WeightedPearsonCorrelationDistance (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- elki.distance.AbstractNumberVectorDistance
- - elki.distance.correlation.WeightedPearsonCorrelationDistance

All Implemented Interfaces:

Distance<NumberVector>, NumberVectorDistance<NumberVector>, PrimitiveDistance<NumberVector>, WeightedNumberVectorDistance<NumberVector>
```
public class WeightedPearsonCorrelationDistance
extends AbstractNumberVectorDistance
implements WeightedNumberVectorDistance<NumberVector>
```
Pearson correlation distance function for feature vectors. The Pearson correlation distance is computed from the Pearson correlation coefficient r as: 1-r. 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.

Since:

0.4.0

Author:

Arthur Zimek, Erich Schubert

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static class WeightedPearsonCorrelationDistance.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

WeightedPearsonCorrelationDistance(double[] weights)
Constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`double`	`distance(NumberVector v1, NumberVector v2)`	Computes the Pearson correlation distance for two given feature vectors.
`boolean`	`equals(java.lang.Object obj)`
`int`	`hashCode()`
`java.lang.String`	`toString()`

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, 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
  - WeightedPearsonCorrelationDistance
```
public WeightedPearsonCorrelationDistance(double[] weights)
```
    Constructor.
    
    Parameters:
    
    weights - Weights
- Method Detail
  - distance
```
public double distance(NumberVector v1,
                       NumberVector v2)
```
    Computes the Pearson correlation distance for two given feature vectors. The Pearson correlation distance is computed from the Pearson correlation coefficient r as: 1-r. Hence, possible values of this distance are between 0 and 2.
    
    Specified by:
    
    distance in interface NumberVectorDistance<NumberVector>
    
    Specified by:
    
    distance in interface PrimitiveDistance<NumberVector>
    
    Parameters:
    
    v1 - first feature vector
    
    v2 - second feature vector
    
    Returns:
    
    the Pearson correlation distance for two given feature vectors v1 and v2
  - 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
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object