Awesome Open Source
Awesome Open Source

Neuron class

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.

Dependencies

Neuron class needs pandas and numpy to work propertly.

Example of usage

Consider Y are targets and X are inputs.

## LNUGD

neuron = LNUGD()
prediction = 1
yn, 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)

QNULM

neuron = QNULM()
prediction = 1
yn, 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)

RBF

neuron = RBF()
prediction = 1
neuron.train(Y_train, X_train, prediction=prediction)
yn = neuron.count(Y,X, logging=True, beta=0.01, prediction=prediction)

MLPGD

neuron = MLPGD()
prediction = 1
yn = neuron.count(Y_train, X_train, prediction=prediction, epochs=5)
yn = neuron.count(Y, X, prediction=prediction, epochs=1)

MLPELM

neuron = MLPELM()
prediction = 1
yn = neuron.count(Y_train, X_train, prediction = prediction, epochs = 10)
yn = neuron.count(Y, X, prediction = prediction)

MLPLMWL

neuron = MLPLMWL()
prediction = 1
yn = neuron.count(Y, X, learningWindow = 50, overLearn = 10,  prediction = prediction)

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