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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 .
 */

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

package weka.classifiers.bayes.net.search.global;

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

import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.ParentSet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 *  This Bayes Network learning algorithm repeatedly
 * uses hill climbing starting with a randomly generated network structure and
 * return the best structure of the various runs.
 * 

* * * Valid options are: *

* *

 * -U <integer>
 *  Number of runs
 * 
* *
 * -A <seed>
 *  Random number seed
 * 
* *
 * -P <nr of parents>
 *  Maximum number of parents
 * 
* *
 * -R
 *  Use arc reversal operation.
 *  (default false)
 * 
* *
 * -N
 *  Initial structure is empty (instead of Naive Bayes)
 * 
* *
 * -mbc
 *  Applies a Markov Blanket correction to the network structure, 
 *  after a network structure is learned. This ensures that all 
 *  nodes in the network are part of the Markov blanket of the 
 *  classifier node.
 * 
* *
 * -S [LOO-CV|k-Fold-CV|Cumulative-CV]
 *  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
 * 
* *
 * -Q
 *  Use probabilistic or 0/1 scoring.
 *  (default probabilistic scoring)
 * 
* * * * @author Remco Bouckaert ([email protected]) * @version $Revision: 10154 $ */ public class RepeatedHillClimber extends HillClimber { /** for serialization */ static final long serialVersionUID = -7359197180460703069L; /** number of runs **/ int m_nRuns = 10; /** random number seed **/ int m_nSeed = 1; /** random number generator **/ Random m_random; /** * search determines the network structure/graph of the network with the * repeated hill climbing. * * @param bayesNet the network to use * @param instances the data to use * @throws Exception if something goes wrong **/ @Override protected void search(BayesNet bayesNet, Instances instances) throws Exception { m_random = new Random(getSeed()); // keeps track of score pf best structure found so far double fBestScore; double fCurrentScore = calcScore(bayesNet); // keeps track of best structure found so far BayesNet bestBayesNet; // initialize bestBayesNet fBestScore = fCurrentScore; bestBayesNet = new BayesNet(); bestBayesNet.m_Instances = instances; bestBayesNet.initStructure(); copyParentSets(bestBayesNet, bayesNet); // go do the search for (int iRun = 0; iRun < m_nRuns; iRun++) { // generate random nework generateRandomNet(bayesNet, instances); // search super.search(bayesNet, instances); // calculate score fCurrentScore = calcScore(bayesNet); // keep track of best network seen so far if (fCurrentScore > fBestScore) { fBestScore = fCurrentScore; copyParentSets(bestBayesNet, bayesNet); } } // restore current network to best network copyParentSets(bayesNet, bestBayesNet); // free up memory bestBayesNet = null; } // search /** * * @param bayesNet * @param instances */ void generateRandomNet(BayesNet bayesNet, Instances instances) { int nNodes = instances.numAttributes(); // clear network for (int iNode = 0; iNode < nNodes; iNode++) { ParentSet parentSet = bayesNet.getParentSet(iNode); while (parentSet.getNrOfParents() > 0) { parentSet.deleteLastParent(instances); } } // initialize as naive Bayes? if (getInitAsNaiveBayes()) { int iClass = instances.classIndex(); // initialize parent sets to have arrow from classifier node to // each of the other nodes for (int iNode = 0; iNode < nNodes; iNode++) { if (iNode != iClass) { bayesNet.getParentSet(iNode).addParent(iClass, instances); } } } // insert random arcs int nNrOfAttempts = m_random.nextInt(nNodes * nNodes); for (int iAttempt = 0; iAttempt < nNrOfAttempts; iAttempt++) { int iTail = m_random.nextInt(nNodes); int iHead = m_random.nextInt(nNodes); if (bayesNet.getParentSet(iHead).getNrOfParents() < getMaxNrOfParents() && addArcMakesSense(bayesNet, instances, iHead, iTail)) { bayesNet.getParentSet(iHead).addParent(iTail, instances); } } } // generateRandomNet /** * copyParentSets copies parent sets of source to dest BayesNet * * @param dest destination network * @param source source network */ void copyParentSets(BayesNet dest, BayesNet source) { int nNodes = source.getNrOfNodes(); // clear parent set first for (int iNode = 0; iNode < nNodes; iNode++) { dest.getParentSet(iNode).copy(source.getParentSet(iNode)); } } // CopyParentSets /** * Returns the number of runs * * @return number of runs */ public int getRuns() { return m_nRuns; } // getRuns /** * Sets the number of runs * * @param nRuns The number of runs to set */ public void setRuns(int nRuns) { m_nRuns = nRuns; } // setRuns /** * Returns the random seed * * @return random number seed */ public int getSeed() { return m_nSeed; } // getSeed /** * Sets the random number seed * * @param nSeed The number of the seed to set */ public void setSeed(int nSeed) { m_nSeed = nSeed; } // setSeed /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ @Override public Enumeration




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