservoir_1}) - & \pageref{image_graphem_reservoir_1} \\ \hline - \lambda_1 & 1 & (\ref{image_graphem_seg_eq_4}) - & \pageref{image_graphem_seg_eq_4} \\ \hline - \lambda_2 & 1000 & (\ref{image_graphem_seg_eq_4}) - & \pageref{image_graphem_seg_eq_4} \\ \hline - \lambda_3 & 1 & (\ref{image_graphem_seg_eq_4}) - & \pageref{image_graphem_seg_eq_4} \\ \hline - \lambda_4 & 1 & (\ref{image_graphem_seg_eq_4}) - & \pageref{image_graphem_seg_eq_4} \\ \hline - \lambda_5 & 0,1 & (\ref{image_graphem_seg_eq_4}) - & \pageref{image_graphem_seg_eq_4} \\ \hline - \eta_1 & 1 & (\ref{image_valley_eta}) - & \pageref{image_valley_eta} \\ \hline - \eta_2 & 0,5 & (\ref{image_valley_eta}) - & \pageref{image_valley_eta} \\ \hline - \mu & 5 & (\ref{image_graphem_reservoir_2}) - & \pageref{image_graphem_reservoir_2} \\ \hline - \zeta_1 & 1 & (\ref{image_segmentation_connexe_graphem}) - & \pageref{image_segmentation_connexe_graphem} \\ \hline - \zeta_2 & 1 & (\ref{image_segmentation_connexe_graphem}) - & \pageref{image_segmentation_connexe_graphem} \\ \hline - \end{array} - $$ - \caption{ Liste des paramtres et valeurs utiliss pour la segmentation - d'un mot en graphmes, ces paramtres sont ajusts manuellement la vue des rsultats - obtenus sur quelques images prises - au hasard dans une large base de donnes ou slectionnes de manire automatique - en assimilant les graphmes mal segments des graphmes peu probables - (voir paragraphe~\ref{reco_densite_valeur_aberrante}, page~\pageref{reco_densite_valeur_aberrante}).} - \label{image_graphem_segmentation_parametre} - \indexfrr{graphme}{paramtre} - \end{table} - - - \begin{figure}[t] - $$\begin{tabular}{|c|c|c|} \hline - \includegraphics[height=2cm, width=6cm]{\filext{../image/image/finalgrm1}} & - \includegraphics[height=2cm, width=4cm]{\filext{../image/image/finalgrm2}} & - \includegraphics[height=2cm, width=6cm]{\filext{../image/image/finalgrm3}} \\ - \textit{(a)} & \textit{(b)} & \textit{(c)} \\ \hline - \includegraphics[height=2cm, width=5cm]{\filext{../image/image/finalgrm4}} & & - \includegraphics[height=2cm, width=5cm]{\filext{../image/image/finalgrm5}} \\ - \textit{(d)} & & \textit{(e)} \\ \hline - \end{tabular}$$ - \caption{ Rsultat final de la segmentation graphme. Les valeurs des paramtres utilises - pour cet exemple sont donnes par le tableau~\ref{image_graphem_segmentation_parametre}. - Il reste encore des erreurs. Le seul accent segment comme tel est celui de l'image "Lahsne". - } - \label{image_graphem_resultat} - \end{figure} - - -La figure~\ref{image_graphem_resultat} prsente quelques rsultats de cette segmentation obtenue pour les paramtres de la table~\ref{image_graphem_segmentation_parametre} qui ont aussi servi produire les illustrations intermdiaires. Il subsiste encore des erreurs. L'exprience montre qu'il est impossible d'ajuster les paramtres afin de les faire disparatre sans gnrer des erreurs sur d'autres documents. - -Afin d'valuer la pertinence d'un traitement dissoci des accents, l'exprience suivante anticipe celle du paragraphe~\ref{reco_reco_knn_sequence}\footnote{Le paragraphe~\ref{reco_reco_knn_sequence} (page~\pageref{reco_reco_knn_sequence}) prcise la source des donnes ainsi que la manire dont ont t constitues les bases d'apprentissage et de test.}. Elle consiste comparer les rsultats d'une reconnaissance mot, ralise avec une mthode des plus proches voisins, effectue sur des images non segmentes dans un premier temps et segmentes en graphmes dans un second temps. La table~\ref{image_kppv_word_recognition} reprend ces rsultats. - - - - \begin{table}[ht] - $$\begin{tabular}{|l|l|l|c|} \hline - base & exprience & jeu & - \begin{minipage}[l]{2.5cm}taux de reconnaissance \smallskip \end{minipage} \\ \hline \hline - % ------------------------------------------------------ - & non segmente & $Mat\pa{10,5}$ & 68,20 \% \\ - & non segmente & $Mat\pa{20,10}$ & 69,45 \% \\ - ICDAR - & segmente (accents inclus) & $Mat\pa{5,5}$ & 52,12 \% \\ - & segmente (accents spars) & $Mat\pa{5,5}$ & 52,06 \% \\ - & segmente (accents spars + dist) & $Mat\pa{5,5}$ & 52,07 \% \\ \hline \hline - % ------------------------------------------------------ - & non segmente & $Mat\pa{10,5}$ & 48,16 \% \\ - prnoms - & non segmente & $Mat\pa{20,10}$ & 57,59 \% \\ - franais - & segmente (accents inclus) & $Mat\pa{5,5}$ & 40,21 \% \\ - & segmente (accents spars) & $Mat\pa{5,5}$ & 42,04 \% \\ - & segmente (accents spars + dist) & $Mat\pa{5,5}$ & 41,96 \% \\ - % ------------------------------------------------------ - \hline \end{tabular}$$ - \caption{ Taux de reconnaissance pour une reconnaissance de mot l'aide de plus proches voisins. - Les bases d'apprentissage et de tests contiennent chacune 15000 mots anglais cursifs - appartenant un vocabulaire de 116 mots diffrents pour la base ICDAR. Elles contiennent - galement 15000 prnoms franais cursifs parmi une liste de 157 - pour la base des prnoms franais dont 13,3\% contiennent des accents. - Les bases d'apprentissage et de test contiennent chacune au moins plus de 100 - occurrences d'un mot pour - la base ICDAR et au moins plus de 50 occurrences pour la base des prnoms franais. - Chaque exemple de la base d'apprentissage - est class selon les plus proches voisins dans la base d'apprentissage. Ces voisins - sont recherchs partir d'une distance calcule sur l'image non segmente ou segmente.} - \label{image_kppv_word_recognition} - \end{table} + \begin{table}[t] + $$ + \begin{array}{|c|c|c|c|} \hline + \text{param�tre} & \text{valeur} & \text{�quation} & \text{page} \\ \hline + \beta & 1,5 & (\ref{image_graphem_seg_eq_2}) + & \pageref{image_graphem_seg_eq_2} \\ \hline + \tau_1 & 1 & (\ref{image_graphem_seg_eq_3}) + & \pageref{image_graphem_seg_eq_3} \\ \hline + \tau_2 & 1 & (\ref{image_graphem_seg_eq_3}) + & \pageref{image_graphem_seg_eq_3} \\ \hline + \tau_3 & 0,2 & (\ref{image_graphem_seg_eq_3}) + & \pageref{image_graphem_seg_eq_3} \\ \hline + \tau_4 & 0,3 & (\ref{image_graphem_seg_eq_3}) + & \pageref{image_graphem_seg_eq_3} \\ \hline + \tau_5 & 1 & (\ref{image_graphem_reservoir_1}) + & \pageref{image_graphem_reservoir_1} \\ \hline + \lambda_1 & 1 & (\ref{image_graphem_seg_eq_4}) + & \pageref{image_graphem_seg_eq_4} \\ \hline + \lambda_2 & 1000 & (\ref{image_graphem_seg_eq_4}) + & \pageref{image_graphem_seg_eq_4} \\ \hline + \lambda_3 & 1 & (\ref{image_graphem_seg_eq_4}) + & \pageref{image_graphem_seg_eq_4} \\ \hline + \lambda_4 & 1 & (\ref{image_graphem_seg_eq_4}) + & \pageref{image_graphem_seg_eq_4} \\ \hline + \lambda_5 & 0,1 & (\ref{image_graphem_seg_eq_4}) + & \pageref{image_graphem_seg_eq_4} \\ \hline + \eta_1 & 1 & (\ref{image_valley_eta}) + & \pageref{image_valley_eta} \\ \hline + \eta_2 & 0,5 & (\ref{image_valley_eta}) + & \pageref{image_valley_eta} \\ \hline + \mu & 5 & (\ref{image_graphem_reservoir_2}) + & \pageref{image_graphem_reservoir_2} \\ \hline + \zeta_1 & 1 & (\ref{image_segmentation_connexe_graphem}) + & \pageref{image_segmentation_connexe_graphem} \\ \hline + \zeta_2 & 1 & (\ref{image_segmentation_connexe_graphem}) + & \pageref{image_segmentation_connexe_graphem} \\ \hline + \end{array} + $$ + \caption{ Liste des param�tres et valeurs utilis�s pour la segmentation + d'un mot en graph�mes, ces param�tres sont ajust�s manuellement � la vue des r�sultats + obtenus sur quelques images prises + au hasard dans une large base de donn�es ou s�lectionn�es de mani�re automatique + en assimilant les graph�mes mal segment�s � des graph�mes peu probables + (voir paragraphe~\ref{reco_densite_valeur_aberrante}, page~\pageref{reco_densite_valeur_aberrante}).} + \label{image_graphem_segmentation_parametre} + \indexfrr{graph�me}{param�tre} + \end{table} + + + \begin{figure}[t] + $$\begin{tabular}{|c|c|c|} \hline + \includegraphics[height=2cm, width=6cm]{\filext{../image/image/finalgrm1}} & + \includegraphics[height=2cm, width=4cm]{\filext{../image/image/finalgrm2}} & + \includegraphics[height=2cm, width=6cm]{\filext{../image/image/finalgrm3}} \\ + \textit{(a)} & \textit{(b)} & \textit{(c)} \\ \hline + \includegraphics[height=2cm, width=5cm]{\filext{../image/image/finalgrm4}} & & + \includegraphics[height=2cm, width=5cm]{\filext{../image/image/finalgrm5}} \\ + \textit{(d)} & & \textit{(e)} \\ \hline + \end{tabular}$$ + \caption{ R�sultat final de la segmentation graph�me. Les valeurs des param�tres utilis�es + pour cet exemple sont donn�es par le tableau~\ref{image_graphem_segmentation_parametre}. + Il reste encore des erreurs. Le seul accent segment� comme tel est celui de l'image "Lahs�ne". + } + \label{image_graphem_resultat} + \end{figure} + + +La figure~\ref{image_graphem_resultat} pr�sente quelques r�sultats de cette segmentation obtenue pour les param�tres de la table~\ref{image_graphem_segmentation_parametre} qui ont aussi servi � produire les illustrations interm�diaires. Il subsiste encore des erreurs. L'exp�rience montre qu'il est impossible d'ajuster les param�tres afin de les faire dispara�tre sans g�n�rer des erreurs sur d'autres documents. + +Afin d'�valuer la pertinence d'un traitement dissoci� des accents, l'exp�rience suivante anticipe celle du paragraphe~\ref{reco_reco_knn_sequence}\footnote{Le paragraphe~\ref{reco_reco_knn_sequence} (page~\pageref{reco_reco_knn_sequence}) pr�cise la source des donn�es ainsi que la mani�re dont ont �t� constitu�es les bases d'apprentissage et de test.}. Elle consiste � comparer les r�sultats d'une reconnaissance mot, r�alis�e avec une m�thode des plus proches voisins, effectu�e sur des images non segment�es dans un premier temps et segment�es en graph�mes dans un second temps. La table~\ref{image_kppv_word_recognition} reprend ces r�sultats. + + + + \begin{table}[ht] + $$\begin{tabular}{|l|l|l|c|} \hline + base & exp�rience & jeu & + \begin{minipage}[l]{2.5cm}taux de reconnaissance \smallskip \end{minipage} \\ \hline \hline + % ------------------------------------------------------ + & non segment�e & $Mat\pa{10,5}$ & 68,20 \% \\ + & non segment�e & $Mat\pa{20,10}$ & 69,45 \% \\ + ICDAR + & segment�e (accents inclus) & $Mat\pa{5,5}$ & 52,12 \% \\ + & segment�e (accents s�par�s) & $Mat\pa{5,5}$ & 52,06 \% \\ + & segment�e (accents s�par�s + dist) & $Mat\pa{5,5}$ & 52,07 \% \\ \hline \hline + % ------------------------------------------------------ + & non segment�e & $Mat\pa{10,5}$ & 48,16 \% \\ + pr�noms + & non segment�e & $Mat\pa{20,10}$ & 57,59 \% \\ + fran�ais + & segment�e (accents inclus) & $Mat\pa{5,5}$ & 40,21 \% \\ + & segment�e (accents s�par�s) & $Mat\pa{5,5}$ & 42,04 \% \\ + & segment�e (accents s�par�s + dist) & $Mat\pa{5,5}$ & 41,96 \% \\ + % ------------------------------------------------------ + \hline \end{tabular}$$ + \caption{ Taux de reconnaissance pour une reconnaissance de mot � l'aide de plus proches voisins. + Les bases d'apprentissage et de tests contiennent chacune 15000 mots anglais cursifs + appartenant � un vocabulaire de 116 mots diff�rents pour la base ICDAR. Elles contiennent + �galement 15000 pr�noms fran�ais cursifs parmi une liste de 157 + pour la base des pr�noms fran�ais dont 13,3\% contiennent des accents. + Les bases d'apprentissage et de test contiennent chacune au moins plus de 100 + occurrences d'un mot pour + la base ICDAR et au moins plus de 50 occurrences pour la base des pr�noms fran�ais. + Chaque exemple de la base d'apprentissage + est class� selon les plus proches voisins dans la base d'apprentissage. Ces voisins + sont recherch�s � partir d'une distance calcul�e sur l'image non segment�e ou segment�e.} + \label{image_kppv_word_recognition} + \end{table} \indexfrr{dictionnaire}{dynamique} \indexfrr{dictionnaire}{statique} -L'exprience utilise le jeu de caractristiques $Mat$ dcrit au paragraphe paragraphe~\ref{reco_graphem_matrice} car ils sont aussi pertinents sur l'image d'un mot que sur l'image d'un graphme. Tout d'abord, le tableau~\ref{image_kppv_word_recognition} montre que la segmentation fait dcrotre les performances obtenues pour cette exprience de reconnaissance avec dictionnaire statique, la fois pour une base d'images de mots anglais et une base d'images de prnoms franais. La segmentation peut donc tre perue comme une perte d'information nanmoins ncessaire dans le cas des vocabulaires dynamiques pour lesquels on ne dispose pas d'exemple pour chacun des mots qu'ils contiennent. +L'exp�rience utilise le jeu de caract�ristiques $Mat$ d�crit au paragraphe paragraphe~\ref{reco_graphem_matrice} car ils sont aussi pertinents sur l'image d'un mot que sur l'image d'un graph�me. Tout d'abord, le tableau~\ref{image_kppv_word_recognition} montre que la segmentation fait d�cro�tre les performances obtenues pour cette exp�rience de reconnaissance avec dictionnaire statique, � la fois pour une base d'images de mots anglais et une base d'images de pr�noms fran�ais. La segmentation peut donc �tre per�ue comme une perte d'information n�anmoins n�cessaire dans le cas des vocabulaires dynamiques pour lesquels on ne dispose pas d'exemple pour chacun des mots qu'ils contiennent. -Le second rsultat concerne trois types de traitements des accents. La premire segmentation en graphmes ne spare pas les accents comme il est dcrit au paragraphe~\ref{image_segmentation_connexe_graphem}. Le second traitement enlve les accents de la squence de graphmes. La troisime option inclut dans la squence de caractristiques lies aux graphmes des caractristiques dcrivant les accents selon le mcanisme dcrit au paragraphe~\ref{reco_sel_feat_acc}. Ces trois traitements aboutissent des performances similaires sur des bases de mots anglais qui ne contiennent comme accents que des points (sur les lettres "i" et "j"). En revanche, pour une base de prnoms franais, le traitement dissoci des accents permet d'accrotre lgrement les performances. Toutefois, tenir compte des accents au niveau des caractristiques ou les oublier ne semble pas faire de diffrence. +Le second r�sultat concerne trois types de traitements des accents. La premi�re segmentation en graph�mes ne s�pare pas les accents comme il est d�crit au paragraphe~\ref{image_segmentation_connexe_graphem}. Le second traitement enl�ve les accents de la s�quence de graph�mes. La troisi�me option inclut dans la s�quence de caract�ristiques li�es aux graph�mes des caract�ristiques d�crivant les accents selon le m�canisme d�crit au paragraphe~\ref{reco_sel_feat_acc}. Ces trois traitements aboutissent � des performances similaires sur des bases de mots anglais qui ne contiennent comme accents que des points (sur les lettres "i" et "j"). En revanche, pour une base de pr�noms fran�ais, le traitement dissoci� des accents permet d'accro�tre l�g�rement les performances. Toutefois, tenir compte des accents au niveau des caract�ristiques ou les oublier ne semble pas faire de diff�rence. -Ces expriences montrent que le traitement des accents n'apporte rien lorsque la langue elle-mme n'en contient pas mais il n'altre rien non plus. Pour une langue incluant des accents, il apparat prfrable d'en tenir compte, soit de les nettoyer dans les images o ils apparaissent, soit de les inclure dans les caractristiques. Les rsultats obtenus ne permettent pas de dterminer si une mthode est prfrable une autre. Il reste qu'un traitement dissoci des accents n'est justifi que par leur importance dans la langue tudie. +Ces exp�riences montrent que le traitement des accents n'apporte rien lorsque la langue elle-m�me n'en contient pas mais il n'alt�re rien non plus. Pour une langue incluant des accents, il appara�t pr�f�rable d'en tenir compte, soit de les nettoyer dans les images o� ils apparaissent, soit de les inclure dans les caract�ristiques. Les r�sultats obtenus ne permettent pas de d�terminer si une m�thode est pr�f�rable � une autre. Il reste qu'un traitement dissoci� des accents n'est justifi� que par leur importance dans la langue �tudi�e. @@ -1712,14 +1712,14 @@ \subsection{Illustration et r \subsection{Prolongements} \label{image_prolongement_segmentation_grapheme} -La segmentation en graphmes propose ici utilise un grand nombre de seuils de dcision (var table~\ref{image_graphem_segmentation_parametre}) que l'exprience permet d'ajuster. Au final, le rsultat est obtenu aprs l'application successive d'algorithmes varis de segmentation ou de regroupement. La mthode prsente dans les articles \citeindex{Desolneux2000}, \citeindex{Desolneux2002}, \citeindex{Desolneux2003} (galement aborde au paragraphe~\ref{image_nettoyage_desolneux}) offre une direction de recherche intressante. Plutt que de varier les algorithmes, il serait possible de n'utiliser qu'une seule mthode ddie la dtection de diffrentes formes gomtriques simples telles que les boucles, les ascendants et descendants, les liaisons et autres formes rcurrentes de l'criture. La segmentation s'appuierait sur les frontires des formes dtectes. Une telle mthode aurait galement l'avantage de ne pas utiliser la connexit entre pixels. +La segmentation en graph�mes propos�e ici utilise un grand nombre de seuils de d�cision (var table~\ref{image_graphem_segmentation_parametre}) que l'exp�rience permet d'ajuster. Au final, le r�sultat est obtenu apr�s l'application successive d'algorithmes vari�s de segmentation ou de regroupement. La m�thode pr�sent�e dans les articles \citeindex{Desolneux2000}, \citeindex{Desolneux2002}, \citeindex{Desolneux2003} (�galement abord�e au paragraphe~\ref{image_nettoyage_desolneux}) offre une direction de recherche int�ressante. Plut�t que de varier les algorithmes, il serait possible de n'utiliser qu'une seule m�thode d�di�e � la d�tection de diff�rentes formes g�om�triques simples telles que les boucles, les ascendants et descendants, les liaisons et autres formes r�currentes de l'�criture. La segmentation s'appuierait sur les fronti�res des formes d�tect�es. Une telle m�thode aurait �galement l'avantage de ne pas utiliser la connexit� entre pixels. \indexfrr{segmentation}{apprentissage} -\indexfrr{segmentation}{graphme} +\indexfrr{segmentation}{graph�me} -Cette segmentation apparat comme une multitude de petites recettes appliques les unes la suite des autres afin de corriger les imperfections des couches prcdentes. Cette premire tape, mme imparfaite, est nanmoins ncessaire afin de construire une premire version des modles de reconnaissance. Il n'existe pas de dfinition prcise de ce qu'est un graphme mais ce premier jeu de modles de reconnaissance permet d'extraire les segmentations qui ont particip une bonne reconnaissance. Il serait possible alors de construire une segmentation en graphmes apprise partir de ces bons exemples. La conception d'un tel algorithme est une autre direction de recherche possible pour la poursuite de ces travaux. +Cette segmentation appara�t comme une multitude de petites recettes appliqu�es les unes � la suite des autres afin de corriger les imperfections des couches pr�c�dentes. Cette premi�re �tape, m�me imparfaite, est n�anmoins n�cessaire afin de construire une premi�re version des mod�les de reconnaissance. Il n'existe pas de d�finition pr�cise de ce qu'est un graph�me mais ce premier jeu de mod�les de reconnaissance permet d'extraire les segmentations qui ont particip� � une bonne reconnaissance. Il serait possible alors de construire une segmentation en graph�mes apprise � partir de ces bons exemples. La conception d'un tel algorithme est une autre direction de recherche possible pour la poursuite de ces travaux. -\indexfrr{directions de recherche}{segmentation graphme apprise} +\indexfrr{directions de recherche}{segmentation graph�me apprise} @@ -1742,20 +1742,20 @@ \section{Segmentation en mots} \indexfr{histogramme} -Il est possible de segmenter en mots avant ou aprs la segmentation en graphmes. Dans le premier cas, la segmentation est semblable un dcoupage en lignes et utilise des projections de l'image selon une direction verticale. Seuls les seuils sont diffrents. Dans le cas d'une segmentation en mots s'appuyant sur celle en graphmes, il s'agit de dterminer les graphmes conscutifs qui appartiennent deux mots diffrents. +Il est possible de segmenter en mots avant ou apr�s la segmentation en graph�mes. Dans le premier cas, la segmentation est semblable � un d�coupage en lignes et utilise des projections de l'image selon une direction verticale. Seuls les seuils sont diff�rents. Dans le cas d'une segmentation en mots s'appuyant sur celle en graph�mes, il s'agit de d�terminer les graph�mes cons�cutifs qui appartiennent � deux mots diff�rents. -\indexfr{rseau de neurones} +\indexfr{r�seau de neurones} -S'il existe des bases de donnes contenant des images de lignes dj segmentes en mots, la seconde mthode utilisant les graphmes est mieux adapte. Par exemple, la figure~\ref{image_graphe_noel} contient 17 graphmes, soit au plus seize coupures entre deux mots. Le principe consiste affecter chacune de ces coupures une probabilit de sparer deux mots, celle-ci est apprise partir de la base de donnes et dpend de paramtres tels que la distance entre les deux graphmes qui l'entourent, leurs tailles, leurs formes... S'il y a $N$ graphmes, on obtient $N-1$ probabilits de csure $\vecteur{p_1}{p_{N-1}}$. A chaque point de csure, on associe la variable alatoire $Y_i \in \acc{0,1}$ vrifiant $\pr{Y_i = 1} = p_i$. Une segmentation en mots est alors compltement dcrite par la donne de $\vecteur{Y_1}{Y_{N-1}}$. Comme ces variables alatoires sont indpendantes, la probabilit associe cette segmentation est~: +S'il existe des bases de donn�es contenant des images de lignes d�j� segment�es en mots, la seconde m�thode utilisant les graph�mes est mieux adapt�e. Par exemple, la figure~\ref{image_graphe_noel} contient 17 graph�mes, soit au plus seize coupures entre deux mots. Le principe consiste � affecter � chacune de ces coupures une probabilit� de s�parer deux mots, celle-ci est apprise � partir de la base de donn�es et d�pend de param�tres tels que la distance entre les deux graph�mes qui l'entourent, leurs tailles, leurs formes... S'il y a $N$ graph�mes, on obtient $N-1$ probabilit�s de c�sure $\vecteur{p_1}{p_{N-1}}$. A chaque point de c�sure, on associe la variable al�atoire $Y_i \in \acc{0,1}$ v�rifiant $\pr{Y_i = 1} = p_i$. Une segmentation en mots est alors compl�tement d�crite par la donn�e de $\vecteur{Y_1}{Y_{N-1}}$. Comme ces variables al�atoires sont ind�pendantes, la probabilit� associ�e � cette segmentation est~: - \begin{eqnarray} - \pr{\vecteurno{Y_1}{Y_{N-1}}} &=& \prody{i=1}{N-1} \; p_i\pa{\theta}^{Y_i} \, \pa{1-p_i\pa{\theta}}^{1-Y_i} - \label{image_vraisemblance_seg_mot} - \end{eqnarray} + \begin{eqnarray} + \pr{\vecteurno{Y_1}{Y_{N-1}}} &=& \prody{i=1}{N-1} \; p_i\pa{\theta}^{Y_i} \, \pa{1-p_i\pa{\theta}}^{1-Y_i} + \label{image_vraisemblance_seg_mot} + \end{eqnarray} -\indexfr{rseau de neurones} +\indexfr{r�seau de neurones} -Chaque $p_i\pa{\theta}$ est fonction valeur dans $\cro{0,1}$ et qui dpend de caractristiques $\theta$ extraites de l'image. Cette fonction peut tre par exemple un rseau de neurones\seeannex{annexe_reseau_neurone}{rseau de neurones} estim en maximisant la vraisemblance~(\ref{image_vraisemblance_seg_mot}) par rapport $\theta$ sur une base d'images pour laquelle les valeurs $\pa{Y_i}_i$ sont connues. Une fois cette fonction apprise, cette criture permet de trouver la segmentation en mots la plus probable. Il est galement parfois utile de conserver les segmentations les plus probables lorsque l'criture dcouper est ambigu. +Chaque $p_i\pa{\theta}$ est fonction � valeur dans $\cro{0,1}$ et qui d�pend de caract�ristiques $\theta$ extraites de l'image. Cette fonction peut �tre par exemple un r�seau de neurones\seeannex{annexe_reseau_neurone}{r�seau de neurones} estim� en maximisant la vraisemblance~(\ref{image_vraisemblance_seg_mot}) par rapport � $\theta$ sur une base d'images pour laquelle les valeurs $\pa{Y_i}_i$ sont connues. Une fois cette fonction apprise, cette �criture permet de trouver la segmentation en mots la plus probable. Il est �galement parfois utile de conserver les segmentations les plus probables lorsque l'�criture � d�couper est ambigu�. @@ -1766,11 +1766,11 @@ \section{Segmentation en mots} %-------------------------------------------------------------------------------------------------------------- -\section{Post-traitement des graphmes} +\section{Post-traitement des graph�mes} %-------------------------------------------------------------------------------------------------------------- -Avant de pouvoir reconnatre un graphme ou un caractre, il faut dcrire son image l'aide de caractristiques qui sont gnralement un vecteur de $\R^n$ o $n$ est le nombre de caractristiques (voir paragraphe~\ref{reco_description_grapheme}, page~\pageref{reco_description_grapheme}). Les graphmes sont parfois trs bruits et ce bruit se rpercute sur la qualit de leur description. Diminuer l'importance de ce bruit peut amliorer les performances de reconnaissance (voir paragraphe~\ref{reco_restauration_image_graheme}). Ces graphmes peuvent galement inclure plusieurs composantes connexes qui nuisent certaines extractions de caractristiques bases sur le contour de la forme (voir paragraphe~\ref{reco_connexion_composante_connexe}). +Avant de pouvoir reconna�tre un graph�me ou un caract�re, il faut d�crire son image � l'aide de caract�ristiques qui sont g�n�ralement un vecteur de $\mathbb{R}^n$ o� $n$ est le nombre de caract�ristiques (voir paragraphe~\ref{reco_description_grapheme}, page~\pageref{reco_description_grapheme}). Les graph�mes sont parfois tr�s bruit�s et ce bruit se r�percute sur la qualit� de leur description. Diminuer l'importance de ce bruit peut am�liorer les performances de reconnaissance (voir paragraphe~\ref{reco_restauration_image_graheme}). Ces graph�mes peuvent �galement inclure plusieurs composantes connexes qui nuisent � certaines extractions de caract�ristiques bas�es sur le contour de la forme (voir paragraphe~\ref{reco_connexion_composante_connexe}). @@ -1780,260 +1780,260 @@ \section{Post-traitement des graph -\subsection{Restauration de l'image des graphmes} +\subsection{Restauration de l'image des graph�mes} \label{reco_restauration_image_graheme} -\indexfrr{graphme}{restauration} -\indexfrr{restauration}{graphme} -\indexfrr{restauration}{caractre} +\indexfrr{graph�me}{restauration} +\indexfrr{restauration}{graph�me} +\indexfrr{restauration}{caract�re} \indexfr{contour} -\indexfrr{caractre}{bruit} +\indexfrr{caract�re}{bruit�} \indexfr{squelette} -Les caractres manuscrits sont parfois mal scannriss, la binarisation de l'image aboutit parfois des caractres bruits qu'il est prfrable de restaurer. L'article \citeindex{Whichello1996} se penche sur un bruit diffus qui se manifeste par la dissmination de pixels blancs travers le caractre reconnatre (voir figure~\ref{image_restauration_mbruit}). La squelettisation et en particulier l'extraction de contour d'une telle forme est impossible et mne souvent myriade de petits morceaux proches les uns des autres. La restauration propose dans \citeindex{Whichello1996} s'intresse l'extraction du contour de la forme bruite. +Les caract�res manuscrits sont parfois mal scann�ris�s, la binarisation de l'image aboutit parfois � des caract�res bruit�s qu'il est pr�f�rable de restaurer. L'article \citeindex{Whichello1996} se penche sur un bruit diffus qui se manifeste par la diss�mination de pixels blancs � travers le caract�re � reconna�tre (voir figure~\ref{image_restauration_mbruit}). La squelettisation et en particulier l'extraction de contour d'une telle forme est impossible et m�ne souvent � myriade de petits morceaux proches les uns des autres. La restauration propos�e dans \citeindex{Whichello1996} s'int�resse � l'extraction du contour de la forme bruit�e. - \begin{figure}[ht] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=1cm, width=1.5cm]{\filext{../image/image/mbruit}} - \\ \hline \end{tabular}$$ - \caption{ Lettre "m" bruite, la binarisation a conserv environ un pixel noir sur deux.} - \label{image_restauration_mbruit} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=1cm, width=1.5cm]{\filext{../image/image/mbruit}} + \\ \hline \end{tabular}$$ + \caption{ Lettre "m" bruit�e, la binarisation a conserv� environ un pixel noir sur deux.} + \label{image_restauration_mbruit} + \end{figure} \indexfr{composante connexe} \indexfr{masque} -La mthode s'appuie sur des masques dits $\pa{N,M}$, partir d'un pixel du contour, on cherche le pixel suivant de ce contour non plus sur un voisinage $\pa{3,3}$ comme c'est le cas pour une composante connexe mais sur un voisinage $\pa{N,M}$. La table~\ref{image_restauration_mbruit_masque} illustre les masques $\pa{1,1}$ et $\pa{3,3}$. En partant d'un premier pixel, le pixel suivant est alors le pixel noir dont le numro est le plus faible. Le masque est ensuite tourn selon la direction du dplacement prcdemment trouv. - - \begin{table}[ht] - $$ \begin{tabular}{ccc} - \begin{tabular}{|c|c|c|} \hline - 4 & 3 & 2 \\ \hline - 5 & 0 & 1 \\ \hline - 6 & 7 & 8 \\ \hline - \end{tabular} - & & - \begin{tabular}{|c|c|c|c|c|c|c|} \hline - 19 & 18 & 16 & 13 & 12 & 10 & 7 \\ \hline - 22 & 20 & 17 & 14 & 11 & 8 & 6 \\ \hline - 24 & 23 & 21 & 15 & 9 & 5 & 4 \\ \hline - 25 & 26 & 27 & 0 & 3 & 2 & 1 \\ \hline - 28 & 29 & 33 & 39 & 45 & 47 & 48 \\ \hline - 30 & 32 & 35 & 38 & 41 & 44 & 46 \\ \hline - 31 & 34 & 36 & 37 & 40 & 42 & 43 \\ \hline - \end{tabular} \\ - masque $\pa{1,1}$ & & masque $\pa{3,3}$ - \end{tabular} $$ - \caption{ Masques de diffrentes tailles pour la recherche du contour, les cases sont numrotes par - angle croissant et par distance au centre dcroissante. Les autres masques sont obtenus en effectuant - des rotations des positions.} - \label{image_restauration_mbruit_masque} - \end{table} - +La m�thode s'appuie sur des masques dits $\pa{N,M}$, � partir d'un pixel du contour, on cherche le pixel suivant de ce contour non plus sur un voisinage $\pa{3,3}$ comme c'est le cas pour une composante connexe mais sur un voisinage $\pa{N,M}$. La table~\ref{image_restauration_mbruit_masque} illustre les masques $\pa{1,1}$ et $\pa{3,3}$. En partant d'un premier pixel, le pixel suivant est alors le pixel noir dont le num�ro est le plus faible. Le masque est ensuite tourn� selon la direction du d�placement pr�c�demment trouv�. + + \begin{table}[ht] + $$ \begin{tabular}{ccc} + \begin{tabular}{|c|c|c|} \hline + 4 & 3 & 2 \\ \hline + 5 & 0 & 1 \\ \hline + 6 & 7 & 8 \\ \hline + \end{tabular} + & & + \begin{tabular}{|c|c|c|c|c|c|c|} \hline + 19 & 18 & 16 & 13 & 12 & 10 & 7 \\ \hline + 22 & 20 & 17 & 14 & 11 & 8 & 6 \\ \hline + 24 & 23 & 21 & 15 & 9 & 5 & 4 \\ \hline + 25 & 26 & 27 & 0 & 3 & 2 & 1 \\ \hline + 28 & 29 & 33 & 39 & 45 & 47 & 48 \\ \hline + 30 & 32 & 35 & 38 & 41 & 44 & 46 \\ \hline + 31 & 34 & 36 & 37 & 40 & 42 & 43 \\ \hline + \end{tabular} \\ + masque $\pa{1,1}$ & & masque $\pa{3,3}$ + \end{tabular} $$ + \caption{ Masques de diff�rentes tailles pour la recherche du contour, les cases sont num�rot�es par + angle croissant et par distance au centre d�croissante. Les autres masques sont obtenus en effectuant + des rotations des positions.} + \label{image_restauration_mbruit_masque} + \end{table} + \indexfr{composante connexe} \indexfr{squelette} -L'article \citeindex{Wang1999} s'attaque un autre type de dtrioration des caractres. La connexit peut tre brise lorsque le caractre dpasse du cadre de l'image ou qu'une partie est escamote aprs une binarisation trop rugueuse (voir figure~\ref{image_restauration_mbruitwang}a). L'algorithme suppose que l'image ne contient qu'une seule composante connexe et cherche recoller les morceaux si elle en contient plus d'un. Les extrmits du squelette sont prolonges afin d'atteindre une autre composante connexe. Le prolongement est cependant contraint par la courbure du squelette ses extrmits. +L'article \citeindex{Wang1999} s'attaque � un autre type de d�t�rioration des caract�res. La connexit� peut �tre bris�e lorsque le caract�re d�passe du cadre de l'image ou qu'une partie est escamot�e apr�s une binarisation trop rugueuse (voir figure~\ref{image_restauration_mbruitwang}a). L'algorithme suppose que l'image ne contient qu'une seule composante connexe et cherche � recoller les morceaux si elle en contient plus d'un. Les extr�mit�s du squelette sont prolong�es afin d'atteindre une autre composante connexe. Le prolongement est cependant contraint par la courbure du squelette � ses extr�mit�s. - \begin{figure}[ht] - $$\begin{tabular}{|c|c|} \hline - \includegraphics[height=4cm, width=4cm]{\filext{../image/image/cutwang}} & - \includegraphics[height=2cm, width=2cm]{\filext{../image/image/cutwang2}} \\ - $(a)$ & $(b)$ - \\ \hline \end{tabular}$$ - \caption{ Figure extraite de \citeindexfig{Wang1999}, les deux chiffres sont incomplets. Les extremits - du squelette sont alors prolonges. La figure $b$ illustre le cot d'un changement de direction par - rapport une direction verticale.} - \label{image_restauration_mbruitwang} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|} \hline + \includegraphics[height=4cm, width=4cm]{\filext{../image/image/cutwang}} & + \includegraphics[height=2cm, width=2cm]{\filext{../image/image/cutwang2}} \\ + $(a)$ & $(b)$ + \\ \hline \end{tabular}$$ + \caption{ Figure extraite de \citeindexfig{Wang1999}, les deux chiffres sont incomplets. Les extremit�s + du squelette sont alors prolong�es. La figure $b$ illustre le co�t d'un changement de direction par + rapport � une direction verticale.} + \label{image_restauration_mbruitwang} + \end{figure} -La figure~\ref{image_restauration_mbruitwang}b permet d'illustrer le cot d'un changement de direction lors du prolongement. A chaque pixel est tout d'abord associe une distance nulle s'il est une extrmit du squelette, infinie dans le cas contraire et un vecteur tangente tenant compte de l'orientation du squelette son extremit. Cette information est propage par l'intermdiaire d'une carte de distance\seeannex{ske_carte_distance_sec}{carte de distance} utilisant un masque calcul partir du schma~\ref{image_restauration_mbruitwang}b. Les liaisons les moins coteuses sont conserves de manire ne former plus qu'une seule composante connexe. Une fois le squelette reconstitu, ce dernier est enrob d'une paisseur de pixels conforme celle du reste de la figure. +La figure~\ref{image_restauration_mbruitwang}b permet d'illustrer le co�t d'un changement de direction lors du prolongement. A chaque pixel est tout d'abord associ�e une distance nulle s'il est une extr�mit� du squelette, infinie dans le cas contraire et un vecteur tangente tenant compte de l'orientation du squelette � son extremit�. Cette information est propag�e par l'interm�diaire d'une carte de distance\seeannex{ske_carte_distance_sec}{carte de distance} utilisant un masque calcul� � partir du sch�ma~\ref{image_restauration_mbruitwang}b. Les liaisons les moins co�teuses sont conserv�es de mani�re � ne former plus qu'une seule composante connexe. Une fois le squelette reconstitu�, ce dernier est enrob� d'une �paisseur de pixels conforme � celle du reste de la figure. \indexfr{ondelettes} -L'article \citeindex{Hwang1998} s'intresse aux documents imprims dont les caractres apparaissent en traits trop gras. Les boucles caractres ne sont dcelables, noyes par l'paisseur des traits. Les auteurs utilisent une mthode fonde sur des ondelettes, ces dernires permettant de dtecter la prsence de segments rectilignes dans une image en niveaux de gris. Cette dtection termine, leur configuration permet de supposer la prsence de boucles et ainsi de binariser l'image sans commettre trop d'erreurs (voir figure~\ref{image_restauration_hwang}). +L'article \citeindex{Hwang1998} s'int�resse aux documents imprim�s dont les caract�res apparaissent en traits trop gras. Les boucles caract�res ne sont d�celables, noy�es par l'�paisseur des traits. Les auteurs utilisent une m�thode fond�e sur des ondelettes, ces derni�res permettant de d�tecter la pr�sence de segments rectilignes dans une image en niveaux de gris. Cette d�tection termin�e, leur configuration permet de supposer la pr�sence de boucles et ainsi de binariser l'image sans commettre trop d'erreurs (voir figure~\ref{image_restauration_hwang}). - \begin{figure}[ht] - $$\begin{tabular}{|c|c|c|} \hline - \includegraphics[height=3cm, width=3cm]{\filext{../image/image/hawang1}} & - \includegraphics[height=3cm, width=3cm]{\filext{../image/image/hawang2}} & - \includegraphics[height=3cm, width=3cm]{\filext{../image/image/hawang3}} - \\ \hline \end{tabular}$$ - \caption{ Figure extraite de \citeindexfig{Hwang1998}, la premire image est l'image originale tandis - que la seconde est le rsultat du traitement propos dans \citeindexfig{Hwang1998}. Cette - binarisation est difficilement accessible aux mthodes reposant sur les simples - histogrammes reprsentant la densit des niveaux de gris (troisime image). - } - \label{image_restauration_hwang} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|c|} \hline + \includegraphics[height=3cm, width=3cm]{\filext{../image/image/hawang1}} & + \includegraphics[height=3cm, width=3cm]{\filext{../image/image/hawang2}} & + \includegraphics[height=3cm, width=3cm]{\filext{../image/image/hawang3}} + \\ \hline \end{tabular}$$ + \caption{ Figure extraite de \citeindexfig{Hwang1998}, la premi�re image est l'image originale tandis + que la seconde est le r�sultat du traitement propos� dans \citeindexfig{Hwang1998}. Cette + binarisation est difficilement accessible aux m�thodes reposant sur les simples + histogrammes repr�sentant la densit� des niveaux de gris (troisi�me image). + } + \label{image_restauration_hwang} + \end{figure} -La figure~\ref{image_restauration_o}a montre le dessin d'une lettre "o" partiellement escamote par la scannerisation. L'\oe il humain peut facilement reconnatre la lettre "o" mme si elle est compose de deux morceaux. Toutefois, la figure~\ref{image_restauration_o}b montre un exemple o il est parfois impossible d'effectuer cette restauration sans avoir connaissance du contexte. +La figure~\ref{image_restauration_o}a montre le dessin d'une lettre "o" partiellement escamot�e par la scannerisation. L'\oe il humain peut facilement reconna�tre la lettre "o" m�me si elle est compos�e de deux morceaux. Toutefois, la figure~\ref{image_restauration_o}b montre un exemple o� il est parfois impossible d'effectuer cette restauration sans avoir connaissance du contexte. - \begin{figure}[ht] - $$\begin{array}{|c|c|c|} \hline - \includegraphics[height=2cm, width=5cm]{\filext{../image/image/restaure}} & - \includegraphics[height=2cm, width=2.5cm]{\filext{../image/image/restaure_au}} & - \includegraphics[height=2cm, width=2cm]{\filext{../image/image/restm}} \\ - $(a)$ & $(b)$ & $(c)$ \\ \hline - \end{array}$$ - \caption{ Restauration souhaite de l'image d'une lettre "o" et restauration ambigu d'une lettre - qui pourrait tre soit~"a" soit~"u". L'image~(c) montre le rsultat obtenu pour une lettre~$M$ - et une valeur de $\alpha$ ngligeable (voir expression~\ref{image_restauration_equation}). - La perte de connexit a t corrige en altrant toutefois le reste de l'image. De petits ergots - se sont accrochs sur la partie suprieure de la lettre de faon crer artificiellement - des lignes trois transitions comme c'est habituellement le cas pour une lettre~"M".} - \label{image_restauration_o} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|c|c|} \hline + \includegraphics[height=2cm, width=5cm]{\filext{../image/image/restaure}} & + \includegraphics[height=2cm, width=2.5cm]{\filext{../image/image/restaure_au}} & + \includegraphics[height=2cm, width=2cm]{\filext{../image/image/restm}} \\ + $(a)$ & $(b)$ & $(c)$ \\ \hline + \end{array}$$ + \caption{ Restauration souhait�e de l'image d'une lettre "o" et restauration ambigu� d'une lettre + qui pourrait �tre soit~"a" soit~"u". L'image~(c) montre le r�sultat obtenu pour une lettre~$M$ + et une valeur de $\alpha$ n�gligeable (voir expression~\ref{image_restauration_equation}). + La perte de connexit� a �t� corrig�e en alt�rant toutefois le reste de l'image. De petits ergots + se sont accroch�s sur la partie sup�rieure de la lettre de fa�on � cr�er artificiellement + des lignes � trois transitions comme c'est habituellement le cas pour une lettre~"M".} + \label{image_restauration_o} + \end{figure} -A partir d'une classification non supervise des graphmes obtenue grce un jeu de caractristiques tels que ceux prsents aux paragraphes~\ref{reco_graphem_matrice} ou~\ref{reco_graphem_histo}, il est possible de dterminer des formes litigieuses, pour lesquelles la classification est ambigu. Plutt que de laisser ce doute, la reconnaissance pourrait tre amliore si l'image de dpart tait modifie de faon se rapprocher de l'une des classes avoisinant ce graphme. +A partir d'une classification non supervis�e des graph�mes obtenue gr�ce � un jeu de caract�ristiques tels que ceux pr�sent�s aux paragraphes~\ref{reco_graphem_matrice} ou~\ref{reco_graphem_histo}, il est possible de d�terminer des formes litigieuses, pour lesquelles la classification est ambigu�. Plut�t que de laisser ce doute, la reconnaissance pourrait �tre am�lior�e si l'image de d�part �tait modifi�e de fa�on � se rapprocher de l'une des classes avoisinant ce graph�me. -Soit $v\pa{G}$ un vecteur de caractristiques attach un graphme~$G$ et~$v\pa{H}$ le vecteur attach au graphme~$H$ qui est un exemple reprsentatif d'une classe quelconque, est-il possible de trouver une forme $G'$ obtenue par une transformation $f$ de cot $c_f$ telle que~: +Soit $v\pa{G}$ un vecteur de caract�ristiques attach� � un graph�me~$G$ et~$v\pa{H}$ le vecteur attach� au graph�me~$H$ qui est un exemple repr�sentatif d'une classe quelconque, est-il possible de trouver une forme $G'$ obtenue par une transformation $f$ de co�t $c_f$ telle que~: - $$ - d\pa{v\pa{G'},v\pa{H}} + c_f \infegal d\pa{v\pa{G},v\pa{H}} - $$ + $$ + d\pa{v\pa{G'},v\pa{H}} + c_f \leqslant d\pa{v\pa{G},v\pa{H}} + $$ \indexfrr{carte}{distance} -$G$ est une image dont il est possible d'extraire le contour. A partir de celui-ci, on construit une carte de distance $D_G$ selon la mthode utilise en annexe\seeannex{ske_def_cart_dist_def}{carte de distance}, cette carte contient pour chaque pixel la distance au pixel noir le plus proche. Pour $\alpha > 0$, la forme $G^*_\alpha$ restaure est celle qui permet d'atteindre le minimum suivant $G^*_\alpha$~: +$G$ est une image dont il est possible d'extraire le contour. A partir de celui-ci, on construit une carte de distance $D_G$ selon la m�thode utilis�e en annexe\seeannex{ske_def_cart_dist_def}{carte de distance}, cette carte contient pour chaque pixel la distance au pixel noir le plus proche. Pour $\alpha > 0$, la forme $G^*_\alpha$ restaur�e est celle qui permet d'atteindre le minimum suivant $G^*_\alpha$~: - \begin{eqnarray} - G^*_\alpha \in \underset{G'}{\arg \min} \cro{ d\pa{v\pa{G'},v\pa{H}} + - \alpha \; \summyone{x,y} \; \abs{G'\pa{x,y} - G\pa{x,y}} \; D_G\pa{x,y} } - \label{image_restauration_equation} - \end{eqnarray} + \begin{eqnarray} + G^*_\alpha \in \underset{G'}{\arg \min} \cro{ d\pa{v\pa{G'},v\pa{H}} + + \alpha \; \summyone{x,y} \; \abs{G'\pa{x,y} - G\pa{x,y}} \; D_G\pa{x,y} } + \label{image_restauration_equation} + \end{eqnarray} -Les diffrences entre $G^*_\alpha$ et $G$ sont pondres par leur loignement par rapport au contour de la forme initiale. Il reste ajuster $\alpha$ de telle sorte que la restauration ne soit pas trop loigne de la forme d'origine ni trop discrte. Le meilleur moyen de mesurer l'apport d'une telle mthode est de comparer les performances en reconnaissance entre l'image non restaure et l'image restaure. Il est galement possible de changer l'quation (\ref{image_restauration_equation}) en~(\ref{image_restauration_equation_2})~: +Les diff�rences entre $G^*_\alpha$ et $G$ sont pond�r�es par leur �loignement par rapport au contour de la forme initiale. Il reste � ajuster $\alpha$ de telle sorte que la restauration ne soit pas trop �loign�e de la forme d'origine ni trop discr�te. Le meilleur moyen de mesurer l'apport d'une telle m�thode est de comparer les performances en reconnaissance entre l'image non restaur�e et l'image restaur�e. Il est �galement possible de changer l'�quation (\ref{image_restauration_equation}) en~(\ref{image_restauration_equation_2})~: - \begin{eqnarray} - G^*_\alpha &\in& \underset{G'}{\arg \min} \cro{ f\pa{v\pa{G'}} + - \alpha \; \summyone{x,y} \; \abs{G'\pa{x,y} - G\pa{x,y}} \; D_G\pa{x,y} } - \label{image_restauration_equation_2} \\ - && \text{ avec } f\pa{v\pa{G'}} \text{ densit du vecteur } v\pa{G'} - \text{ (voir paragraphe~\ref{reco_densite_valeur_aberrante})} \nonumber - \end{eqnarray} + \begin{eqnarray} + G^*_\alpha &\in& \underset{G'}{\arg \min} \cro{ f\pa{v\pa{G'}} + + \alpha \; \summyone{x,y} \; \abs{G'\pa{x,y} - G\pa{x,y}} \; D_G\pa{x,y} } + \label{image_restauration_equation_2} \\ + && \text{ avec } f\pa{v\pa{G'}} \text{ densit� du vecteur } v\pa{G'} + \text{ (voir paragraphe~\ref{reco_densite_valeur_aberrante})} \nonumber + \end{eqnarray} \indexfr{homotope} -En supposant raisonnablement que la forme $G^*$ doit rester homotope\seeannex{annexe_squelettisation}{squelettisation} $G$, il est possible de rduire la complexit lors de la recherche du minimum des quations (\ref{image_restauration_equation}) et (\ref{image_restauration_equation_2}) en classant les pixels par ordre croissant de distance $D_G\pa{x,y}$. Ceci aboutit l'algorithme approch suivant~: - - \begin{xalgorithm}{restauration} - Soient $G$ et $H$ deux graphmes, l'objectif est de restaurer $G$ en prenant $H$ comme modle. - Soit $\alpha > 0$. - La carte de distance $D_G\pa{x,y}$ est construite partir de l'image du contour en utilisant - l'algorithme~\ref{ske_algo_cart_dist}. On suppose galement que $\vecteur{p_1}{p_n}$ est une suite - de pixels vrifiant~: - - $$ - \begin{array}{rl} - \forall i, & G\pa{p_i} \neq H\pa{p_i} \\ - \forall \pa{i,j}, & i \infegal j \Longrightarrow D_G\pa{p_i} \infegal D_G\pa{p_j} - \end{array} - $$ - - - \begin{xalgostep}{initialisation} - $\begin{array}{lll} - G' &\longleftarrow& G \\ - m &\longleftarrow& d\pa{v\pa{G},v\pa{H}} - \end{array}$ - \end{xalgostep} - - \begin{xalgostep}{restauration} - \begin{xfor}{i}{1}{n} - $\begin{array}{lll} - G^t &\longleftarrow& G' \\ - G^t\pa{p_i} &\longleftarrow& H\pa{p_i} \\ - m^t &\longleftarrow& d\pa{v\pa{G^t},v\pa{H}} + \alpha D_G\pa{p_i} - \end{array}$ \\ - \begin{xif}{$m^t < m$} - $\begin{array}{lll} - G' &\longleftarrow& G^t \\ - m &\longleftarrow& m^t - \end{array}$ - \end{xif} - \end{xfor} - \end{xalgostep} - - \end{xalgorithm} - - - -\indexfr{caractristiques} -\indexfrr{classification}{non supervise} - - -Pour tester cet algorithme de restauration, la mthode utilise s'inspire de celle permettant de slectionner le meilleur jeu de caractristiques (voir paragraphe~\ref{reco_selection_caracteristique}, page~\pageref{reco_selection_caracteristique}). Un premier jeu de caractristiques est choisi de manire effectuer une classification non supervise dont le nombre de classes est choisi d'aprs le critre de Davies-Bouldin\seeannex{classification_selection_nb_classe_bouldin}{Davies-Bouldin}. Un second jeu de caractristiques est choisi de manire effectuer une classification par la mthodes des plus proches voisins. Quatre tests sont effectus~: - - \begin{enumerate} - \item Le premier test sert de repre~: un caractre non restaur de la base de test est class - par rapport ses voisins non restaurs dans la base d'apprentissage. Ce test est nomm $App \, Test$. - \item Le second test est un compromis~: un caractre non restaur de la base de test est class - par rapport ses voisins restaurs dans la base d'apprentissage. Ce test est nomm $App^r \, Test$. - \item Le troisime test est un autre compromis~: un caractre restaur de la base de test est class - par rapport ses voisins non restaurs dans la base d'apprentissage. Ce test est nomm $App \, Test^r$. - \item Le dernier test~: un caractre restaur de la base de test est class - par rapport ses voisins restaurs dans la base d'apprentissage. Ce test est nomm $App^r \, Test^r$. - \end{enumerate} - - +En supposant raisonnablement que la forme $G^*$ doit rester homotope\seeannex{annexe_squelettisation}{squelettisation} � $G$, il est possible de r�duire la complexit� lors de la recherche du minimum des �quations (\ref{image_restauration_equation}) et (\ref{image_restauration_equation_2}) en classant les pixels par ordre croissant de distance $D_G\pa{x,y}$. Ceci aboutit � l'algorithme approch� suivant~: + + \begin{xalgorithm}{restauration} + Soient $G$ et $H$ deux graph�mes, l'objectif est de restaurer $G$ en prenant $H$ comme mod�le. + Soit $\alpha > 0$. + La carte de distance $D_G\pa{x,y}$ est construite � partir de l'image du contour en utilisant + l'algorithme~\ref{ske_algo_cart_dist}. On suppose �galement que $\vecteur{p_1}{p_n}$ est une suite + de pixels v�rifiant~: + + $$ + \begin{array}{rl} + \forall i, & G\pa{p_i} \neq H\pa{p_i} \\ + \forall \pa{i,j}, & i \leqslant j \Longrightarrow D_G\pa{p_i} \leqslant D_G\pa{p_j} + \end{array} + $$ + + + \begin{xalgostep}{initialisation} + $\begin{array}{lll} + G' &\longleftarrow& G \\ + m &\longleftarrow& d\pa{v\pa{G},v\pa{H}} + \end{array}$ + \end{xalgostep} + + \begin{xalgostep}{restauration} + \begin{xfor}{i}{1}{n} + $\begin{array}{lll} + G^t &\longleftarrow& G' \\ + G^t\pa{p_i} &\longleftarrow& H\pa{p_i} \\ + m^t &\longleftarrow& d\pa{v\pa{G^t},v\pa{H}} + \alpha D_G\pa{p_i} + \end{array}$ \\ + \begin{xif}{$m^t < m$} + $\begin{array}{lll} + G' &\longleftarrow& G^t \\ + m &\longleftarrow& m^t + \end{array}$ + \end{xif} + \end{xfor} + \end{xalgostep} + + \end{xalgorithm} + + + +\indexfr{caract�ristiques} +\indexfrr{classification}{non supervis�e} + + +Pour tester cet algorithme de restauration, la m�thode utilis�e s'inspire de celle permettant de s�lectionner le meilleur jeu de caract�ristiques (voir paragraphe~\ref{reco_selection_caracteristique}, page~\pageref{reco_selection_caracteristique}). Un premier jeu de caract�ristiques est choisi de mani�re � effectuer une classification non supervis�e dont le nombre de classes est choisi d'apr�s le crit�re de Davies-Bouldin\seeannex{classification_selection_nb_classe_bouldin}{Davies-Bouldin}. Un second jeu de caract�ristiques est choisi de mani�re � effectuer une classification par la m�thodes des plus proches voisins. Quatre tests sont effectu�s~: + + \begin{enumerate} + \item Le premier test sert de rep�re~: un caract�re non restaur� de la base de test est class� + par rapport � ses voisins non restaur�s dans la base d'apprentissage. Ce test est nomm� $App \, Test$. + \item Le second test est un compromis~: un caract�re non restaur� de la base de test est class� + par rapport � ses voisins restaur�s dans la base d'apprentissage. Ce test est nomm� $App^r \, Test$. + \item Le troisi�me test est un autre compromis~: un caract�re restaur� de la base de test est class� + par rapport � ses voisins non restaur�s dans la base d'apprentissage. Ce test est nomm� $App \, Test^r$. + \item Le dernier test~: un caract�re restaur� de la base de test est class� + par rapport � ses voisins restaur�s dans la base d'apprentissage. Ce test est nomm� $App^r \, Test^r$. + \end{enumerate} + + \indexfrr{test}{$App \, Test$} \indexfrr{test}{$App^r \, Test$} \indexfrr{test}{$App \, Test^r$} \indexfrr{test}{$App^r \, Test^r$} - \begin{table}[ht] - $$\begin{tabular}{|c|c|c|cccc|} \hline - & nombre de & & & & & \\ - $1^{er}$ jeu & classes & $2^{\text{me}}$ jeu & $App \, Test$ & $App^r \, Test$ & - $App \, Test^r$ & $App^r \, Test^r$ \\ \hline - $Prof\pa{5,5}$ & 8 & $Prof\pa{5,5}$ & 90,88 \% & 87,84 \% & 90,58 \% & 91,79 \% \\ \hline - \end{tabular}$$ - \caption{ Rsultats obtenus concernant la restauration d'images - (la dsignation du jeu de caractristiques reprend celle de la figure~\ref{reco_carac_distance_assoc}, - page~\pageref{reco_carac_distance_assoc}) pour les quatre tests - $App \, Test$, $App^r \, Test$, $App \, Test^r$, $App^r \, Test^r$. Ces rsultats ont t obtenus - avec environ 2000~images dans les bases d'apprentissage et de test et quatre classes de caractres, - "M", "N", "U", "V".} - \label{image_restau_test_app_test_feat} - \end{table} - - -Pour chaque test, le taux de reconnaissance est estim, les rsultats de ces quatre tests sont rsums dans la table~\ref{image_restau_test_app_test_feat}. Etant donn les temps de traitements, ces rsultats ont t obtenus sur de petites bases d'apprentissage et de test (2000~images chacune) et quatre classes de caractres identifier. Les rsultats sont meilleurs que pour une reconnaissance ne prenant pas en compte la restauration. Afin d'expliquer ce gain, on dnombre dans chacune des deux bases d'apprentissage restaure et non restaure le nombre d'images pour lesquelles les $k$ plus proches voisins appartiennent la mme classe (voir table~\ref{image_reco_kppv_restauration}). Cette proportion dcrot avec $k$ mais reste toujours suprieure pour la base d'images restaures, la restauration des images spare mieux les classes. + \begin{table}[ht] + $$\begin{tabular}{|c|c|c|cccc|} \hline + & nombre de & & & & & \\ + $1^{er}$ jeu & classes & $2^{\text{�me}}$ jeu & $App \, Test$ & $App^r \, Test$ & + $App \, Test^r$ & $App^r \, Test^r$ \\ \hline + $Prof\pa{5,5}$ & 8 & $Prof\pa{5,5}$ & 90,88 \% & 87,84 \% & 90,58 \% & 91,79 \% \\ \hline + \end{tabular}$$ + \caption{ R�sultats obtenus concernant la restauration d'images + (la d�signation du jeu de caract�ristiques reprend celle de la figure~\ref{reco_carac_distance_assoc}, + page~\pageref{reco_carac_distance_assoc}) pour les quatre tests + $App \, Test$, $App^r \, Test$, $App \, Test^r$, $App^r \, Test^r$. Ces r�sultats ont �t� obtenus + avec environ 2000~images dans les bases d'apprentissage et de test et quatre classes de caract�res, + "M", "N", "U", "V".} + \label{image_restau_test_app_test_feat} + \end{table} + + +Pour chaque test, le taux de reconnaissance est estim�, les r�sultats de ces quatre tests sont r�sum�s dans la table~\ref{image_restau_test_app_test_feat}. Etant donn� les temps de traitements, ces r�sultats ont �t� obtenus sur de petites bases d'apprentissage et de test (2000~images chacune) et quatre classes de caract�res � identifier. Les r�sultats sont meilleurs que pour une reconnaissance ne prenant pas en compte la restauration. Afin d'expliquer ce gain, on d�nombre dans chacune des deux bases d'apprentissage restaur�e et non restaur�e le nombre d'images pour lesquelles les $k$ plus proches voisins appartiennent � la m�me classe (voir table~\ref{image_reco_kppv_restauration}). Cette proportion d�cro�t avec $k$ mais reste toujours sup�rieure pour la base d'images restaur�es, la restauration des images s�pare mieux les classes. \indexfr{kPPV} \indexfr{k plus proche voisins} - \begin{table}[ht] - $$\begin{tabular}{|c|cccc|}\hline - $k$ & 1 & 2 & 3 & 4 \\ \hline - base non restaure & 91,3\% & 88,2\% & 85,2\% & 81,7\% \\ - base restaure & 93,9\% & 91,7\% & 89,1\% & 88,2\% \\ \hline - \end{tabular}$$ - \caption{ Nombre d'images dont les $k$ plus proches voisins appartiennent la mme classe. - Cette proportion dcrot avec $k$ mais reste toujours suprieure pour la base d'images - restaures.} - \label{image_reco_kppv_restauration} - \end{table} - - + \begin{table}[ht] + $$\begin{tabular}{|c|cccc|}\hline + $k$ & 1 & 2 & 3 & 4 \\ \hline + base non restaur�e & 91,3\% & 88,2\% & 85,2\% & 81,7\% \\ + base restaur�e & 93,9\% & 91,7\% & 89,1\% & 88,2\% \\ \hline + \end{tabular}$$ + \caption{ Nombre d'images dont les $k$ plus proches voisins appartiennent � la m�me classe. + Cette proportion d�cro�t avec $k$ mais reste toujours sup�rieure pour la base d'images + restaur�es.} + \label{image_reco_kppv_restauration} + \end{table} + + - - - - + + + + @@ -2042,94 +2042,94 @@ \subsection{Connexion de plusieurs composantes connexes} \indexfr{abscisse curviligne} \indexfr{composante connexe} -\indexfr{centre de gravit} +\indexfr{centre de gravit�} \indexfr{contour} \indexfrr{recollement}{composante connexe} -Certaines descriptions de graphmes utilisent des caractristiques extraites partir du contour de l'image. Le contour est alors considr comme une fonction continue~: $f : s \in \cro{0,1} \rightarrow \R$ o $s$ est l'abscisse curviligne. Dans le cas des caractristiques dcrites aux paragraphes~\ref{reco_profil_polair} et~\ref{reco_feature_fourier_contour} (pages~\pageref{reco_profil_polair} et~\pageref{reco_feature_fourier_contour}), la fonction $f$ est la distance du point du contour dont l'abscisse curviligne est $s$, au centre de gravit de l'image. Cette mthode n'est pas applicable dans le cas o l'image contient plusieurs composantes connexes. Il devient ncessaire de les relier entre elles afin d'extraire un seul contour. Le paragraphe~\ref{reco_restauration_image_graheme} mentionne l'article \citeindex{Wang1999} qui recolle les morceaux d'une lettre, la mthode utilise ici est plus simple mais peut tre considre comme un cas particulier. +Certaines descriptions de graph�mes utilisent des caract�ristiques extraites � partir du contour de l'image. Le contour est alors consid�r� comme une fonction continue~: $f : s \in \cro{0,1} \rightarrow \mathbb{R}$ o� $s$ est l'abscisse curviligne. Dans le cas des caract�ristiques d�crites aux paragraphes~\ref{reco_profil_polair} et~\ref{reco_feature_fourier_contour} (pages~\pageref{reco_profil_polair} et~\pageref{reco_feature_fourier_contour}), la fonction $f$ est la distance du point du contour dont l'abscisse curviligne est $s$, au centre de gravit� de l'image. Cette m�thode n'est pas applicable dans le cas o� l'image contient plusieurs composantes connexes. Il devient n�cessaire de les relier entre elles afin d'extraire un seul contour. Le paragraphe~\ref{reco_restauration_image_graheme} mentionne l'article \citeindex{Wang1999} qui recolle les morceaux d'une lettre, la m�thode utilis�e ici est plus simple mais peut �tre consid�r�e comme un cas particulier. \indexfrr{carte}{distance} -L'ide dveloppe ici s'inspire en partie des travaux de \citeindex{Wang1999}. Une carte des distances\seeannex{ske_carte_distance_sec}{carte de distance} est d'abord extraite de l'image $I$. A chaque pixel $\pa{x,y}$ sont alors associes deux informations~: +L'id�e d�velopp�e ici s'inspire en partie des travaux de \citeindex{Wang1999}. Une carte des distances\seeannex{ske_carte_distance_sec}{carte de distance} est d'abord extraite de l'image $I$. A chaque pixel $\pa{x,y}$ sont alors associ�es deux informations~: - $$\begin{tabular}{ll} - $pix_I\pa{x,y}$ & est le pixel noir le plus proche du pixel $\pa{x,y}$ \\ - $dist_I\pa{x,y} = d\pa{\pa{x,y}, \; pix_I\pa{x,y}}$ & - est la distance du pixel $\pa{x,y}$ au pixel noir le plus proche - \end{tabular}$$ - -L'objectif consiste relier deux composantes connexes diffrentes par une ligne. Ces lignes doivent tre les plus courtes possible afin de ne pas trop altrer l'image originale. Par consquent, on cherche d'abord les pixels voisins dont les prdcesseurs appartiennent des composantes connexes diffrentes. Les lignes qui doivent les relier passent ncessairement pas ces points situs mi-chemin entre deux composantes connexes, il suffit alors de slectionner ceux pour lesquels la distance $dist_I\pa{x,y}$ est la plus courte. Ceci dbouche sur l'algorithme suivant~: + $$\begin{tabular}{ll} + $pix_I\pa{x,y}$ & est le pixel noir le plus proche du pixel $\pa{x,y}$ \\ + $dist_I\pa{x,y} = d\pa{\pa{x,y}, \; pix_I\pa{x,y}}$ & + est la distance du pixel $\pa{x,y}$ au pixel noir le plus proche + \end{tabular}$$ + +L'objectif consiste � relier deux composantes connexes diff�rentes par une ligne. Ces lignes doivent �tre les plus courtes possible afin de ne pas trop alt�rer l'image originale. Par cons�quent, on cherche d'abord les pixels voisins dont les pr�d�cesseurs appartiennent � des composantes connexes diff�rentes. Les lignes qui doivent les relier passent n�cessairement pas ces points situ�s � mi-chemin entre deux composantes connexes, il suffit alors de s�lectionner ceux pour lesquels la distance $dist_I\pa{x,y}$ est la plus courte. Ceci d�bouche sur l'algorithme suivant~: - \begin{xalgorithm}{connexion de composantes connexes} - Les notations sont celles utilises dans ce paragraphe, chaque pixel de l'image $I$ est associ le pixel - $pix_I\pa{x,y}$ dfini plus haut. On suppose galement que $C\pa{x,y}$ est l'indice de la composante - connexe laquelle appartient le pixel $\pa{x,y}$. On dsigne le voisinage d'un pixel $\pa{x,y}$ - par l'ensemble~: - - $$ - V\pa{x,y} = \acc{ \pa{x',y'} \sac \pa{x',y'} \neq \pa{x,y}, \; \abs{x'-x} \infegal 1, - \; \abs{y'-y} \infegal 1 } - $$ + \begin{xalgorithm}{connexion de composantes connexes} + Les notations sont celles utilis�es dans ce paragraphe, � chaque pixel de l'image $I$ est associ� le pixel + $pix_I\pa{x,y}$ d�fini plus haut. On suppose �galement que $C\pa{x,y}$ est l'indice de la composante + connexe � laquelle appartient le pixel $\pa{x,y}$. On d�signe le voisinage d'un pixel $\pa{x,y}$ + par l'ensemble~: + + $$ + V\pa{x,y} = \acc{ \pa{x',y'} \sac \pa{x',y'} \neq \pa{x,y}, \; \abs{x'-x} \leqslant 1, + \; \abs{y'-y} \leqslant 1 } + $$ - \begin{xalgostep}{tri de l'ensemble $F$} - $F \longleftarrow \emptyset$ \\ - \begin{xforeach}{\pa{x,y}}{I} - \begin{xforeach}{\pa{x',y'}}{V\pa{x,y}} - \begin{xif}{$C\pa{pix_I\pa{x,y}} \neq C\pa{pix_I\pa{x',y'}}$} - $F \longleftarrow F \cup \acc{ \pa{x,y} }$ - \end{xif} - \end{xforeach} - \end{xforeach} - \end{xalgostep} + \begin{xalgostep}{tri de l'ensemble $F$} + $F \longleftarrow \emptyset$ \\ + \begin{xforeach}{\pa{x,y}}{I} + \begin{xforeach}{\pa{x',y'}}{V\pa{x,y}} + \begin{xif}{$C\pa{pix_I\pa{x,y}} \neq C\pa{pix_I\pa{x',y'}}$} + $F \longleftarrow F \cup \acc{ \pa{x,y} }$ + \end{xif} + \end{xforeach} + \end{xforeach} + \end{xalgostep} - \begin{xalgostep}{connexion des composantes connexes} - L'ensemble $F$ est tri, on note $F = \vecteur{p_1}{p_M}$, il vrifie~: - $$ - \forall i, \; dist_I\pa{p_i} \infegal dist_I\pa{p_{i+1}} - $$ - \end{xalgostep} - - %\possiblecut - - \begin{xalgostep}{choix des pixels sur les frontires} - $c \longleftarrow $ le nombre de composantes connexes \\ - $i \longleftarrow 1 $\\ - \begin{xwhile}{$c > 1$} - $c_1 \longleftarrow C\pa{pix_I\pa{p_i}}$ \\ - \begin{xif}{ il existe un voisin $q$ de $p_i$ tel que $C\pa{pix_I\pa{q}} \neq c_1$} - $c_2 \longleftarrow C\pa{pix_I\pa{q}}$ \\ - On trace la ligne reliant les points $pix_I\pa{p_i}$ et $pix_I\pa{q}$. \\ - $c \longleftarrow c-1$ \\ - \begin{xforeach}{\pa{x,y}}{I} - \begin{xif}{$C\pa{x,y} = c_2$} - $C\pa{x,y} \longleftarrow c_1$ - \end{xif} - \end{xforeach} - \end{xif} \\ - $i \longleftarrow i + 1$ - \end{xwhile} - \end{xalgostep} - \end{xalgorithm} + \begin{xalgostep}{connexion des composantes connexes} + L'ensemble $F$ est tri�, on note $F = \vecteur{p_1}{p_M}$, il v�rifie~: + $$ + \forall i, \; dist_I\pa{p_i} \leqslant dist_I\pa{p_{i+1}} + $$ + \end{xalgostep} + + %\possiblecut + + \begin{xalgostep}{choix des pixels sur les fronti�res} + $c \longleftarrow $ le nombre de composantes connexes \\ + $i \longleftarrow 1 $\\ + \begin{xwhile}{$c > 1$} + $c_1 \longleftarrow C\pa{pix_I\pa{p_i}}$ \\ + \begin{xif}{ il existe un voisin $q$ de $p_i$ tel que $C\pa{pix_I\pa{q}} \neq c_1$} + $c_2 \longleftarrow C\pa{pix_I\pa{q}}$ \\ + On trace la ligne reliant les points $pix_I\pa{p_i}$ et $pix_I\pa{q}$. \\ + $c \longleftarrow c-1$ \\ + \begin{xforeach}{\pa{x,y}}{I} + \begin{xif}{$C\pa{x,y} = c_2$} + $C\pa{x,y} \longleftarrow c_1$ + \end{xif} + \end{xforeach} + \end{xif} \\ + $i \longleftarrow i + 1$ + \end{xwhile} + \end{xalgostep} + \end{xalgorithm} -Un exemple est donn par la figure~\ref{image_connexion_composante_connexion_a}. La lettre $A$ est compose de cinq composantes connexes, chacune est relie la plus grande d'entre elles. Il est maintenant possible de n'extraire qu'un seul contour de cette image. +Un exemple est donn� par la figure~\ref{image_connexion_composante_connexion_a}. La lettre $A$ est compos�e de cinq composantes connexes, chacune est reli�e � la plus grande d'entre elles. Il est maintenant possible de n'extraire qu'un seul contour de cette image. - \begin{figure}[ht] - $$\begin{array}{|c|c|} \hline - \includegraphics[height=2cm, width=2cm]{\filext{../image/image/conal1}} & - \includegraphics[height=2cm, width=2cm]{\filext{../image/image/conal2}} \\ - $(a)$ & $(b)$ \\ \hline - \end{array}$$ - \caption{ Connexion de composantes connexes, la premire image contient quatre composantes - connexes dont un pixel isol. Aprs leur connexion, il n'en reste plus qu'une~: - toutes ont t relies la plus grande d'entre elles.} - \label{image_connexion_composante_connexion_a} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|c|} \hline + \includegraphics[height=2cm, width=2cm]{\filext{../image/image/conal1}} & + \includegraphics[height=2cm, width=2cm]{\filext{../image/image/conal2}} \\ + $(a)$ & $(b)$ \\ \hline + \end{array}$$ + \caption{ Connexion de composantes connexes, la premi�re image contient quatre composantes + connexes dont un pixel isol�. Apr�s leur connexion, il n'en reste plus qu'une~: + toutes ont �t� reli�es � la plus grande d'entre elles.} + \label{image_connexion_composante_connexion_a} + \end{figure} @@ -2150,25 +2150,25 @@ \section{Conclusion} \label{image_conclusion} -Disposant dj d'une segmentation en graphmes\footnote{Celle utilise dans la thse \citeindex{Augustin2001}.} fonde sur des heuristiques, l'objectif tait d'inclure lors de cette tape une partie apprentissage. Cependant, les mthodes actuelles de segmentations d'image fondes sur des optimisations concernent l'extraction de rgions ou la segmentation de textures. La premire direction de recherche fut alors l'laboration d'une segmentation dont les paramtres seraient appris en tenant compte notamment du voisinage des frontires. Mais l'ide dveloppe n'a pas obtenu de rsultats satisfaisants. +Disposant d�j� d'une segmentation en graph�mes\footnote{Celle utilis�e dans la th�se \citeindex{Augustin2001}.} fond�e sur des heuristiques, l'objectif �tait d'inclure lors de cette �tape une partie apprentissage. Cependant, les m�thodes actuelles de segmentations d'image fond�es sur des optimisations concernent l'extraction de r�gions ou la segmentation de textures. La premi�re direction de recherche fut alors l'�laboration d'une segmentation dont les param�tres seraient appris en tenant compte notamment du voisinage des fronti�res. Mais l'id�e d�velopp�e n'a pas obtenu de r�sultats satisfaisants. -Ces travaux se sont ensuite orients vers le problme des accents en langue franaise qui occasionnent souvent des erreurs de segmentation. A l'aide d'une segmentation en graphmes inspirs d'algorithmes existants, des expriences ont alors montr qu'un traitement dissoci amliore lgrement les performances en reconnaissance et ne les dtriore pas lorsque la langue de l'exprience ne contient pas d'accents. La dernire contribution concerne la restauration des graphmes aborde ici d'un point de vue statistique, la mthode propose est lente mais obtient des rsultats attrayants condition de rellement acclrer ce processus. +Ces travaux se sont ensuite orient�s vers le probl�me des accents en langue fran�aise qui occasionnent souvent des erreurs de segmentation. A l'aide d'une segmentation en graph�mes inspir�s d'algorithmes existants, des exp�riences ont alors montr� qu'un traitement dissoci� am�liore l�g�rement les performances en reconnaissance et ne les d�t�riore pas lorsque la langue de l'exp�rience ne contient pas d'accents. La derni�re contribution concerne la restauration des graph�mes abord�e ici d'un point de vue statistique, la m�thode propos�e est lente mais obtient des r�sultats attrayants � condition de r�ellement acc�l�rer ce processus. -De plus, cette partie s'est attache dtailler les prtraitements permettant de dcrire l'information contenue dans l'image sous une forme plus exploitable, une squence de graphmes, ceux-ci sont ensuite utiliss par les modles de reconnaissance statistique. Ce processus permet donc de diviser le problme de la reconnaissance d'un paragraphe en une succession de reconnaissances de mots isols. Il inclut les tapes suivantes~: - - \begin{enumerate} - \item extraction de la zone reconnatre - \item binarisation - \item nettoyage (soulignement par exemple) - \item correction de l'inclinaison des lignes - \item segmentation en lignes - \item correction de l'inclinaison des lettres - \item segmentation en graphmes - \item segmentation en mots - \end{enumerate} +De plus, cette partie s'est attach�e � d�tailler les pr�traitements permettant de d�crire l'information contenue dans l'image sous une forme plus exploitable, une s�quence de graph�mes, ceux-ci sont ensuite utilis�s par les mod�les de reconnaissance statistique. Ce processus permet donc de diviser le probl�me de la reconnaissance d'un paragraphe en une succession de reconnaissances de mots isol�s. Il inclut les �tapes suivantes~: + + \begin{enumerate} + \item extraction de la zone � reconna�tre + \item binarisation + \item nettoyage (soulignement par exemple) + \item correction de l'inclinaison des lignes + \item segmentation en lignes + \item correction de l'inclinaison des lettres + \item segmentation en graph�mes + \item segmentation en mots + \end{enumerate} -La suite concerne la modlisation probabiliste des squences de graphmes au travers de modles de Markov cachs. +La suite concerne la mod�lisation probabiliste des s�quences de graph�mes au travers de mod�les de Markov cach�s. @@ -2182,9 +2182,9 @@ \section{Conclusion} \newpage \firstpassagedo{ - \begin{thebibliography}{99} - \input{image_article.tex} - \end{thebibliography} + \begin{thebibliography}{99} + \input{image_article.tex} + \end{thebibliography} } diff --git a/_todo/image/image_article.tex b/_todo/image/image_article.tex index ac132ef3..ac79eb0d 100644 --- a/_todo/image/image_article.tex +++ b/_todo/image/image_article.tex @@ -1,12 +1,12 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin @@ -16,12 +16,12 @@ {Pattern Recognition}{29}{1161}{1177} \bibitemstyle{Augustin2001}{2001}{E. Augustin} -{Reconnaissance de mots manuscrits par systmes hybrides rseaux de neurones et modles de Markov cachs} -{Thse de l'Universit Paris V}{}{0}{} +{Reconnaissance de mots manuscrits par syst�mes hybrides r�seaux de neurones et mod�les de Markov cach�s} +{Th�se de l'Universit� Paris V}{}{0}{} \bibitemstyle{Baret1991}{1991}{O. Baret} -{Rgularits, singularits de reprsentations et leur complmentarit~: application la reconnaissance de l'criture manuscrite non contrainte} -{Thse de l'Universit Paris VI}{}{0}{} +{R�gularit�s, singularit�s de repr�sentations et leur compl�mentarit�~: application � la reconnaissance de l'�criture manuscrite non contrainte} +{Th�se de l'Universit� Paris VI}{}{0}{} \bibitemstyle{Bloomberg1995}{1995} {D. S. Blommberg, G. E. Kopec, L. Dasari} {Measuring document image skew and orientation} @@ -47,19 +47,19 @@ {A robust algorithm for image principal curve detection} {Pattern Recognition Letters}{25}{1303}{1313} -\bibitemstyle{Ct1997}{1997}{M. Ct, M. Cheriet, E. Lecolinet, C. Y. Suen} -{Dtection des Lignes de Rfrence de Mots Cursifs l'aide de l'entropie} -{Les techniques de l'I.A. appliques aux technologies de l'information, Richard Lepage and Rita Noumeir (Eds.), Cahiers Scientifiques de l'ACFAS}{90}{184}{193} +\bibitemstyle{C�t�1997}{1997}{M. C�t�, M. Cheriet, E. Lecolinet, C. Y. Suen} +{D�tection des Lignes de R�f�rence de Mots Cursifs � l'aide de l'entropie} +{Les techniques de l'I.A. appliqu�es aux technologies de l'information, Richard Lepage and Rita Noumeir (Eds.), Cahiers Scientifiques de l'ACFAS}{90}{184}{193} -\bibitemstyle{Desolneux2000} {2000} {Agns Desolneux, Lionel Moisan, Jean-Michel Morel} +\bibitemstyle{Desolneux2000} {2000} {Agn�s Desolneux, Lionel Moisan, Jean-Michel Morel} {Meaningful Alignments} {International Journal of Computer Vision}{40(1)}{7}{23} -\bibitemstyle{Desolneux2002} {2002} {Agns Desolneux, Lionel Moisan, Jean-Michel Morel} +\bibitemstyle{Desolneux2002} {2002} {Agn�s Desolneux, Lionel Moisan, Jean-Michel Morel} {Gestalt theory and Computer Vision} -{Publication du Centre de Mathmatiques et de Leurs Applications, disponible l'adresse http://www.cmla.ens-cachan.fr/Cmla/Publications/}{N 2002-06}{0}{} +{Publication du Centre de Math�matiques et de Leurs Applications, disponible � l'adresse http://www.cmla.ens-cachan.fr/Cmla/Publications/}{N� 2002-06}{0}{} -\bibitemstyle{Desolneux2003} {2003} {Agns Desolneux, Lionel Moisan, Jean-Michel Morel} +\bibitemstyle{Desolneux2003} {2003} {Agn�s Desolneux, Lionel Moisan, Jean-Michel Morel} {A Grouping Principle and Four Applications} {IEEE Transactions on Pattern Analysis and Machine Intelligence}{25(4)}{508}{513} @@ -67,9 +67,9 @@ {Hierarchical ordering of sequential processes} {Acta Informatica}{1,2}{115}{138} -\bibitemstyle{Dupr2000}{2000} {X. Dupr} -{Reconnaissance de mots-cl dans un document manuscrit} -{Mmoire de DEA de l'Universit Paris VI}{}{0}{} +\bibitemstyle{Dupr�2000}{2000} {X. Dupr�} +{Reconnaissance de mots-cl� dans un document manuscrit} +{M�moire de DEA de l'Universit� Paris VI}{}{0}{} \bibitemstyle{Elnagar2003}{2003} {Ashraf Elnagar, Reda Alhajj} {Segmentation of connected handwritten numeral strings} @@ -135,7 +135,7 @@ {Machine-printed and hand-written text lines identification} {Pattern Recognition Letters}{22}{431}{441} -\bibitemstyle{Pal2003}{2003}{U. Pal, A. Belad, Ch. Choisy} +\bibitemstyle{Pal2003}{2003}{U. Pal, A. Bela�d, Ch. Choisy} {Touching numeral segmentation using water reservoir concept} {Pattern Recognition Letters}{24}{261}{272} diff --git a/_todo/ngrams/ngrams.tex b/_todo/ngrams/ngrams.tex index fe5e41e6..338143a0 100644 --- a/_todo/ngrams/ngrams.tex +++ b/_todo/ngrams/ngrams.tex @@ -4,12 +4,12 @@ \firstpassagedo{\input{ngrams_chapter.tex}} -Les \emph{n-grammes}\indexfr{n-grammes} sont une modlisation statistique du langage, l'ide est d'observer la -frquence des enchanements de lettres l'intrieur des mots, ou la frquence des enchanements de mots l'intrieur -d'une phrase. Plus gnralement, les n-grammes modlisent sous forme de chanes de Markov toute squence de symboles appartenant un ensemble fini. +Les \emph{n-grammes}\indexfr{n-grammes} sont une mod�lisation statistique du langage, l'id�e est d'observer la +fr�quence des encha�nements de lettres � l'int�rieur des mots, ou la fr�quence des encha�nements de mots � l'int�rieur +d'une phrase. Plus g�n�ralement, les n-grammes mod�lisent sous forme de cha�nes de Markov toute s�quence de symboles appartenant � un ensemble fini. -\indexfrr{squence}{symbols} -\indexfrr{chane}{Markov} +\indexfrr{s�quence}{symbols} +\indexfrr{cha�ne}{Markov} \indexfr{Markov} \label{annexe_ngrams} @@ -23,70 +23,70 @@ %--------------------------------------------------------------------------------------------------------------------- -\section{Dfinition} +\section{D�finition} %--------------------------------------------------------------------------------------------------------------------- -Les dfinitions qui vont suivre s'adaptent toutes squences d'lments appartement $E$, un ensemble fini. +Les d�finitions qui vont suivre s'adaptent � toutes s�quences d'�l�ments appartement � $E$, un ensemble fini. - \begin{xdefinition}{n-grammes}\label{n_grammes_definition} + \begin{xdefinition}{n-grammes}\label{n_grammes_definition} \indexfr{suite} - Soit $A = \pa{e,f,\vecteurno{a_1}{a_N}}$ un ensemble fini nomm \emph{alphabet}, les symboles $e$ et $f$ dbutent - et terminent toute squence de symboles appartenant $A$. Cette convention permet de traiter les probabilits - d'entre et de sortie d'une squence comme des probabilits de transitions. Soit $M_A \subset A^{\N}$ - l'ensemble des suites $u=\pa{u_i}_{i \supegal 0}$ de $A$ dfinies comme suit~: - $$ - u \in M_A \Longleftrightarrow \left\{ - \begin{array}{l} - u_1 = e \\ - \exists N > 2 \text{ tel que } \forall i \supegal N, \; u_i = f \text{ et } \forall i < N, \; u_i \neq f - \end{array} - \right. - $$ - Par la suite, $M_A$ sera appel l'ensemble des mots de $A$. \newline \indexfr{mot}% - - Soit $n \supegal 2$, $M_A$ est muni d'une distribution de probabilit vrifiant les hypothses, si $u \in M_A$ : - \begin{eqnarray_xd} - \pr{u_1 = e} &=& 1 &\numequation\\ - \forall t > 1, \; \pr{u_t = f \; | \; u_{t-1} = f} &=& 1 &\numequation \\ - \forall t > 1, \; \pr{u_t = e } &=& 0 &\numequation \\ - \forall t \supegal n, \; \pr{u_t \; | \; \vecteurno{u_{t-1}}{u_1} } &=& P\pa{u_t \; | \; - \vecteurno{u_{t-1}}{u_{t-n+1}} } &\numequation - \end{eqnarray_xd} - \end{xdefinition} - -Si les n-grammes sont connus, ces hypothses simplificatrices permettent d'exprimer la probabilit d'un mot de manire -diffrente~: - - - - - - \begin{xproperty}{expression de la probabilit}\label{n_grammes_propriete_001}% - Avec les notations de la dfinition~\ref{n_grammes_definition}, soit $u \in M_C$, on dfinit $l\pa{u}$ : - $$ - l\pa{u} = \min \acc {i \in \N \; | \; u_i = f } - $$ - Par dfinition de $u$, $l\pa{u}$ existe et la probabilit de $u$ peut s'exprimer diffremment : - $$ - \pr{u} = - \left\{ - \begin{array}{ll} - \pr{u} & \text{ si } l\pa{u} < n \\ - \pr{ \vecteurno{u_1}{u_{n-1}} } \; \prody{t=n}{l\pa{u}} \pr{u_t \; | \; \vecteurno{u_{t-1}}{u_{t-n+1}}} & - \text{ si } l\pa{u} \supegal n - \end{array} - \right. - $$ - \end{xproperty} - - - - - - -\begin{xdemo}{proprit}{\ref{n_grammes_propriete_001}} -La dfinition~\ref{n_grammes_definition} s'inspire de celle d'une chane de Markov d'ordre $n$ (voir -paragraphe~\ref{definition_mmc_ordre_n}, page~\pageref{definition_mmc_ordre_n}), la dmonstration aussi. + Soit $A = \pa{e,f,\vecteurno{a_1}{a_N}}$ un ensemble fini nomm� \emph{alphabet}, les symboles $e$ et $f$ d�butent + et terminent toute s�quence de symboles appartenant � $A$. Cette convention permet de traiter les probabilit�s + d'entr�e et de sortie d'une s�quence comme des probabilit�s de transitions. Soit $M_A \subset A^{\N}$ + l'ensemble des suites $u=\pa{u_i}_{i \supegal 0}$ de $A$ d�finies comme suit~: + $$ + u \in M_A \Longleftrightarrow \left\{ + \begin{array}{l} + u_1 = e \\ + \exists N > 2 \text{ tel que } \forall i \supegal N, \; u_i = f \text{ et } \forall i < N, \; u_i \neq f + \end{array} + \right. + $$ + Par la suite, $M_A$ sera appel� l'ensemble des mots de $A$. \newline \indexfr{mot}% + + Soit $n \supegal 2$, $M_A$ est muni d'une distribution de probabilit� v�rifiant les hypoth�ses, si $u \in M_A$ : + \begin{eqnarray_xd} + \pr{u_1 = e} &=& 1 &\numequation\\ + \forall t > 1, \; \pr{u_t = f \; | \; u_{t-1} = f} &=& 1 &\numequation \\ + \forall t > 1, \; \pr{u_t = e } &=& 0 &\numequation \\ + \forall t \supegal n, \; \pr{u_t \; | \; \vecteurno{u_{t-1}}{u_1} } &=& P\pa{u_t \; | \; + \vecteurno{u_{t-1}}{u_{t-n+1}} } &\numequation + \end{eqnarray_xd} + \end{xdefinition} + +Si les n-grammes sont connus, ces hypoth�ses simplificatrices permettent d'exprimer la probabilit� d'un mot de mani�re +diff�rente~: + + + + + + \begin{xproperty}{expression de la probabilit�}\label{n_grammes_propriete_001}% + Avec les notations de la d�finition~\ref{n_grammes_definition}, soit $u \in M_C$, on d�finit $l\pa{u}$ : + $$ + l\pa{u} = \min \acc {i \in \N \; | \; u_i = f } + $$ + Par d�finition de $u$, $l\pa{u}$ existe et la probabilit� de $u$ peut s'exprimer diff�remment : + $$ + \pr{u} = + \left\{ + \begin{array}{ll} + \pr{u} & \text{ si } l\pa{u} < n \\ + \pr{ \vecteurno{u_1}{u_{n-1}} } \; \prody{t=n}{l\pa{u}} \pr{u_t \; | \; \vecteurno{u_{t-1}}{u_{t-n+1}}} & + \text{ si } l\pa{u} \supegal n + \end{array} + \right. + $$ + \end{xproperty} + + + + + + +\begin{xdemo}{propri�t�}{\ref{n_grammes_propriete_001}} +La d�finition~\ref{n_grammes_definition} s'inspire de celle d'une cha�ne de Markov d'ordre $n$ (voir +paragraphe~\ref{definition_mmc_ordre_n}, page~\pageref{definition_mmc_ordre_n}), la d�monstration aussi. \end{xdemo} @@ -107,33 +107,33 @@ \section{Estimation} \indexfrr{n-grammes}{estimation}% -L'estimation des n-grammes s'effectue pour un sous-ensemble $C \subset M_A$ donn, on dfinit deux types de -probabilits~: +L'estimation des n-grammes s'effectue pour un sous-ensemble $C \subset M_A$ donn�, on d�finit deux types de +probabilit�s~: \begin{enumerate} -\indexfrr{n-grammes}{probabilit de commencer}% -\indexfrr{n-grammes}{probabilit de transiter}% -\item La probabilit de commencer un mot par la squence $x$~: +\indexfrr{n-grammes}{probabilit� de commencer}% +\indexfrr{n-grammes}{probabilit� de transiter}% +\item La probabilit� de commencer un mot par la s�quence $x$~: $$ \forall x \in A^n, \; p_e\pa{x,C} = \widehat{P}\pa{u_1^{n-1} = x} $$ -\item La probabilit de transiter l'intrieur d'un mot de la squence $x$ l'lment $y$~: +\item La probabilit� de transiter � l'int�rieur d'un mot de la s�quence $x$ � l'�l�ment $y$~: $$ \forall x \in A^n, \; \forall y \in A, \; \forall t > n, \; p_t\pa{x,y,C} = \widehat{P}\pa{u_t = y \; | \; u_{t-n+1}^{t-1} = x } $$ \end{enumerate} -Ces deux probabilits peuvent tre estimes l'aide de l'ensemble $C$ comme suit : +Ces deux probabilit�s peuvent �tre estim�es � l'aide de l'ensemble $C$ comme suit : \begin{eqnarray*} p_e\pa{x,C} &=& \dfrac { card \acc {u \in C \sachant u_1^{n-1} = x }} { \card{C}} \\ \\ p_t\pa{x,y,C} &=& \left\{\begin{array}{l} 0 \text{ si } card \acc { \; \pa{u,t} \sachant u \in C, \; - n \infegal t \infegal l\pa{u}, \; u_{t-n+1}^{n-1} = x} = 0 \\ \\ + n \leqslant t \leqslant l\pa{u}, \; u_{t-n+1}^{n-1} = x} = 0 \\ \\ \text{sinon } \dfrac { card \acc { \; \pa{u,t} \sachant u \in C, \; - n \infegal t \infegal l\pa{u}, \; u_{t-n+1}^{n-1} = x, \; u_t = y}} - { card \acc { \; \pa{u,t} \sachant u \in C, \; n \infegal t \infegal - l\pa{u}, \; u_{t-n+1}^{n-1} = x}} + n \leqslant t \leqslant l\pa{u}, \; u_{t-n+1}^{n-1} = x, \; u_t = y}} + { card \acc { \; \pa{u,t} \sachant u \in C, \; n \leqslant t \leqslant + l\pa{u}, \; u_{t-n+1}^{n-1} = x}} \end{array} \right. \end{eqnarray*} @@ -160,33 +160,33 @@ \section{Prolongations} -\subsection{Densit d'un dictionnaire} -\indexfrr{dictionnaire}{densit} -\indexfrr{densit}{dictionnaire} +\subsection{Densit� d'un dictionnaire} +\indexfrr{dictionnaire}{densit�} +\indexfrr{densit�}{dictionnaire} -L'ide d'associer une densit un dictionnaire est tire de l'article~\citeindex{Govindaraju2002}. Effectue sur une mme base d'images de mots cursifs, la reconnaissance de l'criture voit ses performances dcrotre au fur et mesure que la taille du dictionnaire augmente. Le choix est plus vaste, par consquent, la possibilit de se tromper est plus grande. Toutefois, la taille n'est pas le seul paramtre prendre en compte, un dictionnaire dans les mots sont trs proches les uns des autres propose de nombreux choix similaires. Soit $D$ un dictionnaire, la densit de $D=\vecteur{m_1}{m_N}$ note $\rho\pa{D}$ est dfinie par~: +L'id�e d'associer une densit� � un dictionnaire est tir�e de l'article~\citeindex{Govindaraju2002}. Effectu�e sur une m�me base d'images de mots cursifs, la reconnaissance de l'�criture voit ses performances d�cro�tre au fur et � mesure que la taille du dictionnaire augmente. Le choix est plus vaste, par cons�quent, la possibilit� de se tromper est plus grande. Toutefois, la taille n'est pas le seul param�tre � prendre en compte, un dictionnaire dans les mots sont tr�s proches les uns des autres propose de nombreux choix similaires. Soit $D$ un dictionnaire, la densit� de $D=\vecteur{m_1}{m_N}$ not�e $\rho\pa{D}$ est d�finie par~: - \begin{eqnarray} - \rho\pa{D} &=& \underset{ v_R\pa{D} } - {\underbrace{\frac{N\pa{N-1}} { \summyone { i \neq j} \, d_R \pa{m_i,m_j} } } } - \; f_R\pa{N} - \end{eqnarray} - + \begin{eqnarray} + \rho\pa{D} &=& \underset{ v_R\pa{D} } + {\underbrace{\frac{N\pa{N-1}} { \summyone { i \neq j} \, d_R \pa{m_i,m_j} } } } + \; f_R\pa{N} + \end{eqnarray} + -$f_R\pa{N}$ est une fonction croissante de $N$ telle que $f_R\pa{N} = \pa{\ln N}^p + \delta_R$ ou $f_R\pa{N} = N^p + \delta_R$ avec $p > 0$. $d_R\pa{m_i,m_j}$ est une distance qui mesure la confusion entre ces deux mots via un systme de reconnaissance $R$, elle sera dfinie plus loin. L'article~\citeindex{Govindaraju2002} montre de manire pratique que les performances $p_R\pa{D}$ du systme de reconnaissance $R$ voluent linairement par rapport la densit\footnote{Pour une base donne, $p_R\pa{D}$ correspond au nombre de mots reconnus correctement sur le nombre de documents dans la base.}~: +$f_R\pa{N}$ est une fonction croissante de $N$ telle que $f_R\pa{N} = \pa{\ln N}^p + \delta_R$ ou $f_R\pa{N} = N^p + \delta_R$ avec $p > 0$. $d_R\pa{m_i,m_j}$ est une distance qui mesure la confusion entre ces deux mots via un syst�me de reconnaissance $R$, elle sera d�finie plus loin. L'article~\citeindex{Govindaraju2002} montre de mani�re pratique que les performances $p_R\pa{D}$ du syst�me de reconnaissance $R$ �voluent lin�airement par rapport � la densit�\footnote{Pour une base donn�e, $p_R\pa{D}$ correspond au nombre de mots reconnus correctement sur le nombre de documents dans la base.}~: - \begin{eqnarray} - p_R\pa{D} \sim a \rho\pa{D} + b - \end{eqnarray} + \begin{eqnarray} + p_R\pa{D} \sim a \rho\pa{D} + b + \end{eqnarray} -\indexfrr{probabilit}{mission} +\indexfrr{probabilit�}{�mission} \indexfr{Kullback-Leiber} -La distance $d_R\pa{w_i,w_j}$ est gale la distance entre les deux modles de reconnaissance associs aux mots $w_i$ et $w_j$. Soient deux tats $e_i^k$ et $e_j^l$ des modles de reconnaissances associs aux mots $w_i$ et $w_j$, ils diffrent par leurs probabilits d'mission que l'on peut comparer grce une distance de Kullback-Leiber. Il est ensuite possible de construire une distance entre graphes de sorte qu'elle soit la somme des distances d'ditions entre tous les chemins du premier graphe et tous ceux du second. +La distance $d_R\pa{w_i,w_j}$ est �gale � la distance entre les deux mod�les de reconnaissance associ�s aux mots $w_i$ et $w_j$. Soient deux �tats $e_i^k$ et $e_j^l$ des mod�les de reconnaissances associ�s aux mots $w_i$ et $w_j$, ils diff�rent par leurs probabilit�s d'�mission que l'on peut comparer gr�ce � une distance de Kullback-Leiber. Il est ensuite possible de construire une distance entre graphes de sorte qu'elle soit la somme des distances d'�ditions entre tous les chemins du premier graphe et tous ceux du second. @@ -199,98 +199,98 @@ \subsection{Densit \subsection{Classes de symboles} \indexfrr{n-grammes}{classes de symboles} -\indexfr{chane de Markov} +\indexfr{cha�ne de Markov} \indexfr{HMM}\indexfr{MMC} -Plutt que de modliser l'ensemble des n-grammes, il peut paratre judicieux de regrouper certains symboles en classes puis de ne s'intresser qu'aux transitions entre classes de symboles, ce que proposent les articles \citeindex{Yamamoto2003} et~\citeindex{Perraud2003}. Jusqu'ici, les n-grammes reprsents sont assimilables des chanes de Markov, mais les classes de symboles pourraient tre les tats de la chane de Markov cache. Les mots peuvent par exemple tre classs par rapport leur fonction grammaticale dans la phrase, cette classe serait dans le cas prsent l'observation cache. On peut donc imaginer que les tats de la chane de Markov reprsentent des classes de mots et mettent des mots. Le modle ainsi form est une modlisation du langage. Soit $D = \vecteur{m_1}{m_d}$ une liste de symboles ou plus concrtement de mots, on dsire modliser les squences de mots. Les n-grammes des paragraphes prcdents modlisent la probabilit d'un squence $S=\vecteur{s_1}{s_T}$ par~: - - $$ - \pr{S} = \pr{ \vecteurno{s_1}{s_d} } \; \prody {i = d+1} {T} \, - \pr{ s_i \sac \vecteurno{s_{i-1}}{s_{i-d}} } - $$ - -En classant les mots dans une liste de classes $\vecteur{C_1}{C_X}$ considre comme les tats d'une chane de Markov cache, soit $c = \vecteur{c_1}{c_T}$ une squence de classes, la probabilit de la squence $S$ s'crit maintenant~: - - $$ - \begin{array}{l} - \pr {S} = \summyone{c} \; \cro { \prody{i=1}{T} \pr { s_i \sac c_i } } - \pr{ \vecteurno{c_1}{c_d} } \; - \cro{ \prody {i = d+1} {T} - \pr{ c_i \sac \vecteurno{c_{i-1}}{c_{i-d}} } - } \\ - \text{Cette expression est calculable grce l'algorithme~\ref{hmm_algo_forward} (page~\pageref{hmm_algo_forward}). } - \end{array} - $$ - -\indexfr{perplexit} +Plut�t que de mod�liser l'ensemble des n-grammes, il peut para�tre judicieux de regrouper certains symboles en classes puis de ne s'int�resser qu'aux transitions entre classes de symboles, ce que proposent les articles \citeindex{Yamamoto2003} et~\citeindex{Perraud2003}. Jusqu'ici, les n-grammes repr�sent�s sont assimilables � des cha�nes de Markov, mais les classes de symboles pourraient �tre les �tats de la cha�ne de Markov cach�e. Les mots peuvent par exemple �tre class�s par rapport � leur fonction grammaticale dans la phrase, cette classe serait dans le cas pr�sent l'observation cach�e. On peut donc imaginer que les �tats de la cha�ne de Markov repr�sentent des classes de mots et �mettent des mots. Le mod�le ainsi form� est une mod�lisation du langage. Soit $D = \vecteur{m_1}{m_d}$ une liste de symboles ou plus concr�tement de mots, on d�sire mod�liser les s�quences de mots. Les n-grammes des paragraphes pr�c�dents mod�lisent la probabilit� d'un s�quence $S=\vecteur{s_1}{s_T}$ par~: + + $$ + \pr{S} = \pr{ \vecteurno{s_1}{s_d} } \; \prody {i = d+1} {T} \, + \pr{ s_i \sac \vecteurno{s_{i-1}}{s_{i-d}} } + $$ + +En classant les mots dans une liste de classes $\vecteur{C_1}{C_X}$ consid�r�e comme les �tats d'une cha�ne de Markov cach�e, soit $c = \vecteur{c_1}{c_T}$ une s�quence de classes, la probabilit� de la s�quence $S$ s'�crit maintenant~: + + $$ + \begin{array}{l} + \pr {S} = \summyone{c} \; \cro { \prody{i=1}{T} \pr { s_i \sac c_i } } + \pr{ \vecteurno{c_1}{c_d} } \; + \cro{ \prody {i = d+1} {T} + \pr{ c_i \sac \vecteurno{c_{i-1}}{c_{i-d}} } + } \\ + \text{Cette expression est calculable gr�ce � l'algorithme~\ref{hmm_algo_forward} (page~\pageref{hmm_algo_forward}). } + \end{array} + $$ + +\indexfr{perplexit�} \indexfr{entropie} - -Alors que l'article \citeindex{Perraud2003} labore les classes de manire smantique (les mots sont classs selon leur fonction grammaticale), l'article~\citeindex{Yamamoto2003} propose une mthode permettant de dterminer le nombre de classes ainsi qu'un critre d'valuation nomm \emph{perplexit} et dfini comme suit pour une liste de squence de symboles ${\vecteur{s_1^k}{s_{T_s}^k}}_{ 1 \infegal k \infegal K}$~: + +Alors que l'article \citeindex{Perraud2003} �labore les classes de mani�re s�mantique (les mots sont class�s selon leur fonction grammaticale), l'article~\citeindex{Yamamoto2003} propose une m�thode permettant de d�terminer le nombre de classes ainsi qu'un crit�re d'�valuation nomm� \emph{perplexit�} et d�fini comme suit pour une liste de s�quence de symboles ${\vecteur{s_1^k}{s_{T_s}^k}}_{ 1 \leqslant k \leqslant K}$~: - \begin{eqnarray} - H &=& - \frac{1}{K} \; \summy{k=1}{K} \ln \pr{ \vecteurno {s_1^k}{s_{T_s}^k} } \nonumber\\ - P &=& 2^H \label{ngram_perplexite} - \end{eqnarray} + \begin{eqnarray} + H &=& - \frac{1}{K} \; \summy{k=1}{K} \ln \pr{ \vecteurno {s_1^k}{s_{T_s}^k} } \nonumber\\ + P &=& 2^H \label{ngram_perplexite} + \end{eqnarray} -Par rapport \citeindex{Perraud2003}, l'article \citeindex{Yamamoto2003} propose une modlisation plus complexe, alliant probabilits de transitions pour les sous-squences centrales de symboles et probabilit de transitions entre classes pour les sous-squences au bord. Soit $n$ la dimension des n-grammes et $s = \vecteur{s_1}{s_T}$ une squence de symboles dont les classes associes sont $\vecteur{c_1}{c_T}$ (les classes sont connues)~: +Par rapport � \citeindex{Perraud2003}, l'article \citeindex{Yamamoto2003} propose une mod�lisation plus complexe, alliant probabilit�s de transitions pour les sous-s�quences centrales de symboles et probabilit� de transitions entre classes pour les sous-s�quences au bord. Soit $n$ la dimension des n-grammes et $s = \vecteur{s_1}{s_T}$ une s�quence de symboles dont les classes associ�es sont $\vecteur{c_1}{c_T}$ (les classes sont connues)~: - \begin{eqnarray*} - \pr{ \vecteurno{s_1}{s_d}, \vecteurno{s_{d+1}}{s_{T-d}}, \vecteurno {s_{T-d+1}}{s_T}} &=& - \prody{i=1}{d} \pr { c_i \sac \vecteurno{c_1}{c_i} } \; \pr{ s_i \sac s_i} \\ - && \prody{i=d+1}{T-d} \pr{ s_i \sac \vecteurno{s_{i-1}}{s_{i_d}} } \\ - && \prody{i=T-d+1}{T} \pr { c_i \sac \vecteurno{c_1}{c_i} } \; \pr{ s_i \sac s_i} - \end{eqnarray*} + \begin{eqnarray*} + \pr{ \vecteurno{s_1}{s_d}, \vecteurno{s_{d+1}}{s_{T-d}}, \vecteurno {s_{T-d+1}}{s_T}} &=& + \prody{i=1}{d} \pr { c_i \sac \vecteurno{c_1}{c_i} } \; \pr{ s_i \sac s_i} \\ + && \prody{i=d+1}{T-d} \pr{ s_i \sac \vecteurno{s_{i-1}}{s_{i_d}} } \\ + && \prody{i=T-d+1}{T} \pr { c_i \sac \vecteurno{c_1}{c_i} } \; \pr{ s_i \sac s_i} + \end{eqnarray*} -Dans cette expression, les dbuts et fin de mots, supposs moins fiables pour une estimation, sont modliss par des classes de caractres tandis que pour la partie centrale, les caractres sont directement modliss. +Dans cette expression, les d�buts et fin de mots, suppos�s moins fiables pour une estimation, sont mod�lis�s par des classes de caract�res tandis que pour la partie centrale, les caract�res sont directement mod�lis�s. \subsection{Choix de la dimension de n-grammes} -\indexfr{perplexit} +\indexfr{perplexit�} \indexfrr{n-grammes}{dimension} -La dfinition de la perplexit (\ref{ngram_perplexite}) implique ncessaire sa dcroissance lorsque la dimension $n$ crot ainsi que le montre la table~\ref{ngrams_perplexite_dimension} regroupant les calculs de perplexit pour diffrentes valeurs de la dimension. Comme dans toute modlisation, la question du choix de la dimension approprie se pose. +La d�finition de la perplexit� (\ref{ngram_perplexite}) implique n�cessaire sa d�croissance lorsque la dimension $n$ cro�t ainsi que le montre la table~\ref{ngrams_perplexite_dimension} regroupant les calculs de perplexit� pour diff�rentes valeurs de la dimension. Comme dans toute mod�lisation, la question du choix de la dimension appropri�e se pose. \indexfr{BIC} -A l'instar de l'article \citeindex{Bicego2003}, il est possible d'utiliser un critre d'information comme le BIC -~ou Bayesian Information Criterion~- afin de mettre en rapport la baisse de la perplexit avec le nombre de coefficients ajouts aux n-grammes lorsqu'on augmente leur dimension. Les notations utilises sont celles de l'expression (\ref{ngram_perplexite}). On dfinit $N_k$ comme tant le nombre de paramtres libres pour le modle de dimension $k$ et $S$ reprsente la somme des longueurs des squences d'observations. Le meilleur modle maximise le critre suivant~: +A l'instar de l'article \citeindex{Bicego2003}, il est possible d'utiliser un crit�re d'information comme le BIC -~ou Bayesian Information Criterion~- afin de mettre en rapport la baisse de la perplexit� avec le nombre de coefficients ajout�s aux n-grammes lorsqu'on augmente leur dimension. Les notations utilis�es sont celles de l'expression (\ref{ngram_perplexite}). On d�finit $N_k$ comme �tant le nombre de param�tres libres pour le mod�le de dimension $k$ et $S$ repr�sente la somme des longueurs des s�quences d'observations. Le meilleur mod�le maximise le crit�re suivant~: - \begin{eqnarray} - BIC\pa{k} &=& \summy{k=1}{K} \ln \pr{ \vecteurno {s_1^k}{s_{T_s}^k} } - \frac{N_k}{2} \ln S - \end{eqnarray} + \begin{eqnarray} + BIC\pa{k} &=& \summy{k=1}{K} \ln \pr{ \vecteurno {s_1^k}{s_{T_s}^k} } - \frac{N_k}{2} \ln S + \end{eqnarray} -La table~\ref{ngrams_perplexite_dimension} montre les rsultats obtenus pour un dictionnaire de cinq mille mots anglais courants. Le critre est maximum pour une dimension gale trois. +La table~\ref{ngrams_perplexite_dimension} montre les r�sultats obtenus pour un dictionnaire de cinq mille mots anglais courants. Le crit�re est maximum pour une dimension �gale � trois. - \begin{table}[ht] - $$\begin{array}{|rrr|} \hline - \text{dimension} & \log_2 \text{-perplexit } & \frac{BIC\pa{dimension}}{K} \\ \hline - 2 & 19,75 & -20,29 \\ - 3 & 16,20 & -19,64 \\ - 4 & 12,23 & -21,61 \\ - 5 & 9,64 & -24,12 \\ - 6 & 8,81 & -26,47 \\ - 7 & 8,60 & -27,96 \\ - 8 & 8,54 & -28,69 \\ - 9 & 8,52 & -28,99 \\ - 10 & 8,52 & -29,14 \\ \hline - \end{array}$$ - \caption{ Log-perplexit estime pour diffrentes dimensions et sur un dictionnaire de 5000 mots anglais - employ de manire courante et contenant en moyenne entre cinq et six lettres. - La perplexit dcrot lorsque la dimension augmente tandis que le critre $BIC$ prconise - une dimension gale trois pour laquelle il est minimum.} - \label{ngrams_perplexite_dimension} - \indexfr{perplexit} - \end{table} - -\indexfr{bi-grammes} -\indexfr{tri-grammes} + \begin{table}[ht] + $$\begin{array}{|rrr|} \hline + \text{dimension} & \log_2 \text{-perplexit� } & \frac{BIC\pa{dimension}}{K} \\ \hline + 2 & 19,75 & -20,29 \\ + 3 & 16,20 & -19,64 \\ + 4 & 12,23 & -21,61 \\ + 5 & 9,64 & -24,12 \\ + 6 & 8,81 & -26,47 \\ + 7 & 8,60 & -27,96 \\ + 8 & 8,54 & -28,69 \\ + 9 & 8,52 & -28,99 \\ + 10 & 8,52 & -29,14 \\ \hline + \end{array}$$ + \caption{ Log-perplexit� estim�e pour diff�rentes dimensions et sur un dictionnaire de 5000 mots anglais + employ� de mani�re courante et contenant en moyenne entre cinq et six lettres. + La perplexit� d�cro�t lorsque la dimension augmente tandis que le crit�re $BIC$ pr�conise + une dimension �gale � trois pour laquelle il est minimum.} + \label{ngrams_perplexite_dimension} + \indexfr{perplexit�} + \end{table} + +\indexfr{bi-grammes} +\indexfr{tri-grammes} -Il est possible de raffiner la mthode afin de slectionner la meilleure dimension locale. Par exemple, dans le cas d'un mot incluant la lettre~"Z", il n'est pas ncessaire de connatre la lettre prcdant la lettre "Z" pour prvoir celle qui suit. Pour la lettre "Z" les 2-grammes ou bi-grammes suffisent alors qu'avec la lettre "A", il est prfrable de choisir des 3-grammes ou tri-grammes. Il s'agit donc ici d'estimer un modle de n-grammes avec un $n$ assez grand puis de supprimer certains coefficients jusqu' ce que le critre $BIC$ ait atteint son minimum. Dans ce cas, les n-grammes peuvent tre considrs comme les tats d'une chane de Markov, tre classs par ordre dcroissant de probabilit a posteriori\seeannex{hmm_ditribution_temporelle_etat}{probabilit des tats} puis tre supprims selon cet ordre tant que le critre $BIC$ crot. +Il est possible de raffiner la m�thode afin de s�lectionner la meilleure dimension locale. Par exemple, dans le cas d'un mot incluant la lettre~"Z", il n'est pas n�cessaire de conna�tre la lettre pr�c�dant la lettre "Z" pour pr�voir celle qui suit. Pour la lettre "Z" les 2-grammes ou bi-grammes suffisent alors qu'avec la lettre "A", il est pr�f�rable de choisir des 3-grammes ou tri-grammes. Il s'agit donc ici d'estimer un mod�le de n-grammes avec un $n$ assez grand puis de supprimer certains coefficients jusqu'� ce que le crit�re $BIC$ ait atteint son minimum. Dans ce cas, les n-grammes peuvent �tre consid�r�s comme les �tats d'une cha�ne de Markov, �tre class�s par ordre d�croissant de probabilit� a posteriori\seeannex{hmm_ditribution_temporelle_etat}{probabilit� des �tats} puis �tre supprim�s selon cet ordre tant que le crit�re $BIC$ cro�t. @@ -299,82 +299,82 @@ \subsection{Choix de la dimension de n-grammes} -\subsection{Groupe de lettres rcurrents} +\subsection{Groupe de lettres r�currents} \indexfr{groupe de lettres} \indexfrr{n-grammes}{groupe de lettres} -Lors du traitement des erreurs de segmentation\seeannex{hmm_bi_lettre}{erreur graphme}, la reconnaissance de l'criture ncessite la slection des groupes de lettres les plus frquents. Si le "." signifie le dbut ou la fin d'un mot ou l'espace, ".a.", ".de.", "tion." reviennent frquemment dans la langue franaise. On s'intresse ici des probabilits de transition entre des groupes de plusieurs lettres. Jusqu' prsent, les modles de n-grammes prsents estime la probabilit de la lettre suivant sachant la ou les lettres prcdentes. Dans ce cas, on cherche la probabilit des lettres suivantes sachant un pass d'une ou plusieurs lettres. - - -\indexfr{Astrix le Gaulois} - -La table~\ref{ngrams_asterix_gaulois} prsente un extrait des noms utiliss dans la bande dessine \textit{Astrix le Gaulois} dans laquelle les suffixes $ix.$ et $us.$ sont couramment employs. Il est naturel d'envisager la probabilit de ces triplets -~deux lettres plus la fin du mot~- sachant la lettre prcdente. Il reste estimer des probabilits comme $\pr{ u \sac t}$ et $ \pr{ us. \sac t }$. - - \begin{table}[ht] - $$\begin{tabular}{|l|l|l|l|} \hline - Astrix & Oblix & Panoramix & Abraracourcix \\ - Assurancetourix & Agcanonix & Tragicomix & Ctautomatix \\ - Idfix & Plaintecontrix & Ordralfabtix & Pneumatix \\ - Plantaquatix & Elvedelix & Analgsix & Monosyllabix \\ - Uniprix & Linguistix & Arrierboutix & Oblodalix \\ - Harenbaltix & Choucroutgarnix & Bellodalix & Zrozrosix \\ - Allgorix & Boulimix & Porqupix & Aplusbgalix \\ - Thorix & Homopatix & Tournedix & Squinotix \\ \hline - % - Cumulonimbus & Pleindastus & Fleurdelotus & Langlus \\ - Yenapus & Roideprus & Fanfrelus & Faipalgugus \\ - Dtritus & Diplodocus & Garovirus & Cubitus \\ - Diplodocus & Infarctus & Suelburnus & Saugrenus \\ - Volfgangamadus & Soutienmordicus & pinedecactus & Ctinconsensus \\ \hline - \end{tabular}$$ - \caption{ Prnoms gaulois et romains extraits de la bande dessine - \textit{Astrix le Gaulois}. Pour cet extrait, $\pr{ ix. \sac t} = \frac{3}{10}$, - $\pr{ us. \sac t} = \frac{2}{10}$.} - \label{ngrams_asterix_gaulois} - \end{table} - -\indexfr{alphabet tendu} +Lors du traitement des erreurs de segmentation\seeannex{hmm_bi_lettre}{erreur graph�me}, la reconnaissance de l'�criture n�cessite la s�lection des groupes de lettres les plus fr�quents. Si le "." signifie le d�but ou la fin d'un mot ou l'espace, ".a.", ".de.", "tion." reviennent fr�quemment dans la langue fran�aise. On s'int�resse ici � des probabilit�s de transition entre des groupes de plusieurs lettres. Jusqu'� pr�sent, les mod�les de n-grammes pr�sent�s estime la probabilit� de la lettre suivant sachant la ou les lettres pr�c�dentes. Dans ce cas, on cherche la probabilit� des lettres suivantes sachant un pass� d'une ou plusieurs lettres. + + +\indexfr{Ast�rix le Gaulois} + +La table~\ref{ngrams_asterix_gaulois} pr�sente un extrait des noms utilis�s dans la bande dessin�e \textit{Ast�rix le Gaulois} dans laquelle les suffixes $ix.$ et $us.$ sont couramment employ�s. Il est naturel d'envisager la probabilit� de ces triplets -~deux lettres plus la fin du mot~- sachant la lettre pr�c�dente. Il reste � estimer des probabilit�s comme $\pr{ u \sac t}$ et $ \pr{ us. \sac t }$. + + \begin{table}[ht] + $$\begin{tabular}{|l|l|l|l|} \hline + Ast�rix & Ob�lix & Panoramix & Abraracourcix \\ + Assurancetourix & Ag�canonix & Tragicomix & C�tautomatix \\ + Id�fix & Plaintecontrix & Ordralfab�tix & Pneumatix \\ + Plantaquatix & El�vedelix & Analg�six & Monosyllabix \\ + Uniprix & Linguistix & Arrierboutix & Ob�lodalix \\ + Harenbaltix & Choucroutgarnix & Bellodalix & Z�roz�rosix \\ + All�gorix & Boulimix & Porqu�pix & Aplusb�galix \\ + Th�orix & Hom�opatix & Tournedix & Squinotix \\ \hline + % + Cumulonimbus & Pleindastus & Fleurdelotus & Lang�lus \\ + Yenapus & Roideprus & Fanfrelus & Faipalgugus \\ + D�tritus & Diplodocus & Garovirus & Cubitus \\ + Diplodocus & Infarctus & Suelburnus & Saugrenus \\ + Volfgangamad�us & Soutienmordicus & �pinedecactus & C�tinconsensus \\ \hline + \end{tabular}$$ + \caption{ Pr�noms gaulois et romains extraits de la bande dessin�e + \textit{Ast�rix le Gaulois}. Pour cet extrait, $\pr{ ix. \sac t} = \frac{3}{10}$, + $\pr{ us. \sac t} = \frac{2}{10}$.