Toy ML

Welcome to toy_ml, a minimalist machine learning library in Rust designed to serve as a "Hello World" introduction to machine learning concepts. This project is akin to the first simple program you write when learning a new programming language; it's not meant to do much, but it ignites hope and curiosity in the field of machine learning.

Overview

Rust's steep learning curve can be intimidating, especially when venturing into the realm of machine learning. toy_ml aims to change that perception by offering a straightforward implementation of the gradient descent algorithm. It's not meant to be comprehensive but rather a beacon for the curious minds looking to explore machine learning in Rust.

Installation

Add toy_ml to your Cargo.toml dependencies:

[dependencies]
toy_ml = "0.1.0"

Usage

Here's how you can use toy_ml to perform gradient descent:

use toy_ml::gradient_descent;

fn main() {
    // Initialize the slope (m) and y-intercept (c) to 0.
    let mut m = 0.0;
    let mut b = 0.0;
    // Define the learning rate and number of epochs.
    let learning_rate = 0.0001;
    let epochs = 1000000;

    // Define your data points here.
    let x_coords = vec![...]; // x-coordinates
    let y_coords = vec![...]; // y-coordinates

    // Run the gradient descent algorithm.
    for _ in 0..epochs {
        let (new_m, new_c) = gradient_descent(m, c, learning_rate, &x_coords, &y_coords);
        m = new_m;
        c = new_c;
    }

    println!("Calculated values - Slope: {:?}, Intercept: {:?}", m, c);

    let new_x = vec![3.0, 6.0]; // New values of X to be predicted
    let mut predictions:Vec<f64> = Vec::new();

    for x in &new_x{
        predictions.push(x * m + c);
    }

    println!("{:?}", predictions);
}

Contributing

Contributions are welcome! If you have ideas for improvements or want to help refine the crate, feel free to create issues or submit pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

toy_ml is a humble project with grand aspirations. It doesn't promise the stars, but it does hope to launch a thousand journeys into machine learning with Rust. If it inspires even one person to start their journey, it has succeeded in its mission.

Happy coding!