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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.
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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 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.
 */

/*
 *    DiscreteEstimator.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 symbolic probability estimator based on symbol counts.
 *
 * @author Len Trigg ([email protected])
 * @version $Revision: 1.10 $
 */
public class DiscreteEstimator extends Estimator implements IncrementalEstimator {
  
  /** for serialization */
  private static final long serialVersionUID = -5526486742612434779L;

  /** Hold the counts */
  private double [] m_Counts;
  
  /** Hold the sum of counts */
  private double m_SumOfCounts;
  
  
  /**
   * Constructor
   *
   * @param numSymbols the number of possible symbols (remember to include 0)
   * @param laplace if true, counts will be initialised to 1
   */
  public DiscreteEstimator(int numSymbols, boolean laplace) {
    
    m_Counts = new double [numSymbols];
    m_SumOfCounts = 0;
    if (laplace) {
      for(int i = 0; i < numSymbols; i++) {
        m_Counts[i] = 1;
      }
      m_SumOfCounts = (double)numSymbols;
    }
  }
  
  /**
   * Constructor
   *
   * @param nSymbols the number of possible symbols (remember to include 0)
   * @param fPrior value with which counts will be initialised
   */
  public DiscreteEstimator(int nSymbols, double fPrior) {    
    
    m_Counts = new double [nSymbols];
    for(int iSymbol = 0; iSymbol < nSymbols; iSymbol++) {
      m_Counts[iSymbol] = fPrior;
    }
    m_SumOfCounts = fPrior * (double) nSymbols;
  }
  
  /**
   * 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_Counts[(int)data] += weight;
    m_SumOfCounts += weight;
  }
  
  /**
   * 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) {
    
    if (m_SumOfCounts == 0) {
      return 0;
    }
    return (double)m_Counts[(int)data] / m_SumOfCounts;
  }
  
  /**
   * Gets the number of symbols this estimator operates with
   *
   * @return the number of estimator symbols
   */
  public int getNumSymbols() {
    
    return (m_Counts == null) ? 0 : m_Counts.length;
  }
  
  
  /**
   * Get the count for a value
   *
   * @param data the value to get the count of
   * @return the count of the supplied value
   */
  public double getCount(double data) {
    
    if (m_SumOfCounts == 0) {
      return 0;
    }
    return m_Counts[(int)data];
  }
  
  
  /**
   * Get the sum of all the counts
   *
   * @return the total sum of counts
   */
  public double getSumOfCounts() {
    
    return m_SumOfCounts;
  }
  
  
  /**
   * Display a representation of this estimator
   */
  public String toString() {
    
    StringBuffer result = new StringBuffer("Discrete Estimator. Counts = ");
    if (m_SumOfCounts > 1) {
      for(int i = 0; i < m_Counts.length; i++) {
        result.append(" ").append(Utils.doubleToString(m_Counts[i], 2));
      }
      result.append("  (Total = " ).append(Utils.doubleToString(m_SumOfCounts, 2));
      result.append(")\n"); 
    } else {
      for(int i = 0; i < m_Counts.length; i++) {
        result.append(" ").append(m_Counts[i]);
      }
      result.append("  (Total = ").append(m_SumOfCounts).append(")\n"); 
    }
    return result.toString();
  }
  
  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    
    // attributes
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    return result;
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 1.10 $");
  }
  
  /**
   * Main method for testing this class.
   *
   * @param argv should contain a sequence 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 current = Integer.parseInt(argv[0]);
      int max = current;
      for(int i = 1; i < argv.length; i++) {
        current = Integer.parseInt(argv[i]);
        if (current > max) {
          max = current;
        }
      }
      DiscreteEstimator newEst = new DiscreteEstimator(max + 1, true);
      for(int i = 0; i < argv.length; i++) {
        current = Integer.parseInt(argv[i]);
        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());
    }
  }
}