} + \label{ngrams_asterix_gaulois} + \end{table} + +\indexfr{alphabet �tendu} \indexfr{relation d'ordre partiel} -Pour ce faire, on dfinit un alphabet tendu $A = \vecteur{s_1}{s_N}$ incluant tous les groupes de lettres dont on veut estimer les transitions. On dfinit galement $s + t$ comme tant la concatnation de deux sympboles de l'alphabet, par exemple~: $t + us = tus$. Pour un mot donn, on dsigne par $E_A\pa{m}$ toutes les manires possibles d'crire le mot $m$ en utilisant les symboles inclus dans~$A$. On dispose d'une base de mots $\vecteur{m_1}{m_K}$. Les probabilits de transitions sont alors dfinies par~: +Pour ce faire, on d�finit un alphabet �tendu $A = \vecteur{s_1}{s_N}$ incluant tous les groupes de lettres dont on veut estimer les transitions. On d�finit �galement $s + t$ comme �tant la concat�nation de deux sympboles de l'alphabet, par exemple~: $t + us = tus$. Pour un mot donn�, on d�signe par $E_A\pa{m}$ toutes les mani�res possibles d'�crire le mot $m$ en utilisant les symboles inclus dans~$A$. On dispose d'une base de mots $\vecteur{m_1}{m_K}$. Les probabilit�s de transitions sont alors d�finies par~: - \begin{eqnarray} - N &=& \summy{k=1}{K} \card{ E_A\pa{m_k}} \nonumber \\ - \pr{ s } &=& \frac{1}{N} \; \summy{k=1}{K} \; \cro{ \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m_k} } \; - \indicatrice{s_1 = s }} \\ - \pr{ t \sac s } &=& \frac{ \summy{k=1}{K} \; \cro{ \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m_k} } \; - \summy{i = 2}{n} \indicatrice{s_{i-1} = s \text{ et } s_i = t } - }} - { - \summy{k=1}{K} \; \cro{ \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m_k} } \; - \summy{i = 2}{n} \indicatrice{s_{i-1} = s } - } - } - \end{eqnarray} + \begin{eqnarray} + N &=& \summy{k=1}{K} \card{ E_A\pa{m_k}} \nonumber \\ + \pr{ s } &=& \frac{1}{N} \; \summy{k=1}{K} \; \cro{ \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m_k} } \; + \indicatrice{s_1 = s }} \\ + \pr{ t \sac s } &=& \frac{ \summy{k=1}{K} \; \cro{ \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m_k} } \; + \summy{i = 2}{n} \indicatrice{s_{i-1} = s \text{ et } s_i = t } + }} + { + \summy{k=1}{K} \; \cro{ \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m_k} } \; + \summy{i = 2}{n} \indicatrice{s_{i-1} = s } + } + } + \end{eqnarray} -Cet ensemble n'est pas forcment rduit un seul lment (voir table~\ref{ngrams_boa}). Avec ce formalisme, il est maintenant possible d'exprimer la probabilit d'un mot $m$ comme tant~: +Cet ensemble n'est pas forc�ment r�duit � un seul �l�ment (voir table~\ref{ngrams_boa}). Avec ce formalisme, il est maintenant possible d'exprimer la probabilit� d'un mot $m$ comme �tant~: - \begin{eqnarray} - \pr{ m } = \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m} } \; \pr{ s_1} \, \prody{i = 2}{n} \, \pr{ s_i \sac s_{i-1} } - \end{eqnarray} + \begin{eqnarray} + \pr{ m } = \summyone{ \vecteur{s_1}{s_n} \in E_A\pa{m} } \; \pr{ s_1} \, \prody{i = 2}{n} \, \pr{ s_i \sac s_{i-1} } + \end{eqnarray} - \begin{table}[ht] - $$\begin{tabular}{|l|l|} \hline - alphabet & B - BO - O - OA - A \\ \hline - mot & BOA \\ \hline - criture 1 & B - OA \\ - criture 2 & BO - A \\ - criture 3 & B - O - A \\ \hline - \end{tabular}$$ - \caption{ Diffrentes manires d'crire le mot "BOA". } - \label{ngrams_boa} - \end{table} + \begin{table}[ht] + $$\begin{tabular}{|l|l|} \hline + alphabet & B - BO - O - OA - A \\ \hline + mot & BOA \\ \hline + �criture 1 & B - OA \\ + �criture 2 & BO - A \\ + �criture 3 & B - O - A \\ \hline + \end{tabular}$$ + \caption{ Diff�rentes mani�res d'�crire le mot "BOA". } + \label{ngrams_boa} + \end{table} -\indexfrr{segmentation}{graphme} -\indexfrr{graphme}{segmentation} +\indexfrr{segmentation}{graph�me} +\indexfrr{graph�me}{segmentation} -Cet outil permet d'estimer des probabilits de transitions entre des modles de Markoc caches modlisant des groupes de lettres\seeannex{hmm_seq_modele_mot}{groupes de lettres} prsents au paragraphes~\ref{hmm_bi_lettre} (page~\pageref{hmm_bi_lettre}). L'ensemble $E_A\pa{m}$ n'est ici pas prcis et est suppos tre l'ensemble des critures possibles et admises par l'aphabet $A$. Cependant, pour la reconnaissance de l'criture, toutes les critures ne sont pas quiprobables puisqu'une criture est dfinie comme tant le rsultat de la segmentation en graphmes dont les erreurs (voir figure~\ref{image_grapheme_erreur}, page~\pageref{image_grapheme_erreur}) dterminent l'ensemble $E_A\pa{m}$. +Cet outil permet d'estimer des probabilit�s de transitions entre des mod�les de Markoc cach�es mod�lisant des groupes de lettres\seeannex{hmm_seq_modele_mot}{groupes de lettres} pr�sent�s au paragraphes~\ref{hmm_bi_lettre} (page~\pageref{hmm_bi_lettre}). L'ensemble $E_A\pa{m}$ n'est ici pas pr�cis� et est suppos� �tre l'ensemble des �critures possibles et admises par l'aphabet $A$. Cependant, pour la reconnaissance de l'�criture, toutes les �critures ne sont pas �quiprobables puisqu'une �criture est d�finie comme �tant le r�sultat de la segmentation en graph�mes dont les erreurs (voir figure~\ref{image_grapheme_erreur}, page~\pageref{image_grapheme_erreur}) d�terminent l'ensemble $E_A\pa{m}$. @@ -386,17 +386,17 @@ \subsection{Lissage des n-grammes} \indexfrr{lissage}{n-grammes} \indexfrr{n-grammes}{lissage} -L'article \citeindex{Bchet2004} propose une mthode permettant de lisser des probabilits de transitions entre mots et d'obtenir par exemple des probabilits non nulles de transitions entre couples de caractres non reprsents dans l'ensemble d'estimation. A partir des nombres de transitions $c_{ij}$ d'un contexte $h_i$ (un ou plusieurs mots) vers un mot $w_j$, les auteurs construisent un espace vectoriel $E_C$ de reprsentation des contextes. L'objectif est de construire des compteurs de transitions augments $a_{ij}$ prenant en compte non seulement les transitions du contexte $h_i$ vers le mot $w_j$ mais aussi les transitions des contextes proches de $h_i$ vers un mot proche de $w_j$, la proximit tant mesure dans l'espace $E_C$ par une distance. L'article montre empiriquement que les performances dans une tche de reconnaissance sont d'autant plus accrues par un tel lissage que la taille du vocabulaire est grande. +L'article \citeindex{B�chet2004} propose une m�thode permettant de lisser des probabilit�s de transitions entre mots et d'obtenir par exemple des probabilit�s non nulles de transitions entre couples de caract�res non repr�sent�s dans l'ensemble d'estimation. A partir des nombres de transitions $c_{ij}$ d'un contexte $h_i$ (un ou plusieurs mots) vers un mot $w_j$, les auteurs construisent un espace vectoriel $E_C$ de repr�sentation des contextes. L'objectif est de construire des compteurs de transitions augment�s $a_{ij}$ prenant en compte non seulement les transitions du contexte $h_i$ vers le mot $w_j$ mais aussi les transitions des contextes proches de $h_i$ vers un mot proche de $w_j$, la proximit� �tant mesur�e dans l'espace $E_C$ par une distance. L'article montre empiriquement que les performances dans une t�che de reconnaissance sont d'autant plus accrues par un tel lissage que la taille du vocabulaire est grande. \firstpassagedo{ - \begin{thebibliography}{99} - \input{ngrams_biblio.tex} - \end{thebibliography} - \input{../xthese/nb_citations.tex} + \begin{thebibliography}{99} + \input{ngrams_biblio.tex} + \end{thebibliography} + \input{../xthese/nb_citations.tex} } diff --git a/_todo/ngrams/ngrams_biblio.tex b/_todo/ngrams/ngrams_biblio.tex index 4dd6a6ee..813baae0 100644 --- a/_todo/ngrams/ngrams_biblio.tex +++ b/_todo/ngrams/ngrams_biblio.tex @@ -1,15 +1,15 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin -\bibitemstyle{Bchet2004}{2004}{F. Bchet, R. De Mori, D. Janiszek} +\bibitemstyle{B�chet2004}{2004}{F. B�chet, R. De Mori, D. Janiszek} {Data augmentation and language model adaptation using singular value decomposition} {Pattern Recognition Letters}{24}{15}{19} @@ -27,5 +27,5 @@ \bibitemstyle{Yamamoto2003} {2003} {Hirofumi Yamamoto, Shuntara Isogai, Yoshinori Sagisaka} {Multi-class composite N-gram language model} -{Speech Communication}{}{0}{ paratre} +{Speech Communication}{}{0}{� para�tre} diff --git a/_todo/space_metric/space_metric.tex b/_todo/space_metric/space_metric.tex index 86241ee3..7fb05b01 100644 --- a/_todo/space_metric/space_metric.tex +++ b/_todo/space_metric/space_metric.tex @@ -23,13 +23,13 @@ \sloppy -Chercher des mots identiques ou similaires dans un dictionnaire est un problme classique et peut tre dfini pour tout espace mtrique. \indexfr{dictionnaire}\indexfr{lexique}\indexfr{plus proches voisins}\indexfrr{distance}{dition} Soit $\pa{E,d}$ un espace mtrique quelconque et $D \subset E$ un ensemble fini quelconque, $x \in E$ est un lment de $E$ et $s \in \R_+$ un rel positif. L'objectif est de trouver le sous-ensemble $D'\pa{x,s} \subset D$ des voisins les plus proches de $x$ tels que~: +Chercher des mots identiques ou similaires dans un dictionnaire est un probl�me classique et peut �tre d�fini pour tout espace m�trique. \indexfr{dictionnaire}\indexfr{lexique}\indexfr{plus proches voisins}\indexfrr{distance}{�dition} Soit $\pa{E,d}$ un espace m�trique quelconque et $D \subset E$ un ensemble fini quelconque, $x \in E$ est un �l�ment de $E$ et $s \in \mathbb{R}_+$ un r�el positif. L'objectif est de trouver le sous-ensemble $D'\pa{x,s} \subset D$ des voisins les plus proches de $x$ tels que~: $$ - D'\pa{x,s} = \acc{ y \in D \sac d\pa{x,y} \infegal s} + D'\pa{x,s} = \acc{ y \in D \sac d\pa{x,y} \leqslant s} $$ -Afin de dterminer les voisins de $x$, une mthode simple consiste estimer toutes les distances entre $x$ et les lments de $D$. Le cot de cette mthode est proportionnel au nombre d'lments de $D$ et la complexit du calcul de la distance. On distingue gnralement deux directions afin d'amliorer la rapidit des algorithmes de recherche~: +Afin de d�terminer les voisins de $x$, une m�thode simple consiste � estimer toutes les distances entre $x$ et les �l�ments de $D$. Le co�t de cette m�thode est proportionnel au nombre d'�l�ments de $D$ et � la complexit� du calcul de la distance. On distingue g�n�ralement deux directions afin d'am�liorer la rapidit� des algorithmes de recherche~: \begin{enumerate} \item L'optimisation du calcul de la distance. @@ -37,7 +37,7 @@ \end{enumerate} -Les mthodes prsentes dans ce chapitre concerne la seconde direction, plus gnrale que la premire. +Les m�thodes pr�sent�es dans ce chapitre concerne la seconde direction, plus g�n�rale que la premi�re. @@ -47,116 +47,116 @@ %------------------------------------------------------------------------------------------------------------- -\section{Classification ascendante hirarchique} +\section{Classification ascendante hi�rarchique} %------------------------------------------------------------------------------------------------------------- -Cette mthode reprend celle dcrite dans l'article \citeindex{Dupr2003}. +Cette m�thode reprend celle d�crite dans l'article \citeindex{Dupr�2003}. \subsection{Arbre de partitionnement} \label{section_partitionning_tree} \indexfr{partitionnement}\indexfrr{arbre}{partitionnement} -L'objectif de cette partie est de construire un arbre de partitionnement qui sera utilis ensuite afin d'amliorer la recherche des plus proches voisins au paragraphe~\ref{section_optimisation_distance}. +L'objectif de cette partie est de construire un arbre de partitionnement qui sera utilis� ensuite afin d'am�liorer la recherche des plus proches voisins au paragraphe~\ref{section_optimisation_distance}. - \begin{xdefinition}{rayon et centre d'un ensemble discret} - \indexfr{centre}\indexfr{rayon} - \label{definition_center_radius}% - Soit $D = \vecteur{y_1}{y_N} \subset E$ un ensemble fini de $E$, le centre $C\pa{D}$ de $D$ - est dfini par~: - $$ - C\pa{D} \in \underset {x \in D} {\arg \min} \cro{ \underset{y \in D} {\max} \; d\pa{x,y}} - $$ - o $d\pa{x,y}$ est la distance entre les lments $x$ et $y$. On dfinit aussi le rayon $R\pa{D}$ - de $D$ par~: - $$ - R\pa{D} = \underset{x \in D} {\max} \; d\pa{C\pa{D},y} - $$ - \end{xdefinition} + \begin{xdefinition}{rayon et centre d'un ensemble discret} + \indexfr{centre}\indexfr{rayon} + \label{definition_center_radius}% + Soit $D = \vecteur{y_1}{y_N} \subset E$ un ensemble fini de $E$, le centre $C\pa{D}$ de $D$ + est d�fini par~: + $$ + C\pa{D} \in \underset {x \in D} {\arg \min} \cro{ \underset{y \in D} {\max} \; d\pa{x,y}} + $$ + o� $d\pa{x,y}$ est la distance entre les �l�ments $x$ et $y$. On d�finit aussi le rayon $R\pa{D}$ + de $D$ par~: + $$ + R\pa{D} = \underset{x \in D} {\max} \; d\pa{C\pa{D},y} + $$ + \end{xdefinition} \begin{xremark}{cas particulier} -Si $A,B \subset D$ et $A \subset B$, cela n'implique pas que $R\pa{A} \infegal R\pa{B}$ comme le montre la -figure~\ref{figure_partition_inclusion} o $$R\pa{A} = d\pa{x,y} > d\pa{x,z} = R\pa{B}$$. +Si $A,B \subset D$ et $A \subset B$, cela n'implique pas que $R\pa{A} \leqslant R\pa{B}$ comme le montre la +figure~\ref{figure_partition_inclusion} o� $$R\pa{A} = d\pa{x,y} > d\pa{x,z} = R\pa{B}$$. \end{xremark} - \begin{figure}[ht] + \begin{figure}[ht] \[ \unitlength 1mm \fbox{ \filefig{../space_metric/fig_ray} } \] - \caption{Exemple o ajouter un lment un sous-ensemble aboutit une rduction du rayon.} + \caption{Exemple o� ajouter un �l�ment � un sous-ensemble aboutit � une r�duction du rayon.} \label{figure_partition_inclusion} - \end{figure} + \end{figure} - \begin{xproperty}{rayon d'un couple d'lments}\label{property_001}% - Soit $\pa{x,y} \in E^2$ deux lments de $E$, alors $R\acc{x,y} = d\pa{x,y}$.\\ - \end{xproperty} + \begin{xproperty}{rayon d'un couple d'�l�ments}\label{property_001}% + Soit $\pa{x,y} \in E^2$ deux �l�ments de $E$, alors $R\acc{x,y} = d\pa{x,y}$.\\ + \end{xproperty} L'algorithme~\ref{algorithm_AHC}\footnote{ Autre formulation~: - \begin{xalgorithm}{Arbre de partionnement} - \label{algorithm_AHC_prime}% - Soit $D= \vecteur{y_1}{y_N}$ un ensemble fini de $\pa{E,d}$. Soit $N\pa{n_1,n_2,C,R}$ un n\oe ud - li ses deux prcdesseurs $n_1,n_2$ et qui dfinit une partie dont le centre est $C$ et le rayon $R$. - $L$ reprsente un ensemble de n\oe uds. Si $x$ est un n\oe ud, $P\pa{x}$ dsigne la partie runion - de tous les centres des anctres de $x$. - - \begin{xalgostep}{initialisation} - Pour t$ y \in D$, on ajoute le n\oe ud$N\pa{ \emptyset,\emptyset,y,0}$ $L$. - \end{xalgostep} - - \begin{xalgostep}{recherche de la meilleure runion}\label{space_metric_step_cah_cah_2} - Soit $\pa{x,y} \in \underset{ x,y \in L, \, x \neq y } - {\arg \min} \; R\pa{ P\pa{x} \cup P\pa{y} }$. - Le n\oe ud \\ - $ z = N\pa{ x,y, - C\pa{ P\pa{x} \cup P\pa{y} } , - R\pa{ P\pa{x} \cup P\pa{y} } }$ est cr et - $L \longleftarrow L \cup {z} - \acc{x,y}$ - \end{xalgostep} - - \begin{xalgostep}{terminaison} - Si $L$ contient plus d'un n\oe ud, retour l'tape~\ref{space_metric_step_cah_cah_2}. - \end{xalgostep} - \end{xalgorithm} -} est bas sur une classification ascendante hirarchique (voir \citeindex{Saporta1990}, \citeindex{Reinert1979}),\indexfr{CAH} il construit une hirarchie de partitions dcrite par un graphe. \indexfr{n\oe ud} + \begin{xalgorithm}{Arbre de partionnement} + \label{algorithm_AHC_prime}% + Soit $D= \vecteur{y_1}{y_N}$ un ensemble fini de $\pa{E,d}$. Soit $N\pa{n_1,n_2,C,R}$ un n\oe ud + li� � ses deux pr�c�desseurs $n_1,n_2$ et qui d�finit une partie dont le centre est $C$ et le rayon $R$. + $L$ repr�sente un ensemble de n\oe uds. Si $x$ est un n\oe ud, $P\pa{x}$ d�signe la partie r�union + de tous les centres des anc�tres de $x$. + + \begin{xalgostep}{initialisation} + Pour t$ y \in D$, on ajoute le n\oe ud$N\pa{ \emptyset,\emptyset,y,0}$ � $L$. + \end{xalgostep} + + \begin{xalgostep}{recherche de la meilleure r�union}\label{space_metric_step_cah_cah_2} + Soit $\pa{x,y} \in \underset{ x,y \in L, \, x \neq y } + {\arg \min} \; R\pa{ P\pa{x} \cup P\pa{y} }$. + Le n\oe ud \\ + $ z = N\pa{ x,y, + C\pa{ P\pa{x} \cup P\pa{y} } , + R\pa{ P\pa{x} \cup P\pa{y} } }$ est cr�� et + $L \longleftarrow L \cup {z} - \acc{x,y}$ + \end{xalgostep} + + \begin{xalgostep}{terminaison} + Si $L$ contient plus d'un n\oe ud, retour � l'�tape~\ref{space_metric_step_cah_cah_2}. + \end{xalgostep} + \end{xalgorithm} +} est bas� sur une classification ascendante hi�rarchique (voir \citeindex{Saporta1990}, \citeindex{Reinert1979}),\indexfr{CAH} il construit une hi�rarchie de partitions d�crite par un graphe. \indexfr{n\oe ud} - \begin{xalgorithm}{classification ascendante hirarchique}\label{algorithm_AHC}% - Soit $D= \vecteur{y_1}{y_N}$ un sous-ensemble fini de $\pa{E,d}$. On note $P_n$ - une partie contenant les lments $P_n = \acc{p_{n,1} \; , ... , \; p_{n, \cro{card\pa{D}-n+1}}}$. - L'algorithme a pour but de construire la suite de partitions $\pa{P_n}_{n \supegal 1}$ comme suit~: - - \begin{xalgostep}{initialisation} - $n = 1$ et $P_1$ est la partition o chaque lment de $D$ est un lment de $P_1$ - \end{xalgostep} - - \begin{xalgostep}{rcurrence}\label{space_cah_algo_step} - \begin{xfor}{n}{1}{N-1} - soit $\pa{i^*_n,j^*_n} \in \underset{ i \neq j }{\arg \min} \; R\pa{p_{ni} \cup p_{nj} }$, alors - $P_{n+1} = \acc{ \pa{p_{nk}}_{k \neq i^*_n,j^*_n}, p_{ni^*} \cup p_{nj^*} }$ - \end{xfor} - \end{xalgostep} - - \end{xalgorithm} + \begin{xalgorithm}{classification ascendante hi�rarchique}\label{algorithm_AHC}% + Soit $D= \vecteur{y_1}{y_N}$ un sous-ensemble fini de $\pa{E,d}$. On note $P_n$ + une partie contenant les �l�ments $P_n = \acc{p_{n,1} \; , ... , \; p_{n, \cro{card\pa{D}-n+1}}}$. + L'algorithme a pour but de construire la suite de partitions $\pa{P_n}_{n \supegal 1}$ comme suit~: + + \begin{xalgostep}{initialisation} + $n = 1$ et $P_1$ est la partition o� chaque �l�ment de $D$ est un �l�ment de $P_1$ + \end{xalgostep} + + \begin{xalgostep}{r�currence}\label{space_cah_algo_step} + \begin{xfor}{n}{1}{N-1} + soit $\pa{i^*_n,j^*_n} \in \underset{ i \neq j }{\arg \min} \; R\pa{p_{ni} \cup p_{nj} }$, alors + $P_{n+1} = \acc{ \pa{p_{nk}}_{k \neq i^*_n,j^*_n}, p_{ni^*} \cup p_{nj^*} }$ + \end{xfor} + \end{xalgostep} + + \end{xalgorithm} -La suite $\pa{P_n}_{1 \infegal n \infegal N}$ dfinit un graphe d'inclusion illustr par la -figure~\ref{figure_partition_inclusion_graph}) pour cinq lments.\\ +La suite $\pa{P_n}_{1 \leqslant n \leqslant N}$ d�finit un graphe d'inclusion illustr� par la +figure~\ref{figure_partition_inclusion_graph}) pour cinq �l�ments.\\ @@ -167,7 +167,7 @@ \subsection{Arbre de partitionnement} \begin{xremark}{plusieurs minima} -L'tape~\ref{space_cah_algo_step} impose de choisir un lment dans un ensemble de minima. Lorsque celui-ci contient plus d'un lment, une rgle simple consiste choisir le plus petit regroupement de deux parties parmi celles de rayon minimum. Cette rgle a peu d'influence lorsque la construction de l'arbre s'effectue dans un espace continu. En revanche, pour un espace de mot muni d'une distance d'dition comme celle de Levenstein (voir~\citeindex{Levenstein1966}), ce cas se produit frquemment puisque la distance est valeurs entires. Cette rgle accrot sensiblement les performances. +L'�tape~\ref{space_cah_algo_step} impose de choisir un �l�ment dans un ensemble de minima. Lorsque celui-ci contient plus d'un �l�ment, une r�gle simple consiste � choisir le plus petit regroupement de deux parties parmi celles de rayon minimum. Cette r�gle a peu d'influence lorsque la construction de l'arbre s'effectue dans un espace continu. En revanche, pour un espace de mot muni d'une distance d'�dition comme celle de Levenstein (voir~\citeindex{Levenstein1966}), ce cas se produit fr�quemment puisque la distance est � valeurs enti�res. Cette r�gle accro�t sensiblement les performances. \end{xremark} @@ -175,77 +175,77 @@ \subsection{Arbre de partitionnement} Chaque n\oe ud du graphe obtenu avec l'algorithme~\ref{algorithm_AHC} satisfait les conditions suivantes~: \begin{enumerate} -\item Il n'a aucun prdcesseur\indexfr{prdcesseur} et la partie dsigne par ce n\oe ud est un +\item Il n'a aucun pr�d�cesseur\indexfr{pr�d�cesseur} et la partie d�sign�e par ce n\oe ud est un singleton\indexfr{singleton} dont le rayon est nul. \indexfr{rayon} -\item Il a deux prdcesseurs et la partie pointe par ce n\oe ud contient plus d'un lment, son rayon est strictement positif si au moins deux lments sont diffrents. -\item Il n'a aucun successeur\indexfr{successeur} et la partie que le n\oe ud dsigne est le sous-ensemble $D$. +\item Il a deux pr�d�cesseurs et la partie point�e par ce n\oe ud contient plus d'un �l�ment, son rayon est strictement positif si au moins deux �l�ments sont diff�rents. +\item Il n'a aucun successeur\indexfr{successeur} et la partie que le n\oe ud d�signe est le sous-ensemble $D$. \end{enumerate} - \begin{figure}[ht] + \begin{figure}[ht] \[\frame{ \filefig{../space_metric/fig_cah} }\] - \caption{Graphe d'inclusion, la premire partition contient une partie par lment de $D$ (les + \caption{Graphe d'inclusion, la premi�re partition contient une partie par �l�ment de $D$ (les feuilles)\indexfr{feuille}, la seconde partition regroupe ensemble les plus - proches lments, - la troisime partition regroupe ensemble les couples d'lments les plus proches, - la quatrime partition + proches �l�ments, + la troisi�me partition regroupe ensemble les couples d'�l�ments les plus proches, + la quatri�me partition construit la partition de rayon minimum, le choix est entre - $\acc{x_1,x_2,x_3}$, $\acc{x_1,x_4,x_5}$, $\acc{x_2,x_3,x_4,x_5}$, la cinquime partition est + $\acc{x_1,x_2,x_3}$, $\acc{x_1,x_4,x_5}$, $\acc{x_2,x_3,x_4,x_5}$, la cinqui�me partition est l'ensemble $D$ complet.} \label{figure_partition_inclusion_graph} - \end{figure} + \end{figure} - \begin{xproperty}{nombre de n\oe uds}\label{property_node} - L'arbre construit par l'algorithme~\ref{algorithm_AHC} contient exactement $2N-1 = 2 * card\pa{D}-1$ - n\oe uds. - \end{xproperty} + \begin{xproperty}{nombre de n\oe uds}\label{property_node} + L'arbre construit par l'algorithme~\ref{algorithm_AHC} contient exactement $2N-1 = 2 * card\pa{D}-1$ + n\oe uds. + \end{xproperty} -\begin{xdemo}{proprit}{\ref{property_node}} -L'arbre construit par l'algorithme~\ref{algorithm_AHC} contient $N$ n\oe uds sans prdcesseur, -\indexfr{prdcesseur} soit un n\oe ud par mot de $D$. A chaque itration, un n\oe ud est cr pour -assembler deux parties entre elles. $N-1$ itrations sont ncessaires pour passer de $N$ parties une seule. Donc, le nombre de n\oe ud de l'arbre est~: +\begin{xdemo}{propri�t�}{\ref{property_node}} +L'arbre construit par l'algorithme~\ref{algorithm_AHC} contient $N$ n\oe uds sans pr�d�cesseur, +\indexfr{pr�d�cesseur} soit un n\oe ud par mot de $D$. A chaque it�ration, un n\oe ud est cr�� pour +assembler deux parties entre elles. $N-1$ it�rations sont n�cessaires pour passer de $N$ parties � une seule. Donc, le nombre de n\oe ud de l'arbre est~: $$ N + \pa{N-1} = 2N-1 $$ \end{xdemo} -Le thorme~\ref{theorem_hierarchy} montre que l'arbre construit par l'algorithme~\ref{algorithm_AHC} peut tre -considr comme une hirarchie pour un certain indice dfini par le thorme. +Le th�or�me~\ref{theorem_hierarchy} montre que l'arbre construit par l'algorithme~\ref{algorithm_AHC} peut �tre +consid�r� comme une hi�rarchie pour un certain indice d�fini par le th�or�me. - \begin{xtheorem}{hirarchie}\label{theorem_hierarchy} - \indexfr{hirarchie} - Soit $\pa{P_n}_{1 \infegal n \infegal N}$ la suite construite par l'algorithme~\ref{algorithm_AHC}. - Soit $i\pa{P_n}$ dfini par~: - $$ - i\pa{P_n} = \underset{p \in P_n} {\max} \, R\pa{p} - $$ - Alors, la suite $\pa{i\pa{P_n}}_{1 \infegal n \infegal N}$ est croissante. - \indexfrr{suite}{croissante} - \end{xtheorem} + \begin{xtheorem}{hi�rarchie}\label{theorem_hierarchy} + \indexfr{hi�rarchie} + Soit $\pa{P_n}_{1 \leqslant n \leqslant N}$ la suite construite par l'algorithme~\ref{algorithm_AHC}. + Soit $i\pa{P_n}$ d�fini par~: + $$ + i\pa{P_n} = \underset{p \in P_n} {\max} \, R\pa{p} + $$ + Alors, la suite $\pa{i\pa{P_n}}_{1 \leqslant n \leqslant N}$ est croissante. + \indexfrr{suite}{croissante} + \end{xtheorem} -\begin{xdemo}{thorme}{\ref{theorem_hierarchy}} +\begin{xdemo}{th�or�me}{\ref{theorem_hierarchy}} -Afin que la dmonstration soit plus claire, les partitions notes~: +Afin que la d�monstration soit plus claire, les partitions not�es~: $$ - \pa{P_{ni}}_{ \begin{subarray}{l} 1 \infegal n \infegal N \\ 1 \infegal i \infegal N-n+1 \end{subarray} }% + \pa{P_{ni}}_{ \begin{subarray}{l} 1 \leqslant n \leqslant N \\ 1 \leqslant i \leqslant N-n+1 \end{subarray} }% \text{ avec } \forall \pa{n,i}, \; P_{ni} \neq \emptyset $$ -sont maintenant notes~: +sont maintenant not�es~: $$ - \pa{P_{ni}}_{\begin{subarray}{l} 1 \infegal n \infegal N \\ 1 \infegal i \infegal N \end{subarray}} % + \pa{P_{ni}}_{\begin{subarray}{l} 1 \leqslant n \leqslant N \\ 1 \leqslant i \leqslant N \end{subarray}} % \text{ avec } \forall n, \; card \acc{ i | P_{ni} = \emptyset } = N-n+1 $$ @@ -256,7 +256,7 @@ \subsection{Arbre de partitionnement} \end{eqnarray*} Selon l'algorithme~\ref{algorithm_AHC}, $\exists \pa{a_{k+1},b_{k+1}} \in \intervalle{1}{N}^2$ tel que $a_{k+1} < b_{k+1}$ et~: - $$ + $$ \begin{array}{l} P_{k+1}^{a_{k+1}} = P_{k}^{a_{k+1}} \cup P_{k}^{b_{k+1}} \; , \quad P_{k+1}^{b_{k+1}} = \emptyset \; , \quad @@ -266,19 +266,19 @@ \subsection{Arbre de partitionnement} \end{array} $$ -Soit $R_k^i = R \pa{P_k^i}$ et $C_k^i = C \pa{P^k_i}$. On impose aussi que si $P^k_i = \emptyset$ alors $R_k^i = 0$. Si l'assertion (\ref{equation_1}) est vraie alors le thorme~\ref{theorem_hierarchy} le sera aussi~: +Soit $R_k^i = R \pa{P_k^i}$ et $C_k^i = C \pa{P^k_i}$. On impose aussi que si $P^k_i = \emptyset$ alors $R_k^i = 0$. Si l'assertion (\ref{equation_1}) est vraie alors le th�or�me~\ref{theorem_hierarchy} le sera aussi~: \begin{eqnarray} \forall k \in \intervalle{1}{N-1}, \, \forall i \in \intervalle{1}{N}, \, R_{k+1}^{a_{k+1}} \supegal R_{k}^i \label{equation_1} \end{eqnarray} -Cette dmonstratin s'effectue par rcurrence. L'assertion (\ref{equation_1}) est vraie de manire vidente pour $k=2$ puisque~: +Cette d�monstratin s'effectue par r�currence. L'assertion (\ref{equation_1}) est vraie de mani�re �vidente pour $k=2$ puisque~: $$ \forall i \in \intervalle{1}{N}, \, R_{1}^i = 0 $$ -Donc, on suppose (\ref{equation_1}) est pour tout $k < N$, on cherche montrer que (\ref{equation_1}) est aussi vraie pour $k+1$. +Donc, on suppose (\ref{equation_1}) est pour tout $k < N$, on cherche � montrer que (\ref{equation_1}) est aussi vraie pour $k+1$. \itemdemo @@ -295,8 +295,8 @@ \subsection{Arbre de partitionnement} \begin{eqnarray} \forall i \in \intervalle{1}{N}, \, && - i \notin \acc{a_k,b_k} \Longrightarrow R_k^{a_k} \supegal R_{k-1}^{i} = R_{k}^{i} - \label{equation_2} + i \notin \acc{a_k,b_k} \Longrightarrow R_k^{a_k} \supegal R_{k-1}^{i} = R_{k}^{i} + \label{equation_2} \end{eqnarray} Mais l'algorithme~\ref{theorem_hierarchy} implique que~: @@ -310,7 +310,7 @@ \subsection{Arbre de partitionnement} {\arg \min} \, R\pa{P_{k}^i \bigcup P_{k}^j} \end{eqnarray*} -Par consquent, $\acc{a_k,b_k} \bigcap \acc{a_{k+1},b_{k+1}} = \emptyset$ implique que~: +Par cons�quent, $\acc{a_k,b_k} \bigcap \acc{a_{k+1},b_{k+1}} = \emptyset$ implique que~: \begin{eqnarray*} \acc{a_{k+1},b_{k+1}} \in \underset{ \begin{subarray}{c} i < j \\ i \neq a_k, j\neq b_k\\ @@ -319,11 +319,11 @@ \subsection{Arbre de partitionnement} &\Longrightarrow& \acc{a_{k+1},b_{k+1}} \in \underset{ \begin{subarray}{c} i < j \\ i \neq a_k, j\neq b_k\\ P_{k-1}^i \neq \emptyset \\ P_{k-1}^j \neq \emptyset\end{subarray} } {\arg \min} \, R\pa{P_{k-1}^i \bigcup P_{k-1}^j}\\ - &\Longrightarrow& R\pa{P_k^{a_{k-1}} \bigcup P_k^{b_{k-1}}} \infegal R\pa{P_k^{a_{k+1}} \bigcup P_k^{b_{k+1}}} \\ - &\Longrightarrow& R\pa{ P_k^{a_{k}} } \infegal R\pa{ P_{k+1}^{a_{k+1}} } + &\Longrightarrow& R\pa{P_k^{a_{k-1}} \bigcup P_k^{b_{k-1}}} \leqslant R\pa{P_k^{a_{k+1}} \bigcup P_k^{b_{k+1}}} \\ + &\Longrightarrow& R\pa{ P_k^{a_{k}} } \leqslant R\pa{ P_{k+1}^{a_{k+1}} } \end{eqnarray*} -D'o~: +D'o�~: \begin{eqnarray} \forall i \in \intervalle{1}{N}, \, R_{k+1}^{a_k} \supegal R_{k}^{i} \label{equation_reca_1} \end{eqnarray} @@ -335,7 +335,7 @@ \subsection{Arbre de partitionnement} \quad \para{$2^\circ$ cas : $\acc{a_k,b_k} \bigcap \acc{a_{k+1},b_{k+1}} \neq \emptyset$} -Si $\acc{a_k,b_k} \bigcap \acc{a_{k+1},b_{k+1}} \neq \emptyset$, alors $a_{k+1} = a_k$ or $b_{k+1} = a_k$. Pour ces deux cas, la preuve est la mme donc on suppose que $a_{k+1} = a_k$, alors : +Si $\acc{a_k,b_k} \bigcap \acc{a_{k+1},b_{k+1}} \neq \emptyset$, alors $a_{k+1} = a_k$ or $b_{k+1} = a_k$. Pour ces deux cas, la preuve est la m�me donc on suppose que $a_{k+1} = a_k$, alors : $$ \forall i \in \intervalle{1}{N}, \, R_k^{a_k} \supegal R_{k}^{i} \quad \text{ (voir (\ref{equation_2}))} $$ @@ -358,21 +358,21 @@ \subsection{Arbre de partitionnement} \item si $C_{k+1}^{a_k} \in P_{k}^{a_{k}}$, alors : \begin{eqnarray} R_k^{a_k} &=& \underset{y \in P_{k}^{a_{k}}}{\max} \, d\pa{C_{k}^{a_k},y} %\nonumber\\ - \infegal \underset{y \in P_{k}^{a_{k}}}{\max} \, d\pa{C_{k+1}^{a_k},y} - \infegal \underset{y \in P_{k+1}^{a_{k}}}{\max} \, d\pa{C_{k+1}^{a_k},y} %\nonumber\\ - \infegal R_{k+1}^{a_k} \label{equation_reca_2} + \leqslant \underset{y \in P_{k}^{a_{k}}}{\max} \, d\pa{C_{k+1}^{a_k},y} + \leqslant \underset{y \in P_{k+1}^{a_{k}}}{\max} \, d\pa{C_{k+1}^{a_k},y} %\nonumber\\ + \leqslant R_{k+1}^{a_k} \label{equation_reca_2} \end{eqnarray} \item si $C_{k+1}^{a_k} \in P_{k}^{b_{k+1}}$, alors l'algorithm~\ref{algorithm_AHC} implique que~: \begin{eqnarray} R_{k+1}^{a_k} &=& \max \Bigg\{ - \underset{y \in P_{k}^{a_{k}}}{\max} \, d\pa{C_{k+1}^{a_k},y} , %\nonumber\\ + \underset{y \in P_{k}^{a_{k}}}{\max} \, d\pa{C_{k+1}^{a_k},y} , %\nonumber\\ %&& \quad \quad \quad \quad \underset{y \in P_{k}^{b_{k+1}}}{\max} \, d\pa{C_{k+1}^{a_k},y} \Bigg \}\nonumber\\ &\supegal& \max \acc{ \underset{y \in P_{k}^{a_{k}}}{\max} \, - d\pa{C_{k+1}^{a_k},y} , R_k^{b_{k+1}} }\nonumber\\ + d\pa{C_{k+1}^{a_k},y} , R_k^{b_{k+1}} }\nonumber\\ &\supegal& \max \Bigg\{ \underset{y \in P_{k-1}^{a_{k}}}{\max} \, - d\pa{C_{k+1}^{a_k},y} , + d\pa{C_{k+1}^{a_k},y} , \underset{y \in P_{k-1}^{b_{k}}}{\max} \, d\pa{C_{k+1}^{a_k},y} , R_k^{b_{k+1}} \Bigg\}\nonumber\\ &\supegal& \max \acc{ R_k^{a_{k}} , R_k^{b_{k}} , R_k^{b_{k+1}} }\nonumber\\ @@ -381,8 +381,8 @@ \subsection{Arbre de partitionnement} \end{enumerate} -En conclusion, la rcurrence est dmontre par les ingalits (\ref{equation_reca_1}), (\ref{equation_reca_2}), -(\ref{equation_reca_3}). Par consquent, la suite d'indices $\pa{i\pa{P_n}}_{1 \infegal n \infegal N}$ est croissante. +En conclusion, la r�currence est d�montr�e par les in�galit�s (\ref{equation_reca_1}), (\ref{equation_reca_2}), +(\ref{equation_reca_3}). Par cons�quent, la suite d'indices $\pa{i\pa{P_n}}_{1 \leqslant n \leqslant N}$ est croissante. \end{xdemo} @@ -392,129 +392,129 @@ \subsection{Arbre de partitionnement} - \begin{xcorollary}{majoration du rayon}\label{corollary_AHC}% - Soit $D$ un sous-ensemble fini de $E$ et $A$ l'arbre obtenu grce l'algorithme~\ref{algorithm_AHC}, - soit $n$ un n\oe ud quelconque de cet arbre, alors~:\indexfr{successeur} - \begin{eqnarray} - \textnormal{le successeur } s\pa{n} \text { de } n\; { existe} - \Longrightarrow R\pa{n} \infegal R\pa{s\pa{n}} - \end{eqnarray} - \end{xcorollary} + \begin{xcorollary}{majoration du rayon}\label{corollary_AHC}% + Soit $D$ un sous-ensemble fini de $E$ et $A$ l'arbre obtenu gr�ce � l'algorithme~\ref{algorithm_AHC}, + soit $n$ un n\oe ud quelconque de cet arbre, alors~:\indexfr{successeur} + \begin{eqnarray} + \textnormal{le successeur } s\pa{n} \text { de } n\; { existe} + \Longrightarrow R\pa{n} \leqslant R\pa{s\pa{n}} + \end{eqnarray} + \end{xcorollary} Finalement, si $p_1$ et $p_2$ sont deux partitions de l'arbre de partitionnement, et $p_1 \subset p_2$, alors -$R\pa{p_1} \infegal R\pa{p_2}$. Cette conclusion n'tait pas vidente d'aprs le cas particulier de la figure~\ref{figure_partition_inclusion}. +$R\pa{p_1} \leqslant R\pa{p_2}$. Cette conclusion n'�tait pas �vidente d'apr�s le cas particulier de la figure~\ref{figure_partition_inclusion}. -L'objectif de cet algorithme est de grouper ensemble les lments ou les parties les plus proches chaque itration. La consquence attendue est que le voisinage d'un mot soit concentr dans une branche de l'arbre. Nanmoins, le principal inconvnient de cet algorithme est son cot. Si on suppose que le cot de la distance est approche par une constante $c$ et que l'ensemble hirarchiser contient $n$~lments, le cot de l'algorithme est en $O\pa{c\,n^5}$. Ce cot peut tre rduit en factorisant les calculs d'une itration l'autre puisque la liste $L$ conserve $n-k-1$ n\oe uds inchangs. Cette remarque permet de ne calculer le centre et le rayon que pour les parties nouvellement cres. De la mme manire, il est possible de conserver pour chaque n\oe ud, le meilleur voisin qui, l'itration suivante, peut tre rest le mme ou tre la partie qui vient d'tre runie. Finalement, il est possible de faire descendre le cot de l'algorithme $O\pa{c \, n^3}$. +L'objectif de cet algorithme est de grouper ensemble les �l�ments ou les parties les plus proches � chaque it�ration. La cons�quence attendue est que le voisinage d'un mot soit concentr� dans une branche de l'arbre. N�anmoins, le principal inconv�nient de cet algorithme est son co�t. Si on suppose que le co�t de la distance est approch�e par une constante $c$ et que l'ensemble � hi�rarchiser contient $n$~�l�ments, le co�t de l'algorithme est en $O\pa{c\,n^5}$. Ce co�t peut �tre r�duit en factorisant les calculs d'une it�ration � l'autre puisque la liste $L$ conserve $n-k-1$ n\oe uds inchang�s. Cette remarque permet de ne calculer le centre et le rayon que pour les parties nouvellement cr��es. De la m�me mani�re, il est possible de conserver pour chaque n\oe ud, le meilleur voisin qui, � l'it�ration suivante, peut �tre rest� le m�me ou �tre la partie qui vient d'�tre r�unie. Finalement, il est possible de faire descendre le co�t de l'algorithme � $O\pa{c \, n^3}$. -Toutefois, pour des ensembles de plusieurs milliers d'lments, l'algorithme~\ref{algorithm_AHC} demeure trs long. Une optimisation consiste adapter l'algorithme des centres mobiles\seeannex{emission_continue_centre_mobile}{centres mobiles}. L'algorithme s'inspire de l'algorithme~\ref{algo_centre_mobile} en remplaant toutefois la notion de barycentre d'une partie par son centre (dfinition~\ref{definition_center_radius}) et l'inertie d'une partie par son rayon. +Toutefois, pour des ensembles de plusieurs milliers d'�l�ments, l'algorithme~\ref{algorithm_AHC} demeure tr�s long. Une optimisation consiste � adapter l'algorithme des centres mobiles\seeannex{emission_continue_centre_mobile}{centres mobiles}. L'algorithme s'inspire de l'algorithme~\ref{algo_centre_mobile} en rempla�ant toutefois la notion de barycentre d'une partie par son centre (d�finition~\ref{definition_center_radius}) et l'inertie d'une partie par son rayon. - + -\subsection{Ajouter un lment au graphe} +\subsection{Ajouter un �l�ment au graphe} \label{section_alternative} -L'algorithme~\ref{algorithm_AHC} ne permet d'insrer de nouveaux n\oe uds une fois que celui-ci est construit, inconvnient auquel remdie l'algorithme~\ref{algorithm_insertion}. Ce dernier ajoute des n\oe uds au graphe de partitionnement et pourrait tre galement utilis pour construire l'arbre entier en insrant un un tous les lments de $D$ mais cette mthode est moins efficace. - - - - \begin{xalgorithm}{insertion d'un n\oe ud}\label{algorithm_insertion} - - Soit $D$ un sous-ensemble fini de $E$ et $A$ un arbre binaire, soit $n$ un n\oe quelconque de $A$, alors~: - \begin{enumerate} - \item $n$ dfinit une partie note $P\pa{n}$ dont le rayon est $R\pa{n}$ et - dont le centre est $C\pa{n}$. - \item $n$ n'a pas de prdcesseur\indexfr{prdcesseur} si $P\pa{n}$ est - un singleton ou deux predecessors - sinon. Dans ce second cas, $P\pa{n}$ est la runion des parties de deux prdcesseurs~: - $Pr\pa{n} = \acc{ p_1\pa{n} , p_2\pa{n}}$ - \item $n$ n'a pas de successeur\indexfr{successeur} et il dfinit la partiet $D$ - ou un successeur not $Su\pa{n} = s\pa{n}$ - \end{enumerate} - - Le seul n\oe ud sans successeur est appel \emph{racine}.\indexfr{racine} et not $r$. - Soit $m$ un lment et $x$ - le n\oe ud associ, alors $P\pa{x} = \acc{m}$, $C\pa{x} = m$, $R\pa{x} = 0$, et $x$ n'a - pour le moment aucun succeseur - et aucun prdcesseur. $N$ dfinit un ensemble de n\oe ud, le n\oe ud $x$ est insr dans l'arbre $A$ - selon les rgles suivantes~: - - \begin{xalgostep}{intialisation} - $N \longleftarrow \acc{r}$ - \end{xalgostep} - - %\possiblecut - - \begin{xalgostep}{insertion} - \begin{xwhile}{non fin} - - \begin{xif}{$ - \begin{array}{l} d\pa{m,C\pa{n}} > R\pa{n} - \hspace{10cm} - \refstepcounter{equation}(\theequation) - \label{amelioration_insertion_1} - \end{array} - $} - - \begin{enumerate} - \item soit $ n\in \arg \min \acc{ d\pa{m,C\pa{n'}} \;|\; n' \in N}$, le n\oe ud $y$ est cr - \item le successeur de $y$ devient : $s\pa{y} \longleftarrow s\pa{n}$ - \item le prdcesseur de $y$ devient : $p_1\pa{y} \longleftarrow x$ - and $p_2\pa{y} \longleftarrow n$ - \item si le successeur de $n$ existe alors : - $$ - \exists i \in \acc{1,2} \text{ tel que } p_i\pa{s\pa{n}} = n \text{ et } - p_i\pa{s\pa{n}} \longleftarrow p_i\pa{s\pa{n}} = y - $$ - \item le successeur de $n$ devient : $s\pa{n} \longleftarrow y$ - \item le successeur de $x$ devient : $s\pa{x} \longleftarrow y$ - \item si $r = n$, alors $r \longleftarrow y$ - \item le centre et le rayon sont reestims pour toutes les parties dfinies par - les lments $\pa{n_1,...}$ de la suite the serie : - $$ - n_0 = n \textnormal{ et pour } k \supegal 0, \; - n_{k+1} = \left\{ - \begin{array}{l} - s\pa{n_k} \textnormal{ si } r \neq n_k \\ - r \textnormal{ sinon } - \end{array} - \right. - $$ - \item fin - \end{enumerate} - - \xelse - $$ - \begin{array}{lr} - \exists n \in N \text{ tel que } d\pa{m,C\pa{n}} \infegal R\pa{n} & - \hspace{6.5cm}\refstepcounter{equation}(\theequation) - \label{amelioration_insertion_2} \\ - \text{et } N \longleftarrow \acc{N - \acc{n} } \cup \acc{p_1\pa{n},p_2\pa{n}} & - \end{array} - $$ - \end{xif} - \end{xwhile} - \end{xalgostep} - \end{xalgorithm} - - -\begin{xremark}{hirarchie} -Il n'est pas dmontr que l'arbre obtenu aprs une ou plusieurs applications de l'algorithme~\ref{algorithm_insertion} vrifie le corollaire~\ref{corollary_AHC}. \indexfr{hirarchie} +L'algorithme~\ref{algorithm_AHC} ne permet d'ins�rer de nouveaux n\oe uds une fois que celui-ci est construit, inconv�nient auquel rem�die l'algorithme~\ref{algorithm_insertion}. Ce dernier ajoute des n\oe uds au graphe de partitionnement et pourrait �tre �galement utilis� pour construire l'arbre entier en ins�rant un � un tous les �l�ments de $D$ mais cette m�thode est moins efficace. + + + + \begin{xalgorithm}{insertion d'un n\oe ud}\label{algorithm_insertion} + + Soit $D$ un sous-ensemble fini de $E$ et $A$ un arbre binaire, soit $n$ un n\oe quelconque de $A$, alors~: + \begin{enumerate} + \item $n$ d�finit une partie not�e $P\pa{n}$ dont le rayon est $R\pa{n}$ et + dont le centre est $C\pa{n}$. + \item $n$ n'a pas de pr�d�cesseur\indexfr{pr�d�cesseur} si $P\pa{n}$ est + un singleton ou deux predecessors + sinon. Dans ce second cas, $P\pa{n}$ est la r�union des parties de deux pr�d�cesseurs~: + $Pr\pa{n} = \acc{ p_1\pa{n} , p_2\pa{n}}$ + \item $n$ n'a pas de successeur\indexfr{successeur} et il d�finit la partiet $D$ + ou un successeur not� $Su\pa{n} = s\pa{n}$ + \end{enumerate} + + Le seul n\oe ud sans successeur est appel� \emph{racine}.\indexfr{racine} et not� $r$. + Soit $m$ un �l�ment et $x$ + le n\oe ud associ�, alors $P\pa{x} = \acc{m}$, $C\pa{x} = m$, $R\pa{x} = 0$, et $x$ n'a + pour le moment aucun succeseur + et aucun pr�d�cesseur. $N$ d�finit un ensemble de n\oe ud, le n\oe ud $x$ est ins�r� dans l'arbre $A$ + selon les r�gles suivantes~: + + \begin{xalgostep}{intialisation} + $N \longleftarrow \acc{r}$ + \end{xalgostep} + + %\possiblecut + + \begin{xalgostep}{insertion} + \begin{xwhile}{non fin} + + \begin{xif}{$ + \begin{array}{l} d\pa{m,C\pa{n}} > R\pa{n} + \hspace{10cm} + \refstepcounter{equation}(\theequation) + \label{amelioration_insertion_1} + \end{array} + $} + + \begin{enumerate} + \item soit $ n\in \arg \min \acc{ d\pa{m,C\pa{n'}} \;|\; n' \in N}$, le n\oe ud $y$ est cr�� + \item le successeur de $y$ devient : $s\pa{y} \longleftarrow s\pa{n}$ + \item le pr�d�cesseur de $y$ devient : $p_1\pa{y} \longleftarrow x$ + and $p_2\pa{y} \longleftarrow n$ + \item si le successeur de $n$ existe alors : + $$ + \exists i \in \acc{1,2} \text{ tel que } p_i\pa{s\pa{n}} = n \text{ et } + p_i\pa{s\pa{n}} \longleftarrow p_i\pa{s\pa{n}} = y + $$ + \item le successeur de $n$ devient : $s\pa{n} \longleftarrow y$ + \item le successeur de $x$ devient : $s\pa{x} \longleftarrow y$ + \item si $r = n$, alors $r \longleftarrow y$ + \item le centre et le rayon sont reestim�s pour toutes les parties d�finies par + les �l�ments $\pa{n_1,...}$ de la suite the serie : + $$ + n_0 = n \textnormal{ et pour } k \supegal 0, \; + n_{k+1} = \left\{ + \begin{array}{l} + s\pa{n_k} \textnormal{ si } r \neq n_k \\ + r \textnormal{ sinon } + \end{array} + \right. + $$ + \item fin + \end{enumerate} + + \xelse + $$ + \begin{array}{lr} + \exists n \in N \text{ tel que } d\pa{m,C\pa{n}} \leqslant R\pa{n} & + \hspace{6.5cm}\refstepcounter{equation}(\theequation) + \label{amelioration_insertion_2} \\ + \text{et } N \longleftarrow \acc{N - \acc{n} } \cup \acc{p_1\pa{n},p_2\pa{n}} & + \end{array} + $$ + \end{xif} + \end{xwhile} + \end{xalgostep} + \end{xalgorithm} + + +\begin{xremark}{hi�rarchie} +Il n'est pas d�montr� que l'arbre obtenu apr�s une ou plusieurs applications de l'algorithme~\ref{algorithm_insertion} v�rifie le corollaire~\ref{corollary_AHC}. \indexfr{hi�rarchie} \end{xremark} - \begin{figure}[ht] + \begin{figure}[ht] \[ \begin{tabular}{|c|c|} \hline @@ -527,29 +527,29 @@ \subsection{Ajouter un \hline \end{tabular} \] - \caption{Graphe d'inclusion pour deux ordres diffrents d'inclusion, le second est bien sr meilleur.} + \caption{Graphe d'inclusion pour deux ordres diff�rents d'inclusion, le second est bien s�r meilleur.} \label{partition_inclusion_graphe_ordre_insertion} - \end{figure} + \end{figure} \begin{xremark}{ordre d'insertion} -L'arbre final dpend de l'ordre d'insertion des lments comme le montre la \indexfrr{ordre}{insertion} -figure~\ref{partition_inclusion_graphe_ordre_insertion}. L'algorithme~\ref{algorithm_insertion} peut tre amlior et devenir l'algorithme~$\ref{algorithm_insertion}^*$ en remplaant les lignes (\ref{amelioration_insertion_1}) et (\ref{amelioration_insertion_2}) par les suivantes, respectivement (\ref{amelioration_insertion_1_p}) et (\ref{amelioration_insertion_2_p})~: +L'arbre final d�pend de l'ordre d'insertion des �l�ments comme le montre la \indexfrr{ordre}{insertion} +figure~\ref{partition_inclusion_graphe_ordre_insertion}. L'algorithme~\ref{algorithm_insertion} peut �tre am�lior� et devenir l'algorithme~$\ref{algorithm_insertion}^*$ en rempla�ant les lignes (\ref{amelioration_insertion_1}) et (\ref{amelioration_insertion_2}) par les suivantes, respectivement (\ref{amelioration_insertion_1_p}) et (\ref{amelioration_insertion_2_p})~: \begin{eqnarray} \text{si } && \forall n \in N, \; d\pa{m,ArgC\pa{n}} > R\pa{n} \label{amelioration_insertion_1_p} \\ - \text{sinon } && \exists n \in N \text{ tel que } d\pa{m,ArgC\pa{n}} \infegal R\pa{n} + \text{sinon } && \exists n \in N \text{ tel que } d\pa{m,ArgC\pa{n}} \leqslant R\pa{n} \label{amelioration_insertion_2_p} \end{eqnarray} -o~: +o�~: \begin{eqnarray*} ArgC\pa{n} &=& \underset{x \in P\pa{n}} {\arg \min} - \cro{ \underset{y \in P\pa{n}} {\max} \; d\pa{x,y}} \text{ et } + \cro{ \underset{y \in P\pa{n}} {\max} \; d\pa{x,y}} \text{ et } d\pa{m,ArgC\pa{n}} = \underset{y \in ArgC\pa{n}} {\min } d\pa{m,y} \end{eqnarray*} -On ne considre pas seulement un centre mais l'ensemble des centres possibles, gale distance de l'lment insrer. Comme la distance de Levenstein\indexfr{Levenstein} est valeurs entires, cet ensemble est rarement rduit un singleton comme le montrera le paragraphe~\ref{section_test}. Cette version de l'algorithme~\ref{algorithm_insertion} est note~$\ref{algorithm_insertion}^*$. La construction de l'arbre de partitionnement est effectue par la rptition de l'algorithme~\ref{algorithm_insertion} ou~$\ref{algorithm_insertion}^*$ tant qu'il reste des lments classer. +On ne consid�re pas seulement un centre mais l'ensemble des centres possibles, � �gale distance de l'�l�ment � ins�rer. Comme la distance de Levenstein\indexfr{Levenstein} est � valeurs enti�res, cet ensemble est rarement r�duit � un singleton comme le montrera le paragraphe~\ref{section_test}. Cette version de l'algorithme~\ref{algorithm_insertion} est not�e~$\ref{algorithm_insertion}^*$. La construction de l'arbre de partitionnement est effectu�e par la r�p�tition de l'algorithme~\ref{algorithm_insertion} ou~$\ref{algorithm_insertion}^*$ tant qu'il reste des �l�ments � classer. \end{xremark} @@ -569,66 +569,66 @@ \subsection{Ajouter un \subsection{Optimisation de la recherche des plus proches voisins} \label{section_optimisation_distance} -Cette optimisation de la recherche utilise un des arbres construits par l'algorithme~\ref{algorithm_AHC} ou la rptition de l'algorithme~\ref{algorithm_insertion} ou~\ref{algorithm_insertion}$^*$. Chaque n\oe ud dfinit une partie dcrite par un centre et un rayon. Le problme rsoudre consiste ici trouver pour un lment $m$ la liste $B\pa{s}$ des voisins inclus dans le sous-ensemble $D\vecteur{y_1}{y_N}$ vrifiant~: +Cette optimisation de la recherche utilise un des arbres construits par l'algorithme~\ref{algorithm_AHC} ou la r�p�tition de l'algorithme~\ref{algorithm_insertion} ou~\ref{algorithm_insertion}$^*$. Chaque n\oe ud d�finit une partie d�crite par un centre et un rayon. Le probl�me � r�soudre consiste ici � trouver pour un �l�ment $m$ la liste $B\pa{s}$ des voisins inclus dans le sous-ensemble $D\vecteur{y_1}{y_N}$ v�rifiant~: $$ - B\pa{s} = \acc{ x \in D \sachant d\pa{x,m} \infegal s } + B\pa{s} = \acc{ x \in D \sachant d\pa{x,m} \leqslant s } $$ -Soit $P \subset E$ une partie dont le centre est $C\pa{P}$ et le rayon $R\pa{P}$, l'optimisation est base sur les deux remarques suivantes~: +Soit $P \subset E$ une partie dont le centre est $C\pa{P}$ et le rayon $R\pa{P}$, l'optimisation est bas�e sur les deux remarques suivantes~: \begin{eqnarray} - d\pa{m,C\pa{P}} > s + R\pa{P} &\Longrightarrow& - \forall w \in P, \; d\pa{m,w} > s - \Longrightarrow B\pa{s} \cap P = \emptyset \label{equation_un} \\ - d\pa{m,C\pa{P}} + R\pa{P} \infegal s &\Longrightarrow& - \forall w \in P, \; d\pa{m,w} \infegal s - \Longrightarrow B\pa{s} \subset P \label{equation_deux} + d\pa{m,C\pa{P}} > s + R\pa{P} &\Longrightarrow& + \forall w \in P, \; d\pa{m,w} > s + \Longrightarrow B\pa{s} \cap P = \emptyset \label{equation_un} \\ + d\pa{m,C\pa{P}} + R\pa{P} \leqslant s &\Longrightarrow& + \forall w \in P, \; d\pa{m,w} \leqslant s + \Longrightarrow B\pa{s} \subset P \label{equation_deux} \end{eqnarray} - \begin{xalgorithm}{recherche rapide}\label{algorithm_optimisation}% - Soit $r$ la racine de l'arbre $A$ obtenu par un des algorithmes~\ref{algorithm_AHC}, - \ref{algorithm_insertion_all}, - $\ref{algorithm_insertion}^*$. $N$ est un ensemble de n\oe uds. Soit $s \in \R_+$, $B\pa{s}$ est - l'ensemble cherch, il est dfini par $B\pa{s} = \acc{ x \in D \sachant d\pa{x,m} \infegal s }$. - - \begin{xalgostep}{initialisation} - $N \longleftarrow r$ \\ - $B\pa{s} \longleftarrow \emptyset$ - \end{xalgostep} - - \begin{xalgostep}{suite}\label{space_algo_step_B} - \begin{xwhile}{$N \neq \emptyset$} - Soit $n \in N$ et $p$ la partie dfinie par $n$, $n$ est retir de $N$ : - $N \longleftarrow N \backslash n$ - et~: \\ - \begin{xif}{$d\pa{m, C\pa{p}} + R\pa{p}\infegal s $} - $B\pa{s} \longleftarrow B\pa{s} \bigcup p$ - - \xelseif{$ d\pa{m, C\pa{p}} > s + R\pa{p} $} - ne rien faire - \xelse - \begin{xif}{$p = \acc{ w \in D}$} - \begin{xif}{$d\pa{m, w} \infegal s$} - $B\pa{s} \longleftarrow B\pa{s} \bigcup \acc{w}$ - \end{xif} - \xelse - $N \longleftarrow N \bigcup \acc{p_1\pa{n},p_2\pa{n}}$\\ - o $\acc{p_1\pa{n},p_1\pa{n}}$ sont les deux prdcesseurs de $n$ - \end{xif} - \end{xif} - \end{xwhile} - \end{xalgostep} - - La liste $B\pa{s}$ contient tous les voisins de $m$ sans aucune approximation. - \end{xalgorithm} - - -\begin{xremark}{mmorisation des distances} -Durant l'tape~\ref{space_algo_step_B} de l'algorithme~\ref{algorithm_optimisation}, il est ncessaire de calculer les distance entre l'lment $m$ et le centre de certaines parties. Ces centres appartiennent au sous-ensemble $D$ et plusieurs parties peuvent avoir le mme centre si elles sont incluses les unes dans les autres. Si le calcul de la distance $d$ est coteux, il est intressant de conserver en mmoire les rsultats du calcul des distances de $m$ aux centres visits. Cette mmorisation\indexfr{mmorisation} implique qu'il ne peut y avoir plus de $N$ calculs de distance si $N$ est le nombre d'lments de $D$. + \begin{xalgorithm}{recherche rapide}\label{algorithm_optimisation}% + Soit $r$ la racine de l'arbre $A$ obtenu par un des algorithmes~\ref{algorithm_AHC}, + \ref{algorithm_insertion_all}, + $\ref{algorithm_insertion}^*$. $N$ est un ensemble de n\oe uds. Soit $s \in \mathbb{R}_+$, $B\pa{s}$ est + l'ensemble cherch�, il est d�fini par $B\pa{s} = \acc{ x \in D \sachant d\pa{x,m} \leqslant s }$. + + \begin{xalgostep}{initialisation} + $N \longleftarrow r$ \\ + $B\pa{s} \longleftarrow \emptyset$ + \end{xalgostep} + + \begin{xalgostep}{suite}\label{space_algo_step_B} + \begin{xwhile}{$N \neq \emptyset$} + Soit $n \in N$ et $p$ la partie d�finie par $n$, $n$ est retir� de $N$ : + $N \longleftarrow N \backslash n$ + et~: \\ + \begin{xif}{$d\pa{m, C\pa{p}} + R\pa{p}\leqslant s $} + $B\pa{s} \longleftarrow B\pa{s} \bigcup p$ + + \xelseif{$ d\pa{m, C\pa{p}} > s + R\pa{p} $} + ne rien faire + \xelse + \begin{xif}{$p = \acc{ w \in D}$} + \begin{xif}{$d\pa{m, w} \leqslant s$} + $B\pa{s} \longleftarrow B\pa{s} \bigcup \acc{w}$ + \end{xif} + \xelse + $N \longleftarrow N \bigcup \acc{p_1\pa{n},p_2\pa{n}}$\\ + o� $\acc{p_1\pa{n},p_1\pa{n}}$ sont les deux pr�d�cesseurs de $n$ + \end{xif} + \end{xif} + \end{xwhile} + \end{xalgostep} + + La liste $B\pa{s}$ contient tous les voisins de $m$ sans aucune approximation. + \end{xalgorithm} + + +\begin{xremark}{m�morisation des distances} +Durant l'�tape~\ref{space_algo_step_B} de l'algorithme~\ref{algorithm_optimisation}, il est n�cessaire de calculer les distance entre l'�l�ment $m$ et le centre de certaines parties. Ces centres appartiennent au sous-ensemble $D$ et plusieurs parties peuvent avoir le m�me centre si elles sont incluses les unes dans les autres. Si le calcul de la distance $d$ est co�teux, il est int�ressant de conserver en m�moire les r�sultats du calcul des distances de $m$ aux centres visit�s. Cette m�morisation\indexfr{m�morisation} implique qu'il ne peut y avoir plus de $N$ calculs de distance si $N$ est le nombre d'�l�ments de $D$. \end{xremark} @@ -636,95 +636,95 @@ \subsection{Optimisation de la recherche des plus proches voisins} -\subsection{Critre d'efficacit} +\subsection{Crit�re d'efficacit�} \label{section_criterion} -Quelque soit la mthode choisie pour construire l'arbre de partitionnement, l'algorithme~\ref{algorithm_optimisation} mne la solution exacte. D'un autre ct, on peut se demander s'il existe des arbres meilleurs que d'autres lors de la recherche des plus proches voisins et s'il existe un arbre optimal. La premire ide est de considrer que plus les parties dfinies par l'arbre sont petites, plus la recherche sera rapide. Selon cette ide, le critre (\ref{critere_optimalite}) essaye d'valuer la pertinence\indexfr{pertinence}\indexfr{efficacit} d'un arbre $A$ construit partir du sous-ensemble fini $D$~: +Quelque soit la m�thode choisie pour construire l'arbre de partitionnement, l'algorithme~\ref{algorithm_optimisation} m�ne � la solution exacte. D'un autre c�t�, on peut se demander s'il existe des arbres meilleurs que d'autres lors de la recherche des plus proches voisins et s'il existe un arbre optimal. La premi�re id�e est de consid�rer que plus les parties d�finies par l'arbre sont petites, plus la recherche sera rapide. Selon cette id�e, le crit�re (\ref{critere_optimalite}) essaye d'�valuer la pertinence\indexfr{pertinence}\indexfr{efficacit�} d'un arbre $A$ construit � partir du sous-ensemble fini $D$~: \begin{eqnarray} Cr_1\pa{A} &=& \left\{ \begin{array}{l} 0 \text{ si } R\pa{D} = 0 \text{ ou } - card\pa{D} \infegal 1 \\ \\ \textnormal{sinon } + card\pa{D} \leqslant 1 \\ \\ \textnormal{sinon } \dfrac{\summyone{n \in A} R\pa{n}}{\pa{card\pa{D}-1} * R\pa{D}} \end{array}% \right. \label{critere_optimalite} \\ - \text{o }&& \nonumber\\ - card\pa{D} && \text{est le nombre d'lments de } D \nonumber \\ + \text{o� }&& \nonumber\\ + card\pa{D} && \text{est le nombre d'�l�ments de } D \nonumber \\ R\pa{D} && \text{est le rayon de } D \nonumber \\ n && \text{est un n\oe ud de } A \nonumber \\ - R\pa{n} && \text{est le centre de la partie dfinie par } n \nonumber + R\pa{n} && \text{est le centre de la partie d�finie par } n \nonumber \end{eqnarray} -Si l'arbre $A$ choisi est construit par l'algorithme~\ref{algorithm_AHC}, le corollaire~\ref{corollary_AHC} permet d'affirmer que si $n$ est un n\oe ud de $A$, alors $R\pa{n} \infegal R\pa{D}$. De plus, l'arbre contient au plus $card\pa{D}-1$ n\oe uds dont le rayon est strictement positif~: +Si l'arbre $A$ choisi est construit par l'algorithme~\ref{algorithm_AHC}, le corollaire~\ref{corollary_AHC} permet d'affirmer que si $n$ est un n\oe ud de $A$, alors $R\pa{n} \leqslant R\pa{D}$. De plus, l'arbre contient au plus $card\pa{D}-1$ n\oe uds dont le rayon est strictement positif~: $$ - card \acc{n \in A \; | \; R \pa{n} > 0} \infegal card\pa{D}-1 + card \acc{n \in A \; | \; R \pa{n} > 0} \leqslant card\pa{D}-1 $$ -Par consquent, l'arbre $A$ construit par l'algorithme~\ref{algorithm_AHC} satisfait~: +Par cons�quent, l'arbre $A$ construit par l'algorithme~\ref{algorithm_AHC} satisfait~: \begin{eqnarray} - R\pa{D} \infegal \summyone{n \in A} R\pa{n} \infegal \pa{card\pa{D}-1} * R\pa{D} - \label{inegalite_critere} + R\pa{D} \leqslant \summyone{n \in A} R\pa{n} \leqslant \pa{card\pa{D}-1} * R\pa{D} + \label{inegalite_critere} \end{eqnarray} -L'ingalit (\ref{inegalite_critere}) explique l'expression du critre (\ref{critere_optimalite}) puisque~: +L'in�galit� (\ref{inegalite_critere}) explique l'expression du crit�re (\ref{critere_optimalite}) puisque~: \begin{eqnarray} - R\pa{D} > 0 \Longrightarrow \dfrac{1}{card\pa{D}-1} \infegal Cr_1\pa{A} \infegal 1 && + R\pa{D} > 0 \Longrightarrow \dfrac{1}{card\pa{D}-1} \leqslant Cr_1\pa{A} \leqslant 1 && \label{inegalite_critere2} \end{eqnarray} \begin{xremark}{limites} -Les deux limites de l'ingalit (\ref{inegalite_critere2}) sont atteintes pour un sous-ensemble $D$ ne contenant que deux lments distincts. Pour un ensemble contenant plus de trois lments, la proprit suivante rpond partiellement la question. +Les deux limites de l'in�galit� (\ref{inegalite_critere2}) sont atteintes pour un sous-ensemble $D$ ne contenant que deux �l�ments distincts. Pour un ensemble contenant plus de trois �l�ments, la propri�t� suivante r�pond partiellement � la question. \end{xremark} - \begin{xproperty}{limites} \label{property_bornes_atteintes}% - Soit $\pa{E,d}$ un espace mtrique quelconque,\indexfrr{espace}{mtrique} soit $D \neq - \emptyset$ un sous-ensemble fini de $E$, soit $A_D$ l'arbre construit par - l'algorithme~\ref{algorithm_AHC}, alors les trois propositions suivantes sont vraies~: - - \begin{description} - \item[(1) ] $\forall n > 1,$ il existe $D_1 \subset E$ tel que~: - $$ - \left\{ - \begin{array}{rcl} - card\pa{D_1} &=& n \\ - Cr_1\pa{A_{D_1}} &=& \dfrac{1}{n-1} - \end{array} - \right. - $$ - \item[(2) ] $\forall n > 1,$ il existe $D_2 \subset E$ tel que~: - $$ - \left\{ - \begin{array}{l} - card\pa{D_2} = n \\ %\\ - \forall \pa{x,y} \in D_2^2, \; x \neq y \Longrightarrow d\pa{x,y} = R\pa{D_2} - \end{array} - \right. - $$ - alors $Cr_1\pa{A_{D_2}} = 1$. \newline - \item[(3) ] si $E$ est un espace vectoriel de dimension infinie, alors, $\forall n > 1,$ - il existe $D_3 \subset E$ tel que~: - $$ - \left\{ - \begin{array}{rcl} - card\pa{D_3} &=& n \\ - Cr_1\pa{A_{D_3}} &=& 1 - \end{array} - \right. - $$ - \end{description} - \end{xproperty} - -\begin{xdemo}{proprit}{\ref{property_bornes_atteintes}} - -Pour prouver \textbf{(1)}, il faut considrer l'ensemble $D_1 = \vecteur{x_1}{x_n}$ dfini par~: + \begin{xproperty}{limites} \label{property_bornes_atteintes}% + Soit $\pa{E,d}$ un espace m�trique quelconque,\indexfrr{espace}{m�trique} soit $D \neq + \emptyset$ un sous-ensemble fini de $E$, soit $A_D$ l'arbre construit par + l'algorithme~\ref{algorithm_AHC}, alors les trois propositions suivantes sont vraies~: + + \begin{description} + \item[(1) ] $\forall n > 1,$ il existe $D_1 \subset E$ tel que~: + $$ + \left\{ + \begin{array}{rcl} + card\pa{D_1} &=& n \\ + Cr_1\pa{A_{D_1}} &=& \dfrac{1}{n-1} + \end{array} + \right. + $$ + \item[(2) ] $\forall n > 1,$ il existe $D_2 \subset E$ tel que~: + $$ + \left\{ + \begin{array}{l} + card\pa{D_2} = n \\ %\\ + \forall \pa{x,y} \in D_2^2, \; x \neq y \Longrightarrow d\pa{x,y} = R\pa{D_2} + \end{array} + \right. + $$ + alors $Cr_1\pa{A_{D_2}} = 1$. \newline + \item[(3) ] si $E$ est un espace vectoriel de dimension infinie, alors, $\forall n > 1,$ + il existe $D_3 \subset E$ tel que~: + $$ + \left\{ + \begin{array}{rcl} + card\pa{D_3} &=& n \\ + Cr_1\pa{A_{D_3}} &=& 1 + \end{array} + \right. + $$ + \end{description} + \end{xproperty} + +\begin{xdemo}{propri�t�}{\ref{property_bornes_atteintes}} + +Pour prouver \textbf{(1)}, il faut consid�rer l'ensemble $D_1 = \vecteur{x_1}{x_n}$ d�fini par~: $$ \left\{ @@ -735,11 +735,11 @@ \subsection{Crit \right. $$ -De manire vidente~: $Cr_1\pa{A_{D_1}} = \frac{1}{n-1}$. +De mani�re �vidente~: $Cr_1\pa{A_{D_1}} = \frac{1}{n-1}$. -Prouver \textbf{(2)} est aussi vident parce que si un tel ensemble $D_2$ existe, pour une partie $P$ quelconque de $D$, $R\pa{P} = R\pa{D_2} = Cr_1\pa{A}$. +Prouver \textbf{(2)} est aussi �vident parce que si un tel ensemble $D_2$ existe, pour une partie $P$ quelconque de $D$, $R\pa{P} = R\pa{D_2} = Cr_1\pa{A}$. -Pour prouver \textbf{(3)}, on n'utilise \textbf{(2)} puisqu'un tel ensemble $D_2$ existe dans un espace vectoriel de dimension infinie. C'est prcisment le cas de l'espace des mots. +Pour prouver \textbf{(3)}, on n'utilise \textbf{(2)} puisqu'un tel ensemble $D_2$ existe dans un espace vectoriel de dimension infinie. C'est pr�cis�ment le cas de l'espace des mots. \end{xdemo} @@ -750,28 +750,28 @@ \subsection{Crit \comment{ -Pour prendre en compte la pertinence de la runion de deux parties, un second critre est dfini~: +Pour prendre en compte la pertinence de la r�union de deux parties, un second crit�re est d�fini~: - \begin{eqnarray} - Cr_2\pa{A} &=& \left\{\begin{array}{l} - 0 \text{ si } R\pa{D} = 0 \text{ ou } card\pa{D} \infegal 1 \\ + \begin{eqnarray} + Cr_2\pa{A} &=& \left\{\begin{array}{l} + 0 \text{ si } R\pa{D} = 0 \text{ ou } card\pa{D} \leqslant 1 \\ \dfrac{ \summyone{n \in A} \biggcro{ 2 R\pa{n} - d\pa{n} }} - {\pa{card\pa{D}-1} * R\pa{D}} \text{ sinon} - \end{array}% + {\pa{card\pa{D}-1} * R\pa{D}} \text{ sinon} + \end{array}% \right. \label{critere_optimalite_2} \\ - \text{o }&& \nonumber\\ - p_i\pa{n} && \text{est un des deux prdcesseurs du n\oe ud } n \\ - \text{et }d\pa{n} &=& d\pa{C\pa{p_1\pa{n}}, C\pa{p_2\pa{n}}} \nonumber\\ - \end{eqnarray} + \text{o� }&& \nonumber\\ + p_i\pa{n} && \text{est un des deux pr�d�cesseurs du n\oe ud } n \\ + \text{et }d\pa{n} &=& d\pa{C\pa{p_1\pa{n}}, C\pa{p_2\pa{n}}} \nonumber\\ + \end{eqnarray} -De manire vidente, ce critre vrifie (\ref{inegalite_critere3})~: +De mani�re �vidente, ce crit�re v�rifie (\ref{inegalite_critere3})~: \begin{eqnarray} - R\pa{D} > 0 \Longrightarrow 0 \infegal Cr_2\pa{A} \infegal 2 && \label{inegalite_critere3} + R\pa{D} > 0 \Longrightarrow 0 \leqslant Cr_2\pa{A} \leqslant 2 && \label{inegalite_critere3} \end{eqnarray} -Ce second critre ne sera pas voqu par la suite car il corrobore les dductions obtenus avec le premier critre. +Ce second crit�re ne sera pas �voqu� par la suite car il corrobore les d�ductions obtenus avec le premier crit�re. } %------------------------------------------------------------------------------------------------------------------- @@ -785,154 +785,154 @@ \subsection{Crit -\subsection{Rsultats exprimentaux} +\subsection{R�sultats exp�rimentaux} \label{section_test} -La premire exprience consiste chercher les voisins dans un ensemble de points tirs alatoirement dans le carr $\cro{0,1} \times \cro{0,1}$ (figure~\ref{space_metric_rnd_01_01}). L'exprience consiste d'abord tirer $N$ points alatoires dans ce carr. Pour diffrentes valeurs de seuil~$s$, $N$~points sont de nouveau tirs alatoirement pour lesquels le voisinage $V_s\pa{x}=\acc{y \sac d\pa{x,y} \infegal s}$ est calcul selon les deux algorithmes~\ref{algorithm_AHC} et~$\ref{algorithm_insertion}^*$. Si $X$ est une variable alatoire de l'espace mtrique~$E$ -~les lments de $E$ sont quiprobables~-, l'objectif est d'estimer le nombre moyen de calculs de distance $r_s\pa{N}$ effectus pour dterminer les voisins d'un lment~: - - \begin{eqnarray} - r_s\pa{N} = \dfrac{1}{N} \; \esp{ \text{nombre de distances calcules pour $V_s\pa{X}$}} - \label{gain_mot} - \end{eqnarray} - - \begin{figure}[ht] - $$ - \begin{array}{|c|}\hline - \includegraphics[height=3cm, width=3cm]{\filext{../space_metric/image/rnd}} \\ \hline - \end{array} - $$ - \caption{Tirage alatoire de points dans le carr $\cro{0,1} \times \cro{0,1}$.} - \label{space_metric_rnd_01_01} - \end{figure} - -L'algorithme~\ref{algorithm_AHC} est de loin le meilleur et ce quelle que soit la valeur du seuil $s$ choisi. Cette supriorit est galement traduite par la valeur de $Cr_1$ obtenu pour chacun des arbres (voir table~\ref{space_metric_rnd_gain}). - - - \begin{table}[ht] - \[ - \begin{tabular}{|c|c|c|c|c|c|} \hline - seuil & $\frac{\esp{\card{V_s\pa{X}}}}{N}$ & - $\begin{subarray}{c} r_s\pa{N=2000} \\ - algorithme~\ref{algorithm_insertion}^* \end{subarray}$ & - $\begin{subarray}{c} r_s\pa{N=2000} \\ - algorithme~\ref{algorithm_AHC} \end{subarray}$ & - $\begin{subarray}{c} r_s\pa{N=5000} \\ - algorithme~\ref{algorithm_AHC} \end{subarray}$ & - $\begin{subarray}{c} r_s\pa{N=10000} \\ - algorithme~\ref{algorithm_AHC} \end{subarray}$ \\ \hline - 0,001 & 0 \,\% & 6,4 \,\% & 1,2 \,\% & 0,6 \,\% & 0,3 \,\% \\ %\hline - 0,01 & 0 \,\% & 6,8 \,\% & 1,4 \,\% & 0,7 \,\% & 0,4 \,\% \\ %\hline - 0,1 & 2 \,\% & 11,8 \,\% & 4,1 \,\% & 2,7 \,\% & 1,9 \,\% \\ %\hline - 0,2 & 10 \,\% & 17,2 \,\% & 6,8 \,\% & 4,5 \,\% & 3,2 \,\% \\ %\hline - 0,3 & 21 \,\% & 21,7 \,\% & 8,7 \,\% & 5,7 \,\% & 4,0 \,\% \\ %\hline - 0,4 & 34 \,\% & 25,1 \,\% & 9,6 \,\% & 6,3 \,\% & 4,5 \,\% \\ %\hline - 0,5 & 48 \,\% & 26,9 \,\% & 10,0 \,\% & 6,5 \,\% & 4,6 \,\% \\ %\hline - 0,6 & 62 \,\% & 27,9 \,\% & 9,4 \,\% & 6,1 \,\% & 4,4 \,\% \\ %\hline - 0,7 & 74 \,\% & 27,0 \,\% & 8,3 \,\% & 5,4 \,\% & 3,9 \,\% \\ %\hline - 0,8 & 85 \,\% & 24,0 \,\% & 6,7 \,\% & 4,3 \,\% & 3,1 \,\% \\ %\hline - 0,9 & 92 \,\% & 19,2 \,\% & 4,6 \,\% & 3,0 \,\% & 2,1 \,\% \\ %\hline - 1 & 98 \,\% & 10,9 \,\% & 2,3 \,\% & 1,4 \,\% & 1,0 \,\% \\ %\hline - 2 & 100 \,\% & 0,1 \,\% & 0,1 \,\% & 0,0 \,\% & 0,0 \,\% \\ \hline - $Cr_1$ & - & 0,135 & 0,039 & 0,023 & 0,017 \\ \hline - \begin{minipage}[c]{3cm} - temps de calcul (arbre) - \end{minipage} - & - & $\sim$ 2 sec & $\sim $30 sec& $\sim $2 min & $\sim $10 min \\ \hline - \end{tabular} - \] - \caption{ Gain apport lors de la recherche du voisinage pour diffrentes valeurs de seuil. - Le premier test utilise un nuage de 2000 points tirs - alatoirement dans l'ensemble $\cro{0,1}^2$, le second en utilise 5000, - le dernier 10000. A seuil fixe, - la part du voisinage observ dcrot lorsque $N$ diminue. Dans les trois cas, - lorsque le seuil est fix, les rapports tailles de - voisinages sur nombre d'lments sont sensiblement gales quel que soit $N$. - On s'aperoit que le critre $Cr_1$ dcrot galement lorsque $N$ augmente. - Les temps de calcul sont estimes avec un processeur Intel Pentium~III 1~GHz et - dsignent le temps ncessaire la construction de l'arbre.} - \indexfr{Intel} - \indexfr{Pentium} - \indexfr{temps de calcul} - \label{space_metric_rnd_gain} - \end{table} - - - -Une exprience similaire est effectue dans un espace de mots et pour mesurer l'amlioration obtenu par l'optimisation dcrite au paragraphe~\ref{section_optimisation_distance}, le test suivant est ralis~: - - \begin{itemize} - \item Un dictionnaire $D$ de 2178 prnoms\indexfr{prnom} est utilis, son rayon est 10. +La premi�re exp�rience consiste � chercher les voisins dans un ensemble de points tir�s al�atoirement dans le carr� $\cro{0,1} \times \cro{0,1}$ (figure~\ref{space_metric_rnd_01_01}). L'exp�rience consiste d'abord � tirer $N$ points al�atoires dans ce carr�. Pour diff�rentes valeurs de seuil~$s$, $N$~points sont de nouveau tir�s al�atoirement pour lesquels le voisinage $V_s\pa{x}=\acc{y \sac d\pa{x,y} \leqslant s}$ est calcul� selon les deux algorithmes~\ref{algorithm_AHC} et~$\ref{algorithm_insertion}^*$. Si $X$ est une variable al�atoire de l'espace m�trique~$E$ -~les �l�ments de $E$ sont �quiprobables~-, l'objectif est d'estimer le nombre moyen de calculs de distance $r_s\pa{N}$ effectu�s pour d�terminer les voisins d'un �l�ment~: + + \begin{eqnarray} + r_s\pa{N} = \dfrac{1}{N} \; \esp{ \text{nombre de distances calcul�es pour $V_s\pa{X}$}} + \label{gain_mot} + \end{eqnarray} + + \begin{figure}[ht] + $$ + \begin{array}{|c|}\hline + \includegraphics[height=3cm, width=3cm]{\filext{../space_metric/image/rnd}} \\ \hline + \end{array} + $$ + \caption{Tirage al�atoire de points dans le carr� $\cro{0,1} \times \cro{0,1}$.} + \label{space_metric_rnd_01_01} + \end{figure} + +L'algorithme~\ref{algorithm_AHC} est de loin le meilleur et ce quelle que soit la valeur du seuil $s$ choisi. Cette sup�riorit� est �galement traduite par la valeur de $Cr_1$ obtenu pour chacun des arbres (voir table~\ref{space_metric_rnd_gain}). + + + \begin{table}[ht] + \[ + \begin{tabular}{|c|c|c|c|c|c|} \hline + seuil & $\frac{\esp{\card{V_s\pa{X}}}}{N}$ & + $\begin{subarray}{c} r_s\pa{N=2000} \\ + algorithme~\ref{algorithm_insertion}^* \end{subarray}$ & + $\begin{subarray}{c} r_s\pa{N=2000} \\ + algorithme~\ref{algorithm_AHC} \end{subarray}$ & + $\begin{subarray}{c} r_s\pa{N=5000} \\ + algorithme~\ref{algorithm_AHC} \end{subarray}$ & + $\begin{subarray}{c} r_s\pa{N=10000} \\ + algorithme~\ref{algorithm_AHC} \end{subarray}$ \\ \hline + 0,001 & 0 \,\% & 6,4 \,\% & 1,2 \,\% & 0,6 \,\% & 0,3 \,\% \\ %\hline + 0,01 & 0 \,\% & 6,8 \,\% & 1,4 \,\% & 0,7 \,\% & 0,4 \,\% \\ %\hline + 0,1 & 2 \,\% & 11,8 \,\% & 4,1 \,\% & 2,7 \,\% & 1,9 \,\% \\ %\hline + 0,2 & 10 \,\% & 17,2 \,\% & 6,8 \,\% & 4,5 \,\% & 3,2 \,\% \\ %\hline + 0,3 & 21 \,\% & 21,7 \,\% & 8,7 \,\% & 5,7 \,\% & 4,0 \,\% \\ %\hline + 0,4 & 34 \,\% & 25,1 \,\% & 9,6 \,\% & 6,3 \,\% & 4,5 \,\% \\ %\hline + 0,5 & 48 \,\% & 26,9 \,\% & 10,0 \,\% & 6,5 \,\% & 4,6 \,\% \\ %\hline + 0,6 & 62 \,\% & 27,9 \,\% & 9,4 \,\% & 6,1 \,\% & 4,4 \,\% \\ %\hline + 0,7 & 74 \,\% & 27,0 \,\% & 8,3 \,\% & 5,4 \,\% & 3,9 \,\% \\ %\hline + 0,8 & 85 \,\% & 24,0 \,\% & 6,7 \,\% & 4,3 \,\% & 3,1 \,\% \\ %\hline + 0,9 & 92 \,\% & 19,2 \,\% & 4,6 \,\% & 3,0 \,\% & 2,1 \,\% \\ %\hline + 1 & 98 \,\% & 10,9 \,\% & 2,3 \,\% & 1,4 \,\% & 1,0 \,\% \\ %\hline + 2 & 100 \,\% & 0,1 \,\% & 0,1 \,\% & 0,0 \,\% & 0,0 \,\% \\ \hline + $Cr_1$ & - & 0,135 & 0,039 & 0,023 & 0,017 \\ \hline + \begin{minipage}[c]{3cm} + temps de calcul (arbre) + \end{minipage} + & - & $\sim$ 2 sec & $\sim $30 sec& $\sim $2 min & $\sim $10 min \\ \hline + \end{tabular} + \] + \caption{ Gain apport� lors de la recherche du voisinage pour diff�rentes valeurs de seuil. + Le premier test utilise un nuage de 2000 points tir�s + al�atoirement dans l'ensemble $\cro{0,1}^2$, le second en utilise 5000, + le dernier 10000. A seuil fixe, + la part du voisinage observ� d�cro�t lorsque $N$ diminue. Dans les trois cas, + lorsque le seuil est fix�, les rapports tailles de + voisinages sur nombre d'�l�ments sont sensiblement �gales quel que soit $N$. + On s'aper�oit que le crit�re $Cr_1$ d�cro�t �galement lorsque $N$ augmente. + Les temps de calcul sont estim�es avec un processeur Intel Pentium~III � 1~GHz et + d�signent le temps n�cessaire � la construction de l'arbre.} + \indexfr{Intel} + \indexfr{Pentium} + \indexfr{temps de calcul} + \label{space_metric_rnd_gain} + \end{table} + + + +Une exp�rience similaire est effectu�e dans un espace de mots et pour mesurer l'am�lioration obtenu par l'optimisation d�crite au paragraphe~\ref{section_optimisation_distance}, le test suivant est r�alis�~: + + \begin{itemize} + \item Un dictionnaire $D$ de 2178 pr�noms\indexfr{pr�nom} est utilis�, son rayon est 10. \item Le test consiste en l'obtention du voisinage $V_s\pa{m}$ de n'importe quel mot $m$ du dictionnaire. - \item La distance utilise est cette de Levenstein (\citeindex{Levenstein1966}, - \citeindex{Wagner1974}).\indexfr{Levenstein} - \end{itemize} + \item La distance utilis�e est cette de Levenstein (\citeindex{Levenstein1966}, + \citeindex{Wagner1974}).\indexfr{Levenstein} + \end{itemize} -Sans optimisation, pour un mot donn $m$, toutes les distances de $m$ avec les autres mots doivent tre calcules. En utilisant l'optimisation propose, il n'est pas ncessaire de les calculer toutes. Les rsultats sont illustrs par le tableau~\ref{metric_test_optimisation}. +Sans optimisation, pour un mot donn� $m$, toutes les distances de $m$ avec les autres mots doivent �tre calcul�es. En utilisant l'optimisation propos�e, il n'est pas n�cessaire de les calculer toutes. Les r�sultats sont illustr�s par le tableau~\ref{metric_test_optimisation}. - \begin{table}[ht] + \begin{table}[ht] %\newline $$ \begin{tabular}{|c|c|cc|} \hline \textnormal{seuil} & $\frac{\esp{\card{V_s\pa{X}}}}{N}$ & - $\begin{subarray}{c} r_s\pa{N=2178} \\ algorithme~\ref{algorithm_AHC} \end{subarray}$ & + $\begin{subarray}{c} r_s\pa{N=2178} \\ algorithme~\ref{algorithm_AHC} \end{subarray}$ & $\begin{subarray}{c} r_s\pa{N=2178} \\ algorithme~\ref{algorithm_insertion}^* \end{subarray}$ \\ \hline - 1 & 0,1 \,\% & 17,3 \,\% & 34,1 \,\% \\ - 2 & 0,3 \,\% & 30,2 \,\% & 46,9 \,\% \\ - 3 & 1,5 \,\% & 45,7 \,\% & 60,4 \,\% \\ - 4 & 7,0 \,\% & 63,1 \,\% & 73,3 \,\% \\ - 5 & 21,8 \,\% & 76,3 \,\% & 83,0 \,\% \\ - 6 & 45,2 \,\% & 79,8 \,\% & 86,0 \,\% \\ - 7 & 68,2 \,\% & 74,0 \,\% & 81,2 \,\% \\ - 8 & 82,8 \,\% & 59,6 \,\% & 71,1 \,\% \\ - 9 & 90,9 \,\% & 45,2 \,\% & 58,0 \,\% \\ - 10 & 95,5 \,\% & 30,7 \,\% & 43,5 \,\% \\ - 11 & 96,7 \,\% & 21,0 \,\% & 28,8 \,\% \\ - 12 & 98,5 \,\% & 12,5 \,\% & 18,3 \,\% \\ - 13 & 99,3 \,\% & 7,7 \,\% & 11,0 \,\% \\ - 14 & 99,4 \,\% & 4,9 \,\% & 7,3 \,\% \\ - 15 & 99,5 \,\% & 3,0 \,\% & 4,2 \,\% \\ - 16 & 99,5 \,\% & 2,2 \,\% & 2,7 \,\% \\ - 17 & 99,6 \,\% & 1,5 \,\% & 2,0 \,\% \\ - 18 & 99,8 \,\% & 0,9 \,\% & 1,5 \,\% \\ - 19 & 99,7 \,\% & 0,9 \,\% & 1,3 \,\% \\ - 20 & 99,9 \,\% & 0,6 \,\% & 1,2 \,\% \\ \hline - $Cr_1$ & - & 0,153 & 0,221 \\ \hline - \begin{minipage}[c]{3cm} - temps de calcul (arbre) - \end{minipage} - & - & $\sim$5 min & $\sim$1 h\\ \hline + 1 & 0,1 \,\% & 17,3 \,\% & 34,1 \,\% \\ + 2 & 0,3 \,\% & 30,2 \,\% & 46,9 \,\% \\ + 3 & 1,5 \,\% & 45,7 \,\% & 60,4 \,\% \\ + 4 & 7,0 \,\% & 63,1 \,\% & 73,3 \,\% \\ + 5 & 21,8 \,\% & 76,3 \,\% & 83,0 \,\% \\ + 6 & 45,2 \,\% & 79,8 \,\% & 86,0 \,\% \\ + 7 & 68,2 \,\% & 74,0 \,\% & 81,2 \,\% \\ + 8 & 82,8 \,\% & 59,6 \,\% & 71,1 \,\% \\ + 9 & 90,9 \,\% & 45,2 \,\% & 58,0 \,\% \\ + 10 & 95,5 \,\% & 30,7 \,\% & 43,5 \,\% \\ + 11 & 96,7 \,\% & 21,0 \,\% & 28,8 \,\% \\ + 12 & 98,5 \,\% & 12,5 \,\% & 18,3 \,\% \\ + 13 & 99,3 \,\% & 7,7 \,\% & 11,0 \,\% \\ + 14 & 99,4 \,\% & 4,9 \,\% & 7,3 \,\% \\ + 15 & 99,5 \,\% & 3,0 \,\% & 4,2 \,\% \\ + 16 & 99,5 \,\% & 2,2 \,\% & 2,7 \,\% \\ + 17 & 99,6 \,\% & 1,5 \,\% & 2,0 \,\% \\ + 18 & 99,8 \,\% & 0,9 \,\% & 1,5 \,\% \\ + 19 & 99,7 \,\% & 0,9 \,\% & 1,3 \,\% \\ + 20 & 99,9 \,\% & 0,6 \,\% & 1,2 \,\% \\ \hline + $Cr_1$ & - & 0,153 & 0,221 \\ \hline + \begin{minipage}[c]{3cm} + temps de calcul (arbre) + \end{minipage} + & - & $\sim$5 min & $\sim$1 h\\ \hline \end{tabular} $$ - \caption{ Amlioration moyenne mesure par (\ref{gain_mot}), comparaison des - algorithmes~\ref{algorithm_AHC}, - $\ref{algorithm_insertion}^*$. Le centre du dictionnaire est - MARIE-LOUISE et son rayon est 17. Dans le pire des cas, $s=6$, - 20\% des calculs de distances sont vits. - Les temps de calcul correspondand la construction de l'arbre - sont estims avec un processeur Intel Pentium 1~GHz.} - \indexfr{Intel} - \indexfr{Pentium} - \indexfr{temps de calcul} + \caption{ Am�lioration moyenne mesur�e par (\ref{gain_mot}), comparaison des + algorithmes~\ref{algorithm_AHC}, + $\ref{algorithm_insertion}^*$. Le centre du dictionnaire est + MARIE-LOUISE et son rayon est 17. Dans le pire des cas, $s=6$, + 20\% des calculs de distances sont �vit�s. + Les temps de calcul correspondand � la construction de l'arbre + sont estim�s avec un processeur Intel Pentium 1~GHz.} + \indexfr{Intel} + \indexfr{Pentium} + \indexfr{temps de calcul} \label{metric_test_optimisation} - \end{table} + \end{table} \begin{xremark}{ordre d'insertion} -L'ordre d'insertion des mots dans l'arbre affecte les rsultats. En ce qui concerne les \indexfrr{ordre}{insertion} -algorithmes~\ref{algorithm_insertion} et~$\ref{algorithm_insertion}^*$, les mots ont t insrs par ordres croissant et dcroissant de taille, les diffrences sont rendues par le tableau~\ref{test_optimisation_taille_2}. +L'ordre d'insertion des mots dans l'arbre affecte les r�sultats. En ce qui concerne les \indexfrr{ordre}{insertion} +algorithmes~\ref{algorithm_insertion} et~$\ref{algorithm_insertion}^*$, les mots ont �t� ins�r�s par ordres croissant et d�croissant de taille, les diff�rences sont rendues par le tableau~\ref{test_optimisation_taille_2}. \end{xremark} - \begin{table}[ht] - %\newline + \begin{table}[ht] + %\newline $$ \fbox{$ \begin{array}{ccccc} \text{seuil} & \begin{subarray}{c} r \textnormal{ moyen} \\ algorithme~\ref{algorithm_insertion}^* \\ - \textnormal{longueurs dcroissantes} \end{subarray} & + \textnormal{longueurs d�croissantes} \end{subarray} & \begin{subarray}{c} r \textnormal{ moyen} \\ algorithme~\ref{algorithm_insertion}^* \\ \textnormal{longueurs croissantes} \end{subarray} \\ @@ -944,51 +944,51 @@ \subsection{R \end{array} $} $$ - \caption{Amlioration moyenne mesure par (\ref{gain_mot}), comparaison des ordres d'insertion} + \caption{Am�lioration moyenne mesur�e par (\ref{gain_mot}), comparaison des ordres d'insertion} \label{test_optimisation_taille_2} - \end{table} + \end{table} - \begin{table}[ht] + \begin{table}[ht] %\newline $$ \begin{tabular}{|c|cc|} \hline \textnormal{seuil} & $\frac{\esp{\card{V_s\pa{X}}}}{N}$ & - $\begin{subarray}{c} r_s\pa{N=4987} \\ algorithme~\ref{algorithm_AHC} \end{subarray}$ + $\begin{subarray}{c} r_s\pa{N=4987} \\ algorithme~\ref{algorithm_AHC} \end{subarray}$ \\ \hline - 1 & 0,0 \,\% & 5,7 \,\% \\ - 2 & 0,0 \,\% & 10,4 \,\% \\ - 3 & 0,0 \,\% & 17,1 \,\% \\ - 4 & 0,1 \,\% & 26,4 \,\% \\ - 5 & 0,4 \,\% & 42,2 \,\% \\ - 6 & 2,0 \,\% & 61,6 \,\% \\ - 7 & 9,0 \,\% & 82,1 \,\% \\ - 8 & 34,1 \,\% & 94,7 \,\% \\ - 9 & 77,4 \,\% & 91,3 \,\% \\ - 10 & 97,7 \,\% & 73,1 \,\% \\ - 11 & 99,4 \,\% & 48,4 \,\% \\ - 12 & 99,9 \,\% & 31,0 \,\% \\ - 13 & 100,0 \,\% & 20,1 \,\% \\ - 14 & 100,0 \,\% & 11,1 \,\% \\ - 15 & 100,0 \,\% & 6,0 \,\% \\ - 16 & 100,0 \,\% & 3,0 \,\% \\ - 17 & 100,0 \,\% & 1,3 \,\% \\ - 18 & 100,0 \,\% & 0,0 \,\% \\ \hline - $Cr_1$ & - & 0,210 \\ \hline - temps de calcul & - & $\sim$3 h \\ \hline + 1 & 0,0 \,\% & 5,7 \,\% \\ + 2 & 0,0 \,\% & 10,4 \,\% \\ + 3 & 0,0 \,\% & 17,1 \,\% \\ + 4 & 0,1 \,\% & 26,4 \,\% \\ + 5 & 0,4 \,\% & 42,2 \,\% \\ + 6 & 2,0 \,\% & 61,6 \,\% \\ + 7 & 9,0 \,\% & 82,1 \,\% \\ + 8 & 34,1 \,\% & 94,7 \,\% \\ + 9 & 77,4 \,\% & 91,3 \,\% \\ + 10 & 97,7 \,\% & 73,1 \,\% \\ + 11 & 99,4 \,\% & 48,4 \,\% \\ + 12 & 99,9 \,\% & 31,0 \,\% \\ + 13 & 100,0 \,\% & 20,1 \,\% \\ + 14 & 100,0 \,\% & 11,1 \,\% \\ + 15 & 100,0 \,\% & 6,0 \,\% \\ + 16 & 100,0 \,\% & 3,0 \,\% \\ + 17 & 100,0 \,\% & 1,3 \,\% \\ + 18 & 100,0 \,\% & 0,0 \,\% \\ \hline + $Cr_1$ & - & 0,210 \\ \hline + temps de calcul & - & $\sim$3 h \\ \hline \end{tabular} $$ - \caption{ Amlioration moyenne mesure par (\ref{gain_mot}), le test est effectu - sur un dictionnaire de 4987 mots anglais de centre "POSITIVELY" de rayon 13. - L'optimisation est plus pertinente dans ce cas o le dictionnaire - contient plus du double de mots que celui utilis - pour le test~\ref{metric_test_optimisation}. - } + \caption{ Am�lioration moyenne mesur�e par (\ref{gain_mot}), le test est effectu� + sur un dictionnaire de 4987 mots anglais de centre "POSITIVELY" de rayon 13. + L'optimisation est plus pertinente dans ce cas o� le dictionnaire + contient plus du double de mots que celui utilis� + pour le test~\ref{metric_test_optimisation}. + } \label{metric_test_optimisation_dicos} - \end{table} + \end{table} @@ -1003,7 +1003,7 @@ \section{Voisinage dans un espace vectoriel} %------------------------------------------------------------------------------------------------------------------- -Lorsque l'espace mtrique est aussi vectoriel, la recherche des plus proches voisins est facilit car il est possible d'utiliser les coordonnes des lments comme dans l'algorithme \emph{Branch and Bound}. Ces coordonnes permettent galement d'obtenir des rsultats thorique plus avancs en ce qui concerne le cot de cette recherche (voir \citeindex{Arya1994}. +Lorsque l'espace m�trique est aussi vectoriel, la recherche des plus proches voisins est facilit� car il est possible d'utiliser les coordonn�es des �l�ments comme dans l'algorithme \emph{Branch and Bound}. Ces coordonn�es permettent �galement d'obtenir des r�sultats th�orique plus avanc�s en ce qui concerne le co�t de cette recherche (voir \citeindex{Arya1994}. @@ -1012,32 +1012,32 @@ \section{Voisinage dans un espace vectoriel} \subsection{B+ tree} \indexfr{B+ tree} -Ce premier algorithme s'applique dans le cas rel afin d'ordonner des nombres dans un arbre de sorte que chaque n\oe ud ait un pre et pas plus de $n$ fils (voir figure~\ref{space_metric_btree}). - - - \begin{figure}[ht] - $$\begin{array}{|c|}\hline - \includegraphics[height=5cm, width=7cm]{\filext{../space_metric/image/btree}} \\ \hline - \end{array}$$ - \caption{Illustration d'un B+ tree.} - \label{space_metric_btree} - \end{figure} - - \begin{xdefinition}{B+ tree} - Soit $B_n$ un B+ tree, soit $N$ un n\oe ud de $B_n$, il contient un vecteur $V\pa{N} = \vecteur{x_1}{x_t}$ - avec $0 \infegal t \infegal n$ et $x_1 < ... < x_t$. Ce n\oe ud contient aussi exactement $t-1$ n\oe uds fils - nots $\vecteur{N_1}{N_{t-1}}$. On dsigne par $D\pa{N_t}$ l'ensemble des descendants du n\oe ud $N_t$ et - $G\pa{N_t} = \acc{ V\pa{M} \sac M \in D\pa{N_t}}$. Le n\oe ud $N$ vrifie~: - \begin{eqnarray*} - && \forall x \in G\pa{N_t}, \; x_{t} \infegal x < x_{t+1} \\ - && \text{avec par convention } x_0 = -\infty \text{ et } x_{t+1} = + \infty - \end{eqnarray*} - \end{xdefinition} - +Ce premier algorithme s'applique dans le cas r�el afin d'ordonner des nombres dans un arbre de sorte que chaque n\oe ud ait un p�re et pas plus de $n$ fils (voir figure~\ref{space_metric_btree}). + + + \begin{figure}[ht] + $$\begin{array}{|c|}\hline + \includegraphics[height=5cm, width=7cm]{\filext{../space_metric/image/btree}} \\ \hline + \end{array}$$ + \caption{Illustration d'un B+ tree.} + \label{space_metric_btree} + \end{figure} + + \begin{xdefinition}{B+ tree} + Soit $B_n$ un B+ tree, soit $N$ un n\oe ud de $B_n$, il contient un vecteur $V\pa{N} = \vecteur{x_1}{x_t}$ + avec $0 \leqslant t \leqslant n$ et $x_1 < ... < x_t$. Ce n\oe ud contient aussi exactement $t-1$ n\oe uds fils + not�s $\vecteur{N_1}{N_{t-1}}$. On d�signe par $D\pa{N_t}$ l'ensemble des descendants du n\oe ud $N_t$ et + $G\pa{N_t} = \acc{ V\pa{M} \sac M \in D\pa{N_t}}$. Le n\oe ud $N$ v�rifie~: + \begin{eqnarray*} + && \forall x \in G\pa{N_t}, \; x_{t} \leqslant x < x_{t+1} \\ + && \text{avec par convention } x_0 = -\infty \text{ et } x_{t+1} = + \infty + \end{eqnarray*} + \end{xdefinition} + \indexfr{quicksort} \indexfrr{tri}{quicksort} - -Cet arbre permet de trier une liste de nombres, c'est une gnralisation du tri "quicksort" pour lequel $n=2$. Comme pour le tri quicksort, l'arbre est construit partir d'une srie d'insertions et de cet ordre dpend la rapidit du tri. L'esprance du cot (moyenne sur tous les permutations possibles de $k$ lments), le cot de l'algorithme est en $O\pa{k \log_n k}$. + +Cet arbre permet de trier une liste de nombres, c'est une g�n�ralisation du tri "quicksort" pour lequel $n=2$. Comme pour le tri quicksort, l'arbre est construit � partir d'une s�rie d'insertions et de cet ordre d�pend la rapidit� du tri. L'esp�rance du co�t (moyenne sur tous les permutations possibles de $k$ �l�ments), le co�t de l'algorithme est en $O\pa{k \log_n k}$. @@ -1049,116 +1049,116 @@ \subsection{B+ tree} \subsection{R-tree ou Rectangular Tree} \indexfr{R-tree} -L'arbre R-tree est l'adaptation du mcanisme du B+ tree au cas multidimensionnel (voir \citeindex{Guttman1984}). La construction de cet arbre peut se faire de manire globale -~construction de l'arbre sachant l'ensemble de points classer~- ou de manire progressive -~insertion des points dans l'arbre les uns la suite des autres~-. Ces arbres sont comparables l'arbre de partitionnement construit par l'algorithme~\ref{algorithm_AHC} ceci prs que la forme des ensembles est constitue de rectangles et non plus de cercles. L'appartenance d'un point un rectangle dpend dornavant des comparaisons entre coordonnes tandis que l'appartenance d'un point un cercle ncessite le calcul d'une distance, ce qui est plus lent. Toutefois, ces mthodes sont resteintes des espaces vectoriels. - - \begin{figure}[ht] - $$\begin{array}{|c|c|}\hline - \includegraphics[height=6cm, width=6cm]{\filext{../space_metric/image/rtree1}} & - \includegraphics[height=5cm, width=11cm]{\filext{../space_metric/image/rtree2}} \\ \hline - \end{array}$$ - \caption{ Illustration d'un R-tree en deux dimensions, - figure extraite de \citeindexfig{Sellis1987}, la premire image montre des rectangles - pointills englobant d'autres rectangles en trait plein. Chaque style de trait correspond - un niveau dans le graphe de la seconde image. - } - \label{space_metric_rtree} - \end{figure} - -\indexfrr{bote}{englobante} -\indexfrr{bote}{objet} -\indexfrr{bote}{fentre} - -Il n'existe pas une seule manire de construire un R-tree, les n\oe uds de ces arbres suivent toujours la contrainte des B+~Tree qui est d'avoir un pre et au plus $n$ fils. Les R-Tree ont la mme structure que les B+~Tree te de leurs contraintes d'ordonnancement des fils. De plus, ces arbres organisent spatialement des rectangles ou botes en plusieurs dimensions comme le suggre la figure~\ref{space_metric_rtree}. Les botes organiser seront nomms les objets, ces objets sont ensuite regroups dans des botes englobantes. Un n\oe ud $n$ d'un R-tree est donc soit une feuille, auquel cas la bote qu'il dsigne est un objet, dans ce cas, il n'a aucun fils, soit le n\oe ud dsigne une bote englobante $B\pa{n}$. On dsigne par $\mathcal{B}$ l'ensemble des botes d'un espace vectoriel quelconque et $v\pa{b}$ dsigne son volume. Pour un n\oe ud $n$ non feuille, $A\pa{n}$ dsigne l'ensemble des descendants de ce n\oe ud. $B\pa{n}$ est dfini par~: - - $$ - B\pa{n} = \arg \min \acc{ v\pa{b} \sac b \in \mathcal{B} \text{ et } - \forall n' \in A\pa{n'}, \; B\pa{n'} \subset B\pa{n} } - $$ - - - -La recherche dans un R-tree consiste trouver toutes les objets ayant une intersection avec une autre bote ou fentre $W$, soit l'ensemble $L$~: - - $$ - L = \acc{ B\pa{n} \sac B\pa{n} \text{ est un objet et } B\pa{n} \cap W \neq \emptyset } - $$ - - -Cet ensemble est construit grce l'algorithme suivant~: - - - \begin{xalgorithm}{recherche dans un R-tree} \label{space_metric_algo_r_tree_search} - Les notations sont celles utilises dans ce paragraphe. On dsigne par $r$ le n\oe ud racine d'un R-tree. - Soit $n$ un n\oe ud, on dsigne par $F\pa{n}$ l'ensemble des fils de ce n\oe ud. - - \begin{xalgostep}{initialisation} - $L \longleftarrow 0$ \\ - $N \longleftarrow \acc{r}$ - \end{xalgostep} - - \begin{xalgostep}{itration} - \begin{xwhile}{$N \neq \emptyset$} - \begin{xforeach}{n}{N} - \begin{xif}{$W \cap B\pa{n} \neq \emptyset$} - $N \longleftarrow N \cup F\pa{n}$ \\ - \begin{xif}{$B\pa{n}$ est un objet} - $L \longleftarrow B\pa{n}$ - \end{xif} - \end{xif} - \end{xforeach} - \end{xwhile} - \end{xalgostep} - - $L$ est l'ensemble cherch. - - \end{xalgorithm} - - -Il reste construire le R-tree, opration effectue par la rptition successive de l'algorithme~\ref{space_metric_algo_r_tree_insert} permettant d'insrer un objet dans un R-tree. - - \begin{xalgorithm}{insertion d'un objet dans un R-tree} \label{space_metric_algo_r_tree_insert} - Les notations utilises sont les mmes que celles de l'algorithme~\ref{space_metric_algo_r_tree_search}. - On cherche insrer l'object $E$ dsign par son n\oe ud feuille $e$. On suppose que l'arbre contient au - moins un n\oe ud, sa racine $r$. On dsigne galement par $p\pa{n}$ le pre du n\oe ud $n$. Chaque n\oe ud - ne peut contenir plus de $s$ fils. On dsigne par - $v^*\pa{G} = \min \acc{ P \sac P \in \mathcal{B} \text{ et } - \unionone{g \in G} B\pa{g} \subset P }$. - - \begin{xalgostep}{slection du n\oe ud d'insertion} - $n^* \longleftarrow r$ \\ - \begin{xwhile}{$n^*$ n'est pas un n\oe ud feuille} - On choisit le fils $f$ de $n^*$ qui minimise l'accroissement $v_f - v\pa{B\pa{f}}$ - du volume avec $v_f$ dfini par~: - \begin{eqnarray} - v_f = \min \acc{ v\pa{P} \sac P \in \mathcal{B} \text{ et } B\pa{f} \cup B\pa{e} \subset P } - \label{space_metric_r_tree_b_n_update} - \end{eqnarray} - $n^* \longleftarrow f$ - \end{xwhile} - \end{xalgostep} - - \begin{xalgostep}{ajout du n\oe ud} - Si $p\pa{n^*}$ a moins de $s$ fils, alors le n\oe ud $e$ devient le fils de $p\pa{n^*}$ et $B\pa{p\pa{n^*}}$ est - mis jour d'aprs l'expression (\ref{space_metric_r_tree_b_n_update}). L'insertion est termine. - Dans le cas contraire, on spare dcoupe le n\oe ud $p\pa{n^*}$ en deux grce l'tape suivante. - \end{xalgostep} - - %\possiblecut - - \begin{xalgostep}{dcoupage des n\oe uds} \label{space_metric_insertion_decoupage_r_tree} - L'objectif est de diviser le groupe $G$ compos de $s+1$ n\oe uds en deux groupes $G_1$ et $G_1$. - Tout d'abord, on cherche - le couple $\pa{n_1,n_2}$ qui minimise le critre $$ d = v^*\pa{\acc{n_1,n_2}} - v\pa{B\pa{n_1}} - v\pa{B\pa{n_2}}$$ Alors~: - $G_1 \longleftarrow n_1$, $G_2 \longleftarrow n_2$ et $G \longleftarrow G - G_1 \cup G_2$ \\ - \begin{xwhile}{$G \neq \emptyset$} - On choisit un n\oe ud $n \in G$, on dtermine $i^*$ tel que $v\pa{\acc{n} \cup G_i} - v\pa{G_i}$ soit minimal. \\ - $G \longleftarrow G - \acc{n}$ \\ - $G_{i^*} \longleftarrow G_{i^*} \cup \acc{n}$ - \end{xwhile} - \end{xalgostep} - - - \end{xalgorithm} +L'arbre R-tree est l'adaptation du m�canisme du B+ tree au cas multidimensionnel (voir \citeindex{Guttman1984}). La construction de cet arbre peut se faire de mani�re globale -~construction de l'arbre sachant l'ensemble de points � classer~- ou de mani�re progressive -~insertion des points dans l'arbre les uns � la suite des autres~-. Ces arbres sont comparables � l'arbre de partitionnement construit par l'algorithme~\ref{algorithm_AHC} � ceci pr�s que la forme des ensembles est constitu�e de rectangles et non plus de cercles. L'appartenance d'un point � un rectangle d�pend dor�navant des comparaisons entre coordonn�es tandis que l'appartenance d'un point � un cercle n�cessite le calcul d'une distance, ce qui est plus lent. Toutefois, ces m�thodes sont resteintes � des espaces vectoriels. + + \begin{figure}[ht] + $$\begin{array}{|c|c|}\hline + \includegraphics[height=6cm, width=6cm]{\filext{../space_metric/image/rtree1}} & + \includegraphics[height=5cm, width=11cm]{\filext{../space_metric/image/rtree2}} \\ \hline + \end{array}$$ + \caption{ Illustration d'un R-tree en deux dimensions, + figure extraite de \citeindexfig{Sellis1987}, la premi�re image montre des rectangles + pointill�s englobant d'autres rectangles en trait plein. Chaque style de trait correspond + � un niveau dans le graphe de la seconde image. + } + \label{space_metric_rtree} + \end{figure} + +\indexfrr{bo�te}{englobante} +\indexfrr{bo�te}{objet} +\indexfrr{bo�te}{fen�tre} + +Il n'existe pas une seule mani�re de construire un R-tree, les n\oe uds de ces arbres suivent toujours la contrainte des B+~Tree qui est d'avoir un p�re et au plus $n$ fils. Les R-Tree ont la m�me structure que les B+~Tree �t�e de leurs contraintes d'ordonnancement des fils. De plus, ces arbres organisent spatialement des rectangles ou bo�tes en plusieurs dimensions comme le sugg�re la figure~\ref{space_metric_rtree}. Les bo�tes � organiser seront nomm�s les objets, ces objets sont ensuite regroup�s dans des bo�tes englobantes. Un n\oe ud $n$ d'un R-tree est donc soit une feuille, auquel cas la bo�te qu'il d�signe est un objet, dans ce cas, il n'a aucun fils, soit le n\oe ud d�signe une bo�te englobante $B\pa{n}$. On d�signe par $\mathcal{B}$ l'ensemble des bo�tes d'un espace vectoriel quelconque et $v\pa{b}$ d�signe son volume. Pour un n\oe ud $n$ non feuille, $A\pa{n}$ d�signe l'ensemble des descendants de ce n\oe ud. $B\pa{n}$ est d�fini par~: + + $$ + B\pa{n} = \arg \min \acc{ v\pa{b} \sac b \in \mathcal{B} \text{ et } + \forall n' \in A\pa{n'}, \; B\pa{n'} \subset B\pa{n} } + $$ + + + +La recherche dans un R-tree consiste � trouver toutes les objets ayant une intersection avec une autre bo�te ou fen�tre $W$, soit l'ensemble $L$~: + + $$ + L = \acc{ B\pa{n} \sac B\pa{n} \text{ est un objet et } B\pa{n} \cap W \neq \emptyset } + $$ + + +Cet ensemble est construit gr�ce � l'algorithme suivant~: + + + \begin{xalgorithm}{recherche dans un R-tree} \label{space_metric_algo_r_tree_search} + Les notations sont celles utilis�es dans ce paragraphe. On d�signe par $r$ le n\oe ud racine d'un R-tree. + Soit $n$ un n\oe ud, on d�signe par $F\pa{n}$ l'ensemble des fils de ce n\oe ud. + + \begin{xalgostep}{initialisation} + $L \longleftarrow 0$ \\ + $N \longleftarrow \acc{r}$ + \end{xalgostep} + + \begin{xalgostep}{it�ration} + \begin{xwhile}{$N \neq \emptyset$} + \begin{xforeach}{n}{N} + \begin{xif}{$W \cap B\pa{n} \neq \emptyset$} + $N \longleftarrow N \cup F\pa{n}$ \\ + \begin{xif}{$B\pa{n}$ est un objet} + $L \longleftarrow B\pa{n}$ + \end{xif} + \end{xif} + \end{xforeach} + \end{xwhile} + \end{xalgostep} + + $L$ est l'ensemble cherch�. + + \end{xalgorithm} + + +Il reste � construire le R-tree, op�ration effectu�e par la r�p�tition successive de l'algorithme~\ref{space_metric_algo_r_tree_insert} permettant d'ins�rer un objet dans un R-tree. + + \begin{xalgorithm}{insertion d'un objet dans un R-tree} \label{space_metric_algo_r_tree_insert} + Les notations utilis�es sont les m�mes que celles de l'algorithme~\ref{space_metric_algo_r_tree_search}. + On cherche � ins�rer l'object $E$ d�sign� par son n\oe ud feuille $e$. On suppose que l'arbre contient au + moins un n\oe ud, sa racine $r$. On d�signe �galement par $p\pa{n}$ le p�re du n\oe ud $n$. Chaque n\oe ud + ne peut contenir plus de $s$ fils. On d�signe par + $v^*\pa{G} = \min \acc{ P \sac P \in \mathcal{B} \text{ et } + \unionone{g \in G} B\pa{g} \subset P }$. + + \begin{xalgostep}{s�lection du n\oe ud d'insertion} + $n^* \longleftarrow r$ \\ + \begin{xwhile}{$n^*$ n'est pas un n\oe ud feuille} + On choisit le fils $f$ de $n^*$ qui minimise l'accroissement $v_f - v\pa{B\pa{f}}$ + du volume avec $v_f$ d�fini par~: + \begin{eqnarray} + v_f = \min \acc{ v\pa{P} \sac P \in \mathcal{B} \text{ et } B\pa{f} \cup B\pa{e} \subset P } + \label{space_metric_r_tree_b_n_update} + \end{eqnarray} + $n^* \longleftarrow f$ + \end{xwhile} + \end{xalgostep} + + \begin{xalgostep}{ajout du n\oe ud} + Si $p\pa{n^*}$ a moins de $s$ fils, alors le n\oe ud $e$ devient le fils de $p\pa{n^*}$ et $B\pa{p\pa{n^*}}$ est + mis � jour d'apr�s l'expression (\ref{space_metric_r_tree_b_n_update}). L'insertion est termin�e. + Dans le cas contraire, on s�pare d�coupe le n\oe ud $p\pa{n^*}$ en deux gr�ce � l'�tape suivante. + \end{xalgostep} + + %\possiblecut + + \begin{xalgostep}{d�coupage des n\oe uds} \label{space_metric_insertion_decoupage_r_tree} + L'objectif est de diviser le groupe $G$ compos� de $s+1$ n\oe uds en deux groupes $G_1$ et $G_1$. + Tout d'abord, on cherche + le couple $\pa{n_1,n_2}$ qui minimise le crit�re $$ d = v^*\pa{\acc{n_1,n_2}} - v\pa{B\pa{n_1}} - v\pa{B\pa{n_2}}$$ Alors~: + $G_1 \longleftarrow n_1$, $G_2 \longleftarrow n_2$ et $G \longleftarrow G - G_1 \cup G_2$ \\ + \begin{xwhile}{$G \neq \emptyset$} + On choisit un n\oe ud $n \in G$, on d�termine $i^*$ tel que $v\pa{\acc{n} \cup G_i} - v\pa{G_i}$ soit minimal. \\ + $G \longleftarrow G - \acc{n}$ \\ + $G_{i^*} \longleftarrow G_{i^*} \cup \acc{n}$ + \end{xwhile} + \end{xalgostep} + + + \end{xalgorithm} @@ -1170,7 +1170,7 @@ \subsection{R-tree ou Rectangular Tree} \indexfr{R$^*$ tree} \indexfr{R$^*$ tree} \indexfr{R+ Tree} -Si la recherche est identique quel que soit l'arbre construit, chaque variante de la construction de l'arbre tente de minimiser les intersections des botes et leur couverture. Plus prcisment, l'tape~\ref{space_metric_insertion_decoupage_r_tree} qui permet de dcouper les n\oe uds est conue de manire obtenir des botes englobantes de volume minimale et/ou d'intersection minimale avec d'autres botes englobantes. L'algorithme R+~Tree (voir \citeindex{Sellis1987}) essaye de minimiser les intersections entre botes et les objets organiser sont supposs n'avoir aucune intersection commune. La variante R$^*$~Tree (voir \citeindex{Beckmann1990}) effectue un compromis entre l'intersection et la couverture des botes englobantes. L'algorithme X-Tree (voir \citeindex{Berchtold1996}) conserve l'historique de la construction de l'arbre ce qui lui permet de mieux viter les intersections communes entre botes. +Si la recherche est identique quel que soit l'arbre construit, chaque variante de la construction de l'arbre tente de minimiser les intersections des bo�tes et leur couverture. Plus pr�cis�ment, l'�tape~\ref{space_metric_insertion_decoupage_r_tree} qui permet de d�couper les n\oe uds est con�ue de mani�re � obtenir des bo�tes englobantes de volume minimale et/ou d'intersection minimale avec d'autres bo�tes englobantes. L'algorithme R+~Tree (voir \citeindex{Sellis1987}) essaye de minimiser les intersections entre bo�tes et les objets � organiser sont suppos�s n'avoir aucune intersection commune. La variante R$^*$~Tree (voir \citeindex{Beckmann1990}) effectue un compromis entre l'intersection et la couverture des bo�tes englobantes. L'algorithme X-Tree (voir \citeindex{Berchtold1996}) conserve l'historique de la construction de l'arbre ce qui lui permet de mieux �viter les intersections communes entre bo�tes. @@ -1182,33 +1182,33 @@ \subsection{R-tree ou Rectangular Tree} \subsection{Branch and Bound} \indexfr{Branch and Bound} -\indexfr{hirarchie} -\indexfrr{dcomposition}{hirarchique} +\indexfr{hi�rarchie} +\indexfrr{d�composition}{hi�rarchique} \indexfr{espace vectoriel} -Les algorithmes regroups sous cette terminaison \emph{Branch and Bound} englobe la plupart des mthodes prsentes dans ce document, elles dsignent tout algorithme de recherche ncessitant une premire tape permettant de construire une dcomposition hirarchique de l'ensemble des exemples ou exemples d'apprentissage, cet ensemble tant inclus dans un espace vectoriel. La premire version de cette famille algorithme a t dveloppe dans \citeindex{Fukunaga1975}. +Les algorithmes regroup�s sous cette terminaison \emph{Branch and Bound} englobe la plupart des m�thodes pr�sent�es dans ce document, elles d�signent tout algorithme de recherche n�cessitant une premi�re �tape permettant de construire une d�composition hi�rarchique de l'ensemble des exemples ou exemples d'apprentissage, cet ensemble �tant inclus dans un espace vectoriel. La premi�re version de cette famille algorithme a �t� d�velopp�e dans \citeindex{Fukunaga1975}. \indexfrr{loi}{normale} -La mthode prsente dans \citeindex{D'Haes2003} utilise une analyse en composantes principales afin de construire une hirarchique adapte un nuage de points obissant une loi normale multidimensionnelle (voir figure~\ref{space_metric_dheas_1}). +La m�thode pr�sent�e dans \citeindex{D'Haes2003} utilise une analyse en composantes principales afin de construire une hi�rarchique adapt�e � un nuage de points ob�issant � une loi normale multidimensionnelle (voir figure~\ref{space_metric_dheas_1}). - \begin{figure}[ht] - $$\begin{array}{|c|}\hline - \includegraphics[height=6cm, width=12cm]{\filext{../space_metric/image/dhaes}} \\ \hline - \end{array}$$ - \caption{ Figure extraite de \citeindexfig{D'Haes2003} reprsentant l'arbre de dcomposition - de deux nuages de points obissant des loi normales, uniforme dans le premier cas, - avec deux variables corrles dans le second cas.} - \label{space_metric_dheas_1} - \end{figure} + \begin{figure}[ht] + $$\begin{array}{|c|}\hline + \includegraphics[height=6cm, width=12cm]{\filext{../space_metric/image/dhaes}} \\ \hline + \end{array}$$ + \caption{ Figure extraite de \citeindexfig{D'Haes2003} repr�sentant l'arbre de d�composition + de deux nuages de points ob�issant � des loi normales, uniforme dans le premier cas, + avec deux variables corr�l�es dans le second cas.