Ruthril Framework

A powerful AI/ML framework for Rust providing statistical utilities, model training, and prediction capabilities.

🚀 Features

• 📊 Statistical Analysis - Mean, variance, standard deviation calculations
• 🛠️ CLI Tools - Project scaffolding and management
• 🔧 Extensible Architecture - Ready for ML models and algorithms
• ⚡ High Performance - Built with Rust's speed and safety
• 🎯 Easy to Use - Simple API for common ML operations

📦 Installation

Add to your Cargo.toml:

[dependencies]
ruthril = "0.1.0"

🏃‍♂️ Quick Start

Using Statistics

use ruthril::Statistics;

fn main() {
    let data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    
    // Calculate mean
    if let Some(mean) = Statistics::mean(&data) {
        println!("Mean: {}", mean);  // Output: Mean: 3
    }
    
    // Calculate standard deviation
    if let Some(std_dev) = Statistics::std_deviation(&data) {
        println!("Standard Deviation: {}", std_dev);
    }
    
    // Calculate variance
    if let Some(variance) = Statistics::variance(&data) {
        println!("Variance: {}", variance);
    }
}

Creating New Projects

Install the CLI tool:

cargo install ruthril

Create a new AI project:

ruthril new-project my_ai_project
cd my_ai_project
cargo run

📚 API Reference

Statistics Module

• Statistics::mean(&[f64]) -> Option<f64> - Calculate arithmetic mean
• Statistics::variance(&[f64]) -> Option<f64> - Calculate sample variance
• Statistics::std_deviation(&[f64]) -> Option<f64> - Calculate standard deviation

🛠️ Development

git clone https://github.com/Ruthril/ruthril.git
cd ruthril
cargo build
cargo test

🤝 Contributing

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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Roadmap

• Machine Learning algorithms
• Deep Learning utilities
• Data preprocessing tools
• Model serialization
• GPU acceleration support

Built with ❤️ by the Ruthril team