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 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. */ /* * NBTreeNoSplit.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package j48; import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.classifiers.bayes.NaiveBayesUpdateable; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionUtils; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import java.util.Random; /** * Class implementing a "no-split"-split (leaf node) for naive bayes * trees. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.4 $ */ public final class NBTreeNoSplit extends ClassifierSplitModel { /** for serialization */ private static final long serialVersionUID = 7824804381545259618L; /** the naive bayes classifier */ private NaiveBayesUpdateable m_nb; /** the discretizer used */ private Discretize m_disc; /** errors on the training data at this node */ private double m_errors; public NBTreeNoSplit() { m_numSubsets = 1; } /** * Build the no-split node * * @param instances an <code>Instances</code> value * @exception Exception if an error occurs */ public final void buildClassifier(Instances instances) throws Exception { m_nb = new NaiveBayesUpdateable(); m_disc = new Discretize(); m_disc.setInputFormat(instances); Instances temp = Filter.useFilter(instances, m_disc); m_nb.buildClassifier(temp); if (temp.numInstances() >= 5) { m_errors = crossValidate(m_nb, temp, new Random(1)); } m_numSubsets = 1; } /** * Return the errors made by the naive bayes model at this node * * @return the number of errors made */ public double getErrors() { return m_errors; } /** * Return the discretizer used at this node * * @return a <code>Discretize</code> value */ public Discretize getDiscretizer() { return m_disc; } /** * Get the naive bayes model at this node * * @return a <code>NaiveBayesUpdateable</code> value */ public NaiveBayesUpdateable getNaiveBayesModel() { return m_nb; } /** * Always returns 0 because only there is only one subset. */ public final int whichSubset(Instance instance) { return 0; } /** * Always returns null because there is only one subset. */ public final double[] weights(Instance instance) { return null; } /** * Does nothing because no condition has to be satisfied. */ public final String leftSide(Instances instances) { return ""; } /** * Does nothing because no condition has to be satisfied. */ public final String rightSide(int index, Instances instances) { return ""; } /** * Returns a string containing java source code equivalent to the test * made at this node. The instance being tested is called "i". * * @param index index of the nominal value tested * @param data the data containing instance structure info * @return a value of type 'String' */ public final String sourceExpression(int index, Instances data) { return "true"; // or should this be false?? } /** * Return the probability for a class value * * @param classIndex the index of the class value * @param instance the instance to generate a probability for * @param theSubset the subset to consider * @return a probability * @exception Exception if an error occurs */ public double classProb(int classIndex, Instance instance, int theSubset) throws Exception { m_disc.input(instance); Instance temp = m_disc.output(); return m_nb.distributionForInstance(temp)[classIndex]; } /** * Return a textual description of the node * * @return a <code>String</code> value */ public String toString() { return m_nb.toString(); } /** * Utility method for fast 5-fold cross validation of a naive bayes * model * * @param fullModel a <code>NaiveBayesUpdateable</code> value * @param trainingSet an <code>Instances</code> value * @param r a <code>Random</code> value * @return a <code>double</code> value * @exception Exception if an error occurs */ public static double crossValidate(NaiveBayesUpdateable fullModel, Instances trainingSet, Random r) throws Exception { // make some copies for fast evaluation of 5-fold xval Classifier[] copies = Classifier.makeCopies(fullModel, 5); Evaluation eval = new Evaluation(trainingSet); // make some splits for (int j = 0; j < 5; j++) { Instances test = trainingSet.testCV(5, j); // unlearn these test instances for (int k = 0; k < test.numInstances(); k++) { test.instance(k).setWeight(-test.instance(k).weight()); ((NaiveBayesUpdateable) copies[j]).updateClassifier(test.instance(k)); // reset the weight back to its original value test.instance(k).setWeight(-test.instance(k).weight()); } eval.evaluateModel(copies[j], test); } return eval.incorrect(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.4 $"); } }