content: /*
   *    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.
   */
  
  /*
   *    RBFKernel.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *    Copyright (C) 2005 J. Lindgren
   *
   */
  
  package weka.classifiers.functions.supportVector;
  
  import weka.core.Capabilities;
  import weka.core.Capabilities.Capability;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * The RBF kernel. K(x, y) = e^-(gamma * &lt;x-y, x-y&gt;^2)
   * <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), 0 for full cache and 
   *  -1 to turn it off.
   *  (default: 250007)</pre>
   * 
   * <pre> -G &lt;num&gt;
   *  The Gamma parameter.
   *  (default: 0.01)</pre>
   * 
   <!-- options-end -->
   *
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @author Shane Legg (shane@intelligenesis.net) (sparse vector code)
   * @author Stuart Inglis (stuart@reeltwo.com) (sparse vector code)
   * @author J. Lindgren (jtlindgr{at}cs.helsinki.fi) (RBF kernel)
   * @version $Revision: 5450 $
   */
  public class RBFKernel 
    extends CachedKernel {
    
    /** for serialization */
    static final long serialVersionUID = 5247117544316387852L;
  
    /** The precalculated dotproducts of &lt;inst_i,inst_i&gt; */
    protected double m_kernelPrecalc[];
  
    /** Gamma for the RBF kernel. */
    protected double m_gamma = 0.01;
  
    /**
     * default constructor - does nothing.
     */
    public RBFKernel() {
      super();
    }
    
    /**
     * Constructor. Initializes m_kernelPrecalc[].
     * 
     * @param data	the data to use
     * @param cacheSize	the size of the cache
     * @param gamma	the bandwidth
     * @throws Exception	if something goes wrong
     */
    public RBFKernel(Instances data, int cacheSize, double gamma)
      throws Exception {
  
      super();
      
      setCacheSize(cacheSize);
      setGamma(gamma);
      
      buildKernel(data);
    }
    
    /**
     * Returns a string describing the kernel
     * 
     * @return a description suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
      return 
          "The RBF kernel. K(x, y) = e^-(gamma * <x-y, x-y>^2)";
    }
    
    /**
     * Returns an enumeration describing the available options.
     *
     * @return 		an enumeration of all the available options.
     */
    public Enumeration listOptions() {
      Vector		result;
      Enumeration		en;
      
      result = new Vector();
  
      en = super.listOptions();
      while (en.hasMoreElements())
        result.addElement(en.nextElement());
  
      result.addElement(new Option(
  	"\tThe Gamma parameter.\n"
  	+ "\t(default: 0.01)",
  	"G", 1, "-G <num>"));
  
      return result.elements();
    }
  
    /**
     * Parses a given list of options. <p/>
     * 
     <!-- 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), 0 for full cache and 
     *  -1 to turn it off.
     *  (default: 250007)</pre>
     * 
     * <pre> -G &lt;num&gt;
     *  The Gamma parameter.
     *  (default: 0.01)</pre>
     * 
     <!-- options-end -->
     * 
     * @param options 	the list of options as an array of strings
     * @throws Exception 	if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {
      String	tmpStr;
      
      tmpStr = Utils.getOption('G', options);
      if (tmpStr.length() != 0)
        setGamma(Double.parseDouble(tmpStr));
      else
        setGamma(0.01);
      
      super.setOptions(options);
    }
  
    /**
     * Gets the current settings of the Kernel.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {
      int       i;
      Vector    result;
      String[]  options;
  
      result = new Vector();
      options = super.getOptions();
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
  
      result.add("-G");
      result.add("" + getGamma());
  
      return (String[]) result.toArray(new String[result.size()]);	  
    }
  
    /**
     * 
     * @param id1   	the index of instance 1
     * @param id2		the index of instance 2
     * @param inst1	the instance 1 object
     * @return 		the dot product
     * @throws Exception 	if something goes wrong
     */
    protected double evaluate(int id1, int id2, Instance inst1)
      throws Exception {
  
      if (id1 == id2) {
        return 1.0;
      } else {
        double precalc1;
        if (id1 == -1)
          precalc1 = dotProd(inst1, inst1);
        else
          precalc1 = m_kernelPrecalc[id1];
        Instance inst2 = m_data.instance(id2);
        double result = Math.exp(m_gamma
    			     * (2. * dotProd(inst1, inst2) - precalc1 - m_kernelPrecalc[id2]));
        
        return result;
      }
    }
      
    /**
     * Sets the gamma value.
     * 
     * @param value	the gamma value
     */
    public void setGamma(double value) {
      m_gamma = value;
    }
    
    /**
     * Gets the gamma value.
     * 
     * @return		the gamma value
     */
    public double getGamma() {
      return m_gamma;
    }
  
    /**
     * Returns the tip text for this property
     * 
     * @return 		tip text for this property suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String gammaTipText() {
      return "The Gamma value.";
    }
  
    /**
     * initializes variables etc.
     * 
     * @param data	the data to use
     */
    protected void initVars(Instances data) {
      super.initVars(data);
      
      m_kernelPrecalc = new double[data.numInstances()];
    }
  
    /** 
     * Returns the Capabilities of this kernel.
     *
     * @return            the capabilities of this object
     * @see               Capabilities
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
      
      result.enable(Capability.NUMERIC_ATTRIBUTES);
      result.enableAllClasses();
      result.enable(Capability.MISSING_CLASS_VALUES);
      
      return result;
    }
    
    /**
     * builds the kernel with the given data. Initializes the kernel cache. 
     * The actual size of the cache in bytes is (64 * cacheSize).
     * 
     * @param data	the data to base the kernel on
     * @throws Exception	if something goes wrong
     */
    public void buildKernel(Instances data) throws Exception {
      // does kernel handle the data?
      if (!getChecksTurnedOff())
        getCapabilities().testWithFail(data);
      
      initVars(data);
      
      for (int i = 0; i < data.numInstances(); i++)
        m_kernelPrecalc[i] = dotProd(data.instance(i), data.instance(i));
    }
    
    /**
     * returns a string representation for the Kernel
     * 
     * @return 		a string representaiton of the kernel
     */
    public String toString() {
      return "RBF kernel: K(x,y) = e^-(" + getGamma() + "* <x-y,x-y>^2)";
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5450 $");
    }
  }
  
  