Project 5 - Decision Trees.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "iris = load_iris()\n",
    "\n",
    "X = iris.data[ : , 2: ]\n",
    "y = iris.target\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(max_depth=2)",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2)</pre></div></div></div></div></div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_classifier = DecisionTreeClassifier(max_depth = 2)\n",
    "tree_classifier.fit(X, y)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.90740741 0.09259259]]\n"
     ]
    }
   ],
   "source": [
    "print(tree_classifier.predict_proba([[5, 1.5]]))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_classifier.predict([[5, 1.5]])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def plot_decision_boundary(classifier, X, y, axes, cmap):\n",
    "    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 150),\n",
    "                         np.linspace(axes[2], axes[3], 150))\n",
    "\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = classifier.predict(X_new).reshape(x1.shape)\n",
    "\n",
    "    plt.contourf(x1, x2, y_pred, alpha = 0.3, cmap = cmap)\n",
    "    plt.contour(x1, x2, y_pred, alpha = 0.8, cmap = \"Greys\")\n",
    "\n",
    "    colors = {\"Wistia\": [\"#78785c\", \"#c47b27\"], \"Pastel1\": [\"red\", \"blue\"]}\n",
    "    markers = (\"o\", \"^\")\n",
    "    for idx in (0, 1):\n",
    "        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],\n",
    "                 color=colors[cmap][idx], marker=markers[idx], linestyle=\"none\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(min_samples_leaf=5, random_state=42)",
      "text/html": "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(min_samples_leaf=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(min_samples_leaf=5, random_state=42)</pre></div></div></div></div></div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X, y = make_moons(n_samples=150, noise = 0.2, random_state=42)\n",
    "\n",
    "tree1 = DecisionTreeClassifier(random_state=42)\n",
    "tree2 = DecisionTreeClassifier(min_samples_leaf=5, random_state=42)\n",
    "\n",
    "tree1.fit(X, y)\n",
    "tree2.fit(X, y)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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xdctbaA/RNwpQ/v2ZvWMxkV6GyroULViFzFFjAQBJKRmwaRC4AAxeyCBq35jVfto3w9O2Vn1H9LgRG/n9hbtatdLvT4+OxURmNmRfF0i4ULUfuSWLNB0mDTB4IYOofWPW4mm/702+taUNbe3tSE9PRWpqCmRZ1vypW8u+I3rciI3IVkSyWjVHChEpI2QvutzNCBy4AFo3FfXF4IUMocWNWYunfZvNho6OTuz82991X9fGLH1HoqF3tiLcGZrN/v0RmYktIREzVz6I7s7g55iWTUV9MXghQyi7MWeEfWNR+2lfj5l2gzFD3xGrCbcJjd8fUXjs6Vmwp2cZXQ0uzEjGULLIY3dXD04cPxXRewdaYC9cSkf7qLlY40DBFlZ0ODM1DZysSmmmLi09ld8fkYUx80KG8d2Y/7J5Z8AZczs6OjXPboRilnVtONJFOSUZvbT0VDz08L1ITOTlj8iqePUjQ5VMmYDEhISQZbTObgRjppli1comxTolGb2Vtyxh4EJkcTF8Bkuf/CMzqzlTj9Yhbv692Y163YepZmRmDF3IX47HmllMnTYZt935T9i+bXeAjtvadrImPTGAj2cxG7yIniKIHofR1aAhtLY0KSyXDtEzQePa9FdYUASH429wuZqDlnE4hqGw4FqIHl5IzWRK8QQUT7oRNTVVaGtrQUaGE2PHToTNZoPoMbp2pBbZ2wFZlg3JzJKxYjZ4+fs7ZUhP5/wNZnflSoOiclWVZ3H5UrfGtRlszJiZKC9/J8TvZ+CdvR/oWCOKxOVLl1Bz5pLR1SCVtbW14a233kJFRQUgCQzPUpYtJeuL2eBl48aNSEpKMroapEBRUQoSE6WAMzIKIdDTI/Dqq38xoGa9MjISMHJkEpKSrmZXurtlXLzYjZMnd0T13oVpbbgu9zzebshDbTsvvETh6OrqwsWLFwEIjM0bgYe+cpPRVSKdxGzwUl9fP2RHUDIHjycD06fnQQjRL4ARnyz6deJEAy5dVD7xmBYqTgBZWalItieiy9OD5uYOFd5V4F/muzHc7sX8rHPYdzId7DtDFB6bDbhx0XQ8/NXlSLUnG10d0knMBi/LPzcDKTyQLaMbPfCgG6LPtNM22GBHEhZNn2hgzbQzTGpCYcJRAECh04vV14/FFZFtcK2IrEOSJCycNQHXzi3WfC0dMpeYDV7yR2YhNcVudDUoDEIItHd3wSt7kWBLQFpScuxekIRAgesjCO