HousingPrices_convolutons_pooling_callbacks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>Intro to Supervised Learning</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "So, imagine if house pricing was as easy as a house costs **50k + 100k** per bedroom, so that a 1 bedroom house costs 150k, a 2 bedroom house costs 250k etc.\n",
    "\n",
    "How would you create a neural network that learns this relationship so that it would predict the cost of a 7 bedroom house to be750k.\n",
    "\n",
    "Hint: Your network might work better if you scale the house price down. You don't have to give the answer 750...it might be better to create something that predicts the number 7.5, and then your answer is in the 'hundreds of thousands' etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keyboard Shortcuts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Shift + Enter** -- run cell block\n",
    "\n",
    "**M**             -- Markdown\n",
    "\n",
    "**B**             -- New block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##                                     Part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf  # library for machine learning\n",
    "import numpy as np       # library for mathematics (linear algebra)\n",
    "print(tf.__version__)    # prints the version of the library (here tensorflow 1.14.0)\n",
    "# %matplotlib notebook     \n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt   # library for plotting\n",
    "from mpl_toolkits import mplot3d  # # library for 3-D plotting\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [12, 6]\n",
    "plt.rcParams['figure.dpi'] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1.0, 2.0, 3.0, ])  # number of bedrooms\n",
    "y = np.array([150, 250, 350])  # House price\n",
    "\n",
    "x1 = np.array([0., 1.])\n",
    "y1 = np.array([50., 150])\n",
    "\n",
    "x2 = np.array([3., 4., 5.,])\n",
    "y2 = np.array([350., 450., 550.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'House price Vs Bedroom')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y, 'r^')\n",
    "ax.set_xlim([0, 5])\n",
    "ax.set_ylim([0, 600])\n",
    "ax.set_yticks(np.arange(0, 600, 100.0))\n",
    "ax.grid()\n",
    "ax.set_xlabel('Num of Bedrooms')\n",
    "ax.set_ylabel('Cost, x 10^3 £')\n",
    "ax.set_title('House price Vs Bedroom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cost, x 10^3 £')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HAOt0WYcJ50ygtKS00fKwIy9JkiRJUi0pJZ786Emmz59eHTty4yPp0rYLq7Vf7SvbNmYRDxbykiRJkiT9lyP/fSR73LgHN7x+Q3VsYN+BTDp3Er8e+OscM7OQlyRJkiS1cCklXpjwAhWVFdWxQesOolPrTswrn1cdK4kS2pW1yyPFr3CMvCRJkiSpxUopsceNe/DUx0/x4OAH2b/f/gCcuMWJHLfZcXRq0ynnDP+bHXlJkiRJUosydurY6p8jgq16bkW7Vu0YN21cdbxdWbsmWcSDhbwkSZIkqYVYVLGIHa7egQ0u3YD3vnyvOv6znX/GxHMn8v1vfT/H7JafhbwkSZIkqdmaMmdK9c+tS1uzeofVaV3ampcnvVwdX73D6qzabtU80lshjpGXJEmSJDU7X877ksNuP4zXPnuNSedOqr5M/i/7/IXObTqzeofVc85wxdmRlyRJkiQ1C3MXza3+uVu7bnwx9wvml8/nufHPVcfX77p+URfxYEdekiRJklTkPpz+IUPuH8KEWRN458x3iAgighsOvYHenXuzRqc18k6xXtmRlyRJkiQVncWVi6t/7t6hOy9NfImxU8fy+pTXq+PfWutbza6IBzvykiRJkqQi8ubnb/LTx35KSZRw37H3AdCxdUduOvwmNu+xOet0WSffBBuBhbwkSZIkqUlLKRERALQpbcOD7z9ISZQwZc4UenTsAcDBGxycZ4qNykvrJUmSJElN0iuTX+HIfx3JTx77SXWsX7d+/GP/f/DOme9UF/EtjYW8JEmSJKlJmjJnCne+cydXv3I1CxcvrI4P3W4o/bv1zzGzfFnIS5IkSZJyN2rSKI676ziueeWa6tje6+3Nz3f+Oc+c/AxtWrXJMbumxTHykiRJkqTcPT/+eW554xbemPIG393qu0QEpSWl/G7P3+WdWpNjR16SJEmS1KhGTxrNKfeewpMfPVkdO2GLExiy9RCuPeTa6onttHR25CVJkiRJjeq6167j2teuZebCmQzsOxCALm27cMVBV+ScWXGwIy9JkiRJajBjpozhzAfP5KPpH1XHztj2DI7f/HjOHXBujpkVLzvykiRJkqQG86MRP2L4uOF0btuZ3+/5ewA27b4pww4blnNmxcuOvCRJkiSpXrz35Xv8eMSPmVc+rzp25nZnctTGR7F/v/1zzKx5sSMvSZIkSVppKSX2v2V/Ppz+IRutthEnb3UyAAdvcDAHb3Bwztk1L3bkJUmSJEl19tH0j/jT838ipQRARDB026EcvMHB9O/WP+fsmjc78pIkSZKkOplXPo/NL9+cOYvmMGCtAezUZycAfjDgB/xwxx/mnF3zZ0dekiRJkvS1Js6ayG1v3la93L6sPcdscgx7r7c3ZaVl1XHv/944cu3IR8QFwC9rhd9LKW1YWN8W+DNwDNAGeBT4XkppSo199AH+CQwE5gA3AD9LKS1u8DcgSZIkSc3chFkTWOeSdQDYpc8urLnKmgBcfuDllJaU5phZy9UUOvJvAb1qPHause4vwEHAUcBuwBrAXVUrI6IUeBBoDewInAicBPy6EfKWJEmSpGbn87mf89THT1Uvr7XKWuzYe0d26rMT0+ZPq45bxOenKYyRX5xS+qx2MCI6A6cAg1NKTxRiJwPvRMQOKaUXgL2BjYG9Cl361yLifOAPEXFBSmlR470NSZIkSSpuoyaNYsdrdqRL2y6MP2c8bVq1AeDR4x+lXVm7nLNTlaZQyPeLiEnAAmAk2WXxnwLbAGXAY1UbppTejYhPgQHAC4XnN2peak92+f0/gU2AV5d2wIhoQ3apfpVOAOXl5ZSXl9fX+5KalKpz23NczZnnuVoCz3O1BJ7njWf6/OlMmjOJTVbfBIBNum1C9w7dWbPTmnwy/RP6dukLQCta+fuoZyvzeUbVrQLyEBH7AR2B98guq/8lsCawKdkl9dellNrUes1LwJMppZ9ExJXA2imlfWqsbw/MBfZPKT28jONewH+PzeeWW26hffv29fHWJEmSJKlJGzVrFH/86I/0aduHP23wp+r4zMUz6dyqc46ZtQzz5s1j8ODBAJ1TSrPq8tpcO/K1Cu0xEfEi8AlwNDC/AQ99IXBxjeVOwISBAwfSrVu3BjyslJ/y8nJGjBjBoEGDKCsr++YXSEXI81wtgee5WgLP84Yxe+FsZi2axZqdssnqtpu7HX+89I+069SOAQMHsGq7VXPOsGWZOnXqCr+2KVxaXy2lNCMixgLrAyOA1hHRJaU0o8ZmPYCqMfWfAdvX2k2PGuuWdZyFwMKq5apbJJSVlfkHhZo9z3O1BJ7nagk8z9USeJ7Xn5vG3MTQB4dyUP+DuOWIWwBYo8savP29t1l31XW9bVwOVubcbgqz1leLiI7AesBkYDRQDuxZY/0GQB+ysfQUnjeLiO41djMImAW83Rg5S5IkSVJTM698HrMWLrlae6PVNmLOojm8/cXbLK5ccqfu9bquZxFfhHIt5CPiTxGxW0SsExE7AncDFcCtKaWZwDXAxRExMCK2Aa4DRhZmrAcYTlawD4uILSJiH+C3wGWFrrskSZIktSj/ePkfrPHnNbh45JLRxNussQ0vnvoir57+Kq1KmtSF2VoBeXfk1wJuJZvs7l/AVGCHlNIXhfXnAA8AdwLPkF0uf3jVi1NKFcCBZMX/SOAm4EbgF42UvyRJkiTlauHihZRXLJkBvWu7rsxcOJPHP3r8K9ttv+b2dt+bibwnuzvmG9YvAM4sPJa1zSfA/vWcmiRJkiQ1eX96/k/84bk/cMk+l3Dc5scBcNiGh/HIcY8waL1BOWenhpJ3R16SJEmStJwWVy6m5i3E55XP48t5X3LXu3dVx9q0asM+6+9DSVjuNVf+ZiVJkiSpCPz+P7+nz1/6MGrSqOrYaVufxj3fvofbj7w9x8zU2CzkJUmSJKkJqtl5B3jny3eYPGcyN75+Y3WsV6deHLLhIU5g18JYyEuSJElSE1JRWcHv//N7Nrh0Az6f+3l1/IcDfsjtR97On/f5c47ZqSmwkJckSZKkJqS0pJR737uX96e9zw2v3VAd36LnFhy9ydG0Lm2dY3ZqCrz+QpIkSZJyMr98Pv8c9U/ue+8+RnxnBGWlZQD8crdf8uW8Lzlq46NyzlBNkYW8JEmSJOWktKSUPzz3Bz6f+zn3j72fwzc6HID9+3mHbS2bhbwkSZIkNYI5i+Zww2s3MHryaK495FoAWpe25le7/4rSKGXv9fbOOUMVCwt5SZIkSWoEsxfO5uxHz2Zx5WLO2eEcNuuxGQBnbHtGzpmp2FjIS5IkSVI9m7toLre9eRtT5k7h57v8HMhuFXfuDuey1ipr0adzn5wzVDGzkJckSZKkejZmyhhOvf9U2rZqy9Bth7Jqu1UB+MOgP+ScmZoDC3lJkiRJWgkLFi/gjrfvoCRKGLzZYAB2WGsHDt3wUHbqvROlJaU5Z6jmxkJekiRJklbCv9/6NyfccwLrdFmHb2/ybUpLSokI7v723XmnpmaqJO8EJEmSJKlYlFeUc8fbd/D0x09Xx47c+Ei27Lklp2x1CosqFuWYnVoKO/KSJEmStJwuevYifvHUL9iz757sts5uALQra8erp7+ac2ZqSezIS5IkSdJSVFRWcP979zN26tjq2AlbnMCandZkx947Upkqc8xOLZkdeUmSJElaijMfOpMrRl/B0G2H8o8D/gHA2l3W5pOzP3ECO+XKjrwkSZKkFq8yVTJ83HCmzZ9WHTt6k6Pp1q4bPTr0+Mq2FvHKm4W8JEmSpBbviH8dwT437cONr99YHRu4zkAmnDuBX+7+yxwzk/6bhbwkSZKkFiWlxLOfPktFZUV1bN/19mWVNquwcPHC6lhE0LZV2zxSlL6WY+QlSZIktRgpJXa7fjf+8+l/eODYBzig/wFANond8ZsfT4fWHXLOUPpmduQlSZIkNVspJd7+4u3q5YhguzW2o31Zez6e8XF1vF1ZO4t4FQ0LeUmSJEnN0qKKRWx/9fZs8o9NeO/L96rjP9vlZ0w6dxJnbn9mjtlJK85CXpIkSVKzMXn25OqfW5e2plfHXrQpbcOoSaOq46u1X43ObTvnkZ5ULxwjL0mSJKnofTnvSw6+9WDe+PwNJp07iU5tOgHwl33+Qpe2XejWvlvOGUr1x468JEmSpKI0Z9Gc6p+7tevGtPnTWLB4Ac+Pf746vl7X9Szi1ezYkZckSZJUVMZNG8ep95/KpNmTePfMd4kIIoIbD7uRPp370LNjz7xTlBqUHXlJkiRJTV55RXn1zz069mD0pNF8MO0DxkwZUx3ffs3tLeLVItiRlyRJktRkvTHlDX782I9pVdKK+4+9H4COrTtyyxG3sEWPLejduXfOGUqNz0JekiRJUpOSUqr+uV1ZOx754BFKo5Qpc6bQo2MPAA7sf2Be6Um5s5CXJEmS1CSMmjSK3z7zW9btsi67sRsA63ddnysOvII9++5ZXcRLLZ2FvCRJkqQm4ct5X3Lve/fSpW0Xduy/Y3V8yDZDcsxKanqc7E6SJElSo3txwoscc8cxXDn6yurY3uvtzfm7ns/TJzxNWUlZjtlJTZuFvCRJkqRG9+LEF7n9rdu59KVLq8fEl0QJvx74azZabaOcs5OaNgt5SZIkSQ3qpYkvceI9J/LER09Ux07Y4gS+t+33uPGwG4mIHLOTio9j5CVJkiQ1qGGvD+PG129kzqI57NF3DwC6tO3CZQdclnNmUnGyIy9JkiSp3rw6+VVOv/90Ppz+YXXs9G1P56QtT+LHO/44x8yk5sOOvCRJkqR689PHf8rwccPp2q4rF+51IQCbdt+U6w65LufMpObDjrwkSZKkFfL2F2/zg0d/wLzyedWxs7Y7i2M2PYaDNjgox8yk5s2OvCRJkqQ6Sylx8K0HM276ODbtviknb3UyAAdtcJBFvNTA7MhLkiRJ+kYfTPuAPz73x+pbxUUE39vuexy24WFsuNqGOWcntSx25CVJkiR9rXnl89jy8i2ZWz6XHXvvyM59dgbg3AHncu6Ac3POTmp57MhLkiRJ+opPZ37KzWNurl5uX9aewZsNZr/196NNaZscM5MEduQlSZIk1TBh1gT6/rUvQbD7Oruz5iprAnD5gZdTEvYBpabA/xMlSZKkFmzy7Mk88dET1ctrrbIWu/TZhd3W2Y3pC6ZXxy3ipabDjrwkSZLUQr088WV2vHZHurTtwoRzJtCmVXbZ/CPHP0LbVm1zzk7Ssvi1miRJktRCfDnvS8ZMGVO9vFWvrejZsSf9u/Vn8pzJ1XGLeKlpsyMvSZIktQAPjH2AI/91JJt235RRQ0YB0KqkFa+f8Tpd23XNOTtJdWFHXpIkSWqGZi6YyfiZ46uXv7Xmt0iF/2YsmFEdt4iXio+FvCRJktTMDHt9GGtcvAY/eewn1bHVO6zOu2e+y+gho+nStkt+yUlaaRbykiRJUpGbs2gOMxfMrF7etPumzCufx7tfvsviysXV8b6r9s0jPUn1zEJekiRJKmKXvnQpa168JhePvLg6tlWvrRh12ihGDxlNqxKnxZKaGwt5SZIkqYgsWLyARRWLqpdXb786sxbO4ulPnv7KdtussQ0R0djpSWoEFvKSJElSkfjjc39kzYvX5N9v/bs6dthGhzHiOyN44sQncsxMUmOykJckSZKaqPKKclJK1csLFy9k2vxp3P3u3dWx1qWt2WvdvSgJ/2kvtRT+3y5JkiQ1Qb95+jf0/ktvRk0aVR07bZvTeODYB7j9yNtzzExS3izkJUmSpCagZucd4P1p7zNl7hSGjRlWHevZsScH9D+A0pLSxk5PUhNiIS9JkiTlqKKygt88/Rv6/b0fU+ZMqY7/cMcfcsdRd/Dnvf+cY3aSmiILeUmSJClHpSWlPPj+g4ybPu4r3ffNe2zOERsfQVlpWY7ZSWqKvKmkJEmS1Ejmlc/j0pcu5f6x9/PECU9UF+kX7H4B0+ZP44iNjsg5Q0nFwEJekiRJaiStSlpx8ciLmTJ3Cve9dx9HbJwV7vuuv2/OmUkqJhbykiRJUgOYvXA21712Ha9MfoXrD70eyG4V95uBv6G0pNTiXdIKs5CXJEmSGsDc8rn8YPgPWFy5mHMHnMvmPTYHslvISdLKsJCXJEmSVtLshbO55Y1b+GLeF5y363lAdqu4Hw74IX0696Fvl745ZyipObGQlyRJklbSW1+8xRkPnkHbVm05c7szWbXdqgBcuNeFOWcmqTlqMrefi4ifRkSKiEtqxNpGxGURMTUi5kTEnRHRo9br+kTEgxExLyI+j4j/iwi/oJAkSVKDmF8+nxteu4GbxtxUHfvWmt/iiI2O4Pd7/J5WJf5TVFLDahJ/ykTEdsDpwJhaq/4CHAAcBcwELgXuAnYqvK4UeBD4DNgR6AXcCJQDP2+M3CVJktSy3PH2HZx070ms3Xltjt30WEpLSokI7jj6jrxTk9RC5N6Rj4iOwM3AacD0GvHOwCnAuSmlJ1JKo4GTgR0jYofCZnsDGwPHp5ReSyk9DJwPnBkRrRvzfUiSJKn5WVSxiNvfvJ2nPn6qOnbkxkeyda+tOX2b0ymvLM8vOUktVlPoyF8GPJhSeiwizqsR3wYoAx6rCqSU3o2IT4EBwAuF5zdSSlNqvO5R4J/AJsCrSztgRLQB2tQIdQIoLy+nvNw/jNU8VZ3bnuNqzjzP1RJ4njeu3z/7e371zK8YuPZAdjpuJwBa0YoXTn4h2yD5u2gInudqCVbm/M61kI+IY4Ctge2WsronsCilNKNWfEphXdU2U5aynhrbLM3PgF/WDj755JO0b9/+G7KWituIESPyTkFqcJ7nagk8z+tfRarg5Zkv07ttb9ZsuyYAay5ak9XKVqPHwh488OADlETuF7S2KJ7nas7mzZu3wq/NrZCPiN7AX4FBKaUFjXz4C4GLayx3AiYMHDiQbt26NXIqUuMoLy9nxIgRDBo0iLKysrzTkRqE57laAs/zhjP0oaFc8/E1nL716Zy275J7vZ+QTrCAb2Se52oJpk6dusKvzbMjvw3QHXglIqpipcCuEXEWsA/QOiK61OrK9yCb3I7C8/a19tujxrqlSiktBBZWLVcdv6yszD8o1Ox5nqsl8DxXS+B5vnIqUyXDxw1n+zW3p2u7rgAct/lx3Df2PtZcZU0/2ybC81zN2cqc23l+tfg4sBmwZY3HKLKJ76p+Lgf2rHpBRGwA9AFGFkIjgc0ionuN/Q4CZgFvN2DukiRJKmKH3X4Y+928H9e/dn11bPd1dmf8OeM5f7fz80tMkpZDboV8Sml2SunNmg9gLjC1sDwTuAa4OCIGRsQ2wHXAyJRSYXYRhpMV7MMiYouI2Af4LXBZoesuSZKkFi6lxNMfP01FZUV1bP/196dL2y5fiUUEbVq1WdouJKlJaQqz1n+dc4BK4E6yWeYfBb5XtTKlVBERB5LNUj+S7IuAG4BfNH6qkiRJampSSux83c48P/557j/2fg7sfyAAJ255IidscQLtytrlnKEk1V2TKuRTSrvXWl4AnFl4LOs1nwD7N2xmkiRJKgYpJd78/E0267EZkHXZB6w1gDFTxjB+5vjq7dq2aptXipK00px+U5IkSc3CoopFbHPlNmx++ea8++W71fGf7fwzJp07iaHbDc0xO0mqPxbykiRJKloTZ02s/rl1aWvWWmUt2rVqx6uTX62Od2vfjU5tOuWRniQ1iCZ1ab0kSZK0PL6Y+wUH3HIAb3/xNpN+MIlV2qwCwF/3/Std2nZh1Xar5pyhJDUcO/KSJEkqCrMWzqr+ebX2qzF70WwWVSzihQkvVMf7rtrXIl5Ss2dHXpIkSU3auGnjOPnek5k8ZzLvnfUeJVFCRDDssGH06dyH7h26552iJDUqO/KSJElqchZVLKr+uUfHHrw+5XU+mv4Rb0x5ozq+7RrbWsRLapHsyEuSJKnJGDNlDD8Y/gNal7bmwcEPAtCxdUduO+I2tui5BWt0WiPnDCUpfxbykiRJylVKiYgAoH1Zex778DFKo5Qpc6bQo2MPAPbrt1+eKUpSk+Kl9ZIkScrFSxNf4qBbD+IHw39QHVu/6/pcfdDVfPA/H1QX8ZKkr7KQlyRJUi6mzZ/GA2Mf4PrXrmfh4oXV8VO2PoV1uqyTX2KS1MRZyEuSJKnBjRw/kqP+fRRXjLqiOrb3envzq91/xQunvkCbVm1yzE6Siotj5CVJktTgRk0axR1v38G7X77LkG2GEBGURAm/2O0XeacmSUXHjrwkSZLq1cjxIzn+ruN54qMnqmPf2eI7fH/773PL4bdUT2wnSVoxduQlSZJUr2554xZufuNm5i+ezx599wCgS9su/G2/v+WcmSQ1D3bkJUmStMJGTRrFqfedyrhp46pjQ7cbyilbncLPdv5ZjplJUvNlR16SJEkr7LwnzuPRcY+yWvvVuGiviwDYePWNufrgq3POTJKaLzvykiRJWi5jpozh7EfOZu6iudWxs7Y/i+M3P57DNjwsx8wkqWWxIy9JkqRvlFLi8NsPZ9z0cWzeY3O+u9V3ATiw/4Ec2P/AnLOTpJbFjrwkSZL+y3tfvsfv//N7UkoARARnbncmR258JJusvknO2UlSy7bcHfmI6AxUpJTmLGN9GTAgpfRMfSUnSZKkxjevfB7bXLkNc8vnskufXdhl7V0AOGfAOZzDOTlnJ0mqS0f+PmBQ1UJErBURNV/fFXiyvhKTJElS4/ho+kcMe31Y9XL7svYcv/nxHNj/QNqXtc8xM0nS0tRljPzWwKs1lt8GtgQ+rBGLeshJkiRJjWT8zPGs//f1CYKBfQey1iprAfCPA/5BSTgKU5Kaorr86TyfrOteZWlFe1q5dCRJktSQJs6ayGMfPla93Ltzb3Zde1f26LsHMxfMrI5bxEtS01WXjvzTwA0RcSswp/DakyLiy8L6TvWdnCRJkurPSxNfYsdrdmTVdqsy4ZwJtGnVBoBHjnuk+mdJUtNXl0L+TOCPwGCgA1AGnAwsLqwvrd/UJEmStDI+n/s5k2dPZoueWwCwda+t6dWpF3279OWzOZ+xdpe1ASziJanILHchn1L6HDipajkiZgO7pZQ+LCz3BCbVd4KSJEmquwfGPsDhtx/O5j02Z9SQUQC0KmnFG0PfoEvbLvkmJ0laKSsz+Gk/YGKN5dnAr1YuHUmSJH2dePxxBp51FvH441+JT58/nU9nflq9vMNaO1ASJZSWlDJjwYzquEW8JBW/FS7kU0rPppQW1liem1KykJckSWooKVFy3nmsMmECJeedBymbZ/iG125gjYvX4CeP/aR609Xar8bY74/lxVNftHiXpGbG6UglSZKKxfDhlIwezezWMOut0TB8OACb99icBYsX8MG0D6iorKjevE/nPnllKklqQBbykiRJxSAl5v3gfC75VrDGD+DPOwacfz6kxFa9tuLV01/lpVNforTE+YclqbmzkJckSWrC5s2D66+HszceTvu3XqbXnMScNvBc7wQvv1zdld+y55ZERL7JSpIaRV1uPydJkqRG8s47cMUVcMMNMGNG4kXOZzGlHPZuBU9eD7t9DJSWZl35vfcGi3hJajFWuiMfET0iwgFYkiRJK2nhQrjtNth9d9h4Y/