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Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/EuclideanDistanceFunction.java')
-rw-r--r-- | src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/EuclideanDistanceFunction.java | 167 |
1 files changed, 167 insertions, 0 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/EuclideanDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/EuclideanDistanceFunction.java new file mode 100644 index 00000000..03116430 --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/minkowski/EuclideanDistanceFunction.java @@ -0,0 +1,167 @@ +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) 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 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.optionhandling.AbstractParameterizer; + +/** + * Provides the Euclidean distance for FeatureVectors. + * + * @author Arthur Zimek + */ +@Alias({ "euclidean", "euclid", "l2", "EuclideanDistanceFunction", "de.lmu.ifi.dbs.elki.distance.distancefunction.EuclideanDistanceFunction" }) +public class EuclideanDistanceFunction extends LPNormDistanceFunction { + /** + * Static instance. Use this! + */ + public static final EuclideanDistanceFunction STATIC = new EuclideanDistanceFunction(); + + /** + * Provides a Euclidean distance function that can compute the Euclidean + * distance (that is a DoubleDistance) for FeatureVectors. + * + * @deprecated Use static instance! + */ + @Deprecated + public EuclideanDistanceFunction() { + super(2.); + } + + @Override + public double doubleDistance(NumberVector<?> v1, NumberVector<?> v2) { + final int dim = dimensionality(v1, v2); + double agg = 0.; + for (int d = 0; d < dim; d++) { + final double delta = v1.doubleValue(d) - v2.doubleValue(d); + agg += delta * delta; + } + return Math.sqrt(agg); + } + + @Override + public double doubleNorm(NumberVector<?> v) { + final int dim = v.getDimensionality(); + double agg = 0.; + for (int d = 0; d < dim; d++) { + final double val = v.doubleValue(d); + agg += val * val; + } + return Math.sqrt(agg); + } + + protected double doubleMinDistObject(NumberVector<?> v, SpatialComparable mbr) { + final int dim = dimensionality(mbr, v); + double agg = 0.; + for (int d = 0; d < dim; d++) { + final double value = v.doubleValue(d), min = mbr.getMin(d); + final double diff; + if (value < min) { + diff = min - value; + } else { + final double max = mbr.getMax(d); + if (value > max) { + diff = value - max; + } else { + continue; + } + } + agg += diff * diff; + } + return Math.sqrt(agg); + } + + @Override + public double doubleMinDist(SpatialComparable mbr1, SpatialComparable mbr2) { + // Some optimizations for simpler cases. + if (mbr1 instanceof NumberVector) { + if (mbr2 instanceof NumberVector) { + return doubleDistance((NumberVector<?>) mbr1, (NumberVector<?>) mbr2); + } else { + return doubleMinDistObject((NumberVector<?>) mbr1, mbr2); + } + } else if (mbr2 instanceof NumberVector) { + return doubleMinDistObject((NumberVector<?>) mbr2, mbr1); + } + final int dim = dimensionality(mbr1, mbr2); + + double agg = 0.; + for (int d = 0; d < dim; d++) { + final double diff; + final double d1 = mbr2.getMin(d) - mbr1.getMax(d); + if (d1 > 0.) { + diff = d1; + } else { + final double d2 = mbr1.getMin(d) - mbr2.getMax(d); + if (d2 > 0.) { + diff = d2; + } else { + continue; + } + } + agg += diff * diff; + } + return Math.sqrt(agg); + } + + @Override + public boolean isMetric() { + return true; + } + + @Override + public String toString() { + return "EuclideanDistance"; + } + + @Override + public boolean equals(Object obj) { + if (obj == null) { + return false; + } + if (obj == this) { + return true; + } + if (this.getClass().equals(obj.getClass())) { + return true; + } + return super.equals(obj); + } + + /** + * Parameterization class. + * + * @author Erich Schubert + * + * @apiviz.exclude + */ + public static class Parameterizer extends AbstractParameterizer { + @Override + protected EuclideanDistanceFunction makeInstance() { + return EuclideanDistanceFunction.STATIC; + } + } +} |