} + \label{space_metric_dheas_1} + \end{figure} \indexfr{ACP} -\indexfr{mdiane} +\indexfr{m�diane} \indexfr{analyse en composantes principales} -La mthode propose dans cet article effectue une analyse en composantes principales afin de reprer l'axe principal du nuage qui correspond au vecteur propre $\vec{V}$ associ la plus grande des valeurs propres de la matrice $X'X$ o chaque ligne de la matrice $X$ est un lment du nuage de points $\vecteur{\vec{x_1}}{\vec{x_n}}$. On dtermine la mdiane $m$ de l'ensemble $\acc{ < \vec{V}, \vec{x_i} > \sac 1 \infegal i \infegal n}$. Le nuage de points est alors divise en deux sous-nuages de cardinaux gaux selon que le produit scalaire $< \vec{V}, \vec{x_i} >$ est infrieur ou suprieur $m$. Chaque sous-nuage est nouveau divis selon le mme processus incluant une analyse en composantes principales et la recherche de la mdiane. L'algorithme s'arrte lorsque les sous-ensembles sont rduits un seul lment. +La m�thode propos�e dans cet article effectue une analyse en composantes principales afin de rep�rer l'axe principal du nuage qui correspond au vecteur propre $\vec{V}$ associ� � la plus grande des valeurs propres de la matrice $X'X$ o� chaque ligne de la matrice $X$ est un �l�ment du nuage de points $\vecteur{\vec{x_1}}{\vec{x_n}}$. On d�termine la m�diane $m$ de l'ensemble $\acc{ < \vec{V}, \vec{x_i} > \sac 1 \leqslant i \leqslant n}$. Le nuage de points est alors divis�e en deux sous-nuages de cardinaux �gaux selon que le produit scalaire $< \vec{V}, \vec{x_i} >$ est inf�rieur ou sup�rieur � $m$. Chaque sous-nuage est � nouveau divis� selon le m�me processus incluant une analyse en composantes principales et la recherche de la m�diane. L'algorithme s'arr�te lorsque les sous-ensembles sont r�duits � un seul �l�ment. @@ -1216,10 +1216,10 @@ \subsection{Branch and Bound} -\subsection{Mthodes approches} -\indexfrr{kPPV}{mthode approche} +\subsection{M�thodes approch�es} +\indexfrr{kPPV}{m�thode approch�e} -Toutes les mthodes proposes jusqu' prsent permettent de dterminer le voisinage exact d'un lment inclus dans un ensemble d'exemples, les diffrences concernant l'organisation de cet ensemble afin d'optimiser la recherche. L'autre direction de recherche concerne la recherche du voisinage notamment par des mthodes approches, plus rapide, mais ne retournant pas le voisinage exact. Ces mthodes ne seront pas plus dveloppes ici, l'article \citeindex{Arya1994} propose une tude thorique de ce problme. L'article \citeindex{Ramasubramanian2000} compare plusieurs mthodes de recherche du voisinage. +Toutes les m�thodes propos�es jusqu'� pr�sent permettent de d�terminer le voisinage exact d'un �l�ment inclus dans un ensemble d'exemples, les diff�rences concernant l'organisation de cet ensemble afin d'optimiser la recherche. L'autre direction de recherche concerne la recherche du voisinage notamment par des m�thodes approch�es, plus rapide, mais ne retournant pas le voisinage exact. Ces m�thodes ne seront pas plus d�velopp�es ici, l'article \citeindex{Arya1994} propose une �tude th�orique de ce probl�me. L'article \citeindex{Ramasubramanian2000} compare plusieurs m�thodes de recherche du voisinage. @@ -1245,7 +1245,7 @@ \subsection{M \section{Autres alternatives} %------------------------------------------------------------------------------------------------------------- -Il existe de nombreuses alternatives en ce qui concerne la recherche des plus proches voisins dans un espace mtrique quelconque, passes en revue dans les articles \citeindex{Bustos2001}, \citeindex{Chavez1999}, \citeindex{Navarro2001}, certaines utilisant plus particulirement les arbres comme \citeindex{Ulhmann1991} ou \citeindex{Yianilos1993}. Lorsque cette recherche s'applique aux mots ou aux squences, l'optimisation de la distance est envisage (voir \citeindex{Apostolico1985} ou \citeindex{Madhvanath2001}). L'algorithme qui suit est une de ces alternatives prfre aux autres en raison de sa simplicit. Il ne ncessite pas la construction d'un arbre et son cot est aisment calculable. +Il existe de nombreuses alternatives en ce qui concerne la recherche des plus proches voisins dans un espace m�trique quelconque, pass�es en revue dans les articles \citeindex{Bustos2001}, \citeindex{Chavez1999}, \citeindex{Navarro2001}, certaines utilisant plus particuli�rement les arbres comme \citeindex{Ulhmann1991} ou \citeindex{Yianilos1993}. Lorsque cette recherche s'applique aux mots ou aux s�quences, l'optimisation de la distance est envisag�e (voir \citeindex{Apostolico1985} ou \citeindex{Madhvanath2001}). L'algorithme qui suit est une de ces alternatives pr�f�r�e aux autres en raison de sa simplicit�. Il ne n�cessite pas la construction d'un arbre et son co�t est ais�ment calculable. \subsection{LAESA} @@ -1253,263 +1253,263 @@ \subsection{LAESA} \label{space_metric_laesa_laesa} \indexfr{LAESA}\indexfr{pivot} -Cet algorithme permet de chercher les plus proches voisins dans un ensemble inclus dans un espace mtrique quelconque. Il s'appuie encore sur l'ingalit triangulaire applique de manire semblable (\ref{equation_un}). Le voisinage d'un point $x$ doit tre cherch dans un ensemble $E$. L'algorithme LAESA (Linear Approximating Eliminating Search Algorithm, voir \citeindex{Rico-Juan2003}) consiste viter un trop grand nombre de calculs de distances en se servant de distances dj calcules entre les lments de $E$ et un sous-ensemble $B$ inclus dans $E$ contenant des "pivots". - -La slection des pivots demeure un problme ouvert. Ceux-ci pourrait tre les n\oe uds d'un coupe de l'arbre construit par l'algorithme~\ref{algorithm_AHC}. Il existe d'autres possibilits comme l'algorithme~\ref{space_metric_algo_laesa_prime} plus simple et nettement moins coteux -~les deux algorithmes n'ont pas t compars en terme de performances en classification. - - - - - \begin{xalgorithm}{LAESA} - \label{space_metric_algo_laesa} - - Soit $E = \ensemble{y_1}{y_N}$ un ensemble de points, $B = \ensemble{p_1}{p_P} \subset E$ - un ensemble de pivots inclus dans $E$. On cherche dterminer le voisinage $V\pa{x}$ de $x$ - inclus dans $E$ vrifiant~: - - $$ - \forall y \in V\pa{x}, \; d\pa{x,y} \infegal \rho - $$ - - On suppose que la matrice $M = \pa{m_{ij}}_ { \begin{subarray} 1 \infegal i \infegal P \\ - 1 \infegal j \infegal N \end{subarray} }$ a t calcule pralablement comme suit~: - - $$ - \forall \pa{i,j}, m_{ij} = d\pa{p_i, y_j} - $$ - - \begin{xalgostep}{initialisation} - $\begin{array}{ll} - \forall y \in E, & g\pa{y} \longleftarrow 0 \\ - \forall y \in E, & h\pa{y} \longleftarrow 1 - \end{array}$ - \end{xalgostep} - - \begin{xalgostep}{choix d'un pivot et mise jour de la fonction $g$} \label{classif_laesa_step_b} - $B' \longleftarrow B \cap \acc{ y \in E \sac h\pa{y} = 0}$ \\ - \begin{xif}{$B' \neq \emptyset$} - Soit $i$ tel que $p_i$ soit un lment de $B'$ tir au hasard tel que~: \\ - $$p_i \in \arg \min \acc{ g\pa{y} \sac y \in B'}$$ - $\begin{array}{lll} - \alpha &\longleftarrow& d\pa{p_i,x} \\ - h\pa{p_i} &\longleftarrow& 1 - \end{array}$ \\ - $\begin{array}{rl} - \forall y_j \in E \text{ tel que } h\pa{y_j} = 0, \; g\pa{y_j} \longleftarrow & - \max \acc { g\pa{y_j} , \abs{ \alpha - m_{ij} } } \\ = & - \max \acc { g\pa{y_j} , \abs{ d\pa{x,p_i} - d\pa{p_i, y_j} } } - \end{array}$ - \xelse - Choisir un lment $s$ de $E$ tel que $h\pa{s} = 0$. \\ - $\begin{array}{lll} - \alpha &\longleftarrow& d\pa{s,x} \\ - h\pa{s} &\longleftarrow& 1 - \end{array}$ \\ - $\begin{array}{rl} - \forall y \in E \text{ tel que } h\pa{y} = 0, \; g\pa{y} \longleftarrow & d\pa{x,y} - \end{array}$ - - \end{xif} - - - - \end{xalgostep} - - \possiblecut - - - \begin{xalgostep}{limination} - $\begin{array}{rl} - \forall y_j \in E \text{ tel que } h\pa{y_j} = 0, \text{ si } g\pa{y_j} > \rho \text{ alors } - h\pa{y_j} = 1 - \end{array}$ - \end{xalgostep} - - \begin{xalgostep}{terminaison} - $A \longleftarrow \acc{ y \in E \sac h\pa{y} = 0 }$ \\ - \begin{xif}{$A \neq \emptyset$} - Retour l'tape~\ref{classif_laesa_step_b}. - \xelse - Fin, l'ensemble cherch correspond $\acc{y \in E \sac g\pa{y}} \infegal \rho$. - \end{xif} - \end{xalgostep} - - \end{xalgorithm} - - - - -La slection des pivots est assure par un autre algorithme dcrit dans l'article~\citeindex{Moreno2003}. - - - \begin{xalgorithm}{LAESA : slection des pivots} - \label{space_metric_algo_laesa_pivtos_sel} - \indexfrr{pivot}{slection} - - Soit $E = \ensemble{y_1}{y_N}$ un ensemble de points, on cherche dterminer - l'ensemble $B = \ensemble{p_1}{p_P} \subset E$ utilis par l'algorithme~\ref{space_metric_algo_laesa}. - - \begin{xalgostep}{initialisation} - $B \longleftarrow y \in E$ choisi arbitrairement. - \end{xalgostep} - - \begin{xalgostep}{calcul de la fonction $g$} \label{space_metric_laesa_pivots_sel_b} - \begin{xforeach}{y}{E - B} - $g\pa{y} \longleftarrow 0$ \\ - \begin{xforeach}{p}{B} - $g\pa{y} \longleftarrow g\pa{y} + d\pa{y,p}$ - \end{xforeach} - \end{xforeach} - \end{xalgostep} - - \begin{xalgostep}{mise jour de $B$} - Trouver $p^* \in \arg \max \acc { g\pa{p} \sac p \in E - B}$\\ - $B \longleftarrow B \cup \acc{ p^*}$ \\ - Si $\card{B} < P$, retour l'tape~\ref{space_metric_laesa_pivots_sel_b} sinon fin. - \end{xalgostep} - - \end{xalgorithm} - - - - -Cet article~\citeindex{Moreno2003} amliore galement l'algorithme~\ref{space_metric_algo_laesa} par le suivant~: - - - - \begin{xalgorithm}{LAESA'} - \label{space_metric_algo_laesa_prime} - \indexfr{LAESA'} - - Soit $E = \ensemble{y_1}{y_N}$ un ensemble de points, $B = \ensemble{p_1}{p_P} \subset E$ - un ensemble de pivots inclus dans $E$. On cherche dterminer le voisinage $V\pa{x}$ de $x$ - inclus dans $E$ vrifiant~: - - $$ - \forall y \in V\pa{x}, \; d\pa{x,y} \infegal \rho - $$ - - On suppose que la matrice $M = \pa{m_{ij}}_ { \begin{subarray} 1 \infegal i \infegal P \\ - 1 \infegal j \infegal N \end{subarray} }$ a t calcule pralablement comme suit~: - - $$ - \forall \pa{i,j}, \; m_{ij} = d\pa{p_i, y_j} - $$ - - \begin{xalgostep}{initialisation} - $\forall i \in \ensemble{1}{P}, \; d_i \longleftarrow d\pa{x,p_i}$ - \end{xalgostep} - - \begin{xalgostep}{fonction $g$} \label{classif_laesa_prime_step_b} - $\forall j \in \ensemble{1}{N}, \; g\pa{y_j} \longleftarrow \underset{ i \in \ensemble{1}{P} }{\min} \abs{ m_{ij} - d_i} $ - \end{xalgostep} - - \begin{xalgostep}{tri} - Tri l'ensemble $g\pa{y_i}$ par ordre croissant $\longrightarrow g\pa{y_{\sigma\pa{j}}}$. \\ - \begin{xfor}{j}{1}{N} - \begin{xif}{$g\pa{y_{\sigma\pa{j}}} \infegal \rho$} - $g\pa{y_{\sigma\pa{j}}} = d\pa{x,y_{\sigma\pa{j}}}$ - \end{xif} - \end{xfor} - - Fin, l'ensemble cherch correspond $\acc{y \in E \sac g\pa{y}} \infegal \rho$. - \end{xalgostep} +Cet algorithme permet de chercher les plus proches voisins dans un ensemble inclus dans un espace m�trique quelconque. Il s'appuie encore sur l'in�galit� triangulaire appliqu�e de mani�re semblable � (\ref{equation_un}). Le voisinage d'un point $x$ doit �tre cherch� dans un ensemble $E$. L'algorithme LAESA (Linear Approximating Eliminating Search Algorithm, voir \citeindex{Rico-Juan2003}) consiste � �viter un trop grand nombre de calculs de distances en se servant de distances d�j� calcul�es entre les �l�ments de $E$ et un sous-ensemble $B$ inclus dans $E$ contenant des "pivots". + +La s�lection des pivots demeure un probl�me ouvert. Ceux-ci pourrait �tre les n\oe uds d'un coupe de l'arbre construit par l'algorithme~\ref{algorithm_AHC}. Il existe d'autres possibilit�s comme l'algorithme~\ref{space_metric_algo_laesa_prime} plus simple et nettement moins co�teux -~les deux algorithmes n'ont pas �t� compar�s en terme de performances en classification. + + + + + \begin{xalgorithm}{LAESA} + \label{space_metric_algo_laesa} + + Soit $E = \ensemble{y_1}{y_N}$ un ensemble de points, $B = \ensemble{p_1}{p_P} \subset E$ + un ensemble de pivots inclus dans $E$. On cherche � d�terminer le voisinage $V\pa{x}$ de $x$ + inclus dans $E$ v�rifiant~: + + $$ + \forall y \in V\pa{x}, \; d\pa{x,y} \leqslant \rho + $$ + + On suppose que la matrice $M = \pa{m_{ij}}_ { \begin{subarray} 1 \leqslant i \leqslant P \\ + 1 \leqslant j \leqslant N \end{subarray} }$ a �t� calcul�e pr�alablement comme suit~: + + $$ + \forall \pa{i,j}, m_{ij} = d\pa{p_i, y_j} + $$ + + \begin{xalgostep}{initialisation} + $\begin{array}{ll} + \forall y \in E, & g\pa{y} \longleftarrow 0 \\ + \forall y \in E, & h\pa{y} \longleftarrow 1 + \end{array}$ + \end{xalgostep} + + \begin{xalgostep}{choix d'un pivot et mise � jour de la fonction $g$} \label{classif_laesa_step_b} + $B' \longleftarrow B \cap \acc{ y \in E \sac h\pa{y} = 0}$ \\ + \begin{xif}{$B' \neq \emptyset$} + Soit $i$ tel que $p_i$ soit un �l�ment de $B'$ tir� au hasard tel que~: \\ + $$p_i \in \arg \min \acc{ g\pa{y} \sac y \in B'}$$ + $\begin{array}{lll} + \alpha &\longleftarrow& d\pa{p_i,x} \\ + h\pa{p_i} &\longleftarrow& 1 + \end{array}$ \\ + $\begin{array}{rl} + \forall y_j \in E \text{ tel que } h\pa{y_j} = 0, \; g\pa{y_j} \longleftarrow & + \max \acc { g\pa{y_j} , \abs{ \alpha - m_{ij} } } \\ = & + \max \acc { g\pa{y_j} , \abs{ d\pa{x,p_i} - d\pa{p_i, y_j} } } + \end{array}$ + \xelse + Choisir un �l�ment $s$ de $E$ tel que $h\pa{s} = 0$. \\ + $\begin{array}{lll} + \alpha &\longleftarrow& d\pa{s,x} \\ + h\pa{s} &\longleftarrow& 1 + \end{array}$ \\ + $\begin{array}{rl} + \forall y \in E \text{ tel que } h\pa{y} = 0, \; g\pa{y} \longleftarrow & d\pa{x,y} + \end{array}$ + + \end{xif} + + + + \end{xalgostep} + + \possiblecut + + + \begin{xalgostep}{�limination} + $\begin{array}{rl} + \forall y_j \in E \text{ tel que } h\pa{y_j} = 0, \text{ si } g\pa{y_j} > \rho \text{ alors } + h\pa{y_j} = 1 + \end{array}$ + \end{xalgostep} + + \begin{xalgostep}{terminaison} + $A \longleftarrow \acc{ y \in E \sac h\pa{y} = 0 }$ \\ + \begin{xif}{$A \neq \emptyset$} + Retour � l'�tape~\ref{classif_laesa_step_b}. + \xelse + Fin, l'ensemble cherch� correspond � $\acc{y \in E \sac g\pa{y}} \leqslant \rho$. + \end{xif} + \end{xalgostep} + + \end{xalgorithm} + + + + +La s�lection des pivots est assur�e par un autre algorithme d�crit dans l'article~\citeindex{Moreno2003}. + + + \begin{xalgorithm}{LAESA : s�lection des pivots} + \label{space_metric_algo_laesa_pivtos_sel} + \indexfrr{pivot}{s�lection} + + Soit $E = \ensemble{y_1}{y_N}$ un ensemble de points, on cherche � d�terminer + l'ensemble $B = \ensemble{p_1}{p_P} \subset E$ utilis� par l'algorithme~\ref{space_metric_algo_laesa}. + + \begin{xalgostep}{initialisation} + $B \longleftarrow y \in E$ choisi arbitrairement. + \end{xalgostep} + + \begin{xalgostep}{calcul de la fonction $g$} \label{space_metric_laesa_pivots_sel_b} + \begin{xforeach}{y}{E - B} + $g\pa{y} \longleftarrow 0$ \\ + \begin{xforeach}{p}{B} + $g\pa{y} \longleftarrow g\pa{y} + d\pa{y,p}$ + \end{xforeach} + \end{xforeach} + \end{xalgostep} + + \begin{xalgostep}{mise � jour de $B$} + Trouver $p^* \in \arg \max \acc { g\pa{p} \sac p \in E - B}$\\ + $B \longleftarrow B \cup \acc{ p^*}$ \\ + Si $\card{B} < P$, retour � l'�tape~\ref{space_metric_laesa_pivots_sel_b} sinon fin. + \end{xalgostep} + + \end{xalgorithm} + + + + +Cet article~\citeindex{Moreno2003} am�liore �galement l'algorithme~\ref{space_metric_algo_laesa} par le suivant~: + + + + \begin{xalgorithm}{LAESA'} + \label{space_metric_algo_laesa_prime} + \indexfr{LAESA'} + + Soit $E = \ensemble{y_1}{y_N}$ un ensemble de points, $B = \ensemble{p_1}{p_P} \subset E$ + un ensemble de pivots inclus dans $E$. On cherche � d�terminer le voisinage $V\pa{x}$ de $x$ + inclus dans $E$ v�rifiant~: + + $$ + \forall y \in V\pa{x}, \; d\pa{x,y} \leqslant \rho + $$ + + On suppose que la matrice $M = \pa{m_{ij}}_ { \begin{subarray} 1 \leqslant i \leqslant P \\ + 1 \leqslant j \leqslant N \end{subarray} }$ a �t� calcul�e pr�alablement comme suit~: + + $$ + \forall \pa{i,j}, \; m_{ij} = d\pa{p_i, y_j} + $$ + + \begin{xalgostep}{initialisation} + $\forall i \in \ensemble{1}{P}, \; d_i \longleftarrow d\pa{x,p_i}$ + \end{xalgostep} + + \begin{xalgostep}{fonction $g$} \label{classif_laesa_prime_step_b} + $\forall j \in \ensemble{1}{N}, \; g\pa{y_j} \longleftarrow \underset{ i \in \ensemble{1}{P} }{\min} \abs{ m_{ij} - d_i} $ + \end{xalgostep} + + \begin{xalgostep}{tri} + Tri l'ensemble $g\pa{y_i}$ par ordre croissant $\longrightarrow g\pa{y_{\sigma\pa{j}}}$. \\ + \begin{xfor}{j}{1}{N} + \begin{xif}{$g\pa{y_{\sigma\pa{j}}} \leqslant \rho$} + $g\pa{y_{\sigma\pa{j}}} = d\pa{x,y_{\sigma\pa{j}}}$ + \end{xif} + \end{xfor} + + Fin, l'ensemble cherch� correspond � $\acc{y \in E \sac g\pa{y}} \leqslant \rho$. + \end{xalgostep} \end{xalgorithm} \indexfr{TLAESA} -Il existe d'autres versions de l'algorithme LAESA comme TLAESA (Tree - LAESA) (voir \citeindex{Mic\'o1996}). Cet algorithme associe un arbre l'algorithme LAESA et fait le lien entre les algorithmes~\ref{algorithm_AHC} et~\ref{space_metric_algo_laesa}. +Il existe d'autres versions de l'algorithme LAESA comme TLAESA (Tree - LAESA) (voir \citeindex{Mic\'o1996}). Cet algorithme associe un arbre � l'algorithme LAESA et fait le lien entre les algorithmes~\ref{algorithm_AHC} et~\ref{space_metric_algo_laesa}. -\subsection{Rsultats thoriques} +\subsection{R�sultats th�oriques} -\indexfr{mesure}\indexfr{densit} -L'article~\citeindex{Farag\'o1993} dmontre galement qu'il existe une majoration du nombre moyen de calcul de distances pour peu que la mesure de l'espace contenant l'ensemble $E$ et l'lment $x$ soit connue et que l'ensemble $B = \ensemble{p_1}{p_P}$ des pivots vrifie~: +\indexfr{mesure}\indexfr{densit�} +L'article~\citeindex{Farag\'o1993} d�montre �galement qu'il existe une majoration du nombre moyen de calcul de distances pour peu que la mesure de l'espace contenant l'ensemble $E$ et l'�l�ment $x$ soit connue et que l'ensemble $B = \ensemble{p_1}{p_P}$ des pivots v�rifie~: - \begin{eqnarray} - \exists \pa{\alpha,\beta} \in \R^+_* \text{ tels que } && \nonumber\\ - \forall \pa{x,y} \in E^2, \; \forall i\, && \alpha \, d\pa{x,y} \supegal - \abs{d\pa{x,p_i} - d\pa{p_i,y}} \label{space_metric_cond_1} \\ - \forall \pa{x,y} \in E^2, && \underset{i}{\max} \; \abs{d\pa{x,p_i} - d\pa{p_i,y}} \supegal - \beta \, d\pa{x,y} \label{space_metric_cond_1} - \end{eqnarray} + \begin{eqnarray} + \exists \pa{\alpha,\beta} \in \mathbb{R}^+_* \text{ tels que } && \nonumber\\ + \forall \pa{x,y} \in E^2, \; \forall i\, && \alpha \, d\pa{x,y} \supegal + \abs{d\pa{x,p_i} - d\pa{p_i,y}} \label{space_metric_cond_1} \\ + \forall \pa{x,y} \in E^2, && \underset{i}{\max} \; \abs{d\pa{x,p_i} - d\pa{p_i,y}} \supegal + \beta \, d\pa{x,y} \label{space_metric_cond_1} + \end{eqnarray} -L'algorithme dvelopp dans~\citeindex{Farag\'o1993} permet de trouver le point de plus proche d'un lment $x$ dans un ensemble $E = \ensemble{x_1}{x_N}$ selon l'algorithme suivant~: +L'algorithme d�velopp� dans~\citeindex{Farag\'o1993} permet de trouver le point de plus proche d'un �l�ment $x$ dans un ensemble $E = \ensemble{x_1}{x_N}$ selon l'algorithme suivant~: - \begin{xalgorithm}{plus proche voisin d'aprs [Farag\'o1993]}\label{space_metric_algo_farago} - Soit $E = \ensemble{x_1}{x_N}$ et $B = \ensemble{p_1}{p_P} \subset E \subset X$. Soit $x \in X$ - un lment quelconque. - On suppose que les valeurs $m_{ij} = d\pa{x_i, p_j}$ ont t pralablement calcules. - - \begin{xalgostep}{initialisation} - On calcule pralablement les coefficients $\gamma\pa{x_i}$~: - $$ - \forall i \in \ensemble{1}{N}, \; \gamma\pa{x_i} \longleftarrow \underset{j - \in \ensemble{1}{P} } {\max} \; - \abs{ m_{ij} - d\pa{x,p_j} } - $$ - \end{xalgostep} - - \begin{xalgostep}{laguage} - On dfinit $t_0 \longleftarrow \underset{i} {\min} \; \gamma\pa{x_i}$. \\ - Puis on construit l'ensemble $F\pa{x} = \acc{ x_i \in E \sac \gamma\pa{x_i} }\infegal - \frac{\alpha}{\beta} \, t_0$. - \end{xalgostep} - - \begin{xalgostep}{plus proche voisin} - Le plus proche $x^*$ voisin est dfini par~: $x^* \in \arg \min \acc{ d\pa{x,y} \sac y \in F\pa{x}}$. - \end{xalgostep} - - \end{xalgorithm} + \begin{xalgorithm}{plus proche voisin d'apr�s [Farag\'o1993]}\label{space_metric_algo_farago} + Soit $E = \ensemble{x_1}{x_N}$ et $B = \ensemble{p_1}{p_P} \subset E \subset X$. Soit $x \in X$ + un �l�ment quelconque. + On suppose que les valeurs $m_{ij} = d\pa{x_i, p_j}$ ont �t� pr�alablement calcul�es. + + \begin{xalgostep}{initialisation} + On calcule pr�alablement les coefficients $\gamma\pa{x_i}$~: + $$ + \forall i \in \ensemble{1}{N}, \; \gamma\pa{x_i} \longleftarrow \underset{j + \in \ensemble{1}{P} } {\max} \; + \abs{ m_{ij} - d\pa{x,p_j} } + $$ + \end{xalgostep} + + \begin{xalgostep}{�laguage} + On d�finit $t_0 \longleftarrow \underset{i} {\min} \; \gamma\pa{x_i}$. \\ + Puis on construit l'ensemble $F\pa{x} = \acc{ x_i \in E \sac \gamma\pa{x_i} }\leqslant + \frac{\alpha}{\beta} \, t_0$. + \end{xalgostep} + + \begin{xalgostep}{plus proche voisin} + Le plus proche $x^*$ voisin est d�fini par~: $x^* \in \arg \min \acc{ d\pa{x,y} \sac y \in F\pa{x}}$. + \end{xalgostep} + + \end{xalgorithm} - \begin{xtheorem}{[Farag\'o1993]$^1$} - \label{space_metric_farago_1} - Les notations sont celles de l'algorithme~\ref{space_metric_algo_farago}. - L'algorithme~\ref{space_metric_algo_farago} retourne le plus proche voisin $x^*$ de $x$ inclus dans $E$. - Autrement dit, $\forall x \in X, \; x^* \in F\pa{x}$. - \end{xtheorem} + \begin{xtheorem}{[Farag\'o1993]$^1$} + \label{space_metric_farago_1} + Les notations sont celles de l'algorithme~\ref{space_metric_algo_farago}. + L'algorithme~\ref{space_metric_algo_farago} retourne le plus proche voisin $x^*$ de $x$ inclus dans $E$. + Autrement dit, $\forall x \in X, \; x^* \in F\pa{x}$. + \end{xtheorem} -\begin{xremark}{mesure de dissimilarit} -L'algorithme~\ref{space_metric_algo_farago} est en fait valable pour une distance mais aussi pour une mesure de dissimilarit. Contrairement une distance, une mesure de dissimalit ne vrifie pas l'ingalit triangulaire. -\indexfrr{mesure}{dissimilarit}\indexfr{dissimilarit} -\indexfr{ingalit triangulaire} +\begin{xremark}{mesure de dissimilarit�} +L'algorithme~\ref{space_metric_algo_farago} est en fait valable pour une distance mais aussi pour une mesure de dissimilarit�. Contrairement � une distance, une mesure de dissimalit� ne v�rifie pas l'in�galit� triangulaire. +\indexfrr{mesure}{dissimilarit�}\indexfr{dissimilarit�} +\indexfr{in�galit� triangulaire} \end{xremark} - \begin{xtheorem}{[Farag\'o1993]$^2$} - \label{space_metric_farago_2} - Les notations sont celles de l'algorithme~\ref{space_metric_algo_farago}. On dfinit une mesure - sur l'ensemble $X$, $B\pa{x,r}$ dsigne la boule de centre $x$ et de rayon $r$, $Z \in X$ une variable - alatoire, de plus~: - $$ - p\pa{x,r} = P_X \pa{B\pa{x,r}} = \pr{ Z \in B\pa{x,r}} - $$ - - On suppose qu'il existe $d > 0$ et une fonction $f : X \longrightarrow \R$ tels que~: - $$ - \underset { r \rightarrow 0 } { \lim } \; \frac{ p\pa{x,r} } { r^d } = f\pa{x} > 0 - $$ - La convergence doit tre uniforme et presque sre. - On note galement $F_N$ le nombre de calculs de dissimilarit effectus par - l'algorithme~\ref{space_metric_algo_farago} o $N$ est le nombre d'lment de $E$, - $P$ dsigne toujours le nombre de pivots, alors~: - - $$ - \underset{ n \rightarrow \infty } { \lim \sup } \; - \esp{F_N} \infegal k + \pa{\frac{\alpha}{\beta}}^{2d} - $$ - - \end{xtheorem} + \begin{xtheorem}{[Farag\'o1993]$^2$} + \label{space_metric_farago_2} + Les notations sont celles de l'algorithme~\ref{space_metric_algo_farago}. On d�finit une mesure + sur l'ensemble $X$, $B\pa{x,r}$ d�signe la boule de centre $x$ et de rayon $r$, $Z \in X$ une variable + al�atoire, de plus~: + $$ + p\pa{x,r} = P_X \pa{B\pa{x,r}} = \pr{ Z \in B\pa{x,r}} + $$ + + On suppose qu'il existe $d > 0$ et une fonction $f : X \longrightarrow \mathbb{R}$ tels que~: + $$ + \underset { r \rightarrow 0 } { \lim } \; \frac{ p\pa{x,r} } { r^d } = f\pa{x} > 0 + $$ + La convergence doit �tre uniforme et presque s�re. + On note �galement $F_N$ le nombre de calculs de dissimilarit� effectu�s par + l'algorithme~\ref{space_metric_algo_farago} o� $N$ est le nombre d'�l�ment de $E$, + $P$ d�signe toujours le nombre de pivots, alors~: + + $$ + \underset{ n \rightarrow \infty } { \lim \sup } \; + \esp{F_N} \leqslant k + \pa{\frac{\alpha}{\beta}}^{2d} + $$ + + \end{xtheorem} @@ -1524,65 +1524,65 @@ \subsection{Suppression des voisins inutiles} \indexfr{voisins inutiles} \indexfr{plus proches voisins} \indexfr{classification} -\indexfr{capacit d'attraction} +\indexfr{capacit� d'attraction} -L'article \citeindex{Wu2002} propose une ide intressante qui consiste supprimer les voisins inutiles. Cette mthode s'applique dans le cas d'une classification l'aide de plus proches voisins. A un lment classer, cette mthode attribue la classe du point le plus proche, il faut donc a priori calculer les distances du point en question tous ceux dj classs. Certains points de cet ensemble ont une forte "capacit d'attraction"~: ils sont le centre d'une rgion dans laquelle seuls des points de la mme classe figurent. Plus concrtement, soient un point $y$ classer et une base de points classs not $\vecteur{x_1}{x_N}$ de classe $\vecteur{c\pa{x_1}}{c\pa{x_N}}$, on suppose que $x_i$ et $x_j$ sont deux points, enfin, on dfinit~: +L'article \citeindex{Wu2002} propose une id�e int�ressante qui consiste � supprimer les voisins inutiles. Cette m�thode s'applique dans le cas d'une classification � l'aide de plus proches voisins. A un �l�ment � classer, cette m�thode attribue la classe du point le plus proche, il faut donc a priori calculer les distances du point en question � tous ceux d�j� class�s. Certains points de cet ensemble ont une forte "capacit� d'attraction"~: ils sont le centre d'une r�gion dans laquelle seuls des points de la m�me classe figurent. Plus concr�tement, soient un point $y$ � classer et une base de points class�s not� $\vecteur{x_1}{x_N}$ de classe $\vecteur{c\pa{x_1}}{c\pa{x_N}}$, on suppose que $x_i$ et $x_j$ sont deux points, enfin, on d�finit~: - \begin{eqnarray} - y^* = \arg \min \acc{ d\pa{x_k, y} \sac 1 \infegal k \infegal n } - \end{eqnarray} + \begin{eqnarray} + y^* = \arg \min \acc{ d\pa{x_k, y} \sac 1 \leqslant k \leqslant n } + \end{eqnarray} -On suppose galement que $x_i$ est un point de forte capacit et que~: +On suppose �galement que $x_i$ est un point de forte capacit� et que~: - \begin{eqnarray} - \forall y, \; y^* = x_i \Longrightarrow \exists l \text{ tel que } - x_l = \arg \min \acc{ d\pa{x_k, y} \sac k \neq i } \text{ et } c\pa{x_l} = c\pa{x_i} - \end{eqnarray} + \begin{eqnarray} + \forall y, \; y^* = x_i \Longrightarrow \exists l \text{ tel que } + x_l = \arg \min \acc{ d\pa{x_k, y} \sac k \neq i } \text{ et } c\pa{x_l} = c\pa{x_i} + \end{eqnarray} -Autrement dit, $x_i$ est un point attracteur si quel que soit le point $y$ proche de $x_i$, il sera toujours possible de trouver un voisin de $x_i$ proche et $y$ et appartenant la mme classe. Dans ce cas, il ne sert rien de calculer la distance de $x_i$ $y$, le point $x_i$ peut alors tre limin de la base des points classs. Il reste maintenant traiter la base de points classs de manire en garder le moins possible. De cette faon, l'ensemble des points classs ne gardera que des points situs prs des frontires entre classes. +Autrement dit, $x_i$ est un point attracteur si quel que soit le point $y$ proche de $x_i$, il sera toujours possible de trouver un voisin de $x_i$ proche et $y$ et appartenant � la m�me classe. Dans ce cas, il ne sert � rien de calculer la distance de $x_i$ � $y$, le point $x_i$ peut alors �tre �limin� de la base des points class�s. Il reste maintenant � traiter la base de points class�s de mani�re � en garder le moins possible. De cette fa�on, l'ensemble des points class�s ne gardera que des points situ�s pr�s des fronti�res entre classes. -L'article \citeindex{Wu2002} dfinit le rayon d'un point~: +L'article \citeindex{Wu2002} d�finit le rayon d'un point~: - \begin{eqnarray} - r\pa{x} = \max\acc { 0, \max \acc{ d\pa{x, y} \sac c\pa{x} = c\pa{y} } } - \end{eqnarray} - + \begin{eqnarray} + r\pa{x} = \max\acc { 0, \max \acc{ d\pa{x, y} \sac c\pa{x} = c\pa{y} } } + \end{eqnarray} + L'algorithme de suppression des points attracteurs est le suivant~: - \begin{xalgorithm}{suppression des points attracteurs} - Soit $\Gamma$ un seuil positif, les notations sont celles utilises dans les paragraphes qui prcdent, - on note galement $\Omega$ l'ensemble des points classs. - - \begin{xalgostep}{calcul des rayons}\label{space_metric_attracteur_step_a} - Pour chaque $x \in \Omega$, on calcule $r\pa{x}$, et on dsigne par $x^*$ le point qui vrifie~: - $r\pa{x^*} = \underset{x \in \Omega} {\max} \; { r\pa{x} }$ - \end{xalgostep} - - \begin{xalgostep}{suppression} - Si $r\pa{x^*} \supegal \Gamma$, alors le point $x^*$ est supprim de l'ensemble $\Omega$, et retour - l'tape~\ref{space_metric_attracteur_step_a}. Dans le cas contraire, l'algorithme s'arrte. - \end{xalgostep} - - \end{xalgorithm} - - - -\begin{xremark}{mthode approche} -Le fait de supprimer les points dont le rayon attracteur est suprieur un certain seuil peut entraner une modification de la classification si ce seuil est trop petit. + \begin{xalgorithm}{suppression des points attracteurs} + Soit $\Gamma$ un seuil positif, les notations sont celles utilis�es dans les paragraphes qui pr�c�dent, + on note �galement $\Omega$ l'ensemble des points class�s. + + \begin{xalgostep}{calcul des rayons}\label{space_metric_attracteur_step_a} + Pour chaque $x \in \Omega$, on calcule $r\pa{x}$, et on d�signe par $x^*$ le point qui v�rifie~: + $r\pa{x^*} = \underset{x \in \Omega} {\max} \; { r\pa{x} }$ + \end{xalgostep} + + \begin{xalgostep}{suppression} + Si $r\pa{x^*} \supegal \Gamma$, alors le point $x^*$ est supprim� de l'ensemble $\Omega$, et retour + � l'�tape~\ref{space_metric_attracteur_step_a}. Dans le cas contraire, l'algorithme s'arr�te. + \end{xalgostep} + + \end{xalgorithm} + + + +\begin{xremark}{m�thode approch�e} +Le fait de supprimer les points dont le rayon attracteur est sup�rieur � un certain seuil peut entra�ner une modification de la classification si ce seuil est trop petit. \end{xremark} \indexfr{LVQ}\indexfrr{prototype}{LVQ} -Cette mthode poursuit le mme objectif que celui des mthodes LVQ\seeannex{clas_super_principe_lvq}{LVQ} ou Learning Vector Quantization qui permettent de rduire un ensemble de points utiliss dans une classification de plus proches voisins un ensemble de prototypes. +Cette m�thode poursuit le m�me objectif que celui des m�thodes LVQ\seeannex{clas_super_principe_lvq}{LVQ} ou Learning Vector Quantization qui permettent de r�duire un ensemble de points utilis�s dans une classification de plus proches voisins � un ensemble de prototypes. - + \subsection{Lien vers la classification} -Dterminer le voisinage d'un point est un passage oblig lorsqu'on applique une classification l'aide de plus proches voisins puisque chaque lment est class partir des classes de ses voisins\seeannex{clas_super_kppv_simple}{classification k-PPV}. Toutefois, les rsultats obtenus par cette mthode dpendent fortement de la distance utilise. Sans a priori sur celle-ci, c'est souvent une distance euclidienne qui est choisie. +D�terminer le voisinage d'un point est un passage oblig� lorsqu'on applique une classification � l'aide de plus proches voisins puisque chaque �l�ment est class� � partir des classes de ses voisins\seeannex{clas_super_kppv_simple}{classification k-PPV}. Toutefois, les r�sultats obtenus par cette m�thode d�pendent fortement de la distance utilis�e. Sans a priori sur celle-ci, c'est souvent une distance euclidienne qui est choisie. -Dans le cas des espaces vectoriels, il est possible d'utiliser une distance pondrant diffremment chaque dimension et d'estimer cette pondration partir d'un chantillon reprsentatif du problme de classification rsoudre. Deux mthodes sont prsentes aux chapitre~\ref{classification_graphem_carac_dist} et~\ref{classification_distance_voisinage}. +Dans le cas des espaces vectoriels, il est possible d'utiliser une distance pond�rant diff�remment chaque dimension et d'estimer cette pond�ration � partir d'un �chantillon repr�sentatif du probl�me de classification � r�soudre. Deux m�thodes sont pr�sent�es aux chapitre~\ref{classification_graphem_carac_dist} et~\ref{classification_distance_voisinage}. @@ -1604,9 +1604,9 @@ \subsection{Lien vers la classification} \firstpassagedo{ - \begin{thebibliography}{99} - \input{space_metric_biblio.tex} - \end{thebibliography} + \begin{thebibliography}{99} + \input{space_metric_biblio.tex} + \end{thebibliography} } \input{../../common/livre_table_end.tex}% diff --git a/_todo/space_metric/space_metric_biblio.tex b/_todo/space_metric/space_metric_biblio.tex index e8b0afbe..ea9c6e76 100644 --- a/_todo/space_metric/space_metric_biblio.tex +++ b/_todo/space_metric/space_metric_biblio.tex @@ -1,12 +1,12 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin \bibitemstyle{Apostolico1985}{1985} {A. Apostolico} {The Myriad virtues of subword trees} @@ -36,7 +36,7 @@ {PCA-based branch and bound search algorithms for computing K nearest neighbours} {Pattern Recognition Letters}{24}{1437}{1451} -\bibitemstyle{Dupr2003}{2003}{X. Dupr} +\bibitemstyle{Dupr�2003}{2003}{X. Dupr�} {Optimization of cursive words recognition and nearest neighbors search in metric spaces} {International Conference on Image and Signal Processing (ICISP), Agadir, Morroco}{}{608}{615} diff --git a/_todo/squelette/fig_choi.tex b/_todo/squelette/fig_choi.tex index 83139bd6..86905ceb 100644 --- a/_todo/squelette/fig_choi.tex +++ b/_todo/squelette/fig_choi.tex @@ -3,38 +3,38 @@ \begin{picture}(190,100)(-25,-8) -\put(1,1) {\line(1,0){140}} -\put(1,80) {\line(4,-1){140}} -\put(1,40) {\line(8,-1){140}} -\put(130,-8) {\makebox(3,3){\small bord infrieur}} -\put(130,60) {\makebox(3,3){\small bord suprieur}} -\put(-10,40) {\makebox(3,3){\small squelette}} - -\put(15,50) {\line(1,0){12}} -\put(15,54) {\line(1,0){12}} -\put(15,58) {\line(1,0){12}} -\put(15,62) {\line(1,0){12}} -\put(15,50) {\line(0,1){12}} -\put(19,50) {\line(0,1){12}} -\put(23,50) {\line(0,1){12}} -\put(28,50) {\line(0,1){12}} - -\put(17,60) {\line(1,8){2}} -%\put(21,56) {\vector(1,8){2.3}} -\put(25,52) {\line(1,8){2.65}} - -\put(35,30) {\line(1,0){12}} -\put(35,34) {\line(1,0){12}} -\put(35,38) {\line(1,0){12}} -\put(35,42) {\line(1,0){12}} -\put(35,30) {\line(0,1){12}} -\put(39,30) {\line(0,1){12}} -\put(43,30) {\line(0,1){12}} -\put(48,30) {\line(0,1){12}} - -\put(37,40) {\line(1,8){3.9}} -%\put(21,56) {\vector(1,8){2.3}} -\put(41,32) {\line(0,-1){32}} +\put(1,1) {\line(1,0){140}} +\put(1,80) {\line(4,-1){140}} +\put(1,40) {\line(8,-1){140}} +\put(130,-8) {\makebox(3,3){\small bord inf�rieur}} +\put(130,60) {\makebox(3,3){\small bord sup�rieur}} +\put(-10,40) {\makebox(3,3){\small squelette}} + +\put(15,50) {\line(1,0){12}} +\put(15,54) {\line(1,0){12}} +\put(15,58) {\line(1,0){12}} +\put(15,62) {\line(1,0){12}} +\put(15,50) {\line(0,1){12}} +\put(19,50) {\line(0,1){12}} +\put(23,50) {\line(0,1){12}} +\put(28,50) {\line(0,1){12}} + +\put(17,60) {\line(1,8){2}} +%\put(21,56) {\vector(1,8){2.3}} +\put(25,52) {\line(1,8){2.65}} + +\put(35,30) {\line(1,0){12}} +\put(35,34) {\line(1,0){12}} +\put(35,38) {\line(1,0){12}} +\put(35,42) {\line(1,0){12}} +\put(35,30) {\line(0,1){12}} +\put(39,30) {\line(0,1){12}} +\put(43,30) {\line(0,1){12}} +\put(48,30) {\line(0,1){12}} + +\put(37,40) {\line(1,8){3.9}} +%\put(21,56) {\vector(1,8){2.3}} +\put(41,32) {\line(0,-1){32}} \put(52,40) {\makebox(3,3){\small $B$}} \put(32,60) {\makebox(3,3){\small $A$}} diff --git a/_todo/squelette/squelette.tex b/_todo/squelette/squelette.tex index 23aad154..a5832881 100644 --- a/_todo/squelette/squelette.tex +++ b/_todo/squelette/squelette.tex @@ -9,18 +9,18 @@ \indexfrr{homotope}{squelette} -La squelettisation est une opration qui permet de passer d'une image aux traits pais une reprsentation en "fil de fer" dont la premire apparition date de \citeindex{Blum1967}. La figure~\ref{squelette_fig2} reprsente la fois l'image initiale et le rsultat obtenu en filigrane. Cette reprsentation "fil de fer" est appele \emph{squelette}. Il n'existe pas de dfinition unique du squelette, il doit seulement respecter la connexit de la forme qu'il est cens reprsenter ou plus prcisment, une forme et son squelette doivent tre \emph{homotopes}. Pour une composante connexe, le squelette correspondant ne forme galement qu'une seule composante connexe incluse dans la premire. +La squelettisation est une op�ration qui permet de passer d'une image aux traits �pais � une repr�sentation en "fil de fer" dont la premi�re apparition date de \citeindex{Blum1967}. La figure~\ref{squelette_fig2} repr�sente � la fois l'image initiale et le r�sultat obtenu en filigrane. Cette repr�sentation "fil de fer" est appel�e \emph{squelette}. Il n'existe pas de d�finition unique du squelette, il doit seulement respecter la connexit� de la forme qu'il est cens� repr�senter ou plus pr�cis�ment, une forme et son squelette doivent �tre \emph{homotopes}. Pour une composante connexe, le squelette correspondant ne forme �galement qu'une seule composante connexe incluse dans la premi�re. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=6.5cm] - {\filext{../squelette/image/ske_example}}\end{array}$}$$ - \caption{Squelettisation d'une figure quelconque} - \label{squelette_fig2} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=6.5cm] + {\filext{../squelette/image/ske_example}}\end{array}$}$$ + \caption{Squelettisation d'une figure quelconque} + \label{squelette_fig2} + \end{figure} -L'article \citeindex{Lam1992} propose une revue des algorithmes de squelettisation. Ce chapitre prsente quelques-unes des mthodes qui y sont prsentes comme la squelettisation par rosion (voir paragraphe~\ref{ske_par_erosion}), ainsi que d'autres issues d'articles plus rcents et mieux adaptes la reconnaissance de l'criture comme la modlisation des intersections (voir paragraphe~\ref{squelette_modelisation_intersection_modele}). +L'article \citeindex{Lam1992} propose une revue des algorithmes de squelettisation. Ce chapitre pr�sente quelques-unes des m�thodes qui y sont pr�sent�es comme la squelettisation par �rosion (voir paragraphe~\ref{ske_par_erosion}), ainsi que d'autres issues d'articles plus r�cents et mieux adapt�es � la reconnaissance de l'�criture comme la mod�lisation des intersections (voir paragraphe~\ref{squelette_modelisation_intersection_modele}). @@ -37,71 +37,71 @@ \section{Squelette d'une forme continue} \indexfrr{squelette}{continu} \indexfrr{forme}{continue} -On se place ici dans le plan $\R^2$ et on considre une partie $F$ du plan qu'on appellera par la suite \emph{forme continue}. C'est en gnral une partie connexe d'intrieur non vide. L'objectif est ici de dfinir le squelette de cette forme, c'est--dire une partie d'intrieur vide la reprsentant le mieux possible. On dfinit une boule comme un disque de rayon strictement positif du plan. Les dfinitions qui suivent restent valables pour des espaces de dimension suprieure deux (voir \citeindex{Blum1973}, \citeindex{Thiel1994}). +On se place ici dans le plan $\mathbb{R}^2$ et on consid�re une partie $F$ du plan qu'on appellera par la suite \emph{forme continue}. C'est en g�n�ral une partie connexe d'int�rieur non vide. L'objectif est ici de d�finir le squelette de cette forme, c'est-�-dire une partie d'int�rieur vide la repr�sentant le mieux possible. On d�finit une boule comme un disque de rayon strictement positif du plan. Les d�finitions qui suivent restent valables pour des espaces de dimension sup�rieure � deux (voir \citeindex{Blum1973}, \citeindex{Thiel1994}). - \begin{xdefinition}{boule maximale} - \indexfr{boule maximale} - \label{ske_def_boule_max} - - Soit $F$ une forme continue, on note $\mathcal{B}_F$ l'ensemble des boules incluses dans $F$. - Une boule $B$ est dite maximale si et seulement si~: - - $$ - B \text{ est maximale } \Longleftrightarrow \forall B' \in \mathcal{B}_F, - \; B \subset B' \Longrightarrow B = B' - $$ - - \end{xdefinition} + \begin{xdefinition}{boule maximale} + \indexfr{boule maximale} + \label{ske_def_boule_max} + + Soit $F$ une forme continue, on note $\mathcal{B}_F$ l'ensemble des boules incluses dans $F$. + Une boule $B$ est dite maximale si et seulement si~: + + $$ + B \text{ est maximale } \Longleftrightarrow \forall B' \in \mathcal{B}_F, + \; B \subset B' \Longrightarrow B = B' + $$ + + \end{xdefinition} -Par consquent, une boule $B$ est maximale pour la forme $F$ si aucune autre boule incluse dans cette forme n'inclut $B$. +Par cons�quent, une boule $B$ est maximale pour la forme $F$ si aucune autre boule incluse dans cette forme n'inclut $B$. - \begin{xdefinition}{axe mdian} - \indexfr{axe mdian} - \label{ske_def_axe_med} + \begin{xdefinition}{axe m�dian} + \indexfr{axe m�dian} + \label{ske_def_axe_med} - L'axe mdian d'une forme $F$ est le lieu des centres des boules maximales de cette forme. + L'axe m�dian d'une forme $F$ est le lieu des centres des boules maximales de cette forme. - \end{xdefinition} + \end{xdefinition} -Un exemple d'axe mdian est donn par la figure~\ref{squelette_continu}. L'axe mdian est aussi un moyen de compresser l'information puisque la description de cet axe ainsi que la donne du rayon de la boule maximale pour chaque point qui en est le centre permet de reconstituer exactement la forme (\citeindex{Rosenfeld1986}). +Un exemple d'axe m�dian est donn� par la figure~\ref{squelette_continu}. L'axe m�dian est aussi un moyen de compresser l'information puisque la description de cet axe ainsi que la donn�e du rayon de la boule maximale pour chaque point qui en est le centre permet de reconstituer exactement la forme (\citeindex{Rosenfeld1986}). -\indexfr{connexit} +\indexfr{connexit�} \indexfr{composante connexe} \indexfr{homotope} -L'axe mdian n'est pourtant pas encore le squelette car, mme si une forme $F$ n'a qu'une composante connexe, l'axe mdian peut en avoir plusieurs. Il suffit, pour s'en convaincre, de considrer deux disques tangents comme l'illustre la figure~\ref{squelette_deux_cercles}. Axe mdian et forme ne sont donc pas forcment homotopes. +L'axe m�dian n'est pourtant pas encore le squelette car, m�me si une forme $F$ n'a qu'une composante connexe, l'axe m�dian peut en avoir plusieurs. Il suffit, pour s'en convaincre, de consid�rer deux disques tangents comme l'illustre la figure~\ref{squelette_deux_cercles}. Axe m�dian et forme ne sont donc pas forc�ment homotopes. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=6cm] - {\filext{../squelette/image/ske_continu}}\end{array}$}$$ - \caption{ Axe mdian d'une forme continue (trait pointill) dont seul le contour est reprsent, - figure extraite de~\citeindexfig{Thiel1994}.} - \label{squelette_continu} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=6cm] + {\filext{../squelette/image/ske_continu}}\end{array}$}$$ + \caption{ Axe m�dian d'une forme continue (trait pointill�) dont seul le contour est repr�sent�, + figure extraite de~\citeindexfig{Thiel1994}.} + \label{squelette_continu} + \end{figure} - \begin{figure}[ht] - \[ - \unitlength 1mm - \fbox{ - \filefig{../squelette/fig_circle} - } - \] - \caption{ Deux disques tangents~: cette forme ne contient qu'une seule composante - connexe alors que son axe - mdian n'est constitu que de deux points, les deux centres des disques.} - \label{squelette_deux_cercles} - \end{figure} + \begin{figure}[ht] + \[ + \unitlength 1mm + \fbox{ + \filefig{../squelette/fig_circle} + } + \] + \caption{ Deux disques tangents~: cette forme ne contient qu'une seule composante + connexe alors que son axe + m�dian n'est constitu� que de deux points, les deux centres des disques.} + \label{squelette_deux_cercles} + \end{figure} -L'axe mdian, qui est unique, sert de base la construction d'un squelette, qui ne l'est pas. Il est possible de prolonger ses extremits afin d'obtenir un ensemble homotope la forme d'origine. Mais il n'est pas toujours vident de les prolonger et c'est pourquoi il est parfois prfrable de rogner la forme jusqu'au squelette, en particulier dans le cas du traitement d'images discrtises qui est l'objet de ce chapitre. +L'axe m�dian, qui est unique, sert de base � la construction d'un squelette, qui ne l'est pas. Il est possible de prolonger ses extremit�s afin d'obtenir un ensemble homotope � la forme d'origine. Mais il n'est pas toujours �vident de les prolonger et c'est pourquoi il est parfois pr�f�rable de rogner la forme jusqu'au squelette, en particulier dans le cas du traitement d'images discr�tis�es qui est l'objet de ce chapitre. @@ -109,101 +109,101 @@ \section{Squelette d'une forme continue} %------------------------------------------------------------------------------------------------------------ -\section{4-connexit ou 8-connexit} +\section{4-connexit� ou 8-connexit�} %------------------------------------------------------------------------------------------------------------ -\indexfr{4-connexit} -\indexfr{8-connexit} -\indexfrr{connexit}{4} -\indexfrr{connexit}{8} -\indexfrr{connexit}{par arcs} - -Afin d'tre capable de construire le squelette d'une forme, la notion de connexit doit tre transpose du plan l'ensemble discret de pixels que forme une image. La figure~\ref{squelette_connexe48} reprsente un disque et un carr qui peuvent, selon la dfinition de la connexit par arc dans une image, tre ou ne pas tre scinds. Les deux figures (disque et carr) n'en forment qu'une seule si tout point de l'une peut tre reli tout point de l'autre par un chemin inclus dans la runion des deux. - - \begin{xdefinition}{chemin} - \indexfr{chemin} - Un chemin $C$ allant du pixel $x$ au pixel $y$ est une succession de petits dplacements - $\pa{v_i}_{1 \infegal i \infegal n}$ telle que~: - - $$ - y = x + \summy{i=1}{n} \; v_i - $$ - - Ce chemin $C$ est inclus dans un ensemble de pixels $P$ si tous les points emprunts par - ce chemin appartiennent $P$~: - - $$ - C \text{ est inclus dans } P \Longleftrightarrow x \in P \text{ et } - \forall k \in \ensemble{1}{n}, \; - x + \summy{i=1}{k} v_i \in P - $$ - - \end{xdefinition} - - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=3cm] - {\filext{../squelette/image/ske_connexe}}\end{array}$}$$ - \caption{ Un disque et un carr qui se "touchent" en 8-connexit mais qui sont spars en 4-connexit.} - \label{squelette_connexe48} - \end{figure} - - -On dfinit deux ensembles de dplacements lmentaires, $E_4$ et $E_8$~: - - \begin{eqnarray*} - E_4 &=& \acc{ \vecteurim{1}{0}, \vecteurim{0}{1}, \vecteurim{-1}{0}, \vecteurim{0}{-1} } \\ - E_8 &=& E_4 \cup \acc{ \vecteurim{1}{1}, \vecteurim{-1}{1}, \vecteurim{-1}{1}, \vecteurim{-1}{-1} } - \end{eqnarray*} - - - \begin{xdefinition}{chemin k-connexe} - Le chemin $C= \pa{v_i}_{1 \infegal i \infegal n}$ est dit $k$-connexe - si $\forall i \in \ensemble{1}{n}, \; v_i \in E_k$. - \end{xdefinition} - -Ceci nous permet de dfinir la 4-connexit et la 8-connexit. - - \begin{xdefinition}{4-connexit et 8-connexit} - On considre une image $I$ et $P$ une partie de cette image. On dit que $P$ - est $k$-connexe ($k \in \acc{4,8}$) si et seulement si pour tout couple de pixels - $\pa{x,y} \in P^2$, il existe un chemin $k$-connexe inclus dans $P$ allant de $x$ $y$. - \end{xdefinition} +\indexfr{4-connexit�} +\indexfr{8-connexit�} +\indexfrr{connexit�}{4} +\indexfrr{connexit�}{8} +\indexfrr{connexit�}{par arcs} + +Afin d'�tre capable de construire le squelette d'une forme, la notion de connexit� doit �tre transpos�e du plan � l'ensemble discret de pixels que forme une image. La figure~\ref{squelette_connexe48} repr�sente un disque et un carr� qui peuvent, selon la d�finition de la connexit� par arc dans une image, �tre ou ne pas �tre scind�s. Les deux figures (disque et carr�) n'en forment qu'une seule si tout point de l'une peut �tre reli� � tout point de l'autre par un chemin inclus dans la r�union des deux. + + \begin{xdefinition}{chemin} + \indexfr{chemin} + Un chemin $C$ allant du pixel $x$ au pixel $y$ est une succession de petits d�placements + $\pa{v_i}_{1 \leqslant i \leqslant n}$ telle que~: + + $$ + y = x + \summy{i=1}{n} \; v_i + $$ + + Ce chemin $C$ est inclus dans un ensemble de pixels $P$ si tous les points emprunt�s par + ce chemin appartiennent � $P$~: + + $$ + C \text{ est inclus dans } P \Longleftrightarrow x \in P \text{ et } + \forall k \in \ensemble{1}{n}, \; + x + \summy{i=1}{k} v_i \in P + $$ + + \end{xdefinition} + + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=3cm] + {\filext{../squelette/image/ske_connexe}}\end{array}$}$$ + \caption{ Un disque et un carr� qui se "touchent" en 8-connexit� mais qui sont s�par�s en 4-connexit�.} + \label{squelette_connexe48} + \end{figure} + + +On d�finit deux ensembles de d�placements �l�mentaires, $E_4$ et $E_8$~: + + \begin{eqnarray*} + E_4 &=& \acc{ \vecteurim{1}{0}, \vecteurim{0}{1}, \vecteurim{-1}{0}, \vecteurim{0}{-1} } \\ + E_8 &=& E_4 \cup \acc{ \vecteurim{1}{1}, \vecteurim{-1}{1}, \vecteurim{-1}{1}, \vecteurim{-1}{-1} } + \end{eqnarray*} + + + \begin{xdefinition}{chemin k-connexe} + Le chemin $C= \pa{v_i}_{1 \leqslant i \leqslant n}$ est dit $k$-connexe + si $\forall i \in \ensemble{1}{n}, \; v_i \in E_k$. + \end{xdefinition} + +Ceci nous permet de d�finir la 4-connexit� et la 8-connexit�. + + \begin{xdefinition}{4-connexit� et 8-connexit�} + On consid�re une image $I$ et $P$ une partie de cette image. On dit que $P$ + est $k$-connexe ($k \in \acc{4,8}$) si et seulement si pour tout couple de pixels + $\pa{x,y} \in P^2$, il existe un chemin $k$-connexe inclus dans $P$ allant de $x$ � $y$. + \end{xdefinition} \indexfr{Freeman} -Le code de \emph{Freeman} est trs utilis pour reprsenter les chemins 4-connexe ou 8-connexe. Il consiste associer chaque dplacement de $E_8$ un numro en tournant dans le sens inverse des aiguilles d'une montre. +Le code de \emph{Freeman} est tr�s utilis� pour repr�senter les chemins 4-connexe ou 8-connexe. Il consiste � associer � chaque d�placement de $E_8$ un num�ro en tournant dans le sens inverse des aiguilles d'une montre. - \begin{figure} - $$ - \begin{tabular}{|c|c|c|}\hline - $ - \begin{array}{c|c} - \vecteurim{1}{0} & 0 \\ \hline - \vecteurim{0}{1} & 2 \\ \hline - \vecteurim{-1}{0} & 4 \\ \hline - \vecteurim{0}{-1} & 6 - \end{array} - $ - & - $ - \begin{array}{c|c} - \vecteurim{1}{1} & 1 \\ \hline - \vecteurim{-1}{1} & 3 \\ \hline - \vecteurim{-1}{-1} & 5 \\ \hline - \vecteurim{1}{-1} & 7 - \end{array} - $ - & - \filefig{../squelette/fig_freeman} - \\ - \hline - \end{tabular} - $$ - \caption{ Code de Freeman permettant de coder les huit directions lments d'un pixel vers - un de ses huit voisins en 8-connexit.} - \indexfr{Freeman} - \end{figure} + \begin{figure} + $$ + \begin{tabular}{|c|c|c|}\hline + $ + \begin{array}{c|c} + \vecteurim{1}{0} & 0 \\ \hline + \vecteurim{0}{1} & 2 \\ \hline + \vecteurim{-1}{0} & 4 \\ \hline + \vecteurim{0}{-1} & 6 + \end{array} + $ + & + $ + \begin{array}{c|c} + \vecteurim{1}{1} & 1 \\ \hline + \vecteurim{-1}{1} & 3 \\ \hline + \vecteurim{-1}{-1} & 5 \\ \hline + \vecteurim{1}{-1} & 7 + \end{array} + $ + & + \filefig{../squelette/fig_freeman} + \\ + \hline + \end{tabular} + $$ + \caption{ Code de Freeman permettant de coder les huit directions �l�ments d'un pixel vers + un de ses huit voisins en 8-connexit�.} + \indexfr{Freeman} + \end{figure} @@ -226,88 +226,88 @@ \section{Carte de distance} \indexfrr{distance}{carte} \label{ske_carte_distance_sec} - \begin{xdefinition}{carte de distance} - \label{ske_def_cart_dist_def} - - Soit $I_{XY}$ une image binaire, avec $Y$ lignes et $X$ colonnes. $I_{XY}\pa{x,y} \in \acc{0,1}$ - dsigne le pixel de coordonnes $\pa{x,y}$. Soit $d$ une distance entre pixels. On dsigne par - $F$ l'ensemble des pixels noirs de l'image $I_{XY}$, soit $F = \acc{ \pa{x,y} \sac I_{XY}\pa{x,y} = 1}$. - La carte de distance $C^{I_{XY}}$ est - une matrice de mmes dimensions que l'image vrifiant~: - - \begin{eqnarray} - \forall \pa{x,y}, \; C^{I_{XY}}\pa{x,y} = \min \acc{ d\cro{ \pa{x,y}, \pa{x',y'}} - \sac \pa{x',y'} \in \overline{F} } - \label{ske_eq_carte_dist} - \end{eqnarray} - - \end{xdefinition} + \begin{xdefinition}{carte de distance} + \label{ske_def_cart_dist_def} + + Soit $I_{XY}$ une image binaire, avec $Y$ lignes et $X$ colonnes. $I_{XY}\pa{x,y} \in \acc{0,1}$ + d�signe le pixel de coordonn�es $\pa{x,y}$. Soit $d$ une distance entre pixels. On d�signe par + $F$ l'ensemble des pixels noirs de l'image $I_{XY}$, soit $F = \acc{ \pa{x,y} \sac I_{XY}\pa{x,y} = 1}$. + La carte de distance $C^{I_{XY}}$ est + une matrice de m�mes dimensions que l'image v�rifiant~: + + \begin{eqnarray} + \forall \pa{x,y}, \; C^{I_{XY}}\pa{x,y} = \min \acc{ d\cro{ \pa{x,y}, \pa{x',y'}} + \sac \pa{x',y'} \in \overline{F} } + \label{ske_eq_carte_dist} + \end{eqnarray} + + \end{xdefinition} \indexfrr{masque}{distance} \indexfrr{distance}{masque} -La figure~\ref{ske_cart_dist} illustre une carte de distance pour une distance qui associe deux pixels le nombre de dplacements verticaux ou horizontaux ncessaires pour aller de l'un l'autre. Pour cette figure, la valeur d'une case correspond la distance entre le pixel noir considr et le pixel blanc le plus proche. Cette distance est introduite car ses maxima locaux ont une forte probabilit d'appartenir au squelette. Il n'existe pas une unique distance (voir \citeindex{Arcelli1985}), elles sont dfinies en gnral par leur masque (table~\ref{ske_tab_masque_dist}) qui donne leur valeur dans un petit voisinage. Masque et distance sont dfinis comme suit~: - - - \begin{table} - $$ - \begin{tabular}{|c|c|c|} \hline - $\begin{array}{ccc} - 2 & 1 & 2 \\ - 1 & 0 & 1 \\ - 2 & 1 & 2 - \end{array}$ - & - $\begin{array}{ccc} - \sqrt{2} & 1 & \sqrt{2} \\ - 1 & 0 & 1 \\ - \sqrt{2} & 1 & \sqrt{2} - \end{array}$ ou - $\begin{array}{ccc} - 4 & 3 & 4 \\ - 3 & 0 & 3 \\ - 4 & 3 & 4 - \end{array}$ - & - $\begin{array}{ccccc} - 7 & 6 & 5 & 6 & 7 \\ - 6 & 4 & 3 & 4 & 6 \\ - 5 & 3 & 0 & 3 & 5 \\ - 6 & 4 & 3 & 4 & 6 \\ - 7 & 6 & 5 & 6 & 7 \\ - \end{array}$ - \\ \hline - \begin{minipage}{3cm} - Masque de la distance utilise pour calculer la carte de la figure~\ref{ske_cart_dist}. - \end{minipage} - & - \begin{minipage}{6.5cm} - Masques correspondant la distance euclidienne, le premier est peu utilis - car il implique des calculs rels plus longs - que des calculs sur des entiers. $\sqrt{2}$ est de prfrence estim par un rationnel, - $\frac{3}{4}$ ou $\frac{5}{7}$. - \end{minipage} - & - \begin{minipage}{6.5cm} - Exemple de masque 5x5, si ce masque est not - $M \in M_5\pa{\R} = \pa{m_{ij}}_{ -2 \infegal i,j \infegal 2}$, - lorsque $m_{20} > 2 m_{10}$, le calcul de distance dfini en~\ref{squelette_def_dist_masque} ne fera jamais - intervenir le dplacement $\vecteurim{2}{0}$. - \end{minipage} - \\ \hline - \end{tabular} - $$ - \caption{Masques de distance~: chaque case contient la distance d'un pixel au pixel central de la matrice.} - \label{ske_tab_masque_dist} - \end{table} - - - - - - \begin{figure} - \tiny - $$ +La figure~\ref{ske_cart_dist} illustre une carte de distance pour une distance qui associe � deux pixels le nombre de d�placements verticaux ou horizontaux n�cessaires pour aller de l'un � l'autre. Pour cette figure, la valeur d'une case correspond � la distance entre le pixel noir consid�r� et le pixel blanc le plus proche. Cette distance est introduite car ses maxima locaux ont une forte probabilit� d'appartenir au squelette. Il n'existe pas une unique distance (voir \citeindex{Arcelli1985}), elles sont d�finies en g�n�ral par leur masque (table~\ref{ske_tab_masque_dist}) qui donne leur valeur dans un petit voisinage. Masque et distance sont d�finis comme suit~: + + + \begin{table} + $$ + \begin{tabular}{|c|c|c|} \hline + $\begin{array}{ccc} + 2 & 1 & 2 \\ + 1 & 0 & 1 \\ + 2 & 1 & 2 + \end{array}$ + & + $\begin{array}{ccc} + \sqrt{2} & 1 & \sqrt{2} \\ + 1 & 0 & 1 \\ + \sqrt{2} & 1 & \sqrt{2} + \end{array}$ ou + $\begin{array}{ccc} + 4 & 3 & 4 \\ + 3 & 0 & 3 \\ + 4 & 3 & 4 + \end{array}$ + & + $\begin{array}{ccccc} + 7 & 6 & 5 & 6 & 7 \\ + 6 & 4 & 3 & 4 & 6 \\ + 5 & 3 & 0 & 3 & 5 \\ + 6 & 4 & 3 & 4 & 6 \\ + 7 & 6 & 5 & 6 & 7 \\ + \end{array}$ + \\ \hline + \begin{minipage}{3cm} + Masque de la distance utilis�e pour calculer la carte de la figure~\ref{ske_cart_dist}. + \end{minipage} + & + \begin{minipage}{6.5cm} + Masques correspondant � la distance euclidienne, le premier est peu utilis� + car il implique des calculs r�els plus longs + que des calculs sur des entiers. $\sqrt{2}$ est de pr�f�rence estim� par un rationnel, + $\frac{3}{4}$ ou $\frac{5}{7}$. + \end{minipage} + & + \begin{minipage}{6.5cm} + Exemple de masque 5x5, si ce masque est not� + $M \in M_5\pa{\mathbb{R}} = \pa{m_{ij}}_{ -2 \leqslant i,j \leqslant 2}$, + lorsque $m_{20} > 2 m_{10}$, le calcul de distance d�fini en~\ref{squelette_def_dist_masque} ne fera jamais + intervenir le d�placement $\vecteurim{2}{0}$. + \end{minipage} + \\ \hline + \end{tabular} + $$ + \caption{Masques de distance~: chaque case contient la distance d'un pixel au pixel central de la matrice.} + \label{ske_tab_masque_dist} + \end{table} + + + + + + \begin{figure} + \tiny + $$ \begin{tabular}{|p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}@{}p{2mm}|}\hline .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .\\ .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& 1& 1& 1& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .\\ @@ -329,138 +329,138 @@ \section{Carte de distance} .& .& .& .& .& .& .& .& .& .& 1& 1& 1& 1& 1& 1& 1& 1& 1& 1& 1& 1& 1& 1& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .\\ .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .\\ .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .& .\\ - \hline - \end{tabular} - $$ - \caption{ Carte de distance~: la distance entre deux pixels correspond au nombre de dplacements - horizontaux et verticaux ncessaires pour aller de l'un l'autre. - La valeur d'une case correspond la distance entre le pixel noir considr et le pixel blanc - le plus proche. Les maxima locaux (en gras) ont de forte chance d'appartenir au squelette. - Son masque est le - premier de la table~\ref{ske_tab_masque_dist}. Le contour de la forme est l'ensemble des points - pour lesquels la carte retourne la valeur 1.} - \label{ske_cart_dist} - \indexfr{contour} - \end{figure} - - - - - - \begin{xdefinition}{masque de distance} - \indexfrr{masque}{distance} - - Soit $M \in M_{2n+1}\pa{\R} = \pa{m_{ij}}_{ -n \infegal i,j \infegal n}$ une matrice carre - de dimension $2n+1$. La matrice $M$ est un masque de distance de dimension $n$ si~: - - \begin{itemize} - \item $\forall \pa{i,j} \neq \pa{0,0}, \; m_{ij} > 0$ - \item $m_{00} = 0$ - \item $M$ est symtrique selon le pixel central - \end{itemize} - - \end{xdefinition} - - - - - - \begin{xdefinition}{distance induite par un masque} - \indexfrr{distance}{induite par un masque} - \label{squelette_def_dist_masque} - Soit $M$ un masque de dimension $n$, soient deux pixels $p_1$, $p_2$ et $C\pa{p_1,p_2}$, - l'ensemble des chemins allant de $p_1$ et $p_2$ dont les vecteurs sont de longueur infrieure ou - gale $n$. Alors~: - - $$ - d_M\pa{p_1,p_2} = \underset{c \in C\pa{p_1,p_2}}{\min} \; \summyone{v = \pa{ \begin{subarray} {c} - v_x \\ v_y \end{subarray} } - \in c} m_{v_x, v_y} - $$ - - \end{xdefinition} - - -L'application dfinie en~\ref{squelette_def_dist_masque} est bien une distance. Comme le masque est symtrique, elle est aussi symtrique. De plus, $d_M\pa{p_1,p_2} = 0 \Longleftrightarrow p_1 = p_2$. L'ingalit triangulaire est aussi facile vrifier puisque pour trois points $p_1,p_2,p$, la concatnation des chemins allant de $p_1$ $p$, puis de $p$ $p_2$ forme un chemin allant de $p_1$ $p_2$. Par consquent~: $d_M\pa{p_1,p_2} \infegal d_M\pa{p_1,p} + d_M\pa{p_2,p}$. + \hline + \end{tabular} + $$ + \caption{ Carte de distance~: la distance entre deux pixels correspond au nombre de d�placements + horizontaux et verticaux n�cessaires pour aller de l'un � l'autre. + La valeur d'une case correspond � la distance entre le pixel noir consid�r� et le pixel blanc + le plus proche. Les maxima locaux (en gras) ont de forte chance d'appartenir au squelette. + Son masque est le + premier de la table~\ref{ske_tab_masque_dist}. Le contour de la forme est l'ensemble des points + pour lesquels la carte retourne la valeur 1.} + \label{ske_cart_dist} + \indexfr{contour} + \end{figure} + + + + + + \begin{xdefinition}{masque de distance} + \indexfrr{masque}{distance} + + Soit $M \in M_{2n+1}\pa{\mathbb{R}} = \pa{m_{ij}}_{ -n \leqslant i,j \leqslant n}$ une matrice carr�e + de dimension $2n+1$. La matrice $M$ est un masque de distance de dimension $n$ si~: + + \begin{itemize} + \item $\forall \pa{i,j} \neq \pa{0,0}, \; m_{ij} > 0$ + \item $m_{00} = 0$ + \item $M$ est sym�trique selon le pixel central + \end{itemize} + + \end{xdefinition} + + + + + + \begin{xdefinition}{distance induite par un masque} + \indexfrr{distance}{induite par un masque} + \label{squelette_def_dist_masque} + Soit $M$ un masque de dimension $n$, soient deux pixels $p_1$, $p_2$ et $C\pa{p_1,p_2}$, + l'ensemble des chemins allant de $p_1$ et $p_2$ dont les vecteurs sont de longueur inf�rieure ou + �gale � $n$. Alors~: + + $$ + d_M\pa{p_1,p_2} = \underset{c \in C\pa{p_1,p_2}}{\min} \; \summyone{v = \pa{ \begin{subarray} {c} + v_x \\ v_y \end{subarray} } + \in c} m_{v_x, v_y} + $$ + + \end{xdefinition} + + +L'application d�finie en~\ref{squelette_def_dist_masque} est bien une distance. Comme le masque est sym�trique, elle est aussi sym�trique. De plus, $d_M\pa{p_1,p_2} = 0 \Longleftrightarrow p_1 = p_2$. L'in�galit� triangulaire est aussi facile � v�rifier puisque pour trois points $p_1,p_2,p$, la concat�nation des chemins allant de $p_1$ � $p$, puis de $p$ � $p_2$ forme un chemin allant de $p_1$ � $p_2$. Par cons�quent~: $d_M\pa{p_1,p_2} \leqslant d_M\pa{p_1,p} + d_M\pa{p_2,p}$. \indexfr{passe d'image} -L'algorithme qui suit permet d'obtenir la carte~\ref{ske_cart_dist} partir d'une distance dfinie en~\ref{squelette_def_dist_masque} de manire rapide, soit en deux "passes" d'image. Plus prcisment, il est ncessaire de parcourir deux fois l'ensemble des pixels de l'image afin de construire la carte de distance. Auparavant, on dfinit les deux voisinages de pixels suivants~: - - \begin{eqnarray*} - V_h\pa{n} &=& \acc{ v = \vecteurimm{v_x}{v_y} \; \left| \; - -n \infegal v_y \infegal 0 \text{ et } - \left\{ \begin{array}{l} -n \infegal v_x < 0 \text{ si } v_y = 0 \\ - -n \infegal v_x \infegal n \text{ si } v_y < 0 - \end{array} \right. - \right. } - \\ - V_b\pa{n} &=& \acc{ v = \vecteurimm{v_x}{v_y} \; \left| \; - 0 \infegal v_y \infegal n \text{ et } - \left\{ \begin{array}{l} 0 < v_x \infegal n \text{ si } v_y = 0 \\ - -n \infegal v_x \infegal n \text{ si } v_y > 0 - \end{array} \right. - \right. } - \end{eqnarray*} - -Ces deux voisinages sont rsums par la figure~\ref{ske_vois_vb_vh}. - - - - - \begin{figure} - $$ - \begin{tabular}{|c|}\hline - \filefig{../squelette/fig_neib} - \\ \hline - \end{tabular} - $$ - \caption{ Voisinages $V_h\pa{n}$ et $V_b\pa{n}$, le voisinage $V_h\pa{n}$ dsigne - la partie situe au-dessus et - gauche du pixel central, $V_b\pa{n}$ la partie situe au-dessous et droite.} - \label{ske_vois_vb_vh} - \end{figure} - - - - - - \begin{xalgorithm}{carte de distance} - \indexfrr{carte}{distance} - \label{ske_algo_cart_dist} - - L'objectif est de construire la carte de distance $C$ pour la forme $F$ et dfinie par - (\ref{ske_eq_carte_dist}) pour la distance induite par le masque $M$ de dimension $n$. - $I_{XY}$ est une image avec $X$ colonnes et $Y$ lignes. - $\forall \pa{x,y}, \; I_{XY}\pa{x,y} = \indicatrice{\pa{x,y} \in F}$. - Pour ne pas alourdir les notations, $I = I_{XY}$ et $C = C^{I_{XY}}$. - On impose que $C\pa{x,y} = I\pa{x,y} = \infty$ si $\pa{x,y} \notin \intervalle{1}{X} \times \intervalle{1}{Y}$. - - \begin{xalgostep}{premire passe d'image} - \begin{xfor}{y}{1}{Y} - \begin{xfor}{x}{1}{X} - $C\pa{x,y} \longleftarrow \left\{ \begin{array}{l} - 0 \text{ si } I\pa{x,y} = 0 \\ - \min \acc{ C\cro{ \pa{x,y} - v} + M_{v_x,v_y} - \sac v \in V_h\pa{n}} \text{ sinon} - \end{array}\right.$ - \end{xfor} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{seconde passe d'image} - \begin{xfor}{y}{Y}{1} - \begin{xfor}{x}{X}{1} - $C\pa{x,y} \longleftarrow \min\acc{ C\pa{x,y}, \min - \acc{ C\cro{ \pa{x,y} - v} + M_{v_x,v_y} \sac v \in V_b\pa{n}}}$ - \end{xfor} - \end{xfor} - \end{xalgostep} - - \end{xalgorithm} - - - -L'algorithme~\ref{ske_algo_cart_dist} mne bien la carte de distance dfinie par (\ref{ske_eq_carte_dist}). Cette dmonstration est assez simple et s'effectue par rcurrence sur $x$ et $y$. La premire passe dtermine pour un pixel noir (1) la distance au pixel blanc (0) le plus proche situ dans une moiti suprieure de l'image. La seconde passe dtermine ce minimum dans la moiti infrieure et choisit le minimum des deux. +L'algorithme qui suit permet d'obtenir la carte~\ref{ske_cart_dist} � partir d'une distance d�finie en~\ref{squelette_def_dist_masque} de mani�re rapide, soit en deux "passes" d'image. Plus pr�cis�ment, il est n�cessaire de parcourir deux fois l'ensemble des pixels de l'image afin de construire la carte de distance. Auparavant, on d�finit les deux voisinages de pixels suivants~: + + \begin{eqnarray*} + V_h\pa{n} &=& \acc{ v = \vecteurimm{v_x}{v_y} \; \left| \; + -n \leqslant v_y \leqslant 0 \text{ et } + \left\{ \begin{array}{l} -n \leqslant v_x < 0 \text{ si } v_y = 0 \\ + -n \leqslant v_x \leqslant n \text{ si } v_y < 0 + \end{array} \right. + \right. } + \\ + V_b\pa{n} &=& \acc{ v = \vecteurimm{v_x}{v_y} \; \left| \; + 0 \leqslant v_y \leqslant n \text{ et } + \left\{ \begin{array}{l} 0 < v_x \leqslant n \text{ si } v_y = 0 \\ + -n \leqslant v_x \leqslant n \text{ si } v_y > 0 + \end{array} \right. + \right. } + \end{eqnarray*} + +Ces deux voisinages sont r�sum�s par la figure~\ref{ske_vois_vb_vh}. + + + + + \begin{figure} + $$ + \begin{tabular}{|c|}\hline + \filefig{../squelette/fig_neib} + \\ \hline + \end{tabular} + $$ + \caption{ Voisinages $V_h\pa{n}$ et $V_b\pa{n}$, le voisinage $V_h\pa{n}$ d�signe + la partie situ�e au-dessus et + � gauche du pixel central, $V_b\pa{n}$ la partie situ�e au-dessous � et � droite.} + \label{ske_vois_vb_vh} + \end{figure} + + + + + + \begin{xalgorithm}{carte de distance} + \indexfrr{carte}{distance} + \label{ske_algo_cart_dist} + + L'objectif est de construire la carte de distance $C$ pour la forme $F$ et d�finie par + (\ref{ske_eq_carte_dist}) pour la distance induite par le masque $M$ de dimension $n$. + $I_{XY}$ est une image avec $X$ colonnes et $Y$ lignes. + $\forall \pa{x,y}, \; I_{XY}\pa{x,y} = \indicatrice{\pa{x,y} \in F}$. + Pour ne pas alourdir les notations, $I = I_{XY}$ et $C = C^{I_{XY}}$. + On impose que $C\pa{x,y} = I\pa{x,y} = \infty$ si $\pa{x,y} \notin \intervalle{1}{X} \times \intervalle{1}{Y}$. + + \begin{xalgostep}{premi�re passe d'image} + \begin{xfor}{y}{1}{Y} + \begin{xfor}{x}{1}{X} + $C\pa{x,y} \longleftarrow \left\{ \begin{array}{l} + 0 \text{ si } I\pa{x,y} = 0 \\ + \min \acc{ C\cro{ \pa{x,y} - v} + M_{v_x,v_y} + \sac v \in V_h\pa{n}} \text{ sinon} + \end{array}\right.$ + \end{xfor} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{seconde passe d'image} + \begin{xfor}{y}{Y}{1} + \begin{xfor}{x}{X}{1} + $C\pa{x,y} \longleftarrow \min\acc{ C\pa{x,y}, \min + \acc{ C\cro{ \pa{x,y} - v} + M_{v_x,v_y} \sac v \in V_b\pa{n}}}$ + \end{xfor} + \end{xfor} + \end{xalgostep} + + \end{xalgorithm} + + + +L'algorithme~\ref{ske_algo_cart_dist} m�ne bien � la carte de distance d�finie par (\ref{ske_eq_carte_dist}). Cette d�monstration est assez simple et s'effectue par r�currence sur $x$ et $y$. La premi�re passe d�termine pour un pixel noir (1) la distance au pixel blanc (0) le plus proche situ� dans une moiti� sup�rieure de l'image. La seconde passe d�termine ce minimum dans la moiti� inf�rieure et choisit le minimum des deux. @@ -469,13 +469,13 @@ \section{Carte de distance} %------------------------------------------------------------------------------------------------------------ -\section{Squelettisation discrte} +\section{Squelettisation discr�te} %------------------------------------------------------------------------------------------------------------ \indexfr{squelette} \indexfr{squelettisation} -La squelettisation doit aboutir une image style "fil de fer" comme celle de la figure~\ref{squelette_fig2}. Il existe plusieurs mthodes de squelettisation discrte, certaines s'appuient sur une carte de distance comme celle de la figure~\ref{ske_cart_dist}. D'autres approches sont cependant possibles comme par l'intermdiaire d'un graphe de Vorono (paragraphe~\ref{ske_voronoi_ske_ske}). +La squelettisation doit aboutir � une image style "fil de fer" comme celle de la figure~\ref{squelette_fig2}. Il existe plusieurs m�thodes de squelettisation discr�te, certaines s'appuient sur une carte de distance comme celle de la figure~\ref{ske_cart_dist}. D'autres approches sont cependant possibles comme par l'interm�diaire d'un graphe de Vorono� (paragraphe~\ref{ske_voronoi_ske_ske}). @@ -487,85 +487,85 @@ \section{Squelettisation discr -\subsection{Erosion partir de masques $\pa{3,3}$} -\indexfr{morphomathmatique} -\indexfr{rosion} +\subsection{Erosion � partir de masques $\pa{3,3}$} +\indexfr{morphomath�matique} +\indexfr{�rosion} \label{ske_par_erosion} \indexfrr{masque}{$\pa{3,3}$} -L'rosion d'une forme consiste lui retirer l'ensemble des pixels qui forme son contour. Toutefois, l'rosion d'une forme compose d'une seule composante connexe peut aboutir plusieurs composantes. Son squelette est donc obtenu aprs plusieurs rosions successives tout en conservant chaque tape des formes homotopes la forme initiale. +L'�rosion d'une forme consiste � lui retirer l'ensemble des pixels qui forme son contour. Toutefois, l'�rosion d'une forme compos�e d'une seule composante connexe peut aboutir � plusieurs composantes. Son squelette est donc obtenu apr�s plusieurs �rosions successives tout en conservant � chaque �tape des formes homotopes � la forme initiale. -Tout d'abord, on dfinit la fonction $f_k$ qui associe un pixel un ensemble de pixels correspondant un de ses voisinages dfinis par la figure~\ref{ske_vois_48con} (4~ou~8 connexit). On dfinit galement pour une forme $F$~: +Tout d'abord, on d�finit la fonction $f_k$ qui associe � un pixel un ensemble de pixels correspondant � un de ses voisinages d�finis par la figure~\ref{ske_vois_48con} (4~ou~8 connexit�). On d�finit �galement pour une forme $F$~: - $$ - f_k \pa{F} = \underset{p \in F}{\cup} \; f_k\pa{p} - $$ - -Les algorithmes qui suivent supposent frquement que la forme squelettiser ne contient qu'une seule composante connexe. Il suffit de rpter cet algorithme pour chaque composante connexe si ce n'est pas le cas. - + $$ + f_k \pa{F} = \underset{p \in F}{\cup} \; f_k\pa{p} + $$ + +Les algorithmes qui suivent supposent fr�quement que la forme � squelettiser ne contient qu'une seule composante connexe. Il suffit de r�p�ter cet algorithme pour chaque composante connexe si ce n'est pas le cas. + - \begin{xalgorithm}{squelettisation par rosion (1)} - \indexfr{squelettisation} - \indexfr{rosion} - \label{ske_algo_ske_ero_1} - - Pour une forme $F$, on construit son squelette $S_k$ en $k$-connexit rosions successives. - $\overline{S_k}$ dsigne le complmentaire de $S_k$ dans l'image. On suppose que $F$ n'a qu'une composante connexe. - Le squelette est l'ensemble $S_k$ final. - - \begin{xalgostep}{initialistion} - $S_k = F$ - \end{xalgostep} - - \begin{xalgostep}{rosion}\label{ske_algo_ske_ero_a} - $X \longleftarrow f_{12-k}\pa{\overline{S_k}} \cap S_k$ \\ - $L \longleftarrow \emptyset$ \\ - \begin{xforeach}{p}{X} - \begin{xif}{$S_k - \acc{p}$ ne contient qu'une composante connexe} - $L \longleftarrow L \cup \acc{p}$ - \end{xif} - \end{xforeach} \\ - $S_k \longleftarrow S_k - L$ - \end{xalgostep} - - \begin{xalgostep}{terminaison} - \begin{xif}{$L \neq \emptyset$} - retour l'tape~\ref{ske_algo_ske_ero_a} - \end{xif} - \end{xalgostep} - - \end{xalgorithm} + \begin{xalgorithm}{squelettisation par �rosion (1)} + \indexfr{squelettisation} + \indexfr{�rosion} + \label{ske_algo_ske_ero_1} + + Pour une forme $F$, on construit son squelette $S_k$ en $k$-connexit� �rosions successives. + $\overline{S_k}$ d�signe le compl�mentaire de $S_k$ dans l'image. On suppose que $F$ n'a qu'une composante connexe. + Le squelette est l'ensemble $S_k$ final. + + \begin{xalgostep}{initialistion} + $S_k = F$ + \end{xalgostep} + + \begin{xalgostep}{�rosion}\label{ske_algo_ske_ero_a} + $X \longleftarrow f_{12-k}\pa{\overline{S_k}} \cap S_k$ \\ + $L \longleftarrow \emptyset$ \\ + \begin{xforeach}{p}{X} + \begin{xif}{$S_k - \acc{p}$ ne contient qu'une composante connexe} + $L \longleftarrow L \cup \acc{p}$ + \end{xif} + \end{xforeach} \\ + $S_k \longleftarrow S_k - L$ + \end{xalgostep} + + \begin{xalgostep}{terminaison} + \begin{xif}{$L \neq \emptyset$} + retour � l'�tape~\ref{ske_algo_ske_ero_a} + \end{xif} + \end{xalgostep} + + \end{xalgorithm} - \begin{figure} - $$ - \begin{tabular}{|c|c|}\hline - $\begin{array}{c|c|c} - 2 & 1 & 2 \\ \hline - 1 & 0 & 1 \\ \hline - 2 & 1 & 2 \medskip - \end{array}$ - & - $\begin{array}{c|c|c} - 1 & 1 & 1 \\ \hline - 1 & 0 & 1 \\ \hline - 1 & 1 & 1 \medskip - \end{array}$ - \\ \hline - $M_4$ & $M_8$\\ \hline - \end{tabular} - $$ - \caption{Masques de distance $M_4$ et $M_8$ utiliss pour construire un squelette en $k$-connexit.} - \label{ske_masque_ske_k_con} - \end{figure} + \begin{figure} + $$ + \begin{tabular}{|c|c|}\hline + $\begin{array}{c|c|c} + 2 & 1 & 2 \\ \hline + 1 & 0 & 1 \\ \hline + 2 & 1 & 2 \medskip + \end{array}$ + & + $\begin{array}{c|c|c} + 1 & 1 & 1 \\ \hline + 1 & 0 & 1 \\ \hline + 1 & 1 & 1 \medskip + \end{array}$ + \\ \hline + $M_4$ & $M_8$\\ \hline + \end{tabular} + $$ + \caption{Masques de distance $M_4$ et $M_8$ utilis�s pour construire un squelette en $k$-connexit�.} + \label{ske_masque_ske_k_con} + \end{figure} -L'ensemble $X$ correspond au squelette de la forme en cours d'rosion. Cet algorithme est assez coteux puisque, chaque tape~\ref{ske_algo_ske_ero_a}, une passe d'image est effectue pour construire l'ensemble $f_{12-k}\pa{\overline{S_k}}$. L'ide est alors d'utiliser la carte de distance (dfinie en~\ref{ske_def_cart_dist_def}) construite l'aide du masque $M_4$ ou $M_8$ (figure~\ref{ske_masque_ske_k_con}). Cette dernire contient pour chaque pixel de la forme $F$ un nombre qui correspond presque l'itration de l'tape~\ref{ske_algo_ske_ero_a} de l'algorithme~\ref{ske_algo_ske_ero_1} dans laquelle il sera trait. +L'ensemble $X$ correspond au squelette de la forme en cours d'�rosion. Cet algorithme est assez co�teux puisque, � chaque �tape~\ref{ske_algo_ske_ero_a}, une passe d'image est effectu�e pour construire l'ensemble $f_{12-k}\pa{\overline{S_k}}$. L'id�e est alors d'utiliser la carte de distance (d�finie en~\ref{ske_def_cart_dist_def}) construite � l'aide du masque $M_4$ ou $M_8$ (figure~\ref{ske_masque_ske_k_con}). Cette derni�re contient pour chaque pixel de la forme $F$ un nombre qui correspond presque � l'it�ration de l'�tape~\ref{ske_algo_ske_ero_a} de l'algorithme~\ref{ske_algo_ske_ero_1} dans laquelle il sera trait�. @@ -573,281 +573,281 @@ \subsection{Erosion - \begin{figure} - $$ - \begin{tabular}{|c|c|}\hline - \filefig{../squelette/fig_mask1} - & - \filefig{../squelette/fig_mask2} - \\ \hline - $V_4$ & $V_8$ \\ \hline - \end{tabular} - $$ - \caption{ Voisinages 4-connexe et 8-connexe~: le voisinage du point central correspond l'ensemble des - cases coches.} - \label{ske_vois_48con} - \end{figure} + \begin{figure} + $$ + \begin{tabular}{|c|c|}\hline + \filefig{../squelette/fig_mask1} + & + \filefig{../squelette/fig_mask2} + \\ \hline + $V_4$ & $V_8$ \\ \hline + \end{tabular} + $$ + \caption{ Voisinages 4-connexe et 8-connexe~: le voisinage du point central correspond � l'ensemble des + cases coch�es.} + \label{ske_vois_48con} + \end{figure} \indexfr{composante connexe} -Raliser une rosion sur une forme $F$ revient chercher tous les points du contour qu'on peut supprimer sans accrotre le nombre de composantes connexes. Pour cela, il suffit de se limiter un voisinage 3x3 d'un point du contour et de vrifier s'il correspond une des configurations de la figure~\ref{ske_masque_clean_k_con}. Ceci mne l'algorithme suivant~: - - - - - \begin{xalgorithm}{squelettisation par rosion (2)} - \indexfr{squelettisation} - \indexfr{rosion} - \indexfrr{carte}{distance} - \label{ske_algo_ske_ero_algo_2} - \indexfr{masque} - - Pour une forme $F$ incluse dans l'image $I$, on construit son squelette $S_4$ en $4$-connexit, - par rosions successives. Le squelette est l'ensemble $L$ final. - - \begin{xalgostep}{carte de distance} - On construit la carte de distance avec l'algorithme~\ref{ske_algo_cart_dist} et le masque $M_4$ de la - figure~\ref{ske_masque_ske_k_con}. La forme $F$ est constitue des pixel $p$ - pour lesquels $C^I\pa{p} > 0$ o - $C$ est la carte de distance. Puis on construit la liste des pixels $L=\vecteur{p_1}{p_n}$. - Cette liste est trie - par ordre de distance croissante~: $i \infegal j \Longrightarrow C^I\pa{p_i} \infegal C^I\pa{p_j}$. - \end{xalgostep} - - \begin{xalgostep}{rosion}\label{ske_algo_ske_2_a} - $A \longleftarrow \emptyset$ \\ - \begin{xforeach}{p}{L} - Si le voisinage du pixel $p$ correspond une des configurations $N_1$ $N_7$ de la - figure~\ref{ske_masque_clean_k_con} (ou leurs transformations par symtrie axiale ou rotation) alors~: - $A \longleftarrow A \cup \acc{p}$ - \end{xforeach} - \end{xalgostep} - - \begin{xalgostep}{suppression} - \begin{xif}{$A \neq \emptyset$} - $L \longleftarrow L - A$ \\ - L'ordre initial des pixels dans la liste $L$ doit tre conserv. \\ - Retour l'tape~\ref{ske_algo_ske_2_a}. - \end{xif} - \end{xalgostep} - - - \end{xalgorithm} - - - - - - - - \begin{figure} - $$ - \begin{tabular}{|c|c|c|c|c|c|c|}\hline - \filefig{../squelette/fig_8mask1} - & - \filefig{../squelette/fig_8mask2} - & - \filefig{../squelette/fig_8mask3} - & - \filefig{../squelette/fig_8mask4} - & - \filefig{../squelette/fig_8mask5} - & - \filefig{../squelette/fig_8mask6} - & - \filefig{../squelette/fig_8mask7} - \\ \hline - $N_1$ & $N_2$ & $N_3$ & $N_4$ & $N_5$ & $N_6$ & $N_7$ \\ \hline - \end{tabular} - $$ - \caption{ Masques de nettoyage pour construire un squelette en $4$ ou $8$-connexit. Pour chaque masque, - l'ensemble des cases coches correspond des pixels appartenant la figure $F$ dont il faut obtenir - le squelette. Si le voisinage du pixel central correspond une de ces figures, il est nettoy et - prend la couleur du fond. Il faut galement envisager les transformations - (par symtrie axiale ou rotation) - de ces figures, 4 images par rotation, 4 images par symtrie axiale, - soit 36 figures diffrentes au total.} - \label{ske_masque_clean_k_con} - \end{figure} - - - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=6cm] - {\filext{../squelette/image/ske_decentre}}\end{array}$}$$ - \caption{ Squelette dcentr obtenu lorsque l'rosion n'est pas effectue - en liminant d'abord les pixels noirs - les plus proches de pixels blancs, soit lorsque l'algorithme~\ref{ske_algo_ske_ero_algo_2} - ne trie pas l'ensemble $L$. Ce rsultat est comparer avec - celui de la figure~\ref{squelette_fig2}.} - \label{squelette_non_centre} - \end{figure} +R�aliser une �rosion sur une forme $F$ revient � chercher tous les points du contour qu'on peut supprimer sans accro�tre le nombre de composantes connexes. Pour cela, il suffit de se limiter � un voisinage 3x3 d'un point du contour et de v�rifier s'il correspond � une des configurations de la figure~\ref{ske_masque_clean_k_con}. Ceci m�ne � l'algorithme suivant~: + + + + + \begin{xalgorithm}{squelettisation par �rosion (2)} + \indexfr{squelettisation} + \indexfr{�rosion} + \indexfrr{carte}{distance} + \label{ske_algo_ske_ero_algo_2} + \indexfr{masque} + + Pour une forme $F$ incluse dans l'image $I$, on construit son squelette $S_4$ en $4$-connexit�, + par �rosions successives. Le squelette est l'ensemble $L$ final. + + \begin{xalgostep}{carte de distance} + On construit la carte de distance avec l'algorithme~\ref{ske_algo_cart_dist} et le masque $M_4$ de la + figure~\ref{ske_masque_ske_k_con}. La forme $F$ est constitu�e des pixel $p$ + pour lesquels $C^I\pa{p} > 0$ o� + $C$ est la carte de distance. Puis on construit la liste des pixels $L=\vecteur{p_1}{p_n}$. + Cette liste est tri�e + par ordre de distance croissante~: $i \leqslant j \Longrightarrow C^I\pa{p_i} \leqslant C^I\pa{p_j}$. + \end{xalgostep} + + \begin{xalgostep}{�rosion}\label{ske_algo_ske_2_a} + $A \longleftarrow \emptyset$ \\ + \begin{xforeach}{p}{L} + Si le voisinage du pixel $p$ correspond � une des configurations $N_1$ � $N_7$ de la + figure~\ref{ske_masque_clean_k_con} (ou leurs transformations par sym�trie axiale ou rotation) alors~: + $A \longleftarrow A \cup \acc{p}$ + \end{xforeach} + \end{xalgostep} + + \begin{xalgostep}{suppression} + \begin{xif}{$A \neq \emptyset$} + $L \longleftarrow L - A$ \\ + L'ordre initial des pixels dans la liste $L$ doit �tre conserv�. \\ + Retour � l'�tape~\ref{ske_algo_ske_2_a}. + \end{xif} + \end{xalgostep} + + + \end{xalgorithm} + + + + + + + + \begin{figure} + $$ + \begin{tabular}{|c|c|c|c|c|c|c|}\hline + \filefig{../squelette/fig_8mask1} + & + \filefig{../squelette/fig_8mask2} + & + \filefig{../squelette/fig_8mask3} + & + \filefig{../squelette/fig_8mask4} + & + \filefig{../squelette/fig_8mask5} + & + \filefig{../squelette/fig_8mask6} + & + \filefig{../squelette/fig_8mask7} + \\ \hline + $N_1$ & $N_2$ & $N_3$ & $N_4$ & $N_5$ & $N_6$ & $N_7$ \\ \hline + \end{tabular} + $$ + \caption{ Masques de nettoyage pour construire un squelette en $4$ ou $8$-connexit�. Pour chaque masque, + l'ensemble des cases coch�es correspond � des pixels appartenant � la figure $F$ dont il faut obtenir + le squelette. Si le voisinage du pixel central correspond � une de ces figures, il est nettoy� et + prend la couleur du fond. Il faut �galement envisager les transformations + (par sym�trie axiale ou rotation) + de ces figures, 4 images par rotation, 4 images par sym�trie axiale, + soit 36 figures diff�rentes au total.} + \label{ske_masque_clean_k_con} + \end{figure} + + + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=3cm, width=6cm] + {\filext{../squelette/image/ske_decentre}}\end{array}$}$$ + \caption{ Squelette d�centr� obtenu lorsque l'�rosion n'est pas effectu�e + en �liminant d'abord les pixels noirs + les plus proches de pixels blancs, soit lorsque l'algorithme~\ref{ske_algo_ske_ero_algo_2} + ne trie pas l'ensemble $L$. Ce r�sultat est � comparer avec + celui de la figure~\ref{squelette_fig2}.} + \label{squelette_non_centre} + \end{figure} \begin{xremark}{tri de la liste $L$} \indexfr{tri} -Le tri de la liste $L$ par ordre de distance croissante permet de supprimer d'abord les points du contour avant ceux de l'intrieur. Si ce tri n'est pas fait, le rsultat n'est plus aussi parfait (figure~\ref{squelette_non_centre}). +Le tri de la liste $L$ par ordre de distance croissante permet de supprimer d'abord les points du contour avant ceux de l'int�rieur. Si ce tri n'est pas fait, le r�sultat n'est plus aussi parfait (figure~\ref{squelette_non_centre}). \end{xremark} \begin{xremark}{masques $N_6$ et $N_7$} -Les masques $N_6$ et $N_7$ (voir figure~\ref{ske_masque_clean_k_con}) sont ncessaires afin d'viter que le squelette ne prsente des configurations comme celle de la figure~\ref{squelette_non_parfait_recoin} o il reste une case nettoyer. +Les masques $N_6$ et $N_7$ (voir figure~\ref{ske_masque_clean_k_con}) sont n�cessaires afin d'�viter que le squelette ne pr�sente des configurations comme celle de la figure~\ref{squelette_non_parfait_recoin} o� il reste une case � nettoyer. \end{xremark} - \begin{figure} - $$ - \begin{tabular}{|c|}\hline - \filefig{../squelette/fig_maskfail} - \\ \hline - \end{tabular} - $$ - \caption{ L'rosion de la forme n'est pas termine sans l'application - des masques $N_6$ ou $N_7$ de la figure~\ref{ske_masque_clean_k_con}.} - \label{squelette_non_parfait_recoin} - \end{figure} - - -\begin{xremark}{quatre points rsistants} -L'algorithme de squelettisation~\ref{ske_algo_ske_ero_algo_2} produit une figure particulire qui peut tre dlicate traiter si l'objectif est de vectoriser le squelette. La figure~\ref{squelette_4_mousquetaires} montre quatre branches qui se rejoignent en un bloc de quatre pixels dont on aurait prfr qu'ils ne soient qu'un. + \begin{figure} + $$ + \begin{tabular}{|c|}\hline + \filefig{../squelette/fig_maskfail} + \\ \hline + \end{tabular} + $$ + \caption{ L'�rosion de la forme n'est pas termin�e sans l'application + des masques $N_6$ ou $N_7$ de la figure~\ref{ske_masque_clean_k_con}.} + \label{squelette_non_parfait_recoin} + \end{figure} + + +\begin{xremark}{quatre points r�sistants} +L'algorithme de squelettisation~\ref{ske_algo_ske_ero_algo_2} produit une figure particuli�re qui peut �tre d�licate � traiter si l'objectif est de vectoriser le squelette. La figure~\ref{squelette_4_mousquetaires} montre quatre branches qui se rejoignent en un bloc de quatre pixels dont on aurait pr�f�r� qu'ils ne soient qu'un. \end{xremark} - \begin{figure} - $$ - \begin{tabular}{|c|}\hline - \filefig{../squelette/fig_maskfail2} - \\ \hline - \end{tabular} - $$ - \caption{ Quatre branches lies par un bloc de quatre pixels, cette figure - est obtenue pour un squelette - $4$-connexe. } - \label{squelette_4_mousquetaires} - \end{figure} - - - -Le passage un squelette $8$-connexit s'effectue en appliquant de nouveaux masques au squelette $4$-connexe. Il s'agit de l'algorithme suivant~: - - - - \begin{xalgorithm}{squelettisation par rosion (3)} - \indexfr{squelettisation} - \indexfr{rosion} - \indexfr{masque} - \label{ske_algo_ske_ero_algo_3} - - Pour une forme $F$ incluse dans l'image $I$, on construit son squelette $S_4$ en - $4$-connexit par rosions successives. Le squelette est l'ensemble $L$ final. - - \begin{xalgostep}{squelette $4$-connexe} - On construit le squelette $S_4$ ($4$-connexe) grce l'algorithme~\ref{ske_algo_ske_ero_algo_2}. \\ - $L \longleftarrow S_4$ - \end{xalgostep} - - \begin{xalgostep}{rosion}\label{ske_algo_ske_3_a} - $A \longleftarrow \emptyset$ \\ - \begin{xforeach}{p}{L} - Si le voisinage du pixel $p$ correspond la configuration $N'_1$ de la - figure~\ref{ske_masque_clean_k_con_8} (ou ses transformations) alors~: - $A \longleftarrow A \cup \acc{p}$ - \end{xforeach} - \end{xalgostep} - - \begin{xalgostep}{suppression}\label{ske_algo_ske_3_b} - \begin{xif}{$A \neq \emptyset$} - $L \longleftarrow L - A$ \\ - Retour l'tape~\ref{ske_algo_ske_3_a}. - \end{xif} - \end{xalgostep} - - - \end{xalgorithm} - - - - - - - - \begin{figure} - $$ - \begin{tabular}{|c|}\hline - \filefig{../squelette/fig_masksp2} - \\ \hline - $N'_1$ \\ \hline - \end{tabular} - $$ - \caption{ Masque de nettoyage pour construire un squelette en $8$-connexit. - Pour ce masque, l'ensemble des - cases coches correspond des pixels appartenant la figure $F$, les cases marques - d'un point d'interrogation peuvent tre coches ou non. Si le voisinage du pixel central - correspond cette figure (ou ses transformations par sysmtrie ou rotation), - il est nettoy et prend la couleur du fond. - } - \label{ske_masque_clean_k_con_8} - \end{figure} - - - - - + \begin{figure} + $$ + \begin{tabular}{|c|}\hline + \filefig{../squelette/fig_maskfail2} + \\ \hline + \end{tabular} + $$ + \caption{ Quatre branches li�es par un bloc de quatre pixels, cette figure + est obtenue pour un squelette + $4$-connexe. } + \label{squelette_4_mousquetaires} + \end{figure} + + + +Le passage � un squelette $8$-connexit� s'effectue en appliquant de nouveaux masques au squelette $4$-connexe. Il s'agit de l'algorithme suivant~: + + + + \begin{xalgorithm}{squelettisation par �rosion (3)} + \indexfr{squelettisation} + \indexfr{�rosion} + \indexfr{masque} + \label{ske_algo_ske_ero_algo_3} + + Pour une forme $F$ incluse dans l'image $I$, on construit son squelette $S_4$ en + $4$-connexit� par �rosions successives. Le squelette est l'ensemble $L$ final. + + \begin{xalgostep}{squelette $4$-connexe} + On construit le squelette $S_4$ ($4$-connexe) gr�ce � l'algorithme~\ref{ske_algo_ske_ero_algo_2}. \\ + $L \longleftarrow S_4$ + \end{xalgostep} + + \begin{xalgostep}{�rosion}\label{ske_algo_ske_3_a} + $A \longleftarrow \emptyset$ \\ + \begin{xforeach}{p}{L} + Si le voisinage du pixel $p$ correspond � la configuration $N'_1$ de la + figure~\ref{ske_masque_clean_k_con_8} (ou � ses transformations) alors~: + $A \longleftarrow A \cup \acc{p}$ + \end{xforeach} + \end{xalgostep} + + \begin{xalgostep}{suppression}\label{ske_algo_ske_3_b} + \begin{xif}{$A \neq \emptyset$} + $L \longleftarrow L - A$ \\ + Retour � l'�tape~\ref{ske_algo_ske_3_a}. + \end{xif} + \end{xalgostep} + + + \end{xalgorithm} + + + + + + + + \begin{figure} + $$ + \begin{tabular}{|c|}\hline + \filefig{../squelette/fig_masksp2} + \\ \hline + $N'_1$ \\ \hline + \end{tabular} + $$ + \caption{ Masque de nettoyage pour construire un squelette en $8$-connexit�. + Pour ce masque, l'ensemble des + cases coch�es correspond � des pixels appartenant � la figure $F$, les cases marqu�es + d'un point d'interrogation peuvent �tre coch�es ou non. Si le voisinage du pixel central + correspond � cette figure (ou ses transformations par sysm�trie ou rotation), + il est nettoy� et prend la couleur du fond. + } + \label{ske_masque_clean_k_con_8} + \end{figure} + + + + + \indexfr{Marthon} -Dans les algorithmes prcdents (\ref{ske_algo_ske_ero_algo_2} et~\ref{ske_algo_ske_ero_algo_3}), il faut vrifier le voisinage de chaque pixel correspond des configurations. L'algorithme qui suit propose une expression simplifie de ces vrifications, il est tir de~\citeindex{Marthon1979}. +Dans les algorithmes pr�c�dents (\ref{ske_algo_ske_ero_algo_2} et~\ref{ske_algo_ske_ero_algo_3}), il faut v�rifier le voisinage de chaque pixel correspond � des configurations. L'algorithme qui suit propose une expression simplifi�e de ces v�rifications, il est tir� de~\citeindex{Marthon1979}. - \begin{xalgorithm}{squelettisation de Marthon} - \indexfr{squelettisation} - \indexfrr{carte}{distance} - \indexfr{rosion} - \label{ske_algo_ske_ero_algo_marthon} - - Pour une forme $F$ incluse dans l'image $I$, on construit son squelette $S_k$ en $k$-connexit. - - \begin{xalgostep}{carte de distance} - On construit la carte de distance avec l'algorithme~\ref{ske_algo_cart_dist} et le masque $M_k$ de la - figure~\ref{ske_masque_ske_k_con}. La forme $F$ est constitue des pixels - $p$ pour lesquels $C^I\pa{p} > 0$ o - $C$ est la carte de distance. Puis on construit la liste des pixels $L=\vecteur{p_1}{p_n}$. - Cette liste est trie - par ordre de distance croissante~: $i \infegal j \Longrightarrow C^I\pa{p_i} \infegal C^I\pa{p_j}$. - \end{xalgostep} - - \begin{xalgostep}{rosion}\label{ske_algo_ske_2_a} - $A \longleftarrow \emptyset$ \\ - \begin{xforeach}{p}{L} - Soit $V_k$ le voisinage de la figure~\ref{ske_vois_48con} pour le pixel $p$.\\ - $\begin{array}{lll} - U &\longleftarrow& V_k \cap L \\ - x &\longleftarrow& \summyone{p' \in U} \; p'_x - p_x \text{ et } - y \longleftarrow \summyone{p' \in U} \; p'_y - p_y \\ - z &\longleftarrow& \abs{x} + \abs{y} - \end{array}$ \\ - \begin{xif}{$z = 4$} - $A \longleftarrow A \cup \acc{p}$ - \xelse - \begin{xif}{$z = 3$} - selon ses voisins, $A \longleftarrow A \cup \acc{p}$ - \end{xif} - \end{xif} - \end{xforeach} - \end{xalgostep} - - \begin{xalgostep}{suppression} - \begin{xif}{$A \neq \emptyset$} - $L \longleftarrow L - A$ \\ - L'ordre initial des pixels dans la liste $L$ doit tre conserv. \\ - Retour l'tape~\ref{ske_algo_ske_2_a}. - \end{xif} - \end{xalgostep} - - - \end{xalgorithm} - + \begin{xalgorithm}{squelettisation de Marthon} + \indexfr{squelettisation} + \indexfrr{carte}{distance} + \indexfr{�rosion} + \label{ske_algo_ske_ero_algo_marthon} + + Pour une forme $F$ incluse dans l'image $I$, on construit son squelette $S_k$ en $k$-connexit�. + + \begin{xalgostep}{carte de distance} + On construit la carte de distance avec l'algorithme~\ref{ske_algo_cart_dist} et le masque $M_k$ de la + figure~\ref{ske_masque_ske_k_con}. La forme $F$ est constitu�e des pixels + $p$ pour lesquels $C^I\pa{p} > 0$ o� + $C$ est la carte de distance. Puis on construit la liste des pixels $L=\vecteur{p_1}{p_n}$. + Cette liste est tri�e + par ordre de distance croissante~: $i \leqslant j \Longrightarrow C^I\pa{p_i} \leqslant C^I\pa{p_j}$. + \end{xalgostep} + + \begin{xalgostep}{�rosion}\label{ske_algo_ske_2_a} + $A \longleftarrow \emptyset$ \\ + \begin{xforeach}{p}{L} + Soit $V_k$ le voisinage de la figure~\ref{ske_vois_48con} pour le pixel $p$.\\ + $\begin{array}{lll} + U &\longleftarrow& V_k \cap L \\ + x &\longleftarrow& \summyone{p' \in U} \; p'_x - p_x \text{ et } + y \longleftarrow \summyone{p' \in U} \; p'_y - p_y \\ + z &\longleftarrow& \abs{x} + \abs{y} + \end{array}$ \\ + \begin{xif}{$z = 4$} + $A \longleftarrow A \cup \acc{p}$ + \xelse + \begin{xif}{$z = 3$} + selon ses voisins, $A \longleftarrow A \cup \acc{p}$ + \end{xif} + \end{xif} + \end{xforeach} + \end{xalgostep} + + \begin{xalgostep}{suppression} + \begin{xif}{$A \neq \emptyset$} + $L \longleftarrow L - A$ \\ + L'ordre initial des pixels dans la liste $L$ doit �tre conserv�. \\ + Retour � l'�tape~\ref{ske_algo_ske_2_a}. + \end{xif} + \end{xalgostep} + + + \end{xalgorithm} + @@ -863,30 +863,30 @@ \subsection{Erosion -\subsection{Erosion partir de masques plus larges} -\indexfr{rosion} +\subsection{Erosion � partir de masques plus larges} +\indexfr{�rosion} \indexfrr{masque}{$\pa{4,4}$...} \indexfr{voisinage} -Les rosions dcrites au paragraphe~\ref{ske_par_erosion} liminent les pixels en s'appuyant sur un voisinage $\pa{3,3}$ centr sur le pixel considr, elles mnent parfois des configurations indsirables (figure~\ref{squelette_4_mousquetaires}) ou liminent trop de pixels (voir figure~\ref{squelette_elimination_trop}). Pour des algorithmes plus minutieux, il faut avoir recours un voisinage plus grand. Les problmes lis la taille du voisinage rencontrs ici sont similaires ceux concernant le lissage du contour voqu au paragraphe~\ref{image_lissage_contour__} (page~\pageref{image_lissage_contour__}). Certains masques utiliss pour cette tche sont dfinis sur des voisinages plus grands que~3x3 (voir figure~\ref{image_lissage_contour}, page~\pageref{image_lissage_contour}) et donnent une ide de ce qu'il est possible de faire partir des mthodes de squelettisation par rosion. +Les �rosions d�crites au paragraphe~\ref{ske_par_erosion} �liminent les pixels en s'appuyant sur un voisinage $\pa{3,3}$ centr� sur le pixel consid�r�, elles m�nent parfois � des configurations ind�sirables (figure~\ref{squelette_4_mousquetaires}) ou �liminent trop de pixels (voir figure~\ref{squelette_elimination_trop}). Pour des algorithmes plus minutieux, il faut avoir recours � un voisinage plus grand. Les probl�mes li�s � la taille du voisinage rencontr�s ici sont similaires � ceux concernant le lissage du contour �voqu� au paragraphe~\ref{image_lissage_contour__} (page~\pageref{image_lissage_contour__}). Certains masques utilis�s pour cette t�che sont d�finis sur des voisinages plus grands que~3x3 (voir figure~\ref{image_lissage_contour}, page~\pageref{image_lissage_contour}) et donnent une id�e de ce qu'il est possible de faire � partir des m�thodes de squelettisation par �rosion. - \begin{figure} - $$ - \begin{tabular}{|c|c|c|}\hline - \filefig{../squelette/fig_nmask1} - & - \filefig{../squelette/fig_nmask2} - & - \filefig{../squelette/fig_nmask3} - \\ masque 1 & masque 2 & forme squelettiser \\ \hline - \end{tabular} - $$ - \caption{ Le nettoyage de cette forme par les deux masques de gauche ne laisse que trois pixels. } - \label{squelette_elimination_trop} - \end{figure} + \begin{figure} + $$ + \begin{tabular}{|c|c|c|}\hline + \filefig{../squelette/fig_nmask1} + & + \filefig{../squelette/fig_nmask2} + & + \filefig{../squelette/fig_nmask3} + \\ masque 1 & masque 2 & forme � squelettiser \\ \hline + \end{tabular} + $$ + \caption{ Le nettoyage de cette forme par les deux masques de gauche ne laisse que trois pixels. } + \label{squelette_elimination_trop} + \end{figure} @@ -899,21 +899,21 @@ \subsection{Erosion -\subsection{Ligne de crte} -\indexfrr{ligne}{crte} +\subsection{Ligne de cr�te} +\indexfrr{ligne}{cr�te} \label{ske_par_crete} -La carte de distance associe chaque pixel d'une forme $F$ la distance au pixel blanc le plus proche. Si cette distance est considre comme une altitude, il est possible de dfinir les lignes de crtes du paysage form par la carte. Cette mthode de squelettisation est compose de deux tapes~: +La carte de distance associe � chaque pixel d'une forme $F$ la distance au pixel blanc le plus proche. Si cette distance est consid�r�e comme une altitude, il est possible de d�finir les lignes de cr�tes du paysage form� par la carte. Cette m�thode de squelettisation est compos�e de deux �tapes~: - \begin{enumerate} - \item Recherche des maximas locaux~: le squelette inclut les points dont l'altitude est suprieure toutes - celles de ses 4 ou 8 voisins. - \item Prolongations des lignes formes l'tape~1~: la premire tape aboutit la formation de lignes - discontinues qui doivent tre prolonges afin de retrouver un squelette homotope - la forme d'origine. - \end{enumerate} - -Cette mthode est plus rapide qu'un algorithme bas sur des rosions succesives lorsque la forme dont il faut extraire le squelette est "paisse" car l'algorithme se concentre tout de suite sur les points essentiels. + \begin{enumerate} + \item Recherche des maximas locaux~: le squelette inclut les points dont l'altitude est sup�rieure � toutes + celles de ses 4 ou 8 voisins. + \item Prolongations des lignes form�es � l'�tape~1~: la premi�re �tape aboutit � la formation de lignes + discontinues qui doivent �tre prolong�es afin de retrouver un squelette homotope + � la forme d'origine. + \end{enumerate} + +Cette m�thode est plus rapide qu'un algorithme bas� sur des �rosions succesives lorsque la forme dont il faut extraire le squelette est "�paisse" car l'algorithme se concentre tout de suite sur les points essentiels. @@ -923,14 +923,14 @@ \subsection{Ligne de cr -\subsection{Algorithmes parallles de squelettisation} -\indexfrr{rosion}{parallle} -\indexfrr{squelettisation}{parallle} +\subsection{Algorithmes parall�les de squelettisation} +\indexfrr{�rosion}{parall�le} +\indexfrr{squelettisation}{parall�le} \indexfr{processus} \label{ske_squelettisation_parallele} -Les algorithmes de squelettisation bass sur une rosion l'aide de masques incluent de nombreux tests sur les pixels. En divisant cet ensemble de masques en plusieurs groupes disjoints (ou de faible intersection), il est possible de parallliser ces algorithmes~: dans ce cas, plusieurs processus -~autant qu'il y a de groupes~- rodent l'image, chacun capable de n'enlever que des pixels vrifiant la configuration dcrite par le groupe de masques qui lui est associ. La paralllisation ne rend pas l'algorithme moins coteux mais permet de dimininuer son temps d'excution. L'article \citeindex{ZhangY1997} s'intresse quatre de ces algorithmes utiliss avec deux processus. Il compare leur cot et leur redondance, qui est dfinie ici comme l'intersection entre les deux groupes de masques utiliss. +Les algorithmes de squelettisation bas�s sur une �rosion � l'aide de masques incluent de nombreux tests sur les pixels. En divisant cet ensemble de masques en plusieurs groupes disjoints (ou de faible intersection), il est possible de parall�liser ces algorithmes~: dans ce cas, plusieurs processus -~autant qu'il y a de groupes~- �rodent l'image, chacun capable de n'enlever que des pixels v�rifiant la configuration d�crite par le groupe de masques qui lui est associ�. La parall�lisation ne rend pas l'algorithme moins co�teux mais permet de dimininuer son temps d'ex�cution. L'article \citeindex{ZhangY1997} s'int�resse � quatre de ces algorithmes utilis�s avec deux processus. Il compare leur co�t et leur redondance, qui est d�finie ici comme l'intersection entre les deux groupes de masques utilis�s. @@ -941,76 +941,76 @@ \subsection{Algorithmes parall -\subsection{Extraction du squelette base sur un critre de connexit} -\indexfrr{critre}{connexit} +\subsection{Extraction du squelette bas�e sur un crit�re de connexit�} +\indexfrr{crit�re}{connexit�} \label{ske_critere_connexite} -La mthode est tire de l'article \citeindex{Choi2003}. Si on considre un point $P$ d'une forme squelettiser, on note $Q\pa{P}$ le point le plus proche de $P$ appartenant au contour, on note galement $\pa{P_i}_{1 \infegal i \infegal 8}$ les huits voisins de $P$. A priori, si le point $P$ n'appartient pas au squelette, l'ensemble de points $\pa{Q\pa{P_i}}_{1 \infegal i \infegal 8}$ seront proches les uns des autres. En revanche, si le point $P$ appartient au squelette, l'ensemble $\pa{Q\pa{P_i}}_{1 \infegal i \infegal 8}$ sera dispers sur deux bords opposs du contour (voir figure~\ref{ske_choi2003}). +La m�thode est tir�e de l'article \citeindex{Choi2003}. Si on consid�re un point $P$ d'une forme � squelettiser, on note $Q\pa{P}$ le point le plus proche de $P$ appartenant au contour, on note �galement $\pa{P_i}_{1 \leqslant i \leqslant 8}$ les huits voisins de $P$. A priori, si le point $P$ n'appartient pas au squelette, l'ensemble de points $\pa{Q\pa{P_i}}_{1 \leqslant i \leqslant 8}$ seront proches les uns des autres. En revanche, si le point $P$ appartient au squelette, l'ensemble $\pa{Q\pa{P_i}}_{1 \leqslant i \leqslant 8}$ sera dispers� sur deux bords oppos�s du contour (voir figure~\ref{ske_choi2003}). - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \filefig{../squelette/fig_choi} - \\ \hline \end{tabular}$$ - \caption{ Ide sous-jacente de la squelettisation propose par~\citeindexfig{Choi2003}. - Le point $A$ n'est pas situ sur le squelette, tous ses voisins sont plus proches - du bord suprieur que du bord suprieur. A l'inverse, le point $B$ appartient au - squelette, ses voisins, selon leur position par rapport $A$, sont plus proches - soit du bord suprieur, soit du bord infrieur.} - \label{ske_choi2003} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \filefig{../squelette/fig_choi} + \\ \hline \end{tabular}$$ + \caption{ Id�e sous-jacente de la squelettisation propos�e par~\citeindexfig{Choi2003}. + Le point $A$ n'est pas situ� sur le squelette, tous ses voisins sont plus proches + du bord sup�rieur que du bord sup�rieur. A l'inverse, le point $B$ appartient au + squelette, ses voisins, selon leur position par rapport � $A$, sont plus proches + soit du bord sup�rieur, soit du bord inf�rieur.} + \label{ske_choi2003} + \end{figure} \indexfrr{carte}{distance} -L'algorithme suppose de connatre pour chaque pixel de la forme le point le plus proche appartenant au contour, cette information peut tre obtenue facilement partir des cartes de distance (voir paragraphe~\ref{ske_carte_distance_sec}) en conservant le pixel ayant permis d'atteindre le minimum de distance (la carte de distance est ici estime pour une forme rduite son contour). - - \begin{xalgorithm}{squelettisation (Choi2003)} - La forme squelettiser est note $F$, son contour est not $\overline{F}$ et son squelette $S\pa{F}$. - On note $\rho > 0$ un paramtre rel et positif. On note galement $X\pa{P}$ et $Y\pa{P}$ - respectivement l'abscisse et l'ordonne de $P$. - - \begin{xalgostep}{carte de distance} - Calcul d'une carte de distance permettant d'associer chaque pixel $P \in F$ - le point $Q\pa{P} \in \overline{F}$. - \end{xalgostep} - - \begin{xalgostep}{squelettisation} - Pour tout point $P \in F$, soit $\pa{P_i}_{1 \infegal i \infegal 8}$ les voinsins de $P$ - en 8-connexit, on dfinit~: - - $$ - \forall i \in \ensemble{1}{8}, \; Q_i = Q\pa{P_i} + P - P_i - $$ - - Alors $P$ appartient au squelette s'il existe $i \in \ensemble{1}{8}$ tel que~: - - \begin{eqnarray} - \norme{Q_i - Q\pa{P}}^2 &\supegal& \rho \label{squelette_choi_condition_1}\\ - \text{et } \norme{Q_i}^2 - \norme{Q}^2 &\infegal& \max \acc{ X\pa{Q_i - Q}, \; Y\pa{Q_i - Q}} - \end{eqnarray} - - \end{xalgostep} - \end{xalgorithm} - - -Le cot de cet algorithme est linaire par rapport au nombre de pixels de la forme squelettiser, ce qui reprsente un avantage certain par rapport aux algorithmes bass sur une rosion (voir paragraphe~\ref{ske_par_erosion}). Il reste ajuster la valeur $\rho$, petite pour des squelettes fournis, grande pour des squelettes dgarnis (voir figure~\ref{squelette_choi_connexite}). Sans la seconde condition de l'algorithme, le squelette obtenu a trois pixels d'paisseur, cette condition permet de ramener cette paisseur sur un pixel dans la majorit des cas. Il est parfois souhaitable d'affiner le rsultat par une tape d'rosion afin d'obtenir l'epaisseur ou la connexit voulues. - - - - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=12cm] - {\filext{../squelette/image/choi}}\end{array}$}$$ - \caption{ Squelettisation partir d'un critre de connexit, squelettes obtenus pour - diffrentes valeurs du paramtre $\rho$ (figure extraite de~\citeindexfig{Choi2003}).} - \label{squelette_choi_connexite} - \end{figure} - - - -\begin{xremark}{pertes de connexit} -Cet algortihme n'est pas bien adapt la squelettisation des caractres manuscrits. Ces formes sont souvent trs fines et le rsultat final prsente de nombreuses barbules et quelques pertes de connexit dues la condition~(\ref{squelette_choi_condition_1}) qui dtruit le squelette dans les zones dont l'paisseur est trop fine. +L'algorithme suppose de conna�tre pour chaque pixel de la forme le point le plus proche appartenant au contour, cette information peut �tre obtenue facilement � partir des cartes de distance (voir paragraphe~\ref{ske_carte_distance_sec}) en conservant le pixel ayant permis d'atteindre le minimum de distance (la carte de distance est ici estim�e pour une forme r�duite � son contour). + + \begin{xalgorithm}{squelettisation (Choi2003)} + La forme � squelettiser est not�e $F$, son contour est not� $\overline{F}$ et son squelette $S\pa{F}$. + On note $\rho > 0$ un param�tre r�el et positif. On note �galement $X\pa{P}$ et $Y\pa{P}$ + respectivement l'abscisse et l'ordonn�e de $P$. + + \begin{xalgostep}{carte de distance} + Calcul d'une carte de distance permettant d'associer � chaque pixel $P \in F$ + le point $Q\pa{P} \in \overline{F}$. + \end{xalgostep} + + \begin{xalgostep}{squelettisation} + Pour tout point $P \in F$, soit $\pa{P_i}_{1 \leqslant i \leqslant 8}$ les voinsins de $P$ + en 8-connexit�, on d�finit~: + + $$ + \forall i \in \ensemble{1}{8}, \; Q_i = Q\pa{P_i} + P - P_i + $$ + + Alors $P$ appartient au squelette s'il existe $i \in \ensemble{1}{8}$ tel que~: + + \begin{eqnarray} + \norme{Q_i - Q\pa{P}}^2 &\supegal& \rho \label{squelette_choi_condition_1}\\ + \text{et } \norme{Q_i}^2 - \norme{Q}^2 &\leqslant& \max \acc{ X\pa{Q_i - Q}, \; Y\pa{Q_i - Q}} + \end{eqnarray} + + \end{xalgostep} + \end{xalgorithm} + + +Le co�t de cet algorithme est lin�aire par rapport au nombre de pixels de la forme � squelettiser, ce qui repr�sente un avantage certain par rapport aux algorithmes bas�s sur une �rosion (voir paragraphe~\ref{ske_par_erosion}). Il reste � ajuster la valeur $\rho$, petite pour des squelettes fournis, grande pour des squelettes d�garnis (voir figure~\ref{squelette_choi_connexite}). Sans la seconde condition de l'algorithme, le squelette obtenu a trois pixels d'�paisseur, cette condition permet de ramener cette �paisseur sur un pixel dans la majorit� des cas. Il est parfois souhaitable d'affiner le r�sultat par une �tape d'�rosion afin d'obtenir l'epaisseur ou la connexit� voulues. + + + + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=12cm] + {\filext{../squelette/image/choi}}\end{array}$}$$ + \caption{ Squelettisation � partir d'un crit�re de connexit�, squelettes obtenus pour + diff�rentes valeurs du param�tre $\rho$ (figure extraite de~\citeindexfig{Choi2003}).} + \label{squelette_choi_connexite} + \end{figure} + + + +\begin{xremark}{pertes de connexit�} +Cet algortihme n'est pas bien adapt� � la squelettisation des caract�res manuscrits. Ces formes sont souvent tr�s fines et le r�sultat final pr�sente de nombreuses barbules et quelques pertes de connexit� dues � la condition~(\ref{squelette_choi_condition_1}) qui d�truit le squelette dans les zones dont l'�paisseur est trop fine. \end{xremark} @@ -1023,114 +1023,114 @@ \subsection{Extraction du squelette bas -\subsection{Squelettisation partir de filtre de Gabor} +\subsection{Squelettisation � partir de filtre de Gabor} \indexfr{Gabor} \indexfrr{filtrage}{Gabor} \indexfrr{squelettisation}{Gabor} \label{ske_squelettisation_gabor} -L'article \citeindex{Su2003} propose une mthode fonde sur les filtres de Gabor et l'applique au cas des caractres chinois. Ces caractres sont principalement composs des traits verticaux, horizontaux et diagonaux, une premire tape de filtrage permet d'extraire ces quatre types de traits comme le montre la figure~\ref{squelette_su_gabor_1}, chaque trait est ensuite vectoris ce qui permet d'obtenir une premire approximation du squelette (avant-dernire colonne de la figure~\ref{squelette_su_gabor_1}). Les traits sont ensuite reconnects entre eux pour obtenir le squelette final selon des critres de proximits et de direction. L'avantage de cette mthode est sa grande stabilit par rapport au bruit comme le montre les deux dernires lignes de la figure~\ref{squelette_su_gabor_1}. Les post-traitements regroupant le nettoyage et la connexion des traits succdent au filtrage de Gabor. Comme ils sont indpendants de la squelettisation, seul le filtrage de Gabor sera dcrit. +L'article \citeindex{Su2003} propose une m�thode fond�e sur les filtres de Gabor et l'applique au cas des caract�res chinois. Ces caract�res sont principalement compos�s des traits verticaux, horizontaux et diagonaux, une premi�re �tape de filtrage permet d'extraire ces quatre types de traits comme le montre la figure~\ref{squelette_su_gabor_1}, chaque trait est ensuite vectoris� ce qui permet d'obtenir une premi�re approximation du squelette (avant-derni�re colonne de la figure~\ref{squelette_su_gabor_1}). Les traits sont ensuite reconnect�s entre eux pour obtenir le squelette final selon des crit�res de proximit�s et de direction. L'avantage de cette m�thode est sa grande stabilit� par rapport au bruit comme le montre les deux derni�res lignes de la figure~\ref{squelette_su_gabor_1}. Les post-traitements regroupant le nettoyage et la connexion des traits succ�dent au filtrage de Gabor. Comme ils sont ind�pendants de la squelettisation, seul le filtrage de Gabor sera d�crit. - \begin{figure}[ht] - $$\begin{tabular}[c]{|rc|}\hline - image nette & \includegraphics[height=2cm, width=10cm]{\filext{../squelette/image/su1}} \\ - image bruite & \includegraphics[height=2cm, width=10cm]{\filext{../squelette/image/su2}} \\ - contour altr & \includegraphics[height=2cm, width=10cm]{\filext{../squelette/image/su3}} - \\ \hline \end{tabular}$$ - \caption{ Extraction des traits d'une image selon quatre directions, verticale, horizontale, diagonales, - l'aide de filtres de Gabor, figure extraite de \citeindexfig{Su2003}). La premire ligne - montre le rsultat sur une image nette, la seconde ligne sur une image bruite alatoirement, - la dernire ligne, sur une image dont le contour a t altr.} - \label{squelette_su_gabor_1} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}[c]{|rc|}\hline + image nette & \includegraphics[height=2cm, width=10cm]{\filext{../squelette/image/su1}} \\ + image bruit�e & \includegraphics[height=2cm, width=10cm]{\filext{../squelette/image/su2}} \\ + contour alt�r� & \includegraphics[height=2cm, width=10cm]{\filext{../squelette/image/su3}} + \\ \hline \end{tabular}$$ + \caption{ Extraction des traits d'une image selon quatre directions, verticale, horizontale, diagonales, + � l'aide de filtres de Gabor, figure extraite de \citeindexfig{Su2003}). La premi�re ligne + montre le r�sultat sur une image nette, la seconde ligne sur une image bruit�e al�atoirement, + la derni�re ligne, sur une image dont le contour a �t� alt�r�.} + \label{squelette_su_gabor_1} + \end{figure} -On note $i\pa{x,y} \in \cro{0,1}$ l'intensit de l'image, et $h\pa{x,y}$ le filtre de Gabor dfini par~: +On note $i\pa{x,y} \in \cro{0,1}$ l'intensit� de l'image, et $h\pa{x,y}$ le filtre de Gabor d�fini par~: - \begin{eqnarray} - h\pa{x,y} &=&\exp\pa{ -\pi \; \frac{x^2 + y^2}{\sigma^2} } \; - \exp\pa { 2i \pi \, f \, \pa{ x \, \cos \theta + j \, \sin \theta } } - \end{eqnarray} - -La rponse de l'image au filtre de Gabor est note $I\pa{x,y}$~: + \begin{eqnarray} + h\pa{x,y} &=&\exp\pa{ -\pi \; \frac{x^2 + y^2}{\sigma^2} } \; + \exp\pa { 2i \pi \, f \, \pa{ x \, \cos \theta + j \, \sin \theta } } + \end{eqnarray} + +La r�ponse de l'image au filtre de Gabor est not�e $I\pa{x,y}$~: - \begin{eqnarray} - I\pa{x,y} &=& \abs{ i\pa{x,y} \otimes h\pa{x,y} } - \end{eqnarray} + \begin{eqnarray} + I\pa{x,y} &=& \abs{ i\pa{x,y} \otimes h\pa{x,y} } + \end{eqnarray} -Les paramtres utilises sont les suivants~: +Les param�tres utilis�es sont les suivants~: - $$ - \begin{array}{cc} - \sigma = \frac{ \sqrt{2} }{f} & f = 0,9857 \frac{ e } {dh } - \end{array} - $$ - -$e$ est l'paisseur moyenne des traits (voir paragraphe~\ref{image_epaisseur_trace}, page~\pageref{image_epaisseur_trace}), $h$ est la hauteur de l'image, $d$ est la densit de l'image o le rapport entre le nombre de pixels noirs et la taille de d'image. L'angle $\theta$ prend quatre valeurs pour les quatre directions dsires~: $\theta \in \acc{0, \frac{\pi}{4}, \frac{\pi}{2}, \frac{3\pi}{4} }$, le rsultat de ces quatre filtres est illustr par la figure~\ref{squelette_su_gabor_2}. + $$ + \begin{array}{cc} + \sigma = \frac{ \sqrt{2} }{f} & f = 0,9857 \frac{ e } {dh } + \end{array} + $$ + +$e$ est l'�paisseur moyenne des traits (voir paragraphe~\ref{image_epaisseur_trace}, page~\pageref{image_epaisseur_trace}), $h$ est la hauteur de l'image, $d$ est la densit� de l'image o� le rapport entre le nombre de pixels noirs et la taille de d'image. L'angle $\theta$ prend quatre valeurs pour les quatre directions d�sir�es~: $\theta \in \acc{0, \frac{\pi}{4}, \frac{\pi}{2}, \frac{3\pi}{4} }$, le r�sultat de ces quatre filtres est illustr� par la figure~\ref{squelette_su_gabor_2}. - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \includegraphics[height=3cm, width=14cm]{\filext{../squelette/image/su4}} - \\ \hline \end{tabular}$$ - \caption{ Rponses des quatres filtres de Gabor correspondant aux quatre directions, - $0^\circ$, $45^\circ$, $90^\circ$, $135^\circ$, figure extraite de \citeindexfig{Su2003}.} - \label{squelette_su_gabor_2} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \includegraphics[height=3cm, width=14cm]{\filext{../squelette/image/su4}} + \\ \hline \end{tabular}$$ + \caption{ R�ponses des quatres filtres de Gabor correspondant aux quatre directions, + $0^\circ$, $45^\circ$, $90^\circ$, $135^\circ$, figure extraite de \citeindexfig{Su2003}.