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macro no-std concision-macros

Concision is a complete data-science toolkit written in Rust

9 releases

0.1.14 May 25, 2024
0.1.13 May 10, 2024
0.1.11 Aug 1, 2022
0.1.10 Jul 18, 2022

#12 in #scsys

Download history 11/week @ 2024-02-26 6/week @ 2024-03-11 10/week @ 2024-04-01 263/week @ 2024-05-06 13/week @ 2024-05-13 140/week @ 2024-05-20 13/week @ 2024-05-27

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Used in concision




crates.io docs.rs

clippy rust

The library is currently in the early stages of development and is not yet ready for production use.

Concision is designed to be a complete toolkit for building machine learning models in Rust.

Concision is a machine learning library for building powerful models in Rust prioritizing ease-of-use, efficiency, and flexability. The library is built to make use of the both the upcoming autodiff experimental feature and increased support for generics in the 2024 edition of Rust.

Getting Started

Building from the source

Start by cloning the repository

git clone https://github.com/FL03/concision.git
cd concision
cargo build --features full -r --workspace


Example: Linear Model (biased)

    extern crate concision as cnc;

    use cnc::prelude::{linarr, Linear, Result, Sigmoid};
    use ndarray::Ix2;

    fn main() -> Result<()> {
        tracing::info!("Starting linear model example");

        let (samples, d_in, d_out) = (20, 5, 3);
        let data = linarr::<f64, Ix2>((samples, d_in)).unwrap();

        let model = Linear::<f64>::from_features(d_in, d_out).uniform();
        // let model = Linear::<f64, cnc::linear::Unbiased>::from_features(d_in, d_out).uniform();


        let y = model.activate(&data, Sigmoid::sigmoid).unwrap();
        assert_eq!(y.dim(), (samples, d_out));
        println!("Predictions:\n{:?}", &y);



Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.



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