public class WeightedSquaredPearsonCorrelationDistanceFunction extends AbstractVectorDoubleDistanceFunction
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).
This variation is for weighted dimensions.Modifier and Type | Field and Description |
---|---|
private double[] |
weights
Weights
|
Constructor and Description |
---|
WeightedSquaredPearsonCorrelationDistanceFunction(double[] weights)
Provides a SquaredPearsonCorrelationDistanceFunction.
|
Modifier and Type | Method and Description |
---|---|
double |
doubleDistance(NumberVector<?> v1,
NumberVector<?> v2)
Computes the squared 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 WeightedSquaredPearsonCorrelationDistanceFunction(double[] weights)
weights
- Weightspublic double doubleDistance(NumberVector<?> v1, NumberVector<?> v2)
r
as: 1-r
2
. Hence, possible values of this distance are between 0
and 1.v1
- first feature vectorv2
- second feature vector