Hi All,

Prasant and Aleksandr have been working all summer making Clojure even 
better for data science / numerical computing. On behalf of myself and my 
co-mentor Alex Ott I want to offer congratulations to them both for 
successfully completing their Google Summer of Code projects!

Thanks to Prasant, we now have full, idiomatic, linear algebra support for 
Clojure. The implementation (part of vectorz-clj) is pure JVM code - i.e. 
no need for native dependencies. In terms of performance, we are already 
competitive with many Java matrix implementations and with some further 
optimisation could easily be one of the fastest linear algebra libaries on 
the JVM. To see the results of Prasant's work, check out core.matrix, 
vectorz-clj and vectorz.

https://github.com/mikera/core.matrix
https://github.com/mikera/vectorz
https://github.com/mikera/vectorz-clj

Thanks to Aleksandr, we have completed the Incanter / core.matrix 
implementation work which will make it possible to use core.matrix 
implementations seamlessly within Incanter 2.0. You can use regular Clojure 
vectors (nested persistent vectors of numbers), vectorz-clj (pure JVM, 
double precision), Clatrix (native BLAS support) or any combination of 
these. There is also new, improved "Dataset" support which makes it 
possible to create custom dataset types by implementing some new dataset 
protocols. To see the results of Aleksandr's work, check out Incanter, 
Clatrix and core.matrix

https://github.com/incanter/incanter
https://github.com/tel/clatrix/commits/master
https://github.com/mikera/core.matrix

Taken together, these projects represent a significant step towards making 
Clojure a leading language for data science and analytics.

Well done Prasant and Aleksandr!

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