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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 .
 */

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

package weka.gui.beans;

import java.util.EventObject;

import weka.clusterers.Clusterer;

/**
 * Class encapsulating a built clusterer and a batch of instances to
 * test on.
 *
 * @author Stefan Mutter
 * @version $Revision: 8034 $
 * @since 1.0
 * @see EventObject
 */
public class BatchClustererEvent
  extends EventObject {

  /** for serialization */
  private static final long serialVersionUID = 7268777944939129714L;

  /**
   * The clusterer
   */
  protected Clusterer m_clusterer;
  //  protected Instances m_trainingSet;

  /**
   * Training or Test Instances
   */
  //  protected Instances m_testSet;
  protected DataSetEvent m_testSet;

  /**
   * The set number for the test set
   */
  protected int m_setNumber;

  /**
   * Indicates if m_testSet is a training or a test set. 0 for test, >0 for training
   */
  protected int m_testOrTrain;

  /**
   * The last set number for this series
   */
  protected int m_maxSetNumber;
  
  public static int TEST = 0;
  public static int TRAINING = 1;

  /**
   * Creates a new BatchClustererEvent instance.
   *
   * @param source the source object
   * @param scheme a Clusterer
   * @param tstI the training/test instances
   * @param setNum the set number of the training/testinstances
   * @param maxSetNum the last set number in the series
   * @param testOrTrain 0 if the set is a test set, >0 if it is a training set
   */
  public BatchClustererEvent(Object source, Clusterer scheme, DataSetEvent tstI, int setNum, int maxSetNum, int testOrTrain) {
    super(source);
    //    m_trainingSet = trnI;
    m_clusterer = scheme;
    m_testSet = tstI;
    m_setNumber = setNum;
    m_maxSetNumber = maxSetNum;
    if(testOrTrain == 0)
        m_testOrTrain = TEST;
    else
        m_testOrTrain = TRAINING;
  }


  /**
   * Get the clusterer
   *
   * @return the clusterer
   */
  public Clusterer getClusterer() {
    return m_clusterer;
  }

  /**
   * Get the training/test set
   *
   * @return the training/testing instances
   */
  public DataSetEvent getTestSet() {
    return m_testSet;
  }

  /**
   * Get the set number (ie which fold this is)
   *
   * @return the set number for the training and testing data sets
   * encapsulated in this event
   */
  public int getSetNumber() {
    return m_setNumber;
  }

  /**
   * Get the maximum set number (ie the total number of training
   * and testing sets in the series).
   *
   * @return the maximum set number
   */
  public int getMaxSetNumber() {
    return m_maxSetNumber;
  }
  
  /**
   * Get whether the set of instances is a test or a training set
   *
   * @return 0 for test set, >0 fro training set
   */
  public int getTestOrTrain(){
    return m_testOrTrain;
  }
}