Ridge regression 1D

Here, we implement one-dimensional Ridge Regression*in several styles, using increasing levels of abstraction. It's designed as a learning path for beginners, and focuses on writing idiomatic, clear, and type-safe Rust code. We focus on minimizing this loss function:

$$\mathcal{L}(\beta )=\frac{1}{n}\sum _{i=1}^n(y_i-\beta x_i{)}^2+\lambda {\beta }^2,$$

where: $x_i\in \mathbb{R}$ is an input covariate, $y_i\in \mathbb{R}$ is the associated output, $\beta$ is the Ridge coefficient, $\lambda$ is the $L^2$ regularization strength.

What you'll learn

This module is perfect if you're just starting with Rust and want to:

• Write beginner-friendly numerical code
• Understand how to manipulate vectors (Vec<f64>) and slices (&[f64])
• Write clean and safe code using the standard library only
• Structure your functions with clear responsibilities
• Learn good Rust patterns: immutability, iterators, ownership
• How to make a simple crate out of it
• How to expose a public API

If you want to implement and run this example while you read but are not familiar with Cargo yet, have a look at Cargo 101 for how to set up your project.