/*
* 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.
*/
/*
* ZeroOneLossFunction.java
* Copyright (C) 2004 Stijn Lievens
*
*/
package weka.classifiers.misc.monotone;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
/**
* Class implementing the zero-one loss function, this is
* an incorrect prediction always accounts for one unit loss.
*
* <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 $Revision$
*/
public class ZeroOneLossFunction
implements NominalLossFunction, RevisionHandler {
/**
* Returns the zero-one loss function between two class values.
*
* @param actual the actual class value
* @param predicted the predicted class value
* @return 1 if the actual and predicted value differ, 0 otherwise
*/
public final double loss(double actual, double predicted) {
return actual == predicted ? 0 : 1;
}
/**
* Returns a string with the name of the loss function.
*
* @return a string with the name of the loss function
*/
public String toString() {
return "ZeroOneLossFunction";
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision$");
}
}