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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) 2011
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.ids.DBID;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractIndexBasedDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.PreferenceVectorBasedCorrelationDistance;
import de.lmu.ifi.dbs.elki.index.IndexFactory;
import de.lmu.ifi.dbs.elki.index.preprocessed.preference.PreferenceVectorIndex;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterEqualConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
/**
* Abstract super class for all preference vector based correlation distance
* functions.
*
* @author Arthur Zimek
* @param <V> the type of NumberVector to compute the distances in between
* @param <P> the type of Preprocessor used
*/
public abstract class AbstractPreferenceVectorBasedCorrelationDistanceFunction<V extends NumberVector<?, ?>, P extends PreferenceVectorIndex<V>> extends AbstractIndexBasedDistanceFunction<V, P, PreferenceVectorBasedCorrelationDistance> {
/**
* Parameter to specify the maximum distance between two vectors with equal
* preference vectors before considering them as parallel, must be a double
* equal to or greater than 0.
* <p>
* Default value: {@code 0.001}
* </p>
* <p>
* Key: {@code -pvbasedcorrelationdf.epsilon}
* </p>
*/
public static final OptionID EPSILON_ID = OptionID.getOrCreateOptionID("distancefunction.epsilon", "The maximum distance between two vectors with equal preference vectors before considering them as parallel.");
/**
* Holds the value of {@link #EPSILON_ID}.
*/
private double epsilon;
/**
* Constructor.
*
* @param indexFactory Index factory
* @param epsilon Epsilon value
*/
public AbstractPreferenceVectorBasedCorrelationDistanceFunction(IndexFactory<V, P> indexFactory, double epsilon) {
super(indexFactory);
this.epsilon = epsilon;
}
@Override
public PreferenceVectorBasedCorrelationDistance getDistanceFactory() {
return PreferenceVectorBasedCorrelationDistance.FACTORY;
}
/**
* Returns epsilon.
*
* @return epsilon
*/
public double getEpsilon() {
return epsilon;
}
@Override
public boolean equals(Object obj) {
if(obj == null) {
return false;
}
if(!this.getClass().equals(obj.getClass())) {
return false;
}
AbstractPreferenceVectorBasedCorrelationDistanceFunction<?, ?> other = (AbstractPreferenceVectorBasedCorrelationDistanceFunction<?, ?>) obj;
return (this.indexFactory.equals(other.indexFactory)) && this.epsilon == other.epsilon;
}
/**
* Instance to compute the distances on an actual database.
*
* @author Erich Schubert
*/
abstract public static class Instance<V extends NumberVector<?, ?>, P extends PreferenceVectorIndex<V>> extends AbstractIndexBasedDistanceFunction.Instance<V, P, PreferenceVectorBasedCorrelationDistance, AbstractPreferenceVectorBasedCorrelationDistanceFunction<? super V, ?>> {
/**
* The epsilon value
*/
final double epsilon;
/**
* Constructor.
*
* @param database Database
* @param preprocessor Preprocessor
* @param epsilon Epsilon
* @param distanceFunction parent distance function
*/
public Instance(Relation<V> database, P preprocessor, double epsilon, AbstractPreferenceVectorBasedCorrelationDistanceFunction<? super V, ?> distanceFunction) {
super(database, preprocessor, distanceFunction);
this.epsilon = epsilon;
}
@Override
public PreferenceVectorBasedCorrelationDistance distance(DBID id1, DBID id2) {
BitSet preferenceVector1 = index.getPreferenceVector(id1);
BitSet preferenceVector2 = index.getPreferenceVector(id2);
V v1 = relation.get(id1);
V v2 = relation.get(id2);
return correlationDistance(v1, v2, preferenceVector1, preferenceVector2);
}
/**
* 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
*/
public abstract PreferenceVectorBasedCorrelationDistance correlationDistance(V v1, V v2, BitSet pv1, BitSet pv2);
/**
* Computes the weighted distance between the two specified vectors
* according to the given preference vector.
*
* @param v1 the first vector
* @param v2 the second vector
* @param weightVector the preference vector
* @return the weighted distance between the two specified vectors according
* to the given preference vector
*/
public double weightedDistance(V v1, V v2, BitSet weightVector) {
if(v1.getDimensionality() != v2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors\n first argument: " + v1.toString() + "\n second argument: " + v2.toString());
}
double sqrDist = 0;
for(int i = 1; i <= v1.getDimensionality(); i++) {
if(weightVector.get(i - 1)) {
double manhattanI = v1.doubleValue(i) - v2.doubleValue(i);
sqrDist += manhattanI * manhattanI;
}
}
return Math.sqrt(sqrDist);
}
/**
* Computes the weighted distance between the two specified vectors
* according to the given preference vector.
*
* @param id1 the id of the first vector
* @param id2 the id of the second vector
* @param weightVector the preference vector
* @return the weighted distance between the two specified vectors according
* to the given preference vector
*/
public double weightedDistance(DBID id1, DBID id2, BitSet weightVector) {
return weightedDistance(relation.get(id1), relation.get(id2), weightVector);
}
/**
* Computes the weighted distance between the two specified data vectors
* according to their preference vectors.
*
* @param id1 the id of the first vector
* @param id2 the id of the second vector
* @return the weighted distance between the two specified vectors according
* to the preference vector of the first data vector
*/
public double weightedPrefereneceVectorDistance(DBID id1, DBID id2) {
final V v1 = relation.get(id1);
final V v2 = relation.get(id2);
double d1 = weightedDistance(v1, v2, index.getPreferenceVector(id1));
double d2 = weightedDistance(v2, v1, index.getPreferenceVector(id2));
return Math.max(d1, d2);
}
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static abstract class Parameterizer<F extends IndexFactory<?, ?>> extends AbstractIndexBasedDistanceFunction.Parameterizer<F> {
protected double epsilon = 0.0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
configEpsilon(config);
}
protected void configEpsilon(Parameterization config) {
final DoubleParameter epsilonP = new DoubleParameter(EPSILON_ID, new GreaterEqualConstraint(0), 0.001);
if(config.grab(epsilonP)) {
epsilon = epsilonP.getValue();
}
}
}
}
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