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https://github.com/anishpras/rust_scicrate
Rust SciCrate is a scientific computing library for Rust, inspired by SciPy.
https://github.com/anishpras/rust_scicrate
computing mathematics rust-science scientific scientific-computing
Last synced: 26 days ago
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Rust SciCrate is a scientific computing library for Rust, inspired by SciPy.
- Host: GitHub
- URL: https://github.com/anishpras/rust_scicrate
- Owner: Anishpras
- License: other
- Created: 2024-08-12T16:44:01.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-12T17:06:15.000Z (5 months ago)
- Last Synced: 2024-12-09T13:01:01.114Z (about 1 month ago)
- Topics: computing, mathematics, rust-science, scientific, scientific-computing
- Language: Rust
- Homepage: https://crates.io/crates/rust_scicrate
- Size: 6.84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Rust SciCrate
Rust SciCrate is a scientific computing library for Rust, inspired by SciPy. It aims to provide a comprehensive set of tools for numerical operations, statistical analysis, optimization, and more, all implemented in pure Rust for maximum performance and safety.
## Features
- **Linear Algebra**: Basic matrix operations, including multiplication and vector norms.
- **Statistics**: Fundamental statistical functions like mean and standard deviation.
- **Optimization**: Implementation of optimization algorithms, including Golden Section Search.
- **Numerical Integration**: Trapezoidal rule for numerical integration.
## Installation
Add this to your `Cargo.toml`:
```toml
[dependencies]
rust_scicrate = "0.1.0"
```
## Usage
Here are some examples of how to use Rust SciCrate:
### Linear Algebra
```rust
use rust_scicrate::linalg::matrix_multiply;
use ndarray::array;
fn main() {
let a = array![[1., 2.], [3., 4.]];
let b = array![[5., 6.], [7., 8.]];
let c = matrix_multiply(&a, &b).unwrap();
println!("Result of matrix multiplication:\n{}", c);
}
```
### Statistics
```rust
use rust_scicrate::stats::{mean, standard_deviation};
use ndarray::array;
fn main() {
let data = array![1., 2., 3., 4., 5.];
println!("Mean: {}", mean(&data).unwrap());
println!("Standard Deviation: {}", standard_deviation(&data).unwrap());
}
```
### Optimization
```rust
use rust_scicrate::optimize::golden_section_search;
fn main() {
let f = |x: f64| (x - 2.).powi(2);
let min = golden_section_search(f, 0., 4., 1e-6, 100).unwrap();
println!("Minimum found at x = {}", min);
}
```
### Numerical Integration
```rust
use rust_scicrate::integrate::trapezoidal;
use std::f64::consts::PI;
fn main() {
let f = |x: f64| x.sin();
let integral = trapezoidal(f, 0., PI, 1000).unwrap();
println!("Integral of sin(x) from 0 to pi: {}", integral);
}
```
## Documentation
For more detailed documentation, please visit [docs.rs/rust_scicrate](https://docs.rs/rust_scicrate).
## Contributing
We welcome contributions to Rust SciCrate! Here are ways you can contribute:
1. Implement new features or improve existing ones
2. Write tests to increase code coverage
3. Improve documentation
4. Report bugs and suggest enhancements
To contribute:
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
Please make sure to update tests as appropriate and adhere to the existing coding style.
## Roadmap
- Implement more advanced linear algebra operations (e.g., eigenvalue computation, matrix decompositions)
- Add more statistical functions and distributions
- Implement additional optimization algorithms
- Add support for solving ordinary differential equations
- Implement Fourier transforms
## License
This project is licensed under
- MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)
at your option.
## Acknowledgments
- Inspired by the SciPy library for Python
- Built with Rust and love for scientific computing
## Contact
If you want to contact me you can reach me at .
## Disclaimer
This library is in early stages of development. While we strive for correctness and efficiency, it may contain bugs or inefficiencies. Use with caution in production environments.