/*
* 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 (trigg@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
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;
}
}
/**
* 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;
}
/**
* 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());
}
}
}