randomsearch.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

/*
 *    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.
 */

/*
 *    RandomSearch.java
 *    Copyright (C) 1999 Mark Hall
 *
 */

package  weka.attributeSelection;

import  java.io.*;
import  java.util.*;
import  weka.core.*;

/** 
 * Class for performing a random search. <p>
 *
 * Valid options are: <p>
 *
 * -P <start set> <br>
 * Specify a starting set of attributes. Eg 1,4,7-9. <p>
 *
 * -F <percent) <br>
 * Percentage of the search space to consider. (default = 25). <p>
 *
 * -V <br>
 * Verbose output. Output new best subsets as the search progresses. <p>
 *
 * @author Mark Hall ([e-mail])
 * @version $Revision: 1.1.1.1 $
 */
public class RandomSearch extends ASSearch 
  implements StartSetHandler, OptionHandler {

  /** 
   * holds a starting set as an array of attributes.
   */
  private int[] m_starting;
  
  /** holds the start set as a range */
  private Range m_startRange;

  /** the best feature set found during the search */
  private BitSet m_bestGroup;

  /** the merit of the best subset found */
  private double m_bestMerit;

  /** 
   * only accept a feature set as being "better" than the best if its
   * merit is better or equal to the best, and it contains fewer
   * features than the best (this allows LVF to be implimented).
   */
  private boolean m_onlyConsiderBetterAndSmaller;

 /** does the data have a class */
  private boolean m_hasClass;
 
  /** holds the class index */
  private int m_classIndex;
 
  /** number of attributes in the data */
  private int m_numAttribs;

  /** seed for random number generation */
  private int m_seed;

  /** percentage of the search space to consider */
  private double m_searchSize;

  /** the number of iterations performed */
  private int m_iterations;

  /** random number object */
  private Random m_random;

  /** output new best subsets as the search progresses */
  private boolean m_verbose;

  /**
   * Returns a string describing this search method
   * @return a description of the search suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "RandomSearch : \n\nPerforms a Random search in "
      +"the space of attribute subsets. If no start set is supplied, Random "
      +"search starts from a random point and reports the best subset found. "
      +"If a start set is supplied, Random searches randomly for subsets "
      +"that are as good or better than the start point with the same or "
      +"or fewer attributes. Using RandomSearch in conjunction with a start "
      +"set containing all attributes equates to the LVF algorithm of Liu "
      +"and Setiono (ICML-96).\n";
  }

  /**
   * Constructor
   */
  public RandomSearch () {
    resetOptions();
  }

  /**
   * Returns an enumeration describing the available options.
   * @return an enumeration of all the available options.
   **/
  public Enumeration listOptions () {
    Vector newVector = new Vector(3);
    
    newVector.addElement(new Option("\tSpecify a starting set of attributes." 
				    + "\n\tEg. 1,3,5-7."
				    +"\n\tIf a start point is supplied,"
				    +"\n\trandom search evaluates the start"
				    +"\n\tpoint and then randomly looks for"
				    +"\n\tsubsets that are as good as or better"
				    +"\n\tthan the start point with the same"
				    +"\n\tor lower cardinality."
				    ,"P",1
				    , "-P <start set>"));

    newVector.addElement(new Option("\tPercent of search space to consider."
				    +"\n\t(default = 25%)."
				    , "F", 1
				    , "-F <percent> "));
    newVector.addElement(new Option("\tOutput subsets as the search progresses."
				    +"\n\t(default = false)."
				    , "V", 0
				    , "-V"));
    return  newVector.elements();
  }

  /**
   * Parses a given list of options.
   *
   * Valid options are: <p>
   *
   * -P <start set> <br>
   * Specify a starting set of attributes. Eg 1,4,7-9. <p>
   *
   * -F <percent) <br>
   * Percentage of the search space to consider. (default = 25). <p>
   *
   * -V <br>
   * Verbose output. Output new best subsets as the search progresses. <p>
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   *
   **/
  public void setOptions (String[] options)
    throws Exception {
    String optionString;
    resetOptions();
    
    optionString = Utils.getOption('P', options);
    if (optionString.length() != 0) {
      setStartSet(optionString);
    }

    optionString = Utils.getOption('F',options);
    if (optionString.length() != 0) {
      setSearchPercent((new Double(optionString)).doubleValue());
    }

    setVerbose(Utils.getFlag('V',options));
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String startSetTipText() {
    return "Set the start point for the search. This is specified as a comma "
      +"seperated list off attribute indexes starting at 1. It can include "
      +"ranges. Eg. 1,2,5-9,17. If specified, Random searches for subsets "
      +"of attributes that are as good as or better than the start set with "
      +"the same or lower cardinality.";
  }

  /**
   * Sets a starting set of attributes for the search. It is the
   * search method's responsibility to report this start set (if any)
   * in its toString() method.
   * @param startSet a string containing a list of attributes (and or ranges),
   * eg. 1,2,6,10-15. "" indicates no start point.
   * If a start point is supplied, random search evaluates the
   * start point and then looks for subsets that are as good as or better 
   * than the start point with the same or lower cardinality.
   * @exception Exception if start set can't be set.
   */
  public void setStartSet (String startSet) throws Exception {
    m_startRange.setRanges(startSet);
  }

  /**
   * Returns a list of attributes (and or attribute ranges) as a String
   * @return a list of attributes (and or attribute ranges)
   */
  public String getStartSet () {
    return m_startRange.getRanges();
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String verboseTipText() {
    return "Print progress information. Sends progress info to the terminal "
      +"as the search progresses.";
  }

  /**
   * set whether or not to output new best subsets as the search proceeds
   * @param v true if output is to be verbose
   */
  public void setVerbose(boolean v) {
    m_verbose = v;
  }

  /**
   * get whether or not output is verbose
   * @return true if output is set to verbose
   */
  public boolean getVerbose() {
    return m_verbose;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String searchPercentTipText() {
    return "Percentage of the search space to explore.";
  }

  /**
   * set the percentage of the search space to consider
   * @param p percent of the search space ( 0 < p <= 100)
   */
  public void setSearchPercent(double p) {
    p = Math.abs(p);
    if (p == 0) {
      p = 25;
    }

    if (p > 100.0) {
      p = 100;
    }

    m_searchSize = (p/100.0);
  }

  /**
   * get the percentage of the search space to consider
   * @return the percent of the search space explored
   */
  public double getSearchPercent() {
    return m_searchSize;
  }

  /**
   * Gets the current settings of RandomSearch.
   * @return an array of strings suitable for passing to setOptions()
   */
  public String[] getOptions () {
    String[] options = new String[5];
    int current = 0;

    if (m_verbose) {
      options[current++] = "-V";
    }