N-dimensional matrix class for Rust
General-purpose N-dimensional matrix class for Rust. It links to OpenBLAS and
LAPACK to make tensor operations fast (such as matrix multiplications and
linear solvers). It utilizes Rust’s move semantics as much as possible to avoid
unnecessary copies.
Some of the completed and planned features:
Tensor<bool>
to Tensor<f64>
)Recent progress is summarized in CHANGELOG.md. For planned
features, take a look at TODO.md.
#[macro_use(tensor)]
extern crate numeric;
use numeric::Tensor;
fn main() {
let a: Tensor<f64> = Tensor::range(6).reshape(&[2, 3]);
let b = tensor![7.0, 3.0, 2.0; -3.0, 2.0, -5.0];
let c = tensor![7.0, 3.0, 2.0];
let d = &a + &b; // a new tensor is returned
println!("d = {}", d);
let e = a.dot(&c); // matrix multiplication (returns a new tensor)
println!("e = {}", e);
let f = a + &b; // a is moved (no new memory is allocated)
println!("f = {}", f);
// Higher-dimensional
let g: Tensor<f64> = Tensor::ones(&[2, 3, 4, 5]);
println!("g = {}", g);
}
Output:
d =
7 4 4
0 6 0
[Tensor<f64> of shape 2x3]
e =
7 43
[Tensor<f64> of shape 2]
f =
7 4 4
0 6 0
[Tensor<f64> of shape 2x3]
g =
...
[Tensor<f64> of shape 2x3x4x5]
We love pull requests and there are tons of things to work on for this project.
If you want suggestions for contributions, check out TODO.md (a
non-exhaustive list of what would be useful additions). Add your name to the
CONTRIBUTORS.md file as part of your PR, no matter how small
it may seem.
Numeric Rust is primarily inspired by Numpy and borrows heavily from that
project. Even the name is a play on Numeric Python. Another source of
some inspiration is Torch7.