/* * 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: 5922 $ */ 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: 5922 $"); } }