weka.classifiers.mi.supportVector.MIRBFKernel Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of multiInstanceLearning Show documentation
Show all versions of multiInstanceLearning Show documentation
A collection of multi-instance learning classifiers. Includes the Citation KNN method, several variants of the diverse density method, support vector machines for multi-instance learning, simple wrappers for applying standard propositional learners to multi-instance data, decision tree and rule learners, and some other methods.
The newest version!
/*
* 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 -D
* Enables debugging output (if available) to be printed.
* (default: off)
*
* -no-checks
* Turns off all checks - use with caution!
* (default: checks on)
*
* -C <num>
* The size of the cache (a prime number), 0 for full cache and
* -1 to turn it off.
* (default: 250007)
*
* -G <num>
* The Gamma parameter.
* (default: 0.01)
*
*
* @author Eibe Frank ([email protected])
* @author Shane Legg ([email protected]) (sparse vector code)
* @author Stuart Inglis ([email protected]) (sparse vector code)
* @author J. Lindgren (jtlindgr{at}cs.helsinki.fi) (RBF kernel)
* @author Lin Dong ([email protected]) (MIkernel)
* @version $Revision: 9142 $
*/
public class MIRBFKernel
extends RBFKernel
implements MultiInstanceCapabilitiesHandler {
/** for serialiation */
private static final long serialVersionUID = -8711882393708956962L;
/** The precalculated dotproducts of <inst_i,inst_i> */
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.disable(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: 9142 $");
}
}