RustWise 🦀📚

A lightweight collection of machine learning models implemented in pure Rust.

RustWise is an educational and experimental library for implementing classic machine learning models in Rust.
The goal is to provide a clean, minimal foundation for learning, extending, and experimenting with ML in Rust.

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🚀 Features

• 📈 Logistic Regression
• 🔁 Bayesian Logistic Regression
• 🧠 Perceptron
• 🐦 Naive Bayes
• 🧰 Shared tools: metrics, traits, utilities

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🗂 Project Structure

rustwise/
├── Cargo.toml
├── src/
│   ├── common/               # Shared utilities and traits
│   ├── logistic/             # Classic logistic regression
│   ├── bayesian_logistic/    # Bayesian logistic regression
│   ├── perceptron/           # Perceptron model
│   └── naive_bayes/          # Naive Bayes classifier
├── examples/                 # Example training scripts
└── tests/                    # Integration tests

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🛠 Usage

Build

make build

make test

make run example=run_logistic

💡 Contributing

RustWise is an open playground — feel free to contribute improvements, new models, or refactorings. If you're learning Rust or ML, this repo is a great starting point!

📚 References

Chapelle, O. (2009). Semi-supervised learning with Bayesian logistic regression. Elements of Statistical Learning, Hastie et al. 🐛 License

MIT License. Feel free to use and modify — just mention the source if it helps you!