/VJc4mJ5/FOcf43qXmdZKZZsfnF01HRlqKqu/b1d2Dnfs+xuVm7YeRU/ySJCBnuCN2rxMUlCR8ufkY4XK54HQ6MXfuXCQmstkoGllZqUhOTkRXl1rNJORTnN2N++a0D3r9hYNpqGjSr6+WBIHcEVn49wdvRUlRvirvebaxCeue+W+cPtcIoUIzGI9DCiUpQcJdKxbh67cuRkICR/1Zmau1HVlzbkdLSwscjtCjhWM2ePn85z/PDrsRcjiSkJ+fhuTkq8FfV5cX9fXtcLn0H/ETewTunlyH3DQPbH3u7bIAGtrt2HhyNPTq++JyudDV1QV7kg33f+l63L5sQVRPsX97/yie/K9taOv0IjExEU6nM+L343FIQ+nu7kJLiws2CZhVPAb/9u1bOeLIwhi8OJ34wx9+j7S0NKOrYzlNTXWoqvpH0N9PnDgf2dlchTcayS0nkVW1MejvmyfejS7nZF3qcuJEBfbu3YsrV64gJUnCzx++C3OmjovovWrqL+FrP/w12jwCmZmZWLRoEa65ZnZE78XjkJRoa3Nj586dqKqqgpB7sHheCdav+aLR1aIIhRO8xGyfl+uvv3bInaf+ZFnGc889FrLMhQsn8aUv3cXp6SMkhMDJ3/0eHf6eLgNJyHOXYfK/fEPzdnxZFigungy7PRnbt+9Ae5sL9RevYA7GRfR+5y82o9sLpKamoLS0FCtWLMf4CeOQGGYqX6vjUJZl1Nae8k9aV1g4gcexxbW2tiMzMwObN2/BiePHca5R2aSXZH0xG7wkpdYiKZXpw3BUnz6L1tbmkGVcrmbUN76n+1T9sULu6Ua3qx6BAxcAEOhprUdichVsiVebPWVZ/mRhxjZkZGaosDCjDTYxAUlJSbDb7ar2FUhISIDdnoykpESkpiYDCacBKJ8BVYvj8Fh55eDlAhwZWHbz9VwuwLJsSO7KQ1JSMrsIxKGYDV56bw4x1SKmubZWZXOp9JbjdxsJW2Iipt7/U/S4XUHLJKU7YEtMhO877r3xvh3gxhvNOj16TqcuI5zjRe3j8Fh5JTa9vHXQ6y5XGza9/BfDVi2naHFJgHgWw8ELhSsjU9lyCkrLReNqpsGNjMx0FTIN5mF3DofdOVxR2dA33q0xeeNV8ziUZRnbt70dssz2N/agZAqbkIishMEL+Y0dNxoOR0a/J/yBHM5MjB2nbUdJbTIN1hOvN141j8OaM3Uh3wfwrVpex6ZQIguJnSseRc1ms2HZzdeFLLNsRammN0pfpmHgDceXaThWXqnZZ5tNODfeWKLmcdjWqmySPKXliMgcGLxQP1OnTcJtd66Ew9G/s7PDmal5E4XSTIMsx0dbdzzfeNU6DrVuCpVlGdWnz+LoRydQffps3BybREZjsxENMnXaJJRMmaB7nxOm+PszUx8kI6hxHGrZFMrmTSLjMPNCAdlsNhSNH4MZs0pQNH6MLn0q4jnTEIjvxhuKHn2QjBTtcahVUyibN4mMxeCFTCPeMw0DmaEPUixQuymUzZtExmOzEZmGWUY7mYnvxjuoecKZiWUrStk8oZCaTaFs3iQyHoMXMg1fpiHQvCY+8ZhpMKoPUqzxNUFFyjf3kNImoXhp3iQyAoMXMhVmGgKL9sZL0QnUOXco8dK8SWQEBi8EwFwz2jLTQGYSbJbjUOKteZNIbwxeyJRDPplp0E+G5yzun1aP148nG10V01HSOTeQeGzepOg1159EddkWFC24BVn5k42ujqnx7IpzHPIZf3wTqx356EOcr69GnmsfRqZ2Y9nETkBwwc2+lHTO7UuPyRwpNgkhUHNgOzpaLqDmwHYInoshMfMSx+J17Zx4NjDLNhItWJBwAQAwxuFFa8d5I6tnOko73c7/9GxMnTaJzZsUseb6k3BfPgcAcF8+h+b6kxg2utjgWpkXz7I4pvfaOZxK3ViDs2wCxbZzkD95wJMFkHrlCJ/4+lDa6XbqtEm6TeZIsUcIgdqDOwBJ6n1BklB7cAfPxRCYeYljes5oa8Z+NfEkUJZtJFzIktr9P9skwNbVxCe+Pjj3EOmhb9YFACAEsy9D4GNCHAtnRttosibsV2O8wVm2/lmXq6+CT3x9qDnLcSTnELOVsW9Q1sWH2ZeQmHnRiJmGHgej9KnS7e7AM0+9GFHWhP1qzGFg9mxg1sVHAtvbB1Jj7qFIMo/MVsaHQVkXH2ZfQmLwogGrXHSUzGg7Y2YxXn1l26DXfVmToUZWcCp1c+ifZevNuggx+GGvV+8TX1b+ZEiBC8SdaOYeCjZPTKhzKJJtyHr8WRdI6M17DsRzMRg+6qrMLE0kStPNoRat+9LtN+PoRydCfs5QC9BxpWhz6LtCtQ0CqegKErgAgECXuwVC9upWPyuIZIXrSBZxNHLhRzZT6UvIXnS5mxE4cAF4LgbHzIuKenp6sHXzzpBl9GgiCTfzE+ypUo2sCVeKNoe+WTYZNvxdnopk9FwtIAROn76EL5TOxefmFiMpJQO2BF4eohXJOWRUttIqGeNYYktIxMyVD6K7M/jfm+diYPxGVHKsvBJbt7yF9vbOkOW0biKJNN0caEZbNbImHK1hHv37bgCdsAMAEhKSUVl5EefOduGf7NnIGF5gcE1jRyTnkBHZSjZTGceengV7epbR1bCcGA5epE/+aS/ctU96Lzrq101559iJijI/GZkZQ5a5Wi7w/thsCVh28/XY9PJfgm6/bMV1sNkSFH0WRWfqtMkomTIRNWfq4XZloK3NgyNHTmL//sH9mtRjAxCfzQ+RnENqnHfhUPu6oR8z1YX0FrPBi7tlNBJEpuafI8sy/rr1xbC2SbQVwd2i/tNtbW2VonRzRXkXCgsnDvl+I4blIzPzTbS2tgQtk5mZhRHDFsDdEvxCMragAKtWDceuXa/1e6/MzCzccMMXMLZgJtzBP4JU0FbzIRp2P4vc69cgY+ynMGp4IeRsgXNnz6Gt7SC6u7tV+6ye7h64XK1ob++Aq6UdCQn5qr231URyDql13iml9nVDT+3uDrS1tcHtZp+5eBOzwcu2bW8gNTVV889pb78S8iIzUGKiHfv3f4wDB8pVr0tra6Oicnv27EZmprLPz8goCLl/GRmjsWWLsqxTTs5sOBzN8Hq7kJCQjNTULJSXV6O8vFrR9hQhIVBY9wekdjWg4i9PoHb0//IPMzp5shJlZWVob29HWrKESYW5EX9MUcFIpCRJcHV4sHfvXrS2ujBz5ky19sKyIjmH1DzvhqLFdUMvbW1u7N27F3V1dZAkgSnj4zdQjjeSiLEZcFwuF5xOJ6699lokJmofm2Vnp2LixGFDlvN9zVVVV3DlSuh+MZHKzEzGlCkjhix3/PgltLZ2KX7fYcNSUFjohN1+tWnH4+lBba1Ls30h9Ux0duIrUy/7f37p2HBUtaQAADo6OiB7vXCmJ+HR//3PWDQ7un4N5afO4f/+4jU0XnZBstmQlpYW1fvFikjOIb3OO62uG3ro6elBZ2cnEmwSln9uJr53902wJycZXS2KkKu1HVlzbkdLSwscDkfIsjGbeelwu5GYqH