/*
* Encog(tm) Core v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 Heaton Research, Inc.
*
* 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.networks.training.simple;
import org.encog.mathutil.error.ErrorCalculation;
import org.encog.ml.MLMethod;
import org.encog.ml.TrainingImplementationType;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.BasicTraining;
import org.encog.neural.NeuralNetworkError;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.training.LearningRate;
import org.encog.neural.networks.training.propagation.TrainingContinuation;
/**
* Train an ADALINE neural network.
*/
public class TrainAdaline extends BasicTraining implements LearningRate {
/**
* The network to train.
*/
private final BasicNetwork network;
/**
* The training data to use.
*/
private final MLDataSet training;
/**
* The learning rate.
*/
private double learningRate;
/**
* Construct an ADALINE trainer.
*
* @param network
* The network to train.
* @param training
* The training data.
* @param learningRate
* The learning rate.
*/
public TrainAdaline(final BasicNetwork network, final MLDataSet training,
final double learningRate) {
super(TrainingImplementationType.Iterative);
if (network.getLayerCount() > 2) {
throw new NeuralNetworkError(
"An ADALINE network only has two layers.");
}
this.network = network;
this.training = training;
this.learningRate = learningRate;
}
/**
* {@inheritDoc}
*/
@Override
public final boolean canContinue() {
return false;
}
/**
* {@inheritDoc}
*/
@Override
public final double getLearningRate() {
return this.learningRate;
}
/**
* {@inheritDoc}
*/
@Override
public final MLMethod getMethod() {
return this.network;
}
/**
* {@inheritDoc}
*/
@Override
public final void iteration() {
final ErrorCalculation errorCalculation = new ErrorCalculation();
for (final MLDataPair pair : this.training) {
// calculate the error
final MLData output = this.network.compute(pair.getInput());
for (int currentAdaline = 0; currentAdaline < output.size(); currentAdaline++) {
final double diff = pair.getIdeal().getData(currentAdaline)
- output.getData(currentAdaline);
// weights
for (int i = 0; i <= this.network.getInputCount(); i++) {
final double input;
if (i == this.network.getInputCount()) {
input = 1.0;
} else {
input = pair.getInput().getData(i);
}
this.network.addWeight(0, i, currentAdaline,
this.learningRate * diff * input);
}
}
errorCalculation.updateError(output.getData(), pair.getIdeal()
.getData(),pair.getSignificance());
}
// set the global error
setError(errorCalculation.calculate());
}
/**
* {@inheritDoc}
*/
@Override
public final TrainingContinuation pause() {
return null;
}
/**
* {@inheritDoc}
*/
@Override
public void resume(final TrainingContinuation state) {
}
/**
* Set the learning rate.
*
* @param rate
* The new learning rate.
*/
@Override
public final void setLearningRate(final double rate) {
this.learningRate = rate;
}
}