jhpro.nnet.jknnl

Class WTALearningFunction

java.lang.Object

jhpro.nnet.jknnl.WTALearningFunction

Direct Known Subclasses:

WTALearningFunctionWithTired

public class WTALearningFunction
extends java.lang.Object

Winner Takes All - algorithm there only wining neuron weights are changed according to the formula w(k+1) = w(k) + n * (x-w) where
w(k+1) - neuron weight in k +1 iteration
w(k) - neuron weight for k iteration
n - value of learning function factor for k iteration
x - learning vector of data w - neuron weight

See Also:

WTMLearningFunction

Constructor Summary

Constructors

Constructor and Description
WTALearningFunction(NetworkModel networkModel, int maxIteration, MetricModel metrics, LearningDataModel learningData, LearningFactorFunctionalModel functionalModel) Creates a new instance of WTALearningFunction.

Method Summary

Modifier and Type	Method and Description
int	getBestNeuron(double[] vector) Return number of the best neuron for specified input vector
LearningFactorFunctionalModel	getFunctionalModel() Return function model
LearningDataModel	getLearningData() Return reference to learning data
int	getMaxIteration() Return maximal number of iteration
MetricModel	getMetrics() Return metrics
NetworkModel	getNetworkModel() Return network model
boolean	isShowComments() Return information if learning process displays information about learning process.
void	learn() Start learning process
void	setFunctionalModel(LearningFactorFunctionalModel functionalModel) Set functional learning factor model
void	setLearningData(LearningDataModel learningData) Set reference to learning data
void	setMaxIteration(int maxIteration) Set max iteration
void	setMetrics(MetricModel metrics) Set metrics
void	setNetworkModel(NetworkModel networkModel) Set network model
void	setShowComments(boolean showComments) Set if comments during learning process must be shown.

Methods inherited from class java.lang.Object

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

Constructor Detail

WTALearningFunction

public WTALearningFunction(NetworkModel networkModel,
                           int maxIteration,
                           MetricModel metrics,
                           LearningDataModel learningData,
                           LearningFactorFunctionalModel functionalModel)

Creates a new instance of WTALearningFunction.

Parameters:

networkModel - network model

maxIteration - iteration number

metrics - metrics

learningData - learning data

functionalModel - functional model

See Also:

MetricModel, LearningData, NetworkModel, LearningFactorFunctionalModel

Method Detail

isShowComments

public boolean isShowComments()

Return information if learning process displays information about learning process.

Returns:

true if learning process display information

setShowComments

public void setShowComments(boolean showComments)

Set if comments during learning process must be shown.

Parameters:

showComments - true if comments must be shown, false otherwise

getMetrics

public MetricModel getMetrics()

Return metrics

Returns:

metrics

See Also:

MetricModel

setMetrics

public void setMetrics(MetricModel metrics)

Set metrics

Parameters:

metrics - metrics

setNetworkModel

public void setNetworkModel(NetworkModel networkModel)

Set network model

Parameters:

networkModel - network model

getNetworkModel

public NetworkModel getNetworkModel()

Return network model

Returns:

network model

setMaxIteration

public void setMaxIteration(int maxIteration)

Set max iteration

Parameters:

maxIteration - max iteration

getMaxIteration

public int getMaxIteration()

Return maximal number of iteration

Returns:

maximal number of iteration

setLearningData

public void setLearningData(LearningDataModel learningData)

Set reference to learning data

Parameters:

learningData - reference to learning data

getLearningData

public LearningDataModel getLearningData()

Return reference to learning data

Returns:

reference to learning data

setFunctionalModel

public void setFunctionalModel(LearningFactorFunctionalModel functionalModel)

Set functional learning factor model

Parameters:

functionalModel - functional learning factor model

getFunctionalModel

public LearningFactorFunctionalModel getFunctionalModel()

Return function model

Returns:

function model

getBestNeuron

public int getBestNeuron(double[] vector)

Return number of the best neuron for specified input vector

Parameters:

vector - input vector

Returns:

Neuron number

learn

public void learn()

Start learning process