Artificial Neural Net

links-own [weight]

breed [bias-nodes bias-node]
breed [input-nodes input-node]
breed [output-nodes output-node]
breed [hidden-nodes hidden-node]

turtles-own [activation err]

globals [
  epoch-error
  input-node-1   ;; keep the input and output nodes
  input-node-2   ;; in global variables so we can
  output-node-1  ;; refer to them directly
]

;;;
;;; SETUP PROCEDURES
;;;

to setup
  clear-all
  ask patches [ set pcolor gray + 2 ]
  set-default-shape bias-nodes "bias-node"
  set-default-shape input-nodes "circle"
  set-default-shape output-nodes "output-node"
  set-default-shape hidden-nodes "output-node"
  setup-nodes
  setup-links
  propagate
  reset-ticks
end 

to setup-nodes
  create-bias-nodes 1 [ setxy -5 5 ]
  ask bias-nodes [ set activation 1 ]
  create-input-nodes 1
  [ setxy -5 -1
    set input-node-1 self ]
  create-input-nodes 1
  [ setxy -5 1
    set input-node-2 self ]
  ask input-nodes [ set activation random 2 ]
  create-hidden-nodes 1 [ setxy 0 -1 ]
  create-hidden-nodes 1 [ setxy 0 1 ]
  ask hidden-nodes
  [ set activation random 2
    set size 1.5 ]
  create-output-nodes 1
  [ setxy 5 0
    set output-node-1 self ]
  ask output-nodes [ set activation random 2 ]
end 

to setup-links
  connect-all bias-nodes hidden-nodes
  connect-all bias-nodes output-nodes
  connect-all input-nodes hidden-nodes
  connect-all hidden-nodes output-nodes
end 

to connect-all [nodes1 nodes2]
  ask nodes1 [
    create-links-to nodes2 [
      set weight random-float 0.2 - 0.1
    ]
  ]
end 

to recolor
  ask turtles [
    set color item (step activation) [black white]
  ]
  ask links [
    set thickness 0.1 * abs weight
    ifelse weight > 0
      [ set color red ]
      [ set color blue ]
  ]
end 

;;;
;;; TRAINING PROCEDURES
;;;

to train
  set epoch-error 0
  repeat examples-per-epoch [
    ask input-nodes [ set activation random 2 ]
    propagate
    back-propagate
  ]
  tick
  set epoch-error epoch-error / examples-per-epoch
  plotxy ticks epoch-error
end 

;;;
;;; FUNCTIONS TO LEARN
;;;

to-report target-answer
  let a [activation] of input-node-1 = 1
  let b [activation] of input-node-2 = 1
  report ifelse-value run-result
    (word "a " target-function " b") [1][0]
end 

;;;
;;; PROPAGATION PROCEDURES
;;;

;; carry out one calculation from beginning to end

to propagate
  ask hidden-nodes [ set activation new-activation ]
  ask output-nodes [ set activation new-activation ]
  recolor
end 

to-report new-activation  ;; node procedure
  report sigmoid sum [[activation] of end1 * weight] of my-in-links
end 

;; changes weights to correct for errors

to back-propagate
  let example-error 0
  let answer target-answer

  ask output-node-1 [
    set err activation * (1 - activation) * (answer - activation)
    set example-error example-error + ( (answer - activation) ^ 2 )
  ]
  set epoch-error epoch-error + example-error
  ask hidden-nodes [
    set err activation * (1 - activation) * sum [weight * [err] of end2] of my-out-links
  ]
  ask links [
    set weight weight + learning-rate * [err] of end2 * [activation] of end1
  ]
end 

;;;
;;; MISC PROCEDURES
;;;

;; computes the sigmoid function given an input value and the weight on the link

to-report sigmoid [input]
  report 1 / (1 + e ^ (- input))
end 

;; computes the step function given an input value and the weight on the link

to-report step [input]
  report ifelse-value (input > 0.5) [1][0]
end 

;;;
;;; TESTING PROCEDURES
;;;

;; test runs one instance and computes the output

to test
  ;; output the result
  ifelse test-success? input-1 input-2
    [ user-message "Correct." ]
    [ user-message "Incorrect." ]
end 

to-report test-success? [n1 n2]
  ask input-node-1 [ set activation n1 ]
  ask input-node-2 [ set activation n2 ]
  propagate
  report target-answer = step [activation] of one-of output-nodes
end