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.
   */
  
  /*
   * ClassificationGenerator.java
   * Copyright (C) 2000 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.datagenerators;
  
  import weka.core.Option;
  import weka.core.Utils;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /** 
   * Abstract class for data generators for classifiers. <p/>
   *
   * @author Gabi Schmidberger (gabi@cs.waikato.ac.nz)
   * @author FracPete (fracpete at waikato dot ac dot nz)
   * @version $Revision: 1.4 $
   */
  public abstract class ClassificationGenerator 
    extends DataGenerator {
  
    /** for serialization */
    private static final long serialVersionUID = -5261662546673517844L;
  
    /** Number of instances*/
    protected int m_NumExamples;
  
    /**
     * initializes with default values
     */
    public ClassificationGenerator() {
      super();
  
      setNumExamples(defaultNumExamples());
    }
    
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
      Vector result = enumToVector(super.listOptions());
      
      result.addElement(new Option(
          "\tThe number of examples to generate (default " 
          + defaultNumExamples() + ")",
          "n", 1, "-n <num>"));
      
      return result.elements();
    }
  
    /**
     * Sets the options.
     *
     * @param options the options 
     * @throws Exception if invalid option
     */
    public void setOptions(String[] options) throws Exception { 
      String        tmpStr;
     
      super.setOptions(options);
  
      tmpStr = Utils.getOption('n', options);
      if (tmpStr.length() != 0)
        setNumExamples(Integer.parseInt(tmpStr));
      else
        setNumExamples(defaultNumExamples());
    }
    
    /**
     * Gets the current settings of the classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {
      Vector        result;
      String[]      options;
      int           i;
      
      result  = new Vector();
      options = super.getOptions();
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
      
      result.add("-n");
      result.add("" + getNumExamples());
      
      return (String[]) result.toArray(new String[result.size()]);
    }
  
    /**
     * returns the default number of examples
     * 
     * @return the default number of examples
     */
    protected int defaultNumExamples() {
      return 100;
    }
  
    /**
     * Sets the number of examples, given by option.
     * @param numExamples the new number of examples
     */
    public void setNumExamples(int numExamples) { 
      m_NumExamples = numExamples; 
    }
  
    /**
     * Gets the number of examples, given by option.
     * @return the number of examples, given by option 
     */
    public int getNumExamples() { 
      return m_NumExamples; 
    }
    
    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String numExamplesTipText() {
      return "The number of examples to generate.";
    }
  }
  