infomeasure-rs

High-performance Rust library for information-theoretic measures with multiple estimation approaches.

What This Does

infomeasure-rs computes entropy, mutual information, and transfer entropy from data using four different estimation strategies:

• Discrete: For categorical data with 11+ bias-corrected estimators
• Kernel: For continuous data with optional GPU acceleration
• Ordinal: For time series using permutation patterns
• Exponential Family: For high-dimensional data using k-NN

Installation

[dependencies]
infomeasure = "0.1.0"

Optional Features

Enable GPU acceleration for large datasets:

infomeasure = { version = "0.1.0", features = ["gpu"] }

Enable fast exponential approximations:

infomeasure = { version = "0.1.0", features = ["fast_exp"] }

use infomeasure::estimators::entropy::Entropy;
use ndarray::array;

// Discrete entropy
let data = array!(1, 2, 1, 3, 2, 1);
let entropy = Entropy::new_discrete(data).global_value();
println!("Entropy: {}", entropy);

// Continuous data with kernel estimation
let continuous = array![[1.0, 1.5], [2.0, 3.0], [4.0, 5.0]];
let kernel_entropy = Entropy::nd_kernel::<2>(continuous, 1.0).global_value();
println!("Kernel entropy: {}", kernel_entropy);

Feature Status

Feature	Discrete	Kernel	Ordinal	k-NN
Entropy	✅	✅	✅	✅
Mutual Information	✅	✅	✅	✅
Transfer Entropy	✅	✅	✅	✅

✅ = Available | 🔄 = In Development | ❌ = Planned

Documentation

• API Reference - Complete documentation
• Examples - Usage examples

Advanced Features

GPU Acceleration

Enable GPU computation for large datasets:

infomeasure = { version = "0.1.0", features = ["gpu"] }

Performance Optimizations

Fast exponential approximations:

infomeasure = { version = "0.1.0", features = ["fast_exp"] }

Python Compatibility

This crate maintains API compatibility with the infomeasure Python package while providing 10-100x performance improvements.

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

Development Setup

Prerequisites

• Rust 1.70+ (for building)
• uv Python package manager (for validation tests)

Python Environment Setup

The validation tests require a Python environment with infomeasure package. Set it up once before running tests:

# Create virtual environment in validation crate directory
cd tests/validation_crate
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt

Running Tests

# Run all tests (includes Python validation)
cargo test

# Run only Rust unit tests (skip Python validation)
cargo test --lib

Testing and Validation

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

Benchmarks

Performance benchmarks are available for different estimation methods:

cargo bench

Contributing

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

License

MIT OR Apache-2.0