public class UncenteredCorrelationDistanceFunction extends AbstractNumberVectorDistanceFunction
PearsonCorrelationDistanceFunction
, but
uses a fixed mean of 0 instead of the sample mean.Modifier and Type | Class and Description |
---|---|
static class |
UncenteredCorrelationDistanceFunction.Parameterizer
Parameterization class.
|
Modifier and Type | Field and Description |
---|---|
static UncenteredCorrelationDistanceFunction |
STATIC
Static instance.
|
Constructor and Description |
---|
UncenteredCorrelationDistanceFunction()
Deprecated.
Use static instance!
|
Modifier and Type | Method and Description |
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double |
distance(NumberVector v1,
NumberVector v2)
Computes the Pearson correlation distance for two given feature vectors.
|
boolean |
equals(Object obj) |
String |
toString() |
static double |
uncenteredCorrelation(NumberVector x,
NumberVector y)
Compute the uncentered correlation of two vectors.
|
dimensionality, dimensionality, dimensionality, dimensionality, getInputTypeRestriction
instantiate, isMetric, isSymmetric
clone, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
instantiate, isMetric, isSymmetric
public static final UncenteredCorrelationDistanceFunction STATIC
@Deprecated public UncenteredCorrelationDistanceFunction()
STATIC
instead.public static double uncenteredCorrelation(NumberVector x, NumberVector y)
x
- first NumberVectory
- second NumberVectorpublic double distance(NumberVector v1, NumberVector v2)
r
as: 1-r
. Hence, possible values of
this distance are between 0 and 2.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.