rs-ml

ML framework for the rust programming language. It includes traits for transfomers, models, and an implementation for scalers, and a gaussian Naive Bayesian classifier.

Usage

This library requires a compute backend to perform matrix operations. Compute backends are exposed with provided feature flags. Refer to the ndarray_linalg docs for more information.

Design

Classifiers

• iterative
  • Can be trained with streaming data that does not fit in memory at the same time
• non-iterative
  • Must have the entire dataset at one time to train the model

let a = csv::read_csv("filename.csv")?;
let features = Dataset::from_struct(a, |r| arr1[r.f1, r.f2, r.f3], |r| r.label)?;
let model = GaussianNB::fit(features)?;

lib.rs:

rs-ml is a simple ML framework for the Rust language. it includes train test splitting, scalers, and a guassian naive bayes model. It also includes traits to add more transfomers and models to the framework.

Usage

This library requires a compute backend to perform matrix operations. Compute backends are exposed with provided feature flags. Refer to the ndarray_linalg docs for more information.