03_lecture_tryouts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction with a multiple network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight = [0.1, 0.2, 0.0]\n",
    "\n",
    "def neural_network(input_, weight):\n",
    "    pred = w_sum(input_, weight)\n",
    "    return pred\n",
    "\n",
    "\n",
    "def w_sum(a, b): \n",
    "    assert(len(a) == len(b)) \n",
    "    output = 0\n",
    "    for i in range(len(a)):\n",
    "        output += (a[i] * b[i])\n",
    "    return output\n",
    "\n",
    "offsides = [8.5, 9.5, 9.9, 9.0]\n",
    "wlrec = [0.65, 0.8, 0.8, 0.9]\n",
    "nfans = [1.2, 1.3, 0.5, 1.0]\n",
    "\n",
    "win_or_lose_binary = [1, 1, 0, 1]\n",
    "\n",
    "true = win_or_lose_binary[0]\n",
    "input_ = [offsides[0],wlrec[0],nfans[0]]\n",
    "pred = neural_network(input_, weight)\n",
    "\n",
    "error = (pred - true) ** 2\n",
    "delta = pred - true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0003999999999999963, -0.019999999999999907)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error, delta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient descent for NN with multiple INPUTS ( 3 in - 1 out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True : 1 | Pred : 0.8600000000000001 \n",
      "Weight_deltas: [-1.189999999999999, -0.09099999999999994, -0.16799999999999987] \n",
      "Weights: [0.1119, 0.20091, -0.09832], \n",
      "error : 0.01959999999999997, \n",
      "delta: -0.1399999999999999\n"
     ]
    }
   ],
   "source": [
    "def neural_network(input, weights):\n",
    "    \"\"\"\n",
    "    Outputs sum of each input * weight pair\n",
    "    \"\"\"\n",
    "    out = 0\n",
    "    for i in range(len(input)):\n",
    "        out += (input[i] * weights[i])\n",
    "    return out\n",
    "\n",
    "\n",
    "def ele_mul(delta_, input_vector):\n",
    "    \"\"\"\n",
    "    Calculates derivative\n",
    "    \"\"\"\n",
    "    out = [0,0,0]\n",
    "    for i in range(len(out)):\n",
    "        out[i] = input_vector[i] * delta_\n",
    "    return out\n",
    "\n",
    "\n",
    "# Featured\n",
    "offsides = [8.5, 9.5, 9.9, 9.0]\n",
    "wlrec = [0.65, 0.8, 0.8, 0.9]\n",
    "nfans = [1.2, 1.3, 0.5, 1.0]\n",
    "\n",
    "# Y\n",
    "win_or_lose_binary = [1, 1, 0, 1]\n",
    "\n",
    "true = win_or_lose_binary[0]\n",
    "\n",
    "alpha = 0.01\n",
    "\n",
    "# initial weights\n",
    "weights = [0.1, 0.2, -.1]\n",
    "input_ = [offsides[0],wlrec[0],nfans[0]]\n",
    "\n",
    "for iter in range(1):\n",
    "    pred = neural_network(input_, weights)\n",
    "    error = (pred - true) ** 2\n",
    "    # deriction of the slope \n",
    "    # derivative = the ratio of the function gain to the argument gain\n",
    "    # delta - function gain | \n",
    "    delta = pred - true\n",
    "    # derivative\n",
    "    weight_deltas=ele_mul(delta, input_)\n",
    "    for i in range(len(weights)):\n",
    "        weights[i]-=alpha*weight_deltas[i]\n",
    "        \n",
    "print(\"True : {} | Pred : {} \\nWeight_deltas: {} \\nWeights: {}, \\nerror : {}, \\ndelta: {}\".format(true, pred, weight_deltas, weights, error, delta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Going through last cell step by step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1119, 0.20091, -0.09832]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = 0.1 * 8.5 + 0.2 * 0.65 + 1.2 * (-0.1) # ==> 0.8600000000000001\n",
    "error = (pred - 1) ** 2 # ==> 0.01959999999999997\n",
    "delta = pred - true # ==> -0.1399999999999999\n",
    "\n",
    "weight_deltas = [0, 0, 0]\n",
    "weight_deltas[0] = delta * 8.5\n",
