/*
* 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.
*/
/*
* DiscreteEstimatorFullBayes.java
*
*/
package weka.classifiers.bayes.net.estimate;
import weka.core.RevisionUtils;
import weka.estimators.DiscreteEstimator;
/**
* Symbolic probability estimator based on symbol counts and a prior.
*
* @author Remco Bouckaert (rrb@xm.co.nz)
* @version $Revision: 1.3 $
*/
public class DiscreteEstimatorFullBayes
extends DiscreteEstimatorBayes {
/** for serialization */
static final long serialVersionUID = 6774941981423312133L;
/**
* Constructor
*
* @param nSymbols the number of possible symbols (remember to include 0)
* @param w1
* @param w2
* @param EmptyDist
* @param ClassDist
* @param fPrior
*/
public DiscreteEstimatorFullBayes(int nSymbols,
double w1, double w2,
DiscreteEstimatorBayes EmptyDist,
DiscreteEstimatorBayes ClassDist,
double fPrior) {
super(nSymbols, fPrior);
m_SumOfCounts = 0.0;
for (int iSymbol = 0; iSymbol < m_nSymbols; iSymbol++) {
double p1 = EmptyDist.getProbability(iSymbol);
double p2 = ClassDist.getProbability(iSymbol);
m_Counts[iSymbol] = w1 * p1 + w2 * p2;
m_SumOfCounts += m_Counts[iSymbol];
}
} // DiscreteEstimatorFullBayes
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.3 $");
}
/**
* 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());
}
} // main
} // class DiscreteEstimatorFullBayes