content: /*
   *    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.
   */
  
  /*
   *    EntropyBasedSplitCrit.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.trees.j48;
  
  /**
   * "Abstract" class for computing splitting criteria
   * based on the entropy of a class distribution.
   *
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @version $Revision: 1.8 $
   */
  public abstract class EntropyBasedSplitCrit
    extends SplitCriterion {
  
    /** for serialization */
    private static final long serialVersionUID = -2618691439791653056L;
  
    /** The log of 2. */
    protected static double log2 = Math.log(2);
  
    /**
     * Help method for computing entropy.
     */
    public final double logFunc(double num) {
  
      // Constant hard coded for efficiency reasons
      if (num < 1e-6)
        return 0;
      else
        return num*Math.log(num)/log2;
    }
  
    /**
     * Computes entropy of distribution before splitting.
     */
    public final double oldEnt(Distribution bags) {
  
      double returnValue = 0;
      int j;
  
      for (j=0;j<bags.numClasses();j++)
        returnValue = returnValue+logFunc(bags.perClass(j));
      return logFunc(bags.total())-returnValue; 
    }
  
    /**
     * Computes entropy of distribution after splitting.
     */
    public final double newEnt(Distribution bags) {
      
      double returnValue = 0;
      int i,j;
  
      for (i=0;i<bags.numBags();i++){
        for (j=0;j<bags.numClasses();j++)
  	returnValue = returnValue+logFunc(bags.perClassPerBag(i,j));
        returnValue = returnValue-logFunc(bags.perBag(i));
      }
      return -returnValue;
    }
  
    /**
     * Computes entropy after splitting without considering the
     * class values.
     */
    public final double splitEnt(Distribution bags) {
  
      double returnValue = 0;
      int i;
  
      for (i=0;i<bags.numBags();i++)
        returnValue = returnValue+logFunc(bags.perBag(i));
      return logFunc(bags.total())-returnValue;
    }
  }
  
  