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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 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 .
 */

/*
 *    IteratedSingleClassifierEnhancer.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.Instances;
import weka.core.Option;
import weka.core.Utils;

/**
 * Abstract utility class for handling settings common to
 * meta classifiers that build an ensemble from a single base learner.
 *
 * @author Eibe Frank ([email protected])
 * @version $Revision: 12505 $
 */
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 = defaultNumberOfIterations();

  /**
   * The default number of iterations to perform.
   */
  protected int defaultNumberOfIterations() {
    return 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
   *
   * @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(defaultNumberOfIterations());
    }

    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;
  }
}