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java.lang.Objectorg.apache.commons.math3.stat.correlation.StorelessBivariateCovariance
class StorelessBivariateCovariance
Bivariate Covariance implementation that does not require input data to be stored in memory.
This class is based on a paper written by Philippe Pébay:
Formulas for Robust, One-Pass Parallel Computation of Covariances and
Arbitrary-Order Statistical Moments, 2008, Technical Report SAND2008-6212,
Sandia National Laboratories. It computes the covariance for a pair of variables.
Use StorelessCovariance
to estimate an entire covariance matrix.
Note: This class is package private as it is only used internally in
the StorelessCovariance
class.
Field Summary | |
---|---|
private boolean |
biasCorrected
flag for bias correction |
private double |
covarianceNumerator
the running covariance estimate |
private double |
meanX
the mean of variable x |
private double |
meanY
the mean of variable y |
private double |
n
number of observations |
Constructor Summary | |
---|---|
StorelessBivariateCovariance()
Create an empty StorelessBivariateCovariance instance with
bias correction. |
|
StorelessBivariateCovariance(boolean biasCorrection)
Create an empty StorelessBivariateCovariance instance. |
Method Summary | |
---|---|
double |
getN()
Returns the number of observations. |
double |
getResult()
Return the current covariance estimate. |
void |
increment(double x,
double y)
Update the covariance estimation with a pair of variables (x, y). |
Methods inherited from class java.lang.Object |
---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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private double meanX
private double meanY
private double n
private double covarianceNumerator
private boolean biasCorrected
Constructor Detail |
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public StorelessBivariateCovariance()
StorelessBivariateCovariance
instance with
bias correction.
public StorelessBivariateCovariance(boolean biasCorrection)
StorelessBivariateCovariance
instance.
biasCorrection
- if true
the covariance estimate is corrected
for bias, i.e. n-1 in the denominator, otherwise there is no bias correction,
i.e. n in the denominator.Method Detail |
---|
public void increment(double x, double y)
x
- the x valuey
- the y valuepublic double getN()
public double getResult() throws NumberIsTooSmallException
NumberIsTooSmallException
- if the number of observations
is < 2
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