org.apache.commons.math4.stat.correlation

## Class StorelessCovariance

• public class StorelessCovariance
extends Covariance
Covariance implementation that does not require input data to be stored in memory. The size of the covariance matrix is specified in the constructor. Specific elements of the matrix are incrementally updated with calls to incrementRow() or increment Covariance().

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.

Note: the underlying covariance matrix is symmetric, thus only the upper triangular part of the matrix is stored and updated each increment.

Since:
3.0
• ### Constructor Summary

Constructors
Constructor and Description
StorelessCovariance(int dim)
Create a bias corrected covariance matrix with a given dimension.
StorelessCovariance(int dim, boolean biasCorrected)
Create a covariance matrix with a given number of rows and columns and the indicated bias correction.
• ### Method Summary

All Methods
Modifier and Type Method and Description
void append(StorelessCovariance sc)
Appends sc to this, effectively aggregating the computations in sc with this.
double getCovariance(int xIndex, int yIndex)
Get the covariance for an individual element of the covariance matrix.
RealMatrix getCovarianceMatrix()
Returns the covariance matrix
double[][] getData()
Return the covariance matrix as two-dimensional array.
int getN()
This Covariance method is not supported by a StorelessCovariance, since the number of bivariate observations does not have to be the same for different pairs of covariates - i.e., N as defined in Covariance.getN() is undefined.
void increment(double[] data)
Increment the covariance matrix with one row of data.
• ### Methods inherited from class org.apache.commons.math4.stat.correlation.Covariance

computeCovarianceMatrix, computeCovarianceMatrix, computeCovarianceMatrix, computeCovarianceMatrix, covariance, covariance
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

• #### StorelessCovariance

public StorelessCovariance(int dim)
Create a bias corrected covariance matrix with a given dimension.
Parameters:
dim - the dimension of the square covariance matrix
• #### StorelessCovariance

public StorelessCovariance(int dim,
boolean biasCorrected)
Create a covariance matrix with a given number of rows and columns and the indicated bias correction.
Parameters:
dim - the dimension of the covariance matrix
biasCorrected - 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

• #### getCovariance

public double getCovariance(int xIndex,
int yIndex)
throws NumberIsTooSmallException
Get the covariance for an individual element of the covariance matrix.
Parameters:
xIndex - row index in the covariance matrix
yIndex - column index in the covariance matrix
Returns:
the covariance of the given element
Throws:
NumberIsTooSmallException - if the number of observations in the cell is < 2
• #### increment

public void increment(double[] data)
throws DimensionMismatchException
Increment the covariance matrix with one row of data.
Parameters:
data - array representing one row of data.
Throws:
DimensionMismatchException - if the length of rowData does not match with the covariance matrix
• #### append

public void append(StorelessCovariance sc)
throws DimensionMismatchException
Appends sc to this, effectively aggregating the computations in sc with this. After invoking this method, covariances returned should be close to what would have been obtained by performing all of the increment(double[]) operations in sc directly on this.
Parameters:
sc - externally computed StorelessCovariance to add to this
Throws:
DimensionMismatchException - if the dimension of sc does not match this
Since:
3.3
• #### getCovarianceMatrix

public RealMatrix getCovarianceMatrix()
throws NumberIsTooSmallException
Returns the covariance matrix
Overrides:
getCovarianceMatrix in class Covariance
Returns:
covariance matrix
Throws:
NumberIsTooSmallException - if the number of observations in a cell is < 2
• #### getData

public double[][] getData()
throws NumberIsTooSmallException
Return the covariance matrix as two-dimensional array.
Returns:
a two-dimensional double array of covariance values
Throws:
NumberIsTooSmallException - if the number of observations for a cell is < 2