ch3_classification_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check if python >= 3.5\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# check if scikit-learn >= 0.20\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= '0.20'\n",
    "\n",
    "# common import\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# plot pretty figure\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# where to save figure\n",
    "project_root_dir = '.'\n",
    "chapter_name = 'classification'\n",
    "images_path = os.path.join(project_root_dir, 'images', chapter_name)\n",
    "os.makedirs(images_path, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, \n",
    "             tight_layout=True, \n",
    "             fig_extension='png',\n",
    "             fig_resolution=300\n",
    "             ):\n",
    "    \n",
    "    fig_path = os.path.join(images_path, fig_id + '.' + fig_extension)\n",
    "    \n",
    "    print('saving figure', '\\t:', fig_id)\n",
    "    \n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "        \n",
    "    plt.savefig(fig_path, format=fig_extension, dpi=fig_resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "outputs": [],
   "source": [
    "# MNIST\n",
    "\n",
    "The MNIST dataset is a set of 70,000 handwritten digits. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 'details', 'categories', 'url'])"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "mnist.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "feature 1 is \t: pixel1\nfeature 2 is \t: pixel2\nfeature 3 is \t: pixel3\nfeature 4 is \t: pixel4\nfeature 5 is \t: pixel5\nfeature 6 is \t: pixel6\nfeature 7 is \t: pixel7\nfeature 8 is \t: pixel8\nfeature 9 is \t: pixel9\nfeature 10 is \t: pixel10\n"
    }
   ],
   "source": [
    "mnist_feature_names = mnist['feature_names']\n",
    "\n",
    "for i in range(10):\n",
    "    print('feature', str(i+1), 'is', '\\t:', mnist_feature_names[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \n**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown  \n**Please cite**:  \n\nThe MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \n\nIt is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \n\nWith some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \n\nThe MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\n\nDownloaded from openml.org.\n"
    }
   ],
   "source": [
    "mnist_descr = mnist['DESCR']\n",
