weka.classifiers.functions.supportVector.RBFKernel.java Source code

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Here is the source code for weka.classifiers.functions.supportVector.RBFKernel.java

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/*
 *   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 3 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, see <http://www.gnu.org/licenses/>.
 */

/*
 *    RBFKernel.java
 *    Copyright (C) 1999-2017 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.functions.supportVector;

import weka.core.*;
import weka.core.Capabilities.Capability;

/**
 * <!-- globalinfo-start -->
 * The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2)
 * <br><br>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start -->
 * Valid options are: <p>
 * 
 * <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;double&gt;
 *  The value to use for the gamma parameter (default: 0.01).</pre>
 * 
 * <pre> -output-debug-info
 *  Enables debugging output (if available) to be printed.
 *  (default: off)</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)
 * @version $Revision$
 */
public class RBFKernel extends CachedKernel {

    /** for serialization (value needs to be consistent with J. Lindgren's implementation) */
    static final long serialVersionUID = 5247117544316387852L;

    /** The gamma parameter for the RBF kernel. */
    protected double m_gamma = 0.01;

    /** The diagonal values of the dot product matrix (name needs to be consistent with J. Lindgren's implementation). */
    protected double[] m_kernelPrecalc;

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

    /**
     * Creates a new <code>RBFKernel</code> instance.
     * 
     * @param data the training dataset used.
     * @param cacheSize the size of the cache (a prime number)
     * @param gamma the gamma to use
     * @throws Exception if something goes wrong
     */
    public RBFKernel(Instances data, int cacheSize, double gamma) throws Exception {

        super();

        setCacheSize(cacheSize);
        setGamma(gamma);

        buildKernel(data);
    }

    /**
     * Builds the kernel. Calls the super class method and then also initializes the cache for
     * the diagonal of the dot product matrix.
     */
    public void buildKernel(Instances data) throws Exception {
        super.buildKernel(data);

        m_kernelPrecalc = new double[data.numInstances()];
        for (int i = 0; i < data.numInstances(); i++) {
            double sum = 0;
            Instance inst = data.instance(i);
            for (int j = 0; j < inst.numValues(); j++) {
                if (inst.index(j) != data.classIndex()) {
                    sum += inst.valueSparse(j) * inst.valueSparse(j);
                }
            }
            m_kernelPrecalc[i] = sum;
        }
    }

    /**
     * Returns a string describing the kernel
     * 
     * @return a description suitable for displaying in the explorer/experimenter
     *         gui
     */
    @Override
    public String globalInfo() {
        return "The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2)";
    }

    /**
     * 
     * @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
     */
    @Override
    protected double evaluate(int id1, int id2, Instance inst1) throws Exception {

        if (id1 == id2) {
            return 1.0;
        } else {
            if (id1 == -1) {
                return Math.exp(-m_gamma * (dotProd(inst1, inst1) - 2 * dotProd(inst1, m_data.instance(id2))
                        + m_kernelPrecalc[id2]));
            } else {
                return Math.exp(-m_gamma
                        * (m_kernelPrecalc[id1] - 2 * dotProd(inst1, m_data.instance(id2)) + m_kernelPrecalc[id2]));
            }
        }
    }

    /**
     * Returns the Capabilities of this kernel.
     * 
     * @return the capabilities of this object
     * @see Capabilities
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();

        result.enable(Capability.NUMERIC_ATTRIBUTES);
        result.enableAllClasses();
        result.enable(Capability.MISSING_CLASS_VALUES);
        result.enable(Capability.NO_CLASS);

        return result;
    }

    /**
     * Sets the gamma value.
     * 
     * @param value the gamma value
     */
    @OptionMetadata(description = "The value to use for the gamma parameter (default: 0.01).", displayName = "gamma", commandLineParamName = "G", commandLineParamSynopsis = "-G <double>", displayOrder = 1)
    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.";
    }

    /**
     * returns a string representation for the Kernel
     * 
     * @return a string representaiton of the kernel
     */
    @Override
    public String toString() {
        return "RBF Kernel: K(x,y) = exp(-" + m_gamma + "*(x-y)^2)";
    }

    /**
     * Returns the revision string.
     * 
     * @return the revision
     */
    @Override
    public String getRevision() {
        return RevisionUtils.extract("$Revision$");
    }
}