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wrichertwrichert
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Cleaned charts/data handling; fixed outdated norm.rvs() parameterization
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6 files changed

+42
-48
lines changed

6 files changed

+42
-48
lines changed

‎ch11/Makefile‎

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This file was deleted.

‎ch11/demo_corr.py‎

Lines changed: 9 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -12,8 +12,7 @@
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importscipy
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fromscipy.statsimportnorm, pearsonr
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DATA_DIR=os.path.join("..", "data")
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CHART_DIR=os.path.join("..", "charts")
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fromutilsimportCHART_DIR
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def_plot_correlation_func(x, y):
@@ -39,19 +38,19 @@ def plot_correlation_demo():
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x=np.arange(0, 10, 0.2)
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pylab.subplot(221)
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y=0.5*x+norm.rvs(1, loc=0, scale=.01, size=len(x))
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y=0.5*x+norm.rvs(1, scale=.01, size=len(x))
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_plot_correlation_func(x, y)
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pylab.subplot(222)
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y=0.5*x+norm.rvs(1, loc=0, scale=.1, size=len(x))
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y=0.5*x+norm.rvs(1, scale=.1, size=len(x))
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_plot_correlation_func(x, y)
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pylab.subplot(223)
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y=0.5*x+norm.rvs(1, loc=0, scale=1, size=len(x))
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y=0.5*x+norm.rvs(1, scale=1, size=len(x))
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_plot_correlation_func(x, y)
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pylab.subplot(224)
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y=norm.rvs(1, loc=0, scale=10, size=len(x))
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y=norm.rvs(1, scale=10, size=len(x))
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_plot_correlation_func(x, y)
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pylab.autoscale(tight=True)
@@ -66,19 +65,19 @@ def plot_correlation_demo():
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x=np.arange(-5, 5, 0.2)
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pylab.subplot(221)
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y=0.5*x**2+norm.rvs(1, loc=0, scale=.01, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=.01, size=len(x))
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_plot_correlation_func(x, y)
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pylab.subplot(222)
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y=0.5*x**2+norm.rvs(1, loc=0, scale=.1, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=.1, size=len(x))
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_plot_correlation_func(x, y)
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pylab.subplot(223)
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y=0.5*x**2+norm.rvs(1, loc=0, scale=1, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=1, size=len(x))
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_plot_correlation_func(x, y)
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pylab.subplot(224)
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y=0.5*x**2+norm.rvs(1, loc=0, scale=10, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=10, size=len(x))
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_plot_correlation_func(x, y)
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pylab.autoscale(tight=True)

‎ch11/demo_mds.py‎

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Original file line numberDiff line numberDiff line change
@@ -14,8 +14,7 @@
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fromsklearnimportlinear_model, manifold, decomposition, datasets
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logistic=linear_model.LogisticRegression()
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CHART_DIR=os.path.join("..", "charts")
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fromutilsimportCHART_DIR
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np.random.seed(3)
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‎ch11/demo_mi.py‎

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Original file line numberDiff line numberDiff line change
@@ -11,8 +11,7 @@
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importnumpyasnp
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fromscipy.statsimportnorm, entropy
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DATA_DIR=os.path.join("..", "data")
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CHART_DIR=os.path.join("..", "charts")
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fromutilsimportCHART_DIR
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defmutual_info(x, y, bins=10):
@@ -73,19 +72,19 @@ def plot_mi_demo():
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x=np.arange(0, 10, 0.2)
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pylab.subplot(221)
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y=0.5*x+norm.rvs(1, loc=0, scale=.01, size=len(x))
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y=0.5*x+norm.rvs(1, scale=.01, size=len(x))
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_plot_mi_func(x, y)
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pylab.subplot(222)
80-
y=0.5*x+norm.rvs(1, loc=0, scale=.1, size=len(x))
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y=0.5*x+norm.rvs(1, scale=.1, size=len(x))
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_plot_mi_func(x, y)
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pylab.subplot(223)
84-
y=0.5*x+norm.rvs(1, loc=0, scale=1, size=len(x))
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y=0.5*x+norm.rvs(1, scale=1, size=len(x))
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_plot_mi_func(x, y)
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pylab.subplot(224)
88-
y=norm.rvs(1, loc=0, scale=10, size=len(x))
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y=norm.rvs(1, scale=10, size=len(x))
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_plot_mi_func(x, y)
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pylab.autoscale(tight=True)
@@ -100,19 +99,19 @@ def plot_mi_demo():
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x=np.arange(-5, 5, 0.2)
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pylab.subplot(221)
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y=0.5*x**2+norm.rvs(1, loc=0, scale=.01, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=.01, size=len(x))
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_plot_mi_func(x, y)
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pylab.subplot(222)
107-
y=0.5*x**2+norm.rvs(1, loc=0, scale=.1, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=.1, size=len(x))
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_plot_mi_func(x, y)
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pylab.subplot(223)
111-
y=0.5*x**2+norm.rvs(1, loc=0, scale=1, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=1, size=len(x))
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_plot_mi_func(x, y)
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pylab.subplot(224)
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y=0.5*x**2+norm.rvs(1, loc=0, scale=10, size=len(x))
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y=0.5*x**2+norm.rvs(1, scale=10, size=len(x))
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_plot_mi_func(x, y)
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pylab.autoscale(tight=True)

‎ch11/demo_pca.py‎

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@@ -16,12 +16,12 @@
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logistic=linear_model.LogisticRegression()
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CHART_DIR=os.path.join("..", "charts")
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fromutilsimportCHART_DIR
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np.random.seed(3)
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x1=np.arange(0, 10, .2)
24-
x2=x1+np.random.normal(loc=0, scale=1, size=len(x1))
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x2=x1+np.random.normal(scale=1, size=len(x1))
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defplot_simple_demo_1():
@@ -35,7 +35,7 @@ def plot_simple_demo_1():
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pylab.ylabel("$X_2$")
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x1=np.arange(0, 10, .2)
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x2=x1+np.random.normal(loc=0, scale=1, size=len(x1))
38+
x2=x1+np.random.normal(scale=1, size=len(x1))
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good= (x1>5) | (x2>5)
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bad=~good
@@ -89,7 +89,7 @@ def plot_simple_demo_2():
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pylab.ylabel("$X_2$")
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9191
x1=np.arange(0, 10, .2)
92-
x2=x1+np.random.normal(loc=0, scale=1, size=len(x1))
92+
x2=x1+np.random.normal(scale=1, size=len(x1))
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good=x1>x2
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bad=~good

‎ch11/utils.py‎

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# This code is supporting material for the book
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# Building Machine Learning Systems with Python
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# by Willi Richert and Luis Pedro Coelho
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# published by PACKT Publishing
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#
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# It is made available under the MIT License
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importos
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DATA_DIR=os.path.join(
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os.path.dirname(os.path.realpath(__file__)), "data")
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CHART_DIR=os.path.join(
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os.path.dirname(os.path.realpath(__file__)), "charts")
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fordin [DATA_DIR, CHART_DIR]:
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ifnotos.path.exists(d):
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os.mkdir(d)
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