ex1-linear regression/ex1-scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 线性回归（单变量）\n",
    "预测food truck的收益值。\n",
    "ex1data1.txt：数据集，第一列表示城市人数，第二列该城市的food truck收益"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.1101</td>\n",
       "      <td>17.5920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5277</td>\n",
       "      <td>9.1302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5186</td>\n",
       "      <td>13.6620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0032</td>\n",
       "      <td>11.8540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8598</td>\n",
       "      <td>6.8233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population   Profit\n",
       "0      6.1101  17.5920\n",
       "1      5.5277   9.1302\n",
       "2      8.5186  13.6620\n",
       "3      7.0032  11.8540\n",
       "4      5.8598   6.8233"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'ex1data1.txt'\n",
    "data = pd.read_csv(path, header=None, names=['Population', 'Profit'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='scatter', x='Population', y='Profit', figsize=(12,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新增一例，x0\n",
    "data.insert(0, 'Ones', 1)\n",
    "data.head()\n",
    "\n",
    "cols = data.shape[1]\n",
    "X = data.iloc[:, 0:cols-1]\n",
    "Y = data.iloc[:, cols-1:cols]\n",
    "\n",
    "X = np.matrix(X.values)\n",
    "Y = np.matrix(Y.values)\n",
    "theta = np.matrix(np.array([0, 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tYhttn82ZM0cPP/ywnn76aUnS66+/rieffFIHHnig/va3v+mZZ56RpKxQnXLcccfpO9/5jiRpZGREr776qiZPnqwNGzbkvP873/lO3XTTTZKkJ598Us8995xaW1sr/bIAAADqCqHZZ1OnTtX111+vs88+W4ceeqjmzJmjVatWaeLEibrmmmt00kkn6eijj9a+++6b8/FXXXWV7r//fs2cOVOHH364Vq5cqV133VVz587VjBkz1NXVlXb/T3/60xoZGdHMmTN11lln6frrr0+bYQYAAED5rNDH+WHR3t7uent708aeeOIJHXTQQQFVhFz4mQAAgKgzs+XOufbMcWaaAQAAgCL8XD0DAAAAKKi7L67FPQMaHEqouSmmro5Wdba1BF1WFkIzAAAAAtHdF9eCpf1KDI9IkuJDCS1Y2i9JoQvOtGcAAAAgEIt7BrYG5pTE8IgW9wwEVFF+hGYAAAAEYnAoUdZ4kAjNAAAACERzU6ys8SARmsdo/fr1mjVrlmbNmqU999xTLS0tW7/ftGnTuI59++23a/HixRWp85xzztH06dN12GGH6YADDtC5556rwcHBoo9bsmSJ3njjjYrUAAAAkEtXR6tijQ1pY7HGBnV1hG9jNi4EHKNdd9116xbal1xyiSZNmqQvfelLafdxzsk5pwkTyntvcvrpp1esTkm68sor1dnZqS1btmjJkiU69thj1d/fr8bGxryPWbJkif71X/9VEydOrGgtAAAAKamL/aKwekbdzDR398U1d9EyTZ9/l+YuWqbuvrgvz/P0009rxowZ+uQnP6nZs2dr7dq1Ov/889Xe3q5DDjlEX//617fed++999Yll1yitrY2HXrooXryySclST/4wQ904YUXSvJmij//+c/r7W9/u97ylrfo9ttvl+Rtqf3JT35ShxxyiE455RSdeOKJ6u7uLljbhAkT9KUvfUm77LKL7rnnHknKWduVV16pF198Ue94xzv0nve8J+/9AAAAxquzrUUPzz9Wf1t0kh6ef2woA7NUJ6E5tZxJfCghp23LmfgVnB9//HGdd9556uvrU0tLixYtWqTe3l79+c9/1r333qvHH39863332GMP9fX16eMf/7iWLFmS83gvvviiHn74YXV3d2vBggWSpFtvvVXxeFz9/f363ve+p9///vcl1zd79mytWrVKknLW9oUvfEG77767HnzwQf3617/Oez8AAIB6URehudrLmbz1rW/V2972tq3f33LLLZo9e7Zmz56tJ554Ii1wzps3T5J0+OGHa/Xq1TmP19