/* 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.pipe.*;
import cc.mallet.types.*;
/**
@author Andrew McCallum <a href="mailto:mccallum@cs.umass.edu">mccallum@cs.umass.edu</a>
*/
public class BaggingClassifier extends Classifier
{
Classifier[] baggedClassifiers;
double[] weights; // Not yet implemented!
public BaggingClassifier (Pipe instancePipe, Classifier[] baggedClassifiers)
{
super (instancePipe);
this.baggedClassifiers = baggedClassifiers;
}
public Classification classify (Instance inst)
{
int numClasses = getLabelAlphabet().size();
double[] scores = new double[numClasses];
int bestIndex;
double sum = 0;
for (int i = 0; i < baggedClassifiers.length; i++) {
Labeling labeling = baggedClassifiers[i].classify(inst).getLabeling();
labeling.addTo (scores);
}
MatrixOps.normalize (scores);
return new Classification (inst, this, new LabelVector (getLabelAlphabet(), scores));
}
}