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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.

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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 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.
 */

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

package weka.classifiers;

import weka.core.Option;
import weka.core.Randomizable;
import weka.core.Utils;

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

/**
 * Abstract utility class for handling settings common to randomizable
 * meta classifiers that build an ensemble from multiple classifiers based
 * on a given random number seed.  
 *
 * @author Eibe Frank ([email protected])
 * @version $Revision: 1.4 $
 */
public abstract class RandomizableMultipleClassifiersCombiner 
  extends MultipleClassifiersCombiner implements Randomizable {

  /** for serialization */
  private static final long serialVersionUID = 5057936555724785679L;
  
  /** The random number seed. */
  protected int m_Seed = 1;

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(2);

    newVector.addElement(new Option(
	      "\tRandom number seed.\n"
	      + "\t(default 1)",
	      "S", 1, "-S "));

    Enumeration enu = super.listOptions();
    while (enu.hasMoreElements()) {
      newVector.addElement(enu.nextElement());
    }
    return newVector.elements();
  }

  /**
   * Parses a given list of options. Valid options are:

* * -B classifierstring
* Classifierstring should contain the full class name of a scheme * included for selection followed by options to the classifier * (required, option should be used once for each classifier).

* * -S num
* Set the random number seed (default 1).

* * @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 seed = Utils.getOption('S', options); if (seed.length() != 0) { setSeed(Integer.parseInt(seed)); } else { setSeed(1); } super.setOptions(options); } /** * Gets the current settings of the classifier. * * @return an array of strings suitable for passing to setOptions */ public String [] getOptions() { String [] superOptions = super.getOptions(); String [] options = new String [superOptions.length + 2]; int current = 0; options[current++] = "-S"; options[current++] = "" + getSeed(); System.arraycopy(superOptions, 0, options, current, superOptions.length); return options; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String seedTipText() { return "The random number seed to be used."; } /** * Set the seed for random number generation. * * @param seed the seed */ public void setSeed(int seed) { m_Seed = seed; } /** * Gets the seed for the random number generations * * @return the seed for the random number generation */ public int getSeed() { return m_Seed; } }





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