A B D E F G I L M N O P R S T U W

A

activation(double, double[], int) - Method in interface weka.classifiers.functions.activation.ActivationFunction

Computes activation function.

activation(double, double[], int) - Method in class weka.classifiers.functions.activation.ApproximateSigmoid

Computes approximate sigmoid function.

activation(double, double[], int) - Method in class weka.classifiers.functions.activation.Sigmoid

Computes sigmoid function.

activation(double, double[], int) - Method in class weka.classifiers.functions.activation.Softplus

Computes softplus activation function.

ActivationFunction - Interface in weka.classifiers.functions.activation

Interface to be implemented for activation functions.

activationFunctionTipText() - Method in class weka.classifiers.functions.MLPModel

ApproximateAbsoluteError - Class in weka.classifiers.functions.loss

Approximate absolute error for MLPRegressor and MLPClassifier:
loss(a, b) = sqrt((a-b)^2+epsilon)

Valid options are:

ApproximateAbsoluteError() - Constructor for class weka.classifiers.functions.loss.ApproximateAbsoluteError

ApproximateSigmoid - Class in weka.classifiers.functions.activation

Computes approximate (fast) version of sigmoid activation function f(x) = 1 / (1 + e^(-x))

ApproximateSigmoid() - Constructor for class weka.classifiers.functions.activation.ApproximateSigmoid

B

buildClassifier(Instances) - Method in class weka.classifiers.functions.MLPModel

Builds the MLP network classifier based on the given dataset.

D

derivative(double, double) - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

The derivative of the loss with respect to the predicted value

derivative(double, double) - Method in interface weka.classifiers.functions.loss.LossFunction

The derivative of the loss with respect to the predicted value

derivative(double, double) - Method in class weka.classifiers.functions.loss.SquaredError

The derivative of the loss with respect to the predicted value

distributionForInstance(Instance) - Method in class weka.classifiers.functions.MLPModel

Calculates the output of the network after the instance has been piped through the fliters to replace missing values, etc.

E

epsilonTipText() - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

F

FILTER_NONE - Static variable in class weka.filters.unsupervised.attribute.MLPAutoencoder

FILTER_NORMALIZE - Static variable in class weka.filters.unsupervised.attribute.MLPAutoencoder

FILTER_STANDARDIZE - Static variable in class weka.filters.unsupervised.attribute.MLPAutoencoder

filterTypeTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Returns the tip text for this property

G

getActivationFunction() - Method in class weka.classifiers.functions.MLPModel

Returns the ActivationFunction object.

getCapabilities() - Method in class weka.classifiers.functions.MLPClassifier

Returns default capabilities of the classifier.

getCapabilities() - Method in class weka.classifiers.functions.MLPModel

Returns default capabilities of the classifier.

getCapabilities() - Method in class weka.classifiers.functions.MLPRegressor

Returns default capabilities of the classifier.

getCapabilities() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Returns default capabilities of the filter.

getEpsilon() - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

Returns the value of the epsilon parameter.

getFilterType() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets how the training data will be transformed.

getLambda() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets the value of the lambda parameter.

getLossFunction() - Method in class weka.classifiers.functions.MLPModel

Returns the LossFunction object.

getNumFunctions() - Method in class weka.classifiers.functions.MLPModel

Gets the number of functions.

getNumFunctions() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets the number of functions.

getNumThreads() - Method in class weka.classifiers.functions.MLPModel

Gets the number of threads.

getNumThreads() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets the number of threads.

getOptions() - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

Gets the current settings of the loss function.

getOptions() - Method in class weka.classifiers.functions.MLPModel

Gets the current settings of the Classifier.

getOptions() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets the current settings of the Filter.

getOutputInOriginalSpace() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets whether to use original space.

getPoolSize() - Method in class weka.classifiers.functions.MLPModel

Gets the number of threads.

getPoolSize() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets the number of threads.

