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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 .
 */

/*
 * SimulatedAnnealing.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.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;

/**
 *  This Bayes Network learning algorithm uses the
 * general purpose search method of simulated annealing to find a well scoring
 * network structure.
*
* For more information see:
*
* R.R. Bouckaert (1995). Bayesian Belief Networks: from Construction to * Inference. Utrecht, Netherlands. *

* * * BibTeX: * *

 * @phdthesis{Bouckaert1995,
 *    address = {Utrecht, Netherlands},
 *    author = {R.R. Bouckaert},
 *    institution = {University of Utrecht},
 *    title = {Bayesian Belief Networks: from Construction to Inference},
 *    year = {1995}
 * }
 * 
*

* * * Valid options are: *

* *

 * -A <float>
 *  Start temperature
 * 
* *
 * -U <integer>
 *  Number of runs
 * 
* *
 * -D <float>
 *  Delta temperature
 * 
* *
 * -R <seed>
 *  Random number seed
 * 
* *
 * -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: 11267 $ */ public class SimulatedAnnealing extends GlobalScoreSearchAlgorithm implements TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = -5482721887881010916L; /** start temperature **/ double m_fTStart = 10; /** change in temperature at every run **/ double m_fDelta = 0.999; /** number of runs **/ int m_nRuns = 10000; /** use the arc reversal operator **/ boolean m_bUseArcReversal = false; /** random number seed **/ int m_nSeed = 1; /** random number generator **/ Random m_random; /** * Returns an instance of a TechnicalInformation object, containing detailed * information about the technical background of this class, e.g., paper * reference or book this class is based on. * * @return the technical information about this class */ @Override public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.PHDTHESIS); result.setValue(Field.AUTHOR, "R.R. Bouckaert"); result.setValue(Field.YEAR, "1995"); result.setValue(Field.TITLE, "Bayesian Belief Networks: from Construction to Inference"); result.setValue(Field.INSTITUTION, "University of Utrecht"); result.setValue(Field.ADDRESS, "Utrecht, Netherlands"); return result; } /** * * @param bayesNet the bayes net to use * @param instances the data to use * @throws Exception if something goes wrong */ @Override public void search(BayesNet bayesNet, Instances instances) throws Exception { m_random = new Random(m_nSeed); // determine base scores double fCurrentScore = calcScore(bayesNet); // keep track of best scoring network double fBestScore = fCurrentScore; BayesNet bestBayesNet = new BayesNet(); bestBayesNet.m_Instances = instances; bestBayesNet.initStructure(); copyParentSets(bestBayesNet, bayesNet); double fTemp = m_fTStart; for (int iRun = 0; iRun < m_nRuns; iRun++) { boolean bRunSucces = false; double fDeltaScore = 0.0; while (!bRunSucces) { // pick two nodes at random int iTailNode = m_random.nextInt(instances.numAttributes()); int iHeadNode = m_random.nextInt(instances.numAttributes()); while (iTailNode == iHeadNode) { iHeadNode = m_random.nextInt(instances.numAttributes()); } if (isArc(bayesNet, iHeadNode, iTailNode)) { bRunSucces = true; // either try a delete bayesNet.getParentSet(iHeadNode).deleteParent(iTailNode, instances); double fScore = calcScore(bayesNet); fDeltaScore = fScore - fCurrentScore; // System.out.println("Try delete " + iTailNode + "->" + iHeadNode + // " dScore = " + fDeltaScore); if (fTemp * Math .log((Math.abs(m_random.nextInt()) % 10000) / 10000.0 + 1e-100) < fDeltaScore) { // System.out.println("success!!!"); fCurrentScore = fScore; } else { // roll back bayesNet.getParentSet(iHeadNode).addParent(iTailNode, instances); } } else { // try to add an arc if (addArcMakesSense(bayesNet, instances, iHeadNode, iTailNode)) { bRunSucces = true; double fScore = calcScoreWithExtraParent(iHeadNode, iTailNode); fDeltaScore = fScore - fCurrentScore; // System.out.println("Try add " + iTailNode + "->" + iHeadNode + // " dScore = " + fDeltaScore); if (fTemp * Math .log((Math.abs(m_random.nextInt()) % 10000) / 10000.0 + 1e-100) < fDeltaScore) { // System.out.println("success!!!"); bayesNet.getParentSet(iHeadNode).addParent(iTailNode, instances); fCurrentScore = fScore; } } } } if (fCurrentScore > fBestScore) { copyParentSets(bestBayesNet, bayesNet); } fTemp = fTemp * m_fDelta; } copyParentSets(bayesNet, bestBayesNet); } // buildStructure /** * 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 /** * @return double */ public double getDelta() { return m_fDelta; } /** * @return double */ public double getTStart() { return m_fTStart; } /** * @return int */ public int getRuns() { return m_nRuns; } /** * Sets the m_fDelta. * * @param fDelta The m_fDelta to set */ public void setDelta(double fDelta) { m_fDelta = fDelta; } /** * Sets the m_fTStart. * * @param fTStart The m_fTStart to set */ public void setTStart(double fTStart) { m_fTStart = fTStart; } /** * Sets the m_nRuns. * * @param nRuns The m_nRuns to set */ public void setRuns(int nRuns) { m_nRuns = nRuns; } /** * @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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