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Hi @elise_huard - I don't know the reason - but I suspect it is just because the maintainers have moved onto other interests
The Clojure community does need a collection of good libs to support Data Science - either clojure based or good interop with java libs
@gigasquid I agree. I wonder whether Incanter is close enough that it's worth pushing that, or whether other tools need to be pushed
@elise_huard - there is also an interesting comment by nblumoe (one of the (former?) developer) of Incanter https://clojurians-log.clojureverse.org/rdf/2017-02-14.html starting at 09:55:30
so it seems that some developers moved on and some don’t need Incanter anymore (i.e are using directly matrix.core).
Also what @elise_huard said: Incanter might better be split up into multiple, composable libs.
what kind of contribution are you looking for? and it looks like that for a while now, pull requests haven’t been merged.
kixi.stats was made by someone who collaborates with the company I work for (Mastodon C) - it's very early days
kixi.stats does some basic stats with transducers, but doesn't go much further than that (cc @henrygarner )
I've used Weka before - it's pretty good. Here are some other ones http://www.datasciencecentral.com/profiles/blogs/25-java-machine-learning-tools-libraries
of course there is a plethora of things for basic data handling, visualisation, etc. then there are basic number crunching libs (linear algebra etc.) and there are also higher level “data science” libs/frameworks for ML etc
maybe before the python era took off, Java was the prime environment for industrial data science with a general purpose language (excluding more specialized envs like Matlab etc.)?!
I see. Well I guess there are multiple but I am not sure how well they all play together, I mean stuff like jblas, nd4j, etc for the matrices/array processing. For plotting there are GRAL, JFreeChart, JavaFx, etc. etc.
So things definitely exist but I would be very skeptical about getting the same convenience as you get in the Python or R ecosystem
core.matrix also has multiple, pluggable backends which might be worth having a look at to get a feel for the underlying (Java) numerical libs
@miikka core.matrix covers the numpy/pandas stuff (it may or may not at the moment have everything you may need). That, of course, is Clojure (but sits on various impls, including Java libs like vectorz). For matplotlib (ggplot2 is better imo), gyptis https://github.com/dvdt/gyptis may be sufficient, and it is browser vega/d3 based making it very dynamic in principal.
@elise_huard congrats on your EuroClojure talk http://2017.euroclojure.org/the-return-of-clojure/
our company really does want to do everything data in clojure, so it's a practical objective 🙂
@elise_huard also just saw you on the speaker list too. and even mentioning incanter in your abstract 😄
let’s definitely meet at the conference then. I am up to helping with DS tools for Clojure
as a first effort I think I'll try to reproduce some R work in clojure (possibly with incanter develop branch) and see how far that goes
we have quite a good share of AI/DS talks on the EuroClojure program. Will try to contact people to meet and discuss 🙂
great. also don’t hesitate getting in touch regarding your thoughts and issues with Incanter.
and I AM looking forward to your talk as I really went the lazy route in the last year or so and just did stuff with R and Python
now that I got your attention already. any experience on your side or mastodonC with probabilistic programming? maybe even Anglican?
I do wonder about using core.matrix only when you want to do something very matrix-oriented. I wonder if taking a "everything is a dataframe/dataset" approach like in R doesn't work for clojure where the powertools are all related to seqs and maps (partly why I find kixi.stats interesting)
there is powderkeg for mixing transducers with spark which I find interesting too https://github.com/HCADatalab/powderkeg