jrX2HGDDihx3C252VaUUHrCtj9YwiAioqvdOUlSS3DchfyEdEpIm6KiE8i4oaIaB0RlwGTgY8i4umIWKXhUpUkSWqePvwQfvpT6N0bjj0Wnn4aSkrgkEPg4YcS1/c+PwssTUlJ9Vh5SVLLUJdL638PbAP8CTgc+BewHrALUAr8E/gJ8P/qOUdJkqRmZ/FieOABuPxyePTRJfE114TTToNTToG11gIWLoKTP4XKyqXvqLISxo+HRYugTZtGyV2SlK+6FPKHACemlJ6MiDuBCcDBKaXnACLix8CfsZCXJElapgkT4Kqr4OqrYdKkLBYB++wDZ5wBBxwArWr+C61Nm+zy+S++AKB88WKee/ZZdtp5Z8qqNuze3SJeklqQuhTy3YEPAFJKkyJiPjC2xvo3gd71mJskSVKzUFGRDWO//PKsC1/VXF999azzftppsO66X7OD3r2zB0B5OTMnT4attoKysgbPXZLU9NSlkJ8KrA6MLyzfC8yosb4jsLB+0pIkSSp+U6bAtdfClVfCxx8vie++e9Z9P+wwaN06r+wkScWqLoX8GGA74BWAlNLgWuu3A96pp7wkSZKKUkrw1FNZ9/2uu7Kx8ABdusBJJ8Hpp8OGG+aYoCSp6NWlkD8OWMYsKwBMwfHxkiSphZo2Lbvn++WXw9gagw8HDMiK96OPhnbt8stPktR8LHchn1Ka9g3rH175dCRJkopHSjByZFa8/+tf2X3gATp2hO98Jyvgt9gi3xwlSc1PXTrykiRJAmbNgptuygr4N95YEt9ySxg6NLsXfKdOuaUnSWrm6lzIR8QlKaWzGyAXSZKkJu2VV7Li/ZZbYO7cLNauHRxzTDZ53XbbZbeSkySpIS13IR8RpcD1wPoNlo0kSVITM3cu3H57VsC//PKS+MYbZ5fOf+c7sOqq+eUnSWp5lquQj4i2wF1AL2Bgg2YkSZLUBLz5JlxxBQwbBjNnZrHWreHII7Pu+847232XJOVjeTvyjwFdgd1SSjMaLh1JkqT8LFgAd96Zdd+ffXZJfL31su77SSfB6qvnlp4kScDyF/I7At9PKX3RkMlIkiTl4f334cor4brrYOrULFZaCoccknXf99wTSkryzVGSpCrLW8j/L/CniPg4pfRgQyYkSZLUGMrL4b77su77Y48tiffuDaedBqecAmuskV9+kiQty3IV8imlv0fENOCWiDgspfREA+clSZLUID79FK66Cq6+Gj77LItFwP77Z933/fbLuvGSJDVVyz1rfUrp5oiYDtwK9Gi4lCRJkupXRQU8/HA2ed1DD0FlZRbv0QNOPTV7rLNOrilKkrTc6nQf+ZTSQxFxWEMlI0mSVJ8mT4Zrrsk68J9+uiS+555Z9/2QQ6CsLL/8JElaEXUq5AFSSs83RCKSJEn1obISnngiG/t+772weHEW79oVTj4ZhgyB/v3zzVGSpJVR50JekiSpKfryS7j++uzy+Q8+WBLfaaes+37kkdC2bW7pSZJUb+pUyEfExsBZwACgZyH8GTASuDSl9Hb9pidJkrRsKWX3e7/iCvj3v2HRoiy+yipwwgnZvd833TTfHCVJqm/LXchHxH7APcArwL3AlMKqHsAg4JWIOCSl9Gh9JylJklTTjBkwbFh2+fzbNdoI226bdd+POQY6dMgtPUmSGlRdOvIXAX9IKf1iKesuiIgLgP8DLOQlSVK9SwlGjcqK91tvhfnzs3j79jB4cNZ933bbfHOUJKkx1KWQ7w/c/DXrbwV+snLpSJIkfdWcOVnhfvnl8MorS+Kbbpp1348/Hjp3zi8/SZIaW10K+Y+BA4D3lrH+AOCTlU1IkiQJYMyYbOz7sGEwe3YWa9MGjj46K+AHDICIfHOUJCkPdSnkfwHcEhG7A4/x1THyewL7AoPrMzlJktSyzJ8Pd9yRdd+fr3HD2379suL9xBOhW7f88pMkqSlY7kI+pfTviJgI/A/wA/571vrdU0oj6z9FSZLU3L33XtZ9v/56mD49i7VqBYcdlhXwAwfafZckqUqdbj+XUnoeeP4bN5QkSfoGixbBPfdk3fcnn1wSX3ttGDIEvvtd6NlzmS+XJKnFqlMhL0mStLI++giuugquuQY+/zyLlZTAAQdk3fd99oHS0nxzlCSpKau3Qj4iNgIeTCmtW1/7lCRJzcPixfDQQ1n3/ZFHslvJAfTqBaedBqeeCr1755ujJEnFoj478q2Btetxf5IkqchNnAhXX5114CdOXBLfe++s+37ggVBWll9+kiQVo+Uu5CPi4m/YZPW6HjwihgJDgXUKobeAX6eUHi6sbwv8GTgGaAM8CnwvpTSlxj76AP8EBgJzgBuAn6WUFtc1H0mStPIqK2HEiKz7fv/9UFGRxVdbLRv3PmQIrLdevjlKklTM6tKR/1/gNWDWMtZ3XIHjTwB+CrwPBHAicG9EbJVSegv4C9n96Y8CZgKXAncBOwFERCnwINnM+TsCvYAbgXLg5yuQjyRJWkGffw7XXQdXXgkffrgkvuuuWff98MOz+8BLkqSVU5dC/gPgLymlm5a2MiK2BEbX5eAppftrhf5foUu/Q0RMAE4BBqeUnigc42TgnYjYIaX0ArA3sDGwV6FL/1pEnA/8ISIuSCktqks+kiSpblKCZ57Juu933gnl5Vm8S5fsnu9DhsDGG+eaoiRJzU5dCvlRwDbAUgt5IJF11VdIobt+FNCB7L702wBlwGPVB0jp3Yj4FBgAvFB4fqPmpfZkl9//E9gEeHUZx2pDdql+lU4A5eXllFf9C0RqZqrObc9xNWee541n+nS46aYSrryyhPfeW/LX//bbVzJkSCVHHplo3z6L+euoX57nagk8z9USrMz5Halq2thv2jCiJ9AmpfTJCh9t6fvdjKxwb0s2xn1wSumhiBgMXJdSalNr+5eAJ1NKP4mIK4G1U0r71FjfHpgL7F811n4px7wA+GXt+C233EL7qn91SJKkr0gJxo5dlUceWYfnnluTRYuye8S1bbuY3XYbzz77fMK6687MOUtJkorDvHnzGDx4MEDnlNKyhrAv1XJ35FNKn9U1seX0HrAl0Bk4ErghInZroGNVuRCoOXlfJ2DCwIED6datWwMfWspHeXk5I0aMYNCgQZQ5RbSaKc/zhjF7Ntx6a9Z9HzNmSfd9880TQ4ZUcswxiVVWWQtYK78kWxDPc7UEnudqCaZOnbrCr63P28+tkMI49g8Ki6MjYjuyifVuB1pHRJeU0owaL+lBNrkdhefta+2yR411yzrmQmBh1XJE9o+SsrIy/6BQs+d5rpbA87x+vPZaNvb95pthzpws1rYtfPvb2eR13/pWkI2MK80zzRbL81wtgee5mrOVObdzL+SXooRs/Ppostnn9wTuBIiIDYA+ZJfiU3j+fxHRPaX0eSE2iGxm/bcbM2lJkpqDefPgX//KCvgXX1wS33BDOP10OOEE6No1v/wkSVLOhXxEXAg8DHxKdnn7YGB3YJ+U0syIuAa4OCKmkRXnfwdGFmasBxhOVrAPi4gfAz2B3wKXFbrukiRpObz9NlxxBdx4I8yYkcXKyuCII7Lu+667QqzwlLaSJKk+5d2R70523/deZPeJH0NWxI8orD8HqCTryLchm5H+e1UvTilVRMSBZLPUjySb5O4G4BeN9QYkSSpWCxfCXXdl3fdnnlkS79s3676ffDJ0755ffpIkaenqXMhHRNuU0oJlrOuVUpq8vPtKKZ3yDesXAGcWHsva5hNg/+U9piRJLd24cXDllXDttfDll1mstBQOOijrvg8aBCUl+eYoSZKWbUU68q9ExOCU0ms1gxFxBHA5sHp9JCZJkupPeTk88EDWfR8+fEl8zTXhtNPglFNgLSedlySpKKxIIf8U8EJE/DKl9IeI6ABcBhwN/L/6TE6SJK2c8ePh6quzx6RJWSwC9t03677vvz+0ynugnSRJqpM6/9WdUvpeRDwIXF0Yn94LmANsn1J6s74TlCRJdVNRAY8+mk1e98ADUFmZxbt3zzrvp52WjYOXJEnFaUW/g38YuAsYCiwGDrKIlyQpX599lo17v/JK+OSTJfGBA7Pu+6GHQuvWuaUnSZLqyYpMdrcecAvZrd72AXYD7ouIvwL/L6VUXr8pSpKkZUkJnnwyG/t+992weHEWX3VVOOkkGDIkuwe8JElqPlakI/8a8CDZbeJmACMi4iGy28gNAraqt+wkSdJSTZ0KN9yQXT4/duyS+IABWff9qKOgXbv88pMkSQ1nRQr576WUhtUMpJSej4itgEvqJStJkvRfUoLnn8+K93/9K7sPPECnTvCd72T3ft9883xzlCRJDW9FJrsbtoz4bOBr7wsvSZLqbuZMuOmm7PL5N2vMSLPVVjB0KBx7LHTsmF9+kiSpcXnDGUmSmqjRo7Pi/ZZbYN68LNauXVa4n3EGbLttdis5SZLUsljIS5LUhMydC7fdlhXwo0YtiW+8cVa8f+c70KVLbulJkqQmwEJekqQm4M03s7HvN94Is2Zlsdats0nrzjgDdtrJ7rskScpYyEuSlJMFC+COO7Lu+3PPLYmvt15WvJ90Eqy2Wm7pSZKkJspCXpKkRvb++1n3/frrs9vIAZSWwqGHZgX8HntASUmeGUqSpKasXgv5iPgIeAI4P6U0qT73LUlSMSsvh3vvzbrvjz++JN67NwwZAt/9LqyxRn75SZKk4lHfHfkbgHWA54C+9bxvSZKKziefwFVXwTXXwGefZbEI2H//rPu+335ZN16SJGl51Wshn1K6oD73J0lSMaqogIcfzrrvDz0EKWXxnj3h1FOzx9pr55ujJEkqXnUu5CPiF8CfUkrzasXbAT9KKf26vpKTJKmYTJqUdd6vugrGj18S32uvrPt+8MFQVpZffpIkqXlYkY78L4HLgXm14u0L6yzkJUktRmVlNub98suzMfAVFVm8Wzc4+eRs/Hu/fvnmKEmSmpcVKeQDSEuJbwFMW7l0JEkqDl98kc06f8UVMG7ckvjOO2fd9yOOgLZtc0tPkiQ1Y8tdyEfEdLICPgFjI6JmMV8KdCTr1EuS1CylBM8+m3Xf77gDFi3K4qusAieeCKefDptskm+OkiSp+atLR/5ssm78tWSX0M+ssW4R8HFKaWT9pSZJUtMwYwYMG5YV8G+/vSS+3XZZ9/3b34YOHXJLT5IktTDLXcinlG6A6nvFP5dSWtxgWUmSlLOU4OWXs+L9tttg/vws3r49HHdc1n3fZpt8c5QkSS3TioyRnw1sBLwBEBGHACcDbwMXpJQW1V96kiQ1rjlz4JZbsgL+1VeXxDfbLOu+H3ccdO6cX36SJEkrUshfAVwEvBER6wK3A3cBR5HNXH92vWUnSVIjGTMmK95vuglmz85ibdrA0UdnBfyAARCRb46SJEmwYoV8f+C1ws9HAU+nlAZHxE7AbVjIS5KKxPz58O9/ZwX8yBqzvPTvn106f+KJ2W3kJEmSmpIVvf1cSeHnvYAHCj+PB1arj6QkSWpI776b3Tbuhhtg+vQs1qoVHH541n3ffXe775IkqelakUJ+FHBeRDwG7AYMLcT7AlPqKzFJkurTokVw991Z9/2pp5bE1147676ffDL07JlbepIkScttRQr5s4GbgUOB36WUPijEjwSer5+0JEmqu3j8cQaedRZx1VWw774AfPQRXHklXHstfP55tl1JCRx4YNZ933tvKC3NMWlJkqQ6qnMhn1IaA2y2lFU/AipWOiNJklZESpScdx6rTJhA5Xnnce+Cfbj8iuDRR7NbyQGssQacemr26N0733QlSZJW1Ip05AGIiG3IbkMH8HZK6ZX6SUmSpBUwfDglo0cDUDJ6NP84bDjD2QeAffbJuu8HHpiNhZckSSpmdf7nTER0J7vl3G7AjEK4S0Q8CRyTUvqi/tKTJOnrVVbC8EcT6w0+n76U0ooKFlPKRa3OZ6tz9+a0IcF66+WdpSRJUv0p+eZN/svfgY7AJimlrimlrsCmwCrA3+ozOUmSlmXKFLjoIlh/ffjL/sPpN+NlWhVGeLWigq0Wv8xFewy3iJckSc3OilxguC+wV0rpnapASuntiDgTGF5vmUmSVEtK8PTT2czzd90F5eUAiTtKz6eyspSSVGOqltJSOP/8bDY77yUnSZKakRXpyJcA5UuJl6/g/iRJ+lrTpsEll8BGG8HAgXD77VkRv8MOMPwHw9m64uWvFvEAFRXw8ssw3O+YJUlS87IihfcTwF8jYo2qQESsCfwFeLy+EpMktWwpwciRcNJJsOaacM458N570LFjNnHdq6/CyOcTg545P7uf3NKUlGRd+app6yVJkpqBFbm0/izgPuDjiBhfiPUG3gSOr6/EJEkt06xZcPPN2eXzY8YsiW+xBQwdCoMHQ6dOheDCRfDpp9mMd0tTWQnjx8OiRdCmTYPnLkmS1BhW5D7y4yNia2AvYMNC+J2U0mP1mpkkqUV59dWseL/5Zpg7N4u1bQvHHJN14LfffilD3du0yS6f/yK7YUr54sU89+yz7LTzzpRV3Weue3eLeEmS1Kys0N10U0oJGFF4SJK0QubNy8a7X345vPTSkviGG2bF+wknwKqrfsNOevfOHgDl5cycPBm22grKyhosb0mSpDwtdyEfEXsAlwI7pJRm1VrXGXgeOCOl9J/6TVGS1Ny8/TZccQXccAPMnJnFysrgyCOzAn6XXZxoXpIkaVnq0pE/G7iqdhEPkFKaGRFXAOcCFvKSpP+ycCHceWfWff9Pjb8p1l0XTj89m9Sue/fc0pMkSSoadSnktwB+8jXrhwM/XLl0JEnNzQcfwJVXwnXXwZdfZrHSUjj44Kz7vtdey550XpIkSf+tLoV8D5Z+//gqi4HVVy4dSVJzUF4O99+fdd9H1JhNZa214LTT4JRTslvKSZIkqe7qUshPBDYFPljG+s2BySudkSSpaH36KVx9dfaYXPgbIQL23Tfrvu+/P7RaoWlWJUmSVKUu/5x6CPhNRDySUlpQc0VEtAN+BTxQn8lJkpq+igp49NGs+/7gg0tu6d69O5x6ataBX2edXFOUJElqVupSyP8WOBwYGxGXAu8V4hsCZwKlwO/qNz1JUlP12WdwzTXZ+PdPP10S32OPrPt+yCHQunV++UmSJDVXy13Ip5SmRMSOwD+BC4GqGwMl4FHgzJTSlPpPUZLUVFRWwpNPZt33e+6BxYuz+Kqrwsknw5AhsMEGuaYoSZLU7NVppGJK6RNg/4hYFVifrJh/P6U0vSGSkyQ1DVOnwvXXZ/d+f//9JfEdd8y670ceCe3a5ZaeJElSi7JCUw4VCveX6zkXSVITkhI8/3zWff/3v7P7wAN06gQnnJDd+32zzfLNUZIkqSVy7mBJ0lfMnAnDhmUF/FtvLYlvvTUMHQrHHAMdO+aXnyRJUktnIS9JAmDUqKx4v/VWmDcvi7VrB4MHZ5fPb7ttvvlJkiQpYyEvSS3Y3LlZ4X755TB69JL4Jptkxfvxx0OXLrmlJ0mSpKWwkJekFuiNN7KJ64YNg1mzsljr1nD00dnY9512goiv34ckSZLyYSEvSS3EggVwxx1Z9/2555bE118/676feCKstlp++UmSJGn5WMhLUjM3dmzWfb/+epg2LYu1agWHHpoV8AMHQklJnhlKkiSpLizkJakZWrQI7r03674/8cSSeJ8+MGQIfPe70KtXfvlJkiRpxVnIS1Iz8vHHcNVVcM01MGVKFouAAw7Iuu/77gulpbmmKEmSpJVkIS9JRa6iAh56KOu+P/wwpJTFe/WCU06BU0+FtdfON0dJkiTVHwt5SSpSEydmnferr4bx45fEBw3Kuu8HHQRlZfnlJ0mSpIZhIS9JRaSyEh57LOu+33df1o0H6NYtG/c+ZEg2C70kSZKaLwt5SSoCX3wB112XzT7/4YdL4rvsknXfDz8c2rbNLz9JkiQ1Hgt5SWqiUoL//Cfrvt95ZzYTPUDnznDCCXD66bDJJvnmKEmSpMZnIS9JTcz06TBsWFbAv/POkvj222fd929/G9q3zy8/SZIk5ctCXpKagJTgpZeyS+dvuw3mz8/iHTrAccdl3fett843R0mSJDUNFvKSlKPZs+GWW7Lu+2uvLYlvthkMHZoV8ausklt6kiRJaoIs5CUpB6+/nhXvN90Ec+ZksTZtssvmzzgDdtgBIvLNUZIkSU1TSZ4Hj4ifRcTLETE7Ij6PiHsiYoNa27SNiMsiYmpEzImIOyOiR61t+kTEgxExr7Cf/4sIv6SQ1KTMnw833AADBsCWW2aF/Jw50L8/XHwxTJq0ZL1FvCRJkpYl72J3N+Ay4OVCLr8HhkfEximluYVt/gIcABwFzAQuBe4CdgKIiFLgQeAzYEegF3AjUA78vNHeiSQtw7vvZmPfr78eZszIYmVl2S3jzjgDdtvNwl2SJEnLL9dCPqW0b83liDgJ+BzYBngmIjoDpwCDU0pPFLY5GXgnInZIKb0A7A1sDOyVUpoCvBYR5wN/iIgLUkqLGu8dSVJm4UK4++6s6/7000vi66yTTVx38snQo8cyXy5JkiQtU94d+do6F56nFZ63AcqAx6o2SCm9GxGfAgOAFwrPbxSK+CqPAv8ENgFerX2QiGgDtKkR6gRQXl5OeXl5/bwTqYmpOrc9xxvWhx/C1VeXcMMNJXzxRdZmLylJHHBAYsiQSgYNSpQUBjX5q6h/nudqCTzP1RJ4nqslWJnzO1JK9ZjKiouIEuA+oEtKaedCbDBwXUqpTa1tXwKeTCn9JCKuBNZOKe1TY317YC6wf0rp4aUc6wLgl7Xjt9xyC+29ObOkOqqoCF5+uQePProOr766pM3etet8Bg36hEGDPmG11RbkmKEkSZKamnnz5jF48GCAzimlWXV5bVPqyF8GbArs3AjHuhC4uMZyJ2DCwIED6datWyMcXmp85eXljBgxgkGDBlFWVpZ3Os3ChAlw7bUlXHttCZMmZd33iMTeeydOO62S/fdvRatW6wHr5ZtoC+J5rpbA81wtgee5WoKpU6eu8GubRCEfEZcCBwK7ppQm1Fj1GdA6IrqklGbUiPcorKvaZvtau+xRY91/SSktBBbWOD4AZWVl/kGhZs/zfOVUVsLw4dnY9/vvz5YBVl8dTjkFTjstWHfdIOebgrR4nudqCTzP1RJ4nqs5W5lzO9dCPrIK+u/AYcDuKaWPam0ymmz2+T2BOwuv2QDoA4wsbDMS+H8R0T2l9HkhNgiYBbzdsO9AUksxZQpcey1ceSV8/PGS+O67ZzPPH3podh94SZIkqaHl3ZG/DBgMHALMjoiehfjMlNL8lNLMiLgGuDgippEV538HRhZmrAcYTlawD4uIHwM9gd8ClxU675K0QlKCp57Kuu93371kcrouXeCkk7LZ5zfcMMcEJUmS1CLlXcgPLTw/VSt+MnB94edzgEqyjnwbshnpv1e1YUqpIiIOJJulfiTZJHc3AL9oqKQlNW/TpsENN2QF/NixS+I77JB1348+Gtq1yy8/SZIktWx530c+lmObBcCZhceytvkE2L8eU5PUwqQEL7yQFe+3357dBx6gY0f4zney7vsWW+SboyRJkgT5d+QlKVezZsFNN2UF/BtvLIlvuSUMHQrHHgudOuWWniRJkvRfLOQltUivvJIV77fcAnPnZrF27eCYY7LL57fbDuIbrxmSJEmSGp+FvKQWY+7c7LL5yy+Hl19eEt9oo6x4/853YNVV88tPkiRJWh4W8pKavbfegiuugBtvhJkzs1jr1nDEEVkBv8sudt8lSZJUPCzkJTVLCxfCnXdm3ff//GdJfL31sonrTjoJVl89t/QkSZKkFWYhL6lZef99uPJKuP56+PLLLFZaCoccknXf99wTSkpyTVGSJElaKRbykopeeTncd1/WfX/ssSXxtdaCIUPglFNgjTXyy0+SJEmqTxbykorWp5/CVVfB1VfDZ59lsQjYb7+s+77fftDKP+UkSZLUzPhPXElFpaICHnkk674/9BBUVmbxHj2yzvtpp8E66+SaoiRJktSgLOQlFYXJk+Haa7Px759+uiS+555Z9/3gg7OZ6CVJkqTmzkJeUpNVWQlPPJF13++9FxYvzuJdu8LJJ2fj3/v3zzdHSZIkqbFZyEtqcr78Mpt1/oor4IMPlsR32inrvh95JLRtm1t6kiRJUq4s5CU1CSnBc89l3fd//xsWLcriq6wC3/lOdu/3zTbLN0dJkiSpKbCQl5SrGTPgppuyAv6tt5bEt902674fcwx06JBbepIkSVKTYyEvKRcvv5wV77feCvPnZ7H27WHw4Kz7vu22+eYnSZIkNVUW8pIazZw5WeF++eXwyitL4ptumnXfjz8eOnfOLz9JkiSpGFjIS2pwY8ZkE9cNGwazZ2exNm3gqKOyAn7HHSEi3xwlSZKkYmEhL6lBzJ8Pd9yRdd+ff35JvF+/7NL5k06Cbt1yS0+SJEkqWhbykurVe+/BlVdmt4+bNi2LtWoFhx2WFfADB0JJSa4pSpIkSUXNQl7SSlu0CO65J+u+P/nkkvjaa8OQIfDd70LPnrmlJ0mSJDUrFvKSVthHH8FVV8E118Dnn2exkhI44IBs7Ps++0Bpab45SpIkSc2NhbykOlm8GB56KOu+P/IIpJTFe/WCU0/NHn365JujJEmS1JxZyEtaLhMnZp33q66CCROWxPfeOxv7ftBBUFaWX36SJElSS2EhL2mZKivhscey7vt990FFRRZfbbVs3Ptpp8H66+eboyRJktTSWMhL+i+ffw7XXZfNPv/hh0viu+6ajX0//PDsPvCSJEmSGp+FvCQgG+v+zDNZ9/3OO6G8PIt37gwnnphdPr/xxvnmKEmSJMlCXmox4vHHGXjWWcRVV8G++1bHp0+HG2/MCvh3312y/fbbZ933b38b2rfPIWFJkiRJS2UhL7UEKVFy3nmsMmECleedR9p7H156Obj8crjtNliwINusQwc47ris+7711vmmLEmSJGnpLOSllmD4cEpGjwagZPRovtdvOP/8cJ/q1ZtvDkOHwuDBsMoqeSUpSZIkaXmU5J2ApAaWEiN//QPeWD37330xpZz04fm0bZM48UQYORJeey27jN4iXpIkSWr67MhLzdxVw85hyN5vcdSa8K9/Qysq2J6X+fym4XQ6cp9v3oEkSZKkJsWOvNTMvPPFO4ybNi5bSIkDr3qK9ougzWJIVRuVltLpj+dnU9VLkiRJKioW8lIz8rtnfsfG/9iY3/3nd1lg+HB6Pfs6n/0Jht0NUbVhRQW8/DIMH55XqpIkSZJWkIW8VMTGTRvHtPnTqpcH9h1ISZSwsGIhqbISzj8fSkrotGgpLy4pydbblZckSZKKioW8VKR+OPyHrP/39bli1BXVsQFrDWDCORO4+fCbifJy+PRTqKxc+g4qK2H8eFi0tCpfkiRJUlPlZHdSkRg/czw9O/akrLQMgE27b0oQfDj9w+ptIoJenXplC23aZJfPf/EFAOWLF/Pcs8+y0847U9aq8L9+9+7ZdpIkSZKKhoW8VAROu+80rn3tWv515L84YuMjAPj2Jt9m17V3Zd1V1132C3v3zh4A5eXMnDwZttoKysoaIWtJkiRJDcFL66UmaOq8qV9Z7t6hO5WpkhcmvFAda1fW7uuLeEmSJEnNkoW81ISklDj+ruPp9edejJkypjr+/W99n3fOfIf/2/v/csxOkiRJUlNgIS/lbMHiBdU/RwQLKxZSXlnOIx88Uh3v2bEnG662YR7pSZIkSWpiLOSlnMwrn8cJd59Arz/3+sot5C7Y7QJeO/01frzTj3PMTpIkSVJTZSEvNaLKtORWcO1atWPMlDHMWDCD+967rzq+SfdN2KLnFnmkJ0mSJKkIOGu91Ai+nPclP3/85zw3/jnGnDGG0pJSIoJL9r2E9mXt2W6N7fJOUZIkSVKRsJCXGkGHsg7c+c6dTJs/jSc/fpK91t0LgN3X2T3fxCRJkiQVHQt5qZ5NnDWRi569iImzJ3LXt+8CslvF/W3fv9Gncx927rNzzhlKkiRJKmYW8lI9q0gV/GPUP6hMlYydOpb+3foDcNzmx+WcmSRJkqTmwEJeWgmfzvyUv7/4d1qVtOLCvS4EoE/nPvxuj9+xTa9tWL/r+jlnKEmSJKm5sZCXVsK4aeP408g/0bF1R36+y8/p1KYTAD/d+ac5ZyZJkiSpubKQl5bTpzM/5crRV9K3S19O2foUIJusbsjWQzhog4NoX9Y+5wwlSZIktQQW8tJyevSDR/ndf35Hv679+O5W3yUiiAiuOOiKvFOTJEmS1IKU5J2A1BRNnj2Z3z7zWx7/8PHq2LGbHcuB/Q/k93v+nspUmWN2kiRJkloyO/LSUlw88mL+NPJP7N9vf/Zcd08AOrbuyP3H3p9zZpIkSZJaOjvyavG+nPclf3r+T4ydOrY6NmSbIezUeyeO28xbxkmSJElqWuzIq8Ubcv8Q7n73bibOmshf9v0LAP269ePZ7z6bc2aSJEmS9N/syKtFmbFgBpe+dCmzFs6qjp229Wlsu8a2bL/m9jlmJkmSJEnLx468WpS9btyL0ZNHUxqlDN1uKAD7rr8v+/XbL+fMJEmSJGn52JFXszVn0RyGvT7sKzPMn7DFCWzafVNWa79adSwi8khPkiRJklaIHXk1SxWVFWx02UZMmDWBXp16sde6ewHwve2+x/e3/77FuyRJkqSiZUdezcL88vmMGDeierm0pJRDNjiEfl37sWDxgup4q5JWFvGSJEmSipodeRW9mQtmst7f1mPa/GmM+59x9F21LwB/2OsP/H2/v1u4S5IkSWpW7Mir6CyqWMSrk1+tXu7ctjPbrLENfTr34eMZH1fHO7TuYBEvSZIkqdmxI6+iMm7aOHa8dkcWLl7IpB9Mon1ZewCGHTaMbu26UVpSmnOGkiRJktSw7MirSVtcuZhPZnxSvdx31b50KOtA+7L2vPfle9Xx7h26W8RLkiRJahHsyKvJemniSxx+++Gs2m5VxpwxhoigJEp49PhHWafLOpSVluWdoiRJkiQ1ulw78hGxa0TcHxGTIiJFxKG11kdE/DoiJkfE/Ih4LCL61dqma0TcHBGzImJGRFwTER0b9Y2oXlSmSqbNn1a9vEG3DZi+YDqfzfmM8bPGV8f7detnES9JkiSpxcr70voOwOvAmctY/2Pgf4AzgG8Bc4FHI6JtjW1uBjYBBgEHArsCVzZUwmoYj334GOv/bX1Ove/U6ljntp154oQnmHDOBPp07pNjdpIkSZLUdOR6aX1K6WHgYeC/ZhePLHA28NuU0r2F2AnAFOBQ4LaI2AjYF9gupTSqsM33gYci4ocppUmN805UVyklFlYspG2r7DuZXh178dGMj5i1cBZzF82lQ+sOAHxrrW/lmaYkSZIkNTlNeYx8X6An8FhVIKU0MyJeBAYAtxWeZ1QV8QWPAZVkHfy7l7bjiGgDtKkR6gRQXl5OeXl5fb4HLcX9Y+/n50/+nAP7HciFe1wIQP9V+3PXUXexxzp70Dpa+3toAFWfqZ+tmjPPc7UEnudqCTzP1RKszPndlAv5noXnKbXiU2qs6wl8XnNlSmlxREyrsc3S/Az4Ze3gk08+Sfv27VcsWy1TSolKKimNbFb5l2e8zHtT32PG7BnsNH8nSiIb4VFCCU+9/1SOmbYMI0aMyDsFqcF5nqsl8DxXS+B5ruZs3rx5K/zaplzIN6QLgYtrLHcCJgwcOJBu3brllFLz9O+3/80fnv8DZ213FidtcRIAe1fuTd8xfTlioyNYpc0q+SbYgpSXlzNixAgGDRpEWZmTBap58jxXS+B5rpbA81wtwdSpU1f4tU25kP+s8NwDmFwj3gN4rcY23Wu+KCJaAV1rvP6/pJQWAgtrvAaAsrIy/6CoZxPnTGTM52O48Y0bOW3b0wAoo4wh2w3JObOWy/NcLYHnuVoCz3O1BJ7nas5W5tzOe9b6r/MRWTG+Z1UgIlYhG/s+shAaCXSJiG1qvG4Psvf1YiPlqYJb37iVHa7egWc+eaY6dtKWJ/GXff7Cvcfcm2NmkiRJktR85H0f+Y4RsWVEbFkI9S0s90kpJeAS4LyIODgiNgNuBCYB9wCklN4BHgGuiojtI2In4FLgNmesb3xPfPQEL058kStGX1EdW73D6py9w9l0bdc1x8wkSZIkqfnI+9L6bYEnayxXjVu/ATgJ+CPZveavBLoAzwL7ppQW1HjNcWTF++Nks9XfSXbveTWQlBK3vXkbV75yJcMOG8Zaq6wFwFnbn8X6Xdfn5K1OzjlDSZIkSWq+8r6P/FNAfM36BPyi8FjWNtOAwfWenJYpIrhi9BU8/cnTXP3K1Vyw+wUAbNFzC7bouUW+yUmSJElSM9eUx8irCaiorODud+7myH8dyYLFSy6E+OGOP+SC3S7g1K1PzTE7SZIkSWp58r60XkXgfx/5X8bPGs/h7xzO4M2yix8O7H8gB/Y/MOfMJEmSJKnlsZBXtYrKCh4d9ygjxo3g4n0uJiIoLSnlRzv+iMlzJrNT753yTlGSJEmSWjwLeVWbsWAGh99+OAsrFjJ4s8Fst+Z2AHz/W9/POTNJkiRJUhUL+RYqpcSTHz/JG1Pe4H93+F8AurXvxpBthtCqpBU9OvbIOUNJkiRJ0tJYyLdQb37+JnveuCdlJWUcu9mxdO/QHYC/7fe3nDOTJEmSJH0dC/kWIKXE8+OfZ8rcKRy+0eEAbNZjM/Zady/6de3H4srFOWcoSZIkSVpeFvItwIPvP8hBtx5Er469OKj/QZSVlgEw/PjhRETO2UmSJEmS6sL7yDdDoyeNZuT4kdXLe6+3N3279GW/9fdj9qLZ1XGLeEmSJEkqPnbkm5krR1/J6Q+czk69d+LZ7z4LQOvS1oz9/lhalfjrliRJkqRiZ0e+yL35+Zt8MO2D6uWD+h9Eh7IOrN1lbRYuXlgdt4iXJEmSpObBQr6I/faZ37LZPzfjd//5XXWsV6deTPnhFG4+/GbatGqTY3aSJEmSpIZgIV9Exk4dy9R5U6uX9+y7J6VRyuLKxaSUquMdWnfIIz1JkiRJUiOwkC8S5zxyDhtcugFXjr6yOrbDWjsw8dyJDDtsmBPXSZIkSVILYSHfRH0y4xPKK8qrl7fqtRVB8MnMT6pjEUGPjj3ySE+SJEmSlBML+SbolHtPoe9f+3Lve/dWx47a+Cg++t+PuPzAy3PMTJIkSZKUNwv5JuCLuV98ZblXp14kEqMmjaqOtStrx9pd1m7s1CRJkiRJTYyFfI5SShxzxzH0+nMvXv/s9er497f/PmPPGstFe12UY3aSJEmSpKbIQr6RzSufV/1zRJBIVKQKho8bXh3v0bEH/br1yyM9SZIkSVITZyHfSOaVz+O4u45jjT+vwbT506rjF+x2AW8MfYMf7fSjHLOTJEmSJBULC/kGVFFZUf1zu1btePuLt5m5cCYPjH2gOr7R6huxafdN80hPkiRJklSEWuWdQHP0xdwv+MljP2HkhJG8OfRNSktKiQgu2ecSOrbuyDZrbJN3ipIkSZKkImUhX09SSkQEAJ3adOLe9+5l2vxpPPXxU+y57p4A7LbObnmmKEmSJElqBizkV9L4meO58NkLmTR7Evcccw8AbVu15bL9L6NP5z4MWGtAvglKkiRJkpoVC/mVlEhcMfoKKlMl7099v3q2+WM2PSbnzCRJkiRJzZGFfB18PONj/vrCX2nTqk31Pd77dO7DRXtexDZrbMP6XdfPOUNJkiRJUnNnIV8Hn8z4hEtevIQOZR34+S4/Z5U2qwB46zhJkiRJUqOxkF+Gj2d8zBWjrmC9rutx6tanArDr2rsydNuhHNT/IDqUdcg5Q0mSJElSS2Qhvwwjxo3goucuol/Xfpyy1SlEBBHBPw74R96pSZIkSZJasJK8E2hKnvrkqeqfj93sWA7d8FD+sNcfSKT8kpIkSZIkqQYL+RouH3159c8dW3fk7m/fzWEbHUZJ+DFJkiRJkpoGK9Qajt746LxTkCRJkiTpa1nI13D4hofnnYIkSZIkSV/LQl6SJEmSpCJiIS9JkiRJUhGxkJckSZIkqYhYyEuSJEmSVEQs5CVJkiRJKiIW8pIkSZIkFRELeUmSJEmSioiFvCRJkiRJRcRCXpIkSZKkImIhL0mSJElSEbGQlyRJkiSpiFjIS5IkSZJURCzkJUmSJEkqIhbykiRJkiQVEQt5SZIkSZKKiIW8JEmSJElFxEJekiRJkqQiYiEvSZIkSVIRsZCXJEmSJKmIWMhLkiRJklRELOQlSZIkSSoiFvKSJEmSJBURC3lJkiRJkoqIhbwkSZIkSUXEQl6SJEmSpCJiIS9JkiRJUhGxkJckSZIkqYhYyEuSJEmSVEQs5CVJkiRJKiIW8pIkSZIkFRELeUmSJEmSioiFvCRJkiRJRaTZFPIRcWZEfBwRCyLixYjYPu+cJEmSJEmqb82ikI+IbwMXA78CtgZeBx6NiO65JiZJkiRJUj1rFoU8cC5wVUrpupTS28AZwDzgu/mmJUmSJElS/WqVdwIrKyJaA9sAF1bFUkqVEfEYMGAZr2kDtKkR6gQwbdq0BsxUyld5eTnz5s1j6tSplJWV5Z2O1CA8z9USeJ6rJfA8V0uwMvVn0RfywGpAKTClVnwKsOEyXvMz4Je1g/3796/fzCRJkiRJ+npdgVl1eUFzKORXxIVkY+qrdAImAGsBs3PJSGp4nudqCTzP1RJ4nqsl8DxXS1B1nte5Nd8cCvkvgQqgR614D+Czpb0gpbQQWFi1HBFVP85OKdXpmxCpWHieqyXwPFdL4HmulsDzXC1BjfO8zop+sruU0iJgNLBnVSwiSgrLI/PKS5IkSZKkhtAcOvKQXSZ/Q0SMAl4CzgY6ANflmZQkSZIkSfWtWRTyKaXbI2J14NdAT+A1YN+UUu0J8JZlIdk96Bd+04ZSEfM8V0vgea6WwPNcLYHnuVqCFT7PI6VU/+lIkiRJkqQGUfRj5CVJkiRJakks5CVJkiRJKiIW8pIkSZIkFRELeUmSJEmSikiLL+Qj4syI+DgiFkTEixGxfd45SfUpInaNiPsjYlJEpIg4NO+cpPoUET+LiJcjYnZEfB4R90TEBnnnJdWniBgaEWMiYlbhMTIi9ss7L6khRcRPC/92uSTvXKT6EhEXFM7rmo9367qfFl3IR8S3ye5B/ytga+B14NGI6J5rYlL96kB2bp+ZdyJSA9kNuAzYARgElAHDI6JDrllJ9WsC8FNgG2Bb4Ang3ojYJNespAYSEdsBpwNj8s5FagBvAb1qPHau6w5a9O3nIuJF4OWU0lmF5RJgPPD3lNJFuSYnNYCISMBhKaV78s5FaigRsTrwObBbSumZvPORGkpETAN+lFK6Ju9cpPoUER2BV4DvAecBr6WUzs41KameRMQFwKEppS1XZj8ttiMfEa3JvtV+rCqWUqosLA/IKy9J0krrXHielmsWUgOJiNKIOIbsiquReecjNYDLgAdTSo9945ZScepXGPb6YUTcHBF96rqDVg2RVZFYDSgFptSKTwE2bPx0JEkrq3Bl1SXAcymlN3NOR6pXEbEZWeHeFphDdoXV2/lmJdWvwpdUWwPb5Z2L1EBeBE4C3iO7rP6XwH8iYtOU0uzl3UlLLuQlSc3PZcCmrMBYM6kIvAdsSXbVyZHADRGxm8W8mouI6A38FRiUUlqQdz5SQ0gpPVxjcUxhuPcnwNHAcg+VasmF/JdABdCjVrwH8FnjpyNJWhkRcSlwILBrSmlC3vlI9S2ltAj4oLA4ujAZ2P+STQgmNQfbAN2BVyKiKlYK7BoRZwFtUkoVeSUnNYSU0oyIGAusX5fXtdgx8oW/DEcDe1bFCpdk7onjzSSpaETmUuAwYI+U0kd55yQ1khKgTd5JSPXocWAzsitPqh6jgJuBLS3i1RwVJndcD5hcl9e15I48ZLeeuyEiRgEvAWeTTRxzXZ5JSfWp8IdDzW/4+kbElsC0lNKn+WQl1avLgMHAIcDsiOhZiM9MKc3PLy2p/kTEhcDDwKdAJ7JzfndgnxzTkupVYXzwV+Y3iYi5wFTnPVFzERF/Au4nu5x+DbJboVcAt9ZlPy26kE8p3V64TdGvgZ7Aa8C+KaXaE+BJxWxb4MkayxcXnm8gm2hDKnZDC89P1YqfDFzfqJlIDac