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https://issues.apache.org/jira/browse/MATH-449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13049943#comment-13049943
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Patrick Meyer commented on MATH-449:
------------------------------------
I've added the comment to the code. If you have better language for the
comment, pleas send it to me and I will include it.
Do you have any suggestions for how to best integrate this code into the
Covariance class? It's not so easy given that the class allows for computation
of a covariance matrix.
> Storeless covariance
> --------------------
>
> Key: MATH-449
> URL: https://issues.apache.org/jira/browse/MATH-449
> Project: Commons Math
> Issue Type: Improvement
> Reporter: Patrick Meyer
> Fix For: 3.0
>
> 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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