/*
* 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.ml.train.strategy;
import org.encog.ml.MLEncodable;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.training.TrainingError;
import org.encog.util.logging.EncogLogging;
/**
* A simple greedy strategy. If the last iteration did not improve training,
* then discard it. Care must be taken with this strategy, as sometimes a
* training algorithm may need to temporarily decrease the error level before
* improving it.
*
* @author jheaton
*
*/
public class Greedy implements Strategy {
/**
* The training algorithm that is using this strategy.
*/
private MLTrain train;
/**
* The error rate from the previous iteration.
*/
private double lastError;
/**
* The last state of the network, so that we can restore to this
* state if needed.
*/
private double[] lastNetwork;
/**
* Has one iteration passed, and we are now ready to start
* evaluation.
*/
private boolean ready;
private MLEncodable method;
/**
* Initialize this strategy.
* @param train The training algorithm.
*/
public void init(final MLTrain train) {
this.train = train;
this.ready = false;
if( !(train.getMethod() instanceof MLEncodable) ) {
throw new TrainingError("To make use of the Greedy strategy the machine learning method must support MLEncodable.");
}
this.method = ((MLEncodable)train.getMethod());
this.lastNetwork = new double[this.method.encodedArrayLength()];
}
/**
* Called just after a training iteration.
*/
public void postIteration() {
if (this.ready) {
if (this.train.getError() > this.lastError) {
EncogLogging.log(EncogLogging.LEVEL_DEBUG,"Greedy strategy dropped last iteration.");
this.train.setError(this.lastError);
this.method.decodeFromArray(this.lastNetwork);
}
} else {
this.ready = true;
}
}
/**
* Called just before a training iteration.
*/
public void preIteration() {
if (this.method != null) {
this.lastError = this.train.getError();
this.method.encodeToArray(this.lastNetwork);
this.train.setError(this.lastError);
}
}
}