/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.nnet.learning;
import org.neuroph.core.Connection;
import org.neuroph.core.Layer;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.learning.LearningRule;
import org.neuroph.core.learning.TrainingElement;
import org.neuroph.core.learning.TrainingSet;
/**
* Learning algorithm for the Hopfield neural network.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class HopfieldLearning extends LearningRule {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L;
/**
* Creates new HopfieldLearning
*/
public HopfieldLearning() {
super();
}
/**
* Calculates weights for the hopfield net to learn the specified training
* set
*
* @param trainingSet
* training set to learn
*/
public void learn(TrainingSet trainingSet) {
int M = trainingSet.size();
int N = neuralNetwork.getLayerAt(0).getNeuronsCount();
Layer hopfieldLayer = neuralNetwork.getLayerAt(0);
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
if (j == i)
continue;
Neuron ni = hopfieldLayer.getNeuronAt(i);
Neuron nj = hopfieldLayer.getNeuronAt(j);
Connection cij = nj.getConnectionFrom(ni);
Connection cji = ni.getConnectionFrom(nj);
double w = 0;
for (int k = 0; k < M; k++) {
TrainingElement trainingElement = trainingSet.elementAt(k);
double pki = trainingElement.getInput()[i];
double pkj = trainingElement.getInput()[j];
w = w + pki * pkj;
} // k
cij.getWeight().setValue(w);
cji.getWeight().setValue(w);
} // j
} // i
}
}