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package de.lmu.ifi.dbs.elki.distance.distancefunction.subspace;
/*
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 java.util.BitSet;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.data.type.VectorFieldTypeInformation;
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.distancefunction.DoubleNorm;
import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.SpatialPrimitiveDoubleDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.LPNormDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
/**
* Provides a distance function that computes the Euclidean distance between
* feature vectors only in specified dimensions.
*
* @author Elke Achtert
*/
public class SubspaceLPNormDistanceFunction extends AbstractDimensionsSelectingDoubleDistanceFunction<NumberVector<?>> implements SpatialPrimitiveDoubleDistanceFunction<NumberVector<?>>, DoubleNorm<NumberVector<?>>, NumberVectorDistanceFunction<DoubleDistance> {
/**
* Value of p
*/
private double p;
/**
* Constructor.
*
* @param dimensions Selected dimensions
* @param p p value
*/
public SubspaceLPNormDistanceFunction(double p, BitSet dimensions) {
super(dimensions);
this.p = p;
}
/**
* Get the value of p.
*
* @return p
*/
public double getP() {
return p;
}
/**
* Provides the Euclidean distance between two given feature vectors in the
* selected dimensions.
*
* @param v1 first feature vector
* @param v2 second feature vector
* @return the Euclidean distance between two given feature vectors in the
* selected dimensions
*/
@Override
public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) {
if(v1.getDimensionality() != v2.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of FeatureVectors\n " + "first argument: " + v1 + "\n " + "second argument: " + v2);
}
double sqrDist = 0;
for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
double delta = Math.abs(v1.doubleValue(d) - v2.doubleValue(d));
sqrDist += Math.pow(delta, p);
}
return Math.pow(sqrDist, 1. / p);
}
protected double doubleMinDistObject(SpatialComparable mbr, NumberVector<?> v) {
if(mbr.getDimensionality() != v.getDimensionality()) {
throw new IllegalArgumentException("Different dimensionality of objects\n " + "first argument: " + mbr.toString() + "\n " + "second argument: " + v.toString());
}
double sqrDist = 0;
for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
final double delta;
final double value = v.doubleValue(d);
final double omin = mbr.getMin(d);
if(value < omin) {
delta = omin - value;
}
else {
final double omax = mbr.getMax(d);
if(value > omax) {
delta = value - omax;
}
else {
continue;
}
}
sqrDist += Math.pow(delta, p);
}
return Math.pow(sqrDist, 1. / p);
}
@Override
public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) {
if(mbr1.getDimensionality() != 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 = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
final double delta;
final double max1 = mbr1.getMax(d);
final double min2 = mbr2.getMin(d);
if(max1 < min2) {
delta = min2 - max1;
}
else {
final double min1 = mbr1.getMin(d);
final double max2 = mbr2.getMax(d);
if(min1 > max2) {
delta = min1 - max2;
}
else { // The mbrs intersect!
continue;
}
}
sqrDist += Math.pow(delta, p);
}
return Math.pow(sqrDist, 1. / p);
}
@Override
public DoubleDistance minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
return new DoubleDistance(doubleMinDist(mbr1, mbr2));
}
@Override
public DoubleDistance norm(NumberVector<?> obj) {
return new DoubleDistance(doubleNorm(obj));
}
@Override
public double doubleNorm(NumberVector<?> obj) {
double sqrDist = 0;
for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) {
double delta = Math.abs(obj.doubleValue(d));
sqrDist += Math.pow(delta, p);
}
return Math.pow(sqrDist, 1. / p);
}
@Override
public <T extends NumberVector<?>> SpatialPrimitiveDistanceQuery<T, DoubleDistance> instantiate(Relation<T> database) {
return new SpatialPrimitiveDistanceQuery<>(database, this);
}
@Override
public VectorFieldTypeInformation<? super NumberVector<?>> getInputTypeRestriction() {
return TypeUtil.NUMBER_VECTOR_FIELD;
}
@Override
public boolean isMetric() {
return true;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractDimensionsSelectingDoubleDistanceFunction.Parameterizer {
/**
* Value of p.
*/
private double p;
@Override
protected void makeOptions(Parameterization config) {
final DoubleParameter paramP = new DoubleParameter(LPNormDistanceFunction.P_ID);
paramP.addConstraint(CommonConstraints.GREATER_THAN_ZERO_DOUBLE);
if(config.grab(paramP)) {
p = paramP.getValue();
}
super.makeOptions(config);
}
@Override
protected SubspaceLPNormDistanceFunction makeInstance() {
if(p == 2.0) {
return new SubspaceEuclideanDistanceFunction(dimensions);
}
if(p == 1.0) {
return new SubspaceManhattanDistanceFunction(dimensions);
}
return new SubspaceLPNormDistanceFunction(p, dimensions);
}
}
}
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