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.PrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.distance.similarityfunction.AbstractPrimitiveSimilarityFunction;
/**
* Provides a linear Kernel function that computes a similarity between the two
* feature vectors V1 and V2 defined by V1^T*V2.
*
* @author Simon Paradies
* @param <O> vector type
*/
public class LinearKernelFunction<O extends NumberVector<?, ?>> extends AbstractPrimitiveSimilarityFunction<O, DoubleDistance> implements PrimitiveDistanceFunction<O, DoubleDistance> {
/**
* Provides a linear Kernel function that computes a similarity between the
* two vectors V1 and V2 defined by V1^T*V2.
*/
public LinearKernelFunction() {
super();
}
/**
* Provides a linear Kernel function that computes a similarity between the
* two feature vectors V1 and V2 definded by V1^T*V2
*
* @param o1 first feature vector
* @param o2 second feature vector
* @return the linear kernel similarity between the given two vectors as an
* instance of {@link DoubleDistance DoubleDistance}.
*/
@Override
public DoubleDistance similarity(final O o1, final O o2) {
if(o1.getDimensionality() != o2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of Feature-Vectors" + "\n first argument: " + o1.toString() + "\n second argument: " + o2.toString());
}
double sim = 0;
for(int i = 1; i <= o1.getDimensionality(); i++) {
sim += o1.doubleValue(i) * o2.doubleValue(i);
}
return new DoubleDistance(sim);
}
@Override
public DoubleDistance distance(final O fv1, final O fv2) {
return new DoubleDistance(Math.sqrt(similarity(fv1, fv1).doubleValue() + similarity(fv2, fv2).doubleValue() - 2 * similarity(fv1, fv2).doubleValue()));
}
@Override
public VectorFieldTypeInformation<? super O> getInputTypeRestriction() {
return TypeUtil.NUMBER_VECTOR_FIELD;
}
@Override
public DoubleDistance getDistanceFactory() {
return DoubleDistance.FACTORY;
}
@Override
public boolean isMetric() {
return false;
}
@Override
public <T extends O> DistanceSimilarityQuery<T, DoubleDistance> instantiate(Relation<T> database) {
return new PrimitiveDistanceSimilarityQuery<T, DoubleDistance>(database, this, this);
}
}