[jira] [Commented] (MATH-449) Storeless covariance

["Phil Steitz (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MATH-449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13049850#comment-13049850 ] 

Phil Steitz commented on MATH-449:
----------------------------------

Can you please either add a comment indicating the code pasted to the ticket is granted for inclusion under terms of the ASL or add it as an attachment and check the "for inclusion" box?  Also, some unit tests would be great, including validation tests against the stored data version.



> 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}
> 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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