0/irYkL4Chg5fubi8qKy7g0iXlK9SGq8MNFBU5YbcnBpwTQAiB7m4vhNyN5CQvWpo7FL5vG+rPXYIzKxXJyYno6upRvC1pZ1J2D26Z3IEtJ1NR2RTsVBYoneqGLAM2GyDLQOnoZhytTwcgQZKA6ZNG49+/cytGZoe+WDTXn0R12RYULbgFWfmTA5aZNqEAv19/H/71/21B2ZFKdLi1O96tJJJzSK/zTsl1w+PpwYWGJtU/Ww2ZqYn4/ldX4MZFM4yuCulIl8zL888/j6eeegoNDQ2YNWsWnnvuOcyfPz9g2Y0bN+Kee+7p95rdbkdnp7InDV/m5c2N/4b0tJSo6z4UIQR2fXwMnSH6DCQnJmDJ9Gm6dHY7f6UZB6rPKCqbkpSEaQWjkTcsS9M6kQaEACo3Ax2XgNQRwKRVgWecc50FqncMfr3oJsAxBmmpdkyfVIDEhNCBvhACR7b9Eu7L55A+vAAzb/5OyEmzhBD4uPIcWt0Mcq1gqOvG3KJxpr1OTC7Kw4gs7fs3kvZMlXnZtGkT1q5diw0bNmDBggV49tlnsXTpUlRUVGDUqFEBt3E4HKioqPD/HMnMgq/s+BDJOqUPk1JsSB/W+1l96+qLC5sudOCPf/2HLnXx1SfNmQRbwuC69K1fR1cX9p+uhvtKN7o743M0iFXlpbRiychLvT90XMJbu97C+c6BF3CBZaNOITu5d9FFH1kATSf2YvuFCVA6WqX383qnMHdfPoefPP2fAT6PrCzQdUPuEWh3deOt+tCTVRpFkoAFM8bjrpsXDhmAU2zRPHj5+c9/jnvvvdefTdmwYQPeeOMN/OY3v8EjjzwScBtJkpCbG3nHQQA4UnkOCToezCNHZmDSpJFISbkaMHk8PaisvIiLF41JnWdlpcJuT8SkSSORlJQwKAiUJAlCCCSmSfjH4VpD6kiREFg8v39T0OTks/jr4d6mIJ/i4d0YMWZw5sMmASPsHWi/eBoVl5UE+Mo+j2KD77rh8fSg2QLNw4dP1OLdQ5VY/+CtGDEs9NM6xQ5Ng5euri4cOHAA69at879ms9mwZMkS7Nu3L+h2bW1tGDt2LGRZxpw5c/Dv//7vmDZtWsCyHo8HHo/H/7PL5QIATJo0CcnJySrtiTJNTQLJyUBCAuD1Al1dyRg+vADDh+tajX6SkwWSk4O3DEqShJSUJMycWYyuLt+NaOB+ALxJmUdBSjMKnVczkzYbUOj0YsnsPJzrzPrkVYFVo8qDrl8kBPCFaRI2XyjGUH9bZZ9HsSjKZ0jNdXZ24uzZs/i4sg5ffuT/4cmH7sCMyYVGV4t0oGnwcunSJXi9XuTk5PR7PScnBydOBE5DFhcX4ze/+Q1mzpyJlpYWPP3001i0aBHKy8tRUDB4bpT169fj8ccfH/T61772NaSnc8r5c+cqcfBg6CULAODGG5egoGAS6utP4eOP30Vn59V5E1JS0jF9+meRnz9By6qSAkIIiPd+ArQMXMhNwvKJXZA+843ejJq3G2L394Agg0MkCRjlSMQ3bvk6pITg2Reln0dkhPZ2N3bufAtlZWVodrvx/Cu7seHRu42uFunAdKONFi5ciIULF/p/XrRoEaZMmYJf//rX+MlPfjKo/Lp167B27Vr/zy6XC2PGjMGsawrgcLBNftjwbhw8OHS5GbPGoaPDhf373xz0u85ON/bvfxNfuv1/YerU6RrUkpRqqfoQlS2B5sURQEs1Jg67AOfETwEAukp+je725qDvlZQ+DMmOkap9HulDlmXU1FSjrbUVGZmZGDu2yGQz3+pDkhLgarGhs7MTly9fxtEjR+Du8Ay9IcUETYOXESNGICEhAY2N/SdBamxsVNynJSkpCddccw2qqqoC/t5ut8Nutw96XUq4CCnB/O21Whs3Pl3R2kKF41Lxi5/9KeR77di+BVOmjYjLC6UZCCFQ9/YLvWmTQIMEJQl1b78A5+RCSJIEezZgz84K9Y4ALqj2eaQ9Lp7YVwpsthFgk3Z80vQulJycjLlz52LXrl3+12RZxq5du/plV0Lxer04evQo8vLytKpmzOm7rH3NmTosXV4asvyyFaU4W3te8Uq2ZAzh7UFXy+XAgQQACIFu12UIb0/g35v88yg03xpqA89T3+KJx8orDaoZkf40bzZau3YtVq9ejXnz5mH+/Pl49tln4Xa7/aOPvvKVr2D06NFYv349AODHP/4xPv3pT2PixIlobm7GU089hZqaGnz961/XuqoxIdiT2Wc+Nw9HPzrR/3VnJpatKMXUaZNw9CNlQyHVXMmWwmNLTMLU+3+KHrcraJmkdAdsiepMEaD359Fgsiyj5kwdXK5W7HhjT8iyvYsnTmBmlOKC5sHLbbfdhosXL+LRRx9FQ0MDZs+ejR07dvg78dbW1vY72a5cuYJ7770XDQ0NGDZsGObOnYv3338fU6dO1bqqlhdqWfv3/r4fX7r9ZqSlp6Kt1Y2MzHSMHTfa/91nZCrr3Ky0HGnD7hwOu1O/4Wt6fx5dFehBJBRfZrRo/BiNa0ZkPF067D7wwAN44IEHAv5uz549/X5+5pln8Mwzz+hQq9iiZFn7Hdv34rvf+1rAJ7Ox40Yr6hszdtzoqOtKRKEFexAZCjOjFC+YX4wRvanlyPus2Gw2LLv5upDbL1tRypQ0kcaUPIgEw8womU1z/Ukcev0pNNefVPV9eSeKEUqfuEKVmzptEm67cyUcjox+rzucmbjtzpVxOJqBSH9KHkQCYWaUzEYIgZoD29HRcgE1B7ZDzaUUTTfPC0VGrT4rU6dNQsmUCag5UxewbwwRaSvSph9mRslsmutPwn356ppozfUnMWx0sSrvzeAlRqjZZ8Vms7HTH5FBwm366TtqkMgshBCoPbjj6jxRkoTagzuQlT9ZlXmhGLzECF+flVCd/GLpycw3hJTZIdKDnsebkgeRtPRULFtWikxnBo99MqW+WRcAgBCqZl8YvMQQX5+VQfO8xNiTGWcZJT3pfbwpeRBZecsSHusW01x/EtVlW1C04BZk5U82ujqaGpR18VEx+8LgJcbEep+VUHPZbHp5KzsWk6qMOt7M9CDCLGf0BnZcdeZNiuklNQZlXXxUzL4weIlBsdpnRckQUs4ySmox+ngzw4MIs5zq0LLjqu/9zZLV8WddMHAleh91si+8wpNlRDuXDVE4zHC8+R5EZswqQdH4MboHLlxLKXr9mlAAf9OJWsOGtRyOHFF9ZC+63M0IHLgAgECXuwVC9kb1Ocy8kGUon8vGAyBZ28pQzOs9jpSWS0bvxbpbyyrpxuisUyzRuuOq1lmdcNkSEjFz5YPo7gwe+CelZMCWEF34weCFLEPpENKerpG4cD5T49pQrOvpGqm43IXzmUiwJcDhSERiygVIkrVX2g4n6xSLTdRq0brjqtbDkSNlT8+CPT1L089g8EKWoWQIqSQlYc+eMkjSP3Spk7OnDuM7P8DplE+jJZGzm8YSIQQkKQlCBM+m9D3ebDYbJkyYgEWfmY9hw92AZN11htSYsZu077iqdVbHzBi8kGWEGkLa+1AjUF5+FpcundCpRgLfnudCmsMLR8Nb+N1+B3o7qVGsGDEiHVOn5gBAvydZX7+CvsebJEnIzc1Fc3Mzrr++FHmjhwO2y/pXWgVcZT56VzuuBhNdlkSP4chmxuBFIxxeqI1gQ0h7emScONGAlqY2JOl0wk7K7kaho7fTWaHDi6nDu1DZlKTLZ5M+Wi63ouKYjKKJI2G3X/3bdnl6UH3qIlouu5H0yeHW5RWoq6tDZWUlSkpKMCpnKhKSrRm8cJX56AnZi87WUH//qx1XpQj6f+gxHNnMGLxogMMLteUbQlpb3YbamhacOHESb765F02XWrHi2lm45wvXal4HIQTq3/stulra4etVf+8iOwoW3x/TTzvxShYC9ZevoL3Tg7QUO/KHD4Otz9/Z5e7At//tt2jtkCHLMoSQYeUsXLzN2K0FyZaA5DQnOro6/K+lOEdh0udu918jIu24qtdwZDNj8KIyTqKmj94hpONhT/aitvai//XcEVnIG5ml+edfqatAV8v5fq/1uJuQ0tWI7IISzT+f9Dd61LCgv0tPtVs4VAnMTBPlWVFz/Ul0NDf0e62z5QJ6PO1RZ0TCGY4cSVbHCmJzrwzC4YXxIWhbM4Dqsi0YNro4Zp92omGmibRIGTNMlGdFWvdH0Ws4spnF7p4ZgMML40PQtmYAntZLuFJXwezLAPE2PXosidUZu7WkR38UPYYjmxnDZxVxeGHs69/WHFh12RbDZ7k0m0ATaVEvWZZRffosjn50AtWnz0KWZaOrRFEY+hqh7gy78YqZlygMHFGUlp6maDsOL7SuoduaAU9bE2RvDxISOfIIMO9EWmbAzv2xh/1R9MFvLkLBLjqpqSno6OgMuh2HF1qbr625qbYc1WWbAxcSMlyNp2N6mGI44nkirVDYuT82sT+KPvjtRSDURWcoHF5ofclpTlyo2o94HqaoVLxPpBUMO/fHtnjvj6IHnhVhUnLRSU1LQeaApiGHM5NPUjpprj+JQ68/pVm/Cr1WTY0F/qzLwPb9PtmXeGSGFauJrIyZlzApueh0tHdi9VdvhSTZOLxQbzqMamFaWBlOpBUcO/cTRSe+r64RUHoxcbd1YMYsDpfVW7r3oi7LwzMtPDR2XAyOawcRRSe+rhgq4EXHzARGdJ7gqBaTYIYqOK4dRBSd+LtqRIkXHfOanO1Fqtx69QWOajEcM1SBKVk76KZlizmzLVEQDF7CxAXLzEpg6YROCAyYGorZFzKpUGsHzZhZjB1/3cP5X4iCYPASAS5YZj5Fme0odAaYmZTZFzKxQGsHud0dePWVbYPKcv4XoqsYvESIC5aZiBD4XO5lyAKwBUyuMPtC5tV37SBZlvHMUy+GLM/5X4gYvERFqwXLBi47wKAoNAleOJJ6ggQuQDyPaiFr4eKuRMrwSm4yXOskfEJKxO8qx6C79SJuuX4Obrl+zqAy8TqqhayF878QKcOruYlEs9ZJvGdrWruTcLk1AZ6ELGQMLzC6OqST5vqTqC7bgqIFtyArf7LR1YmaWlMxxPv1gGIfgxeTiGatE7Nma3gBJS0JHWZTVkM454EaUzGY9XpApCYGLyYRaVu3WVem5QWUtNZ3tWqzjCjLykoF0I5Ll+ogy9NQEeZ5EO1UDGa9HlBsMFOmk4/BJhFJW7fSbI0sBxhCrCHfBXRgMOa7gB4rr9S1PhR7+q1WDfjn8xEDF4DUScW585gxZyyumTMGknQF+/Ztxc+e/kFE54FvKgaHI6Pf60Mt7mrW6wHFhoGZTqPONR9mXkwikrZuM45MiKb5i0ipvlkXAIbO5/PxmXN4/b39yLO3Y7qtFuVyIS7BiY6O9pDbhToPIpmKwYzXA4odZst08u5hEr627lAGtnWbcWRCOBdQokgMyrr4GJB9kWUZ2z44BEBgiu0cMqVOlNjOIfhilFcNdR74pmKYMasERePHDBnsm/F6QLHBbJlOgMGLafjaukMZ2NZtxkUieQElrfmfAAdeOPtkX/RypvESXO4OjIQLWVJvpiVLasdIuBRtr+Z5YMbrAcWGQeecAefaQAxeTCTctu5IsjVa4wWUtOR/AkSwUUX6PhG2tncCECi2nYP8yUfKAihWmH1R8zyI9HogyzKqT5/F0Y9OoPr0WfaJoX7MlOnsi31eTCactm4zLhLJVbdJS0L2osvdjOCBgb6zKWempfTLugC9S1RkoTf7chHOoNuqfR5Ecj3gqEAayqD+ZT4GrxvH4MWEwll2wGyLRJoxoIoHZhrCqCVbQiJmrnwQ3Z3Bg2M9Z1MeO2o4piTWQ5b7r6vly75clB0IliXS4jwI53rAYdU0lP6ZzkAPDMatG8fgJQaYbZFIowIqAeC1Xftx6txFVd5PkoDPzJ6Emz47w5STn/lYZbI2tdjTs2BPzzK6GgAAV0MVHKJtUHzSN/vSmpKHzs6rmZnMzCzcsGQVxhXORHur+nUaV1iAb9y/GOfOnkab24WMdAcKxoyHzWbzf54sy/jr1v8K+T5/3fp3FBYsNvWDRke7Bx0d7ejs7DS6KopY7SHDbJnOvhi8xAitFomMlB4BlYAHKSlpyMzMhMPhwIULF3C5uR27y46r9hlvlx3H3gMn8aNvrER6ql2191WT2YYwxouhnkoFgBkpTWgo+he0dzSjp8eDxEQ70tKGofLkeVSePK9TTS/iyJFT/V5xu5vQ2toScqvW1ma8+udNSE/P1rJyUWlra8Pu3btRW1sLSRKYOCbH6CoFZcWHDLNlOvti8EKa0TygklrhzE7A/PmfQkdHB1JSUuB2qzd6o7OzEw0NDdj74QlU1jTg6YduQ1HBSNXeXw39OtMJ4e9EZ0QaN94M9VQqAfC6m/DHP7wErzDX3yI7OwXjx2cNWW737p1oajJvVqOnpwft7e1IkIDFn5qC79+zzOgqBRXsIcPs2RgzZTr7YvBClmZLaEZugR0rV96E4uISuN2h55gJh9vtxt/+thOHDx9G/cUr+MUf/4Znf3CXau+vBjNN1hZvAj2V9vR48cqOf+DtsmOQBdDWJaHFo0HbUJQSE7oBZA1ZztXSirbWDs3rE410ewIeuOtGrLp+jmkD9mAPGc68SZbLxpgFgxeyPEnyIN15HtfMHQ0hklR6Vxtczb1P1G63G8fKy9HsCj1jqt4GXRB9mH3RTaCn0vtXj8V119aj5vxlYyqlgBACH1RVwdPTE7SMPTERD97xeVMfQxIkTJ9UgLyRWUZXJaRgDxl1R99mk2+EGLzQINZcDVoAtotBZ/8Il4QU2BJGQJIk2GzmvHibdQgjAcVF+Sguyje6GiGNHp2Nl3e9H/T3ty6ej+njCnSsUWwK+pABoO7ILjb5RojBC/XDeR+swcxDGMkapo8rwJ03LMK2Dw7B5b7aNORMT8WKT1/DwEUlQR8yAMje7qs/8KEjLAxeyM8q8z5YMzOkLjMPYSTrmD6uAFML83Gm8RJa2zuRmZaCcTkj4u580srQDxkDSTixeyNKrr+bAcwQeFUjANZZDZqZoV5mHsJoJWYf6aEHm82G8XmjjK5GTBr6IWPQFhDeHpz+YDPm/PPDirOm8Xgc88pGAMJbDdqo+WSskhnSi1mHMFqFFefdIGsJ9JAhhEDV3zeho6Ux6Hae1ku4UleB7IKSIT8jXo9j5gYJgPlXg1aaGeKicqRUoHk3iNRmT89CxvAC/7/0YXno8Qx9HVW66GG8HsfMvBAA868GbYXMEFkHJ/cjowTKxrgu1OBM2eZ+5dqb6obsvBvPxzEzLwTg6mrQoRi5GrTZM0NW11x/Eodefypuntr8T6u+J9s+Iz2ItObLxvR42lH5zp/QcOzd3gCkr08CkVDZl3g+jhm8EICrq0GHYuRq0GbPDFnZwDZzJalqK+v3tNqXgpsFkVr6nnedrZcGzQEzVCAS78cxgxfy860GPTAD43BmGt4Z1uyZISuLtzbzQU+rPnH01ErhUzs7GWr+l6uCByLxfhyzzwv1o8dq0JHwZYYCjTbyMTIzZFVGt5nrPcTTbJP7ybLMOVYsQO0RPaFm3R1QMuB8TWY7jo3A4IUG0Xw16Aj5MkOD5nlxZmLZitK4GiatFiMXdjRiiKeZJvf7+My5QbPbOtJTcTNntzWdYCtCq/F+fRUtWIXMUWP7vRZoviYzHcdGic29ophl1syQFUW7sGO0WRO1bwhKaDW5nyzLOFF9Hu4Oj6LydU1X8I/KU4Ned7k78PKu9zF/0gSMzh4WVh2saHhWBsYXqDtBntrZPLWzk6HOuwtV+5FbsmjI9+UklQxeyILMmhmymmgWdow2a2Jkc5Xak/u1tLbj0ec34+Cx05AV9pFc8Oki2O2JAfdVCIF3jp5A2QfVqtXRrBJsEpZfOxtrv7IUyUnR3460yOapnZ1Ua0HVeJ+kksELURyKts082qyJkc1VavqoohY/fO51XLrSCslmQ2pq6pDbZGYmIyUlKejvJUlCSkoSRo4ahtbWLjWraypCCLS3t2PL24fwcVUd/mPNv6AgJzuq91Q7mxdtdjLo+8VxXxW1MHghikPRtJlHmzVR+4ZglDN1F/HQk39AW6eMlJQUzJs3D8XFQzdTtLVdxMWLFUOWW7z4s8jIGKlGVTUlhEBnZwu83m4kJCQhJcWp6O8nywIHDx5EeXk5TtVewLd/8iL+8NSDSE+1R1wPtbN5amVJ/Juxr4pqdPl2nn/+eTz11FNoaGjArFmz8Nxzz2H+/PlBy7/66qv40Y9+hDNnzmDSpEl44oknsHz5cj2qShQXomkzjzZrovYNwSgnaxrg6ZaRlpaK0tLrcPPNKzB27BhIttA3ytraKvx509DBy3XXX4/CwolqVVcTJ08ewe5dm9HW1uJ/LSPDietvWIXJk2eG3LanW0ZJSQm2bduKffs+QIu7G+camlBclDfk5wbq16J2Nk+LLAn7qqhH829o06ZNWLt2LTZs2IAFCxbg2WefxdKlS1FRUYFRowZ31Hr//fdxxx13YP369bj55pvx8ssvY9WqVTh48CCmT5+udXWJ4kYkbebRZk1iMW2enJSMrCwnMjLS4cgCYKsLWX6Kww7H9oyQy104nJmYMt0O2xDvZaRj5ZX4y5bBUxe0tbXgL1t+N+TcUKJnHByOTDidWUhISACEV9HnBurXAkD1bJ5WWZJ476uiFs2Dl5///Oe49957cc899wAANmzYgDfeeAO/+c1v8Mgjjwwq/4tf/AI33XQTvv/97wMAfvKTn2Dnzp341a9+hQ0bNgwq7/F44PFc7eHvcrk02hMi61JrBEa0WROmzWNjziKlC6WWTJmg+n4Em1RR7WwesyTmpum33tXVhQMHDmDdunX+12w2G5YsWYJ9+/YF3Gbfvn1Yu3Ztv9eWLl2KzZs3Byy/fv16PP7446rVmSjWqDUCQ42sCW8Ivaw+Z5FRC6UG6tdSc2D7J8eb+tk8ZknMS9MrxKVLl+D1epGTk9Pv9ZycHJw4cSLgNg0NDQHLNzQ0BCy/bt26fsGOy+XCmDEcRkvko9YIDLWyJrwh9LLynEVGLZQaqF9Le1MdEpJTEc/ZvHhk+b+k3W6H3R5Z73SiWKfmCAxmTdSn95xFsiyrEiwZsVBqqP5WyWlOTLzx3qDHtO+41Hs5CtKOpleZESNGICEhAY2Njf1eb2xsRG5ubsBtcnNzwypPpJeLV1zY9cHHhn2+zV2PpMZ/oDtnPuT0fGXbtNXBHmAExp5df4OcocUils0AhlpsLjbs/fCE4knpzOBYeeXgZipHBpbdfF3YzVS+hVKH6nQczkKpsgB2ffAxzjVeDvj7QceyjxDoaG7A/sPHQhzTzYA4C3vNG7B1XsbH77wGz9gVg1dkNqlIzn0rcnd0Ki6rafCSnJyMuXPnYteuXVi1ahWA3sh/165deOCBBwJus3DhQuzatQtr1qzxv7Zz504sXLhQy6oS9SPgRUKCDcnJybDbUyAANLk68KNfbTasRt/5lBuFTi8aP96NX36Yjt42/qG3KcgE+j5cyzLQWP62wvegUIQAEpMSYbenIDExEYCyETN6O1ZeGbCDsMvVhk0vbw171XhVOh1LXiQlJSE5ORmJiYlod3vwhzfKghT+5Fh2AIFGosti6GN6cnY37pvT3lv/zsv4/Z/+Gyebgk8WaB6RnPvW1NOj/PzRPL+7du1arF69GvPmzcP8+fPx7LPPwu12+0cffeUrX8Ho0aOxfv16AMCDDz6IxYsX42c/+xlWrFiBV155Bfv378cLL7ygdVWJ+uhGWnobJk+ehHnz5uHy5cu4fDnwE6EeJjg7UOjsHUlX6PRiVkEyTrWEns217zZ92WzK34NCS0pKwuzZszFz5gzk5GYBtssAko2uVj+9I4P2hCzTOzJoSlhNSFOnTcNtdyZi+7a34HK1+l/v7XS8BKNTOnH0ue+jcPlX4Zwwa/Ab2K6gYEwurrnmGtTV1eHkyQp4vXLAz0qQBLJT2wIGLkBvQDMsVcDpcMArAhUSWDG5EbLoLSsLYMXkbtSXZ8PsgUAk575V9fT0KC4rCRFyPW5V/OpXv/JPUjd79mz88pe/xIIFCwAApaWlGDduHDZu3Ogv/+qrr+KHP/yhf5K6J598UvEkdS6XC06nExevvAOHI0OL3aF4IlLhas7CoYNHUV9fb1AdBArO/g52TwMkCAhI8NhzcW7M6uBpb/825wNemgUAjz0v9HvQkFJTUzF58mSMGDkcNpN+j+fOncbrr/9/Q5b7whfuRUHB+LDfX5Zl1NefQXu7C2lpDuTnj4MkSaj/7wfQdbECySOLkf8vvwraH8XrlVFffx6nTp1Cd3d30M9J7HYhwdse9Pc9CWnwJjkC/i7NfRr59ZsGvV6ffxva08PfZ91Ecu5bWEdHB775zW+ipaUFDkfgv6WPLsGLnhi8kJqEdxjqz3nx3nv7cP78eUPqkNV9DlPb3xz0+rG0pWhOKgi4jSS8mNu6CcmiI+j7dkmpOJB5G4SUoFpdyXw8nitwu88OWS49fQzsdnVWsh54zIY6VjUnBGa4/4IM72VIfUYkCUhoSxiOo+n/ZNpAIJJz38o6Ozvx6KOPKgpeOCyAKAjRk4eqyot4882/YefOtwyaAFHg23NbIGcM7reS2fAWNh5wIljae7fdjoyk4E0Ybd0SWjzB+yxQbHA6UzBr1tCdPN9/vwwtLco7TAbXe8yKzKsDg4Y6VrU0ObsLi2a1DnpdgkCm9xKO/f1VnGwyV1Nfr8jPfasKp9mIwQtRQHa0uxNRU3MWJ06cQFNTExIkIDFBm4vFhGHdWDmpA1srU3HqytVOhJOyuzHGMbgTm80GjHF4UZLtQWWQTodtHqDNE7q+CSbtYErqaWtxw+PpRnJyYsCmGyEEujw9aGtxQ40c3MBjVpKGPla1I7C0qN3f12UgWQBLi9pxqskGswUC0Zz7ViXCuB4xeCEKQIIEWQjIsheyLEMCMDI7E7/6v19W/bOEEGjc9zt0tbjxvxc7kLNwNSRJ6vd6MPdfe7U8UTCnzl/AmweOBPydJEn4p8/MxXf/ZVnUnyOEQMP7G9HtGnzMfuNzmchddLeux6rw9qBuz39C7gp8DtkkYOyIFPxh/X2mmsAuXs/9VncHpi09oKisef5aRCaXlZmGgpxs1d/3Sl0Fzrb09qfpajmPDO8lDBtdDNnbgwbP4HR3X8LThtEjnZwYjkIqyMnG8KwMbPvgEFzuq/2gnOmpWPHpazB9nDr9J67UVaDbFXg29G5Xg//Y1tOoW9YEnVix9WItzpe/g0w0IyvHPJPWxeu572oN3iF7oNjZayILCjUDLme0NYdYmZV1+rgCTC3Mx5nGS2ht70RmWgrG5YxQbTmCq2tfBWfEiuHBlqMQQuDU+/+DztbLUa35pQWe+0OL3z0nMoFAa7X0XX+I6wAZS61FLc3CZrNhfN4oTd5byF50toaeC6mz9bJp1hhSa80vrfDcD838K4ARxah+WZe+Psm+xNgsBpYU6AZHgUm2BCSnOUOWSU5zQrLpNzS/uf4kDr3+1KC/26Bzz6TnXLD6E4MXIsP4b4wDL5h9si9kHKvc4MxCyF70hGjmAICeTjeErM8It4FZs75/t0HnngnPuVD1JzYbERniav8ACUCgi1Lkqz+TOoZq0qP+zNZPI1izUKjVqc10zpm9WctoDF6IDCBkL7rczQgcuACAQJe7xTT9A+KNVW5wZmOWfhqhOsIPCkqvbmSaICFU/Xnc9eJVkcgAZntKpf6scIOj4IJlza7UVeDsoTdh9owns35D45WRyCBmeUr1iZUhwdFik561DZU1625vgZkznsz6KcPghYhibkhwNNikZ22hsmbtTXWY+LnbkZaVE3R7ozOezPopwzOPiNg5sA826VmXkqzZ+WPvYubN3zFlcM6sn3I8+4jiXLx3DgzUXGa2Jj1SxupZM6vXX0/xvfdEFNedA9lcFlusnjWzev31xG+AKI7Fe+dANpfFHqtnzaxef71whl2iOBbPs/xyBl0i62LwQhSn+ncODMS4m7kea7pYYYp4IgqMwQtRnAqnc6Ce9FjThYtikta4qKK22OeFKE6ZtXOgHv1QOJcGaYkdwbXH4IUojpmtc6Aew7ajmUuDsxCTEuwIrj02GxGRaejRDyXS5jI9mrPI+tgRXB/MvBCRKeg1bDvS5jI+TZMS8Txvkp4YvBCRKejZDyXc5rJ4n4WYlIn3eZP0xGYjE5JlGdWnz+LoRydQffosZFk2ukpEmjLzsG2Aw6rjgRqjg+J53iS9MfNiMsfKK7F929twua6mtB2ODCy7+TpMnTbJwJoRacfMa7rwaTr2qTE6iIsq6ovBi4kcK6/Eppe3Dnrd5WrDppe34rY7VzKAoZgUqB9K68Va1H+8F/nTFyNzZKFha7qYfVg1R0BFT43+TGYOwGMRv0GTkGUZ27e9HbLM9jf2oGTKBNhsbO2j2NO3H4oQAqfe/x942ppwofJD5BYvNORp1exP05xPJHpq9Wcy67xJsYp3QZOoOVPXr6koEFdLK2rO1OlUIyLjBHoSNoJZZyH2Mcv3FC0jZ6NVsz+TPT0LGcMLgv4z05xKVscQ0CTaWt2qliOyKjON7DHz07SZvqdoGJk9Yn8m62LmxSQyMtNVLUdkVWYb2WPWp2k9vyctMyNGZo84Osi6GLyYxNhxo+FwZIQs43BmYuy40TrViEh/XDBRGT2/Jy1nFjZyNlqzD8+n0Bi8mITNZsOym68LWWbZilJ21qWYZrUnYaP6auj5PQXKjKi130Zm2czen4lCY58XE5k6bRJuu3Pl4HlenJlYtqKUw6Qpppl9ZM9ARvXV0PN7CtavRggR9X4b3d/EzP2ZaGj8q5jM1GmTUDJlAmrO1KGt1Y2MzHSMHTeaGReKeVabJ8OotY70/J6CrdPjE81+m2H+HLOtqk7KGX8FoEFsNhuKxo8xuhpEurLSk7Av69JXzYHtumSF9PqegmZG+oowSxJO9qjlfCUn4aNBjL8KEBF9wipPws31J9He1H/OpfamOt2yL3p8T0EzI31FmCVRmj2SvT2chI8CYvBCRBSGQFkXH72yL1obOjPSX/UHm5H1zw8r3m+l2SNX42lDmubI/Bi8EBGFIVDWxUfP7IuWhs6M9NfZeglX6iqQXVCi+DOGyh4JIXBi9+9CTsLHdZ3iF4MXIiKFQmVdfGIh+zIwMyKEQOXfX0Fny4Wg21SXbcGw0cWq7XewzsK+4JDrOsU3DmEhIlJIyF54Wi+HLONpbYqJuUH6ziycPiwPXk97yPKetibI3h5VPlvJJHyxsq4TRYaZFyIihSRbAuwZw9B+5XzQMvaMLEi2BB1rpb2BmZjWCzWoLtvcv5CQ4Wo8rUqT2VDDqK/UVeDsoTctv64TRY6ZFyIihYTsRXdHa8gy3R1tMZF5GciXiUnPHo0LVfs1W5pAybT91WVbTLX+FemPmRciIoWsNBeNVrSeXE7JMGpPWxNXgo5zsXuGERFpwCpz0WhBj6UJhgoQAzZZ9VaOw6njCIMXIiJSRK+lCYIFiEIInHr/f2CV9a9IOwxeiIhIEaObzay2/hVph39dIiJSzMhmM6ODJzIP/oWJiMgy4rnPEV3FodJERERkKQxeiIiIyFIYvBAREZGlMHghIqKoNdefxKHXn1J1llst3pNiA4MXIjIN3qysaeAKz9EuEaDVe1LsYPBCRKbAm5V1abHCM1eNplAYvBCRKfBmZU3+JQN8M9qqsECjFu9JsYXBCxEZjjcrfanZPOcPOlVc4VmL96TYwuCFiAzHm5V+1GyeGxR0+kQRfGrxnpFiHyzzYvBCRIYy080qHqjZPDco6PSJIvjU4j0jwT5Y5sbghYgMZZabVTxQs3nO/14Itnpz+O+txXtGin2wzI3BCxEZxkw3q3igZvNcOCs8G/mekWAfLPPjwoxEZJhwblZSBCsFN9efRHXZFhQtuAVZ+ZOjqarl9bsh970Jf3JjzsqfDGlg010IWqzwbJZVo/tmXQD0C/KGjS7W9LNJGU2PgKamJnz729/G1q1bYbPZcOutt+IXv/gFMjIygm5TWlqKvXv39nvtG9/4BjZs2KBlVYnIAFrerAb2WXDmTQrr5hxrBt2QfaK4MWuxwrPRq0arHeSRNjQNXu666y6cP38eO3fuRHd3N+655x7cd999