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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 .
 */

/*
 *    RandomCommittee.java
 *    Copyright (C) 2003-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

import java.util.Random;
import java.util.ArrayList;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Randomizable;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.PartitionGenerator;

/**
 
 * Class for building an ensemble of randomizable base classifiers. Each base classifiers is built using a different random number seed (but based one the same data). The final prediction is a straight average of the predictions generated by the individual base classifiers.
 * 

* * Valid options are:

* *

 -S <num>
 *  Random number seed.
 *  (default 1)
* *
 -I <num>
 *  Number of iterations.
 *  (default 10)
* *
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* *
 -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.RandomTree)
* *
 
 * Options specific to classifier weka.classifiers.trees.RandomTree:
 * 
* *
 -K <number of attributes>
 *  Number of attributes to randomly investigate
 *  (<1 = int(log(#attributes)+1)).
* *
 -M <minimum number of instances>
 *  Set minimum number of instances per leaf.
* *
 -S <num>
 *  Seed for random number generator.
 *  (default 1)
* *
 -depth <num>
 *  The maximum depth of the tree, 0 for unlimited.
 *  (default 0)
* *
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * Options after -- are passed to the designated classifier.

* * @author Eibe Frank ([email protected]) * @version $Revision: 11461 $ */ public class RandomCommittee extends RandomizableParallelIteratedSingleClassifierEnhancer implements WeightedInstancesHandler, PartitionGenerator { /** for serialization */ static final long serialVersionUID = -9204394360557300093L; /** training data */ protected Instances m_data; /** * Constructor. */ public RandomCommittee() { m_Classifier = new weka.classifiers.trees.RandomTree(); } /** * String describing default classifier. * * @return the default classifier classname */ protected String defaultClassifierString() { return "weka.classifiers.trees.RandomTree"; } /** * Returns a string describing classifier * @return a description suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for building an ensemble of randomizable base classifiers. Each " + "base classifiers is built using a different random number seed (but based " + "one the same data). The final prediction is a straight average of the " + "predictions generated by the individual base classifiers."; } /** * Builds the committee of randomizable classifiers. * * @param data the training data to be used for generating the * bagged classifier. * @exception Exception if the classifier could not be built successfully */ public void buildClassifier(Instances data) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(data); // get fresh instances m_data = new Instances(data); super.buildClassifier(m_data); if (!(m_Classifier instanceof Randomizable)) { throw new IllegalArgumentException("Base learner must implement Randomizable!"); } m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_NumIterations); Random random = m_data.getRandomNumberGenerator(m_Seed); // Resample data based on weights if base learner can't handle weights if (!(m_Classifier instanceof WeightedInstancesHandler)) { m_data = m_data.resampleWithWeights(random); } for (int j = 0; j < m_Classifiers.length; j++) { // Set the random number seed for the current classifier. ((Randomizable) m_Classifiers[j]).setSeed(random.nextInt()); // Build the classifier. // m_Classifiers[j].buildClassifier(m_data); } buildClassifiers(); // save memory m_data = null; } /** * Returns a training set for a particular iteration. * * @param iteration the number of the iteration for the requested training set. * @return the training set for the supplied iteration number * @throws Exception if something goes wrong when generating a training set. */ protected synchronized Instances getTrainingSet(int iteration) throws Exception { // we don't manipulate the training data in any way. return m_data; } /** * Calculates the class membership probabilities for the given test * instance. * * @param instance the instance to be classified * @return preedicted class probability distribution * @exception Exception if distribution can't be computed successfully */ public double[] distributionForInstance(Instance instance) throws Exception { double [] sums = new double [instance.numClasses()], newProbs; double numPreds = 0; for (int i = 0; i < m_NumIterations; i++) { if (instance.classAttribute().isNumeric() == true) { double pred = m_Classifiers[i].classifyInstance(instance); if (!Utils.isMissingValue(pred)) { sums[0] += pred; numPreds++; } } else { newProbs = m_Classifiers[i].distributionForInstance(instance); for (int j = 0; j < newProbs.length; j++) sums[j] += newProbs[j]; } } if (instance.classAttribute().isNumeric() == true) { if (numPreds == 0) { sums[0] = Utils.missingValue(); } else { sums[0] /= numPreds; } return sums; } else if (Utils.eq(Utils.sum(sums), 0)) { return sums; } else { Utils.normalize(sums); return sums; } } /** * Returns description of the committee. * * @return description of the committee as a string */ public String toString() { if (m_Classifiers == null) { return "RandomCommittee: No model built yet."; } StringBuffer text = new StringBuffer(); text.append("All the base classifiers: \n\n"); for (int i = 0; i < m_Classifiers.length; i++) text.append(m_Classifiers[i].toString() + "\n\n"); return text.toString(); } /** * Builds the classifier to generate a partition. */ public void generatePartition(Instances data) throws Exception { if (m_Classifier instanceof PartitionGenerator) buildClassifier(data); else throw new Exception("Classifier: " + getClassifierSpec() + " cannot generate a partition"); } /** * Computes an array that indicates leaf membership */ public double[] getMembershipValues(Instance inst) throws Exception { if (m_Classifier instanceof PartitionGenerator) { ArrayList al = new ArrayList(); int size = 0; for (int i = 0; i < m_Classifiers.length; i++) { double[] r = ((PartitionGenerator)m_Classifiers[i]). getMembershipValues(inst); size += r.length; al.add(r); } double[] values = new double[size]; int pos = 0; for (double[] v: al) { System.arraycopy(v, 0, values, pos, v.length); pos += v.length; } return values; } else throw new Exception("Classifier: " + getClassifierSpec() + " cannot generate a partition"); } /** * Returns the number of elements in the partition. */ public int numElements() throws Exception { if (m_Classifier instanceof PartitionGenerator) { int size = 0; for (int i = 0; i < m_Classifiers.length; i++) { size += ((PartitionGenerator)m_Classifiers[i]).numElements(); } return size; } else throw new Exception("Classifier: " + getClassifierSpec() + " cannot generate a partition"); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 11461 $"); } /** * Main method for testing this class. * * @param argv the options */ public static void main(String [] argv) { runClassifier(new RandomCommittee(), argv); } }





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