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.
   */
  
  /*
   *    InfoGainSplitCrit.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.trees.j48;
  
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  
  /**
   * Class for computing the information gain for a given distribution.
   *
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @version $Revision: 1.10 $
   */
  public final class InfoGainSplitCrit
    extends EntropyBasedSplitCrit{
  
    /** for serialization */
    private static final long serialVersionUID = 4892105020180728499L;
  
    /**
     * This method is a straightforward implementation of the information
     * gain criterion for the given distribution.
     */
    public final double splitCritValue(Distribution bags) {
  
      double numerator;
          
      numerator = oldEnt(bags)-newEnt(bags);
  
      // Splits with no gain are useless.
      if (Utils.eq(numerator,0))
        return Double.MAX_VALUE;
          
      // We take the reciprocal value because we want to minimize the
      // splitting criterion's value.
      return bags.total()/numerator;
    }
  
    /**
     * This method computes the information gain in the same way 
     * C4.5 does.
     *
     * @param bags the distribution
     * @param totalNoInst weight of ALL instances (including the
     * ones with missing values).
     */
    public final double splitCritValue(Distribution bags, double totalNoInst) {
      
      double numerator;
      double noUnknown;
      double unknownRate;
      int i;
      
      noUnknown = totalNoInst-bags.total();
      unknownRate = noUnknown/totalNoInst;
      numerator = (oldEnt(bags)-newEnt(bags));
      numerator = (1-unknownRate)*numerator;
      
      // Splits with no gain are useless.
      if (Utils.eq(numerator,0))
        return 0;
      
      return numerator/bags.total();
    }
  
    /**
     * This method computes the information gain in the same way 
     * C4.5 does.
     *
     * @param bags the distribution
     * @param totalNoInst weight of ALL instances 
     * @param oldEnt entropy with respect to "no-split"-model.
     */
    public final double splitCritValue(Distribution bags,double totalNoInst,
                                       double oldEnt) {
      
      double numerator;
      double noUnknown;
      double unknownRate;
      int i;
      
      noUnknown = totalNoInst-bags.total();
      unknownRate = noUnknown/totalNoInst;
      numerator = (oldEnt-newEnt(bags));
      numerator = (1-unknownRate)*numerator;
      
      // Splits with no gain are useless.
      if (Utils.eq(numerator,0))
        return 0;
      
      return numerator/bags.total();
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.10 $");
    }
  }
  