/*
* 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.
*/
/*
* BayesNet.java
* Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.bayes.net.estimate;
import weka.classifiers.bayes.BayesNet;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.estimators.Estimator;
import java.util.Enumeration;
/**
<!-- globalinfo-start -->
* SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned. Estimates probabilities directly from data.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -A <alpha>
* Initial count (alpha)
* </pre>
*
<!-- options-end -->
*
* @author Remco Bouckaert (rrb@xm.co.nz)
* @version $Revision: 1.6 $
*/
public class SimpleEstimator
extends BayesNetEstimator {
/** for serialization */
static final long serialVersionUID = 5874941612331806172L;
/**
* Returns a string describing this object
* @return a description of the classifier suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"SimpleEstimator is used for estimating the conditional probability "
+ "tables of a Bayes network once the structure has been learned. "
+ "Estimates probabilities directly from data.";
}
/**
* estimateCPTs estimates the conditional probability tables for the Bayes
* Net using the network structure.
*
* @param bayesNet the bayes net to use
* @throws Exception if something goes wrong
*/
public void estimateCPTs(BayesNet bayesNet) throws Exception {
initCPTs(bayesNet);
// Compute counts
Enumeration enumInsts = bayesNet.m_Instances.enumerateInstances();
while (enumInsts.hasMoreElements()) {
Instance instance = (Instance) enumInsts.nextElement();
updateClassifier(bayesNet, instance);
}
} // estimateCPTs
/**
* Updates the classifier with the given instance.
*
* @param bayesNet the bayes net to use
* @param instance the new training instance to include in the model
* @throws Exception if the instance could not be incorporated in
* the model.
*/
public void updateClassifier(BayesNet bayesNet, Instance instance) throws Exception {
for (int iAttribute = 0; iAttribute < bayesNet.m_Instances.numAttributes(); iAttribute++) {
double iCPT = 0;
for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getNrOfParents(); iParent++) {
int nParent = bayesNet.getParentSet(iAttribute).getParent(iParent);
iCPT = iCPT * bayesNet.m_Instances.attribute(nParent).numValues() + instance.value(nParent);
}
bayesNet.m_Distributions[iAttribute][(int) iCPT].addValue(instance.value(iAttribute), instance.weight());
}
} // updateClassifier
/**
* initCPTs reserves space for CPTs and set all counts to zero
*
* @param bayesNet the bayes net to use
* @throws Exception if something goes wrong
*/
public void initCPTs(BayesNet bayesNet) throws Exception {
Instances instances = bayesNet.m_Instances;
// Reserve space for CPTs
int nMaxParentCardinality = 1;
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
if (bayesNet.getParentSet(iAttribute).getCardinalityOfParents() > nMaxParentCardinality) {
nMaxParentCardinality = bayesNet.getParentSet(iAttribute).getCardinalityOfParents();
}
}
// Reserve plenty of memory
bayesNet.m_Distributions = new Estimator[instances.numAttributes()][nMaxParentCardinality];
// estimate CPTs
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getCardinalityOfParents(); iParent++) {
bayesNet.m_Distributions[iAttribute][iParent] =
new DiscreteEstimatorBayes(instances.attribute(iAttribute).numValues(), m_fAlpha);
}
}
} // initCPTs
/**
* Calculates the class membership probabilities for the given test
* instance.
*
* @param bayesNet the bayes net to use
* @param instance the instance to be classified
* @return predicted class probability distribution
* @throws Exception if there is a problem generating the prediction
*/
public double[] distributionForInstance(BayesNet bayesNet, Instance instance) throws Exception {
Instances instances = bayesNet.m_Instances;
int nNumClasses = instances.numClasses();
double[] fProbs = new double[nNumClasses];
for (int iClass = 0; iClass < nNumClasses; iClass++) {
fProbs[iClass] = 1.0;
}
for (int iClass = 0; iClass < nNumClasses; iClass++) {
double logfP = 0;
for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
double iCPT = 0;
for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute).getNrOfParents(); iParent++) {
int nParent = bayesNet.getParentSet(iAttribute).getParent(iParent);
if (nParent == instances.classIndex()) {
iCPT = iCPT * nNumClasses + iClass;
} else {
iCPT = iCPT * instances.attribute(nParent).numValues() + instance.value(nParent);
}
}
if (iAttribute == instances.classIndex()) {
// fP *=
// m_Distributions[iAttribute][(int) iCPT].getProbability(iClass);
logfP += Math.log(bayesNet.m_Distributions[iAttribute][(int) iCPT].getProbability(iClass));
} else {
// fP *=
// m_Distributions[iAttribute][(int) iCPT]
// .getProbability(instance.value(iAttribute));
logfP
+= Math.log(bayesNet.m_Distributions[iAttribute][(int) iCPT].getProbability(instance.value(iAttribute)));
}
}
// fProbs[iClass] *= fP;
fProbs[iClass] += logfP;
}
// Find maximum
double fMax = fProbs[0];
for (int iClass = 0; iClass < nNumClasses; iClass++) {
if (fProbs[iClass] > fMax) {
fMax = fProbs[iClass];
}
}
// transform from log-space to normal-space
for (int iClass = 0; iClass < nNumClasses; iClass++) {
fProbs[iClass] = Math.exp(fProbs[iClass] - fMax);
}
// Display probabilities
Utils.normalize(fProbs);
return fProbs;
} // distributionForInstance
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.6 $");
}
} // SimpleEstimator