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package de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski;
/*
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 de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
import de.lmu.ifi.dbs.elki.utilities.Alias;
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
*/
@Alias({ "taxicab", "cityblock", "l1", "ManhattanDistanceFunction", "de.lmu.ifi.dbs.elki.distance.distancefunction.ManhattanDistanceFunction" })
public class ManhattanDistanceFunction extends LPIntegerNormDistanceFunction {
/**
* 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);
}
private final double doublePreDistance(NumberVector<?> v1, NumberVector<?> v2, int start, int end, double agg) {
for (int d = start; d < end; d++) {
final double xd = v1.doubleValue(d), yd = v2.doubleValue(d);
final double delta = (xd >= yd) ? xd - yd : yd - xd;
agg += delta;
}
return agg;
}
private final double doublePreDistanceVM(NumberVector<?> v, SpatialComparable mbr, int start, int end, double agg) {
for (int d = start; d < end; d++) {
final double value = v.doubleValue(d), min = mbr.getMin(d);
double delta = min - value;
if (delta < 0.) {
delta = value - mbr.getMax(d);
}
if (delta > 0.) {
agg += delta;
}
}
return agg;
}
private final double doublePreDistanceMBR(SpatialComparable mbr1, SpatialComparable mbr2, int start, int end, double agg) {
for (int d = start; d < end; d++) {
double delta = mbr2.getMin(d) - mbr1.getMax(d);
if (delta < 0.) {
delta = mbr1.getMin(d) - mbr2.getMax(d);
}
if (delta > 0.) {
agg += delta;
}
}
return agg;
}
private final double doublePreNorm(NumberVector<?> v, int start, int end, double agg) {
for (int d = start; d < end; d++) {
final double xd = v.doubleValue(d);
final double delta = (xd >= 0.) ? xd : -xd;
agg += delta;
}
return agg;
}
private final double doublePreNormMBR(SpatialComparable mbr, int start, int end, double agg) {
for (int d = start; d < end; d++) {
double delta = mbr.getMin(d);
if (delta < 0.) {
delta = -mbr.getMax(d);
}
if (delta > 0.) {
agg += delta;
}
}
return agg;
}
@Override
public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) {
final int dim1 = v1.getDimensionality(), dim2 = v2.getDimensionality();
final int mindim = (dim1 < dim2) ? dim1 : dim2;
double agg = doublePreDistance(v1, v2, 0, mindim, 0.);
if (dim1 > mindim) {
agg = doublePreNorm(v1, mindim, dim1, agg);
} else if (dim2 > mindim) {
agg = doublePreNorm(v2, mindim, dim2, agg);
}
return agg;
}
@Override
public double doubleNorm(NumberVector<?> v) {
return doublePreNorm(v, 0, v.getDimensionality(), 0.);
}
@Override
public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
final int dim1 = mbr1.getDimensionality(), dim2 = mbr2.getDimensionality();
final int mindim = (dim1 < dim2) ? dim1 : dim2;
final NumberVector<?> v1 = (mbr1 instanceof NumberVector) ? (NumberVector<?>) mbr1 : null;
final NumberVector<?> v2 = (mbr2 instanceof NumberVector) ? (NumberVector<?>) mbr2 : null;
double agg = 0.;
if (v1 != null) {
if (v2 != null) {
agg = doublePreDistance(v1, v2, 0, mindim, agg);
} else {
agg = doublePreDistanceVM(v1, mbr2, 0, mindim, agg);
}
} else {
if (v2 != null) {
agg = doublePreDistanceVM(v2, mbr1, 0, mindim, agg);
} else {
agg = doublePreDistanceMBR(mbr1, mbr2, 0, mindim, agg);
}
}
// first object has more dimensions.
if (dim1 > mindim) {
if (v1 != null) {
agg = doublePreNorm(v1, mindim, dim1, agg);
} else {
agg = doublePreNormMBR(v1, mindim, dim1, agg);
}
}
// second object has more dimensions.
if (dim2 > mindim) {
if (v2 != null) {
agg = doublePreNorm(v2, mindim, dim2, agg);
} else {
agg = doublePreNormMBR(mbr2, mindim, dim2, agg);
}
}
return agg;
}
@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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