    "weight_deltas[1] = delta * 0.65\n",
    "weight_deltas[2] = delta * 1.2\n",
    "\n",
    "weights = [0, 0, 0]\n",
    "weights[0] = 0.1 - 0.01 * weight_deltas[0]\n",
    "weights[1] = 0.2 - 0.01 * weight_deltas[1]\n",
    "weights[2] = -0.1 - 0.01 * weight_deltas[2]\n",
    "\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient descent for NN with multiple OUTPUTS (1 in - 3 out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pred :  [0.07273500000000001, 0.1305915, -0.063908]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.107172225, 0.2574215525, -0.09416598000000001]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight = [0.3, 0.2, 0.9]\n",
    "\n",
    "def neural_network(input_, weight):\n",
    "    pred = ele_mul(input_, weight) \n",
    "    return pred\n",
    "\n",
    "def ele_mul(number,vector): \n",
    "    output = [0,0,0]\n",
    "    assert(len(output) == len(vector)) \n",
    "    for i in range(len(vector)):\n",
    "        output[i] = number * vector[i] \n",
    "    return output\n",
    "\n",
    "input1 = wlrec[0]\n",
    "true = [0, 1, 0]\n",
    "\n",
    "pred = neural_network(input1, weights)\n",
    "print('Pred : ', pred)\n",
    "error = [0, 0, 0]\n",
    "delta = [0, 0, 0]\n",
    "\n",
    "for i in range(len(true)):\n",
    "    error[i] = (pred[i] - true[i]) ** 2\n",
    "    delta[i] = pred[i] - true[i]\n",
    "    \n",
    "def scalar_ele_mul(number,vector):\n",
    "    output = [0,0,0]\n",
    "    assert(len(output) == len(vector))\n",
    "    for i in range(len(vector)):\n",
    "        output[i] = number * vector[i]\n",
    "    return output\n",
    "\n",
    "weight_deltas = scalar_ele_mul(input1, delta)\n",
    "\n",
    "alpha = 0.1\n",
    "for i in range(len(weights)):\n",
    "    weights[i] -= (weight_deltas[i] * alpha)\n",
    "\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient descent for NN with multiple INPUTS and OUTPUTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_ : [8.5, 0.65, 1.2] \n",
      "delta: [0.45500000000000007, -0.019999999999999907, 0.8650000000000001] \n",
      "error: [0.20702500000000007, 0.0003999999999999963, 0.7482250000000001]\n",
      "weight_deltas : \n",
      " [[ 3.8675  -0.17     7.3525 ]\n",
      " [ 0.29575 -0.013    0.56225]\n",
      " [ 0.546   -0.024    1.038  ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[0.061325, 0.1017, -0.373525],\n",
       " [0.0970425, 0.20013, -0.005622500000000002],\n",
       " [-0.0054600000000000004, 1.30024, 0.08962]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "            #offside #win #fans\n",
    "weights = [ [0.1, 0.1, -0.3], #injuries?\n",
    "            [0.1, 0.2, 0.0], #win? \n",
    "            [0.0, 1.3, 0.1] ] #happy\n",
    "\n",
    "def w_sum(a,b): \n",
    "    \"\"\"\n",
    "    Sum of multiplication of one output node\n",
    "    \"\"\"\n",
    "    assert(len(a) == len(b)) \n",
    "    output = 0\n",
    "    for i in range(len(a)):\n",
    "        output += (a[i] * b[i]) \n",
    "    return output\n",
    "\n",
    "\n",
    "def vec_mat_mul(vect,matrix): \n",
    "    \"\"\"\n",
    "    Sum of multiplication of all output nodes\n",
    "    \"\"\"\n",
    "    assert(len(vect) == len(matrix)) \n",
    "    output = [0,0,0]\n",
    "    for i in range(len(vect)):\n",
    "        output[i] = w_sum(vect,matrix[i]) \n",
    "    return output\n",
    "\n",
    "def neural_network(input_, weight): \n",
    "    pred = vec_mat_mul(input_, weight) \n",
    "    return pred\n",
    "\n",
    "Offsides = [8.5, 9.5, 9.9, 9.0]\n",
    "wlrec = [0.65,0.8, 0.8, 0.9]\n",
    "nfans = [1.2, 1.3, 0.5, 1.0]\n",
    "hurt = [0.1, 0.0, 0.0, 0.1]\n",