7cCPZxEgzye6tvU9KaUSuWUmS6motsqK9G/AF8CywQ0rpi7rspEXfR16SJEmSpGLTYsfIS5IkSZJUjCzkJUmSJEkqIhbykiRJkiQVEQt5SZIkSZKKiIW8JEmSJElFxEJekiRJkqQiYiEvSZIkSVIRsZCXJEmSJKmIWMhLktTCRcROEfFGRJRHxD05HP+piLiksY8rSVKxspCXJKmeRMT1EZEi4qe14odGRMorr+VwMfAa0Bc4aWkbFIrtVOMxJSL+HRFrN2KekiQJC3lJkurbAuAnEbFq3onUwXrAEymlCSmlGV+z3VVAL2AN4BCgN3BTQycXEa0b+hiSJBUTC3lJkurXY8BnwM+WtUFEXBARr9WKnR0RH9dYvj4i7omInxe63zMi4hcR0Soi/i8ipkXEhIg4+euSiYg2EfG3iPg8IhZExLMRsV1h3TqFKwW6AdcWOu0nfc3u5qWUPkspTU4pvQBcCmxd63ibRsTDETGnkPewiFitxvoOEXFjYf3kiPjBUnL+OCLOL2w3C7iyED8iIt6KiIWFbX5Q63WrFl4zPSLmFfLoV2P9SYXP8cCIeK+wzR0R0T4iTizsc3rh8yqt8brvRcT7hc9vSkTc8XWfuSRJDc1CXpKk+lUB/Bz4fkSstZL72oOs+70rcC7wK+ABYDrwLeBy4IpvOM4fgSOAE8mK7g+ARyOiKzCerMM+Czi78PPty5NY4fVHAy/WiHUBngBeBbYF9gV6AP+q8dL/A3Yj6+jvDexOrS8DCn4IvA5sBfwmIrYp7Oc2YDPggkL8pBqvub5w3IOBAUAAD0VEWY1t2gP/AxxTyG934G5g/8LjO8DpwJGF97Qt8DfgF8AGhdc88w0fjyRJDapV3glIktTcpJTuLnTcfwWcshK7mgb8T0qpEngvIn4MtE8p/R4gIi4EfgrsTFbgfkVEdACGAiellB4uxE4DBgGnpJT+D/is0JWfmVL67Bvy+V5EnEpWILcHxgL71Fh/FvBqSunnNXL4LjA+IvoDk8g+j+NTSo8X1p8ITFjKsZ5IKf25xn5uBh5PKf2mEBobERsDPwKuL3TeDwZ2Sik9X3jNcWRfVhwK/LvwujJgaEppXGGbO8iK9x4ppTnA2xHxJDCQ7EuNPsBc4IGU0mzgE7IvKiRJyo0deUmSGsZPgBMjYqOV2MdbhSK+yhTgjaqFlFIFMBXovozXr0dWuD5X4zXlwEvAiuR1M7AlsAXZlwcfAMMjolNh/RbAwMJl83MiYg7wbo1c1gNaU6OLn1KaBry3lGONqrW8Uc33UfAc0K9wGfxGwOJa+55a2HfN9zqvqogvmAJ8XCjia8aqPtMRZMX7h4VhAsdFRPul5CtJUqOxkJckqQGklJ4BHgUuXMrqSrKudk1lS9muvPZulxFrrL/PZ6aUPig8niPrrvcDvl1Y3xG4n6zYr/noR90vR5+78ukuVZ0+00IXfmvgWGAy8Gvg9cIwAkmScmEhL0lSw/kpcBDZeO2avgB6RkTNYn7LBjj+OGARsFNVoDBefDvg7XrYf0XhuV3h+RVgE7IO9we1HnML+ZSTje+vymdVoP9yHOsdaryPgp2AsYUrE94hGzJYc9/dyMa1r9R7TSktTik9llL6MbA5sA7Z/AWSJOXCMfKSJDWQlNIbhbHd/1Nr1VPA6sCPC2O09wX2I5t0rj6PPzci/gn8X0RMAz4Ffkw2vv2aFdhl+4joWfi5B3A+2e32hhdilwGnAbdGxB/JxvivTzax3KkppTkRcU0hn6nA58DvyK5Q+CZ/Bl6OiPPJxq4PIBuT/73Ce30/Iu4FroqI04HZwEXARODeFXivAETEgcC6ZFcUTCebEK+EpQ8HkCSpUdiRlySpYf2CWn/fppTeIStAzySbmX174E8NdPyfAncCw8g65usD+6SUpq/Avk4ju7x8MvAksBqwf0rpPYCU0iSyLnkpWXH/BnAJMIMlxfqPgP+QXYL/GPAsMPqbDpxSeoVslvxjgDfJLnH/RUrp+hqbnVzY1wPASLLhC/sX5gVYUTOAw8lm438HOAM4NqX01krsU5KklRIppbxzkCRJkiRJy8mOvCRJkiRJRcRCXpIkSZKkImIhL0mSJElSEbGQlyRJkiSpiFjIS5IkSZJURCzkJUmSJEkqIhbykiRJkiQVEQt5SZIkSZKKiIW8JEmSJElFxEJekiRJkqQiYiEvSZIkSVIR+f9vEFwaqOTNUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib notebook\n",
    "# plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, y, 'b')\n",
    "ax.plot(x, y, 'r^')\n",
    "ax.plot(x1, y1, 'g:')\n",
    "ax.plot(x2, y2, 'g:')\n",
    "ax.set_xlim([0, 5])\n",
    "ax.set_ylim([0, 600])\n",
    "ax.set_yticks(np.arange(0, 600, 100.0))\n",
    "ax.grid()\n",
    "ax.set_title('House price Vs Bedroom')\n",
    "ax.set_xlabel('Num of Bedrooms')\n",
    "ax.set_ylabel('Cost, x 10^3 £')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](pics/slope_intercept.png)\n",
    "\n",
    "<p style=\"color:black;font-size:24px;\">Cost of the house as a function of number of bedrooms can be expressed as </p>    \n",
    "\n",
    "$$ y = \\color{red}{\\theta_0} + \\color{red}{\\theta_1} \\times n_B$$\n",
    "\n",
    "$$ y = 50 + 100 \\times n_B$$\n",
    "<h3><center>Or</center></h3>\n",
    "\n",
    "$$ y = bias (intercept) + slope * n_B $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:black;font-size:24px;\">The mathematical model in matrix form can be expressed as</p>    \n",
    "\n",
    "## $$ \\begin{pmatrix}\n",
    "\t150\\\\\n",
    "\t250\\\\\n",
    "\t350\n",
    "\\end{pmatrix}\n",
    " = \\begin{pmatrix}\n",
    "\t1 & 1\\\\\n",
    "\t1 & 2\\\\\n",
    "\t1& 3\n",
    "\\end{pmatrix}  \\color{red}{\\begin{pmatrix}\n",
    "\t50\\\\\n",
    "\t100\n",
    "\\end{pmatrix}} $$\n",
    "\n",
    "## $$ Y = X \\color{red}{\\theta} $$\n",
    "\n",
    "More commonly, this is seen in literature as **$$ Y = \\theta^T X$$** where $Y$ is called the *True Label* or simply *Label* (Cost history), $X$'s are the *Features* (Number of Bedrooms) and **$\\theta$'s are parameters** we need to find out through some 'techniques'. (Here, it's quite easy since it's a hypothetical example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $$Questions?$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.array([1.0, 2.0, 3.0, ])  # Num bedrooms\n",
    "yy = np.array([40.0, 60.0, 70.0])  # Floor area\n",
    "zz = np.array([150.0, 300.0, 375.0])  # Cost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1. 40.]\n",
      " [ 1.  2. 60.]\n",
      " [ 1.  3. 70.]]\n",
      "[-1.50000000e+02 -4.95248287e-12  7.50000000e+00]\n"
     ]
    }
   ],
   "source": [
    "s = np.ones(len(xx)).reshape(-1,1)\n",
    "X1 = np.hstack((xx.reshape(-1, 1), yy.reshape(-1, 1)))\n",
    "X1 = np.hstack((s, X1))\n",
    "print(X1)\n",
    "a = X1.transpose() @ X1\n",
    "b = X1.transpose() @ zz\n",
    "theta = np.linalg.solve(a, b)  # To find parameters\n",
    "print(theta)\n",
    "Z1 = X1 @ theta "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above, there are two feature vectors, viz., number of bedrooms and floor area and the label remains the same.\n",
    "<p style=\"color:black;font-size:20px;\">Following the above, The mathematical model in matrix form can be expressed as</p>   \n",
    "\n",
    "## $$ y = \\color{red}{\\theta_0} + \\color{red}{\\theta_1} x_1 + \\color{red}{\\theta_2} x_2$$\n",
    "\n",
    "## $$ \\begin{pmatrix}\n",
    "\t150\\\\\n",
    "\t300\\\\\n",
    "\t375\n",
    "\\end{pmatrix}\n",
    " = \\begin{pmatrix}\n",
    "\t1 & 1 & 40\\\\\n",
    "\t1 & 2 & 60\\\\\n",
    "\t1& 3 & 70\n",
    "\\end{pmatrix} \\color{red}{\\begin{pmatrix}\n",
    "\t\\theta_1\\\\\n",
    "\t\\theta_2\\\\\n",
    "    \\theta_3\n",
    "\\end{pmatrix}} $$\n",
    "\n",
    "The parameter vector $\\theta = [\\theta_1 ~\\theta_2~  \\theta_3]^T$ can be found out by several methods. Here we will explore **Linear Regression**. \n",
    "\n",
    "If there exists a **perfect $\\theta$** then the error, $e = Y - X\\theta = 0$. But it's hardly the case. So in Regression, we try to minimize the error between the true label $Y$ and the quantity $X\\theta$. This is done by constructing a loss function, $L$ which is defined as\n",
