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https://issues.apache.org/jira/browse/STATISTICS-7?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16805888#comment-16805888
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Ben Nguyen commented on STATISTICS-7:
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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' (such as linear)
to specific use as Mr. [~erans] mentioned earlier. This would simplify usage
but increase overall workload with repeating code (a future problem to come if
everyone does this).... to my understanding, this is the debate, but is there
some kind of consensus regarding an approach style (extent of 'lightweight')
for the entire new commons porting which we should be consistent with? Or are
we truly free to have something up and running as effective as possible (using
Java 8 features, etc) asap like Mr. [~ericbarnhill] mentioned? I'm new to
apache (and open-source) and would like to learn more about how things should
operate :D
Thank you
> 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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