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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 .
 */

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

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

import java.util.Enumeration;

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;

/**
 *  This Bayes Network learning algorithm determines
 * the maximum weight spanning tree and returns a Naive Bayes network augmented
 * with a tree.
*
* For more information see:
*
* N. Friedman, D. Geiger, M. Goldszmidt (1997). Bayesian network classifiers. * Machine Learning. 29(2-3):131-163. *

* * * BibTeX: * *

 * @article{Friedman1997,
 *    author = {N. Friedman and D. Geiger and M. Goldszmidt},
 *    journal = {Machine Learning},
 *    number = {2-3},
 *    pages = {131-163},
 *    title = {Bayesian network classifiers},
 *    volume = {29},
 *    year = {1997}
 * }
 * 
*

* * * Valid options are: *

* *

 * -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 * @version $Revision: 10154 $ */ public class TAN extends GlobalScoreSearchAlgorithm implements TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 1715277053980895298L; /** * 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.ARTICLE); result .setValue(Field.AUTHOR, "N. Friedman and D. Geiger and M. Goldszmidt"); result.setValue(Field.YEAR, "1997"); result.setValue(Field.TITLE, "Bayesian network classifiers"); result.setValue(Field.JOURNAL, "Machine Learning"); result.setValue(Field.VOLUME, "29"); result.setValue(Field.NUMBER, "2-3"); result.setValue(Field.PAGES, "131-163"); return result; } /** * buildStructure determines the network structure/graph of the network using * the maximimum weight spanning tree algorithm of Chow and Liu * * @param bayesNet * @param instances * @throws Exception if something goes wrong */ @Override public void buildStructure(BayesNet bayesNet, Instances instances) throws Exception { m_BayesNet = bayesNet; m_bInitAsNaiveBayes = true; m_nMaxNrOfParents = 2; super.buildStructure(bayesNet, instances); int nNrOfAtts = instances.numAttributes(); // TAN greedy search (not restricted by ordering like K2) // 1. find strongest link // 2. find remaining links by adding strongest link to already // connected nodes // 3. assign direction to links int nClassNode = instances.classIndex(); int[] link1 = new int[nNrOfAtts - 1]; int[] link2 = new int[nNrOfAtts - 1]; boolean[] linked = new boolean[nNrOfAtts]; // 1. find strongest link int nBestLinkNode1 = -1; int nBestLinkNode2 = -1; double fBestDeltaScore = 0.0; int iLinkNode1; for (iLinkNode1 = 0; iLinkNode1 < nNrOfAtts; iLinkNode1++) { if (iLinkNode1 != nClassNode) { for (int iLinkNode2 = 0; iLinkNode2 < nNrOfAtts; iLinkNode2++) { if ((iLinkNode1 != iLinkNode2) && (iLinkNode2 != nClassNode)) { double fScore = calcScoreWithExtraParent(iLinkNode1, iLinkNode2); if ((nBestLinkNode1 == -1) || (fScore > fBestDeltaScore)) { fBestDeltaScore = fScore; nBestLinkNode1 = iLinkNode2; nBestLinkNode2 = iLinkNode1; } } } } } link1[0] = nBestLinkNode1; link2[0] = nBestLinkNode2; linked[nBestLinkNode1] = true; linked[nBestLinkNode2] = true; // 2. find remaining links by adding strongest link to already // connected nodes for (int iLink = 1; iLink < nNrOfAtts - 2; iLink++) { nBestLinkNode1 = -1; for (iLinkNode1 = 0; iLinkNode1 < nNrOfAtts; iLinkNode1++) { if (iLinkNode1 != nClassNode) { for (int iLinkNode2 = 0; iLinkNode2 < nNrOfAtts; iLinkNode2++) { if ((iLinkNode1 != iLinkNode2) && (iLinkNode2 != nClassNode) && (linked[iLinkNode1] || linked[iLinkNode2]) && (!linked[iLinkNode1] || !linked[iLinkNode2])) { double fScore = calcScoreWithExtraParent(iLinkNode1, iLinkNode2); if ((nBestLinkNode1 == -1) || (fScore > fBestDeltaScore)) { fBestDeltaScore = fScore; nBestLinkNode1 = iLinkNode2; nBestLinkNode2 = iLinkNode1; } } } } } link1[iLink] = nBestLinkNode1; link2[iLink] = nBestLinkNode2; linked[nBestLinkNode1] = true; linked[nBestLinkNode2] = true; } // System.out.println(); // for (int i = 0; i < 3; i++) { // System.out.println(link1[i] + " " + link2[i]); // } // 3. assign direction to links boolean[] hasParent = new boolean[nNrOfAtts]; for (int iLink = 0; iLink < nNrOfAtts - 2; iLink++) { if (!hasParent[link1[iLink]]) { bayesNet.getParentSet(link1[iLink]).addParent(link2[iLink], instances); hasParent[link1[iLink]] = true; } else { if (hasParent[link2[iLink]]) { throw new Exception("Bug condition found: too many arrows"); } bayesNet.getParentSet(link2[iLink]).addParent(link1[iLink], instances); hasParent[link2[iLink]] = true; } } } // buildStructure /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ @Override public Enumeration




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