Brainz

Brainz is a Artificial Neural Network (ANN) library written by Loren Segal. Neural networks are generally used in pattern recognition, signal processing and other data intensive processing problems. ANN's benefit by not having to explicitly define the procedural steps involved in the problem, but rather by training the neural network to return the correct output for the respective inputs. This means that the same neural network can be applied to many different problem sets without much (sometimes any) modification, and therefore make a good general solution to a large set of problem domains. The drawback, however, is that these neural networks require large sets of data to be trained and this training process can be processor intensive.

More information on Artificial Neural Networks can be found on Wikipedia and elsewhere:

• http://en.wikipedia.org/wiki/Artificial_Neural_Network
• http://en.wikipedia.org/wiki/Artificial_Neuron
• http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html

Installation & Requirements

Note: this library requires Ruby 1.9

sudo gem install brainz

Examples

A simple neural network to calculate the bitwise AND operator, 1 & 1, can be defined as:

# Define a 2-2-1 neural network
net = Brainz::Network.new(2, 2, 1) 

# We must train the system first
1000.times do 
  net.train([0, 0], [0])
  net.train([0, 1], [0])
  net.train([1, 0], [0])
  net.train([1, 1], [1])
end

# Now some tests:
p net.run([0, 1]).map(&:round) # => [0]
p net.run([1, 1]).map(&:round) # => [1]

Simply changing the dataset used in training can create a neural network designed to calculate the OR or XOR operation.

License & Copyright

MIT License. Copyright 2009.