Ruby Neural Network example

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DATE: May 22, 2015, 6:27 a.m.

FORMAT: Ruby

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  1. require 'ruby-fann'
  3. train = RubyFann::TrainData.new(
  4. inputs: [
  5. [0,0,1,0,1,1,0,0,1,0,0,1,0,0,1], [1,1,1,0,0,1,1,1,1,1,0,0,1,1,1],
  6. [1,1,1,0,0,1,1,1,1,0,0,1,1,1,1], [1,0,1,1,0,1,1,1,1,0,0,1,0,0,1],
  7. [1,1,1,1,0,0,1,1,1,0,0,1,1,1,1], [1,1,1,1,0,0,1,1,1,1,0,1,1,1,1],
  8. [1,1,1,0,0,1,0,1,0,1,0,0,1,0,0], [1,1,1,1,0,1,1,1,1,1,0,1,1,1,1],
  9. [1,1,1,1,0,1,1,1,1,0,0,1,1,1,1]
  10. ],
  11. desired_outputs: [ [1,0,0,0,0,0,0,0,0], [0,1,0,0,0,0,0,0,0],
  12. [0,0,1,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0],
  13. [0,0,0,0,1,0,0,0,0], [0,0,0,0,0,1,0,0,0],
  14. [0,0,0,0,0,0,1,0,0], [0,0,0,0,0,0,0,1,0],
  15. [0,0,0,0,0,0,0,0,1] ]
  16. )
  18. fann = RubyFann::Standard.new(
  19. num_inputs: 15,
  20. hidden_neurons: [30],
  21. num_outputs: 9
  22. )
  24. fann.learning_rate = 0.5
  25. fann.momentum = 0.5
  27. fann.train_on_data(train, 10000, 1000, 0.001)
  29. outputs = fann.run([0,0,1,0,1,1,0,0,1,0,0,1,0,0,1])
  30. m = outputs.max
  31. puts "Result: #{( outputs.find_index { |x| x == m } ) + 1}"
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