moa.classifiers.functions.MajorityClass.java Source code

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Here is the source code for moa.classifiers.functions.MajorityClass.java

Source

/*
 *    MajorityClass.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 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 moa.classifiers.functions;

import moa.classifiers.AbstractClassifier;
import moa.core.DoubleVector;
import moa.core.Measurement;
import moa.core.StringUtils;
import weka.core.Instance;

/**
 * Majority class learner. This is the simplest classifier.
 *
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 7 $
 */
public class MajorityClass extends AbstractClassifier {

    private static final long serialVersionUID = 1L;

    @Override
    public String getPurposeString() {
        return "Majority class classifier: always predicts the class that has been observed most frequently the in the training data.";
    }

    protected DoubleVector observedClassDistribution;

    @Override
    public void resetLearningImpl() {
        this.observedClassDistribution = new DoubleVector();
    }

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        this.observedClassDistribution.addToValue((int) inst.classValue(), inst.weight());
    }

    public double[] getVotesForInstance(Instance i) {
        return this.observedClassDistribution.getArrayCopy();
    }

    @Override
    protected Measurement[] getModelMeasurementsImpl() {
        return null;
    }

    @Override
    public void getModelDescription(StringBuilder out, int indent) {
        StringUtils.appendIndented(out, indent, "Predicted majority ");
        out.append(getClassNameString());
        out.append(" = ");
        out.append(getClassLabelString(this.observedClassDistribution.maxIndex()));
        StringUtils.appendNewline(out);
        for (int i = 0; i < this.observedClassDistribution.numValues(); i++) {
            StringUtils.appendIndented(out, indent, "Observed weight of ");
            out.append(getClassLabelString(i));
            out.append(": ");
            out.append(this.observedClassDistribution.getValue(i));
            StringUtils.appendNewline(out);
        }
    }

    public boolean isRandomizable() {
        return false;
    }
}