NumRS 🦀

A lightweight, high-performance linear algebra and tensor library built from scratch in Rust. NumRS provides an intuitive API, automatic parallelism for large matrices, and a clean modular design.

• 2D Matrix & N-D Tensor: Specialized Matrix for linear algebra and generic Tensor<T> for multi-dimensional data.
• Intuitive Syntax: Create matrices with the ns_array! macro — just like Python/NumPy.
• Automatic Parallelism: Silently scales across CPU cores using Rayon for large-scale computations.
• Robust Math: Transpose, Determinant, Inverse (Gauss-Jordan), Norm, and Hadamard product.
• Beautiful Output: Colored terminal display with automatic column alignment.
• Reference-First Operators: Clean arithmetic (a + b, a * b) with zero unnecessary clones.

use numrs::{ns_array, Matrix};
fn main() {
 let a = ns_array![[1, 2], [3, 4]];
 let b = Matrix::eye(2);
 let result = &a * &b;
 println!("A x I =\n{}", result);
}

• Usage Guide — Detailed API documentation for all modules.
• Examples — Practical recipes for solving linear systems, Markov chains, transformations, and more.
• Changelog — History of releases and recent changes.

• Matrix struct with colored Display
• Full operator support (Add, Sub, Mul, Scalar)
• Constructors (zeros, ones, eye, rand)
• Determinant and Inverse
• Automatic parallelism via Rayon
• Comprehensive error handling
• Tensor Support — N-D generic Tensor<T> with full error handling (TensorError, try_* methods)
• Eigenvalues and eigenvectors
• LU / QR decomposition
• ML layer: ReLU, softmax, gradient tracking

MIT