    "win = [ 1, 1, 0, 1]\n",
    "happy = [0.1, 0.0, 0.1, 0.2]\n",
    "alpha = 0.01\n",
    "\n",
    "input_ = [offsides[0],wlrec[0],nfans[0]]\n",
    "true = [hurt[0], win[0], happy[0]]\n",
    "pred = neural_network(input_, weights)\n",
    "error = [0, 0, 0]\n",
    "delta = [0, 0, 0]\n",
    "\n",
    "for i in range(len(true)):\n",
    "    error[i] = (pred[i] - true[i]) ** 2\n",
    "    delta[i] = pred[i] - true[i]\n",
    "    \n",
    "def outer_prod(vec_a, vec_b):\n",
    "    out = np.zeros((len(vec_a),len(vec_b)))\n",
    "    for i in range(len(vec_a)):\n",
    "        for j in range(len(vec_b)):\n",
    "            out[i][j] = vec_a[i]*vec_b[j]\n",
    "    print(\"weight_deltas : \\n\", out)\n",
    "    return out\n",
    "\n",
    "print(\"input_ : {} \\ndelta: {} \\nerror: {}\".format(input_, delta, error))\n",
    "\n",
    "weight_deltas = outer_prod(input_, delta)\n",
    "\n",
    "for i in range(len(weights)):\n",
    "    for j in range(len(weights[0])):\n",
    "        weights[i][j] -= alpha * weight_deltas[i][j]\n",
    "        \n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Correlation - streetlights example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "streetlights.shape:  (6, 3)\n",
      "Prediction:-0.19999999999999996\n",
      "Prediction:-0.19999999999999996\n",
      "Prediction:-0.5599999999999999\n",
      "Prediction:0.6160000000000001\n",
      "Prediction:0.17279999999999995\n",
      "Prediction:0.17552\n",
      "Error:2.6561231104\n",
      "\n",
      "Prediction:0.14041599999999999\n",
      "Prediction:0.3066464\n",
      "Prediction:-0.34513824\n",
      "Prediction:1.006637344\n",
      "Prediction:0.4785034751999999\n",
      "Prediction:0.26700416768\n",
      "Error:0.9628701776715985\n",
      "\n",
      "Prediction:0.213603334144\n",
      "Prediction:0.5347420299776\n",
      "Prediction:-0.26067345110016\n",
      "Prediction:1.1319428845096962\n",
      "Prediction:0.6274723921901568\n",
      "Prediction:0.25433999330650114\n",
      "Error:0.5509165866836797\n",
      "\n",
      "Prediction:0.20347199464520088\n",
      "Prediction:0.6561967149569552\n",
      "Prediction:-0.221948503950995\n",
      "Prediction:1.166258650532124\n",
      "Prediction:0.7139004922542389\n",
      "Prediction:0.21471099528371604\n",
      "Error:0.36445836852222424\n",
      "\n",
      "Prediction:0.17176879622697283\n",
      "Prediction:0.7324724146523222\n",
      "Prediction:-0.19966478845083285\n",
      "Prediction:1.1697769945341199\n",
      "Prediction:0.7719890116601171\n",
      "Prediction:0.17297997428859369\n",
      "Error:0.2516768662079895\n",
      "\n",
      "Prediction:0.13838397943087496\n",
      "Prediction:0.7864548139561468\n",
      "Prediction:-0.1836567869927348\n",
      "Prediction:1.163248019006011\n",
      "Prediction:0.8148799260629888\n",
      "Prediction:0.1362897844408577\n",
      "Error:0.17797575048089034\n",
      "\n",
      "Prediction:0.10903182755268614\n",
      "Prediction:0.8273717796510367\n",
      "Prediction:-0.17037324196481937\n",
      "Prediction:1.1537962739591756\n",
      "Prediction:0.8481754931254761\n",
      "Prediction:0.1059488041691444\n",
      "Error:0.12864460733422164\n",
      "\n",
      "Prediction:0.0847590433353155\n",
      "Prediction:0.859469609749935\n",
      "Prediction:-0.1585508402022421\n",
      "Prediction:1.1438418857156731\n",
      "Prediction:0.8746623946770374\n",
      "Prediction:0.08148074110264475\n",
      "Error:0.09511036950476208\n",
      "\n",
      "Prediction:0.06518459288211581\n",
      "Prediction:0.8850633823431538\n",
