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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) 2014
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.SparseNumberVector;
import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.Norm;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
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;
/**
* LP-Norm, optimized for {@link SparseNumberVector}s.
*
* @author Erich Schubert
*/
// TODO: implement SpatialDistanceFunction
public class SparseLPNormDistanceFunction extends AbstractPrimitiveDistanceFunction<SparseNumberVector> implements Norm<SparseNumberVector> {
/**
* P parameter and its inverse.
*/
private double p, invp;
/**
* Constructor.
*/
public SparseLPNormDistanceFunction(double p) {
super();
this.p = p;
this.invp = 1. / p;
}
@Override
public double distance(SparseNumberVector v1, SparseNumberVector v2) {
// Get the bit masks
double accu = 0.;
int i1 = v1.iter(), i2 = v2.iter();
while(v1.iterValid(i1) && v2.iterValid(i2)) {
final int d1 = v1.iterDim(i1), d2 = v2.iterDim(i2);
if(d1 < d2) {
// In first only
final double val = Math.abs(v1.iterDoubleValue(i1));
accu += Math.pow(val, p);
i1 = v1.iterAdvance(i1);
}
else if(d2 < d1) {
// In second only
final double val = Math.abs(v2.iterDoubleValue(i2));
accu += Math.pow(val, p);
i2 = v2.iterAdvance(i2);
}
else {
// Both vectors have a value.
final double val = Math.abs(v1.iterDoubleValue(i1) - v2.iterDoubleValue(i2));
accu += Math.pow(val, p);
i1 = v1.iterAdvance(i1);
i2 = v2.iterAdvance(i2);
}
}
while(v1.iterValid(i1)) {
// In first only
final double val = Math.abs(v1.iterDoubleValue(i1));
accu += Math.pow(val, p);
i1 = v1.iterAdvance(i1);
}
while(v2.iterValid(i2)) {
// In second only
final double val = Math.abs(v2.iterDoubleValue(i2));
accu += Math.pow(val, p);
i2 = v2.iterAdvance(i2);
}
return Math.pow(accu, invp);
}
@Override
public double norm(SparseNumberVector v1) {
double accu = 0.;
for(int it = v1.iter(); v1.iterValid(it); it = v1.iterAdvance(it)) {
final double val = Math.abs(v1.iterDoubleValue(it));
accu += Math.pow(val, p);
}
return Math.pow(accu, invp);
}
@Override
public SimpleTypeInformation<? super SparseNumberVector> getInputTypeRestriction() {
return TypeUtil.SPARSE_VECTOR_VARIABLE_LENGTH;
}
@Override
public boolean isMetric() {
return (p >= 1.);
}
/**
* Parameterizer
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
/**
* Value for p
*/
double p = 2.;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
DoubleParameter pP = new DoubleParameter(LPNormDistanceFunction.Parameterizer.P_ID);
pP.addConstraint(CommonConstraints.GREATER_THAN_ZERO_DOUBLE);
if(config.grab(pP)) {
p = pP.getValue();
}
}
@Override
protected SparseLPNormDistanceFunction makeInstance() {
if(p == 2.) {
return SparseEuclideanDistanceFunction.STATIC;
}
if(p == 1.) {
return SparseManhattanDistanceFunction.STATIC;
}
if(p == Double.POSITIVE_INFINITY) {
return SparseMaximumDistanceFunction.STATIC;
}
return new SparseLPNormDistanceFunction(p);
}
}
}
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