Here is the API documentation for the rust crate. Currently up to date for version 0.3.1.
And here is a document detailing development efforts. Including a projected timeline for immediate features. Please feel free to give feedback and let me know if there any features you believe should take precedence.
Rusty-machine is a general purpose machine learning library implemented entirely in Rust. It aims to combine speed and ease of use - without requiring a huge number of external dependencies.
This project began as a way for me to learn Rust and brush up on some less familiar machine learning algorithms and techniques. Now the project aims to provide a complete, easy to use, machine learning library for Rust.
This library is still very much in early stages of development. Although there are a good number of algorithms many other things are missing. Rusty-machine is probably not the best choice for any serious projects - but hopefully that can change in the near future!
This project is currently looking for contributors of all capacities!
I have now created a dedicated page for contributing. If you're interested please take a look.
This project is implemented using Rust. Currently there are no other dependencies! Though, we are planning on introducing optional BLAS/LAPACK dependencies soon.
The linear algebra library is now fairly filled out. But there is still lots of room for optimization and we should provide BLAS/LAPACK support.
There is also a stats module behind an optional features flag.
- Generic data matrices
- Concatenation
- Data manipulation (row and column selection/repetition etc.)
- Matrix arithmetic
- Efficient matrix slicing
- Linear Regression
- Logistic Regression
- Generalized Linear Models
- K-Means Clustering
- Neural Networks
- Gaussian Process Regression
- Support Vector Machines
- Gaussian Mixture Models
- Naive Bayes Classifiers
The library usage is described well in the API documentation - including example code. I will provide a brief overview of the library in it's current state and intended usage.
The library is most easily used with cargo. Simply include the following in your Cargo.toml file:
[dependencies.rusty-machine]
version="0.3.0"And then import the library using:
extern crate rusty_machine as rm;The library consists of two core components. The linear algebra module and the learning module.
The linear algebra module contains the Matrix and Vector data structures and related methods - such as matrix decomposition. Usage looks like this:
extern crate rusty_machine as rm;
use rm::linalg::matrix::Matrix;
let a = Matrix::new(2,2, vec![1.0, 2.0, 3.0, 4.0]); // Create a 2x2 matrix [[1,2],[3,4]]
let b = Matrix::new(2,3, vec![1.0,2.0,3.0,4.0,5.0,6.0]); // Create a 2x3 matrix [[1.0,2.0,3.0],[4.0,5.0,6.0]]
let c = &a * &b; // Matrix product of a and bMore detailed coverage can be found in the API documentation.
The learning module contains machine learning models. The machine learning implementations are designed with customizability in mind. This means you can control the optimization algorithms but still retain the ease of using default values. This is an area I am actively trying to improve on!
The models all provide predict and train methods - similar to sklearn.
The models focus on modularity - you can plug in the pieces you want and easily construct new pieces from existing traits.