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.
   */
  
  /*
   *    PoissonEstimator.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.estimators;
  
  import weka.core.Capabilities.Capability;
  import weka.core.Capabilities;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  
  /** 
   * Simple probability estimator that places a single Poisson distribution
   * over the observed values.
   *
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @version $Revision: 5490 $
   */
  public class PoissonEstimator
    extends Estimator
    implements IncrementalEstimator {
  
    /** for serialization */
    private static final long serialVersionUID = 7669362595289236662L;
    
    /** 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";
    }
    
    /**
     * Returns default capabilities of the classifier.
     *
     * @return      the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
      
      // class
      if (!m_noClass) {
        result.enable(Capability.NOMINAL_CLASS);
        result.enable(Capability.MISSING_CLASS_VALUES);
      } else {
        result.enable(Capability.NO_CLASS);
      }
      
      // attributes
      result.enable(Capability.NUMERIC_ATTRIBUTES);
      return result;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5490 $");
    }
    
    /**
     * 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());
      }
    }
  }
  