; Lab 1
(def n2 (create-net [2 1] :bias true)) ; Создание сети с одним нейроном и двумя входами

(def train-set-and
  [[[0 0] [0]] [[0 1] [0]]
   [[1 0] [0]] [[1 1] [1]]])

(def train-set-or
  [[[0 0] [0]] [[0 1] [1]]
   [[1 0] [1]] [[1 1] [1]]])

(def train-set-vecs
  [[[0.2 0.1] [0]] [[0.3 0.2] [0]]
   [[0.9 0.6] [1]] [[0.9 0.8] [1]]])

; And
(def n2t (training 0.7 0.9 :sigmoid train-set-and 150 n2))

(def errors (reverse (:errors n2t)))

(def results (vec (apply concat (test-results (map first train-set-and) (:current-s n2t)))))

(def my-plot (function-plot (partial nth errors) 0 (dec (count errors-or))))
(view my-plot)

; Or
(def n2t-or (training 0.7 0.9 :sigmoid train-set-or 150 n2))

(def errors-or (reverse (:errors n2t-or)))

(def results-or (vec (apply concat (test-results (map first train-set-or) (:current-s n2t-or)))))

(def my-plot-or (function-plot (partial nth errors-or) 0 (dec (count errors-or))))
(view my-plot-or)

; Vecs
(def n2t-vecs (training 0.7 0.9 :sigmoid train-set-vecs 150 n2))

(def errors-vecs (reverse (:errors n2t-vecs)))

(def results-vecs (vec (apply concat (test-results (map first train-set-vecs) (:current-s n2t-vecs)))))

(def my-plot-vecs (function-plot (partial nth errors-vecs) 0 (dec (count errors-vecs))))
(view my-plot-vecs)