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package de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.data.type.VectorFieldTypeInformation;
import de.lmu.ifi.dbs.elki.database.query.DistanceSimilarityQuery;
import de.lmu.ifi.dbs.elki.database.query.distance.PrimitiveDistanceSimilarityQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.distance.similarityfunction.PrimitiveSimilarityFunction;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
/**
* Provides an experimental KernelDistanceFunction for NumberVectors. Currently
* only supports 2D data and x1^2 ~ x2 correlations.
*
* @author Simon Paradies
*/
public class FooKernelFunction extends AbstractPrimitiveDistanceFunction<NumberVector<?, ?>, DoubleDistance> implements PrimitiveSimilarityFunction<NumberVector<?, ?>, DoubleDistance> {
/**
* The default max_degree.
*/
public static final int DEFAULT_MAX_DEGREE = 2;
/**
* Parameter for the maximum degree
*/
public static final OptionID MAX_DEGREE_ID = OptionID.getOrCreateOptionID("fookernel.max_degree", "The max degree of the" + FooKernelFunction.class.getSimpleName() + ". Default: " + DEFAULT_MAX_DEGREE);
/**
* Degree of the polynomial kernel function
*/
private int max_degree;
/**
* Constructor.
*
* @param max_degree Maximum degree-
*/
public FooKernelFunction(int max_degree) {
super();
this.max_degree = max_degree;
}
/**
* Provides an experimental kernel similarity between the given two vectors.
*
* @param o1 first vector
* @param o2 second vector
* @return the experimental kernel similarity between the given two vectors as
* an instance of {@link DoubleDistance DoubleDistance}.
*/
@Override
public DoubleDistance similarity(final NumberVector<?, ?> o1, final NumberVector<?, ?> o2) {
if(o1.getDimensionality() != o2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors\n first argument: " + o1.toString() + "\n second argument: " + o2.toString());
}
double sim = 0.0;
// iterate over differently powered dimensions
for(int degree = 1; degree <= max_degree; degree++) {
sim += Math.pow(o1.doubleValue(degree) * o2.doubleValue(degree), degree);
}
return new DoubleDistance(sim);
}
@Override
public DoubleDistance distance(final NumberVector<?, ?> fv1, final NumberVector<?, ?> fv2) {
return new DoubleDistance(Math.sqrt(similarity(fv1, fv1).doubleValue() + similarity(fv2, fv2).doubleValue() - 2 * similarity(fv1, fv2).doubleValue()));
}
@Override
public VectorFieldTypeInformation<? super NumberVector<?, ?>> getInputTypeRestriction() {
return TypeUtil.NUMBER_VECTOR_FIELD;
}
@Override
public DoubleDistance getDistanceFactory() {
return DoubleDistance.FACTORY;
}
@Override
public <T extends NumberVector<?, ?>> DistanceSimilarityQuery<T, DoubleDistance> instantiate(Relation<T> database) {
return new PrimitiveDistanceSimilarityQuery<T, DoubleDistance>(database, this, this);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
protected int max_degree = 0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final IntParameter max_degreeP = new IntParameter(MAX_DEGREE_ID, DEFAULT_MAX_DEGREE);
if(config.grab(max_degreeP)) {
max_degree = max_degreeP.getValue();
}
}
@Override
protected FooKernelFunction makeInstance() {
return new FooKernelFunction(max_degree);
}
}
}
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