getRidge() - Method in class weka.classifiers.functions.MLPModel

Gets the value of the ridge parameter.

getSeed() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets the seed for the random number generations

getTechnicalInformation() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

getTolerance() - Method in class weka.classifiers.functions.MLPModel

Gets the tolerance parameter for the delta values.

getTolerance() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets the tolerance parameter for the delta values.

getUseCGD() - Method in class weka.classifiers.functions.MLPModel

Gets whether to use CGD.

getUseCGD() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets whether to use CGD.

getUseContractiveAutoencoder() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets whether to use ContractiveAutoencoder.

getUseExactSigmoid() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets whether to use exact sigmoid.

getWeightsFile() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Gets current weights file.

globalInfo() - Method in class weka.classifiers.functions.activation.ApproximateSigmoid

Returns info for this class.

globalInfo() - Method in class weka.classifiers.functions.activation.Sigmoid

Returns info for this class.

globalInfo() - Method in class weka.classifiers.functions.activation.Softplus

Returns info for this class.

globalInfo() - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

This will return a string describing the classifier.

globalInfo() - Method in class weka.classifiers.functions.loss.SquaredError

This will return a string describing the classifier.

globalInfo() - Method in class weka.classifiers.functions.MLPModel

This will return a string describing the classifier.

globalInfo() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

This will return a string describing the filter.

I

initFilter(Instances) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Builds the autoencoder network based on the given data.

L

lambdaTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

listOptions() - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

Returns an enumeration describing the available options.

listOptions() - Method in class weka.classifiers.functions.MLPModel

Returns an enumeration describing the available options.

listOptions() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Returns an enumeration describing the available options.

loss(double, double) - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

Returns the loss.

loss(double, double) - Method in interface weka.classifiers.functions.loss.LossFunction

Returns the loss.

loss(double, double) - Method in class weka.classifiers.functions.loss.SquaredError

Returns the loss.

LossFunction - Interface in weka.classifiers.functions.loss

Interface implemented by loss functions for MLPRegressor and MLPClassifier.

lossFunctionTipText() - Method in class weka.classifiers.functions.MLPModel

M

main(String[]) - Static method in class weka.classifiers.functions.MLPClassifier

Main method to run the code from the command-line using the standard WEKA options.

main(String[]) - Static method in class weka.classifiers.functions.MLPRegressor

Main method to run the code from the command-line using the standard WEKA options.

main(String[]) - Static method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Main method to run the code from the command-line using the standard WEKA options.

MLPAutoencoder - Class in weka.filters.unsupervised.attribute

Implements an autoencoder with one hidden layer and tied weights using WEKA's Optimization class by minimizing the squared error plus a quadratic penalty (weight decay) with the BFGS method.

MLPAutoencoder() - Constructor for class weka.filters.unsupervised.attribute.MLPAutoencoder

MLPClassifier - Class in weka.classifiers.functions

Trains a multilayer perceptron with one hidden layer using WEKA's Optimization class by minimizing the given loss function plus a quadratic penalty with the BFGS method.

MLPClassifier() - Constructor for class weka.classifiers.functions.MLPClassifier

MLPModel - Class in weka.classifiers.functions

Abstract super class for MLPClassifier and MLPRegressor.

MLPModel() - Constructor for class weka.classifiers.functions.MLPModel

MLPRegressor - Class in weka.classifiers.functions

Trains a multilayer perceptron with one hidden layer using WEKA's Optimization class by minimizing the given loss function plus a quadratic penalty with the BFGS method.

MLPRegressor() - Constructor for class weka.classifiers.functions.MLPRegressor

modelType() - Method in class weka.classifiers.functions.MLPClassifier

Returns the model type as a string.

modelType() - Method in class weka.classifiers.functions.MLPModel

Returns the model type as a string.

modelType() - Method in class weka.classifiers.functions.MLPRegressor

Returns the model type as a string.

