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.
   */
  
  /*
   *    DDConditionalEstimator.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.estimators;
  
  import weka.core.RevisionUtils;
  
   
  /** 
   * Conditional probability estimator for a discrete domain conditional upon
   * a discrete domain.
   *
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @version $Revision: 1.8 $
   */
  public class DDConditionalEstimator implements ConditionalEstimator {
  
    /** Hold the sub-estimators */
    private DiscreteEstimator [] m_Estimators;
  
    /**
     * Constructor
     *
     * @param numSymbols the number of possible symbols (remember to include 0)
     * @param numCondSymbols the number of conditioning symbols 
     * @param laplace if true, sub-estimators will use laplace
     */
    public DDConditionalEstimator(int numSymbols, int numCondSymbols,
  				boolean laplace) {
      
      m_Estimators = new DiscreteEstimator [numCondSymbols];
      for(int i = 0; i < numCondSymbols; i++) {
        m_Estimators[i] = new DiscreteEstimator(numSymbols, laplace);
      }
    }
  
    /**
     * Add a new data value to the current estimator.
     *
     * @param data the new data value 
     * @param given the new value that data is conditional upon 
     * @param weight the weight assigned to the data value 
     */
    public void addValue(double data, double given, double weight) {
      
      m_Estimators[(int)given].addValue(data, weight);
    }
  
    /**
     * Get a probability estimator for a value
     *
     * @param given the new value that data is conditional upon 
     * @return the estimator for the supplied value given the condition
     */
    public Estimator getEstimator(double given) {
      
      return m_Estimators[(int)given];
    }
  
    /**
     * Get a probability estimate for a value
     *
     * @param data the value to estimate the probability of
     * @param given the new value that data is conditional upon 
     * @return the estimated probability of the supplied value
     */
    public double getProbability(double data, double given) {
      
      return getEstimator(given).getProbability(data);
    }
  
    /** Display a representation of this estimator */
    public String toString() {
      
      String result = "DD Conditional Estimator. " 
        + m_Estimators.length + " sub-estimators:\n";
      for(int i = 0; i < m_Estimators.length; i++) {
        result += "Sub-estimator " + i + ": " + m_Estimators[i];
      }
      return result;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.8 $");
    }
  
    /**
     * Main method for testing this class.
     *
     * @param argv should contain a sequence of pairs of integers which
     * will be treated as symbolic.
     */
    public static void main(String [] argv) {
      
      try {
        if (argv.length == 0) {
  	System.out.println("Please specify a set of instances.");
  	return;
        }
        int currentA = Integer.parseInt(argv[0]);
        int maxA = currentA;
        int currentB = Integer.parseInt(argv[1]);
        int maxB = currentB;
        for(int i = 2; i < argv.length - 1; i += 2) {
  	currentA = Integer.parseInt(argv[i]);
  	currentB = Integer.parseInt(argv[i + 1]);
  	if (currentA > maxA) {
  	  maxA = currentA;
  	}
  	if (currentB > maxB) {
  	  maxB = currentB;
  	}
        }
        DDConditionalEstimator newEst = new DDConditionalEstimator(maxA + 1,
  								 maxB + 1,
  								 true);
        for(int i = 0; i < argv.length - 1; i += 2) {
  	currentA = Integer.parseInt(argv[i]);
  	currentB = Integer.parseInt(argv[i + 1]);
  	System.out.println(newEst);
  	System.out.println("Prediction for " + currentA + '|' + currentB 
  			   + " = "
  			   + newEst.getProbability(currentA, currentB));
  	newEst.addValue(currentA, currentB, 1);
        }
      } catch (Exception e) {
        System.out.println(e.getMessage());
      }
    }
  }
  