This library provides a Ruby interface to the FANN (Fast Artificial Neural Network) library, enabling developers to integrate and utilize neural networks within Ruby or Rails applications. It offers a convenient way to leverage FANN's capabilities for tasks like pattern recognition and prediction, with the heavy computation handled natively.

How It Works

RubyFann acts as a binding layer to the C-based FANN library. It exposes FANN's core functionalities, such as creating and configuring neural network architectures (fully-connected and sparsely-connected), training networks using provided data, and running predictions. The advantage lies in performing computationally intensive neural network operations in native C, while allowing developers to interact with them seamlessly through Ruby.

Quick Start & Requirements

• Install via RubyGems: gem install ruby-fann or add gem 'ruby-fann' to your Gemfile and run bundle.
• Requires the FANN library to be installed on the system.
• Documentation: http://tangledpath.github.io/ruby-fann/index.html
• Example project: https://github.com/bigohstudios/tictactoe

Highlighted Details

• Supports standard neural network training and execution.
• Allows saving and loading of training data and trained network configurations.
• Provides a training_callback mechanism for custom training procedures and visualization.
• Can be used without a Rails environment.

Maintenance & Community

Contributors include Steven Miers, Ole Krüger, dignati, Michal Pokorny, Scott Li, and alex.slotty. A sample project using RubyFann for tic-tac-toe is available.

Licensing & Compatibility

The license is not explicitly stated in the README, but it is a Ruby Gem. Compatibility with commercial or closed-source applications would depend on the underlying FANN library's license and any explicit licensing for this Ruby wrapper.

Limitations & Caveats

The README does not specify the license for the RubyFann gem itself, which could impact commercial use. It also assumes the user will understand the underlying FANN library, directing them to external documentation for core concepts.