Package weka.classifiers.mi.supportVector

Source Code of weka.classifiers.mi.supportVector.MIRBFKernel

/*
*    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.
*/

/*
* MIRBFKernel.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/

package weka.classifiers.mi.supportVector;

import weka.classifiers.functions.supportVector.RBFKernel;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.RevisionUtils;
import weka.core.Capabilities.Capability;

/**
<!-- 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)
* @author Lin Dong (ld21@cs.waikato.ac.nz) (MIkernel)
* @version $Revision: 1.4 $
*/
public class MIRBFKernel
  extends RBFKernel
  implements MultiInstanceCapabilitiesHandler {

  /** for serialiation */
  private static final long serialVersionUID = -8711882393708956962L;
 
  /** The precalculated dotproducts of &lt;inst_i,inst_i&gt; */
  protected double m_kernelPrecalc[][];

  /**
   * default constructor - does nothing.
   */
  public MIRBFKernel() {
    super();
  }

  /**
   * Constructor.
   *
   * @param data  the data to use
   * @param cacheSize  the size of the cache
   * @param gamma  the bandwidth
   * @throws Exception  if something goes wrong
   */
  public MIRBFKernel(Instances data, int cacheSize, double gamma)
    throws Exception {

    super(data, cacheSize, gamma);
  }

  /**
   *
   * @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 {

    double result = 0;
    Instances insts1, insts2;
    if (id1 == -1)
      insts1 = new Instances(inst1.relationalValue(1));
    else
      insts1 = new Instances(m_data.instance(id1).relationalValue(1));
    insts2 = new Instances (m_data.instance(id2).relationalValue(1));

    double precalc1=0;
    for(int i = 0; i < insts1.numInstances(); i++){
      for (int j = 0; j < insts2.numInstances(); j++){
        if (id1 == -1)
          precalc1 = dotProd(insts1.instance(i), insts1.instance(i));
        else
          precalc1 =  m_kernelPrecalc[id1][i];

        double res = Math.exp(m_gamma*(2. * dotProd(insts1.instance(i), insts2.instance(j)) -precalc1 -  m_kernelPrecalc[id2][j] ) );

        result += res;
      }
    }

    return result;
  }  

  /**
   * 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();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.enableAllClasses();

    // other
    result.enable(Capability.ONLY_MULTIINSTANCE);
   
    return result;
  }

  /**
   * Returns the capabilities of this multi-instance kernel for the
   * relational data.
   *
   * @return            the capabilities of this object
   * @see               Capabilities
   */
  public Capabilities getMultiInstanceCapabilities() {
    Capabilities result = super.getCapabilities();
   
    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);
   
    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++){
      Instances insts = new Instances(data.instance(i).relationalValue(1));
      m_kernelPrecalc[i] = new double [insts.numInstances()];
      for (int j = 0; j < insts.numInstances(); j++)
        m_kernelPrecalc[i][j] = dotProd(insts.instance(j), insts.instance(j));
    }
  }
 
  /**
   * Returns the revision string.
   *
   * @return    the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 1.4 $");
  }
}
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