} + \label{squelette_su_gabor_2} + \end{figure} -Ces quatre images sont ensuite binarises pour obtenir les quatre images $K_n\pa{x,y}_{1 \infegal n \infegal 4}$~: +Ces quatre images sont ensuite binaris�es pour obtenir les quatre images $K_n\pa{x,y}_{1 \leqslant n \leqslant 4}$~: - \begin{eqnarray} - \forall \pa{x,y}, \; K_n\pa{x,y} &=& \indicatrice{ I_n\pa{x,y} \supegal \alpha } - \end{eqnarray} + \begin{eqnarray} + \forall \pa{x,y}, \; K_n\pa{x,y} &=& \indicatrice{ I_n\pa{x,y} \supegal \alpha } + \end{eqnarray} -Le seuil $\alpha$ est calcul de manire itrative de faon ce que les quatre images $K_n$ ne contiennent pas d'informations redondantes, deux autres images sont alors construites~: +Le seuil $\alpha$ est calcul� de mani�re it�rative de fa�on � ce que les quatre images $K_n$ ne contiennent pas d'informations redondantes, deux autres images sont alors construites~: - \begin{eqnarray} - \forall \pa{x,y}, \; M_\alpha \pa{x,y} &=& \summy{n=1}{4} \; K_n\pa{x,y} \\ - \forall \pa{x,y}, \; N_\alpha \pa{x,y} &=& \summy{n=1}{4} \; \indicatrice{ M_\alpha \pa{x,y} \supegal 1} - \end{eqnarray} + \begin{eqnarray} + \forall \pa{x,y}, \; M_\alpha \pa{x,y} &=& \summy{n=1}{4} \; K_n\pa{x,y} \\ + \forall \pa{x,y}, \; N_\alpha \pa{x,y} &=& \summy{n=1}{4} \; \indicatrice{ M_\alpha \pa{x,y} \supegal 1} + \end{eqnarray} \indexfrr{erreur}{perte} \indexfrr{erreur}{recouvrement} \indexfr{recouvrement} -Les quatre images $K_n$ permettent de reconstruire l'image originale, deux types d'erreurs sont alors quantifis, l'erreur en perte $E_p\pa{\alpha}$ et l'erreur de recouvrement $E_r\pa{\alpha}$~: +Les quatre images $K_n$ permettent de reconstruire l'image originale, deux types d'erreurs sont alors quantifi�s, l'erreur en perte $E_p\pa{\alpha}$ et l'erreur de recouvrement $E_r\pa{\alpha}$~: - \begin{eqnarray} - \begin{array}{ccc} - E_p\pa{\alpha} = \frac{ \summyone{x,y} \; \abs{ N_\alpha \pa{x,y} - i\pa{x,y} } } - { \summyone{x,y} \; i\pa{x,y} } && - E_r\pa{\alpha} = \frac{ \summyone{M_\alpha \pa{x,y} > 1} \; M_\alpha \pa{x,y} - i\pa{x,y} } - { \summyone{x,y} \; i\pa{x,y} } - \end{array} - \end{eqnarray} - -L'erreur globale est dfinie comme la moyenne des deux~: $E\pa{\alpha} = \frac{1}{2} \pa{ E_r\pa{\alpha} + E_p\pa{\alpha}}$. Le seuil $\alpha$ est choisi comme la limite de la suite $\alpha_t$ dfinie par~: + \begin{eqnarray} + \begin{array}{ccc} + E_p\pa{\alpha} = \frac{ \summyone{x,y} \; \abs{ N_\alpha \pa{x,y} - i\pa{x,y} } } + { \summyone{x,y} \; i\pa{x,y} } && + E_r\pa{\alpha} = \frac{ \summyone{M_\alpha \pa{x,y} > 1} \; M_\alpha \pa{x,y} - i\pa{x,y} } + { \summyone{x,y} \; i\pa{x,y} } + \end{array} + \end{eqnarray} + +L'erreur globale est d�finie comme la moyenne des deux~: $E\pa{\alpha} = \frac{1}{2} \pa{ E_r\pa{\alpha} + E_p\pa{\alpha}}$. Le seuil $\alpha$ est choisi comme la limite de la suite $\alpha_t$ d�finie par~: - \begin{eqnarray} - \alpha_0 &\in& \cro{0,1} \\ - \alpha_{t+1} &=& \alpha_t + \cro{ \indicatrice{ E\pa{\alpha_t} \supegal E\pa{\alpha_{t-1}}} - - \indicatrice{ E\pa{\alpha_t} < E\pa{\alpha_{t-1}}} } - \; \tanh \pa{ \frac{ E\pa{\alpha} }{2} } - \end{eqnarray} - -Les images obtenus aprs ce seuillage ne sont pas encore parfaites (voir figure~\ref{squelette_su_gabor_3}), elles sont ensuite nettoyes des trop petits segments en tenant compte des attributs tels que la surface et la longueur, leur prsence sur plus d'une image. + \begin{eqnarray} + \alpha_0 &\in& \cro{0,1} \\ + \alpha_{t+1} &=& \alpha_t + \cro{ \indicatrice{ E\pa{\alpha_t} \supegal E\pa{\alpha_{t-1}}} - + \indicatrice{ E\pa{\alpha_t} < E\pa{\alpha_{t-1}}} } + \; \tanh \pa{ \frac{ E\pa{\alpha} }{2} } + \end{eqnarray} + +Les images obtenus apr�s ce seuillage ne sont pas encore parfaites (voir figure~\ref{squelette_su_gabor_3}), elles sont ensuite nettoy�es des trop petits segments en tenant compte des attributs tels que la surface et la longueur, leur pr�sence sur plus d'une image. - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \includegraphics[height=3cm, width=14cm]{\filext{../squelette/image/su5}} - \\ \hline \end{tabular}$$ - \caption{ Rponses seuilles des quatres filtres de Gabor - correspondant aux quatres directions verticale, horizontale, - diagonales, figure extraite de \citeindexfig{Su2003}. - Les petits segments entours vont tre supprims par le nettoyage.} - \label{squelette_su_gabor_3} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \includegraphics[height=3cm, width=14cm]{\filext{../squelette/image/su5}} + \\ \hline \end{tabular}$$ + \caption{ R�ponses seuill�es des quatres filtres de Gabor + correspondant aux quatres directions verticale, horizontale, + diagonales, figure extraite de \citeindexfig{Su2003}. + Les petits segments entour�s vont �tre supprim�s par le nettoyage.} + \label{squelette_su_gabor_3} + \end{figure} @@ -1169,44 +1169,44 @@ \section{Squelettisation d'une forme vectorielle} -Ces mthodes diffrent des prcdentes car elles utilisent uniquement le contour de la forme squelettiser, ce dernier tant dcrit comme une succession de segments. Les mthodes d'rosion, quant elles, partent d'une forme dcrite par un ensemble de pixels connexes. Il est bien sr possible d'appliquer les mthodes vectorielles ces ensembles de pixels en les rduisant leur contour. Les pixels du contour obtenus sont alors les sommets des segments. Afin de rduire la complexit des mthodes vectorielles, l'ensemble des pixels formant le contour est souvent rduit. Les sommets utiliss pour la squelettisation sont rpartis gale distance le long de la courbe (voir figure~\ref{squelette_voronoi}) ou celle-ci peut-tre vectorise de manire regrouper ensemble des segments successifs colinaires (voir figure~\ref{squelette_reseau_bissecteur}). +Ces m�thodes diff�rent des pr�c�dentes car elles utilisent uniquement le contour de la forme � squelettiser, ce dernier �tant d�crit comme une succession de segments. Les m�thodes d'�rosion, quant � elles, partent d'une forme d�crite par un ensemble de pixels connexes. Il est bien s�r possible d'appliquer les m�thodes vectorielles � ces ensembles de pixels en les r�duisant � leur contour. Les pixels du contour obtenus sont alors les sommets des segments. Afin de r�duire la complexit� des m�thodes vectorielles, l'ensemble des pixels formant le contour est souvent r�duit. Les sommets utilis�s pour la squelettisation sont r�partis � �gale distance le long de la courbe (voir figure~\ref{squelette_voronoi}) ou celle-ci peut-�tre vectoris�e de mani�re � regrouper ensemble des segments successifs colin�aires (voir figure~\ref{squelette_reseau_bissecteur}). -\subsection{Diagramme de Vorono} -\indexfr{Vorono}\indexfr{mdiatrice} +\subsection{Diagramme de Vorono�} +\indexfr{Vorono�}\indexfr{m�diatrice} \label{ske_voronoi_ske_ske} - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=8cm, width=12cm] - {\filext{../squelette/image/ske_voronoi}}\end{array}$}$$ - \caption{ Squelettisation partir d'un graphe de Vorono, figure extraite de~\citeindexfig{Attali1995}.} - \label{squelette_voronoi} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=8cm, width=12cm] + {\filext{../squelette/image/ske_voronoi}}\end{array}$}$$ + \caption{ Squelettisation � partir d'un graphe de Vorono�, figure extraite de~\citeindexfig{Attali1995}.} + \label{squelette_voronoi} + \end{figure} -\indexfr{axe mdian} +\indexfr{axe m�dian} -La dfinition de l'axe mdian (\ref{ske_def_axe_med}) fait intervenir le centre de boules tangentes en au moins deux points du contour de cette forme. Par consquent, en considrant deux points du contour, le squelette est susceptible de passer par la mdiatrice de ces deux points. Le diagramme de Vorono est justement un ensemble de mdiatrices. Il suffit alors de disposer des points parpills sur le contour, de dterminer le diagramme de Vorono puis de l'laguer pour ne garder que les segments de mdiatrice emprunts par le squelette. Cette ide a t developpe dans~\citeindex{Ogniewicz1992}, \citeindex{Ogniewicz1995}, ou encore~\citeindex{Attali1995}. La figure~\ref{squelette_voronoi} montre que le squelette est plus prcis lorsque les points sur le contour sont plus nombreux mais videmment plus lent calculer. Une fois que le diagramme de Vorono associ une forme est construit, le squelette est tout simplement constitu des seuls segments inclus dans l'intrieur de cette forme. +La d�finition de l'axe m�dian (\ref{ske_def_axe_med}) fait intervenir le centre de boules tangentes en au moins deux points du contour de cette forme. Par cons�quent, en consid�rant deux points du contour, le squelette est susceptible de passer par la m�diatrice de ces deux points. Le diagramme de Vorono� est justement un ensemble de m�diatrices. Il suffit alors de disposer des points �parpill�s sur le contour, de d�terminer le diagramme de Vorono� puis de l'�laguer pour ne garder que les segments de m�diatrice emprunt�s par le squelette. Cette id�e a �t� developp�e dans~\citeindex{Ogniewicz1992}, \citeindex{Ogniewicz1995}, ou encore~\citeindex{Attali1995}. La figure~\ref{squelette_voronoi} montre que le squelette est plus pr�cis lorsque les points sur le contour sont plus nombreux mais �videmment plus lent � calculer. Une fois que le diagramme de Vorono� associ� � une forme est construit, le squelette est tout simplement constitu� des seuls segments inclus dans l'int�rieur de cette forme. -\subsection{Rseau bissecteur} -\indexfrr{rseau}{bissecteur} +\subsection{R�seau bissecteur} +\indexfrr{r�seau}{bissecteur} \label{ske_reseau_bissecteur} -La squelettisation par rseau bissecteur est assez proche de celle developpe partir d'un diagramme de Vorono (voir~\citeindex{Cloppet2000}). Une forme est dfinie par son contour lui-mme dfini comme une succession d'artes. Les premiers n\oe uds du rseau bissecteur sont forms par les bissectrices de chaque angle. Lorsque deux bissectrices s'interceptent, elles forment un angle dont on peut nouveau tracer la bissectrice (voir figure~\ref{squelette_reseau_bissecteur}). Ce graphe est similaire celui obtenu par le diagramme de Vorono, le squelette est simplement une sous-partie de ce diagramme. +La squelettisation par r�seau bissecteur est assez proche de celle developp�e � partir d'un diagramme de Vorono� (voir~\citeindex{Cloppet2000}). Une forme est d�finie par son contour lui-m�me d�fini comme une succession d'ar�tes. Les premiers n\oe uds du r�seau bissecteur sont form�s par les bissectrices de chaque angle. Lorsque deux bissectrices s'interceptent, elles forment un angle dont on peut � nouveau tracer la bissectrice (voir figure~\ref{squelette_reseau_bissecteur}). Ce graphe est similaire � celui obtenu par le diagramme de Vorono�, le squelette est simplement une sous-partie de ce diagramme. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=4cm] - {\filext{../squelette/image/ske_bis}}\end{array}$}$$ - \caption{ Squelettisation partir d'un rseau bissecteur, figure extraite de~\citeindexfig{Cloppet2000}.} - \label{squelette_reseau_bissecteur} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=4cm] + {\filext{../squelette/image/ske_bis}}\end{array}$}$$ + \caption{ Squelettisation � partir d'un r�seau bissecteur, figure extraite de~\citeindexfig{Cloppet2000}.} + \label{squelette_reseau_bissecteur} + \end{figure} @@ -1224,7 +1224,7 @@ \subsection{R %------------------------------------------------------------------------------------------------------------ -\section{Affinement du rsultat} +\section{Affinement du r�sultat} %------------------------------------------------------------------------------------------------------------ @@ -1238,35 +1238,35 @@ \subsection{Nettoyage des barbules} \indexfr{barbule} -\indexfr{axe mdian}\indexfr{composante connexe} +\indexfr{axe m�dian}\indexfr{composante connexe} -Le squelette d'une image est sa reprsentation en "fil de fer", celle-ci peut-tre plus ou moins prcise. Aprs la squelettisation, un grand nombre de petits segments du squelette peuvent s'avrer non pertinents comme le montre la figure~\ref{squelette_barbule}. Le squelette d'un mot manuscrit devrait tre proche du mouvement du stylo. Toutefois, la squelettisation conserve un ensemble de petits segments sans importance dont la suppression ne modifie pas le nombre de composantes connexes. Nettoy de ces barbules, le squelette est en quelque sorte plus "lisible" mais il n'y a pas de rgles pour ce nettoyage, il dpend la fois de l'algorithme de squelettisation employ et de la prcision dsire. +Le squelette d'une image est sa repr�sentation en "fil de fer", celle-ci peut-�tre plus ou moins pr�cise. Apr�s la squelettisation, un grand nombre de petits segments du squelette peuvent s'av�rer non pertinents comme le montre la figure~\ref{squelette_barbule}. Le squelette d'un mot manuscrit devrait �tre proche du mouvement du stylo. Toutefois, la squelettisation conserve un ensemble de petits segments sans importance dont la suppression ne modifie pas le nombre de composantes connexes. Nettoy� de ces barbules, le squelette est en quelque sorte plus "lisible" mais il n'y a pas de r�gles pour ce nettoyage, il d�pend � la fois de l'algorithme de squelettisation employ� et de la pr�cision d�sir�e. - \begin{figure}[ht] - $$\begin{tabular}{|c|c|}\hline - \includegraphics[height=4cm, width=4cm]{\filext{../squelette/image/ske_barbule2}}& - \includegraphics[height=4cm, width=4cm]{\filext{../squelette/image/ske_barbule1}} \\ \hline - \end{tabular}$$ - \caption{ La seconde image reprsente le squelette de la premire image nettoy de ses barbules, - la seconde image contient simplement l'information pertinente.} - \label{squelette_barbule} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|}\hline + \includegraphics[height=4cm, width=4cm]{\filext{../squelette/image/ske_barbule2}}& + \includegraphics[height=4cm, width=4cm]{\filext{../squelette/image/ske_barbule1}} \\ \hline + \end{tabular}$$ + \caption{ La seconde image repr�sente le squelette de la premi�re image nettoy� de ses barbules, + la seconde image contient simplement l'information pertinente.} + \label{squelette_barbule} + \end{figure} -Ce nettoyage peut s'appuyer sur des critres gomtriques tel que celui propos dans \citeindex{Jang1992} qui mesure le rapport entre la forme $F$ squelettiser et l'aire forme par l'ensemble $G$ des boules maximales incluses dans $F$ dont le centre appartient au squelette. Par dfinition, $G \subset F$, le critre de \citeindex{Jang1992} est gal ~: +Ce nettoyage peut s'appuyer sur des crit�res g�om�triques tel que celui propos� dans \citeindex{Jang1992} qui mesure le rapport entre la forme $F$ � squelettiser et l'aire form�e par l'ensemble $G$ des boules maximales incluses dans $F$ dont le centre appartient au squelette. Par d�finition, $G \subset F$, le crit�re de \citeindex{Jang1992} est �gal �~: - \begin{eqnarray} - c_J = \frac{ aire\pa{G} } { aire \pa{F} } \infegal 1 - \end{eqnarray} - -Le squelette est rogn ses extrmits tant que le critre $c_J$ est suprieur un certain seuil. Un autre critre provient de \citeindex{Huang2003} qui compare les longueurs (en pixel) du squelette et du contour~: + \begin{eqnarray} + c_J = \frac{ aire\pa{G} } { aire \pa{F} } \leqslant 1 + \end{eqnarray} + +Le squelette est rogn� � ses extr�mit�s tant que le crit�re $c_J$ est sup�rieur � un certain seuil. Un autre crit�re provient de \citeindex{Huang2003} qui compare les longueurs (en pixel) du squelette et du contour~: - \begin{eqnarray} - c_H = \frac{ aire\pa{squelette} } { aire \pa{contour} } \infegal 1 - \label{squelette_haung_critere} - \end{eqnarray} + \begin{eqnarray} + c_H = \frac{ aire\pa{squelette} } { aire \pa{contour} } \leqslant 1 + \label{squelette_haung_critere} + \end{eqnarray} @@ -1278,19 +1278,19 @@ \subsection{Squelette d'une boucle} \indexfrr{squelette}{boucle} -Cet article \citeindex{Huang2003} propose galement d'effectuer l'rosion de la forme tant que le critre $c_H$ (\ref{squelette_haung_critere}) est suprieur un certain seuil. Par consquent, pour un seuil bien ajust, les zones peu paisses sont bien squelettises tandis que les zones paisses sont un compromis entre squelette et contour, la figure~\ref{squelette_barbule_six_stop} montre le rsultat qu'il est possible d'obtenir avec ce genre de mthode. Le squelette du chiffre "6" est obtenu avec sa boucle bien que celle-ci soit comble de pixels noirs. +Cet article \citeindex{Huang2003} propose �galement d'effectuer l'�rosion de la forme tant que le crit�re $c_H$ (\ref{squelette_haung_critere}) est sup�rieur � un certain seuil. Par cons�quent, pour un seuil bien ajust�, les zones peu �paisses sont bien squelettis�es tandis que les zones �paisses sont un compromis entre squelette et contour, la figure~\ref{squelette_barbule_six_stop} montre le r�sultat qu'il est possible d'obtenir avec ce genre de m�thode. Le squelette du chiffre "6" est obtenu avec sa boucle bien que celle-ci soit combl�e de pixels noirs. - \begin{figure}[ht] - $$\begin{tabular}{|c|}\hline - \includegraphics[height=2cm, width=1.2cm]{\filext{../squelette/image/sixstop}} - \\ \hline \end{tabular}$$ - \caption{ Le squelette de cet image est une tape intermdiaire entre le contour et le vritable squelette. - Ce rsultat est pourtant proche de celui qu'il faut obtenir puisque la boucle du chiffre "6" - disparat avec un algorithme de squelettisation classique.} - \label{squelette_barbule_six_stop} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|}\hline + \includegraphics[height=2cm, width=1.2cm]{\filext{../squelette/image/sixstop}} + \\ \hline \end{tabular}$$ + \caption{ Le squelette de cet image est une �tape interm�diaire entre le contour et le v�ritable squelette. + Ce r�sultat est pourtant proche de celui qu'il faut obtenir puisque la boucle du chiffre "6" + dispara�t avec un algorithme de squelettisation classique.} + \label{squelette_barbule_six_stop} + \end{figure} @@ -1302,41 +1302,41 @@ \subsection{Squelette d'une boucle} -\subsection{Amliorer la reprsentation des intersections} +\subsection{Am�liorer la repr�sentation des intersections} \indexfrr{instersection}{squelette} \indexfrr{squelette}{instersection} \indexfrr{squelette}{embranchement} -Lors de l'extraction du squelette d'un caractre, les intersections entre deux segments de droites sont souvent scindes comme le montre la figure~\ref{squelette_zhong_intersection}b. La mthode dveloppe dans \citeindex{Zhong1999} propose de corriger le squelette obtenu en figure~\ref{squelette_zhong_intersection}b pour aboutir celui~\ref{squelette_zhong_intersection}c. La mthode est applique sur des caractres chinois qui prsentent souvent des intersections croisant un trait horizontal et un trait vertical. En explorant l'image initiale selon des lignes parallles aux diagonales, il est possible de marquer quatre types de points caractristiques d'une intersection (voir figure~\ref{squelette_zhong_intersection}d qui indique le dbut ou la fin d'un embranchement). - - \begin{figure}[ht] - $$\begin{tabular}{|c|c|c|c|} \hline - \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross1}} & - \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross2}} & - \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross3}} & - \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross4}} \\ - $(a)$ & $(b)$ & $(c)$ & $(d)$ - \\ \hline \end{tabular}$$ - \caption{ Correction du squelette afin de mieux reprsenter les intersections, figures extraites - de~\citeindexfig{Zhong1999}). L'image~(\textit{d}) montre les quatre points cardinaux - d'une intersection. L'objectif de la mthode est de passer l'image~(\textit{b}) - l'image~(\textit{c}). Avant l'rosion, des points cardinaux sont dtects, symbolisant - chacun un embranchement. Quatre d'entre eux la figure~(\textit{d}) - permettent de reprer une intersection, le squelette l'intrieur de la zone encadre par - ces quatre points ne sera pas issu d'une rosion mais deux droites joignant les quatre extrmits - du squelette situes aux limites de cette zone. } - \label{squelette_zhong_intersection} - \end{figure} +Lors de l'extraction du squelette d'un caract�re, les intersections entre deux segments de droites sont souvent scind�es comme le montre la figure~\ref{squelette_zhong_intersection}b. La m�thode d�velopp�e dans \citeindex{Zhong1999} propose de corriger le squelette obtenu en figure~\ref{squelette_zhong_intersection}b pour aboutir � celui~\ref{squelette_zhong_intersection}c. La m�thode est appliqu�e sur des caract�res chinois qui pr�sentent souvent des intersections croisant un trait horizontal et un trait vertical. En explorant l'image initiale selon des lignes parall�les aux diagonales, il est possible de marquer quatre types de points caract�ristiques d'une intersection (voir figure~\ref{squelette_zhong_intersection}d qui indique le d�but ou la fin d'un embranchement). + + \begin{figure}[ht] + $$\begin{tabular}{|c|c|c|c|} \hline + \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross1}} & + \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross2}} & + \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross3}} & + \includegraphics[height=3cm, width=3cm]{\filext{../squelette/image/cross4}} \\ + $(a)$ & $(b)$ & $(c)$ & $(d)$ + \\ \hline \end{tabular}$$ + \caption{ Correction du squelette afin de mieux repr�senter les intersections, figures extraites + de~\citeindexfig{Zhong1999}). L'image~(\textit{d}) montre les quatre points cardinaux + d'une intersection. L'objectif de la m�thode est de passer l'image~(\textit{b}) + � l'image~(\textit{c}). Avant l'�rosion, des points cardinaux sont d�tect�s, symbolisant + chacun un embranchement. Quatre d'entre eux � la figure~(\textit{d}) + permettent de rep�rer une intersection, le squelette � l'int�rieur de la zone encadr�e par + ces quatre points ne sera pas issu d'une �rosion mais deux droites joignant les quatre extr�mit�s + du squelette situ�es aux limites de cette zone. } + \label{squelette_zhong_intersection} + \end{figure} \indexfr{run-length} \indexfrr{squelette}{divergence} \indexfrr{squelette}{convergence} -Le squelette est alors corrig en effaant tout d'abord la partie incluse entre ces quatre points puis en faisant converger vers un mme et unique point les quatre extremits du squelette le reliant cette zone. +Le squelette est alors corrig� en effa�ant tout d'abord la partie incluse entre ces quatre points puis en faisant converger vers un m�me et unique point les quatre extremit�s du squelette le reliant � cette zone. -Les points caractristiques sont dtects l'aide des "run-length" dfinis dans l'article comme des segments de points contigs ou ensembles de pixels noirs contigs positionns sur la mme ligne. Les embranchements sont dtects en tudiant le nombre de "run-length" et leur chevauchement. Un "run-length" chevauchant deux "run-length" de la ligne suivante dsignent une divergence. La configuration oppose dsigne une convergence. +Les points caract�ristiques sont d�tect�s � l'aide des "run-length" d�finis dans l'article comme des segments de points contig�s ou ensembles de pixels noirs contig�s positionn�s sur la m�me ligne. Les embranchements sont d�tect�s en �tudiant le nombre de "run-length" et leur chevauchement. Un "run-length" chevauchant deux "run-length" de la ligne suivante d�signent une divergence. La configuration oppos�e d�signe une convergence. @@ -1346,47 +1346,47 @@ \subsection{Am -\subsection{Modliser les intersections dans les caractres} +\subsection{Mod�liser les intersections dans les caract�res} \indexfrr{intersection}{squelette} -\indexfr{reconstruction du trac} -\indexfrr{trac}{reconstruction} +\indexfr{reconstruction du trac�} +\indexfrr{trac�}{reconstruction} \label{squelette_modelisation_intersection_modele} -L'article \citeindex{L'Homer2000} propose une modlisation intressante du squelette. Cet article s'intresse tout particulirement la squelettisation de caractres manuscrits et cherche extraire un squelette dcrit comme une fonction dpendant du temps et proche de l'volution du stylo sur la feuille de papier. L'auteur construit un modle permettant de dcomposer le trac d'une lettre sous forme d'arcs continus et rguliers (la drive est borne) reprsents dans la troisime ligne de la figure~\ref{squelette_lhomer_intersection}. +L'article \citeindex{L'Homer2000} propose une mod�lisation int�ressante du squelette. Cet article s'int�resse tout particuli�rement � la squelettisation de caract�res manuscrits et cherche � extraire un squelette d�crit comme une fonction d�pendant du temps et proche de l'�volution du stylo sur la feuille de papier. L'auteur construit un mod�le permettant de d�composer le trac� d'une lettre sous forme d'arcs continus et r�guliers (la d�riv�e est born�e) repr�sent�s dans la troisi�me ligne de la figure~\ref{squelette_lhomer_intersection}. - \begin{figure}[ht] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=6cm, width=8cm]{\filext{../squelette/image/lhomer}} - \\ \hline \end{tabular}$$ - \caption{ Figure extraite de~\citeindexfig{L'Homer2000} reprsentant la modlisation de diffrentes lettres "a". - Les arcs formant ces lettres peuvent se rejoindre dans un point de rebroussement (premier colonne), - s'intercepter comme pour la barre d'un "T" (seconde colonne), se croiser (dernire colonne).} - \label{squelette_lhomer_intersection} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=6cm, width=8cm]{\filext{../squelette/image/lhomer}} + \\ \hline \end{tabular}$$ + \caption{ Figure extraite de~\citeindexfig{L'Homer2000} repr�sentant la mod�lisation de diff�rentes lettres "a". + Les arcs formant ces lettres peuvent se rejoindre dans un point de rebroussement (premier colonne), + s'intercepter comme pour la barre d'un "T" (seconde colonne), se croiser (derni�re colonne).} + \label{squelette_lhomer_intersection} + \end{figure} -\indexfrr{arc}{rgulier}\indexfr{jointure}\indexfr{croisement}\indexfrr{point}{rebroussement} +\indexfrr{arc}{r�gulier}\indexfr{jointure}\indexfr{croisement}\indexfrr{point}{rebroussement} -L'algorithme dtecte les arcs rguliers (premire ligne de la figure~\ref{squelette_lhomer_intersection}) ainsi que leurs extremits devant tre jointes. L'attrait de cet article rside essentiellement dans la faon de raliser ces jointures. Certains croisements reprsentent deux traits qui se rejoignent un point de rebroussement (premire lettre~"a" de la figure~\ref{squelette_lhomer_intersection}), d'autres reprsentent des traits qui se croisent (dernire lettre~"a" de la figure~\ref{squelette_lhomer_intersection}). La liste des modles de jointures est illustre par la figure~\ref{squelette_lhomer_intersection_modele}. Le modle le plus probable tend conserver les courbes les plus rgulires possibles. +L'algorithme d�tecte les arcs r�guliers (premi�re ligne de la figure~\ref{squelette_lhomer_intersection}) ainsi que leurs extremit�s devant �tre jointes. L'attrait de cet article r�side essentiellement dans la fa�on de r�aliser ces jointures. Certains croisements repr�sentent deux traits qui se rejoignent � un point de rebroussement (premi�re lettre~"a" de la figure~\ref{squelette_lhomer_intersection}), d'autres repr�sentent des traits qui se croisent (derni�re lettre~"a" de la figure~\ref{squelette_lhomer_intersection}). La liste des mod�les de jointures est illustr�e par la figure~\ref{squelette_lhomer_intersection_modele}. Le mod�le le plus probable tend � conserver les courbes les plus r�guli�res possibles. - \begin{figure}[ht] - $$\begin{tabular}{|c|c|} \hline - \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/lhomer3}} & - \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/lhomer4}} - \\ \hline \end{tabular}$$ - \caption{ Figure extraite de~\citeindexfig{L'Homer2000} reprsentant les diffrentes - types des jointures entre arcs. - Un mme trait peut cacher deux passages du stylo et c'est ce que ces modles tentent de dtecter. - La premire image prsente les huit possibilits de branchements lorsque trois arcs se croisent. - La seconde image prsente les seize possibilits de branchements lorsque quatre arcs se croisent.} - \label{squelette_lhomer_intersection_modele} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|} \hline + \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/lhomer3}} & + \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/lhomer4}} + \\ \hline \end{tabular}$$ + \caption{ Figure extraite de~\citeindexfig{L'Homer2000} repr�sentant les diff�rentes + types des jointures entre arcs. + Un m�me trait peut cacher deux passages du stylo et c'est ce que ces mod�les tentent de d�tecter. + La premi�re image pr�sente les huit possibilit�s de branchements lorsque trois arcs se croisent. + La seconde image pr�sente les seize possibilit�s de branchements lorsque quatre arcs se croisent.} + \label{squelette_lhomer_intersection_modele} + \end{figure} -Cette reprsentation du squelette est conue pour des caractres manuscrits et propose une description du squelette plus volue que les prcdentes. La dtection de figures particulires incluses dans les caractres se contente souvent des boucles. Cet article propose une description sous forme d'arcs rguliers ainsi que les types d'intersections les reliant entre eux. Il pourrait tre intressant d'tudier l'apport de telles caractristiques pour la reconnaissance des caractres. +Cette repr�sentation du squelette est con�ue pour des caract�res manuscrits et propose une description du squelette plus �volu�e que les pr�c�dentes. La d�tection de figures particuli�res incluses dans les caract�res se contente souvent des boucles. Cet article propose une description sous forme d'arcs r�guliers ainsi que les types d'intersections les reliant entre eux. Il pourrait �tre int�ressant d'�tudier l'apport de telles caract�ristiques pour la reconnaissance des caract�res. -Cette mthode propose la fois une reprsentation paramtre du squelette ainsi qu'une amlioration de la description des intersections. Cette reprsentation sous forme d'arcs est guide par la forme des caractres et mme si la plupart des mthodes abordes dans ce document ont traits ce domaine, il est possible d'adopter des reprsentations paramtriques du squelette plus simple mais plus gnrales, par exemple, sous forme de droites. Ce processus s'appelle la vectorisation et est prsente dans les paragraphes qui suivent. +Cette m�thode propose � la fois une repr�sentation param�tr�e du squelette ainsi qu'une am�lioration de la description des intersections. Cette repr�sentation sous forme d'arcs est guid�e par la forme des caract�res et m�me si la plupart des m�thodes abord�es dans ce document ont traits � ce domaine, il est possible d'adopter des repr�sentations param�triques du squelette plus simple mais plus g�n�rales, par exemple, sous forme de droites. Ce processus s'appelle la vectorisation et est pr�sent�e dans les paragraphes qui suivent. @@ -1408,134 +1408,134 @@ \subsection{Vectorisation du squelette} \indexfrr{vectorisation}{squelette} -Le squelette obtenu est une suite de pixels. Il peut tre intressant de le vectoriser, autrement dit de le dcrire l'aide de segments de droites. Cette vectorisation peut consister en la recherche des droites qui auraient permis le trac du squelette comme le suggre les thses~\citeindex{Reveills1991} et \citeindex{Vittone1999}. Ces travaux utilisent la dfinition suivante (extraite de~\citeindex{Reveills1991})~: - - - - \begin{xdefinition}{droite discrte} - \indexfrr{droite}{discrte} - \label{squelette_droite_discrete_def} - - Soient $\pa{a,b,r} \in \mathbb{Z}^2$ et $\omega \in \N^*$. Une droite de vecteur directeur $\pa{b,a}$ - avec $\pa{a,b} \neq \pa{0,0}$ et $pgcd\pa{a,b}=1$, de paramtre de translation $r$ et d'paisseur - arithmtique $\omega$ est l'ensemble not $D\pa{a,b,r,\omega}$ des points $\pa{x,y} \in \mathbb{Z}^2$ - satisfaisant l'ingalit~: - - $$ - 0 \infegal ax + by + r < \omega - $$ - - De plus~: - - $$ - \begin{tabular}{lcl} - si $1 \infegal w \infegal max\pa{\abs{a},\abs{b}}$ & alors & - la droite n'est pas connexe et est dite dconnecte. \\ - si $\omega = max\pa{\abs{a},\abs{b}}$ & alors & la droite est 8-connexe. \\ - si $max\pa{\abs{a},\abs{b}} \infegal \omega \abs{a} + \abs{b}$ & alors & - la droite est 8-connexe et 4-connexe par moment. \\ - si $\omega = \abs{a} + \abs{b}$ & alors & la droite est 4-connexe. \\ - si $\omega > \abs{a} + \abs{b}$ & alors & la droite est paisse - \end{tabular} - $$ - - \end{xdefinition} - - -D'un point de vue plus pragmatique, l'article~\citeindex{Freeman1970} dcrit les trois proprits vrifies par le code de Freeman\indexfr{Freeman} d'une ligne $8$-connexe~: - - - \begin{enumerate} - \item Au plus deux directions peuvent tre prsentes dans le code et ne peuvent diffrer que d'une - unit modulo $8$. - \item Une des deux directions apparat toujours de manire isole. - \item La direction isole apparat de manire uniforme dans le code. - \end{enumerate} - -La premire proprit permet d'isoler les portions de code susceptibles de reprsenter des droites, les deux suivantes permettent d'estimer les paramtres de son quation. En effet, les occurences des deux directions intervenant dans la description mnent directement au vecteur directeur de la droite. Les algorithmes dvelopps dans~\citeindex{Vittone1999} ou~\citeindex{Breton2002} utilisent la dfinition~\ref{squelette_droite_discrete_def} et retrouvent les segments de droite au pixel prs comme le montre la figure~\ref{squelette_vector}~: la droite est recouverte par l'ensemble de pixels vectoriss par cette droite. - - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] - {\filext{../squelette/image/ske_vector}}\end{array}$}$$ - \caption{ Vectorisation d'un contour : figure extraite de~\citeindexfig{Breton2002}, le contour vectoris - est recouvert par l'ensemble de pixels formant le contour. } - \label{squelette_vector} - \end{figure} - - -Une autre mthode permet de vectoriser un arc quelconque de manire approche. Si cet arc est proche d'une droite alors la corde reliant ses deux extrmits est une bonne approximation (voir figure~\ref{squelette_vector_corde}). En revanche, si cet arc n'est pas une droite, la corde est une mauvaise approximation, l'arc est alors divis en deux parties dont l'extrmit commune est le point de l'arc le plus loign de sa corde. Ceci mne l'algorithme suivant~: - - - \begin{xalgorithm}{vectorisation approche} - \indexfrr{vectorisation}{approche}\label{algo_vecto_appro} - - Soit un arc $k$-connexe dfini par une suite de pixels $\vecteur{p_1}{p_n}$ vrifiant~: - - $$ - \forall i \in \intervalle{2}{n}, \; p_{i-1} \in V_k\pa{p_i} - $$ - - On calcule le critre $c$ dfini par~: - - $$ - c = \underset{i \in \intervalle{1}{n} }{\max} \; d\pa{p_i, D\pa{p_1,p_n} } - $$ - - o $d\pa{p_i, D\pa{p_1,p_n}}$ est la distance du point $p_i$ la droite passant par les points $p_1$ et - $p_n$. Si $c$ est trop grand (suprieur un seuil), alors l'arc est divis en deux parties - $\vecteur{p_1}{p_j}$ et $\vecteur{p_j}{p_n}$ o $p_j$ vrifie~: - - $$ - p_j \in \underset{i \in \intervalle{1}{n} }{\arg \max} \; d\pa{p_i, D\pa{p_1,p_n} } \\ - $$ - - L'arc est ainsi dcoup jusqu' ce que plus aucune division ne soit possible. - - \end{xalgorithm} +Le squelette obtenu est une suite de pixels. Il peut �tre int�ressant de le vectoriser, autrement dit de le d�crire � l'aide de segments de droites. Cette vectorisation peut consister en la recherche des droites qui auraient permis le trac� du squelette comme le sugg�re les th�ses~\citeindex{Reveill�s1991} et \citeindex{Vittone1999}. Ces travaux utilisent la d�finition suivante (extraite de~\citeindex{Reveill�s1991})~: + + + + \begin{xdefinition}{droite discr�te} + \indexfrr{droite}{discr�te} + \label{squelette_droite_discrete_def} + + Soient $\pa{a,b,r} \in \mathbb{Z}^2$ et $\omega \in \N^*$. Une droite de vecteur directeur $\pa{b,a}$ + avec $\pa{a,b} \neq \pa{0,0}$ et $pgcd\pa{a,b}=1$, de param�tre de translation $r$ et d'�paisseur + arithm�tique $\omega$ est l'ensemble not� $D\pa{a,b,r,\omega}$ des points $\pa{x,y} \in \mathbb{Z}^2$ + satisfaisant l'in�galit�~: + + $$ + 0 \leqslant ax + by + r < \omega + $$ + + De plus~: + + $$ + \begin{tabular}{lcl} + si $1 \leqslant w \leqslant max\pa{\abs{a},\abs{b}}$ & alors & + la droite n'est pas connexe et est dite d�connect�e. \\ + si $\omega = max\pa{\abs{a},\abs{b}}$ & alors & la droite est 8-connexe. \\ + si $max\pa{\abs{a},\abs{b}} \leqslant \omega \abs{a} + \abs{b}$ & alors & + la droite est 8-connexe et 4-connexe par moment. \\ + si $\omega = \abs{a} + \abs{b}$ & alors & la droite est 4-connexe. \\ + si $\omega > \abs{a} + \abs{b}$ & alors & la droite est �paisse + \end{tabular} + $$ + + \end{xdefinition} + + +D'un point de vue plus pragmatique, l'article~\citeindex{Freeman1970} d�crit les trois propri�t�s v�rifi�es par le code de Freeman\indexfr{Freeman} d'une ligne $8$-connexe~: + + + \begin{enumerate} + \item Au plus deux directions peuvent �tre pr�sentes dans le code et ne peuvent diff�rer que d'une + unit� modulo $8$. + \item Une des deux directions appara�t toujours de mani�re isol�e. + \item La direction isol�e appara�t de mani�re uniforme dans le code. + \end{enumerate} + +La premi�re propri�t� permet d'isoler les portions de code susceptibles de repr�senter des droites, les deux suivantes permettent d'estimer les param�tres de son �quation. En effet, les occurences des deux directions intervenant dans la description m�nent directement au vecteur directeur de la droite. Les algorithmes d�velopp�s dans~\citeindex{Vittone1999} ou~\citeindex{Breton2002} utilisent la d�finition~\ref{squelette_droite_discrete_def} et retrouvent les segments de droite au pixel pr�s comme le montre la figure~\ref{squelette_vector}~: la droite est recouverte par l'ensemble de pixels vectoris�s par cette droite. + + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=4cm] + {\filext{../squelette/image/ske_vector}}\end{array}$}$$ + \caption{ Vectorisation d'un contour : figure extraite de~\citeindexfig{Breton2002}, le contour vectoris� + est recouvert par l'ensemble de pixels formant le contour. } + \label{squelette_vector} + \end{figure} + + +Une autre m�thode permet de vectoriser un arc quelconque de mani�re approch�e. Si cet arc est proche d'une droite alors la corde reliant ses deux extr�mit�s est une bonne approximation (voir figure~\ref{squelette_vector_corde}). En revanche, si cet arc n'est pas une droite, la corde est une mauvaise approximation, l'arc est alors divis� en deux parties dont l'extr�mit� commune est le point de l'arc le plus �loign� de sa corde. Ceci m�ne � l'algorithme suivant~: + + + \begin{xalgorithm}{vectorisation approch�e} + \indexfrr{vectorisation}{approch�e}\label{algo_vecto_appro} + + Soit un arc $k$-connexe d�fini par une suite de pixels $\vecteur{p_1}{p_n}$ v�rifiant~: + + $$ + \forall i \in \intervalle{2}{n}, \; p_{i-1} \in V_k\pa{p_i} + $$ + + On calcule le crit�re $c$ d�fini par~: + + $$ + c = \underset{i \in \intervalle{1}{n} }{\max} \; d\pa{p_i, D\pa{p_1,p_n} } + $$ + + o� $d\pa{p_i, D\pa{p_1,p_n}}$ est la distance du point $p_i$ � la droite passant par les points $p_1$ et + $p_n$. Si $c$ est trop grand (sup�rieur � un seuil), alors l'arc est divis� en deux parties + $\vecteur{p_1}{p_j}$ et $\vecteur{p_j}{p_n}$ o� $p_j$ v�rifie~: + + $$ + p_j \in \underset{i \in \intervalle{1}{n} }{\arg \max} \; d\pa{p_i, D\pa{p_1,p_n} } \\ + $$ + + L'arc est ainsi d�coup� jusqu'� ce que plus aucune division ne soit possible. + + \end{xalgorithm} \begin{xremark}{choix de $p_j$} -Dans le prcdent algorithme, $p_j$ est un des points les plus loigns de la corde. Si plusieurs points sont gale distance de la corde, le point $p_j$ choisi est de prfrence celui qui est le plus au centre de l'arc. +Dans le pr�c�dent algorithme, $p_j$ est un des points les plus �loign�s de la corde. Si plusieurs points sont � �gale distance de la corde, le point $p_j$ choisi est de pr�f�rence celui qui est le plus au centre de l'arc. \end{xremark} - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=8cm] - {\filext{../squelette/image/ske_veccorde}}\end{array}$}$$ - \caption{Vectorisation approche d'un arc~: l'arc est divis en deux parties dont l'extrmit - commune est le point de l'arc le plus loign de sa corde.} - \label{squelette_vector_corde} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=8cm] + {\filext{../squelette/image/ske_veccorde}}\end{array}$}$$ + \caption{Vectorisation approch�e d'un arc~: l'arc est divis� en deux parties dont l'extr�mit� + commune est le point de l'arc le plus �loign� de sa corde.} + \label{squelette_vector_corde} + \end{figure} \indexfr{composante connexe} -L'article \citeindex{Yeung1996} effectue ce travail dans le cadre de caractres chinois avec une mthode diffrente, les arcs du squelette sont scinds en plusieurs segments de droites en dtectant les changements abruptes d'orientation, le dcoupage n'est pas plus conditionn par des contraintes de distances mais des contraintes angulaires. Comme ce travail s'articule autour de la reconnaissance de caractres chinois comprenant souvent plusieurs composantes connexes, il propose galement une mthode pour connecter les squelettes de deux composantes connexes lorsque certains configurations apparaissent -~par exemple la lettre "T" dont la barre est dissocie de son support~-. +L'article \citeindex{Yeung1996} effectue ce travail dans le cadre de caract�res chinois avec une m�thode diff�rente, les arcs du squelette sont scind�s en plusieurs segments de droites en d�tectant les changements abruptes d'orientation, le d�coupage n'est pas plus conditionn� par des contraintes de distances mais des contraintes angulaires. Comme ce travail s'articule autour de la reconnaissance de caract�res chinois comprenant souvent plusieurs composantes connexes, il propose �galement une m�thode pour connecter les squelettes de deux composantes connexes lorsque certains configurations apparaissent -~par exemple la lettre "T" dont la barre est dissoci�e de son support~-. -Il est possible d'aller plus loin dans la vectorisation du squelette et c'est ce que propose l'article~\citeindex{Chakravarthy2003}. Des points caractristiques sont dtects et tiquets le long du squelette. La figure~\ref{squelette_vector_etendu} illustre cette description sous forme de graphe l'aide des cinq points caractristiques parmi douze possibles~: +Il est possible d'aller plus loin dans la vectorisation du squelette et c'est ce que propose l'article~\citeindex{Chakravarthy2003}. Des points caract�ristiques sont d�tect�s et �tiquet�s le long du squelette. La figure~\ref{squelette_vector_etendu} illustre cette description sous forme de graphe � l'aide des cinq points caract�ristiques parmi douze possibles~: - \begin{itemize} - \item A~: (Angle), deux arcs se rejoignent pour former un point de rebroussement. - \item B~: (Bump), milieu d'un arc courbe. - \item C~: (Cusp), jonction d'une courbe et d'un segment de droite. - \item T~: (T point), un segment de droite vient en intercepter un autre en son milieu. - \item P~: (Peck), un point de rebroussement situs au milieu d'un segment de droite. - \end{itemize} + \begin{itemize} + \item A~: (Angle), deux arcs se rejoignent pour former un point de rebroussement. + \item B~: (Bump), milieu d'un arc courbe. + \item C~: (Cusp), jonction d'une courbe et d'un segment de droite. + \item T~: (T point), un segment de droite vient en intercepter un autre en son milieu. + \item P~: (Peck), un point de rebroussement situ�s au milieu d'un segment de droite. + \end{itemize} - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=6cm, width=6cm] - {\filext{../squelette/image/skevect2}}\end{array}$}$$ - \caption{ Vectorisation tendue du squelette~: le squelette est vectoris et chaque arc - reoit une tiquette choisie (A,B,C,T,P).} - \label{squelette_vector_etendu} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=6cm, width=6cm] + {\filext{../squelette/image/skevect2}}\end{array}$}$$ + \caption{ Vectorisation �tendue du squelette~: le squelette est vectoris� et chaque arc + re�oit une �tiquette choisie (A,B,C,T,P).} + \label{squelette_vector_etendu} + \end{figure} -Ces descriptions complexes d'un squelette sous forme de graphe tendent tirer le plus d'informations possibles de l'image elle-mme de manire pouvoir, depuis cette structure, identifier la forme reprsente. Cette identification peut tre effectue grce une distance d'dition entre graphe, celui obtenu et un modle reprsentant au mieux la forme identifier. \indexfrr{distance}{dition} +Ces descriptions complexes d'un squelette sous forme de graphe tendent � tirer le plus d'informations possibles de l'image elle-m�me de mani�re � pouvoir, depuis cette structure, identifier la forme repr�sent�e. Cette identification peut �tre effectu�e gr�ce � une distance d'�dition entre graphe, celui obtenu et un mod�le repr�sentant au mieux la forme � identifier. \indexfrr{distance}{�dition} @@ -1554,25 +1554,25 @@ \subsection{Vectorisation et intersection} \indexfr{intersection} \indexfr{zones d'attraction} -L'article \citeindex{Abuhaiba1996} suppose que lorsque deux traits s'interceptent comme c'est souvent le cas pour une image contenant de l'criture, la zone de l'intersection est plus paisse que les zones o seul un trait apparat. La figure~\ref{squelette_vector_Abuhaiba1996} illustre ceci dans le cas d'une toile. Le squelette sans aucun post-traitement contient six points reliant trois branches. A partir d'une estimation de l'paisseur du trait (voir paragraphe~\ref{image_epaisseur_trace}, page~\pageref{image_epaisseur_trace}), il est possible de dterminer la zone d'attraction de l'intersection, zone o la carte des distances contient des valeurs suprieures cette paisseur. +L'article \citeindex{Abuhaiba1996} suppose que lorsque deux traits s'interceptent comme c'est souvent le cas pour une image contenant de l'�criture, la zone de l'intersection est plus �paisse que les zones o� seul un trait appara�t. La figure~\ref{squelette_vector_Abuhaiba1996} illustre ceci dans le cas d'une �toile. Le squelette sans aucun post-traitement contient six points reliant trois branches. A partir d'une estimation de l'�paisseur du trait (voir paragraphe~\ref{image_epaisseur_trace}, page~\pageref{image_epaisseur_trace}), il est possible de d�terminer la zone d'attraction de l'intersection, zone o� la carte des distances contient des valeurs sup�rieures � cette �paisseur. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=10cm] - {\filext{../squelette/image/abus}}\end{array}$}$$ - \caption{ Figure extraite de \citeindexfig{Abuhaiba1996}, l'paisseur du trait est plus pais aux intersections, - la vectorisation utilise ces zones tendues pour relier entre eux les arcs du squelette. La premire - image est l'image originale, la seconde reprsente le squelette, la troisime le squelette - vectoris.} - \label{squelette_vector_Abuhaiba1996} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=4cm, width=10cm] + {\filext{../squelette/image/abus}}\end{array}$}$$ + \caption{ Figure extraite de \citeindexfig{Abuhaiba1996}, l'�paisseur du trait est plus �pais aux intersections, + la vectorisation utilise ces zones �tendues pour relier entre eux les arcs du squelette. La premi�re + image est l'image originale, la seconde repr�sente le squelette, la troisi�me le squelette + vectoris�.} + \label{squelette_vector_Abuhaiba1996} + \end{figure} -Les six points reliant chacun trois branches appartiennent tous cette zone d'attraction et sont alors considrs comme tant une seule et mme intersection. La vectorisation de ce squelette associe donc les huit segments sortant de la zone d'attraction un seul point de recoupement. +Les six points reliant chacun trois branches appartiennent tous � cette zone d'attraction et sont alors consid�r�s comme �tant une seule et m�me intersection. La vectorisation de ce squelette associe donc les huit segments sortant de la zone d'attraction � un seul point de recoupement. \subsection{Squelette d'une image de texte} -Dans certains cas, certains a priori permettent d'amliorer la qualit du rsultat. C'est le cas de la mthode dveloppe au paragraphe~\ref{dla_squelette}, page~\pageref{dla_squelette} qui tient compte du fait que l'image est celle d'un ou plusieurs mots. L'hypothse simplificatrice est dans ce cas une relative homognit de l'paisseur de la forme squelettiser. +Dans certains cas, certains a priori permettent d'am�liorer la qualit� du r�sultat. C'est le cas de la m�thode d�velopp�e au paragraphe~\ref{dla_squelette}, page~\pageref{dla_squelette} qui tient compte du fait que l'image est celle d'un ou plusieurs mots. L'hypoth�se simplificatrice est dans ce cas une relative homog�n�it� de l'�paisseur de la forme � squelettiser. @@ -1581,81 +1581,81 @@ \subsection{Squelette d'une image de texte} \subsection{Appariement squelette - image originale} \indexfr{appariement} -\indexfr{graphme} - -La segmentation en graphmes utilise le squelette afin de dcouper l'image d'un mot en lettres ou morceaux de lettres. Il s'agit maintenant de propager ce dcoupage l'ensemble de l'image, donc de retrouver de quelle partie de la forme initiale un morceau est le squelette. Il est possible d'utiliser la carte de distance dfinie en~\ref{ske_def_cart_dist_def}. Pour chaque pixel noir, le pixel du squelette qui en est le plus proche apparat en se dplaant dans la carte de distance selon la plus grande pente. Il suffit de ritrer ce procd pour chaque pixel apparier. - -Une autre approche permet de rsoudre un problme plus gnral. On suppose que l'image contient un ensemble de pixels $\vecteur{p_1}{p_n}$ rpartis en $C$ classes. Chaque pixel $p_i$ est donc tiquet par $c_i \in \intervalle{1}{C}$. Pour un pixel $p$ quelconque de l'image, le point le plus proche dans la suite $\vecteur{p_1}{p_n}$ dtermine sa classe. L'algorithme qui suit permet d'effectuer cet tiquetage de manire analogue l'algorithme~\ref{ske_algo_cart_dist} utilis pour calculer une carte de distance. - - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=8cm] - {\filext{../squelette/image/ske_appari}}\end{array}$}$$ - \caption{ Appariement : les frontires (gris ple et fonc) dlimitent - les zones de pixels (noirs) apparis au mme morceau du squelette. } - \label{squelette_appariement} - \end{figure} - - - - \begin{xalgorithm}{appariement} - \indexfr{appariement} - \label{ske_algo_appariement} - - Soit $P=\vecteur{p_1}{p_n}$ une suite de points, et $\vecteur{c_1}{c_n} \in \intervalle{1}{C}^n$ - leurs classes associes. On note $D\pa{x,y}$ la distance au point le plus proche de l'ensemble $P$ - et $C\pa{x,y}$ la classe de ce point. On impose que $D\pa{x,y} = \infty$ si $\pa{x,y} \notin - \intervalle{1}{X} \times \intervalle{1}{Y}$. - - - \begin{xalgostep}{premire passe d'image} - \begin{xfor}{y}{1}{Y} - \begin{xfor}{x}{1}{X} - $ - \begin{array}{lll} - D\pa{x,y} &\longleftarrow& \left\{ \begin{array}{l} - 0 \text{ si } \exists i \text{ tel que } p_i = \pa{x,y} \\ - \min \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} - \sac v \in V_h\pa{k}} \text{ sinon} - \end{array}\right. \\ \\ - C\pa{x,y} &\longleftarrow& \left\{ \begin{array}{l} - \text{n'est pas dfini si } D\pa{x,y} = \infty \\ - c_i \text{ si } \exists i \text{ tel que } p_i = \pa{x,y} \\ - C\pa{\pa{x,y} - v^*} \\ - \quad \text{ o } v^* \in \arg \min - \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} \sac v \in V_h\pa{k}} - \text{ sinon} - \end{array}\right. - \end{array} - $ - \end{xfor} - \end{xfor} - \end{xalgostep} - - \begin{xalgostep}{seconde passe d'image} - \begin{xfor}{y}{Y}{1} - \begin{xfor}{x}{X}{1} - $ - \begin{array}{lll} - D\pa{x,y} &\longleftarrow& \min\acc{ D\pa{x,y}, \min \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} - \sac v \in V_b\pa{k}}} \\ \\ - C\pa{x,y} &\longleftarrow& C\pa{\pa{x,y} - v^*} \\ - && \quad \text{ o } v^* \in \arg \min - \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} - \sac v \in V_b\pa{k} \cup \acc{\pa{0,0}}} - \end{array} - $ - \end{xfor} - \end{xfor} - \end{xalgostep} - - - \end{xalgorithm} - - - - -\begin{xremark}{lien avec le diagramme de Vorono} -Si pour chaque point $p_i$, $c_i = i$, alors cet algorithme aboutit la construction de rgions de "Vorono" qui serviront construire le diagramme illustr dans la figure~\ref{squelette_voronoi}. Le squelette sera constitu des segments sparant deux pixels appartenant des rgions diffrentes.\indexfr{Vorono} +\indexfr{graph�me} + +La segmentation en graph�mes utilise le squelette afin de d�couper l'image d'un mot en lettres ou morceaux de lettres. Il s'agit maintenant de propager ce d�coupage � l'ensemble de l'image, donc de retrouver de quelle partie de la forme initiale un morceau est le squelette. Il est possible d'utiliser la carte de distance d�finie en~\ref{ske_def_cart_dist_def}. Pour chaque pixel noir, le pixel du squelette qui en est le plus proche appara�t en se d�pla�ant dans la carte de distance selon la plus grande pente. Il suffit de r�it�rer ce proc�d� pour chaque pixel � apparier. + +Une autre approche permet de r�soudre un probl�me plus g�n�ral. On suppose que l'image contient un ensemble de pixels $\vecteur{p_1}{p_n}$ r�partis en $C$ classes. Chaque pixel $p_i$ est donc �tiquet� par $c_i \in \intervalle{1}{C}$. Pour un pixel $p$ quelconque de l'image, le point le plus proche dans la suite $\vecteur{p_1}{p_n}$ d�termine sa classe. L'algorithme qui suit permet d'effectuer cet �tiquetage de mani�re analogue � l'algorithme~\ref{ske_algo_cart_dist} utilis� pour calculer une carte de distance. + + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=5cm, width=8cm] + {\filext{../squelette/image/ske_appari}}\end{array}$}$$ + \caption{ Appariement : les fronti�res (gris p�le et fonc�) d�limitent + les zones de pixels (noirs) appari�s au m�me morceau du squelette. } + \label{squelette_appariement} + \end{figure} + + + + \begin{xalgorithm}{appariement} + \indexfr{appariement} + \label{ske_algo_appariement} + + Soit $P=\vecteur{p_1}{p_n}$ une suite de points, et $\vecteur{c_1}{c_n} \in \intervalle{1}{C}^n$ + leurs classes associ�es. On note $D\pa{x,y}$ la distance au point le plus proche de l'ensemble $P$ + et $C\pa{x,y}$ la classe de ce point. On impose que $D\pa{x,y} = \infty$ si $\pa{x,y} \notin + \intervalle{1}{X} \times \intervalle{1}{Y}$. + + + \begin{xalgostep}{premi�re passe d'image} + \begin{xfor}{y}{1}{Y} + \begin{xfor}{x}{1}{X} + $ + \begin{array}{lll} + D\pa{x,y} &\longleftarrow& \left\{ \begin{array}{l} + 0 \text{ si } \exists i \text{ tel que } p_i = \pa{x,y} \\ + \min \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} + \sac v \in V_h\pa{k}} \text{ sinon} + \end{array}\right. \\ \\ + C\pa{x,y} &\longleftarrow& \left\{ \begin{array}{l} + \text{n'est pas d�fini si } D\pa{x,y} = \infty \\ + c_i \text{ si } \exists i \text{ tel que } p_i = \pa{x,y} \\ + C\pa{\pa{x,y} - v^*} \\ + \quad \text{ o� } v^* \in \arg \min + \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} \sac v \in V_h\pa{k}} + \text{ sinon} + \end{array}\right. + \end{array} + $ + \end{xfor} + \end{xfor} + \end{xalgostep} + + \begin{xalgostep}{seconde passe d'image} + \begin{xfor}{y}{Y}{1} + \begin{xfor}{x}{X}{1} + $ + \begin{array}{lll} + D\pa{x,y} &\longleftarrow& \min\acc{ D\pa{x,y}, \min \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} + \sac v \in V_b\pa{k}}} \\ \\ + C\pa{x,y} &\longleftarrow& C\pa{\pa{x,y} - v^*} \\ + && \quad \text{ o� } v^* \in \arg \min + \acc{ D\cro{ \pa{x,y} - v} + M_{v_x,v_y} + \sac v \in V_b\pa{k} \cup \acc{\pa{0,0}}} + \end{array} + $ + \end{xfor} + \end{xfor} + \end{xalgostep} + + + \end{xalgorithm} + + + + +\begin{xremark}{lien avec le diagramme de Vorono�} +Si pour chaque point $p_i$, $c_i = i$, alors cet algorithme aboutit � la construction de r�gions de "Vorono�" qui serviront � construire le diagramme illustr� dans la figure~\ref{squelette_voronoi}. Le squelette sera constitu� des segments s�parant deux pixels appartenant � des r�gions diff�rentes.\indexfr{Vorono�} \end{xremark} @@ -1671,129 +1671,129 @@ \subsection{Construction d'un graphe pour une classification} \indexfrr{point}{singulier} \indexfrr{singulier}{point} -L'article \citeindex{Ruberto2004} propose la construction d'un graphe rsumant le squelette. Les arcs reprsentent des parties du squelette tandis que les n\oe uds sont ses points singuliers (voir figure~\ref{squelette_point_singuliers}). +L'article \citeindex{Ruberto2004} propose la construction d'un graphe r�sumant le squelette. Les arcs repr�sentent des parties du squelette tandis que les n\oe uds sont ses points singuliers (voir figure~\ref{squelette_point_singuliers}). - \begin{figure}[ht] - $$\begin{tabular}{|c|} \hline - \includegraphics[height=5cm, width=4cm] {\filext{../squelette/image/sing}} - \\ \hline \end{tabular}$$ - \caption{ Ce squelette prsente trois points singuliers~: le premier est une extremit, le second - est la jonction de quatre arcs, le dernier est la jonction de trois arcs.} - \label{squelette_point_singuliers} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|} \hline + \includegraphics[height=5cm, width=4cm] {\filext{../squelette/image/sing}} + \\ \hline \end{tabular}$$ + \caption{ Ce squelette pr�sente trois points singuliers~: le premier est une extremit�, le second + est la jonction de quatre arcs, le dernier est la jonction de trois arcs.} + \label{squelette_point_singuliers} + \end{figure} \indexfr{reconnaissance} \indexfr{barbules} -La mthode dveloppe dans cet article utilise un tel graphe qui est d'abord nettoy des petits arcs ou barbules\seeannex{ske_par_barbule}{barbules}. Il propose ensuite d'associer chaque arc des caractristiques qui sont utilises afin d'effectuer une tche de reconnaissance via le calcul d'une distance entre graphe, celle-ci permettant de comparer le squelette de deux formes entre elles. - -Le graphe obtenu contient une liste d'arcs $\vecteur{A_1}{A_n}$, la longueur $l$ du squelette est la somme des longueurs de chaque arcs~: $l = \sum_{i=1}^{n} l\pa{A_i}$. Les arcs $A_i$ dont la longueur vrifie $l\pa{A_i} < \alpha l$ et ne contenant aucune extrmit sont supprims puis ajouts aux arcs auxquels ils sont connects. $\alpha$ est choisi gal 5\%. - -Chaque arc $S$ est ensuite dcrit par six caractristiques. $S$ est dfini par ses deux extrmits $\pa{x_1,y_1}$, $\pa{x_2,y_2}$ et une fonction $t \in \cro{0,1} \longrightarrow \pa{x\pa{t},y\pa{t} }$. Ces six caractristiques sont donnes par la table~\ref{squelette_ruberto_carac}. - - - \begin{table}[ht] - $$\begin{tabular}{|l|l|} \hline - \begin{tabular}{l} variation de la courbure \\ - moyenne, soit les valeurs \\ extrmes de la fonction~$c$ - \end{tabular} & - $v_1= \underset{t \in \cro{0,1}}{\max} c(t) - - \underset{t \in \cro{0,1}}{\min} c(t)$ - o - $ c(t) = \frac{x'y'' - x''y'}{\pa{ (x')^2 + (y')^2} ^{\frac{3}{2}} } $ - \\ \hline - \begin{tabular}{l} l'orientation de l'arc \\ par rapport celle du squelette - \end{tabular} & - $v_2 = \arctan \frac{ y_2 - y_1 } { x_2 - x_1} $ - \\ \hline - \begin{tabular}{l} la taille de l'arc \\ par rapport celle du squelette - \end{tabular} & - $v_3 = \frac{ l(S) } { l }$ - \quad \begin{minipage}{8cm} o $l(S)$ est la longueur de l'arc - et $l$ longueur du squelette \end{minipage} - \\ \hline - \begin{tabular}{l} la "raideur" de l'arc - \end{tabular} & - $v_4 = \frac{ l(S) }{ \sqrt{ \pa{x_2-x_1}^2 + \pa{y_2-y_1}^2 } }$ - \\ \hline - \begin{tabular}{l} la variation de l'paisseur \\ distance le long de l'arc - \end{tabular} & - $v_5= \underset{t \in \cro{0,1}}{\max} e(t) - - \underset{t \in \cro{0,1}}{\min} e(t)$ - \quad \begin{minipage}{5.8cm} o $e(t)$ est l'paisseur de la forme le long de l'arc, - estime par exemple l'aide d'une carte de distance - (voir paragraphe~\ref{ske_carte_distance_sec}) - \end{minipage} - \\ \hline - \begin{tabular}{l} la taille de la rgion $R$ \\ par rapport celle du squelette - \end{tabular} & - $v_6 = \frac{ A(S) }{A}$ - \quad \begin{minipage}{8cm} o $A(S)$ est la surface de la partie de la forme - dont l'arc dont $S$ est le squelette, $A$ est la surface de la forme squelettise. - \end{minipage} - \\ \hline - \end{tabular}$$ - \caption{ Six caractristiques $\vecteur{v_1}{v_6}$ dcrivant un arc extrait du squelette d'une forme, - elles sont extraites de \citeindexfig{Ruberto2004}. - L'arc $S$ est dfini par ses deux extrmits $\pa{x_1,y_1}$, $\pa{x_2,y_2}$ - et une fonction $t \in \cro{0,1} \longrightarrow \pa{x\pa{t},y\pa{t} }$.} - \label{squelette_ruberto_carac} - \indexfrr{paisseur}{trait} - \end{table} - - - -\indexfrr{graphe}{attribu} +La m�thode d�velopp�e dans cet article utilise un tel graphe qui est d'abord nettoy� des petits arcs ou barbules\seeannex{ske_par_barbule}{barbules}. Il propose ensuite d'associer � chaque arc des caract�ristiques qui sont utilis�es afin d'effectuer une t�che de reconnaissance via le calcul d'une distance entre graphe, celle-ci permettant de comparer le squelette de deux formes entre elles. + +Le graphe obtenu contient une liste d'arcs $\vecteur{A_1}{A_n}$, la longueur $l$ du squelette est la somme des longueurs de chaque arcs~: $l = \sum_{i=1}^{n} l\pa{A_i}$. Les arcs $A_i$ dont la longueur v�rifie $l\pa{A_i} < \alpha l$ et ne contenant aucune extr�mit� sont supprim�s puis ajout�s aux arcs auxquels ils sont connect�s. $\alpha$ est choisi �gal � 5\%. + +Chaque arc $S$ est ensuite d�crit par six caract�ristiques. $S$ est d�fini par ses deux extr�mit�s $\pa{x_1,y_1}$, $\pa{x_2,y_2}$ et une fonction $t \in \cro{0,1} \longrightarrow \pa{x\pa{t},y\pa{t} }$. Ces six caract�ristiques sont donn�es par la table~\ref{squelette_ruberto_carac}. + + + \begin{table}[ht] + $$\begin{tabular}{|l|l|} \hline + \begin{tabular}{l} variation de la courbure \\ + moyenne, soit les valeurs \\ extr�mes de la fonction~$c$ + \end{tabular} & + $v_1= \underset{t \in \cro{0,1}}{\max} c(t) - + \underset{t \in \cro{0,1}}{\min} c(t)$ + o� + $ c(t) = \frac{x'y'' - x''y'}{\pa{ (x')^2 + (y')^2} ^{\frac{3}{2}} } $ + \\ \hline + \begin{tabular}{l} l'orientation de l'arc \\ par rapport � celle du squelette + \end{tabular} & + $v_2 = \arctan \frac{ y_2 - y_1 } { x_2 - x_1} $ + \\ \hline + \begin{tabular}{l} la taille de l'arc \\ par rapport � celle du squelette + \end{tabular} & + $v_3 = \frac{ l(S) } { l }$ + \quad \begin{minipage}{8cm} o� $l(S)$ est la longueur de l'arc + et $l$ longueur du squelette \end{minipage} + \\ \hline + \begin{tabular}{l} la "raideur" de l'arc + \end{tabular} & + $v_4 = \frac{ l(S) }{ \sqrt{ \pa{x_2-x_1}^2 + \pa{y_2-y_1}^2 } }$ + \\ \hline + \begin{tabular}{l} la variation de l'�paisseur \\ distance le long de l'arc + \end{tabular} & + $v_5= \underset{t \in \cro{0,1}}{\max} e(t) - + \underset{t \in \cro{0,1}}{\min} e(t)$ + \quad \begin{minipage}{5.8cm} o� $e(t)$ est l'�paisseur de la forme le long de l'arc, + estim�e par exemple � l'aide d'une carte de distance + (voir paragraphe~\ref{ske_carte_distance_sec}) + \end{minipage} + \\ \hline + \begin{tabular}{l} la taille de la r�gion $R$ \\ par rapport � celle du squelette + \end{tabular} & + $v_6 = \frac{ A(S) }{A}$ + \quad \begin{minipage}{8cm} o� $A(S)$ est la surface de la partie de la forme + dont l'arc dont $S$ est le squelette, $A$ est la surface de la forme squelettis�e. + \end{minipage} + \\ \hline + \end{tabular}$$ + \caption{ Six caract�ristiques $\vecteur{v_1}{v_6}$ d�crivant un arc extrait du squelette d'une forme, + elles sont extraites de \citeindexfig{Ruberto2004}. + L'arc $S$ est d�fini par ses deux extr�mit�s $\pa{x_1,y_1}$, $\pa{x_2,y_2}$ + et une fonction $t \in \cro{0,1} \longrightarrow \pa{x\pa{t},y\pa{t} }$.} + \label{squelette_ruberto_carac} + \indexfrr{�paisseur}{trait} + \end{table} + + + +\indexfrr{graphe}{attribu�} \indexfrr{matrice}{adjacence} \indexfrr{adjacence}{matrice} -L'auteur de l'article \citeindex{Ruberto2004} propose d'utiliser ce graphe "attribu" afin de calculer une distance entre deux formes pour lesquels ce graphe $G$ aura t pralablement estim. Ce graphe inclut un ensemble d'artes dcrites par les caractristiques de la table~\ref{squelette_ruberto_carac} et $n$ n\oe uds qui sont les points singuliers du squelette. On dfinit $A = \pa{a_{ij}} _ { 1 \infegal i,j \infegal n } \in \cro{0,1} ^ {n^2}$ la matrice d'adjacence du graphe $G$, le graphe est donc entirement dfini par $G = \acc{ A, \vecteur{v_{i,j,1}}{v_{i,j,6}} \sac 1 \infegal i,j \infegal n}$. La distance entre deux graphes~$G_1$ et~$G_2$ est dfinie par~: - - - \begin{eqnarray} - d\pa{G_1,G_2,\alpha} &=& \inf \acc{ E\pa{G_1,G_,M = \pa{m_{ik}}_ { - \begin{subarray} \, 1 \infegal i \infegal n_1 \\ - 1 \infegal k \infegal n_2 \end{subarray} } - ,\alpha} \left | - \begin{array}{l} - \forall i,k, \, m_{ik} \in \acc{0,1} \\ - \forall k, \, \sum_{i=1}^{n_1} m_{ik} \infegal 1 \\ - \forall i, \, \sum_{k=1}^{n_2} m_{ik} \infegal 1 - \end{array} \right. - } - \label{squelettisation_graphe_distance_matching} - \\ - \text{ avec } - E\pa{G_1,G_2,M,\alpha} &=& - \frac{1}{2} \; \summy{i=1}{n_1} \; \summy{j=1}{n_1} \; - \summy{j=1}{n_2} \; \summy{l=1}{n_2} \; - m_{ik} \, m_{jl} \, e\pa{i \rightarrow j, \, k \rightarrow l} + - \alpha \; \summy{i=1}{n_1} \; \summy{j=1}{n_2} \; - m_{ik} \, e\pa{i,k} - \nonumber - \end{eqnarray} - - -$\alpha$ est un terme permettant d'ajuster la prpondrance de l'association entre les n\oe uds par rapport celle entre les artes. La fonction $e\pa{i \rightarrow j, \, k \rightarrow l}$ mesure la vraisemblance de l'association entre l'arte $i \rightarrow j$ du premier graphe et l'arte $k \rightarrow l$ du second graphe tandis que $e\pa{i,k}$ mesure la vraisemblance de l'association entre le n\oe ud $i$ de premier graphe et le n\oe ud $j$ du second graphe. La premire fonction est dfinie comme suit~: - - - \begin{eqnarray} - e\pa{i \rightarrow j, \, k \rightarrow l} &=& \left \{ \begin{array}{ll} - 0 & \text{si } a_{ij}^1 a_{kl}^2 = 0 \\ - \summy{d=1}{6} L_d \pa{ 1 - \abs{ \frac{v_{i,j,d,1}}{L_d} - \frac{v_{k,l,d,2}}{L_d} }} - & \text{sinon } - \end{array} \right. \\ - \text{avec } L_d &=& \frac{1}{2} \, \max \acc{ - \summy{i=1}{n_1} \; \summy{j=1}{n_1} \; v_{i,j,d,1}, \; - \summy{k=1}{n_2} \; \summy{l=1}{n_2} \; v_{k,l,d,2} } - \nonumber - \end{eqnarray} +L'auteur de l'article \citeindex{Ruberto2004} propose d'utiliser ce graphe "attribu�" afin de calculer une distance entre deux formes pour lesquels ce graphe $G$ aura �t� pr�alablement estim�. Ce graphe inclut un ensemble d'ar�tes d�crites par les caract�ristiques de la table~\ref{squelette_ruberto_carac} et $n$ n\oe uds qui sont les points singuliers du squelette. On d�finit $A = \pa{a_{ij}} _ { 1 \leqslant i,j \leqslant n } \in \cro{0,1} ^ {n^2}$ la matrice d'adjacence du graphe $G$, le graphe est donc enti�rement d�fini par $G = \acc{ A, \vecteur{v_{i,j,1}}{v_{i,j,6}} \sac 1 \leqslant i,j \leqslant n}$. La distance entre deux graphes~$G_1$ et~$G_2$ est d�finie par~: + + + \begin{eqnarray} + d\pa{G_1,G_2,\alpha} &=& \inf \acc{ E\pa{G_1,G_,M = \pa{m_{ik}}_ { + \begin{subarray} \, 1 \leqslant i \leqslant n_1 \\ + 1 \leqslant k \leqslant n_2 \end{subarray} } + ,\alpha} \left | + \begin{array}{l} + \forall i,k, \, m_{ik} \in \acc{0,1} \\ + \forall k, \, \sum_{i=1}^{n_1} m_{ik} \leqslant 1 \\ + \forall i, \, \sum_{k=1}^{n_2} m_{ik} \leqslant 1 + \end{array} \right. + } + \label{squelettisation_graphe_distance_matching} + \\ + \text{ avec } + E\pa{G_1,G_2,M,\alpha} &=& - \frac{1}{2} \; \summy{i=1}{n_1} \; \summy{j=1}{n_1} \; + \summy{j=1}{n_2} \; \summy{l=1}{n_2} \; + m_{ik} \, m_{jl} \, e\pa{i \rightarrow j, \, k \rightarrow l} + + \alpha \; \summy{i=1}{n_1} \; \summy{j=1}{n_2} \; + m_{ik} \, e\pa{i,k} + \nonumber + \end{eqnarray} + + +$\alpha$ est un terme permettant d'ajuster la pr�pond�rance de l'association entre les n\oe uds par rapport � celle entre les ar�tes. La fonction $e\pa{i \rightarrow j, \, k \rightarrow l}$ mesure la vraisemblance de l'association entre l'ar�te $i \rightarrow j$ du premier graphe et l'ar�te $k \rightarrow l$ du second graphe tandis que $e\pa{i,k}$ mesure la vraisemblance de l'association entre le n\oe ud $i$ de premier graphe et le n\oe ud $j$ du second graphe. La premi�re fonction est d�finie comme suit~: + + + \begin{eqnarray} + e\pa{i \rightarrow j, \, k \rightarrow l} &=& \left \{ \begin{array}{ll} + 0 & \text{si } a_{ij}^1 a_{kl}^2 = 0 \\ + \summy{d=1}{6} L_d \pa{ 1 - \abs{ \frac{v_{i,j,d,1}}{L_d} - \frac{v_{k,l,d,2}}{L_d} }} + & \text{sinon } + \end{array} \right. \\ + \text{avec } L_d &=& \frac{1}{2} \, \max \acc{ + \summy{i=1}{n_1} \; \summy{j=1}{n_1} \; v_{i,j,d,1}, \; + \summy{k=1}{n_2} \; \summy{l=1}{n_2} \; v_{k,l,d,2} } + \nonumber + \end{eqnarray} \indexfr{NP-complet} -\indexfr{affectation gradue} +\indexfr{affectation gradu�e} -La fonction $e\pa{i,k}$ est construite de manire analogue en prenant comme caractristique l'paisseur au point singulier par exemple. Le problme de minimisation est malheureusement NP-complet mais il peut tre rsolu selon une mthode approche appele "affection gradue" dveloppe dans \citeindex{Gold1996}. En dfinitive, la distance~$d$ dfinie en~(\ref{squelettisation_graphe_distance_matching}) permet d'effectuer une classification par plus proches voisins. Etant donn son cot lev, il est prfrable d'utiliser d'viter un trop grand nombre de calculs par le biais de mthodes comme celles dveloppes en annexe~\ref{classification_non_supervisee}. +La fonction $e\pa{i,k}$ est construite de mani�re analogue en prenant comme caract�ristique l'�paisseur au point singulier par exemple. Le probl�me de minimisation est malheureusement NP-complet mais il peut �tre r�solu selon une m�thode approch�e appel�e "affection gradu�e" d�velopp�e dans \citeindex{Gold1996}. En d�finitive, la distance~$d$ d�finie en~(\ref{squelettisation_graphe_distance_matching}) permet d'effectuer une classification par plus proches voisins. Etant donn� son co�t �lev�, il est pr�f�rable d'utiliser d'�viter un trop grand nombre de calculs par le biais de m�thodes comme celles d�velopp�es en annexe~\ref{classification_non_supervisee}. @@ -1806,151 +1806,151 @@ \section{Squelette d'un nuage de points} \indexfrr{squelette}{nuage de points} \label{ske_nuage_point_squelette} -\subsection{Squelettisation partir d'un treillis de Kohonen} +\subsection{Squelettisation � partir d'un treillis de Kohonen} \indexfrr{Kohonen}{treillis} -Le nettoyage des barbules peut s'avrer complexe. De plus, la squelettisation par rosion est souvent sensible aux bruits du contour. C'est pourquoi il est possible de s'inspirer de mthodes qui permettent de dterminer le squelette d'un nuage de points. La figure~\ref{squelette_nuage_points} montre la construction du squelette de la lettre "A" la mthode dveloppe par~\citeindex{Datta1997} part d'un squelette dont la topologie est linaire. Elle supprime des neurones si ceux-ci sont trop rapprochs et en ajoute si ceux-ci sont trop loigns. Elle cre des points "aiguillage" ou barres de "T" lorsque l'angle entre deux segments devient trop ferm. +Le nettoyage des barbules peut s'av�rer complexe. De plus, la squelettisation par �rosion est souvent sensible aux bruits du contour. C'est pourquoi il est possible de s'inspirer de m�thodes qui permettent de d�terminer le squelette d'un nuage de points. La figure~\ref{squelette_nuage_points} montre la construction du squelette de la lettre "A" la m�thode d�velopp�e par~\citeindex{Datta1997} part d'un squelette dont la topologie est lin�aire. Elle supprime des neurones si ceux-ci sont trop rapproch�s et en ajoute si ceux-ci sont trop �loign�s. Elle cr�e des points "aiguillage" ou barres de "T" lorsque l'angle entre deux segments devient trop ferm�. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=12cm, width=6cm] - {\filext{../squelette/image/nuage}}\end{array}$}$$ - \caption{ Squelette d'un nuage de points : diffrentes tapes dans - la construction du squelette de la lettre "A", figure extraite de~\citeindexfig{Datta1997}.} - \label{squelette_nuage_points} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=12cm, width=6cm] + {\filext{../squelette/image/nuage}}\end{array}$}$$ + \caption{ Squelette d'un nuage de points : diff�rentes �tapes dans + la construction du squelette de la lettre "A", figure extraite de~\citeindexfig{Datta1997}.} + \label{squelette_nuage_points} + \end{figure} -Soit $\vecteur{P_1}{P_N}$ un nuage de points, la topologie initiale est linaire et le rseau est constitu de $n$ vecteurs (ou neurones) $W = \vecteur{W_1}{W_n}$ o $W_{i-1}$ et $W_{i+1}$ sont les voisins du vecteur $W_i$. A chaque itration~$t$, on tire alatoirement un point $P_i$ puis on dtermine le vecteur $W^t_{k^*}$ qui en est le plus proche l'itration~$t$~: +Soit $\vecteur{P_1}{P_N}$ un nuage de points, la topologie initiale est lin�aire et le r�seau est constitu� de $n$ vecteurs (ou neurones) $W = \vecteur{W_1}{W_n}$ o� $W_{i-1}$ et $W_{i+1}$ sont les voisins du vecteur $W_i$. A chaque it�ration~$t$, on tire al�atoirement un point $P_i$ puis on d�termine le vecteur $W^t_{k^*}$ qui en est le plus proche � l'it�ration~$t$~: - - \begin{eqnarray*} - W^t_{k^*} \in \underset {k \in \ensemble {1}{n} } { \arg \min } d \pa{ W_k^t, P_i } - \end{eqnarray*} + + \begin{eqnarray*} + W^t_{k^*} \in \underset {k \in \ensemble {1}{n} } { \arg \min } d \pa{ W_k^t, P_i } + \end{eqnarray*} -$d \pa{ W_k, P_i }$ est la distance entre $W_k$ et $P_i$. On procde ensuite la mise jour de $W_{k^*}$ et de l'ensemble de ses voisins not $N\pa{W_{k^*}}$~: +$d \pa{ W_k, P_i }$ est la distance entre $W_k$ et $P_i$. On proc�de ensuite � la mise � jour de $W_{k^*}$ et de l'ensemble de ses voisins not� $N\pa{W_{k^*}}$~: - - \begin{eqnarray*} - \forall k \in N\pa{W_{k^*}}, \; - W^{t+1}_k = W^{t}_k + \alpha_t \cro { P_i - W_k } - \end{eqnarray*} + + \begin{eqnarray*} + \forall k \in N\pa{W_{k^*}}, \; + W^{t+1}_k = W^{t}_k + \alpha_t \cro { P_i - W_k } + \end{eqnarray*} -La suite $\pa{\alpha_t}_{t \supegal 0}$ vrifie~: +La suite $\pa{\alpha_t}_{t \supegal 0}$ v�rifie~: - - \begin{eqnarray*} - \summyone{t \supegal 0} \alpha_t = \infty \text { and } \summyone{t \supegal 0} - \alpha_t^2 \infegal \infty - \end{eqnarray*} - + + \begin{eqnarray*} + \summyone{t \supegal 0} \alpha_t = \infty \text { and } \summyone{t \supegal 0} + \alpha_t^2 \leqslant \infty + \end{eqnarray*} + Par exemple~: - $$ - \alpha_t = \frac{\alpha_0}{1+\beta t} - $$ + $$ + \alpha_t = \frac{\alpha_0}{1+\beta t} + $$ -L'algorithme s'arrte lorsque la condition suivante est vrifie~: +L'algorithme s'arr�te lorsque la condition suivante est v�rifi�e~: - \begin{eqnarray*} - \forall i \in \ensemble{1}{n}, \; d\pa{W_i^{t+1}, W_i^{t} } \infegal \epsilon - \end{eqnarray*} + \begin{eqnarray*} + \forall i \in \ensemble{1}{n}, \; d\pa{W_i^{t+1}, W_i^{t} } \leqslant \epsilon + \end{eqnarray*} -Il reste grer la suppression de deux neurones trop proches, l'insertion d'un neurone entre deux autres trop loigns, l'insertion d'un neurone reliant trois voisins ou neurones "T". Ces oprations sont effectues une fois que les tapes prcdentes ont abouti une configuration ayant converg. La suppression d'un neurone est effectue si la condition suivante est vrifie~: +Il reste � g�rer la suppression de deux neurones trop proches, l'insertion d'un neurone entre deux autres trop �loign�s, l'insertion d'un neurone reliant trois voisins ou neurones "T". Ces op�rations sont effectu�es une fois que les �tapes pr�c�dentes ont abouti � une configuration ayant converg�. La suppression d'un neurone est effectu�e si la condition suivante est v�rifi�e~: - \begin{eqnarray} - \underset{ i \in \ensemble{1}{n} } { min }\; \cro{ - \underset{ j \in N\pa{W_i} } { min }\; d\pa{W_i,W_j} } - < \delta_1 - \label{ske_cloud_point_merge} - \end{eqnarray} + \begin{eqnarray} + \underset{ i \in \ensemble{1}{n} } { min }\; \cro{ + \underset{ j \in N\pa{W_i} } { min }\; d\pa{W_i,W_j} } + < \delta_1 + \label{ske_cloud_point_merge} + \end{eqnarray} -Dans ce cas, les deux neurones permettant d'atteindre le minimum de (\ref{ske_cloud_point_merge}) sont regroups ensemble. L'insertion d'un neurone deux voisins est effectue si la condition suivante est vrifie~: +Dans ce cas, les deux neurones permettant d'atteindre le minimum de (\ref{ske_cloud_point_merge}) sont regroup�s ensemble. L'insertion d'un neurone � deux voisins est effectu�e si la condition suivante est v�rifi�e~: - \begin{eqnarray} - \underset{ i \in \ensemble{1}{n} } { max }\; \cro{ - \underset{ j \in N\pa{W_i} } { max }\; d\pa{W_i,W_j} } - > \delta_2 - \label{ske_cloud_point_insert} - \end{eqnarray} + \begin{eqnarray} + \underset{ i \in \ensemble{1}{n} } { max }\; \cro{ + \underset{ j \in N\pa{W_i} } { max }\; d\pa{W_i,W_j} } + > \delta_2 + \label{ske_cloud_point_insert} + \end{eqnarray} -Dans ce cas, un neurone est insre au milieu du segment form par les deux voisins permettant d'obtenir le maximum de (\ref{ske_cloud_point_insert}). Un neurone "T" trois voisins est insr lorsque l'angle entre les deux voisins d'un neurone forme un angle ferm. La figure \ref{squelette_nuage_points}\textit{(b)} montre trois points $X$,$Y$,$Z$. Les droites $(XZ)$ et $(XY)$ forment un angle ferm, un point trois voisins est alors ajout entre les points $Y$ et $Z$. +Dans ce cas, un neurone est ins�r�e au milieu du segment form� par les deux voisins permettant d'obtenir le maximum de (\ref{ske_cloud_point_insert}). Un neurone "T" � trois voisins est ins�r� lorsque l'angle entre les deux voisins d'un neurone forme un angle ferm�. La figure \ref{squelette_nuage_points}\textit{(b)} montre trois points $X$,$Y$,$Z$. Les droites $(XZ)$ et $(XY)$ forment un angle ferm�, un point � trois voisins est alors ajout� entre les points $Y$ et $Z$. -\subsection{Squelettisation partir d'un treillis de Kohonen} -\indexfrr{segmentation}{graphme} +\subsection{Squelettisation � partir d'un treillis de Kohonen} +\indexfrr{segmentation}{graph�me} \indexfr{Kohonen} -\indexfr{connexit} +\indexfr{connexit�} -Cette segmentation s'inspire de l'article~\citeindex{Datta1997} (voir aussi~\citeindex{Singh2000}). Cet article permet de construire le squelette d'un nuage de points. L'indpendance par rapport la connexit permet d'tre moins sensible au bruit. Afin de simplifier l'algorithme dcrit dans~\citeindex{Datta1997}, un treillis en deux dimensions est d'abord superpos l'image du mot ainsi que le montre la figure~\ref{image_grapheme_kohonen1}. +Cette segmentation s'inspire de l'article~\citeindex{Datta1997} (voir aussi~\citeindex{Singh2000}). Cet article permet de construire le squelette d'un nuage de points. L'ind�pendance par rapport � la connexit� permet d'�tre moins sensible au bruit. Afin de simplifier l'algorithme d�crit dans~\citeindex{Datta1997}, un treillis en deux dimensions est d'abord superpos� � l'image du mot ainsi que le montre la figure~\ref{image_grapheme_kohonen1}. - \begin{figure}[ht] - $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=6cm] - {\filext{../image/image/cloud_1}}\end{array}$}$$ - \caption{ Treillis de Kohonen initial superpos au mot segmenter en graphmes.} - \label{image_grapheme_kohonen1} - \end{figure} + \begin{figure}[ht] + $$\frame{$\begin{array}[c]{c}\includegraphics[height=2cm, width=6cm] + {\filext{../image/image/cloud_1}}\end{array}$}$$ + \caption{ Treillis de Kohonen initial superpos� au mot � segmenter en graph�mes.} + \label{image_grapheme_kohonen1} + \end{figure} \indexfrr{arbre}{poids minimal} \indexfr{Kruskal} -Le treillis volue puis converge vers le rsultat figure~\ref{image_grapheme_kohonen2}$a$ en utilisant l'algorithme~\ref{reco_algo_carac_kohonen_____}\footnote{ -Pour mmoire~: - \begin{xalgorithm}{caractristiques de Kohonen} - \indexfrr{caractristiques}{Kohonen} - \label{reco_algo_carac_kohonen_____} - - \begin{xalgostep}{initialisation} - $\forall \pa{i,j} \in \ensemble{1}{I} \times \ensemble{1}{J}, \; P_{ij} = \pa{ \frac{iX}{I}, \frac{jY}{J} }'$ \\ - $t \leftarrow 0$ \\ - $\delta \leftarrow \sqrt{ \pa{\frac{X}{I}}^2 + \pa{\frac{Y}{J}}^2 }$ - \end{xalgostep} - - \begin{xalgostep}{point caractristique le plus proche et mise jour} \label{reco_algo_carac_kohonen_conv} - $\alpha \leftarrow \frac{0.2}{1 + \frac{t}{XY} }$ \\ - On tire alatoirement un pixel $p$ de l'image, si ce pixel $p$ est noir, alors~:\\ - $\pa{i^*,j^*} \leftarrow \underset{i,j} {\arg \max } \; d\pa{P_{ij}, p}$ \\ - $P_{i^*,j^*} \leftarrow P_{i^*,j^*} + \alpha \pa{ p - P_{i^*,j^*} }$ - \end{xalgostep} - - \begin{xalgostep}{mise jour des voisins} - $\epsilon \leftarrow \exp \pa { \frac{1}{\delta} \norme{P_{i^*,j^*} - p} - 1} $\\ - \begin{xforeach}{P}{V_c\pa{i^*,j^*}} - $\beta \leftarrow \alpha \, \epsilon \, \exp \pa{ - \frac{1}{\delta} \norme{P - p} } $ \\ - $P \leftarrow P + \beta \pa{ p - P}$ - \end{xforeach} - \end{xalgostep} - - \begin{xalgostep}{terminaison} - $t \leftarrow t+1$ \\ - Tant que l'algorithme n'a pas converg, retour l'tape~\ref{reco_algo_carac_kohonen_conv}. - \end{xalgostep} - - - \end{xalgorithm} -} dj dcrit au paragraphe~\ref{reco_point_caracteristique_kohonen} (page~\pageref{reco_point_caracteristique_kohonen}). Le rsultat obtenu inclut le squelette recherch qui sera obtenu en utilisant un algorithme de recherche de l'arbre de poids minimal (voir \citeindex{Kruskal1956}). - - - \begin{figure}[ht] - $$\begin{tabular}{|c|c|} \hline - \includegraphics[height=2cm, width=6cm]{\filext{../image/image/cloud_2}} & - \includegraphics[height=2cm, width=6cm]{\filext{../image/image/cloud_3}} \\ - ($a$) & ($b$) \\ \hline - \end{tabular}$$ - \caption{ La figure ($a$) montre le rsultat obtenu aprs convergence de - l'algorithme~\ref{reco_algo_carac_kohonen}. La figure ($b$) montre le mme arbre - lagu aprs l'application de l'algorithme de Kruskal.} - \indexfr{Kruskal} - \label{image_grapheme_kohonen2} - \end{figure} +Le treillis �volue puis converge vers le r�sultat figure~\ref{image_grapheme_kohonen2}$a$ en utilisant l'algorithme~\ref{reco_algo_carac_kohonen_____}\footnote{ +Pour m�moire~: + \begin{xalgorithm}{caract�ristiques de Kohonen} + \indexfrr{caract�ristiques}{Kohonen} + \label{reco_algo_carac_kohonen_____} + + \begin{xalgostep}{initialisation} + $\forall \pa{i,j} \in \ensemble{1}{I} \times \ensemble{1}{J}, \; P_{ij} = \pa{ \frac{iX}{I}, \frac{jY}{J} }'$ \\ + $t \leftarrow 0$ \\ + $\delta \leftarrow \sqrt{ \pa{\frac{X}{I}}^2 + \pa{\frac{Y}{J}}^2 }$ + \end{xalgostep} + + \begin{xalgostep}{point caract�ristique le plus proche et mise � jour} \label{reco_algo_carac_kohonen_conv} + $\alpha \leftarrow \frac{0.2}{1 + \frac{t}{XY} }$ \\ + On tire al�atoirement un pixel $p$ de l'image, si ce pixel $p$ est noir, alors~:\\ + $\pa{i^*,j^*} \leftarrow \underset{i,j} {\arg \max } \; d\pa{P_{ij}, p}$ \\ + $P_{i^*,j^*} \leftarrow P_{i^*,j^*} + \alpha \pa{ p - P_{i^*,j^*} }$ + \end{xalgostep} + + \begin{xalgostep}{mise � jour des voisins} + $\epsilon \leftarrow \exp \pa { \frac{1}{\delta} \norme{P_{i^*,j^*} - p} - 1} $\\ + \begin{xforeach}{P}{V_c\pa{i^*,j^*}} + $\beta \leftarrow \alpha \, \epsilon \, \exp \pa{ - \frac{1}{\delta} \norme{P - p} } $ \\ + $P \leftarrow P + \beta \pa{ p - P}$ + \end{xforeach} + \end{xalgostep} + + \begin{xalgostep}{terminaison} + $t \leftarrow t+1$ \\ + Tant que l'algorithme n'a pas converg�, retour � l'�tape~\ref{reco_algo_carac_kohonen_conv}. + \end{xalgostep} + + + \end{xalgorithm} +} d�j� d�crit au paragraphe~\ref{reco_point_caracteristique_kohonen} (page~\pageref{reco_point_caracteristique_kohonen}). Le r�sultat obtenu inclut le squelette recherch� qui sera obtenu en utilisant un algorithme de recherche de l'arbre de poids minimal (voir \citeindex{Kruskal1956}). + + + \begin{figure}[ht] + $$\begin{tabular}{|c|c|} \hline + \includegraphics[height=2cm, width=6cm]{\filext{../image/image/cloud_2}} & + \includegraphics[height=2cm, width=6cm]{\filext{../image/image/cloud_3}} \\ + ($a$) & ($b$) \\ \hline + \end{tabular}$$ + \caption{ La figure ($a$) montre le r�sultat obtenu apr�s convergence de + l'algorithme~\ref{reco_algo_carac_kohonen}. La figure ($b$) montre le m�me arbre + �lagu� apr�s l'application de l'algorithme de Kruskal.} + \indexfr{Kruskal} + \label{image_grapheme_kohonen2} + \end{figure} @@ -1958,58 +1958,58 @@ \subsection{Squelettisation \subsection{Squelettisation d'images floues} -\indexfr{rseau de neurones} +\indexfr{r�seau de neurones} \indexfr{image floue} -\indexfrr{pixel}{intensit} -\indexfr{intensit} +\indexfrr{pixel}{intensit�} +\indexfr{intensit�} \indexfr{niveaux de gris} -\indexfr{rosion} +\indexfr{�rosion} -L'article \citeindex{Kalm\'ar1999} propose une squelettisation d'images floues base sur un rseau de neurones. Sa mthode propose l'rosion d'une image en niveaux de gris. On suppose que l'image est forme d'une suite de pixels $\pa{x_i}_i$ et $f\pa{x_i} \in \cro{0,1}$ est l'intensit du pixel. Les neurones de la premire couche sont nots $\pa{n_i^1}_i$, deux points $p_i, \, a_i \in \R^2$ sont associs chacun de ces neurones, $\pa{a_i}$ est calcul comme suit~: +L'article \citeindex{Kalm\'ar1999} propose une squelettisation d'images floues bas�e sur un r�seau de neurones. Sa m�thode propose l'�rosion d'une image en niveaux de gris. On suppose que l'image est form�e d'une suite de pixels $\pa{x_i}_i$ et $f\pa{x_i} \in \cro{0,1}$ est l'intensit� du pixel. Les neurones de la premi�re couche sont not�s $\pa{n_i^1}_i$, deux points $p_i, \, a_i \in \mathbb{R}^2$ sont associ�s � chacun de ces neurones, $\pa{a_i}$ est calcul� comme suit~: - \begin{eqnarray} - \forall i, \; a_i &=& \summyone{j} \; x_j \; f\pa{x_j} \, \exp \cro{ \frac{ - \norme{p_i - x_j}^2} {d_0^2}} - \end{eqnarray} + \begin{eqnarray} + \forall i, \; a_i &=& \summyone{j} \; x_j \; f\pa{x_j} \, \exp \cro{ \frac{ - \norme{p_i - x_j}^2} {d_0^2}} + \end{eqnarray} -\indexfr{paramtre d'chelle} +\indexfr{param�tre d'�chelle} \indexfr{hexagonal} \indexfrr{voisinage}{hexagonal} -$d_0$ est un paramtre d'chelle. Les points $p_i$ sont parpills sur l'image et connects entre eux selon un systme de voisinage qui peut tre aussi bien carr (4,~8-connexit) qu'hexagonal. La connexion entre les neurones $n_i$ et $n_j$ est note $w_{ij} \in \cro{0,1}$. A chaque neurone est associ un paramtre d'activation qui dpend du temps $\sigma_i\pa{t} \in \cro{0,1}$. $\sigma_i\pa{0}$ est estim partir de $a_i$. Par exemple~: +$d_0$ est un param�tre d'�chelle. Les points $p_i$ sont �parpill�s sur l'image et connect�s entre eux selon un syst�me de voisinage qui peut �tre aussi bien carr� (4,~8-connexit�) qu'hexagonal. La connexion entre les neurones $n_i$ et $n_j$ est not�e $w_{ij} \in \cro{0,1}$. A chaque neurone est associ� un param�tre d'activation qui d�pend du temps $\sigma_i\pa{t} \in \cro{0,1}$. $\sigma_i\pa{0}$ est estim� � partir de $a_i$. Par exemple~: - \begin{eqnarray} - \sigma_i\pa{0} &=& f \pa{a_i} - \end{eqnarray} + \begin{eqnarray} + \sigma_i\pa{0} &=& f \pa{a_i} + \end{eqnarray} -L'volution de $\sigma_i\pa{t}$ est dfinie comme suit~: +L'�volution de $\sigma_i\pa{t}$ est d�finie comme suit~: - \begin{eqnarray} - \partialfrac{\sigma_i}{t}\pa{t} &=& \pa{1 - \sigma_i\pa{t} } \pa{ - \summyone{k,j} \; w_{ij} \cro{ \sigma_k\pa{t} - \sigma_i \pa{t} } } - \end{eqnarray} + \begin{eqnarray} + \partialfrac{\sigma_i}{t}\pa{t} &=& \pa{1 - \sigma_i\pa{t} } \pa{ + \summyone{k,j} \; w_{ij} \cro{ \sigma_k\pa{t} - \sigma_i \pa{t} } } + \end{eqnarray} -On dfinit le coefficient $\Sigma_i\pa{t}$ pour chaque neurone~: +On d�finit le coefficient $\Sigma_i\pa{t}$ pour chaque neurone~: - \begin{eqnarray} - \Sigma_i\pa{t} &=& \int_0^t \; \summyone{k} \; \cro { \sigma_k\pa{u} - \sigma_i\pa{u} } du - \end{eqnarray} + \begin{eqnarray} + \Sigma_i\pa{t} &=& \int_0^t \; \summyone{k} \; \cro { \sigma_k\pa{u} - \sigma_i\pa{u} } du + \end{eqnarray} - -A partir d'un certain temps $\tau$, la fonction $\Sigma_i\pa{t}$ converge pour tous les neurones et la suite $\pa{\Sigma_i\pa{\tau}}_i$ est alors la distribution du squelette sur l'ensemble des neurones. Cette mthode peut galement tre applique sur des images binaires mais le rsultat n'est plus un squelette d'un pixel d'paisseur un (ou "fil de fer"). En contrepartie, les branches non significatives sont moins probables (voir figure~\ref{image_kalmar_squelette_hexa}). + +A partir d'un certain temps $\tau$, la fonction $\Sigma_i\pa{t}$ converge pour tous les neurones et la suite $\pa{\Sigma_i\pa{\tau}}_i$ est alors la distribution du squelette sur l'ensemble des neurones. Cette m�thode peut �galement �tre appliqu�e sur des images binaires mais le r�sultat n'est plus un squelette d'un pixel d'�paisseur un (ou "fil de fer"). En contrepartie, les branches non significatives sont moins probables (voir figure~\ref{image_kalmar_squelette_hexa}). - \begin{figure}[ht] - $$\begin{tabular}{|c|c|} \hline - \includegraphics[height=2cm, width=6cm]{\filext{../squelette/image/kalmar1}} & - \includegraphics[height=2cm, width=6cm]{\filext{../squelette/image/kalmar2}} \\ - $(a)$ & $(b)$ \\ \hline - \end{tabular}$$ - \caption{ Figure extraite de \citeindexfig{Kalm\'ar1999}, la forme $(b)$ est ampute d'un bout dans sa moiti - suprieure, les artefacts qui en rsultent au niveau du squelette sont moins probables que les autres. - } - \label{image_kalmar_squelette_hexa} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|} \hline + \includegraphics[height=2cm, width=6cm]{\filext{../squelette/image/kalmar1}} & + \includegraphics[height=2cm, width=6cm]{\filext{../squelette/image/kalmar2}} \\ + $(a)$ & $(b)$ \\ \hline + \end{tabular}$$ + \caption{ Figure extraite de \citeindexfig{Kalm\'ar1999}, la forme $(b)$ est amput�e d'un bout dans sa moiti� + sup�rieure, les artefacts qui en r�sultent au niveau du squelette sont moins probables que les autres. + } + \label{image_kalmar_squelette_hexa} + \end{figure} @@ -2019,29 +2019,29 @@ \subsection{Squelettisation d'images floues} \subsection{Squelettisation et classification} \label{squelette_cem_classification} -\indexfrr{classification}{non supervise} +\indexfrr{classification}{non supervis�e} \indexfrr{RPCL}{local based PCA} \indexfr{Competitive EM algorithm} \indexfrr{algorithme}{EM} -La squelettisation peut tre galement traite comme une classification non supervise. L'article \citeindex{Liu2003} propose une mthode\seeannex{classification_rpcl_local_pca}{classification} qui considre chaque segment de l'image comme une classe suivant une loi normale multidimensionnelle et dgnre, la forme de cette classe reprsente une ellipse dont le petit axe est trs infrieur au grand axe. L'algorithme RPCL-local based PCA permet la fois de dterminer le nombre de segments et d'estimer les paramtres de la loi qui le modlise. L'article \citeindex{ZhangB2004} prsente un nouvel algorithme EM appel Competitive~EM\seeannex{classification_CEM}{algorithme CEM} permettant d'viter les maxima locaux lors de l'optimisation. Le rsultat obtenu sur des caractres chinois est prsent par la figure~\ref{image_zhangb_cem}. +La squelettisation peut �tre �galement trait�e comme une classification non supervis�e. L'article \citeindex{Liu2003} propose une m�thode\seeannex{classification_rpcl_local_pca}{classification} qui consid�re chaque segment de l'image comme une classe suivant une loi normale multidimensionnelle et d�g�n�r�e, la forme de cette classe repr�sente une ellipse dont le petit axe est tr�s inf�rieur au grand axe. L'algorithme RPCL-local based PCA permet � la fois de d�terminer le nombre de segments et d'estimer les param�tres de la loi qui le mod�lise. L'article \citeindex{ZhangB2004} pr�sente un nouvel algorithme EM appel� Competitive~EM\seeannex{classification_CEM}{algorithme CEM} permettant d'�viter les maxima locaux lors de l'optimisation. Le r�sultat obtenu sur des caract�res chinois est pr�sent� par la figure~\ref{image_zhangb_cem}. - \begin{figure}[ht] - $$\begin{tabular}{|c|c|c|} \hline - \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/zhangs1}} & - \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/zhangs2}} & - \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/zhangs3}} \\ \hline - \end{tabular}$$ - \caption{ Figures extraites de \citeindexfig{ZhangB2004} prsentant les rsultats d'une classification - effectue par l'algorithme Competitive Expectation Maximization (CEM). - La premire image reprsente le caractre chinois segmenter. - La seconde image illustre les classes obtenues aprs que l'image a t chantillonne - (environ 1500 points). La dernire traduit chaque classe par un segment gal - au grand axe de l'ellipse. } - \label{image_zhangb_cem} - \end{figure} + \begin{figure}[ht] + $$\begin{tabular}{|c|c|c|} \hline + \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/zhangs1}} & + \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/zhangs2}} & + \includegraphics[height=5cm, width=5cm]{\filext{../squelette/image/zhangs3}} \\ \hline + \end{tabular}$$ + \caption{ Figures extraites de \citeindexfig{ZhangB2004} pr�sentant les r�sultats d'une classification + effectu�e par l'algorithme Competitive Expectation Maximization (CEM). + La premi�re image repr�sente le caract�re chinois � segmenter. + La seconde image illustre les classes obtenues apr�s que l'image a �t� �chantillonn�e + (environ 1500 points). La derni�re traduit chaque classe par un segment �gal + au grand axe de l'ellipse. } + \label{image_zhangb_cem} + \end{figure} @@ -2062,9 +2062,9 @@ \subsection{Squelettisation et classification} \firstpassagedo{ - \begin{thebibliography}{99} - \input{squelette_article.tex} - \end{thebibliography} + \begin{thebibliography}{99} + \input{squelette_article.tex} + \end{thebibliography} } diff --git a/_todo/squelette/squelette_article.tex b/_todo/squelette/squelette_article.tex index 37a1b434..2d50f734 100644 --- a/_todo/squelette/squelette_article.tex +++ b/_todo/squelette/squelette_article.tex @@ -1,12 +1,12 @@ -% insre une entre dans la bibliographie -% 1 - identifiant -% 2 - anne -% 3 - auteurs -% 4 - titre -% 5 - revue -% 6 - volume -% 7 - page dbut -% 8 - page fin +% ins�re une entr�e dans la bibliographie +% 1 - identifiant +% 2 - ann�e +% 3 - auteurs +% 4 - titre +% 5 - revue +% 6 - volume +% 7 - page d�but +% 8 - page fin \bibitemstyle{Abuhaiba1996}{1996}{I. S. I. Abuhaiba, M. J. J. Holt, S. Datta} @@ -18,8 +18,8 @@ {IEEE Transactions on Pattern Analysis and Machine Intelligence}{7(4)}{463}{474} \bibitemstyle{Attali1995}{1995}{D. Attali} -{Squelette et Graphes de Vorono 2D et 3D} -{Thse de l'Universti Joseph Fourier, Grenoble I}{}{0}{} +{Squelette et Graphes de Vorono� 2D et 3D} +{Th�se de l'Universti� Joseph Fourier, Grenoble I}{}{0}{} \bibitemstyle{Blum1967} {1967} {Harry Blum} {A transformation for extracting new descriptors of shape} @@ -30,7 +30,7 @@ {Journal of Theoretical Biology}{38}{205}{287} \bibitemstyle{Breton2002}{2002}{R. Breton, E. Andres} -{Vectorisation d'une courbe discrte standard 2D} +{Vectorisation d'une courbe discr�te standard 2D} {AFIG}{}{0}{} \bibitemstyle{Chakravarthy2003}{2003} { V. Srinivasa Chakravarthy, Bhaskar Kompella } @@ -42,7 +42,7 @@ {Pattern Recognition}{36}{721}{729} \bibitemstyle{Cloppet2000}{2000}{F. Cloppet, J. M. Oliva, G. Stamon} -{Angular Bisector Network, a Simplified Generalized Vorono Diagram: Application to Processing Complex Intersections in Biomedical Images} +{Angular Bisector Network, a Simplified Generalized Vorono� Diagram: Application to Processing Complex Intersections in Biomedical Images} {IEEE Transactions on Pattern Analysis and Machine Intelligence}{22(1)}{120}{128} \bibitemstyle{Datta1997}{1997} {Amitava Datta, S.K. Parui } @@ -65,7 +65,7 @@ {One-pass Parallel Thinning Analysis, Properties, and Quantitative evaluation} {IEEE Transactions on Pattern Analysis and Machine Intelligence}{14(11)}{869}{885} -\bibitemstyle{Kalm\'ar1999}{1999}{Zsolt Kalm\'ar, Zsolt Marczell, Csaba Szepesv\'ari, Andr\'as Lrincz} +\bibitemstyle{Kalm\'ar1999}{1999}{Zsolt Kalm\'ar, Zsolt Marczell, Csaba Szepesv\'ari, Andr\'as L�rincz} {Parallel and robust skeletonization built on self-organizing elements} {Neural Networks}{12}{163}{173} @@ -87,7 +87,7 @@ \bibitemstyle{Marthon1979}{1979} {P. Marthon, A. Bruel, G. Biguet} {Squelettisation par calcul d'une fonction discriminante sur un voisinage de 8 points} -{Actes du second congrs AFCET~: Reconnaissance des Formes et Intelligence Articielles}{107}{114} +{Actes du second congr�s AFCET~: Reconnaissance des Formes et Intelligence Articielles}{107}{114} \bibitemstyle{Ogniewicz1992}{1992} {R. L. Ogniewicz} {Discrete Voronoi Skeletons} @@ -97,9 +97,9 @@ {Hierarchic Voronoi skeletons} {Pattern Recognition}{28}{343}{359} -\bibitemstyle{Reveills1991}{1991}{J. P. Reveills} -{Gomtrie discrte, calculs en nombre entiers et algorithmique} -{Thse, Universit Louis Pasteur, Strasbourg}{}{0}{} +\bibitemstyle{Reveill�s1991}{1991}{J. P. Reveill�s} +{G�om�trie discr�te, calculs en nombre entiers et algorithmique} +{Th�se, Universit� Louis Pasteur, Strasbourg}{}{0}{} \bibitemstyle{Rosenfeld1986}{1986} {A. Rosenfeld} {Axial representations of shape} @@ -119,11 +119,11 @@ \bibitemstyle{Thiel1994}{1994}{E. Thiel} {Les distances de chanfrein en analyse d'images~: fondements et applications} -{Thse, Universit Joseph Fourier, Grenoble I}{}{0}{} +{Th�se, Universit� Joseph Fourier, Grenoble I}{}{0}{} \bibitemstyle{Vittone1999}{1999} {J. Vittone} -{Caractrisation et reconnaissance de droites et de plans en gomtrie discrte} -{Thse, Universit Joseph Fourier, Grenoble I}{}{0}{} +{Caract�risation et reconnaissance de droites et de plans en g�om�trie discr�te} +{Th�se, Universit� Joseph Fourier, Grenoble I}{}{0}{} \bibitemstyle{Yeung1996}{1996} {Daniel S. Yeung, H. S. Fong} {A fuzzy substroke extractor for handwritten chinese characters} diff --git a/_todo/svm/svm.tex b/_todo/svm/svm.tex index 8ae50abf..c52e8df0 100644 --- a/_todo/svm/svm.tex +++ b/_todo/svm/svm.tex @@ -8,146 +8,146 @@ \label{annexe_svm} \indexfr{SVM} \indexsee{Support Vector Machine}{SVM} -\indexsee{Sparateur Vastes Marges}{SVM} +\indexsee{S�parateur � Vastes Marges}{SVM} \indexfrr{ACP}{SVM} -\indexfr{mthodes noyaux} +\indexfr{m�thodes � noyaux} -Les \emph{Support Vector Machine} ou \emph{Sparateurs Vastes Marges} (SVM) ont t pour la premire fois prsents par V. Vapnik ds 1979 (voir \citeindex{Vapnik1979}) et sont plus amplement dvelopps dans \citeindex{Vapnik1998}. Les dfinitions et rsultats proposs sont extraits de \citeindex{Burges1998}, document plus didactique d'aprs son auteur, \citeindex{Smola2004} -~ cet article existe en une version plus tendue (voir \citeindex{Smola1998})~- document plus complet qui prsente la rgression partir de SVM et l'article \citeindex{Mller2001}, document plus rcent qui voque notamment l'analyse en composantes principales partir de SVM. Ce dernier document applique les SVM la reconnaissance de caractres. Cette annexe n'a pas pour but de dcrire en dtail ces modles mais seulement de les introduire sommairement. Le site internet \textit{http://www.kernel-machines.org/} rfrence tous ces documents et recense les derniers dveloppements autour des mthodes noyaux dont font partie les SVM. Il rfrence galement un large panel d'applications ou de code informatique permettant d'utiliser les mthodes noyaux. +Les \emph{Support Vector Machine} ou \emph{S�parateurs � Vastes Marges} (SVM) ont �t� pour la premi�re fois pr�sent�s par V. Vapnik d�s 1979 (voir \citeindex{Vapnik1979}) et sont plus amplement d�velopp�s dans \citeindex{Vapnik1998}. Les d�finitions et r�sultats propos�s sont extraits de \citeindex{Burges1998}, document plus didactique d'apr�s son auteur, \citeindex{Smola2004} -~ cet article existe en une version plus �tendue (voir \citeindex{Smola1998})~- document plus complet qui pr�sente la r�gression � partir de SVM et l'article \citeindex{M�ller2001}, document plus r�cent qui �voque notamment l'analyse en composantes principales � partir de SVM. Ce dernier document applique les SVM � la reconnaissance de caract�res. Cette annexe n'a pas pour but de d�crire en d�tail ces mod�les mais seulement de les introduire sommairement. Le site internet \textit{http://www.kernel-machines.org/} r�f�rence tous ces documents et recense les derniers d�veloppements autour des m�thodes � noyaux dont font partie les SVM. Il r�f�rence �galement un large panel d'applications ou de code informatique permettant d'utiliser les m�thodes � noyaux. %------------------------------------------------------------------------------------------------------------------ -\section{Sparateur linaire} +\section{S�parateur lin�aire} %------------------------------------------------------------------------------------------------------------------ \label{svm_separateur_lineaire} -\subsection{Ensemble sparable} -\indexfrr{ensemble}{sparable} +\subsection{Ensemble s�parable} +\indexfrr{ensemble}{s�parable} -On s'intresse tout d'abord l'hyperplan sparateur d'un ensemble de points rpartis en deux classes. Cet ensemble est not $\pa{X_i,Y_i}_{1 \infegal i \infegal N}$ o, $\forall i$, $X_i \in \R^d$ et $Y_i \in \acc{-1,1}$. Pour simplifier les expressions par la suite, les deux classes sont donc labelles -1 et~1. On cherche alors un vecteur $w$ et une constante $b$ qui vrifient~: +On s'int�resse tout d'abord � l'hyperplan s�parateur d'un ensemble de points r�partis en deux classes. Cet ensemble est not� $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N}$ o�, $\forall i$, $X_i \in \mathbb{R}^d$ et $Y_i \in \acc{-1,1}$. Pour simplifier les expressions par la suite, les deux classes sont donc labell�es -1 et~1. On cherche alors un vecteur $w$ et une constante $b$ qui v�rifient~: - $$ - \forall i, \; 1 \infegal i \infegal N, \; - Y_i = \left\{ \begin{array}{rl} - -1 & \text{ si } w.X_i + b \supegal 1 \\ - 1 & \text{ si } w.X_i + b \infegal -1 - \end{array} \right. - $$ + $$ + \forall i, \; 1 \leqslant i \leqslant N, \; + Y_i = \left\{ \begin{array}{rl} + -1 & \text{ si } w.X_i + b \supegal 1 \\ + 1 & \text{ si } w.X_i + b \leqslant -1 + \end{array} \right. + $$ On cherche donc $w$ et $b$ tels que~: - $$ - \forall i, \; 1 \infegal i \infegal N, \; - Y_i \pa{ w.X_i + b} - 1 \supegal 0 - $$ + $$ + \forall i, \; 1 \leqslant i \leqslant N, \; + Y_i \pa{ w.X_i + b} - 1 \supegal 0 + $$ -Comme on cherche galement un vecteur $w$ de norme minimum, l'hyperplan cherch est la solution du problme de minimsation suivant~: +Comme on cherche �galement un vecteur $w$ de norme minimum, l'hyperplan cherch� est la solution du probl�me de minimsation suivant~: - \begin{xproblem}{meilleur hyperplan sparateur, cas sparable}\label{svm_problem_def} - \indexfr{sparable}\indexfrr{hyperplan}{sparateur} - Le meilleur hyperplan sparateur de l'ensemble de points labells - $\pa{X_i,Y_i}_{1 \infegal i \infegal N} \in \pa{ \R^d \times \acc{-1,1} }^N$ est la solution - d'un problme de minimisation. Cet hyperplan a pour quation $x.w^* + b^* = 0$ o - $w^*$ et $b^*$ vrifient~: - $$ - \begin{array}{rcl} \pa{w^*,b^*} &=& \underset{w,b}{\arg \min} \frac{1}{2} \norme{w}^2 \\ - && \text{avec } \forall i, \; Y_i \pa{ X_i .w + b } -1 \supegal 0 - \end{array} - $$ - \end{xproblem} + \begin{xproblem}{meilleur hyperplan s�parateur, cas s�parable}\label{svm_problem_def} + \indexfr{s�parable}\indexfrr{hyperplan}{s�parateur} + Le meilleur hyperplan s�parateur de l'ensemble de points labell�s + $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \in \pa{ \mathbb{R}^d \times \acc{-1,1} }^N$ est la solution + d'un probl�me de minimisation. Cet hyperplan a pour �quation $x.w^* + b^* = 0$ o� + $w^*$ et $b^*$ v�rifient~: + $$ + \begin{array}{rcl} \pa{w^*,b^*} &=& \underset{w,b}{\arg \min} \frac{1}{2} \norme{w}^2 \\ + && \text{avec } \forall i, \; Y_i \pa{ X_i .w + b } -1 \supegal 0 + \end{array} + $$ + \end{xproblem} \indexfrr{Lagrange}{multiplicateurs} -La rsolution d'un tel problme s'effectue l'aide des multiplicateurs de Lagrange, on affecte chaque contrainte le coefficient $\alpha_i$, il s'agit alors de minimiser l'expression~: +La r�solution d'un tel probl�me s'effectue � l'aide des multiplicateurs de Lagrange, on affecte � chaque contrainte le coefficient $\alpha_i$, il s'agit alors de minimiser l'expression~: - \begin{eqnarray} - L_P = \frac{1}{2} \norme{w}^2 - \summy{i=1}{N} \alpha_i Y_i \pa{ X_i . w + b } + \summy{i=1}{N} \alpha_i - \label{svm_lagrange_lineaire} - \end{eqnarray} + \begin{eqnarray} + L_P = \frac{1}{2} \norme{w}^2 - \summy{i=1}{N} \alpha_i Y_i \pa{ X_i . w + b } + \summy{i=1}{N} \alpha_i + \label{svm_lagrange_lineaire} + \end{eqnarray} -En drivant par rapport $w$ et $b$, on obtient que~: +En d�rivant par rapport � $w$ et $b$, on obtient que~: - \begin{eqnarray} - w &=& \sum_{i=1}^N \alpha_i Y_i X_i \\ - \summy{i=1}{N} \alpha_i Y_i &=& 0 - \end{eqnarray} - -Par consquent, on peut substituer l'expression~\ref{svm_lagrange_lineaire} par~: + \begin{eqnarray} + w &=& \sum_{i=1}^N \alpha_i Y_i X_i \\ + \summy{i=1}{N} \alpha_i Y_i &=& 0 + \end{eqnarray} + +Par cons�quent, on peut substituer l'expression~\ref{svm_lagrange_lineaire} par~: - \begin{eqnarray} - L_D = \frac{1}{2} \summy{i=1}{N}\summy{j=1}{N} - \alpha_i \alpha_j \, Y_i Y_j \, X_i . X_j - - \summy{i=1}{N} \alpha_i - \label{svm_lagrange_lineaire_2} - \end{eqnarray} + \begin{eqnarray} + L_D = \frac{1}{2} \summy{i=1}{N}\summy{j=1}{N} + \alpha_i \alpha_j \, Y_i Y_j \, X_i . X_j - + \summy{i=1}{N} \alpha_i + \label{svm_lagrange_lineaire_2} + \end{eqnarray} \indexfr{noyau}\indexfrr{fonction}{noyau} -Cette dernire quation (\ref{svm_lagrange_lineaire_2}) est importante puisqu'elle permet d'introduire les SVM non linaires pour lesquels le produit scalaire $X_i. X_j$ sera remplac par une fonction noyau $K\pa{X_i, X_j}$. +Cette derni�re �quation (\ref{svm_lagrange_lineaire_2}) est importante puisqu'elle permet d'introduire les SVM non lin�aires pour lesquels le produit scalaire $X_i. X_j$ sera remplac� par une fonction noyau $K\pa{X_i, X_j}$. -\subsection{Ensemble non sparable} -\indexfrr{ensemble}{non sparable} +\subsection{Ensemble non s�parable} +\indexfrr{ensemble}{non s�parable} -Le paragraphe prcdent supposait que l'ensemble $\pa{X_i,Y_i}_{1 \infegal i \infegal N} \in \pa{ \R^d \times \acc{-1,1} }^N$ tait sparable ce qui, d'aprs le paragraphe~\ref{svm_dimension_vc_lin} implique dans la plupart des cas que $N \infegal d+1$. Pour un ensemble non sparable (voir figure~\ref{svm_non_separable_fig}), il est impossible de trouver un hyperplan sparateur. Par consquent, il n'existe pas de solution au problme~\ref{svm_problem_def} vrifiant les contraintes telles qu'elles ont t exprimes. La recherche du meilleur hyperplan sparateur devient alors l'nonc~\ref{svm_problem_def_2}. +Le paragraphe pr�c�dent supposait que l'ensemble $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \in \pa{ \mathbb{R}^d \times \acc{-1,1} }^N$ �tait s�parable ce qui, d'apr�s le paragraphe~\ref{svm_dimension_vc_lin} implique dans la plupart des cas que $N \leqslant d+1$. Pour un ensemble non s�parable (voir figure~\ref{svm_non_separable_fig}), il est impossible de trouver un hyperplan s�parateur. Par cons�quent, il n'existe pas de solution au probl�me~\ref{svm_problem_def} v�rifiant les contraintes telles qu'elles ont �t� exprim�es. La recherche du meilleur hyperplan s�parateur devient alors l'�nonc�~\ref{svm_problem_def_2}. - \begin{figure}[ht] + \begin{figure}[ht] $$\frame{$\begin{array}[c|c]{c}\includegraphics[height=3cm, width=3cm] {\filext{../svm/image/non}}\end{array}$}$$ - \caption{ Exemple d'un nuage de points non sparable par un hyperplan.