meka.classifiers.multilabel.meta.BaggingML.java Source code

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Here is the source code for meka.classifiers.multilabel.meta.BaggingML.java

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/*
 *   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 3 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, see <http://www.gnu.org/licenses/>.
 */

package meka.classifiers.multilabel.meta;

import java.util.Random;

import meka.classifiers.multilabel.ProblemTransformationMethod;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Randomizable;
import weka.core.RevisionUtils;

/**
 * BaggingML.java - Combining several multi-label classifiers using Bootstrap AGGregatING.
 * @author Jesse Read (jmr30@cs.waikato.ac.nz)
 */

public class BaggingML extends MetaProblemTransformationMethod {

    /** for serialization. */
    private static final long serialVersionUID = -6208337124420497991L;

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "Combining several multi-label classifiers using Bootstrap AGGregatING";
    }

    public BaggingML() {
        // default for Bagging
        this.m_BagSizePercent = 100;
    }

    @Override
    public void buildClassifier(Instances train) throws Exception {
        testCapabilities(train);

        if (getDebug())
            System.out.print("-: Models: ");

        train = new Instances(train);
        m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier,
                m_NumIterations);

        for (int i = 0; i < m_NumIterations; i++) {
            Random r = new Random(m_Seed + i);
            Instances bag = new Instances(train, 0);
            if (m_Classifiers[i] instanceof Randomizable)
                ((Randomizable) m_Classifiers[i]).setSeed(m_Seed + i);
            if (getDebug())
                System.out.print("" + i + " ");

            int ixs[] = new int[train.numInstances()];
            for (int j = 0; j < ixs.length; j++) {
                ixs[r.nextInt(ixs.length)]++;
            }
            for (int j = 0; j < ixs.length; j++) {
                if (ixs[j] > 0) {
                    Instance instance = train.instance(j);
                    instance.setWeight(ixs[j]);
                    bag.add(instance);
                }
            }

            m_Classifiers[i].buildClassifier(bag);
        }
        if (getDebug())
            System.out.println(":-");
    }

    @Override
    public String getRevision() {
        return RevisionUtils.extract("$Revision: 9117 $");
    }

    public static void main(String args[]) {
        ProblemTransformationMethod.evaluation(new BaggingML(), args);
    }

}