gistfile1.as

require 'rubygems'
require 'ruby_fann/neural_network'

# Create Training data with 2 each of inputs(array of 3) & desired outputs(array of 1).
#

inputs = []

(1..100000).each do |i|
  inputs << [(Random.rand(100)/100.0).round(1), (Random.rand(100)/100.0).round(1), (Random.rand(100)/100.0).round(1)]
end

def process(set)
  #puts 'test: '+set[0].to_s
  (set[0] + set[1] + set[2])/3.0
end

outputs = Array.new

inputs.each do |i|
  outputs << [process(i)]
end


training_data = RubyFann::TrainData.new(
  :inputs=>inputs, 
  :desired_outputs=>outputs)

# Create FANN Neural Network to match appropriate training data:
fann = RubyFann::Standard.new(
  :num_inputs=>3, 
  :hidden_neurons=>[3,2], 
  :num_outputs=>1)

# Training using data created above:
fann.train_on_data(training_data, 500, 100, 1e-5)

test_values = inputs[1]

# Run with different input data:
puts fann.run(test_values)
puts 'Result should be: ' + process(test_values).to_s