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) 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 .
*/
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;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Squared Pearson correlation distance function for feature vectors.
*
* The squared Pearson correlation distance is computed from the Pearson
* correlation coefficient r
as: 1-r
* 2
. Hence, possible values of this distance are between 0
* and 1.
*
* The distance between two vectors will be low (near 0), if their attribute
* values are dimension-wise strictly positively or negatively correlated. For
* Features with uncorrelated attributes, the distance value will be high (near
* 1).
*
* @author Arthur Zimek
*/
public class SquaredPearsonCorrelationDistanceFunction extends AbstractVectorDoubleDistanceFunction {
/**
* Static instance.
*/
public static final SquaredPearsonCorrelationDistanceFunction STATIC = new SquaredPearsonCorrelationDistanceFunction();
/**
* Provides a SquaredPearsonCorrelationDistanceFunction.
*
* @deprecated use static instance!
*/
@Deprecated
public SquaredPearsonCorrelationDistanceFunction() {
super();
}
/**
* Computes the squared Pearson correlation distance for two given feature
* vectors.
*
* The squared Pearson correlation distance is computed from the Pearson
* correlation coefficient r
as: 1-r
* 2
. Hence, possible values of this distance are between 0
* and 1.
*
* @param v1 first feature vector
* @param v2 second feature vector
* @return the squared Pearson correlation distance for two given feature
* vectors v1 and v2
*/
@Override
public double doubleDistance(NumberVector> v1, NumberVector> v2) {
final double pcc = MathUtil.pearsonCorrelationCoefficient(v1, v2);
return 1 - pcc * pcc;
}
@Override
public String toString() {
return "SquaredPearsonCorrelationDistance";
}
@Override
public boolean equals(Object obj) {
if(obj == null) {
return false;
}
return this.getClass().equals(obj.getClass());
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected SquaredPearsonCorrelationDistanceFunction makeInstance() {
return SquaredPearsonCorrelationDistanceFunction.STATIC;
}
}
}