/**
 *   
 *   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 org.agentacademy.modules.dataminer.core.*;

/**
 * Class for computing the gain ratio for a given distribution.
 *
 */
public final class GainRatioSplitCrit extends EntropyBasedSplitCrit{

  /**
   * This method is a straightforward implementation of the gain
   * ratio criterion for the given distribution.
   */
  public final double splitCritValue(Distribution bags) {

    double numerator;
    double denumerator;

    numerator = oldEnt(bags)-newEnt(bags);

    // Splits with no gain are useless.
    if (Utils.eq(numerator,0))
      return Double.MAX_VALUE;
    denumerator = splitEnt(bags);

    // Test if split is trivial.
    if (Utils.eq(denumerator,0))
      return Double.MAX_VALUE;

    //  We take the reciprocal value because we want to minimize the
    // splitting criterion's value.
    return denumerator/numerator;
  }

  /**
   * This method computes the gain ratio in the same way C4.5 does.
   *
   * @param bags the distribution
   * @param totalnoInst the weight of ALL instances
   * @param numerator the info gain
   */
  public final double splitCritValue(Distribution bags, double totalnoInst,
				     double numerator){

    double denumerator;
    double noUnknown;
    double unknownRate;
    int i;

    // Compute split info.
    denumerator = splitEnt(bags,totalnoInst);

    // Test if split is trivial.
    if (Utils.eq(denumerator,0))
      return 0;
    denumerator = denumerator/totalnoInst;

    return numerator/denumerator;
  }

  /**
   * Help method for computing the split entropy.
   */
  private final double splitEnt(Distribution bags,double totalnoInst){

    double returnValue = 0;
    double noUnknown;
    int i;

    noUnknown = totalnoInst-bags.total();
    if (Utils.gr(bags.total(),0)){
      for (i=0;i<bags.numBags();i++)
	returnValue = returnValue-logFunc(bags.perBag(i));
      returnValue = returnValue-logFunc(noUnknown);
      returnValue = returnValue+logFunc(totalnoInst);
    }
    return returnValue;
  }
}