Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.special import gamma\n",
    "import numpy as np\n",
    "seaborn.set_context(\"talk\", font_scale=1.5)\n",
    "seaborn.set_style(\"whitegrid\")\n",
    "%matplotlib inline\n",
    "det = np.linalg.det\n",
    "inv = np.linalg.inv"
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   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (15, 6)\n",
    "seaborn.set_context(\"talk\")"
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 100\n",
    "m = 2\n",
    "X = np.random.uniform(-5, 5, [n, m])\n",
    "eps = np.random.normal(0, 10, size = [n])\n",
    "y_t = X.dot(np.array([1,-1])) + eps"
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   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sklearn.datasets\n",
    "import sklearn.linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston House Prices dataset\n",
      "\n",
      "Notes\n",
      "------\n",
      "Data Set Characteristics:  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive\n",
      "    \n",
      "    :Median Value (attribute 14) is usually the target\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      "**References**\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bos = sklearn.datasets.load_boston()\n",
    "print(bos[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = bos[\"data\"][:, 0:1]\n",
    "y = bos[\"target\"]\n",
    "model = sklearn.linear_model.LinearRegression().fit(X, y)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 71,
   "metadata": {},
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   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
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   "outputs": [
    {
     "data": {
      "text/plain": [
<<<<<<< HEAD
       "array([[-2.15041117,  6.55815018],\n",
       "       [-9.50044274, -4.90082273],\n",
       "       [ 7.82498259, -0.50614231],\n",
       "       [-9.95087225, -4.90338961],\n",
       "       [-6.6988994 , -6.76605522],\n",
       "       [-4.38158872,  0.4345616 ],\n",
       "       [ 4.78315277,  8.37718135],\n",
       "       [-6.00449303, -0.18639599],\n",
       "       [-1.212048  ,  9.78179806],\n",
       "       [ 7.11581946, -7.97778446]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(-10, 10, [10,2])"
=======
       "<matplotlib.text.Text at 0x7f092f39b550>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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P35YkSbXIYWWltnHjhaxbd2tJKRtJklSbDA6VmqVsJEmaPgwOlZqrlSVJmj4MDpWaq5Ul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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f092f3dc128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y)\n",
    "plt.ylabel(\"price\")\n",
    "plt.xlabel(\"median crime rate\")"
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 72,
   "metadata": {},
=======
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
   "outputs": [
    {
     "data": {
      "text/plain": [
<<<<<<< HEAD
       "(2,)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_hat = inv(X.T.dot(X)).dot(X.T).dot(y_t) \n",
    "w_hat.shape"
=======
       "<matplotlib.text.Text at 0x7f092f235cc0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DzaOJ4KSgwEt29oGJNc3nPIYVgMYAXtxu+6etteuNiIgcThQ8SpgNAJKxMqyt\nrGovU3ipxsVLnMKpjOE2XmYsK0immvG1BYzfUkDdwB9R/elA6v7nsqAKfzefUwfkYE2BlwH13fAZ\nDy6Y4FdERCRaaNpawiweOI3A4A+OAzKAp/iCz5jCcwxgCwuPyWRXYm/rVWv/Tq8JY3GfcSrZtX/A\nze7G11ujiYHT2F999Q0ezxdYI479gRnAjcBMSkr6deNnbZt2vRERkcOJRh4lzFKAz4HFwD7gF8Am\nrJHBGKCGlJTZfH9ELHm153P7d+/w/3ib25nPhfwfMdu3cSPbuN7Znz/2PJN3T/ohBfQkP38W/pG8\n9eunUVx8HzAP2ELgtHUk7DCTkVFGQUFktUlERKSjQg4eDcM4BzCA1cAgYKNpmnVd3TA5XBQDz9O8\nu8xErCCqN+AlNXUHa9bMJD29L6NGraCBvbzJf3mTa/mBK4O//BTcb71BQlUlV5f+k6s//if/dh3P\n45zC61zFfuIa60UejbWLzW6Sk+9k8OAhTesL7ZaTM5rs7Lktdr0RERGJVkEHj4Zh9AXeBL6P9dv/\nfeBRYKhhGBebpvlteJoo0W0gsAerlmNPIBEoJT4+jh49bmfVqvFNySPWCN2L+Lcy/Hf1RK7ZM5dX\nPn+UxFdW4MpdTMx3m/lB9SZe5Rq2059nmcLqpG8xa/0je6mcdVY/8vLG2vNx25Ce3ldrHEVE5LAR\n9PaEhmGswMp8GAcUYi1k2wvkAfWmaV4erkZ2AW1PaJO0tJnAibTc23o68BSttyT0eIo4++w8Kipm\nYe06s5yEhDrOOGM/tbVlfPN1LRc3lDEj4Z+cU7ap6T0a4uLITx3GEtfFFB93lLKZ26Btweyl+28/\n9YH91Af266rtCUOZtr4IuMA0zQrDMAAwTXOvYRgzgH90tiFyuKrBWusYmDCT2vTvwOSR9PS+jBgR\nS35+864ztbVO1q71B5zzeQ0nr9V4ObP39fzpJ3G4//Aazop