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package de.lmu.ifi.dbs.elki.distance.distancefunction.subspace;
/*
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 java.util.BitSet;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancevalue.PreferenceVectorBasedCorrelationDistance;
import de.lmu.ifi.dbs.elki.index.preprocessed.preference.DiSHPreferenceVectorIndex;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
import de.lmu.ifi.dbs.elki.utilities.DatabaseUtil;
import de.lmu.ifi.dbs.elki.utilities.FormatUtil;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
/**
* Distance function used in the DiSH algorithm.
*
* @author Elke Achtert
*
* @apiviz.has Instance
*/
public class DiSHDistanceFunction extends AbstractPreferenceVectorBasedCorrelationDistanceFunction<NumberVector<?, ?>, DiSHPreferenceVectorIndex<NumberVector<?, ?>>> {
/**
* Logger for debug.
*/
static Logging logger = Logging.getLogger(DiSHDistanceFunction.class);
/**
* Constructor.
*
* @param indexFactory
* @param epsilon
*/
public DiSHDistanceFunction(DiSHPreferenceVectorIndex.Factory<NumberVector<?, ?>> indexFactory, double epsilon) {
super(indexFactory, epsilon);
}
@Override
public <T extends NumberVector<?, ?>> Instance<T> instantiate(Relation<T> database) {
// We can't really avoid these warnings, due to a limitation in Java
// Generics (AFAICT)
@SuppressWarnings("unchecked")
DiSHPreferenceVectorIndex<T> indexinst = (DiSHPreferenceVectorIndex<T>) indexFactory.instantiate((Relation<NumberVector<?, ?>>) database);
return new Instance<T>(database, indexinst, getEpsilon(), this);
}
/**
* Get the minpts value.
*
* @return the minpts parameter
*/
public int getMinpts() {
// TODO: get rid of this cast?
return ((DiSHPreferenceVectorIndex.Factory<NumberVector<?, ?>>) indexFactory).getMinpts();
}
/**
* The actual instance bound to a particular database.
*
* @author Erich Schubert
*/
public static class Instance<V extends NumberVector<?, ?>> extends AbstractPreferenceVectorBasedCorrelationDistanceFunction.Instance<V, DiSHPreferenceVectorIndex<V>> {
/**
* Constructor.
*
* @param database Database
* @param index Preprocessed index
* @param epsilon Epsilon
* @param distanceFunction parent distance function
*/
public Instance(Relation<V> database, DiSHPreferenceVectorIndex<V> index, double epsilon, DiSHDistanceFunction distanceFunction) {
super(database, index, epsilon, distanceFunction);
}
/**
* Computes the correlation distance between the two specified vectors
* according to the specified preference vectors.
*
* @param v1 first vector
* @param v2 second vector
* @param pv1 the first preference vector
* @param pv2 the second preference vector
* @return the correlation distance between the two specified vectors
*/
@Override
public PreferenceVectorBasedCorrelationDistance correlationDistance(V v1, V v2, BitSet pv1, BitSet pv2) {
BitSet commonPreferenceVector = (BitSet) pv1.clone();
commonPreferenceVector.and(pv2);
int dim = v1.getDimensionality();
// number of zero values in commonPreferenceVector
Integer subspaceDim = dim - commonPreferenceVector.cardinality();
// special case: v1 and v2 are in parallel subspaces
if(commonPreferenceVector.equals(pv1) || commonPreferenceVector.equals(pv2)) {
double d = weightedDistance(v1, v2, commonPreferenceVector);
if(d > 2 * epsilon) {
subspaceDim++;
if(logger.isDebugging()) {
//Representation<String> rep = database.getObjectLabelQuery();
StringBuffer msg = new StringBuffer();
msg.append("d ").append(d);
//msg.append("\nv1 ").append(rep.get(v1.getID()));
//msg.append("\nv2 ").append(rep.get(v2.getID()));
msg.append("\nsubspaceDim ").append(subspaceDim);
msg.append("\ncommon pv ").append(FormatUtil.format(dim, commonPreferenceVector));
logger.debugFine(msg.toString());
}
}
}
// flip commonPreferenceVector for distance computation in common subspace
BitSet inverseCommonPreferenceVector = (BitSet) commonPreferenceVector.clone();
inverseCommonPreferenceVector.flip(0, dim);
return new PreferenceVectorBasedCorrelationDistance(DatabaseUtil.dimensionality(relation), subspaceDim, weightedDistance(v1, v2, inverseCommonPreferenceVector), commonPreferenceVector);
}
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractPreferenceVectorBasedCorrelationDistanceFunction.Parameterizer<DiSHPreferenceVectorIndex.Factory<NumberVector<?, ?>>> {
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
Class<DiSHPreferenceVectorIndex.Factory<NumberVector<?, ?>>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DiSHPreferenceVectorIndex.Factory.class);
factory = config.tryInstantiate(cls);
}
@Override
protected DiSHDistanceFunction makeInstance() {
return new DiSHDistanceFunction(factory, epsilon);
}
}
}
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