kohonen
Class WTALearningFunction

java.lang.Object
  kohonen.WTALearningFunction

Direct Known Subclasses:

WTALearningFunctionWithTired

public class WTALearningFunction
extends java.lang.Object

Winner Takes All - algorytm there only winnig 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 interation
w(k) - neruon weight for k iteration
n - value of learning function factor for k iteriation
x - learning vector od data w - neuron weight

See Also:

WTMLearningFunction

Field Summary

protected LearningFactorFunctionalModel	functionalModel reference to function model
protected LearningDataModel	learningData reference to learning data
protected int	maxIteration max number of iteration
protected MetricModel	metrics reference to metrics
protected NetworkModel	networkModel reference to network model

Constructor Summary

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

Method Summary

protected void	changeNeuronWeight(int neuronNumber, double[] vector, int iteration) Change neuron weights for specified neuron number,iteration and input data vector
protected 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 dispalys 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 interation
void	setMetrics(MetricModel metrics) Set metrics
void	setNetworkModel(NetworkModel networkModel) Set network model
void	setShowComments(boolean showComments) Set if comments durring learning process must be shown.

Methods inherited from class java.lang.Object

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

Field Detail

metrics

protected MetricModel metrics

reference to metrics

networkModel

protected NetworkModel networkModel

reference to network model

maxIteration

protected int maxIteration

max number of iteration

learningData

protected LearningDataModel learningData

reference to learning data

functionalModel

protected LearningFactorFunctionalModel functionalModel

reference to function model

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 - learnig data

functionalModel - functional model

See Also:

MetricModel, LearningData, NetworkModel, LearningFactorFunctionalModel

Method Detail

isShowComments

public boolean isShowComments()

Return information if learning process dispalys information about learning process.

Returns:

true if learning process display information

setShowComments

public void setShowComments(boolean showComments)

Set if comments durring 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 interation

Parameters:

maxIteration - max interation

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

protected int getBestNeuron(double[] vector)

Return number of the best neuron for specified input vector

Parameters:

vector - input vector

Returns:

Neuron number

changeNeuronWeight

protected void changeNeuronWeight(int neuronNumber,
                                  double[] vector,
                                  int iteration)

Change neuron weights for specified neuron number,iteration and input data vector

Parameters:

neuronNumber - neuron number

vector - input vector

iteration - iteration number

learn

public void learn()

Start learning process