commons-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Patrick Meyer (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MATH-449) Storeless covariance
Date Thu, 18 Aug 2011 03:01:28 GMT

     [ https://issues.apache.org/jira/browse/MATH-449?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Patrick Meyer updated MATH-449:
-------------------------------

    Attachment: MATH-449.patch

This patch includes three new classes, StorelessCovariance.java, StorelessCovarianceMatrix.java,
and StorelessCovarianceTest.java. For the test cases, I used the same data as in CovarianceTest.java.
However, I reduced the accuracy to 10E-7 because the tests failed the Longley data when using
10E-9.

> 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: 3.1
>
>         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}

--
This message is automatically generated by JIRA.
For more information on JIRA, see: http://www.atlassian.com/software/jira

        

Mime
View raw message