public class WeightedPearsonCorrelationDistanceFunction extends AbstractVectorDoubleDistanceFunction
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.Modifier and Type | Field and Description |
---|---|
private double[] |
weights
Weights
|
Constructor and Description |
---|
WeightedPearsonCorrelationDistanceFunction(double[] weights)
Provides a PearsonCorrelationDistanceFunction.
|
Modifier and Type | Method and Description |
---|---|
double |
doubleDistance(NumberVector<?,?> v1,
NumberVector<?,?> v2)
Computes the Pearson correlation distance for two given feature vectors.
|
boolean |
equals(Object obj) |
distance, getDistanceFactory, getInputTypeRestriction
instantiate, isMetric, isSymmetric
clone, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
instantiate, isMetric, isSymmetric
public WeightedPearsonCorrelationDistanceFunction(double[] weights)
weights
- Weightspublic double doubleDistance(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 vector