org.encog.neural.som
Class SOM

java.lang.Object
  extended by org.encog.ml.BasicML
      extended by org.encog.neural.som.SOM
All Implemented Interfaces:
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
static double VERYSMALL
          Do not allow patterns to go below this very small number.
 
Constructor Summary
SOM()
          Default constructor.
SOM(int inputCount, int outputCount)
          The constructor.
 
Method Summary
 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.


Copyright © 2014. All Rights Reserved.