geneticsearch.java

数据挖掘聚类算法：bayes源代码,使用JAVA语言实现

/*
 * 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 2 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, write to the Free Software
 * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 * GeneticSearch.java
 * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
 * 
 */
 
package weka.classifiers.bayes.net.search.global;

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

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

/** 
 <!-- globalinfo-start -->
 * This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure. Genetic search works by having a population of Bayes network structures and allow them to mutate and apply cross over to get offspring. The best network structure found during the process is returned.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -L &lt;integer&gt;
 *  Population size</pre>
 * 
 * <pre> -A &lt;integer&gt;
 *  Descendant population size</pre>
 * 
 * <pre> -U &lt;integer&gt;
 *  Number of runs</pre>
 * 
 * <pre> -M
 *  Use mutation.
 *  (default true)</pre>
 * 
 * <pre> -C
 *  Use cross-over.
 *  (default true)</pre>
 * 
 * <pre> -O
 *  Use tournament selection (true) or maximum subpopulatin (false).
 *  (default false)</pre>
 * 
 * <pre> -R &lt;seed&gt;
 *  Random number seed</pre>
 * 
 * <pre> -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.</pre>
 * 
 * <pre> -S [LOO-CV|k-Fold-CV|Cumulative-CV]
 *  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)</pre>
 * 
 * <pre> -Q
 *  Use probabilistic or 0/1 scoring.
 *  (default probabilistic scoring)</pre>
 * 
 <!-- options-end -->
 * 
 * @author Remco Bouckaert ([e-mail])
 * @version $Revision: 1.5 $
 */
public class GeneticSearch 
    extends GlobalScoreSearchAlgorithm {

    /** for serialization */
    static final long serialVersionUID = 4236165533882462203L;
  
    /** number of runs **/
    int m_nRuns = 10;

	/** size of population **/
	int m_nPopulationSize = 10;

	/** size of descendant population **/
	int m_nDescendantPopulationSize = 100;

	/** use cross-over? **/
	boolean m_bUseCrossOver = true;

	/** use mutation? **/
	boolean m_bUseMutation = true;
	
	/** use tournament selection or take best sub-population **/
	boolean m_bUseTournamentSelection = false;	
	
	/** random number seed **/
	int m_nSeed = 1;
	
	/** random number generator **/
	Random m_random = null;


	/** used in BayesNetRepresentation for efficiently determining
	 * whether a number is square  
	 */
	static boolean [] g_bIsSquare;
	
	class BayesNetRepresentation implements RevisionHandler {
		/** number of nodes in network **/		
		int m_nNodes = 0;

		/** bit representation of parent sets 
		 * m_bits[iTail + iHead * m_nNodes] represents arc iTail->iHead
		 */
		boolean [] m_bits;
		
		/** score of represented network structure **/
		double m_fScore = 0.0f;
		
		/** 
		 * return score of represented network structure
		 * 
		 * @return the score
		 */
		public double getScore() {
			return m_fScore;
		} // getScore

		/** 
		 * c'tor 
		 * 
		 * @param nNodes the number of nodes
		 */
		BayesNetRepresentation (int nNodes) {
			m_nNodes = nNodes;
		} // c'tor
		
		/** initialize with a random structure by randomly placing
		 * m_nNodes arcs.
		 */
		public void randomInit() {
			do {
				m_bits = new boolean [m_nNodes * m_nNodes];
				for (int i = 0; i < m_nNodes; i++) {
					int iPos;
					do {
						iPos = m_random.nextInt(m_nNodes * m_nNodes);
					} while (isSquare(iPos));
					m_bits[iPos] = true;
				}
			} while (hasCycles());
			calcGlobalScore();
		}

		/** calculate score of current network representation
		 * As a side effect, the parent sets are set
		 */
		void calcGlobalScore() {
			// clear current network
			for (int iNode = 0; iNode < m_nNodes; iNode++) {
				ParentSet parentSet = m_BayesNet.getParentSet(iNode);
				while (parentSet.getNrOfParents() > 0) {
					parentSet.deleteLastParent(m_BayesNet.m_Instances);
				}
			}
			// insert arrows
			for (int iNode = 0; iNode < m_nNodes; iNode++) {
				ParentSet parentSet = m_BayesNet.getParentSet(iNode);
				for (int iNode2 = 0; iNode2 < m_nNodes; iNode2++) {
					if (m_bits[iNode2 + iNode * m_nNodes]) {
						parentSet.addParent(iNode2, m_BayesNet.m_Instances);
					}
				}
			}
			// calc score
			try {
				m_fScore = calcScore(m_BayesNet);
			} catch (Exception e) {
				// ignore
			}
		} // calcScore

		/** check whether there are cycles in the network
 		* 
 		* @return true if a cycle is found, false otherwise
		 */
		public boolean hasCycles() {
			// check for cycles
			boolean[] bDone = new boolean[m_nNodes];
			for (int iNode = 0; iNode < m_nNodes; iNode++) {

				// find a node for which all parents are 'done'
				boolean bFound = false;

				for (int iNode2 = 0; !bFound && iNode2 < m_nNodes; iNode2++) {
					if (!bDone[iNode2]) {
						boolean bHasNoParents = true;
						for (int iParent = 0; iParent < m_nNodes; iParent++) {
							if (m_bits[iParent + iNode2 * m_nNodes] && !bDone[iParent]) {
								bHasNoParents = false;
							}
						}
						if (bHasNoParents) {
							bDone[iNode2] = true;
							bFound = true;
						}
					}
				}
				if (!bFound) {
					return true;
				}
			}
			return false;
		} // hasCycles

