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https://issues.apache.org/jira/browse/MATH-449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13088843#comment-13088843
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Phil Steitz commented on MATH-449:
----------------------------------
We could allow NaNs in the input vectors and skip updating the bivariate
covariances for pairs including a NaN.
> 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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