/* *    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.functions.supportVector;import weka.core.Instance;import weka.core.Instances;/** <!-- globalinfo-start --> * The normalized polynomial kernel.<br/> * K(x,y) = &lt;x,y&gt;/sqrt(&lt;x,x&gt;&lt;y,y&gt;) where &lt;x,y&gt; = PolyKernel(x,y) * <p/> <!-- globalinfo-end --> *  <!-- options-start --> * Valid options are: <p/> *  * <pre> -D *  Enables debugging output (if available) to be printed. *  (default: off)</pre> *  * <pre> -no-checks *  Turns off all checks - use with caution! *  (default: checks on)</pre> *  * <pre> -C &lt;num&gt; *  The size of the cache (a prime number). *  (default: 250007)</pre> *  * <pre> -E &lt;num&gt; *  The Exponent to use. *  (default: 1.0)</pre> *  * <pre> -L *  Use lower-order terms. *  (default: no)</pre> *  <!-- options-end --> * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 1.5 $ */public class NormalizedPolyKernel   extends PolyKernel {  /** for serialization */  static final long serialVersionUID = 1248574185532130851L;  /**   * default constructor - does nothing   */  public NormalizedPolyKernel() {    super();    setExponent(2.0);  }    /**   * Creates a new <code>NormalizedPolyKernel</code> instance.   *   * @param dataset	the training dataset used.   * @param cacheSize	the size of the cache (a prime number)   * @param exponent	the exponent to use   * @param lowerOrder	whether to use lower-order terms   * @throws Exception	if something goes wrong   */  public NormalizedPolyKernel(Instances dataset, int cacheSize,       double exponent, boolean lowerOrder) throws Exception {	    super(dataset, cacheSize, exponent, lowerOrder);  }    /**   * Returns a string describing the kernel   *    * @return a description suitable for displaying in the   *         explorer/experimenter gui   */  public String globalInfo() {    return         "The normalized polynomial kernel.\n"      + "K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)";  }     /**   * Computes the result of the kernel function for two instances.   * If id1 == -1, eval use inst1 instead of an instance in the dataset.   * Redefines the eval function of PolyKernel.   *   * @param id1 the index of the first instance in the dataset   * @param id2 the index of the second instance in the dataset   * @param inst1 the instance corresponding to id1 (used if id1 == -1)   * @return the result of the kernel function   * @throws Exception if something goes wrong   */  public double eval(int id1, int id2, Instance inst1)     throws Exception {    double div = Math.sqrt(super.eval(id1, id1, inst1) * ((m_keys != null)                           ? super.eval(id2, id2, m_data.instance(id2))                           : super.eval(-1, -1, m_data.instance(id2))));    if(div != 0){            return super.eval(id1, id2, inst1) / div;    } else {      return 0;    }  }        /**   * Sets the exponent value (must be different from 1.0).   *    * @param value	the exponent value   */  public void setExponent(double value) {    if (value != 1.0)      super.setExponent(value);    else      System.out.println("A linear kernel, i.e., Exponent=1, is not possible!");  }    /**   * returns a string representation for the Kernel   *    * @return 		a string representaiton of the kernel   */  public String toString() {    String	result;        if (getUseLowerOrder())      result = "Normalized Poly Kernel with lower order: K(x,y) = (<x,y>+1)^" + getExponent() + "/" +       	       "((<x,x>+1)^" + getExponent() + "*" + "(<y,y>+1)^" + getExponent() + ")^(1/2)";    else      result = "Normalized Poly Kernel: K(x,y) = <x,y>^" + getExponent() + "/" + "(<x,x>^" +                getExponent() + "*" + "<y,y>^" + getExponent() + ")^(1/2)";        return result;  }}