    "$$ L = e^2 = e^Te = (Y - X\\theta)^T (Y - X\\theta)$$\n",
    "\n",
    "Then a gradient descent is carried out\n",
    "\n",
    "$$\\begin{align}\n",
    "\t\\frac{\\partial L}{\\partial \\theta} &= \\frac{\\partial L}{\\partial e}  \\frac{\\partial e}{\\partial \\theta} \\nonumber\\\\\n",
    "\t&= -2e^TX\\nonumber\\\\\n",
    "\t&= 2(X\\theta - Y)^TX = \\mathbf{0} \n",
    "\\end{align}$$\n",
    "\n",
    "**Note:** In the above, $\\mathbf{0}$ is a row vector\n",
    "\n",
    "Taking the transpose of both sides, we get\n",
    "$$\n",
    "\\begin{align}\n",
    "\tX^T(X\\theta - Y) = \\mathbf{0}  \n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, the formula for finding $\\theta$ is \n",
    "\n",
    "## $$ \\theta = (X^T X)^{-1}X^T Y$$\n",
    "\n",
    "where the quantity $(X^TX)^{-1}X^T$ is called the left pseudo-inverse\n",
    "\n",
    "and using this, we obtain $\\theta$ as follows\n",
    "\n",
    "## $$ \\begin{pmatrix}\n",
    "\t150\\\\\n",
    "\t300\\\\\n",
    "\t375\n",
    "\\end{pmatrix}\n",
    " = \\begin{pmatrix}\n",
    "\t1 & 1 & 40\\\\\n",
    "\t1 & 2 & 60\\\\\n",
    "\t1& 3 & 70\n",
    "\\end{pmatrix} \\color{red}{\\begin{pmatrix}\n",
    "\t-150\\\\\n",
    "\t0.0\\\\\n",
    "    7.5\n",
    "\\end{pmatrix}} $$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "For a detailed tutorial on Linear Regression, you can refer [here](https://github.com/ashBabu/Machine-Learning/blob/master/Linear-Regression.ipynb). \n",
    "\n",
    "In General, the mathematical model will be,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $$ \\begin{pmatrix}\n",
    "\ty_1\\\\\n",
    "\ty_2\\\\\n",
    "\t...\\\\\n",
    "    ...\\\\\n",
    "    y_n\n",
    "\\end{pmatrix}=  \\begin{pmatrix}\n",
    "\t1 & x_{11} & x_{21} \\\\\n",
    "    1 & x_{12} & x_{22} \\\\\n",
    "    .. & .. & .. \\\\\n",
    "    .. & .. & .. \\\\\n",
    "    1 & x_{1n} & x_{2n}\n",
    "\\end{pmatrix}\\begin{pmatrix}\n",
    "\t\\theta_1\\\\\n",
    "\t\\theta_2\\\\\n",
    "    ...\\\\\n",
    "    ...\\\\\n",
    "    \\theta_n\n",
    "\\end{pmatrix} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Mathematical models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $$ \\begin{pmatrix}\n",
    "\ty_1\\\\\n",
    "\ty_2\\\\\n",
    "\t...\\\\\n",
    "    ...\\\\\n",
    "    y_n\n",
    "\\end{pmatrix}=  \\begin{pmatrix}\n",
    "\t1 & x_{11}^2 & x_{21}^2 \\\\\n",
    "    1 & x_{12}^2 & x_{22}^2 \\\\\n",
    "    .. & .. & .. \\\\\n",
    "    .. & .. & .. \\\\\n",
    "    1 & x_{1n}^2 & x_{2n}^2\n",
    "\\end{pmatrix}\\begin{pmatrix}\n",
    "\t\\theta_1\\\\\n",
    "\t\\theta_2\\\\\n",
    "    ...\\\\\n",
    "    ...\\\\\n",
    "    \\theta_n\n",
    "\\end{pmatrix} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0e+00 1.0e+00 1.6e+03]\n",
      " [1.0e+00 4.0e+00 3.6e+03]\n",
      " [1.0e+00 9.0e+00 4.9e+03]]\n",
      "[19.67213115 -7.37704918  0.08606557]\n"
     ]
    }
   ],
   "source": [
    "# Using the squared model\n",
    "X2 = np.hstack(((xx**2).reshape(-1, 1), (yy**2).reshape(-1, 1)))\n",
    "X2 = np.hstack((s, X2))\n",
    "print(X2)\n",
    "a = X2.transpose() @ X2\n",
    "b = X2.transpose() @ zz\n",
    "par2 = np.linalg.solve(a, b)\n",
    "print(par2)\n",
    "Z2 = X2 @ par2 \n",
    "# print(Z2, Z1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following the 'squared' model, we get $\\theta$ as\n",
    "\n",
    "## $$ \\begin{pmatrix}\n",
    "\t150\\\\\n",
    "\t300\\\\\n",
    "\t375\n",
    "\\end{pmatrix}\n",
    " = \\begin{pmatrix}\n",
    "\t1 & 1 & 40\\\\\n",
    "\t1 & 2 & 60\\\\\n",
    "\t1& 3 & 70\n",
    "\\end{pmatrix} \\color{red}{\\begin{pmatrix}\n",
    "\t19.67\\\\\n",
    "\t-7.37\\\\\n",
    "    0.086\n",
    "\\end{pmatrix}} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 3\n",
    "tx, ty = np.linspace(xx.min(), xx.max(), n), np.linspace(yy.min(), yy.max(), n)\n",
    "t1 = np.ones(n).reshape(-1, 1)\n",
    "X = np.vstack((tx, ty)).transpose()\n",
    "X = np.hstack((t1, X**2))\n",
    "# print(X)\n",
    "Z = X @ par2\n",
    "# print(Z)\n",
    "\n",
    "X1 = np.vstack((tx, ty)).transpose()\n",
    "X1 = np.hstack((t1, X1))\n",
    "# print(X1)\n",
    "# Z1 = X1 @ par1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'House price Vs Bedroom')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib notebook\n",
    "%matplotlib inline\n",
    "# plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.plot(xx, yy, zz, 'r^')\n",
    "# ax.plot(xx, yy, zz, 'b')\n",
    "for i in range(len(xx)):\n",
    "    ax.plot([xx[i], xx[i]], [0, yy[i]], ':')\n",
    "    ax.plot([0, xx[i]], [yy[i], yy[i]], ':')\n",
    "    ax.plot([xx[i], xx[i]], [yy[i], yy[i]], [0, zz[i]],':')\n",
    "\n",
    "# ax.plot(xx1, yy1, zz1, 'g:')\n",
    "# ax.plot(xx2, yy2, zz2, 'g:')\n",
    "# ax.plot(xx, yy, 'b:')\n",
    "# ax.plot(xx1, yy1, 'g:')\n",
    "ax.plot(tx, ty, Z, 'c')\n",
    "ax.plot(tx, ty, Z1, 'g')\n",
    "ax.set_xlim([0, 5])\n",
    "ax.set_ylim([0., 150])\n",
    "ax.set_zlim([0, 800])\n",
    "ax.grid()\n",
    "\n",
    "ax.set_xlabel('Num of Bedrooms')\n",
    "ax.set_ylabel('Floor Area, m^2')\n",
    "ax.set_zlabel('House price, x 10^3 £')\n",
    "ax.set_title('House price Vs Bedroom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "* 1. Understood what **Label (Y), Feature Matrix (X)** and **Parameters ($\\theta$)** are\n",
    "* 2. Creating the error equation, $Y - X\\theta$\n",
    "* 3. Define a Loss function, $L = (Y - X\\theta)^T(Y - X\\theta)$\n",
    "* 4. Perform gradient descent to find out parameters $\\theta$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $$Questions?$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we have dealt with simple models. As the complexity of the problem increases, linear regression will turn out to be a poor choice of mathematical model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Networks\n",
