/*
* 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.cpn.training;
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.cpn.CPN;
import org.encog.neural.networks.training.LearningRate;
import org.encog.neural.networks.training.propagation.TrainingContinuation;
import org.encog.util.EngineArray;
/**
* Used for Instar training of a CPN neural network. A CPN network is a hybrid
* supervised/unsupervised network. The Outstar training handles the supervised
* portion of the training.
*
*/
public class TrainOutstar extends BasicTraining implements LearningRate {
/**
* The learning rate.
*/
private double learningRate;
/**
* The network being trained.
*/
private final CPN network;
/**
* The training data. Supervised training, so both input and ideal must be
* provided.
*/
private final MLDataSet training;
/**
* If the weights have not been initialized, then they must be initialized
* before training begins. This will be done on the first iteration.
*/
private boolean mustInit = true;
/**
* Construct the outstar trainer.
*
* @param theNetwork
* The network to train.
* @param theTraining
* The training data, must provide ideal outputs.
* @param theLearningRate
* The learning rate.
*/
public TrainOutstar(final CPN theNetwork, final MLDataSet theTraining,
final double theLearningRate) {
super(TrainingImplementationType.Iterative);
this.network = theNetwork;
this.training = theTraining;
this.learningRate = theLearningRate;
}
/**
* {@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;
}
/**
* Approximate the weights based on the input values.
*/
private void initWeight() {
for (int i = 0; i < this.network.getOutstarCount(); i++) {
int j = 0;
for (final MLDataPair pair : this.training) {
this.network.getWeightsInstarToOutstar().set(j++, i,
pair.getIdeal().getData(i));
}
}
this.mustInit = false;
}
/**
* {@inheritDoc}
*/
@Override
public final void iteration() {
if (this.mustInit) {
initWeight();
}
final ErrorCalculation error = new ErrorCalculation();
for (final MLDataPair pair : this.training) {
final MLData out = this.network.computeInstar(pair.getInput());
final int j = EngineArray.indexOfLargest(out.getData());
for (int i = 0; i < this.network.getOutstarCount(); i++) {
final double delta = this.learningRate
* (pair.getIdeal().getData(i) - this.network
.getWeightsInstarToOutstar().get(j, i));
this.network.getWeightsInstarToOutstar().add(j, i, delta);
}
final MLData out2 = this.network.computeOutstar(out);
error.updateError(out2.getData(), pair.getIdeal().getData(), pair.getSignificance());
}
setError(error.calculate());
}
/**
* {@inheritDoc}
*/
@Override
public final TrainingContinuation pause() {
return null;
}
/**
* {@inheritDoc}
*/
@Override
public final void resume(final TrainingContinuation state) {
}
/**
* {@inheritDoc}
*/
@Override
public final void setLearningRate(final double rate) {
this.learningRate = rate;
}
}