2 unstable releases
0.2.0 | Feb 8, 2025 |
---|---|
0.1.0 | Jan 28, 2025 |
#217 in Math
235 downloads per month
Used in sciru
41KB
743 lines
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 = arr![42, -17, 256, 3, 99, -8];
println!("a.shape() = {:?}", a.shape());
a.visualize();
let b = arr![[TAU, -PI, 1.61], [E, 0.98, -7.42], [4.67, -0.45, 8.88]];
println!("\nb.shape() = {:?}", b.shape());
b.visualize();
let c = arr![
[[101, 202, 303], [404, 505, 606]],
[[-707, -808, -909], [111, 222, 333]]
];
println!("\nc.shape() = {:?}", c.shape());
c.visualize();
}
Output of the code above:
a.shape() = Shape=Ix { dims: [6] }
[42, -17, 256, 3, 99, -8]
b.shape() = Shape=Ix { dims: [3, 3] }
[
[6.283185307179586, -3.141592653589793, 1.61 ]
[2.718281828459045, 0.98 , -7.42]
[4.67 , -0.45 , 8.88 ]
]
c.shape() = Shape=Ix { dims: [2, 2, 3] }
[
[
[101 , 202 , 303 ]
[404 , 505 , 606 ]
]
[
[-707, -808, -909]
[111 , 222 , 333 ]
]
]
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]) |
arr![1, 2, 3] |
Zeros Array | np.zeros((3, 3, 3)) |
🚧 |
Ones Array | np.ones((3, 3, 3)) |
🚧 |
Arange | np.arange(start, stop, step) |
🚧 |
Linspace | np.linspace(start, stop, num) |
🚧 |
Mean | np.mean(a) |
a.mean().compute() |
Min | np.min(a) |
a.min().compute() |
Max | np.max(a) |
a.max().compute() |
Exp | np.exp(a) |
🚧 |
Log | np.log(a) |
🚧 |
Sigmoid | 1 / (1 + np.exp(-a)) |
🚧 |
Dot Product | np.dot(a, b) |
🚧 |
Reshape | a.reshape((4, 3, 3)) |
🚧 |
Concatenate | np.concatenate([a, b], axis=0) |
🚧 |
Element-wise Add | a + b |
🚧 |
Element-wise Sub | a - b |
🚧 |
Element-wise Mul | a * b |
🚧 |
Element-wise Div | a / b |
🚧 |
Fancy Indexing | np.ones((3, 3, 3))[0, :] |
🚧 |
Fancy Flipping | np.array([1, 2, 3])[::-1] |
🚧 |
License
The MIT License.
Dependencies
~235–680KB
~16K SLoC