      "Prediction:-0.14771905585408038\n",
      "Prediction:1.1341830033853888\n",
      "Prediction:0.8959860107828534\n",
      "Prediction:0.0619780399014222\n",
      "Error:0.07194564247043436\n",
      "\n",
      "Prediction:0.04958243192113776\n",
      "Prediction:0.9056327614440267\n",
      "Prediction:-0.13768337501215525\n",
      "Prediction:1.1250605910610996\n",
      "Prediction:0.9132624284442169\n",
      "Prediction:0.04653264583708144\n",
      "Error:0.05564914990717743\n",
      "\n",
      "Prediction:0.03722611666966513\n",
      "Prediction:0.922234066504699\n",
      "Prediction:-0.12834662236261596\n",
      "Prediction:1.116526024487899\n",
      "Prediction:0.9273167105424409\n",
      "Prediction:0.03435527296969987\n",
      "Error:0.04394763937673939\n",
      "\n",
      "Prediction:0.027484218375759886\n",
      "Prediction:0.9356694192994068\n",
      "Prediction:-0.11964712469387503\n",
      "Prediction:1.1085678053734553\n",
      "Prediction:0.9387866868342218\n",
      "Prediction:0.024792915481941458\n",
      "Error:0.035357967050948465\n",
      "\n",
      "Prediction:0.019834332385553155\n",
      "Prediction:0.946566624680628\n",
      "Prediction:-0.11153724870006754\n",
      "Prediction:1.1011550767549563\n",
      "Prediction:0.948176009263518\n",
      "Prediction:0.017315912033043404\n",
      "Error:0.02890700056547436\n",
      "\n",
      "Prediction:0.013852729626434732\n",
      "Prediction:0.9554239432448665\n",
      "Prediction:-0.10397589092234266\n",
      "Prediction:1.0942524239871314\n",
      "Prediction:0.9558862588907013\n",
      "Prediction:0.011498267782398985\n",
      "Error:0.023951660591138853\n",
      "\n",
      "Prediction:0.009198614225919194\n",
      "Prediction:0.9626393189117293\n",
      "Prediction:-0.09692579020989642\n",
      "Prediction:1.087824783849832\n",
      "Prediction:0.9622390773804066\n",
      "Prediction:0.006998674002545002\n",
      "Error:0.020063105176016144\n",
      "\n",
      "Prediction:0.005598939202035996\n",
      "Prediction:0.9685315005838672\n",
      "Prediction:-0.09035250869077546\n",
      "Prediction:1.0818389613301889\n",
      "Prediction:0.9674926590701334\n",
      "Prediction:0.003544193999268516\n",
      "Error:0.016952094519447087\n",
      "\n",
      "Prediction:0.0028353551994148157\n",
      "Prediction:0.9733561723362383\n",
      "Prediction:-0.0842239920152223\n",
      "Prediction:1.0762639960116431\n",
      "Prediction:0.9718545378681842\n",
      "Prediction:0.0009168131382832068\n",
      "Error:0.014420818295271236\n",
      "\n",
      "Prediction:0.0007334505106265654\n",
      "Prediction:0.9773186039296565\n",
      "Prediction:-0.07851033295953944\n",
      "Prediction:1.0710711494147542\n",
      "Prediction:0.9754916865567282\n",
      "Prediction:-0.0010574652271341245\n",
      "Error:0.012331739998443648\n",
      "\n",
      "Prediction:-0.0008459721817072885\n",
      "Prediction:0.9805836929862668\n",
      "Prediction:-0.07318360881847627\n",
      "Prediction:1.066233777045345\n",
      "Prediction:0.9785385598617921\n",
      "Prediction:-0.0025173975573930946\n",
      "Error:0.010587393171639842\n",
      "\n",
      "Prediction:-0.002013918045914484\n",
      "Prediction:0.9832839794497644\n",
      "Prediction:-0.06821774801198803\n",
      "Prediction:1.0617271739912904\n",
      "Prediction:0.9811035235627523\n",
      "Prediction:-0.0035735447350425317\n",
      "Error:0.009117233405426495\n",
      "\n",
      "Prediction:-0.002858835788034024\n",
      "Prediction:0.9855260569025094\n",
      "Prediction:-0.06358841060413677\n",