} + \caption{ Exemple d'un nuage de points non s�parable par un hyperplan.} \label{svm_non_separable_fig} - \end{figure} - - - - \begin{xproblem}{meilleur hyperplan sparateur, cas non sparable}\label{svm_problem_def_2} - \indexfr{non sparable} - Soit $C \in \R^*_+$ une constante et $k \in \N^*$ un entier, - le meilleur hyperplan sparateur de l'ensemble de points labells - $\pa{X_i,Y_i}_{1 \infegal i \infegal N} \in \pa{ \R^d \times \acc{-1,1} }^N$ est la solution - d'un problme de minimisation. Cet hyperplan a pour quation $x.w^* + b^* = 0$ o - $w^*$ et $b^*$ vrifient~: - $$ - \begin{array}{rcl} \pa{w^*,b^*} &=& \underset{w,b}{\arg \min} \dfrac{1}{2} \norme{w}^2 + - C \pa{\summy{i=1}{N} \xi_i}^k \\ - \text{avec } && \forall i, \; Y_i \pa{ X_i .w + b + \xi_i } - 1 \supegal 0 \\ - \text{et } && \forall i, \; \xi_i \supegal 0 - \end{array} - $$ - \end{xproblem} - -$C$ et $k$ sont des constantes dterminer. Toutefois, dans le cas o $k = 1$, la solution du problme prcdent est identique celle du problme suivant~: - - - \begin{xproblem}{meilleur hyperplan sparateur, cas non sparable, problme dual} - \label{svm_problem_def_2p}\indexfr{non sparable}\indexfrr{problme}{dual} - Soit $C \in \R^*_+$ une constante, - le meilleur hyperplan sparateur de l'ensemble de points labells - $\pa{X_i,Y_i}_{1 \infegal i \infegal N} \in \pa{ \R^d \times \acc{-1,1} }^N$ est la solution - d'un problme de minimisation. - $$ - \begin{array}{rcl} \pa{\alpha_i^*} &=& \underset{\alpha_i}{\arg \min} \dfrac{1}{2} - \summy{i=1}{N}\summy{j=1}{N} - \alpha_i \alpha_j \, - Y_i Y_j \, - X_i . X_j - - \summy{i=1}{N} \alpha_i \\ - \text{avec } && \forall i, \; 1 \infegal \alpha_i \infegal C \\ - \text{et } && \summy{i=1}{N} Y_i \, \alpha_i = 0 - \end{array} - $$ - L'hyperplan sparateur est donn par l'quation $ x.w + b = 0$ o - $w = \summy{i=1}{N} \alpha_i Y_i X_i$. - \end{xproblem} + \end{figure} + + + + \begin{xproblem}{meilleur hyperplan s�parateur, cas non s�parable}\label{svm_problem_def_2} + \indexfr{non s�parable} + Soit $C \in \mathbb{R}^*_+$ une constante et $k \in \N^*$ un entier, + le meilleur hyperplan s�parateur de l'ensemble de points labell�s + $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \in \pa{ \mathbb{R}^d \times \acc{-1,1} }^N$ est la solution + d'un probl�me de minimisation. Cet hyperplan a pour �quation $x.w^* + b^* = 0$ o� + $w^*$ et $b^*$ v�rifient~: + $$ + \begin{array}{rcl} \pa{w^*,b^*} &=& \underset{w,b}{\arg \min} \dfrac{1}{2} \norme{w}^2 + + C \pa{\summy{i=1}{N} \xi_i}^k \\ + \text{avec } && \forall i, \; Y_i \pa{ X_i .w + b + \xi_i } - 1 \supegal 0 \\ + \text{et } && \forall i, \; \xi_i \supegal 0 + \end{array} + $$ + \end{xproblem} + +$C$ et $k$ sont des constantes � d�terminer. Toutefois, dans le cas o� $k = 1$, la solution du probl�me pr�c�dent est identique � celle du probl�me suivant~: + + + \begin{xproblem}{meilleur hyperplan s�parateur, cas non s�parable, probl�me dual} + \label{svm_problem_def_2p}\indexfr{non s�parable}\indexfrr{probl�me}{dual} + Soit $C \in \mathbb{R}^*_+$ une constante, + le meilleur hyperplan s�parateur de l'ensemble de points labell�s + $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \in \pa{ \mathbb{R}^d \times \acc{-1,1} }^N$ est la solution + d'un probl�me de minimisation. + $$ + \begin{array}{rcl} \pa{\alpha_i^*} &=& \underset{\alpha_i}{\arg \min} \dfrac{1}{2} + \summy{i=1}{N}\summy{j=1}{N} + \alpha_i \alpha_j \, + Y_i Y_j \, + X_i . X_j + - \summy{i=1}{N} \alpha_i \\ + \text{avec } && \forall i, \; 1 \leqslant \alpha_i \leqslant C \\ + \text{et } && \summy{i=1}{N} Y_i \, \alpha_i = 0 + \end{array} + $$ + L'hyperplan s�parateur est donn� par l'�quation $ x.w + b = 0$ o� + $w = \summy{i=1}{N} \alpha_i Y_i X_i$. + \end{xproblem} \indexfr{dual} -Ce dernier problme est appele la forme duale du problme~\ref{svm_problem_def_2}. +Ce dernier probl�me est appel�e la forme duale du probl�me~\ref{svm_problem_def_2}. %------------------------------------------------------------------------------------------------------------------ \section{Dimension de Vapnik-Chervonenkis (VC)} @@ -156,246 +156,246 @@ \section{Dimension de Vapnik-Chervonenkis (VC)} -\subsection{Dfinition} +\subsection{D�finition} \indexfr{dimension de Vapnik-Chervonenkis} -Dans le problme de classification introduit au chapitre~\ref{svm_separateur_lineaire}, la dimension de Vapnik-Chervonenkis sert majorer le risque d'erreur de classification empirique au risque d'erreur thorique. Nous allons tout d'abord dfinir la dimension de Vapnik-Chervonenkis pour un ensemble de points donn et not $\pa{X_i}_{1 \infegal i \infegal N}$ et une classe de fonction $f\pa{x,\alpha}$ paramtre par $\alpha$. +Dans le probl�me de classification introduit au chapitre~\ref{svm_separateur_lineaire}, la dimension de Vapnik-Chervonenkis sert � majorer le risque d'erreur de classification empirique au risque d'erreur th�orique. Nous allons tout d'abord d�finir la dimension de Vapnik-Chervonenkis pour un ensemble de points donn� et not� $\pa{X_i}_{1 \leqslant i \leqslant N}$ et une classe de fonction $f\pa{x,\alpha}$ param�tr�e par $\alpha$. - \begin{xdefinition}{dimension de Vapnik-Chervonenkis} - Soit $\pa{X_i}_{1 \infegal i \infegal N}$ un ensemble de points appartenant $\R^d$. On dfinit une - fonction $f\pa{x,\alpha} : \R^d \times \Omega \longmapsto \R$ - o $x \in \R^d$ et $\alpha \in \Omega$. - $\Omega$ est appel l'ensemble des paramtres. - On dfinit la dimension de Vapnik-Chervonenkis comme tant le nombre de suites - $\pa{Y_i}_{1 \infegal i \infegal N} \in \acc{-1,1}^N$ vrifiant~: - $$ - \exists \alpha \in \Omega, \text{ tel que } \forall i, \; 1 \infegal i \infegal N, \; - sgn\pa{ f\pa{X_i,\alpha} } = Y_i - $$ - La fonction $sgn\pa{x}$ dsigne le signe de $x$~: $sgn\pa{x} = \left\{ \begin{array}{rl} - 1 & \text{si } x \supegal 0 \\ - -1 & \text{si } x < 0 - \end{array} \right. $ - - Par dfinition, cette dimension est infrieure $2^N$. - \end{xdefinition} + \begin{xdefinition}{dimension de Vapnik-Chervonenkis} + Soit $\pa{X_i}_{1 \leqslant i \leqslant N}$ un ensemble de points appartenant � $\mathbb{R}^d$. On d�finit une + fonction $f\pa{x,\alpha} : \mathbb{R}^d \times \Omega \longmapsto \mathbb{R}$ + o� $x \in \mathbb{R}^d$ et $\alpha \in \Omega$. + $\Omega$ est appel� l'ensemble des param�tres. + On d�finit la dimension de Vapnik-Chervonenkis comme �tant le nombre de suites + $\pa{Y_i}_{1 \leqslant i \leqslant N} \in \acc{-1,1}^N$ v�rifiant~: + $$ + \exists \alpha \in \Omega, \text{ tel que } \forall i, \; 1 \leqslant i \leqslant N, \; + sgn\pa{ f\pa{X_i,\alpha} } = Y_i + $$ + La fonction $sgn\pa{x}$ d�signe le signe de $x$~: $sgn\pa{x} = \left\{ \begin{array}{rl} + 1 & \text{si } x \supegal 0 \\ + -1 & \text{si } x < 0 + \end{array} \right. $ + + Par d�finition, cette dimension est inf�rieure � $2^N$. + \end{xdefinition} -\subsection{Rsultats} +\subsection{R�sultats} \label{svm_dimension_vc_lin} -Dans le cas o la fonction $f$ est linaire, il existe quelques rsultats intressants. +Dans le cas o� la fonction $f$ est lin�aire, il existe quelques r�sultats int�ressants. - \begin{xtheorem}{dimension VC d'un ensemble de vecteurs linairement indpendants} - Soit un ensemble de $N$ points inclus dans l'espace vectoriel $\R^d$ dont un dfinit l'origine. - Alors les $N$ points peuvent tre spars de n'importe quel manire en deux classes - par des hyperplans orients si et seulement si les vecteurs positions sont linairement indpendants. - \end{xtheorem} + \begin{xtheorem}{dimension VC d'un ensemble de vecteurs lin�airement ind�pendants} + Soit un ensemble de $N$ points inclus dans l'espace vectoriel $\mathbb{R}^d$ dont un d�finit l'origine. + Alors les $N$ points peuvent �tre s�par�s de n'importe quel mani�re en deux classes + par des hyperplans orient�s si et seulement si les vecteurs positions sont lin�airement ind�pendants. + \end{xtheorem} - \begin{xcorollary}{dimension VC d'un ensemble de vecteurs linairement indpendants} - La dimension de Vapnik-Chervonenkis d'un ensemble d'hyperplans sparateurs de $\R^d$ est $d+1$ - puisqu'il est toujours possible de choisir $d+1$ points linairement indpendants qui puissent - tre spars quelque soit leurs classes. - \end{xcorollary} + \begin{xcorollary}{dimension VC d'un ensemble de vecteurs lin�airement ind�pendants} + La dimension de Vapnik-Chervonenkis d'un ensemble d'hyperplans s�parateurs de $\mathbb{R}^d$ est $d+1$ + puisqu'il est toujours possible de choisir $d+1$ points lin�airement ind�pendants qui puissent + �tre s�par�s quelque soit leurs classes. + \end{xcorollary} \subsection{Exemple} -On dfinit la suite de points $\pa{X_i}_{1 \infegal i \infegal N}$ par $\forall i, \, 1 \infegal i \infegal N, \; X_i = 10^{-i}$ et l'ensemble de fonctions~: +On d�finit la suite de points $\pa{X_i}_{1 \leqslant i \leqslant N}$ par $\forall i, \, 1 \leqslant i \leqslant N, \; X_i = 10^{-i}$ et l'ensemble de fonctions~: - $$ - \acc{\alpha \in \R, \; f\pa{x,\alpha} = \left\{ - \begin{array}{rl} - 1 & \text{ si } \sin \alpha x \supegal 0 \\ - -1 & \text{ si } \sin \alpha x < 0 - \end{array} \right.} - $$ + $$ + \acc{\alpha \in \mathbb{R}, \; f\pa{x,\alpha} = \left\{ + \begin{array}{rl} + 1 & \text{ si } \sin \alpha x \supegal 0 \\ + -1 & \text{ si } \sin \alpha x < 0 + \end{array} \right.} + $$ -Quelque soit la suite $\pa{Y_i}_{1 \infegal i \infegal N} \in \acc{-1,1}^N$, il est possible de choisir~: +Quelque soit la suite $\pa{Y_i}_{1 \leqslant i \leqslant N} \in \acc{-1,1}^N$, il est possible de choisir~: - $$ - \alpha = \pi \pa{ 1 + \summy{i=1}{N} \frac{ \pa{ 1 - Y_i} 10^i}{ 2 } } - $$ + $$ + \alpha = \pi \pa{ 1 + \summy{i=1}{N} \frac{ \pa{ 1 - Y_i} 10^i}{ 2 } } + $$ -De telle sorte que~: $\forall i, \; f\pa{X_i,\alpha} = Y_i$. Par consquent, la dimension VC cet ensemble de points associs l'ensemble de fonctions $f$ est $2^N$. +De telle sorte que~: $\forall i, \; f\pa{X_i,\alpha} = Y_i$. Par cons�quent, la dimension VC cet ensemble de points associ�s � l'ensemble de fonctions $f$ est $2^N$. \subsection{Risque} -\indexfrr{risque}{thorique} -On dfinit maintenant le risque thorique de classification comme tant~: +\indexfrr{risque}{th�orique} +On d�finit maintenant le risque th�orique de classification comme �tant~: - \begin{eqnarray} - R\pa{\alpha} = \int \frac{1}{2} \abs{y - f\pa{x,\alpha}} dP\pa{x,y} - \label{svm_risque_theorique} - \end{eqnarray} + \begin{eqnarray} + R\pa{\alpha} = \int \frac{1}{2} \abs{y - f\pa{x,\alpha}} dP\pa{x,y} + \label{svm_risque_theorique} + \end{eqnarray} \indexfrr{risque}{empirique} -Et le risque empirique pour le nuage de points $\pa{X_i,Y_i}_{1 \infegal i \infegal N}$ par~: +Et le risque empirique pour le nuage de points $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N}$ par~: - \begin{eqnarray} - R_{emp}\pa{\alpha} = \frac{1}{2N} \; \summy{i=1}{N} \abs{ Y_i - f\pa{X_i,\alpha}} - \label{svm_risque_empirique} - \end{eqnarray} + \begin{eqnarray} + R_{emp}\pa{\alpha} = \frac{1}{2N} \; \summy{i=1}{N} \abs{ Y_i - f\pa{X_i,\alpha}} + \label{svm_risque_empirique} + \end{eqnarray} - \begin{xtheorem}{majoration du risque empirique} - En reprenant les notations utilises dans les expressions - (\ref{svm_risque_theorique}) et (\ref{svm_risque_empirique}). Pour - un nuage de points $\pa{X_i,Y_i}_{1 \infegal i \infegal N} \in \pa{\R^d \times \acc{-1,1} }^N$, - on dmontre (voir \citeindex{Vapnik1995}) que $\forall \eta \in \cro{0,1}$~: - $$ - \pr{ - R\pa{\alpha} \infegal R_{emp}\pa{\alpha} + - \sqrt{\frac {h \pa{ 1+ \ln \frac{2N}{h} } - \ln \frac{\eta}{4} } - {N} - } - } = 1 - \eta - $$ - o $h$ est la dimension de Vapnik-Chervonenkis. - \end{xtheorem} - + \begin{xtheorem}{majoration du risque empirique} + En reprenant les notations utilis�es dans les expressions + (\ref{svm_risque_theorique}) et (\ref{svm_risque_empirique}). Pour + un nuage de points $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \in \pa{\mathbb{R}^d \times \acc{-1,1} }^N$, + on d�montre (voir \citeindex{Vapnik1995}) que $\forall \eta \in \cro{0,1}$~: + $$ + \pr{ + R\pa{\alpha} \leqslant R_{emp}\pa{\alpha} + + \sqrt{\frac {h \pa{ 1+ \ln \frac{2N}{h} } - \ln \frac{\eta}{4} } + {N} + } + } = 1 - \eta + $$ + o� $h$ est la dimension de Vapnik-Chervonenkis. + \end{xtheorem} + %------------------------------------------------------------------------------------------------------------------ -\section{Sparateur non linaire} +\section{S�parateur non lin�aire} %------------------------------------------------------------------------------------------------------------------ \subsection{Principe} -Il est possible d'tendre les SVM au cas non linaire partir du problme~\ref{svm_problem_def_2} d'aprs \citeindex{Boser1992} en remplaant le produit scalaire $X_i . X_j$ par une fonction noyau telle qu'une fonction gaussienne\footnote{$K\pa{X,Y} = \exp\pa{ - \frac{ \norme{ X - Y }^2 }{2 \sigma^2}}$}. +Il est possible d'�tendre les SVM au cas non lin�aire � partir du probl�me~\ref{svm_problem_def_2} d'apr�s \citeindex{Boser1992} en rempla�ant le produit scalaire $X_i . X_j$ par une fonction noyau telle qu'une fonction gaussienne\footnote{$K\pa{X,Y} = \exp\pa{ - \frac{ \norme{ X - Y }^2 }{2 \sigma^2}}$}. - \begin{xproblem}{meilleur hyperplan, cas non sparable, non linaire, problme dual}\label{svm_problem_def_3} - \indexfr{non sparable} - \indexfrr{problme}{dual} - Soit $C \in \R^*_+$ une constante, soit $K : \R^d \times \R^d \longmapsto \R^+$ une fonction noyau, - le meilleur hyperplan sparateur de l'ensemble de points labells - $\pa{X_i,Y_i}_{1 \infegal i \infegal N} \in \pa{ \R^d \times \acc{-1,1} }^N$ est la solution - d'un problme de minimisation~: - $$ - \begin{array}{rcl} \pa{\alpha_i^*} &=& \underset{\alpha_i}{\arg \min} \dfrac{1}{2} - \summy{i=1}{N}\summy{j=1}{N} - \alpha_i \alpha_j \, - Y_i Y_j \, - K\pa{X_i,X_j} - - \summy{i=1}{N} \alpha_i \\ - \text{avec } && \forall i, \; 1 \infegal \alpha_i \infegal C \\ - \text{et } && \summy{i=1}{N} Y_i \, \alpha_i = 0 - \end{array} - $$ - La classification d'un lment $x \in \R^d$ dpend du signe de la fonction~: - $$ - f\pa{x} = \summy{i=1}{N} \alpha_i Y_i K\pa{X_i,x} + b - $$ - \end{xproblem} + \begin{xproblem}{meilleur hyperplan, cas non s�parable, non lin�aire, probl�me dual}\label{svm_problem_def_3} + \indexfr{non s�parable} + \indexfrr{probl�me}{dual} + Soit $C \in \mathbb{R}^*_+$ une constante, soit $K : \mathbb{R}^d \times \mathbb{R}^d \longmapsto \mathbb{R}^+$ une fonction noyau, + le meilleur hyperplan s�parateur de l'ensemble de points labell�s + $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \in \pa{ \mathbb{R}^d \times \acc{-1,1} }^N$ est la solution + d'un probl�me de minimisation~: + $$ + \begin{array}{rcl} \pa{\alpha_i^*} &=& \underset{\alpha_i}{\arg \min} \dfrac{1}{2} + \summy{i=1}{N}\summy{j=1}{N} + \alpha_i \alpha_j \, + Y_i Y_j \, + K\pa{X_i,X_j} + - \summy{i=1}{N} \alpha_i \\ + \text{avec } && \forall i, \; 1 \leqslant \alpha_i \leqslant C \\ + \text{et } && \summy{i=1}{N} Y_i \, \alpha_i = 0 + \end{array} + $$ + La classification d'un �l�ment $x \in \mathbb{R}^d$ d�pend du signe de la fonction~: + $$ + f\pa{x} = \summy{i=1}{N} \alpha_i Y_i K\pa{X_i,x} + b + $$ + \end{xproblem} -\subsection{Interprtation, exemple} +\subsection{Interpr�tation, exemple} -Le problme~\ref{svm_problem_def_3} revient en fait projeter l'lment $X_i \in \R^d$ dans un autre espace de dimension gnralement suprieure $\R^{d'}$ dans lequel la sparation sera un hyperplan. Par exemple, on dfinit le noyau $K : \R^2 \times \R^2 \longmapsto \R^+$ par~: +Le probl�me~\ref{svm_problem_def_3} revient en fait � projeter l'�l�ment $X_i \in \mathbb{R}^d$ dans un autre espace de dimension g�n�ralement sup�rieure $\mathbb{R}^{d'}$ dans lequel la s�paration sera un hyperplan. Par exemple, on d�finit le noyau $K : \mathbb{R}^2 \times \mathbb{R}^2 \longmapsto \mathbb{R}^+$ par~: - $$ - K\pa{X_i,X_j} = \pa{X_i.X_j}^2 - $$ + $$ + K\pa{X_i,X_j} = \pa{X_i.X_j}^2 + $$ -On dfinit galement la fonction $\Phi : \R^2 \longmapsto \R^3$ par~: +On d�finit �galement la fonction $\Phi : \mathbb{R}^2 \longmapsto \mathbb{R}^3$ par~: - $$ - \Phi\pa{x_1,x_2} = \pa{ \begin{array}{c} x_1^2 \\ \sqrt{2} x_1 x_2 \\ x_2^2 \end{array} } - $$ + $$ + \Phi\pa{x_1,x_2} = \pa{ \begin{array}{c} x_1^2 \\ \sqrt{2} x_1 x_2 \\ x_2^2 \end{array} } + $$ -On vrifie alors que~: +On v�rifie alors que~: - $$ - K\pa{X_i,X_j} = \Phi\pa{X_i} . \Phi\pa{X_j} - $$ + $$ + K\pa{X_i,X_j} = \Phi\pa{X_i} . \Phi\pa{X_j} + $$ \indexfr{Mercer} -Plus gnralement, pour qu'un noyau $K$ corresponde un produit scalaire dans un espace de dimension suprieure, il suffit qu'il vrifie la conditions de Mercer (voir \citeindex{Vapnik1995})~: - - $$ - \begin{tabular}{l} - Pour toute fonction $g$ telle que~: $\displaystyle\int g(x)^2 dx < + \infty$ alors \\ - $\displaystyle\int K\pa{x,y} g(x) g(y) dx dy \supegal 0$ - \end{tabular} - $$ - - - +Plus g�n�ralement, pour qu'un noyau $K$ corresponde � un produit scalaire dans un espace de dimension sup�rieure, il suffit qu'il v�rifie la conditions de Mercer (voir \citeindex{Vapnik1995})~: + + $$ + \begin{tabular}{l} + Pour toute fonction $g$ telle que~: $\displaystyle\int g(x)^2 dx < + \infty$ alors \\ + $\displaystyle\int K\pa{x,y} g(x) g(y) dx dy \supegal 0$ + \end{tabular} + $$ + + + \subsection{Autre formulation} -Le problme~\ref{svm_problem_def_3} peut tre formul d'une manire diffrente proche de celle du problme~\ref{svm_problem_def_2}. - - - \begin{xproblem}{meilleur hyperplan sparateur, cas non sparable, non linaire}\label{svm_problem_def_4} - \indexfr{non sparable} - Soit $C \in \R^*_+$ une constante, soit $K : \R^d \times \R^d \longmapsto \R^+$ une fonction noyau, - le meilleur hyperplan sparateur de l'ensemble de points labells - $\pa{X_i,Y_i}_{1 \infegal i \infegal N} \in \pa{ \R^d \times \acc{-1,1} }^N$ est la solution - d'un problme de minimisation~: - $$ - \begin{array}{rcl} \pa{\alpha_i^*, \xi_i^*} &=& \underset{\alpha_i}{\arg \min} \dfrac{1}{2} - \summy{i=1}{N}\summy{j=1}{N} - \alpha_i \alpha_j \, - Y_i Y_j \, - K\pa{X_i,X_j} - + C \pa{\summy{i=1}{N} \xi_i}^k \\ \\ - \text{avec } && \forall i, \; Y_i \pa{ \summy{k=1}{N} \alpha_k Y_k K\pa{X_k,x} + b + \xi_i } - 1 \supegal 0 \\ - \text{et } && \forall i, \; \xi_i \supegal 0 - \end{array} - $$ - La classification d'un lment $x \in \R^d$ dpend du signe de la fonction~: - $$ - f\pa{x} = \summy{i=1}{N} \alpha_i Y_i K\pa{X_i,x} + b - $$ - \end{xproblem} - - +Le probl�me~\ref{svm_problem_def_3} peut �tre formul� d'une mani�re diff�rente proche de celle du probl�me~\ref{svm_problem_def_2}. + + + \begin{xproblem}{meilleur hyperplan s�parateur, cas non s�parable, non lin�aire}\label{svm_problem_def_4} + \indexfr{non s�parable} + Soit $C \in \mathbb{R}^*_+$ une constante, soit $K : \mathbb{R}^d \times \mathbb{R}^d \longmapsto \mathbb{R}^+$ une fonction noyau, + le meilleur hyperplan s�parateur de l'ensemble de points labell�s + $\pa{X_i,Y_i}_{1 \leqslant i \leqslant N} \in \pa{ \mathbb{R}^d \times \acc{-1,1} }^N$ est la solution + d'un probl�me de minimisation~: + $$ + \begin{array}{rcl} \pa{\alpha_i^*, \xi_i^*} &=& \underset{\alpha_i}{\arg \min} \dfrac{1}{2} + \summy{i=1}{N}\summy{j=1}{N} + \alpha_i \alpha_j \, + Y_i Y_j \, + K\pa{X_i,X_j} + + C \pa{\summy{i=1}{N} \xi_i}^k \\ \\ + \text{avec } && \forall i, \; Y_i \pa{ \summy{k=1}{N} \alpha_k Y_k K\pa{X_k,x} + b + \xi_i } - 1 \supegal 0 \\ + \text{et } && \forall i, \; \xi_i \supegal 0 + \end{array} + $$ + La classification d'un �l�ment $x \in \mathbb{R}^d$ d�pend du signe de la fonction~: + $$ + f\pa{x} = \summy{i=1}{N} \alpha_i Y_i K\pa{X_i,x} + b + $$ + \end{xproblem} + + %------------------------------------------------------------------------------------------------------------------ \section{Extensions} %------------------------------------------------------------------------------------------------------------------ \subsection{Classification en plusieurs classes} -Jusqu' prsent, le seul problme voqu concerne une classification en deux classes. Une classification en $N$ classes est nanmoins possible selon deux stratgies. La premire consiste isoler une classe contre toutes les autres puis procder rcusivement de cette manire jusqu' finalement obtenir un problme de classification en deux classes. La seconde stratgie consiste regrouper le nombre de classes en deux groupes puis appliquer la mthode des SVM. Ensuite, l'intrieur de chaque groupe, on ritre cette mthode de manire diviser le nombre de classes jusqu' obtenir un problme de classification deux classes. +Jusqu'� pr�sent, le seul probl�me �voqu� concerne une classification en deux classes. Une classification en $N$ classes est n�anmoins possible selon deux strat�gies. La premi�re consiste � isoler une classe contre toutes les autres puis � proc�der r�cusivement de cette mani�re jusqu'� finalement obtenir un probl�me de classification en deux classes. La seconde strat�gie consiste � regrouper le nombre de classes en deux groupes puis � appliquer la m�thode des SVM. Ensuite, � l'int�rieur de chaque groupe, on r�it�re cette m�thode de mani�re � diviser le nombre de classes jusqu'� obtenir un probl�me de classification � deux classes. -Il existe une autre possibilit plus coteuse et plus fiable. Si on dsire raliser une classification en $N$ classes, plutt que de raliser au plus $N-1$ classifications en deux classes, on ralise $\frac{(N-1)^2}{2}$ classifications pour tous les couples de deux diffrentes classes. Il suffit de prendre la classe qui ressort le plus souvent vainqueur. +Il existe une autre possibilit� plus co�teuse et plus fiable. Si on d�sire r�aliser une classification en $N$ classes, plut�t que de r�aliser au plus $N-1$ classifications en deux classes, on r�alise $\frac{(N-1)^2}{2}$ classifications pour tous les couples de deux diff�rentes classes. Il suffit de prendre la classe qui ressort le plus souvent vainqueur. -\subsection{Ensembles rduits} +\subsection{Ensembles r�duits} -L'article \citeindex{Burges1997} propose une mthode permettant de rduire l'ensemble de point $\pa{X_i}$ afin d'acclerer la rsolution du problme de minimisation, tout en n'accroissant que lgrement l'erreur ($\sim$1\% d'aprs les auteurs). +L'article \citeindex{Burges1997} propose une m�thode permettant de r�duire l'ensemble de point $\pa{X_i}$ afin d'acc�lerer la r�solution du probl�me de minimisation, tout en n'accroissant que l�g�rement l'erreur ($\sim$1\% d'apr�s les auteurs). \indexfr{courbure} -L'article \citeindex{Zhan2005} propose une autre mthode qui supprime des points. Plusieurs optimisations sont ralises et, aprs chaque tape, les points proches des zones de forte courbure de la frontire sont enlevs de l'ensemble d'apprentissage. L'article tudie la perte de performances en fonction du nombre de points supprims. +L'article \citeindex{Zhan2005} propose une autre m�thode qui supprime des points. Plusieurs optimisations sont r�alis�es et, apr�s chaque �tape, les points proches des zones de forte courbure de la fronti�re sont enlev�s de l'ensemble d'apprentissage. L'article �tudie la perte de performances en fonction du nombre de points supprim�s. -\subsection{Slection des paramtres} +\subsection{S�lection des param�tres} -Les problmes de minimisation~\ref{svm_problem_def_2}, \ref{svm_problem_def_2p}, \ref{svm_problem_def_3} et~\ref{svm_problem_def_4} mentionnent une constante $C$ dont l'article \citeindex{Mattera1999} (voir galement \citeindex{Cherkassky2004}) discute le choix dans le cas non pas d'un problme de classification mais dans celui d'une rgression l'aide des SVM. -\indexfr{rgression} +Les probl�mes de minimisation~\ref{svm_problem_def_2}, \ref{svm_problem_def_2p}, \ref{svm_problem_def_3} et~\ref{svm_problem_def_4} mentionnent une constante $C$ dont l'article \citeindex{Mattera1999} (voir �galement \citeindex{Cherkassky2004}) discute le choix dans le cas non pas d'un probl�me de classification mais dans celui d'une r�gression � l'aide des SVM. +\indexfr{r�gression} -\subsection{Rgression} -\indexfr{rgression} +\subsection{R�gression} +\indexfr{r�gression} @@ -407,9 +407,9 @@ \subsection{R \newpage \firstpassagedo{ - \begin{thebibliography}{99} - \input{svm_biblio.tex} - \end{thebibliography} + \begin{thebibliography}{99} + \input{svm_biblio.tex} + \end{thebibliography} } diff --git a/_unittests/ut_data/test_LONG_wikipedia_pageviews.py b/_unittests/ut_data/test_LONG_wikipedia_pageviews.py deleted file mode 100644 index 5b80357b..00000000 --- a/_unittests/ut_data/test_LONG_wikipedia_pageviews.py +++ /dev/null @@ -1,28 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=60s) -""" -import unittest -from datetime import datetime -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder, ExtTestCase -from mlstatpy.data.wikipedia import download_pageviews - - -class TestLONGWikipediaPageViews(ExtTestCase): - - def test_wikipedia_page_views(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - temp = get_temp_folder(__file__, "temp_wikipedia_pageviews") - name = download_pageviews( - datetime(2016, 5, 6, 10), folder=temp, fLOG=fLOG) - self.assertLesser(len(name), 2000) - self.assertExists(name) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_data/test_LONG_wikipedia_titles.py b/_unittests/ut_data/test_LONG_wikipedia_titles.py deleted file mode 100644 index 8d18b8ad..00000000 --- a/_unittests/ut_data/test_LONG_wikipedia_titles.py +++ /dev/null @@ -1,26 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=60s) -""" -import unittest -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder, ExtTestCase -from mlstatpy.data.wikipedia import download_titles - - -class TestLONGWikipediaPageCount(ExtTestCase): - - def test_wikipedia_page_count(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - temp = get_temp_folder(__file__, "temp_wikipedia_title") - name = download_titles("fr", folder=temp, fLOG=fLOG) - self.assertLesser(len(name), 2000) - self.assertExists(name) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_data/test_wikipedia_dump.py b/_unittests/ut_data/test_wikipedia_dump.py index 572ae557..0e62c235 100644 --- a/_unittests/ut_data/test_wikipedia_dump.py +++ b/_unittests/ut_data/test_wikipedia_dump.py @@ -1,38 +1,22 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=60s) -""" import unittest -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder, ExtTestCase +from mlstatpy.ext_test_case import get_temp_folder, ExtTestCase from mlstatpy.data.wikipedia import download_dump class TestWikipediaDump(ExtTestCase): - def test_wikipedia_dump(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - temp = get_temp_folder(__file__, "temp_wikipedia_abstract_gz") - name = download_dump("fr", "latest-abstract.xml.gz-rss.xml", - folder=temp, fLOG=fLOG, unzip=False) - fLOG(name) + name = download_dump( + "fr", "latest-page.sql.gz-rss.xml", folder=temp, unzip=False + ) + # print(name) self.assertTrue(name is not None) self.assertExists(name) def test_wikipedia_dump_zipped(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - temp = get_temp_folder(__file__, "temp_wikipedia_dump_gz") - name = download_dump("fr", "latest-site_stats.sql.gz", - folder=temp, fLOG=fLOG, unzip=True) - fLOG(name) + name = download_dump("fr", "latest-site_stats.sql.gz", folder=temp, unzip=True) + # print(name) self.assertTrue(name is not None) self.assertExists(name) self.assertTrue(not name.endswith("gz")) diff --git a/_unittests/ut_documentation/test_1_2_3_coverage_notebook.py b/_unittests/ut_documentation/test_1_2_3_coverage_notebook.py deleted file mode 100644 index c65671c9..00000000 --- a/_unittests/ut_documentation/test_1_2_3_coverage_notebook.py +++ /dev/null @@ -1,53 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=21s) -""" -import os -import unittest -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder, add_missing_development_version -from pyquickhelper.ipythonhelper import execute_notebook_list, execute_notebook_list_finalize_ut, get_additional_paths -import mlstatpy as thismodule - - -class TestNotebook123Coverage(unittest.TestCase): - - def setUp(self): - add_missing_development_version(["pyensae", "jyquickhelper"], - __file__, hide=True) - - def a_test_notebook_runner(self, name, folder, valid=None): - temp = get_temp_folder(__file__, "temp_notebook_123_{0}".format(name)) - doc = os.path.join(temp, "..", "..", "..", "_doc", "notebooks", folder) - self.assertTrue(os.path.exists(doc)) - keepnote = [os.path.join(doc, _) for _ in os.listdir(doc) if name in _] - self.assertTrue(len(keepnote) > 0) - - import pyquickhelper # pylint: disable=C0415 - import jyquickhelper # pylint: disable=C0415 - import pyensae # pylint: disable=C0415 - add_path = get_additional_paths( - [jyquickhelper, pyquickhelper, pyensae, thismodule]) - res = execute_notebook_list( - temp, keepnote, additional_path=add_path, valid=valid) - execute_notebook_list_finalize_ut(res, fLOG=fLOG, dump=thismodule) - - def test_notebook_roc(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - self.a_test_notebook_runner("roc", "metric") - - def test_notebook_pvalue(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - self.a_test_notebook_runner("pvalue", "metric") - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_documentation/test_LONG_run_notebooks_nlp.py b/_unittests/ut_documentation/test_LONG_run_notebooks_nlp.py deleted file mode 100644 index f5aeab50..00000000 --- a/_unittests/ut_documentation/test_LONG_run_notebooks_nlp.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=571s) -""" -import os -import unittest -from pyquickhelper.loghelper import fLOG, CustomLog -from pyquickhelper.pycode import get_temp_folder, add_missing_development_version -from pyquickhelper.ipythonhelper import execute_notebook_list, execute_notebook_list_finalize_ut -import mlstatpy - - -class TestLONGRunNotebooksNLP(unittest.TestCase): - - def setUp(self): - add_missing_development_version(["pymyinstall", "pyensae", "pymmails", "jyquickhelper"], - __file__, hide=True) - - def test_long_run_notebook(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - temp = get_temp_folder(__file__, "temp_run_notebooks_nlp") - - # selection of notebooks - fnb = os.path.normpath(os.path.join( - os.path.abspath(os.path.dirname(__file__)), "..", "..", "_doc", "notebooks", "nlp")) - keepnote = [] - for f in os.listdir(fnb): - if os.path.splitext(f)[-1] == ".ipynb" and "_long" in f: - keepnote.append(os.path.join(fnb, f)) - - # function to tell that a can be run - def valid(cell): - if "open_html_form" in cell: - return False - if "open_html_form" in cell: - return False - if "[50000, 100000, 200000, 500000, 500000, 1000000, 2000000, None]" in cell: - return False - if '
0) - - # function to tell that a can be run - def valid(cell): - if "open_html_form" in cell: - return False - if "open_window_params" in cell: - return False - if '
0) def test_image_video_kohonen(self): - temp = get_temp_folder(__file__, "temp_graph_distance") - - graph1 = [("a", "b"), ("b", "c"), ("b", "d"), ("d", "e"), - ("e", "f"), ("b", "f"), ("b", "g"), ("f", "g"), - ("a", "g"), ("a", "g"), ("c", "d"), ("d", "g"), - ("d", "h"), ("aa", "h"), ("aa", "c"), ("f", "h"), ] - graph2 = copy.deepcopy(graph1) + \ - [("h", "m"), ("m", "l"), ("l", "C"), ("C", "r"), - ("a", "k"), ("k", "l"), ("k", "C"), - ] + graph1 = [ + ("a", "b"), + ("b", "c"), + ("b", "d"), + ("d", "e"), + ("e", "f"), + ("b", "f"), + ("b", "g"), + ("f", "g"), + ("a", "g"), + ("a", "g"), + ("c", "d"), + ("d", "g"), + ("d", "h"), + ("aa", "h"), + ("aa", "c"), + ("f", "h"), + ] + graph2 = copy.deepcopy(graph1) + [ + ("h", "m"), + ("m", "l"), + ("l", "C"), + ("C", "r"), + ("a", "k"), + ("k", "l"), + ("k", "C"), + ] graph1 = GraphDistance(graph1) graph2 = GraphDistance(graph2) @@ -38,80 +54,95 @@ def test_image_video_kohonen(self): graph2["C"].label = "c" store = {} if len(list(graph1.enumerate_all_paths(True))) != 17: - raise Exception("expecting 17 here") + raise AssertionError("expecting 17 here") if len(list(graph2.enumerate_all_paths(True))) != 19: - raise Exception("expecting 19 here") + raise AssertionError("expecting 19 here") - distance, graph = graph1.distance_matching_graphs_paths(graph2, - use_min=False, store=store) + distance, graph = graph1.distance_matching_graphs_paths( + graph2, use_min=False, store=store + ) if graph["h"].Label != "h": - raise Exception("we expect this node to be merged in the process") + raise AssertionError("we expect this node to be merged in the process") if distance is None: - raise Exception("expecting something different from None") - - outfile1 = os.path.join(temp, "unittest_GraphDistance4_sub1.png") - outfile2 = os.path.join(temp, "unittest_GraphDistance4_sub2.png") - outfilef = os.path.join(temp, "unittest_GraphDistance4_subf.png") - - if is_travis_or_appveyor() == "travis": - warnings.warn("graphviz is not available") - return + raise AssertionError("expecting something different from None") vertices, edges = graph1.draw_vertices_edges() self.assertNotEmpty(vertices) self.assertNotEmpty(edges) - draw_graph_graphviz(vertices, edges, outfile1) vertices, edges = graph2.draw_vertices_edges() self.assertNotEmpty(vertices) self.assertNotEmpty(edges) - draw_graph_graphviz(vertices, edges, outfile2) - self.assertTrue(os.path.exists(outfile2)) vertices, edges = graph.draw_vertices_edges() self.assertNotEmpty(vertices) self.assertNotEmpty(edges) - draw_graph_graphviz(vertices, edges, outfilef) - self.assertTrue(os.path.exists(outfilef)) def test_unittest_GraphDistance2(self): - graph1 = [("a", "b"), ("b", "c"), ("b", "X"), ("X", "c"), - ("c", "d"), ("d", "e"), ("0", "b")] - graph2 = [("a", "b"), ("b", "c"), ("b", "X"), ("X", "c"), - ("c", "t"), ("t", "d"), ("d", "e"), ("d", "g")] + graph1 = [ + ("a", "b"), + ("b", "c"), + ("b", "X"), + ("X", "c"), + ("c", "d"), + ("d", "e"), + ("0", "b"), + ] + graph2 = [ + ("a", "b"), + ("b", "c"), + ("b", "X"), + ("X", "c"), + ("c", "t"), + ("t", "d"), + ("d", "e"), + ("d", "g"), + ] graph1 = GraphDistance(graph1) graph2 = GraphDistance(graph2) store = {} - distance, graph = graph1.distance_matching_graphs_paths(graph2, - use_min=False, store=store) + res, out, err = self.capture( + lambda: graph1.distance_matching_graphs_paths( + graph2, use_min=False, store=store, verbose=True + ) + ) + self.assertIn("[distance_matching_graphs_paths]", out) + self.assertIn("#", err) + distance, graph = res if distance is None: raise TypeError("expecting something different from None") allPaths = list(graph.enumerate_all_paths(True)) - if len(allPaths) == 0: + if not allPaths: raise ValueError("the number of paths should not be null") if distance == 0: raise ValueError("expecting a distance > 0") vertices, edges = graph.draw_vertices_edges() self.assertNotEmpty(vertices) self.assertNotEmpty(edges) - #GV.drawGraphEdgesVertices (vertices,edges, "unittest_GraphDistance2.png") + # GV.drawGraphEdgesVertices (vertices,edges, "unittest_GraphDistance2.png") node = graph.vertices["X"] if None in node.pair: - raise RuntimeError( - "unexpected, this node should be part of the common set") + raise RuntimeError("unexpected, this node should be part of the common set") vertices, edges = graph1.draw_vertices_edges() self.assertNotEmpty(vertices) - #GV.drawGraphEdgesVertices (vertices,edges, "unittest_GraphDistance2_sub1.png") + # GV.drawGraphEdgesVertices (vertices,edges, "unittest_GraphDistance2_sub1.png") vertices, edges = graph2.draw_vertices_edges() self.assertNotEmpty(vertices) - #GV.drawGraphEdgesVertices (vertices,edges, "unittest_GraphDistance2_sub2.png") + # GV.drawGraphEdgesVertices (vertices,edges, "unittest_GraphDistance2_sub2.png") def test_unittest_common_paths(self): - graph1 = [("a", "b"), ("b", "c"), ("b", "X"), ("X", "c"), - ("c", "d"), ("d", "e"), ("0", "b")] + graph1 = [ + ("a", "b"), + ("b", "c"), + ("b", "X"), + ("X", "c"), + ("c", "d"), + ("d", "e"), + ("0", "b"), + ] graph2 = graph1 graph1 = GraphDistance(graph1) graph2 = GraphDistance(graph2) @@ -128,4 +159,4 @@ def test_unittest_common_paths(self): if __name__ == "__main__": - unittest.main() + unittest.main(verbosity=2) diff --git a/_unittests/ut_graph/test_graphviz.py b/_unittests/ut_graph/test_graphviz.py deleted file mode 100644 index bc787ebc..00000000 --- a/_unittests/ut_graph/test_graphviz.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -@brief test log(time=2s) - -""" -import os -import unittest -from pyquickhelper.pycode import get_temp_folder, skipif_travis, skipif_appveyor -from mlstatpy.graph.graphviz_helper import draw_graph_graphviz - - -class TestGraphviz(unittest.TestCase): - - @skipif_appveyor("no graphviz") - @skipif_travis("no graphviz") - def test_make_video(self): - temp = get_temp_folder(__file__, "temp_graphviz") - fout = os.path.join(temp, "image.png") - - draw_graph_graphviz([(1, "eee", "red")], - [(1, 2, "blue"), (3, 4), (1, 3)], fout) - - self.assertTrue(os.path.exists(fout)) - self.assertTrue(os.path.exists(fout + ".gv")) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_image/test_binom.py b/_unittests/ut_image/test_binom.py index b5c78e74..6af21706 100644 --- a/_unittests/ut_image/test_binom.py +++ b/_unittests/ut_image/test_binom.py @@ -1,17 +1,24 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=38s) -""" import unittest from mlstatpy.image.detection_segment import tabule_queue_binom class TestQueueBinom(unittest.TestCase): - def test_queue(self): b = tabule_queue_binom(2, 2) - self.assertEqual(b, {(0, 1): 0.0, (1, 2): 0.0, (0, 0): 1.0, (2, 3): 0.0, - (2, 0): 1.0, (1, 0): 1.0, (2, 2): 4.0, (1, 1): 2.0, (2, 1): 0.0}) + self.assertEqual( + b, + { + (0, 1): 0.0, + (1, 2): 0.0, + (0, 0): 1.0, + (2, 3): 0.0, + (2, 0): 1.0, + (1, 0): 1.0, + (2, 2): 4.0, + (1, 1): 2.0, + (2, 1): 0.0, + }, + ) if __name__ == "__main__": diff --git a/_unittests/ut_image/test_geometrie.py b/_unittests/ut_image/test_geometrie.py index 4c6c970d..a885483c 100644 --- a/_unittests/ut_image/test_geometrie.py +++ b/_unittests/ut_image/test_geometrie.py @@ -1,14 +1,9 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=38s) -""" import unittest import math from mlstatpy.image.detection_segment import Point, Segment class TestGeometrie(unittest.TestCase): - def test_point(self): p = Point(2, 2) pp = Point(3, 5) @@ -23,7 +18,7 @@ def test_point(self): ar = pp.arrondi() self.assertEqual(ar, Point(3, 4)) sc = ar.scalaire(ar) - no = ar.norme()**2 + no = ar.norme() ** 2 self.assertEqual(sc, no) a = Point(1, 1).angle() b = Point(-1, 1).angle() diff --git a/_unittests/ut_image/test_random_image.py b/_unittests/ut_image/test_random_image.py index 2b85f119..0e9a5189 100644 --- a/_unittests/ut_image/test_random_image.py +++ b/_unittests/ut_image/test_random_image.py @@ -1,18 +1,16 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=2s) -""" import os import unittest import numpy -from pyquickhelper.pycode import ExtTestCase, get_temp_folder -from mlstatpy.image.detection_segment.random_image import random_noise_image, random_segment_image +from mlstatpy.ext_test_case import ExtTestCase, get_temp_folder +from mlstatpy.image.detection_segment.random_image import ( + random_noise_image, + random_segment_image, +) from mlstatpy.image.detection_segment import convert_array2PIL, convert_PIL2array from mlstatpy.image.detection_segment.detection_segment import detect_segments class TestRandomImage(ExtTestCase): - def test_random_noise_image(self): img = random_noise_image((100, 100), 0.1) total = img.sum() @@ -21,38 +19,38 @@ def test_random_noise_image(self): def test_random_segment_image(self): img = random_noise_image((12, 10), 0.0) - seg = random_segment_image(img, lmin=0.5, density=2.) + seg = random_segment_image(img, lmin=0.5, density=2.0) total = img.sum() self.assertGreater(total, 0) self.assertLesser(total, 3000) self.assertIsInstance(seg, dict) fimg = img.astype(numpy.float32) - img255 = (- fimg + 1) * 255 + img255 = (-fimg + 1) * 255 timg255 = img255.astype(numpy.uint8) pil = convert_array2PIL(timg255) img2 = convert_PIL2array(pil) temp = get_temp_folder(__file__, "temp_random_segment_image") - outfile = os.path.join(temp, 'img.png') + outfile = os.path.join(temp, "img.png") pil.save(outfile) self.assertEqual(timg255, img2) - pil2 = convert_array2PIL(img, mode='binary') + pil2 = convert_array2PIL(img, mode="binary") img3 = convert_PIL2array(pil2) self.assertEqual(timg255, img3) - for _ in range(0, 100): - seg = random_segment_image(img, lmin=0.5, density=2.) - self.assertGreater(seg['x1'], 0) - self.assertGreater(seg['y1'], 0) - self.assertGreater(seg['x2'], 0) - self.assertGreater(seg['y2'], 0) + for _ in range(100): + seg = random_segment_image(img, lmin=0.5, density=2.0) + self.assertGreater(seg["x1"], 0) + self.assertGreater(seg["y1"], 0) + self.assertGreater(seg["x2"], 0) + self.assertGreater(seg["y2"], 0) def test_segment_random_image(self): img = random_noise_image((100, 100)) - random_segment_image(img, density=3., lmin=0.3) - random_segment_image(img, density=5., lmin=0.3) - random_segment_image(img, density=5., lmin=0.3) + random_segment_image(img, density=3.0, lmin=0.3) + random_segment_image(img, density=5.0, lmin=0.3) + random_segment_image(img, density=5.0, lmin=0.3) seg = detect_segments(img, seuil_nfa=10, seuil_norme=1, verbose=0) # self.assertNotEmpty(seg) self.assertTrue(seg is not None) diff --git a/_unittests/ut_image/test_segments.py b/_unittests/ut_image/test_segments.py index 0f9c28e7..aa19c8f0 100644 --- a/_unittests/ut_image/test_segments.py +++ b/_unittests/ut_image/test_segments.py @@ -1,20 +1,21 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=10s) -""" import os import unittest import math -from pyquickhelper.pycode import ExtTestCase, get_temp_folder +from mlstatpy.ext_test_case import ExtTestCase, get_temp_folder from mlstatpy.image.detection_segment.geometrie import Point from mlstatpy.image.detection_segment.detection_segment_segangle import SegmentBord -from mlstatpy.image.detection_segment.detection_segment import detect_segments, plot_segments -from mlstatpy.image.detection_segment.detection_segment import _calcule_gradient, plot_gradient +from mlstatpy.image.detection_segment.detection_segment import ( + detect_segments, + plot_segments, +) +from mlstatpy.image.detection_segment.detection_segment import ( + _calcule_gradient, + plot_gradient, +) from mlstatpy import __file__ as rootfile class TestSegments(ExtTestCase): - visual = False def test_segment_bord(self): @@ -50,17 +51,16 @@ def attendre_clic(screen): reste = False break - import pygame # pylint: disable=C0415 + import pygame + pygame.init() screen = pygame.display.set_mode((xx * 4, yy * 4)) screen.fill((255, 255, 255)) pygame.display.flip() for i in range(1, 4): - pygame.draw.line(screen, (255, 0, 0), - (0, i * yy), (xx * 4, i * yy)) - pygame.draw.line(screen, (255, 0, 0), - (xx * i, 0), (xx * i, 4 * yy)) + pygame.draw.line(screen, (255, 0, 0), (0, i * yy), (xx * 4, i * yy)) + pygame.draw.line(screen, (255, 0, 0), (xx * i, 0), (xx * i, 4 * yy)) s = SegmentBord(Point(xx, yy), math.pi / 6) s.premier() @@ -69,13 +69,21 @@ def attendre_clic(screen): n = True angle = 0 x, y = 0, 0 - couleur = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255), - (255, 0, 255), (0, 0, 0), (128, 128, 128)] + couleur = [ + (255, 0, 0), + (0, 255, 0), + (0, 0, 255), + (255, 255, 0), + (0, 255, 255), + (255, 0, 255), + (0, 0, 0), + (128, 128, 128), + ] segs = [] c = 0 while n: if TestSegments.visual and __name__ == "__main__" and i % 100 == 0: - print("i={0} s={1}".format(i, s)) + print(f"i={i} s={s}") x = s.bord1 y = s.calcul_bord2() @@ -89,8 +97,7 @@ def attendre_clic(screen): n = s.next() if angle != s.angle: if TestSegments.visual and __name__ == "__main__": - print("changement angle = ", angle, - " --> ", s.angle, " clic ", s) + print("changement angle = ", angle, " --> ", s.angle, " clic ", s) pygame.draw.line(screen, couleur[c % len(couleur)], a, b) pygame.display.flip() # attendre_clic(screen) @@ -111,13 +118,15 @@ def attendre_clic(screen): self.assertEqual(seg.b.y, 122) def test_gradient_profile(self): - img = os.path.join(os.path.dirname(__file__), - "data", "eglise_zoom2.jpg") - rootrem = os.path.normpath(os.path.abspath( - os.path.join(os.path.dirname(rootfile), '..'))) - _, res = self.profile(lambda: _calcule_gradient( - img, color=0), rootrem=rootrem) - short = "\n".join(res.split('\n')[:15]) + img = os.path.join(os.path.dirname(__file__), "data", "eglise_zoom2.jpg") + rootrem = os.path.normpath( + os.path.abspath(os.path.join(os.path.dirname(rootfile), "..")) + ) + _, res = self.profile( + lambda: _calcule_gradient(img, color=0), + rootrem=rootrem, + ) + short = "\n".join(res.split("\n")[:15]) self.assertIn("_calcule_gradient", short) def test_gradient(self): @@ -131,22 +140,24 @@ def test_gradient(self): imgrad.save(grfile) self.assertExists(grfile) - with open(os.path.join(temp, "..", "data", "gradient--2.png"), 'rb') as f: + with open(os.path.join(temp, "..", "data", "gradient--2.png"), "rb") as f: c1 = f.read() - with open(os.path.join(temp, "..", "data", "gradient--2b.png"), 'rb') as f: + with open(os.path.join(temp, "..", "data", "gradient--2b.png"), "rb") as f: c1b = f.read() - with open(os.path.join(temp, "gradient--2.png"), 'rb') as f: + with open(os.path.join(temp, "gradient--2.png"), "rb") as f: c2 = f.read() self.assertIn(c2, (c1, c1b)) def test_segment_detection_profile(self): - img = os.path.join(os.path.dirname(__file__), - "data", "eglise_zoom2.jpg") - rootrem = os.path.normpath(os.path.abspath( - os.path.join(os.path.dirname(rootfile), '..'))) - _, res = self.profile(lambda: detect_segments( - img, stop=100), rootrem=rootrem) - short = "\n".join(res.split('\n')[:25]) + img = os.path.join(os.path.dirname(__file__), "data", "eglise_zoom2.jpg") + rootrem = os.path.normpath( + os.path.abspath(os.path.join(os.path.dirname(rootfile), "..")) + ) + _, res = self.profile( + lambda: detect_segments(img, stop=100), + rootrem=rootrem, + ) + short = "\n".join(res.split("\n")[:25]) if __name__ == "__main__": print(short) self.assertIn("detect_segments", short) diff --git a/_unittests/ut_ml/test_logreg.py b/_unittests/ut_ml/test_logreg.py index ad4d60c6..f192c172 100644 --- a/_unittests/ut_ml/test_logreg.py +++ b/_unittests/ut_ml/test_logreg.py @@ -1,14 +1,9 @@ -""" -@brief test log(time=2s) -@author Xavier Dupre -""" import unittest -from pyquickhelper.pycode import ExtTestCase +from mlstatpy.ext_test_case import ExtTestCase from mlstatpy.ml.logreg import criteria, criteria2, random_set_1d, plot_ds class TestLogReg(ExtTestCase): - def test_criteria(self): for b in [2, 3, 4]: with self.subTest(kind=b): @@ -24,13 +19,12 @@ def test_criteria_plot(self): df2 = criteria(X2, y2) import matplotlib.pyplot as plt + _, ax = plt.subplots(1, 2, figsize=(12, 6), sharey=True) plot_ds(X1, y1, ax=ax[0], title="easy") plot_ds(X2, y2, ax=ax[1], title="difficult") - df1.plot(x='X', y=['Gini', 'Gain', 'p1', 'p2'], - ax=ax[0], lw=5.) - df2.plot(x='X', y=['Gini', 'Gain', 'p1', 'p2'], - ax=ax[1], lw=5.) + df1.plot(x="X", y=["Gini", "Gain", "p1", "p2"], ax=ax[0], lw=5.0) + df2.plot(x="X", y=["Gini", "Gain", "p1", "p2"], ax=ax[1], lw=5.0) plt.clf() def test_criteria2(self): diff --git a/_unittests/ut_ml/test_matrices.py b/_unittests/ut_ml/test_matrices.py index 80c20cbf..32af670f 100644 --- a/_unittests/ut_ml/test_matrices.py +++ b/_unittests/ut_ml/test_matrices.py @@ -1,17 +1,18 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=2s) -""" import unittest import numpy import numpy.random as rnd -from pyquickhelper.pycode import ExtTestCase -from mlstatpy.ml.matrices import gram_schmidt, linear_regression, streaming_gram_schmidt -from mlstatpy.ml.matrices import streaming_linear_regression, streaming_linear_regression_gram_schmidt +from mlstatpy.ext_test_case import ExtTestCase +from mlstatpy.ml.matrices import ( + gram_schmidt, + linear_regression, + streaming_gram_schmidt, + streaming_linear_regression, + streaming_linear_regression_gram_schmidt, + norm2, +) class TestMatrices(ExtTestCase): - def test_gram_schmidt(self): mat1 = numpy.array([[1, 0], [0, 1]], dtype=float) res = gram_schmidt(mat1) @@ -19,13 +20,13 @@ def test_gram_schmidt(self): mat = numpy.array([[1, 0.5], [0.5, 1]], dtype=float) res = gram_schmidt(mat) - self.assertEqualArray(mat1, res @ res.T) + self.assertEqualArray(mat1, res @ res.T, atol=1e-10) res2 = gram_schmidt(mat.T).T res2, change2 = gram_schmidt(mat, change=True) self.assertEqual(res, res2) res3 = change2 @ mat - self.assertEqual(res3, res2) + self.assertEqual(res3, res2, atol=1e-8) mat1 = numpy.array([[1, 0, 0], [0, 0, 1]], dtype=float) res = gram_schmidt(mat1) @@ -35,10 +36,9 @@ def test_gram_schmidt(self): res = gram_schmidt(mat1) self.assertEqual(res[0, 2], 0) - mat1 = numpy.array( - [[1, 0.5, 0], [0, 0.5, 1], [1, 0.5, 1]], dtype=float) + mat1 = numpy.array([[1, 0.5, 0], [0, 0.5, 1], [1, 0.5, 1]], dtype=float) res = gram_schmidt(mat1) - self.assertEqualArray(numpy.identity(3), res @ res.T) + self.assertEqualArray(numpy.identity(3), res @ res.T, atol=1e-10) def test_gram_schmidt_xx(self): X = numpy.array([[1, 0.5, 0], [0, 0.4, 2]], dtype=float).T @@ -47,18 +47,18 @@ def test_gram_schmidt_xx(self): T = T.T m = P.T @ X.T z = m @ m.T - self.assertEqual(z, numpy.identity(2)) + self.assertEqualArray(z, numpy.identity(2), atol=1e-10) m = X @ P - self.assertEqual(m, T) + self.assertEqualArray(m, T, atol=1e-10) z2 = m.T @ m - self.assertEqual(z2, numpy.identity(2)) + self.assertEqualArray(z2, numpy.identity(2), atol=1e-10) def test_linear_regression(self): X = numpy.array([[1, 0.5, 0], [0, 0.4, 2]], dtype=float).T y = numpy.array([1, 1.3, 3.9]) b1 = linear_regression(X, y) b2 = linear_regression(X, y, algo="gram") - self.assertEqualArray(b1, b2) + self.assertEqualArray(b1, b2, atol=1e-8) def test_linear_regression_qr(self): X = numpy.array([[1, 0.5, 0], [0, 0.4, 2]], dtype=float).T @@ -66,43 +66,40 @@ def test_linear_regression_qr(self): b1 = linear_regression(X, y) b3 = linear_regression(X, y, algo="gram") b2 = linear_regression(X, y, algo="qr") - self.assertEqualArray(b1, b3) - self.assertEqualArray(b1, b2) + self.assertEqualArray(b1, b3, atol=1e-8) + self.assertEqualArray(b1, b2, atol=1e-8) def test_linear_regression_qr3(self): - X = numpy.array([[1, 0.5, 0], [0, 0.4, 2], [ - 0, 0.4, 2.1]], dtype=float).T + X = numpy.array([[1, 0.5, 0], [0, 0.4, 2], [0, 0.4, 2.1]], dtype=float).T y = numpy.array([1, 1.3, 3.9]) b1 = linear_regression(X, y) b3 = linear_regression(X, y, algo="gram") b2 = linear_regression(X, y, algo="qr") - self.assertEqualArray(b1, b3) - self.assertEqualArray(b1, b2) + self.assertEqualArray(b1, b3, atol=1e-8) + self.assertEqualArray(b1, b2, atol=1e-8) def test_dim_lin_reg(self): X = rnd.randn(100, 7) eps = rnd.randn(100, 1) / 3 - y = X.sum(axis=1).reshape( # pylint: disable=E1101 - (X.shape[0], 1)) + eps # pylint: disable=E1101 + y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps y = y.ravel() b1 = linear_regression(X, y) b3 = linear_regression(X, y, algo="gram") b2 = linear_regression(X, y, algo="qr") - self.assertEqualArray(b1.ravel(), b3.ravel()) - self.assertEqualArray(b1.ravel(), b2.ravel()) + self.assertEqualArray(b1.ravel(), b3.ravel(), atol=1e-8) + self.assertEqualArray(b1.ravel(), b2.ravel(), atol=1e-8) def test_inner_code(self): - - X = numpy.array([[1., 2., 3., 4.], - [5., 6., 6., 6.], - [5., 6., 7., 8.]]).T + X = numpy.array( + [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 6.0, 6.0], [5.0, 6.0, 7.0, 8.0]] + ).T y = numpy.array([0.1, 0.2, 0.19, 0.29]) Xt = X.T Tt = numpy.empty(Xt.shape) Pt = numpy.identity(X.shape[1]) - for i in range(0, Xt.shape[0]): + for i in range(Xt.shape[0]): Tt[i, :] = Xt[i, :] - for j in range(0, i): + for j in range(i): d = numpy.dot(Tt[j, :], Xt[i, :]) Tt[i, :] -= Tt[j, :] * d Pt[i, :] -= Pt[j, :] * d @@ -115,18 +112,23 @@ def test_inner_code(self): self.assertEqual(Tt.shape, Xt.shape) self.assertEqual(Pt.shape, (X.shape[1], X.shape[1])) _Tt = Pt @ Xt - self.assertEqualArray(_Tt, Tt) - self.assertEqualArray(Tt @ Tt.T, numpy.identity(Tt.shape[0])) + self.assertEqualArray(_Tt, Tt, atol=1e-8) + self.assertEqualArray(Tt @ Tt.T, numpy.identity(Tt.shape[0]), atol=1e-10) beta1 = numpy.linalg.inv(Xt @ X) @ Xt @ y beta2 = Tt @ y @ Pt - self.assertEqualArray(beta1, beta2) + self.assertEqualArray(beta1, beta2, atol=1e-8) def test_streaming_gram_schmidt(self): - X0 = numpy.array([[1, 0.5, 10., 5., -2.], - [0, 0.4, 20, 4., 2.], - [0, 0.4, 19, 4.2, 2.], - [0, 0.7, 18, 4.1, 1.4]], dtype=float).T + X0 = numpy.array( + [ + [1, 0.5, 10.0, 5.0, -2.0], + [0, 0.4, 20, 4.0, 2.0], + [0, 0.4, 19, 4.2, 2.0], + [0, 0.7, 18, 4.1, 1.4], + ], + dtype=float, + ).T for dim in (3, 2, 4): X = X0[:, :dim] Xt = X.T @@ -138,26 +140,33 @@ def test_streaming_gram_schmidt(self): algo1_t.append(t) algo1.append(p) t_ = X[:i] @ p.T - self.assertEqualArray(t @ t.T, idd) - self.assertEqualArray(t_.T @ t_, idd) + self.assertEqualArray(t @ t.T, idd, atol=1e-10) + self.assertEqualArray(t_.T @ t_, idd, atol=1e-10) algo2 = [] - self.assertRaise(lambda: list(streaming_gram_schmidt(X)), # pylint: disable=W0640 - RuntimeError) + self.assertRaise( + lambda X=X: list(streaming_gram_schmidt(X)), + RuntimeError, + ) for i, p in enumerate(streaming_gram_schmidt(Xt)): p2 = p.copy() - t2 = X[:i + dim] @ p2.T + t2 = X[: i + dim] @ p2.T algo2.append(p2) self.assertNotEmpty(t2) self.assertNotEmpty(p2) - self.assertEqualArray(t2.T @ t2, idd) + self.assertEqualArray(t2.T @ t2, idd, atol=1e-10) self.assertEqual(len(algo1), len(algo2)) def test_streaming_linear_regression(self): - X0 = numpy.array([[1, 0.5, 10., 5., -2.], - [0, 0.4, 20, 4., 2.], - [0, 0.4, 19, 4.2, 2.], - [0, 0.7, 18, 4.1, 1.4]], dtype=float).T - y0 = numpy.array([1., 2.1, 3.1, 3.9, 5.]) + X0 = numpy.array( + [ + [1, 0.5, 10.0, 5.0, -2.0], + [0, 0.4, 20, 4.0, 2.0], + [0, 0.4, 19, 4.2, 2.0], + [0, 0.7, 18, 4.1, 1.4], + ], + dtype=float, + ).T + y0 = numpy.array([1.0, 2.1, 3.1, 3.9, 5.0]) for dim in (2, 3, 4): X = X0[:, :dim] @@ -167,20 +176,27 @@ def test_streaming_linear_regression(self): bk = linear_regression(X[:i], y[:i]) algo1.append(bk) algo2 = [] - self.assertRaise(lambda: list(streaming_linear_regression(X.T, y)), # pylint: disable=W0640 - RuntimeError) + self.assertRaise( + lambda X=X, y=y: list(streaming_linear_regression(X.T, y)), + RuntimeError, + ) for i, bk in enumerate(streaming_linear_regression(X, y)): algo2.append(bk.copy()) self.assertNotEmpty(bk) - self.assertEqualArray(algo1[i], algo2[i]) + self.assertEqualArray(algo1[i], algo2[i], atol=1e-8) self.assertEqual(len(algo1), len(algo2)) def test_streaming_linear_regression_graph_schmidt(self): - X0 = numpy.array([[1, 0.5, 10., 5., -2.], - [0, 0.4, 20, 4., 2.], - [0, 0.4, 19, 4.2, 2.], - [0, 0.7, 18, 4.1, 1.4]], dtype=float).T - y0 = numpy.array([1., 2.1, 3.1, 3.9, 5.]) + X0 = numpy.array( + [ + [1, 0.5, 10.0, 5.0, -2.0], + [0, 0.4, 20, 4.0, 2.0], + [0, 0.4, 19, 4.2, 2.0], + [0, 0.7, 18, 4.1, 1.4], + ], + dtype=float, + ).T + y0 = numpy.array([1.0, 2.1, 3.1, 3.9, 5.0]) for dim in (3, 2, 4): X = X0[:, :dim] @@ -190,31 +206,32 @@ def test_streaming_linear_regression_graph_schmidt(self): bk = linear_regression(X[:i], y[:i]) algo1.append(bk) algo2 = [] - self.assertRaise(lambda: list(streaming_linear_regression_gram_schmidt(X.T, y)), # pylint: disable=W0640 - RuntimeError) + self.assertRaise( + lambda X=X, y=y: list(streaming_linear_regression_gram_schmidt(X.T, y)), + RuntimeError, + ) for i, bk in enumerate(streaming_linear_regression_gram_schmidt(X, y)): algo2.append(bk.copy()) self.assertNotEmpty(bk) - self.assertEqualArray(algo1[i], algo2[i]) + self.assertEqualArray(algo1[i], algo2[i], atol=1e-8) self.assertEqual(len(algo1), len(algo2)) def test_profile(self): N = 1000 X = rnd.randn(N, 10) eps = rnd.randn(N, 1) / 3 - y = X.sum(axis=1).reshape( # pylint: disable=E1101 - (X.shape[0], 1)) + eps # pylint: disable=E1101 + y = X.sum(axis=1).reshape((X.shape[0], 1)) + eps y = y.ravel() - res = self.profile(lambda: list( - streaming_linear_regression_gram_schmidt(X, y))) - if __name__ == "__main__": - print(res[1]) + res = self.profile(lambda: list(streaming_linear_regression_gram_schmidt(X, y))) self.assertIn("streaming", res[1]) res = self.profile(lambda: list(streaming_linear_regression(X, y))) - if __name__ == "__main__": - print(res[1]) self.assertIn("streaming", res[1]) + def test_norm2(self): + X = numpy.array([[1, 0.5, 0], [0, 0.4, 2]], dtype=float).T + n2 = norm2(X) + self.assertNotEmpty(n2) + if __name__ == "__main__": - unittest.main() + unittest.main(verbosity=2) diff --git a/_unittests/ut_ml/test_ml_grid_benchmark.py b/_unittests/ut_ml/test_ml_grid_benchmark.py deleted file mode 100644 index 153506e2..00000000 --- a/_unittests/ut_ml/test_ml_grid_benchmark.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -@brief test log(time=2s) -@author Xavier Dupre -""" -import os -import unittest -from sklearn.cluster import AgglomerativeClustering, KMeans -from sklearn.datasets import make_blobs -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder, fix_tkinter_issues_virtualenv -from mlstatpy.ml import MlGridBenchMark - - -class TestMlGridBenchMark(unittest.TestCase): - - def test_ml_benchmark(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - fix_tkinter_issues_virtualenv(fLOG=fLOG) - import matplotlib.pyplot as plt # pylint: disable=C0415 - import dill # pylint: disable=C0415 - self.assertTrue(plt is not None) - temp = get_temp_folder(__file__, "temp_ml_grid_benchmark") - - params = [dict(model=lambda: KMeans(n_clusters=3), name="KMeans-3", - shortname="km-3"), - dict(model=lambda: AgglomerativeClustering(), - name="AgglomerativeClustering", shortname="aggclus")] - - datasets = [dict(X=make_blobs(100, centers=3)[0], Nclus=3, - name="blob-100-3", shortname="b-100-3", no_split=True), - dict(X=make_blobs(100, centers=5)[0], Nclus=5, - name="blob-100-5", shortname="b-100-5", no_split=True)] - - for no_split in [True, False]: - bench = MlGridBenchMark("TestName", datasets, fLOG=fLOG, clog=temp, - path_to_images=temp, - cache_file=os.path.join( - temp, "cache.pickle"), - pickle_module=dill, repetition=3, - graphx=["_time", "time_train", "Nclus"], - graphy=["silhouette", "Nrows"], - no_split=no_split) - bench.run(params) - df = bench.to_df() - ht = df.to_html(float_format="%1.3f", index=False) - self.assertTrue(len(df) > 0) - self.assertIsNotNone(ht) - self.assertEqual(df.shape[0], 12) - report = os.path.join(temp, "report.html") - csv = os.path.join(temp, "report.csv") - rst = os.path.join(temp, "report.rst") - bench.report(filehtml=report, filecsv=csv, filerst=rst, - title="A Title", description="description") - self.assertTrue(os.path.exists(report)) - self.assertTrue(os.path.exists(csv)) - self.assertTrue(os.path.exists(rst)) - - graph = bench.plot_graphs() - self.assertIsNotNone(graph) - self.assertEqual(graph.shape, (3, 2)) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_ml/test_neural_tree.py b/_unittests/ut_ml/test_neural_tree.py index 02599811..1cc7d330 100644 --- a/_unittests/ut_ml/test_neural_tree.py +++ b/_unittests/ut_ml/test_neural_tree.py @@ -1,28 +1,29 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=3s) -""" import io import unittest import pickle import numpy -from sklearn.tree import DecisionTreeClassifier, export_graphviz +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, export_graphviz from sklearn.datasets import load_iris -from pyquickhelper.pycode import ExtTestCase +from sklearn.tree import export_text +from mlstatpy.ext_test_case import ExtTestCase, ignore_warnings +from onnx_array_api.plotting.text_plot import onnx_simple_text_plot from mlstatpy.ml.neural_tree import ( - NeuralTreeNode, NeuralTreeNet, label_class_to_softmax_output) + NeuralTreeNode, + NeuralTreeNet, + label_class_to_softmax_output, + NeuralTreeNetClassifier, + NeuralTreeNetRegressor, +) class TestNeuralTree(ExtTestCase): - def test_neural_tree_node(self): - self.assertRaise(lambda: NeuralTreeNode([0, 1], 0.5, 'identity2')) - neu = NeuralTreeNode([0, 1], 0.5, 'identity') + self.assertRaise(lambda: NeuralTreeNode([0, 1], 0.5, "identity2")) + neu = NeuralTreeNode([0, 1], 0.5, "identity") res = neu.predict(numpy.array([4, 5])) self.assertEqual(res, 5.5) st = repr(neu) - self.assertEqual("NeuralTreeNode(weights=array([0., 1.]), " - "bias=0.5, activation='identity')", st) + self.assertIn("NeuralTreeNode(weights=array([0., 1.]), ", st) st = io.BytesIO() pickle.dump(neu, st) st = io.BytesIO(st.getvalue()) @@ -34,19 +35,18 @@ def test_neural_tree_network(self): X = numpy.random.randn(2, 3) got = net.predict(X) exp = X.sum(axis=1) - self.assertEqual(exp.reshape((-1, 1)), got[:, -1:]) + self.assertEqualArray(exp.reshape((-1, 1)), got[:, -1:]) rep = repr(net) - self.assertEqual(rep, 'NeuralTreeNet(3)') + self.assertEqual(rep, "NeuralTreeNet(3)") net.clear() self.assertEqual(len(net), 0) def test_neural_tree_network_append(self): net = NeuralTreeNet(3, empty=False) self.assertRaise( - lambda: net.append( - NeuralTreeNode(2, activation='identity'), inputs=[3])) - net.append(NeuralTreeNode(1, activation='identity'), - inputs=[3]) + lambda: net.append(NeuralTreeNode(2, activation="identity"), inputs=[3]) + ) + net.append(NeuralTreeNode(1, activation="identity"), inputs=[3]) self.assertEqual(net.size_, 5) last_node = net.nodes[-1] X = numpy.random.randn(2, 3) @@ -54,12 +54,11 @@ def test_neural_tree_network_append(self): exp = X.sum(axis=1) * last_node.input_weights[0] + last_node.bias self.assertEqual(exp.reshape((-1, 1)), got[:, -1:]) rep = repr(net) - self.assertEqual(rep, 'NeuralTreeNet(3)') + self.assertEqual(rep, "NeuralTreeNet(3)") def test_neural_tree_network_copy(self): net = NeuralTreeNet(3, empty=False) - net.append(NeuralTreeNode(1, activation='identity'), - inputs=[3]) + net.append(NeuralTreeNode(1, activation="identity"), inputs=[3]) net2 = net.copy() X = numpy.random.randn(2, 3) self.assertEqualArray(net.predict(X), net2.predict(X)) @@ -67,21 +66,24 @@ def test_neural_tree_network_copy(self): def test_neural_tree_network_append_dim2(self): net = NeuralTreeNet(3, empty=False) self.assertRaise( - lambda: net.append( - NeuralTreeNode(2, activation='identity'), inputs=[3])) - net.append(NeuralTreeNode(numpy.ones((2, 1), dtype=numpy.float64), - activation='identity'), - inputs=[3]) + lambda: net.append(NeuralTreeNode(2, activation="identity"), inputs=[3]) + ) + net.append( + NeuralTreeNode( + numpy.ones((2, 1), dtype=numpy.float64), activation="identity" + ), + inputs=[3], + ) self.assertEqual(net.size_, 6) last_node = net.nodes[-1] X = numpy.random.randn(2, 3) got = net.predict(X) exp = X.sum(axis=1) * last_node.input_weights[1, :] + last_node.bias[0] - self.assertEqual(exp.reshape((-1, )), got[:, -2: -1].reshape((-1, ))) + self.assertEqual(exp.reshape((-1,)), got[:, -2:-1].reshape((-1,))) exp = X.sum(axis=1) * last_node.input_weights[1, :] + last_node.bias[1] - self.assertEqual(exp.reshape((-1, )), got[:, -1:].reshape((-1, ))) + self.assertEqual(exp.reshape((-1,)), got[:, -1:].reshape((-1,))) rep = repr(net) - self.assertEqual(rep, 'NeuralTreeNet(3)') + self.assertEqual(rep, "NeuralTreeNet(3)") def test_convert(self): X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) @@ -91,8 +93,7 @@ def test_convert(self): tree = DecisionTreeClassifier(max_depth=2) tree.fit(X, y2) - self.assertRaise( - lambda: NeuralTreeNet.create_from_tree(tree), RuntimeError) + self.assertRaise(lambda: NeuralTreeNet.create_from_tree(tree), RuntimeError) tree = DecisionTreeClassifier(max_depth=2) tree.fit(X, y) @@ -101,7 +102,7 @@ def test_convert(self): exp = tree.predict_proba(X) got = root.predict(X) self.assertEqual(exp.shape[0], got.shape[0]) - self.assertEqualArray(exp, got[:, -2:]) + self.assertEqualArray(exp, got[:, -2:], atol=1e-10) def test_dot(self): data = load_iris() @@ -117,7 +118,7 @@ def test_dot(self): dot2 = root.to_dot() self.assertIn("digraph", dot2) x = X[:1].copy() - x[0, 3] = 1. + x[0, 3] = 1.0 dot2 = root.to_dot(X=x.ravel()) self.assertIn("digraph", dot2) exp = tree.predict_proba(X)[:, -1] @@ -130,12 +131,11 @@ def test_dot(self): def test_neural_tree_network_training_weights(self): net = NeuralTreeNet(3, empty=False) - net.append(NeuralTreeNode(1, activation='identity'), - inputs=[3]) + net.append(NeuralTreeNode(1, activation="identity"), inputs=[3]) w = net.training_weights - self.assertEqual(w.shape, (6, )) + self.assertEqual(w.shape, (6,)) self.assertEqual(w[0], 0) - self.assertEqualArray(w[1:4], [1, 1, 1]) + self.assertEqualArray(w[1:4], numpy.array([1, 1, 1], dtype=float)) delta = numpy.arange(6) - 0.5 net.update_training_weights(delta) w2 = net.training_weights @@ -152,7 +152,7 @@ def test_training_weights(self): root = NeuralTreeNet.create_from_tree(tree, 10) v1 = root.predict(X[:1]) w = root.training_weights - self.assertEqual(w.shape, (11, )) + self.assertEqual(w.shape, (11,)) delta = numpy.arange(11) + 0.5 root.update_training_weights(delta) v2 = root.predict(X[:1]) @@ -162,16 +162,15 @@ def test_gradients(self): X = numpy.array([0.1, 0.2, -0.3]) w = numpy.array([10, 20, 3]) g = numpy.array([-0.7], dtype=numpy.float64) - for act in ['sigmoid', 'sigmoid4', 'expit', 'identity', - 'relu', 'leakyrelu']: + for act in ["sigmoid", "sigmoid4", "expit", "identity", "relu", "leakyrelu"]: with self.subTest(act=act): neu = NeuralTreeNode(w, bias=-4, activation=act) pred = neu.predict(X) self.assertEqual(pred.shape, tuple()) grad = neu.gradient_backward(g, X) - self.assertEqual(grad.shape, (4, )) + self.assertEqual(grad.shape, (4,)) grad = neu.gradient_backward(g, X, inputs=True) - self.assertEqual(grad.shape, (3, )) + self.assertEqual(grad.shape, (3,)) ww = neu.training_weights neu.update_training_weights(-ww) w0 = neu.training_weights @@ -181,58 +180,56 @@ def test_gradients(self): w = numpy.array([[10, 20, 3], [-10, -20, 3]]) b = numpy.array([-3, 4], dtype=numpy.float64) g = numpy.array([-0.7, 0.2], dtype=numpy.float64) - for act in ['softmax', 'softmax4']: + for act in ["softmax", "softmax4"]: with self.subTest(act=act): neu = NeuralTreeNode(w, bias=b, activation=act) pred = neu.predict(X) - self.assertAlmostEqual(numpy.sum(pred), 1.) + self.assertAlmostEqual(numpy.sum(pred), 1.0, atol=1e-10) self.assertEqual(pred.shape, (2,)) grad = neu.gradient_backward(g, X) self.assertEqual(grad.shape, (2, 4)) grad = neu.gradient_backward(g, X, inputs=True) - self.assertEqual(grad.shape, (3, )) + self.assertEqual(grad.shape, (3,)) ww = neu.training_weights neu.update_training_weights(-ww) w0 = neu.training_weights self.assertEqualArray(w0, numpy.zeros(w0.shape)) def test_optim_regression(self): - X = numpy.abs(numpy.random.randn(10, 2)) - w0 = numpy.random.randn(3) + state = numpy.random.RandomState(seed=0) + X = numpy.abs(state.randn(10, 2)) + w0 = state.randn(3) w1 = numpy.array([-0.5, 0.8, -0.6]) - noise = numpy.random.randn(X.shape[0]) / 10 + noise = state.randn(X.shape[0]) / 10 noise[0] = 0 noise[1] = 0.07 X[1, 0] = 0.7 X[1, 1] = -0.5 y = w1[0] + X[:, 0] * w1[1] + X[:, 1] * w1[2] + noise - for act in ['identity', 'relu', 'leakyrelu', - 'sigmoid', 'sigmoid4', 'expit']: + for act in ["identity", "relu", "leakyrelu", "sigmoid", "sigmoid4", "expit"]: with self.subTest(act=act): neu = NeuralTreeNode(w1[1:], bias=w1[0], activation=act) loss = neu.loss(X, y).sum() / X.shape[0] - if act == 'identity': + if act == "identity": self.assertGreater(loss, 0) self.assertLess(loss, 0.1) grad = neu.gradient(X[0], y[0]) - if act == 'identity': + if act == "identity": self.assertEqualArray(grad, numpy.zeros(grad.shape)) grad = neu.gradient(X[1], y[1]) ming, maxg = grad[:2].min(), grad[:2].max() if ming == maxg: - raise AssertionError( - "Gradient is wrong\nloss={}\ngrad={}".format( - loss, grad)) + raise AssertionError(f"Gradient is wrong\nloss={loss}\ngrad={grad}") self.assertEqual(grad.shape, w0.shape) neu.fit(X, y, verbose=False) c1 = neu.training_weights neu = NeuralTreeNode(w0[1:], bias=w0[0], activation=act) - neu.fit(X, y, verbose=False, lr_schedule='constant') + neu.fit(X, y, verbose=False, lr_schedule="constant") c2 = neu.training_weights diff = numpy.abs(c2 - c1) - if act == 'identity': + if act == "identity": self.assertLess(diff.max(), 0.16) def test_optim_clas(self): @@ -242,22 +239,21 @@ def test_optim_clas(self): noise = numpy.random.randn(*X.shape) / 10 noise[0] = 0 noise[1] = 0.07 - y0 = (X[:, :1] @ w1[:, 1:2].T + - X[:, 1:] @ w1[:, 2:3].T + w1[:, 0].T + noise) + y0 = X[:, :1] @ w1[:, 1:2].T + X[:, 1:] @ w1[:, 2:3].T + w1[:, 0].T + noise y = numpy.exp(y0) y /= numpy.sum(y, axis=1, keepdims=1) y[:-1, 0] = (y[:-1, 0] >= 0.5).astype(numpy.float64) y[:-1, 1] = (y[:-1, 1] >= 0.5).astype(numpy.float64) y /= numpy.sum(y, axis=1, keepdims=1) - for act in ['softmax', 'softmax4']: + for act in ["softmax", "softmax4"]: with self.subTest(act=act): neu2 = NeuralTreeNode(2, activation=act) neu = NeuralTreeNode(w1[:, 1:], bias=w1[:, 0], activation=act) - self.assertEqual(neu2.training_weights.shape, - neu.training_weights.shape) - self.assertEqual(neu2.input_weights.shape, - neu.input_weights.shape) + self.assertEqual( + neu2.training_weights.shape, neu.training_weights.shape + ) + self.assertEqual(neu2.input_weights.shape, neu.input_weights.shape) loss = neu.loss(X, y).sum() / X.shape[0] self.assertNotEmpty(loss) self.assertFalse(numpy.isinf(loss)) @@ -268,18 +264,19 @@ def test_optim_clas(self): neu.fit(X, y, verbose=False) c1 = neu.training_weights neu = NeuralTreeNode(w0[:, 1:], bias=w0[:, 0], activation=act) - neu.fit(X, y, verbose=False, lr_schedule='constant') + neu.fit(X, y, verbose=False, lr_schedule="constant") c2 = neu.training_weights self.assertEqual(c1.shape, c2.shape) def test_label_class_to_softmax_output(self): y_label = numpy.array([0, 1, 0, 0]) - self.assertRaise(lambda: label_class_to_softmax_output(y_label.reshape((-1, 1))), - ValueError) + self.assertRaise( + lambda: label_class_to_softmax_output(y_label.reshape((-1, 1))), ValueError + ) soft_y = label_class_to_softmax_output(y_label) self.assertEqual(soft_y.shape, (4, 2)) - self.assertEqual(soft_y[:, 1], y_label) - self.assertEqual(soft_y[:, 0], 1 - y_label) + self.assertEqualArray(soft_y[:, 1], y_label.astype(float)) + self.assertEqualArray(soft_y[:, 0], 1 - y_label.astype(float)) def test_neural_net_gradient(self): X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) @@ -303,8 +300,7 @@ def test_neural_net_gradient_regression(self): X[1, 1] = -0.5 y = w1[0] + X[:, 0] * w1[1] + X[:, 1] * w1[2] + noise - for act in ['identity', 'relu', 'leakyrelu', - 'sigmoid', 'sigmoid4', 'expit']: + for act in ["identity", "relu", "leakyrelu", "sigmoid", "sigmoid4", "expit"]: with self.subTest(act=act): neu = NeuralTreeNode(w1[1:], bias=w1[0], activation=act) loss1 = neu.loss(X, y) @@ -314,9 +310,10 @@ def test_neural_net_gradient_regression(self): net.append(neu, numpy.arange(0, 2)) loss2 = net.loss(X, y) grad2 = net.gradient(X[0], y[0]) - self.assertEqualArray(loss1, loss2) - self.assertEqualArray(grad1, grad2) + self.assertEqualArray(loss1, loss2, atol=1e-5) + self.assertEqualArray(grad1, grad2, atol=1e-5) + @ignore_warnings(DeprecationWarning) def test_neural_net_gradient_regression_2(self): X = numpy.abs(numpy.random.randn(10, 2)) w1 = numpy.array([-0.5, 0.8, -0.6]) @@ -327,37 +324,38 @@ def test_neural_net_gradient_regression_2(self): X[1, 1] = -0.5 y = w1[0] + X[:, 0] * w1[1] + X[:, 1] * w1[2] + noise - for act in ['relu', 'sigmoid', 'identity', 'leakyrelu', - 'sigmoid4', 'expit']: - + for act in ["relu", "sigmoid", "identity", "leakyrelu", "sigmoid4", "expit"]: with self.subTest(act=act): neu = NeuralTreeNode(w1[1:], bias=w1[0], activation=act) loss1 = neu.loss(X, y) pred1 = neu.predict(X) - if act == 'relu': + if act == "relu": self.assertEqualArray(pred1[1:2], numpy.array([0.36])) pred11 = neu.predict(X) self.assertEqualArray(pred11[1:2], numpy.array([0.36])) net = NeuralTreeNet(X.shape[1], empty=True) net.append(neu, numpy.arange(0, 2)) - ide = NeuralTreeNode(numpy.array([1], dtype=X.dtype), - bias=numpy.array([0], dtype=X.dtype), - activation='identity') + ide = NeuralTreeNode( + numpy.array([1], dtype=X.dtype), + bias=numpy.array([0], dtype=X.dtype), + activation="identity", + ) net.append(ide, numpy.arange(2, 3)) pred2 = net.predict(X) loss2 = net.loss(X, y) - self.assertEqualArray(pred1, pred2[:, -1]) + self.assertEqualArray(pred1, pred2[:, -1], atol=1e-10) self.assertEqualArray(pred2[:, -2], pred2[:, -1]) self.assertEqualArray(pred2[:, 2], pred2[:, 3]) - self.assertEqualArray(loss1, loss2) + self.assertEqualArray(loss1, loss2, atol=1e-7) - for p in range(0, 5): + for p in range(5): grad1 = neu.gradient(X[p], y[p]) grad2 = net.gradient(X[p], y[p]) - self.assertEqualArray(grad1, grad2[:3]) + self.assertEqualArray(grad1, grad2[:3], atol=1e-7) + @ignore_warnings(DeprecationWarning) def test_neural_net_gradient_regression_2_h2(self): X = numpy.abs(numpy.random.randn(10, 2)) w1 = numpy.array([-0.5, 0.8, -0.6]) @@ -368,14 +366,12 @@ def test_neural_net_gradient_regression_2_h2(self): X[1, 1] = -0.5 y = w1[0] + X[:, 0] * w1[1] + X[:, 1] * w1[2] + noise - for act in ['relu', 'sigmoid', 'identity', 'leakyrelu', - 'sigmoid4', 'expit']: - + for act in ["relu", "sigmoid", "identity", "leakyrelu", "sigmoid4", "expit"]: with self.subTest(act=act): neu = NeuralTreeNode(w1[1:], bias=w1[0], activation=act) loss1 = neu.loss(X, y) pred1 = neu.predict(X) - if act == 'relu': + if act == "relu": self.assertEqualArray(pred1[1:2], numpy.array([0.36])) pred11 = neu.predict(X) self.assertEqualArray(pred11[1:2], numpy.array([0.36])) @@ -385,40 +381,46 @@ def test_neural_net_gradient_regression_2_h2(self): # a layer of identity neurons - ide1 = NeuralTreeNode(numpy.array([0.7], dtype=X.dtype), - bias=numpy.array([0], dtype=X.dtype), - activation='identity') + ide1 = NeuralTreeNode( + numpy.array([0.7], dtype=X.dtype), + bias=numpy.array([0], dtype=X.dtype), + activation="identity", + ) net.append(ide1, numpy.arange(2, 3)) - ide2 = NeuralTreeNode(numpy.array([0.3], dtype=X.dtype), - bias=numpy.array([0], dtype=X.dtype), - activation='identity') + ide2 = NeuralTreeNode( + numpy.array([0.3], dtype=X.dtype), + bias=numpy.array([0], dtype=X.dtype), + activation="identity", + ) net.append(ide2, numpy.arange(2, 3)) # sums of the two last neurons - ide3 = NeuralTreeNode(numpy.array([1, 1], dtype=X.dtype), - bias=numpy.array([0], dtype=X.dtype), - activation='identity') + ide3 = NeuralTreeNode( + numpy.array([1, 1], dtype=X.dtype), + bias=numpy.array([0], dtype=X.dtype), + activation="identity", + ) net.append(ide3, numpy.arange(3, 5)) # same verification pred2 = net.predict(X) loss2 = net.loss(X, y) - self.assertEqualArray(pred1, pred2[:, -1]) - self.assertEqualArray(pred2[:, 2], pred2[:, -1]) - self.assertEqualArray(loss1, loss2) + self.assertEqualArray(pred1, pred2[:, -1], atol=1e-8) + self.assertEqualArray(pred2[:, 2], pred2[:, -1], atol=1e-10) + self.assertEqualArray(loss1, loss2, atol=1e-7) - for p in range(0, 5): + for p in range(5): grad1 = neu.gradient(X[p], y[p]) grad2 = net.gradient(X[p], y[p]) - self.assertEqualArray(grad1, grad2[:3]) + self.assertEqualArray(grad1, grad2[:3], atol=1e-7) loss1 = net.loss(X, y).sum() net.fit(X, y, max_iter=20) loss2 = net.loss(X, y).sum() - self.assertLess(loss2, loss1) + self.assertLess(loss2, loss1 + 1e-7) def test_neural_net_gradient_fit(self): X = numpy.arange(16).astype(numpy.float64).reshape((-1, 2)) @@ -430,9 +432,8 @@ def test_neural_net_gradient_fit(self): root = NeuralTreeNet.create_from_tree(tree, 10) loss1 = root.loss(X, ny).sum() self.assertGreater(loss1, -1e-5) - self.assertLess(loss1, 1.) - _, out, err = self.capture( - lambda: root.fit(X, ny, verbose=True, max_iter=20)) + self.assertLess(loss1, 1.0) + _, out, err = self.capture(lambda: root.fit(X, ny, verbose=True, max_iter=20)) self.assertEmpty(err) self.assertNotEmpty(out) loss2 = root.loss(X, ny).sum() @@ -451,10 +452,10 @@ def test_neural_net_gradient_fit2(self): root = NeuralTreeNet.create_from_tree(tree, 0.01) loss1 = root.loss(X, ny).sum() self.assertGreater(loss1, -1e-5) - self.assertLess(loss1, 60.) + self.assertLess(loss1, 60.0) _, out, err = self.capture( - lambda: root.fit(X, ny, verbose=True, max_iter=20, l2=0.001, - momentum=0.1)) + lambda: root.fit(X, ny, verbose=True, max_iter=20, l2=0.001, momentum=0.1) + ) self.assertNotEmpty(out) self.assertEmpty(err) loss2 = root.loss(X, ny).sum() @@ -463,13 +464,214 @@ def test_neural_net_gradient_fit2(self): def test_shape_dim2(self): X = numpy.random.randn(10, 3) w = numpy.array([[10, 20, 3], [-10, -20, 0.5]]) - for act in ['sigmoid', 'sigmoid4', 'expit', 'identity', - 'relu', 'leakyrelu']: + first = None + for act in ["sigmoid", "sigmoid4", "expit", "identity", "relu", "leakyrelu"]: with self.subTest(act=act): neu = NeuralTreeNode(w, bias=[-4, 0.5], activation=act) pred = neu.predict(X) self.assertEqual(pred.shape, (X.shape[0], 2)) + text = str(neu) + self.assertIn("NeuralTreeNode(", text) + if first is None: + first = neu + else: + self.assertFalse(neu == first) + self.assertEqual(neu.ndim, 3) + loss = neu.loss(X[0], 0.0) + self.assertEqual(loss.shape, (2,)) + loss = neu.loss(X, numpy.zeros((X.shape[0], 1), dtype=numpy.float64)) + self.assertEqual(loss.shape, (10, 2)) + + @ignore_warnings(DeprecationWarning) + def test_convert_compact(self): + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = ((X[:, 0] + X[:, 1] * 2) > 10).astype(numpy.int64) + y2 = y.copy() + y2[0] = 2 + + tree = DecisionTreeClassifier(max_depth=2) + tree.fit(X, y2) + self.assertRaise( + lambda: NeuralTreeNet.create_from_tree(tree, arch="k"), ValueError + ) + self.assertRaise( + lambda: NeuralTreeNet.create_from_tree(tree, arch="compact"), RuntimeError + ) + + tree = DecisionTreeClassifier(max_depth=2) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + self.assertNotEmpty(root) + exp = tree.predict_proba(X) + got = root.predict(X) + self.assertEqual(exp.shape[0], got.shape[0]) + self.assertEqualArray(exp + 1e-8, got[:, -2:] + 1e-8) + dot = root.to_dot() + self.assertIn("s3a4:f4 -> s5a6:f6", dot) + + def test_convert_compact_fit(self): + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = ((X[:, 0] + X[:, 1] * 2) > 10).astype(numpy.int64) + y2 = y.copy() + y2[0] = 2 + + tree = DecisionTreeClassifier(max_depth=2) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + self.assertNotEmpty(root) + exp = tree.predict_proba(X) + got = root.predict(X) + self.assertEqual(exp.shape[0], got.shape[0]) + self.assertEqualArray(exp + 1e-8, got[:, -2:] + 1e-8) + ny = label_class_to_softmax_output(y) + loss1 = root.loss(X, ny).sum() + _, out, err = self.capture(lambda: root.fit(X, ny, verbose=True, max_iter=20)) + self.assertEmpty(err) + self.assertNotEmpty(out) + loss2 = root.loss(X, ny).sum() + self.assertLess(loss2, loss1 + 1) + + def test_convert_compact_skl(self): + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = ((X[:, 0] + X[:, 1] * 2) > 10).astype(numpy.int64) + tree = DecisionTreeClassifier(max_depth=2) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + + exp = tree.predict_proba(X) + got = root.predict(X) + self.assertEqual(exp.shape[0], got.shape[0]) + self.assertEqualArray(exp + 1e-8, got[:, -2:] + 1e-8) + + skl = NeuralTreeNetClassifier(root) + prob = skl.predict_proba(X) + self.assertEqualArray(exp, prob, atol=1e-10) + lab = skl.predict(X) + self.assertEqual(lab.shape, (X.shape[0],)) + + def test_convert_compact_skl_fit(self): + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = ((X[:, 0] + X[:, 1] * 2) > 10).astype(numpy.int64) + tree = DecisionTreeClassifier(max_depth=2) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + skl = NeuralTreeNetClassifier(root) + skl.fit(X, y) + exp = tree.predict_proba(X) + got = skl.predict_proba(X) + self.assertEqualArray(exp, got, atol=1e-10) + + def test_convert_compact_skl_onnx(self): + from skl2onnx import to_onnx + from onnx.reference import ReferenceEvaluator + + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = ((X[:, 0] + X[:, 1] * 2) > 10).astype(numpy.int64) + tree = DecisionTreeClassifier(max_depth=3) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + skl = NeuralTreeNetClassifier(root) + got = skl.predict_proba(X) + exp = tree.predict_proba(X) + self.assertEqualArray(exp, got, atol=1e-10) + dec = root.predict(X) + self.assertEqualArray(exp, dec[:, -2:], atol=1e-10) + + x32 = X.astype(numpy.float32) + onx = to_onnx(skl, x32, target_opset=15) + text = onnx_simple_text_plot(onx) + self.assertIn("Sigmoid(", text) + self.assertIn("Softmax(", text) + oinf = ReferenceEvaluator(onx) + got2 = oinf.run(None, {"X": x32})[0] + self.assertEqualArray(exp[:, 1], got2.astype(float).ravel(), atol=1e-5) + + @ignore_warnings(DeprecationWarning) + def test_convert_reg_compact(self): + X = numpy.arange(32).astype(numpy.float64).reshape((-1, 2)) + y = (X[:, 0] + X[:, 1] * 2).astype(numpy.float64) + tree = DecisionTreeRegressor(max_depth=3) + tree.fit(X, y) + text = export_text(tree, feature_names=["x1", "x2"]) + self.assertIn("[5.00]", text) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + # if __name__ == '__main__': + # print(text) + # for n in root.nodes: + # print(n) + # print('--------------') + # t = X[2:3] + # print(t) + # for n in root.nodes: + # print('*') + # ii = n._predict(t) + # print((ii * 10 + 0.01).astype(numpy.int64) / 10.) + # h = n.predict(t) + # print((h * 10 + 0.01).astype(numpy.int64) / 10.) + # t = h + self.assertNotEmpty(root) + exp = tree.predict(X) + got = root.predict(X) + self.assertEqualArray(exp, got[:, -1], atol=1e-6) + dot = root.to_dot() + self.assertIn("9 -> 17", dot) + + @ignore_warnings(DeprecationWarning) + def test_convert_compact_skl_reg(self): + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = X[:, 0] + X[:, 1] * 2 + tree = DecisionTreeRegressor(max_depth=2) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + + exp = tree.predict(X) + got = root.predict(X) + self.assertEqual(exp.shape[0], got.shape[0]) + self.assertEqualArray(exp, got[:, -1], atol=1e-7) + + skl = NeuralTreeNetRegressor(root) + prob = skl.predict(X) + self.assertEqualArray(exp, prob.ravel(), atol=1e-7) + + @ignore_warnings(DeprecationWarning) + def test_convert_compact_skl_fit_reg(self): + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = X[:, 0] + X[:, 1] * 2 + tree = DecisionTreeRegressor(max_depth=2) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + skl = NeuralTreeNetRegressor(root) + skl.fit(X, y) + exp = tree.predict(X) + got = skl.predict(X) + self.assertEqualArray(exp, got.ravel(), atol=1e-7) + + @ignore_warnings(DeprecationWarning) + def test_convert_compact_skl_onnx_reg(self): + from skl2onnx import to_onnx + from onnx.reference import ReferenceEvaluator + + X = numpy.arange(8).astype(numpy.float64).reshape((-1, 2)) + y = X[:, 0] + X[:, 1] * 2 + tree = DecisionTreeRegressor(max_depth=3) + tree.fit(X, y) + root = NeuralTreeNet.create_from_tree(tree, 10, arch="compact") + skl = NeuralTreeNetRegressor(root) + got = skl.predict(X) + exp = tree.predict(X) + self.assertEqualArray(exp, got.ravel(), atol=1e-7) + dec = root.predict(X) + self.assertEqualArray(exp, dec[:, -1], atol=1e-7) + + x32 = X.astype(numpy.float32) + onx = to_onnx(skl, x32, target_opset=15) + text = onnx_simple_text_plot(onx) + self.assertIn("Sigmoid(", text) + self.assertNotIn("Softmax(", text) + oinf = ReferenceEvaluator(onx) + got2 = oinf.run(None, {"X": x32})[0] + self.assertEqualArray(exp, got2.ravel().astype(float)) if __name__ == "__main__": - unittest.main() + unittest.main(verbosity=2) diff --git a/_unittests/ut_ml/test_nuage_points.py b/_unittests/ut_ml/test_nuage_points.py index 1582365c..1b11dc1a 100644 --- a/_unittests/ut_ml/test_nuage_points.py +++ b/_unittests/ut_ml/test_nuage_points.py @@ -1,17 +1,14 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=1s) -""" import unittest import numpy from numpy.testing import assert_array_equal +from mlstatpy.ext_test_case import ExtTestCase, ignore_warnings from sklearn.neighbors import NearestNeighbors from mlstatpy.ml.kppv import NuagePoints from mlstatpy.ml.kppv_laesa import NuagePointsLaesa -class TestNuagePoints(unittest.TestCase): - +class TestNuagePoints(ExtTestCase): + @ignore_warnings(DeprecationWarning) def test_nuage_points_1d(self): X = numpy.array([[0], [3], [1]]) neigh = NearestNeighbors(n_neighbors=1) @@ -25,6 +22,7 @@ def test_nuage_points_1d(self): assert_array_equal(y.ravel(), y2.ravel()) assert_array_equal(dist.ravel(), dist2.ravel()) + @ignore_warnings(DeprecationWarning) def test_nuage_points_1d_leasa(self): X = numpy.array([[0], [3], [1]]) neigh = NearestNeighbors(n_neighbors=1) @@ -38,6 +36,7 @@ def test_nuage_points_1d_leasa(self): assert_array_equal(y.ravel(), y2.ravel()) assert_array_equal(dist.ravel(), dist2.ravel()) + @ignore_warnings(DeprecationWarning) def test_nuage_points_2d(self): X = numpy.array([[0, 0], [3, 3], [1, 1]]) neigh = NearestNeighbors(n_neighbors=1) @@ -51,6 +50,7 @@ def test_nuage_points_2d(self): assert_array_equal(y.ravel(), y2.ravel()) assert_array_equal(dist.ravel(), dist2.ravel()) + @ignore_warnings(DeprecationWarning) def test_nuage_points_2d_leasa(self): X = numpy.array([[0, 0], [3, 3], [1, 1]]) neigh = NearestNeighbors(n_neighbors=1) diff --git a/_unittests/ut_ml/test_roc.py b/_unittests/ut_ml/test_roc.py index d4827d31..d6c82bea 100644 --- a/_unittests/ut_ml/test_roc.py +++ b/_unittests/ut_ml/test_roc.py @@ -1,64 +1,49 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=70s) -""" import os import unittest import random -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder, fix_tkinter_issues_virtualenv, ExtTestCase +from mlstatpy.ext_test_case import get_temp_folder, ExtTestCase from mlstatpy.ml.roc import ROC class TestROC(ExtTestCase): - def test_roc(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - fix_tkinter_issues_virtualenv(fLOG=fLOG) - import matplotlib.pyplot as plt # pylint: disable=C0415 + import matplotlib.pyplot as plt temp = get_temp_folder(__file__, "temp_roc") - data = [random.random() for a in range(0, 1000)] + data = [random.random() for a in range(1000)] data = [(x, 1 if x + random.random() / 3 > 0.7 else 0) for x in data] - test = ROC(y_true=[_[1] for _ in data], - y_score=[_[0] for _ in data]) + test = ROC(y_true=[_[1] for _ in data], y_score=[_[0] for _ in data]) + self.assertNotEmpty(test.Data) + self.assertNotEmpty(repr(test)) self.assertEqual(len(test), len(data)) test = ROC(df=data) - fLOG(test.__str__()) + roc = test.compute_roc_curve() t = test.roc_intersect(roc, 0.2) self.assertTrue(1 >= t >= 0) conf = test.confusion() - s = str(conf) - fLOG(s) + str(conf) + self.assertEqual(conf.shape, (12, 5)) conf = test.confusion(score=0.5) - fLOG(conf) - self.assertEqual(conf.shape, (1, 5)) - fLOG("graph.............. PROBSCORE") + self.assertEqual(conf.shape, (1, 5)) fig, ax = plt.subplots() - ax = test.plot(0, ax=ax, curve=ROC.CurveType.PROBSCORE, - thresholds=True) + ax = test.plot(0, ax=ax, curve=ROC.CurveType.PROBSCORE, thresholds=True) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_PROBSCORE_10.png")) fig, ax = plt.subplots() - test.plot(0, ax=ax, bootstrap=10, - curve=ROC.CurveType.PROBSCORE, thresholds=True) + test.plot( + 0, ax=ax, bootstrap=10, curve=ROC.CurveType.PROBSCORE, thresholds=True + ) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_PROBSCORE_100_b10.png")) - fLOG("graph.............. SKROC") - fig, ax = plt.subplots() ax = test.plot(0, ax=ax, curve=ROC.CurveType.SKROC) self.assertNotEmpty(ax) @@ -69,8 +54,6 @@ def test_roc(self): self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_SKROC_100_b10.png")) - fLOG("graph.............. RECPREC") - fig, ax = plt.subplots() ax = test.plot(100, ax=ax, curve=ROC.CurveType.RECPREC) self.assertNotEmpty(ax) @@ -81,35 +64,28 @@ def test_roc(self): self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_RECPREC_100_b10.png")) - fLOG("graph.............. SKROC True") - fig, ax = plt.subplots() ax = test.plot(0, ax=ax, curve=ROC.CurveType.SKROC, thresholds=True) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_SKROC_T_10.png")) fig, ax = plt.subplots() - test.plot(0, ax=ax, bootstrap=10, - curve=ROC.CurveType.SKROC, thresholds=True) + test.plot(0, ax=ax, bootstrap=10, curve=ROC.CurveType.SKROC, thresholds=True) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_SKROC_T_100_b10.png")) - fLOG("graph.............. RECPREC True") - fig, ax = plt.subplots() - ax = test.plot(100, ax=ax, curve=ROC.CurveType.RECPREC, - thresholds=True) + ax = test.plot(100, ax=ax, curve=ROC.CurveType.RECPREC, thresholds=True) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_RECPREC_T_100.png")) fig, ax = plt.subplots() - ax = test.plot(100, ax=ax, bootstrap=10, - curve=ROC.CurveType.RECPREC, thresholds=True) + ax = test.plot( + 100, ax=ax, bootstrap=10, curve=ROC.CurveType.RECPREC, thresholds=True + ) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_RECPREC_T_100_b10.png")) - fLOG("graph.............. ERRREC") - fig, ax = plt.subplots() ax = test.plot(100, ax=ax, curve=ROC.CurveType.ERRREC) self.assertNotEmpty(ax) @@ -120,19 +96,21 @@ def test_roc(self): self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_ERRREC_100_b10.png")) - fLOG("graph.............. ROC") - fig, ax = plt.subplots() - self.assertRaise(lambda: test.plot(10, ax=ax, label=[ - "r10", "p10"], curve=ROC.CurveType.ROC), ValueError) - ax = test.plot(10, ax=ax, thresholds=True, - label=["r10", "p10"], curve=ROC.CurveType.ROC) + self.assertRaise( + lambda: test.plot(10, ax=ax, label=["r10", "p10"], curve=ROC.CurveType.ROC), + ValueError, + ) + ax = test.plot( + 10, ax=ax, thresholds=True, label=["r10", "p10"], curve=ROC.CurveType.ROC + ) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_ROC_10.png")) fig, ax = plt.subplots() - test.plot(100, ax=ax, label=["r100", "p100"], curve=ROC.CurveType.ROC, - thresholds=True) + test.plot( + 100, ax=ax, label=["r100", "p100"], curve=ROC.CurveType.ROC, thresholds=True + ) self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_ROC_100.png")) @@ -141,24 +119,20 @@ def test_roc(self): self.assertNotEmpty(ax) fig.savefig(os.path.join(temp, "roc_ROC_100_b10.png")) - fLOG("computing rate..............................") values = test.auc_interval(alpha=0.1, bootstrap=20) - for k, v in sorted(values.items()): - fLOG("{0}={1}".format(k, v)) - self.assertEqual(list(sorted(values.keys())), [ - 'auc', 'interval', 'max', 'mean', 'mediane', 'min', 'var']) + self.assertEqual( + list(sorted(values.keys())), + ["auc", "interval", "max", "mean", "mediane", "min", "var"], + ) self.assertTrue(values["min"] <= values["auc"] <= values["max"]) - fLOG("computing rate..............................") - values = test.roc_intersect_interval( - 0.1, 100, bootstrap=50) - for k, v in sorted(values.items()): - fLOG("{0}={1}".format(k, v)) - self.assertEqual(list(sorted(values.keys())), [ - 'interval', 'max', 'mean', 'mediane', 'min', 'var', 'y']) + values = test.roc_intersect_interval(0.1, 100, bootstrap=50) + self.assertEqual( + list(sorted(values.keys())), + ["interval", "max", "mean", "mediane", "min", "var", "y"], + ) self.assertTrue(values["min"] <= values["y"] <= values["max"]) - plt.close('all') - fLOG("end") + plt.close("all") if __name__ == "__main__": diff --git a/_unittests/ut_ml/test_voronoi.py b/_unittests/ut_ml/test_voronoi.py index 6132eb53..162c523d 100644 --- a/_unittests/ut_ml/test_voronoi.py +++ b/_unittests/ut_ml/test_voronoi.py @@ -1,7 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=6s) -""" import math import unittest from io import StringIO @@ -9,66 +5,74 @@ import numpy from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression -from pyquickhelper.pycode import ExtTestCase, add_missing_development_version +from mlstatpy.ext_test_case import ExtTestCase class TestVoronoi(ExtTestCase): - - def setUp(self): - add_missing_development_version(["mlinsights"], __file__, hide=True) - def test_iris(self): from mlstatpy.ml import voronoi_estimation_from_lr + data = load_iris() X, y = data.data[:, :2], data.target clr = LogisticRegression(solver="liblinear") clr.fit(X, y) - C = [1., 0.] - D = 3. - self.assertRaise(lambda: voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, None), ValueError) - self.assertRaise(lambda: voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, [D]), TypeError) + C = [1.0, 0.0] + D = 3.0 + self.assertRaise( + lambda: voronoi_estimation_from_lr(clr.coef_, clr.intercept_, C, None), + ValueError, + ) + self.assertRaise( + lambda: voronoi_estimation_from_lr(clr.coef_, clr.intercept_, C, [D]), + TypeError, + ) std = StringIO() with redirect_stdout(std): points = voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, D, qr=False, verbose=True) + clr.coef_, clr.intercept_, C, D, qr=False, verbose=True + ) self.assertEqual(points.shape, (3, 2)) expected_values = numpy.array( - [[3., 4.137], [5.044, 0.281], [5.497, 0.184]]) - self.assertEqualArray(expected_values, points, decimal=2) + [[3.0, 4.137], [5.044, 0.281], [5.497, 0.184]] + ) + self.assertEqualArray(expected_values, points, atol=1e-2) points = voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, D, qr=True, verbose=True) + clr.coef_, clr.intercept_, C, D, qr=True, verbose=True + ) self.assertEqual(points.shape, (3, 2)) expected_values = numpy.array( - [[3., 4.137], [5.044, 0.281], [5.497, 0.184]]) - self.assertEqualArray(expected_values, points, decimal=2) + [[3.0, 4.137], [5.044, 0.281], [5.497, 0.184]] + ) + self.assertEqualArray(expected_values, points, atol=1e-2) std = std.getvalue() - self.assertIn('[voronoi_estimation_from_lr] iter=', std) + self.assertIn("[voronoi_estimation_from_lr] iter=", std) def test_iris_dim4(self): from mlstatpy.ml.voronoi import voronoi_estimation_from_lr + data = load_iris() X, y = data.data[:, :4], data.target clr = LogisticRegression(solver="liblinear") clr.fit(X, y) - C = [1., 0.] - D = 3. - self.assertRaise(lambda: voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, None), ValueError) - self.assertRaise(lambda: voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, [D]), ValueError) - - C = [1., 0., 0., 0.] - points = voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, D, qr=False) + C = [1.0, 0.0] + D = 3.0 + self.assertRaise( + lambda: voronoi_estimation_from_lr(clr.coef_, clr.intercept_, C, None), + ValueError, + ) + self.assertRaise( + lambda: voronoi_estimation_from_lr(clr.coef_, clr.intercept_, C, [D]), + ValueError, + ) + + C = [1.0, 0.0, 0.0, 0.0] + points = voronoi_estimation_from_lr(clr.coef_, clr.intercept_, C, D, qr=False) self.assertEqual(points.shape, (3, 4)) - points2 = voronoi_estimation_from_lr( - clr.coef_, clr.intercept_, C, D, qr=True) + points2 = voronoi_estimation_from_lr(clr.coef_, clr.intercept_, C, D, qr=True) self.assertEqual(points2.shape, (3, 4)) - self.assertEqualArray(points2, points2, decimal=5) + self.assertEqualArray(points2, points2, atol=1e-5) def test_square(self): from mlstatpy.ml.voronoi import voronoi_estimation_from_lr @@ -76,8 +80,8 @@ def test_square(self): Xs = [] Ys = [] n = 20 - for i in range(0, 4): - for j in range(0, 3): + for i in range(4): + for j in range(3): x1 = numpy.random.rand(n) + i * 1.1 x2 = numpy.random.rand(n) + j * 1.1 Xs.append(numpy.vstack([x1, x2]).T) @@ -88,11 +92,12 @@ def test_square(self): clr = LogisticRegression(solver="liblinear") clr.fit(X, Y) - points = voronoi_estimation_from_lr(clr.coef_, clr.intercept_, qr=True, - verbose=False) + points = voronoi_estimation_from_lr( + clr.coef_, clr.intercept_, qr=True, verbose=False + ) self.assertEqual(points.shape, (12, 2)) self.assertGreater(points.ravel().min(), -35) - self.assertLesser(points.ravel().max(), 5) + self.assertLesser(points.ravel().max(), 10.5) def test_hexa_scale(self): from mlstatpy.ml.voronoi import voronoi_estimation_from_lr @@ -104,7 +109,7 @@ def test_hexa_scale(self): for i in range(n): for j in range(n): dil = ((i + 1) ** 2 + (j + 1) ** 2) ** 0.6 - for _ in range(0, 20): + for _ in range(20): x = i + j * math.cos(a) y = j * math.sin(a) points.append([x * dil, y * dil]) @@ -112,7 +117,7 @@ def test_hexa_scale(self): mi = 0.5 for r in [0.1, 0.3, mi]: nb = 6 if r == mi else 12 - for k2 in range(0, nb): + for k2 in range(nb): ang = math.pi * 2 / nb * k2 + math.pi / 6 x = i + j * math.cos(a) + r * math.cos(ang) y = j * math.sin(a) + r * math.sin(ang) @@ -126,14 +131,15 @@ def test_hexa_scale(self): std = StringIO() with redirect_stdout(std): - points = voronoi_estimation_from_lr(clr.coef_, clr.intercept_, qr=True, - verbose=True, max_iter=20) + points = voronoi_estimation_from_lr( + clr.coef_, clr.intercept_, qr=True, verbose=True, max_iter=20 + ) self.assertEqual(points.shape, (16, 2)) self.assertGreater(points.ravel().min(), -15) self.assertLesser(points.ravel().max(), 16) std = std.getvalue() - self.assertIn('del P', std) - self.assertIn('[voronoi_estimation_from_lr] iter', std) + self.assertIn("del P", std) + self.assertIn("[voronoi_estimation_from_lr] iter", std) if __name__ == "__main__": diff --git a/_unittests/ut_module/test_code_style.py b/_unittests/ut_module/test_code_style.py deleted file mode 100644 index ddb1d4c6..00000000 --- a/_unittests/ut_module/test_code_style.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -@brief test log(time=0s) -""" -import os -import unittest -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import check_pep8, ExtTestCase - - -class TestCodeStyle(ExtTestCase): - """Test style.""" - - def test_style_src(self): - thi = os.path.abspath(os.path.dirname(__file__)) - src_ = os.path.normpath(os.path.join(thi, "..", "..", "src")) - check_pep8(src_, fLOG=fLOG, - pylint_ignore=('C0103', 'C1801', 'R0201', 'R1705', 'W0108', 'W0613', - 'C0111', 'W0201', 'W0212', 'E0203', 'W0107', 'C0415'), - skip=["Too many nested blocks", - "Module 'numpy.random' has no 'RandomState' member", - "Value 'sch' is unsubscriptable", - "Instance of 'tuple' has no ", - "Instance of '_Stat' has no 'next_nodes' member", - "completion.py:125: W0612", - "Parameters differ from overridden '", - "do not assign a lambda expression, use a def", - "Module 'matplotlib.cm' has no 'rainbow' member", - "Value 'self.label' is unsubscriptable", - "Unused variable 'count_edge_left'", - "Unused variable 'k' ", - "Redefining built-in 'format'", - "poulet.py:146: C0200", - "Unable to import 'pygame'", - ]) - - def test_style_test(self): - thi = os.path.abspath(os.path.dirname(__file__)) - test = os.path.normpath(os.path.join(thi, "..", )) - check_pep8(test, fLOG=fLOG, neg_pattern="temp_.*", - pylint_ignore=('C0103', 'C1801', 'R0201', 'R1705', 'W0108', 'W0613', - 'C0111', 'W0212', 'W0212', 'W0107', 'C0415'), - skip=["Module 'pygame' has no 'init' member", - "Module 'pygame' has no 'MOUSEBUTTONUP' member", - "test_graph_distance.py:122: W0612", - "Instance of 'tuple' has no '", - "Unable to import 'pygame'", - ]) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_module/test_convert_notebooks.py b/_unittests/ut_module/test_convert_notebooks.py deleted file mode 100644 index 09fe244f..00000000 --- a/_unittests/ut_module/test_convert_notebooks.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -@brief test log(time=0s) -""" -import os -import unittest -from pyquickhelper.loghelper import fLOG -from pyquickhelper.filehelper import explore_folder_iterfile -from pyquickhelper.ipythonhelper import upgrade_notebook, remove_execution_number - - -class TestConvertNotebooks(unittest.TestCase): - - def test_convert_notebooks(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - fold = os.path.abspath(os.path.dirname(__file__)) - fold2 = os.path.normpath( - os.path.join(fold, "..", "..", "_doc", "notebooks")) - for nbf in explore_folder_iterfile(fold2, pattern=".*[.]ipynb"): - t = upgrade_notebook(nbf) - if t: - fLOG("modified", nbf) - # remove numbers - remove_execution_number(nbf, nbf) - - fold2 = os.path.normpath(os.path.join(fold, "..", "..", "_unittests")) - for nbf in explore_folder_iterfile(fold2, pattern=".*[.]ipynb"): - t = upgrade_notebook(nbf) - if t: - fLOG("modified", nbf) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_module/test_doc_page.py b/_unittests/ut_module/test_doc_page.py deleted file mode 100644 index 85d29bc6..00000000 --- a/_unittests/ut_module/test_doc_page.py +++ /dev/null @@ -1,122 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=38s) -""" -import os -import unittest -from pyquickhelper.helpgen import rst2html -from pyquickhelper.pycode import get_temp_folder, skipif_travis, skipif_appveyor, ExtTestCase -from pyquickhelper.filehelper import synchronize_folder - - -class TestDocPage(ExtTestCase): - - preamble = ''' - \\usepackage{etex} - \\usepackage{fixltx2e} % LaTeX patches, \\textsubscript - \\usepackage{cmap} % fix search and cut-and-paste in Acrobat - \\usepackage[raccourcis]{fast-diagram} - \\usepackage{titlesec} - \\usepackage{amsmath} - \\usepackage{amssymb} - \\usepackage{amsfonts} - \\usepackage{graphics} - \\usepackage{epic} - \\usepackage{eepic} - %\\usepackage{pict2e} - %%% Redefined titleformat - \\setlength{\\parindent}{0cm} - \\setlength{\\parskip}{1ex plus 0.5ex minus 0.2ex} - \\newcommand{\\hsp}{\\hspace{20pt}} - \\newcommand{\\acc}[1]{\\left\\{#1\\right\\}} - \\newcommand{\\cro}[1]{\\left[#1\\right]} - \\newcommand{\\pa}[1]{\\left(#1\\right)} - \\newcommand{\\R}{\\mathbb{R}} - \\newcommand{\\HRule}{\\rule{\\linewidth}{0.5mm}} - %\\titleformat{\\chapter}[hang]{\\Huge\\bfseries\\sffamily}{\\thechapter\\hsp}{0pt}{\\Huge\\bfseries\\sffamily} - '''.replace(" ", "") - - custom_preamble = """\n - \\usepackage[all]{xy} - \\newcommand{\\vecteur}[2]{\\pa{#1,\\dots,#2}} - \\newcommand{\\N}[0]{\\mathbb{N}} - \\newcommand{\\indicatrice}[1]{\\mathbf{1\\!\\!1}_{\\acc{#1}}} - \\newcommand{\\infegal}[0]{\\leqslant} - \\newcommand{\\supegal}[0]{\\geqslant} - \\newcommand{\\ensemble}[2]{\\acc{#1,\\dots,#2}} - \\newcommand{\\fleche}[1]{\\overrightarrow{ #1 }} - \\newcommand{\\intervalle}[2]{\\left\\{#1,\\cdots,#2\\right\\}} - \\newcommand{\\independant}[0] - {\\;\\makebox[3ex]{\\makebox[0ex]{\\rule[-0.2ex]{3ex}{.1ex}}\\!\\!\\!\\!\\makebox[.5ex][l] - {\\rule[-.2ex]{.1ex}{2ex}}\\makebox[.5ex][l]{\\rule[-.2ex]{.1ex}{2ex}}} \\,\\,} - \\newcommand{\\esp}{\\mathbb{E}} - \\newcommand{\\espf}[2]{\\mathbb{E}_{#1}\\pa{#2}} - \\newcommand{\\var}{\\mathbb{V}} - \\newcommand{\\pr}[1]{\\mathbb{P}\\pa{#1}} - \\newcommand{\\loi}[0]{{\\cal L}} - \\newcommand{\\vecteurno}[2]{#1,\\dots,#2} - \\newcommand{\\norm}[1]{\\left\\Vert#1\\right\\Vert} - \\newcommand{\\norme}[1]{\\left\\Vert#1\\right\\Vert} - \\newcommand{\\dans}[0]{\\rightarrow} - \\newcommand{\\partialfrac}[2]{\\frac{\\partial #1}{\\partial #2}} - \\newcommand{\\partialdfrac}[2]{\\dfrac{\\partial #1}{\\partial #2}} - \\newcommand{\\trace}[1]{tr\\pa{#1}} - \\newcommand{\\sac}[0]{|} - \\newcommand{\\abs}[1]{\\left|#1\\right|} - \\newcommand{\\loinormale}[2]{{\\cal N} \\pa{#1,#2}} - \\newcommand{\\loibinomialea}[1]{{\\cal B} \\pa{#1}} - \\newcommand{\\loibinomiale}[2]{{\\cal B} \\pa{#1,#2}} - \\newcommand{\\loimultinomiale}[1]{{\\cal M} \\pa{#1}} - \\newcommand{\\variance}[1]{\\mathbb{V}\\pa{#1}} - \\newcommand{\\scal}[2]{\\left<#1,#2\\right>} - """.replace(" ", "") - - @skipif_travis("latex is not installed") - @skipif_appveyor("latex is not installed") - def test_doc_page(self): - temp = get_temp_folder(__file__, "temp_doc_page") - preamble = TestDocPage.preamble + TestDocPage.custom_preamble - this = os.path.abspath(os.path.dirname(__file__)) - root = os.path.join(this, "..", "..", "_doc", - "sphinxdoc", "source", "c_ml") - image_path = "piecewise" - rst = os.path.join(root, "piecewise.rst") - imgs = os.path.join(root, image_path) - content = self.read_file(rst) - synchronize_folder(imgs, os.path.join( - temp, image_path), create_dest=True) - - epkg_dictionary = { - 'XD': 'http://www.xavierdupre.fr', - 'scikit-learn': 'https://scikit-learn.org/stable/', - 'sklearn': ('http://scikit-learn.org/stable/', - ('http://scikit-learn.org/stable/modules/generated/{0}.html', 1), - ('http://scikit-learn.org/stable/modules/generated/{0}.{1}.html', 2)), - 'ICML 2016': 'link', - } - writer = 'html' - ht = rst2html(content, writer=writer, layout="sphinx", keep_warnings=True, - imgmath_latex_preamble=preamble, outdir=temp, - epkg_dictionary=epkg_dictionary) - ht = ht.replace('src="_images/', 'src="') - ht = ht.replace('/scripts\\bokeh', '../bokeh_plot\\bokeh') - ht = ht.replace('/scripts/bokeh', '../bokeh_plot/bokeh') - rst = os.path.join(temp, "out.{0}".format(writer)) - self.write_file(rst, ht) - - ht = ht.split('
