kohonen
Class WTMLearningFunction

java.lang.Object
  kohonen.WTMLearningFunction

Direct Known Subclasses:

WTMLearningFunctionWithTired

public class WTMLearningFunction
extends java.lang.Object

Winner Takes Most - algorytm where winnig neuron and neurons in neighboorhood weights are changed according to the formula w(k+1) = w(k) + n *N(i,x)* (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
N(i,x) - value of neighboorhood function for i - specified neuron 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 NeighbourhoodFunctionModel	neighboorhoodFunction reference to neighboorhood function model
protected NetworkModel	networkModel reference to network model
protected TopologyModel	topology reference to topology model

Constructor Summary

WTMLearningFunction(NetworkModel networkModel, int maxIteration, MetricModel metrics, LearningDataModel learningData, LearningFactorFunctionalModel functionalModel, NeighbourhoodFunctionModel neighboorhoodFunction)
Creates a new instance of WTMLearningFunction

Method Summary

protected void	changeNeuronWeight(int neuronNumber, double[] vector, int iteration, int distance) Change neuron weights for specified neuron number, iteration, input data vector and distance and distance to winning neuron
void	changeWeight(int neuronNumber, double[] vector, int iteration) Change specified neuron weight
protected int	getBestNeuron(double[] vector) Rerturn number of 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
NeighbourhoodFunctionModel	getNeighboorhoodFunction() Return reference to neighboorhood function
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	setNeighboorhoodFunction(NeighbourhoodFunctionModel neighboorhoodFunction) Set reference to neighboorhood function
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

topology

protected TopologyModel topology

reference to topology model

neighboorhoodFunction

protected NeighbourhoodFunctionModel neighboorhoodFunction

reference to neighboorhood function model

Constructor Detail

WTMLearningFunction

public WTMLearningFunction(NetworkModel networkModel,
                           int maxIteration,
                           MetricModel metrics,
                           LearningDataModel learningData,
                           LearningFactorFunctionalModel functionalModel,
                           NeighbourhoodFunctionModel neighboorhoodFunction)

Creates a new instance of WTMLearningFunction

Parameters:

networkModel - reference to network Model

maxIteration - max number of iteration

metrics - reference to metrics

learningData - reference to learning data

functionalModel - reference to functional Model

neighboorhoodFunction - reference to Neighboorhood Function

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

setNeighboorhoodFunction

public void setNeighboorhoodFunction(NeighbourhoodFunctionModel neighboorhoodFunction)

Set reference to neighboorhood function

Parameters:

neighboorhoodFunction - reference to neighboorhood function

getNeighboorhoodFunction

public NeighbourhoodFunctionModel getNeighboorhoodFunction()

Return reference to neighboorhood function

Returns:

reference to neighboorhood function

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)

Rerturn number of best neuron for specified input vector

Parameters:

vector - input vector

Returns:

NeuronModelnumber

changeNeuronWeight

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

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

Parameters:

distance - distance to winning neuron

neuronNumber - neuron number

vector - input vector

iteration - iteration number

changeWeight

public void changeWeight(int neuronNumber,
                         double[] vector,
                         int iteration)

Change specified neuron weight

Parameters:

neuronNumber - neuron Number

vector - input vector

iteration - iteration number

learn

public void learn()

Start learning process