org.encog.neural.som

Class SOM

java.lang.Object

org.encog.ml.BasicML

org.encog.neural.som.SOM

All Implemented Interfaces:

java.io.Serializable, MLClassification, MLError, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLResettable

public class SOM
extends BasicML
implements MLClassification, MLResettable, MLError

A self organizing map neural network.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	VERYSMALL Do not allow patterns to go below this very small number.

Constructor Summary

Constructors

Constructor and Description
SOM() Default constructor.
SOM(int inputCount, int outputCount) The constructor.

Method Summary

Modifier and Type	Method and Description
double	calculateError(MLDataSet data) Calculate the error of the ML method, given a dataset.
int	classify(MLData input) Classify the input into a group.
int	getInputCount()
int	getOutputCount()
Matrix	getWeights()
void	reset() Reset the weights.
void	reset(int seed) Reset the weights with a seed.
void	setWeights(Matrix weights)
void	updateProperties() Update any objeccts when a property changes.
int	winner(MLData input) An alias for the classify method, kept for compatibility with earlier versions of Encog.

Methods inherited from class org.encog.ml.BasicML

getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty

Methods inherited from class java.lang.Object

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

Field Detail

VERYSMALL

public static final double VERYSMALL

Do not allow patterns to go below this very small number.

See Also:

Constant Field Values

Constructor Detail

SOM

public SOM()

Default constructor.

SOM

public SOM(int inputCount,
           int outputCount)

The constructor.

Parameters:

inputCount - Number of input neurons

outputCount - Number of output neurons

Method Detail

calculateError

public double calculateError(MLDataSet data)

Calculate the error of the ML method, given a dataset.

Specified by:

calculateError in interface MLError

Parameters:

data - The dataset.

Returns:

The error.

classify

public int classify(MLData input)

Classify the input into a group.

Specified by:

classify in interface MLClassification

Parameters:

input - The input data to classify.

Returns:

The group that the data was classified into.

getInputCount

public int getInputCount()

Specified by:

getInputCount in interface MLInput

Returns:

The input.

getOutputCount

public int getOutputCount()

Specified by:

getOutputCount in interface MLOutput

Returns:

The output count.

getWeights

public Matrix getWeights()

Returns:

the weights

reset

public void reset()

Reset the weights.

Specified by:

reset in interface MLResettable

reset

public void reset(int seed)

Reset the weights with a seed.

Specified by:

reset in interface MLResettable

Parameters:

seed - The seed value.

setWeights

public void setWeights(Matrix weights)

Parameters:

weights - the weights to set

updateProperties

public void updateProperties()

Update any objeccts when a property changes.

Specified by:

updateProperties in interface MLProperties

Specified by:

updateProperties in class BasicML

winner

public int winner(MLData input)

An alias for the classify method, kept for compatibility with earlier versions of Encog.

Parameters:

input - The input pattern.

Returns:

The winning neuron.