/**
* 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;
/**
* Hebbian-like learning algorithm used for Hopfield network. Works with [0, 1] values
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class BinaryHebbianLearning extends UnsupervisedHebbianLearning {
/**
* 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 BinaryHebbianLearning
*/
public BinaryHebbianLearning() {
super();
}
/**
* This method implements weights update procedure for the single neuron
*
* @param neuron
* neuron to update weights
*/
@Override
protected void updateNeuronWeights(Neuron neuron) {
double output = neuron.getOutput();
for (Connection connection : neuron.getInputConnections()) {
double input = connection.getInput();
if (((input>0) && (output>0)) || ((input<=0) && (output<=0))) {
connection.getWeight().inc(this.learningRate);
} else {
connection.getWeight().dec(this.learningRate);
}
}
}
}