diff options
author | Andrej Shadura <andrewsh@debian.org> | 2019-03-09 22:30:28 +0000 |
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committer | Andrej Shadura <andrewsh@debian.org> | 2019-03-09 22:30:28 +0000 |
commit | cde76aeb42240f7270bc6605c606ae07d2dc5a7d (patch) | |
tree | c3ebf1d7745224f524da31dbabc5d76b9ea75916 /src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.java |
Import Upstream version 0.4.0~beta1
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.java')
-rw-r--r-- | src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.java | 95 |
1 files changed, 95 insertions, 0 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.java b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.java new file mode 100644 index 00000000..d1bf98c4 --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/distance/distancefunction/correlation/WeightedPearsonCorrelationDistanceFunction.java @@ -0,0 +1,95 @@ +package de.lmu.ifi.dbs.elki.distance.distancefunction.correlation; +/* +This file is part of ELKI: +Environment for Developing KDD-Applications Supported by Index-Structures + +Copyright (C) 2011 +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.Arrays; + +import de.lmu.ifi.dbs.elki.data.NumberVector; +import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractVectorDoubleDistanceFunction; +import de.lmu.ifi.dbs.elki.math.MathUtil; + +/** + * Pearson correlation distance function for feature vectors. + * + * The Pearson correlation distance is computed from the Pearson correlation + * coefficient <code>r</code> as: <code>1-r</code>. Hence, possible values of + * this distance are between 0 and 2. + * + * The distance between two vectors will be low (near 0), if their attribute + * values are dimension-wise strictly positively correlated, it will be high + * (near 2), if their attribute values are dimension-wise strictly negatively + * correlated. For Features with uncorrelated attributes, the distance value + * will be intermediate (around 1). + * + * This variation is for weighted dimensions. + * + * @author Arthur Zimek + * @author Erich Schubert + */ +public class WeightedPearsonCorrelationDistanceFunction extends AbstractVectorDoubleDistanceFunction { + /** + * Weights + */ + private double[] weights; + + /** + * Provides a PearsonCorrelationDistanceFunction. + * + * @param weights Weights + */ + public WeightedPearsonCorrelationDistanceFunction(double[] weights) { + super(); + this.weights = weights; + } + + /** + * Computes the Pearson correlation distance for two given feature vectors. + * + * The Pearson correlation distance is computed from the Pearson correlation + * coefficient <code>r</code> as: <code>1-r</code>. Hence, possible values of + * this distance are between 0 and 2. + * + * @param v1 first feature vector + * @param v2 second feature vector + * @return the Pearson correlation distance for two given feature vectors v1 + * and v2 + */ + @Override + public double doubleDistance(NumberVector<?, ?> v1, NumberVector<?, ?> v2) { + return 1 - MathUtil.weightedPearsonCorrelationCoefficient(v1, v2, weights); + } + + @Override + public boolean equals(Object obj) { + if(this == obj) { + return true; + } + if(obj == null) { + return false; + } + if (!this.getClass().equals(obj.getClass())) { + return false; + } + return Arrays.equals(this.weights, ((WeightedPearsonCorrelationDistanceFunction)obj).weights); + } +}
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