')[0] - - # Tests the content. - self.assertNotIn('runpythonerror', ht) - lines = ht.split('\n') - for i, line in enumerate(lines): - if 'WARNING' in line: - if "contains reference to nonexisting document" in lines[i + 1]: - continue - mes = 'WARNING issue\n File "{0}", line {1}'.format( - rst, i + 1) - raise Exception(mes) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_module/test_readme.py b/_unittests/ut_module/test_readme.py deleted file mode 100644 index c9d365b3..00000000 --- a/_unittests/ut_module/test_readme.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -@brief test tree node (time=50s) -""" -import os -import unittest -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder - - -class TestReadme(unittest.TestCase): - - def test_venv_docutils08_readme(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - fold = os.path.dirname(os.path.abspath(__file__)) - readme = os.path.join(fold, "..", "..", "README.rst") - assert os.path.exists(readme) - with open(readme, "r", encoding="utf8") as f: - content = f.read() - - assert len(content) > 0 - temp = get_temp_folder(__file__, "temp_readme") - - if __name__ != "__main__": - # does not work from a virtual environment - return - - from pyquickhelper.pycode import check_readme_syntax - - check_readme_syntax(readme, folder=temp, fLOG=fLOG) - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_nlp/test_LONG_completion.py b/_unittests/ut_nlp/test_LONG_completion.py deleted file mode 100644 index 3441fb8e..00000000 --- a/_unittests/ut_nlp/test_LONG_completion.py +++ /dev/null @@ -1,92 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=33s) -""" -import os -import unittest -from pyquickhelper.loghelper import fLOG, CustomLog -from pyquickhelper.pycode import get_temp_folder -from mlstatpy.nlp.completion import CompletionTrieNode - - -class TestLONGCompletion(unittest.TestCase): - - def test_build_dynamic_trie_mks_min(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - data = os.path.join(os.path.abspath( - os.path.dirname(__file__)), "data", "sample20000.txt") - with open(data, "r", encoding="utf-8") as f: - lines = [_.strip("\n\r\t ") for _ in f.readlines()] - queries = [(None, _) for _ in lines] - temp = get_temp_folder(__file__, "temp_build_dynamic_trie_mks_min") - clog = CustomLog(temp) - clog("build trie") - trie = CompletionTrieNode.build(queries) - fLOG(len(queries), len(set(_[1] for _ in queries)), - len(list(trie.leaves())), len(set(trie.leaves()))) - - self.assertTrue("Cannes 2005" in set(_[1] for _ in queries)) - self.assertTrue("Cannes 2005" in set(_.value for _ in trie.leaves())) - - clog("precompute") - trie.precompute_stat() - clog("update") - trie.update_stat_dynamic() - clog("loop") - fLOG("loop") - for i, q in enumerate(queries): - if i % 1000 == 0: - clog(i) - fLOG(i) - leave = trie.find(q[1]) - if leave.stat is None: - raise Exception("None for {0}".format(leave)) - - self.assertTrue(hasattr(leave, "stat")) - self.assertTrue(hasattr(leave.stat, "mks0")) - self.assertTrue(hasattr(leave.stat, "mks1")) - - sug = leave.all_mks_completions() - nb_ = [(a.value, len([s.value for _, s in b if s.value == q[1]])) - for a, b in sug] - nbf_ = [(a.value, len(b)) for a, b in sug] - nb = sum(_[1] for _ in nb_) - mnb = max(_[1] for _ in nbf_) - if nb == 0 and len(q[1]) > 10: - info = "nb={0} mnb={2} q='{1}'".format(nb, q[1], mnb) - st = leave.stat.str_mks() - text = leave.str_all_completions() - text2 = leave.str_all_completions(use_precompute=False) - raise Exception( - "{4}\n---\nleave='{0}'\n{1}\n---\n{2}\n---\n{3}".format(leave.value, st, text, text2, info)) - - mk1 = trie.min_keystroke0(leave.value) - try: - mk = trie.min_dynamic_keystroke(leave.value) - mk2 = trie.min_dynamic_keystroke2(leave.value) - except Exception as e: - raise Exception( - "{0}-{1}-{2}-{3}".format(id(trie), id(leave), str(leave), leave.leave)) from e - - if mk[0] > mk1[0]: - st = leave.stat.str_mks() - text = leave.str_all_completions() - text2 = leave.str_all_completions(use_precompute=False) - raise Exception("weird {0} > {1} -- leave='{2}'\n{3}\n---\n{4}\n---\n{5}".format( - mk, mk1, leave.value, st, text, text2)) - if mk2[0] < mk[0]: - st = leave.stat.str_mks() - text = leave.str_all_completions() - text2 = leave.str_all_completions(use_precompute=False) - raise Exception("weird {0} > {1} -- leave='{2}'\n{3}\n---\n{4}\n---\n{5}".format( - mk, mk2, leave.value, st, text, text2)) - clog("end") - fLOG("end") - - -if __name__ == "__main__": - unittest.main() diff --git a/_unittests/ut_nlp/test_completion.py b/_unittests/ut_nlp/test_completion.py index 82131e2a..68091bbc 100644 --- a/_unittests/ut_nlp/test_completion.py +++ b/_unittests/ut_nlp/test_completion.py @@ -1,92 +1,68 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=3s) -""" import os import unittest import itertools -from pyquickhelper.loghelper import fLOG +from mlstatpy.ext_test_case import ExtTestCase from mlstatpy.nlp.completion import CompletionTrieNode from mlstatpy.data.wikipedia import normalize_wiki_text, enumerate_titles from mlstatpy.nlp.normalize import remove_diacritics -class TestCompletion(unittest.TestCase): - +class TestCompletion(ExtTestCase): def test_build_trie(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - queries = [(1, 'a'), (2, 'ab'), (3, 'abc'), (4, 'abcd'), (5, 'bc')] + queries = [(1, "a"), (2, "ab"), (3, "abc"), (4, "abcd"), (5, "bc")] trie = CompletionTrieNode.build(queries) res = list(trie.items()) self.assertEqual(len(res), 2) res = list(trie.iter_leaves()) - self.assertEqual( - res, [(1, 'a'), (2, 'ab'), (3, 'abc'), (4, 'abcd'), (5, 'bc')]) + self.assertEqual(res, [(1, "a"), (2, "ab"), (3, "abc"), (4, "abcd"), (5, "bc")]) lea = list(trie.leaves()) self.assertEqual(len(lea), 5) assert all(_.leave for _ in lea) - node = trie.find('b') + node = trie.find("b") assert node is not None assert not node.leave - node = trie.find('ab') + node = trie.find("ab") assert node is not None assert node.leave - self.assertEqual(node.value, 'ab') + self.assertEqual(node.value, "ab") for _, word in queries: ks = trie.min_keystroke(word) self.assertEqual(ks[0], ks[1]) def test_build_trie_mks(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - queries = [(4, 'a'), (2, 'ab'), (3, 'abc'), (1, 'abcd')] + queries = [(4, "a"), (2, "ab"), (3, "abc"), (1, "abcd")] trie = CompletionTrieNode.build(queries) nodes = trie.items_list() st = [str(_) for _ in nodes] - fLOG(st) self.assertEqual( - st, ['[-::w=1]', '[#:a:w=4]', '[#:ab:w=2]', '[#:abc:w=3]', '[#:abcd:w=1]']) - find = trie.find('a') + st, ["[-::w=1]", "[#:a:w=4]", "[#:ab:w=2]", "[#:abc:w=3]", "[#:abcd:w=1]"] + ) + find = trie.find("a") assert find ms = [(word, trie.min_keystroke(word)) for k, word in queries] - self.assertEqual(ms, [('a', (1, 1)), ('ab', (2, 2)), - ('abc', (3, 3)), ('abcd', (1, 0))]) + self.assertEqual( + ms, [("a", (1, 1)), ("ab", (2, 2)), ("abc", (3, 3)), ("abcd", (1, 0))] + ) def test_build_trie_mks_min(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - queries = [(None, 'a'), (None, 'ab'), (None, 'abc'), (None, 'abcd')] + queries = [(None, "a"), (None, "ab"), (None, "abc"), (None, "abcd")] trie = CompletionTrieNode.build(queries) gain = sum(len(w) - trie.min_keystroke(w)[0] for a, w in queries) self.assertEqual(gain, 0) for per in itertools.permutations(queries): trie = CompletionTrieNode.build(per) gain = sum(len(w) - trie.min_keystroke(w)[0] for a, w in per) - fLOG(gain, per) + self.assertNotEmpty(trie) + self.assertNotEmpty(gain) def test_build_dynamic_trie_mks_min(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - queries = [(None, 'a'), (None, 'ab'), (None, 'abc'), (None, 'abcd')] + queries = [(None, "a"), (None, "ab"), (None, "abc"), (None, "abcd")] trie = CompletionTrieNode.build(queries) trie.precompute_stat() trie.update_stat_dynamic() for leave in trie.leaves(): if leave.stat is None: - raise Exception("None for {0}".format(leave)) + raise AssertionError(f"None for {leave}") find = trie.find(leave.value) self.assertEqual(id(find), id(leave)) assert hasattr(leave, "stat") @@ -97,35 +73,30 @@ def test_build_dynamic_trie_mks_min(self): mk = trie.min_dynamic_keystroke(leave.value) mk2 = trie.min_dynamic_keystroke2(leave.value) except Exception as e: - raise Exception( - "{0}-{1}-{2}-{3}".format(id(trie), id(leave), str(leave), leave.leave)) from e + raise AssertionError( + f"{id(trie)}-{id(leave)}-{str(leave)}-{leave.leave}" + ) from e if mk[0] > mk1[0]: - raise Exception("weird {0} > {1}".format(mk, mk1)) + raise AssertionError(f"weird {mk} > {mk1}") if mk2[0] < mk[0]: - raise Exception("weird {0} > {1}".format(mk, mk2)) - fLOG(leave.value, mk, "-", leave.stat.str_mks()) - self.assertEqual( - mk, (leave.stat.mks0, leave.stat.mks0_, leave.stat.mks1i_)) + raise AssertionError(f"weird {mk} > {mk2}") + # print(leave.value, mk, "-", leave.stat.str_mks()) + self.assertEqual(mk, (leave.stat.mks0, leave.stat.mks0_, leave.stat.mks1i_)) text = leave.str_all_completions() assert text text = leave.str_all_completions(use_precompute=False) assert text def test_permutations(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - queries = ['actuellement', 'actualité', 'actu'] + queries = ["actuellement", "actualité", "actu"] weights = [1, 1, 0] for per in itertools.permutations(zip(queries, weights)): trie = CompletionTrieNode.build([(None, w) for w, p in per]) trie.precompute_stat() trie.update_stat_dynamic() - fLOG("----", per) + # print("----", per) for n in trie.leaves(): - fLOG(" ", n.value, n.stat.str_mks()) + # print(" ", n.value, n.stat.str_mks()) assert n.stat.mks1 <= n.stat.mks0 a = trie.min_dynamic_keystroke(n.value)[0] self.assertEqual(a, n.stat.mks1) @@ -133,23 +104,21 @@ def test_permutations(self): if a != n.stat.mks0: mes = [str(per)] for n2 in trie.leaves(): - mes.append("{0} - {1} || {2}".format(n2.value, - n2.stat.str_mks(), trie.min_keystroke(n2.value))) + mes.append( + "{0} - {1} || {2}".format( + n2.value, + n2.stat.str_mks(), + trie.min_keystroke(n2.value), + ) + ) mes.append("---") for n2 in trie: - mes.append("{0} || {1}".format( - n2.value, n2.stat.str_mks())) + mes.append(f"{n2.value} || {n2.stat.str_mks()}") for i, s in enumerate(n2.stat.completions): - mes.append( - " {0} - {1}:{2}".format(i, s[0], s[1].value)) - raise Exception("difference\n{0}".format("\n".join(mes))) + mes.append(f" {i} - {s[0]}:{s[1].value}") + raise AssertionError("difference\n{0}".format("\n".join(mes))) def test_normalize(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - this = os.path.abspath(os.path.dirname(__file__)) this = os.path.join(this, "data", "wikititles.txt") with open(this, "r", encoding="utf-8") as f: @@ -158,16 +127,11 @@ def test_normalize(self): line = line.strip(" \r\n\t") cl = normalize_wiki_text(line) lo = remove_diacritics(cl).lower() - fLOG(line, cl, lo) + # print(line, cl, lo) assert len(line) >= len(cl) assert len(line) >= len(lo) def test_load_titles(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - this = os.path.abspath(os.path.dirname(__file__)) this = os.path.join(this, "data", "wikititles.txt") titles = sorted(enumerate_titles(this)) @@ -178,14 +142,12 @@ def test_load_titles(self): if wc not in res: res[wc] = w else: - fLOG("duplicated key: '{0}', '{1}', key: '{2}'".format( - w, res[wc], wc)) + # print(f"duplicated key: '{w}', '{res[wc]}', key: '{wc}'") dups += 1 - fLOG("len(titles)=", len(res), "duplicated", dups) + # print("len(titles)=", len(res), "duplicated", dups) titles = list(sorted((None, k, v) for k, v in res.items())) - self.assertEqual(titles[-1], (None, 'grand russe', 'Grand Russe')) - self.assertEqual( - titles[-2], (None, 'grand rue de pera', 'Grand Rue de Pera')) + self.assertEqual(titles[-1], (None, "grand russe", "Grand Russe")) + self.assertEqual(titles[-2], (None, "grand rue de pera", "Grand Rue de Pera")) trie = CompletionTrieNode.build(titles) nodes = list(trie) exp_value = '[-:":w=0]' @@ -193,13 +155,19 @@ def test_load_titles(self): lines = "\n".join(str(_) for _ in nodes[:5]) lines2 = "\n".join(str(_) for _ in titles[:5]) info = ";".join(k for k, v in sorted(trie.children.items())) - raise Exception("{0} != {1}\n{2}\nTITLES\n{3}\nINFO\n{4}".format( - str(nodes[1]), exp_value, lines, lines2, info)) + raise AssertionError( + "{0} != {1}\n{2}\nTITLES\n{3}\nINFO\n{4}".format( + str(nodes[1]), exp_value, lines, lines2, info + ) + ) if str(nodes[-1]) != "[#:grand russe:w=354]": lines = "\n".join(str(_) for _ in nodes[-5:]) lines2 = "\n".join(str(_) for _ in titles[-5:]) - raise Exception("{0} != {1}\n{2}\nTITLES\n{3}".format( - str(nodes[-1]), "[#:grand russe:w=354]", lines, lines2)) + raise AssertionError( + "{0} != {1}\n{2}\nTITLES\n{3}".format( + str(nodes[-1]), "[#:grand russe:w=354]", lines, lines2 + ) + ) self.assertEqual(len(nodes), 3753) def cmks(trie): @@ -215,42 +183,36 @@ def cmks(trie): size += len(n.value) nb += 1 return nb, gmks, gmksd, size - nb, gmks, gmksd, size = cmks(trie) - fLOG(nb, size, gmks / nb, gmksd / nb, gmks / size, gmksd / size) + + nb, gmks, gmksd, _size = cmks(trie) + # print(nb, size, gmks / nb, gmksd / nb, gmks / size, gmksd / size) if gmks > gmksd: - raise Exception("gmks={0} gmksd={1}".format(gmks, gmksd)) + raise AssertionError(f"gmks={gmks} gmksd={gmksd}") if gmksd == 0: i = 0 - for node in trie: - fLOG(node.value, "--", node.stat.str_mks()) + for _node in trie: + # print(node.value, "--", node.stat.str_mks()) if i > 20: break i += 1 - assert False + raise AssertionError("should not happen") trie = CompletionTrieNode.build(titles) - nb2, gmks2, gmksd2, size = cmks(trie) + nb2, gmks2, gmksd2, _size = cmks(trie) self.assertEqual(nb, nb2) self.assertEqual(gmks, gmks2) self.assertEqual(gmksd, gmksd2) assert gmksd > 0.62 - fLOG(nb2, gmks2 / nb2, gmksd2 / nb2) - fLOG("-----") + # print(nb2, gmks2 / nb2, gmksd2 / nb2) + # print("-----") for i in range(1, 20): trie = CompletionTrieNode.build(titles[:i]) - nb, gmks, gmksd, size = cmks(trie) + nb, gmks, gmksd, _size = cmks(trie) if i == 1: self.assertEqual(gmks, 30) - fLOG(i, nb, size, gmks / nb, gmksd / nb, - gmks / size, gmksd / size, gmks) + # print(i, nb, size, gmks / nb, gmksd / nb, gmks / size, gmksd / size, gmks) def test_mks_consistency(self): - - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - def cmks(trie): trie.precompute_stat() trie.update_stat_dynamic() @@ -265,47 +227,40 @@ def cmks(trie): nb += 1 return nb, gmks, gmksd, size - titles = [(None, '"contra el gang del chicharron"', - '"Contra el gang del chicharron')] + titles = [ + (None, '"contra el gang del chicharron"', '"Contra el gang del chicharron') + ] trie = CompletionTrieNode.build(titles) - nb, gmks, gmksd, size = cmks(trie) - fLOG("***", 1, nb, size, gmks / nb, gmksd / - nb, gmks / size, gmksd / size, gmks) + _nb, gmks, _gmksd, _size = cmks(trie) + # print("***", 1, nb, size, gmks / nb, gmksd / nb, + # gmks / size, gmksd / size, gmks) self.assertEqual(gmks, 30) titles.append((None, '"la sequestree"', '"La séquestrée')) trie = CompletionTrieNode.build(titles) - nb, gmks, gmksd, size = cmks(trie) - fLOG("***", 2, nb, size, gmks / nb, gmksd / - nb, gmks / size, gmksd / size, gmks) - for n in trie.leaves(): - fLOG("***", n.value, n.stat.str_mks()) + _nb, gmks, _gmksd, _size = cmks(trie) + # print("***", 2, nb, size, gmks / nb, gmksd / nb, + # gmks / size, gmksd / size, gmks) + # for n in trie.leaves(): + # print("***", n.value, n.stat.str_mks()) self.assertEqual(gmks, 43) def test_duplicates(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - titles = ["abdcf", "abdcf"] try: - fLOG(titles) + # print(titles) trie = CompletionTrieNode.build( - [(None, remove_diacritics(w).lower(), w) for w in titles]) - fLOG(trie) + [(None, remove_diacritics(w).lower(), w) for w in titles] + ) + # print(trie) le = list(trie) assert len(le) == 6 assert trie is not None - except ValueError as e: - fLOG(e) + except ValueError: + # print(e) + pass def test_completions(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - this = os.path.abspath(os.path.dirname(__file__)) data = os.path.join(this, "data", "sample300.txt") with open(data, "r", encoding="utf-8") as f: @@ -319,17 +274,19 @@ def test_completions(self): find = trie.find(q) assert find is not None sug = find.all_mks_completions() - nb_ = [(a.value, len([s.value for _, s in b if s.value == q])) - for a, b in sug] + nb_ = [ + (a.value, len([s.value for _, s in b if s.value == q])) for a, b in sug + ] nb = sum(_[1] for _ in nb_) if nb == 0: - info = "nb={0} q='{1}'".format(nb, q) + info = f"nb={nb} q='{q}'" st = find.stat.str_mks() text = find.str_all_completions() text2 = find.str_all_completions(use_precompute=False) - raise Exception( - "{4}\n---\nleave='{0}'\n{1}\n---\n{2}\n---\n{3}".format(find.value, st, text, text2, info)) + raise AssertionError( + f"{info}\n---\nleave='{find.value}'\n{st}\n---\n{text}\n---\n{text2}" + ) if __name__ == "__main__": - unittest.main() + unittest.main(verbosity=2) diff --git a/_unittests/ut_nlp/test_completion_longer.py b/_unittests/ut_nlp/test_completion_longer.py index 8d9d22a3..3e7e77b7 100644 --- a/_unittests/ut_nlp/test_completion_longer.py +++ b/_unittests/ut_nlp/test_completion_longer.py @@ -1,39 +1,33 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=16s) -""" import os import unittest -from pyquickhelper.loghelper import fLOG from mlstatpy.nlp.completion import CompletionTrieNode class TestCompletionLonger(unittest.TestCase): - def test_check_bug_about_mergeing_completions(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - data = os.path.join(os.path.abspath( - os.path.dirname(__file__)), "data", "sample20000.txt") + data = os.path.join( + os.path.abspath(os.path.dirname(__file__)), "data", "sample20000.txt" + ) with open(data, "r", encoding="utf-8") as f: lines = [_.strip("\n\r\t ") for _ in f.readlines()] queries = [(None, _) for _ in lines] - fLOG("build trie") + # print("build trie") trie = CompletionTrieNode.build(queries) - fLOG(len(queries), len(set(_[1] for _ in queries)), - len(list(trie.leaves())), len(set(trie.leaves()))) + # print( + # len(queries), + # len(set(_[1] for _ in queries)), + # len(list(trie.leaves())), + # len(set(trie.leaves())), + # ) assert "Cannes 2005" in set(_[1] for _ in queries) assert "Cannes 2005" in set(_.value for _ in trie.leaves()) - fLOG("bug precompute") + # print("bug precompute") trie.precompute_stat() - fLOG("bug checking") - find = trie.find('Cann') + # print("bug checking") + find = trie.find("Cann") sug = find.stat.completions self.assertEqual(len(sug), 2) - leave = trie.find('Cannes 2005') + leave = trie.find("Cannes 2005") sugg = leave.all_mks_completions() assert len(sugg) > 0 @@ -44,7 +38,7 @@ def test_check_bug_about_mergeing_completions(self): if s[1].value == "Cannes 2005": verif += 1 if verif == 0: - raise Exception(leave.str_all_completions(use_precompute=True)) + raise AssertionError(leave.str_all_completions(use_precompute=True)) sugg = leave.all_completions() assert len(sugg) > 0 @@ -55,7 +49,7 @@ def test_check_bug_about_mergeing_completions(self): if s == "Cannes 2005": verif += 1 if verif == 0: - raise Exception(leave.str_all_completions(use_precompute=False)) + raise AssertionError(leave.str_all_completions(use_precompute=False)) if __name__ == "__main__": diff --git a/_unittests/ut_nlp/test_completion_mks.py b/_unittests/ut_nlp/test_completion_mks.py index fbc938c0..55ecb81b 100644 --- a/_unittests/ut_nlp/test_completion_mks.py +++ b/_unittests/ut_nlp/test_completion_mks.py @@ -1,22 +1,10 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=3s) -""" import os import unittest -from pyquickhelper.loghelper import fLOG from mlstatpy.nlp.completion import CompletionTrieNode class TestCompletionMks(unittest.TestCase): - def test_mks_consistency(self): - - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - def cmks(trie): trie.precompute_stat() trie.update_stat_dynamic() @@ -26,12 +14,14 @@ def cmks(trie): nb = 0 size = 0 for n in trie.leaves(): - if (True and gmksd2 > gmksd) or \ - (n.value == "baaaab" and n.stat.mks1 != 4): + if (gmksd2 > gmksd) or (n.value == "baaaab" and n.stat.mks1 != 4): info = n.str_all_completions() info2 = n.str_all_completions(use_precompute=True) - raise Exception("issue with query '{0}'\n{1}\n##########\n{2}\n############\n{3}".format( - n.value, n.stat.str_mks(), info, info2)) + raise AssertionError( + "issue with query '{0}'\n{1}\n##########" + "\n{2}\n############\n{3}" + "".format(n.value, n.stat.str_mks(), info, info2) + ) gmks += len(n.value) - n.stat.mks0 gmksd += len(n.value) - n.stat.mks1 @@ -47,27 +37,31 @@ def gain_dynamique_moyen_par_mot(queries, weights): trie.precompute_stat() trie.update_stat_dynamic() wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per] - wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) - for p, w in per] - wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) - for p, w in per] + wks_dyn = [ + (w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for p, w in per + ] + wks_dyn2 = [ + (w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) for p, w in per + ] gain = sum(g * p / total for w, p, g in wks) gain_dyn = sum(g * p / total for w, p, g in wks_dyn) gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2) ave_length = sum(len(w) * p / total for p, w in per) return gain, gain_dyn, gain_dyn2, ave_length - this = os.path.abspath(os.path.join( - os.path.dirname(__file__), "data", "sample_alpha_2.txt")) + this = os.path.abspath( + os.path.join(os.path.dirname(__file__), "data", "sample_alpha_2.txt") + ) with open(this, "r", encoding="utf-8") as f: titles = [_.strip(" \n\r\t") for _ in f.readlines()] - fLOG(titles[:5]) + # print(titles[:5]) trie = CompletionTrieNode.build([(None, q) for q in titles]) - nb, gmks, gmksd, gmksd2, size = cmks(trie) - gain, gain_dyn, gain_dyn2, ave_length = gain_dynamique_moyen_par_mot(titles, [ - 1.0] * len(titles)) - fLOG("***", 1, nb, size, "*", gmks / size, gmksd / size, gmksd2 / size) - fLOG("***", gain, gain_dyn, gain_dyn2, ave_length) + nb, _gmks, _gmksd, _gmksd2, _size = cmks(trie) + _gain, _gain_dyn, _gain_dyn2, _ave_length = gain_dynamique_moyen_par_mot( + titles, [1.0] * len(titles) + ) + # print("***", 1, nb, size, "*", gmks / size, gmksd / size, gmksd2 / size) + # print("***", gain, gain_dyn, gain_dyn2, ave_length) self.assertEqual(nb, 494) diff --git a/_unittests/ut_nlp/test_completion_profiling.py b/_unittests/ut_nlp/test_completion_profiling.py index 9430cf81..ade968f9 100644 --- a/_unittests/ut_nlp/test_completion_profiling.py +++ b/_unittests/ut_nlp/test_completion_profiling.py @@ -1,22 +1,18 @@ -# -*- coding: utf-8 -*- """ -@brief test log(time=2s) - https://dumps.wikimedia.org/frwiki/latest/frwiki-latest-all-titles.gz https://dumps.wikimedia.org/frwiki/latest/frwiki-latest-all-titles-in-ns0.gz """ + import os import unittest import cProfile import pstats import io -from pyquickhelper.loghelper import fLOG -from pyquickhelper.pycode import get_temp_folder +from mlstatpy.ext_test_case import get_temp_folder from mlstatpy.nlp.completion import CompletionTrieNode class TestCompletionProfiling(unittest.TestCase): - def gain_dynamique_moyen_par_mot(self, queries, weights): per = list(zip(weights, queries)) total = sum(weights) * 1.0 @@ -24,10 +20,8 @@ def gain_dynamique_moyen_par_mot(self, queries, weights): trie.precompute_stat() trie.update_stat_dynamic() wks = [(w, p, len(w) - trie.min_keystroke0(w)[0]) for p, w in per] - wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) - for p, w in per] - wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) - for p, w in per] + wks_dyn = [(w, p, len(w) - trie.min_dynamic_keystroke(w)[0]) for p, w in per] + wks_dyn2 = [(w, p, len(w) - trie.min_dynamic_keystroke2(w)[0]) for p, w in per] gain = sum(g * p / total for w, p, g in wks) gain_dyn = sum(g * p / total for w, p, g in wks_dyn) gain_dyn2 = sum(g * p / total for w, p, g in wks_dyn2) @@ -35,11 +29,6 @@ def gain_dynamique_moyen_par_mot(self, queries, weights): return gain, gain_dyn, gain_dyn2, ave_length def test_profiling(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - temp = get_temp_folder(__file__, "temp_profiling") data = os.path.join(temp, "..", "data", "sample1000.txt") with open(data, "r", encoding="utf-8") as f: @@ -55,16 +44,17 @@ def prof(n, show): profile_exe() pr.disable() s = io.StringIO() - ps = pstats.Stats(pr, stream=s).sort_stats('cumulative') + ps = pstats.Stats(pr, stream=s).sort_stats("cumulative") ps.print_stats() rem = os.path.normpath(os.path.join(temp, "..", "..", "..")) res = s.getvalue().replace(rem, "") if show: - fLOG(res) + print(res) with open(os.path.join(temp, "profiling%d.txt" % n), "w") as f: f.write(res) + prof(1, show=False) - prof(2, show=True) + # prof(2, show=True) if __name__ == "__main__": diff --git a/_unittests/ut_nlp/test_completion_simple.py b/_unittests/ut_nlp/test_completion_simple.py index d75bf1c9..082f9e0f 100644 --- a/_unittests/ut_nlp/test_completion_simple.py +++ b/_unittests/ut_nlp/test_completion_simple.py @@ -1,44 +1,32 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=3s) -""" import os import unittest import itertools -from pyquickhelper.loghelper import fLOG from mlstatpy.nlp.completion_simple import CompletionSystem, CompletionElement class TestCompletionSimple(unittest.TestCase): - def test_build_trie_simple(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - queries = [(1, 'a'), (2, 'ab'), (3, 'abc'), (4, 'abcd'), (5, 'bc')] + queries = [(1, "a"), (2, "ab"), (3, "abc"), (4, "abcd"), (5, "bc")] trie = CompletionSystem(queries) res = list(trie.items()) self.assertEqual(len(res), 5) res = list(trie.tuples()) - self.assertEqual( - res, [(1, 'a'), (2, 'ab'), (3, 'abc'), (4, 'abcd'), (5, 'bc')]) - node = trie.find('b') + self.assertEqual(res, [(1, "a"), (2, "ab"), (3, "abc"), (4, "abcd"), (5, "bc")]) + node = trie.find("b") assert node is None - node = trie.find('ab') + node = trie.find("ab") assert node is not None - self.assertEqual(node.value, 'ab') - trie.compute_metrics(fLOG=fLOG) + self.assertEqual(node.value, "ab") + trie.compute_metrics() for el in trie: self.assertEqual(el.mks0, el.mks1) self.assertEqual(el.mks0, el.mks2) s = el.str_mks() assert s is not None - diffs = trie.compare_with_trie(fLOG=fLOG) + diffs = trie.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) r = trie[2] assert r._info s = trie[2].str_all_completions() @@ -46,103 +34,93 @@ def test_build_trie_simple(self): assert isinstance(r._info._log_imp, list) for k, v in sorted(r._info._completions.items()): assert isinstance(v, list) - if k != '' and len(v) > 2: - raise Exception(v) + if k != "" and len(v) > 2: + raise AssertionError(v) assert v - fLOG(k, v) - for _ in v: - fLOG(" ", _.value, ":", _.str_mks()) + # print(k, v) + # for _ in v: + # print(" ", _.value, ":", _.str_mks()) assert "MKS=3 *=3 |'=3 *=3 |\"=3 *=3" in s assert trie.to_dict() def test_permutations(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - queries = ['actuellement', 'actualité', 'actu'] + queries = ["actuellement", "actualité", "actu"] weights = [1, 1, 0] for per in itertools.permutations(zip(queries, weights)): trie = CompletionSystem([(None, w) for w, p in per]) trie.compute_metrics() - # fLOG("----", per) + # # print("----", per) for n in trie: assert n.mks1 <= n.mks0 diffs = trie.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) def test_mks_consistency(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - titles = [(None, '"contra el gang del chicharron"', - '"Contra el gang del chicharron')] + titles = [ + (None, '"contra el gang del chicharron"', '"Contra el gang del chicharron') + ] trie = CompletionSystem(titles) diffs = trie.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) titles.append((None, '"la sequestree"', '"La séquestrée')) trie = CompletionSystem(titles) diffs = trie.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) def test_mks_consistency_port(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - titles = ["por", "por rouge", "por vert", - "por orange", "port", "port blanc", "port rouge"] + titles = [ + "por", + "por rouge", + "por vert", + "por orange", + "port", + "port blanc", + "port rouge", + ] trie = CompletionSystem(titles) diffs = trie.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] - raise Exception("\n".join(res)) - - titles = ["po", "po rouge", "po vert", "po orange", - "port", "port blanc", "port rouge"] + raise AssertionError("\n".join(res)) + + titles = [ + "po", + "po rouge", + "po vert", + "po orange", + "port", + "port blanc", + "port rouge", + ] trie = CompletionSystem(titles) diffs = trie.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) def test_completions(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - this = os.path.abspath(os.path.dirname(__file__)) data = os.path.join(this, "data", "sample300.txt") with open(data, "r", encoding="utf-8") as f: lines = [_.strip(" \n\r\t") for _ in f.readlines()] trie = CompletionSystem([(None, q) for q in lines]) - diffs = trie.compare_with_trie(fLOG=fLOG) + diffs = trie.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] if len(res) > 3: res = res[:3] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) assert len(trie) > 0 def test_exception(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - try: e = CompletionElement(4, 5) except TypeError as e: @@ -154,19 +132,13 @@ def test_exception(self): self.assertEqual(r, "-") def test_mks_consistency_bigger(self): - - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - def cmks(trie): - diffs = trie.compare_with_trie(fLOG=fLOG) + diffs = trie.compare_with_trie() if diffs: if len(diffs) > 3: diffs = diffs[:3] res = [_[-1] for _ in diffs] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) gmks = 0.0 gmksd = 0.0 @@ -177,8 +149,12 @@ def cmks(trie): if n.mks2 < n.mks1 or (n.value == "baaaab" and n.mks1 != 4): info = "" # n.str_all_completions() info2 = "" # n.str_all_completions(use_precompute=True) - raise Exception("issue with query '{0}'\n{1}\n##########\n{2}\n############\n{3}".format( - n.value, n.str_mks(), info, info2)) + raise AssertionError( + "issue with query '{0}'\n{1}\n##########\n" + "{2}\n############\n{3}".format( + n.value, n.str_mks(), info, info2 + ) + ) gmks += len(n.value) - n.mks0 gmksd += len(n.value) - n.mks1 @@ -201,40 +177,35 @@ def gain_dynamique_moyen_par_mot(queries, weights): ave_length = sum(len(w) * p / total for p, w in per) return gain, gain_dyn, gain_dyn2, ave_length - this = os.path.abspath(os.path.join( - os.path.dirname(__file__), "data", "sample_alpha_2.txt")) + this = os.path.abspath( + os.path.join(os.path.dirname(__file__), "data", "sample_alpha_2.txt") + ) with open(this, "r", encoding="utf-8") as f: titles = [_.strip(" \n\r\t") for _ in f.readlines()] - fLOG(titles[:5]) + # print(titles[:5]) trie = CompletionSystem([(None, q) for q in titles]) - trie.compute_metrics(fLOG=fLOG, details=True) - nb, gmks, gmksd, gmksd2, size = cmks(trie) - gain, gain_dyn, gain_dyn2, ave_length = gain_dynamique_moyen_par_mot(titles, [ - 1.0] * len(titles)) - fLOG("***", 1, nb, size, "*", gmks / size, gmksd / size, gmksd2 / size) - fLOG("***", gain, gain_dyn, gain_dyn2, ave_length) + trie.compute_metrics(details=True) + nb, _gmks, _gmksd, _gmksd2, _size = cmks(trie) + _gain, _gain_dyn, _gain_dyn2, _ave_length = gain_dynamique_moyen_par_mot( + titles, [1.0] * len(titles) + ) + # print("***", 1, nb, size, "*", gmks / size, gmksd / size, gmksd2 / size) + # print("***", gain, gain_dyn, gain_dyn2, ave_length) self.assertEqual(nb, 494) def test_completions_bug(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - couleur = ["blanc", "vert", "orange", - "rouge", "noir", "noire", "blanche"] + couleur = ["blanc", "vert", "orange", "rouge", "noir", "noire", "blanche"] key = "portes" - mots = ["porch", "porch rouge", "porch vert", - "porch orange", "pore", "pour"] + mots = ["porch", "porch rouge", "porch vert", "porch orange", "pore", "pour"] mots.append(key) mots += [key + " " + c for c in couleur] ens = CompletionSystem(mots) - diffs = ens.compare_with_trie(fLOG=fLOG) + diffs = ens.compare_with_trie() if diffs: res = [_[-1] for _ in diffs] if len(res) > 3: res = res[:3] - raise Exception("\n".join(res)) + raise AssertionError("\n".join(res)) assert len(ens) > 0 m = ens.find("portes blanche") self.assertEqual(m.mks2, 7.8) diff --git a/_unittests/ut_nlp/test_completion_simple_optim.py b/_unittests/ut_nlp/test_completion_simple_optim.py index ae617971..f1e38a48 100644 --- a/_unittests/ut_nlp/test_completion_simple_optim.py +++ b/_unittests/ut_nlp/test_completion_simple_optim.py @@ -1,23 +1,12 @@ -# -*- coding: utf-8 -*- -""" -@brief test log(time=3s) -""" import unittest -from pyquickhelper.loghelper import fLOG from mlstatpy.nlp.completion_simple import CompletionSystem class TestCompletionSimpleOptimisation(unittest.TestCase): - def test_build_trie_simple(self): - fLOG( - __file__, - self._testMethodName, - OutputPrint=__name__ == "__main__") - - comp = [(1, 'a'), (2, 'ab'), (3, 'abc'), (4, 'abcd'), (5, 'bc')] + comp = [(1, "a"), (2, "ab"), (3, "abc"), (4, "abcd"), (5, "bc")] cset = CompletionSystem(comp) - cset.compute_metrics(fLOG=fLOG) + cset.compute_metrics() queries = [(q, w) for w, q in comp] res = cset.test_metric(queries) self.assertEqual(res["mks1"], res["sum_wlen"]) @@ -26,28 +15,28 @@ def test_build_trie_simple(self): comp = [q for w, q in comp] comp.reverse() cset = CompletionSystem(comp) - cset.compute_metrics(fLOG=fLOG) + cset.compute_metrics() queries = [(q, 1) for q in comp] - for el, found in cset.enumerate_test_metric(queries): - # fLOG(el, found) + for _el, found in cset.enumerate_test_metric(queries): + # print(el, found) assert found is not None res = cset.test_metric(queries) - # for k, v in sorted(res.items()): fLOG(k, "=", v) + # for k, v in sorted(res.items()): print(k, "=", v) assert res["mks1"] < res["sum_wlen"] self.assertEqual(res["n"], 5) self.assertEqual(res["hist"]["l"], {1: 1, 2: 2, 3: 1, 4: 1}) # one suggestion in the completion set - comp = ['a', 'abc', 'bc'] + comp = ["a", "abc", "bc"] cset = CompletionSystem(comp) - cset.compute_metrics(fLOG=fLOG) - queries = [(q, 1) for q in comp] + [('abcd', 1)] + cset.compute_metrics() + queries = [(q, 1) for q in comp] + [("abcd", 1)] for el, found in cset.enumerate_test_metric(queries): - if found is None: - fLOG(el.str_mks(), "*", el.value) - else: - fLOG(el.str_mks(), "*", el.value, "*", found, found.str_mks()) - fLOG(el.weight) + # if found is None: + # print(el.str_mks(), "*", el.value) + # else: + # print(el.str_mks(), "*", el.value, "*", found, found.str_mks()) + # print(el.weight) self.assertEqual(el.weight, 1) if el.value == "abcd": assert found is None @@ -61,8 +50,8 @@ def test_build_trie_simple(self): self.assertEqual(el.mks1, found.mks1) self.assertEqual(el.mks2, found.mks2) res = cset.test_metric(queries) - for k, v in sorted(res.items()): - fLOG(k, "=", v) + # for k, v in sorted(res.items()): + # print(k, "=", v) assert res["mks1"] < res["sum_wlen"] self.assertEqual(res["n"], 4) self.assertEqual(res["hist"]["l"], {1: 1, 2: 1, 3: 1, 4: 1}) diff --git a/_unittests/ut_optim/test_optim.py b/_unittests/ut_optim/test_optim.py index 2543af74..b00f7375 100644 --- a/_unittests/ut_optim/test_optim.py +++ b/_unittests/ut_optim/test_optim.py @@ -1,11 +1,12 @@ """ @brief test log(time=6s) """ + import io from contextlib import redirect_stdout import unittest import numpy -from pyquickhelper.pycode import ExtTestCase +from mlstatpy.ext_test_case import ExtTestCase from mlstatpy.optim import SGDOptimizer @@ -18,7 +19,6 @@ def fct_grad(c, x, y, i): class TestOptim(ExtTestCase): - def test_sgd_optimizer(self): coef = numpy.array([0.5, 0.6, 0.7]) @@ -32,29 +32,28 @@ def test_sgd_optimizer(self): no = numpy.linalg.norm(gr) self.assertLess(no, 1e-10) - gr = fct_grad(numpy.array([0., 0., 0.]), X[0, :], y[0], 0) + gr = fct_grad(numpy.array([0.0, 0.0, 0.0]), X[0, :], y[0], 0) no = numpy.linalg.norm(gr) - self.assertGreater(no, 0.001) + self.assertGreater(no, 0.0001) - sgd = SGDOptimizer(numpy.array([0., 0., 0.]), - lr_schedule='constant', momentum=0.9) + sgd = SGDOptimizer( + numpy.array([0.0, 0.0, 0.0]), lr_schedule="constant", momentum=0.9 + ) buf = io.StringIO() with redirect_stdout(buf): - ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, - verbose=True) + ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, verbose=True) out = buf.getvalue() self.assertIn("15/15: loss", out) - self.assertLess(ls, 0.1) + self.assertLess(ls, 0.11) self.assertEqual(sgd.learning_rate, 0.1) - sgd = SGDOptimizer(numpy.array([0., 0., 0.]), - lr_schedule='invscaling', - momentum=0.9) + sgd = SGDOptimizer( + numpy.array([0.0, 0.0, 0.0]), lr_schedule="invscaling", momentum=0.9 + ) buf = io.StringIO() with redirect_stdout(buf): - ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, - verbose=True) + ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, verbose=True) out = buf.getvalue() self.assertIn("15/15: loss", out) self.assertLess(ls, 1) @@ -66,26 +65,32 @@ def test_sgd_optimizer_l1l2(self): X = numpy.random.randn(10, 3) y = X @ coef - sgd = SGDOptimizer(numpy.array([0., 0., 0.]), - lr_schedule='constant', - l1=0.01, l2=0.01, momentum=0.9) + sgd = SGDOptimizer( + numpy.array([0.0, 0.0, 0.0]), + lr_schedule="constant", + l1=0.01, + l2=0.01, + momentum=0.9, + ) buf = io.StringIO() with redirect_stdout(buf): - ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, - verbose=True) + ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, verbose=True) out = buf.getvalue() self.assertIn("15/15: loss", out) - self.assertLess(ls, 1) + self.assertLess(ls, 1.3) self.assertEqual(sgd.learning_rate, 0.1) - sgd = SGDOptimizer(numpy.array([0., 0., 0.]), - lr_schedule='invscaling', - l1=0.001, l2=0.001, momentum=0.9) + sgd = SGDOptimizer( + numpy.array([0.0, 0.0, 0.0]), + lr_schedule="invscaling", + l1=0.001, + l2=0.001, + momentum=0.9, + ) buf = io.StringIO() with redirect_stdout(buf): - ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, - verbose=True) + ls = sgd.train(X, y, fct_loss, fct_grad, max_iter=15, verbose=True) out = buf.getvalue() self.assertIn("15/15: loss", out) self.assertLess(ls, 1) @@ -94,7 +99,7 @@ def test_sgd_optimizer_l1l2(self): def test_sgd_optimizer_raise(self): coef = numpy.array([0.5, 0.6, 0.7]) - rs = numpy.random.RandomState(seed=0) # pylint: disable=E1101 + rs = numpy.random.RandomState(seed=0) X = rs.randn(10, 3) y = X @ coef @@ -105,34 +110,36 @@ def test_sgd_optimizer_raise(self): no = numpy.linalg.norm(gr) self.assertLess(no, 1e-10) - gr = fct_grad(numpy.array([0., 0., 0.]), X[0, :], y[0], 0) + gr = fct_grad(numpy.array([0.0, 0.0, 0.0]), X[0, :], y[0], 0) no = numpy.linalg.norm(gr) self.assertGreater(no, 0.0007) self.assertRaise(lambda: SGDOptimizer({}), TypeError) - sgd = SGDOptimizer(numpy.array([0., 0., 0.])) + sgd = SGDOptimizer(numpy.array([0.0, 0.0, 0.0])) self.assertRaise( - lambda: sgd.update_coef(numpy.array([0., 0., 0., 0.])), ValueError) - self.assertRaise(lambda: sgd.train( - X, {}, fct_loss, fct_grad), TypeError) - self.assertRaise(lambda: sgd.train( - {}, y, fct_loss, fct_grad), TypeError) - self.assertRaise(lambda: sgd.train( - X[:4], y, fct_loss, fct_grad), ValueError) + lambda: sgd.update_coef(numpy.array([0.0, 0.0, 0.0, 0.0])), ValueError + ) + self.assertRaise(lambda: sgd.train(X, {}, fct_loss, fct_grad), TypeError) + self.assertRaise(lambda: sgd.train({}, y, fct_loss, fct_grad), TypeError) + self.assertRaise(lambda: sgd.train(X[:4], y, fct_loss, fct_grad), ValueError) self.assertRaise( - lambda: SGDOptimizer(numpy.array([0., 0., 0.]), min_threshold="e"), TypeError) + lambda: SGDOptimizer(numpy.array([0.0, 0.0, 0.0]), min_threshold="e"), + TypeError, + ) self.assertRaise( - lambda: SGDOptimizer(numpy.array([0., 0., 0.]), max_threshold="e"), TypeError) + lambda: SGDOptimizer(numpy.array([0.0, 0.0, 0.0]), max_threshold="e"), + TypeError, + ) buf = io.StringIO() with redirect_stdout(buf): X[0, 0] = numpy.nan with self.assertRaises(ValueError): sgd.train(X, y, fct_loss, fct_grad, max_iter=15, verbose=True) - X[0, 0] = 1. + X[0, 0] = 1.0 y[0] = numpy.nan with self.assertRaises(ValueError): sgd.train(X, y, fct_loss, fct_grad, max_iter=15, verbose=True) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/_unittests/ut_xrun_doc/test_documentation_examples.py b/_unittests/ut_xrun_doc/test_documentation_examples.py new file mode 100644 index 00000000..d6f2aea2 --- /dev/null +++ b/_unittests/ut_xrun_doc/test_documentation_examples.py @@ -0,0 +1,97 @@ +import unittest +import os +import sys +import importlib +import subprocess +import time +from mlstatpy import __file__ as mlstatpy_file +from mlstatpy.ext_test_case import ExtTestCase + +VERBOSE = 0 +ROOT = os.path.realpath(os.path.abspath(os.path.join(mlstatpy_file, "..", ".."))) + + +def import_source(module_file_path, module_name): + if not os.path.exists(module_file_path): + raise FileNotFoundError(module_file_path) + module_spec = importlib.util.spec_from_file_location(module_name, module_file_path) + if module_spec is None: + raise FileNotFoundError( + "Unable to find '{}' in '{}'.".format(module_name, module_file_path) + ) + module = importlib.util.module_from_spec(module_spec) + return module_spec.loader.exec_module(module) + + +class TestDocumentationExamples(ExtTestCase): + def run_test(self, fold: str, name: str, verbose=0) -> int: + ppath = os.environ.get("PYTHONPATH", "") + if len(ppath) == 0: + os.environ["PYTHONPATH"] = ROOT + elif ROOT not in ppath: + sep = ";" if sys.platform == "win32" else ":" + os.environ["PYTHONPATH"] = ppath + sep + ROOT + perf = time.perf_counter() + try: + mod = import_source(fold, os.path.splitext(name)[0]) + assert mod is not None + except FileNotFoundError: + # try another way + cmds = [sys.executable, "-u", os.path.join(fold, name)] + p = subprocess.Popen(cmds, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + res = p.communicate() + _out, err = res + st = err.decode("ascii", errors="ignore") + if "No such file or directory" in st: + raise FileNotFoundError(st) # noqa: B904 + if len(st) > 0 and "Traceback" in st: + if '"dot" not found in path.' in st: + # dot not installed, this part + # is tested in onnx framework + if verbose: + print(f"failed: {name!r} due to missing dot.") + return -1 + raise AssertionError( # noqa: B904 + f"Example {name!r} (cmd: {cmds!r} - " + f"exec_prefix={sys.exec_prefix!r}) " + f"failed due to\n{st}" + ) + dt = time.perf_counter() - perf + if verbose: + print(f"{dt:.3f}: run {name!r}") + return 1 + + @classmethod + def add_test_methods(cls): + this = os.path.abspath(os.path.dirname(__file__)) + folds = [ + os.path.normpath(os.path.join(this, "..", "..", "_doc", "examples")), + ] + for fold in folds: + found = os.listdir(fold) + for name in found: + if name.startswith("plot_") and name.endswith(".py"): + short_name = os.path.split(os.path.splitext(name)[0])[-1] + + if sys.platform == "win32" and ( + "protobuf" in name or "td_note_2021" in name + ): + + @unittest.skip("notebook with questions or issues with windows") + def _test_(self, name=name, fold=fold): + res = self.run_test(fold, name, verbose=VERBOSE) + self.assertIn(res, (-1, 1)) + + else: + + def _test_(self, name=name, fold=fold): + res = self.run_test(fold, name, verbose=VERBOSE) + self.assertIn(res, (-1, 1)) + + setattr(cls, f"test_{short_name}", _test_) + + +TestDocumentationExamples.add_test_methods() + +if __name__ == "__main__": + unittest.main(verbosity=2) diff --git a/_unittests/ut_xrun_doc/test_documentation_notebook.py b/_unittests/ut_xrun_doc/test_documentation_notebook.py new file mode 100644 index 00000000..af136bff --- /dev/null +++ b/_unittests/ut_xrun_doc/test_documentation_notebook.py @@ -0,0 +1,151 @@ +import unittest +import os +import shutil +import sys +import importlib +import subprocess +import time +import warnings +from nbconvert import PythonExporter +from mlstatpy import __file__ as mlstatpy_file +from mlstatpy.ext_test_case import ExtTestCase + +VERBOSE = 0 +ROOT = os.path.realpath(os.path.abspath(os.path.join(mlstatpy_file, "..", ".."))) + + +def import_source(module_file_path, module_name): + if not os.path.exists(module_file_path): + raise FileNotFoundError(module_file_path) + module_spec = importlib.util.spec_from_file_location(module_name, module_file_path) + if module_spec is None: + raise RuntimeError( + f"Unable to find or execute {module_name!r} in {module_file_path!r}." + ) + module = importlib.util.module_from_spec(module_spec) + return module_spec.loader.exec_module(module) + + +class TestDocumentationNotebook(ExtTestCase): + def post_process(self, content): + lines = [] + for line in content.split("\n"): + if "get_ipython()" in line: + line = "# " + line + lines.append(line) + return "\n".join(lines) + + def run_test(self, nb_name: str, verbose=0) -> int: + ppath = os.environ.get("PYTHONPATH", "") + if len(ppath) == 0: + os.environ["PYTHONPATH"] = ROOT + elif ROOT not in ppath: + sep = ";" if sys.platform == "win32" else ":" + os.environ["PYTHONPATH"] = ppath + sep + ROOT + + perf = time.perf_counter() + + exporter = PythonExporter() + content = self.post_process(exporter.from_filename(nb_name)[0]) + bcontent = content.encode("utf-8") + + tmp = "temp_notebooks" + if not os.path.exists(tmp): + os.mkdir(tmp) + # with tempfile.NamedTemporaryFile(suffix=".py") as tmp: + name = os.path.splitext(os.path.split(nb_name)[-1])[0] + if os.path.exists(tmp): + tmp_name = os.path.join(tmp, name + ".py") + self.assertEndsWith(tmp_name, ".py") + with open(tmp_name, "wb") as f: + f.write(bcontent) + + fold, name = os.path.split(tmp_name) + if name == "segment_detection.py": + img_name = os.path.join(os.path.split(nb_name)[0], "eglise_zoom2.jpg") + shutil.copy(img_name, fold) + shutil.copy(img_name, ".") + + try: + mod = import_source(fold, os.path.splitext(name)[0]) + assert mod is not None + except (FileNotFoundError, RuntimeError): + # try another way + cmds = [sys.executable, "-u", tmp_name] + p = subprocess.Popen( + cmds, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + res = p.communicate() + _out, err = res + st = err.decode("ascii", errors="ignore") + if "No such file or directory" in st: + raise FileNotFoundError(st) # noqa: B904 + if len(st) > 0 and "Traceback" in st: + msg = ( + f"Example {nb_name!r} (cmd: {cmds} - " + f"exec_prefix={sys.exec_prefix!r}) " + f"failed due to\n{st}" + ) + if "CERTIFICATE_VERIFY_FAILED" in st and sys.platform == "win32": + warnings.warn(msg, stacklevel=0) + else: + raise AssertionError(msg) # noqa: B904 + + dt = time.perf_counter() - perf + if verbose: + print(f"{dt:.3f}: run {name!r}") + return 1 + + @classmethod + def add_test_methods_path(cls, fold): + found = os.listdir(fold) + last = os.path.split(fold)[-1] + for name in found: + if name.endswith(".ipynb"): + fullname = os.path.join(fold, name) + if ( + "interro_rapide_" in name + or ( + sys.platform == "win32" + and ( + "protobuf" in name + or "td_note_2021" in name + or "nb_pandas" in name + ) + ) + or "_long" in name + ): + + @unittest.skip("notebook with questions or issues with windows") + def _test_(self, fullname=fullname): + res = self.run_test(fullname, verbose=VERBOSE) + self.assertIn(res, (-1, 1)) + + else: + + def _test_(self, fullname=fullname): + res = self.run_test(fullname, verbose=VERBOSE) + self.assertIn(res, (-1, 1)) + + lasts = last.replace("-", "_") + names = os.path.splitext(name)[0].replace("-", "_") + setattr(cls, f"test_{lasts}_{names}", _test_) + + @classmethod + def add_test_methods(cls): + this = os.path.abspath(os.path.dirname(__file__)) + folds = [ + os.path.join(this, "..", "..", "_doc", "notebooks", "dsgarden"), + os.path.join(this, "..", "..", "_doc", "notebooks", "image"), + os.path.join(this, "..", "..", "_doc", "notebooks", "metric"), + os.path.join(this, "..", "..", "_doc", "notebooks", "ml"), + os.path.join(this, "..", "..", "_doc", "notebooks", "nlp"), + ] + for fold in folds: + cls.add_test_methods_path(os.path.normpath(fold)) + + +TestDocumentationNotebook.add_test_methods() + +if __name__ == "__main__": + unittest.main(verbosity=2) diff --git a/_unittests/ut_xrun_doc/test_measure_time.py b/_unittests/ut_xrun_doc/test_measure_time.py new file mode 100644 index 00000000..84a4cfc9 --- /dev/null +++ b/_unittests/ut_xrun_doc/test_measure_time.py @@ -0,0 +1,14 @@ +import unittest +from math import cos +from mlstatpy.ext_test_case import ExtTestCase, measure_time + + +class TestMeasureTime(ExtTestCase): + def test_measure_time(self): + res = measure_time(lambda: cos(5)) + self.assertIsInstance(res, dict) + self.assertIn("average", res) + + +if __name__ == "__main__": + unittest.main(verbosity=2) diff --git a/_unittests/ut_xrun_doc/test_normalize_notebook.py b/_unittests/ut_xrun_doc/test_normalize_notebook.py new file mode 100644 index 00000000..1cbfa7a8 --- /dev/null +++ b/_unittests/ut_xrun_doc/test_normalize_notebook.py @@ -0,0 +1,84 @@ +import unittest +import os +import pprint +from nbformat import reader, writes +from nbformat.validator import normalize +from mlstatpy import __file__ as mlstatpy_file +from mlstatpy.ext_test_case import ExtTestCase + +VERBOSE = 0 +ROOT = os.path.realpath(os.path.abspath(os.path.join(mlstatpy_file, "..", ".."))) + + +class TestDocumentationNotebook(ExtTestCase): + def post_process(self, content): + lines = [] + for line in content.split("\n"): + if "get_ipython()" in line: + line = "# " + line + lines.append(line) + return "\n".join(lines) + + def run_test(self, nb_name: str, verbose=0) -> int: + with open(nb_name, "r", encoding="utf-8") as f: + content = f.read() + if "sys.path.append" in content and "module_file_regex.ipynb" not in nb_name: + raise AssertionError( + f"'sys.path.append' was found in notebook {nb_name!r}." + ) + nbdict = reader.reads(content) + new_dict = normalize(nbdict) + try: + new_content = writes(new_dict[1], version=4) + except AttributeError as e: + raise AssertionError( + f"Cannot convert {nb_name!r}\n----\n{pprint.pformat(nbdict)}" + f"\n-----\n{pprint.pformat(new_dict)}" + ) from e + if content != new_content: + if os.environ.get("NB_NORMALIZE", 0) in (1, "1"): + if verbose: + print(f"[nbformat] normalize {nb_name!r}.") + with open(nb_name, "w", encoding="utf-8") as f: + f.write(new_content) + return 1 + raise AssertionError( + f"Normalization should be run on {nb_name!r}. " + f"Set NB_NORMALIZE=1 and rerun this file." + ) + return 1 + + @classmethod + def add_test_methods_path(cls, fold): + found = os.listdir(fold) + last = os.path.split(fold)[-1] + for name in found: + if name.endswith(".ipynb"): + fullname = os.path.join(fold, name) + + def _test_(self, fullname=fullname): + res = self.run_test(fullname, verbose=VERBOSE) + self.assertIn(res, (-1, 1)) + + lasts = last.replace("-", "_") + names = os.path.splitext(name)[0].replace("-", "_") + setattr(cls, f"test_{lasts}_{names}", _test_) + + @classmethod + def add_test_methods(cls): + this = os.path.abspath(os.path.dirname(__file__)) + folds = [ + os.path.join(this, "..", "..", "_doc", "notebooks", "dsgarden"), + os.path.join(this, "..", "..", "_doc", "notebooks", "image"), + os.path.join(this, "..", "..", "_doc", "notebooks", "metric"), + os.path.join(this, "..", "..", "_doc", "notebooks", "ml"), + os.path.join(this, "..", "..", "_doc", "notebooks", "nlp"), + ] + for fold in folds: + cls.add_test_methods_path(os.path.normpath(fold)) + + +TestDocumentationNotebook.add_test_methods() + +if __name__ == "__main__": + unittest.main(verbosity=2) diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100644 index fdf9f2ad..00000000 --- a/appveyor.yml +++ /dev/null @@ -1,38 +0,0 @@ -environment: - - global: - # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the - # /E:ON and /V:ON options are not enabled in the batch script intepreter - # See: http://stackoverflow.com/a/13751649/163740 - WITH_COMPILER: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_compiler.cmd" - - matrix: - - - PYTHON: "C:\\Python38-x64" - PYTHON_VERSION: "3.8.x" - PYTHON_ARCH: "64" - -init: - - "ECHO %PYTHON% %PYTHON_VERSION% %PYTHON_ARCH%" - -install: - - "%PYTHON%\\python -m pip install --upgrade pip" - - "%PYTHON%\\Scripts\\pip install pymyinstall>=1.3.1776" - - "%PYTHON%\\Scripts\\pymy_install3 pycrypto minepy" - - "%PYTHON%\\Scripts\\pymy_install3 --task=tool --source=zip graphviz" - - "%PYTHON%\\Scripts\\pip install -r requirements_conda.txt" - - "%PYTHON%\\Scripts\\pip install -r requirements.txt" - - "set PATH=%PATH%;C:\\projects\\mlstatpy\\build\\update_modules\\Graphviz\\bin" - - "dir C:\\projects\\mlstatpy\\build\\update_modules\\Graphviz\\bin" - - set PYTHONPATH=src -build: off - -test_script: - - "%PYTHON%\\python -u setup.py unittests" - -after_test: - - "%PYTHON%\\python -u setup.py bdist_wheel" - -artifacts: - - path: dist - name: mlstatpy diff --git a/build_script.bat b/build_script.bat deleted file mode 100644 index 9bd4486c..00000000 --- a/build_script.bat +++ /dev/null @@ -1,18 +0,0 @@ -@echo off -if "%1"=="" goto default_value_python: -set pythonexe="%1" -%pythonexe% setup.py write_version -goto custom_python: - -:default_value_python: -set pythonexe="c:\Python372_x64\python.exe" -if not exist %pythonexe% set pythonexe="c:\Python370_x64\python.exe" -if not exist %pythonexe% set pythonexe="c:\Python366_x64\python.exe" -if not exist %pythonexe% set pythonexe="c:\Python365_x64\python.exe" -if not exist %pythonexe% set pythonexe="c:\Python364_x64\python.exe" -if not exist %pythonexe% set pythonexe="c:\Python363_x64\python.exe" -if not exist %pythonexe% set pythonexe="c:\Python36_x64\python.exe" -:custom_python: -@echo [python] %pythonexe% -%pythonexe% -u setup.py build_script -if %errorlevel% neq 0 exit /b %errorlevel% \ No newline at end of file diff --git a/mlstatpy/__init__.py b/mlstatpy/__init__.py new file mode 100644 index 00000000..5a7c90a9 --- /dev/null +++ b/mlstatpy/__init__.py @@ -0,0 +1,5 @@ +__version__ = "0.5.0" +__author__ = "Xavier Dupré" +__github__ = "https://github.com/sdpython/mlstatpy" +__url__ = "https://sdpython.github.io/doc/mlstatpy/dev/" +__license__ = "MIT License" diff --git a/mlstatpy/data/__init__.py b/mlstatpy/data/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/mlstatpy/data/data_exceptions.py b/mlstatpy/data/data_exceptions.py similarity index 54% rename from src/mlstatpy/data/data_exceptions.py rename to mlstatpy/data/data_exceptions.py index 498173e2..4ad1e7a6 100644 --- a/src/mlstatpy/data/data_exceptions.py +++ b/mlstatpy/data/data_exceptions.py @@ -1,11 +1,4 @@ -""" -@file -@brief Exceptions while retrieving data. -""" - - class DataException(Exception): """ raised when retrieving data """ - pass diff --git a/src/mlstatpy/data/wikipedia.py b/mlstatpy/data/wikipedia.py similarity index 50% rename from src/mlstatpy/data/wikipedia.py rename to mlstatpy/data/wikipedia.py index aef8eac2..c35caeed 100644 --- a/src/mlstatpy/data/wikipedia.py +++ b/mlstatpy/data/wikipedia.py @@ -1,28 +1,21 @@ -""" -@file -@brief Functions to retrieve data from Wikipedia -""" import os -from pyquickhelper.loghelper import noLOG -from pyquickhelper.filehelper import get_url_content_timeout, ungzip_files +from mlstatpy.ext_test_case import get_url_content_timeout, ungzip_files from .data_exceptions import DataException -def download_pageviews(dt, folder=".", unzip=True, timeout=-1, - overwrite=False, fLOG=noLOG): +def download_pageviews(dt, folder=".", unzip=True, timeout=-1, overwrite=False): """ Downloads wikipedia pagacount for a precise date (up to the hours), the url follows the pattern:: https://dumps.wikimedia.org/other/pageviews/%Y/%Y-%m/pagecounts-%Y%m%d-%H0000.gz - @param dt datetime - @param folder where to download - @param unzip unzip the file - @param timeout timeout - @param overwrite overwrite - @param fLOG logging function - @return filename + :param dt: datetime + :param folder: where to download + :param unzip: unzip the file + :param timeout: timeout + :param overwrite: overwrite + :return: filename More information on page `pageviews `_. @@ -33,79 +26,77 @@ def download_pageviews(dt, folder=".", unzip=True, timeout=-1, name = os.path.join(folder, file) unzipname = os.path.splitext(name)[0] if overwrite or (not os.path.exists(name) and not os.path.exists(unzipname)): - get_url_content_timeout(url, timeout=timeout, - encoding=None, output=name, chunk=2**20, fLOG=fLOG) + get_url_content_timeout( + url, timeout=timeout, encoding=None, output=name, chunk=2**20 + ) if unzip and not os.path.exists(unzipname): names = ungzip_files(name, unzip=False, where_to=folder) os.remove(name) if isinstance(names, list): if len(names) != 1: - raise DataException( # pragma: no cover - "Expecting only one file, not '{0}'".format(names)) + raise DataException(f"Expecting only one file, not '{names}'") return names[0] return names return name -def download_dump(country, name, folder=".", unzip=True, timeout=-1, - overwrite=False, fLOG=noLOG): +def download_dump(country, name, folder=".", unzip=True, timeout=-1, overwrite=False): """ - Downloads *wikipedia dumps* from - `dumps.wikimedia.org/frwiki/latest/ - `_. - - @param country country - @param name name of the stream to download - @param folder where to download - @param unzip unzip the file - @param timeout timeout - @param overwrite overwrite - @param fLOG logging function + Downloads :epkg:`wikipedia dumps`. + + :param country: country + :param name: name of the stream to download + :param folder: where to download + :param unzip: unzip the file + :param timeout: timeout + :param overwrite: overwrite """ - url = "https://dumps.wikimedia.org/{0}wiki/latest/{0}wiki-{1}".format( - country, name) + url = "https://dumps.wikimedia.org/{0}wiki/latest/{0}wiki-{1}".format(country, name) file = url.split("/")[-1] name = os.path.join(folder, file) unzipname = os.path.splitext(name)[0] if overwrite or (not os.path.exists(name) and not os.path.exists(unzipname)): - get_url_content_timeout(url, timeout=timeout, - encoding=None, output=name, chunk=2**20, fLOG=fLOG) + get_url_content_timeout( + url, timeout=timeout, encoding=None, output=name, chunk=2**20 + ) if unzip and not os.path.exists(unzipname): names = ungzip_files(name, unzip=False, where_to=folder) os.remove(name) if isinstance(names, list): if len(names) != 1: - raise DataException( # pragma: no cover - "Expecting only one file, not '{0}'".format(names)) + raise DataException(f"Expecting only one file, not '{names}'") return names[0] return names - return name[:-3] if name.endswith('.gz') else name + return name[:-3] if name.endswith(".gz") else name -def download_titles(country, folder=".", unzip=True, timeout=-1, - overwrite=False, fLOG=noLOG): +def download_titles(country, folder=".", unzip=True, timeout=-1, overwrite=False): """ Downloads wikipedia titles from `dumps.wikimedia.org/frwiki/latest/latest-all-titles-in-ns0.gz `_. - @param country country - @param folder where to download - @param unzip unzip the file - @param timeout timeout - @param overwrite overwrite - @param fLOG logging function + :param country country + :param folder where to download + :param unzip unzip the file + :param timeout timeout + :param overwrite overwrite """ - return download_dump(country, "latest-all-titles-in-ns0.gz", - folder, unzip=unzip, timeout=timeout, - overwrite=overwrite, fLOG=fLOG) + return download_dump( + country, + "latest-all-titles-in-ns0.gz", + folder, + unzip=unzip, + timeout=timeout, + overwrite=overwrite, + ) def normalize_wiki_text(text): """ Normalizes a text such as a wikipedia title. - @param text text to normalize + :param text text to normalize @return normalized text """ return text.replace("_", " ").replace("''", '"') @@ -115,9 +106,9 @@ def enumerate_titles(filename, norm=True, encoding="utf8"): """ Enumerates titles from a file. - @param filename filename - @param norm normalize in the function - @param encoding encoding + :param filename filename + :param norm normalize in the function + :param encoding encoding """ if norm: with open(filename, "r", encoding=encoding) as f: diff --git a/mlstatpy/ext_test_case.py b/mlstatpy/ext_test_case.py new file mode 100644 index 00000000..535fcc13 --- /dev/null +++ b/mlstatpy/ext_test_case.py @@ -0,0 +1,797 @@ +import os +import stat +import sys +import time +import unittest +import unicodedata +import warnings +from contextlib import redirect_stderr, redirect_stdout +from io import StringIO, BytesIO +from timeit import Timer +from typing import Any, Callable, Dict, List, Optional, Union + +import numpy +from numpy.testing import assert_allclose + + +class InternetException(RuntimeError): + """ + Exception for the function @see fn get_url_content_timeout + """ + + +def get_url_content_timeout( + url, + timeout=10, + output=None, + encoding="utf8", + raise_exception=True, + chunk=None, + fLOG=None, +): + """ + Downloads a file from internet (by default, it assumes + it is text information, otherwise, encoding should be None). + + :param url: (str) url + :param timeout: (int) in seconds, after this time, + the function drops an returns None, -1 for forever + :param output: (str) if None, the content is stored in that file + :param encoding: (str) utf8 by default, but if it is None, + the returned information is binary + :param raise_exception: (bool) True to raise an exception, False to send a warnings + :param chunk: (int|None) save data every chunk (only if output is not None) + :param fLOG: logging function (only applies when chunk is not None) + :return: content of the url + + If the function automatically detects that the downloaded data is in gzip + format, it will decompress it. + + The function raises the exception :class:`InternetException`. + """ + import gzip + import urllib.error as urllib_error + import urllib.request as urllib_request + import http.client as http_client + + try: + from http.client import InvalidURL + except ImportError: + InvalidURL = ValueError + + def save_content(content, append=False): + "local function" + app = "a" if append else "w" + if encoding is not None: + with open(output, app, encoding=encoding) as f: + f.write(content) + else: + with open(output, app + "b") as f: + f.write(content) + + try: + if chunk is not None: + if output is None: + raise ValueError("output cannot be None if chunk is not None") + app = [False] + size = [0] + + def _local_loop(ur): + while True: + res = ur.read(chunk) + size[0] += len(res) # pylint: disable=E1137 + if fLOG is not None: + fLOG("[get_url_content_timeout] downloaded", size, "bytes") + if len(res) > 0: + if encoding is not None: + res = res.decode(encoding=encoding) + save_content(res, app) + else: + break + app[0] = True # pylint: disable=E1137 + + if timeout != -1: + with urllib_request.urlopen(url, timeout=timeout) as ur: + _local_loop(ur) + else: + with urllib_request.urlopen(url) as ur: + _local_loop(ur) + app = app[0] + size = size[0] + else: + if timeout != -1: + with urllib_request.urlopen(url, timeout=timeout) as ur: + res = ur.read() + else: + with urllib_request.urlopen(url) as ur: + res = ur.read() + except ( + urllib_error.HTTPError, + urllib_error.URLError, + ConnectionRefusedError, + TimeoutError, + ConnectionResetError, + http_client.BadStatusLine, + http_client.IncompleteRead, + ValueError, + InvalidURL, + ) as e: + if raise_exception: + raise InternetException(f"Unable to retrieve content url='{url}'") from e + warnings.warn( + f"Unable to retrieve content from '{url}' because of {e}", + ResourceWarning, + stacklevel=0, + ) + return None + except Exception as e: + if raise_exception: + raise InternetException( + f"Unable to retrieve content, url='{url}', exc={e}" + ) from e + warnings.warn( + f"Unable to retrieve content from '{url}' " + f"because of unknown exception: {e}", + ResourceWarning, + stacklevel=0, + ) + raise e + + if chunk is None: + if len(res) >= 2 and res[:2] == b"\x1f\x8b": + # gzip format + res = gzip.decompress(res) + + if encoding is not None: + try: + content = res.decode(encoding) + except UnicodeDecodeError as e: + # it tries different encoding + + laste = [e] + othenc = ["iso-8859-1", "latin-1"] + + for encode in othenc: + try: + content = res.decode(encode) + break + except UnicodeDecodeError as ee: + laste.append(ee) + content = None + + if content is None: + mes = [f"Unable to parse text from '{url}'."] + mes.append("tried:" + str([*encoding, othenc])) + mes.append("beginning:\n" + str([res])[:50]) + for e in laste: + mes.append("Exception: " + str(e)) + raise ValueError("\n".join(mes)) from e + else: + content = res + else: + content = None + + if output is not None and chunk is None: + save_content(content) + + return content + + +def unit_test_going(): + """ + Enables a flag telling the script is running while testing it. + Avois unit tests to be very long. + """ + going = int(os.environ.get("UNITTEST_GOING", 0)) + return going == 1 + + +def ignore_warnings(warns: List[Warning]) -> Callable: + """ + Catches warnings. + + :param warns: warnings to ignore + """ + + def wrapper(fct): + if warns is None: + raise AssertionError(f"warns cannot be None for '{fct}'.") + + def call_f(self): + with warnings.catch_warnings(): + warnings.simplefilter("ignore", warns) + return fct(self) + + return call_f + + return wrapper + + +def measure_time( + stmt: Union[str, Callable], + context: Optional[Dict[str, Any]] = None, + repeat: int = 10, + number: int = 50, + warmup: int = 1, + div_by_number: bool = True, + max_time: Optional[float] = None, +) -> Dict[str, Any]: + """ + Measures a statement and returns the results as a dictionary. + + :param stmt: string or callable + :param context: variable to know in a dictionary + :param repeat: average over *repeat* experiment + :param number: number of executions in one row + :param warmup: number of iteration to do before starting the + real measurement + :param div_by_number: divide by the number of executions + :param max_time: execute the statement until the total goes + beyond this time (approximatively), *repeat* is ignored, + *div_by_number* must be set to True + :return: dictionary + + .. runpython:: + :showcode: + + from mlstatpy.ext_test_case import measure_time + from math import cos + + res = measure_time(lambda: cos(0.5)) + print(res) + + See `Timer.repeat `_ + for a better understanding of parameter *repeat* and *number*. + The function returns a duration corresponding to + *number* times the execution of the main statement. + """ + if not callable(stmt) and not isinstance(stmt, str): + raise TypeError( + f"stmt is not callable or a string but is of type {type(stmt)!r}." + ) + if context is None: + context = {} + + if isinstance(stmt, str): + tim = Timer(stmt, globals=context) + else: + tim = Timer(stmt) + + if warmup > 0: + warmup_time = tim.timeit(warmup) + else: + warmup_time = 0 + + if max_time is not None: + if not div_by_number: + raise ValueError( + "div_by_number must be set to True of max_time is defined." + ) + i = 1 + total_time = 0 + results = [] + while True: + for j in (1, 2): + number = i * j + time_taken = tim.timeit(number) + results.append((number, time_taken)) + total_time += time_taken + if total_time >= max_time: + break + if total_time >= max_time: + break + ratio = (max_time - total_time) / total_time + ratio = max(ratio, 1) + i = int(i * ratio) + + res = numpy.array(results) + tw = res[:, 0].sum() + ttime = res[:, 1].sum() + mean = ttime / tw + ave = res[:, 1] / res[:, 0] + dev = (((ave - mean) ** 2 * res[:, 0]).sum() / tw) ** 0.5 + mes = dict( + average=mean, + deviation=dev, + min_exec=numpy.min(ave), + max_exec=numpy.max(ave), + repeat=1, + number=tw, + ttime=ttime, + ) + else: + res = numpy.array(tim.repeat(repeat=repeat, number=number)) + if div_by_number: + res /= number + + mean = numpy.mean(res) + dev = numpy.mean(res**2) + dev = (dev - mean**2) ** 0.5 + mes = dict( + average=mean, + deviation=dev, + min_exec=numpy.min(res), + max_exec=numpy.max(res), + repeat=repeat, + number=number, + ttime=res.sum(), + ) + + if "values" in context: + if hasattr(context["values"], "shape"): + mes["size"] = context["values"].shape[0] + else: + mes["size"] = len(context["values"]) + else: + mes["context_size"] = sys.getsizeof(context) + mes["warmup_time"] = warmup_time + return mes + + +class ExtTestCase(unittest.TestCase): + _warns = [] + + def assertEndsWith(self, string, suffix): + if not string.endswith(suffix): + raise AssertionError(f"{string!r} does not end with {suffix!r}.") + + def assertExists(self, name): + if not os.path.exists(name): + raise AssertionError(f"File or folder {name!r} does not exists.") + + def assertEqual(self, *args, **kwargs): + if isinstance(args[0], numpy.ndarray): + self.assertEqualArray(*args, **kwargs) + else: + super().assertEqual(*args, **kwargs) + + def assertNotEqualArray( + self, + expected: numpy.ndarray, + value: numpy.ndarray, + atol: float = 0, + rtol: float = 0, + ): + try: + self.assertEqualArray(expected, value, atol=atol, rtol=rtol) + except AssertionError: + return + raise AssertionError("Both arrays are equal.") + + def assertEqualArray( + self, + expected: numpy.ndarray, + value: numpy.ndarray, + atol: float = 0, + rtol: float = 0, + ): + self.assertEqual(expected.dtype, value.dtype) + self.assertEqual(expected.shape, value.shape) + assert_allclose(expected, value, atol=atol, rtol=rtol) + + def assertAlmostEqual( + self, + expected: numpy.ndarray, + value: numpy.ndarray, + atol: float = 0, + rtol: float = 0, + ): + if not isinstance(expected, numpy.ndarray): + expected = numpy.array(expected) + if not isinstance(value, numpy.ndarray): + value = numpy.array(value).astype(expected.dtype) + self.assertEqualArray(expected, value, atol=atol, rtol=rtol) + + def assertRaise( + self, fct: Callable, exc_type: Optional[Exception] = None + ): # noqa: UP045 + exct = exc_type or Exception + try: + fct() + except exct as e: + if exc_type is not None and not isinstance(e, exc_type): + raise AssertionError(f"Unexpected exception {type(e)!r}.") # noqa: B904 + return + raise AssertionError("No exception was raised.") + + def assertEmpty(self, value: Any): + if value is None: + return + if len(value) == 0: + return + raise AssertionError(f"value is not empty: {value!r}.") + + def assertNotEmpty(self, value: Any): + if value is None: + raise AssertionError(f"value is empty: {value!r}.") + if isinstance(value, (list, dict, tuple, set)): + if len(value) == 0: + raise AssertionError(f"value is empty: {value!r}.") + + def assertStartsWith(self, prefix: str, full: str): + if not full.startswith(prefix): + raise AssertionError(f"prefix={prefix!r} does not start string {full!r}.") + + def assertGreater(self, a, b): + if a < b: + raise AssertionError(f"{a} < {b}") + + def assertLesser(self, a, b): + if a > b: + raise AssertionError(f"{a} > {b}") + + @classmethod + def tearDownClass(cls): + for name, line, w in cls._warns: + warnings.warn(f"\n{name}:{line}: {type(w)}\n {str(w)}", stacklevel=0) + + def capture(self, fct: Callable): + """ + Runs a function and capture standard output and error. + + :param fct: function to run + :return: result of *fct*, output, error + """ + sout = StringIO() + serr = StringIO() + with redirect_stdout(sout), redirect_stderr(serr): + res = fct() + return res, sout.getvalue(), serr.getvalue() + + @staticmethod + def profile(fct, sort="cumulative", rootrem=None, return_results=False): + """ + Profiles the execution of a function with function + :func:`profile `. + + :param fct: function to profile + :param sort: see :meth:`pstats.Stats.sort_stats` + :param rootrem: root to remove in filenames + :param return_results: return the results as well + :return: statistics text dump + """ + from onnx_array_api.profiling import profile + + return profile(fct, sort=sort, rootrem=rootrem, return_results=return_results) + + +def remove_folder(top, remove_also_top=True, raise_exception=True): + """ + Removes everything in folder *top*. + + :param top: path to remove + :param remove_also_top: remove also root + :param raise_exception: raise an exception if a file cannot be remove + :return: list of removed files and folders + --> list of tuple ( (name, "file" or "dir") ) + """ + if top in {"", "C:", "c:", "C:\\", "c:\\", "d:", "D:", "D:\\", "d:\\"}: + raise RuntimeError( # pragma: no cover + "top is a root (c: for example), this is not safe" + ) + + res = [] + first_root = None + for root, dirs, files in os.walk(top, topdown=False): + for name in files: + t = os.path.join(root, name) + try: + os.remove(t) + except PermissionError as e: # pragma: no cover + if raise_exception: + raise PermissionError(f"unable to remove file {t}") from e + remove_also_top = False + continue + res.append((t, "file")) + for name in dirs: + t = os.path.join(root, name) + try: + os.rmdir(t) + except OSError as e: + if raise_exception: + raise OSError(f"unable to remove folder {t}") from e + remove_also_top = False # pragma: no cover + continue # pragma: no cover + res.append((t, "dir")) + if first_root is None: + first_root = root + + if top is not None and remove_also_top: + res.append((top, "dir")) + os.rmdir(top) + + return res + + +def get_temp_folder( + thisfile, name=None, clean=True, create=True, persistent=False, path_name="tpath" +): + """ + Creates and returns a local temporary folder to store files + when unit testing. + + :param thisfile: use ``__file__`` or the function which runs the test + :param name: name of the temporary folder + :param clean: if True, clean the folder first, it can also a function + called to determine whether or not the folder should be cleaned + :param create: if True, creates it (empty if clean is True) + :param persistent: if True, create a folder at root level to reduce path length, + the function checks the ``MAX_PATH`` variable and + shorten the test folder is *max_path* is True on :epkg:`Windows`, + on :epkg:`Linux`, it creates a folder three level ahead + :param path_name: test path used when *max_path* is True + :return: temporary folder + + The function extracts the file which runs this test and will name + the temporary folder base on the name of the method. *name* must be None. + + Parameter *clean* can be a function. + Signature is ``def clean(folder)``. + """ + if name is None: + name = thisfile.__name__ + if name.startswith("test_"): + name = "temp_" + name[5:] + elif not name.startswith("temp_"): + name = "temp_" + name + thisfile = os.path.abspath(thisfile.__func__.__code__.co_filename) + final = os.path.split(name)[-1] + + if not final.startswith("temp_") and not final.startswith("temp2_"): + raise NameError(f"the folder '{name}' must begin with temp_") + + local = os.path.join( + os.path.normpath(os.path.abspath(os.path.dirname(thisfile))), name + ) + + if persistent: + if sys.platform.startswith("win"): + from ctypes.wintypes import MAX_PATH + + if MAX_PATH <= 300: + local = os.path.join(os.path.abspath("\\" + path_name), name) + else: + local = os.path.join(local, "..", "..", "..", "..", path_name, name) + else: + local = os.path.join(local, "..", "..", "..", "..", path_name, name) + local = os.path.normpath(local) + + if name == local: + raise NameError(f"The folder '{name}' must be relative, not absolute") + + if not os.path.exists(local): + if create: + os.makedirs(local) + mode = os.stat(local).st_mode + nmode = mode | stat.S_IWRITE + if nmode != mode: + os.chmod(local, nmode) + else: + if (callable(clean) and clean(local)) or (not callable(clean) and clean): + # delayed import to speed up import time of pycode + remove_folder(local) + time.sleep(0.1) + if create and not os.path.exists(local): + os.makedirs(local) + mode = os.stat(local).st_mode + nmode = mode | stat.S_IWRITE + if nmode != mode: + os.chmod(local, nmode) + + return local + + +def noLOG(*args, **kwargs): + pass + + +def unzip_files( + zipf, where_to=None, fLOG=noLOG, fvalid=None, remove_space=True, fail_if_error=True +): + """ + Unzips files from a zip archive. + + :param zipf: archive (or bytes or BytesIO) + :param where_to: destination folder (can be None, the result is a list of tuple) + :param fLOG: logging function + :param fvalid: function which takes two paths (zip name, local name) + and return True if the file + must be unzipped, False otherwise, if None, the default answer is True + :param remove_space: remove spaces in created local path (+ ``',()``) + :param fail_if_error: fails if an error is encountered + (typically a weird character in a filename), + otherwise a warning is thrown. + :return: list of unzipped files + """ + import zipfile + + if isinstance(zipf, bytes): + zipf = BytesIO(zipf) + + try: + with zipfile.ZipFile(zipf, "r"): + pass + except zipfile.BadZipFile as e: # pragma: no cover + if isinstance(zipf, BytesIO): + raise e + raise OSError(f"Unable to read file '{zipf}'") from e + + files = [] + with zipfile.ZipFile(zipf, "r") as file: + for info in file.infolist(): + if fLOG: + fLOG(f"[unzip_files] unzip '{info.filename}'") + if where_to is None: + try: + content = file.read(info.filename) + except zipfile.BadZipFile as e: # pragma: no cover + if fail_if_error: + raise zipfile.BadZipFile( + f"Unable to extract '{info.filename}' due to {e}" + ) from e + warnings.warn( + f"Unable to extract '{info.filename}' due to {e}", + UserWarning, + stacklevel=0, + ) + continue + files.append((info.filename, content)) + else: + clean = remove_diacritics(info.filename) + if remove_space: + clean = ( + clean.replace(" ", "") + .replace("'", "") + .replace(",", "_") + .replace("(", "_") + .replace(")", "_") + ) + tos = os.path.join(where_to, clean) + if not os.path.exists(tos): + if fvalid and not fvalid(info.filename, tos): + fLOG("[unzip_files] skipping", info.filename) + continue + try: + data = file.read(info.filename) + except zipfile.BadZipFile as e: # pragma: no cover + if fail_if_error: + raise zipfile.BadZipFile( + f"Unable to extract '{info.filename}' due to {e}" + ) from e + warnings.warn( + f"Unable to extract '{info.filename}' due to {e}", + UserWarning, + stacklevel=0, + ) + continue + # check encoding to avoid characters not allowed in paths + if not os.path.exists(tos): + if sys.platform.startswith("win"): + tos = tos.replace("/", "\\") + finalfolder = os.path.split(tos)[0] + if not os.path.exists(finalfolder): + fLOG( + "[unzip_files] creating folder (zip)", + os.path.abspath(finalfolder), + ) + try: + os.makedirs(finalfolder) + except FileNotFoundError as e: # pragma: no cover + mes = ( + "Unexpected error\ninfo.filename={0}\ntos={1}" + "\nfinalfolder={2}\nlen(nfinalfolder)={3}" + ).format( + info.filename, tos, finalfolder, len(finalfolder) + ) + raise FileNotFoundError(mes) from e + if not info.filename.endswith("/"): + try: + with open(tos, "wb") as u: + u.write(data) + except FileNotFoundError as e: # pragma: no cover + # probably an issue in the path name + # the next lines are just here to distinguish + # between the two cases + if not os.path.exists(finalfolder): + raise e + newname = info.filename.replace(" ", "_").replace( + ",", "_" + ) + if sys.platform.startswith("win"): + newname = newname.replace("/", "\\") + tos = os.path.join(where_to, newname) + finalfolder = os.path.split(tos)[0] + if not os.path.exists(finalfolder): + fLOG( + "[unzip_files] creating folder (zip)", + os.path.abspath(finalfolder), + ) + os.makedirs(finalfolder) + with open(tos, "wb") as u: + u.write(data) + files.append(tos) + fLOG( + "[unzip_files] unzipped ", info.filename, " to ", tos + ) + elif not tos.endswith("/"): # pragma: no cover + files.append(tos) + elif not info.filename.endswith("/"): # pragma: no cover + files.append(tos) + return files + + +def ungzip_files( + filename, + where_to=None, + fLOG=noLOG, + fvalid=None, + remove_space=True, + unzip=True, + encoding=None, +): + """ + Uncompresses files from a gzip file. + + :param filename: final gzip file (double compression, extension + should something like .zip.gz) + :param where_to: destination folder (can be None, the result is a list of tuple) + :param fLOG: logging function + :param fvalid: function which takes two paths (zip name, local name) + and return True if the file + must be unzipped, False otherwise, if None, the default answer is True + :param remove_space: remove spaces in created local path (+ ``',()``) + :param unzip: unzip file after gzip + :param encoding: encoding + :return: list of unzipped files + """ + import gzip + + if isinstance(filename, bytes): + is_file = False + filename = BytesIO(filename) + else: + is_file = True + + if encoding is None: + f = gzip.open(filename, "rb") # noqa: SIM115 + content = f.read() + f.close() + if unzip: + try: + return unzip_files(content, where_to=where_to, fLOG=fLOG) + except Exception as e: + raise OSError(f"Unable to unzip file '{filename}'") from e + elif where_to is not None: + filename = os.path.split(filename)[-1].replace(".gz", "") + filename = os.path.join(where_to, filename) + with open(filename, "wb") as f: + f.write(content) + return filename + return content + else: + f = gzip.open(filename, "rt", encoding="utf-8") # noqa: SIM115 + content = f.read() + f.close() + if is_file: + filename = filename.replace(".gz", "") + with open(filename, "wb") as f: + f.write(content) + return filename + return content + + +def remove_diacritics(input_str): + """ + Removes diacritics. + + :param input_str: string to clean + :return: cleaned string + + Example:: + + enguérand --> enguerand + """ + nkfd_form = unicodedata.normalize("NFKD", input_str) + only_ascii = nkfd_form.encode("ASCII", "ignore") + return only_ascii.decode("utf8") diff --git a/mlstatpy/garden/__init__.py b/mlstatpy/garden/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/mlstatpy/garden/poulet.py b/mlstatpy/garden/poulet.py similarity index 56% rename from src/mlstatpy/garden/poulet.py rename to mlstatpy/garden/poulet.py index f0ddc7f5..934cf89c 100644 --- a/src/mlstatpy/garden/poulet.py +++ b/mlstatpy/garden/poulet.py @@ -1,8 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Functions for :ref:`l-exemple_optim_alea`. -""" import math import random @@ -21,12 +16,12 @@ def profit(N, X, p, q, s): """ Calcule le profit. - @param N nombre de poulets vendus - @param X nombre de poulets achetés - @param p prix d'achat - @param q prix de vente - @param s prix soldé - @return profit + :param N: nombre de poulets vendus + :param X: nombre de poulets achetés + :param p: prix d'achat + :param q: prix de vente + :param s: prix soldé + :return: profit """ if X <= N: return X * (q - p) @@ -40,7 +35,7 @@ def proba_poisson(lx, i): lorsque :math:`X` suit une loi de Poisson de paramètre :math:`\\lambda`. """ - return math.exp(-lx) * (lx ** i) / factorielle(i) + return math.exp(-lx) * (lx**i) / factorielle(i) def esperance(X, p, q, s, lx): @@ -48,15 +43,15 @@ def esperance(X, p, q, s, lx): Espérance du profit en faisant varier le nombre de poulet vendus. - @param X nombre de poulets achetés - @param p prix d'achat - @param q prix de vente - @param s prix soldé - @param lx paramètre :math:`\\lambda` - @return espérance du profit + :param X: nombre de poulets achetés + :param p: prix d'achat + :param q: prix de vente + :param s: prix soldé + :param lx: paramètre :math:`\\lambda` + :return: espérance du profit """ res = 0.0 - for i in range(0, lx * 2): + for i in range(lx * 2): res += profit(float(i), X, p, q, s) * proba_poisson(lx, i) return res @@ -66,14 +61,14 @@ def maximum(p, q, s, lx): Calcule les espérances de profit pour différents nombres de poulets achetés. - @param p prix d'achat - @param q prix de vente - @param s prix soldé - @param lx paramètre :math:`\\lambda` - @return liste ``(X, profit)`` + :param p: prix d'achat + :param q: prix de vente + :param s: prix soldé + :param lx: paramètre :math:`\\lambda` + :return: liste ``(X, profit)`` """ res = [] - for X in range(0, 2 * lx): + for X in range(2 * lx): r = esperance(X, p, q, s, lx) res.append((X, r)) return res @@ -84,8 +79,9 @@ def find_maximum(res): Trouver le couple (nombre de poulets achetés, profit) lorsque le profit est maximum. - @param res résultat de la fonction :func:`maximum ` - @return ``(X, profit)`` maximum + :param res: résultat de la fonction + :func:`maximum ` + :return: ``(X, profit)`` maximum """ m = (0, 0) for r in res: @@ -99,7 +95,7 @@ def exponentielle(lx): Simule une loi exponentielle de paramètre :math:`\\lambda`. """ u = random.random() - return - 1.0 / lx * math.log(1.0 - u) + return -1.0 / lx * math.log(1.0 - u) def poisson(lx): @@ -118,9 +114,10 @@ def poisson_melange(params, coef): """ Simule une variable selon un mélange de loi de Poisson. - @param params liste de paramètre :math:`\\lambda` - @param coef ``coef[i]`` coefficient associé à la loi de paramètre ``params[i]`` - @return valeur simulée + :param params: liste de paramètre :math:`\\lambda` + :param coef: ``coef[i]`` coefficient associé + à la loi de paramètre ``params[i]`` + :return: valeur simulée """ s = 0 for i, pa in enumerate(params): @@ -133,17 +130,18 @@ def histogramme_poisson_melange(params, coef, n=100000): """ Calcule un histogramme d'un mélange de loi de Poisson. - @param params liste de paramètre :math:`\\lambda` - @param coef ``coef[i]`` coefficient associé à la loi de paramètre ``params[i]`` - @return histogramme + :param params: liste de paramètre :math:`\\lambda` + :param coef: ``coef[i]`` coefficient associé + à la loi de paramètre ``params[i]`` + :return: histogramme """ - h = [0.0 for i in range(0, 4 * max(params))] - for i in range(0, n): + h = [0.0 for i in range(4 * max(params))] + for _i in range(n): x = poisson_melange(params, coef) if x < len(h): h[x] += 1 s = sum(h) - for i in range(0, len(h)): + for i in range(len(h)): h[i] = float(h[i]) / s return h @@ -161,17 +159,18 @@ def local_proba_poisson_melange(params, coef, i): Calcule la probabilité :math:`\\pr{X=i}`` lorsque :math:`X` suit un mélange de lois. - @param params liste de paramètre :math:`\\lambda` - @param coef ``coef[i]`` coefficient associé à la loi de paramètre ``params[i]`` - @return valeur + :param params: liste de paramètre :math:`\\lambda` + :param coef: ``coef[i]`` coefficient associé + à la loi de paramètre ``params[i]`` + :return: valeur """ - if len(proba_poisson_melange_tableau) == 0: + if not proba_poisson_melange_tableau: proba_poisson_melange_tableau.extend( - histogramme_poisson_melange(params, coef)) + histogramme_poisson_melange(params, coef) + ) if i >= len(proba_poisson_melange_tableau): return 0.0 - else: - return proba_poisson_melange_tableau[i] + return proba_poisson_melange_tableau[i] return local_proba_poisson_melange diff --git a/src/mlstatpy/graph/__init__.py b/mlstatpy/graph/__init__.py similarity index 51% rename from src/mlstatpy/graph/__init__.py rename to mlstatpy/graph/__init__.py index c16fa831..be270590 100644 --- a/src/mlstatpy/graph/__init__.py +++ b/mlstatpy/graph/__init__.py @@ -1,6 +1 @@ -""" -@file -@brief shortcut to graph -""" - from .graph_distance import GraphDistance diff --git a/src/mlstatpy/graph/graph_distance.py b/mlstatpy/graph/graph_distance.py similarity index 55% rename from src/mlstatpy/graph/graph_distance.py rename to mlstatpy/graph/graph_distance.py index dc953bde..cb6f3de5 100644 --- a/src/mlstatpy/graph/graph_distance.py +++ b/mlstatpy/graph/graph_distance.py @@ -1,15 +1,17 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief First approach for a edit distance between two graphs. - -See :ref:`l-graph_distance`. -""" import copy import re -class Vertex: +class _Vertex: + __slots__ = ("nb", "label", "weight") # noqa: RUF023 + + def __init__(self, nb, label, weight): + self.nb = nb # kind of id + self.label = label # label + self.weight = weight + + +class Vertex(_Vertex): """ Defines a vertex of a graph. """ @@ -21,25 +23,23 @@ def __init__(self, nb, label, weight): @param label (str) label @para weight (float) """ - self.nb = nb # kind of id - self.label = label # label + _Vertex.__init__(self, nb, label, weight) self.pair = (None, None) self.edges = {} self.predE = {} self.succE = {} - self.weight = weight def __str__(self): """ usual """ - return '{}'.format(self.Label) + return f"{self.Label}" def __repr__(self): """ usual """ - return "Vertex({}, {}, {})".format(repr(self.nb), repr(self.Label), self.weight) + return f"Vertex({repr(self.nb)}, {repr(self.Label)}, {self.weight})" def is_vertex(self): """ @@ -61,14 +61,22 @@ def Label(self): return self.label -class Edge: +class _Edge: + __slots__ = ("from_", "to", "label", "weight", "nb") # noqa: RUF023 + + def __init__(self, from_, to, label, weight): + self.from_, self.to = from_, to + self.nb = from_, to + self.label = label + + +class Edge(_Edge): """ Defines an edge of a graph. """ def __init__(self, from_, to, label, weight): """ - constructor @param from_ (int) @param to (int) @param label (str) @@ -76,9 +84,7 @@ def __init__(self, from_, to, label, weight): ``'00'`` means the beginning of a graph, ``'11'`` the end. """ - self.from_, self.to = from_, to - self.nb = from_, to - self.label = label + _Edge.__init__(self, from_, to, label, weight) self.pair = (None, None) self.weight = weight if self.from_ == "00" and self.to == "00": @@ -90,13 +96,18 @@ def __str__(self): """ usual """ - return "{} -> {} [{}]".format(self.nb[0], self.nb[1], self.Label) + return f"{self.nb[0]} -> {self.nb[1]} [{self.Label}]" def __repr__(self): """ usual """ - return "Edge({}, {}, {}, {})".format(repr(self.nb[0]), repr(self.nb[1]), repr(self.Label), self.weight) + return "Edge({}, {}, {}, {})".format( + repr(self.nb[0]), + repr(self.nb[1]), + repr(self.Label), + self.weight, + ) def is_vertex(self): """ @@ -143,7 +154,8 @@ class GraphDistance: graph1 = GraphDistance(graph1) graph2 = GraphDistance(graph2) - distance, graph = graph1.distance_matching_graphs_paths(graph2, use_min=False) + distance, graph = graph1.distance_matching_graphs_paths( + graph2, use_min=False) print("distance", distance) print("common paths:", graph) @@ -160,18 +172,25 @@ def get_list_of_vertices(graph): vertices.sort() return vertices - def __init__(self, edge_list, vertex_label=None, add_loop=False, - weight_vertex=1., weight_edge=1.): + def __init__( + self, + edge_list, + vertex_label=None, + add_loop=False, + weight_vertex=1.0, + weight_edge=1.0, + ): """ constructor - @param edge_list list of edges - @param add_loop automatically add a loop on each vertex (an edge from a vertex to itself) - @param weight_vertex weight for every vertex - @param weight_edge weight for every edge + @param edge_list list of edges + @param add_loop automatically add a loop on + each vertex (an edge from a vertex to itself) + @param weight_vertex weight for every vertex + @param weight_edge weight for every edge """ if vertex_label is None: - vertex_label = dict() + vertex_label = {} if isinstance(edge_list, str): self.load_from_file(edge_list, add_loop) else: @@ -181,8 +200,7 @@ def __init__(self, edge_list, vertex_label=None, add_loop=False, self.labelEnd = "11" vid = GraphDistance.get_list_of_vertices(edge_list) for u in vid: - self.vertices[u] = Vertex( - u, vertex_label.get(u, str(u)), weight_vertex) + self.vertices[u] = Vertex(u, vertex_label.get(u, str(u)), weight_vertex) for e in edge_list: i, j = e[:2] ls = "" if len(e) < 3 else e[2] @@ -197,10 +215,9 @@ def __getitem__(self, index): """ if isinstance(index, str): return self.vertices[index] - elif isinstance(index, tuple): + if isinstance(index, tuple): return self.edges[index] - else: - raise KeyError("unable to get element " + str(index)) + raise KeyError("unable to get element " + str(index)) @staticmethod def load_from_file(filename, add_loop): @@ -209,10 +226,12 @@ def load_from_file(filename, add_loop): @param filename file name @param add_loop @see me __init__ """ - lines = open(filename, "r").readlines() - regV = re.compile("\\\"?([a-z0-9_]+)\\\"? *[[]label=\\\"(.*)\\\"[]]") - regE = re.compile("\\\"?([a-z0-9_]+)\\\"? *-> *\\\"?" + - "([a-z0-9_]+)\\\"? *[[]label=\\\"(.*)\\\"[]]") + with open(filename, "r") as f: + lines = f.readlines() + regV = re.compile('\\"?([a-z0-9_]+)\\"? *[[]label=\\"(.*)\\"[]]') + regE = re.compile( + '\\"?([a-z0-9_]+)\\"? *-> *\\"?([a-z0-9_]+)\\"? *[[]label=\\"(.*)\\"[]]' + ) edge_list = [] vertex_label = {} for line in lines: @@ -225,8 +244,8 @@ def load_from_file(filename, add_loop): elif ve: g = ve.groups() vertex_label[g[0]] = g[1] - if len(vertex_label) == 0 or len(edge_list) == 0: - raise OSError("unable to parse file " + filename) + if not vertex_label or not edge_list: + raise OSError(f"Unable to parse file {filename!r}.") return GraphDistance(edge_list, vertex_label, add_loop) def _private__init__(self, add_loop, weight_vertex, weight_edge): @@ -250,19 +269,21 @@ def connect_root_and_leave(self, weight_vertex, weight_edge): for r in roots: if self.labelBegin not in self.vertices: self.vertices[self.labelBegin] = Vertex( - self.labelBegin, self.labelBegin, weight_vertex) + self.labelBegin, self.labelBegin, weight_vertex + ) if r != self.labelBegin: self.edges[self.labelBegin, r] = Edge( - self.labelBegin, r, "", weight_edge) + self.labelBegin, r, "", weight_edge + ) leaves = [k for k, v in vert.items() if v == 0] for r in leaves: if self.labelEnd not in self.vertices: self.vertices[self.labelEnd] = Vertex( - self.labelEnd, self.labelEnd, weight_vertex) + self.labelEnd, self.labelEnd, weight_vertex + ) if r != self.labelEnd: - self.edges[r, self.labelEnd] = Edge( - r, self.labelEnd, "", weight_edge) + self.edges[r, self.labelEnd] = Edge(r, self.labelEnd, "", weight_edge) def get_order_vertices(self): edges = self.edges @@ -289,7 +310,7 @@ def __str__(self): li = [] for v in self.vertices.values(): li.append(str(v)) - for k, e in self.edges.items(): + for _k, e in self.edges.items(): li.append(str(e)) return "\n".join(li) @@ -298,9 +319,10 @@ def __repr__(self): usual """ edges = ", ".join(repr(v) for _, v in sorted(self.edges.items())) - vertices = ", ".join("'{}': {}".format(k, repr(v)) - for k, v in sorted(self.vertices.items())) - return "GraphDistance(\n [{0}],\n {{{1}}})".format(edges, vertices) + vertices = ", ".join( + f"'{k}': {repr(v)}" for k, v in sorted(self.vertices.items()) + ) + return f"GraphDistance(\n [{edges}],\n {{{vertices}}})" def compute_predecessor(self): """ @@ -325,16 +347,19 @@ def compute_successor(self): for n in v: self.vertices[k].succE[n] = self.edges[n] - def get_matching_functions(self, function_mach_vertices, function_match_edges, - cost=False): + def get_matching_functions( + self, function_mach_vertices, function_match_edges, cost=False + ): """ - returns default matching functions between two vertices and two edges - @param function_mach_vertices if not None, this function is returned, othewise, it returns a new fonction. - See below. - @param function_match_edges if not None, this function is returned, othewise, it returns a new fonction. - See below. - @param cost if True, the returned function should return a float, otherwise a boolean - @return a pair of functions + Returns default matching functions between two vertices and two edges. + + :param function_mach_vertices: if not None, this function + is returned, othewise, it returns a new fonction. See below. + :param function_match_edges: if not None, this function is returned, + othewise, it returns a new fonction. See below. + :param cost: if True, the returned function should + return a float, otherwise a boolean + :return: a pair of functions Example for * if cost is False: @@ -347,13 +372,16 @@ def get_matching_functions(self, function_mach_vertices, function_match_edges, :: def tempF1 (v1,v2,g1,g2,w1,w2) : - if v1 is not None and not v1.is_vertex() : raise TypeError("should be a vertex") - if v2 is not None and not v2.is_vertex() : raise TypeError("should be a vertex") + if v1 is not None and not v1.is_vertex(): + raise TypeError("should be a vertex") + if v2 is not None and not v2.is_vertex(): + raise TypeError("should be a vertex") if v1 is None and v2 is None : return 0 elif v1 is None or v2 is None : return v2.weight*w2 if v1 is None else v1.weight*w1 else : - return 0 if v1.label == v2.label else 0.5*(v1.weight*w1 + v2.weight*w2) + return 0 if v1.label == v2.label else ( + 0.5*(v1.weight*w1 + v2.weight*w2)) Example for *function_match_edges* if cost is False: @@ -369,37 +397,53 @@ def tempF1 (v1,v2,g1,g2,w1,w2) : :: def tempF2 (e1,e2,g1,g2,w1,w2) : - if e1 is not None and not e1.is_edge() : raise TypeError("should be an edge") - if e2 is not None and not e2.is_edge() : raise TypeError("should be an edge") - if e1 is None and e2 is None : return 0 - elif e1 is None or e2 is None : + if e1 is not None and not e1.is_edge(): + raise TypeError("should be an edge") + if e2 is not None and not e2.is_edge(): + raise TypeError("should be an edge") + if e1 is None and e2 is None: return 0 + elif e1 is None or e2 is None: return e2.weight*w2 if e1 is None else e1.weight*w1 - elif e1.label != e2.label : return 0.5*(e1.weight*w1 + e2.weight*w2) + elif e1.label != e2.label: return 0.5*(e1.weight*w1 + e2.weight*w2) else : lab1 = g1.vertices [e1.from_].label == g2.vertices [e2.from_].label lab2 = g1.vertices [e1.to].label == g2.vertices [e2.to].label - if lab1 and lab2 : return 0 - else : return e1.weight*w1 + e2.weight*w2 + if lab1 and lab2: return 0 + else: return e1.weight*w1 + e2.weight*w2 """ if cost: - if function_mach_vertices is None: - def tempF1(v1, v2, g1, g2, w1, w2): - if v1 is not None and not v1.is_vertex(): - raise TypeError("should be a vertex") - if v2 is not None and not v2.is_vertex(): - raise TypeError("should be a vertex") - if v1 is None and v2 is None: - return 0 - elif v1 is None or v2 is None: - return v2.weight * w2 if v1 is None else v1.weight * w1 + + def tempF1_vertex(v1, v2, g1, g2, w1, w2): + if v1 is None: + if v2 is None: + return 0.0 + if not v2.is_vertex(): + raise TypeError("v2 should be a vertex") + return v2.weight * w2 + elif v2 is None: + if not v1.is_vertex(): + raise TypeError("v1 should be a vertex") + if not v1.is_vertex(): + raise TypeError("v1 should be a vertex") + return v1.weight * w1 else: - return 0 if v1.label == v2.label else 0.5 * (v1.weight * w1 + v2.weight * w2) - function_mach_vertices = tempF1 + if not v1.is_vertex(): + raise TypeError("v1 should be a vertex") + if not v2.is_vertex(): + raise TypeError("v2 should be a vertex") + return ( + 0 + if v1.label == v2.label + else 0.5 * (v1.weight * w1 + v2.weight * w2) + ) + + function_mach_vertices = tempF1_vertex if function_match_edges is None: - def tempF2(e1, e2, g1, g2, w1, w2): + + def tempF2_edge(e1, e2, g1, g2, w1, w2): if e1 is not None and not e1.is_edge(): raise TypeError("should be an edge") if e2 is not None and not e2.is_edge(): @@ -411,73 +455,91 @@ def tempF2(e1, e2, g1, g2, w1, w2): elif e1.label != e2.label: return 0.5 * (e1.weight * w1 + e2.weight * w2) else: - lab1 = g1.vertices[ - e1.from_].label == g2.vertices[e2.from_].label - lab2 = g1.vertices[ - e1.to].label == g2.vertices[e2.to].label + lab1 = ( + g1.vertices[e1.from_].label == g2.vertices[e2.from_].label + ) + lab2 = g1.vertices[e1.to].label == g2.vertices[e2.to].label if lab1 and lab2: return 0 else: return e1.weight * w1 + e2.weight * w2 - function_match_edges = tempF2 + function_match_edges = tempF2_edge else: if function_mach_vertices is None: - function_mach_vertices = \ - lambda v1, v2, g1, g2, w1, w2: \ - v1.label == v2.label + function_mach_vertices = ( + lambda v1, v2, g1, g2, w1, w2: v1.label == v2.label + ) if function_match_edges is None: - function_match_edges = \ - lambda e1, e2, g1, g2, w1, w2: \ - e1.label == e2.label and \ - (e1.from_ != e1.to or e2.from_ != e2.to) and \ - (e1.from_ != self.labelBegin or e1.to != self.labelBegin) and \ - (e1.from_ != self.labelEnd or e1.to != self.labelEnd) + function_match_edges = ( + lambda e1, e2, g1, g2, w1, w2: e1.label == e2.label + and (e1.from_ != e1.to or e2.from_ != e2.to) + and (e1.from_ != self.labelBegin or e1.to != self.labelBegin) + and (e1.from_ != self.labelEnd or e1.to != self.labelEnd) + ) return function_mach_vertices, function_match_edges - def common_paths(self, graph2, - function_mach_vertices=None, - function_match_edges=None, - noClean=False): - function_mach_vertices, function_match_edges = \ - self.get_matching_functions( - function_mach_vertices, function_match_edges) + def common_paths( + self, + graph2, + function_mach_vertices=None, + function_match_edges=None, + noClean=False, + ): + function_mach_vertices, function_match_edges = self.get_matching_functions( + function_mach_vertices, function_match_edges + ) g = GraphDistance([]) - vfirst = Vertex(self.labelBegin, "%s-%s" % (self.labelBegin, self.labelBegin), - (self.vertices[self.labelBegin].weight + - graph2.vertices[self.labelBegin].weight) / 2) + vfirst = Vertex( + self.labelBegin, + f"{self.labelBegin}-{self.labelBegin}", + ( + self.vertices[self.labelBegin].weight + + graph2.vertices[self.labelBegin].weight + ) + / 2, + ) g.vertices[self.labelBegin] = vfirst - vfirst.pair = self.vertices[ - self.labelBegin], graph2.vertices[self.labelBegin] + vfirst.pair = self.vertices[self.labelBegin], graph2.vertices[self.labelBegin] modif = 1 while modif > 0: modif = 0 add = {} - for k, v in g.vertices.items(): - + for _k, v in g.vertices.items(): v1, v2 = v.pair - if len(v.succE) == 0: + if not v.succE: for e1 in v1.succE: for e2 in v2.succE: oe1 = self.edges[e1] oe2 = graph2.edges[e2] - if function_match_edges(oe1, oe2, self, graph2, 1., 1.): + if function_match_edges(oe1, oe2, self, graph2, 1.0, 1.0): tv1 = self.vertices[oe1.to] tv2 = graph2.vertices[oe2.to] - if function_mach_vertices(tv1, tv2, self, graph2, 1., 1.): + if function_mach_vertices( + tv1, tv2, self, graph2, 1.0, 1.0 + ): # we have a match - ii = "%s-%s" % (tv1.nb, tv2.nb) - if tv1.nb == self.labelEnd and tv2.nb == self.labelEnd: + ii = f"{tv1.nb}-{tv2.nb}" + if ( + tv1.nb == self.labelEnd + and tv2.nb == self.labelEnd + ): ii = self.labelEnd - lab = "%s-%s" % (tv1.label, tv2.label) \ - if tv1.label != tv2.label else tv1.label - tv = Vertex( - ii, lab, (tv1.weight + tv2.weight) / 2) - lab = "%s-%s" % (oe1.label, oe2.label) \ - if oe1.label != oe2.label else oe1.label - ne = Edge(v.nb, tv.nb, lab, - (oe1.weight + oe2.weight) / 2) + lab = ( + f"{tv1.label}-{tv2.label}" + if tv1.label != tv2.label + else tv1.label + ) + tv = Vertex(ii, lab, (tv1.weight + tv2.weight) / 2) + lab = ( + f"{oe1.label}-{oe2.label}" + if oe1.label != oe2.label + else oe1.label + ) + ne = Edge( + v.nb, tv.nb, lab, (oe1.weight + oe2.weight) / 2 + ) add[tv.nb] = tv g.edges[ne.from_, ne.to] = ne ne.pair = oe1, oe2 @@ -535,15 +597,17 @@ def clean_dead_ends(self): def enumerate_all_paths(self, edges_and_vertices, begin=None): if begin is None: begin = [] - if len(self.vertices) > 0 and len(self.edges) > 0: + if self.vertices and self.edges: if edges_and_vertices: - last = begin[-1] if len(begin) > 0 \ - else self.vertices[self.labelBegin] + last = begin[-1] if begin else self.vertices[self.labelBegin] else: - last = self.vertices[begin[-1].to] if len(begin) > 0 \ + last = ( + self.vertices[begin[-1].to] + if begin else self.vertices[self.labelBegin] + ) - if edges_and_vertices and len(begin) == 0: + if edges_and_vertices and not begin: begin = [last] for ef in last.succE: @@ -559,68 +623,103 @@ def enumerate_all_paths(self, edges_and_vertices, begin=None): if v.label == self.labelEnd: yield path else: - for p in self.enumerate_all_paths(edges_and_vertices, path): - yield p - - def edit_distance_path(self, p1, p2, g1, g2, - function_mach_vertices=None, - function_match_edges=None, - use_min=False, - debug=False): + yield from self.enumerate_all_paths(edges_and_vertices, path) + + def edit_distance_path( + self, + p1, + p2, + g1, + g2, + function_mach_vertices=None, + function_match_edges=None, + use_min=False, + debug=False, + cache=None, + ): """ Tries to align two paths from two graphs. - @param p1 path 1 (from g1) - @param p2 path 2 (from g2) - @param g1 graph 1 - @param g2 graph 2 - @param function_mach_vertices function which gives a distance bewteen two vertices, - if None, it take the output of @see me get_matching_functions - @param function_match_edges function which gives a distance bewteen two edges, - if None, it take the output of @see me get_matching_functions - @param use_min the returned is based on a edit distance, if this parameter is True, the returned value will be: - - :: - - if use_min : - n = min (len(p1), len(p2)) - d = d*1.0 / n if n > 0 else 0 - - @param debug unused - @return 2-uple: distance, aligned path + :param p1: path 1 (from g1) + :param p2: path 2 (from g2) + :param g1: graph 1 + :param g2: graph 2 + :param function_mach_vertices: function which gives a + distance bewteen two vertices, if None, it take the output of + :meth:`get_matching_functions` + :param function_match_edges: function which gives a distance bewteen + two edges, if None, it take the output of + :meth:`get_matching_functions` + :param use_min: the returned is based on a edit distance, + if this parameter is True, the returned value will be: + + :: + + if use_min : + n = min (len(p1), len(p2)) + d = d*1.0 / n if n > 0 else 0 + + :param debug: unused + :param cache: to cache the costs + :return: 2-uple: distance, aligned path """ - function_mach_vertices, function_match_edges = \ - self.get_matching_functions( - function_mach_vertices, function_match_edges, True) + + def edge_vertex_match(x, y, g1, g2, w1, w2): + if x is None: + if y is None: + raise RuntimeError("Both x and y are None.") + return y.weight * w2 + elif y is None: + return x.weight * w1 + return (x.weight * w1 + y.weight * w2) / 2 + + def cache_cost(func, a, b, g1, g2, w1, w2): + if cache is None: + return func(a, b, g1, g2, w1, w2) + key = (id(a), id(b), w1, w2) + if key in cache: + return cache[key] + cost = func(a, b, g1, g2, w1, w2) + cache[key] = cost + return cost + + function_mach_vertices, function_match_edges = self.get_matching_functions( + function_mach_vertices, function_match_edges, True + ) dist = {(-1, -1): (0, None, None)} - w1 = 1.0 / len(p1) if use_min else 1. - w2 = 1.0 / len(p2) if use_min else 1. + if use_min: + w1 = 1.0 / len(p1) + w2 = 1.0 / len(p2) + else: + w1 = 1.0 + w2 = 1.0 + p2l = list(enumerate(p2)) for i1, eorv1 in enumerate(p1): - for i2, eorv2 in enumerate(p2): + ed1 = eorv1.is_edge() + ve1 = eorv1.is_vertex() + for i2, eorv2 in p2l: np = i1, i2 - posit = [((i1 - 1, i2), (eorv1, None)), - ((i1, i2 - 1), (None, eorv2)), - ((i1 - 1, i2 - 1), (eorv1, eorv2)), ] + posit = [ + ((i1 - 1, i2 - 1), (eorv1, eorv2)), + ((i1 - 1, i2), (eorv1, None)), + ((i1, i2 - 1), (None, eorv2)), + ] - if eorv1.is_edge() and eorv2.is_edge(): + if ed1 and eorv2.is_edge(): func = function_match_edges - elif eorv1.is_vertex() and eorv2.is_vertex(): + elif ve1 and eorv2.is_vertex(): func = function_mach_vertices else: - def func(x, y, g1, g2, w1, w2): - return 0.5 * (x.weight * w1 + y.weight * w2) if x is not None and y is not None \ - else (x.weight * w1 if y is None else y.weight * w2) + func = edge_vertex_match for p, co in posit: if p in dist: c0 = dist[p][0] - c1 = func(co[0], co[1], g1, g2, w1, w2) + c1 = cache_cost(func, co[0], co[1], g1, g2, w1, w2) c = c0 + c1 - if np not in dist: - dist[np] = (c, p, co, (c0, c1)) - elif c < dist[np][0]: + if np not in dist or c < dist[np][0]: dist[np] = (c, p, co, (c0, c1)) last = dist[len(p1) - 1, len(p2) - 1] @@ -640,7 +739,7 @@ def func(x, y, g1, g2, w1, w2): def private_count_left_right(self, valuesInList): countLeft = {} countRight = {} - for k, v in valuesInList: + for _k, v in valuesInList: i, j = v if i not in countRight: countRight[i] = {} @@ -654,64 +753,82 @@ def private_kruskal_matrix(self, matrix, reverse): countLeft, countRight = self.private_count_left_right(matrix) cleft, cright = len(countLeft), len(countRight) matrix.sort(reverse=reverse) - count = max(max([sum(_.values()) for _ in countRight.values()]), - max([sum(_.values()) for _ in countLeft.values()])) + count = max( + max(sum(_.values()) for _ in countRight.values()), + max(sum(_.values()) for _ in countLeft.values()), + ) while count > 1: - k, v = matrix.pop() + _k, v = matrix.pop() i, j = v countRight[i][j] -= 1 countLeft[j][i] -= 1 - count = max(max([max(_.values()) for _ in countRight.values()]), - max([max(_.values()) for _ in countLeft.values()])) + count = max( + max(max(_.values()) for _ in countRight.values()), + max(max(_.values()) for _ in countLeft.values()), + ) mini = min(cleft, cright) if len(matrix) < mini: - raise Exception("impossible: the smallest set should get all" + - "its element associated to at least one coming from the other set") + raise RuntimeError( + "impossible: the smallest set should get all " + "its element associated to at least one coming " + "from the other set" + ) def _private_string_path_matching(self, path, skipEdge=False): temp = [] for p in path: u, v = p[2] - if skipEdge and ((u is not None and u.is_edge()) or - (v is not None and v.is_edge())): + if skipEdge and ( + (u is not None and u.is_edge()) or (v is not None and v.is_edge()) + ): continue su = "-" if u is None else str(u.nb) sv = "-" if v is None else str(v.nb) - s = "(%s,%s)" % (su, sv) + s = f"({su},{sv})" temp.append(s) return " ".join(temp) - def distance_matching_graphs_paths(self, graph2, - function_mach_vertices=None, - function_match_edges=None, - noClean=False, - store=None, - use_min=True, - weight_vertex=1., - weight_edge=1.): + def distance_matching_graphs_paths( + self, + graph2, + function_mach_vertices=None, + function_match_edges=None, + noClean=False, + store=None, + use_min=True, + weight_vertex=1.0, + weight_edge=1.0, + verbose=0, + ): """ Computes an alignment between two graphs. - @param graph2 the other graph - @param function_mach_vertices function which gives a distance bewteen two vertices, - if None, it take the output of @see me get_matching_functions - @param function_match_edges function which gives a distance bewteen two edges, - if None, it take the output of @see me get_matching_functions - @param noClean if True, clean unmatched vertices and edges - @param store if None, does nothing, if it is a dictionary, the function will store here various - information about how th matching was operated - @param use_min @see me edit_distance_path - @param weight_vertex a weight for every vertex - @param weight_edge a weight for every edge - @return 2 tuple (a distance, a graph containing the aligned paths between the two graphs) + :param graph2: the other graph + :param function_mach_vertices: function which gives a distance + between two vertices, if None, it take the output of + :meth:`get_matching_functions` + :param function_match_edges: function which gives a distance + bewteen two edges, if None, it take the output of + :meth:`get_matching_functions` + :param noClean: if True, clean unmatched vertices and edges + :param store: if None, does nothing, if it is a + dictionary, the function will store + here various information about how + the matching was operated + :param use_min: @see me edit_distance_path + :param weight_vertex: a weight for every vertex + :param weight_edge: a weight for every edge + :param verbose: display some progress with :epkg:`tqdm` + :return: 2 tuple (a distance, a graph containing + the aligned paths between the two graphs) See :ref:`l-graph_distance`. """ - function_mach_vertices, function_match_edges = \ - self.get_matching_functions( - function_mach_vertices, function_match_edges, True) + function_mach_vertices, function_match_edges = self.get_matching_functions( + function_mach_vertices, function_match_edges, True + ) paths1 = list(self.enumerate_all_paths(True)) paths2 = list(graph2.enumerate_all_paths(True)) @@ -721,23 +838,52 @@ def distance_matching_graphs_paths(self, graph2, store["nbpath2"] = len(paths2) matrix_distance = {} - for i1, p1 in enumerate(paths1): - for i2, p2 in enumerate(paths2): - matrix_distance[i1, i2] = self.edit_distance_path(p1, p2, self, graph2, - function_mach_vertices, function_match_edges, use_min=use_min) + if verbose > 0: + print("[distance_matching_graphs_paths] builds matrix_distance") + from tqdm import tqdm + + loop1 = tqdm(list(enumerate(paths1))) + else: + loop1 = enumerate(paths1) + loop2 = list(enumerate(paths2)) + if verbose > 0: + print( + "[distance_matching_graphs_paths] len(loop1)=%d" + % len(list(enumerate(paths1))) + ) + print(f"[distance_matching_graphs_paths] len(loop2)={len(loop2)}") + cache = {} + for i1, p1 in loop1: + for i2, p2 in loop2: + matrix_distance[i1, i2] = self.edit_distance_path( + p1, + p2, + self, + graph2, + function_mach_vertices, + function_match_edges, + use_min=use_min, + cache=cache, + ) + if verbose > 0: + print(f"[distance_matching_graphs_paths] len(cache)={len(cache)}") if store is not None: store["matrix_distance"] = matrix_distance reduction = [(v[0], k) for k, v in matrix_distance.items()] if store is not None: store["path_mat1"] = copy.deepcopy(reduction) + if verbose > 0: + print("[distance_matching_graphs_paths] private_kruskal_matrix") self.private_kruskal_matrix(reduction, False) if store is not None: store["path_mat2"] = copy.deepcopy(reduction) + if verbose > 0: + print("[distance_matching_graphs_paths] pair_count_vertex") pair_count_edge = {} pair_count_vertex = {} - for k, v in reduction: + for _k, v in reduction: path = matrix_distance[v][1] for el in path: n1, n2 = el[2] @@ -747,8 +893,7 @@ def distance_matching_graphs_paths(self, graph2, pair_count_edge[add] = pair_count_edge.get(add, 0) + 1 elif n1.is_vertex() and n2.is_vertex(): add = n1.nb, n2.nb - pair_count_vertex[ - add] = pair_count_vertex.get(add, 0) + 1 + pair_count_vertex[add] = pair_count_vertex.get(add, 0) + 1 if store is not None: store["pair_count_vertex"] = pair_count_vertex @@ -768,32 +913,37 @@ def distance_matching_graphs_paths(self, graph2, if store is not None: store["vertex_mat2"] = copy.copy(reduction_vertex) - count_edge_left, count_edge_right = self.private_count_left_right( - reduction_edge) + if verbose > 0: + print("[distance_matching_graphs_paths] private_count_left_right") + _count_edge_left, count_edge_right = self.private_count_left_right( + reduction_edge + ) count_vertex_left, count_vertex_right = self.private_count_left_right( - reduction_vertex) + reduction_vertex + ) res_graph = GraphDistance([]) doneVertex = {} done_edge = {} + if verbose > 0: + print("[distance_matching_graphs_paths] builds merged graph") for k, v in self.vertices.items(): newv = Vertex(v.nb, v.label, weight_vertex) res_graph.vertices[k] = newv if v.nb in count_vertex_right: - ind = list(count_vertex_right[v.nb].keys())[0] + ind = list(count_vertex_right[v.nb].keys())[0] # noqa: RUF015 newv.pair = (v, graph2.vertices[ind]) doneVertex[ind] = newv if newv.pair[0].label != newv.pair[1].label: - newv.label = "%s|%s" % ( - newv.pair[0].label, newv.pair[1].label) + newv.label = f"{newv.pair[0].label}|{newv.pair[1].label}" else: newv.pair = (v, None) for k, v in graph2.vertices.items(): if k in doneVertex: continue - newv = Vertex("2a.