de.lmu.ifi.dbs.elki.math

Class PearsonCorrelation

java.lang.Object

de.lmu.ifi.dbs.elki.math.PearsonCorrelation

public class PearsonCorrelation
extends 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.

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
private double	meanX Current mean for X and Y.
private double	meanY Current mean for X and Y.
private double	sumWe Weight sum.
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	sumYY Aggregation for squared residuals - we are not using sum-of-squares!

Constructor Summary

Constructors

Constructor and Description
PearsonCorrelation() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
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.

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!

meanX

private double meanX

Current mean for X and Y.

meanY

private double meanY

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.