source: src/main/java/weka/classifiers/misc/monotone/NominalLossFunction.java @ 4

/*
 *    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.
 */

/*
 *    NominalLossFunction.java
 *    Copyright (C) 2004 Stijn Lievens
 *
 */

package weka.classifiers.misc.monotone;

/**
 * Interface for incorporating different loss functions.
 * <p>
 * This interface contains only one method, namely <code> loss
 * </code> that measures the error between an actual class
 * value <code> actual </code> and a predicted value <code>
 * predicted. </code>  It is understood that the return value
 * of this method is always be positive and that it is zero
 * if and only if the actual and the predicted value coincide.
 * </p>
 * <p>
 * This implementation is done as part of the master's thesis: "Studie
 * en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd
 * rangschikken", Stijn Lievens, Ghent University, 2004. 
 * </p>
 * 
 * @author Stijn Lievens (stijn.lievens@ugent.be)
 * @version $Revision: 5922 $
 */
public interface NominalLossFunction {

  /**
   * Calculate the loss between an actual and a predicted class value.
   *
   * @param actual the actual class value
   * @param predicted the predicted class value
   * @return a measure for the error of making the prediction
   * <code> predicted </code> instead of <code> actual </code>
   */
  public double loss(double actual, double predicted);
}