/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.core.learning;
import java.io.Serializable;
import org.neuroph.core.NeuralNetwork;
/**
* Base class for all iterative learning algorithms.
* It provides the iterative learning procedure for all of its subclasses.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
abstract public class IterativeLearning extends LearningRule implements
Serializable {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class
*/
private static final long serialVersionUID = 1L;
/**
* Learning rate parametar
*/
protected double learningRate = 0.1d;
/**
* Current iteration counter
*/
protected int currentIteration = 0;
/**
* Max training iterations (when to stopLearning training)
* TODO: this field should be private, to force use of setMaxIterations from derived classes, so
* iterationsLimited flag is also set at the sam etime.Wil that break backward compatibility with serialized networks?
*/
protected int maxIterations = Integer.MAX_VALUE;
/**
* Flag for indicating if the training iteration number is limited
*/
protected boolean iterationsLimited = false;
/**
* Flag for indicating if learning thread is paused
*/
private transient volatile boolean pausedLearning = false;
/**
* Creates new instance of IterativeLearning learning algorithm
*/
public IterativeLearning() {
super();
}
/**
* Returns learning rate for this algorithm
*
* @return learning rate for this algorithm
*/
public double getLearningRate() {
return this.learningRate;
}
/**
* Sets learning rate for this algorithm
*
* @param learningRate
* learning rate for this algorithm
*/
public void setLearningRate(double learningRate) {
this.learningRate = learningRate;
}
/**
* Sets iteration limit for this learning algorithm
*
* @param maxIterations
* iteration limit for this learning algorithm
*/
public void setMaxIterations(int maxIterations) {
this.maxIterations = maxIterations;
this.iterationsLimited = true;
}
/**
* Returns current iteration of this learning algorithm
*
* @return current iteration of this learning algorithm
*/
public Integer getCurrentIteration() {
return new Integer(this.currentIteration);
}
/**
* Returns true if learning thread is paused, false otherwise
* @return true if learning thread is paused, false otherwise
*/
public boolean isPausedLearning() {
return pausedLearning;
}
/**
* Pause the learning
*/
public void pause() {
this.pausedLearning = true;
}
/**
* Resumes the paused learning
*/
public void resume() {
this.pausedLearning = false;
synchronized(this) {
this.notify();
}
}
/**
* Reset the iteration counter
*/
protected void reset() {
this.currentIteration = 0;
}
@Override
public void learn(TrainingSet trainingSet) {
this.reset();
while(!isStopped()) {
doLearningEpoch(trainingSet);
this.currentIteration++;
if (iterationsLimited && (currentIteration == maxIterations)) {
stopLearning();
} else if (!iterationsLimited && (currentIteration == Integer.MAX_VALUE)){
// restart iteration counter since it has reached max value and iteration numer is not limited
this.currentIteration = 1;
}
this.notifyChange(); // notify observers
// Thread safe pause
if (this.pausedLearning)
synchronized (this) {
while (this.pausedLearning) {
try {
this.wait();
}
catch (Exception e) { }
}
}
}
}
/**
* Trains network for the specified training set and number of iterations
* @param trainingSet training set to learn
* @param maxIterations maximum numberof iterations to learn
*
*/
public void learn(TrainingSet trainingSet, int maxIterations) {
this.setMaxIterations(maxIterations);
this.learn(trainingSet);
}
/**
* Runs one learning iteration for the specified training set and notfies observers.
* This method does the the doLearningEpoch() and in addtion notifes observrs when iteration is done.
* @param trainingSet training set to learn
*/
public void doOneLearningIteration(TrainingSet trainingSet) {
this.doLearningEpoch(trainingSet);
this.notifyChange(); // notify observers
}
/**
* Override this method to implement specific learning epoch - one learning iteration, one pass through whole training set
*
* @param trainingSet
* training set
*/
abstract public void doLearningEpoch(TrainingSet trainingSet);
}