    "print(mnist_descr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "key 1 [ id ] is:\n 554 \n\nkey 2 [ name ] is:\n mnist_784 \n\nkey 3 [ version ] is:\n 1 \n\nkey 4 [ format ] is:\n ARFF \n\nkey 5 [ upload_date ] is:\n 2014-09-29T03:28:38 \n\nkey 6 [ licence ] is:\n Public \n\nkey 7 [ url ] is:\n https://www.openml.org/data/v1/download/52667/mnist_784.arff \n\nkey 8 [ file_id ] is:\n 52667 \n\nkey 9 [ default_target_attribute ] is:\n class \n\nkey 10 [ tag ] is:\n ['AzurePilot', 'OpenML-CC18', 'OpenML100', 'study_1', 'study_123', 'study_41', 'study_99', 'vision'] \n\nkey 11 [ visibility ] is:\n public \n\nkey 12 [ status ] is:\n active \n\nkey 13 [ processing_date ] is:\n 2018-10-03 21:23:30 \n\nkey 14 [ md5_checksum ] is:\n 0298d579eb1b86163de7723944c7e495 \n\n"
    }
   ],
   "source": [
    "mnist_details = mnist['details']\n",
    "\n",
    "cnt = 1\n",
    "for key, value in mnist_details.items():\n",
    "    print('key', str(cnt), '[', key, ']', 'is:\\n', value, '\\n')\n",
    "    cnt +=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "MNIST categories are: {}\n"
    }
   ],
   "source": [
    "mnist_categories = mnist['categories']\n",
    "print('MNIST categories are:', mnist_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "MNIST url is: https://www.openml.org/d/554\n"
    }
   ],
   "source": [
    "mnist_url = mnist['url']\n",
    "print('MNIST url is:', mnist_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "X shape \t: (70000, 784)\ny shape \t: (70000,)\n"
    }
   ],
   "source": [
    "# read in the data & target\n",
    "X, y = mnist['data'], mnist['target']\n",
    "print('X shape', '\\t:', X.shape)\n",
    "print('y shape', '\\t:', y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "the raw data in one row reshaped as a 2D-array of 28x28 is: \n\n [[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   3.  18.\n   18.  18. 126. 136. 175.  26. 166. 255. 247. 127.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.  30.  36.  94. 154. 170. 253.\n  253. 253. 253. 253. 225. 172. 253. 242. 195.  64.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.  49. 238. 253. 253. 253. 253. 253.\n  253. 253. 253. 251.  93.  82.  82.  56.  39.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.  18. 219. 253. 253. 253. 253. 253.\n  198. 182. 247. 241.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.  80. 156. 107. 253. 253. 205.\n   11.   0.  43. 154.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.  14.   1. 154. 253.  90.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 139. 253. 190.\n    2.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  11. 190. 253.\n   70.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  35. 241.\n  225. 160. 108.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  81.\n  240. 253. 253. 119.  25.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n   45. 186. 253. 253. 150.  27.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.  16.  93. 252. 