9XLRrHRexjnr3D6n+IJXan14F8fHd\n9zFFRESOEKEEj7FYv/lb6wXs75rmyOGnGqvWY3PCCHgan2vOnPbXQJw168fAXNau9VJbGxhw+vfB\nth5/XHo+1+4q5ZUvHiHh1Vdw5S4iduMG4v79KXH//pSG+2dRff0EasZPoiH96ANaZWftRdV9FBGR\naBbKtHUuVh2UccC3WNPWLuAlYJ1pmpPC1cguoGlrm6Sl3Ye1yqEPVgC4DSgmIeE0Ro50Ultbwdq1\nD+Kf0vZ9cQ8RAAAgAElEQVRPY2dlLSQ/fybNAeeNwFCsv1XKgAqGDj2aNWsa1zb6fMS9/x6u3EXE\nv/tnHI1f177YWGovv4LqSTex/4wzwWGN1lvXv+uA9+0Odry3povspftvP/WB/dQH9rNj2jobyMUa\nNnIAX2EFj28Cd3S2IXK42oH1ZTKfwIxrl2sDhYUj2Lp1P23VQAzMUHa7i/jiCyfl5YHrJh9vWfLG\n4aB+1AXUj7oA53ebcT2/hMSXl+MsKyXx9d+T+PrvqT91ONWTp1J75dW21l5U3UcREYlmQQePpmnu\nA8YYhnEcMKTxtd+YprkhXI2Tw0EycDYt1zyeS2lpMaWlNwFzaKsuY+sM5ZEjX6C8vPkaSUn7ycm5\nts13bDjmWCofnE3lL+8m8fevWlPa3xQQ98V64qbdTMOD9zAr5RR+zSVs41i6ovZiKFPRqvsoIiLR\nLJRSPQ4aC+mZprm88difDcN4xzTNeV3RGMMwRmJFEycDRcBvTdNcbBhGb2ApcAFQCjxkmubSrnhP\nCbd44MPGf/uLhO8B/IHV9S3qMs6a9eMWayD9QVhxcQGBQabLZa2bbOvcJsnJ1Fw/gZrrxhP3z7W4\nchcT/84fcRYX84viNVzD8fxfj9P484lnMOHJX3XqU4ayBaHqPoqISDQLZdr6UeB6YGrAsbeA/zUM\no5dpmg91piGNAeIfgFtM01xpGMbpwF8Nw9gE3IQVefQFhgPvGIbxlWmaH3fmPaU7VAPLaFmmpxpr\nD2poXZcxcD1gYBDmdh9DcfE8oAdQjts9OPiAzeGg/tzzqD/3PJzbt+FalkviS8uIKSnhon3rueiz\n9ey/5p9UT5pCzc+vgaSkkD9lKFPRqvsoIiLRLJS9ra8HrjFN8y3/AdM0n248fmMXtGUw8EfTNFc2\nXnsdsAY4B7gCuM80zXrTND8BXm58X4l4J9Jyyvo0YASwmZiY+xk58pEWI2/tBWGDBvmwAs8bgWwG\nDfK1ea7H03Lfa4+nqEVrGgYMpPKeByhe9w3l85+h/tThAMR+8zU9Zk4ndfjJJD9wD84t34X0Ka2p\nZ2/jI01Fi4jI4SuU4DEFKGnj+C7gqM42xDTNz03TvMH/2DCMo4DzGh/Wm6a5JfB0rKltiXgbCAyq\nYD2wEzgRr/c+EhISWkw1txeE5eSMJjNzLkOHPktm5hxycka3ea5/NLKg4Cby82eSnb267YDS5aL2\n2rGU/vV99v7xr9RcdTW+2FicpaUkLZyP+8zT6Hn9tcS9/x4EUZGgrfYdKpAVERGJRqGU6nkDa5p7\nnGmapY3HemJlYCeZpvmTrmqUYRi9gD9i1WR5HHjdNM0+Ac9PAG40TfOcIC/pKy2tpKFBpQG6W2rq\nlUA/YCTNax6fxyrZ8wxDhz7LBx+Mazrf4yli2rRV7NzZk9TUIny+OkpK+pORUca9917Aww//H1u3\nOikp2UzPnoMoL99GaupQBg6sYMGCMfz853+hoOCmpusNHfosGRllLUrjXHjhXFauvOWAtjoKC0l8\nYSkJy3JxFjUHet6TDGomT6V2zLXQo0fQn33MmKeDet/u4HQ66N07GX0f2EP3337qA/upD+zX2Afd\nWqrnduBvwA7DMPz7WB8LbAZ+2tmG+BmGcSzWWsqNwLVYxf0SW52WBFSEct3evZO7pH0SquOBk7AC\nx55YKw72Yq17nMh//hPP4MHFnHGGg5dfnsyQIcfy17/+EoDLLpvLO+/Mwr+m8YsvplNUdA/wMDCc\n4uJ9wD2ceWYeb79tJbwMHlzZIpN58OBKtm93Ezi97fEchdudcmBT3SfCE4/Cww/AqlWwYAF8/DEx\nG0ySf3knyb95AMaPh9tugxNPPOQn3707yPftRvo+sJfuv/3UB/ZTH0S/UEr1bDMM4xSsbQqHYGU8\nbADeNU2zoSsaYxjG94F3gBdN07yr8dhGIN4wjAGmaW73nwoUhHJt/aVjFyfwBdZooz9pZnLjv4fS\n0HAnFRVO3nuvkGHDHiQ9/RQyMspYsGAMW7YkExh8lZf3B1YACwKuNY8tW5IoKbH+lpg792fU189l\n504rk/lXv7qAq69+HliMf+QzLa2k6fx2XXYlXHYlMf/+lMQli4l/4/c4ysth/nyYP5+6zIuovXEq\n9ZkXgbPt1R9paSUEZogH9b5hor/47aX7bz/1gf3UB/bz90FnHXTa2jCMeNM06/z/PtiF/Od1lGEY\n6VhRxhzTNH/b6rlVWJskTwGGAW8DlzUmzwRDO8zYJC3tFqw6j4HTtdZoIgyiOddqLlat+eZdV8DX\nYpeZ1NTbKS4ejvVl4LeYzMy95OW1PR3cejeX1NRprFkzI+TtAB27d+N6aRmJy3KJ2VXYdHz/scdR\nM/FGarLG4evZq8VrrNqPq1uU5LFrG0Lt7GAv3X/7qQ/spz6wX3ftMFNtGEaGaZq7sYK3tnrb0Xi8\nrX2vQzERaw+7ew3DuK/xmA+YhxVhLAK2Yw0fzQwhcBRb9Qe+puXe1qVYX069Ao73wKr/uBzoyb/+\ntZ233hoH+HeZ2UldXSqlpevwepuvFRv7IbNmNZe9aV2se+tWB4Gjl+npp3QogPOlpVF15y+puv0O\nEt5+C9eSRcR99CGxm78l5d7/JfnR31Az5lqqJ03Fa1i5XKGW5NGe1yIiEg0OFTxeQHOG9Y/D2RDT\nNB/FqiXZnmvC+f4SLoVYOU/zsBL2/4G1VNZJ794F1NXdgdOZTlzcNvbuLceqQ++kstLL7NnNdRuz\nshaydu1dWAHmdOBUYB/79z/C7NnLycsbAhxYrDs1dRpt7WDTYXFx1F7xM2qv+BmxX35OYu5iEl9b\nhaOqEteyXFzLcqk7bxTVk6dSd/H/QEzwf1OFUmhcRETELgcNHk3TfD/g4a3AvaZpmuFtkhxeErES\nXAYCn2PlOk0lNfVhvvmmeWMij6eIs8/Oo6Ki7ULbzTUd07BqRd4Y8Fxb51nXSE0dwvDh4dnNZf8p\np1GR8zSV9z1E4ksv4lq2hJjt24j/YA3xH6zBO2gw1eMnUzP2OnxHuQ95Pe15LSIi0SCUOo+ZQH24\nGiKHq2qsBJdfA/OBKmAqLldSi9qH6el9GTEilvYKbbes6dh2LUiPpwiP54sWzw0cWEle3s2sWTOW\nvLxbwjIN7HOnUj3tDko++YKyZS9Td975AMRs3ULKQ/eSOnwIKXfeTszXXx30Oio0LiIi0SCUOo+z\ngIuBJ7HK81QHPm+a5oYub13XUcKMTdLSfoM12rgXa3fJ74AG4HvAdEaOfJTXXpsOHDzBJPA5t3sH\nkEBJSZ8W51nJMTdglQNKITX18w4lx3SFmG8KcOUuJnH1KziqqpqO1519rjWlfen/g9iWA//hTLDR\nQnV76f7bT31gP/WB/boqYSaU4LGtcjw+GhNmTNPsbMJMOCl4tEla2m3AKfjXMloja7djrZi4h5SU\n5/j222ld8l6jRq04oED4mjVju+TaHeUo3Uti3gpcSxcTE7Dlobdff2pumEj1dRPw9enT/gW6iH5o\n20v3337qA/upD+zXXdnWgY7t7JvJkcflOo7q6pZr+SC18f8L8fmad7zsbLZxRkZZiwLhwUz7Br6n\n210ENO9o0xUjf77eR1F9821UT7mZ+Px3cS1ZRPya/yNm5w6SH32YpN89Qe2VV1M9eSr7Tzu9U++l\nbG0REekOQY88+jUW8h5C40bFpmn+JxwN62IaebTJz36Ww9q1Lqwajv5SPY9hJdJsYuTIVF57zdpR\npnVNxszM5mzjYAKjjkz7tn5PyAFmHPD+XSnmvxtx5S4i4ZWXcVY2Fw2v/+GZVE+eSu3/uwLiD1pW\ntU0Hu3/6i99euv/2Ux/YT31gv24feTQMox/wOnAGVvmeGKCnYRh/A8aYpqnV/XKAhx66hAsueAyr\n1uNxwA6saev3AS87dqSSlbWQnJzRB802DqaMTah1FeHADGdrC8