Catalano.Neuro.Classifier

Class ExtremeLearningMachine

java.lang.Object

Catalano.Neuro.Classifier.ExtremeLearningMachine

All Implemented Interfaces:

IClassifier, java.io.Serializable, java.lang.Cloneable

public class ExtremeLearningMachine
extends java.lang.Object
implements IClassifier, java.io.Serializable

Extreme Learning Machine.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ExtremeLearningMachine() Initializes a new instance of the ExtremeLearningMachine class.
ExtremeLearningMachine(int nHiddenNodes) Initializes a new instance of the ExtremeLearningMachine class.
ExtremeLearningMachine(int nHiddenNodes, double c) Initializes a new instance of the ExtremeLearningMachine class.
ExtremeLearningMachine(int nHiddenNodes, double c, IActivationFunction function) Initializes a new instance of the ExtremeLearningMachine class.
ExtremeLearningMachine(int nHiddenNodes, double c, IActivationFunction function, long seed) Initializes a new instance of the ExtremeLearningMachine class.

Method Summary

Modifier and Type	Method and Description
IClassifier	clone() Clone of the object.
double[]	getBias() Get the bias of the hidden nodes.
IActivationFunction	getFunction() Get the activation function.
double[][]	getInputWeight() Get the input weight.
int	getNumberOfHiddenNodes() Get the number of hidden nodes.
double[][]	getOutputWeight() Get the output weight.
double	getRegulazationFactor() Get regularization factor.
long	getSeed() Get random seed.
void	Learn(DatasetClassification dataset) Learn from a dataset.
void	Learn(double[][] input, int[] output) Learn.
int	Predict(double[] feature) Predict.
void	setBias(double[] bias) Set the bias of the hidden nodes.
void	setFunction(IActivationFunction function) Set the activation function.
void	setInputWeight(double[][] inputWeight) Set the input weight.
void	setNumberOfHiddenNodes(int nHiddenNodes) Set the number of hidden nodes.
void	setOutputWeight(double[][] outputWeight) Set the output weight.
void	setRegularizationFactor(double c) Set regularization factor.
void	setSeed(long seed) Set random seed.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ExtremeLearningMachine

public ExtremeLearningMachine()

Initializes a new instance of the ExtremeLearningMachine class.

ExtremeLearningMachine

public ExtremeLearningMachine(int nHiddenNodes)

Initializes a new instance of the ExtremeLearningMachine class.

Parameters:

nHiddenNodes - Number of hidden nodes.

ExtremeLearningMachine

public ExtremeLearningMachine(int nHiddenNodes,
                              double c)

Initializes a new instance of the ExtremeLearningMachine class.

Parameters:

nHiddenNodes - Number of hidden nodes.

c - Regularization factor.

ExtremeLearningMachine

public ExtremeLearningMachine(int nHiddenNodes,
                              double c,
                              IActivationFunction function)

Initializes a new instance of the ExtremeLearningMachine class.

Parameters:

nHiddenNodes - Number of hidden nodes.

c - Regularization factor.

function - Activation function.

ExtremeLearningMachine

public ExtremeLearningMachine(int nHiddenNodes,
                              double c,
                              IActivationFunction function,
                              long seed)

Initializes a new instance of the ExtremeLearningMachine class.

Parameters:

nHiddenNodes - Number of hidden nodes.

c - Regularization factor.

function - Activation function.

seed - Random seed.

Method Detail

getNumberOfHiddenNodes

public int getNumberOfHiddenNodes()

Get the number of hidden nodes.

Returns:

Number of hidden nodes.

setNumberOfHiddenNodes

public void setNumberOfHiddenNodes(int nHiddenNodes)

Set the number of hidden nodes.

Parameters:

nHiddenNodes - Number of hidden nodes.

getRegulazationFactor

public double getRegulazationFactor()

Get regularization factor.

Returns:

Regularization factor.

setRegularizationFactor

public void setRegularizationFactor(double c)

Set regularization factor.

Parameters:

c - Regularization factor.

getBias

public double[] getBias()

Get the bias of the hidden nodes.

Returns:

Bias of the hidden nodes.

setBias

public void setBias(double[] bias)

Set the bias of the hidden nodes.

Parameters:

bias - Bias of the hidden nodes.

getInputWeight

public double[][] getInputWeight()

Get the input weight.

Returns:

Input weight.

setInputWeight

public void setInputWeight(double[][] inputWeight)

Set the input weight.

Parameters:

inputWeight - Input weight.

getOutputWeight

public double[][] getOutputWeight()

Get the output weight.

Returns:

Output weight.

setOutputWeight

public void setOutputWeight(double[][] outputWeight)

Set the output weight.

Parameters:

outputWeight - Output weight.

getFunction

public IActivationFunction getFunction()

Get the activation function.

Returns:

Activation function.

setFunction

public void setFunction(IActivationFunction function)

Set the activation function.

Parameters:

function - Activation function.

getSeed

public long getSeed()

Get random seed.

Returns:

Seed.

setSeed

public void setSeed(long seed)

Set random seed.

Parameters:

seed - Seed.

Learn

public void Learn(DatasetClassification dataset)

Description copied from interface: IClassifier

Learn from a dataset.

Specified by:

Learn in interface IClassifier

Parameters:

dataset - Dataset.

Learn

public void Learn(double[][] input,
                  int[] output)

Description copied from interface: IClassifier

Learn.

Specified by:

Learn in interface IClassifier

Parameters:

input - Matrix of features.

output - Labels.

Predict

public int Predict(double[] feature)

Description copied from interface: IClassifier

Predict.

Specified by:

Predict in interface IClassifier

Parameters:

feature - Feature.

Returns:

Label.

clone

public IClassifier clone()

Description copied from interface: IClassifier

Clone of the object.

Specified by:

clone in interface IClassifier

Overrides:

clone in class java.lang.Object

Returns:

A new copy of the object.