253. 187.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0. 249. 253. 249.  64.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n   46. 130. 183. 253. 253. 207.   2.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  39. 148.\n  229. 253. 253. 253. 250. 182.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  24. 114. 221. 253.\n  253. 253. 253. 201.  78.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.  23.  66. 213. 253. 253. 253.\n  253. 198.  81.   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.  18. 171. 219. 253. 253. 253. 253. 195.\n   80.   9.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.  55. 172. 226. 253. 253. 253. 253. 244. 133.  11.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0. 136. 253. 253. 253. 212. 135. 132.  16.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]]\n"
    }
   ],
   "source": [
    "# print the raw data of one digit\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# get the first row of data in 'data'\n",
    "some_digit = X[0]\n",
    "\n",
    "# reshape the row into a 2D array of 28 rows and 28 columns\n",
    "some_digit_image = some_digit.reshape(28, 28)\n",
    "print('the raw data in one row reshaped as a 2D-array of 28x28 is:', '\\n\\n', some_digit_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot one digit as image\n",
    "plt.imshow(some_digit_image,\n",
    "           cmap='binary')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "data in y[0] is:\n5\nwhose type is: <class 'str'>\n"
    }
   ],
   "source": [
    "# print the target value corresponding to X[0]\n",
    "print('data in y[0] is:')\n",
    "print(y[0])\n",
    "print('whose type is:', type(y[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digit(data):\n",
    "    image = data.reshape(28,28)\n",
    "    plt.imshow(image, cmap=mpl.cm.binary, interpolation='nearest')\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "after casting, data in y[0] is:\n5\nwhose type is: <class 'numpy.uint8'>\n"
    }
   ],
   "source": [
    "# cast y into integer\n",
    "y = y.astype(np.uint8)\n",
    "print('after casting, data in y[0] is:')\n",
    "print(y[0])\n",
    "print('whose type is:', type(y[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "saving figure \t: the_first_300_digits\n"
    },
    {
     "data": {
