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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.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;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
/**
* Provides a LP-Norm for FeatureVectors.
*
* @author Arthur Zimek
*
* @apiviz.landmark
*/
public class LPNormDistanceFunction extends AbstractVectorDoubleDistanceNorm implements SpatialPrimitiveDoubleDistanceFunction<NumberVector<?>> {
/**
* OptionID for the "p" parameter
*/
public static final OptionID P_ID = new OptionID("lpnorm.p", "the degree of the L-P-Norm (positive number)");
/**
* Keeps the currently set p.
*/
private double p;
/**
* Constructor, internal version.
*
* @param p Parameter p
*/
public LPNormDistanceFunction(double p) {
super();
this.p = p;
}
/**
* Returns the distance between the specified FeatureVectors as a LP-Norm for
* the currently set p.
*
* @param v1 first FeatureVector
* @param v2 second FeatureVector
* @return the distance between the specified FeatureVectors as a LP-Norm for
* the currently set p
*/
@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());
}
double sqrDist = 0;
for(int i = 0; i < dim1; i++) {
final double delta = Math.abs(v1.doubleValue(i) - v2.doubleValue(i));
sqrDist += Math.pow(delta, p);
}
return Math.pow(sqrDist, 1.0 / p);
}
@Override
public double doubleNorm(NumberVector<?> v) {
final int dim = v.getDimensionality();
double sqrDist = 0;
for(int i = 0; i < dim; i++) {
final double delta = v.doubleValue(i);
sqrDist += Math.pow(delta, p);
}
return Math.pow(sqrDist, 1.0 / p);
}
/**
* Get the functions p parameter.
*
* @return p
*/
public double getP() {
return p;
}
@Override
public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
// Optimization for the simplest case
if(mbr1 instanceof NumberVector) {
if(mbr2 instanceof NumberVector) {
return doubleDistance((NumberVector<?>) mbr1, (NumberVector<?>) mbr2);
}
}
// TODO: optimize for more simpler cases: obj vs. rect?
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 += Math.pow(manhattanI, p);
}
return Math.pow(sumDist, 1.0 / p);
}
@Override
public DoubleDistance minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleMinDist(mbr1, mbr2));
}
@Override
public boolean isMetric() {
return (p >= 1);
}
@Override
public String toString() {
return "L_" + p + " Norm";
}
@Override
public boolean equals(Object obj) {
if(obj == null) {
return false;
}
if(obj instanceof LPNormDistanceFunction) {
return this.p == ((LPNormDistanceFunction) obj).p;
}
return false;
}
@Override
public <T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T, DoubleDistance> instantiate(Relation<T> relation) {
return new SpatialPrimitiveDistanceQuery<T, DoubleDistance>(relation, this);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
/**
* The value of p.
*/
protected double p = 0.0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final DoubleParameter paramP = new DoubleParameter(P_ID);
paramP.addConstraint(new GreaterConstraint(0));
if(config.grab(paramP)) {
p = paramP.getValue();
}
}
@Override
protected LPNormDistanceFunction makeInstance() {
if(p == 1.0) {
return ManhattanDistanceFunction.STATIC;
}
if(p == 2.0) {
return EuclideanDistanceFunction.STATIC;
}
if(p == Double.POSITIVE_INFINITY) {
return MaximumDistanceFunction.STATIC;
}
return new LPNormDistanceFunction(p);
}
}
}
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