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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.distance.distancefunction.AbstractSpatialDoubleDistanceNorm;
import de.lmu.ifi.dbs.elki.utilities.Alias;
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
*/
@Alias({ "squaredeuclidean", "de.lmu.ifi.dbs.elki.distance.distancefunction.SquaredEuclideanDistanceFunction" })
public class SquaredEuclideanDistanceFunction extends AbstractSpatialDoubleDistanceNorm {
/**
* 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();
}
@Override
public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) {
final int dim = dimensionality(v1, v2);
double agg = 0.;
for (int d = 0; d < dim; d++) {
final double delta = v1.doubleValue(d) - v2.doubleValue(d);
agg += delta * delta;
}
return agg;
}
@Override
public double doubleNorm(NumberVector<?> v) {
final int dim = v.getDimensionality();
double agg = 0.;
for (int d = 0; d < dim; d++) {
final double val = v.doubleValue(d);
agg += val * val;
}
return agg;
}
protected double doubleMinDistObject(NumberVector<?> v, SpatialComparable mbr) {
final int dim = dimensionality(mbr, v);
double agg = 0.;
for (int d = 0; d < dim; d++) {
final double value = v.doubleValue(d), min = mbr.getMin(d);
final double diff;
if (value < min) {
diff = min - value;
} else {
final double max = mbr.getMax(d);
if (value > max) {
diff = value - max;
} else {
continue;
}
}
agg += diff * diff;
}
return agg;
}
@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((NumberVector<?>) mbr1, mbr2);
}
} else if (mbr2 instanceof NumberVector) {
return doubleMinDistObject((NumberVector<?>) mbr2, mbr1);
}
final int dim = dimensionality(mbr1, mbr2);
double agg = 0.;
for (int d = 0; d < dim; d++) {
final double diff;
final double d1 = mbr2.getMin(d) - mbr1.getMax(d);
if (d1 > 0.) {
diff = d1;
} else {
final double d2 = mbr1.getMin(d) - mbr2.getMax(d);
if (d2 > 0.) {
diff = d2;
} else {
continue;
}
}
agg += diff * diff;
}
return agg;
}
@Override
public boolean isMetric() {
return false;
}
@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;
}
}
}
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