[jira] [Issue Comment Deleted] (STATISTICS-7) Stream-based Java statistical processing

["Ben Nguyen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/STATISTICS-7?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ben Nguyen updated STATISTICS-7:
--------------------------------
    Comment: was deleted

(was: Hello,

I am thinking about some scenarios of approaches and the main one is an approach that would have near zero dependencies (extremely lightweight) with all necessary functionality from dependencies 're-implemented' to specific use as )

> 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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