N

numFunctionsTipText() - Method in class weka.classifiers.functions.MLPModel

numFunctionsTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

numThreadsTipText() - Method in class weka.classifiers.functions.MLPModel

numThreadsTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

O

outputInOriginalSpaceTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

P

poolSizeTipText() - Method in class weka.classifiers.functions.MLPModel

poolSizeTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

R

ridgeTipText() - Method in class weka.classifiers.functions.MLPModel

S

seedTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Returns the tip text for this property

setActivationFunction(ActivationFunction) - Method in class weka.classifiers.functions.MLPModel

Sets the loss function.

setEpsilon(double) - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

Sets the value of the epsilon parameter.

setFilterType(SelectedTag) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets how the training data will be transformed.

setLambda(double) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets the value of the lambda parameter.

setLossFunction(LossFunction) - Method in class weka.classifiers.functions.MLPModel

Sets the loss function.

setNumFunctions(int) - Method in class weka.classifiers.functions.MLPModel

Sets the number of functions.

setNumFunctions(int) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets the number of functions.

setNumThreads(int) - Method in class weka.classifiers.functions.MLPModel

Sets the number of threads

setNumThreads(int) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets the number of threads

setOptions(String[]) - Method in class weka.classifiers.functions.loss.ApproximateAbsoluteError

Parses a given list of options.

setOptions(String[]) - Method in class weka.classifiers.functions.MLPModel

Parses a given list of options.

setOptions(String[]) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Parses a given list of options.

setOutputInOriginalSpace(boolean) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets whether to use original space.

setPoolSize(int) - Method in class weka.classifiers.functions.MLPModel

Sets the number of threads

setPoolSize(int) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets the number of threads

setRidge(double) - Method in class weka.classifiers.functions.MLPModel

Sets the value of the ridge parameter.

setSeed(int) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Set the seed for random number generation.

setTolerance(double) - Method in class weka.classifiers.functions.MLPModel

Sets the tolerance parameter for the delta values.

setTolerance(double) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets the tolerance parameter for the delta values.

setUseCGD(boolean) - Method in class weka.classifiers.functions.MLPModel

Sets whether to use CGD.

setUseCGD(boolean) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets whether to use CGD.

setUseContractiveAutoencoder(boolean) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets whether to use ContractiveAutoencoder.

setUseExactSigmoid(boolean) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets whether to use exact sigmoid.

setWeightsFile(File) - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Sets the weights file to use.

Sigmoid - Class in weka.classifiers.functions.activation

Computes sigmoid activation function f(x) = 1 / (1 + e^(-x))

Sigmoid() - Constructor for class weka.classifiers.functions.activation.Sigmoid

Softplus - Class in weka.classifiers.functions.activation

Computes softplus activation function f(x) = ln(1 + e^(x))

Softplus() - Constructor for class weka.classifiers.functions.activation.Softplus

SquaredError - Class in weka.classifiers.functions.loss

Squared error for MLPRegressor and MLPClassifier:
loss(a, b) = (a-b)^2

SquaredError() - Constructor for class weka.classifiers.functions.loss.SquaredError

T

TAGS_FILTER - Static variable in class weka.filters.unsupervised.attribute.MLPAutoencoder

toleranceTipText() - Method in class weka.classifiers.functions.MLPModel

toleranceTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

toString() - Method in class weka.classifiers.functions.MLPModel

Outputs the network as a string.

U

useCGDTipText() - Method in class weka.classifiers.functions.MLPModel

useCGDTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

useContractiveAutoencoderTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

useExactSigmoidTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

W

weightsFileTipText() - Method in class weka.filters.unsupervised.attribute.MLPAutoencoder

Returns the tip text for this property.

weka.classifiers.functions - package weka.classifiers.functions

weka.classifiers.functions.activation - package weka.classifiers.functions.activation

weka.classifiers.functions.loss - package weka.classifiers.functions.loss

weka.filters.unsupervised.attribute - package weka.filters.unsupervised.attribute

A B D E F G I L M N O P R S T U W