Machine learning algorithms in ruby
The algorithms themselves are not language specific. You can implement them using any language you want. For maximum efficiency you will want to use matrix/vector based computing.
Ruby has a built in Matrix class that you can use to implement these algorithms. The implementation will be very similar to the one using Octave. Everything you need to implement the algorithms yourself is included in the base Standard Library for 1.9+.
Octave is used because it provides a thorough and easy Matrix library.
Be sure to check this gist, it has plenty of information:
- Resources for Machine Learning in Ruby
Moreover, following are some noteworthy algorithms libraries (which might or might not be already listed in the gist above):
AI4R
http://www.ai4r.org/ - https://github.com/SergioFierens/ai4r
AI4R is a collection of ruby algorithm implementations, covering several Artificial intelligence fields, and simple practical examples using them. A Ruby playground for AI researchers. It implements:
Genetic algorithms
Self-organized maps (SOM)
Neural Networks: Multilayer perceptron with Backpropagation learning, Hopfield net.
Automatic classifiers (Machine Learning): ID3 (Decision Trees), PRISM (J. Cendrowska, 1987), Multilayer Perceptron, OneR (AKA One Attribute Rule, 1R), ZeroR, Hyperpipes, Naive Bayes, IB1 (D. Aha, D. Kibler - 1991).
Data clustering: K-means, Bisecting k-means, Single linkage, Complete linkage, Average linkage, Weighted Average linkage, Centroid linkage, Median linkage, Ward's method linkage, Diana (Divisive Analysis)
kmeans-clusterer - k-means clustering in Ruby:
https://github.com/gbuesing/kmeans-clusterer
kmeans-clustering - A simple Ruby gem for parallelized k-means clustering:
https://github.com/vaneyckt/kmeans-clustering
tlearn-rb - Recurrent Neural Network library for Ruby:
https://github.com/josephwilk/tlearn-rb
TensorFlow Ruby wrapper - As of this writing it seems that work is about to begin in building a TensorFlow Ruby API:
https://github.com/tensorflow/tensorflow/issues/50#issuecomment-216200945
If JRuby is a viable alternative to Ruby for you:
weka-jruby - Machine Learning & Data Mining with JRuby based on the Weka Java library:
https://github.com/paulgoetze/weka-jruby
jruby_mahout - JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby:
https://github.com/vasinov/jruby_mahout
UPDATE: the Resources for Machine Learning in Ruby gist above is now starting to be mantained as a repository: https://github.com/arbox/machine-learning-with-ruby