Module models

Lightweight machine‑learning models built on matrices.

Models are intentionally minimal and operate on the Matrix type for inputs and parameters.

use rustframe::compute::models::linreg::LinReg;
use rustframe::matrix::Matrix;

let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
let mut model = LinReg::new(1);
model.fit(&x, &y, 0.01, 1000);
let preds = model.predict(&x);
assert_eq!(preds.rows(), 4);

Modules

activations

Common activation functions used in neural networks.

dense_nn

A minimal dense neural network implementation for educational purposes.

gaussian_nb

Gaussian Naive Bayes classifier for dense matrices.

k_means

Simple k-means clustering working on Matrix data.

linreg

Ordinary least squares linear regression.

logreg

Binary logistic regression classifier.

pca

Principal Component Analysis using covariance matrices.