nZKTPToYceqnjcC/oPPfSQPvCBD2jChAlqbm7WMcccU3J9o7dOL1TbaKXeDwAAoBbVRU9ztZcz2XHHHbd+/dRTT+mqq67SH//4RzU1Nemcc85Ju8Buhx12kCQ1NDRo8+bNOY+Xuo+0LfCODr7lWrFihU466aSitZX6GgAAAGpdXcw0B7mcyauvvqrJkydrp5120tq1a9XT01OR4x599NG67bbb5JzT2rVr9cADDxR9jHNOV155pdavX6/jjz++YG2TJ0/Whg0bfH0NAAAAUVEXM81dHa1pWzRK1VvOZPbs2Tr44IM1Y8YMveUtb9HcuXMrctwPfOADWrZsmWbMmKHW1lYdeeSRmjJlSs77fuELX9DXvvY1JRIJHXXUUVq2bJkaGxsL1nb++efrPe95j/bZZx/de++9vrwGAACAqLDxfMxfLe3t7a63tzdt7IknntBBBx1U8jG6++KRWM6kHBs3btSkSZO0bt06HXnkkXrkkUc0derUwOop92cCAAAQNma23DnXnjleFzPNkrecSdRDcqb3vve9evXVVzU8PKxLL7000MAMAABQy+omNNeiBx98MOgSAAAA6kJdXAgIAAAAjEekQ3MU+rHrBT8LAABQyyIbmidOnKj169cT1kLAOaf169dr4sSJQZcCAADgi8j2NO+9995as2aN1q1bF3QpkPcmZu+99w66DAAAAF9ENjQ3NjZq+vTpQZcBAACAOhDZ9gwAAACgWiI70wwAAOpbLW5chvAiNAMAgMjp7otrwdJ+JYZHJEnxoYQWLO2XJIIzfEF7BgAAiJzFPQNbA3NKYnhEi3sGAqoI4/Laa9LQUNBVFERoBgAAkTM4lChrHCG1fr00bZo0aZK0885BV1MQoRkAAEROc1OsrHGEzLp1UkuLtNtu0rPPemPf+U6wNRVBaAYAAJHT1dGqWGND2lissUFdHa0BVYSSvPCCtMce0u67S4OD3thll0nOSZ/8ZLC1FcGFgAAAIHJSF/uxekZErF0rHXxwet/y5ZdLF10UXE1lIjQDAIBI6mxrISSHXTwuHXigtHHjtrElS6QvfCG4msaI0AwAAIDKeu45af/9pU2bto1961vSBRcEV9M4EZoBAABQGatXS299q7Rly7ax73wn9P3KpSA0AwAAYHz++lcvLI/2/e9LH/94MPX4gNAMAACAsXnqKemAA9LHrr9eOvfcQMrxE6EZAAAA5RkY8C7wG+3HP5Y+/OFg6qkCQjMAAEAVdffFo7tU3uOPS4cckj72k59IZ50VTD1VRGgGAACoku6+uBYs7VdieESSFB9KaMHSfkkKd3Du75cOPTR97LbbpPe/P5h6AsCOgAAAAFWyuGdga2BOSQyPaHHPQEAVFbFihWSWHpi7u70d/OooMEvMNAMAAFTN4FCirPHALF8utbenj/3yl9LJJwdTTwgw0wwAAFAlzU2xssar7o9/9GaWRwfmu+/2ZpbrODBLhGYAAICq6epoVayxIW0s1tigro7WgCpKevhhLywfeeS2sXvu8cLyiScGV1eIEJoBAACqpLOtRQvnzVRLU0wmqaUppoXzZgZ3EeDChV5YPvrobWPLlnlh+fjjg6kppOhpBgAAqKLOtpbgV8q45BLp0kvTx+68UzrppEDKiQJmmgEAAOrFggXezPLowHzFFd7MMoG5IGaaAQAAat0XvyhdeWX62Le/LX32s8HUE0GEZgAAgFr1mc9IV1+dPva970nnnx9MPRFGaAYAAAiIb1tqn3ee9MMfpo9dd5300Y+O/9h1itAMAAAQAF+21P7wh6Wbb04fu+km6UMfGk+pEBcCAgAABKKiW2qfcYZ3gd/owHzrrd4FfgTmimCmGQAAIAAV2VL75JOlu+5KH+vulk47bRyVIRdmmgEAAAIwri213/Meb2Z5dGD+1a+8mWUCsy8IzQAAAAEY05bac+d6Yfm++7aN3XuvF5bf+16fKoVEewYAAEAgUhf7lbR6xuGHS48+mj72m99Ixxzjf6GQRGgGAAAITNEttQ86SFq1Kn3s4Yelt7/d38KQhdAMAAAQNtOmSc8+mz72yCPSEUcEUg4IzQAAAOGx++7SunXpY48+KrW1BVMPtiI0AwAABG3yZGnjxvSxxx6TZs4Mph5kITQDAAAExSx7bOVK6eCDq18LCiI0AwAAVJNz0oQcq/4ODEgHHFD9elAS1mkGAACoBue8meXMwNzb691GYA4130Kzme1jZveb2RNmttLMPp8c38XM7jWzp5L/39mvGgAAAAK3ZUvusPzgg15YPvzwYOpCWfycad4s6d+dcwdJmiPpM2Z2sKT5ku5zzu0v6b7k9wAAALVlZMQLyw3pu/7pkUe8sHz00cHUhTHxLTQ759Y65x5Nfr1B0hOSWiSdJumG5N1ukNTpVw0AAABVNzzsheXtMi4dW7HCC8ustRxJVelpNrNpktokPSJpD+fcWskL1pJ2z/OY882s18x612WuVwgAABA2//ynF5a33z59fOVKLywfdlgwdaEifA/NZjZJ0s8lXeice7XUxznnrnHOtTvn2qdOnepfgQAAAOORSHhheeLE9PGnnvLCMsvH1QRfQ7OZNcoLzDc555Ymh18ws72St+8l6UU/awAAAPDFxo1eWH7Tm9LHV6/2wvJ++wVSFvzh5+oZJulaSU8455aMuukOSecmvz5X0i/8qgEAAKDi/vEPLyxPnpw+Ho97YXnffYOpC77yc3OTuZI+IqnfzFYkx74saZGkn5nZeZKek3SmjzUAAABUxvr10m67ZY+/8IK0e85LtFBDfAvNzrmHJOXYG1KSdJxfzwsAAFBRL7wg7bln9vj