/* Copyright (C) 2002 Univ. of Massachusetts Amherst, Computer Science Dept.
This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
http://www.cs.umass.edu/~mccallum/mallet
This software is provided under the terms of the Common Public License,
version 1.0, as published by http://www.opensource.org. For further
information, see the file `LICENSE' included with this distribution. */
package cc.mallet.classify;
import cc.mallet.types.*;
/**
Bagging Trainer.
@author Andrew McCallum <a href="mailto:mccallum@cs.umass.edu">mccallum@cs.umass.edu</a>
*/
public class BaggingTrainer extends ClassifierTrainer<BaggingClassifier>
{
ClassifierTrainer.Factory underlyingTrainer;
int numBags;
BaggingClassifier classifier;
public BaggingClassifier getClassifier () { return classifier; }
public BaggingTrainer (ClassifierTrainer.Factory underlyingTrainerFactory, int numBags)
{
this.underlyingTrainer = underlyingTrainerFactory;
this.numBags = numBags;
}
public BaggingTrainer (ClassifierTrainer.Factory underlyingTrainerFactory)
{
this (underlyingTrainerFactory, 10);
}
public BaggingClassifier train (InstanceList trainingList)
{
Classifier[] classifiers = new Classifier[numBags];
java.util.Random r = new java.util.Random ();
for (int round = 0; round < numBags; round++) {
InstanceList bag = trainingList.sampleWithReplacement (r, trainingList.size());
classifiers[round] = underlyingTrainer.newClassifierTrainer().train (bag);
}
this.classifier = new BaggingClassifier (trainingList.getPipe(), classifiers);
return classifier;
}
}