neural network exploration

ann.clj

(ns machine-learning.ann
  (:use clojure.core.matrix)
  (:require [clojure.core.matrix.impl.mathsops :as m]
            [clojure.core.matrix.operators :as o]))


(defn make-layers [coll]
  "Takes a coll like [3 4 2] and uses it to make an architecture of 3 layers with an input layer of 3 nodes, hidden layer of 4, etc. "
  (let [make-layer (fn [number-of-nodes] (vec (take number-of-nodes (repeat 0))))
        input-layer (make-layer (first coll))
        output-layer [(make-layer (last coll))]
        hidden-layers (vec (for [n (rest (butlast coll))] (make-layer n)))]
    (into (vec (cons input-layer hidden-layers)) output-layer)))

(defn initialize-weights [layers]
  (loop [head (first layers), tail (rest layers), weights []]
    (if (empty? tail) weights
      (recur (first tail)
             (rest tail) 
             (into weights [(vec (for [i (range (* (count head) (count (first tail))))]
                                   (rand)))])))))

(defn make-neural-network [layers weights] 
  (into (vec (interleave layers weights)) [(last layers)]))

(defn propogate-forward [inputs nn]
  (let [weights (vec (for [i nn :when (not (even? (.indexOf nn i)))] i))]
    (mapv #(m/tanh %)
      (mapv #(reduce + %) (o/* inputs (transpose weights))))))
    
(defn propogate-back [output expected nn] ;;TODO)
    
(defn pprint [coll]
  (println (reverse (into [] (interpose "\n" coll)))))