/* *    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. *//* *    DNConditionalEstimator.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 numeric domain. * * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.4 $ */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 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) {    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;  }  /**   * 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());    }  }}