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package de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2014
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.Cluster;
import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
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.similarityfunction.AbstractPrimitiveSimilarityFunction;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Measure the similarity of clusters via the intersection size.
*
* @author Erich Schubert
*/
public class ClusterIntersectionSimilarityFunction extends AbstractPrimitiveSimilarityFunction<Cluster<?>> implements PrimitiveDistanceFunction<Cluster<?>> {
/**
* Static instance.
*/
public static final ClusterIntersectionSimilarityFunction STATIC = new ClusterIntersectionSimilarityFunction();
/**
* Constructor - use the static instance {@link #STATIC}!
*/
public ClusterIntersectionSimilarityFunction() {
super();
}
@Override
public double similarity(Cluster<?> o1, Cluster<?> o2) {
return DBIDUtil.intersectionSize(o1.getIDs(), o2.getIDs());
}
@Override
public double distance(Cluster<?> o1, Cluster<?> o2) {
int i = DBIDUtil.intersectionSize(o1.getIDs(), o2.getIDs());
return Math.max(o1.size(), o2.size()) - i;
}
@Override
public boolean isMetric() {
return false;
}
@Override
public <T extends Cluster<?>> DistanceSimilarityQuery<T> instantiate(Relation<T> relation) {
return new PrimitiveDistanceSimilarityQuery<>(relation, this, this);
}
@Override
public SimpleTypeInformation<? super Cluster<?>> getInputTypeRestriction() {
return new SimpleTypeInformation<>(Cluster.class);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected ClusterIntersectionSimilarityFunction makeInstance() {
return STATIC;
}
}
}
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