anchor.diff:           do (layout-neurons layer :y y :delta h-delta :margin h-margin)))
       net)
     (:method ((layer t-layer) &key y delta margin)
  -    (declare (float y delta margin))
  +    (declare (real y delta margin))
       (loop
          for neuron across (neuron-array layer)
          for x = (+ margin (/ delta 2)) then (+ x delta)
  
positive.code:           :type integer)
     (biased :reader biased :initarg :biased :initform nil :type boolean)
     (id :accessor id :type integer)
     (input :accessor input :initform 0.0 :type float)
     (output :accessor output :initform 0.0 :type float)
     (err :accessor err :initform 0.0 :type float)
     (x-coor :accessor x-coor :initform 0.0 :type float)
     (y-coor :accessor y-coor :initform 0.0 :type float)
     (cxs :accessor cxs :initform nil :type list))
    (:documentation "Describes a neuron.  NET, required, is an object of type t-net that represents the neural network that this neuron is a part of.  LAYER, required, is an object of type t-layer that represents the neural network layer that this neuron belongs to.  If BIASED is true, the neuron will not have incoming connections.  ID is a distinct integer that identifies the neuron. X-COOR and Y-COOR allow this neuron to be placed in 2-dimentional space.  CXS contains the list of outgoing connections (of type t-cx) to other neurons."))
  
  
negative.code:             (height 100.0)
              (h-margin 20.0)
              (v-margin 20.0))
      (declare (real width height h-margin v-margin))
      (let* ((c-width (- width (* h-margin 2)))
             (v-delta (/ (- height (* v-margin 2))
                                (1- (length (layers net))))))
  