On Mon, Mar 14, 2016 at 11:19 AM, Dragan Djuric <draga...@gmail.com> wrote:

> core.matrix already exists, is widely used and already unifies several
>> different implementations that cover a wide variety of use cases. It
>> provides an extensible toolkit that can be used either directly or by
>> library / tool implementers. It's really very powerful, and it's solving
>> real problems for a lot of people right now. It has the potential to make
>> Clojure one of the best languages for data science.
>>
>
> I agree that this sounds great, but, come on... It sounds like a marketing
> pitch. Clojure currently doesn't offer a single data science library that
> would be even a distant match to the state of the art. Nice toys - sure. I
> hope you didn't mean Incanter?...
>

Interesting thread.

Its a fact that the JVM is not the state of the art for numerical
computing, including big swaths of data science/machine learning. There is
0 chance of this changing until at least Panama and Valhalla come to
fruition (5 year timeline).

Every interesting JVM library for numerical computing uses JNI to interact
with native code. If that is the question, is core.matrix the answer? I'm
not convinced (and Dragan clearly isn't), but its certainly worth thinking
about in great detail. Its not just mutable vs immutable, but memory
management and GC. Big questions.

Now, its also the case that systems optimal for numerical computing are
pretty much a disaster for almost everything else. So there is plenty of
room to add value via Clojure. I am skeptical of a "all things to all
people" approach though. Figuring out how to harness the best of both
platforms (and acknowledging the tradeoffs and compromises necessary) seems
to me the way to go.

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