PearsonCorrelation (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

java.lang.Object
- de.lmu.ifi.dbs.elki.math.PearsonCorrelation

```
public class PearsonCorrelation
extends java.lang.Object
```
Class to incrementally compute pearson correlation. In fact, this actually computes Var(X), Var(Y) and Cov(X, Y), all of which can be obtained from this class. If you need more than two variables, use CovarianceMatrix which uses slightly more memory (by using arrays) but essentially does the same.

Since:

0.5.0

Author:

Erich Schubert

Field Summary

Fields
Modifier and Type	Field and Description
`private double`	`sumWe` Weight sum.
`private double`	`sumX` Current mean for X and Y.
`private double`	`sumXX` Aggregation for squared residuals - we are not using sum-of-squares!
`private double`	`sumXY` Aggregation for squared residuals - we are not using sum-of-squares!
`private double`	`sumY` Current mean for X and Y.
`private double`	`sumYY` Aggregation for squared residuals - we are not using sum-of-squares!

Constructor Summary

Constructors
Constructor and Description

PearsonCorrelation()
Constructor.

Constructors
Constructor and Description
`PearsonCorrelation()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static double`	`coefficient(double[] x, double[] y)` Compute the Pearson product-moment correlation coefficient for two FeatureVectors.
`static double`	`coefficient(NumberVector x, NumberVector y)` Compute the Pearson product-moment correlation coefficient for two NumberVectors.
`double`	`getCorrelation()` Get the Pearson correlation value.
`double`	`getCount()` Get the number of points the average is based on.
`double`	`getMeanX()` Return mean of X
`double`	`getMeanY()` Return mean of Y
`double`	`getNaiveCovariance()` Get the covariance of X and Y (not taking sampling into account)
`double`	`getNaiveStddevX()` Return standard deviation using the non-sample variance Note: usually, you should be using `getSampleStddevX()` instead!
`double`	`getNaiveStddevY()` Return standard deviation using the non-sample variance Note: usually, you should be using `getSampleStddevY()` instead!
`double`	`getNaiveVarianceX()` Return the naive variance (not taking sampling into account) Note: usually, you should be using `getSampleVarianceX()` instead!
`double`	`getNaiveVarianceY()` Return the naive variance (not taking sampling into account) Note: usually, you should be using `getSampleVarianceY()` instead!
`double`	`getSampleCovariance()` Get the covariance of X and Y (with sampling correction)
`double`	`getSampleStddevX()` Return standard deviation
`double`	`getSampleStddevY()` Return standard deviation
`double`	`getSampleVarianceX()` Return sample variance.
`double`	`getSampleVarianceY()` Return sample variance.
`void`	`put(double x, double y)` Put a single value into the correlation statistic.
`void`	`put(double x, double y, double w)` Put a single value into the correlation statistic.
`void`	`reset()` Reset the value.
`static double`	`weightedCoefficient(double[] x, double[] y, double[] weights)` Compute the Pearson product-moment correlation coefficient for two FeatureVectors.
`static double`	`weightedCoefficient(NumberVector x, NumberVector y, double[] weights)` Compute the Pearson product-moment correlation coefficient for two NumberVectors.
`static double`	`weightedCoefficient(NumberVector x, NumberVector y, NumberVector weights)` Compute the Pearson product-moment correlation coefficient for two FeatureVectors.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - sumXX
```
private double sumXX
```
    Aggregation for squared residuals - we are not using sum-of-squares!
  - sumYY
```
private double sumYY
```
    Aggregation for squared residuals - we are not using sum-of-squares!
  - sumXY
```
private double sumXY
```
    Aggregation for squared residuals - we are not using sum-of-squares!
  - sumX
```
private double sumX
```
    Current mean for X and Y.
  - sumY
```
private double sumY
```
    Current mean for X and Y.
  - sumWe
```
private double sumWe
```
    Weight sum.
- Constructor Detail
  - PearsonCorrelation
```
public PearsonCorrelation()
```
    Constructor.
- Method Detail
  - put
```
public void put(double x,
                double y,
                double w)
```
    Put a single value into the correlation statistic.
    
    Parameters:
    
    x - Value in X
    
    y - Value in Y
    
    w - Weight
  - put
```
public void put(double x,
                double y)
```
    Put a single value into the correlation statistic.
    
    Parameters:
    
    x - Value in X
    
    y - Value in Y
  - getCorrelation
```
public double getCorrelation()
```
    Get the Pearson correlation value.
    
    Returns:
    
    Correlation value
  - getCount
```
public double getCount()
```
    Get the number of points the average is based on.
    
