/**
 *   
 *   AgentAcademy - an open source Data Mining framework for
 *   training intelligent agents
 *
 *   Copyright (C)   2001-2003 AA Consortium.
 *
 *   This library is open source software; you can redistribute it 
 *   and/or modify it under the terms of the GNU Lesser General 
 *   Public License as published by the Free Software Foundation;   
 *   either version 2.0 of the License, or (at your option) any later 
 *   version.
 *
 *   This library 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 Lesser General Public
 *   License along with this library; if not, write to the Free 
 *   Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, 
 *   MA  02111-1307 USA
 * 
 */

package org.agentacademy.modules.dataminer.classifiers;

/**
 * <p>Title: The Data Miner prototype</p>
 * <p>Description: A prototype for the DataMiner (DM), the Agent Academy (AA) module responsible for performing data mining on the contents of the Agent Use Repository (AUR). The extracted knowledge is to be sent back to the AUR in the form of a PMML document.</p>
 * <p>Copyright: Copyright (c) 2002</p>
 * <p>Company: CERTH</p>
 * @author asymeon
 * @version 0.3
 */

import java.io.*;

/**
 * Abstract class for computing splitting criteria
 * with respect to distributions of class values.
 *
 */
public abstract class SplitCriterion implements Serializable {

  /**
   * 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;
  }
  }