meka.classifiers.multitarget.meta.EnsembleMT.java Source code

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Here is the source code for meka.classifiers.multitarget.meta.EnsembleMT.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.multitarget.meta;

import meka.classifiers.multilabel.ProblemTransformationMethod;
import meka.classifiers.multilabel.meta.EnsembleML;
import meka.classifiers.multitarget.CC;
import meka.classifiers.multitarget.MultiTargetClassifier;
import meka.core.MLUtils;
import weka.core.Instance;
import weka.core.RevisionUtils;

import java.util.HashMap;

/**
 * The Multi-Target Version of EnsembleML.
 * It takes votes using the confidence outputs of the base classifier.
 * @see meka.classifiers.multilabel.meta.EnsembleML
 * @author Jesse Read
 * @version   Sepetember 2012
 */

public class EnsembleMT extends EnsembleML implements MultiTargetClassifier {

    /** for serialization. */
    private static final long serialVersionUID = 1213045324147680550L;

    public EnsembleMT() {
        // default classifier for GUI
        this.m_Classifier = new CC();
    }

    @Override
    protected String defaultClassifierString() {
        // default classifier for CLI
        return "meka.classifiers.multitarget.CC";
    }

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "The Multi-Target Version of EnsembleML.\n"
                + "It takes votes using the confidence outputs of the base classifier.";
    }

    @Override
    public double[] distributionForInstance(Instance x) throws Exception {

        int L = x.classIndex();

        HashMap<Integer, Double> votes[] = new HashMap[L];
        for (int j = 0; j < L; j++) {
            votes[j] = new HashMap<Integer, Double>();
        }

        double y[] = new double[L];

        for (int m = 0; m < m_NumIterations; m++) {
            double c[] = ((ProblemTransformationMethod) m_Classifiers[m]).distributionForInstance(x);
            // votes[j] = votes[j] + P(j|x)      @TODO: only if c.length > L
            for (int j = 0; j < L; j++) {
                Double w = votes[j].containsKey((int) c[j]) ? votes[j].get((int) c[j]) + c[j + L] : c[j + L];
                votes[j].put((int) c[j], w);
            }
        }

        for (int j = 0; j < L; j++) {
            // get the class with max weight
            y[j] = (Integer) MLUtils.maxItem(votes[j]);
        }

        return y;
    }

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

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

}