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From "Udit Arora (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (STATISTICS-7) Stream-based Java statistical processing
Date Tue, 02 Apr 2019 14:57:00 GMT

    [ https://issues.apache.org/jira/browse/STATISTICS-7?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16807832#comment-16807832
] 

Udit Arora commented on STATISTICS-7:
-------------------------------------

Sir
I am interested in both. But since I have been following this discussion, I plan to stick
to this. But now that I know I might send a proposal there as well. But currently this project
is my priority.

> Stream-based Java statistical processing
> ----------------------------------------
>
>                 Key: STATISTICS-7
>                 URL: https://issues.apache.org/jira/browse/STATISTICS-7
>             Project: Apache Commons Statistics
>          Issue Type: New Feature
>            Reporter: Eric Barnhill
>            Priority: Major
>              Labels: GSoC2019, gsoc2019, statistics, streams
>
> The new component aims to be a library of commons statistics functions synchronized
with the latest developments in the Java language, in particular Java's functional programming
syntax.
> The library will make commonly used statistical functions available to an end user through
a simple grammar comparable to commons-math-statistics or scikit-learn, while under the hood
will implement Java's mapping, streaming, and other producer and consumer functions to ensure
the statistical methods run optimally in new Java implementations.
> Developers working on the project will have the opportunity to demonstrate Java programming,
functional programming, algorithm design, and data science skills and receive authorship on a
commons project that is likely to be widely used.
> The ideal contributor will also be able to help with important architectural decision
making. The old source of these libraries, commons-math, grew too large, hierarchically complex
and interdependent for the commons mission. The developers on this project need to make architectural
choices that will enable the statiscal code to be lightweight and reusable, with a minimum
of outside dependencies while avoiding redundancy.



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