public class SquaredPearsonCorrelationDistanceFunction extends AbstractNumberVectorDistanceFunction
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).Modifier and Type | Class and Description |
---|---|
static class |
SquaredPearsonCorrelationDistanceFunction.Parameterizer
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
static SquaredPearsonCorrelationDistanceFunction |
STATIC
Static instance.
|
Constructor and Description |
---|
SquaredPearsonCorrelationDistanceFunction()
Deprecated.
use static instance!
|
Modifier and Type | Method and Description |
---|---|
double |
distance(NumberVector v1,
NumberVector v2)
Computes the squared Pearson correlation distance for two given feature
vectors.
|
boolean |
equals(Object obj) |
String |
toString() |
dimensionality, dimensionality, dimensionality, dimensionality, getInputTypeRestriction
instantiate, isMetric, isSymmetric
clone, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
instantiate, isMetric, isSymmetric
public static final SquaredPearsonCorrelationDistanceFunction STATIC
@Deprecated public SquaredPearsonCorrelationDistanceFunction()
STATIC
instead.public double distance(NumberVector v1, NumberVector v2)
r
as: 1-r
2
. Hence, possible values of this distance are between 0
and 1.distance
in interface NumberVectorDistanceFunction<NumberVector>
distance
in interface PrimitiveDistanceFunction<NumberVector>
distance
in class AbstractPrimitiveDistanceFunction<NumberVector>
v1
- first feature vectorv2
- second feature vectorCopyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.