/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.bam;
import org.encog.mathutil.matrices.Matrix;
import org.encog.ml.BasicML;
import org.encog.ml.data.MLData;
import org.encog.neural.NeuralNetworkError;
import org.encog.neural.networks.NeuralDataMapping;
/**
* 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.
*
* @author jheaton
*
*/
public class BAM extends BasicML {
/**
* Serial id.
*/
private static final long serialVersionUID = 1L;
/**
* Neurons in the F1 layer.
*/
private int f1Count;
/**
* Neurons in the F2 layer.
*/
private int f2Count;
/**
* The weights between the F1 and F2 layers.
*/
private Matrix weightsF1toF2;
/**
* The weights between the F1 and F2 layers.
*/
private Matrix weightsF2toF1;
/**
* Default constructor, used mainly for persistence.
*/
public BAM() {
}
/**
* Construct the BAM network.
* @param theF1Count The F1 count.
* @param theF2Count The F2 count.
*/
public BAM(final int theF1Count, final int theF2Count) {
this.f1Count = theF1Count;
this.f2Count = theF2Count;
this.weightsF1toF2 = new Matrix(f1Count, f2Count);
this.weightsF2toF1 = new Matrix(f2Count, f1Count);
}
/**
* Add a pattern to the neural network.
*
* @param inputPattern
* The input pattern.
* @param outputPattern
* The output pattern(for this input).
*/
public void addPattern(final MLData inputPattern,
final MLData outputPattern) {
int weight;
for (int i = 0; i < this.f1Count; i++) {
for (int j = 0; j < this.f2Count; j++) {
weight = (int) (inputPattern.getData(i) * outputPattern
.getData(j));
this.weightsF1toF2.add(i, j, weight);
this.weightsF2toF1.add(j, i, weight);
}
}
}
/**
* Clear any connection weights.
*/
public void clear() {
this.weightsF1toF2.clear();
this.weightsF2toF1.clear();
}
/**
* Setup the network logic, read parameters from the network. NOT USED, call
* compute(NeuralInputData).
*
* @param input
* NOT USED
* @return NOT USED
*/
public MLData compute(final MLData input) {
throw new NeuralNetworkError(
"Compute on BasicNetwork cannot be used, rather call"
+ " the compute(NeuralData) method on the BAMLogic.");
}
/**
* Compute the network for the specified input.
*
* @param input
* The input to the network.
* @return The output from the network.
*/
public NeuralDataMapping compute(final NeuralDataMapping input) {
boolean stable1 = true, stable2 = true;
do {
stable1 = propagateLayer(this.weightsF1toF2, input.getFrom(),
input.getTo());
stable2 = propagateLayer(this.weightsF2toF1, input.getTo(),
input.getFrom());
} while (!stable1 && !stable2);
return null;
}
/**
* @return the f1Count
*/
public int getF1Count() {
return this.f1Count;
}
/**
* @return the f2Count
*/
public int getF2Count() {
return this.f2Count;
}
/**
* Get the specified weight.
*
* @param matrix The matrix to use.
* @param input
* The input, to obtain the size from.
* @param x
* The x matrix value. (could be row or column, depending on
* input)
* @param y
* The y matrix value. (could be row or column, depending on
* input)
* @return The value from the matrix.
*/
private double getWeight(final Matrix matrix, final MLData input,
final int x, final int y) {
if (matrix.getRows() != input.size()) {
return matrix.get(x, y);
} else {
return matrix.get(y, x);
}
}
/**
* @return the weightsF1toF2
*/
public Matrix getWeightsF1toF2() {
return this.weightsF1toF2;
}
/**
* @return the weightsF2toF1
*/
public Matrix getWeightsF2toF1() {
return this.weightsF2toF1;
}
/**
* Propagate the layer.
*
* @param matrix
* The matrix for this layer.
* @param input
* The input pattern.
* @param output
* The output pattern.
* @return True if the network has become stable.
*/
private boolean propagateLayer(final Matrix matrix, final MLData input,
final MLData output) {
int i, j;
int sum, out = 0;
boolean stable;
stable = true;
for (i = 0; i < output.size(); i++) {
sum = 0;
for (j = 0; j < input.size(); j++) {
sum += getWeight(matrix, input, i, j) * input.getData(j);
}
if (sum != 0) {
if (sum < 0) {
out = -1;
} else {
out = 1;
}
if (out != (int) output.getData(i)) {
stable = false;
output.setData(i, out);
}
}
}
return stable;
}
/**
* Set the F1 neuron count.
* @param i The count.
*/
public void setF1Count(final int i) {
this.f1Count = i;
}
/**
* Set the F2 neuron count.
* @param i The count.
*/
public void setF2Count(final int i) {
this.f2Count = i;
}
/**
* Set the weights for F1 to F2.
* @param matrix The weight matrix.
*/
public void setWeightsF1toF2(final Matrix matrix) {
this.weightsF1toF2 = matrix;
}
/**
* Set the weights for F2 to F1.
* @param matrix The weight matrix.
*/
public void setWeightsF2toF1(final Matrix matrix) {
this.weightsF2toF1 = matrix;
}
/**
* {@inheritDoc}
*/
@Override
public void updateProperties() {
}
}
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