/* * 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. */ /* * Kernel.java * Copyright (C) 1999 Eibe Frank * */ package weka.classifiers.sparse; import weka.core.*; import java.io.*; /** * Abstract kernel. * Kernels implementing this class must respect Mercer's condition in order * to ensure a correct behaviour of SMOreg. * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $$ */ public abstract class Kernel implements Serializable { /** The dataset */ Instances m_data; /** * Computes the result of the kernel function for two instances. * If id1 == -1, eval use inst1 instead of an instance in the dataset. * * @param id1 the index of the first instance in the dataset * @param id2 the index of the second instance in the dataset * @param inst the instance corresponding to id1 (used if id1 == -1) * @return the result of the kernel function */ public abstract double eval(int id1, int id2, Instance inst1) throws Exception; /** * Frees the memory used by the kernel. * (Usefull with kernels which use cache.) * This function is called when the training is done. * i.e. after that, eval will be called with id1 == -1. */ public abstract void clean(); /** * Returns the number of kernel evaluation performed. * * @return the number of kernel evaluation performed. */ public abstract int numEvals(); }