/*
* 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/>.
*/
/*
* Puk.java
* Copyright (C) 2007-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.functions.supportVector;
import java.util.Enumeration;
import java.util.Vector;
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.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
/**
<!-- globalinfo-start -->
* The Pearson VII function-based universal kernel.<br/>
* <br/>
* For more information see:<br/>
* <br/>
* B. Uestuen, W.J. Melssen, L.M.C. Buydens (2006). Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems. 81:29-40.
* <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 <num>
* The size of the cache (a prime number), 0 for full cache and
* -1 to turn it off.
* (default: 250007)</pre>
*
* <pre> -O <num>
* The Omega parameter.
* (default: 1.0)</pre>
*
* <pre> -S <num>
* The Sigma parameter.
* (default: 1.0)</pre>
*
<!-- options-end -->
*
* @author Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class Puk
extends CachedKernel
implements TechnicalInformationHandler {
/** for serialization */
private static final long serialVersionUID = 1682161522559978851L;
/** The precalculated dotproducts of <inst_i,inst_i> */
protected double m_kernelPrecalc[];
/** Omega for the Puk kernel. */
protected double m_omega = 1.0;
/** Sigma for the Puk kernel. */
protected double m_sigma = 1.0;
/** Cached factor for the Puk kernel. */
protected double m_factor = 1.0;
/**
* default constructor - does nothing.
*/
public Puk() {
super();
}
/**
* Constructor. Initializes m_kernelPrecalc[].
*
* @param data the data to use
* @param cacheSize the size of the cache
* @param omega the exponent
* @param sigma the bandwidth
* @throws Exception if something goes wrong
*/
public Puk(Instances data, int cacheSize, double omega, double sigma)
throws Exception {
super();
setCacheSize(cacheSize);
setOmega(omega);
setSigma(sigma);
buildKernel(data);
}
/**
* Returns a string describing the kernel
*
* @return a description suitable for displaying in the
* explorer/experimenter gui
*/
public String globalInfo() {
return
"The Pearson VII function-based universal kernel.\n\n"
+ "For more information see:\n\n"
+ getTechnicalInformation().toString();
}
/**
* Returns an instance of a TechnicalInformation object, containing
* detailed information about the technical background of this class,
* e.g., paper reference or book this class is based on.
*
* @return the technical information about this class
*/
public TechnicalInformation getTechnicalInformation() {
TechnicalInformation result;
result = new TechnicalInformation(Type.ARTICLE);
result.setValue(Field.AUTHOR, "B. Uestuen and W.J. Melssen and L.M.C. Buydens");
result.setValue(Field.YEAR, "2006");
result.setValue(Field.TITLE, "Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel");
result.setValue(Field.JOURNAL, "Chemometrics and Intelligent Laboratory Systems");
result.setValue(Field.VOLUME, "81");
result.setValue(Field.PAGES, "29-40");
result.setValue(Field.PDF, "http://www.cac.science.ru.nl/research/publications/PDFs/ustun2006.pdf");
return result;
}
/**
* 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 Omega parameter.\n"
+ "\t(default: 1.0)",
"O", 1, "-O <num>"));
result.addElement(new Option(
"\tThe Sigma parameter.\n"
+ "\t(default: 1.0)",
"S", 1, "-S <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 <num>
* The size of the cache (a prime number), 0 for full cache and
* -1 to turn it off.
* (default: 250007)</pre>
*
* <pre> -O <num>
* The Omega parameter.
* (default: 1.0)</pre>
*
* <pre> -S <num>
* The Sigma parameter.
* (default: 1.0)</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('O', options);
if (tmpStr.length() != 0)
setOmega(Double.parseDouble(tmpStr));
else
setOmega(1.0);
tmpStr = Utils.getOption('S', options);
if (tmpStr.length() != 0)
setSigma(Double.parseDouble(tmpStr));
else
setSigma(1.0);
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("-O");
result.add("" + getOmega());
result.add("-S");
result.add("" + getSigma());
return (String[]) result.toArray(new String[result.size()]);
}
/**
* returns the dot product
*
* @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 squaredDifference = -2.0 * dotProd(inst1, inst2) + precalc1 + m_kernelPrecalc[id2];
double intermediate = m_factor * Math.sqrt(squaredDifference);
double result = 1.0 / Math.pow(1.0 + intermediate * intermediate, getOmega());
return result;
}
}
/**
* Sets the omega value.
*
* @param value the omega value
*/
public void setOmega(double value) {
m_omega = value;
m_factor = computeFactor(m_omega, m_sigma);
}
/**
* Gets the omega value.
*
* @return the omega value
*/
public double getOmega() {
return m_omega;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String omegaTipText() {
return "The Omega value.";
}
/**
* Sets the sigma value.
*
* @param value the sigma value
*/
public void setSigma(double value) {
m_sigma = value;
m_factor = computeFactor(m_omega, m_sigma);
}
/**
* Gets the sigma value.
*
* @return the sigma value
*/
public double getSigma() {
return m_sigma;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String sigmaTipText() {
return "The Sigma value.";
}
/**
* computes the factor for curve-fitting (see equation (13) in paper)
*
* @param omega the omega to use
* @param sigma the sigma to use
* @return the factor for curve-fitting
*/
protected double computeFactor(double omega, double sigma) {
double root = Math.sqrt(Math.pow(2.0, 1.0 / omega) - 1);
return 2.0 * root / sigma;
}
/**
* initializes variables etc.
*
* @param data the data to use
*/
protected void initVars(Instances data) {
super.initVars(data);
m_factor = computeFactor(m_omega, m_sigma);
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 "Puk kernel";
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}