anchor.diff:  
   (defmethod initialize-instance :after ((neuron t-neuron) &key)
     (setf (id neuron) (incf (next-id (net neuron))))
  +  (when (biased neuron)
  +    (setf (input neuron) 1.0))
     (setf (transfer-function neuron)
           (transfer-functions (transfer-tag neuron) :function))
     (setf (transfer-derivative neuron)
  
positive.code:    (layer-type :accessor layer-type :initarg :layer-type :initform nil)
     (biased :reader biased :initarg :biased :initform nil :type boolean)
     (id :accessor id :type integer)
     (input :accessor input :initform 0.0 :type real)
     (transfer-tag :accessor transfer-tag :initarg :transfer-tag :initform :sigmoid)
     (transfer-function :accessor transfer-function :initform nil)
  
negative.code:          for c = (sqrt (+ (* x x) (* y y)))
           collect (list (vector x y) (vector (if (> c diameter) 0 1))))))
       
  (defun xor-training ()
    (list (list #(0.0 0.0) #(1.0))
          (list #(0.0 1.0) #(0.0))
          (list #(1.0 0.0) #(0.0))
          (list #(1.0 1.0) #(1.0))))
  