README.md

numru

A high-performance scientific computation library written in Rust.

Motivation

Numru is a scientific computation library that aims to provide a high-performance, easy-to-use, and flexible API for numerical operations. It is inspired by NumPy, a popular numerical computation library in Python. Numru is designed to be a fundamental library for scientific computing with Rust.

Get Started

This getting started guide might change and should not be a source of absolute truth. Check the unit tests if you want to stay up to date with how things should be done. Some APIs will definetely be changed in the future.

[dependencies]
numru = "0.1.0"

And a simple code:

fn main() {
    let a = array![42, -17, 256, 3, 99, -8];
    println!("{:?}", a);

    let b = array![
        [TAU, -PI, 1.61],
        [E,  0.98, -7.42],
        [4.67, -0.45, 8.88],
    ];
    println!("{:?}", b);

    let c = array![
        [[101, 202, 303], [404, 505, 606]],
        [[-707, -808, -909], [111, 222, 333]]
    ];
    println!("{:?}", c);
}

Features

Numru will offer a variety of different numerical operations and data types. It is intended to be a fundamental library for scientific computing with Rust.

Supported Data Types

• i64
• f64

Planned Data Types (Future)

• i8, i16, i32, i128
• u8, u16, u32, u64, u128
• f32
• bool
• String, &str

Supported Operations (planned)

Note that currently we only show the numru equivalents as the ones that are planned. They do not exist yet.

Operation	NumPy Equivalent	Numru Equivalent
Create Array	np.array([1, 2, 3]) or np.array([ [1,2,3], [1,2,3], [[4,5,6], [4,5,6]] ])	array![1, 2, 3] or array![ [[1,2,3], [1,2,3]], [[4,5,6], [4,5,6]] ]
Zeros Array	np.zeros((3, 3, 3))	zeros!(3, 3, 3) or a.zeros()
Ones Array	np.ones((3, 3, 3))	ones!(3, 3, 3) or a.ones()
Arange	np.arange(start, stop, step)	arange!(start, stop, step)
Linspace	np.linspace(start, stop, num)	🚧
Mean	np.mean(a)	a.mean()
Min	np.min(a)	a.min()
Max	np.max(a)	a.max()
Exp	np.exp(a)	🚧
Log	np.log(a)	🚧
Sigmoid	1 / (1 + np.exp(-a))	🚧
Dot Product	np.dot(a, b)	dot!(a, b)
Reshape	a.reshape((4, 3, 3))	a.reshape(4, 3, 3)
Concatenate	np.concatenate([a, b], axis=0)	🚧
Element-wise Add	a + b	a + b
Element-wise Sub	a - b	a - b
Element-wise Mul	a * b	a * b
Element-wise Div	a / b	a / b
Fancy Indexing	np.ones((3, 3, 3))[0, :]	🚧
Fancy Flipping	np.array([1, 2, 3])[::-1]	🚧

License

The MIT License.