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https://issues.apache.org/jira/browse/MATH-449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13209670#comment-13209670
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Thomas Neidhart commented on MATH-449:
--------------------------------------
I had missed some suggestions from Phil at first, and have committed them in
r1245133.
The changes include:
* Drop setEntry and incrementCovariance. Rename incrementRow to increment and
have that the only mutator. (/)
* Replace colDimension and rowDimension with just dimension, forcing the matrix
to be square. (/)
* Store only upper triangular BivariateCovariances. (/)
** Add a transpose method to StorelessBivariateCovariance so getEntry returns
something that can be further
incemented properly. (x)
* Add symmetry tests (/)
* Change getCovariance to return the actual covariance double value instead of
the Storelessxxxx object (/)
* make StorelessBivariateCovariance package private as it is not used outside
StorelessCovariance (/)
Returning the inner StorelessBivariateCovariance elements is dangerous as
incrementing them individually could break the symmetry due to the way they are
now stored internally (as upper triangular matrix). Adding a transpose method
to achieve this somehow as Phil described is at least not obvious to me.
As there seems to be no actual use of the inner elements anyway, this has been
dropped so far.
Do you agree with the changes made so far?
> Storeless covariance
> --------------------
>
> Key: MATH-449
> URL: https://issues.apache.org/jira/browse/MATH-449
> Project: Commons Math
> Issue Type: Improvement
> Reporter: Patrick Meyer
> Assignee: Thomas Neidhart
> Fix For: 3.0
>
> Attachments: MATH-449.patch, MATH-449.patch
>
> Original Estimate: 168h
> Remaining Estimate: 168h
>
> Currently there is no storeless version for computing the covariance.
> However, Pebay (2008) describes algorithms for on-line covariance
> computations, [http://infoserve.sandia.gov/sand_doc/2008/086212.pdf]. I have
> provided a simple class for implementing this algorithm. It would be nice to
> have this integrated into org.apache.commons.math.stat.correlation.Covariance.
> {code}
> //This code is granted for inclusion in the Apache Commons under the terms of
> the ASL.
> public class StorelessCovariance{
> private double deltaX = 0.0;
> private double deltaY = 0.0;
> private double meanX = 0.0;
> private double meanY = 0.0;
> private double N=0;
> private Double covarianceNumerator=0.0;
> private boolean unbiased=true;
> public Covariance(boolean unbiased){
> this.unbiased = unbiased;
> }
> public void increment(Double x, Double y){
> if(x!=null & y!=null){
> N++;
> deltaX = x - meanX;
> deltaY = y - meanY;
> meanX += deltaX/N;
> meanY += deltaY/N;
> covarianceNumerator += ((N-1.0)/N)*deltaX*deltaY;
> }
>
> }
> public Double getResult(){
> if(unbiased){
> return covarianceNumerator/(N-1.0);
> }else{
> return covarianceNumerator/N;
> }
> }
> }
> {code}
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