vote.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    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.
 */

/*
 *    Vote.java
 *    Copyright (C) 2000 Alexander K. Seewald
 *
 */

package weka.classifiers.meta;

import weka.classifiers.Evaluation;
import weka.classifiers.MultipleClassifiersCombiner;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.UnsupportedClassTypeException;

/**
 * Class for combining classifiers using unweighted average of
 * probability estimates (classification) or numeric predictions 
 * (regression).
 *
 * Valid options from the command line are:<p>
 *
 * -B classifierstring <br>
 * Classifierstring should contain the full class name of a scheme
 * included for selection followed by options to the classifier
 * (required, option should be used once for each classifier).<p>
 *
 * @author Alexander K. Seewald ([e-mail])
 * @author Eibe Frank ([e-mail])
 * @version $Revision$
 */

public class Vote extends MultipleClassifiersCombiner {
    
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {

    return "Class for combining classifiers using unweighted average of "
      + "probability estimates (classification) or numeric predictions "
      + "(regression).";
  }

  /**
   * Buildclassifier selects a classifier from the set of classifiers
   * by minimising error on the training data.
   *
   * @param data the training data to be used for generating the
   * boosted classifier.
   * @exception Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // Check for non-nominal classes
    if (!data.classAttribute().isNominal()) {
      throw new UnsupportedClassTypeException("Vote: Nominal class, please.");
    }

    Instances newData = new Instances(data);
    newData.deleteWithMissingClass();

    for (int i = 0; i < m_Classifiers.length; i++) {
      getClassifier(i).buildClassifier(data);
    }
  }

  /**
   * Classifies a given instance using the selected classifier.
   *
   * @param instance the instance to be classified
   * @exception Exception if instance could not be classified
   * successfully
   */
  public double[] distributionForInstance(Instance instance) throws Exception {

    double[] probs = getClassifier(0).distributionForInstance(instance);
    for (int i = 1; i < m_Classifiers.length; i++) {
      double[] dist = getClassifier(i).distributionForInstance(instance);
      for (int j = 0; j < dist.length; j++) {
	probs[j] += dist[j];
      }
    }
    for (int j = 0; j < probs.length; j++) {
      probs[j] /= (double)m_Classifiers.length;
    }
    return probs;
  }

  /**
   * Output a representation of this classifier
   */
  public String toString() {

    if (m_Classifiers == null) {
      return "Vote: No model built yet.";
    }

    String result = "Vote combines";
    result += " the probability distributions of these base learners:\n";
    for (int i = 0; i < m_Classifiers.length; i++) {
      result += '\t' + getClassifierSpec(i) + '\n';
    }

    return result;
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the following arguments:
   * -t training file [-T test file] [-c class index]
   */
  public static void main(String [] argv) {

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

}