Module: aprender::primitives

Public module of the aprender-core crate.

Source

crates/aprender-core/src/primitives.rs or directory.

Example

use aprender::primitives::{Vector, Matrix};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::primitives exposes the two foundational tensor types that every other aprender module is built on: Vector<T> (1-D, contiguous, generic over element type) and Matrix<T> (2-D, row-major, generic). Both are also re-exported at the crate root and via aprender::prelude::* because almost every ML signature takes a Matrix<f32> of features and a Vector<f32> of targets.

Key types

Matrix<f32> adds linear-algebra entry points: transpose, matmul, matvec, add, sub, mul_scalar, and cholesky_solve for SPD systems. Vector<f32> adds reductions (sum, mean, variance, std, norm), inner-product (dot), and argmin / argmax.

Usage patterns

Pattern 1: Build a matrix and inspect shape

use aprender::primitives::Matrix;

let m = Matrix::from_vec(2, 3, vec![
    1.0, 2.0, 3.0,
    4.0, 5.0, 6.0_f32,
]).expect("2x3 matrix");
assert_eq!(m.shape(), (2, 3));
assert_eq!(m.get(1, 2), 6.0);

let t = m.transpose();
assert_eq!(t.shape(), (3, 2));

Pattern 2: Vector reductions and Cholesky solve

use aprender::primitives::{Matrix, Vector};

let v = Vector::from_slice(&[1.0, 2.0, 3.0, 4.0]);
assert!((v.mean() - 2.5).abs() < 1e-6);
assert!((v.norm() - (30.0_f32).sqrt()).abs() < 1e-5);

// SPD system Ax = b
let a = Matrix::from_vec(2, 2, vec![
    4.0, 1.0,
    1.0, 3.0,
]).expect("2x2");
let b = Vector::from_slice(&[1.0, 2.0]);
let x = a.cholesky_solve(&b).expect("SPD solve");
println!("x = {:?}", x.as_slice());

See also

• compute — Trueno-backed SIMD kernels that accelerate primitive ops
• autograd — differentiable Tensor wrapper built on top of Vector/Matrix
• traits — the core traits whose signatures consume these primitives
• format — APR / SafeTensors / GGUF serialization of Matrix-shaped weights

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.