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.
   */
  
  /*
   *    NBTreeNoSplit.java
   *    Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.trees.j48;
  
  import weka.classifiers.Classifier;
  import weka.classifiers.AbstractClassifier;
  import weka.classifiers.Evaluation;
  import weka.classifiers.bayes.NaiveBayesUpdateable;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.RevisionUtils;
  import weka.filters.Filter;
  import weka.filters.supervised.attribute.Discretize;
  
  import java.util.Random;
  
  /**
   * Class implementing a "no-split"-split (leaf node) for naive bayes
   * trees.
   *
   * @author Mark Hall (mhall@cs.waikato.ac.nz)
   * @version $Revision: 5928 $
   */
  public final class NBTreeNoSplit
    extends ClassifierSplitModel {
  
    /** for serialization */
    private static final long serialVersionUID = 7824804381545259618L;
  
    /** the naive bayes classifier */
    private NaiveBayesUpdateable m_nb;
  
    /** the discretizer used */
    private Discretize m_disc;
  
    /** errors on the training data at this node */
    private double m_errors;
  
    public NBTreeNoSplit() {
      m_numSubsets = 1;
    }
  
    /**
     * Build the no-split node
     *
     * @param instances an <code>Instances</code> value
     * @exception Exception if an error occurs
     */
    public final void buildClassifier(Instances instances) throws Exception {
      m_nb = new NaiveBayesUpdateable();
      m_disc = new Discretize();
      m_disc.setInputFormat(instances);
      Instances temp = Filter.useFilter(instances, m_disc);
      m_nb.buildClassifier(temp);
      if (temp.numInstances() >= 5) {
        m_errors = crossValidate(m_nb, temp, new Random(1));
      }
      m_numSubsets = 1;
    }
  
    /**
     * Return the errors made by the naive bayes model at this node
     *
     * @return the number of errors made
     */
    public double getErrors() {
      return m_errors;
    }
  
    /**
     * Return the discretizer used at this node
     *
     * @return a <code>Discretize</code> value
     */
    public Discretize getDiscretizer() {
      return m_disc;
    }
  
    /**
     * Get the naive bayes model at this node
     *
     * @return a <code>NaiveBayesUpdateable</code> value
     */
    public NaiveBayesUpdateable getNaiveBayesModel() {
      return m_nb;
    }
  
    /**
     * Always returns 0 because only there is only one subset.
     */
    public final int whichSubset(Instance instance){
      
      return 0;
    }
  
    /**
     * Always returns null because there is only one subset.
     */
    public final double [] weights(Instance instance){
  
      return null;
    }
    
    /**
     * Does nothing because no condition has to be satisfied.
     */
    public final String leftSide(Instances instances){
  
      return "";
    }
    
    /**
     * Does nothing because no condition has to be satisfied.
     */
    public final String rightSide(int index, Instances instances){
  
      return "";
    }
  
    /**
     * Returns a string containing java source code equivalent to the test
     * made at this node. The instance being tested is called "i".
     *
     * @param index index of the nominal value tested
     * @param data the data containing instance structure info
     * @return a value of type 'String'
     */
    public final String sourceExpression(int index, Instances data) {
  
      return "true";  // or should this be false??
    }
  
    /**
     * Return the probability for a class value
     *
     * @param classIndex the index of the class value
     * @param instance the instance to generate a probability for
     * @param theSubset the subset to consider
     * @return a probability
     * @exception Exception if an error occurs
     */
    public double classProb(int classIndex, Instance instance, int theSubset) 
      throws Exception {
      m_disc.input(instance);
      Instance temp = m_disc.output();
      return m_nb.distributionForInstance(temp)[classIndex];
    }
  
    /**
     * Return a textual description of the node
     *
     * @return a <code>String</code> value
     */
    public String toString() {
      return m_nb.toString();
    }
  
    /**
     * Utility method for fast 5-fold cross validation of a naive bayes
     * model
     *
     * @param fullModel a <code>NaiveBayesUpdateable</code> value
     * @param trainingSet an <code>Instances</code> value
     * @param r a <code>Random</code> value
     * @return a <code>double</code> value
     * @exception Exception if an error occurs
     */
    public static double crossValidate(NaiveBayesUpdateable fullModel,
  			       Instances trainingSet,
  			       Random r) throws Exception {
      // make some copies for fast evaluation of 5-fold xval
      Classifier [] copies = AbstractClassifier.makeCopies(fullModel, 5);
      Evaluation eval = new Evaluation(trainingSet);
      // make some splits
      for (int j = 0; j < 5; j++) {
        Instances test = trainingSet.testCV(5, j);
        // unlearn these test instances
        for (int k = 0; k < test.numInstances(); k++) {
  	test.instance(k).setWeight(-test.instance(k).weight());
  	((NaiveBayesUpdateable)copies[j]).updateClassifier(test.instance(k));
  	// reset the weight back to its original value
  	test.instance(k).setWeight(-test.instance(k).weight());
        }
        eval.evaluateModel(copies[j], test);
      }
      return eval.incorrect();
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5928 $");
    }
  }
  