Package elki.distance.correlation

Class WeightedSquaredPearsonCorrelationDistance

java.lang.Object

elki.distance.AbstractNumberVectorDistance

elki.distance.correlation.WeightedSquaredPearsonCorrelationDistance

All Implemented Interfaces:

Distance<NumberVector>, NumberVectorDistance<NumberVector>, PrimitiveDistance<NumberVector>, WeightedNumberVectorDistance<NumberVector>

public class WeightedSquaredPearsonCorrelationDistance
extends AbstractNumberVectorDistance
implements WeightedNumberVectorDistance<NumberVector>

Weighted 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).

This variation is for weighted dimensions.

Since:

0.4.0

Author:

Arthur Zimek, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	WeightedSquaredPearsonCorrelationDistance.Par	Parameterization class.

Field Summary

Fields

Modifier and Type	Field	Description
private double[]	weights	Weights

Fields inherited from interface elki.distance.WeightedNumberVectorDistance

WEIGHTS_ID

Constructor Summary

Constructors

Constructor	Description
WeightedSquaredPearsonCorrelationDistance(double[] weights)	Constructor.

Method Summary

Modifier and Type	Method	Description
double	distance(NumberVector v1, NumberVector v2)	Computes the distance between two given vectors according to this distance function.
boolean	equals(java.lang.Object obj)
int	hashCode()

Methods inherited from class elki.distance.AbstractNumberVectorDistance

dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, getInputTypeRestriction

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface elki.distance.Distance

isMetric, isSquared, isSymmetric

Methods inherited from interface elki.distance.PrimitiveDistance

getInputTypeRestriction, instantiate

Field Detail

weights

private double[] weights

Weights

Constructor Detail

WeightedSquaredPearsonCorrelationDistance

public WeightedSquaredPearsonCorrelationDistance(double[] weights)

Constructor.

Parameters:

weights - Weights

Method Detail

distance

public double distance(NumberVector v1,
                       NumberVector v2)

Description copied from interface: NumberVectorDistance

Computes the distance between two given vectors according to this distance function.

Specified by:

distance in interface NumberVectorDistance<NumberVector>

Specified by:

distance in interface PrimitiveDistance<NumberVector>

Parameters:

v1 - first vector

v2 - second vector

Returns:

the distance between two given vectors according to this distance function

equals

public boolean equals(java.lang.Object obj)

Overrides:

equals in class java.lang.Object

hashCode

public int hashCode()

Overrides:

hashCode in class java.lang.Object