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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) 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 de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Manhattan distance function to compute the Manhattan distance for a pair of
* FeatureVectors.
*
* @author Arthur Zimek
*/
// TODO: add spatial!
public class ManhattanDistanceFunction extends LPNormDistanceFunction implements SpatialPrimitiveDoubleDistanceFunction<NumberVector<?>> {
/**
* The static instance to use.
*/
public static final ManhattanDistanceFunction STATIC = new ManhattanDistanceFunction();
/**
* Provides a Manhattan distance function that can compute the Manhattan
* distance (that is a DoubleDistance) for FeatureVectors.
*
* @deprecated Use static instance!
*/
@Deprecated
public ManhattanDistanceFunction() {
super(1.0);
}
/**
* Compute the Manhattan distance.
*
* @param v1 first vector
* @param v2 second vector
* @return Manhattan distance value
*/
@Override
public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) {
final int dim = v1.getDimensionality();
if (dim != v2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors" + "\n first argument: " + v1.toString() + "\n second argument: " + v2.toString());
}
double sum = 0;
for (int i = 0; i < dim; i++) {
sum += Math.abs(v1.doubleValue(i) - v2.doubleValue(i));
}
return sum;
}
/**
* Returns the Manhattan norm of the given vector.
*
* @param v the vector to compute the norm of
* @return the Manhattan norm of the given vector
*/
@Override
public double doubleNorm(NumberVector<?> v) {
final int dim = v.getDimensionality();
double sum = 0;
for (int i = 0; i < dim; i++) {
sum += Math.abs(v.doubleValue(i));
}
return sum;
}
private 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 sumDist = 0;
for (int d = 0; d < dim; d++) {
final double value = v.doubleValue(d);
final 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 = Math.abs(value - r);
sumDist += manhattanI;
}
return sumDist;
}
@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 sumDist = 0;
for (int d = 0; d < dim1; d++) {
final double m1, m2;
if (mbr1.getMax(d) < mbr2.getMin(d)) {
m1 = mbr2.getMin(d);
m2 = mbr1.getMax(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;
sumDist += manhattanI;
}
return sumDist;
}
@Override
public boolean isMetric() {
return true;
}
@Override
public String toString() {
return "ManhattanDistance";
}
@Override
public boolean equals(Object obj) {
if (obj == null) {
return false;
}
if (obj == this) {
return true;
}
if (this.getClass().equals(obj.getClass())) {
return true;
}
return super.equals(obj);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected ManhattanDistanceFunction makeInstance() {
return ManhattanDistanceFunction.STATIC;
}
}
}
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