/**
* 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.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.nnet.comp.ThresholdNeuron;
/**
* Delta rule learning algorithm for perceptrons with step functions.
*
* The difference to Perceptronlearning is that Delta Rule calculates error
* before the non-lnear step transfer function
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class BinaryDeltaRule extends PerceptronLearning {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L;
/**
* The errorCorrection parametar of this learning algorithm
*/
private double errorCorrection = 0.1;
/**
* Creates new BinaryDeltaRule learning
*/
public BinaryDeltaRule() {
super();
}
/**
* This method implements weight update procedure for the whole network for
* this learning rule
*
* @param patternError
* single pattern error vector
*
* if the output is 0 and required value is 1, increase rthe weights
* if the output is 1 and required value is 0, decrease the weights
* otherwice leave weights unchanged
*
*/
@Override
protected void updateNetworkWeights(double[] patternError) {
int i = 0;
for(Neuron outputNeuron : neuralNetwork.getOutputNeurons()) {
ThresholdNeuron neuron = (ThresholdNeuron)outputNeuron;
double outputError = patternError[i];
double thresh = neuron.getThresh();
double netInput = neuron.getNetInput();
double threshError = thresh - netInput; // distance from zero
// use output error to decide weathet to inrease, decrase or leave unchanged weights
// add errorCorrection to threshError to move above or below zero
double neuronError = outputError * (Math.abs(threshError) + errorCorrection);
// use same adjustment principle as PerceptronLearning,
// just with different neuronError
neuron.setError(neuronError);
updateNeuronWeights(neuron);
i++;
} // for
}
/**
* Gets the errorCorrection parametar
*
* @return errorCorrection parametar
*/
public double getErrorCorrection() {
return this.errorCorrection;
}
/**
* Sets the errorCorrection parametar
*
* @param errorCorrection
* the value for errorCorrection parametar
*/
public void setErrorCorrection(double errorCorrection) {
this.errorCorrection = errorCorrection;
}
}