Neuron class provides LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neurons learned with Gradient descent or LeLevenberg–Marquardt algorithm. This class is suitable for prediction on time series.
Neuron class needs pandas and numpy to work propertly.
Consider Y are targets and X are inputs.
## LNUGD
neuron=LNUGD() prediction=1yn, w, e, Wall, MSE=neuron.train(Y_train, X_train, epochs=2, prediction=prediction) yn, w, Wall, MSE, e=neuron.countSerie(Y, X, logging=False, prediction=prediction)neuron=QNULM() prediction=1yn, w, e, Wall, MSE=neuron.train(Y_train, X_train, epochs=10, prediction=prediction) yn, w, MSE, e=neuron.countSerie(Y, X, logging=False, prediction=prediction)neuron=RBF() prediction=1neuron.train(Y_train, X_train, prediction=prediction) yn=neuron.count(Y,X, logging=True, beta=0.01, prediction=prediction)neuron=MLPGD() prediction=1yn=neuron.count(Y_train, X_train, prediction=prediction, epochs=5) yn=neuron.count(Y, X, prediction=prediction, epochs=1)neuron=MLPELM() prediction=1yn=neuron.count(Y_train, X_train, prediction=prediction, epochs=10) yn=neuron.count(Y, X, prediction=prediction)neuron=MLPLMWL() prediction=1yn=neuron.count(Y, X, learningWindow=50, overLearn=10, prediction=prediction)If you find this useful, consider supporting independent open-source development and buy me a coffee.
