/*
* 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.
*/
/*
* NormalizedPolyKernel.java
* Copyright (C) 1999 Eibe Frank
*
*/
package weka.classifiers.sparse;
import weka.core.*;
/**
* The normalized polynomial kernel.
* K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
*
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $$ */
public class NormalizedPolyKernel extends PolyKernel {
/**
* Creates a new <code>NormalizedPolyKernel</code> instance.
*
* @param dataset the training dataset used.
* @param cacheSize the size of the cache (a prime number)
*/
public NormalizedPolyKernel(Instances dataset, int cacheSize, double exponent, boolean lowerOrder){
super(dataset, cacheSize, exponent, lowerOrder);
}
/**
* Redefines the eval function of PolyKernel.
*/
public double eval(int id1, int id2, Instance inst1)
throws Exception {
double div = Math.sqrt(super.eval(id1, id1, inst1) *
super.eval(id2, id2, m_data.instance(id2)));
if(div != 0){
return super.eval(id1, id2, inst1) / div;
} else {
return 0;
}
}
}