/**
* 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.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.transfer.TransferFunction;
/**
* Delta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.
*
* TODO: Rename to DeltaRuleContinuous (ContinuousDeltaRule) or something like that, but that will break backward compatibility,
* posibly with backpropagation which is the most used
*
* @see LMS
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class SigmoidDeltaRule extends LMS {
/**
* 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 SigmoidDeltaRule
*/
public SigmoidDeltaRule() {
super();
}
/**
* Creates new SigmoidDeltaRule for the specified neural network
*
* @param neuralNetwork neural network to train
*/
public SigmoidDeltaRule(NeuralNetwork neuralNetwork) {
super(neuralNetwork);
}
/**
* This method implements weight update procedure for the whole network for
* this learning rule
*
* @param patternError
* single pattern error vector
*/
@Override
protected void updateNetworkWeights(Vector<Double> patternError) {
this.adjustOutputNeurons(patternError);
}
/**
* This method implements weights update procedure for the output neurons
*
* @param patternError
* single pattern error vector
*/
protected void adjustOutputNeurons(Vector<Double> patternError) {
int i = 0;
for(Neuron neuron : neuralNetwork.getOutputNeurons()) {
double outputError = patternError.elementAt(i);
if (outputError == 0) {
neuron.setError(0);
i++;
continue;
}
TransferFunction transferFunction = neuron.getTransferFunction();
double neuronInput = neuron.getNetInput();
double delta = outputError * transferFunction.getDerivative(neuronInput);
neuron.setError(delta);
this.updateNeuronWeights(neuron);
i++;
} // for
}
}
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