UD378reU84kYpH51B5930kvJqH\nK3cxsf/dSNynHxP36cd40+62prSvn4gvPT3o6ypbW0REukMo09aLsSo7n2Ca5rcAhmGcBCwFngKu\n6/rmSbS7774/Ahm03FIwB/gWeIbNm51s3mwFgxkZvnannbsqMGo9gul219GygLn/PcOT7dzmCOqE\nG4l7/z1cuYuI/+tfiNntIfm3j5KUM4fay6+getJU9v/wTHAc/I/Fjkzbi4iIhCqU4HEUcJY/cAQr\nw9owjNuwhpFEDvDZZ3ux6jIGju6VYWVgNx/76KN63nzzIvw7yrSuydhVgVHrEcyRIx8kM9O/i00R\nUE9JybNkZJQxa9aPycpa2KVrCNsbQa3/cSb1P87EuflbXM8vIfHl5TjLy0h8bTWJr62m/rTTqZ40\nhdorr4bExDavnZMzmuzs8NS0FBER8QsleNwKnAC0LlbXF/B0WYvksFJb68baZWYO1naEZVjlQosJ\nHPGrqEhg9uw17U47+wOjbduSKC4uYOvWY5qmu0MJ6FqPYJaU9Gs3IztwDWFX7fhyqBHUhmOPo/Kh\nR6j85d0krl6JK3cRseZ/iPt8HXHTbqbhoXupHjeemvGTaOjXv8VrOzJtLyIiEqpQgsc5wKLGqeq1\nwH7gdOBeYKlhGBf7TzRN890ubaVErYSEPVRV9aNlqZ4ngHqczttoaPg+UA5cT2Hha+1exx8YZWUt\nZMOGpygudrJxo5ebb36IhISkoEcHQxnBPFig19HM5qDfPyWFmvGTqLlhInFr/25laf/lbZx79pCc\nM4ekBU9Sd9nlVE+eSv1Z5xxySltERKSrhBI8Lmn8/2NtPHdPwL99WL8ZRfje91x88kkdB5bqieOo\nozZTXDyRUKaiWwZ0xfzzn9toaFhCsKODoUztHizQ6+g+1CFPLTsc1J93PvXnnY9z21Zcy3JJfGkZ\nzr17SXjrDRLeeoP9Q4dRPWkKNVePgaSkQ7ZBRESkM4IOHk3TDGUrQxEAEhJ6AS5aJqX8A/gtbvey\nkPedbhnQvUhDw1mEkkgTytTuwQK9jibwdGZquWHgICrvfZDKmb8m8fXVJC5ZRNxXXxBb8BU9Zkwj\n+eH7qBl7A9UTJtMwaHCH3sNOqlMpIhIdQhl5FAlZSUlf4GfAPKAH1pLZeiCVQYN85OXdcsBrDhZE\nBAZ0W7fWUlGxj8DA1OP5Ao/nYtLT+3Y6GDlYoGdrZrPLRc0vrqMmaxyxH/3Lqhn5pzdxlpaS9PQ8\nXM8soO7iS60p7fPOj5op7Y6O5oqISPcKuUh4lFKRcJtYSSczaR51zAGqyMyMbzeYO1ix6wPPux7I\nA1KAz4F7yMxc3rQ+MpjrdEQ496HuCGfhThJfyMX14jKce4qaju83TqZ64hTqr83CPejoiC7OG4nb\nS3YVFUe2n/rAfuoD+9mxPaFIyHJyRnPKKZOAc4B9wC+Au/jww16cfXYew4c38MwzVoDgHyXcunU/\ngVPC27YlNZXMCdxC0O2uYOTIXD75JIbaWh9wP5DWNIUczqLZkZbZ3JDRj6pf30vVHb8k4Q+v4cpd\nRNy6z4g1/0OPX91Jw+wHYeIEnGPH4x18nN3NbZPqVIqIRAcFjxJW6el9OeOMVD755CPgROA3wECq\nqmYDTtautaYnwRcwSjibwKnooqKv2LBhIS2LjN+EfzRx5Egf+fnZtA46uiIYibp1eAkJ1I7JonZM\nFrH//gTXkkUkvPk6zvIyyMmh17x51GVeZE1pj8oEZ+QsZVadShGR6KBpawm7goL/kJm5FK/XX5an\nAmuauSdQTlLSHgYM6M+GDbc1vuIBrPWRPYEykpKKqap6NOCKzwE3AtbU5sqVF3PzzS+zfr0Dn8/D\n6af34plnJgB0emo5nFPf3cXh8ZC0YhlJLyyFwsKm4/uPO56aSVOouXYsvh7ayjCcNF1nP/WB/dQH\n9tO0tUSNe+/9M17v0zSPHE7BCgCtx1VV0yku/obm0cZ+jedYHI5pHGwLwfT0viQkxFFRYQV5/tHM\nvLybmwK9jo4gRvN+0S0/cw9e+ugTUv7yVxIWP0vcJx8R++0mUmb9iqRHHqZ2zLVUT5qK9yTD7mZL\nlIi6UXkR6TIKHiXs1q1z0rLO4+BWjw3c7uqmsj0ezxcUFzcHi6ef3ouEhLa3EPRPbbYO8gLXSWZk\nlFFbW8HatQ/iz+Q9VHFx/y9Ga/3lHOB6IDWq1uG1zl6eOHUeL700heorrib2i/XWlPbrq3FWVuB6\nfgmu55dQd/6PqZ58E3UXXgwxKtcq7VN2vMiRS8GjhJ3P56HlyOGmVo//w6BBgxrXvK2ivv4YYBqp\nqUMYOLCSnJwJhxzRaL2+sbi4gA0bnsL/iy052b/DDUAM69btpbLyftr7xRf4ixG8uFw3k5QUy7Zt\nQzu0LaIdWgfU27f3aHpu/6nD2Tf/GSrue5jEFS/gen4JMTt3EP/+e8S//x7eQcdQPfFGan4xDl/v\no2xpv0S2aB6VF5HOiZzV8nLYGjo0HpiOtTnRNGJj47DqPi4EbgUq+fDDen70oyfIz7+LjRt/SXHx\nfAYOrCIv75aggrScnNFkZs5l6NBnycycg9t9DIG/2ByONKxAFcCLw5He4vmPPqpn1KgVZGUtxOMp\nOuAXo9OZSHHxU2zYcBv5+TPJzl7dBXcmvKxR0ubPPGBA+QHn+Pr0oXr6DEo+/ZKy3OXUnTMSgJit\n35HywCxSTzuZlBnTiSn4uvsaLlGh9ddXNI3Ki0jnaORRwi4xsRfWftZOoBCv99c012UcBPyaqion\nVVWL6ehIRnp6X3Jyft60Bquk5AsCRzeHDq1h06ZpVFQMICVlG8cfn8rHHzc/X1GRQEHBTU2jkBkZ\nvhYjma2DzWgYZWmdvbx06YT2T46Npe7yK6i7/Apivv4K19LFJK5eiaO6Gtfy53Etf566c8+jeuIU\n6i79CcTqR8eRTtnxIkcu/QaQsNu1KxF4Eit7ej0+3+PA0VjB3WM0B2Utd4s51EhG6wX7gesaoZDU\n1Gmkp5/S+BwUFy8AnNTWehky5EEyM61ffFu2FFBZ6d+ePYatWx2kp+8jOflOHI40hg6tYePG3SG1\nLRIE1qIMzHI8FO/3hlExdz6V9zxA4ssvWVPaW78j/h8fEP+PD/D2H0D1+EnUjBuPLzU13B9DIlSk\n1ToVke6j4FHCrqRkM2CtP7QCsHnAnViBWGBQ9guOOupW6usH4vN5qK3thcdT1O60desF+y3XNWaQ\nnn5K0w4lo0atIHDksKSkHytXXtyYFJMOvIg/KWbz5gI2bsxtau+mTdPYu/fBxnankJr6ObNm3dAi\nIaezayA7k7karqxX31Fuqm+dRvVNtxL/t3dxPfcs8X9/j5gd20mZ/SDJcx6j9qqfUz15KvtPHd7p\n9xMRkeig4FHCrmfPwRQX+0ceywF/fS8vsAun8zYSE/vz/e/7gP6sXfu/tC6505bW6xKb1zUeODrY\nVsHw1kkxMAMYyP79Z7S4bkXFAKyR0jsBSE9/ltmz3+vSTNPOZK6GPes1Joa6Sy6l7pJLidlg4spd\nROLKPBxVlSS+soLEV1ZQf+ZZVE+eSu1PfgpxcV333iIiEnEUPErYlZdvoeXIo7/OYzkwlIaGZJzO\nWhIS4ti2LZmDldwJHFVrHRAOH97QVNIncA2Wx1PEvn0VxMQ8RENDEb16VTNr1s3cdttnLd4L+gLX\nAcsJDEJTUrZRW9sy8Ny61UnzVHw5W7d27h51JnO1O7NevScZVDz+Oypn3W8FjrmLid38LXEf/4u4\nj/+FN/1oasZPovq6CfjS0sLWDhERsY+CRwm7Xr0MiosDg7TTsHaIKQRmAUuoqHCSn+8lNbVlQfDW\nJXcCR9UOXLA/9oBajVlZC/noo/1UVPTCPy1dWprD7NlrDkiKgQTgJeAXTesl3e4iamtd1NRY6x+H\nD28gJ2cso0bNITAgLimZ1ql71JmtFO3YE9rXsxfVU26hevJNxL/3NxKXLCIh/6/EeHaR/Phskp78\nLbU/vYrqSVPY/4Mzwt4eERHpPgoeJezKykwCA8LY2PXEx99NVdUu4CQCR8169OgDNGdF9+jRp0Xg\nGTiqdqgF+wdOS/vXWtawdq2TM86oIinpDqqqTsEaBb0eh2MxbvfDrFo1nmHDhpCVtZC1a3/bdI2E\nhLmkp/clNXVoi3alpg7p1D3qTOZqd2S9truu0umkLvNi6jIvxvntJitLO28Fzn3lJK5eSeLqldSf\n/n2qJ02l9oqfQUJCl7dNRES6l4JHCTu3+xiKi+dh7VddzrHHnkx6ei1r1y7BmvptDiz37SuiuNga\n0bOmiluORIYyqtZ6Otd6f2uEsbZ2JmvXWiOdVVUTm67v87koLr6X0aN/Q3r6KWzZsgPYA6QRGLwO\nHFjJhg3N7Ro4sLJT96gzmavdkfUazLrKhuOOp/I3j1P563tJXPUKrtxFxG4wiVv3GXG3TaXhgXuo\nvn48NTdMoiGjX1jbKyIi4aPgUcJu0CAfGzdOxx9opac/wvr1/i0Lr8MaEawjNXVrmyN6/m0LW4+q\nHSrLuPV0bkzMZzQ0rMPne6Dp+m73YIYPn8vatV5qa+Pxr3ksLl7Q2I5C4EFgOFCG220Fr6GM9h0O\newCHtK4yJYWaCZOpGT+JuA/ex7VkEfHvvoNzTxHJv/stSfOfpPYnP6V60lT2jzgLHI7u+AgiItJF\nFDxK2C1YMIYZM+axZUtSY83FusY1iF6sEb3ppKbezpo1M8nOXnXAiF5e3i1tXret0bDAQuFudwUj\nRz5KSUmfxqDtrsbX+GsTehk0yEde3i1kZS0kPz+b5hFKf6D0MrCA5qnvR4HQRvsOhz2AO7Su0uGg\n/kejqP/RKJxbt+B6fgmJK17AWVpK4h9eI/EPr1E/7FRqJk+l5qqfg8sV7o8hIiJdwOHz+Q59VvTz\nlZRU4PUeEZ814gQWqPZ6fYwatYKCgp9hJaf0IDn5S/71r2zS0/s2jtKtbjGi194onXWdm5oeDx36\nLBkZZS3WOWZmtgzUAq/vdhcBdZSU9Mft3gEkUFLSB4/ni8aC4jHAYqzs8Ob38NeODFZb7Qz1Gp3V\nug9CFUq/HFRVFYmvrcK1ZBGxBV81HW5wu6kZewPVEybTMGBg6NeNcJ29/9J56gP7qQ/s19gHnZ7u\n0cijdDtrFCsVK3nFy1lnzWkKREIZ0WtrNOxQ06uB17dGG2cRGGiuWTMWj+dibr75Udavd1BVtYmG\nhkl0JpP5UKN20TCt3WXrKpOSqBl3AzVjryfuX/8kMXcxCX96E2dJCUkLnsT19Dzq/ucnVE+eSv25\n52lKW0QkAil4lLDyB0a7d7vp3XsXPl8du3enk5o6Dbd7MIMG+Q5YLxhsMNXWusPs7FVBT6+2F2im\np/clISGOioq7sJJlckhJqWHEiNgOZTIfan1kd05r79pVxLRpr9ofqDoc1J99LvVnn0vlzh0kvpCL\n68XncRYXk/D2WyS8/Rb7hwyleuIUan5+DSQnd38bRUSkTZq2lrCyRvcCy+XkYO3kcuCUcnuvae+8\ntoQyvWq9z0z8gebIkQ+SkJBMYWGvgP2urULXXTHV3F5Q3B3T2v7pogsvfLzD9zbsampIeOP3uHIX\nE/f5uqbDDT17UZM1juqJN9Jw7HE2NrDjNF1nP/WB/dQH9tO0tUSFA8vl9Gz6d3sZu53ZMSWU6dVZ\ns37M+vXNNSXr6lJZu7btQLcrCm+3N8LYnUW+u3M3mpAlJlJ77Vhqr/kFsZ9+jCt3EQlvvoGzvIyk\nRU/jWryQugsvpnrSVOpHXQBO56GvKSIiXU7Bo4RV68AI/IFR+0FSdwVTs2e/15gYY9WUrK19jMDA\nKiamnOOPf4qBAyu7pPB2e4FbdxT59rNjN5qQORzsP2ME+84YQeWDj5D4wlISX3yemN0eEv76FxL+\n+hf2n3Ai1RNvpPaaX+DrEUEBsIjIEUDT1hJWHk8Rd9yxGo/nqMY1j/WUlPQ76JRyl2X2ttMe/9Rx\n66np5ORpVFY+SXOg+ySZmQ1dNq3bepo8M3NOu2WIupp/uuibbzZz++2rwnJvw6qujoS33sC1ZBFx\n//6k6XBDSg9qr8mietJUvCecaGMDD07TdfZTH9hPfWC/rpq2VvAoYRdJPzAOtgZz5MgHWLeunMrK\nYVjbFV7H0KGvdWj9YVvrG4GgguJwZF9HUh90Vuz6z3DlLibhjd/jqK1tOl436gKqJ0+l7sJLwj6l\nHWofHU73P1qpD+ynPrDfYR08GoZxJvC6aZr9Gx/3BpYCFwClwEOmaS4N4ZIKHm0UST8wWienpKTM\nZtCg1BbZ2l0xOtiZpJ/OvLY9kdQHXcWxZw+ul5aRuCyXmJ07mo57Bx9jZWn/Yhy+Xr3D8t6h9tHh\neP+jjfrAfuoD+3VV8BhxK84Nw5gI/AWICzi8BNgH9AVGA080BpgiIbHW+HkbH3kZMSKWNWvGkpd3\nC+npfcnJGU1m5lyGDn2WzMw5HV5/2JnEFLuSWjyeIrKyFjJq1Aqyshbi8RR16NxQrtMZvj59qMqe\nScmnX1KW+yJ1Z58LQMyW70i5/25STzuZlJnZxPznmy5/74hOPBKRTumun2HRLKISZgzDuBsrOPwN\n8KvGY8nAFcAJpmnWA58YhvEycD3wsV1tleh0qOSUzhTDDpzK9Hi+wApSD0xMCXVP7raSWsIxtR1K\nvcmDndvt2zHGxlJ3+ZXUXX4lMV99iWvpYhJXr8RRVYXrxaW4XlxK3cgfUT1pKnWXXAqxnf+xFxWJ\nRyLSIYfDlrLhFlHBI5BrmuYjhmGcH3DsJKDONM0tAcdM4KrubZocDrpsp5Q2BP7AgUJSU6eRnn7K\nAUHqoX4wBZN9HY4fbqGMph3sXDtH5bzDTqHidwuovOcBElcsx7VsCTHbthK/9u/Er/073gEDqR4/\nmZpx1+Nzpx76gu3ozgx5Eelemlk4tIgKHk3T9LRxOAmobnWsqvF40JxObXNmF/+9P9z7oOUPnAzS\n00/hgw/GHeI86wdTTEzzvenXL41XXz34OstDXaO1YPqgrdG09q55sHNDuU7Y9O1DXfYd1N0+jbg/\nv+PCiRQAACAASURBVE3iksXE/X0NMdu3kfKb+0me8yh1V4+m5sab8J5yasiXD6aPAh0p3wORTH1g\nv2jpg4j4GRYmXXXvIyp4bEcVkNjqWBJQEcpFevfW9mZ2Oxz7oLBwN5MmLWf79p7s3v0lgVPVgwdX\n4nanHPCawYMrKSgoBF4GerB795fU1VVx9NFpQb+vdY1Dv1drB+uDl16awMSJ89i+vQcDBpSzdOmE\ndq95sHNDuU63uC7L+q+gAJ56Cl58EUdlJQkrlpOwYjmMHAm33w5XXQVxcYe+Xiccjt8D0UZ9YL9I\n74OI+xkWgSI12/p8YJVpmmmGYaQAJcBxpmlub3x+PtBgmmZ2kJf0lZZW0tAQeZ/1SOB0OujdO5lo\n6YPW+z8vWDCm3fWEY8Y83Wqq+jdNU9Xtvc7jKeK88+Y0FSgHLxdeOJeVK4MfyfJ4ipg2bRU7d/Y8\nZBsh+vognBxlpcTnrSAx9zlivt3UdLwhox814ydRe8MEfH27tval7r/91Af2Ux/Yr7EPDv/tCU3T\nrDAM4w/Ao4ZhTAGGAVn/v737jpKiSvs4/u3uyYFJKGZ0d/USFAkqYgQRFeOaMYCSRUVFXYwsKoqu\nGcMKCKiYMACKgUXBlzUjEkQJVxdXTGuanFP3+0f1sGPvAD3QPdXT8/ucw4G+VV319L3TxTN1QwEn\nNuc4fn9ASwO4rLW0wRVXvPi78YRjx255PGFTXdWN14Vs6vO2b9+eDh0OID//v93OP/7Yrll10759\ne5577vcxhfP+1tIGUZWRRd3IS6kYfglJ77xN6oxpJL2zGO9/fiTtzkmk3vc3qk87g8oRo6nr0Sui\np1b9u09t4D61QesXc0v1bMEoIAn4HngJuNZau3zrbxHZPs0ZLB269E+4s263930SQV4vNcceT/Gc\neRR8tIKKkZfgz8jEU1NDyktzyDm+H9kDjyH55RegpsbtaEVEYkZMdltHgRYJd1FrWxi2OY8R3N5H\nKUbzEYxNn+slfvkll513Lmg9jyR0gaeslOQXnid11nQSvvpyc7l/p52pHDKUqouG4d9l12Yft7V9\nB+KR2iA6S3w1h9rAfXH9hJkoUPLootZ2wWjJxO6/54veBT0aT6yJe4EAie8uJXXmNJIWLcQTvE4G\nEhKoPuU0KodfQt3Bh4AnvGtwa/sOxCO1gfvXArWB+yKVPMb8mEeRSNtWstbctSDDSf62tk+0F6TV\nmmXbweOh9uh+1B7dD+83/yb1iRmkPPc03uIiUubPJWX+XGq7dady+CiqTz8LUkIXhAif23eDpO3Q\ntUAipbWMeRSJmIZkbd26S1iy5FquuurlqB9va/uEXtC/+y4too/Giub4yrbwGC//3vtQfusd5K9e\nT+m9U6jr3AWAxDWraXflpeR170T67bfg/f677Tp+pH8eRbZEY60lUnTnUaLup59+5cILp7FpU3pM\n3FmJ9G/f4Rxva/uELkibn7+OL798hEjdiXzwwbMZN+4+fv45Z/OYx0hpU4/xSk+nashQqgZfTOKH\n7zuztBe+jreggLSH7if1kQepGXgylSNGU3vYEWF3aetukLQUPRlJIkXJo0Rd6NI3bicYkX4ucTjH\n29o+oRf0b7/d+3fL+OxoMtGhw0688MKlURlr1CYTH4+H2sOPpPbwI/F+/x2pT80i5ekn8BYUkPzG\nApLfWEBd565UDh9F1ZnnQPrWF0TWc7KlpUTz8azStih5lKiLtQQj0r99h3O8re0TekE/77y/89VX\nrSOZaOuJj3+PPSm/aSLl11xH8itzSZ0xjcQ1q0lYv5bMa68kfdJEqs4fTM3IUZC7f5PH0N0gEWlt\nNNtaoq45S9/EupaY3BCN2d7NmeXYnM/Y0jPTY14gQMLyT0idOZXk117FU1fnFHs8eE4+mZKLRlB9\nZN+wu7QlcjTT131qA/dpqZ7mUfLoot9++41rrpnPpk1prT7BiMRSF81NQLe0f3OO05yLttvLecQL\n70//IeWpWaTOfgLvr79sLq/bdz8qh42i+tzzCGRkuhhh26LExX1qA/dFKnnUbGuJOuf3k/i4UESi\nC765s2u3tH+0ZunG2jCD1sq/y65UXHcT+SvXUjZ1BvTuDUDCV1+SecO15B7YmfSbxuPb+FVEz9sW\nZsCLiLuUPErUXXHFiyxcOC4uliKJxFIXzU3OtrR/tJI8LecRYcnJ1Jx9Lnz8McVvL6Xq7EEEkpLw\nlpaQ9vhUcvv0ImvQGSQtXgR+/w6fTkv/iEi0KXmUqIunO1kPPng2/fvfR5cuU+nf/97tmtzQ3ORs\nS/tHK8mLxGeUptX37EXpo9PJX7We8utvpj74qMOkdxaTdf7Z5B7ag9Rpj+Ip2f62jKfvm4jEJo15\nlKiLpwkzkdDcSSZb2r85x9FYI3dtsf5ra0l+8zVnlvayjzYXB9LSqTpnEJXDR1NvOjXrXPq+NU3f\nAfepDdynCTPNo+TRRa15wky8PDpOF213hVP/CZ9/Rsqsx0mZ+yKeqqrN5TVH9qVyxGhqjjsBfL5t\nnksz4Jum74D71AbuU/LYPEoeXdSaLxjxMvO4NbdBPGhO/XsK8kl5ZjapT87A1+iRh/V7daTy4hFU\nXTCYQE5utEOOO/oOuE9t4D7NthZpARo/Ji0tkJtH5RXjKFi+huInn6PmiKMA8H27iYzbJpB3YCcy\nxl2O74vPXY5URNoqJY8iW6GZx9umpWGixOej5sSTKZ73OgXvLqPyouEE0tLwVFWR+uxsco85nKxT\nTyDptVcguBi5iEhLULe1RF1r7qqIl/Fj0WyDeOnaj6ZI1b+nuIiU558hdeZ0fJu+2Vxev9vuVF00\njMrBQwm0bx+BiONPa74OxQu1gfs05rF5lDy6SBcM90WzDfr2fZZ16y7Z/LpLl6ksXXpBRM/R2kW8\n/uvrSVryFqkzp5P0f0s2FweSk6n+85lUjhhN3YE9dvw8cUTXIfepDdynMY8iEhPaete+K932Ph81\nxw2k+IX5FHy4gsrho/CnZ+CpriblhefIGXA02SceS/K8l6CmJvrxyHbRkA9prXTnUaJOv226L5pt\nEC9d+9srnG77lvgOeEpLSH7xeVJnTCNh4782l9fv3IGqi4ZRNWQo/g67ROXcrUEsXofa2pCPWGyD\ntiZSdx4TIhGMiLRdHTrsFNf/4W1LrMzID2S2o2r4aKqGjiRx6TukzpxG0uK38P3yM+n33Enag/dS\nfcppVA4fTd1Bh4Bnh///kB0UKz87Is2lbmsRkR0Qc932Xi+1xxxLybMvUfDxKipGX4a/XRae2lpS\n5r1MzkkDyD6uL8lznoVGi5FLy4u5nx2RMKnbWqJOXRXuUxtETzjd9q7Xf1kZKS+/QOqs6SRsWL+5\n2J+XR+XgoVRdPBz/bru3fFwtyPU2aEJbG/IRi23Q1mi2dfMoeXSRLhjuUxu4K2bqPxAg8f13SZ0x\njaRFb+Lx+51in4+aE0+hcsRoag89LC67tGOmDdowtYH7NNtaRESax+Oh9sijKXnqOQqWr6Fi7Dj8\nOTl46utJfu0Vsk8bSM4xR5DyzFNQUeF2tCISo5Q8ioi0Qf4996J8wq3kr95A6YOPUrt/NwAS1n5O\n5tVjyevRmfRbJ+D9dpPLkYpIrFHyKCLSlqWmUnX+YIqWvEfhgkVUnXYGAZ8Pb2EhaY9OIfeQA2k3\n5DwS310KbWOYk4hsg5JHEREBj4e6Q/tQ+viTFKxcS/nVf8Hffic8fj/J/3iD7LNOJeeo3qQ8MQPK\nytyOVkRcpORRRER+x7/rblRcP4H8VesoeWQatT16ApBgN5B53dXkde9M+oTr8X690eVIRcQNSh5F\nRKRpyclUn3MeRYuWUrhwCVVnnkMgMRFvSTFp0/5Obp+etDv/LBLfeRuCM7dFJP4peRQRkW2q63Uw\npY/NIH/lOsrH30h9h13wBAIkL36L7EFnknNYL1IffwxPaYnboYpIlCl5FBGRsAU6dKDi2uspWPEF\nJdNmUXtwbwASvt5Ixk3XkdutExnXX4Pvqy9djlREokXJo4iINF9SEtWnn0XRG29TuPhdqgZdQCA5\nGW95GamzHif38IPIOvs0khYthPr6bR9PRFoNJY8iIrJD6rp1p/Shx8hfvYGym2+hfvc9AEj65/+R\nNfhccnv3IPXvD+MpKnQ5UhGJBCWPIiISEYG8PCqvuJqC5WsonvUMNYcfCYDv22/IuOUm8rp3JuOa\nK/GtW+typCKyI5Q8iohIZCUkUHPyqRTPf4OCpR9ROfhiAqmpeCoqSH36CXL79iHr9JNIeu1VqKtz\nO1oRaSYljyIiEjX1XbpSdt9D5K9eT9nE26nfqyMASR+8R9bwweQe3I3Uh+7Hk5/vcqQiEi4ljyIi\nEnWBnFwqL7uCgmWrKZ49h5qj+gHg++F7Mm6/hbzunci48lISPv/M3UBFZJuUPIqISMvx+ag54USK\nX36VgveXUzl0BIG0dDzV1aQ+/ww5/Y8k++TjSH5lLtTWuh2tiDTBE2hFD7o3xvQApgJdgS+BMdba\nZWG8NVBQUEZ9fev5rPHE5/OQm5uB2sA9agN3qf63zlNSTMoLz5EyczoJjR55WN9hF6ouGkblkGEE\ndt55h86hNnCf2sB9wTbw7OhxWs2dR2NMMrAAmAlkAQ8DC4wxaa4GJiIiOyTQLovKkWMo/HAFRXPm\nUn3scQQ8Hnw//0T63ZPJ69GZzDEjSFix3O1QRYRWlDwC/YB6a+10a229tfYJ4GfgRJfjEhGRSPB6\nqT1mACXPvUzBRyupGH0p/sx2eGprSZn7IjkD+5N9fF+SX3weqqvdjlakzWpNyWMnYF1ImQ2Wi4hI\nHPH/4Y+UT7qL/M82UPq3+6kzzqU+cdVK2l0+mrweXUi7axLe//zocqQibU9rSh7TgYqQsgpA3dYi\nIvEqI4OqoSMofHcZRS8voPqEkwh4vXh/+5X0++8ht9f+ZI68mISPP4JWNIZfpDVLcDuAZqgAUkPK\n0oCycN7s9e7w+FDZTg11rzZwj9rAXar/SPDg79eP8n79qPx2E8mzHif56dl4iwpJeXUeKa/Oo+6A\nblSNHE3NGWdD6u//u1AbuE9t4L5I1X2rmW1tjDkBeMRa+6dGZWuAv1prX9nG21vHhxQRkfBVVMBz\nz8HDD8OaNf8tz8uDESNgzBjo2NG9+ERi0w5nkK0peUwCNgJ3AdOAIcBkYB9rbeU23h4oKirH728d\nnzXeeL0esrPTURu4R23gLtV/lAUCJHz0AckzppP0+gI89fVOsddL7cCTqBoxGv/RR5Odk6E2cJG+\nB+4LtkHbSR4BjDH74ySO+wP/Ai6x1oazdoPWeXSR1vZyn9rAXar/luP98QdSnppJ6tNP4v3tt83l\ndZ06k3DlFRScdDr1KRoq7wZ9D9wXqXUeW1XyuAOUPLpIFwz3qQ3cpfp3QVUVya/OI3XmNBJXr9pc\n7G+XRdV5F1I5bCT+ff7gYoBtj74H7mtzi4SLiIiELSWF6nPPp2jRUgrfXEz1WedAQgLekmLSpj1K\n7qE9aHfB2SS+sxj8frejFWlVlDyKiEj88nioO+gQyqfNhG+/pXL8DdTv3AFPIEDy24vIHnQGOYcf\nRMqMqXhKS9yOVqRVUPIoIiJtw667UnndjRSsXEvJ1JnUHnQIAAkb/0XmjePJPbAz6Tf+Bd/Gr1wO\nVCS2KXkUEZG2JSmJ6jPOpujNxRS+tZSqc88nkJSEt6yUtBnTyO3Ti6xzTyfprYXq0hZpgpJHERFp\ns+q696T04ankr95A+Y1/pX633QFI+r8lZF14Lrm9u5P62CN4iotcjlQkdih5FBGRNi/Qvj0VV11L\nwaefUzxzNjV9DgfAt+kbMibeSN6Bnci49ip869e5HKmI+5Q8ioiINEhIoOaUP1P86kIK3vmAygsv\nIpCaiqeigtTZs8g9+lCyzjiZpDdeg7o6t6MVcYWSRxERkSbU738AZfc/TP7q9ZT9dRL1ezmPOkx6\n/12yhl5A7iEHkvrQA3gK8l2OVKRlKXkUERHZikBOLpWXX0nBstUUP/U8NUf1A8D3/Xdk3D6RvO6d\nybjqMnyfr9nGkUTig5JHERGRcPh81Aw8ieKXX6XgvU+ovHg4gbR0PFVVpD73NLn9jyD7lONJfnUe\n1Na6Ha1I1Ch5FBERaaZ604myux8g/7P1lE26k7rgow4Tl31Eu5EXk3vQAaTdfzeeX391OVKRyFPy\nKCIisp0CWdlUjr6Mwo9WUvz8y1T3HwCA7z8/kn7X7eT16EzmZaNIWLXC5UhFIkfJo4iIyI7yeqnp\nfxwlz8+l4KMVVIy8BH9GJp6aGlJemkPO8f3IHngMyS/Ngepqt6MV2SFKHkVERCKo/o/7Un7H3RSs\n2UDpnfdSt+9+ACSu+JR2l40ir2dX0u66He9P/3E5UpHto+RRREQkCgIZmVQNH0Xh+8spevEVqo8f\nSMDjwfvrL6Tffze5PbuSOXooCZ8sg0DA7XBFwqbkUUREJJo8Hmr7HkPJ0y9QsGw1FWPG4s/KxlNX\nR8r8ueScPIDsAUeTPOdZqKpyO1qRbVLyKCIi0kL8e+9D+a13kL96PaX3TqGucxcAEtespt0VY8jr\n0Zn0O27F+8P3LkcqsmVKHkVERFpaejpVQ4ZSuPQjiua/QfVJpxLwevHm55M25T5yDzqAdsMGk/jh\n++rSlpij5FFERMQtHg+1hx9JyRPPUPDp51RceQ3+3Fw89fUkv/4q2X8+kZy+h5Ey+wmoqHA7WhFA\nyaOIiEhM8O+xJ+U3TSR/9QZKHnqM2gMOBCBh/Voyr72SvAM7kT7xJrybvnE3UGnzlDyKiIjEkpQU\nqgddQNHidyl8/W2qTj+TQEIC3uIi0h57mNxDDqTd4HNJXPqOurTFFUoeRUREYpHHQ90hvSmd9gQF\nK9dSfs11+HfaGU8gQPKihWSf82dyjjiYlJnT8ZSVuh2ttCFKHkVERGKcf5ddqbjuJvJXrqXk749T\n2+sgABK++pLMG64lt1sn0m8aj2/jVy5HKm2BkkcREZHWIjmZ6rPOpWjhOxQu+j+qzh5EICkJb1kp\naY9PJbdPL7IGnUHS4kXg97sdrcQpJY8iIiKtUF2PXpQ+Op38Vespv/5m6nfZFYCkdxaTdf7Z5PTp\nSeq0R/GUFLscqcQbJY8iIiKtWGCnnai4ejwFK76g5PEnqe3dB4CEf39NxoQbyOvWiYzx4/DZDS5H\nKvFCyaOIiEg8SEyk+rQzKHptEYVL3qPy/MEEUlLwVJST+uRMco88hKwzTyVp4RtQX+92tNKKKXkU\nERGJM3UHHEjZg4+Sv3o9ZTffSv0eewKQ9N5Ssi46j9ze3Ul9ZAqewgKXI5XWSMmjiIhInArk5lF5\nxTgKPvmM4ieepeaIowDwfbuJjNsmkNe9MxlXj8W39guXI5XWRMmjiIhIvEtIoOakUyie9zoF//yY\nyiHDCKSl4amsJPWZp8jtdxhZp55A0muvQF2d29FKjFPyKCIi0obUd+5C2b0POl3at06mvuPeACR9\n/CFZw4eQe9ABpD1wD57ffnM3UIlZSh5FRETaoEB2DpVjLqfg41UUP/MCNf36A+D78QfS75xEXvdO\nZF4+moTVK12OVGKNkkcREZG2zOej5riBFL8wn4IPV1AxYjT+jEw8NTWkvPg8Ocf1JXtgf5Lnvgg1\nNW5HKzFAyaOIiIgAUP+nfSmffA8Fn62n9M57qPvTvgAkrlhOuzEjyO3ZlbS7J+P9+SeXIxU3KXkU\nERGR3wlktqNq+GgK319O0QvzqT7uBAIeD75ffib93rvI7dmVzEuGkbB8GQQCbocrLUzJo4iIiDTN\n66W2X39KnnmRgo9XUXHJ5fjbZeGprSVl3svknDSA7OP6kjznWaiqcjtaaSFKHkVERGSb/Pv8gfLb\nJpP/2QZK73mQuk6dAUj8bBXtrhhDXs8upE2+De+PP7gcqUSbkkcREREJX3o6VRcNo/CfH1M073Wq\nTzyFgNeL97ffSH/wXnJ77U+74UNI/OgDdWnHKSWPIiIi0nweD7VHHEXJk89SsHwNFWPH4c/JwVNf\nT/Jrr5B92kBy+h1OyjNPQUWF29FKBCl5FBERkR3i33MvyifcSv7qDZQ++Ci1+3cDIGHdF2RePZa8\nHp1JveVm+OYbdwOViIjJ5NEYM8UYc3dI2bHGmM+NMaXGmH8aY/Z1Kz4RERFpQmoqVecPpmjJexQu\nWETVaWcQ8PnwFhaS+vAU+OMfybhwEInvLlWXdisWU8mjMSbXGPMkcHlI+c7AXOA6IAdYAsxv8QBF\nRERk2zwe6g7tQ+njT1Kwci3lV/8Ff/v24PeTtPANss86lZyjepPyxAwoK3M7WmmmmEoegfeBGpxE\nsbEzgFXW2jettXXA7cBuxpiDWjpAERERCZ9/192ouH4CRWs2wOzZ1PXoBUCC3UDmdVeT170z6ROu\nx/v1RpcjlXC1aPJojPEZY7Ka+JMZ3OUYa+0ooDzkrZ2AdQ0vrLV+YGOwXERERGJdcjIMHkzJ4qUU\nLlxC1ZnnEEhMxFtSTNq0v5Pbpyftzj+LxHfeBr/f7WhlK1r6zmNfoBAoCPnzGYC1dkvPO0oHQqdq\nVQBpUYlSREREoqau18GUPjaD/JXrKB9/I/UddsETCJC8+C2yB51JzmG9SH38MTylJW6HKk1IaMmT\nWWuXsH0JawWQGlKWBoQ9UMLr9WzHaSUSGupebeAetYG7VP/uUxu4r8k22G0Xqq+7gepx15D0+gKS\nH59K4ifLSPh6Ixk3XUf65ElUDzqfqhGj8O9nXIo8fkTq598TiMHZTsaYJ4BfrbXjg68vAc621vYP\nvvYC+UA/a+3qMA4Zex9SRERE/tfKlfDww/D881Bd/d/yAQNg7Fg48UTw+dyLr/Xb4QyytSSPHQAL\nXAy8AdwInGmt7RbmIQNFReX4/bH3WdsCr9dDdnY6agP3qA3cpfp3n9rAfc1tA0/+byQ//RTJs2bg\n++H7zeX1HfemevhIqi8YTCA7J5ohx51gG7SN5DFYdjQwBdgHWA0Mt9b+K8xDBgoKyqivj73P2hb4\nfB5yczNQG7hHbeAu1b/71Abu2+42qKsj6R9vkjpzGkkfvLe5OJCWRtWZ51I5fBT1XbpGIeL4E2yD\n+Eweo0DJo4t00Xaf2sBdqn/3qQ3cF4k28K1bS+rM6aS8PAdPZeXm8prDj6Ry2ChqBp4ECS06naNV\niVTyGGvrPIqIiIg0qb5LV8rum0L+6vWUTbyd+r06ApD0wXtkDR9M7sHdSJ1yH578fJcjjW9KHkVE\nRKRVCeTkUnnZFRQsW03x0y9Qc3Q/AHw/fE/GHbeS170TmVeMIWFNOHNqpbmUPIqIiEjr5PNRc/xA\nil96lYIPPqVy2Ej86Rl4qqtJmfMsOcceRfZJA0ie/zLU1rodbdxQ8igiIiKtXv2++1F2130UrNlA\n2R1/o+4PfwQgcfky2o0eRm7PrqTdexeeX35xOdLWT8mjiIiIxI1AZjsqR46h8MMVFM2ZS/WxxwHg\n+/kn0u+eTF6PzmSOGUHCiuUuR9p6KXkUERGR+OP1UnvMAEqee5n8j1dRMfpS/Jnt8NTWkjL3RXIG\n9if7+L4kvxiyGLlsk5JHERERiWv+P/yR8kl3kf/ZBkr/dj91wUcdJq5aSbvLR5PXowtpd96G9z8/\nuhxp66DkUURERNqGjAyqho6g8L1PKHp5AdUnnETA68X726+kP3AvuT27kjniIhI//hDaxjrY20XJ\no4iIiLQtHg+1R/WlZPbzFHzyGRWXXYk/OxtPfT0pC+aTfeoJZPc/kpRnZ0OjxcjFoeRRRERE2iz/\nXh0pnziJ/NUbKH3gEeq6HgBA4hdryBx3OXndO5E+aSLe7751OdLYoeRRREREJC2NqguGUPjO+xQt\n+AdVp55OwOfDW1hI2sMPkHtwNzJHXQzl5W5H6joljyIiIiINPB5qDz2M0hlPUbDiC8rHXYu/fXs8\nfj8pr8wj6d2lbkfoOj09XERERKQJ/t12p+KGv1IxbjzJC+bj/flnavoPcDss1yl5FBEREdmalBSq\nzznP7ShihrqtRURERCRsSh5FREREJGxKHkVEREQkbEoeRURERCRsSh5FREREJGxKHkVEREQkbEoe\nRURERCRsSh5FREREJGxKHkVEREQkbEoeRURERCRsSh5FREREJGxKHkVEREQkbEoeRURERCRsSh5F\nREREJGxKHkVEREQkbEoeRURERCRsSh5FREREJGxKHkVEREQkbEoeRURERCRsSh5FREREJGxKHkVE\nREQkbEoeRURERCRsSh5FREREJGxKHkVEREQkbEoeRURERCRsSh5FREREJGwJbgfQmDHmZmAkkAms\nBsZaa9cGtx0LPADsDawERlhrv3IpVBEREZE2KWbuPBpjLgYuBI4C2gNLgDeC2zoAc4HrgJzgtvmu\nBCoiIiLShsVM8gjkAndYazdZa/3AFGBPY8wewOnAKmvtm9baOuB2YDdjzEEuxisiIiLS5rRot7Ux\nxgdkNLHJb629P6TsNCDfWvu9MaYTsK5hg7XWb4zZCHQCPo1awCIiIiLyOy095rEv8DYQCCnfBPyh\n4YUx5mjgMZzxjwDpQHHIeyqAtKhEKSIiIiJNatHk0Vq7hG10lRtjBgOPApdZa18IFlcAqSG7pgFl\n4Z7b6/U0I1KJpIa6Vxu4R23gLtW/+9QG7lMbuC9SdR9rs60nAFcAp1hr/9lo03rg7Eb7eYE/0agr\nexs82dnpEYtTto/awH1qA3ep/t2nNnCf2qD18wQCoT3I7jDGDAXuBfpYa78M2dYBsMDFODOwbwTO\ntNZ2a+k4RURERNqyWEoeLc4ajtXBIg/O2MiDrbU2OA5yCrAPzhqQw621/3IjVhEREZG2KmaSRxER\nERGJfbG0zqOIiIiIxDgljyIiIiISNiWPIiIiIhI2JY8iIiIiEjYljyIiIiIStphaJDzSjDE34zzi\nMBNneZ+x1tq1wW3HAg/gLA+0Ehhhrf3KpVDjljGmBzAV6Ap8CYyx1i5zN6r4Zow5AmfN1E7AxYr4\nkgAAC3ZJREFUr8A91trpxphsYBZwDFAE3GatneVepPEtuD7tGmCotfZNY0xHYCZwCPAjcI219g03\nY4xXxpjdca47R+E82vYea+3D+g60HGPMYTjL6+2H8/N+m7X2ebVB9BljDgHmW2t3D77eYp0bY5Jw\nHgf9Z6AGeNhaO3lb54jbO4/GmIuBC3EuHu2BJTgLjDdc1OcC1wE5wW3zXQk0jhljkoEFOP9hZgEP\nAwuMMXomeZQELxKvAg9Ya7OBc4DJxpj+wONAKbATzhOb7g5eZCQ6ZgK5jV6/BHyMc825CnjOGLOH\nG4G1Aa8Aa3Hq+nhgojHmUGAG+g5EXfApcPOBydbaLJybOE8ZY/ZCbRBVxphhwCIgsVHx1up8MrAn\n0BE4EhhhjDlrW+eJ2+QR56J9h7V2k7XWj/Mb0J7Bi/XpwCpr7ZvW2jrgdmA3Y8xBLsYbj/oB9dba\n6dbaemvtE8DPwIkuxxXPOgKvNzwX3lq7ClgKHAacBvzVWltrrV0OPAcMcSvQeGaMGY1zsf4u+LoT\nsD8wKfhd+AfwT2CQe1HGJ2NMb2BX4AZrrd9aux7og3P3S9+BlpGNc9OmIYEJ4DwAxI/aIGqMMTcC\nY3FymoaydLZe5xfi5EplwQevPILzNL+tatXd1sYYH5DRxCa/tfb+kLLTgHxr7ffBC/nm52Jba/3G\nmI043XyfRi3gtud39Rxkg+USBdbaz4CLGl4bY3Jwfpv8DKi11m5qvDvOL1ISQcaY/YCrgd7AqmBx\nJ+Aba211o131XYiOnjjXnXuMMRfgdFtPxhlCUKPvQPRZawuMMY8Bc4wxz+I8MW44TkKpNoiemdba\nycEn8jXYjy3UebCnamdgfci2S7d1otZ+57EvUAgUhPz5rPFOwYp8DCcjB0gHKkKOVQGoOzWyVM8u\nMsZk4QwbWI5z97EyZBe1RYQFf6GdjTO+uqjRJn0XWk4uTq/HLzjdcUOBh3BuNOg70AKMMR6cuj0T\nSAVOxen9a4faIGqstT83UZzGlus8vdHr0G1b1arvPFprl7CNBNgYMxh4FLisoSsPp3JSQ3ZNA8oi\nHmTbpnp2iTFmH+A14CucrtEuQErIbmqLyPsrzpCYt0LK9V1oOdU4vUx3B19/ZIyZB9yKvgMt5Qzg\nEGvt+ODrN40xrwO3oDZoaRVsuc4bksZU/tsGYbVHa7/zuFXGmAnA/cAp1tqnG21aT6PuouDg3j/x\nv12ssmPWAyakzKB6jipjTE+ciRkLrbWnB7tKvwKSQiZoqC0i7xxgkDGmwBhTAOwFzMHpOtrbGNN4\nELvqPzoskBC8+9XAh7Oqhr4DLWMvIDmkrA5YgdqgpW3x2m+tLcS5Q29Ct23roJ5AIBDRKGOFMWYo\nznIlfay1X4Zs64BzgbkYZwb2jcCZ1tpuLR1nPAsuAbARuAuYhjNAdzKwj7U29Da6RECj5WHutdbe\nE7LtJaAKGIUzeeNN4MTgAGqJAmPMv4FLrbULjTHLcVZ2+CvQHyep7GKt/cHNGOONMSYFZ1mwWcAk\nnLGnC4EBwF/QdyDqjDH74wyXucRa+1Rw6NirOEvF3IDaIKqC9f2StXbn4Oumrv0DrbWfGmPuBbrj\nzMJujzNT+1pr7bytnSOe7zxejzPG5VNjTIkxpjT4twmOCzgN5xb6bzg/0Ge4F2p8stbWAAOB84F8\n4DKcu8BKHKNnGM4FYELwZ77h534SznIZScD3OMvGXKsLdtQFcCYLgHON6Y7zm/79wCAljpFnra3C\nGQ/fG6eun8EZg/oJzn+e+g5EmbX2C+As4CpjTBHOMm1DrLUrURu4oak6b5gcfDPOL1sbgHeBadtK\nHCGO7zyKiIiISOTF851HEREREYkwJY8iIiIiEjYljyIiIiISNiWPIiIiIhI2JY8iIiIiEjYljyIi\nIiISNiWPIiIiIhI2JY8i0iYYYyYaYz4K/rujMcZvjNkvBuKKmVi2xBgzyhjjczsOEYkNSh5FpC1p\neCrCd8AuOM99ddu3xE4s/8MYcxQwFef50CIiJLgdgIhIS7PW+nEeXec6a22AGIllC7z8/jGLItLG\nKXkUkZhgjPED5+I8a3VfnOesjgAm4zwX+kdgpLX2n8H9d8V5Zu5xQAnwKjDeWlse3N4beAg4AHgP\n5/mtDefqCPwb6GSt/dIYY4D7gCOAFGAdMK7RufzARcBVQBec58CObPR82NDPsmcwtmOAMmC2tfb6\nRseaBFwSjOHckFj+DdwBXAz0BNYAFwBjgaFAMfAXa+0LweNlAg8CpwN1wNvAVdbaX5uIq+FzTwCu\nBpZYa88xxlwEXBus90rgrWDd5wLvBN9eYYzpZ6191xgzFLgB2B34ArjRWrukqboQkfijbmsRiSV3\nApcCRwEH4SROK3GSqLXA3xvtOx+oAA7GSZwOBGYBGGPygIXAh8HyBcDokHMFGv17AfAz0Ct4rm+B\naSH7T8JJmLoBpcBjTX0AY0wSsBjnl/M+OMnhEGPMNY12Oyf4GUc0EQs4yeMdwXhygeVATbBO3gCm\nNRqDOBMnieuHk6ymBz/P1hyHU28TjDGHBz/rJJzk8RygL3BZsB7ODMbXEfjQGHMi8DdgPE5iPht4\nzRjTdRvnFJE4oTuPIhJLHrLWfgBgjFkC7GOtfTD4ehpOkuLBSZT2Aw631tYHtw8DNhhjxuEkkyXA\n1cFu4a+CY/f2bHQuT/B9acB0YLq1tjRY9giwyBjjazg+MMVa+1Zw+z3AK8YYT/D4jQ0A9gB6W2uL\ngvuPBrIb7TPNWmuD2zryv13Cz1prFwa3zweGWGvHB19PAUYBewQTyDOBDtba34LbLwTyjTGHWWs/\n3EI9P2Ct/Tq4fzow3Fr7YnDbd8aYt4Cu1tqAMaYgWP6LtbbOGHMd8Ddr7SvB8keNMYcBVwbjEpE4\np+RRRGLJxkb/rgC+bvS6Eqe3JBHoDGQBRU6P82Z+wAS3fx6S2C3n98kjANbaCmPM34ELjTEHBd/f\nM7jZBzQkj40ntJQE/04AakMO2Rn4uiFxDJ7jtZB9/h0aR4jQetjU6HVl8O9knDuFHuDrYFLdwBf8\nHFtKHjef31q70hhTZoyZgNMl3zX4GV7awnu7AAcbY25pVJYIfLy1DyQi8UPJo4jEktBEzL+F/RKA\nfwEn8L937X7CGSMZWh56bGDznbdlOAnhK8A8IBWYG7JrTRNvb2oSSVP7harcxvbm1EMlTtd8aCz/\nM+axqfMbYwbgdHM/izO+8R6c8ZBbGtaUgDM+8h8h5dVbOZ+IxBGNeRSR1mg9TtdwibX262AXbDJw\nP5AJfA50N8Y0/gW5V8gxGu5KHg/sAxxprb3bWrsI2C24bXtmGH8J7GOMaddQYIy5zBgTmmxFwnqc\nCT6pjeqhEGcCzV5beE9oN/tY4Blr7Qhr7ePW2pX8945mU/uvB/ZuOF/wnCNwhgqISBugO48i0po0\nJDRv48x4nmOMGY/zi/BUoMpa+7MxZg4wEZgaHJ94JHA2sKqJY/2Ak4CdY4z5ADgMuC24LZnm31F7\nC6dbeKYxZiLOGo434NzRixQPQHB29mvAM8aYy3FmYt8H/IktrxsZmhD/ABxujDkQqMKZsHRwsByc\n2eIAvYwxK4G7gWeNMV/i3Kk8FfgLcFIkPpiIxD7deRSRWBF6h2uL+wTHMp6KkywtBRYBFqe7Gmtt\nCc7EFYMzW3sozrI9TR1rGc7SNffhLDszDhiD03Xcq/G+4QiuIXkqkAZ8AjyFMxlnylaOFdjCv7ek\n8T5DcJLiBcAHwbiPs9Zuqfs89PgTcWZVv4dTlx2CZT2C2z/HSYjfAU4MTpS5EidhXAuMBC5smEwk\nIvHPEwiEfU0UERERkTZOdx5FREREJGxKHkVEREQkbEoeRURERCRsSh5FREREJGxKHkVEREQkbEoe\nRURERCRsSh5FREREJGxKHkVEREQkbEoeRURERCRs/w8UJq2H6WXO2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f092f2b5208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "plt.scatter(X, y)\n",
    "test_x = np.linspace(-10, 90, 100).reshape(-1, 1)\n",
    "test_y = model.predict(test_x)\n",
    "plt.plot(test_x, test_y, c =\"red\" )\n",
    "plt.ylabel(\"price\")\n",
    "plt.xlabel(\"median crime rate\")"
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='d1957f8c-12d7-4e29-b4b5-e04a008684f3'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
=======
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "knn_model = sklearn.neighbors.KNeighborsRegressor(n_neighbors= 1).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f092e63d9b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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81J9tnZjYvIuEp93/Nr1zrHsuKrytQYyqPIqIiNRR5VF84qYdP5NMT/LoxmlA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\nqa5uC+SQlGR1VRfsWcVVBVZ3YCSVTN/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+aWafikcRyVluuq2/9Pz/vWzOvZzhaxfupinJ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\nZmYdc/BoZmZmZh37D4NBBtAP5279AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f092e5a2ac8>"
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
      ]
     },
     "metadata": {},
     "output_type": "display_data"
<<<<<<< HEAD
    },
    {
     "ename": "NameError",
     "evalue": "name 'w_hat' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-80ee06fa21b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_surface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-4-80ee06fa21b3>\u001b[0m in \u001b[0;36mplot\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_o\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw_hat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_surface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'w_hat' is not defined"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "def plot():\n",
    "    ax.clear()\n",
    "    ax.scatter(X[:,0], X[:, 1], y_t, s=50)\n",
    "\n",
    "    x_o = np.linspace(-5, 5, 100)\n",
    "    y_o = np.linspace(-5, 5, 100)\n",
    "    x, y = np.meshgrid(x_o, y_o)\n",
    "    p = np.array([x.ravel(), y.ravel()])\n",
    "    Z = w_hat.reshape(1,2).dot(p)\n",
    "    Z = Z.reshape(x.shape)\n",
    "    f = ax.plot_surface(x, y, Z)\n",
    "plot()\n"
=======
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "plt.scatter(X[:, 0:1], y)\n",
    "\n",
    "test_x = np.linspace(-10, 100, 90).reshape(-1, 1)\n",
    "test_y = knn_model.predict(test_x)\n",
    "plt.plot(test_x, test_y, c =\"red\" )\n",
    "\n",
    "test_y = model.predict(test_x)\n",
    "plt.plot(test_x, test_y, c =\"green\" )\n",
    "\n",
    "plt.ylabel(\"price\")\n",
    "plt.xlabel(\"median crime rate\")"
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
   ]
  }
 ],
 "metadata": {
<<<<<<< HEAD
  "kernelspec": {
   "display_name": "Python 3",
=======
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "IPython (Python 3)",
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
   "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",
<<<<<<< HEAD
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
=======
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
>>>>>>> ef03b90fc0c6003371fec40efd87dee6a2633448
}