discreteestimator.java

数据挖掘estimators算法

/*
 *    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 Len Trigg
 *
 */

package weka.estimators;

import java.util.*;
import weka.core.*;

/** 
 * Simple symbolic probability estimator based on symbol counts.
 *
 * @author Len Trigg ([e-mail])
 * @version $Revision: 1.4 $
 */

public class DiscreteEstimator implements Estimator {

  /** 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;
    }
  }

  /**
   * 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;
  }

  /**
   * Display a representation of this estimator
   */
  public String toString() {
    
    String result = "Discrete Estimator. Counts = ";
    if (m_SumOfCounts > 1) {
      for(int i = 0; i < m_Counts.length; i++) {
	result += " " + Utils.doubleToString(m_Counts[i], 2);
      }
      result += "  (Total = " + Utils.doubleToString(m_SumOfCounts, 2)
	+ ")\n"; 
    } else {
      for(int i = 0; i < m_Counts.length; i++) {
	result += " " + m_Counts[i];
      }
      result += "  (Total = " + m_SumOfCounts + ")\n"; 
    }
    return result;
  }

  /**
   * 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());
    }
  }
}