Example of a _very_ simple feed-forward neural network.

Neural_Net_demo.rb

class Link

  def initialize(to, weight)
    @to = to
    @weight = weight
  end

  def propagate(activation)
    puts "  propagating #{activation} * #{@weight} = #{@weight * activation} to #{@to.name}"
    puts "    old activation: #{@to.activation}"

    @to.activation += @weight * activation

    puts "    new activation: #{@to.activation}"
  end
end

class Neuron
  attr_accessor :activation
  attr_reader   :name

  def initialize(name)
    @name = name
    @links = []
    @activation = 0.0

    puts "New Node '#{name}'"
  end

  def add_link_to(neuron, weight)
    puts "Adding Link from #{@name} to #{neuron.name}, weight #{weight}."
    @links << Link.new(neuron, weight)
  end

  def activate
    print "Activating Neuron #{@name}: #{@activation} -> "

    # Simple threshold function
    @activation = (@activation > 0.5 ? 1.0 : 0.0)

    puts "#{@activation}"

    @links.each do |link|
      link.propagate(@activation)
    end
  end
end

if $0 == __FILE__
  # Boring, simple network that computes AND

  nodes = [ Neuron.new("A"), Neuron.new("B"), # Input Neurons
                                              # No Hidden Neurons
            Neuron.new("Result") ]            # Output Neuron

  nodes[0].add_link_to(nodes[2], 0.26) # A -> Result
  nodes[1].add_link_to(nodes[2], 0.26) # B -> Result

  for a in [0.0, 1.0]
    for b in [0.0, 1.0]
      # Reset the network
      nodes.each { |node| node.activation = 0.0 }

      # Feed the input in
      nodes[0].activation = a
      nodes[1].activation = b

      puts "\nComputing #{a} AND #{b}...\n"

      # Evaluate the network by activating each node
      nodes.each { |n| n.activate }

      # Extract output from the activation of the output node
      output = nodes[2].activation

      puts "\n#{a} AND #{b} = #{output}"
    end
  end

end


# Output:
#
# New Node 'A'
# New Node 'B'
# New Node 'Result'
# Adding Link from A to Result, weight 0.26.
# Adding Link from B to Result, weight 0.26.
# 
# Computing 0.0 AND 0.0...
# Activating Neuron A: 0.0 -> 0.0
#   propagating 0.0 * 0.26 = 0.0 to Result
#     old activation: 0.0
#     new activation: 0.0
# Activating Neuron B: 0.0 -> 0.0
#   propagating 0.0 * 0.26 = 0.0 to Result
#     old activation: 0.0
#     new activation: 0.0
# Activating Neuron Result: 0.0 -> 0.0
# 
# 0.0 AND 0.0 = 0.0
# 
# Computing 0.0 AND 1.0...
# Activating Neuron A: 0.0 -> 0.0
#   propagating 0.0 * 0.26 = 0.0 to Result
#     old activation: 0.0
#     new activation: 0.0
# Activating Neuron B: 1.0 -> 1.0
#   propagating 1.0 * 0.26 = 0.26 to Result
#     old activation: 0.0
#     new activation: 0.26
# Activating Neuron Result: 0.26 -> 0.0
# 
# 0.0 AND 1.0 = 0.0
# 
# Computing 1.0 AND 0.0...
# Activating Neuron A: 1.0 -> 1.0
#   propagating 1.0 * 0.26 = 0.26 to Result
#     old activation: 0.0
#     new activation: 0.26
# Activating Neuron B: 0.0 -> 0.0
#   propagating 0.0 * 0.26 = 0.0 to Result
#     old activation: 0.26
#     new activation: 0.26
# Activating Neuron Result: 0.26 -> 0.0
# 
# 1.0 AND 0.0 = 0.0
# 
# Computing 1.0 AND 1.0...
# Activating Neuron A: 1.0 -> 1.0
#   propagating 1.0 * 0.26 = 0.26 to Result
#     old activation: 0.0
#     new activation: 0.26
# Activating Neuron B: 1.0 -> 1.0
#   propagating 1.0 * 0.26 = 0.26 to Result
#     old activation: 0.26
#     new activation: 0.52
# Activating Neuron Result: 0.52 -> 1.0
# 
# 1.0 AND 1.0 = 1.0