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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 .
 */

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

package weka.estimators;

import weka.core.RevisionUtils;

/** 
 * Conditional probability estimator for a discrete domain conditional upon
 * a numeric domain.
 *
 * @author Len Trigg ([email protected])
 * @version $Revision: 8034 $
 */
public class DNConditionalEstimator implements ConditionalEstimator {

  /** Hold the sub-estimators */
  private NormalEstimator [] m_Estimators;

  /** Hold the weights for each of the sub-estimators */
  private DiscreteEstimator m_Weights;

  /**
   * Constructor
   *
   * @param numSymbols the number of symbols 
   * @param precision the  precision to which numeric values are given. For
   * example, if the precision is stated to be 0.1, the values in the
   * interval (0.25,0.35] are all treated as 0.3. 
   */
  public DNConditionalEstimator(int numSymbols, double precision) {

    m_Estimators = new NormalEstimator [numSymbols];
    for(int i = 0; i < numSymbols; i++) {
	m_Estimators[i] = new NormalEstimator(precision);
    }
    m_Weights = new DiscreteEstimator(numSymbols, true);
  }

  /**
   * 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)data].addValue(given, weight);
    m_Weights.addValue((int)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) {

    Estimator result = new DiscreteEstimator(m_Estimators.length,false);
    for(int i = 0; i < m_Estimators.length; i++) {
      result.addValue(i,m_Weights.getProbability(i)
		      *m_Estimators[i].getProbability(given));
    }
    return result;
  }

  /**
   * 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 = "DN Conditional Estimator. " 
      + m_Estimators.length + " sub-estimators:\n";
    for(int i = 0; i < m_Estimators.length; i++) {
      result += "Sub-estimator " + i + ": " + m_Estimators[i];
    }
    result += "Weights of each estimator given by " + m_Weights;
    return result;
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 8034 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain a sequence of pairs of integers which
   * will be treated as pairs of symbolic, numeric.
   */
  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;
	}
      }
      DNConditionalEstimator newEst = new DNConditionalEstimator(maxA + 1,
								 1);
      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());
    }
  }
}