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.
   */
  
  /*
   *    ResidualModelSelection.java
   *    Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.trees.lmt;
  
  import weka.classifiers.trees.j48.ClassifierSplitModel;
  import weka.classifiers.trees.j48.Distribution;
  import weka.classifiers.trees.j48.ModelSelection;
  import weka.classifiers.trees.j48.NoSplit;
  import weka.core.Instances;
  import weka.core.RevisionUtils;
  
  /**
   * Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the 
   * splitting criterion based on residuals.
   * 
   * @author Niels Landwehr
   * @version $Revision: 1.4 $
   */
  public class ResidualModelSelection
    extends ModelSelection {
  
    /** for serialization */
    private static final long serialVersionUID = -293098783159385148L;
  
    /** Minimum number of instances for leaves*/
    protected int m_minNumInstances;
  
    /** Minimum information gain for split*/
    protected double m_minInfoGain;    
  
    /**
     * Constructor to create ResidualModelSelection object. 
     * @param minNumInstances minimum number of instances for leaves
     */
    public ResidualModelSelection(int minNumInstances) {
      m_minNumInstances = minNumInstances;
      m_minInfoGain = 1.0E-4;
    }
  
    /**Method not in use*/
    public void cleanup() {
      //method not in use
    }
  
    /**
     * Selects split based on residuals for the given dataset.
     */
    public final ClassifierSplitModel selectModel(Instances data, 
        double[][] dataZs, double[][] dataWs) throws Exception{
  
      int numAttributes = data.numAttributes();
  
      if (numAttributes < 2) throw new Exception("Can't select Model without non-class attribute");
      if (data.numInstances() < m_minNumInstances) return new NoSplit(new Distribution(data));
  
  
      double bestGain = -Double.MAX_VALUE;
      int bestAttribute = -1;
  
      //try split on every attribute
      for (int i = 0; i < numAttributes; i++) {
        if (i != data.classIndex()) {
  
  	//build split
  	ResidualSplit split = new ResidualSplit(i);	    
  	split.buildClassifier(data, dataZs, dataWs);
  
  	if (split.checkModel(m_minNumInstances)){
  
  	  //evaluate split 
  	  double gain = split.entropyGain();	
  	  if (gain > bestGain) {
  	    bestGain = gain;
  	    bestAttribute = i;
  	  }
  	}
        }    	    
      }     
  
      if (bestGain >= m_minInfoGain){
        //return best split
        ResidualSplit split = new ResidualSplit(bestAttribute);
        split.buildClassifier(data, dataZs, dataWs);	
        return split;	    
      } else {	    
        //could not find any split with enough information gain
        return new NoSplit(new Distribution(data));	    
      }
    }
  
    /**Method not in use*/
    public final ClassifierSplitModel selectModel(Instances train) {
      //method not in use
      return null;
    }
  
    /**Method not in use*/
    public final ClassifierSplitModel selectModel(Instances train, Instances test) {
      //method not in use
      return null;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.4 $");
    }
  }
  