    Returns:
    
    number of data points
  - getMeanX
```
public double getMeanX()
```
    Return mean of X
    
    Returns:
    
    mean
  - getMeanY
```
public double getMeanY()
```
    Return mean of Y
    
    Returns:
    
    mean
  - getNaiveCovariance
```
public double getNaiveCovariance()
```
    Get the covariance of X and Y (not taking sampling into account)
    
    Returns:
    
    Covariance
  - getSampleCovariance
```
public double getSampleCovariance()
```
    Get the covariance of X and Y (with sampling correction)
    
    Returns:
    
    Covariance
  - getNaiveVarianceX
```
public double getNaiveVarianceX()
```
    Return the naive variance (not taking sampling into account) Note: usually, you should be using getSampleVarianceX() instead!
    
    Returns:
    
    variance
  - getSampleVarianceX
```
public double getSampleVarianceX()
```
    Return sample variance.
    
    Returns:
    
    sample variance
  - getNaiveStddevX
```
public double getNaiveStddevX()
```
    Return standard deviation using the non-sample variance Note: usually, you should be using getSampleStddevX() instead!
    
    Returns:
    
    standard deviation
  - getSampleStddevX
```
public double getSampleStddevX()
```
    Return standard deviation
    
    Returns:
    
    standard deviation
  - getNaiveVarianceY
```
public double getNaiveVarianceY()
```
    Return the naive variance (not taking sampling into account) Note: usually, you should be using getSampleVarianceY() instead!
    
    Returns:
    
    variance
  - getSampleVarianceY
```
public double getSampleVarianceY()
```
    Return sample variance.
    
    Returns:
    
    sample variance
  - getNaiveStddevY
```
public double getNaiveStddevY()
```
    Return standard deviation using the non-sample variance Note: usually, you should be using getSampleStddevY() instead!
    
    Returns:
    
    stddev
  - getSampleStddevY
```
public double getSampleStddevY()
```
    Return standard deviation
    
    Returns:
    
    stddev
  - reset
```
public void reset()
```
    Reset the value.
  - coefficient
```
public static double coefficient(double[] x,
                                 double[] y)
```
    Compute the Pearson product-moment correlation coefficient for two FeatureVectors.
    
    Parameters:
    
    x - first FeatureVector
    
    y - second FeatureVector
    
    Returns:
    
    the Pearson product-moment correlation coefficient for x and y
  - coefficient
```
public static double coefficient(NumberVector x,
                                 NumberVector y)
```
    Compute the Pearson product-moment correlation coefficient for two NumberVectors.
    
    Parameters:
    
    x - first NumberVector
    
    y - second NumberVector
    
    Returns:
    
    the Pearson product-moment correlation coefficient for x and y
  - weightedCoefficient
```
public static double weightedCoefficient(double[] x,
                                         double[] y,
                                         double[] weights)
```
    Compute the Pearson product-moment correlation coefficient for two FeatureVectors.
    
    Parameters:
    
    x - first FeatureVector
    
    y - second FeatureVector
    
    weights - Weights
    
    Returns:
    
    the Pearson product-moment correlation coefficient for x and y
  - weightedCoefficient
```
public static double weightedCoefficient(NumberVector x,
                                         NumberVector y,
                                         double[] weights)
```
    Compute the Pearson product-moment correlation coefficient for two NumberVectors.
    
    Parameters:
    
    x - first NumberVector
    
    y - second NumberVector
    
    weights - Weights
    
    Returns:
    
    the Pearson product-moment correlation coefficient for x and y
  - weightedCoefficient
```
public static double weightedCoefficient(NumberVector x,
                                         NumberVector y,
                                         NumberVector weights)
```
    Compute the Pearson product-moment correlation coefficient for two FeatureVectors.
    
    Parameters:
    
    x - first FeatureVector
    
    y - second FeatureVector
    
    weights - Weights
    
    Returns:
    
    the Pearson product-moment correlation coefficient for x and y

Class PearsonCorrelation

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

sumXX

sumYY

sumXY

sumX

sumY

sumWe

Constructor Detail

PearsonCorrelation

Method Detail

put

put

getCorrelation

getCount

getMeanX

getMeanY

getNaiveCovariance

getSampleCovariance

getNaiveVarianceX

getSampleVarianceX

getNaiveStddevX

getSampleStddevX

getNaiveVarianceY

getSampleVarianceY

getNaiveStddevY

getSampleStddevY

reset

coefficient

coefficient

weightedCoefficient

weightedCoefficient

weightedCoefficient