69dIuu1S/HgSCbbQBAAByicelvffOHh8akqZMqX49CBTbaAMAAIy2erXXs5wZmDds8HqWCcx1idAMAAAgeUvEmUnTp6ePv/66F5YnTQqmLoQCoRkAANS3lSu9sHzAAenjb7zhheVYLJi6ECqEZgAAUJ9WrPDC8owZ6eObNnlheYcdgqkLoURoBgAA9eWRR7yw3NaWPr55sxeWGxuDqQuhRmgGAAD14cEHvbA8Z076+MiIF5YbGoKpC5FAaAYAALXt3nu9sPzOd6aPb9niheUJxCEUx58SAABQm+66ywvLJ5yQPp4Ky5ZvDzYgG6EZAADUlttu8wLxySenjztHWMaYEZoBAEBtuOkmLxCfeWb6eCosA+NAaAYAANF27bVeWD7nnPRxwjIqaLugCwAAABiTf/kX6Uc/yh4nKMMHhGYAABAtnZ3SL36RPU5Yho8IzQAAIBqOPVa6//7sccIyqoDQDAAAwu3ww6VHH80eJyyjigjNAAAgnPbbT3rmmexxwjICQGgGAADhsuuu0ssvZ48TlhEgQjMAAAiH7baTRkayxwnLCAFCMwAACFa+HfoIywgRQjMAAAgGYRkRQmgGAADVRVhGBBGaAQBAdRCWQ6m7L67FPQMaHEqouSmmro5Wdba1BF1W6BCaAQCAvwjLodXdF9eCpf1KDHsXYMaHElqwtF+SCM4ZJgRdAAAAqFFmuQOzcwTmkFjcM7A1MKckhke0uGcgoIrCi5lmAABQWcwsR8bgUKKs8XpGaI4o+o8AAKFDWI6c5qaY4jkCcnNTLIBqwo32jAhK9R/FhxJy2tZ/1N0XD7o0AEA9og0jsro6WhVrbEgbizU2qKujNaCKwovQHEH0HwEAQoGwHHmdbS1aOG+mWppiMkktTTEtnDeTT69zoD0jgug/AgAEijaMmtLZ1kJILgEzzRGUr8+I/iMAgK+YWUYdIzRHEP1HAICqyhWWJ04kLKOu0J4RQamPUFg9AwDgq1yzynvtJQ0OVr8WIGCE5oii/wgA4JtcYfnAA6Unnqh+LUBIEJoBAIAnV1ieM0f6/e+rX0sJ2LMA1URoBgCg3uUKyyecIPX0VL+WEqX2LEgtwZras0ASwRm+4EJAAADqVa4L/M44w7u4L8SBWWLPAlQfoRkAgHqTKyz/6796YfnWW4OpqUzsWYBqIzQDAFAvcoXlz33OC8vXXhtMTWPEngWoNkIzAAC1LldY/spXvLB81VXB1DRO7FmAauNCQAAAalWuC/wWLpTmz69+LRXGngWoNkIzAAC1xDlpQo4Pkq+6ymvFqCHsWYBqIjQDAFALtmyRGhqyx6+91rvID8C4EJoBAIiykRFpuxz/nN9yi/TBD1a/HqBGEZoBAIii4WFp++2zx3/xC+nUU6tfD1DjCM0AAETJG29IsRzLqt1zj3T88dWvB6gThGYAAKLgtdekSZOyxx98UDr66OrXA9QZQjMAAGH26qvSlCnZ43/8o/S2t1W/HqBOEZoBAAij9eul3XbLHn/sMWnmzOrXA9Q5QjMAAGHywgvSnntmjw8MSAccUP16AEgiNAMAEA7PPy+9+c3Z43/7mzRtWtXLAZCO0AwAQJCeeUbab7/s8Xhcam6ufj0AcsqxzyYAAPDd449LZtmB+cUXva2wCcxAqBCaAQCopr4+Lywfckj6+Msve2F56tRg6gJQEKEZAIBq+P3vvbA8e3b6+KuvemF5552DqQtASQjNAAD46f77vbD89renj7/+uheWJ08Opi4AZSE0AwDgh1/9ygvLxx6bPv7Pf3phOddW2ABCi9UzAAB5dffFtbhnQINDCTU3xdTV0arOtpagywq3n/9cOuOM7PHhYWk7/tkFooq/vQCAnLr74lqwtF+J4RFJUnwooQVL+yWJ4JzLjTdK556bPT4yIk3gg10g6vhbDADIaXHPwNbAnJIYHtHinoGAKgqp737Xa8PIDMxbtnhtGARmoCbwNxkAkNPgUKKs8bpzxRVeWP7Up9LHU2HZLJi6APiC0AwAyKm5KfeFamqOw9kAACAASURBVPnG68all3qBuKsrfdw5wjJQwwjNAICcujpaFWtsSBuLNTaoq6M1oIoCdtFFXiC+5JL08VRYBlDTfAvNZvZDM3vRzP4yauwSM4ub2Yrkf+/z6/kBAOPT2daihfNmqqUpJpPU0hTTwnkz6+8iwE99ygvLixenjxOWgbri5+oZ10v6H0k3Zoxf6Zy7wsfnBQBUSGdbS/2F5JRzzpFuuil7nKAM1CXfQrNz7gEzm+bX8QEA8MVpp0l33JE9TlgG6loQPc2fNbPHku0bOwfw/AAAZDvmGK8NIzMw04YBQNUPzd+R9FZJsyStlfTNfHc0s/PNrNfMetetW1et+gAA9aatzQvLDzyQPk5YBjBKVUOzc+4F59yIc26LpO9LOqLAfa9xzrU759qnTp1avSIBAPVh2jQvLK9YkT5OWAaQQ1W30TazvZxza5Pfni7pL4XuDwD1qLsvrsU9AxocSqi5Kaaujtb6vRjPD01N0j/+kT1OUAZQgG+h2cxukfQuSbuZ2RpJX5P0LjObJclJWi3p3/x6fgCIou6+uBYs7d+6fXV8KKEFS/slieA8Xvk2HSEsAyiBn6tnnJ1j+Fq/ng8AasHinoGtgTklMTyixT0DhOaxIiwDqICqtmcAAAobHEqUNY4CCMsAKohttAEgRJqbYmWNIwez3IGZC/wAjAOhGQBCpKujVbHGhrSxWGODujpaA6ooQgjLAHxEewYAhEiqb5nVM8pAGwaAKiA0A0DIdLa1EJJLQVgGUEWEZgBAtBCWAQSA0AwAiAbCMoAAEZoBAOFGWAYQAoRmAEA4EZYBhAihGQAQLoRlACFEaAYAhANhGUCIsbkJACBYuTYledOb2JQEQKgw0wwkdffF2VACqKZcM8tHHy09+GD1awGAIgjNgLzAvGBpvxLDI5Kk+FBCC5b2SxLBGai0XGH5lFOkO+6ofi0AUCLaMwB5WxanAnNKYnhEi3sGAqoItaS7L665i5Zp+vy7NHfRMnX3xYMuKRi52jA+/GGvBYPADCDkmGkGJA0OJcoaB0rFpxjKPbN8/vnS975X/VoAYIyYaQYkNTfFyhoHSlXXn2Lkmln+4he9mWUCM4CIITQDkro6WhVrbEgbizU2qKujNaCKUCvq8lOMXGH5q1/1wvI3vxlMTQAwTrRnANr2MTmrZ6DSmptiiucIyDX5KUauNozLL5cuuqj6tQBAhRGagaTOthZCMiquq6M1radZqsFPMXKF5W9/W/rsZ6tfCwD4hNAMAD6q2U8xnJMm5Ojw+8EPpPPOq349AOAzQjMA+KymPsXIF5Zvvlk6++zq1wMAVUJoBgAUt2WL1NCQPb50qXT66dWvBwCqjNAMAMhvZETaLsc/FXffLZ14YvXrAYCAEJoBANmGh6Xtt88ev/9+6V3vqno5ABA01mkGAGzzxhveahiZgfnee71+ZgIzgDrFTDMAQHrtNWnSpOzxhx6S5s6tfj0AEDKEZgCoZ//4h9TUlD3+pz9J7e3VrwcAQorQDAD1aP16abfdssf7+6UZM6pfDwCEHKEZAOrJ3/8u7bVX9vjAgHTAAdWvBwAigtAMAPXgueekfffNHl+9Ovc4ACANoRkAatnTT0v77589Ho9Lzc3VrwcAIool5wCgFq1c6S0dlxmYX3zRWzqOwAwAZSE0A0AtefRRLyxnXsz3yiteWJ46NZi6ACDiaM8Aqqy7L67FPQMaHEqouSmmro5Wdba1BF0Wou53v8u9nvKGDbnXXwYAlIXQDFRRd19cC5b2KzE8IkmKDyW0YGm/JBGcMTbLlknHHZc9/vrrUixW/XrGiDeTAMKO0Axk8PMf78U9A1sDc0pieESLewYICCjPXXdJJ5+cPf7Pf2ZvgR1yvJkEEAX0NAOjpP7xjg8l5LTtH+/uvnhFjj84lChrHMhy7bVez3JmYB4e9nqWIxaYpcJvJgEgLJhpxphE/aPUfPX7PRPc3BRTPEdAbm6KzsfoCMhVV0kXXpg9PjIiTYj2/AdvJgFEQbR/0yIQfs/G+q1Q/X7/493V0apYY0PaWKyxQV0drRU5PmrQN77hzSxnBuYtW7yZ5YgHZin/m0beTAIIk+j/tkXVRf2j1EL1+/2Pd2dbixbOm6mWpphMUktTTAvnzYzULD2qZP58Lyx/5Svp46mwbBZMXT7gzSSAKKA9A2WL+kepheq/8qxZaRckSZX/x7uzrYWQjPw+8xnp6quzx52rfi1Vkvr7EOWWLwC1j9CMskW9L7dQ/fzjjcB85CPSj3+cPV7DYXk03kwCCDtCM8rW1dHq+2ysn4rVzz/eqKpTT5V++cvs8ToJywAQFYRmlC3qs7FRrx814phjpAceyB4nLANAKJmLwC/o9vZ219vbG3QZADB+hx4q9fdnj0fgdzEA1AMzW+6ca88cL2n1DDObW8oYACAPM++/zMDsHIEZACKg1CXnvl3iGABgtFRYzkRYBoBIKdjTbGZHSXq7pKlm9sVRN+0kqSH3owAAeddRJigDQCQVuxBwe0mTkvebPGr8VUln+FUUAEQWYRkAalLB0Oyc+62k35rZ9c65Z6tUEwBED2EZAGpasfaM/3bOXSjpf8ws6ze/c+5U3yoDgCggLANAXSjWnnFj8v9X+F0IAEQKYRkA6kqx0LxY0nGS3uecu7gK9QBAuBGWAaAuFQvNe5nZMZJONbOfSEr718I596hvlQFAmBCWAaCuFQvN/yFpvqS9JS3JuM1JOtaPogAgNAjLAAAVXz3jNkm3mdlXnXP/WaWaACB4hGUAwCjFZpolSc65/zSzUyW9Mzn0G+fcnf6VBQABISwDAHIoaRttM1so6fOSHk/+9/nkGADUBra7BgAUUNJMs6STJM1yzm2RJDO7QVKfpAV+FQYAVcHMMgCgBCXNNCc1jfp6SqULAYCqYmYZAFCGUmeaF0rqM7P75S07904xywwgiphZBgCMQdHQbGYm6SFJcyS9TV5ovtg593efawOAyiEsAwDGoWhods45M+t2zh0u6Y4q1AQAlROCsNzdF9fingENDiXU3BRTV0erOttaqvb8AIDxK7Wn+Q9m9rZyDmxmPzSzF83sL6PGdjGze83sqeT/dy6rWgAoVUh6lrv74lqwtF/xoYScpPhQQguW9qu7L161GgAA41dqaH63vOD8jJk9Zmb9ZvZYkcdcL+nEjLH5ku5zzu0v6b7k9wBQOSEJyymLewaUGB5JG0sMj2hxz0DVawEAjF2pFwK+t9wDO+ceMLNpGcOnSXpX8usbJP1G0sXlHhsAsoSgDSOXwaFEWeMAgHAqGJrNbKKkT0raT1K/pGudc5vH8Xx7OOfWSpJzbq2Z7T6OYwFA7rC8007SP/5R/VpyaG6KKZ4jIDc3xQKoBgAwVsXaM26Q1C4vML9X0jd9ryjJzM43s14z6123bl21nhZAVBRqwwhJYJakro5WxRob0sZijQ3q6mgNqCIAwFgUa8842Dk3U5LM7FpJfxzn871gZnslZ5n3kvRivjs6566RdI0ktbe3syYUAE9I2zDySa2SweoZABBtxULzcOoL59xmy/ePVenukHSupEXJ//9ivAcEUCdy/f6ZOlV6Me9779DobGshJANAxBULzYeZ2avJr01SLPm9yVvCead