/*
* 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.BoundMath;
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.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 Instar training handles the unsupervised
* portion of the training.
*
*/
public class TrainInstar extends BasicTraining implements LearningRate {
/**
* The network being trained.
*/
private final CPN network;
/**
* The training data. This is unsupervised training, so only the input
* portion of the training data will be used.
*/
private final MLDataSet training;
/**
* The learning rate.
*/
private double learningRate;
/**
* If the weights have not been initialized, then they can be initialized
* before training begins. This will be done on the first iteration.
*/
private boolean mustInit;
/**
* Construct the instar training object.
*
* @param theNetwork
* The network to be trained.
* @param theTraining
* The training data.
* @param theLearningRate
* The learning rate.
* @param theInitWeights
* True, if the weights should be initialized from the training
* data. If set to true, then you must have the same number of
* training elements as instar neurons.
*/
public TrainInstar(final CPN theNetwork, final MLDataSet theTraining,
final double theLearningRate, final boolean theInitWeights) {
super(TrainingImplementationType.Iterative);
this.network = theNetwork;
this.training = theTraining;
this.learningRate = theLearningRate;
this.mustInit = theInitWeights;
}
/**
* {@inheritDoc}
*/
@Override
public final boolean canContinue() {
return false;
}
/**
* {@inheritDoc}
*/
@Override
public final double getLearningRate() {
return this.learningRate;
}
/**
* {@inheritDoc}
*/
@Override
public final CPN getMethod() {
return this.network;
}
/**
* Approximate the weights based on the input values.
*/
private void initWeights() {
if (this.training.getRecordCount() != this.network.getInstarCount()) {
throw new NeuralNetworkError(
"If the weights are to be set from the "
+ "training data, then there must be one instar "
+ "neuron for each training element.");
}
int i = 0;
for (final MLDataPair pair : this.training) {
for (int j = 0; j < this.network.getInputCount(); j++) {
this.network.getWeightsInputToInstar().set(j, i,
pair.getInput().getData(j));
}
i++;
}
this.mustInit = false;
}
/**
* {@inheritDoc}
*/
@Override
public final void iteration() {
if (this.mustInit) {
initWeights();
}
double worstDistance = Double.NEGATIVE_INFINITY;
for (final MLDataPair pair : this.training) {
final MLData out = this.network.computeInstar(pair.getInput());
// determine winner
final int winner = EngineArray.indexOfLargest(out.getData());
// calculate the distance
double distance = 0;
for (int i = 0; i < pair.getInput().size(); i++) {
final double diff = pair.getInput().getData(i)
- this.network.getWeightsInputToInstar().get(i, winner);
distance += diff * diff;
}
distance = BoundMath.sqrt(distance);
if (distance > worstDistance) {
worstDistance = distance;
}
// train
for (int j = 0; j < this.network.getInputCount(); j++) {
final double delta = this.learningRate
* (pair.getInput().getData(j) - this.network
.getWeightsInputToInstar().get(j, winner));
this.network.getWeightsInputToInstar().add(j, winner, delta);
}
}
setError(worstDistance);
}
/**
* {@inheritDoc}
*/
@Override
public final TrainingContinuation pause() {
return null;
}
/**
* {@inheritDoc}
*/
@Override
public void resume(final TrainingContinuation state) {
}
/**
* {@inheritDoc}
*/
@Override
public final void setLearningRate(final double rate) {
this.learningRate = rate;
}
}