# `RubyFann`¶ ↑

Fast AI

— Neural Networks in `ruby`

`RubyFann`, or “ruby-fann” is a Ruby Gem (no Rails required) that binds to FANN (Fast Artificial Neural Network) from within a ruby/rails environment. FANN is a is a free native open source neural network library, which implements multilayer artificial neural networks, supporting both fully-connected and sparsely-connected networks. It is easy to use, versatile, well documented, and fast. `RubyFann` makes working with neural networks a breeze using `ruby`, with the added benefit that most of the heavy lifting is done natively.

A talk given by our friend Ethan from Big-Oh Studios at Lone Star Ruby 2013: confreaks.com/videos/2609-lonestarruby2013-neural-networks-with-rubyfann

## Installation¶ ↑

Add this line to your application’s Gemfile:

    gem 'ruby-fann'

And then execute:

    $ bundle

Or install it yourself as:

    $ gem install ruby-fann

## Usage¶ ↑

First, Go here & read about FANN. You don’t need to install it before using the gem, but understanding FANN will help you understand what you can do with the ruby-fann gem: leenissen.dk/fann/

## Documentation:¶ ↑

ruby-fann documentation: tangledpath.github.io/ruby-fann/index.html

### Example training & subsequent execution:¶ ↑

    require 'ruby-fann'
      train = RubyFann::TrainData.new(:inputs=>[[0.3, 0.4, 0.5], [0.1, 0.2, 0.3]], :desired_outputs=>[[0.7], [0.8]])
      fann = RubyFann::Standard.new(:num_inputs=>3, :hidden_neurons=>[2, 8, 4, 3, 4], :num_outputs=>1)
      fann.train_on_data(train, 1000, 10, 0.1) # 1000 max_epochs, 10 errors between reports and 0.1 desired MSE (mean-squared-error)
      outputs = fann.run([0.3, 0.2, 0.4])

### Save training data to file and use it later (continued from above)¶ ↑

    train.save('verify.train')
      train = RubyFann::TrainData.new(:filename=>'verify.train')
      # Train again with 10000 max_epochs, 20 errors between reports and 0.01 desired MSE (mean-squared-error)
      # This will take longer:
      fann.train_on_data(train, 10000, 20, 0.01)

### Save trained network to file and use it later (continued from above)¶ ↑

    fann.save('foo.net')
      saved_nn = RubyFann::Standard.new(:filename=>"foo.net")
      saved_nn.run([0.3, 0.2, 0.4])

### Custom training using a callback method¶ ↑

This callback function can be called during training when using train_on_data, train_on_file or cascadetrain_on_data.

It is very useful for doing custom things during training. It is recommended to use this function when implementing custom training procedures, or when visualizing the training in a GUI etc. The args which the callback function takes is the parameters given to the train_on_data, plus an epochs parameter which tells how many epochs the training have taken so far.

The callback method should return an integer, if the callback function returns -1, the training will terminate.

The callback (training_callback) will be automatically called if it is implemented on your subclass as follows:

    class MyFann < RubyFann::Standard
      def training_callback(args)
        puts "ARGS: #{args.inspect}"
        0
      end
    end

### A sample project using `RubyFann` to play tic-tac-toe¶ ↑

github.com/bigohstudios/tictactoe

## Contributors¶ ↑

1. Steven Miers

2. Ole Krüger

3. dignati

4. Michal Pokorny

5. Scott Li (locksley)

6. alex.slotty

## Contributing¶ ↑

1. Fork it

2. Create your feature branch (`git checkout -b my-new-feature`)

3. Commit your changes (`git commit -am 'Add some feature'`)

4. Push to the branch (`git push origin my-new-feature`)

5. Create new Pull Request