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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 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.
 */

/*
 *    IterativeClassifier.java
 *    Copyright (C) 2001  University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers;

import weka.core.*;

/**
 * Interface for classifiers that can induce models of growing
 * complexity one step at a time.
 *
 * @author Gabi Schmidberger ([email protected])
 * @author Bernhard Pfahringer ([email protected])
 * @version $Revision: 1.3 $
 */

public interface IterativeClassifier {

  /**
   * Inits an iterative classifier.
   *
   * @param instances the instances to be used in induction
   * @exception Exception if the model cannot be initialized
   */
  void initClassifier(Instances instances) throws Exception;

  /**
   * Performs one iteration.
   * 
   * @param iteration the index of the current iteration (0-based)
   * @exception Exception if this iteration fails 
   */  
  void next(int iteration) throws Exception;

  /**
   * Signal end of iterating, useful for any house-keeping/cleanup
   * 
   * @exception Exception if cleanup fails 
   */  
  void done() throws Exception;

  /**
    * Performs a deep copy of the classifier, and a reference copy
    * of the training instances (or a deep copy if required).
    *
    * @return a clone of the classifier
    */
  Object clone() throws CloneNotSupportedException;

}