/*
* OzaBagAdwin.java
* Copyright (C) 2008 University of Waikato, Hamilton, New Zealand
* @author Albert Bifet
*
* 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 tr.gov.ulakbim.jDenetX.core.DoubleVector;
import tr.gov.ulakbim.jDenetX.core.Measurement;
import tr.gov.ulakbim.jDenetX.core.MiscUtils;
import tr.gov.ulakbim.jDenetX.core.SizeOf;
import tr.gov.ulakbim.jDenetX.options.ClassOption;
import tr.gov.ulakbim.jDenetX.options.IntOption;
import weka.core.Instance;
public class OzaBagAdwin extends AbstractClassifier {
private static final long serialVersionUID = 1L;
public ClassOption baseLearnerOption = new ClassOption("baseLearner", 'l',
"Classifier to train.", Classifier.class, "HoeffdingTree");
public IntOption ensembleSizeOption = new IntOption("ensembleSize", 's',
"The number of models in the bag.", 10, 1, Integer.MAX_VALUE);
protected Classifier[] ensemble;
protected ADWIN[] ADError;
@Override
public int measureByteSize() {
int size = (int) SizeOf.sizeOf(this);
for (Classifier classifier : this.ensemble) {
size += classifier.measureByteSize();
}
for (ADWIN adwin : this.ADError) {
size += adwin.measureByteSize();
}
return size;
}
@Override
public void resetLearningImpl() {
this.ensemble = new Classifier[this.ensembleSizeOption.getValue()];
Classifier baseLearner = (Classifier) getPreparedClassOption(this.baseLearnerOption);
baseLearner.resetLearning();
for (int i = 0; i < this.ensemble.length; i++) {
this.ensemble[i] = baseLearner.copy();
}
this.ADError = new ADWIN[this.ensemble.length];
for (int i = 0; i < this.ensemble.length; i++) {
this.ADError[i] = new ADWIN();
}
}
@Override
public void trainOnInstanceImpl(Instance inst) {
boolean Change = false;
for (int i = 0; i < this.ensemble.length; i++) {
int k = MiscUtils.poisson(1.0, this.classifierRandom);
if (k > 0) {
Instance weightedInst = (Instance) inst.copy();
weightedInst.setWeight(inst.weight() * k);
this.ensemble[i].trainOnInstance(weightedInst);
}
boolean correctlyClassifies = this.ensemble[i].correctlyClassifies(inst);
double ErrEstim = this.ADError[i].getEstimation();
if (this.ADError[i].setInput(correctlyClassifies ? 0 : 1))
if (this.ADError[i].getEstimation() > ErrEstim) Change = true;
}
if (Change) {
double max = 0.0;
int imax = -1;
for (int i = 0; i < this.ensemble.length; i++) {
if (max < this.ADError[i].getEstimation()) {
max = this.ADError[i].getEstimation();
imax = i;
}
}
if (imax != -1) {
this.ensemble[imax].resetLearning();
//this.ensemble[imax].trainOnInstance(inst);
this.ADError[imax] = new ADWIN();
}
}
}
public double[] getVotesForInstance(Instance inst) {
DoubleVector combinedVote = new DoubleVector();
for (int i = 0; i < this.ensemble.length; i++) {
DoubleVector vote = new DoubleVector(this.ensemble[i]
.getVotesForInstance(inst));
if (vote.sumOfValues() > 0.0) {
vote.normalize();
combinedVote.addValues(vote);
}
}
return combinedVote.getArrayRef();
}
public boolean isRandomizable() {
return true;
}
@Override
public void getModelDescription(StringBuilder out, int indent) {
// TODO Auto-generated method stub
}
@Override
protected Measurement[] getModelMeasurementsImpl() {
return new Measurement[]{new Measurement("ensemble size",
this.ensemble != null ? this.ensemble.length : 0)};
}
@Override
public Classifier[] getSubClassifiers() {
return this.ensemble.clone();
}
}