8DzSzW+Rd9Lebma2R9DV5YflnZnaepOcknTnO+gHUulxh+eCDpZUrq18LAKBuFQzNzrmGQrcXeezZeW46bqzHBFAnnJMm5Ljk4uijpQcfrH49AIC6V+o6zQDgP+e8meXMwHzaad5tBGYAQEAIzQCCNzKSOyx/9KNeWO7uDqQsAABSCM0AgrNpkxeWt8voFPviF72wfN11wdQFAEAGQjOA6nv9dS8s77BD+vill3ph+ZtVWxIeAICSlLqNNgCM36uvSlOmZI9fdZX0uc9Vvx4AAEpEaK6S7r44mxugfr30kremcqbrrvP6lgEACDlCcxV098W1YGm/EsMjkqT4UEILlvZLEsEZte2FF6Q998wev/VW6Ywzql9PjeBNOABUHz3NVbC4Z2BrYE5JDI9occ9AQBUBPnvuOa9nOTMw332317NMYB6z1Jvw+FBCTtvehHf3xYMuDQBqGqG5CgaHEmWNA5H15JNeWN533/TxRx7xwvKJJwZTVw3hTTgABIPQXAXNTbGyxoHI+fOfvbDc2po+/thjXlg+4ohg6qpBvAkHgGAQmqugq6NVscb0HcljjQ3q6mjN8wggIh55xAvLs2aljz/5pBeWZ84Mpq4axptwAAgGobkKOttatHDeTLU0xWSSWppiWjhvJhfuILqWLfPC8pw56ePPPuuF5f33D6auOsCbcAAIBqtnVElnWwshGdH3y19Kp56aPf73v0t77FH9eupQ6vcIq2cAQHURmgEUd8st0oc+lD3+8svSzjtXv546x5twAKg+2jMA5HfNNV4bRmZg3rDBa8MgMAMA6gShGUC2K67wwvK//Vv6eCLhheVJk4KpCwCAgBCaAWzzH//hheWurvTxTZu8sDxxYjB1AQAQMHqaAUif/7z0rW9lj2/eLDU0ZI8DAFBnCM1APTvuOG/5uExbtngzzgAAQBKhGahPra3eBiSZnKt+LQAARAChGagnu+0mrV+fPU5YBgCgIEIzUA/ytVrUUFju7ouz4QcAwDeEZqCW1UFYlrzAvGBpvxLDI5Kk+FBCC5b2SxLBGQBQESw5B9Qis9yB2bmaC8ySt6V0KjCnJIZHtLhnIKCKAAC1hplmoJaUMbNcS+0Mg0OJssYBACgXM81ALShzZjnVzhAfSshpWztDd1/c/1p90NwUK2scAIByEZqBKBtjG0attTN0dbQq1pi+CUussUFdHa0BVQQAqDW0ZwBRNM4L/GqtnSHVVlIr7SYAgPAhNANRkicsz114nxcSSzxMc1NM8RwBOcrtDJ1tLYRkAIBvCM1AFOQJy9MuvtP7oswl1ro6WtOWaJNoZwAAoBBCMxBmBWaWM2eKUz3JpYRm2hkAACgPoRkIoyI9y4Pz78p5czk9ybQzAABQOkIz6lYo1yku8QK/WuxJBgAgzFhyLqS6++Kau2iZps+/S3MXLYvs+rlhFbp1istcOo4l1gAAqC5CcwiFLtDVoNCsUzzGdZY721q0cN5MtTTFZJJammJaOG9m8DPlAADUKNozQqhQoCMUVUbg6xSPc51lKRo9yaFsgQEAYAyYaQ6hwANdHQhs2+UxzixHEZ+YAABqCaE5hAILdHWk6j3BdRSWU0LTAgMAQAUQmkOoq6NVjQ3pAauxwbjIq4Kq1hNch2E5hU9MAAC1hJ7msMrMUzWcr4Lqe/W1JzhPz/L0i+/0XmNfPDS9vX6df5bFAwDUEkJzCC3uGdDwlvSUPLzF1eSFgKm+19TH+PEC20FH4qKyPGH5oK/cXdJr9FvmOXz3gVP18+VxX2pjq24AQC2hPSOE6ulj7VL7XkN/UVmBNoy5C+8LRW9vrnN40x+e8602lsUDANQSZppDyM+PtcM2W1vqG4SxLsPn++stYem4sLwJynUO83X9VKq2KCyLBwBAKZhpDiG/VnYI42xtqSuFjCV4+vp6y7jALyyroZQThOk7BgAgHaE5hPz6WDuMS4CV+gZhLMHTl9c7htUwwrLldb5zlflq6DsGACAb7Rkh5cfH2mFpExgt9RqLtVCM5aKyir7ecezgV+pr9Fu+c/j+w1t0/6p1oWnZAQAgjAjNdSSsS4CV8gZhLMGzIq+3AttdS+Ho7Q1LeAcAIIoIzXUk6kuAlRs8x/V6KxSWwyYM4R0AgCgiNNeReptpHNPrrdGwDAAAxsdcBMJAe3u76+3tDboM1DLCMgAAkGRmy51z7ZnjzDTXsbCt2RwIwjIAACgBoblOlbN9dc1xTpqQZ7VFwjIAAMiBdZrrVBjXbPbdyIg3s5wrMBdYZxkAAIDQXKfCuGazbzZt8sLydjk+WCEsAwCAEhCa61RYtnb21caNXljeYYesm+YuvE/dj64JoCgAABBF9DSXoBYvmIv6ms0FvfSSNHVq1vD6N03R4Rfc5H1TTz3cAABg3AjNRdTqBXM1uWbzs89K06Zljx9wgOZ+7DtZuwOmergj/ZoBAEBVEJqLKHTBXNTDVs3sDrdypTRjRvb4ccdJv/61JGlw/l05H1qTPdwAAKDiCM1FRPmCuVpsK0nzyCPSnDnZ4+9/v3TbbWlDzU2xrJnm1DgAAEAxhOYiohq2qtVWEkgwv+ceqaMje/yCC6RvfSvnQ6LSw13zb3R8xLkDAPiJ0FxEVMJWpmLrMFciXFS93/vHP5Y+8pHs8csuk/7f/yv40Cj0cNdq/3w1cO4AAH4zF4E1atvb211vb29gzx/FGaxpeXp4JS/0Z74JWDhvZtmvae6iZTln4VuaYnp4/rFlHaugq66SLrwwe/yHP5Q+9rHKPU/AqnY+axDnDgBQKWa23DnXnjnOTHMJonjBnFn+PTsqdWGj7/3eX/6ytHBh9nh3t3TaaZV5jjHy441UlPvng8a5AwD4jdBcoijNNnf3xcve5G4s4cK3fu/zzvNmkTMtWya9+93jO3YF+NUKENX++TDg3AEA/BbIjoBmttrM+s1shZkF13dRolRIig8l5LQtJHX3xYMuLadU33IuDWY5x8cSLro6WhVrbEgbG1e/9xFHeFPkmYF5+XJv2jwEgVkq3i8+VhU/n3WEcwcA8FuQ22i/2zk3K1fPSNj4FZL8UmjW+Owj96lYuOhsa9HCeTPV0hSTyesfHUtvtLbbzgvLf/pT2vC93Q94YXn27LJr85NfrQAVO591iHMHAPAb7RkliFq/ZL6Pqnd+U6Mu65yp9n13qVirybj6vfPMeh/1qeu0dqepivW+poVvjocu+PjZChDF/vmw4NwBAPwUVGh2ku4xMyfpe865awKqoyRR65fMt0ze1045RJL/4aJo/3eesDzrczdrKLbT1u/DuvNiVJchBAAAYxdUe8Zc59xsSe+V9Bkze2fmHczsfDPrNbPedevWVb/CUaLWLxnkR9UF+7/NcgfmDRs0/eI70wJzShhn82kFAACg/gS+TrOZXSJpo3Puinz3CXqdZilaq2cEKdd6uasvPzn3nTdtkhob8z5OYp1dAABQXaFZp9nMdpQ0wTm3Ifn1CZK+Xu06ykW/ZGlGzwznDcsjI9KE9A85aHkAAABhFkRP8x6SbjfvY/rtJN3snPu/AOoIVK3OXDc3xfTwguNy31jgU41yt7mu1fMHAADCKfD2jFKEoT2jkjI3x5DGvpV1qOS5wO+gr9xd0ddWs+cPAAAELl97RpDrNNetqK37XFSeC/ymX3yn5i68r+JhtubOHwAACD3WaQ5A1NZ9zivPzHKqDeNvBR46nvaKmjl/AAAgMgjNAYjaus9ZioTlQrr74rr0lyv1yuvDW8dSy9KlFAvTkT9/AAAgcgjNVZA5q/ruA6fq58vj0VspYhxhWcrdi5ySGB7RJXes1D83b9l6++gwPTo4+7XSBhcXAgCAfOhp9lmuzT5+vjyu9x/eEp3NMfJtSuJcyYFZyt2LPNpQYrikXmU/NhcpuCkLAACoe8w0+yzfRWv3r1oX/k07xjmznGmsPce5Hpdv3eyxzhYXurgwtG9mAABA1RCafRbJi9YqHJZTQbbQo2ONDZrYOCGt1zml1F7lzPaPfO0duUTy5wQAAKqG9gyf5Qt8obxorUJtGKONbnvIpynWqIXzZuprpxyiWGND2m3l9CqPZym6IH5O3X1xzV20TNPn36W5i5bRCgIAQIgx0+yzSGwPXWRmeTwXyBXqY25JHit1v8GhhKbEGjWxcYKGXh+u6lJ01f45jWdWHAAAVB+huQTjCY3lbg9dVSW0YYw33OULrCbp4fnHZh1/KDGsWGODrjxrVtnnaDxL0VX750QPNQAA0UJoLqISM4L5LloLTBk9y+MNd8WCbCXD43hni6v5c6KHGgCAaKGnuYia2rJ5DD3L4w13XR2tBfuUKxke/ViKzi+R6nUHAADMNBdTEzOC41gNY7y77xVre6j07n6hm9XPIxK97gAAYCtCcxGR3rI5T1iedvGdijU2aGFfvGjArES4KxRk6zU8hrrXHQAAZCE0FzGWUBf4dswFwnJKqX3Dfoe7eg6PUZkVBwAAkrkxrr9bTe3t7a63tzew5y8nBGdeOCh5IXt0b61voTpPWJ5+8Z05NxYxSX9bdNL4nxcAAKBGmNly51x75jgzzSUoZ0aw2GoQvqzPW6RnuXnRsqq3mAQ+2w4AAFBBrJ5RYcUuHMwXqi/86Yryd4UrcTWMYitYVNroXQCdtr0xYMc7AAAQVYTmCiu2lFihVTdKDpdlLh1X7aXYamqZPgAAANGeUXHFLhzMtxpHSmJ4RJfcsTJ3oM3ThtH96BqvFWL+XXlbIdi4AwAAYOyYaa6wQrO63X1xvfbPzUWPMZQYTp9tLjCz3P3omtC1QrBxBwAAqDXMNPsg16xurlU1ClncM6DO2XvnvnFUC0Ylt6GulHpdexkAANQuQnOV5Aq3+ay+/OTcN+ToVw5jK0Q9r70MAABqE6G5SkoJseWE5ZSw7ljIxh0AAKCWEJpLUIk1h/OF253f1Ki+r3XkflAJG8/QCgEAAOA/LgQsolJrDudaK3n15SfnDMzdj64pKTBL1V9ODgAAoB6xjXYRc/PsptfSFNPD848t61ipGeuHFxyX+w4R+FkAAADUMrbRHqNKXmjXOXtvdea6gbAMAAAQarRnFFGRNYfL3MEPAAAA4cJMcxHjutAuzw5++YJyJS44rMQxAAAAkI7QXETZaw47J03IM4FfYFY5c/OT1AWHo2sophLHAAAAQDZCcwlKWnN4yxapoSF7fMoUaWio6HNUYme/MO4OCAAAUAvoaR6vkRGvDSMzMJ9wgjezXEJglipzwWEYdwcEAACoBYTmsRoe9sLydhmT9V1dXlju6SnrcJW44LAiFy0CAAAgC6G5XJs2eWF5++3Tx6+6ygvL//VfYzpsrs1Pyt3ZrxLHAAAAQDZ6mku1ebPU2Jg9/rOfSWeeOe7Dl33BoU/HAAAAQDZ2BCzF4KDUkh48Tz/nCq2adsjWLatZ6g0AACD62BFwPF5+eeuX7/i3H+j5pj29b5IrU0hiqTcAAIAaRmguxYwZmn7xnco1Jz84lBjXUm+ZM9TvPnCq7l+1ToNDCTW9qVHOSf9IDDN7DQAAECBCc4mam2KK51i6Ld+4pLzjKbk2I/nxH57bevsrrw+nHYvZawAAgGCwekaJCq1M0ZBnu+x84ym5ZqgLSYxqBwEAAED1EJpL1NnWooXzZqqlKSaT1NIU23oR4EieiynzjaeMZdMRNioBAACoPtozypBvO+2WPC0aLUU2FSnU2lHoMQAAAKguZporYKybiuR6XCFsVAIAABAMZporYKybiuR6XKVWz2DdaAAAgMphc5MalLkqh+TNUqd6sAEAAJAbm5vUgFJnj8ezbjQAAACyEZojIteazvnWbc63wgYrbwAAAIwNFwJGRKHZ40z5Vthg5Q0AAICxITRHRDmzx2NdzWOsuvvimrtomabPv0tzFy1Td1/cl+cBAAAICu0ZVdDdF9elv1y5dVvsplijLjn1kLL6iwtt451prKt5jEU5bSMAAABRRWj2WXdfXF23/VnDI9tWKRlKDKvr1j9LKj1YdnW0quvWP2t4y7bjNE6wvLPH+TZiqTQuOgQAAPWA9gyfLe4ZSAvMKcNbXM5+5IKsyPcB4KJDAABQDwjNPisUHssJlrnC9/DIGIJ3hXHRIQAAqAeEZp8VCo8TzEq+eC6sM7rVvugQAAAgCIRmn3V1tKqxIXcfxYhzctp28Vyh4DyWGd1qrGrR2daihfNmqqUpJpPU0hRj50EAAFBzuBDQR6kd/IZHnCaYlLqGzyRldjkXu3iuq6M159bY+WZ0q7mqRbUuOgQAAAgKM81lKnX29ivd/frCT1dsXSZui/NC7n+fNSvvsQu1WpQ7o1vOZigAAAAojJnmMpQ6e9vdF9dNf3gu72xyvjWXnaS5i5blXVO5nBndsPZAAwAARBEzzWUodfZ2cc9AVmBOGRxK5Lx4LqWU/uZSsKoFAABA5RCay1Dq7G2h2dzmplhaq0UulWijKLaqBVtfAwAAlI7QXIZSZ2/z3c+kraG1s61FD88/Nu/+JONtoyjUA51qM4kPJUpevQMAAKCeEZrLkK+t4vVNm9MCZ677maQPz3lzVk+yn20UqWD+t0Un6eH5x259bi4SBAAAKA+huQyp2dumWGPa+CuvD6fN1Oaa5f3wnDfr/lXrstohgtgcpF4vEqQlBQAAjFUgodnMTjSzATN72szmB1HDWHW2tWjHHbIXHcmcqR09y9vV0aqfL4/nbIcIYnOQerxIkJYUAAAwHlVfcs7MGiT9r6TjJa2R9Cczu8M593i1axmrcmdqC7VDpJaRq+bmIOVulFILiv0MAAAACglipvkISU875/7qnNsk6SeSTgugjjErd6Y2bO0Q9bj1ddh+BgAAIFqC2NykRdLzo75fI+nIAOoYs3JnavNtZhJkO0S9bX0dxp8BAACIjiBmmnOtspa1F4iZnW9mvWbWu27duiqUVbpyZ2qDuNgP6fgZAACA8QhipnmNpH1Gfb+3pMHMOznnrpF0jSS1t7fn22AvMOXM1I5e6m1wKKHmpljerbLhD34GAABgPMy56uZRM9tO0pOSjpMUl/QnSR9yzq3M95j29nbX29tbpQoBAABQr8xsuXOuPXO86jPNzrnNZvZZST2SGiT9sFBgBgAAAIIWRHuGnHO/kvSrIJ4bAAAAKBc7AgIAAABFEJoBAACAIgjNAAAAQBGB9DTXgu6+OMuXAQAA1AlC8xh098XTdgSMDyW0YGm/JBGcAQAAahDtGWOwuGcgbQttSUoMj2hxz0BAFQEAAMBPhOYxGBxKlDUOAACAaCM0j0FzU6yscQAAAEQboXkMujpaFWtsSBuLNTaoq6M1oIoAAADgJy4EHIPUxX6LewYUH0qowSytzNoBRQAACiBJREFUp5mLAQEAAGoLM81j1NnWsnXGecQ5SdtW0ejuiwdcHQAAACqJ0DwOrKIBAABQH2jPGIewr6LBBiwAAACVwUzzOIR5FY3UBizxoYScaB0BAAAYD0LzOIR5FQ1aRwAAACqH9oxxGL2KRthaIMLeOgIAABAlhOZx6mxrCUVIztTcFFM8R0AOQ+sIAABA1NCeUaPC3DoCAAAQNcw016gwt44AAABEDaG5hoW1dQQAACBqaM8AAAAAiiA0AwAAAEUQmgEAAIAiCM0AAABAEYRmAAAAoAhCMwAAAFAEoRkAAAAogtAMAAAAFEFoBgAAAIogNAMAAABFEJoBAACAIrYLuoCw6+6La3HPgAaHEmpuiqmro1WdbS1BlwUAAIAqIjQX0N0X14Kl/UoMj0iS4kMJLVjaL0kEZwAAgDpCe0YBi3sGtgbmlMTwiBb3DARUEQAAAIJAaC5gcChR1jgAAABqE6G5gOamWFnjAAAAqE2E5gK6OloVa2xIG4s1NqirozWgigAAABAELgQsIHWxH6tnAAAA1DdCcxGdbS2EZAAAgDpHewYAAABQBKEZAAAAKILQDAAAABRBaAYAAACKIDQDAAAARRCaAQAAgCIIzQAAAEARhGYAAACgCEIzAAAAUAShGQAAACiC0AwAAAAUQWgGAAAAiiA0AwAAAEUQmgEAAIAiCM0AAABAEYRmAAAAoAhCMwAAAFCEOeeCrqEoM1sn6dkAS9hN0ksBPn+t4/z6i/PrL86vfzi3/uL8+ovz6y8/z+++zrmpmYORCM1BM7Ne51x70HXUKs6vvzi//uL8+odz6y/Or784v/4K4vzSngEAAAAUQWgGAAAAiiA0l+aaoAuocZxff3F+/cX59Q/n1l+cX39xfv1V9fNLTzMAAABQBDPNAAAAQBGE5lHMbLWZ9ZvZCjPrzXG7mdm3zOxpM3vMzGYHUWcUmVlr8rym/nvVzC7MuM+7zOwfo+7zH0HVGwVm9kMze9HM/jJqbBczu9fMnkr+f+c8jz3RzAaSf5bnV6/q6Mhzfheb2ark3//bzawpz2ML/i6pd3nO7SVmFh/19/99eR7Ln90i8pzfn446t6vNbEWex/JntwAz28fM7jezJ8xspZl9PjnO794