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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 .
 */

/*
 * TabuSearch.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.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 tabu
 * search for finding a well scoring Bayes network structure. Tabu search is
 * hill climbing till an optimum is reached. The following step is the least
 * worst possible step. The last X steps are kept in a list and none of the
 * steps in this so called tabu list is considered in taking the next step. The
 * best network found in this traversal is returned.
*
* 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: *

* *

 * -L <integer>
 *  Tabu list length
 * 
* *
 * -U <integer>
 *  Number of runs
 * 
* *
 * -P <nr of parents>
 *  Maximum number of parents
 * 
* *
 * -R
 *  Use arc reversal operation.
 *  (default false)
 * 
* *
 * -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 TabuSearch extends HillClimber implements TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 1176705618756672292L; /** number of runs **/ int m_nRuns = 10; /** size of tabu list **/ int m_nTabuList = 5; /** the actual tabu list **/ Operation[] m_oTabuList = null; /** * 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; } /** * search determines the network structure/graph of the network with the Tabu * search algorithm. * * @param bayesNet the network to use * @param instances the instances to use * @throws Exception if something goes wrong */ @Override protected void search(BayesNet bayesNet, Instances instances) throws Exception { m_oTabuList = new Operation[m_nTabuList]; int iCurrentTabuList = 0; // 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++) { Operation oOperation = getOptimalOperation(bayesNet, instances); performOperation(bayesNet, instances, oOperation); // sanity check if (oOperation == null) { throw new Exception( "Panic: could not find any step to make. Tabu list too long?"); } // update tabu list m_oTabuList[iCurrentTabuList] = oOperation; iCurrentTabuList = (iCurrentTabuList + 1) % m_nTabuList; fCurrentScore += oOperation.m_fScore; // keep track of best network seen so far if (fCurrentScore > fBestScore) { fBestScore = fCurrentScore; copyParentSets(bestBayesNet, bayesNet); } if (bayesNet.getDebug()) { printTabuList(); } } // restore current network to best network copyParentSets(bayesNet, bestBayesNet); // free up memory bestBayesNet = null; } // search /** * 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 /** * check whether the operation is not in the tabu list * * @param oOperation operation to be checked * @return true if operation is not in the tabu list */ @Override boolean isNotTabu(Operation oOperation) { for (int iTabu = 0; iTabu < m_nTabuList; iTabu++) { if (oOperation.equals(m_oTabuList[iTabu])) { return false; } } return true; } // isNotTabu /** * print tabu list for debugging purposes. */ void printTabuList() { for (int i = 0; i < m_nTabuList; i++) { Operation o = m_oTabuList[i]; if (o != null) { if (o.m_nOperation == 0) { System.out.print(" +("); } else { System.out.print(" -("); } System.out.print(o.m_nTail + "->" + o.m_nHead + ")"); } } System.out.println(); } // printTabuList /** * @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 /** * @return the Tabu List length */ public int getTabuList() { return m_nTabuList; } // getTabuList /** * Sets the Tabu List length. * * @param nTabuList The nTabuList to set */ public void setTabuList(int nTabuList) { m_nTabuList = nTabuList; } // setTabuList /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ @Override public Enumeration




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