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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
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* NaiveBayesMultinomialUpdateable.java
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* Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
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* Copyright (C) 2007 Jiang Su (incremental version)
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package weka.classifiers.bayes;
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import weka.classifiers.UpdateableClassifier;
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import weka.core.Instance;
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import weka.core.Instances;
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import weka.core.Utils;
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<!-- globalinfo-start -->
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* Class for building and using a multinomial Naive Bayes classifier. For more information see,<br/>
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* Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.<br/>
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* The core equation for this classifier:<br/>
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* P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)<br/>
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* where Ci is class i and D is a document.<br/>
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* Incremental version of the algorithm.
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<!-- globalinfo-end -->
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<!-- technical-bibtex-start -->
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* @inproceedings{Mccallum1998,
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* author = {Andrew Mccallum and Kamal Nigam},
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* booktitle = {AAAI-98 Workshop on 'Learning for Text Categorization'},
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* title = {A Comparison of Event Models for Naive Bayes Text Classification},
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<!-- technical-bibtex-end -->
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<!-- options-start -->
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* Valid options are: <p/>
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* If set, classifier is run in debug mode and
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* may output additional info to the console</pre>
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* @author Andrew Golightly (acg4@cs.waikato.ac.nz)
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* @author Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
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* @version $Revision: 1.2 $
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public class NaiveBayesMultinomialUpdateable
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extends NaiveBayesMultinomial
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implements UpdateableClassifier {
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/** for serialization */
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private static final long serialVersionUID = -7204398796974263186L;
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/** the word count per class */
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protected double[] m_wordsPerClass;
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* Returns a string describing this classifier
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* @return a description of the classifier suitable for
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* displaying in the explorer/experimenter gui
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public String globalInfo() {
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super.globalInfo() + "\n\n"
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+ "Incremental version of the algorithm.";
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* Generates the classifier.
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* @param instances set of instances serving as training data
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* @throws Exception if the classifier has not been generated successfully
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public void buildClassifier(Instances instances) throws Exception {
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// can classifier handle the data?
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getCapabilities().testWithFail(instances);
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// remove instances with missing class
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instances = new Instances(instances);
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instances.deleteWithMissingClass();
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m_headerInfo = new Instances(instances, 0);
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m_numClasses = instances.numClasses();
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m_numAttributes = instances.numAttributes();
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m_probOfWordGivenClass = new double[m_numClasses][];
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m_wordsPerClass = new double[m_numClasses];
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m_probOfClass = new double[m_numClasses];
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// initialising the matrix of word counts
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// NOTE: Laplace estimator introduced in case a word that does not
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// appear for a class in the training set does so for the test set
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for (int c = 0; c < m_numClasses; c++) {
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m_probOfWordGivenClass[c] = new double[m_numAttributes];
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m_probOfClass[c] = laplace;
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m_wordsPerClass[c] = laplace * m_numAttributes;
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for(int att = 0; att<m_numAttributes; att++) {
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m_probOfWordGivenClass[c][att] = laplace;
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for (int i = 0; i < instances.numInstances(); i++)
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updateClassifier(instances.instance(i));
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* Updates the classifier with the given instance.
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* @param instance the new training instance to include in the model
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* @throws Exception if the instance could not be incorporated in
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public void updateClassifier(Instance instance) throws Exception {
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int classIndex = (int) instance.value(instance.classIndex());
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m_probOfClass[classIndex] += instance.weight();
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for (int a = 0; a < instance.numValues(); a++) {
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if (instance.index(a) == instance.classIndex() ||
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instance.isMissing(a))
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double numOccurences = instance.valueSparse(a) * instance.weight();
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if (numOccurences < 0)
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"Numeric attribute values must all be greater or equal to zero.");
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m_wordsPerClass[classIndex] += numOccurences;
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m_probOfWordGivenClass[classIndex][instance.index(a)] += numOccurences;
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* Calculates the class membership probabilities for the given test
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* @param instance the instance to be classified
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* @return predicted class probability distribution
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* @throws Exception if there is a problem generating the prediction
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public double[] distributionForInstance(Instance instance) throws Exception {
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double[] probOfClassGivenDoc = new double[m_numClasses];
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// calculate the array of log(Pr[D|C])
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double[] logDocGivenClass = new double[m_numClasses];
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for (int c = 0; c < m_numClasses; c++) {
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logDocGivenClass[c] += Math.log(m_probOfClass[c]);
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for (int i = 0; i < instance.numValues(); i++) {
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if (instance.index(i) == instance.classIndex())
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double frequencies = instance.valueSparse(i);
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allWords += frequencies;
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logDocGivenClass[c] += frequencies *
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Math.log(m_probOfWordGivenClass[c][instance.index(i)]);
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logDocGivenClass[c] -= allWords * Math.log(m_wordsPerClass[c]);
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double max = logDocGivenClass[Utils.maxIndex(logDocGivenClass)];
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for (int i = 0; i < m_numClasses; i++)
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probOfClassGivenDoc[i] = Math.exp(logDocGivenClass[i] - max);
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Utils.normalize(probOfClassGivenDoc);
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return probOfClassGivenDoc;
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* Returns a string representation of the classifier.
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* @return a string representation of the classifier
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public String toString() {
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StringBuffer result = new StringBuffer();
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result.append("The independent probability of a class\n");
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result.append("--------------------------------------\n");
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for (int c = 0; c < m_numClasses; c++)
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result.append(m_headerInfo.classAttribute().value(c)).append("\t").
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append(Double.toString(m_probOfClass[c])).append("\n");
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result.append("\nThe probability of a word given the class\n");
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result.append("-----------------------------------------\n\t");
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for (int c = 0; c < m_numClasses; c++)
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result.append(m_headerInfo.classAttribute().value(c)).append("\t");
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for (int w = 0; w < m_numAttributes; w++) {
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result.append(m_headerInfo.attribute(w).name()).append("\t");
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for (int c = 0; c < m_numClasses; c++)
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Double.toString(Math.exp(m_probOfWordGivenClass[c][w]))).append("\t");
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return result.toString();
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* Main method for testing this class.
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* @param args the options
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public static void main(String[] args) {
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runClassifier(new NaiveBayesMultinomialUpdateable(), args);
added
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weka/classifiers/bayes/NaiveBayesMultinomialUpdateable.java
