meka.classifiers.multilabel.incremental.meta.BaggingMLUpdateable.java Source code

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Here is the source code for meka.classifiers.multilabel.incremental.meta.BaggingMLUpdateable.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.incremental.meta;

import meka.classifiers.multilabel.IncrementalMultiLabelClassifier;
import meka.classifiers.multilabel.incremental.BRUpdateable;
import meka.classifiers.multilabel.incremental.IncrementalEvaluation;
import meka.classifiers.multilabel.meta.EnsembleML;
import weka.classifiers.UpdateableClassifier;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;

import java.util.Random;

/**
 * BaggingMLUpdatable.java - Using the OzaBag scheme (see OzaBag.java from MOA)).
 * See also: N. Oza and S. Russell. Online bagging and boosting. In Artificial Intelligence and Statistics 2001, pages 105-112. Morgan Kaufmann, 2001.
 * @version    Jan 2013
 * @author       Jesse Read
 */

public class BaggingMLUpdateable extends EnsembleML
        implements IncrementalMultiLabelClassifier, TechnicalInformationHandler {

    private static final long serialVersionUID = 4978269895923479962L;
    protected Random random = null;

    /**
     * Description to display in the GUI.
     * 
     * @return      the description
     */
    @Override
    public String globalInfo() {
        return "Incremental Bagging";
    }

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

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

    @Override
    public void buildClassifier(Instances D) throws Exception {
        random = new Random(m_Seed);
        super.buildClassifier(D);
    }

    @Override
    public void updateClassifier(Instance x) throws Exception {

        for (int i = 0; i < m_NumIterations; i++) {
            // Oza-Bag style
            int k = poisson(1.0, random);
            if (m_BagSizePercent == 100) {
                // Train on all instances
                k = 1;
            }
            if (k > 0) {
                // Train on this instance only if k > 0
                Instance x_weighted = (Instance) x.copy();
                x_weighted.setWeight(x.weight() * (double) k);
                ((UpdateableClassifier) m_Classifiers[i]).updateClassifier(x_weighted);
            }
        }
    }

    protected static int poisson(double lambda, Random r) {
        if (lambda < 100.0) {
            double product = 1.0;
            double sum = 1.0;
            double threshold = r.nextDouble() * Math.exp(lambda);
            int i = 1;
            int max = Math.max(100, 10 * (int) Math.ceil(lambda));
            while ((i < max) && (sum <= threshold)) {
                product *= (lambda / i);
                sum += product;
                i++;
            }
            return i - 1;
        }
        double x = lambda + Math.sqrt(lambda) * r.nextGaussian();
        if (x < 0.0) {
            return 0;
        }
        return (int) Math.floor(x);
    }

    @Override
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;

        result = new TechnicalInformation(Type.ARTICLE);
        result.setValue(Field.AUTHOR, "N.Oza, S. Russell");
        result.setValue(Field.TITLE, "Online bagging and boosting");
        result.setValue(Field.JOURNAL, "Artificial Intelligence and Statistics");
        result.setValue(Field.PUBLISHER, "Morgan Kaufmann");
        result.setValue(Field.YEAR, "2001");
        result.setValue(Field.PAGES, "105-112");
        return result;
    }

    public static void main(String args[]) {
        IncrementalEvaluation.runExperiment(new BaggingMLUpdateable(), args);
    }

}