{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output after training:  [[  6.55109972e-03   9.93684857e-01   9.93925710e-01   6.62304973e-03]\n",
      " [  1.71082162e-03   9.97516440e-01   9.97766376e-01   1.82685927e-03]\n",
      " [  2.05800960e-03   9.98268211e-01   9.97548919e-01   1.77362990e-03]\n",
      " [  5.35659849e-04   9.99320839e-01   9.99100767e-01   4.87503198e-04]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "#sigmoid\n",
    "def nonlin(x, deriv=False):\n",
    "    if (deriv==True):\n",
    "        return x*(1-x)\n",
    "    return 1/(1+np.exp(-x))\n",
    "\n",
    "#input data\n",
    "x=np.array([[0,0,1],[0,1,1],[1,0,1],[1,1,1]])\n",
    "\n",
    "#output data\n",
    "y=np.array([0,1,1,0]).T\n",
    "\n",
    "#seed random numbers to make calculation\n",
    "np.random.seed(1)\n",
    "\n",
    "#initialize weights with mean=0\n",
    "syn0=2*np.random.random((3,4))-1\n",
    "\n",
    "for iter in range(10000):\n",
    "    #forward propogation\n",
    "    l0=x\n",
    "    l1=nonlin(np.dot(l0,syn0))\n",
    "    l1_error=y-l1\n",
    "    #multiply error by slope of sigmoid at values of l1\n",
    "    l1_delta=l1_error*nonlin(l1,True)\n",
    "    #update weights\n",
    "    syn0+=np.dot(l0.T, l1_delta)\n",
    "    \n",
    "print(\"Output after training: \",l1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
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