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package de.lmu.ifi.dbs.elki.distance.distancefunction.set;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
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.BitVector;
import de.lmu.ifi.dbs.elki.data.FeatureVector;
import de.lmu.ifi.dbs.elki.data.NumberVector;
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.NumberVectorDistanceFunction;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Computes the Hamming distance of arbitrary vectors - i.e. counting, on how
* many places they differ.
*
* Reference:
* <p>
* R. W. Hamming<br />
* Error detecting and error correcting codes<br />
* Bell System technical journal, 29(2)
* </p>
*
* TODO: add a sparse (but not binary) optimized version?
*
* @author Erich Schubert
*/
@Reference(authors = "R. W. Hamming", //
title = "Error detecting and error correcting codes", //
booktitle = "Bell System technical journal, 29(2)", //
url = "http://dx.doi.org/10.1002/j.1538-7305.1950.tb00463.x")
public class HammingDistanceFunction extends AbstractSetDistanceFunction<FeatureVector<?>> implements NumberVectorDistanceFunction<FeatureVector<?>> {
/**
* Static instance.
*/
public static final HammingDistanceFunction STATIC = new HammingDistanceFunction();
@Override
public boolean isMetric() {
return true;
}
@Override
public double distance(FeatureVector<?> o1, FeatureVector<?> o2) {
if(o1 instanceof BitVector && o2 instanceof BitVector) {
return ((BitVector) o1).hammingDistance((BitVector) o2);
}
if(o1 instanceof NumberVector && o2 instanceof NumberVector) {
return hammingDistanceNumberVector((NumberVector) o1, (NumberVector) o2);
}
final int d1 = o1.getDimensionality(), d2 = o2.getDimensionality();
int differences = 0;
int d = 0;
for(; d < d1 && d < d2; d++) {
Object v1 = o1.getValue(d), v2 = o2.getValue(d);
final boolean n1 = isNull(v1), n2 = isNull(v2);
if(n1 && n2) {
continue;
}
if(v1 instanceof Double && Double.isNaN((Double) v1)) {
continue;
}
if(v2 instanceof Double && Double.isNaN((Double) v2)) {
continue;
}
// One must be set.
if(n1 || n2 || !v1.equals(v2)) {
++differences;
}
}
for(; d < d1; d++) {
Object v1 = o1.getValue(d);
if(!isNull(v1)) {
if(v1 instanceof Double && Double.isNaN((Double) v1)) {
continue;
}
++differences;
}
}
for(; d < d2; d++) {
Object v2 = o2.getValue(d);
if(!isNull(v2)) {
if(v2 instanceof Double && Double.isNaN((Double) v2)) {
continue;
}
++differences;
}
}
return differences;
}
@Override
public double distance(NumberVector o1, NumberVector o2) {
if(o1 instanceof BitVector && o2 instanceof BitVector) {
return ((BitVector) o1).hammingDistance((BitVector) o2);
}
return hammingDistanceNumberVector(o1, o2);
}
/**
* Version for number vectors.
*
* @param o1 First vector
* @param o2 Second vector
* @return hamming distance
*/
private double hammingDistanceNumberVector(NumberVector o1, NumberVector o2) {
final int d1 = o1.getDimensionality(), d2 = o2.getDimensionality();
int differences = 0;
int d = 0;
for(; d < d1 && d < d2; d++) {
double v1 = o1.doubleValue(d), v2 = o2.doubleValue(d);
if(v1 != v1 || v2 != v2) { /* NaN */
continue;
}
if(v1 != v2) {
++differences;
}
}
for(; d < d1; d++) {
double v1 = o1.doubleValue(d);
if(v1 != 0. && v1 == v1 /* not NaN */) {
++differences;
}
}
for(; d < d2; d++) {
double v2 = o2.doubleValue(d);
if(v2 != 0. && v2 == v2 /* not NaN */) {
++differences;
}
}
return differences;
}
@Override
public SimpleTypeInformation<? super FeatureVector<?>> getInputTypeRestriction() {
return TypeUtil.FEATURE_VECTORS;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected HammingDistanceFunction makeInstance() {
return STATIC;
}
}
}
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