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https://github.com/tangledpath/ruby-fann
Ruby library for interfacing with FANN (Fast Artificial Neural Network)
https://github.com/tangledpath/ruby-fann
ai c fann machine-learning native neural-network neural-networks nn ruby-fann ruby-gem rubygems
Last synced: 2 months ago
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Ruby library for interfacing with FANN (Fast Artificial Neural Network)
- Host: GitHub
- URL: https://github.com/tangledpath/ruby-fann
- Owner: tangledpath
- License: mit
- Created: 2013-03-26T17:44:46.000Z (almost 12 years ago)
- Default Branch: master
- Last Pushed: 2024-03-25T08:19:23.000Z (10 months ago)
- Last Synced: 2024-10-01T06:27:26.394Z (3 months ago)
- Topics: ai, c, fann, machine-learning, native, neural-network, neural-networks, nn, ruby-fann, ruby-gem, rubygems
- Language: C
- Homepage: https://github.com/tangledpath/ruby-fann
- Size: 2.45 MB
- Stars: 495
- Watchers: 30
- Forks: 41
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: History.txt
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-ruby - ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network). (Machine Learning)
README
# RubyFann
_Fast_ **AI**
---
Neural Networks in `ruby`
[](http://badge.fury.io/rb/ruby-fann)
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: http://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:
http://leenissen.dk/fann/
## Documentation:
*ruby-fann documentation:*
http://tangledpath.github.io/ruby-fann/index.html
### Example training & subsequent execution:
```ruby
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)
```ruby
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)
```ruby
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:
```ruby
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
https://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