adnn.clj

(let [f (fn [args]
          (let [w1 (nth args 0)
                w2 (nth args 1)
                x (nth args 2)
                y (nth args 3)
                h1 (mmul w1 x)
                h2 (mmul w2 h1)
                ;; TODO dot with vector of ones yields sum
                ;; this is euclidian distance or squared error
                error (** (- (dot [1 1] h2) y) 2)]
            error))

      f-inspect (fn [args]
                  (let [w1 (nth args 0)
                        w2 (nth args 1)
                        x (nth args 2)
                        y (nth args 3)
                        h1 (mmul w1 x)
                        h2 (mmul w2 h1)
                        pred (dot [1 1] h2)
                        error (** (- pred  y) 2)]
                    [h1 h2 error pred]))
      f' (gradient-vector-F f)]
  (loop [i 1000
         w1 [[0.3509216367936949 0.8478685293908089]
             [0.04224372281103417 0.7689310188327428]]
         w2 [[0.7021497660571601 0.4090020951653528]
             [0.9584080028515212 0.3988865031355743]]]
    (if (pos? i)
      (let [x [1 2]
            y 2
            [w1' w2'] (f' [w1 w2 x y])]
        (when (= (mod i 100) 0)
          (let [[h1 h2 error pred] (f-inspect [w1 w2 x y])]
            (prn "h1" h1)
            (prn "h2" h2)
            (prn "Pred:" pred)
            (prn "Error:" error)))
        (recur (dec i) (- w1 (* 0.01 w1')) (- w2 (* 0.01 w2'))))
      [w1 w2] ;; trained model
      )))