org.encog.neural.bam

Class BAM

java.lang.Object

org.encog.ml.BasicML

org.encog.neural.bam.BAM

All Implemented Interfaces:

java.io.Serializable, MLMethod, MLProperties

public class BAM
extends BasicML

Bidirectional associative memory (BAM) is a type of neural network developed by Bart Kosko in 1988. The BAM is a recurrent neural network that works similarly that allows patterns of different lengths to be mapped bidirectionally to other patterns. This allows it to act as almost a two-way hash map. During training the BAM is fed pattern pairs. The two halves of each pattern do not have to be the to be of the same length. However all patterns must be of the same overall structure. The BAM can be fed a distorted pattern on either side and will attempt to map to the correct value.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
BAM() Default constructor, used mainly for persistence.
BAM(int theF1Count, int theF2Count) Construct the BAM network.

Method Summary

Modifier and Type	Method and Description
void	addPattern(MLData inputPattern, MLData outputPattern) Add a pattern to the neural network.
void	clear() Clear any connection weights.
MLData	compute(MLData input) Setup the network logic, read parameters from the network.
NeuralDataMapping	compute(NeuralDataMapping input) Compute the network for the specified input.
int	getF1Count()
int	getF2Count()
Matrix	getWeightsF1toF2()
Matrix	getWeightsF2toF1()
void	setF1Count(int i) Set the F1 neuron count.
void	setF2Count(int i) Set the F2 neuron count.
void	setWeightsF1toF2(Matrix matrix) Set the weights for F1 to F2.
void	setWeightsF2toF1(Matrix matrix) Set the weights for F2 to F1.
void	updateProperties() Update any objeccts when a property changes.

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

Constructor Detail

BAM

public BAM()

Default constructor, used mainly for persistence.

BAM

public BAM(int theF1Count,
           int theF2Count)

Construct the BAM network.

Parameters:

theF1Count - The F1 count.

theF2Count - The F2 count.

Method Detail

addPattern

public void addPattern(MLData inputPattern,
                       MLData outputPattern)

Add a pattern to the neural network.

Parameters:

inputPattern - The input pattern.

outputPattern - The output pattern(for this input).

clear

public void clear()

Clear any connection weights.

compute

public MLData compute(MLData input)

Setup the network logic, read parameters from the network. NOT USED, call compute(NeuralInputData).

Parameters:

input - NOT USED

Returns:

NOT USED

compute

public NeuralDataMapping compute(NeuralDataMapping input)

Compute the network for the specified input.

Parameters:

input - The input to the network.

Returns:

The output from the network.

getF1Count

public int getF1Count()

Returns:

the f1Count

getF2Count

public int getF2Count()

Returns:

the f2Count

getWeightsF1toF2

public Matrix getWeightsF1toF2()

Returns:

the weightsF1toF2

getWeightsF2toF1

public Matrix getWeightsF2toF1()

Returns:

the weightsF2toF1

setF1Count

public void setF1Count(int i)

Set the F1 neuron count.

Parameters:

i - The count.

setF2Count

public void setF2Count(int i)

Set the F2 neuron count.

Parameters:

i - The count.

setWeightsF1toF2

public void setWeightsF1toF2(Matrix matrix)

Set the weights for F1 to F2.

Parameters:

matrix - The weight matrix.

setWeightsF2toF1

public void setWeightsF2toF1(Matrix matrix)

Set the weights for F2 to F1.

Parameters:

matrix - The weight matrix.

updateProperties

public void updateProperties()

Update any objeccts when a property changes.

Specified by:

updateProperties in interface MLProperties

Specified by:

updateProperties in class BasicML