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.
   */
  
  /*
   *    IteratedSingleClassifierEnhancer.java
   *    Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers;
  
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.Utils;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /**
   * Abstract utility class for handling settings common to
   * meta classifiers that build an ensemble from a single base learner.
   *
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @version $Revision: 6041 $
   */
  public abstract class IteratedSingleClassifierEnhancer
    extends SingleClassifierEnhancer {
  
    /** for serialization */
    private static final long serialVersionUID = -6217979135443319724L;
  
    /** Array for storing the generated base classifiers. */
    protected Classifier[] m_Classifiers;
  
    /** The number of iterations. */
    protected int m_NumIterations = 10;
  
    /**
     * Stump method for building the classifiers.
     *
     * @param data the training data to be used for generating the
     * bagged classifier.
     * @exception Exception if the classifier could not be built successfully
     */
    public void buildClassifier(Instances data) throws Exception {
  
      if (m_Classifier == null) {
        throw new Exception("A base classifier has not been specified!");
      }
      m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_NumIterations);
    }
  
    /**
     * 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(
            "\tNumber of iterations.\n"
            + "\t(default 10)",
            "I", 1, "-I <num>"));
  
      Enumeration enu = super.listOptions();
      while (enu.hasMoreElements()) {
        newVector.addElement(enu.nextElement());
      }
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. Valid options are:<p>
     *
     * -W classname <br>
     * Specify the full class name of the base learner.<p>
     *
     * -I num <br>
     * Set the number of iterations (default 10). <p>
     *
     * Options after -- are passed to the designated classifier.<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 iterations = Utils.getOption('I', options);
      if (iterations.length() != 0) {
        setNumIterations(Integer.parseInt(iterations));
      } else {
        setNumIterations(10);
      }
  
      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++] = "-I";
      options[current++] = "" + getNumIterations();
  
      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 numIterationsTipText() {
      return "The number of iterations to be performed.";
    }
  
    /**
     * Sets the number of bagging iterations
     */
    public void setNumIterations(int numIterations) {
  
      m_NumIterations = numIterations;
    }
  
    /**
     * Gets the number of bagging iterations
     *
     * @return the maximum number of bagging iterations
     */
    public int getNumIterations() {
  
      return m_NumIterations;
    }
  }
  