Dimensionals

Dimensionals is a Rust library for working with n-dimensional data. It provides a flexible and efficient multidimensional array implementation with a generic storage backend over generic number types.

Features

• Generic over element type T (implementing Num and Copy), number of dimensions N, and storage backend S
• Support for Scalar (0D), Vector (1D), Matrix (2D), and Tensor (N>2 D) types
• Efficient LinearArrayStorage backend with support for row-major and column-major layouts
• Iterators (immutable and mutable) for efficient traversal
• Indexing and slicing operations
• Arithmetic operations (element-wise and scalar) with operator overloading
• Convenient macros for vector and matrix creation (vector! and matrix!)

Usage

Add this to your Cargo.toml:

[dependencies]
dimensionals = "0.1.0"

Here's a basic example of creating and using a matrix:

use dimensionals::{matrix, Dimensional, LinearArrayStorage};

fn main() {
    let m: Dimensional<i32, LinearArrayStorage<i32, 2>, 2> = matrix![
        [1, 2, 3],
        [4, 5, 6]
    ];
    assert_eq!(m[[0, 0]], 1);
    assert_eq!(m[[1, 1]], 5);

    // Element-wise addition
    let m2 = &m + &m;
    assert_eq!(m2[[0, 0]], 2);
    assert_eq!(m2[[1, 1]], 10);

    // Scalar multiplication
    let m3 = &m * 2;
    assert_eq!(m3[[0, 0]], 2);
    assert_eq!(m3[[1, 1]], 10);

    // Iteration
    for &value in m.iter() {
        println!("{}", value);
    }

    // Matrix multiplication
    let m4: Dimensional<i32, LinearArrayStorage<i32, 2>, 2> = matrix![
        [7, 8],
        [9, 10],
        [11, 12]
    ];
    let product = m.dot(&m4);
    assert_eq!(product[[0, 0]], 58);
}

For more examples and usage details, see the API documentation.

Core Concepts

• Element type T: The type of data stored in the array (must implement Num and Copy).
• Storage backend S: The underlying storage mechanism for the array (must implement DimensionalStorage).
• Number of dimensions N: The dimensionality of the array (const generic parameter).

Performance

The LinearArrayStorage backend stores elements in a contiguous Vec<T> and supports both row-major and column-major layouts. This provides good cache locality for traversals. The storage computes strides for efficient indexing.

Roadmap

The following features and improvements are planned for future releases:

• [ * ] Basic N-dimensional array
• [ * ] Basic indexing
• [ * ] Basic iterators
• [ * ] Basic arithmetic operations
• [ * ] Basic slicing
• [ * ] Use safe rust in indexing
• [ * ] Support common arithmetic operations
• [ * ] Use safe rust in arithmetic operations
• Move shape data to type-system for compile-time known dimensions
• Matrix multiplication
• Use safe Rust in iterators (currently uses unsafe code)
• Add tensor macro for creating higher-dimensional arrays
• Remove the need for phantom data markers
• Support reshaping, appending, and removing operations
• Implement comprehensive linear algebra functions
• Add support for common statistical functions
• Implement geometric functions like Brownian motion
• Add support for GPU offloading
• Implement SIMD optimizations
• Support Apache Arrow or safetensors storage backend
• Integrate with Polars, plotly-rs, and argmin-rs
• Add parallel processing support with Rayon
• Implement feature flags for optional functionality
• Support no_std environments
• Add WebAssembly and WebGPU support
• Implement support for SVM targets

Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests on the GitHub repository.

License

This project is licensed under the MIT License.

Contact

Warlock Labs - https://github.com/warlock-labs

Project Link: https://github.com/warlock-labs/dimensionals