Java tutorial
/* * 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 <http://www.gnu.org/licenses/>. */ /* * 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; /** * <!-- globalinfo-start --> BMAEstimator estimates conditional probability * tables of a Bayes network using Bayes Model Averaging (BMA). * <p/> * <!-- globalinfo-end --> * * <!-- options-start --> Valid options are: * <p/> * * <pre> * -k2 * Whether to use K2 prior. * </pre> * * <pre> * -A <alpha> * Initial count (alpha) * </pre> * * <!-- options-end --> * * @author Remco Bouckaert (rrb@xm.co.nz) * @version $Revision$ */ 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<Option> listOptions() { Vector<Option> newVector = new Vector<Option>(1); newVector.addElement(new Option("\tWhether to use K2 prior.\n", "k2", 0, "-k2")); newVector.addAll(Collections.list(super.listOptions())); return newVector.elements(); } // listOptions /** * Parses a given list of options. * <p/> * * <!-- options-start --> Valid options are: * <p/> * * <pre> * -k2 * Whether to use K2 prior. * </pre> * * <pre> * -A <alpha> * Initial count (alpha) * </pre> * * <!-- options-end --> * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ @Override public void setOptions(String[] options) throws Exception { setUseK2Prior(Utils.getFlag("k2", options)); super.setOptions(options); } // setOptions /** * Gets the current settings of the classifier. * * @return an array of strings suitable for passing to setOptions */ @Override public String[] getOptions() { Vector<String> options = new Vector<String>(); if (isUseK2Prior()) { options.add("-k2"); } Collections.addAll(options, super.getOptions()); return options.toArray(new String[0]); } // getOptions /** * Returns the revision string. * * @return the revision */ @Override public String getRevision() { return RevisionUtils.extract("$Revision$"); } } // class BMAEstimator