%s" % v.nb, v.label, weight_vertex) + newv = Vertex(f"2a.{v.nb}", v.label, weight_vertex) res_graph.vertices[newv.nb] = newv newv.pair = (None, v) @@ -801,7 +951,7 @@ def distance_matching_graphs_paths(self, graph2, newe = Edge(e.from_, e.to, e.label, weight_edge) res_graph.edges[k] = newe if e.nb in count_edge_right: - ind = list(count_edge_right[e.nb].keys())[0] + ind = list(count_edge_right[e.nb].keys())[0] # noqa: RUF015 newe.pair = (e, graph2.edges[ind]) done_edge[ind] = newe else: @@ -810,10 +960,16 @@ def distance_matching_graphs_paths(self, graph2, for k, e in graph2.edges.items(): if k in done_edge: continue - from_ = list(count_vertex_left[e.from_].keys())[0] if e.from_ in count_vertex_left \ - else "2a.%s" % e.from_ - to = list(count_vertex_left[e.to].keys())[0] if e.to in count_vertex_left \ - else "2a.%s" % e.to + from_ = ( + list(count_vertex_left[e.from_].keys())[0] # noqa: RUF015 + if e.from_ in count_vertex_left + else f"2a.{e.from_}" + ) + to = ( + list(count_vertex_left[e.to].keys())[0] # noqa: RUF015 + if e.to in count_vertex_left + else f"2a.{e.to}" + ) if from_ not in res_graph.vertices: raise RuntimeError("should not happen " + from_) if to not in res_graph.vertices: @@ -822,13 +978,19 @@ def distance_matching_graphs_paths(self, graph2, res_graph.edges[newe.nb] = newe newe.pair = (None, e) + if verbose > 0: + print( + "[distance_matching_graphs_paths] " + "compute_predecessor, compute_successor" + ) res_graph.compute_predecessor() res_graph.compute_successor() allPaths = list(res_graph.enumerate_all_paths(True)) - temp = [sum([0 if None in _.pair else 1 for _ in p]) * 1.0 / len(p) - for p in allPaths] + temp = [ + sum(0 if None in _.pair else 1 for _ in p) * 1.0 / len(p) for p in allPaths + ] distance = 1.0 - 1.0 * sum(temp) / len(allPaths) return distance, res_graph @@ -837,23 +999,27 @@ def draw_vertices_edges(self): vertices = [] edges = [] for k, v in self.vertices.items(): - if v.pair == (None, None) or (v.pair[0] is not None and v.pair[1] is not None): + if v.pair == (None, None) or ( + v.pair[0] is not None and v.pair[1] is not None + ): vertices.append((k, v.label)) elif v.pair[1] is None: vertices.append((k, "-" + v.label, "red")) elif v.pair[0] is None: vertices.append((k, "+" + v.label, "green")) else: - raise Exception("?") + raise RuntimeError("?") - for k, v in self.edges.items(): - if v.pair == (None, None) or (v.pair[0] is not None and v.pair[1] is not None): + for _k, v in self.edges.items(): + if v.pair == (None, None) or ( + v.pair[0] is not None and v.pair[1] is not None + ): edges.append((v.from_, v.to, v.label)) elif v.pair[1] is None: edges.append((v.from_, v.to, "-" + v.label, "red")) elif v.pair[0] is None: edges.append((v.from_, v.to, "+" + v.label, "green")) else: - raise Exception("?") + raise RuntimeError("?") return vertices, edges diff --git a/mlstatpy/image/__init__.py b/mlstatpy/image/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/mlstatpy/image/detection_segment/__init__.py b/mlstatpy/image/detection_segment/__init__.py similarity index 61% rename from src/mlstatpy/image/detection_segment/__init__.py rename to mlstatpy/image/detection_segment/__init__.py index ed5b7b64..f32f9230 100644 --- a/src/mlstatpy/image/detection_segment/__init__.py +++ b/mlstatpy/image/detection_segment/__init__.py @@ -1,9 +1,9 @@ -""" -@file -@brief shortcut to image -""" - -from .detection_segment import detect_segments, plot_segments, compute_gradient, plot_gradient +from .detection_segment import ( + detect_segments, + plot_segments, + compute_gradient, + plot_gradient, +) from .detection_segment import convert_array2PIL, convert_PIL2array from .geometrie import Point, Segment from .queue_binom import tabule_queue_binom diff --git a/src/mlstatpy/image/detection_segment/detection_nfa.py b/mlstatpy/image/detection_segment/detection_nfa.py similarity index 88% rename from src/mlstatpy/image/detection_segment/detection_nfa.py rename to mlstatpy/image/detection_segment/detection_nfa.py index 541d08ae..88590b50 100644 --- a/src/mlstatpy/image/detection_segment/detection_nfa.py +++ b/mlstatpy/image/detection_segment/detection_nfa.py @@ -1,9 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Ce module determine si un segment est significatif, c'est à dire -si le nombre de fausses alarmes n'est pas trop élevé. -""" from .geometrie import Segment @@ -40,7 +34,7 @@ class InformationPoint: proche du vecteur normal au segment (aligne)""" # voir la classe Point pour __slots__ - __slots__ = "pos", "aligne", "norme" + __slots__ = ("pos", "aligne", "norme") # noqa: RUF023 def __init__(self, pos, aligne, norme): """constructeur, initialisation""" @@ -96,10 +90,13 @@ def extremite(self): """ ext = [] if self.has_aligned_point(): - for i in range(0, len(self)): - if self.info_ligne[i].aligne and \ - (i == 0 or i == len(self) - 1 or not self.info_ligne[i - 1].aligne or - not self.info_ligne[i + 1].aligne): + for i in range(len(self)): + if self.info_ligne[i].aligne and ( + i == 0 + or i == len(self) - 1 + or not self.info_ligne[i - 1].aligne + or not self.info_ligne[i + 1].aligne + ): ext.append(i) return ext @@ -157,7 +154,7 @@ def segments_significatifs(self, binomiale, nb_seg): # premier couple d'extrémités ij = self.premier_chemin(ext) - res = [] # pour memoriser les segments significatifs + res = [] # pour memoriser les segments significatifs while ij is not None: # tant qu'il reste un couple d'extremite # probabilite de fausses alarmes pour ce segment @@ -167,9 +164,11 @@ def segments_significatifs(self, binomiale, nb_seg): # si cette proba est suffisamment faible, # l'agencement est un cas rare (non aleatoire), # il est significatif - seg = SegmentNFA(self.info_ligne[ext[ij[0]]].pos, - self.info_ligne[ext[ij[1]]].pos, - nfa) + seg = SegmentNFA( + self.info_ligne[ext[ij[0]]].pos, + self.info_ligne[ext[ij[1]]].pos, + nfa, + ) # on l'ajoute a la liste res.append(seg) diff --git a/src/mlstatpy/image/detection_segment/detection_segment.py b/mlstatpy/image/detection_segment/detection_segment.py similarity index 67% rename from src/mlstatpy/image/detection_segment/detection_segment.py rename to mlstatpy/image/detection_segment/detection_segment.py index 6a41d22b..7fff854e 100644 --- a/src/mlstatpy/image/detection_segment/detection_segment.py +++ b/mlstatpy/image/detection_segment/detection_segment.py @@ -1,8 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Détecte les segments dans une image. -""" import math import copy import time @@ -19,20 +14,21 @@ def convert_array2PIL(img, mode=None): Convertit une image donnée sous la forme d'un array au format :epkg:`numpy:array`. - @param img :epkg:`numpy:array` - @param mode voir `modes `_, - si None, essaye de deviner. - @return *PIL* + :param img: epkg:`numpy:array` + :param mode: voir `modes + `_, + si None, essaye de deviner. + :return: *PIL* Le mode ``'binary'`` convertit une image issue de la fonction @see fn random_noise_image. """ - if mode == 'binary': + if mode == "binary": fimg = img.astype(numpy.float32) - img255 = (- fimg + 1) * 255 + img255 = (-fimg + 1) * 255 img = img255.astype(numpy.uint8) mode = None - return _load_image(img, 'PIL', mode=mode) + return _load_image(img, "PIL", mode=mode) def convert_PIL2array(img): @@ -43,54 +39,49 @@ def convert_PIL2array(img): @param img :epkg:`Pillow` @return :epkg:`numpy:array` """ - return _load_image(img, 'array') + return _load_image(img, "array") -def _load_image(img, format='PIL', mode=None): +def _load_image(img, format="PIL", mode=None): """ Charge une image en différents formats. @param img image (*array*, *PIL*, filename) @param format *array* ou *PIL* - @param mode voir `modes `_, + @param mode voir `modes `_, si None, essaye de deviner. @return *PIL* """ if isinstance(img, str): img = Image.open(img) return _load_image(img, format) - elif isinstance(img, Image.Image): - if format == 'PIL': + if isinstance(img, Image.Image): + if format == "PIL": return img - elif format == 'array': + if format == "array": d1, d0 = img.size[1], img.size[0] img = numpy.array(img.getdata(), dtype=numpy.uint8) if len(img.shape) == 1: gray = img.shape[0] - d1 * d0 elif len(img.shape) == 2: gray = img.shape[0] * img.shape[1] - d1 * d0 - elif(img.shape) == 3: + elif len(img.shape) == 3: gray = img.shape[0] * img.shape[1] * img.shape[2] - d1 * d0 else: - raise ValueError("Unexpected shape {0}".format(img.shape)) + raise ValueError(f"Unexpected shape {img.shape}") if gray == 0: img = img.reshape((d1, d0)) else: img = img.reshape((d1, d0, 3)) return img - else: - raise ValueError( - "Unexpected value for fomat: '{0}'".format(format)) - elif isinstance(img, numpy.ndarray): - if format == 'array': + raise ValueError(f"Unexpected value for fomat: '{format}'") + if isinstance(img, numpy.ndarray): + if format == "array": return img - elif format == 'PIL': + if format == "PIL": return Image.fromarray(img, mode=mode) - else: - raise ValueError( - "Unexpected value for fomat: '{0}'".format(format)) - else: - raise TypeError("numpy array expected not {0}".format(type(img))) + raise ValueError(f"Unexpected value for fomat: '{format}'") + raise TypeError(f"numpy array expected not {type(img)}") def compute_gradient(img, color=None): @@ -113,7 +104,7 @@ def _calcule_gradient(img, color=None): @return array of *shape (y, x, 2)*, first dimension is *dx*, second one is *dy* """ - img = _load_image(img, 'array') + img = _load_image(img, "array") img = img.astype(numpy.float32) if color is not None: img = img[:, :, color] @@ -125,7 +116,7 @@ def _calcule_gradient(img, color=None): dy1 = img[1:-1, :] - img[:-2, :] dy2 = img[2:, :] - img[1:-1, :] dy = (dy1 + dy2) / 2 - res = numpy.zeros(img.shape + (2,)) + res = numpy.zeros((*img.shape, 2)) res[:, 1:-1, 0] = dx res[1:-1, :, 1] = dy return res @@ -139,15 +130,15 @@ def plot_gradient(image, gradient, more=None, direction=-1): si direction > 0, cette fonction affiche egalement le gradient sur l'image tous les 10 pixels si direction vaut 10. """ - image_ = _load_image(image, 'PIL') + image_ = _load_image(image, "PIL") image = ImageDraw.Draw(image_) X, Y = image_.size if direction != -1: - for x in range(0, X - 1): - for y in range(0, Y - 1): + for x in range(X - 1): + for y in range(Y - 1): n = gradient[y, x] if more is None: - v = int((n[0]**2 + n[1] ** 2)**0.5 + 0.5) + v = int((n[0] ** 2 + n[1] ** 2) ** 0.5 + 0.5) elif more == "x": v = int(n[0] / 2 + 127 + 0.5) else: @@ -157,10 +148,10 @@ def plot_gradient(image, gradient, more=None, direction=-1): pass elif direction > 0: # on dessine des petits gradients dans l'image - for x in range(0, X, direction): - for y in range(0, Y, direction): + for x in range(X, direction): + for y in range(Y, direction): n = gradient[y, x] - t = (n[0]**2 + n[1] ** 2)**0.5 + t = (n[0] ** 2 + n[1] ** 2) ** 0.5 if t == 0: continue m = copy.copy(n) @@ -170,13 +161,12 @@ def plot_gradient(image, gradient, more=None, direction=-1): if t < 2: t = 2 m *= t - image.line([(x, y), (x + int(m[0]), y + int(m[1]))], - fill=(255, 255, 0)) + image.line([(x, y), (x + int(m[0]), y + int(m[1]))], fill=(255, 255, 0)) elif direction == -2: # derniere solution, la couleur represente l'orientation # en chaque point de l'image - for x in range(0, X): - for y in range(0, Y): + for x in range(X): + for y in range(Y): n = gradient[y, x] i = int(-n[0] * 10 + 128) j = int(n[1] * 10 + 128) @@ -184,8 +174,7 @@ def plot_gradient(image, gradient, more=None, direction=-1): i, j = max(i, 0), max(j, 0) image.line([(x, y), (x, y)], fill=(0, j, i)) else: - raise ValueError( - "unexpected value for direction={0}".format(direction)) + raise ValueError(f"Unexpected value for direction={direction}") return image_ @@ -201,50 +190,55 @@ def plot_segments(image, segments, outfile=None, color=(255, 0, 0)): @param color couleur @return nom de fichier ou image """ - image = _load_image(image, 'PIL') + image = _load_image(image, "PIL") draw = ImageDraw.Draw(image) for seg in segments: draw.line([(seg.a.x, seg.a.y), (seg.b.x, seg.b.y)], fill=color) if outfile is not None: image.save(outfile) return outfile - else: - return image - - -def detect_segments(image, proba_bin=1.0 / 16, - cos_angle=math.cos(1.0 / 16 / 2 * (math.pi * 2)), - seuil_nfa=1e-5, seuil_norme=2, angle=math.pi / 24.0, - stop=-1, verbose=False): + return image + + +def detect_segments( + image, + proba_bin=1.0 / 16, + cos_angle=math.cos(1.0 / 16 / 2 * (math.pi * 2)), # noqa: B008 + seuil_nfa=1e-5, + seuil_norme=2, + angle=math.pi / 24.0, + stop=-1, + verbose=False, +): """ Détecte les segments dans une image. - @param image image (*fichier*, *array*, *PIL*) - @param proba_bin est en fait un secteur angulaire (360 / 16) - qui determine la proximite de deux directions - @param cos_angle est le cosinus de l'angle correspondant à ce secteur angulaire - @param seuil_nfa au delà de ce seuil, on considere qu'un segment - génère trop de fausses alertes pour être sélectionné - @param seuil_norme norme en deça de laquelle un gradient est trop - petit pour etre significatif (c'est du bruit) - @param angle lorsqu'on balaye l'image pour détecter les segments, - on tourne en rond selon les angles 0, angle, 2*angle, - 3*angle, ... - @param stop arrête après avoir collecté tant de segments - @param verbose affiche l'avancement - @return les segments + :param image: image (*fichier*, *array*, *PIL*) + :param proba_bin: est en fait un secteur angulaire (360 / 16) + qui determine la proximite de deux directions + :param cos_angle: est le cosinus de l'angle correspondant à ce secteur angulaire + :param seuil_nfa: au delà de ce seuil, on considere qu'un segment + génère trop de fausses alertes pour être sélectionné + :param seuil_norme: norme en deça de laquelle un gradient est trop + petit pour etre significatif (c'est du bruit) + :param angle: lorsqu'on balaye l'image pour détecter les segments, + on tourne en rond selon les angles 0, angle, 2*angle, 3*angle, ... + :param stop: arrête après avoir collecté tant de segments + :param verbose: affiche l'avancement + :return: les segments """ - gray_image = _load_image(image, 'PIL').convert('L') + gray_image = _load_image(image, "PIL").convert("L") grad = _calcule_gradient(gray_image) # on calcule les tables de la binomiale pour eviter d'avoir a le fait a # chaque fois qu'on en a besoin yy, xx = grad.shape[:2] - nbbin = int(math.ceil(math.sqrt(xx * xx + yy * yy))) + nbbin = int(math.ceil(math.sqrt(xx * xx + yy * yy))) # noqa: RUF046 binomiale = tabule_queue_binom(nbbin, proba_bin) # nb_seg est le nombre total de segment de l'image - # il y a xx * yy pixels possibles dont (xx*yy)^2 couples de pixels (donc de segments) + # il y a xx * yy pixels possibles dont (xx*yy)^2 + # couples de pixels (donc de segments) nb_seg = xx * xx * yy * yy # on cree une instance de la classe permettant de parcourir @@ -256,13 +250,12 @@ def detect_segments(image, proba_bin=1.0 / 16, ti = time.perf_counter() # memorise l'heure de depart # pour savoir combien de segments on a deja visite (seg) n = 0 - cont = True # condition d'arret de la boucle + cont = True # condition d'arret de la boucle # on cree une classe permettant de recevoir les informations relatives # a l'image et au gradient pour un segment reliant deux points # du contour de l'image - points = [InformationPoint(Point(0, 0), False, 0) - for i in range(0, xx + yy)] + points = [InformationPoint(Point(0, 0), False, 0) for i in range(xx + yy)] ligne = LigneGradient(points, seuil_norme=seuil_norme, seuil_nfa=seuil_nfa) # premier segment @@ -273,7 +266,6 @@ def detect_segments(image, proba_bin=1.0 / 16, # tant qu'on a pas fini while cont: - # calcule les informations relative a un segment de l'image reliant deux bords # position des pixels, norme du gradient, alignement avec le segment seg.decoupe_gradient(grad, cos_angle, ligne, seuil_norme) @@ -296,8 +288,16 @@ def detect_segments(image, proba_bin=1.0 / 16, # pour verifier que cela avance if verbose and n % 1000 == 0: - print("n = ", n, " ... ", len(segment), " temps ", - "%2.2f" % (time.perf_counter() - ti), " sec", - "nalign", not_aligned) + print( + "n = ", + n, + " ... ", + len(segment), + " temps ", + f"{time.perf_counter() - ti:2.2f}", + " sec", + "nalign", + not_aligned, + ) return segment diff --git a/src/mlstatpy/image/detection_segment/detection_segment_bord.py b/mlstatpy/image/detection_segment/detection_segment_bord.py similarity index 89% rename from src/mlstatpy/image/detection_segment/detection_segment_bord.py rename to mlstatpy/image/detection_segment/detection_segment_bord.py index a996fc69..8aac9233 100644 --- a/src/mlstatpy/image/detection_segment/detection_segment_bord.py +++ b/mlstatpy/image/detection_segment/detection_segment_bord.py @@ -1,11 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Ce module définit un segment qui va parcourir l'image, -en plus d'être un segment, cette classe inclut la dimension de l'image, -et une fonction repérant sur ce segment les gradients presque -orthogonaux à l'image. -""" import copy import numpy from .geometrie import Segment, Point @@ -74,7 +66,7 @@ def decoupe_gradient(self, gradient, cos_angle, ligne_gradient, seuil_norme): g = gradient[a.y, a.x] # on calcul sa norme - t.norme = (g[0] ** 2 + g[1]**2) ** 0.5 + t.norme = (g[0] ** 2 + g[1] ** 2) ** 0.5 # on place les coordonnees du pixel dans t t.pos.x = p.x @@ -90,7 +82,7 @@ def decoupe_gradient(self, gradient, cos_angle, ligne_gradient, seuil_norme): # on passe au pixel suivant p += n - a = p.arrondi() # calcul de l'arrondi + a = p.arrondi() # calcul de l'arrondi i += 1 # on indique a ligne_gradient le nombre de pixel pris en compte diff --git a/src/mlstatpy/image/detection_segment/detection_segment_segangle.py b/mlstatpy/image/detection_segment/detection_segment_segangle.py similarity index 96% rename from src/mlstatpy/image/detection_segment/detection_segment_segangle.py rename to mlstatpy/image/detection_segment/detection_segment_segangle.py index ea4ab935..812e4da7 100644 --- a/src/mlstatpy/image/detection_segment/detection_segment_segangle.py +++ b/mlstatpy/image/detection_segment/detection_segment_segangle.py @@ -1,9 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Ce module inclut une classe qui permet de -parcourir tous les segments de l'image. -""" import math import copy from .detection_segment_bord import SegmentBord_Commun @@ -34,7 +28,7 @@ class SegmentBord(SegmentBord_Commun): """ # voir la remarque de la classe Point a propos de __slots__ - __slots__ = "angle", "fin", "vecteur", "bord1", "dangle" + __slots__ = ("angle", "fin", "vecteur", "bord1", "dangle") # noqa: RUF023 def __init__(self, dim, dangle=math.pi / 24.0): """initialise les dimensions et @@ -48,8 +42,8 @@ def __str__(self): s = SegmentBord_Commun.__str__(self) s += " -- bord " + str(self.bord1) s += " -- fin " + str(self.fin) - s += " -- a " + "%3.1f" % (self.angle * 180 / math.pi) - s += " -- vec " + "%2.2f,%2.2f" % (self.vecteur.x, self.vecteur.y) + s += " -- a " + f"{self.angle * 180 / math.pi:3.1f}" + s += " -- vec " + f"{self.vecteur.x:2.2f},{self.vecteur.y:2.2f}" return s def premier(self): diff --git a/src/mlstatpy/image/detection_segment/geometrie.py b/mlstatpy/image/detection_segment/geometrie.py similarity index 86% rename from src/mlstatpy/image/detection_segment/geometrie.py rename to mlstatpy/image/detection_segment/geometrie.py index f08aa6bf..2070f2f4 100644 --- a/src/mlstatpy/image/detection_segment/geometrie.py +++ b/mlstatpy/image/detection_segment/geometrie.py @@ -1,10 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Définition de petits éléments géométriques tels que les points -et les segments, implemente également des opérations standard -telles le produit scalaire entre deux vecteurs, ... -""" import math import copy import numpy @@ -15,6 +8,7 @@ class Point: Définit un point de l'image ou un vecteur, deux coordonnées *x* et *y* qui sont réelles. """ + __slots__ = "x", "y" def __init__(self, x, y): @@ -24,17 +18,17 @@ def __init__(self, x, y): def __str__(self): """permet d'afficher un point avec l'instruction print""" - return '({0},{1})'.format(self.x, self.y) + return f"({self.x},{self.y})" def __repr__(self): """usuel""" - return 'Point({0}, {1})'.format(self.x, self.y) + return f"Point({self.x}, {self.y})" def normalise(self): """normalise le vecteur, sa norme devient 1""" v = self.x * self.x + self.y * self.y v = math.sqrt(v) - if v > 0: # evite les erreurs si sa norme est nulle + if v > 0: # evite les erreurs si sa norme est nulle self.x /= v self.y /= v @@ -61,7 +55,7 @@ def as_array(self): """ return numpy.array([self.x, self.y]) - def scalaire(self, k: 'Point') -> float: + def scalaire(self, k: "Point") -> float: """ Calcule le produit scalaire. @@ -80,7 +74,7 @@ def __add__(self, ad): """ajoute un vecteur a celui-ci""" return Point(self.x + ad.x, self.y + ad.y) - def arrondi(self) -> 'Point': + def arrondi(self) -> "Point": """ retourne les coordonnées arrondies à l'entier le plus proche """ @@ -119,7 +113,7 @@ def __init__(self, a, b): def __str__(self) -> str: """permet d'afficher le segment avec l'instruction print""" - return "[{0},{1}]".format(self.a, self.b) + return f"[{self.a},{self.b}]" def directeur(self) -> Point: """retourne le vecteur directeur du segment, diff --git a/src/mlstatpy/image/detection_segment/queue_binom.py b/mlstatpy/image/detection_segment/queue_binom.py similarity index 84% rename from src/mlstatpy/image/detection_segment/queue_binom.py rename to mlstatpy/image/detection_segment/queue_binom.py index b5ff58b1..df0cc4aa 100644 --- a/src/mlstatpy/image/detection_segment/queue_binom.py +++ b/mlstatpy/image/detection_segment/queue_binom.py @@ -1,10 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Ce module construit les probabilités d'une loi binomiale :math:`B(n,p)`. -""" - - def tabule_queue_binom(n, p): """ Retourne un dictionnaire dont la clé est couple d'entiers *(a,b)* @@ -19,7 +12,7 @@ def tabule_queue_binom(n, p): et :math:`t[(m,k)] = p * t [ (m-1, k-1)] + (1-p) * t [ (m-1,k) ]` Cette fonction calcule tous les coefficients :math:`t [ (a,b) ]` pour une - probabilité :math:`p` donnée et :math:`b \\infegal a \\infegal n`. + probabilité :math:`p` donnée et :math:`b \\leqslant a \\leqslant n`. Ces probabilités sont stockées dans un dictionnaire car s'ils étaient stockées dans une matrice, celle-ci serait triangulaire inférieure. diff --git a/src/mlstatpy/image/detection_segment/random_image.py b/mlstatpy/image/detection_segment/random_image.py similarity index 60% rename from src/mlstatpy/image/detection_segment/random_image.py rename to mlstatpy/image/detection_segment/random_image.py index 672233e9..5bac8ffc 100644 --- a/src/mlstatpy/image/detection_segment/random_image.py +++ b/mlstatpy/image/detection_segment/random_image.py @@ -1,11 +1,6 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Génère des images aléatoires. -""" import math import numpy -import numpy.random as nprnd # pylint: disable=E1101 +import numpy.random as nprnd def random_noise_image(size, ratio=0.1): @@ -26,7 +21,7 @@ def random_noise_image(size, ratio=0.1): return img -def random_segment_image(image, lmin=0.1, lmax=1., noise=0.01, density=1.): +def random_segment_image(image, lmin=0.1, lmax=1.0, noise=0.01, density=1.0): """ Ajoute un segment aléatoire à une image. Génère des points le long d'un segment aléatoire. @@ -38,6 +33,7 @@ def random_segment_image(image, lmin=0.1, lmax=1., noise=0.01, density=1.): @param noise bruit @return dictionary with *size, angle, x1, y1, x2, y2, nbpoints* """ + def move_coordinate(x1, y1, x2, y2, X, Y): if x2 < 0: x1 -= x2 @@ -46,38 +42,39 @@ def move_coordinate(x1, y1, x2, y2, X, Y): x2 = min(max(x2, 0), X - 1) y1 = min(max(y1, 0), Y - 1) y2 = min(max(y2, 0), Y - 1) - size = int(((x1 - x2)**2 + (y1 - y2) ** 2) ** 0.5) + size = int(((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5) return x1, y1, x2, y2, size mind = min(image.shape) lmin = int(mind * lmin) lmax = int(mind * lmax) size = nprnd.randint(lmin, lmax) - angle = nprnd.random() * math.pi # pylint: disable=E1101 - x1 = nprnd.randint( - image.shape[1] - int(size * abs(math.cos(angle)) - 1)) + angle = nprnd.random() * math.pi + x1 = nprnd.randint(image.shape[1] - int(size * abs(math.cos(angle)) - 1)) y1 = nprnd.randint(image.shape[0] - int(size * math.sin(angle) - 1)) x2 = x1 + size * math.cos(angle) y2 = y1 + size * math.sin(angle) x1, y1, x2, y2, size = move_coordinate( - x1, y1, x2, y2, image.shape[1], image.shape[0]) + x1, y1, x2, y2, image.shape[1], image.shape[0] + ) t = nprnd.randint(0, size, int(size * density)) xs = t * math.cos(angle) + x1 ys = t * math.sin(angle) + x2 - noise = nprnd.randn( # pylint: disable=E1101 - xs.shape[0] * 2).reshape(xs.shape[0], 2) * noise * mind + noise = nprnd.randn(xs.shape[0] * 2).reshape(xs.shape[0], 2) * noise * mind xs += noise[:, 0] ys += noise[:, 1] - xs = numpy.maximum(xs, numpy.zeros(xs.shape[0])) # pylint: disable=E1111 - ys = numpy.maximum(ys, numpy.zeros( - xs.shape[0])) # pylint: disable=E1111,E1101,E1136 - xs = numpy.minimum(xs, numpy.zeros( # pylint: disable=E1101,E1136 - xs.shape[0]) + image.shape[1] - 1) # pylint: disable=E1111,E1101,E1136 - ys = numpy.minimum(ys, numpy.zeros( # pylint: disable=E1101,E1136 - xs.shape[0]) + image.shape[0] - 1) # pylint: disable=E1111,E1101,E1136 - xs = xs.astype(numpy.int32) # pylint: disable=E1101 - ys = ys.astype(numpy.int32) # pylint: disable=E1101 + xs = numpy.maximum(xs, numpy.zeros(xs.shape[0])) + ys = numpy.maximum(ys, numpy.zeros(xs.shape[0])) + xs = numpy.minimum( + xs, + numpy.zeros(xs.shape[0]) + image.shape[1] - 1, + ) + ys = numpy.minimum( + ys, + numpy.zeros(xs.shape[0]) + image.shape[0] - 1, + ) + xs = xs.astype(numpy.int32) + ys = ys.astype(numpy.int32) image[ys, xs] = 1 - res = dict(size=size, angle=angle, x1=x1, y1=y1, x2=x2, y2=y2, - nbpoints=xs.shape[0]) # pylint: disable=E1136 + res = dict(size=size, angle=angle, x1=x1, y1=y1, x2=x2, y2=y2, nbpoints=xs.shape[0]) return res diff --git a/mlstatpy/ml/__init__.py b/mlstatpy/ml/__init__.py new file mode 100644 index 00000000..a98af663 --- /dev/null +++ b/mlstatpy/ml/__init__.py @@ -0,0 +1,2 @@ +from .roc import ROC +from .voronoi import voronoi_estimation_from_lr diff --git a/src/mlstatpy/ml/_neural_tree_api.py b/mlstatpy/ml/_neural_tree_api.py similarity index 71% rename from src/mlstatpy/ml/_neural_tree_api.py rename to mlstatpy/ml/_neural_tree_api.py index b75db81a..5df399dd 100644 --- a/src/mlstatpy/ml/_neural_tree_api.py +++ b/mlstatpy/ml/_neural_tree_api.py @@ -1,8 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Conversion from tree to neural network. -""" import numpy from ..optim import SGDOptimizer @@ -15,8 +10,7 @@ class _TrainingAPI: @property def training_weights(self): "Returns the weights." - raise NotImplementedError( # pragma: no cover - "This should be overwritten.") + raise NotImplementedError("This should be overwritten.") def update_training_weights(self, grad, add=True): """ @@ -25,28 +19,25 @@ def update_training_weights(self, grad, add=True): :param grad: vector to add to the weights such as gradient :param add: addition or replace """ - raise NotImplementedError( # pragma: no cover - "This should be overwritten.") + raise NotImplementedError("This should be overwritten.") def fill_cache(self, X): """ Creates a cache with intermediate results. """ - return None # pragma: no cover + return None def loss(self, X, y, cache=None): """ Computes the loss. Returns a float. """ - raise NotImplementedError( # pragma: no cover - "This should be overwritten.") + raise NotImplementedError("This should be overwritten.") def dlossds(self, X, y, cache=None): """ Computes the loss derivative due to prediction error. """ - raise NotImplementedError( # pragma: no cover - "This should be overwritten.") + raise NotImplementedError("This should be overwritten.") def gradient_backward(self, graddx, X, inputs=False, cache=None): """ @@ -59,8 +50,7 @@ def gradient_backward(self, graddx, X, inputs=False, cache=None): :param cache: cache intermediate results to avoid more computation :return: gradient """ - raise NotImplementedError( # pragma: no cover - "This should be overwritten.") + raise NotImplementedError("This should be overwritten.") def gradient(self, X, y, inputs=False): """ @@ -73,14 +63,27 @@ def gradient(self, X, y, inputs=False): :return: gradient """ if len(X.shape) != 1: - raise ValueError( # pragma: no cover - "X must a vector of one dimension but has shape {}.".format(X.shape)) - cache = self.fill_cache(X) # pylint: disable=E1128 + raise ValueError( + f"X must a vector of one dimension but has shape {X.shape}." + ) + cache = self.fill_cache(X) dlossds = self.dlossds(X, y, cache=cache) return self.gradient_backward(dlossds, X, inputs=inputs, cache=cache) - def fit(self, X, y, optimizer=None, max_iter=100, early_th=None, verbose=False, - lr=None, lr_schedule=None, l1=0., l2=0., momentum=0.9): + def fit( + self, + X, + y, + optimizer=None, + max_iter=100, + early_th=None, + verbose=False, + lr=None, + lr_schedule=None, + l1=0.0, + l2=0.0, + momentum=0.9, + ): """ Fits a neuron. @@ -104,9 +107,13 @@ def fit(self, X, y, optimizer=None, max_iter=100, early_th=None, verbose=False, """ if optimizer is None: optimizer = SGDOptimizer( - self.training_weights, learning_rate_init=lr or 0.002, - lr_schedule=lr_schedule or 'invscaling', - l1=l1, l2=l2, momentum=momentum) + self.training_weights, + learning_rate_init=lr or 0.002, + lr_schedule=lr_schedule or "invscaling", + l1=l1, + l2=l2, + momentum=momentum, + ) def fct_loss(coef, lx, ly, neuron=self): neuron.update_training_weights(coef, False) @@ -120,8 +127,14 @@ def fct_grad(coef, lx, ly, i, neuron=self): return neuron.gradient(lx, ly).ravel() optimizer.train( - X, y, fct_loss, fct_grad, max_iter=max_iter, - early_th=early_th, verbose=verbose) + X, + y, + fct_loss, + fct_grad, + max_iter=max_iter, + early_th=early_th, + verbose=verbose, + ) self.update_training_weights(optimizer.coef, False) return self diff --git a/src/mlstatpy/ml/_neural_tree_node.py b/mlstatpy/ml/_neural_tree_node.py similarity index 67% rename from src/mlstatpy/ml/_neural_tree_node.py rename to mlstatpy/ml/_neural_tree_node.py index 6ffe5242..bd49c467 100644 --- a/src/mlstatpy/ml/_neural_tree_node.py +++ b/mlstatpy/ml/_neural_tree_node.py @@ -1,17 +1,18 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Conversion from tree to neural network. -""" import numpy import numpy.random as rnd -from scipy.special import expit, softmax, kl_div as kl_fct # pylint: disable=E0611 +from scipy.special import expit, softmax, kl_div as kl_fct from ._neural_tree_api import _TrainingAPI class NeuralTreeNode(_TrainingAPI): """ One node in a neural network. + + :param weights: weights + :param bias: bias, if None, draws a random number + :param activation: activation function + :param nodeid: node id + :param tag: unused but to add information on how this node was created """ @staticmethod @@ -28,7 +29,7 @@ def _leakyrelu(x): def _drelu(x): "Derivative of the Relu function." res = numpy.ones(x.shape, dtype=x.dtype) - res[x < 0] = 0. + res[x < 0] = 0.0 return res @staticmethod @@ -55,7 +56,7 @@ def _softmax(x): def _dsoftmax(x): "Derivative of the softmax function." soft = softmax(x) - grad = - soft @ soft.T + grad = -soft @ soft.T diag = numpy.diag(soft) return diag + grad @@ -65,22 +66,21 @@ def get_activation_function(activation): Returns the activation function. It returns a function *y=f(x)*. """ - if activation == 'softmax': + if activation == "softmax": return NeuralTreeNode._softmax - if activation == 'softmax4': + if activation == "softmax4": return lambda x: NeuralTreeNode._softmax(x * 4) - if activation in {'logistic', 'expit', 'sigmoid'}: + if activation in {"logistic", "expit", "sigmoid"}: return expit - if activation == 'sigmoid4': + if activation == "sigmoid4": return lambda x: expit(x * 4) - if activation == 'relu': + if activation == "relu": return NeuralTreeNode._relu - if activation == 'leakyrelu': + if activation == "leakyrelu": return NeuralTreeNode._leakyrelu - if activation == 'identity': + if activation == "identity": return lambda x: x - raise ValueError( - "Unknown activation function '{}'.".format(activation)) + raise ValueError(f"Unknown activation function '{activation}'.") @staticmethod def get_activation_gradient_function(activation): @@ -91,27 +91,26 @@ def get_activation_gradient_function(activation): .. math:: - \\begin{array}{l} - f(x) &=& \frac{1}{1 + e^{-x}} \\\\ - f'(x) &=& \frac{e^{-x}}{(1 + e^{-x})^2} = f(x)(1-f(x)) - \\end{array}} + \\begin{array}{rcl} + f(x) &=& \\frac{1}{1 + e^{-x}} \\\\ + f'(x) &=& \\frac{e^{-x}}{(1 + e^{-x})^2} = f(x)(1-f(x)) + \\end{array} """ - if activation == 'softmax': + if activation == "softmax": return NeuralTreeNode._dsoftmax - if activation == 'softmax4': + if activation == "softmax4": return lambda x: NeuralTreeNode._dsoftmax(x) * 4 - if activation in {'logistic', 'expit', 'sigmoid'}: + if activation in {"logistic", "expit", "sigmoid"}: return NeuralTreeNode._dsigmoid - if activation == 'sigmoid4': + if activation == "sigmoid4": return lambda x: NeuralTreeNode._dsigmoid(x) * 4 - if activation == 'relu': + if activation == "relu": return NeuralTreeNode._drelu - if activation == 'leakyrelu': + if activation == "leakyrelu": return NeuralTreeNode._dleakyrelu - if activation == 'identity': + if activation == "identity": return lambda x: numpy.ones(x.shape, dtype=x.dtype) - raise ValueError( - "Unknown activation gradient function '{}'.".format(activation)) + raise ValueError(f"Unknown activation gradient function '{activation}'.") @staticmethod def get_activation_loss_function(activation): @@ -119,21 +118,21 @@ def get_activation_loss_function(activation): Returns a default loss function based on the activation function. It returns two functions *g=loss(x,y)*. """ - if activation in {'logistic', 'expit', 'sigmoid', 'sigmoid4'}: + if activation in {"logistic", "expit", "sigmoid", "sigmoid4"}: # regression + regularization return lambda x, y: (x - y) ** 2 - if activation in {'softmax', 'softmax4'}: + if activation in {"softmax", "softmax4"}: cst = numpy.finfo(numpy.float32).eps # classification def kl_fct2(x, y): return kl_fct(x + cst, y + cst) + return kl_fct2 - if activation in {'identity', 'relu', 'leakyrelu'}: + if activation in {"identity", "relu", "leakyrelu"}: # regression return lambda x, y: (x - y) ** 2 - raise ValueError( - "Unknown activation function '{}'.".format(activation)) + raise ValueError(f"Unknown activation function '{activation}'.") @staticmethod def get_activation_dloss_function(activation): @@ -142,14 +141,14 @@ def get_activation_dloss_function(activation): on the activation function. It returns a function *df(x,y)/dw, df(w)/dw* where *w* are the weights. """ - if activation in {'logistic', 'expit', 'sigmoid', 'sigmoid4'}: + if activation in {"logistic", "expit", "sigmoid", "sigmoid4"}: # regression + regularization def dregrdx(x, y): return (x - y) * 2 return dregrdx - if activation in {'softmax', 'softmax4'}: + if activation in {"softmax", "softmax4"}: # classification cst = numpy.finfo(numpy.float32).eps @@ -158,28 +157,18 @@ def dclsdx(x, y): return dclsdx - if activation in {'identity', 'relu', 'leakyrelu'}: + if activation in {"identity", "relu", "leakyrelu"}: # regression def dregdx(x, y): return (x - y) * 2 return dregdx - raise ValueError( - "Unknown activation function '{}'.".format(activation)) + raise ValueError(f"Unknown activation function '{activation}'.") - def __init__(self, weights, bias=None, activation='sigmoid', nodeid=-1, - tag=None): - """ - @param weights weights - @param bias bias, if None, draws a random number - @param activation activation function - @param nodeid node id - @param tag unused but to add information - on how this node was created - """ + def __init__(self, weights, bias=None, activation="sigmoid", nodeid=-1, tag=None): self.tag = tag if isinstance(weights, int): - if activation.startswith('softmax'): + if activation.startswith("softmax"): weights = rnd.randn(2, weights) else: weights = rnd.randn(weights) @@ -196,9 +185,6 @@ def __init__(self, weights, bias=None, activation='sigmoid', nodeid=-1, elif len(weights.shape) == 2: self.n_outputs = weights.shape[0] - if self.n_outputs == 1: - raise RuntimeError( # pragma: no cover - "Unexpected unsqueezed weights shape: {}".format(weights.shape)) if bias is None: bias = rnd.randn(self.n_outputs) shape = list(weights.shape) @@ -207,22 +193,19 @@ def __init__(self, weights, bias=None, activation='sigmoid', nodeid=-1, self.coef[:, 1:] = weights self.coef[:, 0] = bias else: - raise RuntimeError( # pragma: no cover - "Unexpected weights shape: {}".format(weights.shape)) + raise RuntimeError(f"Unexpected weights shape: {weights.shape}") self.activation = activation self.nodeid = nodeid self._set_fcts() def _set_fcts(self): - self.activation_ = NeuralTreeNode.get_activation_function( - self.activation) + self.activation_ = NeuralTreeNode.get_activation_function(self.activation) self.gradient_ = NeuralTreeNode.get_activation_gradient_function( - self.activation) - self.losss_ = NeuralTreeNode.get_activation_loss_function( - self.activation) - self.dlossds_ = NeuralTreeNode.get_activation_dloss_function( - self.activation) + self.activation + ) + self.losss_ = NeuralTreeNode.get_activation_loss_function(self.activation) + self.dlossds_ = NeuralTreeNode.get_activation_dloss_function(self.activation) @property def input_weights(self): @@ -241,23 +224,26 @@ def bias(self): def __getstate__(self): "usual" return { - 'coef': self.coef, 'activation': self.activation, - 'nodeid': self.nodeid, 'n_outputs': self.n_outputs, - 'tag': self.tag} + "coef": self.coef, + "activation": self.activation, + "nodeid": self.nodeid, + "n_outputs": self.n_outputs, + "tag": self.tag, + } def __setstate__(self, state): "usual" - self.coef = state['coef'] - self.activation = state['activation'] - self.nodeid = state['nodeid'] - self.n_outputs = state['n_outputs'] - self.tag = state['tag'] + self.coef = state["coef"] + self.activation = state["activation"] + self.nodeid = state["nodeid"] + self.n_outputs = state["n_outputs"] + self.tag = state["tag"] self._set_fcts() def __eq__(self, obj): if self.coef.shape != obj.coef.shape: return False - if any(map(lambda xy: xy[0] != xy[1], zip(self.coef, obj.coef))): + if any(xy[0] != xy[1] for xy in zip(self.coef.ravel(), obj.coef.ravel())): return False if self.activation != obj.activation: return False @@ -267,32 +253,45 @@ def __repr__(self): "usual" if len(self.coef.shape) == 1: return "%s(weights=%r, bias=%r, activation=%r)" % ( - self.__class__.__name__, self.coef[1:], - self.coef[0], self.activation) + self.__class__.__name__, + self.coef[1:], + self.coef[0], + self.activation, + ) return "%s(weights=%r, bias=%r, activation=%r)" % ( - self.__class__.__name__, self.coef[:, 1:], - self.coef[:, 0], self.activation) + self.__class__.__name__, + self.coef[:, 1:], + self.coef[:, 0], + self.activation, + ) def _predict(self, X): "Computes inputs of the activation function." if self.n_outputs == 1: return X @ self.coef[1:] + self.coef[0] - return (X.reshape((1, -1)) @ self.coef[:, 1:].T + self.coef[:, 0]).ravel() + if len(X.shape) == 2: + return X @ self.coef[:, 1:].T + self.coef[:, 0] + res = X.reshape((1, -1)) @ self.coef[:, 1:].T + self.coef[:, 0] + return res.ravel() def predict(self, X): "Computes neuron outputs." - if self.n_outputs == 1: - return self.activation_(X @ self.coef[1:] + self.coef[0]) - if len(X.shape) == 2: - return self.activation_( - (X @ self.coef[:, 1:].T + self.coef[:, 0])) - return self.activation_( - (X.reshape((1, -1)) @ self.coef[:, 1:].T + self.coef[:, 0]).ravel()) + y = self._predict(X) + return self.activation_(y) @property def ndim(self): "Returns the input dimension." - return self.coef.shape[0] - 1 + if len(self.coef.shape) == 1: + return self.coef.shape[0] - 1 + return self.coef.shape[1] - 1 + + @property + def ndim_out(self): + "Returns the output dimension." + if len(self.coef.shape) == 1: + return 1 + return self.coef.shape[0] @property def training_weights(self): @@ -303,7 +302,7 @@ def update_training_weights(self, X, add=True): """ Updates weights. - :param grad: vector to add to the weights such as gradient + :param X: training datasets :param add: addition or replace """ if add: @@ -318,7 +317,7 @@ def fill_cache(self, X): ``aX`` is the results after the activation function, the prediction. """ cache = dict(lX=self._predict(X)) - cache['aX'] = self.activation_(cache['lX']) + cache["aX"] = self.activation_(cache["lX"]) return cache def _common_loss_dloss(self, X, y, cache=None): @@ -326,8 +325,8 @@ def _common_loss_dloss(self, X, y, cache=None): Common beginning to methods *loss*, *dlossds*, *dlossdw*. """ - if cache is not None and 'aX' in cache: - act = cache['aX'] + if cache is not None and "aX" in cache: + act = cache["aX"] else: act = self.predict(X) return act @@ -338,8 +337,8 @@ def loss(self, X, y, cache=None): """ act = self._common_loss_dloss(X, y, cache=cache) if len(X.shape) == 1: - return self.losss_(act, y) # pylint: disable=E1120 - return self.losss_(act, y) # pylint: disable=E1120 + return self.losss_(act, y) + return self.losss_(act, y) def dlossds(self, X, y, cache=None): """ @@ -362,7 +361,7 @@ def gradient_backward(self, graddx, X, inputs=False, cache=None): if cache is None: cache = self.fill_cache(X) - pred = cache['aX'] + pred = cache["aX"] ga = self.gradient_(pred) if len(ga.shape) == 2: f = graddx @ ga @@ -375,8 +374,7 @@ def gradient_backward(self, graddx, X, inputs=False, cache=None): rgrad[:] = self.coef[1:] rgrad *= f else: - rgrad = numpy.sum( - self.coef[:, 1:] * f.reshape((-1, 1)), axis=0) + rgrad = numpy.sum(self.coef[:, 1:] * f.reshape((-1, 1)), axis=0) return rgrad rgrad = numpy.empty(self.coef.shape) diff --git a/src/mlstatpy/ml/kppv.py b/mlstatpy/ml/kppv.py similarity index 85% rename from src/mlstatpy/ml/kppv.py rename to mlstatpy/ml/kppv.py index 14bf1db0..1ee1069c 100644 --- a/src/mlstatpy/ml/kppv.py +++ b/mlstatpy/ml/kppv.py @@ -1,8 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Implements classic k-nn. -""" import numpy import numpy.linalg from scipy.spatial.distance import euclidean @@ -19,7 +14,6 @@ def __init__(self): """ constructeur """ - pass def fit(self, X, y=None): """ @@ -43,15 +37,13 @@ def kneighbors(self, X, n_neighbors=1, return_distance=True): if n_neighbors != 1: raise NotImplementedError("Not implemented when n_neighbors != 1.") if not return_distance: - raise NotImplementedError( - "Not implemented when return_distance is False.") + raise NotImplementedError("Not implemented when return_distance is False.") dist = numpy.zeros(X.shape[0]) ind = numpy.zeros(X.shape[0], dtype=numpy.int64) for i in range(X.shape[0]): row = X[i, :] - row.resize((1, X.shape[1])) r = self.ppv(row) dist[i], ind[i] = r return dist, ind @@ -71,7 +63,13 @@ def distance(self, obj1, obj2): @param obj2 object 2 @return distance """ - return euclidean(obj1, obj2) + try: + return euclidean(obj1, obj2) + except ValueError as e: + raise ValueError( + f"Unable to compute euclidean distance with shapes " + f"{obj1.shape} and {obj2.shape}." + ) from e def label(self, i): """ @@ -89,6 +87,8 @@ def ppv(self, obj): @param obj object @return ``tuple(dist, index)`` """ + if len(obj.shape) == 1: + obj = obj.reshape((1, -1)) ones = numpy.ones((self.nuage.shape[0], 1)) mat = ones @ obj if len(mat.shape) == 1: diff --git a/src/mlstatpy/ml/kppv_laesa.py b/mlstatpy/ml/kppv_laesa.py similarity index 86% rename from src/mlstatpy/ml/kppv_laesa.py rename to mlstatpy/ml/kppv_laesa.py index 6b62fc84..5d4277ab 100644 --- a/src/mlstatpy/ml/kppv_laesa.py +++ b/mlstatpy/ml/kppv_laesa.py @@ -1,17 +1,12 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Implements optimized k-nn. -""" import random import numpy from .kppv import NuagePoints -class NuagePointsLaesa (NuagePoints): +class NuagePointsLaesa(NuagePoints): """ Implémente l'algorithme des plus proches voisins, - version :ref:`LAESA `_ + version :ref:`LAESA `. """ def __init__(self, nb_pivots): @@ -56,7 +51,8 @@ def selection_pivots(self, nb): for i in range(self.nuage.shape[0]): for j in range(len(self.pivots)): self.dist[i, j] = self.distance( - self.nuage[i, :], self.nuage[self.pivots[j], :]) + self.nuage[i, :], self.nuage[self.pivots[j], :] + ) def ppv(self, obj): """ @@ -68,18 +64,19 @@ def ppv(self, obj): """ # initialisation - dp = [(self.distance(obj, self.nuage[p, :]), p, i) - for i, p in enumerate(self.pivots)] + dp = [ + (self.distance(obj, self.nuage[p, :]), p, i) + for i, p in enumerate(self.pivots) + ] # pivots le plus proche dm, im, _ = min(dp) # améliorations - for i in range(0, self.nuage.shape[0]): - + for i in range(self.nuage.shape[0]): # on regarde si un pivot permet d'éliminer l'élément i calcul = True - for d, p, ip in dp: + for d, _p, ip in dp: delta = abs(d - self.dist[i, ip]) if delta > dm: calcul = False diff --git a/src/mlstatpy/ml/logreg.py b/mlstatpy/ml/logreg.py similarity index 68% rename from src/mlstatpy/ml/logreg.py rename to mlstatpy/ml/logreg.py index 9ddeadde..bd80ff51 100644 --- a/src/mlstatpy/ml/logreg.py +++ b/mlstatpy/ml/logreg.py @@ -1,7 +1,3 @@ -""" -@file -@brief Helpers on logistic regression. -""" import numpy from pandas import DataFrame @@ -32,7 +28,7 @@ def random_set_1d(n, kind): y[(x >= 0.8) & (x <= 1.5)] = 0 y[x > 1.5] = 1 else: - raise ValueError('kind must be in (2, 3, 4).') + raise ValueError("kind must be in (2, 3, 4).") x2 = numpy.random.rand(n) return numpy.vstack([x, x2]).T, y @@ -44,10 +40,11 @@ def plot_ds(X, y, ax=None, title=None): """ if ax is None: import matplotlib.pyplot as plt + ax = plt.gca() - colors = {0: '#88CCCC', 1: '#CCCC88'} + colors = {0: "#88CCCC", 1: "#CCCC88"} c = [colors[_] for _ in y] - ax.scatter(X[:, 0], X[:, 1], c=c, s=20, edgecolor='k', lw=0.1) + ax.scatter(X[:, 0], X[:, 1], c=c, s=20, edgecolor="k", lw=0.1) if title is not None: ax.set_title(title) return ax @@ -66,16 +63,17 @@ def logistic(x): """ Computes :math:`\\frac{1}{1 + e^{-x}}`. """ - return 1. / (1. + numpy.exp(-x)) + return 1.0 / (1.0 + numpy.exp(-x)) -def likelihood(x, y, theta=1., th=0.): +def likelihood(x, y, theta=1.0, th=0.0): """ - Computes :math:`\\sum_i y_i f(\\theta (x_i - x_0)) + (1 - y_i) (1 - f(\\theta (x_i - x_0)))` + Computes :math:`\\sum_i y_i f(\\theta (x_i - x_0)) + + (1 - y_i) (1 - f(\\theta (x_i - x_0)))` where :math:`f(x_i)` is :math:`\\frac{1}{1 + e^{-x}}`. """ lr = logistic((x - th) * theta) - return y * lr + (1. - y) * (1 - lr) + return y * lr + (1.0 - y) * (1 - lr) def criteria(X, y): @@ -101,12 +99,12 @@ def criteria(X, y): p2 = numpy.sum(y[i:]) / (y.shape[0] - i) res[i, 2] = p1 res[i, 3] = p2 - res[i, 4] = 1 - p1**2 - (1 - p1)**2 + 1 - p2**2 - (1 - p2)**2 - res[i, 5] = - plog2(p1) - plog2(1 - p1) - plog2(p2) - plog2(1 - p2) + res[i, 4] = 1 - p1**2 - (1 - p1) ** 2 + 1 - p2**2 - (1 - p2) ** 2 + res[i, 5] = -plog2(p1) - plog2(1 - p1) - plog2(p2) - plog2(1 - p2) th = x[i] res[i, 6] = logistic(th) - res[i, 7] = numpy.sum(likelihood(x, y, 1., th)) / res.shape[0] - columns = ['X', 'y', 'p1', 'p2', 'Gini', 'Gain', 'lr', 'LL'] + res[i, 7] = numpy.sum(likelihood(x, y, 1.0, th)) / res.shape[0] + columns = ["X", "y", "p1", "p2", "Gini", "Gain", "lr", "LL"] return DataFrame(res[1:-1], columns=columns) @@ -130,11 +128,26 @@ def criteria2(X, y): for i in range(1, res.shape[0] - 1): # gini th = x[i] - res[i, 2] = max(numpy.sum(likelihood(x, y, 1., th)), - numpy.sum(likelihood(x, y, -1., th))) / res.shape[0] - res[i, 3] = max(numpy.sum(likelihood(x, y, 10., th)), - numpy.sum(likelihood(x, y, -10., th))) / res.shape[0] - res[i, 4] = max(numpy.sum(likelihood(x, y, 100., th)), - numpy.sum(likelihood(x, y, -100., th))) / res.shape[0] - columns = ['X', 'y', 'LL', 'LL-10', 'LL-100'] + res[i, 2] = ( + max( + numpy.sum(likelihood(x, y, 1.0, th)), + numpy.sum(likelihood(x, y, -1.0, th)), + ) + / res.shape[0] + ) + res[i, 3] = ( + max( + numpy.sum(likelihood(x, y, 10.0, th)), + numpy.sum(likelihood(x, y, -10.0, th)), + ) + / res.shape[0] + ) + res[i, 4] = ( + max( + numpy.sum(likelihood(x, y, 100.0, th)), + numpy.sum(likelihood(x, y, -100.0, th)), + ) + / res.shape[0] + ) + columns = ["X", "y", "LL", "LL-10", "LL-100"] return DataFrame(res[1:-1], columns=columns) diff --git a/src/mlstatpy/ml/matrices.py b/mlstatpy/ml/matrices.py similarity index 79% rename from src/mlstatpy/ml/matrices.py rename to mlstatpy/ml/matrices.py index 273af70f..1710131a 100644 --- a/src/mlstatpy/ml/matrices.py +++ b/mlstatpy/ml/matrices.py @@ -1,17 +1,12 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Algorithms about matrices. -""" import warnings import numpy import numpy.linalg -from scipy.linalg.lapack import dtrtri # pylint: disable=E0611 +from scipy.linalg.lapack import dtrtri def gram_schmidt(mat, change=False): """ - Applies the `Gram–Schmidt process + Applies the `Gram-Schmidt process `_. Due to performance, every row is considered as a vector. @@ -42,15 +37,17 @@ def gram_schmidt(mat, change=False): if len(mat.shape) != 2: raise ValueError("mat must be a matrix.") if mat.shape[1] < mat.shape[0]: - raise RuntimeError("The function only works if the number of rows is less " - "than the number of columns.") + raise RuntimeError( + "The function only works if the number of rows is less " + "than the number of columns." + ) if change: base = numpy.identity(mat.shape[0]) # The following code is equivalent to: # res = numpy.empty(mat.shape) - # for i in range(0, mat.shape[0]): + # for i in range(mat.shape[0]): # res[i, :] = mat[i, :] - # for j in range(0, i): + # for j in range(i): # d = numpy.dot(res[j, :], mat[i, :]) # res[i, :] -= res[j, :] * d # if change: @@ -63,7 +60,7 @@ def gram_schmidt(mat, change=False): # base[i, :] /= d # But it is faster to write it this way: res = numpy.empty(mat.shape) - for i in range(0, mat.shape[0]): + for i in range(mat.shape[0]): res[i, :] = mat[i, :] if i > 0: d = numpy.dot(res[:i, :], mat[i, :]) @@ -91,12 +88,11 @@ def linear_regression(X, y, algo=None): :ref:`Arbre de décision optimisé pour les régressions linéaires `. - @param X features - @param y targets - @param algo None to use the standard algorithm - :math:`\\beta = (X'X)^{-1} X'y`, - `'gram'`, `'qr'` - @return beta + :param X: features + :param y: targets + :param algo: None to use the standard algorithm + :math:`\\beta = (X'X)^{-1} X'y`, `'gram'`, `'qr'` + :return: beta .. runpython:: :showcode: @@ -114,8 +110,8 @@ def linear_regression(X, y, algo=None): ``algo=None`` computes :math:`\\beta = (X'X)^{-1} X'y`. ``algo='qr'`` uses a `QR `_ decomposition and calls function - `dtrtri `_ to invert an upper triangular matrix. + `dtrtri `_ + to invert an upper triangular matrix. ``algo='gram'`` uses :func:`gram_schmidt ` and then computes the solution of the linear regression (see above for a link @@ -123,21 +119,22 @@ def linear_regression(X, y, algo=None): """ if len(y.shape) != 1: warnings.warn( - "This function is not tested for a multidimensional linear regression.") + "This function is not tested for a multidimensional linear regression.", + stacklevel=0, + ) if algo is None: inv = numpy.linalg.inv(X.T @ X) return inv @ (X.T @ y) - elif algo == "gram": + if algo == "gram": T, P = gram_schmidt(X.T, change=True) # T = P X return (y.T @ T.T @ P).ravel() - elif algo == "qr": - Q, R = numpy.linalg.qr(X, "full") + if algo == "qr": + Q, R = numpy.linalg.qr(X, "reduced") Ri = dtrtri(R)[0] gamma = (y.T @ Q).ravel() return (gamma @ Ri.T).ravel() - else: - raise ValueError("Unknwown algo='{}'.".format(algo)) + raise ValueError(f"Unknwown algo='{algo}'.") def norm2(X): @@ -156,7 +153,7 @@ def streaming_gram_schmidt_update(Xk, Pk): Updates matrix :math:`P_k` to produce :math:`P_{k+1}` which is the matrix *P* in algorithm :ref:`Streaming Linear Regression - `. + `. The function modifies the matrix *Pk* given as an input. @@ -166,11 +163,11 @@ def streaming_gram_schmidt_update(Xk, Pk): tki = Pk @ Xk idi = numpy.identity(Pk.shape[0]) - for i in range(0, Pk.shape[0]): + for i in range(Pk.shape[0]): val = tki[i] if i > 0: - # for j in range(0, i): + # for j in range(i): # d = tki[j] * val # tki[i] -= tki[j] * d # Pk[i, :] -= Pk[j, :] * d @@ -182,11 +179,11 @@ def streaming_gram_schmidt_update(Xk, Pk): Pk[i, :] -= numpy.multiply(Pk[:i, :], dv).sum(axis=0) idi[i, :] -= numpy.multiply(idi[:i, :], dv).sum(axis=0) - d = numpy.square(idi[i, :]).sum() # pylint: disable=E1101 + d = numpy.square(idi[i, :]).sum() d = tki[i] ** 2 + d if d > 0: d **= 0.5 - d = 1. / d + d = 1.0 / d tki[i] *= d Pk[i, :] *= d idi[i, :] *= d @@ -198,7 +195,7 @@ def streaming_gram_schmidt(mat, start=None): find :math:`\\beta` which minimizes :math:`\\norme{y - X\\beta}`, based on algorithm :ref:`Streaming Gram-Schmidt - `. + `. @param mat matrix @param start first row to start iteration, ``X.shape[1]`` by default @@ -225,8 +222,10 @@ def streaming_gram_schmidt(mat, start=None): if len(mat.shape) != 2: raise ValueError("mat must be a matrix.") if mat.shape[1] < mat.shape[0]: - raise RuntimeError("The function only works if the number of rows is less " - "than the number of columns.") + raise RuntimeError( + "The function only works if the number of rows is less " + "than the number of columns." + ) if start is None: start = mat.shape[0] mats = mat[:, :start] @@ -243,14 +242,13 @@ def streaming_gram_schmidt(mat, start=None): def streaming_linear_regression_update(Xk, yk, XkXk, bk): """ Updates coefficients :math:`\\beta_k` to produce :math:`\\beta_{k+1}` - in :ref:`l-piecewise-linear-regression`. - The function modifies the matrix *Pk* - given as an input. + in :ref:`l-piecewise-linear-regression`. The function modifies + the matrix *Pk* given as an input. - @param Xk kth row - @param yk kth target - @param XkXk matrix :math:`X_{1..k}'X_{1..k}', updated by the function - @param bk current coefficient (updated by the function) + :param Xk: kth row + :param yk: kth target + :param XkXk: matrix :math:`X_{1..k}'X_{1..k}`, updated by the function + :param bk: current coefficient (updated by the function) """ Xk = Xk.reshape((1, XkXk.shape[0])) xxk = Xk.T @ Xk @@ -286,11 +284,15 @@ def streaming_linear_regression(mat, y, start=None): if len(mat.shape) != 2: raise ValueError("mat must be a matrix.") if mat.shape[0] < mat.shape[1]: - raise RuntimeError("The function only works if the number of rows is more " - "than the number of columns.") + raise RuntimeError( + "The function only works if the number of rows is more " + "than the number of columns." + ) if len(y.shape) != 1: warnings.warn( - "This function is not tested for a multidimensional linear regression.") + "This function is not tested for a multidimensional linear regression.", + stacklevel=0, + ) if start is None: start = mat.shape[1] @@ -311,15 +313,14 @@ def streaming_linear_regression_gram_schmidt_update(Xk, yk, Xkyk, Pk, bk): Updates coefficients :math:`\\beta_k` to produce :math:`\\beta_{k+1}` in :ref:`Streaming Linear Regression `. - The function modifies the matrix *Pk* - given as an input. - - @param Xk kth row - @param yk kth target - @param Xkyk matrix :math:`X_{1..k}' y_{1..k}' (updated by the function) - @param Pk Gram-Schmidt matrix produced by the streaming algorithm - (updated by the function) - @param bk current coefficient (updated by the function) + The function modifies the matrix *Pk* given as an input. + + :param Xk: kth row + :param yk: kth target + :param Xkyk: matrix :math:`X_{1..k}' y_{1..k}` (updated by the function) + :param Pk: Gram-Schmidt matrix produced by the streaming algorithm + (updated by the function) + :return: bk current coefficient (updated by the function) """ Xk = Xk.T streaming_gram_schmidt_update(Xk, Pk) @@ -355,11 +356,15 @@ def streaming_linear_regression_gram_schmidt(mat, y, start=None): if len(mat.shape) != 2: raise ValueError("mat must be a matrix.") if mat.shape[0] < mat.shape[1]: - raise RuntimeError("The function only works if the number of rows is more " - "than the number of columns.") + raise RuntimeError( + "The function only works if the number of rows is more " + "than the number of columns." + ) if len(y.shape) != 1: warnings.warn( - "This function is not tested for a multidimensional linear regression.") + "This function is not tested for a multidimensional linear regression.", + stacklevel=0, + ) if start is None: start = mat.shape[1] @@ -371,7 +376,6 @@ def streaming_linear_regression_gram_schmidt(mat, y, start=None): k = start while k < mat.shape[0]: - streaming_linear_regression_gram_schmidt_update( - mat[k], y[k], xyk, Pk, bk) + streaming_linear_regression_gram_schmidt_update(mat[k], y[k], xyk, Pk, bk) yield bk k += 1 diff --git a/mlstatpy/ml/neural_tree.py b/mlstatpy/ml/neural_tree.py new file mode 100644 index 00000000..8bd9a34d --- /dev/null +++ b/mlstatpy/ml/neural_tree.py @@ -0,0 +1,1077 @@ +from io import BytesIO +import pickle +import numpy +from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin +from sklearn.tree import BaseDecisionTree +from ._neural_tree_api import _TrainingAPI +from ._neural_tree_node import NeuralTreeNode + + +def label_class_to_softmax_output(y_label): + """ + Converts a binary class label into a matrix + with two columns of probabilities. + + .. runpython:: + :showcode: + + import numpy + from mlstatpy.ml.neural_tree import label_class_to_softmax_output + + y_label = numpy.array([0, 1, 0, 0]) + soft_y = label_class_to_softmax_output(y_label) + print(soft_y) + """ + if len(y_label.shape) != 1: + raise ValueError(f"y_label must be a vector but has shape {y_label.shape}.") + y = numpy.empty((y_label.shape[0], 2), dtype=numpy.float64) + y[:, 0] = (y_label < 0.5).astype(numpy.float64) + y[:, 1] = 1 - y[:, 0] + return y + + +class NeuralTreeNet(_TrainingAPI): + """ + Node ensemble. + + :param dim: space dimension + :param empty: empty network, other adds an identity node + + .. runpython:: + :showcode: + + import numpy + from mlstatpy.ml.neural_tree import NeuralTreeNode, NeuralTreeNet + + w1 = numpy.array([-0.5, 0.8, -0.6]) + + neu = NeuralTreeNode(w1[1:], bias=w1[0], activation='sigmoid') + net = NeuralTreeNet(2, empty=True) + net.append(neu, numpy.arange(2)) + + ide = NeuralTreeNode(numpy.array([1.]), + bias=numpy.array([0.]), + activation='identity') + + net.append(ide, numpy.arange(2, 3)) + + X = numpy.abs(numpy.random.randn(10, 2)) + pred = net.predict(X) + print(pred) + """ + + def __init__(self, dim, empty=True): + self.dim = dim + if empty: + self.nodes = [] + self.nodes_attr = [] + else: + self.nodes = [ + NeuralTreeNode( + numpy.ones((dim,), dtype=numpy.float64), + bias=numpy.float64(0.0), + activation="identity", + nodeid=0, + ) + ] + self.nodes_attr = [ + dict( + inputs=numpy.arange(0, dim), + output=dim, + coef_size=self.nodes[0].coef.size, + first_coef=0, + ) + ] + self._update_members() + + def copy(self): + st = BytesIO() + pickle.dump(self, st) + cop = BytesIO(st.getvalue()) + return pickle.load(cop) + + def _update_members(self, node=None, attr=None): + "Updates internal members." + if node is None or attr is None: + self.size_ = ( + self.dim + if not self.nodes_attr + else (max(d["output"] for d in self.nodes_attr) + 1) + ) + self.output_to_node_ = {} + self.input_to_node_ = {} + for node2, attr2 in zip(self.nodes, self.nodes_attr): + if isinstance(attr2["output"], list): + for o in attr2["output"]: + self.output_to_node_[o] = node2, attr2 + else: + self.output_to_node_[attr2["output"]] = node2, attr2 + for i in attr2["inputs"]: + self.input_to_node_[i] = node2, attr2 + else: + if len(node.input_weights.shape) == 1: + self.size_ += 1 + else: + self.size_ += node.input_weights.shape[0] + if isinstance(attr["output"], list): + for o in attr["output"]: + self.output_to_node_[o] = node, attr + else: + self.output_to_node_[attr["output"]] = node, attr + for i in attr["inputs"]: + self.input_to_node_[i] = node, attr + + def __repr__(self): + "usual" + return "%s(%d)" % (self.__class__.__name__, self.dim) + + def clear(self): + "Clear all nodes" + del self.nodes[:] + del self.nodes_attr[:] + self._update_members() + + def append(self, node, inputs): + """ + Appends a node into the graph. + + :param node: node to add + :param inputs: index of input nodes + """ + if len(node.input_weights.shape) == 1: + if node.input_weights.shape[0] != len(inputs): + raise RuntimeError( + f"Dimension mismatch between weights " + f"[{node.input_weights.shape[0]}] " + f"and inputs [{len(inputs)}]." + ) + node.nodeid = len(self.nodes) + self.nodes.append(node) + first_coef = ( + 0 + if not self.nodes_attr + else self.nodes_attr[-1]["first_coef"] + + self.nodes_attr[-1]["coef_size"] + ) + attr = dict( + inputs=numpy.array(inputs), + output=self.size_, + coef_size=node.coef.size, + first_coef=first_coef, + ) + self.nodes_attr.append(attr) + elif len(node.input_weights.shape) == 2: + if node.input_weights.shape[1] != len(inputs): + raise RuntimeError( + f"Dimension mismatch between weights " + f"[{node.input_weights.shape[1]}] " + f"and inputs [{len(inputs)}], tag={node.tag!r}, " + f"node={node!r}." + ) + node.nodeid = len(self.nodes) + self.nodes.append(node) + first_coef = ( + 0 + if not self.nodes_attr + else self.nodes_attr[-1]["first_coef"] + + self.nodes_attr[-1]["coef_size"] + ) + attr = dict( + inputs=numpy.array(inputs), + output=list( + range(self.size_, self.size_ + node.input_weights.shape[0]) + ), + coef_size=node.coef.size, + first_coef=first_coef, + ) + self.nodes_attr.append(attr) + else: + raise RuntimeError( + f"Coefficients should have 1 or 2 dimension not " + f"{node.input_weights.shape}." + ) + self._update_members(node, attr) + + def __getitem__(self, i): + "Retrieves node and attributes for node i." + return self.nodes[i], self.nodes_attr[i] + + def __len__(self): + "Returns the number of nodes" + return len(self.nodes) + + def _predict_one(self, X): + res = numpy.zeros((self.size_,), dtype=numpy.float64) + res[: self.dim] = X + for node, attr in zip(self.nodes, self.nodes_attr): + res[attr["output"]] = node.predict(res[attr["inputs"]]) + return res + + def predict(self, X): + if len(X.shape) == 2: + res = numpy.zeros((X.shape[0], self.size_)) + for i, x in enumerate(X): + res[i, :] = self._predict_one(x) + return res + return self._predict_one(X) + + @staticmethod + def create_from_tree(tree, k=1.0, arch="one"): + """ + Creates a :class:`NeuralTreeNet` instance from a + :epkg:`DecisionTreeClassifier` + + :param tree: :epkg:`DecisionTreeClassifier` + :param k: slant of the sigmoïd + :param arch: architecture, see below + :return: :class:`NeuralTreeNet` + + The function only works for binary problems. + Available architecture: + + * `'one'`: the method adds nodes with one output, there + is no soecific definition of layers, + * `'compact'`: the adds two nodes, the first computes + the threshold, the second one computes the leaves + output, a final node merges all outputs into one + + See notebook :ref:`/notebooks/ml/neural_tree.ipynb` for examples. + """ + if arch == "one": + return NeuralTreeNet._create_from_tree_one(tree, k) + if arch == "compact": + return NeuralTreeNet._create_from_tree_compact(tree, k) + raise ValueError(f"Unknown arch value '{arch}'.") + + @staticmethod + def _create_from_tree_one(tree, k=1.0): + "Implements strategy 'one'. See :meth:`create_from_tree`." + + if not isinstance(tree, BaseDecisionTree): + raise TypeError(f"Only decision tree as supported not {type(tree)!r}.") + if not isinstance(tree, ClassifierMixin): + raise TypeError( + f"Only a classifier can be converted by this function " + f"not {type(tree)!r}, arch='compact' should be used." + ) + if tree.n_classes_ > 2: + raise RuntimeError( + "The function only supports binary classification problem." + ) + + n_nodes = tree.tree_.node_count + children_left = tree.tree_.children_left + children_right = tree.tree_.children_right + feature = tree.tree_.feature + threshold = tree.tree_.threshold + value = tree.tree_.value.reshape((-1, 2)) + output_class = (value[:, 1] > value[:, 0]).astype(numpy.int64) + max_features_ = tree.max_features_ + + root = NeuralTreeNet(tree.max_features_, empty=True) + feat_index = numpy.arange(0, max_features_) + predecessor = {} + outputs = {i: [] for i in range(tree.n_classes_)} + for i in range(n_nodes): + if children_left[i] != children_right[i]: + # node with a threshold + # right side + coef = numpy.zeros((max_features_,), dtype=numpy.float64) + coef[feature[i]] = -k + node_th = NeuralTreeNode( + coef, bias=k * threshold[i], activation="sigmoid4", tag="N%d-th" % i + ) + root.append(node_th, feat_index) + + if i in predecessor: + pred = predecessor[i] + node1 = pred + node2 = node_th + attr1 = root[node1.nodeid][1] + attr2 = root[node2.nodeid][1] + + coef = numpy.ones((2,), dtype=numpy.float64) * k + node_true = NeuralTreeNode( + coef, bias=-k * 1.5, activation="sigmoid4", tag="N%d-T" % i + ) + root.append(node_true, [attr1["output"], attr2["output"]]) + + coef = numpy.zeros((2,), dtype=numpy.float64) + coef[0] = k + coef[1] = -k + node_false = NeuralTreeNode( + coef, bias=-k * 0.25, activation="sigmoid4", tag="N%d-F" % i + ) + root.append(node_false, [attr1["output"], attr2["output"]]) + + predecessor[children_left[i]] = node_true + predecessor[children_right[i]] = node_false + else: + coef = numpy.ones((1,), dtype=numpy.float64) * -1 + node_false = NeuralTreeNode( + coef, bias=1, activation="identity", tag="N%d-F" % i + ) + attr = root[node_th.nodeid][1] + root.append(node_false, [attr["output"]]) + + predecessor[children_left[i]] = node_th + predecessor[children_right[i]] = node_false + + elif i in predecessor: + # leave + outputs[output_class[i]].append(predecessor[i]) + + # final node + output = [] + index = [0] + nb = [] + for i in range(tree.n_classes_): + output.extend(outputs[i]) + nb.append(len(outputs[i])) + index.append(len(outputs[i]) + index[-1]) + coef = numpy.zeros((len(nb), len(output)), dtype=numpy.float64) + for i in range(tree.n_classes_): + coef[i, index[i] : index[i + 1]] = k + feat = [root[n.nodeid][1]["output"] for n in output] + root.append( + NeuralTreeNode( + coef, bias=(-k / 2) * len(feat), activation="softmax4", tag="Nfinal" + ), + feat, + ) + + # final + return root + + @staticmethod + def _create_from_tree_compact(tree, k=1.0): + "Implements strategy 'compact'. See :meth:`create_from_tree`." + if not isinstance(tree, BaseDecisionTree): + raise TypeError(f"Only decision tree as supported not {type(tree)!r}.") + if isinstance(tree, ClassifierMixin): + is_classifier = True + if tree.n_classes_ > 2: + raise RuntimeError( + "The function only supports binary classification problem." + ) + else: + is_classifier = False + if tree.n_outputs_ != 1: + raise RuntimeError( + "The function only supports single regression problem." + ) + + n_nodes = tree.tree_.node_count + children_left = tree.tree_.children_left + children_right = tree.tree_.children_right + feature = tree.tree_.feature + threshold = tree.tree_.threshold + if is_classifier: + value = tree.tree_.value.reshape((-1, 2)) + output_class = (value[:, 1] > value[:, 0]).astype(numpy.int64) + else: + output_value = tree.tree_.value.ravel() + max_features_ = tree.max_features_ + feat_index = numpy.arange(0, max_features_) + + root = NeuralTreeNet(tree.max_features_, empty=True) + coef1 = [] + bias1 = [] + parents = {} + rows = {} + + # first pass: threshold + + for i in range(n_nodes): + if children_left[i] == children_right[i]: + # leaves + continue + rows[i] = len(coef1) + parents[children_left[i]] = i + parents[children_right[i]] = i + coef = numpy.zeros((max_features_,), dtype=numpy.float64) + coef[feature[i]] = -k + coef1.append(coef) + bias1.append(k * threshold[i]) + + coef1 = numpy.vstack(coef1) + if len(bias1) == 1: + bias1 = bias1[0] + node1 = NeuralTreeNode( + coef1 if coef1.shape[0] > 1 else coef1[0], + bias=bias1, + activation="sigmoid4", + tag="threshold", + ) + root.append(node1, feat_index) + th_index = numpy.arange(max_features_, max_features_ + coef1.shape[0]) + + # second pass: decision path + coef2 = [] + bias2 = [] + output = [] + paths = [] + + for i in range(n_nodes): + if children_left[i] != children_right[i]: + # not a leave + continue + + path = [] + last = i + if is_classifier: + lr = "class", output_class[i] + output.append(output_class[i]) + else: + lr = "reg", output_value[i] + output.append(output_value[i]) + while last is not None: + path.append((last, lr)) + if last not in parents: + break + par = parents[last] + if children_right[par] == last: + lr = "right" + elif children_left[par] == last: + lr = "left" + else: + raise RuntimeError("Inconsistent tree structure.") + last = par + + coef = numpy.zeros((coef1.shape[0],), dtype=numpy.float64) + # This bias is different from the one implemented in + # _create_from_tree_one where bias=0. + bias = -k * (len(path) - 2) / 2 + for ip, lr in path: + if isinstance(lr, tuple): + lr, value = lr + if lr not in ("class", "reg"): + raise RuntimeError("algorithm issue") + else: + r = rows[ip] + # coefficients are the opposite in _create_from_tree_one + if lr == "right": + coef[r] = -k + bias += k / 2 + else: + coef[r] = k + bias -= k / 2 + coef2.append(coef) + bias2.append(bias) + paths.append(path) + + coef2 = numpy.vstack(coef2) + if len(bias2) == 1: + bias2 = bias2[0] + node2 = NeuralTreeNode( + coef2 if coef2.shape[0] > 1 else coef2[0], + bias=bias2, + activation="sigmoid4", + tag="pathes", + ) + root.append(node2, th_index) + + # final node + n_outputs = tree.n_classes_ if is_classifier else tree.n_outputs_ + + index1 = max_features_ + coef1.shape[0] + index2 = index1 + coef2.shape[0] + findex = numpy.arange(index1, index2) + + if is_classifier: + # coefficients are the opposite in _create_from_tree_one + coef = numpy.zeros((n_outputs, coef2.shape[0]), dtype=numpy.float64) + bias = numpy.zeros(n_outputs, dtype=numpy.float64) + for i, cls in enumerate(output): + coef[cls, i] = k + coef[1 - cls, i] = -k + bias[cls] -= k / 2 + bias[1 - cls] += k / 2 + root.append( + NeuralTreeNode(coef, bias=bias, activation="softmax4", tag="final"), + findex, + ) + else: + coef = numpy.array(output, dtype=numpy.float64) + bias = numpy.zeros(n_outputs, dtype=numpy.float64) + for i, reg in enumerate(output): + coef[i] = reg + root.append( + NeuralTreeNode(coef, bias=bias, activation="identity", tag="final"), + findex, + ) + + # end + return root + + def to_dot(self, X=None): + """ + Exports the neural network into :epkg:`dot`. + + :param X: input as an example + """ + y = None + if X is not None: + y = self.predict(X) + rows = [ + "digraph Tree {", + "node [shape=box, fontsize=10];", + "edge [fontsize=8];", + ] + for i in range(self.dim): + if y is None: + rows.append('{0} [label="X[{0}]"];'.format(i)) + else: + rows.append('{0} [label="X[{0}]=\\n{1:1.2f}"];'.format(i, X[i])) + + labels = {} + + for i in range(len(self)): + o = self[i][1]["output"] + if isinstance(o, int): + lo = str(o) + labels[o] = lo + lof = "%s" + else: + lo = "s" + "a".join(map(str, o)) + for oo in o: + labels[oo] = f"{lo}:f{oo}" + los = "|".join(" {0}".format(oo) for oo in o) + lof = "%s\n" + los + + a = f"a={self[i][0].activation}\n" + stag = "" if self[i][0].tag is None else (self[i][0].tag + "\\n") + bias = str(numpy.array(self[i][0].bias)).replace(" ", "\ ") + if y is None: + lab = lof % f"{stag}{a}id={i} b={bias} s={self[i][0].n_outputs}" + else: + yo = numpy.array(y[o]) + lab = lof % "{}{}id={} b={} s={}\ny={}".format( + stag, a, i, bias, self[i][0].n_outputs, yo + ) + rows.append('{} [label="{}"];'.format(lo, lab.replace("\n", "\n"))) + for ii, inp in enumerate(self[i][1]["inputs"]): + if isinstance(o, int): + w = self[i][0].input_weights[ii] + if w == 0: + c = ", color=grey, fontcolor=grey" + elif w < 0: + c = ", color=red, fontcolor=red" + else: + c = ", color=blue, fontcolor=blue" + rows.append(f'{inp} -> {o} [label="{w}"{c}];') + continue + + w = self[i][0].input_weights[:, ii] + for oi, oo in enumerate(o): + if w[oi] == 0: + c = ", color=grey, fontcolor=grey" + elif w[oi] < 0: + c = ", color=red, fontcolor=red" + else: + c = ", color=blue, fontcolor=blue" + rows.append( + '{} -> {} [label="{}|{}"{}];'.format( + labels.get(inp, inp), labels[oo], oi, w[oi], c + ) + ) + + rows.append("}") + return "\n".join(rows) + + @property + def shape(self): + "Returns the shape of the coefficients." + return (sum(n.coef.size for n in self.nodes),) + + @property + def training_weights(self): + "Returns the weights." + sh = self.shape + res = numpy.empty(sh[0], dtype=numpy.float64) + pos = 0 + for n in self.nodes: + s = n.coef.size + res[pos : pos + s] = n.coef if len(n.coef.shape) == 1 else n.coef.ravel() + pos += s + return res + + def update_training_weights(self, X, add=True): + """ + Updates weights. + + :param X: training dataset + :param add: addition or replace + """ + pos = 0 + if add: + for n in self.nodes: + s = n.coef.size + n.coef += X[pos : pos + s].reshape(n.coef.shape) + pos += s + else: + for n in self.nodes: + s = n.coef.size + numpy.copyto(n.coef, X[pos : pos + s].reshape(n.coef.shape)) + pos += s + + def fill_cache(self, X): + """ + Creates a cache with intermediate results. + """ + big_cache = {} + res = numpy.zeros((self.size_,), dtype=numpy.float64) + res[: self.dim] = X + for node, attr in zip(self.nodes, self.nodes_attr): + cache = node.fill_cache(res[attr["inputs"]]) + big_cache[node.nodeid] = cache + res[attr["output"]] = cache["aX"] + big_cache[-1] = res + return big_cache + + def _get_output_node_attr(self, nb_last): + """ + Retrieves the output nodes. + *nb_last* is the number of expected outputs. + """ + neurones = set( + self.output_to_node_[i][0].nodeid + for i in range(self.size_ - nb_last, self.size_) + ) + if len(neurones) != 1: + raise RuntimeError( + f"Only one output node is implemented not {len(neurones)}" + ) + return self.output_to_node_[self.size_ - 1] + + def _common_loss_dloss(self, X, y, cache=None): + """ + Common beginning to methods *loss*, *dlossds*, + *dlossdw*. + """ + last = 1 if len(y.shape) <= 1 else y.shape[1] + if cache is not None and -1 in cache: + res = cache[-1] + else: + res = self.predict(X) + if len(res.shape) == 2: + pred = res[:, -last:] + else: + pred = res[-last:] + last_node, last_attr = self._get_output_node_attr(last) + return res, pred, last_node, last_attr + + def loss(self, X, y, cache=None): + """ + Computes the loss due to prediction error. Returns a float. + """ + res, _, last_node, last_attr = self._common_loss_dloss(X, y, cache=cache) + if len(res.shape) <= 1: + return last_node.loss(res[last_attr["inputs"]], y) + return last_node.loss(res[:, last_attr["inputs"]], y) + + def dlossds(self, X, y, cache=None): + """ + Computes the loss derivative against the inputs. + """ + res, _, last_node, last_attr = self._common_loss_dloss(X, y, cache=cache) + if len(res.shape) <= 1: + return last_node.dlossds(res[last_attr["inputs"]], y) + return last_node.dlossds(res[:, last_attr["inputs"]], y) + + def gradient_backward(self, graddx, X, inputs=False, cache=None): + """ + Computes the gradient in X. + + :param graddx: existing gradient against the inputs + :param X: computes the gradient in X + :param inputs: if False, derivative against the coefficients, + otherwise against the inputs. + :param cache: cache intermediate results to avoid more computation + :return: gradient + """ + if cache is None: + cache = self.fill_cache(X) + shape = self.training_weights.shape + pred = self.predict(X) + + whole_gradx = numpy.zeros(pred.shape, dtype=numpy.float64) + whole_gradw = numpy.zeros(shape, dtype=numpy.float64) + if not graddx.shape: + whole_gradx[-1] = graddx + else: + whole_gradx[-graddx.shape[0] :] = graddx + + for node, attr in zip(self.nodes[::-1], self.nodes_attr[::-1]): + ch = cache[node.nodeid] + + node_graddx = whole_gradx[attr["output"]] + xi = pred[attr["inputs"]] + + temp_gradw = node.gradient_backward(node_graddx, xi, inputs=False, cache=ch) + temp_gradx = node.gradient_backward(node_graddx, xi, inputs=True, cache=ch) + + whole_gradw[ + attr["first_coef"] : attr["first_coef"] + attr["coef_size"] + ] += temp_gradw.reshape((attr["coef_size"],)) + whole_gradx[attr["inputs"]] += temp_gradx.reshape((len(attr["inputs"]),)) + + if inputs: + return whole_gradx + return whole_gradw + + +class BaseNeuralTreeNet(BaseEstimator): + """ + Classifier or regressor following :epkg:`scikit-learn` API. + + :param estimator: instance of :class:`NeuralTreeNet`. + :param X: training set + :param y: training labels + :param optimizer: optimizer, by default, it is + :class:`SGDOptimizer `. + :param max_iter: number maximum of iterations + :param early_th: early stopping threshold + :param verbose: more verbose + :param lr: to overwrite *learning_rate_init* if + *optimizer* is None (unused otherwise) + :param lr_schedule: to overwrite *lr_schedule* if + *optimizer* is None (unused otherwise) + :param l1: L1 regularization if *optimizer* is None + (unused otherwise) + :param l2: L2 regularization if *optimizer* is None + (unused otherwise) + :param momentum: used if *optimizer* is None + """ + + def __init__( + self, + estimator, + optimizer=None, + max_iter=100, + early_th=None, + verbose=False, + lr=None, + lr_schedule=None, + l1=0.0, + l2=0.0, + momentum=0.9, + ): + if not isinstance(estimator, NeuralTreeNet): + raise ValueError( + f"estimator must be an instance of " + f"NeuralTreeNet not {type(estimator)!r}." + ) + BaseEstimator.__init__(self) + self.estimator = None + self.estimator_ = estimator + self.optimizer = None + self.max_iter = max_iter + self.early_th = early_th + self.verbose = verbose + self.lr = lr + self.lr_schedule = lr_schedule + self.l1 = l1 + self.l2 = l2 + self.momentum = momentum + + def decision_function(self, X): + """ + Returns the classification probabilities. + + :param X: inputs + :return: probabilities + """ + return self.estimator_.predict(X) + + def fit(self, X, y, sample_weights=None): + """ + Trains the estimator. + + :param X: input features + :param y: expected classes (binary) + :param sample_weights: sample weights + :return: self + """ + if sample_weights is not None: + raise NotImplementedError("sample_weights is not supported yet.") + if isinstance(self, ClassifierMixin): + ny = label_class_to_softmax_output(y) if len(y.shape) == 1 else y + else: + ny = y + self.estimator_.fit( + X, + ny, + optimizer=self.optimizer, + max_iter=self.max_iter, + early_th=self.early_th, + verbose=self.verbose, + lr=self.lr, + lr_schedule=self.lr_schedule, + l1=self.l1, + l2=self.l2, + momentum=self.momentum, + ) + return self + + @staticmethod + def onnx_shape_calculator(): + """ + Shape calculator when converting this model into ONNX. + See :epkg:`sklearn-onnx`. + """ + from skl2onnx.common.data_types import Int64TensorType + + def shape_calculator(operator): + op = operator.raw_operator + input_type = operator.inputs[0].type.__class__ + input_dim = operator.inputs[0].get_first_dimension() + output_type = input_type([input_dim, op.estimator_.nodes[-1].ndim_out]) + if isinstance(op, ClassifierMixin): + operator.outputs[0].type = Int64TensorType([input_dim, 1]) + operator.outputs[1].type = output_type + else: + operator.outputs[0].type = output_type + + return shape_calculator + + @staticmethod + def onnx_converter(): + """ + Converts this model into ONNX. + """ + from skl2onnx.common.data_types import guess_numpy_type + from skl2onnx.algebra.onnx_ops import ( + OnnxIdentity, + OnnxArgMax, + OnnxAdd, + OnnxMatMul, + OnnxSigmoid, + OnnxMul, + OnnxSoftmax, + ) + + def converter(scope, operator, container): + op = operator.raw_operator + net = op.estimator_ + out = operator.outputs + opv = container.target_opset + + X = operator.inputs[0] + dtype = guess_numpy_type(X.type) + + res = {"inputs": X} + last = None + for node, attr in zip(net.nodes, net.nodes_attr): + # verification + coef = ( + node.coef.reshape((1, -1)) + if len(node.coef.shape) == 1 + else node.coef + ) + if len(coef.shape) != 2: + raise RuntimeError(f"coef must be a 2D matrix not {coef.shape!r}.") + if coef.shape[1] < 2: + raise RuntimeError( + f"coef must be a 2D matrix with at least 2 columns " + f"not {coef.shape!r}." + ) + + # input, output, names + name = ( + "inputs" + if attr["inputs"][0] == 0 + else "r_%s" % ("_".join(map(str, attr["inputs"]))) + ) + if name not in res: + raise KeyError(f"Unable to find {name!r} in {set(res)}.") + output_name = ( + "r_%d" % attr["output"] + if isinstance(attr["output"], int) + else "r_%s" % ("_".join(map(str, attr["output"]))) + ) + x = res[name] + + # conversion of one node + tr = OnnxAdd( + OnnxMatMul(x, coef[:, 1:].T.astype(dtype), op_version=opv), + coef[:, 0].astype(dtype), + op_version=opv, + ) + + # activation + if node.activation == "sigmoid4": + final = OnnxSigmoid( + OnnxMul(tr, numpy.array([4], dtype=dtype), op_version=opv), + op_version=opv, + ) + elif node.activation == "sigmoid": + final = OnnxSigmoid(tr, op_version=opv) + elif node.activation == "softmax4": + final = OnnxSoftmax( + OnnxMul(tr, numpy.array([4], dtype=dtype), op_version=opv), + op_version=opv, + ) + elif node.activation == "softmax": + final = OnnxSoftmax(tr, op_version=opv) + elif node.activation == "identity": + final = OnnxIdentity(tr, op_version=opv) + else: + raise NotImplementedError( + f"Unable to convert activation {node.activation!r} " + f"function into ONNX." + ) + + res[output_name] = final + last = final + + if isinstance(op, ClassifierMixin): + prob = OnnxIdentity(last, op_version=opv, output_names=[out[1]]) + prob.add_to(scope, container) + labels = OnnxArgMax( + prob, axis=1, keepdims=1, op_version=opv, output_names=[out[0]] + ) + labels.add_to(scope, container) + else: + pred = OnnxIdentity(last, op_version=opv, output_names=[out[0]]) + pred.add_to(scope, container) + + return converter + + +class NeuralTreeNetClassifier(ClassifierMixin, BaseNeuralTreeNet): + """ + Classifier following :epkg:`scikit-learn` API. + + :param estimator: instance of :class:`NeuralTreeNet`. + :param optimizer: optimizer, by default, it is + :class:`SGDOptimizer `. + :param max_iter: number maximum of iterations + :param early_th: early stopping threshold + :param verbose: more verbose + :param lr: to overwrite *learning_rate_init* if + *optimizer* is None (unused otherwise) + :param lr_schedule: to overwrite *lr_schedule* if + *optimizer* is None (unused otherwise) + :param l1: L1 regularization if *optimizer* is None + (unused otherwise) + :param l2: L2 regularization if *optimizer* is None + (unused otherwise) + :param momentum: used if *optimizer* is None + """ + + def __init__( + self, + estimator, + optimizer=None, + max_iter=100, + early_th=None, + verbose=False, + lr=None, + lr_schedule=None, + l1=0.0, + l2=0.0, + momentum=0.9, + ): + if not isinstance(estimator, NeuralTreeNet): + raise ValueError( + f"estimator must be an instance of " + f"NeuralTreeNet not {type(estimator)!r}." + ) + ClassifierMixin.__init__(self) + BaseNeuralTreeNet.__init__( + self, + estimator=estimator, + optimizer=optimizer, + max_iter=max_iter, + early_th=early_th, + verbose=verbose, + lr=lr, + lr_schedule=lr_schedule, + l1=l1, + l2=l2, + momentum=momentum, + ) + + def predict(self, X): + """ + Returns the predicted classes. + + :param X: inputs + :return: classes + """ + probas = self.predict_proba(X) + return numpy.argmax(probas, axis=1) + + def predict_proba(self, X): + """ + Returns the classification probabilities. + + :param X: inputs + :return: probabilities + """ + return self.decision_function(X)[:, -2:] + + +class NeuralTreeNetRegressor(RegressorMixin, BaseNeuralTreeNet): + """ + Regressor following :epkg:`scikit-learn` API. + + :param estimator: instance of :class:`NeuralTreeNet`. + :param optimizer: optimizer, by default, it is + :class:`SGDOptimizer `. + :param max_iter: number maximum of iterations + :param early_th: early stopping threshold + :param verbose: more verbose + :param lr: to overwrite *learning_rate_init* if + *optimizer* is None (unused otherwise) + :param lr_schedule: to overwrite *lr_schedule* if + *optimizer* is None (unused otherwise) + :param l1: L1 regularization if *optimizer* is None + (unused otherwise) + :param l2: L2 regularization if *optimizer* is None + (unused otherwise) + :param momentum: used if *optimizer* is None + """ + + def __init__( + self, + estimator, + optimizer=None, + max_iter=100, + early_th=None, + verbose=False, + lr=None, + lr_schedule=None, + l1=0.0, + l2=0.0, + momentum=0.9, + ): + if not isinstance(estimator, NeuralTreeNet): + raise ValueError( + f"estimator must be an instance of " + f"NeuralTreeNet not {type(estimator)!r}." + ) + RegressorMixin.__init__(self) + BaseNeuralTreeNet.