polynomial-regression-assessing-fit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assessing Fit (polynomial regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fire up graphlab create"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import graphlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial_sframe function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an SFrame consisting of the powers of an SArray up to a specific degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def polynomial_sframe(feature, degree):\n",
    "    # assume that degree >= 1\n",
    "    # initialize the SFrame:\n",
    "    poly_sframe = graphlab.SFrame()\n",
    "    # and set poly_sframe['power_1'] equal to the passed feature\n",
    "    poly_sframe['power_1'] = feature\n",
    "\n",
    "    # first check if degree > 1\n",
    "    if degree > 1:\n",
    "        # then loop over the remaining degrees:\n",
    "        # range usually starts at 0 and stops at the endpoint-1. We want it to start at 2 and stop at degree\n",
    "        for power in range(2, degree+1): \n",
    "            # first we'll give the column a name:\n",
    "            name = 'power_' + str(power)\n",
    "            # then assign poly_sframe[name] to the appropriate power of feature\n",
    "            poly_sframe[name] =  feature.apply(lambda x: x**power)\n",
    "\n",
    "    return poly_sframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1529367630.log\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to nanlee_89@yahoo.com and will expire on December 07, 2018.\n",
      "+---------+---------+---------+\n",
      "| power_1 | power_2 | power_3 |\n",
      "+---------+---------+---------+\n",
      "|   1.0   |   1.0   |   1.0   |\n",
      "|   2.0   |   4.0   |   8.0   |\n",
      "|   3.0   |   9.0   |   27.0  |\n",
      "+---------+---------+---------+\n",
      "[3 rows x 3 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tmp = graphlab.SArray([1., 2., 3.])\n",
    "\n",
    "print polynomial_sframe(tmp, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing polynomial regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use matplotlib to visualize what a polynomial regression looks like on some real data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales = graphlab.SFrame('kc_house_data.gl/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# For plotting purposes (connecting the dots), sort by the values of sqft_living. \n",
    "# For houses with identical square footage, we break the tie by their prices.\n",
    "sales = sales.sort(['sqft_living', 'price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a degree 1 polynomial using 'sqft_living' (i.e. a line) to predict 'price' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly1_data = polynomial_sframe(sales['sqft_living'], 1)\n",
    "poly1_data['price'] = sales['price'] # add price to the data since it's the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model1 = graphlab.linear_regression.create(poly1_data, target = 'price', \n",
    "                                           features = ['power_1'], verbose = False,\n",
    "                                           validation_set = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the weights before plotting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-43579.0852515</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4402.68969743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">280.622770886</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.93639855513</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[2 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 2\n",
       "\n",
       "Data:\n",
       "+-------------+-------+----------------+---------------+\n",
       "|     name    | index |     value      |     stderr    |\n",
       "+-------------+-------+----------------+---------------+\n",
       "| (intercept) |  None | -43579.0852515 | 4402.68969743 |\n",
       "|   power_1   |  None | 280.622770886  | 1.93639855513 |\n",
       "+-------------+-------+----------------+---------------+\n",
       "[2 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.get(\"coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x123951d50>,\n",
       " <matplotlib.lines.Line2D at 0x123951e10>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jk4o8EmtR8oBXQjHdzax3pNYnLzgWj4mO8uoC9I3E5EfKkhc6Fv/MSxLjUojZ45w7TAPm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\nEJGeLbQJeu/nAWVkxthNAvoCK0WkXkTqUQe874S/6YfIjXEA2AkETdX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122a7b7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly1_data['power_1'],poly1_data['price'],'.',\n",
    "        poly1_data['power_1'], model1.predict(poly1_data),'-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unpack that plt.plot() command. The first pair of SArrays we passed are the 1st power of sqft and the actual price we then ask it to print these as dots '.'. The next pair we pass is the 1st power of sqft and the predicted values from the linear model. We ask these to be plotted as a line '-'. \n",
    "\n",
    "We can see, not surprisingly, that the predicted values all fall on a line, specifically the one with slope 280 and intercept -43579. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a second degree polynomial and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly2_data = polynomial_sframe(sales['sqft_living'], 2)\n",
    "my_features = poly2_data.column_names() # get the name of the features\n",
    "poly2_data['price'] = sales['price'] # add price to the data since it's the target\n",
    "model2 = graphlab.linear_regression.create(poly2_data, target = 'price', \n",
    "                                           features = my_features, verbose = False,\n",
    "                                           validation_set = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">199222.496445</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7058.00483552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">67.9940640677</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.28787201316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0385812312789</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.000898246547032</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[3 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 3\n",
       "\n",
       "Data:\n",
       "+-------------+-------+-----------------+-------------------+\n",
       "|     name    | index |      value      |       stderr      |\n",
       "+-------------+-------+-----------------+-------------------+\n",
       "| (intercept) |  None |  199222.496445  |   7058.00483552   |\n",
       "|   power_1   |  None |  67.9940640677  |   5.28787201316   |\n",
       "|   power_2   |  None | 0.0385812312789 | 0.000898246547032 |\n",
       "+-------------+-------+-----------------+-------------------+\n",
       "[3 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.get(\"coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a0a87d0>,\n",
       " <matplotlib.lines.Line2D at 0x1238ff1d0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zhZ7s2ruL9698nxMPPzHeKUk1o64uEZEk0qJF8duJ7L8//Jfu/+zOvoJ9fDDs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\n+BiGYRiGUTSY8DEMwzAMo2j4/8NOVJQ6Px9OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1238f05d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly2_data['power_1'],poly2_data['price'],'.',\n",
    "        poly2_data['power_1'], model2.predict(poly2_data),'-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting model looks like half a parabola. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A degree three polynomial "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly3_data = polynomial_sframe(sales['sqft_living'], 3)\n",
    "my_features = poly3_data.column_names() # get the name of the features\n",
    "poly3_data['price'] = sales['price'] # add price to the data since it's the target\n",
    "model3 = graphlab.linear_regression.create(poly3_data, target = 'price', \n",
    "                                           features = my_features, verbose = False,\n",
    "                                           validation_set = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">336788.117952</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10661.0153713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-90.1476236119</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10.6222891844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.087036715081</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.00296630623148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.8398521196e-06</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.2417490959e-07</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[4 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 4\n",
       "\n",
       "Data:\n",
       "+-------------+-------+-------------------+------------------+\n",
       "|     name    | index |       value       |      stderr      |\n",
       "+-------------+-------+-------------------+------------------+\n",
       "| (intercept) |  None |   336788.117952   |  10661.0153713   |\n",
       "|   power_1   |  None |   -90.1476236119  |  10.6222891844   |\n",
       "|   power_2   |  None |   0.087036715081  | 0.00296630623148 |\n",
       "|   power_3   |  None | -3.8398521196e-06 | 2.2417490959e-07 |\n",
       "+-------------+-------+-------------------+------------------+\n",
       "[4 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.get(\"coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119ec2110>,\n",
       " <matplotlib.lines.Line2D at 0x11aea6e10>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pun8sEhMe5dUYaB2KyQyVJT1wLPKeHiPGVSBmh3NuL2WYNGkSvXv3LitEREQkacWqDFi8\neDEZGRkJKlH8TV1zga6hfV3xOzg7577CSySGRA76nZn7AfP8XTlAYSimK3A0MN/fNR84xO+IHDEE\nL6laEIg5yczaBGKGAvnAZ4GYwX7SFIxZ4ZzLD8QMIdrQQFlERESkgYg38ZkE9DezO8yss5ldBlwL\nTA7EPALcZWbnmdlJwF+BdcAb8H1n56eBh/35czKAZ4C5zrmFfszneB2MnzSzTDMbCPwJyPJHdAG8\ni5fgPO/P1TMMmAhMds7t92NeBPYBz5jZCWZ2CfBL4A+B8j4BdDKzB82sq5ndCFwEPBznsxEREZE6\nLq6mLufcIjO7EHgA+A3e/DhjnXMvBWIeMrPmeHPuHAJ8AJztnNsXuNQ44ADwKpACvAP8InS7y/AS\nqllAkR87NnCfIjM7F6+D9Ty8+YKeBe4JxOwws6HAFGARsAUY75x7OhDztZmdg5fU/RIvSbvGORce\n6SUiIiL1nDnnEl2GesefqTonJydHfXxERETiEOjjk+GcW1zb99daXSIiIpI0lPiIiIhI0lDiIyIi\nIklDiY+IiIgkDSU+IiIikjSU+IiIiEjSUOIjIiIiSUOJj4iIiCQNJT4iIiKSNJT4iIgkkbw8GDQI\nOnf23jdvTnSJRGqXEh8RkSQyciTMnQu5ud77iBGJLpHUR7v37050ESpNiY+ISBLZuLHsbZFYnHN8\nvuVzHp7/MGf+9UxaP9Sarbu2JrpYlRLX6uwiIlK/tW/v1fYEt0Vi2b1/N7NXz2bGFzOY+eVMcrfn\nclCTgzj9mNP5v7P+jyaN6mcKUT9LLSIilTJtmte8tXGjl/RMm5boEkldsvrb1cxcOZMZK2fwz6/+\nye7C3XRM68g5x53D8OOGc/qxp9O8afNEF7NKlPiIiCSRtm3hww8TXQqpK/Yf2M/ctXO/T3Y+++Yz\nmjRqwqCjBzHh9AkMP2443dp0w8wSXdRqo8RHREQkiWz6bhPvfPkOM1bO4N1V77Jj7w7SW6Qz/Ljh\nTDhtAmd2OpO0g9ISXcwao8RHRESkAStyRXy0/qPva3VyNuZgGH2P7MutA25l+HHD6dW+F40sOcY7\nKfEREalGeXnekPFgH5q2bRNdKkk223dvJ3tVNjNXzuSdL9/hm13fcOhBhzKsyzD+p///MKzzMA5v\ncXiii5kQSnxERKpRZJ4c8EZPjRihPjVS85xzfLr50+9rdeatnUeRK6Jnek+u7X0tw48bTv+j+tfb\nkVjVSU9ARJJaddfQaJ4cqS3f7fuO93PfZ+bKmcz8cibrdqyjRdMWnNX5LJ445wnOPu5sjko9KtHF\nrHOU+IhIUqvuGhrNkyM1aeXWld/X6sxePZt9B/bxg8N+wEXdLuKcH5zDKUefQkqTlEQXs05T4iMi\nSa26a2g0T45Up72Fe5m9evb3yc6X276kWeNmnHbMafz+rN8z/LjhdGndJdHFrFeU+IhIUqvuGhrN\nkyNVtTZ/7ffNV7NyZ7Fr/y46pHZg+HHDeXjow5xx7Bm0aNYi0cWst5T4iEhSUw2NJFphUSHz185n\nxsoZzFw5k083f0pja8zAowdy9+C7GX7ccE5se2KDmkQwkZT4iEhSUw2NJMI3O7/h7S/fZubKmWSv\nyubbPd9yePPDOfu4s7lr8F2c1eksDj340EQXs0FS4iMiIlILvtv3HVMXTeWVz17ho/Uf4XBkHpHJ\n2H5jOee4c8g4IiNpJhFMJCU+IiLVTJMYStCOvTuYvHAyD89/mB17d/Dj43/MjX1u5EddfkR6y/RE\nFy/pxJVamtk9ZlYUen0WiplgZhvMbJeZvWdmXULHU8xsipltMbMCM3vVzNqGYg41sxfMLN/MtpvZ\nU2bWIhTTwcxmmNlOM9tkZg+ZRafKZtbDzOaY2W4zW21mt8X4TKeZWY6Z7TGzL8zsynieiYhIWGSI\nfG6u9z5iRKJLJImQvyefibMncswjx3Dv7Hu5pPslrPrlKv7+k79z5Q+vVNKTIJWp8VkKDAEivawK\nIwfM7FfATcAVwNfAb4FsM+vmnNvnhz0CnA2MBHYAU4DXgFMC93gRSPfv0wx4FpgKXO7fpxEwE9gA\n9AeOAJ4H9gF3+TGtgGzgXeB64CTgL2a23Tn3lB9zDPAW8BhwGXAm8JSZbXDOvVeJZyMiokkMk9z2\n3dt5dMGjPPKfR9hTuIfrMq7jVwN/xZGpRya6aELlEp9C59w3pRwbC0x0zr0FYGZXAHnABcArZpYK\njAYudc7N9mOuBpabWV/n3EIz6wYMAzKccx/7MTcDM8zsVufcJv/48cDpzrktwKdm9hvgATMb75wr\nxEuSmgLX+NvLzawXcAvwlF/eMUCuc+52f3uFmQ0CxgFKfESkUjSJYXLaumsrj/znEf648I/sO7CP\nGzJu4PaBt9O+lf4C1CWV6UV1nJmtN7NVZvY3M+sAYGbHAu2A9yOBzrkdwAJggL+rD16yFYxZAawJ\nxPQHtkeSHt8swAH9AjGf+klPRDaQBnQPxMzxk55gTFczSwvEzAp9vuxAWURE4jZtGgwcCJ06ee8a\nIt+wbdm1hTvfv5NjHj2Gh//zMNf1vo6vxn7FpB9NUtJTB8Vb4/Mf4CpgBdAeGA/MMbMT8ZIeh1fD\nE5TnHwOv+WqfnxCVFtMO2Bw86Jw7YGbbQjGx7hM59on/nltGTH4Z10k1sxTn3F5EROKkIfLJYfPO\nzfxh3h+Y8tEUAG7qexO3DLiFti3Uk70uiyvxcc5lBzaXmtlCYDVwMfB5dRasBmkGKBERqbRN323i\n93N/z+OLHqdJoyaM7TeWcQPG0aZ5m0QXTSqgSsPZnXP5ZvYF0AX4N15SkU50LUo6EGm22gQ0M7PU\nUK1Pun8sEhMe5dUYaB2KyQwVJz1wLPIe7jKfjlcrVV7MjorU9owbN460tLSofaNGjWLUqFHlnSoi\nIvXMhoINPDT3IabmTKVZ42bcevKt/E///6H1wa0TXbQ6Kysri6ysrKh9+fn5CSqNp0qJj5m1xEt6\nnnPOfWVmm/BGYi3xj6fi9cuZ4p+SgzcKbAjwuh/TFTgamO/HzAcOMbNegX4+kVFkCwIxd5pZm0A/\nn6F4zVefBWJ+a2aNnXMHAjErnHP5gZizQx9raKAsZZo0aRK9e/euSKiIiNRT63as48EPH+TJxU9y\ncNOD+fXAXzO2/1gOOeiQRBetzotVGbB48WIyMjISVKI4Ex8z+z0wHa9560jgXmA/8JIf8ghwl5l9\niTecfSKwDngDvM7OZvY08LCZbQcKgD8Cc51zC/2Yz80sG3jSzMbgDWf/E5Dlj+gCb4j6Z8Dz/hD6\n9v69Jjvn9vsxLwJ3A8+Y2YN4w9l/iTfyLOIJ4Bf+8WfwEqyLgOHxPBcREWl41uSv4YEPH+Dpj5+m\nRdMW3DX4Lm7uezNpB6WVf7LUWfHW+ByFl1AcBnwDfAj0d85tBXDOPWRmzfHm3DkE+AA4OzCHD3hD\nxQ8ArwIpwDvAL0L3uQyYjDfiqsiP/T5hcc4Vmdm5wOPAPGAn3lw/9wRidpjZULzapkXAFmC8c+7p\nQMzXZnYOMAkvKVqHN/w9PNJLRESSxNfffs39H9zPX/77F1JTUrn3tHu5MfNGUlNSE100qQbmnEt0\nGeodM+sN5OTk5KipS0Skgcjdnst9H9zHc588x6EHHcptJ9/GmMwxtGzWMtFFa1ACTV0ZzrnFtX1/\nrdUlIiJJbeXWldz34X08/8nztGnehgfPfJDrM66nRbMW5Z8s9Y4SHxERSUortqzgdx/8jhc+fYH0\nFun8YegfuC7jOg5uenCiiyY1SImPiIgklc+++YzfffA7Xlr6Eu1btufRHz3Ktb2v5aAmByW6aFIL\nlPiIiEhSWLp5KRPnTOTvy/7OUalHMfnsyYzuNZqUJimJLprUIiU+IiLSoC3JW8KE2RN4bflrdEzr\nyBPnPsFVP7yKZo2bJbpokgBKfEREpEH6eOPHTJgzgX98/g+OPeRYnjrvKa7oeQVNGzdNdNEkgZT4\niIhIheTlwciRsHEjtG/vrTrftg6ux7lowyImzJ7A9C+m06V1F5798bNcdtJlSngEUOIjIiIVNHIk\nzJ3r/ZybCyNG1K1V6BesW8CEOROYuXImXQ/ryvMXPs+lJ15Kk0b6qpNi+tsgIiIVsnFj2duJMm/t\nPCbMnkD2qmy6tenGiyNe5OLuF9O4UeNEF03qICU+IiJSIe3bezU9we1E+mD1B9w7+17e/+p9Tmx7\nIi9f9DIXnXARjaxRYgsmdZoSHxERqZBp07zmrWAfn0T499f/5t7Z9/Lvr/9Nj/QevPqTV7mw24VK\neKRClPiIiEiFtG2buD49zjn++dU/mTBnAnNWz6FXu168fsnrnN/1fCU8Ehf9bRGRhMrLg0GDoHNn\n733z5oodk+TgnOPdVe8y6C+DOPP5M9m1fxfTR00n57ocLjj+AiU9EjfV+IhIQpU1UqiujyKSmuOc\n450v3+He2feyYP0C+h3Zj5mXzeRHXX6EmSW6eFKPKfERkYQqa6RQXR1FJDXHOcdbX7zFhDkTWLRh\nESd3OJnsy7M5q9NZSnikWqiOUEQSKjwyKLhd1jFpWJxz/OPzf5Dx5wzOf+l8Dm5yMLN+NosPr/6Q\noZ2HKumRaqMaHxFJqLJGCtWVUURSc4pcEa8vf52JcybySd4nnH7M6fzryn9x2jGnJbpo0kAp8RGR\nhCprpFAiRxFJzTpQdIDXlr/GxDkTWbp5KWd2OpM5V83hlI6nJLpo0sAp8RERkVpzoOgAryx7hYlz\nJrJ8y3KGdR7G1HOncnKHkxNdNEkSSnxERKTGFRYV8tLSl/jtnN+yYusKhh83nL/8+C/0O6pfoosm\nSUaJj4iI1BjnHK8se4W7/nUXX277kvN+cB5/G/E3+hzRJ9FFkySlxEdERGrEp3mfcvPbNzN79WzO\n+8F5vHLRK/Rq3yvRxZIkp8RHRESq1bd7vuWef93DlI+m0KV1F7Ivz2Zo56GJLpYIoMRHRATwlscY\nOTJ66HzbtokuVf1S5Ip49r/P8utZv2Z34W7uH3I/Y/uPpVnjZokumsj3lPiIiKDlMapq4fqF3Pz2\nzSxcv5CfnvRTHjrrIY5odUSiiyVSghIfERG0PEZlbd65mTvfv5OnP36anuk9NReP1HlKfERE8Jq3\ncnOjt6V0hUWFPP7R4/zmX7/BzJgyfArXZVxHk0b6WpG6TX9DRUTQ8hjxmP31bG5++2aWbl7Ktb2v\n5Xdn/I7DWxye6GKJVEiVFik1s1+bWZGZPRzaP8HMNpjZLjN7z8y6hI6nmNkUM9tiZgVm9qqZtQ3F\nHGpmL5hZvpltN7OnzKxFKKaDmc0ws51mtsnMHjKzRqGYHmY2x8x2m9lqM7stxuc4zcxyzGyPmX1h\nZldW5bmISP0TWR5j1SrvXR2bS9pYsJHLXruM0547jeZNm7Pw5wv583l/VtIj9UqlEx8zywSuAz4J\n7f8VcJN/rC+wE8g2s2C3/keAc4CRwGDgCOC10C1eBLoBQ/zYwcDUwH0aATPxaq36A1cCVwETAjGt\ngGzgK6A3cBsw3syuDcQcA7wFvA/0BB4FnjKzs+J5HiIiDdWBogNMXjiZ46ccz3u57/HM+c8w75p5\nmoRQ6qVKNXWZWUvgb8C1wG9Ch8cCE51zb/mxVwB5wAXAK2aWCowGLnXOzfZjrgaWm1lf59xCM+sG\nDAMynHMf+zE3AzPM7Fbn3Cb/+PHA6c65LcCnZvYb4AEzG++cKwQuB5oC1/jby82sF3AL8JRf3jFA\nrnPudn97hZkNAsYB71Xm+YiINBSLNizihrduIGdjDj/v/XMeOPMBWh/cOtHFEqm0ytb4TAGmO+f+\nGdxpZscC7fBqTwBwzu0AFgAD/F198BKuYMwKYE0gpj+wPZL0+GYBDugXiPnUT3oisoE0oHsgZo6f\n9ARjuppZWiBmVujzZQfKIiL1UF4eDBoEnTt775s3J7pE9Uv+nnxumnkTfZ/sy/6i/cwbPY8/n/dn\nJT1S78Vd42NmlwI/xEtgwtrhJSd5of15/jGAdGCfnxCVFtMOiPpvyjl3wMy2hWJi3Sdy7BP/PbeM\nmPwyrpNqZinOub2ISL2jeXkqxznHS0tf4pZ3b6FgbwH/N/T/+GW/X2q0ljQYcf1NNrOj8PrnnOmc\n218zRap3T3l9AAAgAElEQVQVVh0XGTduHGlpaVH7Ro0axahRo6rj8iJSBZqXJ34rt67kxpk3Mit3\nFiO6jeCRYY/QIa1Doosl9VhWVhZZWVlR+/Lz8xNUGk+8KXwGcDiw2MwiyUNjYLCZ3YTX58bwanWC\ntSjpQKTZahPQzMxSQ7U+6f6xSEx4lFdjoHUoJjNUvvTAsch7eowYV4GYHeXV9kyaNInevXuXFSIi\nCaJ5eSpuT+EeHvjwAe7/8H6OaHUEb416i3N+cE6iiyUNQKzKgMWLF5ORkZGgEsXfx2cWcBJeU1dP\n/7UIr6NzT+dcLl4iMSRygt+ZuR8wz9+VAxSGYroCRwPz/V3zgUP8jsgRQ/CSqgWBmJPMrE0gZihe\n89VngZjBftIUjFnhnMsPxAwh2tBAWUSkHpo2DQYOhE6dvHfNyxPbu6ve5aTHT+K+D+7j1gG3suzG\nZUp6pEGLq8bHObeT4qQCADPbCWx1zi33dz0C3GVmXwJfAxOBdcAb/jV2mNnTwMNmth0oAP4IzHXO\nLfRjPjezbOBJMxsDNAP+BGT5I7oA3vXL8rw/hL69f6/JgWa4F4G7gWfM7EG8pO2XeCPPIp4AfuEf\nfwYvCboIGB7PsxGRuiUyL4/EtrFgI7e8ewsvLX2JUzueypuXvkm3w7slulgiNa46equ5qA3nHjKz\n5nhz7hwCfACc7ZzbFwgbBxwAXgVSgHeAX4SuexkwGa+WqciP/T5hcc4Vmdm5wON4tUk7gWeBewIx\nO8xsKN4otEXAFmC8c+7pQMzXZnYOMAkvKVqHN/w9PNJLJKlp9fKGocgV8cSiJ7jj/TtIaZzCcxc8\nx896/Izi3gsiDZs558qPkihm1hvIycnJUR8fSRqDBhWPkgKv+Ug1KvXLss3L+Pn0nzN/3XzNySMJ\nE+jjk+GcW1zb96/SkhUi0nCUN+9NfRolpTl8ou0p3MPd/7qbXlN7sW33NmZfNVtz8kjS0sQMIgKU\nP+9NfRolpTl8is1ZPYfrpl9H7vZcfj3o19x5yp0c1OSgRBdLJGGU+IgIUH6NTn1avbw+1U7VlG/3\nfMvt793Ok4ufZMBRA3jt4tfo3rZ7+SeKNHBKfEQEKL9Gpz6NkqpPtVPVzTnHa8tf4+a3b2bnvp1M\nGT6FG/rcQCNTzwYRUB8fEfFNmwaZmZCS4r3276+/fWOSdQ6fdTvWccHLF/CTv/+Efkf247NffMaN\nmTcq6REJUI2PiABejU6zZrDXn6984cL62zemPtVOVYciV8TjHz3OHe/fQctmLXnt4te48PgLNURd\nJAb9GiBSj5Q2Wqmqo5gi5y9YEL0/GfvG1DdLNy9l0DODuOntm7jspMv47BefMaLbCCU9IqVQjY9I\nPVLaaKWqjmI67zz46KOS+5Opb0x9s6dwD7+b8zsenPsgnVt3Zs5Vczil4ymJLpZInafER6QeKW20\n0rp10fvD2+VZsqTkvmTqG1PfBIeo3zHoDu485U5SmqQkulgi9YISH5F6JDxa6ZtvvGatbdui48Lb\n8UpJSa4+MvVF/p58bnvvNp5c/CQndziZaZdM44TDT0h0sUTqFfXxEalHpk2DVq2KtwsKvGat1qEJ\neMPb5enZs+xtSbzpK6ZzwmMn8NLSl3hs+GN8cPUHSnpEKkGJj0g90rYtHH549L6NG+Goo6L3hbfL\nM3169PDv6dOrVk6pPlt2beGn037K+S+dzw/b/ZBlNy5jTOYYDVEXqSQ1dYnUM7Em56vqrMrJNvy7\nPnDO8cqyV7j57Zs54A7w1wv+yuU9LtdoLZEqUuIjUs/ESnKUuDQsGwo2cOOMG3ljxRtcdMJFTD57\nMukt0xNdLJEGQYmPSD2jJKfhcs7xl//+hVuyb+GgJgfx2sWvMaLbiEQXS6RBUSOxSJKr6uSHUj2+\n/vZrhv1tGNe8eQ0/Pv7H309EKCLVS4mPSA2rrlmVa+r8yOSHubne+wh919aqIlfE5IWTOfGxE/l8\ny+fMvGwmz13wHK0PjnNonohUiJq6RGpYVWdVrunzS5sUUWreF1u/4Jo3r+HDNR8yps8YHjjzAVJT\nUhNdLJEGTYmPSA2ramJR3eevXevV/EQ6R7dpU3KUmNSswqJCHp7/MHf/6246pHXg31f+m1OPOTXR\nxRJJCkp8RGpYrOHniTx/+3ZYs8b7OTcXMjO9uXsqOxRe4rMkbwmj3xjNx5s+Zlz/cUw4fQLNmzZP\ndLFEkob6+IjUoLw82LfPWwIiJQX69o0/sZg2LXpywaqeH57VeetWr+lr1SrvvW3b+K5fETXRgToR\nnbIres9YcfsO7OOef91Dxp8z2F24m3mj5/F/Q/9PSY9ILVONj0gNGjkyetXzpk3jTyyqOnw9fP6g\nQbB6dfF2bTRtVbWfUm1ds7ruGY4766qFHDh3NCu2ruCOQXfw/075f1pUVCRBlPiI1KC62HG4qrM8\nV0ZNPIdEPNuK3vP7/U13wWn3sCTzYXo3+SGLfr6Inu20EJpIIqmpS6QGhWtTyqpdKa8ZpbqadiI1\nQDXZtBV22GFlb1dGrGcbfkZLl1Zvc1hF/zzbtwc6zoEbekK/P9Fx1X0suHaBkh6ROkA1PiI1KJ7a\nlfKaURLRtFNdwstLVcdyU7Ge7YgR0c/o5JO9Fewj21V9ZhX58/xu33d0HXcHc5dOJmXzyZyw4C3e\n+VtXmujXTJE6QYmPSA0K9q/Jy4u9xlZEec0odbHZrKK2bCl7uzJi9X0KP5Pdu8s+Xh33DPrnV//k\n2jevZdN3m3hk2CPc1PcmGjdqXLWbiki10u8gIrWkvBmSy2tGiafZrK6prbKHr3vwwbVz34K9BYx5\nawxD/jqEDmkdWDJmCWP7j1XSI1IHKfERqYTK9LcprcYmcq1166BVK+jYMfaw9YoOa6+La29VdUh+\nRT3xhPcMmzTx3t96q+bv++6qdznx8RN5fsnzTD57Mv+68l90ad2l+m8kItXDOVfhF3AD8AmQ77/m\nAT8KxUwANgC7gPeALqHjKcAUYAtQALwKtA3FHAq84N9jO/AU0CIU0wGYAewENgEPAY1CMT2AOcBu\nYDVwW4zPdBqQA+wBvgCurMBz6A24nJwcJ8kpM9M5KH5lZpZ/zsCB0ecMHBh7f0qKty8vr3JlK+0+\nyaA2P/u3u79117xxjWM87oznznC523Jr7mYiDUhOTo4DHNDbxZGDVNcr3hqftcCv/C/+DOCfwBtm\n1g3AzH4F3ARcB/T1k5JsM2sWuMYjwDnASGAwcATwWug+LwLdgCF+7GBgauSgmTUCZuL1UeoPXAlc\nhZd0RWJaAdnAV355bwPGm9m1gZhjgLeA94GewKPAU2Z2VpzPRZLMkiVlb8eqdSmt1iNcE7R3b9UW\nC63PfYGqqrY++9sr3+bEx0/klWWvMPXcqcz62SyOPfTYmrmZiFSvqmZOwFbgav/nDcC4wLFUvNqW\niwPbe4ELAzFdgSKgr7/dzd/uFYgZBhQC7fzts4H9QJtAzPV4tUNN/O0xeLVKTQIx9wOfBbYfBJaE\nPk8WMLOcz6wanySXklKyliYonpqHcGzk1alT5cqmGp+a++zbdm1zV75+pWM8bujzQ93qb1dX7w1E\nkkB9q/H5npk1MrNLgebAPDM7FmiHV3sSSap2AAuAAf6uPni1NMGYFcCaQEx/YLtz7uPA7Wb5D6lf\nIOZT51xwbEg2kAZ0D8TMcc4VhmK6mllaIGZW6KNlB8oiElPPnmVvl1fzEKwR2rfPW8oiJTSR7zff\nxO6fU14fnnA/l6lT479GXRJPWWuyL9H0FdPp/lh3Xv/8dZ4+/2ne+ek7HJ12dPXdQERqRdyJj5md\naGYFeDU3j+HV3qzAS3ockBc6Jc8/BpAO7PMTotJi2gFR/7U55w4A20Ixse5DNcWkmpnmk5dSTZ8e\n/QU7fXr08fJGMQVHeH30kbeUxZo1XqISUVAQu7mrvNFhN9zgnVtY6L1ff33816hL4ilrZSZnLC+x\n2rprK5dPu5zzXzqfXu17sezGZYzuNRqrjsmIRKTWVWYen8/x+sOkARcBfzWzwdVaqppVbf9bjRs3\njrS0tKh9o0aNYtSoUdV1C6mD8vK8L+PS5uOB0ie6i5y7YEF0/IIFXnzr1sUT7kHsPipr10Zvr1sX\nvV2Rfi6J6AdUkecWS02XtayJIV9f/jpjZoxh74G9PHfBc/ysx8+U8IjEISsri6ysrKh9+fn5CSqN\nJ+7Ex286yvU3PzazvsBYvFFVhlerE6xFSQcizVabgGZmlhqq9Un3j0Viov47NLPGQOtQTGaoaOmB\nY5H39BgxrgIxO5xzeynHpEmT6N27d3lh0sBUZAbl0ia6C54bVFjo7W/ZMnp/mzYlY7dvj97eti16\nu317r1zB7bCKxFS3ys48XdNljZVYfbPzG25++2ZeXvYy53c9n8fPeZwjWh1RvTcWSQKxKgMWL15M\nRkZGgkpUPfP4NAJSnHNf4SUSQyIHzCwVr1/OPH9XDl4n5WBMV+BoYL6/az5wiJn1CtxjCF5StSAQ\nc5KZBb8WhuINf/8sEDPYT5qCMSucc/mBmCFEGxooiyS5WM0gVamBKC92byjd3revZEzr1rG3Y80H\nlJkJ+/eXbMapbF+YqvQNquxzq+k5gMKJVNOef6f7Y915L/c9XhjxAv+45B9KekQaknh6QgP3AacA\nHYET8UZJFQJn+MdvxxvldR5wEvAPYCXQLHCNx/CGmJ+GNyR+LvBB6D4zgUV4tToDgRXA84HjjfDm\nE3obb66eYXi1TBMDMal4o8yeA04ALgG+A64JxByDN5fQg3ijy24E9gFnlvMcNKorScQaJVSVkUPh\nc1u1it42K3u0WGlliqesmzZ57506xTdf0KZNJctblc9eV0ab5eV5ZTn6hE3usDEjHeNxI14e4TYV\nbEp00Uqo7J+dSF2S6FFd8SY+T+E1c+3Gq915N5L0BGLGUzyBYTaxJzD8E8UTGP6dkhMYHgL8jeIJ\nDJ8EmodiOuDNwfOdn/Q8SMkJDE8EZvtlWQPcGuMzDcaridrtJ2k/q8BzUOKTJDp1iv6y7tSp+Iuy\nMl8+4XOXLo3eLm+YfKxrRO7fsWP0uR07xi5/ZROQWMPu4xlyX5XnVpOKiorci0tedIc9eJhr81Ab\n9/LSl11RUVGiixVTXU0eReKR6MQnrj4+zrlrKxAz3k9+Sju+F7jZf5UW8y1weTn3WQucW07MUuDU\ncmLm4NU8iZQQq39JeQtVliXWucHtfv1g4cLi7fAw+dKuASX7+mzbBj16lCx/ZZucYsXF09+mKs+t\npmws2MiYGWN4Y8UbXNz9YiafPZnDWxye6GKVKpknpxSpLlqrS6QMtbXGVMTTT0fPwfPMMxU/N1bf\nn1jlr+yCoeG4Vq1q/nnUFOccz3/yPN0f6878dfN57eLXePmil+t00gP1e6FakbqiMsPZRZJGbddS\nRObggeI5eCp6/6OOgtWro7djlb+0ofbliXVeRYaj1zXrd6zn+reuZ8bKGVx20mX88Ud/5LDmhyW6\nWBVS2T87ESmmxEekmlV0vppYcVVpyqjol2Jlk7nqSgIrO59PVTnn+Mt//8It2bdwcNODeePSNzi/\n6/k1f+NqVBebC0XqGyU+IlUQ60u8ovPVxIqrypw19eVLMfy5zz0XmjWr2URo9ber+fn0n/Ne7ntc\n0fMKJg2bROuDW5d/oog0OOrjI1JBseawibWcQriWZtGikvPe5OV5+4M2bqz5PkV1YY2u8PNZsqTm\nls8ockVMWTiFEx8/kc+3fM7My2by3AXPVVvSUxeep4jERzU+IhUUq4YmVtNUuNZm715vOzcXjj4a\n+vTxJhUMT1ZY1RFjlf0MtV1LFH4+YdU1Umnl1pVc8+Y1fLDmA27IuIEHz3qQ1JTU6rm4ry48TxGJ\nj2p8RCqotCQnKNJUE6m1Ca+4vnev90X5ySfR+1NSomt3IjUJHTtCaiocc0z11CjUheHQ4Vqt8JD9\nqo5UOlB0gD/M+wM9nujB+oL1/POKf/L4uY9Xe9IDdeN5ikh8VOMjUkGx+t+UNtIp8lv/oEGx1+YK\n69Mnul9LeE2vggJvxFZVaxTK60NUGx2Pw7VamzdX30ilZZuXMfrN0Xy0/iPG9hvLb8/4LS2atah6\noUuRiDXPRKRqlPhI0qnsl3t5SU5Z5yxaFN201aNHyQ69QaXVHFSlRiEvz1v7K1IL1bNnyfsmoumm\nOpr39h/Yz4NzH2TinIl0OrQTH47+kJM7nFw9BSyDhpeL1D9KfKTBCyc6+/cXz44cz5d7Zb6gI+fE\nqtVo27a4bAMGRO8vrR/M+vVeLVJlamJGjoSPPireXr68ZMy6dWVv10Ufb/yY0W+O5tO8T7l94O3c\nferdHNTkoFq5d30ZSScixdTHRxq88MircP+a0mpR8vKgb1846CDv1a9ffH1sgiN+RozwkpV587xj\nAwZ4x84/P/aIpkg/mI4dvRmSI7U0kT5CsUY+lTfCKPw5CwqgS5fo+FjLXtRVewv3ctc/7yLzyUyK\nXBELrl3AfUPuq7WkR0TqJ9X4SINXXvNQaf0ywjUkCxfG1/QTq9kIoveFOz9HyhquSejcOboGKNZn\nKq+ZKlYtUkGB98rN9ZKgXbuij4eXwagrFqxbwOg3R7Ny60ruPvVurj7u14y6uFm9n1VaRGqeanyk\nwQsnNk2aQK9eXh8bM6//TazanFjJRXBfvDUsa9eWnLunvLKWtj9WXHkjjKZNgxZl9PMtKIADB6L3\nbdpUt+an2bV/F7e+eysnP3Mya75sTvo/cnj3/93NRRc2q7G5gESkYVHiIw3etGlec1HEzp3w5Zde\nR1/nvOajSG1OUKzkIrgv1uSFZZ2/fXvJuXtOOKH8CQuDnZJTUrzmt1hx5SVHbdtC9+4lzytLWU1r\n1SGcPC5dWnoyOfvr2fR8oieTF06mw4r7+e7R+axbfFJczZciIkp8pMFr2xYODy26vXt3ybhYNSSZ\nmaUnHBWpYRk40Ju0sFWr2PdcubL00WXBuXw++shLQvbuhaZNYzfjPPFE9MruU6eWjNmyJXo7JcVL\nuoKJYWR/WZ+tuoSTx5NPLplMFuwt4BczfsFpz51Geot0PrnhExr/53YoKr2lXsPKRaQ0Snyk3opn\nuYDwF6FzpcdErjtggNcctmYN7NkDCxZEJxyxaliCHaKPPtobQdaundeMVFhY8p7ffRf9JR/8TMcd\n5+0P1xJt3Bj7s0dWdi8sLF7Zvbzn0KcPrFrldboOJk0nnFD2edW1VEM4oQonh6t4lxMfP5FnP3mW\nP/7oj8y5eg5d23QtUZ4ePWp2qY+yaNkKkXrGOadXnC+gN+BycnKcJM7Agc55KYz3Gjgw+vimTd6+\nTp2cy8x0rm9f7+dWraLPM/OO5eV55/XtG328b9/oaw0c6MXm5ZXcFy4TOJeSUnJfrFfkOuXFDRwY\n+7N36lTyemGxyhzrWWZmxo6r6LMvT+R5hp/N9382B21znD/aMR53xnNnuNxtuRX6HIlQ1Wchkmxy\ncnIc4IDeLgHf4RrVJfVWeU1N4VFOAwd6tRudO3s1IhEdOnjNR5G5dML9RT75pPQRU+ERXhVpEmrR\nwquVifQximjfvuzzU1K8Gppp07yyBq1d6/UhCorV3FPavDPh+378sdfhe/782M1qVV2qITwzdeSz\nPfGE46K7X+PLH9wETXbzwKlT+d9Tf46ZVehzJIKWrRCpX9TUJfVWeZ15S/tCitXpONivZN++kveq\n6JdbrGQj3AzTvbvXfBVMelq29CYL/Oab6HNbtSo+b80a78s+MsFh+DMEk7lWreJr7glfr7Cw7E7N\nFRllVpbw