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From "Patrick Meyer (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MATH-449) Storeless covariance
Date Wed, 15 Jun 2011 18:36:48 GMT

    [ https://issues.apache.org/jira/browse/MATH-449?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13049943#comment-13049943
] 

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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