/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 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;
import java.util.ArrayList;
import java.util.List;
import org.encog.ml.TrainingImplementationType;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.strategy.Strategy;
import org.encog.ml.train.strategy.end.EndTrainingStrategy;
/**
* An abstract class that implements basic training for most training
* algorithms. Specifically training strategies can be added to enhance the
* training.
*
* @author jheaton
*
*/
public abstract class BasicTraining implements MLTrain {
/**
* The training strategies to use.
*/
private final List<Strategy> strategies = new ArrayList<Strategy>();
/**
* The training data.
*/
private MLDataSet training;
/**
* The current error rate.
*/
private double error;
/**
* The current iteration.
*/
private int iteration;
private TrainingImplementationType implementationType;
/**
* Used for serialization.
*/
public BasicTraining() {
}
public BasicTraining(TrainingImplementationType implementationType) {
this.implementationType = implementationType;
}
/**
* Training strategies can be added to improve the training results. There
* are a number to choose from, and several can be used at once.
*
* @param strategy
* The strategy to add.
*/
public void addStrategy(final Strategy strategy) {
strategy.init(this);
this.strategies.add(strategy);
}
/**
* Should be called after training has completed and the iteration method
* will not be called any further.
*/
public void finishTraining() {
}
/**
* {@inheritDoc}
*/
public double getError() {
return this.error;
}
/**
* @return the iteration
*/
public int getIteration() {
return this.iteration;
}
/**
* @return The strategies to use.
*/
public List<Strategy> getStrategies() {
return this.strategies;
}
/**
* @return The training data to use.
*/
public MLDataSet getTraining() {
return this.training;
}
/**
* @return True if training can progress no further.
*/
public boolean isTrainingDone() {
for (Strategy strategy : this.strategies) {
if (strategy instanceof EndTrainingStrategy) {
EndTrainingStrategy end = (EndTrainingStrategy)strategy;
if( end.shouldStop() ) {
return true;
}
}
}
return false;
}
/**
* Perform the specified number of training iterations. This is a basic
* implementation that just calls iteration the specified number of times.
* However, some training methods, particularly with the GPU, benefit
* greatly by calling with higher numbers than 1.
*
* @param count
* The number of training iterations.
*/
public void iteration(final int count) {
for (int i = 0; i < count; i++) {
iteration();
}
}
/**
* Call the strategies after an iteration.
*/
public void postIteration() {
for (final Strategy strategy : this.strategies) {
strategy.postIteration();
}
}
/**
* Call the strategies before an iteration.
*/
public void preIteration() {
this.iteration++;
for (final Strategy strategy : this.strategies) {
strategy.preIteration();
}
}
/**
* @param error
* Set the current error rate. This is usually used by training
* strategies.
*/
public void setError(final double error) {
this.error = error;
}
/**
* @param iteration
* the iteration to set
*/
public void setIteration(final int iteration) {
this.iteration = iteration;
}
/**
* Set the training object that this strategy is working with.
*
* @param training
* The training object.
*/
public void setTraining(final MLDataSet training) {
this.training = training;
}
public TrainingImplementationType getImplementationType() {
return this.implementationType;
}
}