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diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/AbstractPreferenceVectorBasedCorrelationDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/AbstractPreferenceVectorBasedCorrelationDistanceFunction.java
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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) 2013
- 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.ids.DBIDRef;
-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.CommonConstraints;
-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 = new OptionID("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(DBIDRef id1, DBIDRef 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 = 0; i < v1.getDimensionality(); i++) {
- if (weightVector.get(i)) {
- 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 abstract static 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, 0.001);
- epsilonP.addConstraint(CommonConstraints.GREATER_EQUAL_ZERO_DOUBLE);
- if (config.grab(epsilonP)) {
- epsilon = epsilonP.doubleValue();
- }
- }
- }
-}
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