LazyMatrixCenter and scale matrices without materializing them.

Normalized design matrices for Rust. Keep sparse matrices sparse, reuse fitted parameters, and choose when to materialize.

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Compare lazy and eager

API documentation

Preserve sparse storage

Apply centering and scaling through matrix operations without turning structural zeros into stored entries.

Choose your backend

Use faer, nalgebra, ndarray, sprs, or chunked Zarr arrays through a shared trait interface.

Materialize when it helps

Convert to dense storage explicitly, normalize writable dense storage in place, and retain fitted parameters for prediction.

A normalized matrix, used as an operator

LazyMatrix represents

\tilde{X} = (X - 1 c^{⊤}) S^{- 1}

without storing the centered matrix. Here, $c$ holds the column centers, and $S$ is diagonal with the column scales. Products use the original storage and apply normalization algebraically.

cargo add lazymatrix --features ndarray
cargo add ndarray@0.17

rust

use lazymatrix::{Centering, LazyMatrix, MatVec, Normalization, Scaling};
use ndarray::array;

let x = array![[1.0, 0.0], [2.0, 3.0], [0.0, 4.0]];
let lazy = LazyMatrix::new(
    x.view(),
    Normalization::new(Centering::Mean, Scaling::Sd),
).unwrap();

let y = lazy.matvec(&array![1.0, -1.0]).unwrap();

Read the guide for installation and eager conversion, or see how it works for the normalization identities. The benchmark comparisons explain the costs of construction, repeated operations, and additional memory.

LazyMatrix is in early development, and breaking changes are expected. Find releases on crates.io and source code on GitHub.

LazyMatrixCenter and scale matrices without materializing them.

Preserve sparse storage

Choose your backend

Materialize when it helps

A normalized matrix, used as an operator ​

A normalized matrix, used as an operator