public class PearsonCorrelationDistanceFunction extends AbstractNumberVectorDistanceFunction
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).Modifier and Type | Class and Description |
---|---|
static class |
PearsonCorrelationDistanceFunction.Parameterizer
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
static PearsonCorrelationDistanceFunction |
STATIC
Static instance.
|
Constructor and Description |
---|
PearsonCorrelationDistanceFunction()
Deprecated.
Use static instance!
|
Modifier and Type | Method and 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() |
dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, dimensionality, getInputTypeRestriction
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
instantiate
isMetric, isSquared, isSymmetric
public static final PearsonCorrelationDistanceFunction STATIC
@Deprecated public PearsonCorrelationDistanceFunction()
STATIC
instead.public double distance(NumberVector v1, NumberVector v2)
r
as: 1-r
. Hence, possible values of
this distance are between 0 and 2.v1
- first feature vectorv2
- second feature vectorpublic java.lang.String toString()
toString
in class java.lang.Object
public boolean equals(java.lang.Object obj)
equals
in class java.lang.Object
public int hashCode()
hashCode
in class java.lang.Object
Copyright © 2019 ELKI Development Team. License information.