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.data.spatial.SpatialComparable;
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.distance.SpatialPrimitiveDistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.SpatialPrimitiveDoubleDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
/**
* Provides a distance function that computes the Euclidean distance between
* feature vectors only in specified dimensions.
*
* @author Elke Achtert
*/
public class DimensionsSelectingEuclideanDistanceFunction extends AbstractDimensionsSelectingDoubleDistanceFunction<NumberVector<?, ?>> implements SpatialPrimitiveDoubleDistanceFunction<NumberVector<?, ?>> {
/**
* Constructor.
*
* @param dimensions Selected dimensions
*/
public DimensionsSelectingEuclideanDistanceFunction(BitSet dimensions) {
super(dimensions);
}
/**
* Provides the Euclidean distance between two given feature vectors in the
* selected dimensions.
*
* @param v1 first feature vector
* @param v2 second feature vector
* @return the Euclidean distance between two given feature vectors in the
* selected dimensions
*/
@Override
public double doubleDistance(NumberVector<?, ?> v1, NumberVector<?, ?> v2) {
if(v1.getDimensionality() != v2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors\n " + "first argument: " + v1 + "\n " + "second argument: " + v2);
}
if(v1.getDimensionality() < getSelectedDimensions().cardinality()) {
throw new IllegalArgumentException("The dimensionality of the feature space " + "is not consistent with the specified dimensions " + "to be considered for distance computation.\n " + "dimensionality of the feature space: " + v1.getDimensionality() + "\n " + "specified dimensions: " + getSelectedDimensions());
}
double sqrDist = 0;
for(int d = getSelectedDimensions().nextSetBit(0); d >= 0; d = getSelectedDimensions().nextSetBit(d + 1)) {
double manhattanI = v1.doubleValue(d + 1) - v2.doubleValue(d + 1);
sqrDist += manhattanI * manhattanI;
}
return Math.sqrt(sqrDist);
}
protected double doubleMinDistObject(SpatialComparable mbr, NumberVector<?, ?> v) {
if(mbr.getDimensionality() != v.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of objects\n " + "first argument: " + mbr.toString() + "\n " + "second argument: " + v.toString());
}
if(v.getDimensionality() < getSelectedDimensions().size()) {
throw new IllegalArgumentException("The dimensionality of the feature space " + "is not consistent with the specified dimensions " + "to be considered for distance computation.\n " + "dimensionality of the feature space: " + v.getDimensionality() + "\n " + "specified dimensions: " + getSelectedDimensions());
}
double sqrDist = 0;
for(int d = getSelectedDimensions().nextSetBit(0); d >= 0; d = getSelectedDimensions().nextSetBit(d + 1)) {
double value = v.doubleValue(d);
double r;
if(value < mbr.getMin(d)) {
r = mbr.getMin(d);
}
else if(value > mbr.getMax(d)) {
r = mbr.getMax(d);
}
else {
r = value;
}
double manhattanI = value - r;
sqrDist += manhattanI * manhattanI;
}
return Math.sqrt(sqrDist);
}
@Override
public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
if(mbr1.getDimensionality() != mbr2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of objects\n " + "first argument: " + mbr1.toString() + "\n " + "second argument: " + mbr2.toString());
}
if(mbr1.getDimensionality() < getSelectedDimensions().size()) {
throw new IllegalArgumentException("The dimensionality of the feature space " + "is not consistent with the specified dimensions " + "to be considered for distance computation.\n " + "dimensionality of the feature space: " + mbr1.getDimensionality() + "\n " + "specified dimensions: " + getSelectedDimensions());
}
double sqrDist = 0;
for(int d = getSelectedDimensions().nextSetBit(0); d >= 0; d = getSelectedDimensions().nextSetBit(d + 1)) {
final double m1, m2;
if(mbr1.getMax(d) < mbr2.getMin(d)) {
m1 = mbr1.getMax(d);
m2 = mbr2.getMin(d);
}
else if(mbr1.getMin(d) > mbr2.getMax(d)) {
m1 = mbr1.getMin(d);
m2 = mbr2.getMax(d);
}
else { // The mbrs intersect!
continue;
}
double manhattanI = m1 - m2;
sqrDist += manhattanI * manhattanI;
}
return Math.sqrt(sqrDist);
}
@Override
public double doubleCenterDistance(SpatialComparable mbr1, SpatialComparable mbr2) {
if(mbr1.getDimensionality() != mbr2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of objects\n " + "first argument: " + mbr1.toString() + "\n " + "second argument: " + mbr2.toString());
}
if(mbr1.getDimensionality() < getSelectedDimensions().size()) {
throw new IllegalArgumentException("The dimensionality of the feature space " + "is not consistent with the specified dimensions " + "to be considered for distance computation.\n " + "dimensionality of the feature space: " + mbr1.getDimensionality() + "\n " + "specified dimensions: " + getSelectedDimensions());
}
double sqrDist = 0;
for(int d = getSelectedDimensions().nextSetBit(0); d >= 0; d = getSelectedDimensions().nextSetBit(d + 1)) {
double c1 = (mbr1.getMin(d) + mbr1.getMax(d)) / 2;
double c2 = (mbr2.getMin(d) + mbr2.getMax(d)) / 2;
double manhattanI = c1 - c2;
sqrDist += manhattanI * manhattanI;
}
return Math.sqrt(sqrDist);
}
@Override
public DoubleDistance minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleMinDist(mbr1, mbr2));
}
@Override
public DoubleDistance centerDistance(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleCenterDistance(mbr1, mbr2));
}
@Override
public VectorFieldTypeInformation<? super NumberVector<?, ?>> getInputTypeRestriction() {
return TypeUtil.NUMBER_VECTOR_FIELD;
}
@Override
public boolean isMetric() {
return true;
}
@Override
public <T extends NumberVector<?, ?>> SpatialPrimitiveDistanceQuery<T, DoubleDistance> instantiate(Relation<T> database) {
return new SpatialPrimitiveDistanceQuery<T, DoubleDistance>(database, this);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractDimensionsSelectingDoubleDistanceFunction.Parameterizer {
@Override
protected DimensionsSelectingEuclideanDistanceFunction makeInstance() {
return new DimensionsSelectingEuclideanDistanceFunction(dimensions);
}
}
}