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Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java')
| -rw-r--r-- | src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java | 149 |
1 files changed, 149 insertions, 0 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java new file mode 100644 index 00000000..6330ed34 --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/subspace/SubspaceEuclideanDistanceFunction.java @@ -0,0 +1,149 @@ +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) 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 java.util.BitSet; + +import de.lmu.ifi.dbs.elki.data.NumberVector; +import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable; + +/** + * Provides a distance function that computes the Euclidean distance between + * feature vectors only in specified dimensions. + * + * @author Elke Achtert + */ +public class SubspaceEuclideanDistanceFunction extends SubspaceLPNormDistanceFunction { + /** + * Constructor. + * + * @param dimensions Selected dimensions + */ + public SubspaceEuclideanDistanceFunction(BitSet dimensions) { + super(2.0, dimensions); + } + + /** + * 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)) { + final double delta = v1.doubleValue(d + 1) - v2.doubleValue(d + 1); + sqrDist += delta * delta; + } + return Math.sqrt(sqrDist); + } + + @Override + 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 + 1); + final double omin = mbr.getMin(d + 1); + if(value < omin) { + delta = omin - value; + } + else { + final double omax = mbr.getMax(d + 1); + if(value > omax) { + delta = value - omax; + } + else { + continue; + } + } + sqrDist += delta * delta; + } + return Math.sqrt(sqrDist); + } + + @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 + 1); + final double min2 = mbr2.getMin(d + 1); + if(max1 < min2) { + delta = min2 - max1; + } + else { + final double min1 = mbr1.getMin(d + 1); + final double max2 = mbr2.getMax(d + 1); + if(min1 > max2) { + delta = min1 - max2; + } + else { // The mbrs intersect! + continue; + } + } + sqrDist += delta * delta; + } + return Math.sqrt(sqrDist); + } + + @Override + public double doubleNorm(NumberVector<?, ?> obj) { + double sqrDist = 0; + for(int d = dimensions.nextSetBit(0); d >= 0; d = dimensions.nextSetBit(d + 1)) { + final double delta = obj.doubleValue(d + 1); + sqrDist += delta * delta; + } + return Math.sqrt(sqrDist); + } + + /** + * Parameterization class. + * + * @author Erich Schubert + * + * @apiviz.exclude + */ + public static class Parameterizer extends AbstractDimensionsSelectingDoubleDistanceFunction.Parameterizer { + @Override + protected SubspaceEuclideanDistanceFunction makeInstance() { + return new SubspaceEuclideanDistanceFunction(dimensions); + } + } +}
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