8zvySHh55nruWnohK3r+hHN/2J/Vt3/GraddVyLpqWuq+ixEpHYp8ZF6K7zYZfiLPvwF\ntH691/9m167oDsvhuWqaNo3e7tGj9C+3YP+Ovn0hP784xswbNv/MM8X79u0rWaMEXl+f1au95CWY\n7Hz5pVdLFUl4Svvs4c8QK3kLlzfYHyVyvSahOuBwghI5f906r5wdO1auT03U87QirM9UTnjsBBas\nX8CrP3mV1y95nSNTj4zvoglS3t9DEaljEtG+Vt9fqI9PvRDpB1JWH5tY/WVi9W+paN+Yily/In19\n4hXrHrH6mpTXH6Wqxysq8jyP+uHnLnXsYMd43DVvXOO27dpWuQuKSK2I1d8xXurjI1JNYi0++uGH\nJWc9Dpo3D/79b7jzzvIXLY30KcnLK153a/36sstUmf4esZpKgp/tsMO82qQtW4rLO22aN4osOAJs\n7VqvFmrJEm+7Z0/vOmWVr7xFN6urP8uhh+1n+P2/Z8LsCRyVehSvn/c+Zxx7RuUuJiK1JhELGVc3\nJT7SYJT2D7K0BT/Bq7c44wzYsKF40dDwF79zpS9yWp5vvim92Sks2Hk5IpLwBFd3D36W4Ofs0ye6\nw/D27V6/oIiFC0vO1xNrksOy/hMLP8vK9GdZtGER17x5Dcs2L+N/B/wv95x2D82bNo//QpUUK0HW\n8hYiFdMQOvMr8ZEGo7R/kJFajLVrYetWb+bmoAMH4NhjvffgSKvS1tcK97VNSSk51w5Ao0bRHY7B\nG9H1gx94fYHWr48+78gjo2uVwglPeZ87XFuzbl3J++/d69UCBWuL4hG5x9dfe8tZLFgAqane6K82\nbcpOKHbu28k9/76HSf+ZRM/0niz8+UJ6t+8dXwGqQUP4jVUkUarjl59EU+IjDUZp/yAjtRiDBkXX\ngASFF+eMiPXbTCQxiujTx0uYgguatmzpNUmtXh0du3On13l61SqvPMEamljLYVRE+HNGDBpU8v77\n9hXfvzIi90hNLV7Xq6DAG17fo0fpCcV7q97j+reuZ+N3G7l/yP3cMuAWmjRKzH8/DeE3VpFEKa85\nvD5Q4iMNQnA9K/D6s5TXP6UiIklFuKnMzOtTc9RRxfcJ/2cwYkTJxAO8BKlzZy8xKq32Ze3a0svU\nooU3JD7WeeG+QL16eXPyBFXHF314huXdu2MnFNt2b+OW7Ft47pPnOP2Y03n3Z+/SpXWXqhegChrC\nb6wiiVKX5tCqLCU+0iCMHBld4+JcyUSkrL4+YeH+Nl26RDcbOQfbtnn9ZiLNOeH/DCLJT7jmxjmv\nHMFJFcPCkxEGpad7TUyxhJtxGjcu2RQXHIpf2b4uBx8c/TwOPjj8fB1NemXRbco49h3Yx9PnP83V\nP7y6TszJ0xB+YxWRylPiIw1CuLZhyZLozsBdunj9UK6/vrg2JBgT1LIl/Oc/0SuZf/llyVFTBQVl\n9w+J/GbUt290UlZWuSNaty7ZPyeirBqK8PUOHChukiqeHdlrBgt3mI6nr8v8+V7z1u7dXtIzf763\nEOyIEbCmYBUFp47hi8Pe4ycdf8KjP3qU9q3qTrVKQ/iNVUQqL64JDM3sDjNbaGY7zCzPzF43sx/E\niJtgZhvMbJeZvWdmXULHU8xsipltMbMCM3vVzNqGYg41sxfMLN/MtpvZU2bWIhTTwcxmmNlOM9tk\nZg+ZWaNQTA8zm2Nmu81stZndFqO8p5lZjpntMbMvzOzKeJ6LJF55zRWRRTs//NCrYVm40EsCYvnu\nu5ILfLZtGzu+vGajyPDxyISJzUODlw47LPZ5Rx0VvW0We4X4sLKeQ6Tz9A03lL74aUV17w47dngj\n3Hbs8LYPOWwf59x/P99cfCJpnb5gxmUzeOUnr9SppEdEJN6Zm08B/gT0A84EmgLvmtnBkQAz+xVw\nE3Ad0BfYCWSbWbPAdR4BzgFGAoOBI4DXQvd6EegGDPFjBwNTA/dpBMzEq7XqD1wJXAVMCMS0ArKB\nr/AmHbwNGG9m1wZijgHeAt4HegKPAk+Z2VlxPRlJqPDsuT17loyJNWdN5JxI36CIRYtKrrL9xBNe\n01FQeQlXpAlu717vFe4bU1rLz7RpkJlZnDBlZnods8MrxMc6LzxkPVzWeJbbqKi5a+bSe2pvfvOv\n33Bz35tZduMyhh83vPIXFBGpKVWZ/RBoAxQBgwL7NgDjAtupwG7g4sD2XuDCQExX/zp9/e1u/nav\nQMwwoBBo52+fDewH2gRirge2A0387THAlsi2v+9+4LPA9oPAktDnygJmlvG5NXNzHZeXV3IV9rJm\nGa7IzMfhmFatSs5aGp7VtGPHys/SXNlZkiOzInfs6JWxY8eyZ5tOSan8DKzbdm1z1715nWM8rt+T\n/dx/N/43/ouISFJJ9MzNVV2r6xC/8NsAzOxYoB1e7UkksdoBLAAi60n3waulCcasANYEYvoD251z\nwfEos/x79QvEfOqc2xKIyQbSgO6BmDnOucJQTFczSwvEzAp9ruxAWaQWlLaGVNDSpd4w6qZNvfdl\ny0o/t21br19OeWsoBdeeCte+BGtG8vK8WqCgggKv30+/fsXljXQujix4um1b2Z/7sMOiy750afF2\n+H4VbYqK9GH5+muvGSrSEXrAAO/aU6dGP5fg4qdBZf2ZOOfI+jSL46ccz0vLXmLK8CnMHT2Xnu1i\nVLVVUEX+DoiIVFllMybA8JqIZgf2DQAOAOmh2JeBLP/nUcDuGNdbANzv/3wHsDxGTB5wvf/zVODt\n0PGD8WqKhvnb2cDjoZhufhm7+tsrgF+FYs72Y1JK+eyq8almFandCNfgtGpV8XMret/SrlPeeluR\n2p9OnaL3R2pdKrpOV+PGpR+LtYZYdT3beM77cuuXbujzQx3jcT955Sdu/Y71FX/gNVBOEalfEl3j\nU5VRXY8BJwADq3CNRKmWMbXjxo0jLS0tat+oUaMYNWpUdVy+wQsOpw6veRWrdiPW3DGxYufOLZ5N\nODIyKzx0+4knvE6+4WHhKSleJ+DwMOfyalsKCqBDh+IRVMHz9u8v+9yg8PkRLVtCYWHx6LB4RmFV\ndsK+cNyGvH3c/8EfmDBnAm1btGX6qOmc+4NzK3Stigydr+mJBcsqg5axEKkZWVlZZGVlRe3Lz89P\nUGl8lcmWgMnAauDo0P5j8WpceoT2/xuY5P98Ol5tSmoo5mtgrP/z1cDW0PHGeH16fuxv3wssDsUc\n41K+SeMAACAASURBVN+/p7/9HDAtFHOaf/80f3s28HAo5iq8prbSPr9qfKpBRWtbIipa4xM+Hium\ntFqYVq1i16jEu8J6TbzCq8ynpFSs1qdaanw6znbNb+3uGt/b2N2afav7bu93FbtIHGWo6Rqfsq5f\n0XtXx8rUIsku0TU+cffxMbPJwI+B051zUQsAOOe+AjbhjcSKxKfi9cuZ5+/KweukHIzpChwNzPd3\nzQcOMbNegcsPwaupWRCIOcnM2gRihgL5wGeBmMFm1jgUs8I5lx+IGUK0oYGySA0J/0afkuL1mWnV\nyutzE+znkZfn9f2I9MNp2dKr0Vm6FP7739jXLygoPj98r3DtUZMm3n0LCor75xxxRHF/ovvuK+4X\nk5npzYhcU1q18spTnr17i9cSiwj2k+nb1+t/tG6dd82OHUvv6xTLtGmQeXoeLX92JVx9Ksd3asmi\n6xbx+6G/p0WzFuVfIKAitTnhkWz791dvP5+yylDR2qZwH67w8xeReiCeLAmveWs73rD29MDroEDM\n7cBW4DzgJOAfwEqgWeg6X+HVvmQAc4EPQveaCSwCMvGa01YAzweONwI+Ad4GeuCN+soDJgZiUvFG\nmT2H1yx3CfAdcE0g5higAG90V1fgRmAfcGYZz0E1PtUg/Ft2ZmbpI7FixQ4c6JxZ2bUkmZneKxwX\n6z7h/jnBV4sW0b/pf/qpc82aVX+tjplzS5fGrmHKzCxZ6xMeFVZWzVRptVmxFB4odFMWTnFp96e5\n1g+2dn9e9Gd3oOiAc65ytR4VrVGpyVqf6qjxCf8dKWtUnojElugan3gTnyK8ZqLw64pQ3Hg/4diF\n18G4S+h4Ct58QFv8pOPvQNtQzCHA3/BqcLYDTwLNQzEd8DpYf+cnPQ8CjUIxJ+I1Z+3CGzl2a4zP\nNRivJmq3n6T9rJznoMSnGkSGXUe+QPv2Lfll3amT90Ub/sKvbPNQJAFYurQ4kUhJ8e4d6/6lvcrq\nhFzVVySZCJcv8rzCnyWYeJSVvFU0oZj5yQLX4pYMx3hc+rXXus++/ibqeGWSk/CfdWnJUk0mFmWV\noaLlUwdskapLdOJjzvsilziYWW8gJycnh969eye6OA1G584l19Ia6Hedr+hK5WHhdaoi+9asKbmO\nVmYmNGvmNXNUdE2vmnDUUaUvUrp5c8l1wwYOLO7kHF7xvTSdOpVcI2zb7m3c+f6dTF30Z9jUE2Y8\nBusGRF0fSv45Ba8VbyfhcPz+/d6s2hHBP5O60Ol48+aS63ypE7RIfBYvXkxGRgZAhnNucW3fX2t1\nSZ0RXkS0VStv9FVpS0uENWrkzaxcWOh9WUZmbw5+kYKXCJ13XvFyEhGbN3tz34A3QmvduoqXvVEj\nr0/Qxo2lj8yqqA0bSj/Wtq23JlYw8YnMMt22bfQCnIcd5vWJ2rIFvvkm+pzVq715kLp3hyJXxB/n\nPMdt797OAfbRePYjHJh/IxR5/z2E+7uUtbp5eJHUskae5eXBcccVlys310t0Bg6MToQqer3aoHW+\nROo/JT5SKyoylDjSCbd1a6/W4/+3d+bxcdXl/n8/SZppk6bF7rldSVpLaelK2kIRuAKiCHihKiI/\nd6/FDX/4Ehf0XhF/KuD94YbaXhS4IhRFqiziRVHkQmlT2kJDAdlKS1PSKV3okpQkTb73j2cOc+bM\nmclMOsnMZJ7363Ve03POd87ynWnOZ57Ve4iHNRL109AQf8D7jx2NqsCprISOjsT3bNqk2/34iw1O\nnpyd8Onuzo3o8Y6VjqDw8IKcvSKEYQ/mXbtUmHnX19WlBQ3vXtvEe278NAePeQz+cQn8+ft0HUrs\nXRFsZZGuu3lQJPlFWZClS5Mbse7Zk2iJqq9P3J/rFHfDMEqPo63cbBgZkS4bxtu3bZs+CCdMiD/E\ne3rQefVt1q2LH/u88+LHffzxZNHjESjBxKFDMGVKvLqxJ6gyJUz0iEB1dglQiKSvYrx8efJ1bd+e\nvurxmDGB90QOcGjJF3n7b+ZzsHMf3PI3WPVriImeSCS84nU0qp/d9u1qRWpu1nXvfEGRFJZ55hH2\n2Qbf39O6YRhG1uQjsKjYFyy4OWuCQauTJsWDScPq06QLeA7LhAquh1VRDmZIDR2aev+SJUdfu8cL\nfPUClTN9X1lZ6mM5F35dqbLh/BlY5eXOIV2OuTc7vjTW8fUqN+K8axzl7RkHP6eaE/+99pR5lupY\nYb3PMg06NgyjeMh3cLNZfIx+IfhLfd++uAUo6Mpqb49bb5yL13aprFQLTxDnktfr6uDll1Nfz+HD\nauFJxfr12bm6wnjySbUgXXhhdpajMFeXZx3ZvBkeeyx5f3AOm5vjMTTePHeNWwefOAn+5WPI1rdz\n56nPMWPvV6Ar7vPzOsF3diZbj8L6lfnPB2pZCsZkpbLSrFqV2DPsxReTXWKe6+6ll8L7ifUH1kPM\nMAYWFuNj9AvBuJDm5uT4jjD27NHx3oO9o0NFRFDsBGltTb9/8+b0+9vbNfPraGht1WXbtnDhU1bW\nczyPR22tPoDnzg2/96A7b+9eXwxNdRTO/BrMu1mztW5+GLftVH7YEh6v48928wcUL12aOt7KHx+V\nLgbIT7EECmcTsG0YRuFjwsfoF4IPuVNOUUHQE1u3JqeWDxqUOm4nl/Qkro72WN3d4en2QYYOhe98\nR603mQZPjxihvbU46QY47VuaoXXfz2HDv4LTQuYtLcmfS5hVx7M2pYu3GjEi/u9iETSZ0tc9xAzD\n6F/M1WX0C0F3wYoVmsHVE2EWkf4QPf1FT6IH1CV33nmZWcg8Isf/mW3vmgNnXQFNH4KfvED5E5e+\nKXog7oLyfzbTpiVfkzcuXWDxhAnxf0ej2i5j8GBdFi3Kzj1UaK4lC7A2jIGFWXyMfiHoLli2TGM6\nPJfIsGG6/sYbatXIpbWlGIhE9J5TibpMRM/QofCWui20nvJFnh9zN2w9FX57B0TnUFMDax7XeQ+6\noPyfTRhezyzPhbV9u7ogjxzR/XPmJLqzvGw6j3XrsnMPFZprKVPXnWEYxYFZfIycEvZrPcx9sn69\n1pEBbTZaXa2WjSNHBo7oiUSyGx+sK5SOmhqNU/KCgxedepCP33Ylu953PFVTNzDq73fALX+HqFZx\n7OiAc86BpiYVlh0dakWqr08dsOyxbh2ce27chTVxosYutbfrMmhQYtCxVwTSTzbuoUJzLRVCgLVh\nGLnDLD5GTgn7tQ6pM7e8Mfl+uPUFs2drocRMXHOeiEiHiHav94o7OgeOLg5Mu4nt87/BpqYDXHHy\nFXz1lK9S+83E4kH+YO2DB9PHV3ld6v00NcULTTY2Ju7zihQ6p/t37Eg+ZjbuoXSVoQ3DMI4WEz5G\nxmTSh6k3v9ZbWpKLCfopL89NReT+IhJR98+99yb3AzsanIsXdwSYdf6DPD37izD2KWi6hMq13+P2\nmyfyUK3OZ1YxQREYPz7e5iLY5gNSu8Ta27V/WEdHavGWjXto+XI4+WQtOTBkiMaDGYZh5ApzdRkZ\nk676skdYIGhPv9hHjlTLSCqqqrK/1nzit/CsWpW9yws01f2445K3NzbC/LOf5axbzuXpBWdB+zC4\nsRFW/ZrWVye++dns35/d+U48UV05lZXhomfOnPQi9uDB1KKnpiY799Cll+rxjhzR12XLUo8ttEBo\nwzAKHxM+RsaEWXPCsrWWLFGXTE2N1uvp6NAsn7q68Ho2zz6bup5NdbX+6s8lInotfYVzKh7GjdN4\nmEwyt4J0d8NzzwUy36p2c+Ssz/PE4hN45NlnqN9wJ9z0COxYmPT+oUP1cwgTXZWVWqTQ+0z8bSmC\nn3FFhe6/997euZxqajSGKxuysRpmIsYNwzD8mKvLyIhoVHsz+amtDc/WuuuueNdtL55kyRK1KAwe\nnCwE0lVQ9ooA5hLn0ld1zuV5jib13jmdv+rh7RyedQPdb/s24ODB71G76/OMHDGYl1K8d9cu7fJe\nX59cB2nChHCrDiTH1yxaFHetZdr5PRJRC1KYKzQTsonxKbRAaMMwCh8TPkZGBDtp19RoLEawPcH2\n7XHR48d7IFVU9M4CkmuKI3PMwcw7aT3jSjjmZdiwDB76FrSNZvySzFpqBEWEty0V6VK303V+D77n\naDKfskkft0BowzCyxYSP0SNh6eijR2ssRlDE7NsXHlTrtVzo7Oy76yx2Bg3SIO7ubuDYv8GZX4Hx\n6+H5c2Dl3fDaTETgxFgvrXQtNaqqdL47OtS11dmpr8GaO0HCxE00qqnvTU267gVue+Im15Waszme\n1dgxDCNbTPgYPRLWo2nkyPD6L6msOdu2xTN/jHA6O4Gxm+Csr8DUB6B5Edz8d9h22ptjnFMBks5q\nVl6ucTXBQoLe+887D3buVJE6fLgGQo8YoYLDc1/5LTeZFCTMJOOvLxho7TEMw+h7TPgYPRKMmxBJ\n/fBNJWyOttP5gOeYrfDP/wazb4O90+A3v4NnLwSSo8HTiZ6yMo3hWbYsec7b25OFkGedC9b28VdM\nDoubCR47rH7TXXf1LIbyJZgMwyhdLKvLeJNUqcHBuAnnCiNOpxAoO9r/QVW74ezL4XPToe5BuO/n\nLGjcTGTLUsJET090d6uAWb06sWN6b/AET1jcTPDYYUHGmWRcpRpjaeqGYfQVJnyMN0n1EFq1Kt4a\noTc1aTIhm3YNhUSqNPweqTwEb/sOXFYP838JD/87/ORFlgxexv33DuL443s+hEj6Rq/Dh6dOac+E\nUaP0ddWq5M9n+PBEYeKN9aitTRZDXoVnP6mysixN3TCMvsKEj/EmYQ+hzZs1NqexUdOWM3kg94aw\n+j4DkorDsPgH8IU6OP1b8OTH4EcvwSNfh45qWlq0L1a6go4ekyZpY9eGhnDh2NKiWXYVFSp+IhGY\nN0/r90yerKKptlZjgioqkq1XXubbmDF6Dj87diQKE+fi4tirCxS0FLW3JwuYVJ3Pg9/Fxkaz/BiG\nkRssxsd4k1GjElODR43S1gH+OJDnn9cHW0tLcpr00VBenrtjFSTlHTD/F2rlGRqFJz8KD/8b7J+c\nMMzrX5YJY8aokGhqCo+t6upKzvyqqkoMBl60KHXtmz174v/22kh434VgOYA9e7ROk59Vq1Sc+d2i\nwXOlysoKpqkfORK3/Fgws2EYR4MJnxImmKYcFB/Oab8kP62t8YfUzp3Q1paba8nVcQqOsiMw51dw\n2tUw/BVougQe/ibsndrrQ06YoBabzs7s+4D5g5Kj0eRgZz9+a4zXRiKTsR5jxmidJ/81Bselysry\nBFFjo4oeDytQaBjG0WLCp4QJS3f209QU3hw0G6tEySJdMOs3cPpVMPIFePq9cNsf4bWZocPLyjKP\nF5o8WcVCfX3qMakau/qDkpcuTbbciMCxxybXxEklOPxVmsMIWnSWL1eXVU9ZXJ4gOuWU9MLJMAwj\nWyzGp4Tp6ddze3vig7Fk4nCOBumGGXfBp+fA0ktg93RYvhHuvDOl6IHsKkl7VptUIiAS0RihJUuS\nrXj+oOSwOkxes9JHH00UJMFzVVZqjNC4cepme+c7tR1JJKLbp0zR84AeyzvmpZdmF7TsD6z39xTz\n09sMMMscM4wSxTlnS5YLMB9wGzZscMXMkiXO6SO35yUSca6hIfPxJbfIEces2x2fmem4CseHznSM\nX9sn56qu1s8vGtXPMBJJ3L9kSerPuKYm9XFravSYYXjnqqvT14ULM7tW/7U4p+/376+ry/33OHjO\nTN+X7v4Nw8gdGzZscIAD5jvX/8/wrC0+IvI2EblHRHaISLeInB8y5moReVVE2kTkLyIyNbA/IiI/\nFZHdInJQRH4nImMCY94iIreJyH4R2ScivxCR6sCYiSLyRxFpFZGdInKdiJQFxswWkf8RkcMisk1E\nrgi53tNFZIOIvCEiz4vIR7Kdl2Jk+XLtfp4pqSw+JW0JKuuEOf8Fnz0e3vtB2D8Rfrkabv0L7FjU\nJ6f0Yl48d9Arr8StIg2xdhaeFWPFikSLyYgRyceLRHTfiy/GrTxBawgkWm52787sWoNWxVRZXEdD\nbxuVBscdPGhp84ZRCvTG1VUNPAl8BlVsCYjIV4DPAZ8CFgKtwAMi4k+4/SHwbmApcCrwT8BdgUPd\nDswAzoiNPRVY4TtPGXA/Gqe0GPgI8FHgat+YGuAB4GXUSnMFcJWIfNI3ZgpwH/BXYA7wI+AXInJW\nhvNRtHziE6k7nwcF0YwZqVOsXdK3oAQo74D5N8Lnp8MFH4XdM+A/18Ftf4LtJ/fpqdvbNSbIcyst\nXKjb16xRF9S6dXFX0sc+lvjeMOEzfnyya6unOjqZCpbguExcV9nSWzEVNs6Cpw2jBDgacxHQDZwf\n2PYqcLlvfRhwGHi/b70duMA3ZnrsWAtj6zNi6/N8Y84GjgDjYuvvAjqBUb4xy4B9QEVs/dPAbm89\ntu17wDO+9WuBpsA9rATuT3PfRePq2rkz0UXx1FPxdZFk10Qkovs3b46Pa2hwbujQ3LtsinKpOOxo\nuMFx+UTHN8Xxvvc5xj7ZZ+d7+GF1bWUytqEh2ZUUdIOFHSvMNdSTS8rv+mpocG7ePD1XZaV+VyZP\n1v394ToKuuEyPWc0muz6y9RNZhhG78m3qyunWV0iciwwDrWeAOCcOyAijcBJwG+BE1ErjX/McyLy\nSmzMOtSCs88594Tv8A/GJmoRcHdszFPOOb/R/QHg58BMYFNszP84544ExnxZRIY75/bHxjwYuJUH\ngB/0ahIKjGAPpcWLU1t5QK0J69fDxz+uHbidg2nT4NCh/rnegmXw67BgBSz+IVTvgs0XwyNXwmt9\nVNExxpVXZt7RftOmnqs0Bz97kXCrS7COTqZp6Pmgt9cyZoy696y7u2GUFrlOZx+HipNoYHs0tg9g\nLNDhnDuQZsw4ICHHwjnXJSJ7A2PCzuPt2xR7DSZe+8fsT3OcYSIScc4VdVeqoOk+nejxaG9Xd8mU\nKfrvXrdlGAjU7FCxc+IKKG+HTR+B1VdoI9F+IJs6PZ2diYUMa2rURbluXXybSKJrsrIyPJ3cK1h4\n+DAMGaKxQgORQhJwhmH0D6Vax6ckwnGjUW0z4Sf44EtHsHhhSTH6GTj5+9otvbMK1n0OGj8Ph4qn\nkMzo0Wq181s02trgCZ8ddfbs8A7p/oKFBw/CggXxej3WPd0wjGIm18JnJyoqxpJoRRkLPOEbUyki\nwwJWn7Gxfd6YYJZXOTAiMCbQQYixvn3e69iQMS6DMQd6svZcfvnlDB8+PGHbxRdfzMUXX5zubX3K\n5s3xX+rOJRaxKy9XC8DmzXm7vALHwaTVsORamH4fHBgPD34PNnwKOtJ0A+1ngsUOIxG13AQrK+/Y\noaLHL1Z27dJeYF61bhE4//y4VWjLFu3NFhS97e1qfaqv18yudOInTEiZWDKM0mTlypWsXLkyYdv+\n/fvzdDUxjiZAiOyCm9/nW+8puPk4oIvE4OZ3kBjc/E6Sg5s/hQY3D4qtX4oGN5f7xnyXxODma4BN\ngXu4nSILbvaCmMMClv1LVZUGofY0rqSWsg7HrJWOTyzWGjyfOd4x5xZHeXv+ry1kqahIXK+rSw5G\n9i81Nc5NmqSvkycnB/QGA6B7WnoKAO5tXR3DMEqDogtujtXSmUrcXVQnInOAvc657Wiq+jdE5EVg\nK/BtoBkNSMZpsPMvgetFZB9wEPgxsNo5ty425h8i8gBwo4h8GqgEfgKsdM55lpo/A88At8ZS6Gtj\n57rBOeeFg94O/Dtwk4hcC5wAXAZ8wXdLy4HPxvbfhKbPvxc4J9u5ySf+IOZ0DNieWL2hajcs+E9o\n+CkMexW2vB1uvwdeeDe4wi1qPmRIonXHCzxO1Ubk4MFEt1WQsAan6egp5bu3dXX6ArM+GYaRRLZK\nCTgNtc50BZabfGOuQi0/bWiG1NTAMSKokNmNCp87gTGBMccAv0YDkPcBNwJVgTET0Ro8h1DX2rVA\nWWDMLODh2LW8Anwp5J5OBTaglqkXgA/1MAcFY/HxLD3l5fm3RBTNMqbJcf4nHF8frMt5n9Rt+b6u\nDJbqai010NCglppIRKso+8sPpKvOnGqpqcn8vcVk8SmkazEMQ8m3xUecczkVUqWAiMwHNmzYsIH5\n8+fn7TqiUU01T9c124ghXfDW+2Dxj+DYhzR+Z91nYeO/QtuofF9dxohofE+weWd5uaazz5ypcTxe\nQPNrr4V/P4JB7hUVsGiRZm8tW6bvHTlSq0Q//bRmjFVWwpw5GjCdzmriP3++rSz19YmWsLo6jVEy\nDCN/bNy4kQULFgAscM5t7O/zl2pW14Dg/PNN9PRIzasw75ew4EYYvh1eORnuvAOevRC6B+X76rLG\nOX2Y79iRuL2rS0XJq68mpmh7IqS5WTuzjxgBEyaokPGnuR85okLKy95as6b3YqWQUsR7qkcUxFxj\nhjHwKdxABiMlXh8l/4PL8CHdUP9nuOhCuHwSnHINvPhOWLEebloNT19UlKLHY8sWzbIK0tWV3FrC\nEyFbt8ILL6joaWnRuJ6hQ5OP4WVvpetZVUxdzbNtkdFTqw7DMIofs/gUIf70Y8NH9S6Ye7MGLI/Y\nAtET4E8/hqZLoH14z+8fAKQLJM40AD6b42zZouKgUCw8QbK1PhVSYLZhGH2DCZ8iwjPDm+jxUXYE\n6h+AubfAcXeDK4PNF8Hvb4XtJzGQa1UOGpTcziKdKyebh3jYcbzvX2Nj749b6GTrGjMMo/gw4VNE\nmKXHx+inVezMuRWGRmHnbPjLdbDpw3A4pAX5AMQTPTU1WqW5p15TwYe6n3nzoKoqfc+qVBajgSQO\nVq2y3l2GMdAx4VMEmKUnxpC9MGulCp7x66F1FDx1CTz5Udg5N99Xl5Js2oRkQiSSGOMzenRmmUr+\nh3o0mti3raKiZ5dQ0LLjZYINJHFQSIHZhmH0DSZ8ioBsYjMGHIPaNA191kqYdj+UdcHz74Y7Vmmh\nwa7KfF9hj+RS9IQ1Hg1aXFJlJvkf6sE07z17ej530GK0aJGJBMMwig8TPkXAQIqhyIjyds3KOmEl\nTL8HKluheSE8eA089UFoDbZWG7hUVuoycqRmZHnWlXTumEyCj3sTy2JuIMMwBgImfAqcaFSXAU9Z\nJ0z5O8z6Dcy4C4a8DtFZ8MiVGqy8rz7fV5hTgo1GU9HRoa4yT/R4NWUefTRu2TnppETLTiaZSX4R\nM3KkxgvV16evXWNuIMMwBgImfAqMoJuirS0xFmNAMagVpj4Ax/1e3VlDXoe9dfD4Z2HzB2DXrHxf\nYZ+RSvSUl+trV1d8m7+2jl/wrF8fj/XxW3Yyseb4RYy/CnShp6cbhmEcLSZ8Coygm2LAMWQPTL8X\njvuDpqEPekMtO+s+D//4F2iZx0BOQe+JdIHQnuUmVcyXtz9bl9T27Ynrzc3ZXbNhGEYxYcKnwBh4\n8TwOxm2CqX+Cqf8Nk1ZrZeXtJ8FD31axs3dqvi+yYDhyJPU+z3KT6jvi7c/WJbVvX+L63r2Zv9cw\nDKPYMOFTIGzeDCefPEB6bw3eB/V/iYudmp3QPhRePgP++DN47nw4NC7fV9nvlJcnurAyJRLR/lme\n5SasHk9NTe+DjUeMSPzejSiNMkiGYZQoJnwKhKIWPYPaYOJqDU4+9iEY3whl3erCavo/8OK74JVT\niiL1vC/JJJjZw1+UcPlyuPTSeBDzihXaTDRYy6e3zTQnTIBt2xLXDcMwBiomfAqEw4fzfQVZUHEY\nJj7mEzrroLwTWkfD1tPhiY9pU9ADE/N9pQVFpvV8vGaanpAJBh8vW6YWIH+cj+fm6k13cUtTNwyj\nlDDhkye8B9T27bB7d/rYjvzi4JhtMGFtfKndqEKnbaQKnf/+gb6+djylHJicC2pqkuNzwtLT16wJ\nFyu9aSBqaeqGYZQSJnzyQDQK06YVqGurOgrjnoTaJ+JCZ2iskNDeemherO6rrafBazO1KaiRE2pq\nVNAECUtPTyVWrLu4YRhGekz45IGlSwtA9JR3wIgXYewmzboa96Qunshpr4EdC2HjJ1XsNC+CttH5\nveYCZuhQOHSod+9duBDuvTe1SyobV5R1FzcMw0iPCZ880G91UqRbLTgjX4CRz8Go5+Kvb9mifa8A\n9k/UJp8bPqWvO+fA68eaNScL1q5NDjiurIzX5ams1OrI/v2g8Ty5dEVZvI5hGEZ6TPj0A8GA09de\ny8FBK96AqtegepcuNS0w/BUYvk1fj9kGw7ZDRYeO7y5TMbN7Ojx/rr7umQ7R2XDY8pd7S1kZPPQQ\nzJyZHHDc0JAoWHbtgnPPhaYmXZ8zJ/fCxOJ1DMMw0mPCp49Y27yWlU+tpLK8kjtui9BcUQm1EbZ0\nVcLMiKZ2O1GrS9mRxKW8AyoPQuUhiMReKw/qv6t2q9CJhPjKDtbC65Nh/yRoWaCv+ydpbM7eqdAV\n6f+JKHJSVVIOs9T0ZG0ZMyaxq7phGIbR/5jw6SNaDrbw15f/yqHDHTSP6IDR7SpoKmKv5Z3xwV2D\noLsitpSrKOqogY6hGmvTUaNWmf2ToW0UtI7R1PHWMbq0xf5d4nVyck1dHdxzj9bPaWvTbbW1MHly\nuKXGrC2GYRiFjwmfPuKCGRdwwYwLqKkBwoJeJVbNzuJoCpbaWnVhHTiQ7ysxDMMwcoUJnz4mZaaP\nCZ5+IV3TT4BZs7Rq8eHD2hrirW+F/fstMNgwDGOgYsKnj+npwWv0DZWVMHcu3HSTVjpubtbmm8OH\nq7AZMUJbM2RS2dgwDMMYOJjw6WPmzoUnnsj3VQw8ysp08VLG29thyBAtADhzZuJYi7sxDMMwPMzf\n0gdEo9pfqb4eKiqgqirfV1R8/OEPailzTuezoUFdUZGIFvxradG6OK2t6k7s7NRYnKDoMQzDMAw/\nJnz6AK9f0pYt8Pjj+jBuaMj3VfUVK7MaXVamLqYlS2DzZn2tq9PXaDQudt7znvh7vDTwN97QpbGx\nMN1TK1dmNxcDFZsHxeYhjs2FYvNQGJjwiSEinxWRl0XksIisFZFeS5Vgf6Q9e7QlQXX10V5lFlFT\nqAAACLVJREFUIZL+P7Jf6ESj0NWljVkffVQF4aOPwksv6WshiplssD9qis2DYvMQx+ZCsXkoDEz4\nACJyEfD/gW8C84BNwAMiMqo3xwv2R6qtVStQa+tRXmiemTJF42k8qqo0riZorfEvfqFT7MLGMAzD\nKH5M+CiXAyucc79yzv0DuBRoAz7em4OtWpXowlm1qrC7ZFdXq9vJOX31LFMiMG9eXNS8/DJ0d8dF\nTWsrnHmmiRrDMAyjeCj5rC4RGQQsAL7rbXPOORF5EDipN8cMq+Ab7Jrdn1RXw6hRmaVvz5zZ+y7j\nhmEYhlHolLzwAUYB5UA0sD0KTE/xnsEAzz77bMYnueoq+NKXYPdurSXT1aWxLUeO6P7e1vupr1fL\ny4EDMGwYjB0L//EfWqcmjObm3HaH379/Pxs3bszdAYsYmwvF5kGxeYhjc6HYPCi+Z+fgfJxfXIlX\n1xORWmAHcJJzrtG3/VrgVOdcktVHRD4I3NZ/V2kYhmEYA45LnHO39/dJzeIDu4EuYGxg+1hgZ4r3\nPABcAmwF3uizKzMMwzCMgcdgYAr6LO13St7iAyAia4FG59wXYusCvAL82Dn3/bxenGEYhmEYOcMs\nPsr1wC0isgFYh2Z5VQG35POiDMMwDMPILSZ8AOfcb2M1e65GXVxPAmc7517L75UZhmEYhpFLzNVl\nGIZhGEbJYAUMDcMwDMMoGUz4GIZhGIZRMpjwyZJcNjPNNyLyNRFZJyIHRCQqIr8XkbeGjLtaRF4V\nkTYR+YuITA3sj4jIT0Vkt4gcFJHficiYwJi3iMhtIrJfRPaJyC9EpCDbtorIV0WkW0SuD2wviXkQ\nkX8SkVtj99EmIptEZH5gzICfCxEpE5Fvi8iW2H2+KCLfCBk3oOZCRN4mIveIyI7Y/4PzQ8b0yz2L\nyEQR+aOItIrIThG5TkT67bmVbi5EpEJErhWRJhE5FBvzX6K14fzHKPq5yOQ74Ru7PDbmssD2wpkH\n55wtGS7ARWjdng8DxwErgL3AqHxfWy/v537gQ8AM4ATgPrQ20RDfmK/E7vFcYBbwB+AloNI35uex\n952GNnl9DHgkcK4/ARuBE4GTgeeBX+d7DkLmpAHYAjwBXF9q8wAcA7wM/AJt5TIZOBM4tgTn4kpg\nF/BOYBJwIXAA+NxAnovY/V4NvAetcXZ+YH+/3DP6w/wptNbLCcDZsc/j/xXCXADDYte2FJgGLATW\nAusCxyj6uejpO+EbdwH6t3M7cFmhzkO//6cq5iX2pf6Rb12AZuDL+b62HN3fKKAbOMW37VXgct/6\nMOAw8H7fejtwgW/M9NhxFsbWZ8TW5/nGnA0cAcbl+7591zQUeA54O/AQicKnJOYBuAZ4uIcxpTIX\n9wI3Brb9DvhVqcxF7LqCwqdf7hl4F9CJ74clsAzYB1QUwlyEjDkRFQYTBupcpJoHYDxa/24G+uPp\nMt++gpoHc3VliMSbmf7V2+Z01nvdzLQAOQZw6K85RORYYByJ93wAaCR+zyeiZRH8Y55D/wN4YxYD\n+5xzT/jO9WDsXIv64kZ6yU+Be51zf/NvLLF5OA9YLyK/FXV/bhSRT3o7S2wuHgPOEJFpACIyB1iC\nWkpLbS6Afr/nxcBTzrndvjEPAMOBmTm6pVzj/Q19Pba+gBKYCxER4FfAdc65sCaWBTUPJnwyJ10z\n03H9fzm5JfbF/SHwqHPumdjmceiXLt09jwU6Yn/8Uo0Zh5oj38Q514UKrIKYOxH5ADAX+FrI7pKZ\nB6AO+DRq+XoHap7+sYh8KLa/lObiGuA3wD9EpAPYAPzQOXdHbH8pzYVHf97zuBTngcKbF0Qkgn5n\nbnfOHYptHkdpzMVX0fu8IcX+gpoHK2BoePwMOB79RVtSiMgEVPSd6ZzrzPf15JkyNEbh32Lrm0Rk\nFnApcGv+LisvXAR8EPgA8AwqjH8kIq8650ptLow0iEgFcCcqCj+T58vpV0RkAXAZGrdTFJjFJ3N6\n08y0KBCRG4BzgNOdcy2+XTvROKZ097wTqBSRYT2MCUbvlwMjKIy5WwCMBjaKSKeIdKIBeF+I/dKP\nUhrzANACBE3Vz6LBvVA63wmA64BrnHN3Oueeds7dBvyAuFWwlObCoz/veWeK80ABzYtP9EwE3uGz\n9kBpzMUp6N/P7b6/n5OB60VkS2xMQc2DCZ8MiVkCNgBneNti7qEz0FiAoiQmet4D/LNz7hX/Pufc\ny+iXyX/Pw1B/q3fPG9DgM/+Y6eiDck1s0xrgGBHx/yI4A/0D2pjL++klD6IZAnOBObFlPfBrYI5z\nbgulMQ8Aq9GgQz/TgW1QUt8J0H59XYFt3cT+bpbYXAD9fs9rgBNE2wl5vAPYj1rg8o5P9NQBZzjn\n9gWGlMJc/AqYTfxv5xw0AP46NDgZCm0e+jMavNgX4P1AG4np7HuA0fm+tl7ez8/QaPi3oarZWwb7\nxnw5do/noeLgD8ALJKau/gyN4j8dtZ6sJjlN8X5UTDSg7rTngFvzPQdp5iaY1VUS84AGprajVo16\n1NVzEPhACc7FzWjw5TnoL9gL0BiE7w7kuQCq0YfXXFTo/d/Y+sT+vGdUYG5CU5xnow/RKPDtQpgL\nNFTkbvRHwQkk/g0dNJDmoqfvRMj4hKyuQpuHfv9PVewL6r/diqZvrgFOzPc1HcW9dKO/aIPLhwPj\nrkIVfBsaQT81sD8C/AR1Bx5EfwGNCYw5BrWg7EfF1o1AVb7nIM3c/A2f8CmleUAf9E2x+3wa+HjI\nmAE/F7E/9tfH/li3og/3bxFImx1oc4G6ecP+NtzU3/eMCoz7gEPoA+5aoKwQ5gIVw8F93vqpA2ku\nMvlOBMZvIVn4FMw8WJNSwzAMwzBKBovxMQzDMAyjZDDhYxiGYRhGyWDCxzAMwzCMksGEj2EYhmEY\nJYMJH8MwDMMwSgYTPoZhGIZhlAwmfAzDMAzDKBlM+BiGYRiGUTKY8DEMwzAMo2Qw4WMYhmEYRslg\nwscwDMMwjJLhfwFashjOGonSGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae9ad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly3_data['power_1'],poly3_data['price'],'.',\n",
    "        poly3_data['power_1'], model3.predict(poly3_data),'-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try a 15th degree polynomial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "poly15_data = polynomial_sframe(sales['sqft_living'], 15)\n",
    "my_features = poly15_data.column_names() # get the name of the features\n",
    "poly15_data['price'] = sales['price'] # add price to the data since it's the target\n",
    "model15 = graphlab.linear_regression.create(poly15_data, target = 'price', \n",
    "                                            features = my_features, verbose = False,\n",
    "                                            validation_set = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">73619.7521124</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">932386.401678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">410.287462538</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4736.29673944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.230450714433</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.8605324062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.58840542482e-05</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0111697430756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-5.65701802745e-09</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.69719587967e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-4.57028130487e-13</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.4042245283e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.66360206432e-17</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.86624536049e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.38584769256e-21</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.85621317895e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.14723104066e-25</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.13709629311e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-4.65293586333e-30</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.22655023693e-24</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[16 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 16\n",
       "\n",
       "Data:\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "|     name    | index |       value        |       stderr      |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "| (intercept) |  None |   73619.7521124    |   932386.401678   |\n",
       "|   power_1   |  None |   410.287462538    |   4736.29673944   |\n",
       "|   power_2   |  None |  -0.230450714433   |    9.8605324062   |\n",
       "|   power_3   |  None | 7.58840542482e-05  |  0.0111697430756  |\n",
       "|   power_4   |  None | -5.65701802745e-09 | 7.69719587967e-06 |\n",
       "|   power_5   |  None | -4.57028130487e-13 |  3.4042245283e-09 |\n",
       "|   power_6   |  None | 2.66360206432e-17  | 9.86624536049e-13 |\n",
       "|   power_7   |  None | 3.38584769256e-21  | 1.85621317895e-16 |\n",
       "|   power_8   |  None | 1.14723104066e-25  | 2.13709629311e-20 |\n",
       "|   power_9   |  None | -4.65293586333e-30 | 1.22655023693e-24 |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "[16 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model15.get(\"coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1244fc250>,\n",
       " <matplotlib.lines.Line2D at 0x119edb6d0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e+nmICD/v/3O3U1F1kOXJ4q1Nb7mdRkTQFh+lVEyL1BaacDLG8OLaFxndezQd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\nDMMw8ob/B+2fSO0VyFGuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aea6810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly15_data['power_1'],poly15_data['price'],'.',\n",
    "        poly15_data['power_1'], model15.predict(poly15_data),'-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Changing the data and re-learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to split the sales data into four subsets of roughly equal size. Then estimate a 15th degree polynomial model on all four subsets of the data. Print the coefficients and plot the resulting fit. \n",
    "\n",
    "To split the sales data into four subsets, we perform the following steps:\n",
    "* First split sales into 2 subsets with `.random_split(0.5, seed=0)`. \n",
    "* Next split the resulting subsets into 2 more subsets each. Use `.random_split(0.5, seed=0)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset_a, subset_b = sales.random_split(0.5, seed =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "subset_1, subset_2 = subset_a.random_split(0.5, seed =0)\n",
    "subset_3, subset_4 = subset_b.random_split(0.5, seed =0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit a 15th degree polynomial on set_1, set_2, set_3, and set_4 using sqft_living to predict prices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">223312.750249</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1256782.60077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">118.086127587</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6007.14384425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.0473482011344</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.969007003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.25310342469e-05</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0131429551736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.32372152561e-09</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.85414511316e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-9.75830457761e-14</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.83982596813e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.15440303426e-17</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0847728091e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.05145869404e-21</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.93625236102e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.46049616546e-26</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.8950619488e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.09654454168e-30</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[16 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 16\n",
       "\n",
