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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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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 3 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, see .
 */

/*
 * BayesNet.java
 * Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 * 
 */

package weka.classifiers.bayes.net.estimate;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.search.local.K2;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Statistics;
import weka.core.Utils;
import weka.estimators.Estimator;

/**
 *  BMAEstimator estimates conditional probability
 * tables of a Bayes network using Bayes Model Averaging (BMA).
 * 

* * * Valid options are: *

* *

 * -k2
 *  Whether to use K2 prior.
 * 
* *
 * -A <alpha>
 *  Initial count (alpha)
 * 
* * * * @author Remco Bouckaert ([email protected]) * @version $Revision: 10154 $ */ public class BMAEstimator extends SimpleEstimator { /** for serialization */ static final long serialVersionUID = -1846028304233257309L; /** whether to use K2 prior */ protected boolean m_bUseK2Prior = false; /** * Returns a string describing this object * * @return a description of the classifier suitable for displaying in the * explorer/experimenter gui */ @Override public String globalInfo() { return "BMAEstimator estimates conditional probability tables of a Bayes " + "network using Bayes Model Averaging (BMA)."; } /** * estimateCPTs estimates the conditional probability tables for the Bayes Net * using the network structure. * * @param bayesNet the bayes net to use * @throws Exception if an error occurs */ @Override public void estimateCPTs(BayesNet bayesNet) throws Exception { initCPTs(bayesNet); Instances instances = bayesNet.m_Instances; // sanity check to see if nodes have not more than one parent for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) { if (bayesNet.getParentSet(iAttribute).getNrOfParents() > 1) { throw new Exception( "Cannot handle networks with nodes with more than 1 parent (yet)."); } } BayesNet EmptyNet = new BayesNet(); K2 oSearchAlgorithm = new K2(); oSearchAlgorithm.setInitAsNaiveBayes(false); oSearchAlgorithm.setMaxNrOfParents(0); EmptyNet.setSearchAlgorithm(oSearchAlgorithm); EmptyNet.buildClassifier(instances); BayesNet NBNet = new BayesNet(); oSearchAlgorithm.setInitAsNaiveBayes(true); oSearchAlgorithm.setMaxNrOfParents(1); NBNet.setSearchAlgorithm(oSearchAlgorithm); NBNet.buildClassifier(instances); // estimate CPTs for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) { if (iAttribute != instances.classIndex()) { double w1 = 0.0, w2 = 0.0; int nAttValues = instances.attribute(iAttribute).numValues(); if (m_bUseK2Prior == true) { // use Cooper and Herskovitz's metric for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) { w1 += Statistics .lnGamma(1 + ((DiscreteEstimatorBayes) EmptyNet.m_Distributions[iAttribute][0]) .getCount(iAttValue)) - Statistics.lnGamma(1); } w1 += Statistics.lnGamma(nAttValues) - Statistics.lnGamma(nAttValues + instances.numInstances()); for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute) .getCardinalityOfParents(); iParent++) { int nTotal = 0; for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) { double nCount = ((DiscreteEstimatorBayes) NBNet.m_Distributions[iAttribute][iParent]) .getCount(iAttValue); w2 += Statistics.lnGamma(1 + nCount) - Statistics.lnGamma(1); nTotal += nCount; } w2 += Statistics.lnGamma(nAttValues) - Statistics.lnGamma(nAttValues + nTotal); } } else { // use BDe metric for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) { w1 += Statistics .lnGamma(1.0 / nAttValues + ((DiscreteEstimatorBayes) EmptyNet.m_Distributions[iAttribute][0]) .getCount(iAttValue)) - Statistics.lnGamma(1.0 / nAttValues); } w1 += Statistics.lnGamma(1) - Statistics.lnGamma(1 + instances.numInstances()); int nParentValues = bayesNet.getParentSet(iAttribute) .getCardinalityOfParents(); for (int iParent = 0; iParent < nParentValues; iParent++) { int nTotal = 0; for (int iAttValue = 0; iAttValue < nAttValues; iAttValue++) { double nCount = ((DiscreteEstimatorBayes) NBNet.m_Distributions[iAttribute][iParent]) .getCount(iAttValue); w2 += Statistics.lnGamma(1.0 / (nAttValues * nParentValues) + nCount) - Statistics.lnGamma(1.0 / (nAttValues * nParentValues)); nTotal += nCount; } w2 += Statistics.lnGamma(1) - Statistics.lnGamma(1 + nTotal); } } // System.out.println(w1 + " " + w2 + " " + (w2 - w1)); if (w1 < w2) { w2 = w2 - w1; w1 = 0; w1 = 1 / (1 + Math.exp(w2)); w2 = Math.exp(w2) / (1 + Math.exp(w2)); } else { w1 = w1 - w2; w2 = 0; w2 = 1 / (1 + Math.exp(w1)); w1 = Math.exp(w1) / (1 + Math.exp(w1)); } for (int iParent = 0; iParent < bayesNet.getParentSet(iAttribute) .getCardinalityOfParents(); iParent++) { bayesNet.m_Distributions[iAttribute][iParent] = new DiscreteEstimatorFullBayes( instances.attribute(iAttribute).numValues(), w1, w2, (DiscreteEstimatorBayes) EmptyNet.m_Distributions[iAttribute][0], (DiscreteEstimatorBayes) NBNet.m_Distributions[iAttribute][iParent], m_fAlpha); } } } int iAttribute = instances.classIndex(); bayesNet.m_Distributions[iAttribute][0] = EmptyNet.m_Distributions[iAttribute][0]; } // 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. */ @Override public void updateClassifier(BayesNet bayesNet, Instance instance) throws Exception { throw new Exception("updateClassifier does not apply to BMA estimator"); } // 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 */ @Override public void initCPTs(BayesNet bayesNet) throws Exception { // Reserve space for CPTs int nMaxParentCardinality = 1; for (int iAttribute = 0; iAttribute < bayesNet.m_Instances.numAttributes(); iAttribute++) { if (bayesNet.getParentSet(iAttribute).getCardinalityOfParents() > nMaxParentCardinality) { nMaxParentCardinality = bayesNet.getParentSet(iAttribute) .getCardinalityOfParents(); } } // Reserve plenty of memory bayesNet.m_Distributions = new Estimator[bayesNet.m_Instances .numAttributes()][nMaxParentCardinality]; } // initCPTs /** * Returns whether K2 prior is used * * @return true if K2 prior is used */ public boolean isUseK2Prior() { return m_bUseK2Prior; } /** * Sets the UseK2Prior. * * @param bUseK2Prior The bUseK2Prior to set */ public void setUseK2Prior(boolean bUseK2Prior) { m_bUseK2Prior = bUseK2Prior; } /** * Returns an enumeration describing the available options * * @return an enumeration of all the available options */ @Override public Enumeration




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