/* *    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 Len Trigg * */package weka.estimators;import java.util.*;import weka.core.*;/**  * Conditional probability estimator for a discrete domain conditional upon * a discrete domain. * * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.4 $ */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 data the value to estimate the probability of   * @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;  }  /**   * 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());    }  }}