      "Prediction:1.05752842286588\n",
      "Prediction:0.9832740020092452\n",
      "Prediction:-0.004313918034364962\n",
      "Error:0.00786904226904208\n",
      "\n",
      "Prediction:-0.003451134427491974\n",
      "Prediction:0.9873957068535818\n",
      "Prediction:-0.059272877470408075\n",
      "Prediction:1.0536162524729626\n",
      "Prediction:0.9851206027353137\n",
      "Prediction:-0.004808501248434842\n",
      "Error:0.006803273214640502\n",
      "\n",
      "Prediction:-0.0038468009987478735\n",
      "Prediction:0.9889620124129692\n",
      "Prediction:-0.05524994626077355\n",
      "Prediction:1.049970908776931\n",
      "Prediction:0.9867004228010665\n",
      "Prediction:-0.005112871449710697\n",
      "Error:0.005889303541837786\n",
      "\n",
      "Prediction:-0.004090297159768559\n",
      "Prediction:0.9902806551018011\n",
      "Prediction:-0.051499833441728114\n",
      "Prediction:1.0465740376293469\n",
      "Prediction:0.9880596998997442\n",
      "Prediction:-0.0052710974096659285\n",
      "Error:0.0051029252561172675\n",
      "\n",
      "Prediction:-0.004216877927732746\n",
      "Prediction:0.9913965574535352\n",
      "Prediction:-0.048004082062078055\n",
      "Prediction:1.043408578143574\n",
      "Prediction:0.9892359385403211\n",
      "Prediction:-0.005318059364078823\n",
      "Error:0.004424644608684828\n",
      "\n",
      "Prediction:-0.0042544474912630525\n",
      "Prediction:0.992346001517791\n",
      "Prediction:-0.044745474990504665\n",
      "Prediction:1.0404586655589985\n",
      "Prediction:0.9902596156014837\n",
      "Prediction:-0.005281305317687134\n",
      "Error:0.0038385124412518303\n",
      "\n",
      "Prediction:-0.0042250442541497055\n",
      "Prediction:0.9931583274383705\n",
      "Prediction:-0.041707953394155776\n",
      "Prediction:1.0377095425371112\n",
      "Prediction:0.9911555487826897\n",
      "Prediction:-0.005182536193432452\n",
      "Error:0.0033313054558089675\n",
      "\n",
      "Prediction:-0.004146028954745959\n",
      "Prediction:0.9938572955409696\n",
      "Prediction:-0.03887654022599941\n",
      "Prediction:1.0351474779634813\n",
      "Prediction:0.9919439948626794\n",
      "Prediction:-0.00503879377425797\n",
      "Error:0.0028919416227737734\n",
      "\n",
      "Prediction:-0.004031035019406375\n",
      "Prediction:0.9944621787695098\n",
      "Prediction:-0.03623726848360008\n",
      "Prediction:1.032759692455092\n",
      "Prediction:0.9926415313729495\n",
      "Prediction:-0.004863410672429416\n",
      "Error:0.002511053608117256\n",
      "\n",
      "Prediction:-0.003890728537943533\n",
      "Prediction:0.9949886390193969\n",
      "Prediction:-0.03377711399894662\n",
      "Prediction:1.0305342898820642\n",
      "Prediction:0.9932617646389992\n",
      "Prediction:-0.004666769772712614\n",
      "Error:0.0021806703520253884\n",
      "\n",
      "Prediction:-0.003733415818170091\n",
      "Prediction:0.9954494302702878\n",
      "Prediction:-0.03148393251909879\n",
      "Prediction:1.0284601943056741\n",
      "Prediction:0.9938158986070053\n",
      "Prediction:-0.004456911151490314\n",
      "Error:0.0018939739123713475\n",
      "\n",
      "Prediction:-0.003565528921192253\n",
      "Prediction:0.9958549628928723\n",
      "Prediction:-0.029346400840475826\n",
      "Prediction:1.0265270918125804\n",
      "Prediction:0.9943131920358295\n",
      "Prediction:-0.004240016908292479\n",
      "Error:0.0016451096996342332\n",
      "\n",
      "Prediction:-0.0033920135266339822\n",
      "Prediction:0.9962137566721563\n",
      "Prediction:-0.02735396176499221\n",
      "Prediction:1.0247253767906936\n",
