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https://issues.apache.org/jira/browse/MATH-449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13088317#comment-13088317
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Phil Steitz commented on MATH-449:
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Thanks for the patch!
Definitely a useful addition. Looking carefully at the code, I think the
following would be good:
1. StorelessCovarianceMatrix really corresponds to Covariance. The current
implementations of Covariance and PearssonsCorrelation are really
matrix-valued. So I think StorelessCovarianceMatrix should be called
StorelsssCovariance and what is now StorelessCovariance should be
BivariateStorelessCovariance. (Of course, one could argue that it is the
current classes that are misnamed. If people feel strongly that is the case,
we can discuss changing those names and creating bivariate versions. In any
case, we should be consistent.)
2. I think StorelessCovariance (the matrix version) should extend Covariance.
This should work, just omitting array/matrix constructors and overriding
getMatrix as it implements it now. The advantage of this is that it can then
be used, for example, to create a correlation matrix using the method exposed
by PearsonsCorrelation.
3. We need to fill in the missing javadoc.
Thanks again for the patch. I will take care of the items above if there are
no objections and no one beats me to it.
> Storeless covariance
> --------------------
>
> Key: MATH-449
> URL: https://issues.apache.org/jira/browse/MATH-449
> Project: Commons Math
> Issue Type: Improvement
> Reporter: Patrick Meyer
> Assignee: Phil Steitz
> Fix For: 3.1
>
> Attachments: 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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