weka.classifiers.rules.ZeroR.java Source code

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Here is the source code for weka.classifiers.rules.ZeroR.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 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.
 */

/*
 *    ZeroR.java
 *    Copyright (C) 1999 Eibe Frank
 *
 */

package weka.classifiers.rules;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import java.io.*;
import java.util.*;
import weka.core.*;

/**
 * Class for building and using a 0-R classifier. Predicts the mean
 * (for a numeric class) or the mode (for a nominal class).
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision: 1.11 $
 */
public class ZeroR extends Classifier implements WeightedInstancesHandler {

    /** The class value 0R predicts. */
    private double m_ClassValue;

    /** The number of instances in each class (null if class numeric). */
    private double[] m_Counts;

    /** The class attribute. */
    private Attribute m_Class;

    /**
     * Returns a string describing classifier
     * @return a description suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
        return "Class for building and using a 0-R classifier. Predicts the mean "
                + "(for a numeric class) or the mode (for a nominal class).";
    }

    /**
     * Generates the classifier.
     *
     * @param instances set of instances serving as training data 
     * @exception Exception if the classifier has not been generated successfully
     */
    public void buildClassifier(Instances instances) throws Exception {

        double sumOfWeights = 0;

        m_Class = instances.classAttribute();
        m_ClassValue = 0;
        switch (instances.classAttribute().type()) {
        case Attribute.NUMERIC:
            m_Counts = null;
            break;
        case Attribute.NOMINAL:
            m_Counts = new double[instances.numClasses()];
            for (int i = 0; i < m_Counts.length; i++) {
                m_Counts[i] = 1;
            }
            sumOfWeights = instances.numClasses();
            break;
        default:
            throw new Exception("ZeroR can only handle nominal and numeric class" + " attributes.");
        }
        Enumeration enu = instances.enumerateInstances();
        while (enu.hasMoreElements()) {
            Instance instance = (Instance) enu.nextElement();
            if (!instance.classIsMissing()) {
                if (instances.classAttribute().isNominal()) {
                    m_Counts[(int) instance.classValue()] += instance.weight();
                } else {
                    m_ClassValue += instance.weight() * instance.classValue();
                }
                sumOfWeights += instance.weight();
            }
        }
        if (instances.classAttribute().isNumeric()) {
            if (Utils.gr(sumOfWeights, 0)) {
                m_ClassValue /= sumOfWeights;
            }
        } else {
            m_ClassValue = Utils.maxIndex(m_Counts);
            Utils.normalize(m_Counts, sumOfWeights);
        }
    }

    /**
     * Classifies a given instance.
     *
     * @param instance the instance to be classified
     * @return index of the predicted class
     */
    public double classifyInstance(Instance instance) {

        return m_ClassValue;
    }

    /**
     * Calculates the class membership probabilities for the given test instance.
     *
     * @param instance the instance to be classified
     * @return predicted class probability distribution
     * @exception Exception if class is numeric
     */
    public double[] distributionForInstance(Instance instance) throws Exception {

        if (m_Counts == null) {
            double[] result = new double[1];
            result[0] = m_ClassValue;
            return result;
        } else {
            return (double[]) m_Counts.clone();
        }
    }

    /**
     * Returns a description of the classifier.
     *
     * @return a description of the classifier as a string.
     */
    public String toString() {

        if (m_Class == null) {
            return "ZeroR: No model built yet.";
        }
        if (m_Counts == null) {
            return "ZeroR predicts class value: " + m_ClassValue;
        } else {
            return "ZeroR predicts class value: " + m_Class.value((int) m_ClassValue);
        }
    }

    /**
     * Main method for testing this class.
     *
     * @param argv the options
     */
    public static void main(String[] argv) {

        try {
            System.out.println(Evaluation.evaluateModel(new ZeroR(), argv));
        } catch (Exception e) {
            System.err.println(e.getMessage());
        }
    }
}