linfa-clustering
| Crates.io | linfa-clustering |
| lib.rs | linfa-clustering |
| version | |
| source | src |
| created_at | 2019-11-23 17:40:32.83152+00 |
| updated_at | 2025-01-14 15:43:19.471167+00 |
| description | A collection of clustering algorithms |
| homepage | |
| repository | https://github.com/rust-ml/linfa/ |
| max_upload_size | |
| id | 183760 |
| Cargo.toml error: | TOML parse error at line 22, column 1 | 22 | autolib = false | ^^^^^^^ unknown field `autolib`, expected one of `name`, `version`, `edition`, `authors`, `description`, `readme`, `license`, `repository`, `homepage`, `documentation`, `build`, `resolver`, `links`, `default-run`, `default_dash_run`, `rust-version`, `rust_dash_version`, `rust_version`, `license-file`, `license_dash_file`, `license_file`, `licenseFile`, `license_capital_file`, `forced-target`, `forced_dash_target`, `autobins`, `autotests`, `autoexamples`, `autobenches`, `publish`, `metadata`, `keywords`, `categories`, `exclude`, `include` |
| size | 0 |
wg (github:rust-ml:wg)
documentation
README
Clustering
linfa-clustering aims to provide pure Rust implementations of popular clustering algorithms.
The big picture
linfa-clustering is a crate in the linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's scikit-learn.
You can find a roadmap (and a selection of good first issues) here - contributors are more than welcome!
Current state
linfa-clustering currently provides implementation of the following clustering algorithms, in addition to a couple of helper functions:
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K-Means
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DBSCAN
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Approximated DBSCAN (Currently an alias for DBSCAN, due to its superior performance)
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Gaussian Mixture Model
Implementation choices, algorithmic details and a tutorial can be found here.
BLAS/Lapack backend
We found that the pure Rust implementation maintained similar performance to the BLAS/LAPACK version and have removed it with this PR. Thus, to reduce code complexity BLAS support has been removed for this module.
License
Dual-licensed to be compatible with the Rust project.
Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 or the MIT license http://opensource.org/licenses/MIT, at your option. This file may not be copied, modified, or distributed except according to those terms.