Machine Learning

The compute::models module bundles several learning algorithms that operate on Matrix structures. These examples highlight the basic training and prediction APIs. For more end‑to‑end walkthroughs see the examples directory in the repository.

Currently implemented models include:

Linear and logistic regression
K‑means clustering
Principal component analysis (PCA)
Gaussian Naive Bayes
Dense neural networks

Linear Regression

# extern crate rustframe;
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, 100);
let preds = model.predict(&x);
assert_eq!(preds.rows(), 4);

K-means Walkthrough

# extern crate rustframe;
use rustframe::compute::models::k_means::KMeans;
use rustframe::matrix::Matrix;

let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
let (model, _labels) = KMeans::fit(&data, 2, 10, 1e-4);
let new_point = Matrix::from_vec(vec![0.0, 0.0], 1, 2);
let cluster = model.predict(&new_point)[0];

For helper functions and upcoming modules, visit the utilities section.

Logistic Regression

# extern crate rustframe;
use rustframe::compute::models::logreg::LogReg;
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![0.0, 0.0, 1.0, 1.0], 4, 1);
let mut model = LogReg::new(1);
model.fit(&x, &y, 0.1, 200);
let preds = model.predict_proba(&x);
assert_eq!(preds.rows(), 4);

Principal Component Analysis

# extern crate rustframe;
use rustframe::compute::models::pca::PCA;
use rustframe::matrix::Matrix;

let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let pca = PCA::fit(&data, 1, 0);
let transformed = pca.transform(&data);
assert_eq!(transformed.cols(), 1);