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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 .
 */

/*
 *    SplitCriterion.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.j48;

import java.io.Serializable;

import weka.core.RevisionHandler;

/**
 * Abstract class for computing splitting criteria
 * with respect to distributions of class values.
 *
 * @author Eibe Frank ([email protected])
 * @version $Revision: 8034 $
 */
public abstract class SplitCriterion
  implements Serializable, RevisionHandler {

  /** for serialization */
  private static final long serialVersionUID = 5490996638027101259L;

  /**
   * Computes result of splitting criterion for given distribution.
   *
   * @return value of splitting criterion. 0 by default
   */
  public double splitCritValue(Distribution bags){

    return 0;
  }

  /**
   * Computes result of splitting criterion for given training and
   * test distributions.
   *
   * @return value of splitting criterion. 0 by default
   */
  public double splitCritValue(Distribution train, Distribution test){

    return 0;
  }

  /**
   * Computes result of splitting criterion for given training and
   * test distributions and given number of classes.
   *
   * @return value of splitting criterion. 0 by default
   */
  public double splitCritValue(Distribution train, Distribution test,
			       int noClassesDefault){

    return 0;
  }

  /**
   * Computes result of splitting criterion for given training and
   * test distributions and given default distribution.
   *
   * @return value of splitting criterion. 0 by default
   */
  public double splitCritValue(Distribution train, Distribution test,
			       Distribution defC){

    return 0;
  }
}