package de.lmu.ifi.dbs.elki.distance.distancefunction;
/*
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 de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
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.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Provides the squared Euclidean distance for FeatureVectors. This results in
* the same rankings, but saves computing the square root as often.
*
* @author Arthur Zimek
*/
public class SquaredEuclideanDistanceFunction extends AbstractVectorDoubleDistanceFunction implements SpatialPrimitiveDoubleDistanceFunction<NumberVector<?, ?>> {
/**
* Static instance. Use this!
*/
public static final SquaredEuclideanDistanceFunction STATIC = new SquaredEuclideanDistanceFunction();
/**
* Provides a Euclidean distance function that can compute the Euclidean
* distance (that is a DoubleDistance) for FeatureVectors.
*
* @deprecated Use static instance!
*/
@Deprecated
public SquaredEuclideanDistanceFunction() {
super();
}
/**
* Provides the squared Euclidean distance between the given two vectors.
*
* @return the squared Euclidean distance between the given two vectors as raw
* double value
*/
@Override
public double doubleDistance(NumberVector<?, ?> v1, NumberVector<?, ?> v2) {
final int dim1 = v1.getDimensionality();
if(dim1 != v2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors" + "\n first argument: " + v1.toString() + "\n second argument: " + v2.toString() + "\n" + v1.getDimensionality() + "!=" + v2.getDimensionality());
}
double sqrDist = 0;
for(int i = 1; i <= dim1; i++) {
final double delta = v1.doubleValue(i) - v2.doubleValue(i);
sqrDist += delta * delta;
}
return sqrDist;
}
protected double doubleMinDistObject(SpatialComparable mbr, NumberVector<?, ?> v) {
final int dim = mbr.getDimensionality();
if(dim != v.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of objects\n " + "first argument: " + mbr.toString() + "\n " + "second argument: " + v.toString() + "\n" + dim + "!=" + v.getDimensionality());
}
double sqrDist = 0;
for(int d = 1; d <= dim; d++) {
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;
}
final double manhattanI = value - r;
sqrDist += manhattanI * manhattanI;
}
return sqrDist;
}
@Override
public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
// Some optimizations for simpler cases.
if(mbr1 instanceof NumberVector) {
if(mbr2 instanceof NumberVector) {
return doubleDistance((NumberVector<?, ?>) mbr1, (NumberVector<?, ?>) mbr2);
}
else {
return doubleMinDistObject(mbr2, (NumberVector<?, ?>) mbr1);
}
}
else if(mbr2 instanceof NumberVector) {
return doubleMinDistObject(mbr1, (NumberVector<?, ?>) mbr2);
}
final int dim1 = mbr1.getDimensionality();
if(dim1 != mbr2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of objects\n " + "first argument: " + mbr1.toString() + "\n " + "second argument: " + mbr2.toString());
}
double sqrDist = 0;
for(int d = 1; d <= dim1; d++) {
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;
}
final double manhattanI = m1 - m2;
sqrDist += manhattanI * manhattanI;
}
return sqrDist;
}
@Override
public double doubleCenterDistance(SpatialComparable mbr1, SpatialComparable mbr2) {
final int dim1 = mbr1.getDimensionality();
if(dim1 != mbr2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of objects\n " + "first argument: " + mbr1.toString() + "\n " + "second argument: " + mbr2.toString());
}
double sqrDist = 0;
for(int d = 1; d <= dim1; d++) {
final double c1 = (mbr1.getMin(d) + mbr1.getMax(d)) / 2;
final double c2 = (mbr2.getMin(d) + mbr2.getMax(d)) / 2;
final double manhattanI = c1 - c2;
sqrDist += manhattanI * manhattanI;
}
return sqrDist;
}
@Override
public DoubleDistance centerDistance(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleCenterDistance(mbr1, mbr2));
}
@Override
public DoubleDistance minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleMinDist(mbr1, mbr2));
}
@Override
public boolean isMetric() {
return false;
}
@Override
public <T extends NumberVector<?, ?>> SpatialPrimitiveDistanceQuery<T, DoubleDistance> instantiate(Relation<T> relation) {
return new SpatialPrimitiveDistanceQuery<T, DoubleDistance>(relation, this);
}
@Override
public String toString() {
return "SquaredEuclideanDistance";
}
@Override
public boolean equals(Object obj) {
if(obj == null) {
return false;
}
if(obj == this) {
return true;
}
return this.getClass().equals(obj.getClass());
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected SquaredEuclideanDistanceFunction makeInstance() {
return SquaredEuclideanDistanceFunction.STATIC;
}
}
}