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https://issues.apache.org/jira/browse/STATISTICS-7?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16829243#comment-16829243
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Gilles commented on STATISTICS-7:
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Hi [~Udit Arora],
{quote}this is kinda fun...
{quote}
Glad to hear. :)
Such small changes can indeed be a good opportunity to learn the process:
# File an appropriate JIRA report: Almost every modification must be tracked
(a notable exception is made for Javadoc improvement, e.g. correcting typos, or
adding more unit tests, e.g. to improve code coverage).
# The commit message should be prepended by the name of the JIRA ticket (e.g.
"STATISTICS-123: ..."). See the output of the "git log" for examples of how
detailed the message should be.
Long time committers sometimes omit to open a JIRA ticket, but that should not
be emulated. ;)
# Describe the changes in the commit message: It's obvious that the commit
contains a "change", but the reviewer should know, by reading the commit
message, what was the purpose of the change.
# It's always good to specify that you ran the unit test suite, and that the
change is covered, and still produce the expected results. Side-note: We should
ask INFRA to activate [Travis|https://travis-ci.org/apache/commons-statistics]
for "Commons Statistics".
> 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.
> As functional programming grows increasingly central to big data applications
> we believe these libraries will play an important function in the data
> engineering ecosystem. In particular, data engineering is widely done with
> Java, then passed to other languages for data-scientific analyses; however,
> the common availability of functionally implemented statistical mapping and
> reductions in Java could prove very useful at the interface of data science
> and engineering, by enabling teams to more easily perform reductions on the
> engineering side before handing off to the analysis side.
> 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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