KKHB+Q/G7l/aMUcxstaR251zOdf+Sv8QvkPQ+SUdKuso5d2T1KqwNZtYgKS7pSOfcs6PG3yXpS865k4OqLUrM7J2SNkq60Tk3Izn2X5Jeds4tSv5C3tk5d3HG4xokPSnpeElrJP1J0tnOucer+gJCLs/5PUHSMufcZjO7XJIyz2/yfqtV4HdJvctzbi+RtNE5d0WBx/FntwS5zm/G7d+U9A/n3Ndz3LZa/NnNy8z2krSXc+5RM5ssabmkTkkfFb97x63A+d1bIfjdy0xzeU6T90vIOef+IKkp+QNGeY6T9MzowIzyOecekPRyxvBpkm5Ifn2DvF82mY6Q9LRz7q/OuU2SfpJ8HEbJdX7///buLlSuq4zD+POHVDRRolVatVWiWLyoSEKlptVILkRs0WIUpEXs8QMlSC+iqEV6YbRXIlZEpUGttNWiUk00SotVobUKTbVpviTBWgg2JqQoYhotauLrxV5Hx+lM9jme0zkfeX6wmb3XXntmncXinXfWrD2nqu6pqlPt8AG6QK5ZGjN2Z8KxOwNn6t8kAd4JfGuijVomqupYVe1u+08AB4ELMPbOi3H9u1hir0nz/yrgniQPJfngiPMXAI8NHB9pZZqdqxkfsC9LsjfJ3UkunmSjlonzq+oYdMEHOG9EHcfx/HgfcPeYc32xRKNd175+/fqYr7cdu3O3ATheVY+MOe/YnaEka4B1wC6MvfNuqH8HLVjsXTHfT7jEva6qjiY5D/hJkkPtE/u0jLjG9S2zkOQZwFXAJ0ac3k33rytPtqUw3wcummT7zhKO4zlKcgNwCrhjTJW+WKKnuhm4kW4s3gh8ju7NcZBjd+6u4cyzzI7dGUjybOB7wJaqOtFN4PdfNqLM8TvCcP8OlC9o7HWmeUBVHW2PjwM76L5KGXQEeMnA8YXA0cm0btm4AthdVceHT1TViao62fbvAs5J8oJJN3CJOz69ZKg9Pj6ijuN4DpJMAW8B3lVjbgqZQSzRkKo6XlWnq+pfwFcZ3WeO3TlIsgJ4O/CdcXUcu/2SnEOX0N1RVdtbsbF3nozp30URe02amySr2qJzkqwC3gQcGKq2E7g2nfV0N1Icm3BTl7qxsxxJXtjW25HkUrrx+acJtm052AlMtf0p4Acj6vwKuCjJy9rM/9XtOvVI8mbgeuCqqvrbmDoziSUaMnR/yCZG95ljd27eCByqqiOjTjp2+7X3qFuAg1V108ApY+88GNe/iyb2VpVb94Hl5cDetv0GuKGVbwY2t/0AXwYeBfbT3aG54G1fKhuwki4JXj1QNti/17W+30u30P/yhW7zYt7oPnwcA/5JN4PxfuD5wM+AR9rjua3ui4G7Bq69ku4u7kenx7rbjPr3d3RrEve0bdtw/46LJW69ffuNFlf30SUSLxru23bs2P0/+reV3zodbwfqOnZn17evp1tSsW8gDlxp7H3a+3dRxF5/ck6SJEnq4fIMSZIkqYdJsyRJktTDpFmSJEnqYdIsSZIk9TBpliRJknqYNEvShCU5nWRPkgNJ7kyycp6f/z1JvtRTZ2OSyweONye5dj7bIUnLiUmzJE3ek1W1tqpeBfyD7vfKJ20j8J+kuaq2VdXtC9AOSVoSTJolaWHdD7wCIMlH2uzzgSRbWtmaJIeS3JZkX5LvTs9MJzk8/a/mk7wmyb3DT57krUl2JXk4yU+TnJ9kDV2i/uE2470hydYkH23XrE3yQHu9HUme18rvTfKZJA8m+W2SDU9/90jS4mDSLEkLJMkK4Apgf5JLgPcCrwXWAx9Isq5VfSXwlap6NXAC+NAsXuYXwPqqWgd8G/h4VR0GtgGfbzPe9w9dcztwfXu9/cAnB86tqKpLgS1D5ZK0rJk0S9LkPSvJHuDXwO+BW+j+feyOqvprVZ0EtgPTM7mPVdUv2/43W92ZuhD4cZL9wMeAi89UOclq4LlVdV8rug14w0CV7e3xIWDNLNohSUvaioVugCSdhZ6sqrWDBUlyhvo15vgU/538eOaYa78I3FRVO5NsBLbOrqlP8ff2eBrfQySdRZxplqTF4efA25KsTLIK2ES33hngpUkua/vX0C25ADgMXNL23zHmeVcDf2j7UwPlTwDPGa5cVX8B/jywXvndwH3D9STpbGPSLEmLQFXtBm4FHgR2AV+rqofb6YPAVJJ9wLnAza38U8AXktxPN/M7ylbgzlbnjwPlPwQ2Td8IOHTNFPDZ9nprgU/P5W+TpOUgVcPf+kmSFov2Sxc/aj9PJ0laIM40S5IkST2caZYkSZJ6ONMsSZIk9TBpliRJknqYNEuSJEk9TJolSZKkHibNkiRJUg+TZkmSJKnHvwG67KPv4Um4UAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(X[:, 1].A1)\n",
    "y = model.predict(X).flatten()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.plot(x, y, 'r', label='Prediction')\n",
    "ax.scatter(data.Population, data.Profit, label='Traning Data')\n",
    "ax.legend(loc=2)\n",
    "ax.set_xlabel('Population')\n",
    "ax.set_ylabel('Profit')\n",
    "ax.set_title('Predicted Profit vs. Population Size')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}