/**
* 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.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.learning.TrainingSet;
import org.neuroph.core.learning.UnsupervisedLearning;
/**
* Unsupervised hebbian learning rule.
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class UnsupervisedHebbianLearning 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 UnsupervisedHebbianLearning algorithm
*/
public UnsupervisedHebbianLearning() {
super();
this.setLearningRate(0.1);
}
/**
* Creates an instance of UnsupervisedHebbianLearning algorithm for the specified
* neural network
*
* @param neuralNetwork
* neural network to train
*/
public UnsupervisedHebbianLearning(NeuralNetwork neuralNetwork) {
super(neuralNetwork);
this.setLearningRate(0.1);
}
/**
* 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 output neurons
*/
protected void adjustWeights() {
for (Neuron neuron : neuralNetwork.getOutputNeurons()) {
this.updateNeuronWeights(neuron);
}
}
/**
* This method implements weights update procedure for the single neuron
*
* @param neuron
* neuron to update weights
*/
protected void updateNeuronWeights(Neuron neuron) {
double output = neuron.getOutput();
for (Connection connection : neuron.getInputConnections()) {
double input = connection.getInput();
double deltaWeight = input * output * this.learningRate;
connection.getWeight().inc(deltaWeight);
}
}
}
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