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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) 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.NumberVector;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
import de.lmu.ifi.dbs.elki.utilities.Alias;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Bray-Curtis distance function / Sørensen–Dice coefficient for continuous
* spaces.
*
* Reference:
* <p>
* J. R. Bray and J. T. Curtis<br />
* An ordination of the upland forest communities of southern Wisconsin<br />
* Ecological monographs 27.4
* </p>
* Also:
* <p>
* T. Sørensen<br />
* A method of establishing groups of equal amplitude in plant sociology based
* on similarity of species and its application to analyses of the vegetation on
* Danish commons<br />
* Kongelige Danske Videnskabernes Selskab 5 (4)
* </p>
* and:
* <p>
* L. R. Dice<br />
* Measures of the Amount of Ecologic Association Between Species<br />
* Ecology 26 (3)
* </p>
*
*
* Note: we modified the usual definition of Bray-Curtis for use with negative
* values. In essence, this function is defined as:
*
* ManhattanDistance(v1, v2) / (ManhattanNorm(v1) + ManhattanNorm(v2))
*
* This obviously limits the usefulness of this distance function for cases
* where this kind of normalization is desired. In particular in low dimensional
* data it should be used with care.
*
* TODO: add a version optimized for sparse vectors / binary data.
*
* @author Erich Schubert
* @since 0.4.0
*/
@Alias({ "bray-curtis", "braycurtis", "sorensen", "dice", "sorensen-dice" })
@Reference(authors = "J. R. Bray and J. T. Curtis", title = "An ordination of the upland forest communities of southern Wisconsin", booktitle = "Ecological monographs 27.4", url = "http://dx.doi.org/10.2307/1942268")
public class BrayCurtisDistanceFunction extends AbstractSpatialDistanceFunction {
/**
* Static instance.
*/
public static final BrayCurtisDistanceFunction STATIC_CONTINUOUS = new BrayCurtisDistanceFunction();
/**
* Constructor.
*
* @deprecated Use {@link #STATIC_CONTINUOUS} instance instead.
*/
@Deprecated
public BrayCurtisDistanceFunction() {
super();
}
/**
* Dummy method, just to attach a second reference.
*/
@Reference(authors = "T. Sørensen", title = "A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons", booktitle = "Kongelige Danske Videnskabernes Selskab 5 (4)")
static void secondReference() {
// Empty, just to attach a second reference
};
/**
* Dummy method, just to attach a third reference.
*/
@Reference(authors = "L. R. Dice", title = "Measures of the Amount of Ecologic Association Between Species", booktitle = "Ecology 26 (3)")
static void thirdReference() {
// Empty, just to attach a second reference
};
@Override
public double distance(NumberVector v1, NumberVector v2) {
final int dim = dimensionality(v1, v2);
double sumdiff = 0., sumsum = 0.;
for (int d = 0; d < dim; d++) {
double xd = v1.doubleValue(d), yd = v2.doubleValue(d);
sumdiff += Math.abs(xd - yd);
sumsum += Math.abs(xd) + Math.abs(yd);
}
return sumdiff / sumsum;
}
@Override
public double minDist(SpatialComparable mbr1, SpatialComparable mbr2) {
final int dim = dimensionality(mbr1, mbr2);
double sumdiff = 0., sumsum = 0.;
for (int d = 0; d < dim; d++) {
final double min1 = mbr1.getMin(d), max1 = mbr1.getMax(d);
final double min2 = mbr2.getMin(d), max2 = mbr2.getMax(d);
if (max1 < min2) {
sumdiff += min2 - max1;
} else if (min1 > max2) {
sumdiff += min1 - max2;
} else {
// Minimum difference is 0
}
sumsum += Math.max(-min1, max1) + Math.max(-min2, max2);
}
return sumdiff / sumsum;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected BrayCurtisDistanceFunction makeInstance() {
return BrayCurtisDistanceFunction.STATIC_CONTINUOUS;
}
}
}
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