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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 .
 */

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

package weka.classifiers;

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

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

/**
 * Abstract utility class for handling settings common to randomizable
 * classifiers.
 *
 * @author Eibe Frank ([email protected])
 * @version $Revision: 10141 $
 */
public abstract class RandomizableClassifier
  extends AbstractClassifier implements Randomizable {

  /** for serialization */
  private static final long serialVersionUID = -8816375798262351903L;

  /** 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
   *
   * @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() {

    Vector options = new Vector();

    options.add("-S");
    options.add("" + getSeed());

    Collections.addAll(options, super.getOptions());

    return options.toArray(new String[0]);
  }

  /**
   * 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;
  }
}