/* *    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. *//* *    PoissonEstimator.java *    Copyright (C) 1999 Len Trigg * */package weka.estimators;import java.util.*;import weka.core.*;/**  * Simple probability estimator that places a single Poisson distribution * over the observed values. * * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.4 $ */public class PoissonEstimator implements Estimator {  /** The number of values seen */  private double m_NumValues;  /** The sum of the values seen */  private double m_SumOfValues;  /**    * The average number of times   * an event occurs in an interval.   */  private double m_Lambda;  /**   * Calculates the log factorial of a number.   *   * @param x input number.   * @return log factorial of x.   */  private double logFac(double x) {    double result = 0;    for (double i = 2; i <= x; i++) {      result += Math.log(i);    }    return result;  }  /**   * Returns value for Poisson distribution   *   * @param x the argument to the kernel function   * @return the value for a Poisson kernel   */  private double Poisson(double x) {        return Math.exp(-m_Lambda + (x * Math.log(m_Lambda)) - logFac(x));  }    /**   * Add a new data value to the current estimator.   *   * @param data the new data value    * @param weight the weight assigned to the data value    */  public void addValue(double data, double weight) {        m_NumValues += weight;    m_SumOfValues += data * weight;    if (m_NumValues != 0) {      m_Lambda = m_SumOfValues / m_NumValues;    }  }  /**   * Get a probability estimate for a value   *   * @param data the value to estimate the probability of   * @return the estimated probability of the supplied value   */  public double getProbability(double data) {        return Poisson(data);  }  /** Display a representation of this estimator */  public String toString() {        return "Poisson Lambda = " + Utils.doubleToString(m_Lambda, 4, 2) + "\n";  }  /**   * Main method for testing this class.   *   * @param argv should contain a sequence of numeric values   */  public static void main(String [] argv) {        try {      if (argv.length == 0) {	System.out.println("Please specify a set of instances.");	return;      }      PoissonEstimator newEst = new PoissonEstimator();      for(int i = 0; i < argv.length; i++) {	double current = Double.valueOf(argv[i]).doubleValue();	System.out.println(newEst);	System.out.println("Prediction for " + current 			   + " = " + newEst.getProbability(current));	newEst.addValue(current, 1);      }    } catch (Exception e) {      System.out.println(e.getMessage());    }  }}