    "![alt text](pics/nn1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](pics/NeuralNetwork_schematic.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Watch the video](pics/andrew_ng_thumbnail.png)](https://www.youtube.com/watch?v=EVeqrPGfuCY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Tensorflow to Predict house prices\n",
    "\n",
    "[Neural Network Playground](https://playground.tensorflow.org/#activation=tanh&regularization=L1&batchSize=20&dataset=spiral&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=8,8,5&seed=0.64269&showTestData=false&discretize=false&percTrainData=70&x=true&y=true&xTimesY=true&xSquared=true&ySquared=true&cosX=false&sinX=true&cosY=false&sinY=true&collectStats=false&problem=regression&initZero=false&hideText=false&showTestData_hide=false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](pics/neuralPlayground.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model has two hidden layers each having 4 and 6 neurons respectively\n",
    "model = tf.keras.Sequential([\n",
    "                            tf.keras.layers.Dense(input_shape=[1], units=4),\n",
    "                            tf.keras.layers.Dense(units=6),\n",
    "                            tf.keras.layers.Dense(units=1)\n",
    "                            ])\n",
    "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
    "# model.compile(optimizer='Adam', loss='mean_squared_error')\n",
    "# model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# l = 9\n",
    "# ys = .5 + np.array([i for i in range(1, l)], dtype=float) # Your Code Here#\n",
    "# xs = np.arange(1, l) # Your Code Here#\n",
    "\n",
    "xs = np.array([1., 2., 3., 4.,])  # Number of Bedrooms\n",
    "ys = np.array([1.5, 2.5, 3.5, 4.5])  # Cost in 10^5 £\n",
    "model.fit(xs, ys, epochs=100, verbose=0)  # Find out the parameters\n",
    "prediction = model.predict([7.0])  # Predict the value of a 7 bedroom house\n",
    "print('The cost of a 7 bedroom house predicted by the neural network is',  prediction)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
    "<h1><center>End of Part 1 </center></h1>\n",
    "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fashion Data Set & Example of using callbacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss might change during training. The question addressed here is 'wouldn't it be nice if the training could\n",
    "be stopped when a desired value is reached?' -- i.e. 95% accuracy might be enough that after 3 epochs, why sit around\n",
    "waiting for it to finish a lot more epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "    if(logs.get('loss')<0.4):\n",
    "      print(\"\\nReached 60% accuracy so cancelling training!\")\n",
    "      self.model.stop_training = True\n",
    "\n",
    "callbacks = myCallback()\n",
    "mnist = tf.keras.datasets.fashion_mnist\n",
    "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
    "training_images=training_images/255.0\n",
    "test_images=test_images/255.0\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
    "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n",
    "model.fit(training_images, training_labels, epochs=5, verbose=0, callbacks=[callbacks])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifications = model.predict(test_images)\n",
    "\n",
    "print(classifications[0])\n",
    "print(test_labels[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutions and Pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tensorflow as tf\n",
    "# print(tf.__version__)\n",
    "# mnist = tf.keras.datasets.fashion_mnist\n",
    "# (training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
    "training_images=training_images.reshape(60000, 28, 28, 1)  # *training_images.shape gives 60000, 28, 28\n",
    "training_images=training_images / 255.0\n",
    "test_images = test_images.reshape(10000, 28, 28, 1)\n",
    "test_images=test_images/255.0\n",
    "# 64 convolutions of size 3 x 3. They reduce the size of input by 2 each in x & y as can be seen \n",
    "# from the model.summary() output. These corresponds to the corners and edge columns and rows as they have no \n",
    "# neighbours to do the convolutions.\n",
    "\n",
    "# Pooling: Here 2 x 2 which means in the 2x2 matrix, which ever is the maximum value, that is taken and the rest\n",
    "# is ignored. So the overall size becomes halved in both x and y directions (total = 1/4)\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),\n",
    "  tf.keras.layers.MaxPooling2D(2, 2),\n",
    "  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D(2,2),\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()\n",
    "model.fit(training_images, training_labels, epochs=5)\n",
    "test_loss = model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Convolutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "f, axarr = plt.subplots(3,4)\n",
    "FIRST_IMAGE=0\n",
    "SECOND_IMAGE=7\n",
    "THIRD_IMAGE=26\n",
    "CONVOLUTION_NUMBER = 5  # Change this number to see what each convolutions do. \n",
    "from tensorflow.keras import models\n",
    "layer_outputs = [layer.output for layer in model.layers]\n",
    "activation_model = tf.keras.models.Model(inputs = model.input, outputs = layer_outputs)\n",
    "for x in range(0,4):\n",
    "  f1 = activation_model.predict(test_images[FIRST_IMAGE].reshape(1, 28, 28, 1))[x]\n",
    "  axarr[0,x].imshow(f1[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
    "  axarr[0,x].grid(False)\n",
    "  f2 = activation_model.predict(test_images[SECOND_IMAGE].reshape(1, 28, 28, 1))[x]\n",
    "  axarr[1,x].imshow(f2[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
    "  axarr[1,x].grid(False)\n",
    "  f3 = activation_model.predict(test_images[THIRD_IMAGE].reshape(1, 28, 28, 1))[x]\n",
    "  axarr[2,x].imshow(f3[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')\n",
    "  axarr[2,x].grid(False)"
   ]
  }
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