/*
* HoeffdingTreeNBAdaptive.java
* Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
package tr.gov.ulakbim.jDenetX.classifiers;
import weka.core.Instance;
import weka.core.Utils;
public class HoeffdingTreeNBAdaptive extends HoeffdingTreeNB {
private static final long serialVersionUID = 1L;
public static class LearningNodeNBAdaptive extends LearningNodeNB {
private static final long serialVersionUID = 1L;
protected double mcCorrectWeight = 0.0;
protected double nbCorrectWeight = 0.0;
public LearningNodeNBAdaptive(double[] initialClassObservations) {
super(initialClassObservations);
}
@Override
public void learnFromInstance(Instance inst, HoeffdingTree ht) {
int trueClass = (int) inst.classValue();
if (this.observedClassDistribution.maxIndex() == trueClass) {
this.mcCorrectWeight += inst.weight();
}
if (Utils.maxIndex(NaiveBayes.doNaiveBayesPrediction(inst,
this.observedClassDistribution, this.attributeObservers)) == trueClass) {
this.nbCorrectWeight += inst.weight();
}
super.learnFromInstance(inst, ht);
}
@Override
public double[] getClassVotes(Instance inst, HoeffdingTree ht) {
if (this.mcCorrectWeight > this.nbCorrectWeight) {
return this.observedClassDistribution.getArrayCopy();
}
return NaiveBayes.doNaiveBayesPrediction(inst,
this.observedClassDistribution, this.attributeObservers);
}
}
@Override
protected LearningNode newLearningNode(double[] initialClassObservations) {
return new LearningNodeNBAdaptive(initialClassObservations);
}
}