ePnll0Nud++99+LHP/6x/+e0tDQtq0lEBtLqZhWoz0K8PjX3b54LlOXijdlHiyCP1KdZ8HL8+HHs2LEDH374IebNmwcAeO6557B8+XI8/fTTyM/PD7ptWloacnNzFX2Ox+OBx+Px/+xyuaKrOBFZ3qCn5zh/ata6eS5WMMizDs2O0n379iErK8sfuADAkiVLYLPZUFZWhi984QtBt/3jH/+IP/zhD8jNzcXKlSvxox/9KGj2Zf369Xj88cdVrz8RWRf7LPRnlr4kZscgzzo0+/YbGhowatSo/h+WmIjs7Gw0NDQE3e7OO+/E2LFjkZ+fjyNHjuAHP/gBKioq8NprrwUsv27dOqxdu9b/s8vlwpgxY9TZCSKyHK36LFi986/RfUmsIJIgz+rHhVWFHbw88sgjeOKJJ0KWOX78eMQVuu+++/z/nzFjBvLy8nDDDTfg1KlTmDBhwqDydrsddrs94s8jotiiRZ8Fq3b+5Y01fOEEeVY9LmJB2MHLQw89hLvvvjtkmfHjxyM3NxcXLlzo93pPTw+ampoU92cBgAULFgAAqqqqAgYvREQ+WvVZsGLnX95YtWfF4yJWhB28jBw5EiNHjhyy3MKFC9Hc3IwDBw5g7ty5AIDdu3dDlmV/QKLE4cOHAQB5eXnhVpWI4owWfRbU7vyrVzZEqxsrszm92CncWJr1eZkyZQpuuukm3HvvvdiwYQO6u7vxwAMP4Pbbb/ePNKqrq8MNN9yAl156CfPnz8epU6fw8ssvY/ny5Rg+fDiOHDmC7373u7j22msxc+ZMrapKRDFCi46panb+1SsbotWNldmcq9gp3FiaLg/wxz/+ESUlJbjhhhuwfPlyfPazn8ULL7zg/313dzcqKirQ3t4OAEhOTsZbb72FG2+8ESUlJXjooYdw6623YuvWrVpWk4hiiD09CxnDC4L+C6fTqtqLRuq1Xo5Wq3RzvZ9eXEzUeJqO9crOzg45Id24ceP6/ZHHjBkzaHZdIiKjqNn5V69mBq1GW7GZ5CpOZGc8LsxIRBSA2otGapUNGfJzfKL8PL3qb3ZcTNQcGLwQkWaa60/i0OtPWfIGp+YKx3o1M2h1Y2UzyVVmWfk63nGKQCLShNU7d6rZ+VevZgYtRls1159E1XuvfvK+A98u/ppJOFuxOfDbJSJNhBqqa5XhtmrMSqvnejlq31h9AWjAwMUv/vq+cLZi4zF4ISLVhercCcDSGZlw6b1ejpo31qAZo3643g/pj0caEaku1BwYAOJqVlKrNjMMHrUkIdU5ChM/d9ugYNOM9afYxqONiFQVaqhuzYHtvTe+OBtua8VmhsFZF4GOlkb0eNpjOtgka+BoIyJSVaihuu1NdRxuawEcXURmx+CFiFQz9FDdAHhDNB2t5oohUguDFyJSzdCdUwNtxBuimXASNrIC9nkhItUE65wqhEDV3zeho6UxyJbx0ffFCvQeHUUUCR55RKSqQJ1TZW8PejzuEFvxhmgWVh0dRfGFRx8RaY43RGux4ugoii+8UhCRLnhDJCK1sMMuERERWQqDFyIiIrIUBi9ERERkKQxeiIiIyFIYvBAREZGlMHghIiIiS2HwQkRERJbC4IWIiIgshcELERERWQqDFyIiIrIUBi9ERGFqrj+JQ68/heb6k0ZXhSguMXghIgqDEAI1B7ajo+UCag5shxDC6CoRxR0GL0REYWiuPwn35XMAAPflc8y+EBmAwQsRkUJCCNQe3AFIUu8LkoTagzuYfSHSGYMXIiKF/FkXX7AiBLMvRAZg8EJEpMCgrIsPsy9EumPwQkSkwKCsiw+zL0S6Y/BCRDQEf9YFUpASzL4Q6YnBCxHREITsRZe7GUCw4ESgy90CIXt1rBVR/Eo0ugJERGZnS0jEzJUPoruzLWiZpJQM2BJ4SSXSA880IiIF7OlZsKdnGV0NIgKbjYiIqA8ufUBWwOCFiIgAcOkDsg4GL0REBIBLH5B1MHghIiIufUCWwuCFiIi49AFZCoMXIqI4x6UPyGoYvBARxTkufUBWw+CFiCiOcekDsiIGL0REcYxLH5AVcYZdIqI4xqUPyIp4NBIRxTkufUBWw2YjIiIishQGL0RERGQpDF6IiIjIUhi8EBERkaUweCEiIiJLYfBCRERElsLghYiIiCyFwQsRERFZCoMXIiIishQGL0RERGQpDF6IiIjIUhi8EBERkaUweCEiIiJLYfBCRERElsLghYiIiCyFwQsRERFZCoMXIiIishTNgpef/vSnWLRoEdLS0pCVlaVoGyEEHn30UeTl5SE1NRVLlixBZWWlVlUkIiIiC9IseOnq6sIXv/hFfPOb31S8zZNPPolf/vKX2LBhA8rKypCeno6lS5eis7NTq2oSERGRxSRq9caPP/44AGDjxo2Kygsh8Oyzz+KHP/whbrnlFgDASy+9hJycHGzevBm33357wO08Hg88Ho//55aWFgBAq8sdRe0p3knoQavLjvb2dnR1daHH60VXdzdcre1GV42IKCa52nqvr0KIoQsLjf32t78VTqdzyHKnTp0SAMShQ4f6vX7ttdeK73znO0G3e+yxxwQA/uM//uM//uM//ouBf2fPnh0yZtAs8xKuhoYGAEBOTk6/13Nycvy/C2TdunVYu3at/+fm5maMHTsWtbW1cDqd2lTWxFwuF8aMGYOzZ8/C4XAYXR3dxfP+x/O+A/G9//G870B8738s7bsQAq2trcjPzx+ybFjByyOPPIInnngiZJnjx4+jpKQknLeNit1uh91uH/S60+m0/B8yGg6Hg/sfp/sfz/sOxPf+x/O+A/G9/7Gy70qTDmEFLw899BDuvvvukGXGjx8fzlv65ebmAgAaGxuRl5fnf72xsRGzZ8+O6D2JiIgo9oQVvIwcORIjR47UpCJFRUXIzc3Frl27/MGKy+VCWVlZWCOWiIiIKLZpNlS6trYWhw8fRm1tLbxeLw4fPozDhw+jra3NX6akpASvv/46AECSJKxZswb/9m//hr/85S84evQovvKVryA/Px+rVq1S/Ll2ux2PPfZYwKakeMD9j9/9j+d9B+J7/+N534H43v943XdJCCVjksJ3991343e/+92g199++22Ulpb2frgk4be//a2/KUoIgcceewwvvPACmpub8dnPfhb/+Z//icmTJ2tRRSIiIrIgzYIXIiIiIi1wbSMiIiKyFAYvREREZCkMXoiIiMhSGLwQERGRpcRE8PLTn/4UixYtQlpaGrKyshRtc/fdd0OSpH7/brrpJm0rqpFI9l8IgUcffRR5eXlITU3FkiVLUFlZqW1FNdDU1IS77roLDocDWVlZ+NrXvtZvOH4gpaWlg/72999/v041js7zzz+PcePGISUlBQsWLMA//vGPkOVfffVVlJSUICUlBTNmzMBf//pXnWqqjXD2f+PGjYP+zikpKTrWVj3vvPMOVq5cifz8fEiShM2bNw+5zZ49ezBnzhzY7XZMnDhR8SK5ZhPuvu/Zs2fQ312SpJDLzJjZ+vXr8alPfQqZmZkYNWoUVq1ahYqKiiG3i7Vzf6CYCF66urrwxS9+MezJ7G666SacP3/e/+9Pf/qTRjXUViT7/+STT+KXv/wlNmzYgLKyMqSnp2Pp0qXo7OzUsKbqu+uuu1BeXo6dO3di27ZteOedd3DfffcNud29997b72//5JNP6lDb6GzatAlr167FY489hoMHD2LWrFlYunQpLly4ELD8+++/jzvuuANf+9rXcOjQIaxatQqrVq3Cxx9/rHPN1RHu/gO9U6b3/TvX1NToWGP1uN1uzJo1C88//7yi8tXV1VixYgWuu+46HD58GGvWrMHXv/51vPnmmxrXVH3h7rtPRUVFv7/9qFGjNKqhtvbu3Ytvfetb+OCDD7Bz5050d3fjxhtvhNvtDrpNrJ37AQ25dKOFKF3BWgghVq9eLW655RZN66M3pfsvy7LIzc0VTz31lP+15uZmYbfbxZ/+9CcNa6iuY8eOCQDiww8/9L+2fft2IUmSqKurC7rd4sWLxYMPPqhDDdU1f/588a1vfcv/s9frFfn5+WL9+vUBy3/pS18SK1as6PfaggULxDe+8Q1N66mVcPc/nOuBlQAQr7/+esgyDz/8sJg2bVq/12677TaxdOlSDWumPSX7/vbbbwsA4sqVK7rUSW8XLlwQAMTevXuDlom1cz+QmMi8RGrPnj0YNWoUiouL8c1vfhOXL182ukq6qK6uRkNDA5YsWeJ/zel0YsGCBdi3b5+BNQvPvn37kJWVhXnz5vlfW7JkCWw2G8rKykJu+8c//hEjRozA9OnTsW7dOrS3t2td3ah0dXXhwIED/f5mNpsNS5YsCfo327dvX7/yALB06VJL/Y19Itl/AGhra8PYsWMxZswY3HLLLSgvL9ejuoaLpb99pGbPno28vDx8/vOfx3vvvWd0dVTT0tICAMjOzg5aJh7+/mGtbRRLbrrpJvzzP/8zioqKcOrUKfyf//N/sGzZMuzbtw8JCQlGV09TvrbfnJycfq/n5ORYql24oaFhUCo4MTER2dnZIffjzjvvxNixY5Gfn48jR47gBz/4ASoqKvDaa69pXeWIXbp0CV6vN+Df7MSJEwG3aWhosPzf2CeS/S8uLsZvfvMbzJw5Ey0tLXj66aexaNEilJeXo6CgQI9qGybY397lcqGjowOpqakG1Ux7eXl52LBhA+bNmwePx4MXX3wRpaWlKCsrw5w5c4yuXlRkWcaaNWvwmc98BtOnTw9aLpbO/WBMG7w88sgjeOKJJ0KWOX78OEpKSiJ6/9tvv93//xkzZmDmzJmYMGEC9uzZgxtuuCGi91ST1vtvZkr3PVJ9+8TMmDEDeXl5uOGGG3Dq1ClMmDAh4vclc1m4cCEWLlzo/3nRokWYMmUKfv3rX+MnP/mJgTUjLRUXF6O4uNj/86JFi3Dq1Ck888wz+P3vf29gzaL3rW99Cx9//DHeffddo6tiONMGLw899JB/zaNgxo8fr9rnjR8/HiNGjEBVVZUpghct9z83NxcA0NjYiLy8PP/rjY2N/hW9jaR033Nzcwd11uzp6UFTU5N/H5VYsGABAKCqqsq0wcuIESOQkJCAxsbGfq83NjYG3dfc3NywyptZJPs/UFJSEq655hpUVVVpUUVTCfa3dzgcMZ11CWb+/PmWv+E/8MAD/kEJQ2UOY+ncD8a0wcvIkSMxcuRI3T7v3LlzuHz5cr+buZG03P+ioiLk5uZi165d/mDF5XKhrKws7BFbWlC67wsXLkRzczMOHDiAuXPnAgB2794NWZb9AYkShw8fBgDT/O0DSU5Oxty5c7Fr1y7/KuuyLGPXrl144IEHAm6zcOFC7Nq1C2vWrPG/tnPnzn7ZCKuIZP8H8nq9OHr0KJYvX65hTc1h4cKFg4bGWvVvr4bDhw+b+vwORQiBb3/723j99dexZ88eFBUVDblNLJ37QRndY1gNNTU14tChQ+Lxxx8XGRkZ4tChQ+LQoUOitbXVX6a4uFi89tprQgghWltbxfe+9z2xb98+UV1dLd566y0xZ84cMWnSJNHZ2WnUbkQs3P0XQoj/+I//EFlZWWLLli3iyJEj4pZbbhFFRUWio6PDiF2I2E033SSuueYaUVZWJt59910xadIkcccdd/h/f+7cOVFcXCzKysqEEEJUVVWJH//4x2L//v2iurpabNmyRYwfP15ce+21Ru2CYq+88oqw2+1i48aN4tixY+K+++4TWVlZoqGhQQghxJe//GXxyCOP+Mu/9957IjExUTz99NPi+PHj4rHHHhNJSUni6NGjRu1CVMLd/8cff1y8+eab4tSpU+LAgQPi9ttvFykpKaK8vNyoXYhYa2ur/7wGIH7+85+LQ4cOiZqaGiGEEI888oj48pe/7C9/+vRpkZaWJr7//e+L48ePi+eff14kJCSIHTt2GLULEQt335955hmxefNmUVlZKY4ePSoefPBBYbPZxFtvvWXULkTlm9/8pnA6nWLPnj3i/Pnz/n/t7e3+MrF+7gcSE8HL6tWrBYBB/95++21/GQDit7/9rRBCiPb2dnHjjTeKkSNHiqSkJDF27Fhx7733+i+CVhPu/gvRO1z6Rz/6kcjJyRF2u13ccMMNoqKiQv/KR+ny5cvijjvuEBkZGcLhcIh77rmnX9BWXV3d77uora0V1157rcjOzhZ2u11MnDhRfP/73xctLS0G7UF4nnvuOVFYWCiSk5PF/PnzxQcffOD/3eLFi8Xq1av7lf/zn/8sJk+eLJKTk8W0adPEG2+8oXON1RXO/q9Zs8ZfNicnRyxfvlwcPHjQgFpHzzf8d+A/3/6uXr1aLF68eNA2s2fPFsnJyWL8+PH9zn8rCXffn3jiCTFhwgSRkpIisrOzRWlpqdi9e7cxlVdBoH0feD2Ph3N/IEkIIXRI8BARERGpIq7neSEiIiLrYfBCRERElsLghYiIiCyFwQsRERFZCoMXIiIishQGL0RERGQpDF6IiIjIUhi8EBERkaUweCEiIiJLYfBCRERElsLghYiIiCzl/wcRpdnZtj7p8QAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(tree1, X, y,\n",
    "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(tree2, X, y,\n",
    "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_quad = np.random.rand(200, 1)\n",
    "y_quad = X_quad ** 2 + 0.025 * np.random.rand(200, 1)\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "\n",
    "tree_reg.fit(X_quad, y_quad)"
   ],
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    "collapsed": false
   }
  }
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