linfa-clustering 0.8.1

A collection of clustering algorithms
Documentation 
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//! `linfa-clustering` aims to provide pure Rust implementations
//! of popular clustering algorithms.
//!
//! ## The big picture
//!
//! `linfa-clustering` is a crate in the `linfa` ecosystem, a wider effort to
//! bootstrap a toolkit for classical Machine Learning implemented in pure Rust,
//! kin in spirit to Python's `scikit-learn`.
//!
//! You can find a roadmap (and a selection of good first issues)
//! [here](https://github.com/LukeMathWalker/linfa/issues) - contributors are more than welcome!
//!
//! ## Current state
//!
//! Right now `linfa-clustering` provides the following clustering algorithms:
//! * [K-Means](KMeans)
//! * [DBSCAN](Dbscan)
//! * [Approximated DBSCAN](AppxDbscan) (Currently an alias for DBSCAN, due to its superior
//!   performance)
//! * [Gaussian-Mixture-Model](GaussianMixtureModel)
//! * [OPTICS](OpticsAnalysis)
//!
//! Implementation choices, algorithmic details and tutorials can be found in the page dedicated to the specific algorithms.
mod dbscan;
mod gaussian_mixture;
#[allow(clippy::new_ret_no_self)]
mod k_means;
mod optics;

pub use dbscan::*;
pub use gaussian_mixture::*;
pub use k_means::*;
pub use optics::*;

// Approx DBSCAN is currently an alias for DBSCAN, due to the old Approx DBSCAN implementation's
// lower performance and outdated dependencies

use linfa_nn::distance::L2Dist;
pub type AppxDbscanValidParams<F, N> = DbscanValidParams<F, L2Dist, N>;
pub type AppxDbscanParams<F, N> = DbscanParams<F, L2Dist, N>;
pub type AppxDbscanParamsError = DbscanParamsError;
pub type AppxDbscan = Dbscan;