       "Data:\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "|     name    | index |       value        |       stderr      |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "| (intercept) |  None |   223312.750249    |   1256782.60077   |\n",
       "|   power_1   |  None |   118.086127587    |   6007.14384425   |\n",
       "|   power_2   |  None |  -0.0473482011344  |    11.969007003   |\n",
       "|   power_3   |  None | 3.25310342469e-05  |  0.0131429551736  |\n",
       "|   power_4   |  None | -3.32372152561e-09 | 8.85414511316e-06 |\n",
       "|   power_5   |  None | -9.75830457761e-14 | 3.83982596813e-09 |\n",
       "|   power_6   |  None | 1.15440303426e-17  |  1.0847728091e-12 |\n",
       "|   power_7   |  None | 1.05145869404e-21  | 1.93625236102e-16 |\n",
       "|   power_8   |  None | 3.46049616546e-26  |  1.8950619488e-20 |\n",
       "|   power_9   |  None | -1.09654454168e-30 |        nan        |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "[16 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly15_data = polynomial_sframe(subset_1['sqft_living'], 15)\n",
    "my_features = poly15_data.column_names() # get the name of the features\n",
    "poly15_data['price'] = subset_1['price'] # add price to the data since it's the target\n",
    "model15_set1 = graphlab.linear_regression.create(poly15_data, target = 'price', \n",
    "                                                 features = my_features, verbose = False,\n",
    "                                                 validation_set = None)\n",
    "\n",
    "coefficients_model15_set1 = model15_set1.get('coefficients')\n",
    "coefficients_model15_set1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1247837d0>,\n",
       " <matplotlib.lines.Line2D at 0x122c72450>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ATTs2+Z1KjVaRFp8FQDrQMvQTLNlhZrcB1wKXA12AbcAUM6sb9vlHgLOAvkAP\n4FAgcij6WKA90DMU2wN4Ouw8KcBbeE+ldQWGABcD94TFNAamAMuBTOAWYLiZXRYW0wZ4E3gP6AQ8\nCowys9OjuyUiIj9LtkkMt+3eRv/x/Tmq+VGM6DXC73SSVk4gB4dj9urZfqdSo1Wk8NnrnPveObc+\n9LMxbN8NwL3OuTedcwuAi/AKm3MBzKwJcCkwzDn3kXNuPnAJkG1mXUIx7YFewFDn3Fzn3CzgOmCA\nmbUMnacXcCxwgXPuS+fcFOBO4BozK3lEfzBQJ3Schc65V4DHgJvC8r0KWOacu9U5t9g59wQwHhhW\ngfsiIgIk3ySGN75zI9/++C3j+o2jfp36fqeTtNo2a0vLRi2ZsULdXWWpSOFztJl9Z2ZLzexFM8sA\nMLMj8FqA3isJdM5tBuYA3UKbTsZrpQmPWQysDIvpCmwKFUUlpgEOOCUs5kvn3IawmClAGtAhLGa6\nc25vREw7M0sLi5kWcX1TwnIREYlaMk1i+PKClxk1fxQjfzeS4w45zu90kpqZEQwEyVulAc5libbw\n+RivS6kXcCVwBDA9NP6mJV5xUhDxmYLQPvC6yHaHCqL9xbQE9hkK6JwrAjZGxJR2HmIU08TMUhER\nqYCSSQyXLvVeW7Qo/zPxaOnGpVz+xuUMPH4gl5x4id/pCF531yfffcLOvTv9TqXGiqrwcc5Ncc5N\ncM4tcM69C/wOaAacXyXZVQ3zOwERkXi3u2g3AyYMoEXDFjzV+ynM9Ku1JggGguwu2k3+mny/U6mx\nKrVkhXOu0MyWAEcBH+IVFens24qSDpR0W60D6ppZk4hWn/TQvpKYyKe8agHNI2I6R6STHrav5DW9\nlBh3ADGbnXO7KMewYcNIS0vbZ9vAgQMZOHBgeR8VEYlrf572Zz5f9zmzhs6iSWoTv9ORkI7pHWlU\ntxEzVs74aVJDP+Xm5pKbm7vPtsLCQp+y8VSq8DGzRnhFzwvOueVmtg7vSawvQvub4I3LeSL0kXxg\nbyhmUiimHRAASoahzwaamtlJYeN8euIVVXPCYv5iZgeHjfM5AygEvg6L+ZuZ1Qp1lZXELHbOFYbF\nnBlxWWeE5VKmESNGkJmZeSChIiIJY/KSyTz88cM8fMbDnHzoyX6nI2Fqp9Sme0b3GjORYWmNAfPm\nzSMrK8unjKKfx+efZtbDzA43s+54xcse4OVQyCPAX83sbDM7ARgNrAZeg58GOz8DPByaPycLeBaY\n6Zz7JBSuyEZqAAAgAElEQVSzCG+A8X/NrLOZZQOPA7nOuZKWmql4Bc6Y0Fw9vYB7gZHOuT2hmLHA\nbuBZMzvOzPoD1wMPhV3SU0BbM/uHmbUzs6uBfsDD0dwXEZFk8d3m7xjy6hB6H9ObG7ve6Hc6Uopg\nRpCZq2ZS7Ir9TqVGinZwc2u8gmIRXrHzPdDVOfcDgHPuQbwi5Wm81pn6wJnOud1hxxiGN3fOeLzu\nsTV4c/qEGxQ6x7RQ7HTgipKdzrlioDdQBMzCK7CeB+4Ki9mM13rTBpgL/BMY7px7JizmW7x5gk4D\nPgvlNtQ5F/mkl4hI0isqLuKCiReQWjuV5855TuN6aqhgIMiPO3/kq/Vf+Z1KjRRVV5dzrtzBK865\n4cDwMvbvwpuX57oyYn7Em4enrPOswit+yopZAPyqnJjpgH9tbiIiceJv0//GjJUzeP+i9zm4wcF+\npyP7cUrrU6idUpu8lXmckH6C3+nUOFqrS0REyvXRtx9xz/R7+H89/h+/alPm35PiswZ1GpDVKkvz\n+eyHCh8RESnThu0bGDRxEDmBHP7a469+pyMHIBgIagbn/VDhIyIi++Wc4+JXL2bX3l281OclaqXU\n8jslOQDBQJBVm1exsnCl36nUOCp8RERkvx6d8yiT/28yL5z7Aoc1OczvdOQAZWd4c/jUlMfaaxIV\nPiIiUqq5a+Zy67u3MqzrMM465iy/05EoHNLwEI49+Fh1d5VChY+IiPzC5l2bGTB+AB3TO/LAaQ/4\nnY5UQDBDC5aWRoWPiIjswznHlW9eyfpt6xnXbxx1a9X1OyWpgJzDc1iwfgGbdmzyO5UaRYWPiIjs\n47nPniN3QS7/Ofs/HNn8SL/TkQoKBoIAzFw10+dMahYVPiIi8pOF3y/k2reuZehJQxlw/AC/05FK\nOKLpEbRq1EoDnCOo8BEREQB27NnB+ePPp03TNjx25mN+pyOVZGbkHJ6jwieCCh8REQHgpik38c3G\nbxjXbxwN6jTwOx2JgWBGkE/XfMrOvTv9TqXGUOEjIiL876v/8VT+UzzS6xGt75RAgoEgu4t28+l3\nn/qdSo2hwkdEaqyCAggG4cgjvdf16/3OKDEt37ScP77xR/5w3B+4POtyv9ORGOqY3pHGdRuruyuM\nCh8RqbH69oWZM2HZMu+1Tx+/M0o8e4r2MHDCQJrVb8Z/zv4PZuZ3ShJDtVJq0T2jOzNWaiLDEip8\nRKTGWru27PdSeX99/6/kr83n5b4v07ReU7/TkSoQDASZtWoWRcVFfqdSI6jwEZEaq1Wrst9L5Uz5\nZgoPznqQv//m75zS+hS/05EqkhPIoXBXIV99/5XfqdQIKnxEpMaaOBGys6FtW+914kS/M0oca7es\n5cJJF9LryF78qfuf/E5HqlDnwzpTJ6WOxvmEqPARkRqrRQvIy4OlS73XFi38zigxFBUXMXjSYGql\n1GL0eaNJMX0VJLIGdRqQdWiWxvmE6F+7iEiSeSDvAT5Y/gEvnvciLRqqmkwGOYEcZqyYgXPO71R8\np8JHRCSJ5K3M464P7+KOnDvo2ban3+lINQkGgny35TtWFq70OxXfqfAREUkSG3dsZNCEQXRt3ZW7\nTr3L73SkGmVnZAOouwsVPiIiScE5x9DXh7J191bG9h1L7ZTafqck1eigBgdx3CHHaYAzoH/5IiJJ\n4IlPn+DVRa/yav9XCaQF/E5HfBDMCKrwQS0+IiIJb/7a+fxp6p+4rst1nHPsOX6nIz4JBoJ89f1X\n/LD9B79T8ZUKHxGRBLZl1xb6j+9Ph0M68M/T/+l3OuKjYCAIwKxVs3zOxF8qfEREEti1b1/Lmi1r\neLnfy6TWTvU7HfFRm6ZtOKzxYUnf3aUxPiIiCWr056MZ/floxpw3hmMOOsbvdMRnZkYwEEz6J7vU\n4iMikoAWb1jM1ZOvZkinIQzuONjvdKSGCAaCzF0zlx17dvidim8qVfiY2e1mVmxmD0dsv8fM1pjZ\ndjN718yOitifamZPmNkGM9tiZuPNrEVETDMze8nMCs1sk5mNMrOGETEZZjbZzLaZ2Toze9Bs37nX\nzayjmU03sx1mtsLMbinlOk41s3wz22lmS8xsSGXui4iIn3bu3Un/8f1p3aQ1I3830u90pAbJCeSw\np3gPn6751O9UfFPhwsfMOgOXA59HbL8NuDa0rwuwDZhiZnXDwh4BzgL6Aj2AQ4EJEacYC7QHeoZi\newBPh50nBXgLr7uuKzAEuBi4JyymMTAFWA5kArcAw83ssrCYNsCbwHtAJ+BRYJSZnR7N/RARqSlu\nmXoLizYs4uV+L9OobiO/05Ea5PgWx9MktQkzViRvd1eFCh8zawS8CFwG/Bix+wbgXufcm865BcBF\neIXNuaHPNgEuBYY55z5yzs0HLgGyzaxLKKY90AsY6pyb65ybBVwHDDCzlqHz9AKOBS5wzn3pnJsC\n3AlcY2YlY5cGA3VCx1nonHsFeAy4KSzfq4BlzrlbnXOLnXNPAOOBYRW5NyIifpq0cBIjPx3JQ2c8\nxIktT/Q7HalhaqXUontGd/JWJe8A54q2+DwBvOGcez98o5kdAbTEaz0BwDm3GZgDdAttOhmvlSY8\nZjGwMiymK7ApVBSVmAY44JSwmC+dcxvCYqYAaUCHsJjpzrm9ETHtzCwtLGZaxPVNCctFRCQurPhx\nBZe+finnHXseV3e+2u90pIbKCeQwa9UsioqL/E7FF1EXPmY2ADgR+HMpu1viFScFEdsLQvsA0oHd\noYJofzEtgfXhO51zRcDGiJjSzkOMYpqYmZ79FJG4sLd4L4MmDqJJahOe+f0zmJnfKUkNFQwE2bxr\nM1+u/9LvVHwR1ePsZtYab3zOac65PVWTUrWIyW+EYcOGkZaWts+2gQMHMnDgwFgcXkTkgN31wV3M\nWT2H6ZdMp1n9Zn6nIzVY50M7UyelDnkr86q8OzQ3N5fc3Nx9thUWFlbpOcsT7Tw+WcAhwDz7+c+J\nWkAPM7sWb8yN4bXqhLeipAMl3VbrgLpm1iSi1Sc9tK8kJvIpr1pA84iYzhH5pYftK3lNLyXGHUDM\nZufcLsowYsQIMjMzywoREaly05ZN4/68+7nvN/fRPaO73+lIDVe/Tn06H9aZvJV5XNvl2io9V2mN\nAfPmzSMrK6tKz1uWaLu6pgEn4HV1dQr9zMUb6NzJObcMr5DoWfKB0GDmU4CSObLzgb0RMe2AADA7\ntGk20NTMTgo7d0+8ompOWMwJZnZwWMwZQCHwdVhMj1DRFB6z2DlXGBbTk32dEZaLiEiNVbC1gMET\nB9OzbU9uC97mdzoSJ4IZ3kSGzjm/U6l2URU+zrltzrmvw3/wHlf/wTm3MBT2CPBXMzvbzE4ARgOr\ngddCx9gMPAM8HJo/Jwt4FpjpnPskFLMIb4Dxf82ss5llA48Duc65kpaaqXgFzpjQXD29gHuBkWHd\ncGOB3cCzZnacmfUHrgceCrusp4C2ZvYPM2tnZlcD/YB95iYSEalpil0xF716EQ7HmPPGkGKak1YO\nTDAQZM2WNXz747d+p1LtYrFkxT7lonPuQTNrgDfnTlNgBnCmc253WNgwoAjvsfFU4B3gmojjDgJG\n4rUyFYdibwg7T7GZ9Qb+jdeatA14HrgrLGazmZ2B9xTaXGADMNw590xYzLdmdhYwAq8oWo33+Hvk\nk14iIjXKv2b9i6lLpzJl8BRaNmpZ/gdEQrID2QDkrczjiGZH+JxN9bJkbOaqLDPLBPLz8/M1xkdE\nfPHx6o/JeS6HP3X7Ew+c9oDf6UgcOv7J48nOyObps58uPziGwsb4ZDnn5lXrydFaXSKSgAoKIBiE\nI4/0XtevL/8z8eTHnT8yYPwATj70ZO799b1+pyNxKlkXLFXhIyIJp29fmDkTli3zXvv08Tuj2HHO\ncdnrl1G4q5DcvrnUqVXH75QkTuUEcli4YSEbtm8oPziBqPARkYSzdm3Z7+PZ0/lPM2HhBEadPYo2\nTdv4nY7EsWAgCMCsVbPKiUwsKnxEJOG0alX2+3j14bcfcuM7N3LVyVfR97i+fqcjcS6QFqB1k9ZJ\nt2CpCh8R+UmijI2ZOBGys6FtW+914kS/M6q8T777hLNzz6bH4T0Y0WuE3+lIAjAzcgI5SbdgaSwe\nZxeRBFEyNga88TF9+kBeHP5ObNEiPvPenwXrF/DbF39Lx/SOTOo/idTaWkZQYiMYCDL+6/Fs37Od\nBnUa+J1OtVCLj4j8JJHHxsSrpRuXcvqY0wmkBZg8aDIN6zb0OyVJIMFAkD3Fe/jku0/8TqXaqPAR\nkZ8k6tiYeLV682pOG3MaTVKbMPXCqTSt19TvlCTBdDikA2mpaeStTKAm0nKoq0tEfjJxote9tXat\nV/QkwtiYePX9tu85fczpFLtipl04jRYNW5T/IZEo1UqpRXYgW4WPiCSnRBsbE68KdxbS68VebNqx\niRmXzCAjLcPvlCSBBTOC3J93P0XFRdRKqVX+B+KcurpERGqQ7Xu20zu3N8t/XM7UC6dy9EFH+52S\nJLhgIMiW3Vv4ouALv1OpFip8RERqiF17d9FnXB/mr53P2xe8Tcf0jn6nJEmg82GdqVurbtJ0d6nw\nERGpAfYW7+WCiRfwwbcf8NqA1+jauqvfKUmSqFe7Hp0P7Zw063ap8BER8VmxK+aPb/yRVxe9yiv9\nXqFn255+pyRJJhgIkrcyD+ec36lUORU+IiI+cs4x7J1hvPDZC7xw7gucc+w5fqckSSgnkMParWtZ\n/uNyv1Opcip8RER8dNeHd/HYJ4/x5FlPckHHC/xOR5JU94zuAEmxbpcKH5EElyjrbyWih2Y9xL3T\n7+WBng9w5clX+p2OJLFm9ZtxfIvjk2KAswofkQRXsv7WsmXea58+fmckAKPmjeLmd2/mz8E/c1vw\nNr/TEUmaBUtV+IgkOK2/VfOMWzCOy9+4nKtPvpr7fnOf3+mIAN4A50UbFvH9tu/9TqVKqfARSXBa\nf6tmmbxkMoMnDeaCjhfw+O8ex8z8TkkE8AofgJmrZvqcSdVS4SOS4CZOhOxsaNvWe/Vj/a1EGWdU\n2ev46NuP6Pe/fpx19Fk8d85zpJh+BUvNEUgLEEgLJPw4H63VJZLgasL6WyXjjMAba9Snj/85VURl\nruPT7z6ld25vsjOyebnfy9RO0a9fqXlK5vNJZPpzQ0SqXKKMM6rodSxYv4DfvvRbTmhxAq8OeJV6\ntevFPjmRGAhmBMlfm8+23dv8TqXKqPARqSESpTuoNAcdVPb7WIm8hwsWxPaeVmS81NKNSzl9zOlk\nNMlg8qDJNKrbqHJJiFShnMNz2Fu8l0+++8TvVKqM2lpFaohE6Q4qTeT43aoazxt5D7t3hy1bfn5f\n2Xs6caJ3jLVrvaKnvPFSqzev5rQxp9G4bmOmDJ5Cs/rNKn5ykWpw3CHH0bReU/JW5vHrI37tdzpV\nQoWPSA2RKN1Bpdmwoez3sRJ5z3bsKHt/tKIZL/X9tu85fczpFBUX8dHFH5HeKL1yJxepBimWQnZG\ndkIvWKquLpEaIpEfO6+ua4s8bv361XPeSAVbC+j1Yi827tjItIumEUgLVM+JRWIgJ5DD7NWz2Vu8\n1+9UqoQKH5EaoiY8dl5VquvaIs8ze3b139PFGxbT7ZlurN26lncvfJdjDjqm6k8qEkPBQJCtu7fy\nRcEXfqdSJaIqfMzsSjP73MwKQz+zzOy3ETH3mNkaM9tuZu+a2VER+1PN7Akz22BmW8xsvJm1iIhp\nZmYvhc6xycxGmVnDiJgMM5tsZtvMbJ2ZPWi276QYZtbRzKab2Q4zW2Fmt5RyTaeaWb6Z7TSzJWY2\nJJp7IhIrJd0oS5d6ry1alP+ZeFFd1xZ5ng4dqvee5q3Mo/uz3alfpz4fD/2Yjukdq/aEIlXg5ENP\nJrVWasIuWBpti88q4DYgE8gC3gdeM7P2AGZ2G3AtcDnQBdgGTDGzumHHeAQ4C+gL9AAOBSZEnGcs\n0B7oGYrtATxdsjNU4LyFN0apKzAEuBi4JyymMTAFWB7K9xZguJldFhbTBngTeA/oBDwKjDKz06O8\nLyKS5P731f84bfRpnNDiBPIuyePwpof7nZJIhaTWTqXLYV0Sdt2uqAof59xk59w7zrmlzrlvnHN/\nBbbiFR8ANwD3OufedM4tAC7CK2zOBTCzJsClwDDn3EfOufnAJUC2mXUJxbQHegFDnXNznXOzgOuA\nAWbWMnSeXsCxwAXOuS+dc1OAO4FrzKxkwPZgoE7oOAudc68AjwE3hV3SVcAy59ytzrnFzrkngPHA\nsGjui4gkL+cc98+4n/PHn0+f9n309JYkhJKJDJ1zfqcScxUe42NmKWY2AGgAzDKzI4CWeK0nADjn\nNgNzgG6hTSfjtdKExywGVobFdAU2hYqiEtMAB5wSFvOlcy782ZApQBrQISxmunNub0RMOzNLC4uZ\nFnFpU8JyEalxEnm+n3izfc92Bk0cxF/e/wt39riTF/u8SGrtVL/TEqm0YCDIuq3rWLppqd+pxFzU\nj7Ob2fHAbKAesAU4zzm32My64RUnBREfKcAriADSgd2hgmh/MS2BfX6VO+eKzGxjRExp5ynZ93no\ndVkZMYVlHKeJmaU653YhUsMk8nw/8WRV4SrOHXcuizYs4pV+r/CHDn/wOyWRmOme0R3DyFuZx1HN\njyr/A3GkIvP4LMIbD5MG9ANGm1mPmGZVtWI2ddqwYcNIS0vbZ9vAgQMZOHBgrE4hCa6gwCtkwifE\nK28AbrzO91ORa62pZq2aRZ9xfUitncrMS2dyYssT/U5JJKaa1mvKCeknkLcyj4tPvLjCx8nNzSU3\nN3efbYWFhZXMrnKiLnxCXUclLSnzQ2NzbgAexCsq0tm3FSUdKOm2WgfUNbMmEa0+6aF9JTGRT3nV\nAppHxHSOSC09bF/Ja+SMYel4rVLlxWw+kNaeESNGkJmZWV6YyH5VpPWmVSsvNvx9PEiUlqrn5j/H\nlZOvpMthXZhw/gRaNIzT6k2kHMGMINOWR44GiU5pjQHz5s0jKyurUsetjFjM45MCpDrnluMVEj1L\ndoQGM58CzAptygf2RsS0AwJ43WeEXpua2Ulh5+iJV1TNCYs5wcwODos5A6/76uuwmB6hoik8ZrFz\nrjAspif7OiMsF0lyVT2epiKtN/ubE6e0XGvSeKB4bakqsW33Ni557RIuff1ShnQawnsXvaeiRxJa\nMBBkyQ9LWL8twQYSOucO+Af4O5ADHA4cD9yPV8j8JrT/VuAH4GzgBOBV4P+AumHHeBLvEfNT8R6J\nnwnMiDjPW8BcvFadbGAxMCZsfwreOJ63gY54T3kV4D1RVhLTBFgDvAAcB/THewJtaFhMG7xxSv8A\n2gFXA7uB08q5D5mAy8/Pd5LYsrOdg59/srNr7vFLO1ZV51/Z/OLFF+u+cO1HtncN7mvgnp//vG95\nrFvn3be2bb3XggLfUpEksKpwlWM4buLXE2N63Pz8fIfX+5LpoqhBYvUTbVdXi1Ah0QqvdeUL4Azn\n3PuhIupBM2uAN+dOU2AGcKZzbnfYMYYBRXiPjacC7wDXRJxnEDAS74mr4lDsDSU7nXPFZtYb+Dde\na9I24HngrrCYzWZ2BvAEXhG1ARjunHsmLOZbMzsLGAFcD6zGK4wq17YnCaOqWymiXfSyLKtX//J9\nrVr7bvOzlSWW11pdnHOMmjeK69+5nqObH03+5fkce/CxvuWTKN2FEh9aN2nN4WmHM2PlDM5rf57f\n6cRMVIWPc+6yA4gZDgwvY/8uvHl5risj5ke8eXjKOs8qoHc5MQuAX5UTMx2v5UnkF6p6PE00i16W\nZ+PGX77v2LHmjAeK5bVWh827NnPFm1fw8oKXuSLrCkb0GkH9OvXL/2AVivfuQok/JfP5JBKtzi5S\nhnhqpWjeHLZs2fd9POVfk8xbO4/+4/tTsLWAl/u+TP/j+/udEhC/A9slfuUEcnh5wcts272NhnUb\nlv+BOKDCR6QM8dRK0bo1rFix7/t4yr8mcM7xxKdP8Kepf+L4Fsfz9gVv16g5TFTISnULBoIUuSLm\nfDeH3xzxG7/TiQkVPiIJQl+KlbNpxyaGvj6USYsmcX2X63nw9Adr3CzMKmSlurU/pD3N6jVjxooZ\nKnxEpGZNyqcvxYqbvGQyl795Odv3bGdS/0mce+y5fqckUiOkWIo3zieBFiyNxTw+Ikmr5CmbZcu8\n1z59/M5IorFpxyYufvVieuf2pmN6R7648ouoip6aNE+SSFUJBoLMXjWbvcV7yw+OAyp8RCrhQJ+y\n0RdkzTN5yWSO//fxTFo0iWd//yxvDXqLjLSMqI6hwleSQTAQZNuebXy27jO/U4kJFT4ilRD5VM3+\nnrLRF2TNsWnHJoa8OoTeub3plN6Jr67+iktOugSz6Jfx0+PlkgyyWmVRr3a9hHmsXYWPSCXsb/mI\nSPqCrBneXPImHZ7swGuLXuPZ3z/L5EGTad2kdYWPd6CFr0g8S62dSpfDuqjwEYlXsex2KhlQvHSp\n97q/gc36gvTXph2buGjSRZydezYntTqJBVcvqHArT7gDLXxF4l0wI8iMlTNKlm2Kayp8JOn40e0U\nzcKiEjvOOSYtnESHJzvw+uLXef6c53lz4JuVauUJd6CFr0i8yzk8h/Xb1vPNxm/8TqXS9Di7JJ3q\n6nY6kEfdtfZS1Vm+aTnXvX0dk/9vMr2P6c1TZz3FYU0O8zstkbjUrXU3DCNvZR5HH3S03+lUilp8\nJOlUV7fTgbQs1ZSxP4nU8rS7aDf3z7ifDk924POCz5l4/kReH/C6ih6RSkirl0bH9I7MWDnD71Qq\nTYWPJJ2SbqdAABo39lYxr4ov+8giZtWqXxYX1T32Z38FTrw9dba/6