/**
* 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 java.util.Vector;
import org.neuroph.core.Connection;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.learning.TrainingSet;
import org.neuroph.core.learning.UnsupervisedLearning;
import org.neuroph.nnet.comp.CompetitiveLayer;
import org.neuroph.nnet.comp.CompetitiveNeuron;
/**
* Competitive learning rule.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class CompetitiveLearning extends UnsupervisedLearning {
/**
* 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 instance of CompetitiveLearning
*/
public CompetitiveLearning() {
super();
}
/**
* This method does one learning epoch for the unsupervised learning rules.
* It iterates through the training set and trains network weights for each
* element. Stops learning after one epoch.
*
* @param trainingSet
* training set for training network
*/
@Override
public void doLearningEpoch(TrainingSet trainingSet) {
super.doLearningEpoch(trainingSet);
stopLearning(); // stop learning ahter one learning epoch
}
/**
* Adjusts weights for the winning neuron
*/
protected void adjustWeights() {
// find active neuron in output layer
// TODO : change idx, in general case not 1
CompetitiveNeuron winningNeuron = ((CompetitiveLayer) neuralNetwork
.getLayerAt(1)).getWinner();
Vector<Connection> inputConnections = winningNeuron
.getConnectionsFromOtherLayers();
for(Connection connection : inputConnections) {
double weight = connection.getWeight().getValue();
double input = connection.getInput();
double deltaWeight = this.learningRate * (input - weight);
connection.getWeight().inc(deltaWeight);
}
}
}