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A Rust machine learning framework.
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examples Add top-level K-means example Nov 18, 2019
linfa-clustering Fix keywords, again Nov 23, 2019
src Updated READMEs Nov 18, 2019
.gitignore Ignore json files Nov 18, 2019
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Cargo.toml Bump version. Nov 23, 2019


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linfa (Italian) / sap (English):

The vital circulating fluid of a plant.

linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust.

Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks.

Documentation: latest Community chat: Gitter

Current state

Such bold ambitions! Where are we now? Are we learning yet?

Not really: linfa only provides a single algorithm, K-Means, with a couple of helper functions.

There is a long way to go to fulfill its bold mission statement, but there is significant lurking interest in the Rust ecosystem when it comes to ML and its surroundings: sometimes a small spark is all you need to light a beacon fire.

In fact, it is a firm belief of mine that only a significant community effort can nurture, build and sustain an ML ecosystem in Rust - there is no other way forward.

Even this humble beginning, the K-Means algorithm, is the result of a community workshop at RustFest 2019, with a bunch of different people chipping in to provide Python bindings and interesting performance benchmarks.

We just need to keep walking down the same path.

If this strikes a chord with you, please take a look at the roadmap and get involved!


Dual-licensed to be compatible with the Rust project.

Licensed under the Apache License, Version 2.0 or the MIT license, at your option. This file may not be copied, modified, or distributed except according to those terms.

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