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//! `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.
//!
//! ## Current state
//!
//! Such bold ambitions! Where are we now? [Are we learning yet?](http://www.arewelearningyet.com/)
//!
//! Not really: `linfa` only provides a single algorithm, [`K-Means`](clustering/struct.KMeans.html),
//! with a couple of helper functions.
//!
//! There is a long way to go to fulfill its bold mission statement, but there is [significant](https://github.com/rust-ml/discussion/issues/1)
//! [lurking](https://github.com/rust-lang/wg-governance/issues/11) [interest](https://www.reddit.com/r/rust/comments/dvcvo7/rust_2020_scientific_rust/) 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](https://www.youtube.com/watch?v=odI_LY8AIqo&t=8s) 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](clustering/struct.KMeans.html), is the result of [a community workshop](https://github.com/LukeMathWalker/ndarray-koans) at RustFest 2019,
//! with a bunch of different people chipping in to provide [Python bindings](https://github.com/LukeMathWalker/linfa-python) and interesting
//! [performance benchmarks](https://github.com/LukeMathWalker/clustering-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](https://github.com/LukeMathWalker/linfa/issues)
//! and get involved!
//!

/// Clustering algorithms for unlabeled data.
pub mod clustering {
    pub use linfa_clustering::*;
}