		/** create clone of current object 
		 * @return cloned object
		 */
		BayesNetRepresentation copy() {
			BayesNetRepresentation b = new BayesNetRepresentation(m_nNodes);
			b.m_bits = new boolean [m_bits.length];
			for (int i = 0; i < m_nNodes * m_nNodes; i++) {
				b.m_bits[i] = m_bits[i];
			}
			b.m_fScore = m_fScore;
			return b;		
		} // copy

		/** Apply mutation operation to BayesNet
		 * Calculate score and as a side effect sets BayesNet parent sets.
		 */
		void mutate() {
			// flip a bit
			do {				
				int iBit;
				do {
					iBit = m_random.nextInt(m_nNodes * m_nNodes);
				} while (isSquare(iBit));
				
				m_bits[iBit] = !m_bits[iBit];
			} while (hasCycles());

			calcGlobalScore();
		} // mutate

		/** Apply cross-over operation to BayesNet 
		 * Calculate score and as a side effect sets BayesNet parent sets.
		 * @param other BayesNetRepresentation to cross over with
		 */
		void crossOver(BayesNetRepresentation other) {
			boolean [] bits = new boolean [m_bits.length];
			for (int i = 0; i < m_bits.length; i++) {
				bits[i] = m_bits[i];
			}
			int iCrossOverPoint = m_bits.length;
			do {
				// restore to original state
				for (int i = iCrossOverPoint; i < m_bits.length; i++) {
					m_bits[i] = bits[i];
				}
				// take all bits from cross-over points onwards
				iCrossOverPoint = m_random.nextInt(m_bits.length);
				for (int i = iCrossOverPoint; i < m_bits.length; i++) {
					m_bits[i] = other.m_bits[i];
				}
			} while (hasCycles());
			calcGlobalScore();
		} // crossOver
				
		/** check if number is square and initialize g_bIsSquare structure
		 * if necessary
		 * @param nNum number to check (should be below m_nNodes * m_nNodes)
		 * @return true if number is square
		 */
		boolean isSquare(int nNum) {
			if (g_bIsSquare == null || g_bIsSquare.length < nNum) {
				g_bIsSquare = new boolean [m_nNodes * m_nNodes];
				for (int i = 0; i < m_nNodes; i++) {
					g_bIsSquare[i * m_nNodes + i] = true;
				}
			}
			return g_bIsSquare[nNum];
		} // isSquare

		/**
		 * Returns the revision string.
		 * 
		 * @return		the revision
		 */
		public String getRevision() {
		  return RevisionUtils.extract("$Revision: 1.5 $");
		}
	} // class BayesNetRepresentation 
	    	
	/**
	 * search determines the network structure/graph of the network
	 * with a genetic search algorithm.
	 * 
	 * @param bayesNet the network to search
	 * @param instances the instances to use
	 * @throws Exception if population size doesn fit or neither cross-over or mutation was chosen
	 */
	protected void search(BayesNet bayesNet, Instances instances) throws Exception {
		// sanity check
		if (getDescendantPopulationSize() < getPopulationSize()) {
			throw new Exception ("Descendant PopulationSize should be at least Population Size");
		}
		if (!getUseCrossOver() && !getUseMutation()) {
			throw new Exception ("At least one of mutation or cross-over should be used");
		}
		
		m_random = new Random(m_nSeed);

		// keeps track of best structure found so far 
		BayesNet bestBayesNet;
		// keeps track of score pf best structure found so far 
		double fBestScore = calcScore(bayesNet);	

		// initialize bestBayesNet
		bestBayesNet = new BayesNet();
		bestBayesNet.m_Instances = instances;
		bestBayesNet.initStructure();
		copyParentSets(bestBayesNet, bayesNet);
		
                
        // initialize population        
		BayesNetRepresentation  [] population = new BayesNetRepresentation [getPopulationSize()];
        for (int i = 0; i < getPopulationSize(); i++) {
        	population[i] = new BayesNetRepresentation (instances.numAttributes());
			population[i].randomInit();
			if (population[i].getScore() > fBestScore) {
				copyParentSets(bestBayesNet, bayesNet);
				fBestScore = population[i].getScore();
				
			}
        }
        
        // go do the search        
        for (int iRun = 0; iRun < m_nRuns; iRun++) {
        	// create descendants
			BayesNetRepresentation  [] descendantPopulation = new BayesNetRepresentation  [getDescendantPopulationSize()];
			for (int i = 0; i < getDescendantPopulationSize(); i++) {
				descendantPopulation[i] = population[m_random.nextInt(getPopulationSize())].copy();
				if (getUseMutation()) {
					if (getUseCrossOver() && m_random.nextBoolean()) {
						descendantPopulation[i].crossOver(population[m_random.nextInt(getPopulationSize())]);						
					} else {
						descendantPopulation[i].mutate();								
					}
				} else {
					// use crossover
					descendantPopulation[i].crossOver(population[m_random.nextInt(getPopulationSize())]);
				}

				if (descendantPopulation[i].getScore() > fBestScore) {
					copyParentSets(bestBayesNet, bayesNet);
					fBestScore = descendantPopulation[i].getScore();
				}