/jo24848akTufODO7mm8zUs\nvGYh57U/r9JjeUTEW7crEQY4q6tLkk7JuIxgEFau9Bb2XLEi9t1MkQtKbtrknQ9+7taq7mUm9te1\nVlNang5U5HX07r+e9jfcwujPR9M9ozvzrphHx/SO/iYpkmCCgSAjPx1JwdYC0hul+51OhanwkaRV\n1V/2kUXN6tX7rp6+dm31LzOxv2uOt1W/f7qOlD3Q+Unyuw1n6ZIURp09iktOuoQUU2O2SKxlB7IB\nyFuZR9/j+vqcTcXpt4PErcqOS6mqbqaSvLp1897Pnu0VN60jHiTyo7jY3zVH+1i232OCWrUC2r4L\nV54Ivx3GId/3Z/G1ixmaObTaix6/74VIdWndpDVtmraJ++4utfhI3Crviajynqqqqm6m/eVVE1ZP\n318O0bY8+fk02tKNS2n8xz/Bt69RryCHdh/PY+roEzm4QfWcP5KezJNkkhPIifsFS1X4SNwqr6uq\nvC+kqupm2l9eNWH19PJyONDV5v0YE7R191b+PuPvPDT7IdIbpvNy35c5v8P5vg9cjrfxUSKVEQwE\nGfvlWLbu3kqjuo38TqdC1NUlcau8riq/vpDieZbmA326qzqvsdgVM+bzMRzz+DGM+HgEt2ffzqJr\nF9H/+P6+Fz0Q3/+/RaIVDAQpckV8vPpjv1OpMBU+ErfKG5dSHV9I4eM7unSBzEz49FMwg9RUb9tT\nT1XPGJADGWtSXsyBFovVtVTDjBUz6PZMNy569SKCgSCLrlnE3b++mwZ1fOrXKoWWrZBk0v7g9hxU\n/6C4HudjibDuRnUzs0wgPz8/n8zMTL/Tkf1Yv/6X41livaRAMPhzd1ppsr2HIPaJyc6umi6vyFxK\nO095MQdyjOqw5Icl3DbtNl5d9CpZrbJ46IyH+FWbX1V/IiKyj4ICOO6ec9hRtI3MBdMq9Ht13rx5\nZGVlAWQ55+ZVRZ5l0RgfSVj7G89yoONYDkR53Wel7a9Il9uB5Fxaa03k51avLvszfg/A/n7b99z9\n0d08nf80hzY+lJf6vMSA4wfo8XSRGqJvX9jognDqcGbO3kOfPnV8H7sYLRU+knTKGvQcbVEUOf9N\naftLzhO5LVY57y+XVq1++bnGjUvPr4RfA7B37NnBIx8/wv1595NiKfz9N3/nulOuo17tetWfjIjs\n19q1wK5fwYb20KiAtWtjs+BvdVLhI0mnrHEs+ysw9lcQhbeQHHQQ7N0LX3/tfb5Tp59bTKJtRYm2\npQZKb60pmUuoRPPm0LFj5Vt0YtVqVuyKeemLl7jj/TtYu3UtV598NXf+6k4ObnBwxRITkSrVqhUs\nm9kF/jPXe3+CzwlVgAofSTplzVK8v6JofwXRgbSQFBREn2Pk+SKV1mpUWi6R19q6dWxadGIxd837\ny9/n5qk3M3/dfPq278v9Pe+P+1WfRRKd393hsaCOc0kI0cyeW9Yipft7Eqwyj8ZXZAHQVav2v69x\n4wP/ZVNVTxxV5n58/f3X9B7bm56je1K3Vl3yLslj/PnjVfSIxIGSP7CWLv35j794oxYfSQjRtECU\ntUjp/v6aqcxaVhUpEjZt2v++Qw458F82ZbVIVaa7qiL347vN33H3R3fzzPxnaNO0Da/0e4V+x/Wr\nEXPxiEjyUOEjCaEixUVkzNy53piYVq289bVitbxFRYqE5s33XdA08niVUVLwzJ0Lu3Z526Ltrorm\nfvy480f+kfcPHp3zKPXr1Oefp/+TazpfQ2rt1MpdiIhIBUTV1WVmfzazT8xss5kVmNkkMzumlLh7\nzGyNmW03s3fN7KiI/alm9oSZbTCzLWY23sxaRMQ0M7OXzKzQzDaZ2SgzaxgRk2Fmk81sm5mtM7MH\nzfZ97tXMOprZdDPbYWYrzOyWUvI91czyzWynmS0xsyHR3BfxX0UmK4yM2bVr/91RlWnerUh3U+SC\npuETIla2u6qkdayk6CkRTXfVgdyPnXt38q9Z/6Lto215dM6jDOs6jGXXL+Ombjep6BER30Q7xicH\neBw4BTgNqANMNbP6JQFmdhtwLXA50AXYBkwxs7phx3kEOAvoC/QADgUmRJxrLNAe6BmK7QE8HXae\nFOAtvFarrsAQ4GLgnrCYxsAUYDmQCdwCDDezy8Ji2gBvAu8BnYBHgVFmdnpUd0Z8VZHiIvwzqRHf\nw48OdX8AACAASURBVHPn/nKcUEVX4a5I0RR5PevWwc6dMGdO5fvU91fgxGpm66LiIp6b/xxHP340\nt0+7nfM7nM8313/DfT3vI61eWmxOIiJSUc65Cv8ABwPF8P/bO/f4uMoy8X+fpM20SZPSC0kK6YW0\nUAq9X1IggEhdLkpR6CJbXN1dZC1qF8WPssoubqUrK3hBUbCIvxURqIIgCltgRfwJLaWX9F5KLS30\nnumVNL0lTfLuH+85zJkzZyaTZJK5Pd/P53wm55znnPcykznPPO9z4WLPsT3A7Z79MuAE8EnPfhNw\nnUdmtHOfGmd/jLM/ySNzJdACVDr7VwOngMEemTnAYaCXs/954IC77xz7L+Atz/69wDrfuBYCixKM\nezJg6urqjJIb1NYaA9FbbW1iGf/5bME/jlDIHguHu3bftrY289ym58x5D55nmIe54akbzOYDm1PT\naUVRcoa6ujoDGGCy6YIO0tmtq1FdpzmdPwQgImcBlVjriatYHQGWAW5GkalYK41XZjOwwyNzAXDY\nGLPa09YrTlvTPTLrjTEHPDIvA/2B8z0yrxljWnwyo0Wkv0fmFd+4Xvb0RckDnn021urjtYyEw9YK\nFO98IjprKUoV/vYffjjamrRjR9ejMxbvWMzFv7iYT/zmE1SUVLD8luU8dcNTnDMoZiVcURQlrXTa\nuVlsKMYPgcXGGCdlG5VY5cSfuSTsnAOoAJodhSieTCUQ9XgwxrSKyCGfTFA77rm1zqs/C4pXpiHB\nfcpEJGSM8XlCKLlIeTlMnRpdp8q79DNrVqxPTLJLQ6nIedMV/O3PmZO69jfs28Cdf7qT5//6PBMr\nJ/LSp17iipFXaKSWoigZS1eiuh4CzgNqU9SXniQl38q33347/ftH+yzMnj2b2bNnp+L2SgpJJnQ7\nUaSS37oTCiXvZNyVnDepoDva39Gwg2/++Zs8tvYxzhpwFk9e/yQ3jr0xa2tqpbJ+m6IoERYuXMjC\nhQujjjU0NKSpN5ZOKT4i8hPgo8Alxhjv12g9VqmoINqKUgGs9sgUiUiZz+pT4ZxzZfxRXoXAQJ/M\nNF/XKjzn3NeKABmThMyR9qw9999/v1ZnzxKSsbokynnjD0mfOrV7c96kklS2f/D4Qe55/R4eXPEg\n/fv054GrH+BzUz5HUWFR+xdnMOm2yilKrhJkDPBUZ08LHf555ig9Hwc+bIzZ4T1njHkXq0jM8MiX\nYf1y3nAO1WGdlL0yo4FhwFLn0FLgNBGZ5Ln9DKxStcwjM05EvEV9rsAuX73lkbnUUZq8MpuNMQ0e\nmRlEc4WnL0oPEs8fpqt+Ml21enQlA3J3ZU8OImieUtH+seZj3PP6PVQ/UM3PVv2MOy+5k3f+5R3m\n1sxNqdKTLn+odFvlFEXpQTriCY1d3jqMDWuv8Gx9PDJ3AAeBmcA44DlgC1Dku8+7wGXAFGAJ8Lqv\nrUXASqxVpxbYDPzKc74A68fzIjAeG/UVBuZ7ZMqwUWa/xC7L3QgcBT7rkRkBNGKju0YDXwCagY8k\nmAeN6uom4kVOdSWiqr7emNLSrkVk1dfba6qrE0dAJSvXXaQ68qy5pdksWLHAVH6v0vS+u7e5bdFt\nZv274W4bY7oi53IlYk9RsoF0R3V1VPFpA1oDts/45OY5CsdxbITUKN/5EDYf0AFH6XgaKPfJnAY8\njrXgHAYeAYp9MkOxOXiOOkrPvUCBT2Ys8BenLzuArwaM61KsJeqEo6R9up15UMWnm6iujn4AVVcn\nPp4M/odaaWnHH9bJPhjT/QDtyjy51Ncbc1Ftmyn/8FOmz9fONjJPzKee+ZTZemirMaZ7x5iK/neG\ncDi9Cqui5BPpVnw65ONjjElqacwYM89RfuKdbwL+xdniybwP/H077ewErmlHZgPwoXZkXsNanpQ0\nE88fJZW1spqbO96vZJdC0r1kkgp/nhm3vMrG874OZ66ALVcx4cBTPP4fEz84351jTJc/VCL/LkVR\ncovsDMFQcpZ4/ihd8VMJKk1x/fUd8yfx32P/fhg+HMrKYMSI9qu7g22vpgb69LHb9On2mlT6tXR2\nnsJhmHj1aornXMnGqTPACDz6KjzxIo1bJkbJdqY8SLL0pD+Uoij5iRi7dKN0ABGZDNTV1dVpVFcK\n6Goocbzr3eO7dtkkfd6PenW1lfXm7amtjf3V773HoUPQv79tp7U1th+lpba46Zw5wWOZPh2WL4++\nptZJBtFeP7qLcBg++vdbWTPw32k779dwYDT86duw6XrcrA/+/uzbFxv2r6HfiqIkiyeqa4oxZlVP\nt6/V2ZW009VQ4njXe4/7GTIkuDr7yJHRD/OgewQpPWCrqSdKDrh2beyxoGWi9paOUpVzpv5oPZP+\nbT71F/4MjpXDH34Ga/4J2noRCsGZZ8bmM9J8N4qiZDu61KWkna76jMS7Pt59SkvtAzuZ6uz+e5w4\n0bG+uITDwb5FQ4bA4MHRx/z7flxlLF4leX+7/mW0I01HuOvVuxj5wEj2lT8Jr/4n/HgLrPpnaLO/\nhaZODS6q2pG2FUVRMhG1+Chpx+/Qun+/fUB3NUGg/7jLgAH2gb1rl1WCBg601c+9JSlcBcZ/jz59\n4OjR+H3Zvt36/JSXgwgcOGDvcepU9FIbQL9+VgG7xuee397q865d0fvLllnfIW97QRarbdubmP7l\nh2ic+G2OnTrGbTW38Zdvf51lSwZ8cK9QyCo98Xxr0u28nQ7UyqUoOUY6QsmyfUPD2VNKONy1PDvh\nsDHTptkq46GQMTU19pgbohwKmZhwdn9b8UK0/WHOkyZFy02cGHu/oE0kdn/DBtuGP4S7V6/gkGo3\nR5D/XkGb2//qamOQFsOERw1fHmb4ZoG55fe3mJ0NOwPH114Yd7rD9dNBPo5ZUbqTrApnV5TuoLwc\nTj/d+si4dMSS4P76di02y5fD0KEwbVrEcuF1xt21K7atpUuD63T5w5xHjoxue9MmqKyMvl8QxsTu\nu/5AfqtSS4u10gwbFrG+xPM3isfevfZHTWjcC3DlnVCxAd66nkkbX+aRb537gZx/fO7SWDzrRqJ6\nZrlKPlq5FCWXUR8fJSPoSIi0329lwwbrmOyluTnig+I+3F2flaqqaNlBg+xy08qVsHu3XZbaty84\nxDzIL8i/9JQs7gPUDeHu5fsZ0tSU2N8oESXnLuGSX1zCpknXUtZ7MGcsepPa3c/w0uPnJryuPR8e\n/1wGLfl0NTw/XWUr4tGd4fuKoqSBdJiZsn1Dl7pSzvr1dsmoVy/76i4DBRGUiTnekk8oFLuM41/e\nqamJva6wMHh5IxyOXToLki8pCZZLtGTiH5d3DG6fvceLiuzYhw+3S301NcZUTV5vBnxxpmEeZuKC\niebFLS+atra2pN+HVGRO7urSUKYtLWlWZ0VJLele6lKLj9LjBP2iv/VWu1zU0mJfL7ww/i/+jkRa\nBUVq+a0WBw7EXucPWXfbLC+3y09+RKL3KyqC5VxCIWvp8c5Fc7N1Ug6FYsdw/fWxyf127oQjR+C9\n9+Dp/93OmG/8I7uvHc+Aczby5PVPUve5Oq4adRX79knMfMezqqTCuvHee9H727d37PpMW1pKxsql\nKEoWkQ5tK9s31OLTJYJ+0fstDUG/+F3n3qKi6PP9+kXvFxZaK4jf4hLPehHP0hLP6hAOx1p4gixE\nXktBPOftoLkIsirFc3jef2y/uf2l203R/CJT/t1y8+NlPzZNLU3tzneyztze9rwFWF0LU5Bc0Nx0\n9fOhKErukG6Ljzo3Kz1O0C/6wYODQ8+98vGce0eNgpISawE5fNhmVz50KFYunvXCDSlft87u9+4d\nHbIuEvH7KS+3W1VVrCXDDY2vqoo4BbuOw95sx4MGwfHjNjTeG0LvjtW1KnnH6jo8u8kZjzUf4/43\n7+e7b3wXYwz/dsm/8ZULv0K/on5x5y/evvdYoppV/kSRLv6kk37rl3+/PRYsgIsuspa8vn3h4Yc7\ndr2iKEoidKlL6VHCYZunx8uQIbFRT/7zEH/J48gReOYZq/Q0NkaitlylIhRKXPepvNxGgp08aZWZ\nMWOgqChy3hh7ftiwyLLQgAHR92httW3W18dvw10uKSqC1atjlR7vWOM5PO+pP8VDKx5i5AMjmf/a\nfG6eeDNbb9vKNz/0zUClx3tP735nlrQSLTl5z/XtG33Ov98e/mXPOXM6dr2iKEoiVPFRuoV4PiSz\nZkWHfrtZlA8ejL4+FIotVBnv4TxkCMycGT+k/Mwzk/fNmDULVqwIzrLsRlnNnAnvvBN8vT8SK5kI\nNLAKjnesrqI0fbojIG0w9tfsnTWGuYvmcuWoK9k8dzP3X3U/p5ecnnBMQYU/O1MMNJFy5D23dKl9\nX3v1itQv6wiZ5uOjKEpuoUtdSlJ0NHttvPpZ/tDvgQPtffy5bKZOjc0v09xsFSJjrNVk0KDIstKw\nYfH74q+QnmgcyTxkV6xIbKHy3sc/DxddFGzpmT49eInpmWcMl//zH3lnxNdpHrSaS4Zfw/ev/h3j\nKsZ9MJ6ZMyPLdBMmwPPPR48p3vJVRwuhenP4DBoUmyna5fzzrRWus8TLxK0oipIS0uFYlO0beejc\nnKzDqesA26tXtLzrWOx38i0ttcfbCxlur/14Ieb+0PigUPj16yOZn5PJipzMVlpqxzB8ePuybqZp\nP8t3LTfTH7zcMA/DZy8yfc99LWos9fXBofzZ7gys4eOKktuoc7OSFSS7/BDPAdn91T5wYPSS1IkT\ndgno2WcTWyDaq6Q+YYL1w/Hi+t1MnmwtRW5NLi+NjTBxYmz4elFR5Jp9+9ovTuqnsdFaR4KcrL3U\n1saOe+O+jdz157v43du/o+DAefDH38PmmZxAuPBC2LLFzvPKlcHWI3euUlVjKhmrUipJ5GCtKIrS\nVVTxUZLCv/wwaFCktIF32WP37ujrCguhuNgucdXUxPry+KOVkm3fzc+zbZuN6ho40PbBBCxBNTfb\nLZ4PkF/pAfvw3bnT/l1aGr9fiVi5MlbRA6tQnXlm7BLRtsPbmPf/5/H4uscZWjqCszc8xpZnbgJT\n+IHMiRPtl65wlUz/MtuwYTB+fPQS1YIF1pk4kXLk+j25LF/e/vulKIqSqajioySFv0bTqVPBoc1+\niovtg7+xMXEiuyALktdiUVZmq5mfPGkVFa+C494/lXj7c/Jk7PnqahudlqjdpqbgcY0fH22d2tu4\nl/mvzeeRVY8wuHgwP/noT3j8K7ewdHFRzLV9+8a3tolE1yfzyzU1RSswrs+ROwZ/WPoH/UsQ/q4o\nipJtqOKjJEV7xTqDKC21OXWSUUqCHJDjLeUkIp7Vp7OEw7H3K3QMMOeea9tbsyY4CiweGzfaHD6m\n70EGzbyP90f/mD69+vDty7/N3Jq5FPcu5vt7Yq8rKbHWrbfeij4eCkUXM3XxW8mC8C/hBSk0QfdR\nh2NFUbIVDWdXOkUyD77GRmhoSCzjD+OGyBJNR5UeiM6/01WGD7dLUv6lsNZWqwisWGGTHe7caS0t\nybZ9vKWRpunzab61mr1DH6Tl9a+y7KZt3FF7B8W9i4HY+a2ttb5I3vw/bn6iHTuCw/XdkHV/CQwv\n/hw7+/fHpiB49lk7vlDIbjU1+VGVXVGU3EQtPkpC4jnIPvustT60Z80pK7OvJ05EHsDHjkXOB4Vx\nJ1PbqV+/6OzKLqdOtX9tMrS2WoWiPdxMy8uXW2XB63tTWOhTmnqdhKkL4JJ7INQAK74Ai7/BqWPl\n3PhxuyzozvPDD9vEfd55v/DC6Lbd/EQQ/31avDg2a7TXx8fbjrt019gYvezljk9RFCUXUMVHSUi8\nfDzl5cGOu/6lpvr6yMO/pSViGfE+oP0P7T0Byzx+2to6drwj9Opl+5oMXsuMf5moqspuO3e3ED7j\nUVpqv0Vr372w+p/gtbugIZJ8aM2ayLxt22aVEb9CmCi/Tbz3CRJHSYXDkb/9y3Xqx6MoSi6iio+S\nkERh7EGh2n5/GP8y0cGDtmyDSzgMZ58d7WAbRCgUvfR1/HjifidDcbF92BsT3c++fZPzSxKJriPl\nV0wOHm7h+NlP0PjJ+TQVb+XjI2/kf7/xLU7sGh1zL/+8eefZVQx37YqtBxYkH7Qfj0QRYurHoyhK\nLqKKj5KQRFaGIItPMvfz4i9hEY+ios75/CTCqzyVlFgrj18JSoQxcMEFkczT7rLRnvpW9g5ayNHp\nd3N00BbY9An4y2/5Q3hiXMdr/9Kd62szaBBs2hR9bvz4jlmDEuFXkOKF2iuKouQKqvgoCfGHsXsf\nhkEVyoMoLYXTT4+9PhwOrlsVxMCB1jqTauXHpaUlcu+ORGgdPWq3bdvgc3Na+eJPf8Pdf7mbkwc3\nw+aZ8Ntfw97JgE1T6kXE5tapqkrsa+MnyJqT6H1KRHulQrKBziZqTFWCR0VRsgtVfJSEJPIP8T5s\n4+W0KS21BT2DHiizZsVXZPxLW/X11vemuxSfLiFtcP5TrJh2N596dhMfO/tjhBY9zroXpya8zBir\n9Ljz676OHJnYChZkzelstuPOKkyZRCL/pu64TlGU7KbD4ewicomI/EFEdotIm4hcGyBzt4jsEZHj\nIvJHERnlOx8SkQdF5ICINIrIb0Wk3CczQESeEJEGETksIj8XkRKfzFAR+R8ROSYi9SJyn4gU+GTG\ni8hrInJCRLaLyNcC+nuZiNSJyEkR+auI/ENH5yUbiVdBPVnch+3WrVa5mTgxVsZrPfG35y9Y6sUY\nqzS5IeJNTdHRYKmgqMhaXUIhq1R1GEfh4fPj4G9n069lBMtuWcYLN73AHx+dmjCM3GXZsti5T7RM\n5VazTxXe9zDZCvaZRmf9m7QKvKLkJ53J41MCrAG+QKz1HhH5V2Au8DmgBjgGvCwi3iwnPwQ+BswC\nLgXOAJ7x3epJYAwww5G9FPjAldRRcBZhrVYXAP8A/CNwt0emFHgZeBdbWPRrwDwRucUjMwJ4AfgT\nMAH4EfBzEfmbJOcja7n2WvuLd9s2+zpzZufvZYz1k/E/7JuabNj7yJHWidnbXqI6Vm6JiY4sOyUi\nqOyE69jcYaVKWq3Cc+sEuOFGODIUfr6UU48uouT9GsJhaz1IBrdkx7BhEQXIzb9TXW2j4Gpq7N+1\ntfGtZ/mMX1FM1r+ps9cpipLldKXCKdAGXOs7tge43bNfBpwAPunZbwKu88iMdu5V4+yPcfYneWSu\nBFqASmf/auAUMNgjMwc4DPRy9j8PHHD3nWP/Bbzl2b8XWOcbw0JgUYJxZ211drd6enV1bCXyUKjz\n9/VXPU9mGz7cVlBPRTX0ZLbCQmOKipKTDar2TkGzYcKjhrm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ec2ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly15_data['power_1'],poly15_data['price'],'.',\n",
    "        poly15_data['power_1'], model15_set1.predict(poly15_data),'-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">89836.5077327</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1575072.52605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">319.806946764</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9306.12882948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.103315397042</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23.4277359048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.06682476069e-05</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0331642866136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.75577097718e-09</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.9410878859e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-2.5466346476e-13</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.72262411743e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.09641345061e-16</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.8337583788e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-6.36458441713e-21</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.84750094523e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.52560416991e-25</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.35734055021e-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.35082038963e-28</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.79203635463e-23</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[16 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 16\n",
       "\n",
       "Data:\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "|     name    | index |       value        |       stderr      |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "| (intercept) |  None |   89836.5077327    |   1575072.52605   |\n",
       "|   power_1   |  None |   319.806946764    |   9306.12882948   |\n",
       "|   power_2   |  None |  -0.103315397042   |   23.4277359048   |\n",
       "|   power_3   |  None | 1.06682476069e-05  |  0.0331642866136  |\n",
       "|   power_4   |  None | 5.75577097718e-09  |  2.9410878859e-05 |\n",
       "|   power_5   |  None | -2.5466346476e-13  | 1.72262411743e-08 |\n",
