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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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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 .
 */

/*
 *    EntropySplitCrit.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.j48;

import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.ContingencyTables;

/**
 * Class for computing the entropy for a given distribution.
 *
 * @author Eibe Frank ([email protected])
 * @version $Revision: 10055 $
 */
public final class EntropySplitCrit
  extends EntropyBasedSplitCrit {

  /** for serialization */
  private static final long serialVersionUID = 5986252682266803935L;

  /**
   * Computes entropy for given distribution.
   */
  public final double splitCritValue(Distribution bags) {
    
    return newEnt(bags);
  }

  /**
   * Computes entropy of test distribution with respect to training distribution.
   */
  public final double splitCritValue(Distribution train, Distribution test) {

    double result = 0;
    int numClasses = 0;
    int i, j;
    
    // Find out relevant number of classes
    for (j = 0; j < test.numClasses(); j++)
      if (Utils.gr(train.perClass(j), 0) || Utils.gr(test.perClass(j), 0))
	numClasses++;

    // Compute entropy of test data with respect to training data
    for (i = 0; i < test.numBags(); i++)
      if (Utils.gr(test.perBag(i),0)) {
	for (j = 0; j < test.numClasses(); j++)
	  if (Utils.gr(test.perClassPerBag(i, j), 0))
	    result -= test.perClassPerBag(i, j)*
	      Math.log(train.perClassPerBag(i, j) + 1);
	result += test.perBag(i) * Math.log(train.perBag(i) + numClasses);
      }
  
    return result / ContingencyTables.log2;
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10055 $");
  }
}