[−][src]Crate linfa
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?
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!
Re-exports
pub use dataset::Dataset; |
pub use dataset::DatasetBase; |
pub use dataset::DatasetPr; |
pub use dataset::DatasetView; |
pub use dataset::Float; |
pub use dataset::Label; |
Modules
dataset | Datasets |
error | Error types in Linfa |
metrics | Common metrics functions for classification and regression |
prelude | Linfa prelude. |
traits | Provide traits for different classes of algorithms |