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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 .
 */

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

package weka.classifiers.trees.j48;

import java.io.Serializable;

import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.Utils;

/** 
 * Abstract class for classification models that can be used 
 * recursively to split the data.
 *
 * @author Eibe Frank ([email protected])
 * @version $Revision: 11245 $
 */
public abstract class ClassifierSplitModel
  implements Cloneable, Serializable, RevisionHandler {

  /** for serialization */
  private static final long serialVersionUID = 4280730118393457457L;

  /** Distribution of class values. */  
  protected Distribution m_distribution;  

  /** Number of created subsets. */
  protected int m_numSubsets;         

  /**
   * Allows to clone a model (shallow copy).
   */
  public Object clone() {

    Object clone = null;
    
    try {
      clone = super.clone();
    } catch (CloneNotSupportedException e) {
    } 
    return clone;
  }

  /**
   * Builds the classifier split model for the given set of instances.
   *
   * @exception Exception if something goes wrong
   */
  public abstract void buildClassifier(Instances instances) throws Exception;
  
  /**
   * Checks if generated model is valid.
   */
  public final boolean checkModel() {
    
    if (m_numSubsets > 0)
      return true;
    else
      return false;
  }
  
  /**
   * Classifies a given instance.
   *
   * @exception Exception if something goes wrong
   */
  public final double classifyInstance(Instance instance)
       throws Exception {
    
    int theSubset;
    
    theSubset = whichSubset(instance);
    if (theSubset > -1)
      return (double)m_distribution.maxClass(theSubset);
    else
      return (double)m_distribution.maxClass();
  }

  /**
   * Gets class probability for instance.
   *
   * @exception Exception if something goes wrong
   */
  public double classProb(int classIndex, Instance instance, int theSubset) 
       throws Exception {

    if (theSubset > -1) {
      return m_distribution.prob(classIndex,theSubset);
    } else {
      double [] weights = weights(instance);
      if (weights == null) {
	return m_distribution.prob(classIndex);
      } else {
	double prob = 0;
	for (int i = 0; i < weights.length; i++) {
	  prob += weights[i] * m_distribution.prob(classIndex, i);
	}
	return prob;
      }
    }
  }

  /**
   * Gets class probability for instance.
   *
   * @exception Exception if something goes wrong
   */
  public double classProbLaplace(int classIndex, Instance instance,
				 int theSubset) throws Exception {

    if (theSubset > -1) {
      return m_distribution.laplaceProb(classIndex, theSubset);
    } else {
      double [] weights = weights(instance);
      if (weights == null) {
	return m_distribution.laplaceProb(classIndex);
      } else {
	double prob = 0;
	for (int i = 0; i < weights.length; i++) {
	  prob += weights[i] * m_distribution.laplaceProb(classIndex, i);
	}
	return prob;
      }
    }
  }

  /**
   * Returns coding costs of model. Returns 0 if not overwritten.
   */
  public double codingCost() {

    return 0;
  }

  /**
   * Returns the distribution of class values induced by the model.
   */
  public final Distribution distribution() {

    return m_distribution;
  }

  /**
   * Prints left side of condition satisfied by instances.
   *
   * @param data the data.
   */
  public abstract String leftSide(Instances data);

  /**
   * Prints left side of condition satisfied by instances in subset index.
   */
  public abstract String rightSide(int index,Instances data);

  /**
   * Prints label for subset index of instances (eg class).
   *
   * @exception Exception if something goes wrong
   */
  public final String dumpLabel(int index,Instances data) throws Exception {

    StringBuffer text;

    text = new StringBuffer();
    text.append(((Instances)data).classAttribute().
		value(m_distribution.maxClass(index)));
    text.append(" ("+Utils.roundDouble(m_distribution.perBag(index),2));
    if (Utils.gr(m_distribution.numIncorrect(index),0))
      text.append("/"+Utils.roundDouble(m_distribution.numIncorrect(index),2));
    text.append(")");

    return text.toString();
  }
  
  public final String sourceClass(int index, Instances data) throws Exception {

    System.err.println("sourceClass");
    return (new StringBuffer(m_distribution.maxClass(index))).toString();
  }

  public abstract String sourceExpression(int index, Instances data);

  /**
   * Prints the split model.
   *
   * @exception Exception if something goes wrong
   */
  public final String dumpModel(Instances data) throws Exception {

    StringBuffer text;
    int i;

    text = new StringBuffer();
    for (i=0;i -1) {
        subsetSize[subset]++;
      } else {
        double[] weights = weights(instance);
        for (int j = 0; j < m_numSubsets; j++) {
          if (Utils.gr(weights[j], 0)) {
            subsetSize[j]++;
          }
        }
      }
    }
    
    // Create subsets
    Instances [] instances = new Instances [m_numSubsets];
    for (int j = 0; j < m_numSubsets; j++) {
      instances[j] = new Instances(data, subsetSize[j]);
    }
    for (Instance instance : data) {
      int subset = whichSubset(instance);
      if (subset > -1) {
	instances[subset].add(instance);
      } else {
        double[] weights = weights(instance);
        for (int j = 0; j < m_numSubsets; j++) {
	  if (Utils.gr(weights[j], 0)) {
	    instances[j].add(instance);
	    instances[j].lastInstance().
	      setWeight(weights[j] * instance.weight());
	  }
	}
      }
    }
    
    return instances;
  }

  /**
   * Returns weights if instance is assigned to more than one subset.
   * Returns null if instance is only assigned to one subset.
   */
  public abstract double [] weights(Instance instance);
  
  /**
   * Returns index of subset instance is assigned to.
   * Returns -1 if instance is assigned to more than one subset.
   *
   * @exception Exception if something goes wrong
   */
  public abstract int whichSubset(Instance instance) throws Exception;
}