       "|   power_6   |  None | -1.09641345061e-16 |  6.8337583788e-12 |\n",
       "|   power_7   |  None | -6.36458441713e-21 | 1.84750094523e-15 |\n",
       "|   power_8   |  None | 5.52560416991e-25  | 3.35734055021e-19 |\n",
       "|   power_9   |  None | 1.35082038963e-28  | 3.79203635463e-23 |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "[16 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly15_data = polynomial_sframe(subset_2['sqft_living'], 15)\n",
    "my_features = poly15_data.column_names() # get the name of the features\n",
    "poly15_data['price'] = subset_2['price'] # add price to the data since it's the target\n",
    "model15_set2 = graphlab.linear_regression.create(poly15_data, target = 'price', \n",
    "                                                 features = my_features, verbose=False,\n",
    "                                                 validation_set = None)\n",
    "\n",
    "coefficients_model15_set2 = model15_set2.get('coefficients')\n",
    "coefficients_model15_set2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x125063190>,\n",
       " <matplotlib.lines.Line2D at 0x122d96390>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UFRU+IiIiWeAvK/7C0Yqj3DjsxrBTadBU+IiIiISswlVQtLSISadMonub7mGn06Cp8BER\nEQnZ/HXz+fDTD5laMDXsVBo8FT4iIiIhK1xayGldT2PMiWPCTqXBU+EjIiISok1lm3jmg2eYWjBV\n+3LVAxU+IiIiIfpTyZ9o07wNVw+5OuxUGgUVPiIiIiE5fPQw9y27j2tPv5Y2zduEnU6joMJHREQk\nJHNXz2XH/h3al6seqfAREREJSeHSQs7tey6DugwKO5VGQ0tDioiIhGDF9hUs3LyQJy9/MuxUGhW1\n+IiIiISgaGkRx7c9nq+e/NWwU2lUVPiIiIjUs7JDZTy88mHtyxUCFT4iIiL17C8r/kL5sXK+Pezb\nYafS6KjwERERqUfOOYqWFjFx0ER6tO0RdjqNjgofERGRerRg/QLWfLKGKSOmhJ1Ko6TCR0REpB4V\nFRcxuMtgxvUeF3YqjZIKHxERkXqyZe8Wnn7/aaYUTNG+XCFR4SMiIlJP/lTyJ1o2a8m/Dfm3sFNp\ntFT4iIiI1IPyY+Xct+w+vjnkm7TNbxt2Oo2WCh8REZF68NTqp9j+2XamFGhQc5hU+IiISFYpLYWx\nY6F/f+/rjh1hZ5QZRcVFnNP7HAZ3HRx2Ko2alosUEZGsMmkSLFzo/fe6dTBxIrzxRrg51dbK0pW8\ntvE1Hr/s8bBTafTU4iMiIlll27bkz3PRPcX30KNND7528tfCTqXRU+EjIiJZpUeP5M9zzd7De3no\nnYe4afhNNMtrFnY6jZ66ukREJKvMnet1b23b5hU9c+eGnVHtPLTiIQ4eOah9ubKECh8REckqXbvm\n/pieKOcchUsL+drJX+OEdieEnY6gri4REZE68+rGV1m9azVTC6aGnYpE1KrwMbP/Z2YVZva7wOu3\nm9lWMztgZi+Z2YDA+/lmVmhmu8xsn5k9aWZdAzEdzewRMyszs91mNsvMWgdiTjSz58xsv5ltN7M7\nzaxJIGaImb1mZgfNbKOZ/SjO5/iimZWY2SEz+8DMrq3NdREREQEoXFrIoM6D+GKfL4adikTUuPAx\nswLgJmBF4PXbgO9E3hsJ7AfmmVlzX9hdwMXAJGAccDwwJ3CKR4FBwPhI7DjgXt95mgDP43XXnQlc\nC3wLuN0X0xaYB6wHhgE/Aqab2Y2+mD7As8B84HTg98AsMzs/neshIiLit3XfVp5a/ZT25coyNSp8\nzKwN8DBwI7An8Pb3gTucc88651YB38QrbL4W+d52wPXANOfcq8655cB1wBgzGxmJGQRcANzgnCt2\nzr0JfBe40sy6R85zAXAycLVzbqVzbh7wn8BUM4uOXboGaBY5zmrn3OPAH4Af+PK9FVjnnPuxc26N\nc64QeBKYVpNrIyIiAt6+XC2attC+XFmmpi0+hcAzzrkF/hfNrC/QHa/1BADn3F5gMTA68tIIvFYa\nf8waYJMv5kxgd6QoinoZcMAoX8xK59wuX8w8oD0w2BfzmnPuaCBmoJm198W8HPh883y5iIiIpOXI\nsSP8qeRP/NuQf6N9i/bVf4PUm7QLHzO7EhgK/CTO293xipPSwOulkfcAugHlkYIoUUx3IGaRcufc\nMeDTQEy885ChmHZmlo+IiEia/v7+39n22Tbty5WF0prObmY98cbnnOecO1I3KdULdbaKiEidKSou\n4uxeZ3Nat9PCTkUC0l3HZzjQBVhmlSO18oBxZvYdvDE3hteq429F6QZEu622A83NrF2g1adb5L1o\nTHCWVx7QKRBTEMivm++96NducWJcCjF7nXOHSWLatGm0bx/bhDl58mQmT56c7NtERKQBe2/ne/xr\nw7+YPWl22KmEbvbs2cyeHXsdysrKQsrGk27h8zIQLF8fBFYDv3bOrTOz7Xgzsd6Bzwczj8IbFwRQ\nAhyNxDwViRkI9AIWRWIWAR3M7AzfOJ/xeEXVYl/MT82ss2+czwSgDHjPF/NLM8uLdJVFY9Y458p8\nMRcGPtMEXy4JzZgxg2HDhlUXJiIijUjR0iK6te7GxEETw04ldPEaA5YtW8bw4cNDyijNMT7Ouf3O\nuff8D7zp6p8451ZHwu4Cfm5ml5rZacBfgS3A05Fj7AXuB34XWT9nOPAAsNA5tyQS8z7eAOP7zKzA\nzMYAdwOznXPRlpoX8QqchyJr9VwA3AH80dcN9yhQDjxgZqeY2RXA94Df+j7WTKCfmf3GzAaa2RTg\nMiBmbSIREZHq7Du8j7+u+CvfHvZtmuc1r/4bpN5lYssKF/PEuTvNrBXemjsdgNeBC51z5b6wacAx\nvGnj+cALQHBZy6uAP+K1MlVEYr/vO0+FmV0C3AO8iVeAPQj8whez18wm4LU2FQO7gOnOuft9MRvM\n7GJgBl5RtAVv+ntwppeIiEhSD7/zMAeOHODmETeHnYokYM656qMkhpkNA0pKSkrU1SUiIoC3L9eQ\nmUM4qdNJzL0ix3dWrUO+rq7hzrll9X1+7dUlIiKSAa9vep1VO1ZpCnuWU+EjIiKSAUVLixh43EDG\n9x0fdiqShAofERGRWtq2bxtzVs/h1hG3al+uLKfCR0REpJZmLZtF87zmXDv02rBTkWqo8BEREamF\noxVHubfkXq4+7Wo6tOgQdjpSDRU+IiIitfCPNf/g430fa1BzjlDhIyIiUguFSws568SzGNp9aNip\nSAoysYChiIhIo7R652oWrF/AIxMfCTsVSZFafERERGpoZvFMurTqwqRBk8JORVKkwkdERKQGPiv/\njAdXPMi3h32b/Kb5YacjKVLhIyIiUgOPrnyUz8o/075cOUaFj4iISJqccxQuLeTSL1xKr/a9wk5H\n0qDCR0REJE1vbn6Td0rf0RT2HKTCR0QalNJSGDsW+vf3vu7YEXZG0hAVLi1kQKcBnNfvvLBTkTSp\n8BGRBmXSJFi4ENat875OnBh2RtLQlH5WypPvPcmUEVNoYrqN5hr9xESkQdm2LflzkdqatWwWTZs0\n5VtDvxV2KlIDKnxEpEHp0SP5c5HaiO7LddVpV9GxZcew05Ea0MrNItKgzJ3rdW9t2+YVPXPnhp2R\nNCTPfvAsm/du1qDmHKbCR0QalK5d4Y03ws5CGqqipUWc2fNMhvUYFnYqUkPq6hIREUnBB598wEvr\nXmLKCLX25DIVPiIiIim4Z+k9dG7VmcsHXx52KlILKnxERESqsb98P39++8/ccMYNtGjaIux0pBZU\n+IiIiFRj9qrZ7D28l1tG3BJ2KlJLKnxERESSiO7LdfEXLqZPhz5hpyO1pMJHREQkibe2vMXb299m\nasHUsFORDFDhIyIikkRRcRH9OvZjQv8JYaciGaDCR0REJIEd+3fw+LuPc+uIW7UvVwOhn6KISIR2\ndpegB5Y/QBNrwnVDrws7FckQFT4iIhHa2V38jlUcY2bxTK489UqOa3Vc2OlIhqjwERGJ0M7u4vf8\nh8+zsWyjBjU3MCp8REQitLO7+BUuLaTg+AJGHD8i7FQkg7RJqYhIhHZ2l6iPPv2IeWvn8eBXHww7\nFckwFT4iIhHa2V2i7ll6D51aduIbg78RdiqSYerqEhER8Tlw5MDn+3K1bNYy7HQkw1T4iIiI+Dy2\n6jH2HNqjfbkaKBU+IiIiEdF9uS486UL6dewXdjpSBzTGR0REJGLJx0tYtm0Zz05+NuxUpI6oxUdE\nRCSiqLiIvh368uUBXw47FakjKnxEpN5oSwjJZrsO7OJvq/7GLSNuIa9JXtjpSB1R4SMi9UZbQkg2\ne2D5AwBcf8b1IWcidUmFj4jUG20JIdkqui/XFadeQedWncNOR+pQWoWPmd1iZivMrCzyeNPMvhyI\nud3MtprZATN7ycwGBN7PN7NCM9tlZvvM7Ekz6xqI6Whmj0TOsdvMZplZ60DMiWb2nJntN7PtZnan\nmTUJxAwxs9fM7KCZbTSzH8X5TF80sxIzO2RmH5jZtelcE5HGpjbdVdoSQrLVCx+9wPo965kyYkrY\nqUgdS7fFZzNwGzAMGA4sAJ42s0EAZnYb8B3gJmAksB+YZ2bNfce4C7gYmASMA44H5gTO8ygwCBgf\niR0H3Bt9M1LgPI83K+1M4FrgW8Dtvpi2wDxgfSTfHwHTzexGX0wf4FlgPnA68Htglpmdn+Z1EWk0\natNdNXcujBkD/fp5X7UlhGSLouIihvcYzsgTRoaditSxtKazO+eeC7z0czO7Fa/4WA18H7jDOfcs\ngJl9EygFvgY8bmbtgOuBK51zr0ZirgNWm9lI59ySSBF1ATDcObc8EvNd4Dkz+6Fzbnvk/ZOBLznn\ndgErzew/gV+b2XTn3FHgGqAZcEPk+WozOwP4ATArkv+twDrn3I8jz9eY2VhgGvBSOtdGpLGoTXeV\ntoSQbLRu9zr++eE/mfWVWZhZ2OlIHavxGB8za2JmVwKtgDfNrC/QHa/1BADn3F5gMTA68tIIvGLL\nH7MG2OSLORPYHS16Il4GHDDKF7MyUvREzQPaA4N9Ma9Fih5/zEAza++LeTnw0eb5chGRgIbeXaWZ\nZ43PzOKZdGjRgStPvTLsVKQepF34mNmpZrYPOAwUAV+PFC/d8YqT0sC3lEbeA+gGlEcKokQx3YGY\nf2qcc8eATwMx8c5DhmLamVk+IlJFQ++u0syzxuXgkYPcv/x+rht6Ha2atQo7HakHNVm5+X288TDt\ngcuAv5rZuIxmVbcy1o45bdo02rdvH/Pa5MmTmTx5cqZOIZJ1Gnp3lWaeNS6Pv/s4nx78lFsLbg07\nlQZp9uzZzJ49O+a1srKykLLxpF34RLqO1kWeLjezkXhje+7EKyq6EduK0g2IdlttB5qbWbtAq0+3\nyHvRmOAsrzygUyCmIJBaN9970a/d4sS4FGL2OucOU40ZM2YwbNiw6sJEslppqdfKsW2b1201d65X\n3GSzusy5Rw+vtcf/XBquwqWFXND/AgZ0GlB9sKQtXmPAsmXLGD58eEgZZWYdnyZAvnNuPV4hMT76\nRmQw8yjgzchLJcDRQMxAoBewKPLSIqBDZCBy1Hi8omqxL+Y0M/MvtjABKAPe88WMixRN/pg1zrky\nX8x4Yk3w5SLS4OVi105d5tzQu/Kk0tKPl7J061KmFkwNOxWpR2m1+JjZ/wD/xBuM3Ba4GjgHr1gA\nb6r6z83sI2ADcAewBXgavMHOZnY/8Dsz2w3sA/4ALHTOLYnEvG9m84D7IjPGmgN3A7MjM7oAXsQr\ncB6KTKHvETnXH51zRyIxjwL/BTxgZr8BTgO+h9c6FTUTmBp5/wG8Iugy4KJ0rotILsvFrp26zLmh\nd+VJpaLiInq3781FJ+mf/MYk3RafrsBf8Mb5vIy3ls8E59wCAOfcnXhFyr14rTMtgQudc+W+Y0zD\nWzvnSeBfwFa8NX38rvKd41ngNeDm6JvOuQrgEuAYXmvSX4EHgV/4YvbiFWR9gGLgf4Hpzrn7fTEb\n8NYJOg94O5LbDc654EwvkQYrF2dp1VXOmtHVeHxy4BMeW/WY9uVqhMw5F3YOOcfMhgElJSUlGuMj\nOW/HDq+rKJfG+NRVzmPHel1nUWPG1K71JxfHTzUWv33zt/x0wU/ZPG0zXVvrh1KffGN8hjvnltX3\n+Wsyq0tEGpBc7Nqpq5wz3YUWHYsE3nikiRNz71o3RBWugnuK7+HyUy5X0dMIaZNSEZGITHeh5eL4\nqcbgxbUvsnb3Wg1qbqRU+IiIRGR6Rlcujp9qDAqXFjK0+1DO7Hlm2KlICNTVJSISkekutLlzq45F\nknBt2LOB5z54jj9d+ifty9VIqfAREakjuTh+qqGbWTyTdvntmHyqVthvrNTVJSJ1TtPEJRscOnro\n8325WjdvHXY6EhIVPiJS53JxdWhpeJ549wl2HdilfbkaORU+IlLnNLtJskFRcRHn9zufLxz3hbBT\nkRCp8BGRGkulC6u0FHbujH0t22Y3qSuu4Vu2bRlvbXmLKQVTwk5FQqbCR0RqLJUurEmTYN++yudt\n22bf7CZ1xTV8RUuLOLHdiVzyhUvCTkVCpsJHRGoslS6s4GtdumTftg3qimvYdh/czaMrH+Xm4TfT\ntIkmMzd2KnxEpMZSWaAvFxbxy4UcpeYefPtBjlYc5cZhN4adimQBFT4iUmOprHSc6dWQ60I6OWo8\nUG6pcBUUFRdx2SmX0a1Nt7DTkSygNj8RqbFUFujL9kX80t1BXRuP5paX173MR59+xJ+/+uewU5Es\noRYfEcnGZQS0AAAgAElEQVR6ddnKku7A5i1bkj+X7FK0tIgh3YYw5sQxYaciWUKFj4hkvbqcdZXu\nwOZPP03+XLLHprJNPPPBM0wZMUX7csnnVPiISNary1lX6Q5s7tQp+XPJHvcW30ub5m24esjVYaci\nWUSFj4hkvbqcdZXu4OuePZM/l+xw+Ohh7lt2H9eefi1tmrcJOx3JIhrcLJKl0h1025DNnet1b/mv\nRaakO/i6LnORzJmzeg47D+zUSs1ShQofkSwVb/bQnDmNsxjKpplh2ZSLJFa4tJBz+57LyZ1PDjsV\nyTLq6hLJUvHGtWhrBamO1hmCt7e/zZub32RqwdSwU5EspMJHJEvFG9eSjVsr1OWNtqbHbsw3fxXH\n3hT2E9qewFcGfiXsVCQLqfARyVLxBt1m49YKdXmjremxG/PNPxuL4/q059AeHln5iPblkoT0WyGS\npfxjSUpLvZv3li3e7uadOnmzibJhYG1d3miTHTvZ4O/GfPPv0cMr+PzPG5O/vP0Xyo+Va18uSUgt\nPiI5INqCsXEj7NvnFT1vvJEdA5vrshUq2bGTtepkY8tYXfJ37ZWXw8iR2b03Wl1xzlFUXMSkQZPo\n0baB/9ClxtTiI5IDsrkFoy6ndyc7drJr0timnAdnAI4ZA2vXhptTGOavn88Hn3zAfZfeF3YqksVU\n+IjkgGzuvqjL6d3Jjp3smjS2KefZXBjXl3W713HjP25kaPehnN3r7LDTkSymri6RHJDu6sLZpK5m\nWOXyNcm0xta1F/T+rvc5+89n0zyvOf+48h/al0uSUouPSA7I5RaMeAsxZuKz5MI1qa/Vtxtb157f\nO6XvcN5fz6Nr6668/M2X6d6me9gpSZZT4SMidaoxd8PUVdEXlAtFYF0o3lrMhIcm0LdjX+ZdM4/O\nrTqHnZLkAHV1iUhCmeimaszdMI256KtrCzctZPxfxzOw80Dmf3O+ih5JmQofkXqUaysKZ2IhwMY8\nFqcxF311acH6BUx4eALDegzjxWtepEOLDmGnJDlEXV0i9ai+uj4yJRMtFo21GwYa99ibZGoz9un5\nD59n4t8m8sU+X2TuFXNp1axV3SYrDY4KH5F6lGtdH9k8jT4XNOaiL5ma/gEw5705TJ4zmYtOuoi/\nXfY38pvm122i0iCpq0ukHtWm6yNZN5mmjGe/XOvmrEs1+QPgkXce4Yonr2DSKZN44vInVPRIjanF\nR6Qe1abrI9lfyY15yniuyLVuzrqUbkvirGWzuOmZm/jW0G9x36X3kdckr24TlAZNhY9IPapNIZHs\nr+Rc60KrD/W1hk6q9DOqlM4fAHcvvpvvvfA9poyYwt0X3U0TU0eF1I4KH5EckeyvZI3FqSrbWlj0\nM6qUyh8Ah48e5j9e/A8Klxbyw9E/5M7z79SKzJIRKnxEckSyv5I1e6iqbGth0c8odW9vf5vrn76e\nd3e+S+FFhdw64lYVPZIxKnxEckSyv5I1FqeqbGth0c+oep+Vf8YvXvkFv1/8e07ufDKLbljEsB7D\nwk5LGhgVPiLSIKmFJbc8/f7TfPef32XXgV3897n/zQ9G/4Bmec3CTksaoLRGiZnZT8xsiZntNbNS\nM3vKzL4QJ+52M9tqZgfM7CUzGxB4P9/MCs1sl5ntM7MnzaxrIKajmT1iZmVmttvMZplZ60DMiWb2\nnJntN7PtZnanWezINzMbYmavmdlBM9toZj+Kk+8XzazEzA6Z2Qdmdm0610VEsk+0hWXtWu9rmAOb\nJbHNZZv5+t++ztf+9jVO7Xoq7055l9vG3qaiR+pMusPjzwbuBkYB5wHNgBfNrGU0wMxuA74D3ASM\nBPYD88ysue84dwEXA5OAccDxwJzAuR4FBgHjI7HjgHt952kCPI/XanUmcC3wLeB2X0xbYB6wHhgG\n/AiYbmY3+mL6AM8C84HTgd8Ds8zs/LSujEgjl8l1arTmTcN3tOIoMxbN4JSiU1i8ZTGPX/Y4z131\nHH079g07NWnonHM1fgCdgQpgrO+1rcA03/N2wEHgG77nh4Gv+2IGRo4zMvJ8UOT5Gb6YC4CjQPfI\n8wuBI0BnX8zNwG6gaeT5rcCu6PPIa78C3vM9/w3wTuBzzQaeT/K5hwGupKTEiYhnzBjnoPIxZkx2\nHEuyx95De91zHzzn/mPef7iBdw90Nt3cd577jttzcE/YqUk9KikpcYADhrla1CA1fdR2QYQOkeQ/\nBTCzvkB3vNaTaGG1F1gMjI68NAKvlcYfswbY5Is5E9jtnFvuO9fLkXON8sWsdM7t8sXMA9oDg30x\nrznnjgZiBppZe1/My4HPNc+Xi0iDtmoVtGsHzZp5X999t2bHyeQsqmybkVUdtVDFd/DIQeavm8/P\nF/ycs+4/i46/6cjFj17MY6seY+QJI1l842Luvuhu2rdoX/3BRDKkxoObzZtbeBfwhnPuvcjL3fGK\nk9JAeGnkPYBuQHmkIEoU0x2I+afDOXfMzD4NxMQ7T/S9FZGv65LElCU5Tjszy3fOHUakGtm2WF46\nzjoL9u3z/nvfPhg9GvYG/+9MQSZnUWXbjKzqZNuaQWEpP1bOko+X8Mr6V1iwYQFvbn6T8mPldGnV\nhS/1/RLXnn4t5/Y9lwGdBmh6uoSmNrO6ioBTgDEZyqU+ZeT/uGnTptG+fexfKpMnT2by5MmZOLzk\nkFy+8R08GPt83z6v1SLd4i3ZLKp0C8Ncm5GVay1UmXKs4hjLti3jlQ2vsGD9Al7f9DoHjhygQ4sO\nnNP7HP73/P/l3L7nMrjLYBU6jdTs2bOZPXt2zGtlZWUhZeOpUeFjZn8ELgLOds75/xffjldUdCO2\nFaUbsNwX09zM2gVafbpF3ovGBGd55QGdAjEFgdS6+d6Lfu0WJ8alELO3utaeGTNmMGyY1piQ+rvx\n1UXLUsuWlS0+UQsXVhZvyc6Zaj7pFobeULrckWstVDVV4Sp4d8e7LFi/gAUbFvDqhlcpO1xG62at\nObv32Uw/Zzrn9j2Xod2Haj8tAeI3Bixbtozhw4eHlFENCp9I0fNV4Bzn3Cb/e8659Wa2HW8m1juR\n+HZ443IKI2EleIOUxwNPRWIGAr2ARZGYRUAHMzvDN85nPF5RtdgX81Mz6+wb5zMBr/vqPV/ML80s\nzzl3zBezxjlX5ou5MPAxJ/hyEalWfd346qJladEir3srWPxs2VL9OYPvDRgAXbpULYLSLQxzrQUt\n11qoUuWc48NPP/QKnfULeGXDK+w6sIv8vHzOOvEsfnjWDzm377kUHF8Q+vTzXO5ulvqVVuFjZkXA\nZOArwH4zi7aUlDnnDkX++y7g52b2EbABuAPYAjwN3mBnM7sf+J2Z7Qb2AX8AFjrnlkRi3jezecB9\nZnYr0BxvGv1s51y0peZFvALnocgU+h6Rc/3ROXckEvMo8F/AA2b2G+A04HvA930fayYwNfL+A3gF\n1mV4LVoiKUl048v0P8Z10bI0eLA3pqddu9ji59NPqz9n8L19+7zHunVw6aWwOPJnSrqFYa51HTWk\nVZk37tn4eYvOgvUL2LpvK02bNGXkCSO5ZfgtfKnvlxjdczQtm7Ws/mD1KNeKZQlPui0+t+B1E/0r\n8Pp1wF8BnHN3mlkrvDV3OgC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xo+6Ok+ymEZ2NdOml1Z8r2qU2alTimOjg3q98xTvuunXe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\ndyMztbW6WrN7d/o2EqCp5f39WtPHo6FBX3/0aPzY4PNMmOv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12467bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly15_data['power_1'],poly15_data['price'],'.',\n",
    "        poly15_data['power_1'], model15_set2.predict(poly15_data),'-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">87317.9795547</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">356.304911045</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.164817442809</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.40424992697e-05</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.48234876179e-10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-6.75253226587e-13</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-3.36842592661e-17</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.60999704242e-21</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6.46999725625e-25</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.23639388865e-29</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[16 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 16\n",
