Class: Node

Inherits:
Object
  • Object
show all
Defined in:
lib/node.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(sigma) ⇒ Node

Returns a new instance of Node.



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# File 'lib/node.rb', line 4

def initialize sigma
  @input_value = 0
  @forward_nodes = {}
  @weights =[]
  @bias = 0
  @sigma = sigma
end

Instance Attribute Details

#bias ⇒ Object

Returns the value of attribute bias.



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# File 'lib/node.rb', line 3

def bias
  @bias
end

#forward_nodes ⇒ Object (readonly)

Returns the value of attribute forward_nodes.



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# File 'lib/node.rb', line 2

def forward_nodes
  @forward_nodes
end

#input_value ⇒ Object

Returns the value of attribute input_value.



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# File 'lib/node.rb', line 3

def input_value
  @input_value
end

#output_value ⇒ Object (readonly)

Returns the value of attribute output_value.



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# File 'lib/node.rb', line 2

def output_value
  @output_value
end

#sigma ⇒ Object

Returns the value of attribute sigma.



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# File 'lib/node.rb', line 3

def sigma
  @sigma
end

#weights ⇒ Object

Returns the value of attribute weights.



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# File 'lib/node.rb', line 3

def weights
  @weights
end

Instance Method Details

#attach_forward_node(node, sequence) ⇒ Object 



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# File 'lib/node.rb', line 25

def attach_forward_node node, sequence
  if @weights.count <= sequence
    @weights << ((rand * 0.4 ) - 0.2) #sampled from a uniform distribution in range ± 0.2
  end
  
  @forward_nodes[node] = @weights[sequence]
end

#clone ⇒ Object 



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# File 'lib/node.rb', line 57

def clone
  clone = Node.new(self.sigma)
  
  clone.sigma = self.sigma
  clone.bias = self.bias
  
  @weights.each do |weight|
    clone.weights << weight
  end
  
  #being pure we clone the forward node refs as well
  #although in practice these are about to be updated with new nodes
  self.forward_nodes.each do |key,value| 
    clone.forward_nodes[key] = value
  end
  
  clone
end

#detatch_all_forward_nodes ⇒ Object 



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# File 'lib/node.rb', line 33

def detatch_all_forward_nodes
  @forward_nodes = {}
end

#evaluate ⇒ Object

take our input value (sum of weighted outputs of backward connected nodes) , subtract the bias and pass the result through our sigmoid function (tanh) finally then pass on our output value to each of our forward nodes



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# File 'lib/node.rb', line 14

def evaluate
  @output_value = Math.tanh(@input_value - @bias)
  #p "output value #{@output_value}"
  @forward_nodes.each do |node, weight|
    #p "weight #{weight} old input #{node.input_value}"
    node.input_value += @output_value * weight
    #p "new input #{node.input_value}"
  end
  @input_value = 0
end

#fingerprint ⇒ Object 



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# File 'lib/node.rb', line 76

def fingerprint
  fingerprint = "#{@sigma.to_s}:#{@bias.to_s}:"
  @weights.each do |weight|
    fingerprint += "#{weight.to_s},"
  end
  fingerprint = fingerprint.chop + "\n"
end

#mutate ⇒ Object 



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# File 'lib/node.rb', line 37

def mutate
  new_weights = []
  @weights.each do |weight|
    new_weights << weight + @sigma * gaussian_random # new_random_number = average + standard_deviation  * gaussian_rand
  end
  @weights = new_weights
  if @forward_values
    i = 0
    @forward_values.each do |key,value|
      @forward_values[key] = @weights[i]
      i += 1
    end
  end
  
  #mutate the bias
  @bias = @bias + gaussian_random * @sigma

  self
end

#reload(fingerprint) ⇒ Object 



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# File 'lib/node.rb', line 84

def reload fingerprint
  self.detatch_all_forward_nodes
  @weights = []
  self.sigma = fingerprint.split(':')[0].to_f
  self.bias = fingerprint.split(':')[1].to_f
  if fingerprint.split(":").count == 3
    fingerprint.split(":")[2].split(',').each do |weight|
      @weights << weight.to_f
    end
  end
end