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From "Gilles (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (STATISTICS-7) Stream-based Java statistical processing
Date Mon, 01 Apr 2019 15:01:00 GMT

    [ https://issues.apache.org/jira/browse/STATISTICS-7?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16806878#comment-16806878
] 

Gilles commented on STATISTICS-7:
---------------------------------

bq. current "math-linear" will be ported to "Commons Linear" in the future?

Perhaps; we'd need expert advice on how to design a modern implementation of matrix algebra
(?).

In the meantime, it may be worth exploring the implications of having a very focused {{commons-numbers-matrix}}
module in "Commons Numbers".

bq. port necessary functionality into private packages

Yes. But IMO it should be very limited (i.e. code that is not called should be stripped).

bq. just use the current library temporarily for now

I'd rather not, as it will perpetuate the impression that "Commons Math" is still supported.
 A new major version of CM should be released (with "legacy" codes) that will depend on "Commons
Statistics".

bq. "math-exceptions"

No.  I now consider that specific exceptions generated by low-level components should not
be public.
See how it's done in "Commons Numbers".

bq. "math-util"

Anything in there that is still useful is a candidate for "Commons Numbers".  Did you have
a look at what's there already?


By the way, this discussion should be moved to the "dev" ML.

> 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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