content: /*
   * 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.
   */
  
  /*
   * RepeatedHillClimber.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.RevisionUtils;
  import weka.core.Utils;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /** 
   <!-- globalinfo-start -->
   * 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.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -U &lt;integer&gt;
   *  Number of runs</pre>
   * 
   * <pre> -A &lt;seed&gt;
   *  Random number seed</pre>
   * 
   * <pre> -P &lt;nr of parents&gt;
   *  Maximum number of parents</pre>
   * 
   * <pre> -R
   *  Use arc reversal operation.
   *  (default false)</pre>
   * 
   * <pre> -N
   *  Initial structure is empty (instead of Naive Bayes)</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 (rrb@xm.co.nz)
   * @version $Revision: 1.6 $
   */
  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
  	**/
  	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.
  	 */
  	public Enumeration listOptions() {
  		Vector newVector = new Vector(4);
  
  		newVector.addElement(new Option("\tNumber of runs", "U", 1, "-U <integer>"));
  		newVector.addElement(new Option("\tRandom number seed", "A", 1, "-A <seed>"));
  
  		Enumeration enu = super.listOptions();
  		while (enu.hasMoreElements()) {
  			newVector.addElement(enu.nextElement());
  		}
  		return newVector.elements();
  	} // listOptions
  
  	/**
  	 * Parses a given list of options. <p/>
  	 *
  	 <!-- options-start -->
  	 * Valid options are: <p/>
  	 * 
  	 * <pre> -U &lt;integer&gt;
  	 *  Number of runs</pre>
  	 * 
  	 * <pre> -A &lt;seed&gt;
  	 *  Random number seed</pre>
  	 * 
  	 * <pre> -P &lt;nr of parents&gt;
  	 *  Maximum number of parents</pre>
  	 * 
  	 * <pre> -R
  	 *  Use arc reversal operation.
  	 *  (default false)</pre>
  	 * 
  	 * <pre> -N
  	 *  Initial structure is empty (instead of Naive Bayes)</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 -->
  	 *
  	 * @param options the list of options as an array of strings
  	 * @throws Exception if an option is not supported
  	 */
  	public void setOptions(String[] options) throws Exception {
  		String sRuns = Utils.getOption('U', options);
  		if (sRuns.length() != 0) {
  			setRuns(Integer.parseInt(sRuns));
  		}
  		
  		String sSeed = Utils.getOption('A', options);
  		if (sSeed.length() != 0) {
  			setSeed(Integer.parseInt(sSeed));
  		}
  
  		super.setOptions(options);
  	} // setOptions
  
  	/**
  	 * Gets the current settings of the search algorithm.
  	 *
  	 * @return an array of strings suitable for passing to setOptions
  	 */
  	public String[] getOptions() {
  		String[] superOptions = super.getOptions();
  		String[] options = new String[7 + superOptions.length];
  		int current = 0;
  
  		options[current++] = "-U";
  		options[current++] = "" + getRuns();
  
  		options[current++] = "-A";
  		options[current++] = "" + getSeed();
  
  		// insert options from parent class
  		for (int iOption = 0; iOption < superOptions.length; iOption++) {
  			options[current++] = superOptions[iOption];
  		}
  
  		// Fill up rest with empty strings, not nulls!
  		while (current < options.length) {
  			options[current++] = "";
  		}
  		return options;
  	} // getOptions
  
  	/**
  	 * This will return a string describing the classifier.
  	 * 
  	 * @return The string.
  	 */
  	public String globalInfo() {
  		return "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.";
  	} // globalInfo
  	
  	/**
  	 * @return a string to describe the Runs option.
  	 */
  	public String runsTipText() {
  	  return "Sets the number of times hill climbing is performed.";
  	} // runsTipText
  
  	/**
  	 * @return a string to describe the Seed option.
  	 */
  	public String seedTipText() {
  	  return "Initialization value for random number generator." +
  	  " Setting the seed allows replicability of experiments.";
  	} // seedTipText
  
  	/**
  	 * Returns the revision string.
  	 * 
  	 * @return		the revision
  	 */
  	public String getRevision() {
  	  return RevisionUtils.extract("$Revision: 1.6 $");
  	}
  }
  