__init__( + self, + estimator=estimator, + optimizer=optimizer, + max_iter=max_iter, + early_th=early_th, + verbose=verbose, + lr=lr, + lr_schedule=lr_schedule, + l1=l1, + l2=l2, + momentum=momentum, + ) + + def predict(self, X): + """ + Returns the predicted classes. + + :param X: inputs + :return: classes + """ + return self.decision_function(X)[:, -1:] diff --git a/src/mlstatpy/ml/roc.py b/mlstatpy/ml/roc.py similarity index 70% rename from src/mlstatpy/ml/roc.py rename to mlstatpy/ml/roc.py index cf1415a6..547ad067 100644 --- a/src/mlstatpy/ml/roc.py +++ b/mlstatpy/ml/roc.py @@ -1,8 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief About :epkg:`ROC`. -""" import math import itertools from enum import Enum @@ -13,6 +8,20 @@ class ROC: """ Helper to draw a :epkg:`ROC` curve. + + Initialisation with a dataframe and two or three columns: + + * column 1: score (y_score) + * column 2: expected answer (boolean) (y_true) + * column 3: weight (optional) (sample_weight) + + :param y_true: if *df* is None, *y_true*, *y_score*, + *sample_weight* must be filled, *y_true* is whether + or None the answer is true. *y_true* means the prediction is right. + :param y_score: score prediction + :param sample_weight: weights + :param df: dataframe or array or list, it must contains + 2 or 3 columns always in the same order """ class CurveType(Enum): @@ -27,6 +36,7 @@ class CurveType(Enum): (`scikit-learn `_) """ + PROBSCORE = 2 ERRREC = 3 RECPREC = 4 @@ -34,21 +44,6 @@ class CurveType(Enum): SKROC = 6 def __init__(self, y_true=None, y_score=None, sample_weight=None, df=None): - """ - Initialisation with a dataframe and two or three columns: - - * column 1: score (y_score) - * column 2: expected answer (boolean) (y_true) - * column 3: weight (optional) (sample_weight) - - @param y_true if *df* is None, *y_true*, *y_score*, *sample_weight* must be filled, - *y_true* is whether or None the answer is true. - *y_true* means the prediction is right. - @param y_score score prediction - @param sample_weight weights - @param df dataframe or array or list, - it must contains 2 or 3 columns always in the same order - """ if df is None: df = pandas.DataFrame() df["score"] = y_score @@ -60,17 +55,14 @@ def __init__(self, y_true=None, y_score=None, sample_weight=None, df=None): if len(df[0]) == 2: self.data = pandas.DataFrame(df, columns=["score", "label"]) else: - self.data = pandas.DataFrame( - df, columns=["score", "label", "weight"]) + self.data = pandas.DataFrame(df, columns=["score", "label", "weight"]) elif isinstance(df, numpy.ndarray): if df.shape[1] == 2: self.data = pandas.DataFrame(df, columns=["score", "label"]) else: - self.data = pandas.DataFrame( - df, columns=["score", "label", "weight"]) + self.data = pandas.DataFrame(df, columns=["score", "label", "weight"]) elif not isinstance(df, pandas.DataFrame): - raise TypeError( - "df should be a DataFrame, not {0}".format(type(df))) + raise TypeError(f"df should be a DataFrame, not {type(df)}") else: self.data = df.copy() self.data.sort_values(self.data.columns[0], inplace=True) @@ -102,8 +94,7 @@ def __str__(self): Shows first elements, precision rate. """ rows = [] - rows.append("Overall precision: %3.2f - AUC=%f" % - (self.precision(), self.auc())) + rows.append(f"Overall precision: {self.precision():3.2f} - AUC={self.auc():f}") rows.append("--------------") rows.append(str(self.data.head(min(5, len(self))))) rows.append("--------------") @@ -133,7 +124,6 @@ def confusion(self, score=None, nb=10, curve=CurveType.ROC, bootstrap=False): cloud = self.random_cloud() if score is None: - sum_weights = cloud[cloud.columns[2]].sum() if nb <= 0: nb = len(cloud) @@ -153,10 +143,17 @@ def confusion(self, score=None, nb=10, curve=CurveType.ROC, bootstrap=False): pos_roc = 0 pos_seuil = 0 if curve is ROC.CurveType.ROC: - roc = pandas.DataFrame(0, index=numpy.arange( - nb + 2), columns=["True Positive", "False Positive", - "False Negative", "True Negative", - "threshold"]) + roc = pandas.DataFrame( + 0, + index=numpy.arange(nb + 2), + columns=[ + "True Positive", + "False Positive", + "False Negative", + "True Negative", + "threshold", + ], + ) sum_good_weights = cloud.iloc[-1, 5] sum_bad_weights = sum_weights - sum_good_weights roc.iloc[0, 0] = 0 @@ -182,24 +179,25 @@ def confusion(self, score=None, nb=10, curve=CurveType.ROC, bootstrap=False): roc.iloc[pos_roc:, 3] = 0 roc.iloc[pos_roc:, 4] = min(cloud.iloc[:, 0]) return roc - else: - raise NotImplementedError( - "Unexpected type '{0}', only ROC is allowed.".format(curve)) - else: - roc = self.confusion(nb=len(self), curve=curve, - bootstrap=False, score=None) - roc = roc[roc["threshold"] <= score] - if len(roc) == 0: - raise ValueError( - "The requested confusion is empty for score={0}.".format(score)) - return roc[:1] + raise NotImplementedError( + f"Unexpected type '{curve}', only ROC is allowed." + ) + + # if score is not None + roc = self.confusion(nb=len(self), curve=curve, bootstrap=False, score=None) + roc = roc[roc["threshold"] <= score] + if len(roc) == 0: + raise ValueError(f"The requested confusion is empty for score={score}.") + return roc[:1] def precision(self): """ Computes the precision. """ score, weight = self.data.columns[0], self.data.columns[2] - return (self.data[score] * self.data[weight] * 1.0).sum() / self.data[weight].sum() + return (self.data[score] * self.data[weight] * 1.0).sum() / self.data[ + weight + ].sum() def compute_roc_curve(self, nb=100, curve=CurveType.ROC, bootstrap=False): """ @@ -210,7 +208,7 @@ def compute_roc_curve(self, nb=100, curve=CurveType.ROC, bootstrap=False): @param nb number of points for the curve @param curve see :class:`CurveType ` - @param boostrap builds the curve after resampling + @param bootstrap builds the curve after resampling @return DataFrame (metrics and threshold) If *curve* is *SKROC*, the parameter *nb* is not taken into account. @@ -218,14 +216,16 @@ def compute_roc_curve(self, nb=100, curve=CurveType.ROC, bootstrap=False): """ if curve is ROC.CurveType.ERRREC: roc = self.compute_roc_curve( - nb=nb, curve=ROC.CurveType.RECPREC, bootstrap=bootstrap) - roc["error"] = - roc["precision"] + 1 + nb=nb, curve=ROC.CurveType.RECPREC, bootstrap=bootstrap + ) + roc["error"] = -roc["precision"] + 1 return roc[["error", "recall", "threshold"]] - elif curve is ROC.CurveType.PROBSCORE: + if curve is ROC.CurveType.PROBSCORE: roc = self.compute_roc_curve( - nb=nb, curve=ROC.CurveType.ROC, bootstrap=bootstrap) + nb=nb, curve=ROC.CurveType.ROC, bootstrap=bootstrap + ) roc["P(->s)"] = roc["False Positive Rate"] - roc["P(+s)", "threshold"]] if not bootstrap: @@ -237,11 +237,17 @@ def compute_roc_curve(self, nb=100, curve=CurveType.ROC, bootstrap=False): if nb > 0: raise NotImplementedError("nb must be <= 0 si curve is SKROC") from sklearn.metrics import roc_curve - fpr, tpr, thresholds = roc_curve(y_true=cloud[cloud.columns[1]], - y_score=cloud[cloud.columns[0]], - sample_weight=cloud[cloud.columns[2]]) - roc = pandas.DataFrame(0, index=numpy.arange(len(fpr)), - columns=["False Positive Rate", "True Positive Rate", "threshold"]) + + fpr, tpr, thresholds = roc_curve( + y_true=cloud[cloud.columns[1]], + y_score=cloud[cloud.columns[0]], + sample_weight=cloud[cloud.columns[2]], + ) + roc = pandas.DataFrame( + 0, + index=numpy.arange(len(fpr)), + columns=["False Positive Rate", "True Positive Rate", "threshold"], + ) roc_cols = list(roc.columns) roc[roc_cols[0]] = fpr roc[roc_cols[1]] = tpr @@ -269,8 +275,11 @@ def compute_roc_curve(self, nb=100, curve=CurveType.ROC, bootstrap=False): pos_seuil = 0 if curve is ROC.CurveType.ROC: - roc = pandas.DataFrame(0, index=numpy.arange( - nb + 1), columns=["False Positive Rate", "True Positive Rate", "threshold"]) + roc = pandas.DataFrame( + 0, + index=numpy.arange(nb + 1), + columns=["False Positive Rate", "True Positive Rate", "threshold"], + ) sum_good_weights = cloud.iloc[-1, 5] sum_bad_weights = sum_weights - sum_good_weights for i in range(len(cloud)): @@ -282,19 +291,22 @@ def compute_roc_curve(self, nb=100, curve=CurveType.ROC, bootstrap=False): pos_roc += 1 pos_seuil += 1 roc.iloc[pos_roc:, 0] = ( - cloud.iloc[-1, 4] - cloud.iloc[-1, 5]) / sum_bad_weights + cloud.iloc[-1, 4] - cloud.iloc[-1, 5] + ) / sum_bad_weights roc.iloc[pos_roc:, 1] = cloud.iloc[-1, 5] / sum_good_weights roc.iloc[pos_roc:, 2] = cloud.iloc[-1, 0] elif curve is ROC.CurveType.RECPREC: - roc = pandas.DataFrame(0, index=numpy.arange( - nb + 1), columns=["recall", "precision", "threshold"]) + roc = pandas.DataFrame( + 0, + index=numpy.arange(nb + 1), + columns=["recall", "precision", "threshold"], + ) for i in range(len(cloud)): if cloud.iloc[i, 4] > seuil[pos_seuil]: roc.iloc[pos_roc, 0] = cloud.iloc[i, 4] / sum_weights if cloud.iloc[i, 4] > 0: - roc.iloc[pos_roc, 1] = cloud.iloc[ - i, 5] / cloud.iloc[i, 4] + roc.iloc[pos_roc, 1] = cloud.iloc[i, 5] / cloud.iloc[i, 4] else: roc.iloc[pos_roc, 1] = 0.0 roc.iloc[pos_roc, 2] = cloud.iloc[i, 0] @@ -305,7 +317,7 @@ def compute_roc_curve(self, nb=100, curve=CurveType.ROC, bootstrap=False): roc.iloc[pos_roc:, 2] = cloud.iloc[-1, 0] else: - raise NotImplementedError("Unknown curve type '{}'.".format(curve)) + raise NotImplementedError(f"Unknown curve type '{curve}'.") return roc @@ -315,18 +327,26 @@ def random_cloud(self): @return DataFrame """ - res = self.data.sample(len(self.data), weights=self.data[ - self.data.columns[2]], replace=True) + res = self.data.sample( + len(self.data), weights=self.data[self.data.columns[2]], replace=True + ) return res.sort_values(res.columns[0]) - def plot(self, nb=100, curve=CurveType.ROC, bootstrap=0, - ax=None, thresholds=False, **kwargs): + def plot( + self, + nb=100, + curve=CurveType.ROC, + bootstrap=0, + ax=None, + thresholds=False, + **kwargs, + ): """ Plots a :epkg:`ROC` curve. @param nb number of points @param curve see :class:`CurveType ` - @param boostrap number of curves for the boostrap (0 for None) + @param bootstrap number of curves for the boostrap (0 for None) @param ax axis @param thresholds use thresholds for the X axis @param kwargs sent to `pandas.plot `_ @@ -335,43 +355,50 @@ def plot(self, nb=100, curve=CurveType.ROC, bootstrap=0, nb_bootstrap = 0 if bootstrap > 0: ckwargs = kwargs.copy() - if 'color' not in ckwargs: - ckwargs['color'] = 'r' - if 'linewidth' not in kwargs: - ckwargs['linewidth'] = 0.2 - ckwargs['legend'] = False - if 'label' in ckwargs: - del ckwargs['label'] - for _ in range(0, bootstrap): + if "color" not in ckwargs: + ckwargs["color"] = "r" + if "linewidth" not in kwargs: + ckwargs["linewidth"] = 0.2 + ckwargs["legend"] = False + if "label" in ckwargs: + del ckwargs["label"] + for _ in range(bootstrap): roc = self.compute_roc_curve(nb, curve=curve, bootstrap=True) if thresholds: - cols = list(_ for _ in roc.columns if _ != "threshold") + cols = [_ for _ in roc.columns if _ != "threshold"] roc = roc.sort_values("threshold").reset_index(drop=True) - ax = roc.plot(x="threshold", y=cols, - ax=ax, label=['_nolegend_' for i in cols], **ckwargs) + ax = roc.plot( + x="threshold", + y=cols, + ax=ax, + label=["_nolegend_" for i in cols], + **ckwargs, + ) else: - cols = list(_ for _ in roc.columns[ - 1:] if _ != "threshold") - roc = roc.sort_values( - roc.columns[0]).reset_index(drop=True) - ax = roc.plot(x=roc.columns[0], y=cols, - ax=ax, label=['_nolegend_' for i in cols], **ckwargs) + cols = [_ for _ in roc.columns[1:] if _ != "threshold"] + roc = roc.sort_values(roc.columns[0]).reset_index(drop=True) + ax = roc.plot( + x=roc.columns[0], + y=cols, + ax=ax, + label=["_nolegend_" for i in cols], + **ckwargs, + ) nb_bootstrap += len(cols) bootstrap = 0 if bootstrap <= 0: - if 'legend' not in kwargs: - kwargs['legend'] = False + if "legend" not in kwargs: + kwargs["legend"] = False roc = self.compute_roc_curve(nb, curve=curve) if not thresholds: roc = roc[[_ for _ in roc.columns if _ != "threshold"]] - cols = list(_ for _ in roc.columns if _ != "threshold") + cols = [_ for _ in roc.columns if _ != "threshold"] final = 0 if thresholds: - if 'label' in kwargs and len(cols) != len(kwargs['label']): - raise ValueError( - 'label must have {0} values'.format(len(cols))) + if "label" in kwargs and len(cols) != len(kwargs["label"]): + raise ValueError(f"label must have {len(cols)} values") roc = roc.sort_values("threshold").reset_index(drop=True) ax = roc.plot(x="threshold", y=cols, ax=ax, **kwargs) ax.set_ylim([0, 1]) @@ -379,9 +406,8 @@ def plot(self, nb=100, curve=CurveType.ROC, bootstrap=0, final += len(cols) diag = 0 else: - if 'label' in kwargs and len(cols) - 1 != len(kwargs['label']): - raise ValueError( - 'label must have {0} values'.format(len(cols) - 1)) + if "label" in kwargs and len(cols) - 1 != len(kwargs["label"]): + raise ValueError(f"label must have {len(cols) - 1} values") final += len(cols) - 1 roc = roc.sort_values(cols[0]).reset_index(drop=True) ax = roc.plot(x=cols[0], y=cols[1:], ax=ax, **kwargs) @@ -410,9 +436,9 @@ def auc(self, cloud=None): """ Computes the area under the curve (:epkg:`AUC`). - @param cloud data or None to use ``self.data``, the function - assumes the data is sorted. - @return AUC + :param cloud: data or None to use ``self.data``, the function + assumes the data is sorted. + :return: AUC The first column is the label, the second one is the score, the third one is the weight. @@ -428,8 +454,11 @@ def auc(self, cloud=None): elif a[0] >= b[0]: auc += a[2] * b[2] / 2 if auc == 0 and good.shape[0] + wrong.shape[0] < self.data.shape[0]: - raise ValueError("Label are not right, expect 0 and 1 not {0}".format( - set(cloud[cloud.columns[1]]))) + raise ValueError( + "Label are not right, expect 0 and 1 not {0}".format( + set(cloud[cloud.columns[1]]) + ) + ) n = len(wrong) * len(good) if n > 0: auc /= float(n) @@ -439,14 +468,14 @@ def auc_interval(self, bootstrap=10, alpha=0.95): """ Determines a confidence interval for the :epkg:`AUC` with bootstrap. - @param bootstrap number of random estimation + @param bootstrap number of random estimations @param alpha define the confidence interval @return dictionary of values """ if bootstrap <= 1: raise ValueError("Use auc instead, bootstrap < 2") rate = [] - for _ in range(0, bootstrap): + for _ in range(bootstrap): cloud = self.random_cloud() auc = self.auc(cloud) rate.append(auc) @@ -463,9 +492,15 @@ def auc_interval(self, bootstrap=10, alpha=0.95): var += r * r var = float(var) / len(rate) var = var - moy * moy - return dict(auc=ra, interval=(rate[i1], rate[i2]), - min=rate[0], max=rate[len(rate) - 1], - mean=moy, var=math.sqrt(var), mediane=med) + return dict( + auc=ra, + interval=(rate[i1], rate[i2]), + min=rate[0], + max=rate[len(rate) - 1], + mean=moy, + var=math.sqrt(var), + mediane=med, + ) def roc_intersect(self, roc, x): """ @@ -490,22 +525,25 @@ def roc_intersect(self, roc, x): if p1[0] == p2[0]: return (p1[1] + p2[0]) / 2 - else: - return (x - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1] + return (x - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1] - def roc_intersect_interval(self, x, nb, curve=CurveType.ROC, bootstrap=10, alpha=0.05): + def roc_intersect_interval( + self, x, nb, curve=CurveType.ROC, bootstrap=10, alpha=0.05 + ): """ Computes a confidence interval for the value returned by @see me roc_intersect. - @param roc ROC curve @param x x + @param nb number of curves to draw @param curve see :class:`CurveType ` + @param bootstrap number of random estimations + @param alpha confidence interval @return dictionary """ rate = [] - for _ in range(0, bootstrap): + for _ in range(bootstrap): roc = self.compute_roc_curve(nb, curve=curve, bootstrap=True) r = self.roc_intersect(roc, x) rate.append(r) @@ -524,6 +562,12 @@ def roc_intersect_interval(self, x, nb, curve=CurveType.ROC, bootstrap=10, alpha var += r * r var = float(var) / len(rate) var = var - moy * moy - return dict(y=ra, interval=(rate[i1], rate[i2]), - min=rate[0], max=rate[len(rate) - 1], - mean=moy, var=math.sqrt(var), mediane=med) + return dict( + y=ra, + interval=(rate[i1], rate[i2]), + min=rate[0], + max=rate[len(rate) - 1], + mean=moy, + var=math.sqrt(var), + mediane=med, + ) diff --git a/src/mlstatpy/ml/voronoi.py b/mlstatpy/ml/voronoi.py similarity index 76% rename from src/mlstatpy/ml/voronoi.py rename to mlstatpy/ml/voronoi.py index 7e2c8f0b..1f5e0890 100644 --- a/src/mlstatpy/ml/voronoi.py +++ b/mlstatpy/ml/voronoi.py @@ -1,8 +1,3 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief About Voronoi Diagram -""" import warnings import numpy from sklearn.linear_model import LinearRegression @@ -14,10 +9,11 @@ class VoronoiEstimationError(Exception): """ Raised when the algorithm failed. """ - pass -def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=None, verbose=False): +def voronoi_estimation_from_lr( + L, B, C=None, D=None, cl=0, qr=True, max_iter=None, verbose=False +): """ Determines a Voronoi diagram close to a convex partition defined by a logistic regression in *n* classes. @@ -44,9 +40,11 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non .. math:: \\begin{array}{rcl} - & \\Longrightarrow & \\left\\{\\begin{array}{l}\\scal{\\frac{L_i-L_j}{\\norm{L_i-L_j}}}{P_i + P_j} + + & \\Longrightarrow & \\left\\{\\begin{array}{l} + \\scal{\\frac{L_i-L_j}{\\norm{L_i-L_j}}}{P_i + P_j} + 2 \\frac{B_i - B_j}{\\norm{L_i-L_j}} = 0 \\\\ - \\scal{P_i- P_j}{u_{ij}} - \\scal{P_i - P_j}{\\frac{L_i-L_j}{\\norm{L_i-L_j}}} \\scal{\\frac{L_i-L_j}{\\norm{L_i-L_j}}}{u_{ij}}=0 + \\scal{P_i- P_j}{u_{ij}} - \\scal{P_i - P_j}{\\frac{L_i-L_j} + {\\norm{L_i-L_j}}} \\scal{\\frac{L_i-L_j}{\\norm{L_i-L_j}}}{u_{ij}}=0 \\end{array} \\right. \\end{array} @@ -57,7 +55,7 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non is the Voronoï point attached to class *cl*. `Quantile regression `_ is not implemented in :epkg:`scikit-learn`. - We use `QuantileLinearRegression `_. + We use :epkg:`QuantileLinearRegression`. After the first iteration, the function determines the furthest pair of points and removes it from the list @@ -71,11 +69,11 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non nb_constraints = numpy.zeros((L.shape[0],)) matL = [] matB = [] - for i in range(0, L.shape[0]): + for i in range(L.shape[0]): for j in range(i + 1, L.shape[0]): li = L[i, :] lj = L[j, :] - c = (li - lj) + c = li - lj nc = (c.T @ c) ** 0.5 # first condition @@ -85,7 +83,7 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non d = -2 * (B[i] - B[j]) matB.append(d) matL.append(mat.ravel()) - labels_inv[i, j, 'eq1'] = len(matL) - 1 + labels_inv[i, j, "eq1"] = len(matL) - 1 nb_constraints[i] += 1 nb_constraints[j] += 1 @@ -103,7 +101,9 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non found = True if not found: raise ValueError( - "Matrix L has two similar rows {0} and {1}. Problem cannot be solved.".format(i, j)) + "Matrix L has two similar rows {0} and {1}. " + "Problem cannot be solved.".format(i, j) + ) c /= nc c2 = c * c[coor] @@ -115,7 +115,7 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non mat[j, coor] -= 1 matB.append(0) matL.append(mat.ravel()) - labels_inv[i, j, 'eq2'] = len(matL) - 1 + labels_inv[i, j, "eq2"] = len(matL) - 1 nb_constraints[i] += 1 nb_constraints[j] += 1 @@ -129,30 +129,31 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non if nbeq * 2 <= L.shape[0] * L.shape[1]: if C is None and D is None: warnings.warn( - "[voronoi_estimation_from_lr] Additional condition are required.") + "[voronoi_estimation_from_lr] Additional condition are required.", + stacklevel=0, + ) if C is not None and D is not None: matL = numpy.vstack([matL, numpy.zeros((1, matL.shape[1]))]) a = cl * L.shape[1] b = a + L.shape[1] matL[-1, a:b] = C if not isinstance(D, float): - raise TypeError("D must be a float not {0}".format(type(D))) + raise TypeError(f"D must be a float not {type(D)}") matB = numpy.hstack([matB, [D]]) elif C is None and D is None: pass else: - raise ValueError( - "C and D must be None together or not None together.") + raise ValueError("C and D must be None together or not None together.") sample_weight = numpy.ones((matL.shape[0],)) tol = numpy.abs(matL.ravel()).max() * 1e-8 / matL.shape[0] order_removed = [] removed = set() - for it in range(0, max(max_iter, 1)): - + for it in range(max(max_iter, 1)): if qr: clr = QuantileLinearRegression( - fit_intercept=False, max_iter=max(matL.shape)) + fit_intercept=False, max_iter=max(matL.shape) + ) else: clr = LinearRegression(fit_intercept=False) @@ -165,22 +166,26 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non # early stopping if score < tol: if verbose: - print('[voronoi_estimation_from_lr] iter={0}/{1} score={2} tol={3}'.format( - it + 1, max_iter, score, tol)) + print( + "[voronoi_estimation_from_lr] iter={0}/{1} " + "score={2} tol={3}".format(it + 1, max_iter, score, tol) + ) break # defines the best pair of points to remove dist2 = pairwise_distances(res, res) - dist = [(d, n // dist2.shape[0], n % dist2.shape[1]) - for n, d in enumerate(dist2.ravel())] + dist = [ + (d, n // dist2.shape[0], n % dist2.shape[1]) + for n, d in enumerate(dist2.ravel()) + ] dist = [_ for _ in dist if _[1] < _[2]] dist.sort(reverse=True) # test equal points if dist[-1][0] < tol: _, i, j = dist[-1] - eq1 = labels_inv[i, j, 'eq1'] - eq2 = labels_inv[i, j, 'eq2'] + eq1 = labels_inv[i, j, "eq1"] + eq2 = labels_inv[i, j, "eq2"] if sample_weight[eq1] == 0 and sample_weight[eq2] == 0: sample_weight[eq1] = 1 sample_weight[eq2] = 1 @@ -192,8 +197,8 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non while pos >= 0: i, j = order_removed[pos] if i in keep or j in keep: - eq1 = labels_inv[i, j, 'eq1'] - eq2 = labels_inv[i, j, 'eq2'] + eq1 = labels_inv[i, j, "eq1"] + eq2 = labels_inv[i, j, "eq2"] if sample_weight[eq1] == 0 and sample_weight[eq2] == 0: sample_weight[eq1] = 1 sample_weight[eq2] = 1 @@ -202,9 +207,11 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non break pos -= 1 if pos < 0: - forma = 'Two classes have been merged in a single Voronoi point (dist={0} < {1}). max_iter should be lower than {2}' raise VoronoiEstimationError( - forma.format(dist[-1][0], tol, it)) + "Two classes have been merged in a single Voronoi point " + "(dist={0} < {1}). max_iter should be lower than " + "{2}".format(dist[-1][0], tol, it) + ) dmax, i, j = dist[0] pos = 0 @@ -217,15 +224,19 @@ def voronoi_estimation_from_lr(L, B, C=None, D=None, cl=0, qr=True, max_iter=Non break removed.add((i, j)) order_removed.append((i, j)) - eq1 = labels_inv[i, j, 'eq1'] - eq2 = labels_inv[i, j, 'eq2'] + eq1 = labels_inv[i, j, "eq1"] + eq2 = labels_inv[i, j, "eq2"] sample_weight[eq1] = 0 sample_weight[eq2] = 0 nb_constraints[i] -= 1 nb_constraints[j] -= 1 if verbose: - print('[voronoi_estimation_from_lr] iter={0}/{1} score={2:.3g} tol={3:.3g} del P{4},{5} d={6:.3g}'.format( - it + 1, max_iter, score, tol, i, j, dmax)) + print( + "[voronoi_estimation_from_lr] iter={0}/{1} " + "score={2:.3g} tol={3:.3g} del P{4},{5} d={6:.3g}".format( + it + 1, max_iter, score, tol, i, j, dmax + ) + ) return res diff --git a/src/mlstatpy/nlp/__init__.py b/mlstatpy/nlp/__init__.py similarity index 79% rename from src/mlstatpy/nlp/__init__.py rename to mlstatpy/nlp/__init__.py index a4fc1a43..510d8838 100644 --- a/src/mlstatpy/nlp/__init__.py +++ b/mlstatpy/nlp/__init__.py @@ -1,8 +1,3 @@ -""" -@file -@brief shortcut to nlp -""" - from .completion import CompletionTrieNode from .completion_simple import CompletionElement, CompletionSystem from .normalize import remove_diacritics diff --git a/src/mlstatpy/nlp/completion.py b/mlstatpy/nlp/completion.py similarity index 77% rename from src/mlstatpy/nlp/completion.py rename to mlstatpy/nlp/completion.py index 043cce1b..768cdbb6 100644 --- a/src/mlstatpy/nlp/completion.py +++ b/mlstatpy/nlp/completion.py @@ -1,7 +1,3 @@ -""" -@file -@brief About completion -""" from typing import Tuple, List, Iterator from collections import deque @@ -14,8 +10,15 @@ class CompletionTrieNode: It should be done another way (:epkg:`cython`, :epkg:`C++`). """ - __slots__ = ("value", "children", "weight", - "leave", "stat", "parent", "disp") + __slots__ = ( # noqa: RUF023 + "value", + "children", + "weight", + "leave", + "stat", + "parent", + "disp", + ) def __init__(self, value, leave, weight=1.0, disp=None): """ @@ -25,8 +28,7 @@ def __init__(self, value, leave, weight=1.0, disp=None): @param disp original string, use this to identify the node """ if not isinstance(value, str): - raise TypeError( - "value must be str not '{0}' - type={1}".format(value, type(value))) + raise TypeError(f"value must be str not '{value}' - type={type(value)}") self.value = value self.children = None self.weight = weight @@ -49,7 +51,7 @@ def __str__(self): """ usual """ - return "[{2}:{0}:w={1}]".format(self.value, self.weight, "#" if self.leave else "-") + return f"[{'#' if self.leave else '-'}:{self.value}:w={self.weight}]" def _add(self, key, child): """ @@ -63,13 +65,13 @@ def _add(self, key, child): self.children = {key: child} child.parent = self elif key in self.children: - raise KeyError("'{0}' already added".format(key)) + raise KeyError(f"'{key}' already added") else: self.children[key] = child child.parent = self return self - def items_list(self) -> List['CompletionTrieNode']: + def items_list(self) -> List["CompletionTrieNode"]: """ All children nodes inluding itself in a list. @@ -91,8 +93,7 @@ def __iter__(self): node = stack.pop() yield node if node.children: - stack.extend(v for k, v in sorted( - node.children.items(), reverse=True)) + stack.extend(v for k, v in sorted(node.children.items(), reverse=True)) def unsorted_iter(self): """ @@ -105,7 +106,7 @@ def unsorted_iter(self): if node.children: stack.extend(node.children.values()) - def items(self) -> Iterator[Tuple[float, str, 'CompletionTrieNode']]: + def items(self) -> Iterator[Tuple[float, str, "CompletionTrieNode"]]: """ Iterates on children, iterates on weight, key, child. """ @@ -119,17 +120,18 @@ def iter_leaves(self, max_weight=None) -> Iterator[Tuple[float, str]]: @param max_weight keep all value under this threshold or None for all """ + def iter_local(node): if node.leave and (max_weight is None or node.weight <= max_weight): yield node.weight, None, node.value - for w, k, v in sorted(node.items()): - for w_, k_, v_ in iter_local(v): + for _w, _k, v in sorted(node.items()): + for w_, k_, v_ in iter_local(v): # noqa: UP028 yield w_, k_, v_ for w, _, v in sorted(iter_local(self)): yield w, v - def leaves(self) -> Iterator['CompletionTrieNode']: + def leaves(self) -> Iterator["CompletionTrieNode"]: """ Iterators on leaves. """ @@ -141,7 +143,7 @@ def leaves(self) -> Iterator['CompletionTrieNode']: if pop.children: stack.extend(pop.children.values()) - def all_completions(self) -> List[Tuple['CompletionTrieNone', List[str]]]: + def all_completions(self) -> List[Tuple["CompletionTrieNode", List[str]]]: """ Retrieves all completions for a node, the method does not need @see me precompute_stat to be run first. @@ -156,12 +158,14 @@ def all_completions(self) -> List[Tuple['CompletionTrieNone', List[str]]]: nodes.reverse() all_res = [] for node in nodes: - res = list(n[1] for n in node.iter_leaves()) + res = [n[1] for n in node.iter_leaves()] all_res.append((node, res)) all_res.reverse() return all_res - def all_mks_completions(self) -> List[Tuple['CompletionTrieNone', List['CompletionTrieNone']]]: + def all_mks_completions( + self, + ) -> List[Tuple["CompletionTrieNode", List["CompletionTrieNode"]]]: """ Retrieves all completions for a node, the method assumes @see me precompute_stat was run. @@ -181,34 +185,41 @@ def str_all_completions(self, maxn=10, use_precompute=True) -> str: Builds a string with all completions for all prefixes along the paths. - @param maxn maximum number of completions to show - @param use_precompute use intermediate results built by @see me precompute_stat - @return str + :param maxn: maximum number of completions to show + :param use_precompute: use intermediate results + built by @see me precompute_stat + :return: str """ res = self.all_mks_completions() if use_precompute else self.all_completions() rows = [] for node, sug in res: - rows.append("l={3} p='{0}' {1} {2}".format(node.value, "-" * 10, node.stat.str_mks(), - '+' if node.leave else '-')) + rows.append( + "l={3} p='{0}' {1} {2}".format( + node.value, + "-" * 10, + node.stat.str_mks(), + "+" if node.leave else "-", + ) + ) for i, s in enumerate(sug): if isinstance(s, str): - rows.append(" {0}-'{1}'".format(i + 1, s)) + rows.append(f" {i + 1}-'{s}'") else: - rows.append( - " {0}-w{1}-'{2}'".format(i + 1, s[0], s[1].value)) + rows.append(f" {i + 1}-w{s[0]}-'{s[1].value}'") if maxn is not None and i > maxn: break return "\n".join(rows) @staticmethod - def build(words) -> 'CompletionTrieNode': + def build(words) -> "CompletionTrieNode": """ Builds a trie. - @param words list of ``(word)`` or ``(weight, word)`` or ``(weight, word, display string)`` - @return root of the trie (CompletionTrieNode) + :param words: list of ``(word)`` or ``(weight, word)`` + or ``(weight, word, display string)`` + :return: root of the trie (CompletionTrieNode) """ - root = CompletionTrieNode('', False) + root = CompletionTrieNode("", False) nb = 0 minw = None for wword in words: @@ -220,7 +231,10 @@ def build(words) -> 'CompletionTrieNode': w, word, disp = wword else: raise ValueError( - "Unexpected number of values, it should be (weight, word) or (weight, word, dispplay string): {0}".format(wword)) + f"Unexpected number of values, it should be " + f"(weight, word) or (weight, word, " + f"dispplay string): {wword}" + ) else: w = 1.0 word = wword @@ -237,36 +251,37 @@ def build(words) -> 'CompletionTrieNode': node.weight = min(node.weight, w) node = node.children[c] else: - new_node = CompletionTrieNode( - node.value + c, False, weight=w) + new_node = CompletionTrieNode(node.value + c, False, weight=w) node._add(c, new_node) node = new_node if new_node is None: if node.leave: raise ValueError( - "Value '{0}' appears twice in the input list (not allowed).".format(word)) + f"Value '{word}' appears twice in the input list (not allowed)." + ) new_node = node new_node.leave = True new_node.weight = w if disp is not None: new_node.disp = disp - nb += 1 + nb += 1 # noqa: SIM113 root.weight = minw return root - def find(self, prefix: str) -> 'CompletionTrieNode': + def find(self, prefix: str) -> "CompletionTrieNode": """ Returns the node which holds all completions starting with a given prefix. - @param prefix prefix - @return node or None for no result + :param prefix: prefix + :return: node or None for no result """ - if len(prefix) == 0: + if not prefix: if not self.value: return self else: raise ValueError( - "find '{0}' but node is not empty '{1}'".format(prefix, self.value)) + f"find '{prefix}' but node is not empty '{self.value}'" + ) node = self for c in prefix: if node.children is not None and c in node.children: @@ -291,7 +306,7 @@ def min_keystroke(self, word: str) -> Tuple[int, int]: \\begin{eqnarray*} K(q, k, S) &=& \\min\\acc{ i | s_i \\succ q[1..k], s_i \\in S } \\\\ - M(q, S) &=& \\min_{0 \\infegal k \\infegal l(q)} k + K(q, k, S) + M(q, S) &=& \\min_{0 \\leqslant k \\leqslant l(q)} k + K(q, k, S) \\end{eqnarray*} """ nodes = [self] @@ -307,7 +322,7 @@ def min_keystroke(self, word: str) -> Tuple[int, int]: metric = len(word) best = len(word) for node in nodes[1:]: - res = list(n[1] for n in node.iter_leaves()) + res = [n[1] for n in node.iter_leaves()] ind = res.index(word) m = len(node.value) + ind + 1 if m < metric: @@ -322,11 +337,11 @@ def min_keystroke0(self, word: str) -> Tuple[int, int]: """ Returns the minimum keystrokes for a word. - @param word word - @return number, length of best prefix, iteration it stops moving + :param word: word + :return: number, length of best prefix, iteration it stops moving - This function must be called after @see me precompute_stat - and @see me update_stat_dynamic. + This function must be called after :meth:`precompute_stat` + and :meth:`update_stat_dynamic`. See :ref:`l-completion-optim`. @@ -335,18 +350,23 @@ def min_keystroke0(self, word: str) -> Tuple[int, int]: \\begin{eqnarray*} K(q, k, S) &=& \\min\\acc{ i | s_i \\succ q[1..k], s_i \\in S } \\\\ - M(q, S) &=& \\min_{0 \\infegal k \\infegal l(q)} k + K(q, k, S) + M(q, S) &=& \\min_{0 \\leqslant k \\leqslant l(q)} k + K(q, k, S) \\end{eqnarray*} """ node = self.find(word) if node is None: raise NotImplementedError( - "this metric is not yet computed for a query outside the trie: '{0}'".format(word)) + f"this metric is not yet computed for a " + f"query outside the trie: '{word}'" + ) if not hasattr(node, "stat"): raise AttributeError("run precompute_stat and update_stat_dynamic") if not hasattr(node.stat, "mks1"): - raise AttributeError("run precompute_stat and update_stat_dynamic\nnode={0}\n{1}".format( - self, "\n".join(sorted(self.stat.__dict__.keys())))) + raise AttributeError( + "run precompute_stat and update_stat_dynamic\nnode={0}\n{1}".format( + self, "\n".join(sorted(self.stat.__dict__.keys())) + ) + ) return node.stat.mks0, node.stat.mks0_, 0 def min_dynamic_keystroke(self, word: str) -> Tuple[int, int]: @@ -365,18 +385,24 @@ def min_dynamic_keystroke(self, word: str) -> Tuple[int, int]: \\begin{eqnarray*} K(q, k, S) &=& \\min\\acc{ i | s_i \\succ q[1..k], s_i \\in S } \\\\ - M'(q, S) &=& \\min_{0 \\infegal k \\infegal l(q)} \\acc{ M'(q[1..k], S) + K(q, k, S) | q[1..k] \\in S } + M'(q, S) &=& \\min_{0 \\leqslant k \\leqslant l(q)} + \\acc{ M'(q[1..k], S) + K(q, k, S) | q[1..k] \\in S } \\end{eqnarray*} """ node = self.find(word) if node is None: raise NotImplementedError( - "this metric is not yet computed for a query outside the trie: '{0}'".format(word)) + f"this metric is not yet computed for " + f"a query outside the trie: '{word}'" + ) if not hasattr(node, "stat"): raise AttributeError("run precompute_stat and update_stat_dynamic") if not hasattr(node.stat, "mks1"): - raise AttributeError("run precompute_stat and update_stat_dynamic\nnode={0}\n{1}".format( - self, "\n".join(sorted(self.stat.__dict__.keys())))) + raise AttributeError( + "run precompute_stat and update_stat_dynamic\nnode={0}\n{1}".format( + self, "\n".join(sorted(self.stat.__dict__.keys())) + ) + ) return node.stat.mks1, node.stat.mks1_, node.stat.mks1i_ def min_dynamic_keystroke2(self, word: str) -> Tuple[int, int]: @@ -387,7 +413,7 @@ def min_dynamic_keystroke2(self, word: str) -> Tuple[int, int]: @return number, length of best prefix, iteration it stops moving This function must be called after @see me precompute_stat - and @see me update_stat_dynamic. + and :meth`update_stat_dynamic`. See :ref:`Modified Dynamic Minimum Keystroke `. .. math:: @@ -396,20 +422,29 @@ def min_dynamic_keystroke2(self, word: str) -> Tuple[int, int]: \\begin{eqnarray*} K(q, k, S) &=& \\min\\acc{ i | s_i \\succ q[1..k], s_i \\in S } \\\\ M"(q, S) &=& \\min \\left\\{ \\begin{array}{l} - \\min_{1 \\infegal k \\infegal l(q)} \\acc{ M"(q[1..k-1], S) + 1 + K(q, k, S) | q[1..k] \\in S } \\\\ - \\min_{0 \\infegal k \\infegal l(q)} \\acc{ M"(q[1..k], S) + \\delta + K(q, k, S) | q[1..k] \\in S } + \\min_{1 \\leqslant k \\leqslant l(q)} + \\acc{ M"(q[1..k-1], S) + 1 + K(q, k, S) | q[1..k] + \\in S } \\\\ + \\min_{0 \\leqslant k \\leqslant l(q)} + \\acc{ M"(q[1..k], S) + \\delta + K(q, k, S) | q[1..k] + \\in S } \\end{array} \\right . \\end{eqnarray*} """ node = self.find(word) if node is None: raise NotImplementedError( - "this metric is not yet computed for a query outside the trie: '{0}'".format(word)) + f"this metric is not yet computed for a " + f"query outside the trie: '{word}'" + ) if not hasattr(node, "stat"): raise AttributeError("run precompute_stat and update_stat_dynamic") if not hasattr(node.stat, "mks2"): - raise AttributeError("run precompute_stat and update_stat_dynamic\nnode={0}\n{1}".format( - self, "\n".join(sorted(self.stat.__dict__.keys())))) + raise AttributeError( + "run precompute_stat and update_stat_dynamic\nnode={0}\n{1}".format( + self, "\n".join(sorted(self.stat.__dict__.keys())) + ) + ) return node.stat.mks2, node.stat.mks2_, node.stat.mks2i_ def precompute_stat(self): @@ -452,9 +487,9 @@ def update_stat_dynamic(self, delta=0.8): Must be called after @see me precompute_stat and computes dynamic mks (see :ref:`Dynamic Minimum Keystroke `). - @param delta parameter :math:`\\delta` in defintion - :ref:`Modified Dynamic KeyStroke ` - @return number of iterations to converge + :param delta: parameter :math:`\\delta` in defintion + :ref:`Modified Dynamic KeyStroke ` + :return: number of iterations to converge """ for node in self.unsorted_iter(): node.stat.init_dynamic_minimum_keystroke(len(node.value)) @@ -470,7 +505,8 @@ def update_stat_dynamic(self, delta=0.8): if pop.stat.iter_ > itera: continue updates += pop.stat.update_dynamic_minimum_keystroke( - len(pop.value), delta) + len(pop.value), delta + ) if pop.children: stack.extend(pop.children.values()) pop.stat.iter_ += 1 @@ -500,13 +536,15 @@ class _Stat: * *mks2i*: iteration when it converged """ - def merge_completions(self, prefix: int, nodes: '[CompletionTrieNode]'): + def merge_completions(self, prefix: int, nodes: "[CompletionTrieNode]"): """ Merges list of completions and cut the list, we assume given lists are sorted. """ + class Fake: pass + res = [] indexes = [0 for _ in nodes] indexes.append(0) @@ -514,14 +552,22 @@ class Fake: last.value = None last.stat = CompletionTrieNode._Stat() last.stat.completions = list( - sorted((_.weight, _) for _ in nodes if _.leave)) + sorted((_.weight, _) for _ in nodes if _.leave) + ) nodes = list(nodes) nodes.append(last) maxl = 0 while True: - en = [(_.stat.completions[indexes[i]][0], i, _.stat.completions[indexes[i]][1]) - for i, _ in enumerate(nodes) if indexes[i] < len(_.stat.completions)] + en = [ + ( + _.stat.completions[indexes[i]][0], + i, + _.stat.completions[indexes[i]][1], + ) + for i, _ in enumerate(nodes) + if indexes[i] < len(_.stat.completions) + ] if not en: break e = min(en) @@ -530,7 +576,8 @@ class Fake: indexes[i] += 1 maxl = max(maxl, len(res[-1][1].value)) - # maxl - len(prefix) represents the longest list which reduces the number of keystrokes + # maxl - len(prefix) represents the longest list + # which reduces the number of keystrokes # however, as the method aggregates completions at a lower lovel, # we must keep longer completions for lower levels ind = maxl @@ -581,7 +628,7 @@ def update_dynamic_minimum_keystroke(self, lw, delta): sug.stat.mks2i_ = self.mks_iter update += 1 else: - raise Exception("this case should not happen") + raise RuntimeError("this case should not happen") # optimisation of second case of modified metric # in a separate function for profiling @@ -603,7 +650,8 @@ def second_step(update): update = second_step(update) # finally we need to update mks, mks2 for every prefix - # this is not necessary a leave so it does not appear in the list of completions + # this is not necessary a leave so it does not + # appear in the list of completions # but we need to update mks for these strings, we assume it just # requires an extra character, somehow, we propagate the values if hasattr(self, "next_nodes"): @@ -651,7 +699,7 @@ def str_mks0(self) -> str: Returns a string with metric information. """ if hasattr(self, "mks0"): - return "MKS={0} *={1}".format(self.mks0, self.mks0_) + return f"MKS={self.mks0} *={self.mks0_}" else: return "-" @@ -662,6 +710,13 @@ def str_mks(self) -> str: s0 = self.str_mks0() if hasattr(self, "mks1"): return s0 + " |'={0} *={1},{2} |\"={3} *={4},{5} |nn={6}".format( - self.mks1, self.mks1_, self.mks1i_, self.mks2, self.mks2i_, self.mks2i_, '+' if hasattr(self, "next_nodes") else '-') + self.mks1, + self.mks1_, + self.mks1i_, + self.mks2, + self.mks2i_, + self.mks2i_, + "+" if hasattr(self, "next_nodes") else "-", + ) else: return s0 diff --git a/src/mlstatpy/nlp/completion_simple.py b/mlstatpy/nlp/completion_simple.py similarity index 63% rename from src/mlstatpy/nlp/completion_simple.py rename to mlstatpy/nlp/completion_simple.py index bef10875..fa7576d8 100644 --- a/src/mlstatpy/nlp/completion_simple.py +++ b/mlstatpy/nlp/completion_simple.py @@ -1,10 +1,4 @@ -""" -@file -@brief About completion, simple algorithm -""" -import time -from typing import Tuple, List, Iterator, Dict -from pyquickhelper.loghelper import noLOG +from typing import Tuple, List, Iterator, Dict, Optional from .completion import CompletionTrieNode @@ -26,21 +20,29 @@ class CompletionElement: * *mks2*: value of modified dynamic minimum keystroke * *mks2_*: length of the prefix to obtain *mks2* + + :param value: value (a character) + :param weight: ordering (the lower, the first) + :param disp: original string, use this to identify the node """ - __slots__ = "value", "weight", "disp", 'mks0', 'mks0_', 'mks1', 'mks1_', 'mks2', 'mks2_', 'prefix', '_info' + __slots__ = ( # noqa: RUF023 + "value", + "weight", + "disp", + "mks0", + "mks0_", + "mks1", + "mks1_", + "mks2", + "mks2_", + "prefix", + "_info", + ) def __init__(self, value: str, weight=1.0, disp=None): - """ - constructor - - @param value value (a character) - @param weight ordering (the lower, the first) - @param disp original string, use this to identify the node - """ if not isinstance(value, str): - raise TypeError( - "value must be str not '{0}' - type={1}".format(value, type(value))) + raise TypeError(f"value must be str not '{value}' - type={type(value)}") self.value = value self.weight = weight self.disp = disp @@ -53,7 +55,7 @@ def empty_prefix(): return an instance filled with an empty prefix """ if not hasattr(CompletionElement, "static_empty_prefix"): - res = CompletionElement('', None) + res = CompletionElement("", None) res.mks0 = res.mks1 = res.mks2 = 0 res.mks0_ = res.mks1_ = res.mks2_ = 0 CompletionElement.static_empty_prefix = res @@ -66,22 +68,24 @@ def __repr__(self): usual """ if self._info: - return "CompletionElementInfo('{0}'{1}{2})".format(self.value, - ", {0}".format( - self.weight) if self.weight != 1 else "", - ", disp='{0}'" if self.disp else "") + return "CompletionElementInfo('{0}'{1}{2})".format( + self.value, + ", {0}".format(self.weight) if self.weight != 1 else "", + ", disp='{0}'" if self.disp else "", + ) else: - return "CompletionElement('{0}'{1}{2})".format(self.value, - ", {0}".format( - self.weight) if self.weight != 1 else "", - ", disp='{0}'" if self.disp else "") + return "CompletionElement('{0}'{1}{2})".format( + self.value, + ", {0}".format(self.weight) if self.weight != 1 else "", + ", disp='{0}'" if self.disp else "", + ) def str_mks0(self) -> str: """ return a string with metric information """ if hasattr(self, "mks0"): - return "MKS={0} *={1}".format(self.mks0, self.mks0_) + return f"MKS={self.mks0} *={self.mks0_}" else: return "-" @@ -92,7 +96,8 @@ def str_mks(self) -> str: s0 = self.str_mks0() if hasattr(self, "mks1"): return s0 + " |'={0} *={1} |\"={2} *={3}".format( - self.mks1, self.mks1_, self.mks2, self.mks2_) + self.mks1, self.mks1_, self.mks2, self.mks2_ + ) else: return s0 @@ -101,14 +106,14 @@ def str_all_completions(self, maxn=10, use_precompute=True) -> str: builds a string with all completions for all prefixes along the paths, this is only available if parameter *completions* was used when calling - method @see me update_metrics. + method :meth`update_metrics`. - @param maxn maximum number of completions to show - @param use_precompute use intermediate results built by @see me precompute_stat - @return str + :param maxn: maximum number of completions to show + :param use_precompute: use intermediate results built + by @see me precompute_stat + :return: str """ - rows = [ - "{0} -- {1} -- {2}".format(self.weight, self.value, self.str_mks())] + rows = [f"{self.weight} -- {self.value} -- {self.str_mks()}"] if self._info is not None: rows.append("------------------") for el in self._info._log_imp: @@ -116,44 +121,53 @@ def str_all_completions(self, maxn=10, use_precompute=True) -> str: for i in range(len(self.value)): prefix = self.value[:i] rows.append("------------------") - rows.append("i={0} - {1}".format(i, prefix)) + rows.append(f"i={i} - {prefix}") completions = self._info._completions.get(prefix, []) for i2, el in enumerate(completions): ar = " " if el.value != self.value else "-> " - add = "{5}{0}:{1} -- {2}{4}-- {3}".format( - i2, el.weight, el.value, el.str_mks(), " " * (20 - len(el.value)), ar) + add = "{5}{0}:{1} -- {2}{4}-- {3}".format( # noqa: UP030 + i2, + el.weight, + el.value, + el.str_mks(), + " " * (20 - len(el.value)), + ar, + ) rows.append(add) else: rows.append("NO INFO") return "\n".join(rows) - def init_metrics(self, position: int, completions: List['CompletionElement'] = None): + def init_metrics( + self, + position: int, + completions: Optional[List["CompletionElement"]] = None, # noqa: UP045 + ): """ - initiate the metrics + Initializes the metrics. - @param position position in the completion system when prefix is null, - *position starting from 0* - @param completions displayed completions, if not None, the method will - store them in member *_completions* - @return boolean which indicates there was an update + :param position: position in the completion system when prefix is null, + *position starting from 0* + :param completions: displayed completions, if not None, the method will + store them in member *_completions* + :return: boolean which indicates there was an update """ if completions is not None: log_imp = True class c: def __str__(self): - return "{0}-{1}".format(self._completions, self._log_imp) + return f"{self._completions}-{self._log_imp}" self._info = c() self._info._completions = {} self._info._log_imp = [] - if '' not in self._info._completions: - cut = min(15, max(10, len(self.value)), - len(completions[''])) - if len(completions['']) >= cut: - self._info._completions[''] = completions[''][:cut] + if "" not in self._info._completions: + cut = min(15, max(10, len(self.value)), len(completions[""])) + if len(completions[""]) >= cut: + self._info._completions[""] = completions[""][:cut] else: - self._info._completions[''] = completions[''].copy() + self._info._completions[""] = completions[""].copy() else: log_imp = False @@ -176,25 +190,33 @@ def __str__(self): self.mks2_ = 0 if log_imp: self._info._log_imp.append( - (0, "mks0", position, '', "k={0}".format(0), - "p={0}".format(position), "it={0}".format(0))) + (0, "mks0", position, "", f"k={0}", f"p={position}", f"it={0}") + ) return True - def update_metrics(self, prefix: str, position: int, improved: dict, delta: float, - completions: List['CompletionElement'] = None, iteration=-1): - """ - update the metrics - - @param prefix prefix - @param position position in the completion system when prefix has length k, - *position starting from 0* - @param improved if one metrics is < to the completion length, it means - it can be used to improve others queries - @param delta delta in the dynamic modified mks - @param completions displayed completions, if not None, the method will - store them in member *_completions* - @param iteration for debugging purpose, indicates when this improvment was detected - @return boolean which indicates there was an update + def update_metrics( + self, + prefix: str, + position: int, + improved: dict, + delta: float, + completions: Optional[List["CompletionElement"]] = None, # noqa: UP045 + iteration=-1, + ): + """ + Updates the metrics. + + :param prefix: prefix + :param position: position in the completion system + when prefix has length k, *position starting from 0* + :param improved: if one metrics is < to the completion length, it means + it can be used to improve others queries + :param delta: delta in the dynamic modified mks + :param completions: displayed completions, if not None, the method will + store them in member *_completions* + :param iteration: for debugging purpose, indicates + when this improvment was detected + :return: boolean which indicates there was an update """ if self.prefix is not None and len(prefix) < len(self.prefix.value): # no need to look into it @@ -203,13 +225,11 @@ def update_metrics(self, prefix: str, position: int, improved: dict, delta: floa if completions is not None: log_imp = True if prefix not in self._info._completions: - cut = min(15, max(10, len(self.value)), - len(completions[prefix])) + cut = min(15, max(10, len(self.value)), len(completions[prefix])) if len(completions[prefix]) >= cut: self._info._completions[prefix] = completions[prefix][:cut] else: - self._info._completions[ - prefix] = completions[prefix].copy() + self._info._completions[prefix] = completions[prefix].copy() else: log_imp = False @@ -223,8 +243,17 @@ def update_metrics(self, prefix: str, position: int, improved: dict, delta: floa check = True if log_imp: self._info._log_imp.append( - (1, "mks0", mks, prefix, "k={0}".format(k), "p={0}".format(position), - "it={0}".format(iteration), "last={0}".format(self.prefix.value))) + ( + 1, + "mks0", + mks, + prefix, + f"k={k}", + f"p={position}", + f"it={iteration}", + f"last={self.prefix.value}", + ) + ) elif mks == self.mks0 and self.mks0_ < k: self.mks0_ = k if mks < self.mks1: @@ -247,8 +276,17 @@ def update_metrics(self, prefix: str, position: int, improved: dict, delta: floa check = True if log_imp: self._info._log_imp.append( - (4, "mks1", mks, prefix, "k={0}".format(k), "p={0}".format(position), - "it={0}".format(iteration), "last={0}".format(self.prefix.value))) + ( + 4, + "mks1", + mks, + prefix, + f"k={k}", + f"p={position}", + f"it={iteration}", + f"last={self.prefix.value}", + ) + ) mks = v.mks2 + dd if mks < self.mks2: self.mks2 = mks @@ -256,8 +294,17 @@ def update_metrics(self, prefix: str, position: int, improved: dict, delta: floa check = True if log_imp: self._info._log_imp.append( - (5, "mks2", mks, prefix, "k={0}".format(k), "p={0}".format(position), - "it={0}".format(iteration), "last={0}".format(self.prefix.value))) + ( + 5, + "mks2", + mks, + prefix, + f"k={k}", + f"p={position}", + f"it={iteration}", + f"last={self.prefix.value}", + ) + ) if prefix in improved: v = improved[prefix] self.prefix = v @@ -268,8 +315,17 @@ def update_metrics(self, prefix: str, position: int, improved: dict, delta: floa check = True if log_imp: self._info._log_imp.append( - (2, "mks1", mks, prefix, "k={0}".format(k), "p={0}".format(position), - "it={0}".format(iteration), "last={0}".format(self.prefix.value))) + ( + 2, + "mks1", + mks, + prefix, + f"k={k}", + f"p={position}", + f"it={iteration}", + f"last={self.prefix.value}", + ) + ) mks = v.mks2 + min(len(self.value) - len(prefix), pos + delta) if mks < self.mks2: self.mks2 = mks @@ -277,30 +333,36 @@ def update_metrics(self, prefix: str, position: int, improved: dict, delta: floa check = True if log_imp: self._info._log_imp.append( - (3, "mks2", mks, prefix, "k={0}".format(k), "p={0}".format(position), - "it={0}".format(iteration), "last={0}".format(self.prefix.value))) - - if log_imp and self.prefix and self.prefix.value != '': + ( + 3, + "mks2", + mks, + prefix, + f"k={k}", + f"p={position}", + f"it={iteration}", + f"last={self.prefix.value}", + ) + ) + + if log_imp and self.prefix and self.prefix.value != "": self._info._log_imp.append(self.prefix) return check class CompletionSystem: """ - define a completion system + Defines a completion system. """ def __init__(self, elements: List[CompletionElement]): - """ - fill the completion system - """ def create_element(i, e): if isinstance(e, CompletionElement): return e - elif isinstance(e, tuple): + if isinstance(e, tuple): return CompletionElement(e[1], e[0] if e[0] else i) - else: - return CompletionElement(e, i) + return CompletionElement(e, i) + self._elements = [create_element(i, e) for i, e in enumerate(elements)] def __getitem__(self, i): @@ -313,13 +375,13 @@ def find(self, value: str, is_sorted=False) -> CompletionElement: """ Not very efficient, finds an item in a the list. - @param value string to find - @param is_sorted the function will assume the elements are sorted by - alphabetical order - @return element or None + :param value: string to find + :param is_sorted: the function will assume the elements are sorted by + alphabetical order + :return: element or None """ if is_sorted: - raise NotImplementedError() + raise NotImplementedError("No optimisation for the sorted case.") for e in self: if e.value == value: return e @@ -349,42 +411,45 @@ def __iter__(self) -> Iterator[CompletionElement]: """ Iterates over elements. """ - for e in self._elements: - yield e + yield from self._elements def sort_values(self): """ sort the elements by value """ - self._elements = list( - _[-1] for _ in sorted((e.value, e.weight, e) for e in self)) + self._elements = [_[-1] for _ in sorted((e.value, e.weight, e) for e in self)] def sort_weight(self): """ Sorts the elements by value. """ - self._elements = list( - _[-1] for _ in sorted((e.weight, e.value, e) for e in self)) + self._elements = [_[-1] for _ in sorted((e.weight, e.value, e) for e in self)] - def compare_with_trie(self, delta=0.8, fLOG=noLOG): + def compare_with_trie(self, delta=0.8): """ Compares the results with the other implementation. @param delta parameter *delta* in the dynamic modified mks - @param fLOG logging function @return None or differences """ + def format_diff(el, f, diff): - s = "VALUE={0}\nSYST=[{1}]\nTRIE=[{2}]\nMORE SYSTEM:\n{3}\n######\nMORE TRIE:\n{4}".format( - el.value, el.str_mks(), f.stat.str_mks(), - el.str_all_completions(), f.str_all_completions()) + s = ( # noqa: UP030 + "VALUE={0}\nSYST=[{1}]\nTRIE=[{2}]\nMORE SYSTEM:" + "\n{3}\n######\nMORE TRIE:\n{4}" + ).format( + el.value, + el.str_mks(), + f.stat.str_mks(), + el.str_all_completions(), + f.str_all_completions(), + ) if diff: - return "-------\n{0}\n-------".format(s) - else: - return s + return f"-------\n{s}\n-------" + return s trie = CompletionTrieNode.build(self.tuples()) - self.compute_metrics(delta=delta, fLOG=fLOG, details=True) + self.compute_metrics(delta=delta, details=True) trie.precompute_stat() trie.update_stat_dynamic(delta=delta) diffs = [] @@ -408,15 +473,13 @@ def to_dict(self) -> Dict[str, CompletionElement]: """ return {el.value: el for el in self} - def compute_metrics(self, ffilter=None, delta=0.8, - details=False, fLOG=noLOG) -> int: + def compute_metrics(self, ffilter=None, delta=0.8, details=False) -> int: """ Computes the metric for the completion itself. @param ffilter filter function @param delta parameter *delta* in the dynamic modified mks @param details log more details about displayed completions - @param fLOG logging function @return number of iterations The function ends by sorting the set of completion by alphabetical order. @@ -425,30 +488,29 @@ def compute_metrics(self, ffilter=None, delta=0.8, if ffilter is not None: raise NotImplementedError("ffilter not None is not implemented") if details: - store_completions = {'': []} + store_completions = {"": []} improved = {} - to = time.perf_counter() - fLOG("init_metrics:", len(self)) + # to = time.perf_counter() + # print("init_metrics:", len(self)) for i, el in enumerate(self._elements): if details: - store_completions[''].append(el) + store_completions[""].append(el) r = el.init_metrics(i, store_completions) else: r = el.init_metrics(i) if r and el.value not in improved: improved[el.value] = el - t = time.perf_counter() - fLOG( - "interation 0: #={0} dt={1} - log details={2}".format(len(self), t - to, details)) + # t = time.perf_counter() + # print(f"interation 0: #={len(self)} dt={t - to} - log details={details}") updates = 1 it = 1 while updates > 0: displayed = {} updates = 0 - for i, el in enumerate(self._elements): - for k in range(0, len(el.value)): + for _i, el in enumerate(self._elements): + for k in range(len(el.value)): prefix = el.value[:k] if prefix not in displayed: displayed[prefix] = 0 @@ -459,32 +521,37 @@ def compute_metrics(self, ffilter=None, delta=0.8, if details: store_completions[prefix].append(el) r = el.update_metrics( - prefix, displayed[prefix], improved, delta, + prefix, + displayed[prefix], + improved, + delta, completions=(store_completions if details else None), - iteration=it) + iteration=it, + ) if r: if el.value not in improved: improved[el.value] = el updates += 1 - t = time.perf_counter() - fLOG("interation {0}: updates={1} dt={2}".format( - it, updates, t - to)) + # t = time.perf_counter() + # print(f"interation {it}: updates={updates} dt={t - to}") it += 1 self.sort_values() return it - 1 - def enumerate_test_metric(self, qset: Iterator[Tuple[str, float]]) -> Iterator[Tuple[CompletionElement, CompletionElement]]: + def enumerate_test_metric( + self, qset: Iterator[Tuple[str, float]] + ) -> Iterator[Tuple[CompletionElement, CompletionElement]]: """ Evaluates the completion set on a set of queries, the function returns a list of @see cl CompletionElement with the three metrics :math:`M`, :math:`M'`, :math:`M"` for these particular queries. - @param qset list of tuple(str, float) = (query, weight) - @return list of tuple of @see cl CompletionElement, - the first one is the query, the second one is the None or - the matching completion + :param qset: list of tuple(str, float) = (query, weight) + :return: list of tuple of @see cl CompletionElement, + the first one is the query, the second one is the None or + the matching completion The method @see me compute_metric needs to be called first. """ @@ -546,8 +613,7 @@ def test_metric(self, qset: Iterator[Tuple[str, float]]) -> Dict[str, float]: The method @see me compute_metric needs to be called first. It then calls @see me enumerate_metric. """ - res = dict(mks0=0.0, mks1=0.0, mks2=0.0, - sum_weights=0.0, sum_wlen=0.0, n=0) + res = dict(mks0=0.0, mks1=0.0, mks2=0.0, sum_weights=0.0, sum_wlen=0.0, n=0) hist = {k: {} for k in {"mks0", "mks1", "mks2", "l"}} wei = {k: {} for k in hist} res["hist"] = hist diff --git a/mlstatpy/nlp/normalize.py b/mlstatpy/nlp/normalize.py new file mode 100644 index 00000000..1ecd6cf7 --- /dev/null +++ b/mlstatpy/nlp/normalize.py @@ -0,0 +1,17 @@ +import unicodedata + + +def remove_diacritics(input_str): + """ + Removes diacritics. + + :param input_str: string to clean + :return: cleaned string + + Example:: + + enguérand --> enguerand + """ + nkfd_form = unicodedata.normalize("NFKD", input_str) + only_ascii = nkfd_form.encode("ASCII", "ignore") + return only_ascii.decode("utf8") diff --git a/mlstatpy/optim/__init__.py b/mlstatpy/optim/__init__.py new file mode 100644 index 00000000..c47101a6 --- /dev/null +++ b/mlstatpy/optim/__init__.py @@ -0,0 +1 @@ +from .sgd import SGDOptimizer diff --git a/src/mlstatpy/optim/sgd.py b/mlstatpy/optim/sgd.py similarity index 71% rename from src/mlstatpy/optim/sgd.py rename to mlstatpy/optim/sgd.py index ffa03717..0d2ed4e9 100644 --- a/src/mlstatpy/optim/sgd.py +++ b/mlstatpy/optim/sgd.py @@ -1,12 +1,9 @@ -""" -@file -@brief Implements simple stochastic gradient optimisation. -It is inspired from `_stochastic_optimizers.py -`_. -""" import numpy -from numpy.core._exceptions import UFuncTypeError + +try: + from numpy.core._exceptions import UFuncTypeError +except ImportError: + UFuncTypeError = Exception class BaseOptimizer: @@ -29,19 +26,27 @@ class BaseOptimizer: * *l1*: L1 regularization """ - def __init__(self, coef, learning_rate_init=0.1, - min_threshold=None, max_threshold=None, - l1=0., l2=0.): + def __init__( + self, + coef, + learning_rate_init=0.1, + min_threshold=None, + max_threshold=None, + l1=0.0, + l2=0.0, + ): if not isinstance(coef, numpy.ndarray): raise TypeError("coef must be an array.") self.coef = coef self.learning_rate_init = learning_rate_init self.learning_rate = float(learning_rate_init) - if (min_threshold is not None and - not isinstance(min_threshold, (float, numpy.float64, numpy.float32))): + if min_threshold is not None and not isinstance( + min_threshold, (float, numpy.float64, numpy.float32) + ): raise TypeError("min_threshold must be a float") - if (max_threshold is not None and - not isinstance(max_threshold, (float, numpy.float64, numpy.float32))): + if max_threshold is not None and not isinstance( + max_threshold, (float, numpy.float64, numpy.float32) + ): raise TypeError("min_threshold must be a float") self.min_threshold = min_threshold self.max_threshold = max_threshold @@ -60,33 +65,36 @@ def update_coef(self, grad): if self.coef.shape != grad.shape: raise ValueError( "coef and grad must have the same shape coef {} != gradient {}." - "".format(self.coef.shape, grad.shape)) + "".format(self.coef.shape, grad.shape) + ) update = self._get_updates(grad) self.coef += update if self.min_threshold is not None: try: self.coef = numpy.maximum(self.coef, self.min_threshold) - except UFuncTypeError: # pragma: no cover - raise RuntimeError( # pylint: disable=W0707 + except UFuncTypeError: + raise RuntimeError( # noqa: B904 "Unable to compute an upper bound with coef={} " - "max_threshold={}".format(self.coef, self.min_threshold)) + "max_threshold={}".format(self.coef, self.min_threshold) + ) if self.max_threshold is not None: try: self.coef = numpy.minimum(self.coef, self.max_threshold) - except UFuncTypeError: # pragma: no cover - raise RuntimeError( # pylint: disable=W0707 + except UFuncTypeError: + raise RuntimeError( # noqa: B904 "Unable to compute a lower bound with coef={} " - "max_threshold={}".format(self.coef, self.max_threshold)) + "max_threshold={}".format(self.coef, self.max_threshold) + ) def iteration_ends(self, time_step): """ Performs update to learning rate and potentially other states at the end of an iteration. """ - pass # pragma: no cover - def train(self, X, y, fct_loss, fct_grad, max_iter=100, - early_th=None, verbose=False): + def train( + self, X, y, fct_loss, fct_grad, max_iter=100, early_th=None, verbose=False + ): """ Optimizes the coefficients. @@ -128,16 +136,14 @@ def train(self, X, y, fct_loss, fct_grad, max_iter=100, grad = fct_grad(self.coef, X[irow, :], y[irow], irow) self._regularize_gradient(grad) if isinstance(verbose, int) and verbose >= 10: - self._display_progress(0, max_iter, loss, grad, 'grad') + self._display_progress(0, max_iter, loss, grad, "grad") if numpy.isnan(grad).sum() > 0: - raise RuntimeError( # pragma: no cover - "The gradient has nan values.") + raise RuntimeError("The gradient has nan values.") self.update_coef(grad) n_samples += 1 self.iteration_ends(n_samples) - loss = fct_loss(self.coef, X, y) + \ - self.loss_regularization(self.coef) + loss = fct_loss(self.coef, X, y) + self.loss_regularization(self.coef) if verbose: self._display_progress(it + 1, max_iter, loss) self.iter_ = it + 1 @@ -146,7 +152,8 @@ def train(self, X, y, fct_loss, fct_grad, max_iter=100, best_loss = loss best_coef = self.coef.copy() if self._evaluate_early_stopping( - it, max_iter, losses, early_th, verbose=verbose): + it, max_iter, losses, early_th, verbose=verbose + ): break self.coef = best_coef return best_loss @@ -156,7 +163,7 @@ def loss_regularization(self, coef): if self.l1 > 0: loss += numpy.sum(numpy.abs(coef)) * self.l1 if self.l2 > 0: - loss += numpy.sum(coef ** 2) * self.l2 + loss += numpy.sum(coef**2) * self.l2 return loss def _regularize_gradient(self, grad): @@ -169,49 +176,48 @@ def _regularize_gradient(self, grad): if self.l1 > 0: grad += numpy.sign(self.coef) * self.l1 - def _evaluate_early_stopping( - self, - it, - max_iter, - losses, - early_th, - verbose=False): + def _evaluate_early_stopping(self, it, max_iter, losses, early_th, verbose=False): if len(losses) < 5 or early_th is None: return False if numpy.isnan(losses[-5]): if numpy.isnan(losses[-1]): - if verbose: # pragma: no cover - self._display_progress(it + 1, max_iter, losses[-1], - losses=losses[-5:]) + if verbose: + self._display_progress( + it + 1, max_iter, losses[-1], losses=losses[-5:] + ) return True return False if numpy.isnan(losses[-1]): - if verbose: # pragma: no cover - self._display_progress(it + 1, max_iter, losses[-1], - losses=losses[-5:]) + if verbose: + self._display_progress(it + 1, max_iter, losses[-1], losses=losses[-5:]) return True if abs(losses[-1] - losses[-5]) <= early_th: - if verbose: # pragma: no cover - self._display_progress(it + 1, max_iter, losses[-1], - losses=losses[-5:]) + if verbose: + self._display_progress(it + 1, max_iter, losses[-1], losses=losses[-5:]) return True return False def _display_progress(self, it, max_iter, loss, losses=None, msg=None): - 'Displays training progress.' + "Displays training progress." mxc = numpy.abs(self.coef.ravel()).max() l1 = numpy.sum(numpy.abs(self.coef)) l2 = numpy.sum(self.coef * self.coef) vl1 = numpy.sum(numpy.abs(self.velocity_grad)) vl2 = numpy.sum(self.velocity_grad * self.velocity_grad) if losses is None: - print('{}/{}: loss: {:1.4g} max(coef): {:1.2g} ' - 'l1={:1.2g}/{:1.2g} l2={:1.2g}/{:1.2g}'.format( - it, max_iter, loss, mxc, vl1, l1, vl2, l2)) + print( + "{}/{}: loss: {:1.4g} max(coef): {:1.2g} " + "l1={:1.2g}/{:1.2g} l2={:1.2g}/{:1.2g}".format( + it, max_iter, loss, mxc, vl1, l1, vl2, l2 + ) + ) else: - print('{}/{}: loss: {:1.4g} losses: {} max(coef): {:1.4g} ' - 'l1={:1.2g}/{:1.2g} l2={:1.2g}/{:1.2g}'.format( - it, max_iter, loss, losses, mxc, vl1, l1, vl2, l2)) + print( + "{}/{}: loss: {:1.4g} losses: {} max(coef): {:1.4g} " + "l1={:1.2g}/{:1.2g} l2={:1.2g}/{:1.2g}".format( + it, max_iter, loss, losses, mxc, vl1, l1, vl2, l2 + ) + ) class SGDOptimizer(BaseOptimizer): @@ -278,14 +284,27 @@ def fct_grad(c, x, y, i=0): print('optimized coefficients:', sgd.coef) """ - def __init__(self, coef, learning_rate_init=0.1, lr_schedule='invscaling', - momentum=0.9, power_t=0.5, early_th=None, - min_threshold=None, max_threshold=None, - l1=0., l2=0.): - super().__init__(coef, learning_rate_init, - min_threshold=min_threshold, - max_threshold=max_threshold, - l1=l1, l2=l2) + def __init__( + self, + coef, + learning_rate_init=0.1, + lr_schedule="invscaling", + momentum=0.9, + power_t=0.5, + early_th=None, + min_threshold=None, + max_threshold=None, + l1=0.0, + l2=0.0, + ): + super().__init__( + coef, + learning_rate_init, + min_threshold=min_threshold, + max_threshold=max_threshold, + l1=l1, + l2=l2, + ) self.lr_schedule = lr_schedule self.momentum = momentum self.power_t = power_t @@ -302,14 +321,14 @@ def iteration_ends(self, time_step): number of training samples trained on so far, used to update learning rate for 'invscaling' """ - if self.lr_schedule == 'invscaling': - self.learning_rate = (float(self.learning_rate_init) / - (time_step + 1) ** self.power_t) - elif self.lr_schedule == 'constant': + if self.lr_schedule == "invscaling": + self.learning_rate = ( + float(self.learning_rate_init) / (time_step + 1) ** self.power_t + ) + elif self.lr_schedule == "constant": pass else: - raise ValueError( # pragma: no cover - "Unexpected value: lr_schedule='{}'.".format(self.lr_schedule)) + raise ValueError(f"Unexpected value: lr_schedule='{self.lr_schedule}'.") def _get_updates(self, grad): """ @@ -322,20 +341,36 @@ def _get_updates(self, grad): self.velocity = update return update - def _display_progress(self, it, max_iter, loss, losses=None, msg='loss'): - 'Displays training progress.' + def _display_progress(self, it, max_iter, loss, losses=None, msg="loss"): + "Displays training progress." mxc = numpy.abs(self.coef.ravel()).max() l1 = numpy.sum(numpy.abs(self.coef)) l2 = numpy.sum(self.coef * self.coef) vl1 = numpy.sum(numpy.abs(self.velocity_grad)) vl2 = numpy.sum(self.velocity_grad * self.velocity_grad) if losses is None: - print('{}/{}: {}: {:1.4g} lr={:1.3g} max(coef): {:1.2g} ' - 'l1={:1.2g}/{:1.2g} l2={:1.2g}/{:1.2g}'.format( - it, max_iter, msg, loss, self.learning_rate, mxc, - vl1, l1, vl2, l2)) + print( + "{}/{}: {}: {:1.4g} lr={:1.3g} max(coef): {:1.2g} " + "l1={:1.2g}/{:1.2g} l2={:1.2g}/{:1.2g}".format( + it, max_iter, msg, loss, self.learning_rate, mxc, vl1, l1, vl2, l2 + ) + ) else: - print('{}/{}: {}: {:1.4g} lr={:1.3g} {}es: {} ' # pylint: disable=W1308 - 'max(coef): {:1.2g} l1={:1.2g}/{:1.2g} l2={:1.2g}/{:1.2g}'.format( # pylint: disable=W1308 - it, max_iter, msg, loss, self.learning_rate, msg, losses, - mxc, vl1, l1, vl2, l2)) + print( + "{}/{}: {}: {:1.4g} lr={:1.3g} {}es: {} " + "max(coef): {:1.2g} l1={:1.2g}/{:1.2g} " + "l2={:1.2g}/{:1.2g}".format( + it, + max_iter, + msg, + loss, + self.learning_rate, + msg, + losses, + mxc, + vl1, + l1, + vl2, + l2, + ) + ) diff --git a/mlstatpy/render_js_dot.py b/mlstatpy/render_js_dot.py new file mode 100644 index 00000000..b9e7f3ae --- /dev/null +++ b/mlstatpy/render_js_dot.py @@ -0,0 +1,406 @@ +import uuid +import os +import shutil +import urllib.request as liburl +import urllib.error as liberror +import IPython.core.display as ipydisplay +from IPython.display import display_html, display_javascript + + +class UrlNotFoundError(Exception): + """ + Raised when a url does not exist. + """ + + def __init__(self, url, code): + Exception.__init__(self, f"Url not found: returned code={code} for '{url}'") + + +class JavascriptScriptError(ValueError): + """ + Raised when the class does not find what it expects. + """ + + +def check_url(url): + "Checks urls." + try: + liburl.urlopen(url) # pylint: disable=R1732 + return True + except liberror.HTTPError as e: + raise UrlNotFoundError(url, e.code) from e + except liberror.URLError as e: + raise UrlNotFoundError(url, e.reason) from e + except Exception as e: + raise AssertionError(f"Issue with url '{url}'") from e + + +class RenderJSRaw: + """ + Adds :epkg:`javascript` into a noteboook. + + :param script: (str) script + :param width: (str) width + :param height: (str) height + :param style: (str) style (added in ````) + :param divid: (str|None) id of the div + :param css: (list) list of css + :param libs: (list) list of dependencies + :param only_html: (bool) use only function + `display_html `_ + and not `display_javascript + `_ to add + javascript to the page. + :param div_class: (str) class of the section ``div`` which will host the results + of the javascript + :param check_urls: (bool) by default, check url exists + :param local: (bool|False) use local javascript files + """ + + def __init__( + self, + script, + width="100%", + height="100%", + divid=None, + css=None, + libs=None, + style=None, + only_html=False, + div_class=None, + check_urls=True, + local=False, + ): + self.script = script + self.uuid = divid if divid else "M" + str(uuid.uuid4()).replace("-", "") + if style is None: + style = "" + if width is not None and "width" not in style: + style += f"width:{width};" + if height is not None and "height" not in style: + style += f"height:{height};" + if not style: + style = None + else: + if width is not None and "width" not in style: + style += f"width:{width};" + if height is not None and "height" not in style: + style += f"height:{height};" + self.style = style + self.only_html = only_html + self.div_class = div_class + if "__ID__" not in script: + raise JavascriptScriptError( + f"The sript does not contain any string __ID__. " + f"It is replaced by the ID value in script:\n{script}" + ) + self.local = local + self.css, self.libs = self._copy_local(css, libs, local) + if check_urls and not local: + if self.css is not None: + for c in self.css: + check_url(c) + if self.libs is not None: + for lib in self.libs: + if isinstance(lib, dict): + check_url(lib["path"]) + else: + check_url(lib) + + def _copy_local(self, css, libs, local): + """ + If *self.local*, copies javascript dependencies in the local folder. + + :param css: list of css + :param libs: list of libraries + :param local: boolean or new location + :return: tuple (css, libs) + """ + if not self.local: + return css, libs + to_copy = [] + if css: + to_copy.extend(css) + if libs: + for js in libs: + if isinstance(js, dict): + to_copy.append(js["path"]) + else: + to_copy.append(js) + + for js in to_copy: + if not os.path.exists(js): + raise FileNotFoundError(f"Unable to find '{js}'") + dest = local if isinstance(local, str) else os.getcwd() + shutil.copy(js, dest) + + if css: + css = [os.path.split(c)[-1] for c in css] + if libs: + + def proc(d): + "proc" + if isinstance(d, dict): + d = d.copy() + d["path"] = os.path.split(d["path"])[-1] + return d + else: + return os.path.split(d)[-1] + + libs = [proc(c) for c in libs] + return css, libs + + def generate_html(self): + """ + Overloads method + `_ipython_display_ `_. + + :return: `HTML `_ text, + `Javascript `_ text + """ + if self.style: + style = f' style="{self.style}"' + else: + style = "" + if self.div_class: + divcl = f' class="{self.div_class}"' + else: + divcl = "" + if self.css: + css = "".join( + f'' + for c in self.css + ) + ht = ( + '
{css}
' + ).format(uuid=self.uuid, css=css, style=style, divcl=divcl) + else: + ht = ( + '
' + ).format(uuid=self.uuid, style=style, divcl=divcl) + + script = self.script.replace("__ID__", self.uuid) + if self.libs: + names = [] + paths = [] + shims = {} + args = [] + exports = [] + for lib in self.libs: + if isinstance(lib, dict): + name = lib.get("name", None) + if "path" in lib: + p = lib["path"] + if name is None: + name = ".".join((p.split("/")[-1]).split(".")[:-1]) + path = ".".join(p.split(".")[:-1]) + paths.append((name, path)) + else: + raise KeyError(f"unable to find 'path' in {lib}") + names.append(name) + args.append(name) + if "exports" in lib: + if name not in shims: + shims[name] = {} + shims[name]["exports"] = lib["exports"] + if isinstance(lib["exports"], list): + exports.extend(lib["exports"]) + else: + exports.append(lib["exports"]) + if "deps" in lib: + if name not in shims: + shims[name] = {} + shims[name]["deps"] = lib["deps"] + else: + names.append(lib) + if len(names) == 0: + raise ValueError( + ( + "names is empty.\nlibs={0}\npaths={1}" + "\nshims={2}\nexports={3}" + ).format(self.libs, paths, shims, exports) + ) + require = ",".join(f"'{na}'" for na in names) + + config = ["require.config({"] + if len(paths) > 0: + config.append("paths:{") + for name, path in paths: + config.append(f"'{name}':'{path}',") + config.append("},") + if len(shims) > 0: + config.append("shim:{") + + def vd(d): + "vd" + rows = [] + for k, v in sorted(d.items()): + rows.append( + "'{0}':{1}".format( + k, v if isinstance(v, list) else "'{0}'".format(v) + ) + ) + return "{%s}" % ",".join(rows) + + for k, v in sorted(shims.items()): + config.append(f"'{k}':{vd(v)},") + config.append("},") + config.append("});") + if len(config) > 2: + prefix = "\n".join(config) + "\n" + else: + prefix = "" + js = prefix + """\nrequire([%s], function(%s) { %s });\n""" % ( + require, + ",".join(args), + script, + ) + else: + js = script + if self.only_html: + ht += f"\n" + return ht, None + return ht, js + + +class RenderJSObj(RenderJSRaw): + """ + Renders JS using :epkg:`javascript`. + """ + + def _ipython_display_(self): + """ + overloads method + `_ipython_display_ + `_. + """ + if "display" not in dir(ipydisplay): + # Weird bug introduced in IPython 8.0.0 + import IPython.core.display_functions + + ipydisplay.display = IPython.core.display_functions.display + ht, js = self.generate_html() + if js is None: + display_html(ht, raw=True) + else: + display_html(ht, raw=True) + display_javascript(js, raw=True) + + +class RenderJS(RenderJSRaw): + """ + Renders :epkg:`javascript`, only outputs :epkg:`HTML`. + """ + + def _repr_html_(self): + """ + Overloads method *_repr_html_*. + """ + ht, js = self.generate_html() + if js is not None: + ht += f"\n\n" + return ht + + +class RenderJsDot(RenderJS): + """ + Renders a graph in a :epkg:`notebook` + defined in :epkg:`DOT` language + with :epkg:`viz.js`. On `binder + `_, + argument *local* should be set to True to be working. + + :param dot: (str) dot + :param local: (bool) use local path to javascript dependencies + :param script: (str) script + :param width: (str) width + :param height: (str) height + :param style: (str) style (added in ````) + :param divid: (str|None) id of the div + :param only_html: (bool) use only function + `display_html `_ + and not `display_javascript + `_ to add + javascript to the page. + :param div_class: (str) class of the section ``div`` + which will host the results of the javascript + :param check_urls: (bool) by default, check url exists + :param lite: (bool) use lite version + (no `neato `_) + """ + + def __init__( + self, + dot, + local=False, + width="100%", + height="100%", + divid=None, + style=None, + only_html=True, + div_class=None, + check_urls=True, + lite=False, + ): + script = RenderJsDot._build_script(dot) + libs, css = RenderJsDot._get_libs_css(local, lite) + RenderJS.__init__( + self, + script, + width=width, + height=height, + divid=divid, + only_html=only_html, + div_class=div_class, + check_urls=True, + libs=libs, + css=css, + local=local, + ) + + @staticmethod + def _get_libs_css(local, lite): + """ + Returns the dependencies. + + :param local: use local file (True) or remote urls (False) + :param lite: use lite version + :return: tuple *(libs, css)* + """ + jsvers = "viz-lite.js" if lite else "viz.js" + if local: + this = os.path.dirname(__file__) + js = os.path.join(this, "..", "js", "vizjs", jsvers) + libs = [js] + else: + libs = [ + "https://raw.githubusercontent.com/sdpython/jyquickhelper/refs/heads/master/src/jyquickhelper/js/vizjs/" + + jsvers + ] + css = None + return libs, css + + @staticmethod + def _build_script(dot): + """ + Builds the javascript script based wrapping the + :epkg:`DOT` language. + + :param dot: :epkg:`DOT` language + :return: javascript + """ + dot = dot.replace('"', '\\"').replace("\n", "\\n") + script = f"""var svgGraph = Viz("{dot}"); +document.getElementById('__ID__').innerHTML = svgGraph;""" + return script diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..d23b7681 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,138 @@ +[project] +authors = [{name="Xavier Dupré", email="xavier.dupre@gmail.com"}] +classifiers = [ + "Intended Audience :: Science/Research", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: C", + "Programming Language :: Python", + "Topic :: Software Development", + "Topic :: Scientific/Engineering", + "Development Status :: 5 - Production/Stable", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX", + "Operating System :: Unix", + "Operating System :: MacOS", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = ["numpy>=2", "scikit-learn>=1.5", "scipy"] +description = "Points de détails liés au machine learning" +keywords = ["cython", "scikit-learn", "machine-learning"] +license = {file = "LICENSE.txt"} +name = "mlstatpy" +readme = "README.rst" +requires-python = ">=3.10" +version = "0.5.0" + +[project.urls] +homepage = "https://sdpython.github.io/doc/mlstatpy/dev/" +documentation = "https://sdpython.github.io/doc/mlstatpy/dev/" +repository = "https://github.com/sdpython/mlstatpy/" +changelog = "https://sdpython.github.io/doc/mlstatpy/dev/CHANGELOGS.html" + +[tool.rstcheck] +report_level = "INFO" +ignore_directives = [ + "autosignature", + "autoclass", + "autofunction", + "automodule", + "blogpost", + "blogpostagg", + "exref", + "exreflist", + "faqreflist", + "gdot", + "image-sg", + "inheritance-diagram", + "mathdef", + "mathdeflist", + "nbgallery", + "nbgallerylink", + "plot", + "runpython", + "tocdelay", + "todoext", + "todoextlist", +] +ignore_roles = ["epkg", "githublink", "issue"] +ignore_messages = [ + ".*Hyperlink target .* is not referenced.*", + ".*Document or section may not begin with a transition.*", + ".*Unknown target name: .*[0-9]{4}.*", + ".*Duplicate explicit target name: .pdf..*", + ".*Unexpected possible title overline or transition..*", + # + ".*Duplicate implicit target name: .((complétion)|(analyse de survie)|(régression linéaire))..*", + ".*Duplicate implicit target name: .((courbe roc)|(diagramme de voronoï)|(régression logistique))..*", + ".*kppv.rst:11[560].*", + ".*rn_biblio.rst:7.*", +] + +[tool.ruff] + +# Exclude a variety of commonly ignored directories. +exclude = [ + ".eggs", + ".git", + "build", + "dist", +] + +line-length = 88 + +[tool.ruff.lint] +select = [ + "B", # flake8-bugbear + "C4", # flake8-comprehensions + #"D", # pydocstyle + "E", # pycodestyle + "F", # Pyflakes + "G", # flake8-logging-format + #"I", # isort + "ISC", # flake8-implicit-str-concat + "LOG", # flake8-logging + #"N", # pep8-naming + #"NPY", # modern numpy + #"PERF", # Perflint + "PIE", # flake8-pie + "PYI", # flake8-pyi + "RUF", # Ruff-specific rules + "SIM", # flake8-simplify + "SLOT", # flake8-slot + "T10", # flake8-debugger + #"TID", # Disallow relative imports + #"TRY", # flake8-try-except-raise + "UP", # pyupgrade + "W", # pycodestyle + "YTT", # flake8-2020 +] + +[tool.ruff.lint.per-file-ignores] +"**" = [ + "B905", + "C401", "C408", "C413", + "RUF012", "RUF100", "RUF010", + "SIM905", "SIM108", "SIM910", "SIM110", "SIM102", "SIM114", "SIM103", + "UP015", "UP027", "UP031", "UP034", "UP032", "RUF051", "UP006", "UP035", "UP045", "UP007", "UP030", "UP038" +] +"_unittests/**" = ["SIM113", "RUF005", "E402"] +"**/plot*.py" = ["B018"] +"_doc/conf.py" = ["E501"] +"_doc/sphinxdoc/source/conf.py" = ["F821"] +"_doc/notebooks/dsgarden/**" = ["B007", "E402"] +"_doc/notebooks/metric/**" = ["C400", "RUF005", "B007", "C417"] +"_doc/notebooks/ml/**" = ["E402", "B007", "RUF005"] +"_doc/notebooks/nlp/**" = ["RUF005", "E501", "F811", "E401", "E402"] +"mlstatpy/__init__.py" = ["E501"] +"mlstatpy/graph/__init__.py" = ["F401"] +"mlstatpy/graph/graph_distance.py" = ["E731"] +"mlstatpy/image/detection_segment/__init__.py" = ["F401"] +"mlstatpy/ml/__init__.py" = ["F401"] +"mlstatpy/ml/ml_grid_benchmark.py" = ["E731"] +"mlstatpy/nlp/__init__.py" = ["F401"] +"mlstatpy/optim/__init__.py" = ["F401"] diff --git a/requirements-dev.txt b/requirements-dev.txt new file mode 100644 index 00000000..e29c232a --- /dev/null +++ b/requirements-dev.txt @@ -0,0 +1,53 @@ +astroid +black +coverage +Cython +cytoolz +dill +furo +graphviz +hummingbird-ml +ijson +importlib_metadata +ipykernel +ipython +isort +jdcal +jupyter_sphinx +jupyter +jupyter-black +lifelines +matplotlib +memory_profiler +mlinsights +nbconvert +nbsphinx +notebook +onnxscript +onnx-array-api +onnx-diagnostic +onnxruntime>=1.23 +pandas +pillow +psutil +pybind11 +pydata_sphinx_theme +pyinstrument +pytest +ruff +seaborn +snakeviz +scikit-learn>=1.5 +skl2onnx +sphinx +sphinx-gallery +sphinx-issues +sphinx-runpython +stack_data +statsmodels +tqdm +traitlets +transformers +vprof +wheel +xgboost diff --git a/requirements.txt b/requirements.txt index 795f0c57..231d9584 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,23 +1,3 @@ -autopep8 -blockdiag -coverage -cpyquickhelper>=0.2 -dill -jupyter_sphinx -jyquickhelper -mako -memory_profiler -minepy -mlinsights>=0.2.491 -pycodestyle -pyensae>=1.2.788 -pyinstrument -pylint -pyquickhelper>=1.9.3240 -scikit-learn>=0.21 -snakeviz -sphinx-bootstrap-theme -sphinxcontrib.imagesvg -tqdm -vprof -wheel +mlinsights>=0.4 +onnxruntime>=1.23 +skl2onnx>=1.14 diff --git a/requirements_conda.txt b/requirements_conda.txt deleted file mode 100644 index d66e7b98..00000000 --- a/requirements_conda.txt +++ /dev/null @@ -1,15 +0,0 @@ -Cython -cytoolz -ipython -jupyter -matplotlib -nbformat -notebook -numpy>=1.19.0 -pandas>=1.0 -pillow -scipy>=1.4 -seaborn -setuptools -statsmodels -Sphinx diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..18ba137a --- /dev/null +++ b/setup.cfg @@ -0,0 +1,5 @@ +[options] +packages = find: + +[options.packages.find] +include = mlstatpy* diff --git a/setup.py b/setup.py index 328192a8..bed3e6a1 100644 --- a/setup.py +++ b/setup.py @@ -1,164 +1,66 @@ -# -*- coding: utf-8 -*- -import sys import os -from distutils.core import setup -from setuptools import find_packages - -######### -# settings -######### - -project_var_name = "mlstatpy" -versionPython = "%s.%s" % (sys.version_info.major, sys.version_info.minor) -path = "Lib/site-packages/" + project_var_name -readme = 'README.rst' -history = 'HISTORY.rst' -requirements = None - -KEYWORDS = project_var_name + ', Xavier Dupré' -DESCRIPTION = """Lectures about machine learning, mathematics, statistics, programming.""" -CLASSIFIERS = [ - 'Programming Language :: Python :: 3', - 'Intended Audience :: Developers', - 'Topic :: Scientific/Engineering', - 'Topic :: Education', - 'License :: OSI Approved :: MIT License', - 'Development Status :: 5 - Production/Stable' -] - -####### -# data -####### - -packages = find_packages('src', exclude='src') -package_dir = {k: "src/" + k.replace(".", "/") for k in packages} -package_data = {} - -############ -# functions -############ - - -def ask_help(): - return "--help" in sys.argv or "--help-commands" in sys.argv - - -def is_local(): - file = os.path.abspath(__file__).replace("\\", "/").lower() - try: - from pyquickhelper.pycode.setup_helper import available_commands_list - except ImportError: - return False - return available_commands_list(sys.argv) - - -def verbose(): - print("---------------------------------") - print("package_dir =", package_dir) - print("packages =", packages) - print("package_data=", package_data) - print("current =", os.path.abspath(os.getcwd())) - print("---------------------------------") - -########## -# version -########## - - -if is_local() and not ask_help(): - def write_version(): - from pyquickhelper.pycode import write_version_for_setup - return write_version_for_setup(__file__) - - write_version() - - versiontxt = os.path.join(os.path.dirname(__file__), "version.txt") - if os.path.exists(versiontxt): - with open(versiontxt, "r") as f: - lines = f.readlines() - subversion = "." + lines[0].strip("\r\n ") - if subversion == ".0": - raise Exception("Subversion is wrong: '{0}'.".format(subversion)) - else: - raise FileNotFoundError(versiontxt) -else: - # when the module is installed, no commit number is displayed - subversion = "" - -if "upload" in sys.argv and not subversion and not ask_help(): - # avoid uploading with a wrong subversion number - raise Exception( - "Git version is empty, cannot upload, is_local()={0}".format(is_local())) - -############## -# common part -############## - -if os.path.exists(readme): - with open(readme, "r", encoding='utf-8-sig') as f: - long_description = f.read() -else: +from setuptools import setup + +###################### +# beginning of setup +###################### + + +here = os.path.dirname(__file__) +if here == "": + here = "." +package_data = {"mlstatpy": ["*.txt"]} + +try: + with open(os.path.join(here, "requirements.txt"), "r") as f: + requirements = f.read().strip(" \n\r\t").split("\n") +except FileNotFoundError: + requirements = [] +if not requirements or requirements == [""]: + requirements = ["numpy", "mlinsight", "onnxruntime", "skl2onnx"] + +try: + with open(os.path.join(here, "README.rst"), "r", encoding="utf-8") as f: + long_description = "mlstatpy:" + f.read().split("mlstatpy:")[1] +except FileNotFoundError: long_description = "" -if os.path.exists(history): - with open(history, "r", encoding='utf-8-sig') as f: - long_description += f.read() - -if "--verbose" in sys.argv: - verbose() - -if is_local(): - import pyquickhelper - logging_function = pyquickhelper.get_fLOG() - logging_function(OutputPrint=True) - from pyquickhelper.pycode import process_standard_options_for_setup - r = process_standard_options_for_setup( - sys.argv, __file__, project_var_name, - unittest_modules=["pyquickhelper", "jyquickhelper", "mlinsights"], - additional_notebook_path=["pyquickhelper", "pyensae", "cpyquickhelper", - "pymyinstall", "jyquickhelper", "mlinsights"], - additional_local_path=["pyquickhelper", "pyensae", "cpyquickhelper", - "pymyinstall", "jyquickhelper", "mlinsights"], - requirements=["pyquickhelper", "pymyinstall", "cpyquickhelper", - "jyquickhelper", "pyensae", "mlinsights"], - add_htmlhelp=sys.platform.startswith("win"), - coverage_options=dict(omit=["*exclude*.py"]), - github_owner="sdpython", - fLOG=logging_function, covtoken=("ab2da06c-1ff3-4875-97fa-145e594bd7f9", "'_UT_37_std' in outfile")) - if not r and not ({"bdist_msi", "sdist", - "bdist_wheel", "publish", "publish_doc", "register", - "upload_docs", "bdist_wininst", "build_ext"} & set(sys.argv)): - raise Exception("unable to interpret command line: " + str(sys.argv)) -else: - r = False - -if ask_help(): - from pyquickhelper.pycode import process_standard_options_for_setup_help - process_standard_options_for_setup_help(sys.argv) - -if not r: - if len(sys.argv) in (1, 2) and sys.argv[-1] in ("--help-commands",): - from pyquickhelper.pycode import process_standard_options_for_setup_help - process_standard_options_for_setup_help(sys.argv) - from pyquickhelper.pycode import clean_readme - from mlstatpy import __version__ as sversion - long_description = clean_readme(long_description) - setup( - name=project_var_name, - version=sversion, - author='Xavier Dupré', - author_email='xavier.dupre@gmail.com', - license="MIT", - url="http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/index.html", - download_url="https://github.com/sdpython/mlstatpy/", - description=DESCRIPTION, - long_description=long_description, - keywords=KEYWORDS, - classifiers=CLASSIFIERS, - packages=packages, - package_dir=package_dir, - package_data=package_data, - setup_requires=["pyquickhelper"], - install_requires=['numpy>=1.16', 'scipy>=1.4', - 'mlinsights>=0.2', 'cpyquickhelper>=0.2'], - ) +version_str = "0.1.0" +with open(os.path.join(here, "mlstatpy/__init__.py"), "r") as f: + line = [ + _ + for _ in [_.strip("\r\n ") for _ in f.readlines()] + if _.startswith("__version__") + ] + if line: + version_str = line[0].split("=")[1].strip('" ') + +# see https://pypi.org/classifiers/ +setup( + name="mlstatpy", + version=version_str, + description="Points de détails liés au machine learning", + long_description=long_description, + author="Xavier Dupré", + author_email="xavier.dupre@gmail.com", + url="https://github.com/sdpython/mlstatpy", + package_data=package_data, + setup_requires=["numpy"], + install_requires=requirements, + classifiers=[ + "Intended Audience :: Science/Research", + "Intended Audience :: Education", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Mathematics", + "Topic :: Education", + "Development Status :: 5 - Production/Stable", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + ], +) diff --git a/src/mlstatpy/__init__.py b/src/mlstatpy/__init__.py deleted file mode 100644 index fa8f2399..00000000 --- a/src/mlstatpy/__init__.py +++ /dev/null @@ -1,52 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Module *mlstatpy*. -Functions and examples associated to the -content of the :epkg:`mlstatpy`. -""" - -__version__ = "0.2.539" -__author__ = "Xavier Dupré" -__github__ = "https://github.com/sdpython/mlstatpy" -__url__ = "http://www.xavierdupre.fr/app/mlstatpy/helpsphinx/index.html" -__license__ = "MIT License" -__blog__ = """ - - - - blog - - - - - -""" - - -def check(log=False): - """ - Checks the library is working. - It raises an exception. - If you want to disable the logs: - - @param log if True, display information, otherwise - @return 0 or exception - """ - return True - - -def _setup_hook(use_print=False): - """ - if this function is added to the module, - the help automation and unit tests call it first before - anything goes on as an initialization step. - """ - # we can check many things, needed module - # any others things before unit tests are started - if use_print: - print("Success: _setup_hook") diff --git a/src/mlstatpy/data/__init__.py b/src/mlstatpy/data/__init__.py deleted file mode 100644 index 927556cd..00000000 --- a/src/mlstatpy/data/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -""" -@file -@brief shortcut to data -""" diff --git a/src/mlstatpy/garden/__init__.py b/src/mlstatpy/garden/__init__.py deleted file mode 100644 index 0a92658c..00000000 --- a/src/mlstatpy/garden/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -""" -@file -@brief shortcut to garden -""" diff --git a/src/mlstatpy/graph/graphviz_helper.py b/src/mlstatpy/graph/graphviz_helper.py deleted file mode 100644 index fc3a762b..00000000 --- a/src/mlstatpy/graph/graphviz_helper.py +++ /dev/null @@ -1,129 +0,0 @@ -""" -@file -@brief graphviz helper -""" -import os -import sys -from pyquickhelper.loghelper import run_cmd -from pyquickhelper.helpgen.conf_path_tools import find_graphviz_dot - - -def run_graphviz(filename, image, engine="dot"): - """ - Run :epkg:`GraphViz`. - - @param filename filename which contains the graph definition - @param image output image - @param engine *dot* or *neato* - @return output of graphviz - """ - ext = os.path.splitext(image)[-1] - if ext != ".png": - raise Exception("extension should be .png not " + str(ext)) - if sys.platform.startswith("win"): - bin_ = os.path.dirname(find_graphviz_dot()) - # if bin not in os.environ["PATH"]: - # os.environ["PATH"] = os.environ["PATH"] + ";" + bin - cmd = '"{0}\\{3}" -Tpng "{1}" -o "{2}"'.format( - bin_, filename, image, engine) - else: - cmd = '"{0}" -Tpng "{1}" -o "{2}"'.format(engine, filename, image) - out, err = run_cmd(cmd, wait=True) - if len(err) > 0: - raise Exception( - "Unable to run Graphviz\nCMD:\n{0}\nOUT:\n{1}\nERR:\n{2}".format(cmd, out, err)) - return out - - -def edges2gv(vertices, edges): - """ - Converts a graph into a :epkg:`GraphViz` file format. - - @param edges see below - @param vertices see below - @return gv format - - The function creates a file ``.gv``. - - .. runpython:: - :showcode: - - from mlstatpy.graph.graphviz_helper import edges2gv - gv = edges2gv([(1, "eee", "red")], - [(1, 2, "blue"), (3, 4), (1, 3)]) - print(gv) - - """ - memovertex = {} - for v in vertices: - if isinstance(v, tuple): - if len(v) == 1: - memovertex[v[0]] = None - else: - memovertex[v[0]] = v[1:] - else: - memovertex[v] = None - for edge in edges: - i, j = edge[:2] - if i not in memovertex: - memovertex[i] = None - if j not in memovertex: - memovertex[j] = None - - li = ["digraph{"] - for k, v in memovertex.items(): - if v is None: - li.append("%s ;" % k) - elif len(v) == 1: - li.append("\"%s\" [label=\"%s\"];" % (k, v[0])) - elif len(v) == 2: - li.append("\"%s\" [label=\"%s\",fillcolor=%s,color=%s];" % ( - k, v[0], v[1], v[1])) - else: - raise ValueError("unable to understand " + str(v)) - - for edge in edges: - i, j = edge[:2] - if len(edge) == 2: - li.append("\"%s\" -> \"%s\";" % (i, j)) - elif len(edge) == 3: - li.append("\"%s\" -> \"%s\" [label=\"%s\"];" % (i, j, edge[2])) - elif len(edge) == 4: - li.append( - "\"%s\" -> \"%s\" [label=\"%s\",color=%s];" % (i, j, edge[2], edge[3])) - else: - raise ValueError("unable to understand " + str(edge)) - li.append("}") - - text = "\n".join(li) - return text - - -def draw_graph_graphviz(vertices, edges, image, engine="dot"): - """ - Draws a graph using :epkg:`Graphviz`. - - @param edges see below - @param vertices see below - @param image output image - @param engine *dot* or *neato* - @return :epkg:`Graphviz` - - The function creates a file ``.gv``. - :: - - edges = [ (1,2, label, color), (3,4), (1,3), ... ] , liste d'arcs - vertices = [ (1, label, color), (2), ... ] , liste de noeuds - image = nom d'image (format png) - - """ - text = edges2gv(vertices, edges) - filename = image + ".gv" - with open(filename, "w", encoding="utf-8") as f: - f.write(text) - - out = run_graphviz(filename, image, engine=engine) - if not os.path.exists(image): - raise FileNotFoundError( - "GraphViz failed with no reason. '{0}' not found.".format(image)) - return out diff --git a/src/mlstatpy/image/__init__.py b/src/mlstatpy/image/__init__.py deleted file mode 100644 index 51494e59..00000000 --- a/src/mlstatpy/image/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -""" -@file -@brief shortcut to image -""" diff --git a/src/mlstatpy/ml/__init__.py b/src/mlstatpy/ml/__init__.py deleted file mode 100644 index 4ce31ce0..00000000 --- a/src/mlstatpy/ml/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -""" -@file -@brief shortcut to ml -""" - -from .ml_grid_benchmark import MlGridBenchMark -from .roc import ROC -from .voronoi import voronoi_estimation_from_lr diff --git a/src/mlstatpy/ml/ml_grid_benchmark.py b/src/mlstatpy/ml/ml_grid_benchmark.py deleted file mode 100644 index 0b951bc5..00000000 --- a/src/mlstatpy/ml/ml_grid_benchmark.py +++ /dev/null @@ -1,288 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief About Machine Learning Benchmark -""" -import os -import numpy -from sklearn.model_selection import train_test_split -from sklearn.base import ClusterMixin -from sklearn.metrics import silhouette_score -from pyquickhelper.loghelper import noLOG -from pyquickhelper.benchhelper import GridBenchMark - - -class MlGridBenchMark(GridBenchMark): - """ - The class tests a list of model over a list of datasets. - """ - - def __init__(self, name, datasets, clog=None, fLOG=noLOG, path_to_images=".", - cache_file=None, progressbar=None, graphx=None, graphy=None, - **params): - """ - @param name name of the test - @param datasets list of dictionary of dataframes - @param clog see @see cl CustomLog or string - @param fLOG logging function - @param params extra parameters - @param path_to_images path to images and intermediate results - @param cache_file cache file - @param progressbar relies on *tqdm*, example *tnrange* - @param graphx list of variables to use as X axis - @param graphy list of variables to use as Y axis - - If *cache_file* is specified, the class will store the results of the - method :meth:`bench `. - On a second run, the function load the cache - and run modified or new run (in *param_list*). - - *datasets* should be a dictionary with dataframes a values - with the following keys: - - * ``'X'``: features - * ``'Y'``: labels (optional) - """ - GridBenchMark.__init__(self, name=name, datasets=datasets, clog=clog, fLOG=fLOG, - path_to_images=path_to_images, cache_file=cache_file, - progressbar=progressbar, **params) - self._xaxis = graphx - self._yaxis = graphy - - def preprocess_dataset(self, dsi, **params): - """ - Splits the dataset into train and test. - - @param params additional parameters - @return dataset (like info), dictionary for metrics - """ - ds, appe, params = GridBenchMark.preprocess_dataset( - self, dsi, **params) - - no_split = ds["no_split"] if "no_split" in ds else False - - if no_split: - self.fLOG("[MlGridBenchMark.preprocess_dataset] no split") - return (ds, ds), appe, params - - self.fLOG("[MlGridBenchMark.preprocess_dataset] split train test") - spl = ["X", "Y", "weight", "group"] - names = [_ for _ in spl if _ in ds] - if len(names) == 0: - raise ValueError( # pragma: no cover - "No dataframe or matrix was found.") - mats = [ds[_] for _ in names] - - pars = {"train_size", "test_size"} - options = {k: v for k, v in params.items() if k in pars} - for k in pars: - if k in params: - del params[k] - - res = train_test_split(*mats, **options) - - train = {} - for i, n in enumerate(names): - train[n] = res[i * 2] - test = {} - for i, n in enumerate(names): - test[n] = res[i * 2 + 1] - - self.fLOG("[MlGridBenchMark.preprocess_dataset] done") - return (train, test), appe, params - - def bench_experiment(self, ds, **params): - """ - Calls meth *fit*. - """ - if not isinstance(ds, tuple) and len(ds) != 2: - raise TypeError( # pragma: no cover - "ds must a tuple with two dictionaries train, test") - if "model" not in params: - raise KeyError( # pragma: no cover - "params must contains key 'model'") - model = params["model"] - # we assume model is a function which creates a model - model = model() - del params["model"] - return self.fit(ds[0], model, **params) - - def predict_score_experiment(self, ds, model, **params): - """ - Calls method *score*. - """ - if not isinstance(ds, tuple) and len(ds) != 2: - raise TypeError( # pragma: no cover - "ds must a tuple with two dictionaries train, test") - if "model" in params: - raise KeyError( # pragma: no cover - "params must not contains key 'model'") - return self.score(ds[1], model, **params) - - def fit(self, ds, model, **params): - """ - Trains a model. - - @param ds dictionary with the data to use for training - @param model model to train - """ - if "X" not in ds: - raise KeyError( # pragma: no cover - "ds must contain key 'X'") - if "model" in params: - raise KeyError( # pragma: no cover - "params must not contain key 'model', this is the model to train") - X = ds["X"] - Y = ds.get("Y", None) - weight = ds.get("weight", None) - self.fLOG("[MlGridBenchMark.fit] fit", params) - - train_params = params.get("train_params", {}) - - if weight is not None: - model.fit(X=X, y=Y, weight=weight, **train_params) - else: - model.fit(X=X, y=Y, **train_params) - self.fLOG("[MlGridBenchMark.fit] Done.") - return model - - def score(self, ds, model, **params): - """ - Scores a model. - """ - X = ds["X"] - Y = ds.get("Y", None) - - if "weight" in ds: - raise NotImplementedError("weight are not used yet") - - metrics = {} - appe = {} - - if hasattr(model, "score"): - score = model.score(X, Y) - metrics["own_score"] = score - - if isinstance(model, ClusterMixin): - # add silhouette - if hasattr(model, "predict"): - ypred = model.predict(X) - elif hasattr(model, "transform"): - ypred = model.transform(X) - elif hasattr(model, "labels_"): - ypred = model.labels_ - if len(ypred.shape) > 1 and ypred.shape[1] > 1: - ypred = numpy.argmax(ypred, axis=1) - score = silhouette_score(X, ypred) - metrics["silhouette"] = score - - return metrics, appe - - def end(self): - """ - nothing to do - """ - pass - - def graphs(self, path_to_images): - """ - Plots multiples graphs. - - @param path_to_images where to store images - @return list of tuple (image_name, function to create the graph) - """ - import matplotlib.pyplot as plt # pylint: disable=C0415 - import matplotlib.cm as mcm # pylint: disable=C0415 - df = self.to_df() - - def local_graph(vx, vy, ax=None, text=True, figsize=(5, 5)): - btrys = set(df["_btry"]) - ymin = df[vy].min() - ymax = df[vy].max() - decy = (ymax - ymin) / 50 - colors = mcm.rainbow(numpy.linspace(0, 1, len(btrys))) - if len(btrys) == 0: - raise ValueError("The benchmark is empty.") # pragma: no cover - if ax is None: - _, ax = plt.subplots(1, 1, figsize=figsize) - ax.grid(True) - for i, btry in enumerate(sorted(btrys)): - subset = df[df["_btry"] == btry] - if subset.shape[0] > 0: - tx = subset[vx].mean() - ty = subset[vy].mean() - if not numpy.isnan(tx) and not numpy.isnan(ty): - subset.plot(x=vx, y=vy, kind="scatter", - label=btry, ax=ax, color=colors[i]) - if text: - ax.text(tx, ty + decy, btry, size='small', - color=colors[i], ha='center', va='bottom') - ax.set_xlabel(vx) - ax.set_ylabel(vy) - return ax - - res = [] - if self._xaxis is not None and self._yaxis is not None: - for vx in self._xaxis: - for vy in self._yaxis: - self.fLOG("Plotting {0} x {1}".format(vx, vy)) - func_graph = lambda ax=None, text=True, vx=vx, vy=vy, **kwargs: \ - local_graph(vx, vy, ax=ax, text=text, **kwargs) - - if path_to_images is not None: - img = os.path.join( - path_to_images, "img-{0}-{1}x{2}.png".format(self.Name, vx, vy)) - gr = self.LocalGraph( - func_graph, img, root=path_to_images) - self.fLOG("Saving '{0}'".format(img)) - fig, ax = plt.subplots(1, 1, figsize=(8, 8)) - gr.plot(ax=ax, text=True) - fig.savefig(img) - self.fLOG("Done") - res.append(gr) - plt.close('all') - else: - gr = self.LocalGraph(func_graph) - res.append(gr) - return res - - def plot_graphs(self, grid=None, text=True, **kwargs): - """ - Plots all graphs in the same graphs. - - @param grid grid of axes - @param text add legend title on the graph - @return grid - """ - nb = len(self.Graphs) - if nb == 0: - raise ValueError("No graph to plot.") # pragma: no cover - - nb = len(self.Graphs) - if nb % 2 == 0: - size = nb // 2, 2 - else: - size = nb // 2 + 1, 2 - - if grid is None: - import matplotlib.pyplot as plt # pylint: disable=C0415 - fg = kwargs.get('figsize', (5 * size[0], 10)) - _, grid = plt.subplots(size[0], size[1], figsize=fg) - if 'figsize' in kwargs: - del kwargs['figsize'] - else: - shape = grid.shape - if shape[0] * shape[1] < nb: - raise ValueError( # pragma: no cover - "The graph is not big enough {0} < {1}".format(shape, nb)) - - x = 0 - y = 0 - for i, gr in enumerate(self.Graphs): - self.fLOG("Plot graph {0}/{1}".format(i + 1, nb)) - gr.plot(ax=grid[y, x], text=text, **kwargs) - x += 1 - if x >= grid.shape[1]: - x = 0 - y += 1 - return grid diff --git a/src/mlstatpy/ml/neural_tree.py b/src/mlstatpy/ml/neural_tree.py deleted file mode 100644 index d9ae1bb9..00000000 --- a/src/mlstatpy/ml/neural_tree.py +++ /dev/null @@ -1,505 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Conversion from tree to neural network. -""" -from io import BytesIO -import pickle -import numpy -from ._neural_tree_api import _TrainingAPI -from ._neural_tree_node import NeuralTreeNode - - -def label_class_to_softmax_output(y_label): - """ - Converts a binary class label into a matrix - with two columns of probabilities. - - .. runpython:: - :showcode: - - import numpy - from mlstatpy.ml.neural_tree import label_class_to_softmax_output - - y_label = numpy.array([0, 1, 0, 0]) - soft_y = label_class_to_softmax_output(y_label) - print(soft_y) - """ - if len(y_label.shape) != 1: - raise ValueError( - "y_label must be a vector but has shape {}.".format(y_label.shape)) - y = numpy.empty((y_label.shape[0], 2), dtype=numpy.float64) - y[:, 0] = (y_label < 0.5).astype(numpy.float64) - y[:, 1] = 1 - y[:, 0] - return y - - -class NeuralTreeNet(_TrainingAPI): - """ - Node ensemble. - - .. runpython:: - :showcode: - - import numpy - from mlstatpy.ml.neural_tree import NeuralTreeNode, NeuralTreeNet - - w1 = numpy.array([-0.5, 0.8, -0.6]) - - neu = NeuralTreeNode(w1[1:], bias=w1[0], activation='sigmoid') - net = NeuralTreeNet(2, empty=True) - net.append(neu, numpy.arange(2)) - - ide = NeuralTreeNode(numpy.array([1.]), - bias=numpy.array([0.]), - activation='identity') - - net.append(ide, numpy.arange(2, 3)) - - X = numpy.abs(numpy.random.randn(10, 2)) - pred = net.predict(X) - print(pred) - """ - - def __init__(self, dim, empty=True): - """ - @param dim space dimension - @param empty empty network, other adds an identity node - """ - self.dim = dim - if empty: - self.nodes = [] - self.nodes_attr = [] - else: - self.nodes = [ - NeuralTreeNode( - numpy.ones((dim,), dtype=numpy.float64), - bias=numpy.float64(0.), - activation='identity', nodeid=0)] - self.nodes_attr = [dict(inputs=numpy.arange(0, dim), output=dim, - coef_size=self.nodes[0].coef.size, - first_coef=0)] - self._update_members() - - def copy(self): - st = BytesIO() - pickle.dump(self, st) - cop = BytesIO(st.getvalue()) - return pickle.load(cop) - - def _update_members(self, node=None, attr=None): - "Updates internal members." - if node is None or attr is None: - if len(self.nodes_attr) == 0: - self.size_ = self.dim - else: - self.size_ = max(d['output'] for d in self.nodes_attr) + 1 - self.output_to_node_ = {} - self.input_to_node_ = {} - for node2, attr2 in zip(self.nodes, self.nodes_attr): - if isinstance(attr2['output'], list): - for o in attr2['output']: - self.output_to_node_[o] = node2, attr2 - else: - self.output_to_node_[attr2['output']] = node2, attr2 - for i in attr2['inputs']: - self.input_to_node_[i] = node2, attr2 - else: - if len(node.input_weights.shape) == 1: - self.size_ += 1 - else: - self.size_ += node.input_weights.shape[0] - if isinstance(attr['output'], list): - for o in attr['output']: - self.output_to_node_[o] = node, attr - else: - self.output_to_node_[attr['output']] = node, attr - for i in attr['inputs']: - self.input_to_node_[i] = node, attr - - def __repr__(self): - "usual" - return "%s(%d)" % (self.__class__.__name__, self.dim) - - def clear(self): - "Clear all nodes" - del self.nodes[:] - del self.nodes_attr[:] - self._update_members() - - def append(self, node, inputs): - """ - Appends a node into the graph. - - @param node node to add - @param inputs index of input nodes - """ - if len(node.input_weights.shape) == 1: - if node.input_weights.shape[0] != len(inputs): - raise RuntimeError( - "Dimension mismatch between weights [{}] and inputs [{}].".format( - node.input_weights.shape[0], len(inputs))) - node.nodeid = len(self.nodes) - self.nodes.append(node) - first_coef = ( - 0 if len(self.nodes_attr) == 0 else - self.nodes_attr[-1]['first_coef'] + self.nodes_attr[-1]['coef_size']) - attr = dict(inputs=numpy.array(inputs), output=self.size_, - coef_size=node.coef.size, first_coef=first_coef) - self.nodes_attr.append(attr) - elif len(node.input_weights.shape) == 2: - if node.input_weights.shape[1] != len(inputs): - raise RuntimeError( - "Dimension mismatch between weights [{}] and inputs [{}].".format( - node.input_weights.shape[1], len(inputs))) - node.nodeid = len(self.nodes) - self.nodes.append(node) - first_coef = ( - 0 if len(self.nodes_attr) == 0 else - self.nodes_attr[-1]['first_coef'] + self.nodes_attr[-1]['coef_size']) - attr = dict(inputs=numpy.array(inputs), - output=list(range(self.size_, self.size_ + - node.input_weights.shape[0])), - coef_size=node.coef.size, first_coef=first_coef) - self.nodes_attr.append(attr) - else: - raise RuntimeError( - "Coefficients should have 1 or 2 dimension not {}.".format(node.input_weights.shape)) - self._update_members(node, attr) - - def __getitem__(self, i): - "Retrieves node and attributes for node i." - return self.nodes[i], self.nodes_attr[i] - - def __len__(self): - "Returns the number of nodes" - return len(self.nodes) - - def _predict_one(self, X): - res = numpy.zeros((self.size_,), dtype=numpy.float64) - res[:self.dim] = X - for node, attr in zip(self.nodes, self.nodes_attr): - res[attr['output']] = node.predict(res[attr['inputs']]) - return res - - def predict(self, X): - if len(X.shape) == 2: - res = numpy.zeros((X.shape[0], self.size_)) - for i, x in enumerate(X): - res[i, :] = self._predict_one(x) - return res - return self._predict_one(X) - - @staticmethod - def create_from_tree(tree, k=1.): - """ - Creates a @see cl NeuralTreeNet instance from a - :epkg:`DecisionTreeClassifier` - - @param tree :epkg:`DecisionTreeClassifier` - @param k slant of the sigmoïd - @return @see cl NeuralTreeNet - - The function only works for binary problems. - """ - if tree.n_classes_ > 2: - raise RuntimeError( - "The function only support binary classification problem.") - - n_nodes = tree.tree_.node_count - children_left = tree.tree_.children_left - children_right = tree.tree_.children_right - feature = tree.tree_.feature - threshold = tree.tree_.threshold - value = tree.tree_.value.reshape((-1, 2)) - output_class = (value[:, 1] > value[:, 0]).astype(numpy.int64) - max_features_ = tree.max_features_ - - root = NeuralTreeNet(tree.max_features_, empty=True) - feat_index = numpy.arange(0, max_features_) - predecessor = {} - outputs = {i: [] for i in range(0, tree.n_classes_)} - for i in range(n_nodes): - - if children_left[i] != children_right[i]: - # node with a threshold - # right side - coef = numpy.zeros((max_features_,), dtype=numpy.float64) - coef[feature[i]] = -k - node_th = NeuralTreeNode(coef, bias=k * threshold[i], - activation='sigmoid4', tag="N%d-th" % i) - root.append(node_th, feat_index) - - if i in predecessor: - pred = predecessor[i] - node1 = pred - node2 = node_th - attr1 = root[node1.nodeid][1] - attr2 = root[node2.nodeid][1] - - coef = numpy.ones((2,), dtype=numpy.float64) * k - node_true = NeuralTreeNode(coef, bias=-k * 1.5, - activation='sigmoid4', - tag="N%d-T" % i) - root.append(node_true, [attr1['output'], attr2['output']]) - - coef = numpy.zeros((2,), dtype=numpy.float64) - coef[0] = k - coef[1] = -k - node_false = NeuralTreeNode(coef, bias=-k * 0.25, - activation='sigmoid4', - tag="N%d-F" % i) - root.append(node_false, [attr1['output'], attr2['output']]) - - predecessor[children_left[i]] = node_true - predecessor[children_right[i]] = node_false - else: - coef = numpy.ones((1,), dtype=numpy.float64) * -1 - node_false = NeuralTreeNode( - coef, bias=1, activation='identity', tag="N%d-F" % i) - attr = root[node_th.nodeid][1] - root.append(node_false, [attr['output']]) - - predecessor[children_left[i]] = node_th - predecessor[children_right[i]] = node_false - - elif i in predecessor: - # leave - outputs[output_class[i]].append(predecessor[i]) - - # final node - output = [] - index = [0] - nb = [] - for i in range(0, tree.n_classes_): - output.extend(outputs[i]) - nb.append(len(outputs[i])) - index.append(len(outputs[i]) + index[-1]) - coef = numpy.zeros((len(nb), len(output)), dtype=numpy.float64) - for i in range(0, tree.n_classes_): - coef[i, index[i]:index[i + 1]] = k - feat = [root[n.nodeid][1]['output'] for n in output] - root.append( - NeuralTreeNode(coef, bias=-k / 2, - activation='softmax4', tag="Nfinal"), - feat) - - # final - return root - - def to_dot(self, X=None): - """ - Exports the neural network into :epkg:`dot`. - - @param X input as an example - """ - y = None - if X is not None: - y = self.predict(X) - rows = ['digraph Tree {', - "node [shape=box, fontsize=10];", - "edge [fontsize=8];"] - for i in range(self.dim): - if y is None: - rows.append('{0} [label="X[{0}]"];'.format(i)) - else: - rows.append( - '{0} [label="X[{0}]=\\n{1:1.2f}"];'.format(i, X[i])) - - labels = {} - - for i in range(0, len(self)): # pylint: disable=C0200 - o = self[i][1]['output'] - if isinstance(o, int): - lo = str(o) - labels[o] = lo - lof = "%s" - else: - lo = "s" + 'a'.join(map(str, o)) - for oo in o: - labels[oo] = '{}:f{}'.format(lo, oo) - los = "|".join(" {0}".format(oo) for oo in o) - lof = "%s\n" + los - - a = "a={}\n".format(self[i][0].activation) - stag = "" if self[i][0].tag is None else (self[i][0].tag + "\\n") - bias = str(numpy.array(self[i][0].bias)).replace(" ", "\ ") - if y is None: - lab = lof % '{}{}id={} b={} s={}'.format( - stag, a, i, bias, self[i][0].n_outputs) - else: - yo = numpy.array(y[o]) - lab = lof % '{}{}id={} b={} s={}\ny={}'.format( - stag, a, i, bias, self[i][0].n_outputs, yo) - rows.append('{} [label="{}"];'.format( - lo, lab.replace("\n", "\n"))) - for ii, inp in enumerate(self[i][1]['inputs']): - if isinstance(o, int): - w = self[i][0].input_weights[ii] - if w == 0: - c = ', color=grey, fontcolor=grey' - elif w < 0: - c = ', color=red, fontcolor=red' - else: - c = ', color=blue, fontcolor=blue' - rows.append( - '{} -> {} [label="{}"{}];'.format(inp, o, w, c)) - continue - - w = self[i][0].input_weights[:, ii] - for oi, oo in enumerate(o): - if w[oi] == 0: - c = ', color=grey, fontcolor=grey' - elif w[oi] < 0: - c = ', color=red, fontcolor=red' - else: - c = ', color=blue, fontcolor=blue' - rows.append('{} -> {} [label="{}|{}"{}];'.format( - inp, labels[oo], oi, w[oi], c)) - - rows.append('}') - return '\n'.join(rows) - - @property - def shape(self): - "Returns the shape of the coefficients." - return (sum(n.coef.size for n in self.nodes), ) - - @property - def training_weights(self): - "Returns the weights." - sh = self.shape - res = numpy.empty(sh[0], dtype=numpy.float64) - pos = 0 - for n in self.nodes: - s = n.coef.size - res[pos: pos + s] = ( - n.coef if len(n.coef.shape) == 1 else n.coef.ravel()) - pos += s - return res - - def update_training_weights(self, X, add=True): - """ - Updates weights. - - :param grad: vector to add to the weights such as gradient - :param add: addition or replace - """ - pos = 0 - if add: - for n in self.nodes: - s = n.coef.size - n.coef += X[pos: pos + s].reshape(n.coef.shape) - pos += s - else: - for n in self.nodes: - s = n.coef.size - numpy.copyto(n.coef, X[pos: pos + s].reshape(n.coef.shape)) - pos += s - - def fill_cache(self, X): - """ - Creates a cache with intermediate results. - """ - big_cache = {} - res = numpy.zeros((self.size_,), dtype=numpy.float64) - res[:self.dim] = X - for node, attr in zip(self.nodes, self.nodes_attr): - cache = node.fill_cache(res[attr['inputs']]) - big_cache[node.nodeid] = cache - res[attr['output']] = cache['aX'] - big_cache[-1] = res - return big_cache - - def _get_output_node_attr(self, nb_last): - """ - Retrieves the output nodes. - *nb_last* is the number of expected outputs. - """ - neurones = set(self.output_to_node_[i][0].nodeid - for i in range(self.size_ - nb_last, self.size_)) - if len(neurones) != 1: - raise RuntimeError( # pragma: no cover - "Only one output node is implemented not {}".format( - len(neurones))) - return self.output_to_node_[self.size_ - 1] - - def _common_loss_dloss(self, X, y, cache=None): - """ - Common beginning to methods *loss*, *dlossds*, - *dlossdw*. - """ - last = 1 if len(y.shape) <= 1 else y.shape[1] - if cache is not None and -1 in cache: - res = cache[-1] - else: - res = self.predict(X) - if len(res.shape) == 2: - pred = res[:, -last:] - else: - pred = res[-last:] - last_node, last_attr = self._get_output_node_attr(last) - return res, pred, last_node, last_attr - - def loss(self, X, y, cache=None): - """ - Computes the loss due to prediction error. Returns a float. - """ - res, _, last_node, last_attr = self._common_loss_dloss( - X, y, cache=cache) - if len(res.shape) <= 1: - return last_node.loss(res[last_attr['inputs']], y) # pylint: disable=E1120 - return last_node.loss(res[:, last_attr['inputs']], y) # pylint: disable=E1120 - - def dlossds(self, X, y, cache=None): - """ - Computes the loss derivative against the inputs. - """ - res, _, last_node, last_attr = self._common_loss_dloss( - X, y, cache=cache) - if len(res.shape) <= 1: - return last_node.dlossds(res[last_attr['inputs']], y) # pylint: disable=E1120 - return last_node.dlossds(res[:, last_attr['inputs']], y) # pylint: disable=E1120 - - def gradient_backward(self, graddx, X, inputs=False, cache=None): - """ - Computes the gradient in X. - - :param graddx: existing gradient against the inputs - :param X: computes the gradient in X - :param inputs: if False, derivative against the coefficients, - otherwise against the inputs. - :param cache: cache intermediate results to avoid more computation - :return: gradient - """ - if cache is None: - cache = self.fill_cache(X) - shape = self.training_weights.shape - pred = self.predict(X) - - whole_gradx = numpy.zeros(pred.shape, dtype=numpy.float64) - whole_gradw = numpy.zeros(shape, dtype=numpy.float64) - if len(graddx.shape) == 0: - whole_gradx[-1] = graddx - else: - whole_gradx[-graddx.shape[0]:] = graddx - - for node, attr in zip(self.nodes[::-1], self.nodes_attr[::-1]): - ch = cache[node.nodeid] - - node_graddx = whole_gradx[attr['output']] - xi = pred[attr['inputs']] - - temp_gradw = node.gradient_backward( - node_graddx, xi, inputs=False, cache=ch) - temp_gradx = node.gradient_backward( - node_graddx, xi, inputs=True, cache=ch) - - whole_gradw[attr['first_coef']:attr['first_coef'] + - attr['coef_size']] += temp_gradw.reshape((attr['coef_size'],)) - whole_gradx[attr['inputs'] - ] += temp_gradx.reshape((len(attr['inputs']),)) - - if inputs: - return whole_gradx - return whole_gradw diff --git a/src/mlstatpy/nlp/normalize.py b/src/mlstatpy/nlp/normalize.py deleted file mode 100644 index 8ff6065f..00000000 --- a/src/mlstatpy/nlp/normalize.py +++ /dev/null @@ -1,24 +0,0 @@ -# -*- coding: utf-8 -*- -""" -@file -@brief Text normalization -""" -import unicodedata - - -def remove_diacritics(input_str): - """ - remove diacritics - - @param input_str string to clean - @return cleaned string - - Example:: - - enguérand --> enguerand - - .. versionadded:: 1.0 - """ - nkfd_form = unicodedata.normalize('NFKD', input_str) - only_ascii = nkfd_form.encode('ASCII', 'ignore') - return only_ascii.decode("utf8") diff --git a/src/mlstatpy/optim/__init__.py b/src/mlstatpy/optim/__init__.py deleted file mode 100644 index 5ab9ea75..00000000 --- a/src/mlstatpy/optim/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -""" -@file -@brief Shortcuts to *optim*. -""" - -from .sgd import SGDOptimizer