       "\n",
       "Data:\n",
       "+-------------+-------+--------------------+--------+\n",
       "|     name    | index |       value        | stderr |\n",
       "+-------------+-------+--------------------+--------+\n",
       "| (intercept) |  None |   87317.9795547    |  nan   |\n",
       "|   power_1   |  None |   356.304911045    |  nan   |\n",
       "|   power_2   |  None |  -0.164817442809   |  nan   |\n",
       "|   power_3   |  None | 4.40424992697e-05  |  nan   |\n",
       "|   power_4   |  None | 6.48234876179e-10  |  nan   |\n",
       "|   power_5   |  None | -6.75253226587e-13 |  nan   |\n",
       "|   power_6   |  None | -3.36842592661e-17 |  nan   |\n",
       "|   power_7   |  None | 3.60999704242e-21  |  nan   |\n",
       "|   power_8   |  None | 6.46999725625e-25  |  nan   |\n",
       "|   power_9   |  None | 4.23639388865e-29  |  nan   |\n",
       "+-------------+-------+--------------------+--------+\n",
       "[16 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly15_data = polynomial_sframe(subset_3['sqft_living'], 15)\n",
    "my_features = poly15_data.column_names() # get the name of the features\n",
    "poly15_data['price'] = subset_3['price'] # add price to the data since it's the target\n",
    "model15_set3 = graphlab.linear_regression.create(poly15_data, target = 'price', \n",
    "                                                 features = my_features, verbose = False,\n",
    "                                                 validation_set = None)\n",
    "\n",
    "coefficients_model15_set3 = model15_set3.get('coefficients')\n",
    "coefficients_model15_set3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x124e06e10>,\n",
       " <matplotlib.lines.Line2D at 0x124e06ed0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T8DIwF+gF3A9MNrOTk3k2IiJSP23ZsYVPCj7R+J56okmS8b8DljvnLgu8tiwUcx1wm3Pu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\noNGHcEQnChGgtgk+8A1YORGu+jK8uwTuexUeeQ5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hGEbuMMcnj0TVPAlrPVpbYezUfTB1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\nDP8f+6DFIZWcpXsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124ed4990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly15_data['power_1'],poly15_data['price'],'.',\n",
    "        poly15_data['power_1'], model15_set3.predict(poly15_data),'-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">259020.879454</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1545198.28029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-31.7277162076</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9987.4875763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.109702769619</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.738101963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.58383847337e-05</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0392428614089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-4.47660623786e-09</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.515833293e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.13976573482e-12</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.00754862175e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.97669120547e-16</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.24105980967e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-6.15783678661e-21</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.43187601549e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-4.88012304074e-24</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">power_9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-6.62186781351e-28</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">nan</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[16 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tvalue\tfloat\n",
       "\tstderr\tfloat\n",
       "\n",
       "Rows: 16\n",
       "\n",
       "Data:\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "|     name    | index |       value        |       stderr      |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "| (intercept) |  None |   259020.879454    |   1545198.28029   |\n",
       "|   power_1   |  None |   -31.7277162076   |    9987.4875763   |\n",
       "|   power_2   |  None |   0.109702769619   |    26.738101963   |\n",
       "|   power_3   |  None | -1.58383847337e-05 |  0.0392428614089  |\n",
       "|   power_4   |  None | -4.47660623786e-09 |  3.515833293e-05  |\n",
       "|   power_5   |  None | 1.13976573482e-12  | 2.00754862175e-08 |\n",
       "|   power_6   |  None | 1.97669120547e-16  | 7.24105980967e-12 |\n",
       "|   power_7   |  None | -6.15783678661e-21 | 1.43187601549e-15 |\n",
       "|   power_8   |  None | -4.88012304074e-24 |        nan        |\n",
       "|   power_9   |  None | -6.62186781351e-28 |        nan        |\n",
       "+-------------+-------+--------------------+-------------------+\n",
       "[16 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly15_data = polynomial_sframe(subset_4['sqft_living'], 15)\n",
    "my_features = poly15_data.column_names() # get the name of the features\n",
    "poly15_data['price'] = subset_4['price'] # add price to the data since it's the target\n",
    "model15_set4 = graphlab.linear_regression.create(poly15_data, target = 'price', \n",
    "                                                 features = my_features, verbose = False,\n",
    "                                                 validation_set = None)\n",
    "\n",
    "coefficients_model15_set4 = model15_set4.get('coefficients')\n",
    "coefficients_model15_set4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12b062290>,\n",
       " <matplotlib.lines.Line2D at 0x128bcd710>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NzrndwCehMrGOQ5bKdDGzSkREpGjV1sK4cTBwoPe8YUPyz0hpqUhepJmZ9cHLrznZObcr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125328050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly15_data['power_1'],poly15_data['price'],'.',\n",
    "        poly15_data['power_1'], model15_set4.predict(poly15_data),'-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The  power_15 coefficients of the resulting models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value of power_15 for subset_1: {'index': None, 'stderr': 1.4924971572268935e-49, 'name': 'power_15', 'value': 5.019752329333753e-55}\n",
      "value of power_15 for subset_2: {'index': None, 'stderr': 6.729607727195604e-48, 'name': 'power_15', 'value': 1.686366583280963e-52}\n",
      "value of power_15 for subset_3: {'index': None, 'stderr': nan, 'name': 'power_15', 'value': 6.390458859923061e-53}\n",
      "value of power_15 for subset_4: {'index': None, 'stderr': 1.1645426147255294e-47, 'name': 'power_15', 'value': -4.7453533310147055e-51}\n"
     ]
    }
   ],
   "source": [
    "print \"value of power_15 for subset_1:\", coefficients_model15_set1[-1]\n",
    "print \"value of power_15 for subset_2:\", coefficients_model15_set2[-1]\n",
    "print \"value of power_15 for subset_3:\", coefficients_model15_set3[-1]\n",
    "print \"value of power_15 for subset_4:\", coefficients_model15_set4[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting a Polynomial Degree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the sales dataset 3-way into training set, test set, and validation set as follows:\n",
    "\n",
    "* Split our sales data into 2 sets: `training_and_validation` and `testing`. Use `random_split(0.9, seed=1)`.\n",
    "* Further split our training data into two sets: `training` and `validation`. Use `random_split(0.5, seed=1)`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_and_validation_data, testing_data = sales.random_split(0.9, seed=1)\n",
    "training_data, validation_data = training_and_validation_data.random_split(0.5, seed=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can write a loop that does the following:\n",
    "* For degree in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] (to get this in python type range(1, 15+1))\n",
    "    * Build an SFrame of polynomial data of train_data['sqft_living'] at the current degree\n",
    "    * Add train_data['price'] to the polynomial SFrame\n",
    "    * Learn a polynomial regression model to sqft vs price with that degree on TRAIN data\n",
    "    * Compute the RSS on VALIDATION data for that degree and make a polynmial SFrame using validation data.\n",
    "    * Report which degree had the lowest RSS on validation data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The  1 degree polynomial model RSS:  6.76709775198e+14\n",
      "The  2 degree polynomial model RSS:  6.07090530698e+14\n",
      "The  3 degree polynomial model RSS:  6.16714574533e+14\n",
      "The  4 degree polynomial model RSS:  6.09129230654e+14\n",
      "The  5 degree polynomial model RSS:  5.99177138584e+14\n",
      "The  6 degree polynomial model RSS:  5.89182477809e+14\n",
      "The  7 degree polynomial model RSS:  5.91717038418e+14\n",
      "The  8 degree polynomial model RSS:  6.01558237777e+14\n",
      "The  9 degree polynomial model RSS:  6.12563853988e+14\n",
      "The  10 degree polynomial model RSS:  6.21744288936e+14\n",
      "The  11 degree polynomial model RSS:  6.27012012704e+14\n",
      "The  12 degree polynomial model RSS:  6.27757914772e+14\n",
      "The  13 degree polynomial model RSS:  6.24738503262e+14\n",
      "The  14 degree polynomial model RSS:  6.19369705905e+14\n",
      "The  15 degree polynomial model RSS:  6.13089202414e+14\n"
     ]
    }
   ],
   "source": [
    "for n in range(1,16):\n",
    "    poly_data = polynomial_sframe(training_data['sqft_living'], n)\n",
    "    my_features = poly_data.column_names() # get the name of the features\n",
    "    poly_data['price'] = training_data['price'] # add price to the data since it's the target\n",
    "    model = graphlab.linear_regression.create(poly_data, target = 'price', features = my_features, validation_set = None, verbose = False)\n",
    "    poly_validation = polynomial_sframe(validation_data['sqft_living'], n)\n",
    "    predictions = model.predict(poly_validation)\n",
    "    residuals = predictions - validation_data['price']\n",
    "    squared_residuals = residuals*residuals\n",
    "    RSS = squared_residuals.sum()\n",
    "    print \"The \", n , \"degree polynomial model RSS: \",RSS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note that the 6 degree polynomial model had the lowest RSS on Validation data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose the degree of polynomial using validation data, compute the RSS of this model on TEST data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 6 degree polynomial model RSS:  1.25529337848e+14\n"
     ]
    }
   ],
   "source": [
    "poly_data = polynomial_sframe(training_data['sqft_living'], 6)\n",
    "my_features = poly_data.column_names() # get the name of the features\n",
    "poly_data['price'] = training_data['price'] # add price to the data since it's the target\n",
    "model = graphlab.linear_regression.create(poly_data, target = 'price', features = my_features, validation_set = None, verbose = False)\n",
    "poly_testing = polynomial_sframe(testing_data['sqft_living'], 6)\n",
    "predictions = model.predict(poly_testing)\n",
    "residuals = predictions - testing_data['price']\n",
    "squared_residuals = residuals*residuals\n",
    "RSS = squared_residuals.sum()\n",
    "print \"The 6 degree polynomial model RSS: \",RSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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