public class AbsolutePearsonCorrelationDistanceFunction extends AbstractNumberVectorDistanceFunction
r
as: 1-abs(r)
.
The distance between two vectors will be low (near 0), if their attribute
values are dimension-wise strictly positively or negatively correlated, it
will be high (near 1), if their attribute values are dimension-wise
uncorrelated.Modifier and Type | Class and Description |
---|---|
static class |
AbsolutePearsonCorrelationDistanceFunction.Parameterizer
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
static AbsolutePearsonCorrelationDistanceFunction |
STATIC
Static instance.
|
Constructor and Description |
---|
AbsolutePearsonCorrelationDistanceFunction()
Deprecated.
Use static instance!
|
Modifier and Type | Method and Description |
---|---|
double |
distance(NumberVector v1,
NumberVector v2)
Computes the absolute 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 AbsolutePearsonCorrelationDistanceFunction STATIC
@Deprecated public AbsolutePearsonCorrelationDistanceFunction()
STATIC
instead.public double distance(NumberVector v1, NumberVector v2)
r
as: 1-abs(r)
. 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.