https://github.com/lsegal/brainz

Brainz is an Artificial Neural Network library written in Ruby
https://github.com/lsegal/brainz

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Brainz is an Artificial Neural Network library written in Ruby

• Host: GitHub
• URL: https://github.com/lsegal/brainz
• Owner: lsegal
• Created: 2009-08-24T01:46:23.000Z (over 15 years ago)
• Default Branch: master
• Last Pushed: 2009-08-24T01:48:05.000Z (over 15 years ago)
• Last Synced: 2024-10-31T06:51:32.419Z (2 months ago)
• Language: Ruby
• Homepage:
• Size: 71.3 KB
• Stars: 12
• Watchers: 4
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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_Neural_Network)

  * [http://en.wikipedia.org/wiki/Artificial_Neuron](http://en.wikipedia.org/wiki/Artificial_Neuron)

  * [http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html](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.