      "Prediction:0.9947613261560856\n",
      "Prediction:-0.004020798285770878\n",
      "Error:0.0014290353984827077\n",
      "\n",
      "Prediction:-0.003216638628616701\n",
      "Prediction:0.9965328046163073\n",
      "Prediction:-0.025496772653362886\n",
      "Prediction:1.0230461022472208\n",
      "Prediction:0.9951667005089379\n",
      "Prediction:-0.0038028045995257484\n",
      "Error:0.0012413985592149145\n",
      "\n",
      "Prediction:-0.003042243679620596\n",
      "Prediction:0.996817865235065\n",
      "Prediction:-0.023765657359234325\n",
      "Prediction:1.0214809338160067\n",
      "Prediction:0.995534671160774\n",
      "Prediction:-0.0035886696105582767\n",
      "Error:0.0010784359268087556\n",
      "\n",
      "Prediction:-0.0028709356884466207\n",
      "Prediction:0.9970736974585198\n",
      "Prediction:-0.022152061336940452\n",
      "Prediction:1.0200221071408409\n",
      "Prediction:0.9958697426723416\n",
      "Prediction:-0.0033803078583175654\n",
      "Error:0.0009368896209360312\n",
      "\n",
      "Prediction:-0.0027042462866540516\n",
      "Prediction:0.9973042495523706\n",
      "Prediction:-0.02064800972530455\n",
      "Prediction:1.018662388355171\n",
      "Prediction:0.9961757229433927\n",
      "Prediction:-0.0031790709774033414\n",
      "Error:0.0008139366504753339\n",
      "\n",
      "Prediction:-0.002543256781922673\n",
      "Prediction:0.9975128111306469\n",
      "Prediction:-0.019246068219762574\n",
      "Prediction:1.0173950374076535\n",
      "Prediction:0.9964558482449631\n",
      "Prediction:-0.0029858720226535913\n",
      "Error:0.0007071291752624441\n",
      "\n",
      "Prediction:-0.002388697618122871\n",
      "Prediction:0.9977021355600483\n",
      "Prediction:-0.01793930655497516\n",
      "Prediction:1.0162137740080082\n",
      "Prediction:0.9967128843019345\n",
      "Prediction:-0.0028012842268006904\n",
      "Error:0.0006143435674831474\n",
      "\n",
      "Prediction:-0.0022410273814405524\n",
      "Prediction:0.9978745386023716\n",
      "Prediction:-0.016721264429884947\n",
      "Prediction:1.0151127459893812\n",
      "Prediction:0.9969492081270097\n",
      "Prediction:-0.0026256193329783125\n",
      "Error:0.00053373677328488\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#import numpy as np\n",
    "\n",
    "weights = np.array([0.5,0.48,-0.7])\n",
    "alpha = 0.1\n",
    "streetlights = np.array( [ [ 1, 0, 1 ],\n",
    "                           [ 0, 1, 1 ],\n",
    "                           [ 0, 0, 1 ],\n",
    "                           [ 1, 1, 1 ],\n",
    "                           [ 0, 1, 1 ],\n",
    "                           [ 1, 0, 1 ] ] )\n",
    "\n",
    "print(\"streetlights.shape: \", streetlights.shape)\n",
    "walk_vs_stop = np.array( [ 0, 1, 0, 1, 1, 0 ] )\n",
    "\n",
    "for iteration in range(40):\n",
    "    error_for_all_lights = 0\n",
    "    for row_index in range(len(walk_vs_stop)):\n",
    "        input = streetlights[row_index]\n",
    "        goal_prediction = walk_vs_stop[row_index]\n",
    "        prediction = input.dot(weights)\n",
    "        error = (goal_prediction - prediction) ** 2\n",
    "        error_for_all_lights += error\n",
    "        delta = prediction - goal_prediction\n",
    "        weights = weights - (alpha * (input * delta))\n",
    "        print(\"Prediction:\" + str(prediction))\n",
    "    print(\"Error:\" + str(error_for_all_lights) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01389228,  1.0138147 , -0.01599277])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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