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https://github.com/cbueth/infomeasure-rs

Rust library for calculating various information-theoretic measures, including entropy, mutual information, transfer entropy, and more, with support for both discrete and continuous variables. (WIP)

https://github.com/cbueth/infomeasure-rs

Science Score: 26.0%

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Repository

Rust library for calculating various information-theoretic measures, including entropy, mutual information, transfer entropy, and more, with support for both discrete and continuous variables. (WIP)

Basic Info
  • Host: GitHub
  • Owner: cbueth
  • Language: Rust
  • Default Branch: main
  • Size: 75.2 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 12 months ago · Last pushed 12 months ago
Metadata Files
Readme

README.md

infomeasure-rs

infomeasure is a Rust library for computing information-theoretic measures such as entropy, mutual information, and transfer entropy. This project is a Rust implementation of the infomeasure, designed to provide superior performance while maintaining API compatibility.

[!IMPORTANT]
This crate is under development, features will be added step by step.

Features

  • Multiple Estimation Techniques: Supports discrete and kernel-based approaches
  • Future: all features from the parent pacakge, see infomeasure introduction
  • High Performance: Leverages Rust's zero-cost abstractions for efficient computation
  • Python Compatibility: Designed to potentially serve as a backend for the Python package
  • Modular Design: Easily extensible architecture for adding new estimators and methods

Installation

[!NOTE] As of now infomeasure is not on crates.io, yet.

Add this to your Cargo.toml:

toml [dependencies] infomeasure = "0.1.0"

Usage Examples

Calculating Entropy with Gaussian Kernel

```rust use infomeasure::estimators::entropy::Entropy; use ndarray::Array2;

fn main() { // Create or load your data let data = Array2::fromshapevec((1000, 2), vec![/* your data */]).unwrap();

// Calculate entropy with Gaussian kernel
let bandwidth = 0.5;
let entropy = Entropy::nd_kernel_with_type::<2>(
    data.clone(),
    "gaussian".to_string(),
    bandwidth
).global_value();

println!("Entropy: {}", entropy);

} ```

Repository Structure

  • src/ - Main source code
    • estimators/ - Estimation techniques implementations
    • approaches/ - Specific implementations (discrete, kernel, ...)
    • traits/ - Shared interfaces for estimators
  • benches/ - Performance benchmarks using Criterion
  • tests/ - Unit and integration tests
  • examples/ - Example usage and demonstrations

Testing and Validation

The project includes a validation crate that compares results with the Python implementation to ensure compatibility and correctness. Run tests with:

bash cargo test

Benchmarks

Performance benchmarks are available for different estimation methods:

bash cargo bench

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Owner

  • Name: Carlson Büth
  • Login: cbueth
  • Kind: user
  • Location: Palma de Mallorca, Spain

GitHub Events

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  • Create event: 1
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Dependencies

Cargo.lock cargo
  • 181 dependencies
Cargo.toml cargo
  • approx 0.5 development
  • criterion 0.5 development
  • ndarray 0.16 development
  • rand 0.8 development
  • rand_distr 0.4 development
  • kiddo 5.2.0
  • ndarray 0.16
  • plotters 0.3.7
tests/validation_crate/Cargo.toml cargo
tests/validation_crate/environment.yml pypi