      "image/png": 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iEJaQnZ2NlJQUVS3R2rVrMWbMGDRv3pzf6IULF2LBggVG1VmMobZJ5O/vj0aNGinq16elpWHYsGG8mxkcHKzIB1FF1dzWrVs3i2PBW7duYejQoWYFLs+fP29VEv3www+ws7NTlKrz4MEDREVFgTGG2bNnY9u2bTh79iyOHTsGrVarnEQLFy6Er68vb3VWrFgBrVbL30hKs0Z1ocv+evXqybr28uXLCA0NBWMMW7ZskdRlMoeaYyJLYzM/Pz/ZJDpw4AA8PDzw3Xff8a1t27ZwdHQEY8ykRJcpiBm2PXr0QEZGBu7fv4/OnTtDEARFeVmiPoOplshScqE5nD17Fowx9OnTh6uqDhw4ULE9Yzhx4gTs7OwU92amT58OT09PvXHlzJkzlZOosLAQjDG9dNszZ85YlUS6N0quZJafnx+0Wq1k1RlLyMnJ0RPqs0QiuS2RSCBj0+d+fn6YNWuWbJ9jY2PBGNMbA1VWVsLZ2VnR2KBv375mSaRGwFN8nhirTp6Li4t7I/lYTk5ORoU3peDOnTsQBEFvlvj8+fMQBMFssp/ZMRERoW7duno3ZMWKFQgMDERRUZEiR2tCfHPKnZ1jjCEkJMSkXPChQ4dkE33BggX8Rrdo0cLkTT506JBsErm7u4MxBg8PD8TGxvJt1qxZil9Iw4cPN/r5n332mSJ74sRCzW3UqFE4cOCAoul3XYgt7tSpU1XZMQcnJyckJCQovr5u3bp6+g3Pnz9Hs2bN8Pnnn5u8xuJUW1paGnx8fJCQkMAFL6zVEulClCeS06edO3cutFotHBwc0KNHD745ODhAo9Eoni4XyWFMHunp06f84fX398f+/fvN2nr58iWio6Ph5uYmWetAKsQ1KN009cWLF8POzg579uxRZFMUaBRbn5YtW1rLXQDVJWGICCEhIejZsyfS0tL4eNuYkElNODg4wMHBAfb29vxvBwcHTJ48GZMnT0abNm1UkcgUiMjkXICk+eqcnBwwxtCyZUtERUUhISFB9VvJGOSSCKh+U/Tv31/vzVmvXj09IRQlfhhbJ1q+fDkGDBjAj1uagQJ+6cZYUy5XREFBASIiIuDo6Ig2bdpg27ZtiI+PN6mXJwXHjh3Ti1J4E5Ekuq14zUgQOdea2t4EicTn39jEj+RFnxUrVoCIMHHiRFUESklJMZkp+Pr1awQGBlp19k8JFi1axFVlHj9+jIqKCjx+/BguLi78Rnl4eEhqjRMTE8FYtZzTqFGj0K5dO3h6emLSpElYunQpPD094enpqbiaw8OHD7lPzs7OYIypIhFQPcDWfSnl5+erslcTV69exZQpU9CuXTtMmTIFU6dO5ZsliK3j2LFjcf36db6NHTuWb1JaNLlgjGHQoEHGj/3HsV8NHh4e9PTpU2KM0aFDh+j27du0atUq+vHHHwkAMcbo559/Jj8/v1/TLQPs37+fhg0bRo8ePaK///3v9OWXXxJjjLy9vWndunUUExNj0UZJSQm5uLgQEdGrV6/4/rlz5xqcO378eHrrrbes9wVUYs6cObRkyRJ6+vQpERG/N69fv65lz2oHMTExVFhYSCdOnCBHR0e9Y786iY4fP04XLlyguXPn0qNHj/jNIaq+Uc2aNaNLly79mi6ZxNGjR+lvf/sbnTx5kj88f/rTn6hFixa17Nmvg927d9OHH35IpaWl/+dJ9PHHH1NhYSFt2bLF4JhNY8EGszh16hRFRUURUbUcsJI0kD86bCSywQaV+MNmttpgw68FG4lssEEl/lAkunPnDo0cObJWxCps+L+LPxSJTp48SatXr/4/q3pjgzKkp6fTzJkziTFGjDGaOXMmpaenSzdgaZHp9OnTqstBGsPhw4cRHBxs1erRly9ftmp6wu8JBw8etGrC5N69e9G/f3+TsYl/JJCJtI+kpCRp11s64fz5828km/X169cgIkVCIuaglkR5eXl6JVTEUCSpohXGkJqaCkdHRwOh/YyMDAiCgEWLFin2V4QYUSK3Tq0piEXdpJax14WpcJyYmBi9UKn4+HgwxmT9ti9evMCyZcsQGRlptFK8FAF6EceOHQMRGc0gSEpK4nYtZRjUGokA8EBEa0K8YVKENGpi4sSJqF+/Po+bGzNmDMaMGYMePXpIjpUzBjHqe86cOQbHXF1dIQgCcnJyFNnWBRFZzDqVCpFESlJNdIkj/oZiSJKDgwPy8vJw/vx5+Pr68jQYqcmIYgCruU1q5rVIFFMtTufOnblNc7DIjsrKSvj6+kpySi6ICBqNxmQuvhK0a9cOjDGkp6fLuk63mPFnn31mcFPXrFkDQRDQvXt3RcWfGauuMFATX3zxBQRBQHh4uGRbL168MJrfMmjQIPj6+uLRo0ey/asJkUTt27eXfS1jDAMGDNBreU+fPs1b9rCwMAQEBICxaj2L1NRUybbFFHYiwpUrV3Dz5k3cvHkT06ZN48IoUVFRePLkiUVbIonMwSokAqq1FtRWINu+fTuICP369UPXrl3h7+8PBwcH7qSUlGypmD17Nk/1lYIePXrA29tbL4/EGMQU4ri4ONk+OTs7QxAEBAQE4MGDBygoKMClS5d4aryx0u6mIOY9tWjRgud1XblyBQkJCVZLW9+6daukB8gYxMzVDRs24Nq1a3x/RkYGWrdureej3Bb4wIEDvOtWE1VVVejduzeICAMGDJDttzFIIpoUQ8eOHcP48eNVOXPs2DHY2dmZbIKHDRsmy15GRobZh17MGJWCTp064fz58xbPu3jxIhhjiurBfvLJJ3qV9sSxlrjJSTkoKyvTy4oNDAyEl5eX7LQCc3j+/DlatWqliEQ7duzg+n0+Pj745JNPcO7cOa7Zp4ZEZWVlmDx5skmlILFgWJMmTWT7rYukpCS9cZE5SPqF7t69K/shN4aMjAx+o4cMGYKxY8eiefPmICJZlefEh8jd3R0dOnRAWlqagcaCmFQnpeu1ZcsWSfoGHTp0ABFh/fr1kn0Vce7cOTRo0ACCIGDQoEHo3r073nnnHb7JfZi6dOli8EBGRUXxlHFrzFCKXToleP78OT788EPui5jZq9Fo0L59e75fii6hMRjT13v8+DH27NkDIkKLFi0U2QX0x0JWIxEAq0nUOjs78/4sUN2/j4yMxOLFiyXb2LZtG78JR44cQVJSEv+/KH8r/l+3ErYcdOnSxaDFiImJUTQeElFeXo5PPvkEzs7OqrNcc3NzeVHimhg8eDAYY6qXDy5fvqx3r9Ti9evX/N/t27fDzc0NGo0GY8aMUT2Vbm9vDyKCi4uLbMEXYxBn7qxKImtNAPj5+YGIMGHCBL4vMTFR1qzSixcvOEmWLl2K169f4/Hjx5g9ezZmz56NQYMGwcHBgZ+j5MH38/PjKkQzZ87EzJkzrXJzcnJyuN2XL1+qsiVKZNXExYsXodVqVU93v3r1Cp07d7aqKq0udMdHT58+VWxHJDtjDF9//bXV/NPtzh07dszkeZJJRP/JalWLtm3bYvPmzQZvSSJC7969Jds5ceIEoqOjwRjDjBkzjJ4zZswYMMbQvXt3Rdm4P/zwA785Ump3SsXYsWPBGJOsiWcK33zzjdEZPwCIiYkBYww3b95U9Rnp6elW6cobg+7iuNJuXVlZGdq3b48WLVq8EckC4JfunampcFla3KbSY60BIpIdGXHq1CnUrVsXQUFBRjWs58+fz1O6582bp8ivTp06GUwI9OvXD3v37rU4m2cKona0IAiSpmKVYOTIkVYh0b17937TJFq3bp2s6AJzEBdWjbU65rp1kkn03nvvwcvL642VnldCIqB6KtaUOEVubi4iIiLAGMPIkSMV+fXDDz+gXr16JmfVlGLt2rVgTF+px5o4ceKEVUgEAAEBAaq6Ww8ePDDaFdYlUXZ2tiLbIomUTPboQrfrZixKwSok2rVrF4gIW7ZsUeWsKcTExICIsGPHDtnXPn782OT4QtQPb9OmjeoxiLXx5MkTPr1t7XIjAPiYUS3UduUnT54MxhiOHz+ODz74AFlZWcjKyuJjOjVjIpFErVq1QmFhIUpLSxX7WXMyoeZmKvxHMomqqqoQFRWFmTNnKnbSHNSQ6PcMMexHqX62OVirJWrcuLEqEmVkZMDV1dVkTF1sbKzi8czx48fBWLX0lq+vLyIiIhAfH6+4qw0YtkrmunmAzNIqCxcutErf0xh69OgBNzc3qymr/l4ghv28ichzIrIKiYYNG6Z6Uunp06c4cOCA3vKDuLaldkJAXL8TNxcXF6xevVqVTTmwaSzYYINK/KGS8mywoTZgI5ENNqiEjURG8L//+79Up04dqlOnTm27YsPvADYSWUB2dnZtu/CHgahh8EeDjURGYGdnR3Z2dkRE9Pjx4zf+ecXFxeTv7y/rmkGDBvHWUhAECg8Pp3/+859vxkEro6KiorZdsC5+tXnA3xkePHiAwMBACILwRoRadHHp0iUQkeRI5piYGAiCAH9/fyQmJmLatGlo2LAhBEHAV199pdgPNzc3aLVa/q9uZIa42dvbY/r06YrsP3v2DG5ubpKTJS1h//79+OKLL1BcXGwVe5MmTQJjDN26dZMVmWOWRNeuXeObtZCamoqkpCQeyezj44MLFy4oslVaWoq7d++iT58+vMiTmNZdVFSEp0+f4u7duxg5ciQEQcCCBQtk2b969Sp/eBYuXKjIRykQK9RJhRhcqlubdN++fWCMoXnz5orSw69du2aUNC1btsTQoUMxdOhQREdHQxAEhIaGyrYvIikpSVXZSl3Qf9aFHBwcFBWMrokbN27w9Ss5FdhN3rkJEyboLWDFx8fzimZKceXKFaNvOB8fH0WxbcOGDeM2+vTpw/8dNGgQgoKCEBwcjODgYB4xvWvXLtmfoRsz96YwduxYWSSaN28eBEHQS1HYt2+fqgrs3333HQRBwNChQzFu3DhcuXIFV65c0RP9mDt3LgRBUJV6nZubC0EQFNdj0gURoWHDhggJCZH10JtCRUUFJ9E777wj3Y+aOzQajdH4uKKiIuTn5+Pq1avo1q0bQkJCZL2dxbdns2bNsGHDBgCG0kru7u6S0w0GDBiAoKAg3L17F6WlpTh//jxcXFyQkZGB0tJSdOrUCdu3b8eZM2dUNfeDBg3iD6e5kvJycO/ePXzxxRd48OAB7t+/D09PT9lJj2JQbJs2bfDo0SPcvn0bzZo1U0wiAMjMzMS2bdsMrk9JSYGjoyMEQcCPP/6oyLYuSKEAijE7AQEBOHHiBIjIKt3EuLg42ZnBBiSSIsJRXFyMPXv2oEOHDnqVls1B7Mfr3iBxzCEIAt577z1ZkdFxcXHo06ePVcUfjaGwsBCdOnWyihxVQUEB3n//fd66u7u7Y+PGjSAixMTEyLK1bNkyfrPHjRvHu3NEpJhEpuDg4ABBENC2bVur2NNqtfDz88P9+/dV2Rk/fjxXjCIiDBo0CFu2bMGWLVvw+eefo3379pg/f76soOmlS5eqJ9HcuXMlX1xeXg6tVouNGzda7JOKJNLtEvbt25cT58SJE7ySuBQsW7YMgiAoEhdUAjEF4tNPP1V0/YYNG+Dm5gYiQnBwMOLj4xEYGIioqCgQkaKUiNmzZ3PpKd1up7VIVFVVhbVr10IQBPTs2dNsdqccDBgwAEQke4xaE+Xl5Vi7di20Wq3JyGs3NzeMHTtWss3Kyko0aNAAjDFs3bpV0jUGJFIy6Js6darFKGRx0Obv72/Q7KanpytSqVmzZg0YY7h+/bpsn+Xi+vXreloLcnDr1i0QEcLDw/VSHvLz8/nNvnTpkmLfli9frkei6OhoxflTIsTfljGmeiyclpYGNzc3eHp6wsnJCR07dlQsx2UKrVu3BhHh/fffV23ryJEjcHFxQVRUlKTzDb6FnZ2d7MrbU6dOlfTwjx8/nk8k1JyRc3d3V5TodubMGTRs2BBff/21qsQxEXv37jWZjqE0Ge+zzz5Du3btjE5hW4NEnTp14g88/Sed3d3dXXJX2xhatmypOvEQqM4+1mq1aNq0KV6+fImsrCw0atRIEYmmT59uVOUH+GVyxhpjLQCIiIiAj4+PpDwvg28hMlAUMAnbAAAgAElEQVTqTb1//z7Cw8Ph7+9v8dyHDx/yG1OzQvi+ffsQExMj+y0PAF5eXlbJyTl9+jT//g8ePDA4/tlnn8l+sG7evAlnZ2ccOHDA6HEiQoMGDVRlDIvdOQ8PD6Snp/PxZefOnRV1dwsKCiAIArRareru8s6dO0FEejOJ3377rSISmXuoxWUCa5FIFMg0Jv1cE0a/RWFhIQYPHowvvviC75s0aZLBeatXrwYRoVu3bibfEDUhDoAZY1aRuxX9/eijj7jdDh06KLJz584dREZGQhAEuLu749NPP+VrTDXTwrt06SI5UzYkJMRol/PGjRt6qkdKIGaNtmnTRu8BE7vPcrQL7t27h/bt20MQBGzbtk2VXyLy8vLg7u5uMPs4aNAgEJGsZYeCggK+LhQeHq73ohO7cz169LCK3wcPHgRjzKRIpC5MvgqKiopARPD29gYABAcHY926dZg0aRIWLFgAHx8fEJGiaV9xkuFNSDGJD7nSfvySJUuMLjrWJJEgCJK7vRs3bjSa1Pb9999j//79ivwUIS4d1CSLEhKJBLKWxqCI7t27g4j0tPaUkAio7tLVr1+fTxr069cPcXFxcHd3t6p8gbhmZG9vb1Ej0Gx7evXqVdy4cQNA9VhhypQpmDp1Kt+uXr2qyMHs7GwEBATAxcUFK1euVGTDFNq2bQtXV1c4OTkhKytL9vXFxcUICAjgRGnatCmSk5MxZswYZGRkYPr06fz4uXPnJNvVarUYNWqUXhbn999/j3v37sn2URciicLCwpCUlISUlBS+hiSXROJ3rjmgvn37tiofT506hS5dusDR0RGTJk3CmTNnwBhD3bp1FVXvyMvLw6xZswxm4jp27GiVcbEIsWczcOBAs+fVauycqfGRWpSWloKIrLauYS1cuXIFvr6+GDlyJNauXatYnVUXp0+fxr59+zB79my9FlOr1crWGZg7dy6PKOnUqRPi4+OxadMmfp/8/f2RkZFhdLwoBYWFhbh27Rrs7e3h4+OjajLl10Dnzp0lzRjXKonExVZrk6i4uBiMMSQmJlrVrjVQVlaG2NhYNGjQwKokz8nJQWhoKIhILypELpKTk7l4iri5uroiOTkZTZo0gSAI8PT0xGeffWY133+rkBq9UKsaC//zP/9D3333HTHG6P79+1axGRsbSzdu3KDKykrat28fNWzY0Cp2/69h3rx5/O/ExES9/Zs2baLLly/T69eva8O1Xw3//d//TZs3b6auXbvSoUOHTJ5X60Ila9asoY8++sgqN+Ts2bP0448/Uvv27alZs2ZW8M4GGyyj1klkgw2/d9gyW22wQSVsJLLBBpWwkciG3y3q1KlD7777Lr148aJW/bCRyIbfLV6/fk379++nffv21aoftUKitm3bkiAI5O3tXRsfbxTZ2dmUmppKqamptGvXLqvb379/P0VGRhJjjIKCgigwMJACAgLIy8uL+vfvTzdu3JBkJz8/n7RaLQmCYHQ7evSoKj937txJjDEKCAig1NRUVbZqYvr06cQYo1u3blnF3pkzZ8jLy4sGDBhgFXtERK9evaLnz5/T8+fPpV9kbhGpoqICmZmZ6N27N5o0aYJ+/fqhW7duaNKkCSIiIhQXzhJL2UtR0TEW+FoTopZCcHAwAgMDERwczOPSzpw5Y/H6jRs3onHjxtBoNNBoNHB2dkaXLl0wZswYPY2B27dv6/1fDiIiIqDRaPQKpb148QJBQUEgIknhOYsWLeL1lkxt/fv3V+SfiPbt2/N0Cq1Wi5CQEFX2dJGdnY3OnTsjOjoad+/etYrNSZMmGcTlKUFlZSWuXbsGf39/HkZ0+fJlSZnTZkn0ySefmK3XQkQ4fPiwbIcnTJgAxhjc3NxkxZ8Zw4ULF0wGirL/FEJeu3Ytbt26ZVKSijHGCVRzi4yMxLJly5Cens7/rySWrGfPnkZjsO7du4devXpJSgto0qQJJ8vLly/1tp49e/LvriZSQyTR8uXL0bVrV8U1b03hyy+/NEiNUIOioiJERUUpSqERsWrVKgwePNjo8z1nzhyLtXrN3rm5c+eaDMkpKSlB//79FWUnlpaW8oehX79+qkpr6OYoCYKAcePGYfz48Rg/fjz/WySUGDhaEyNGjABjjJOjpKQE69at4wWkdN/0RIT09HTZflZUVGD06NEYP3680eMREREWb5bog7nfSzwnOTlZto8AuOhHbm4u30dE+Pe//63IXkpKCg4ePMj/f+3aNRARunfvrrows4gnT57AwcFBtvTavXv3EBwcDDs7O0yePBk5OTl6xyMiIiTlPSmKncvIyOA1Yb788kvZ1+uSiDH1FZ9nzJhhNlnuxo0buHHjBu7du8ej0nWRkZEBjUaDZs2aGegTdOjQQa9lEiu+KUFmZqZJjYaIiAiLOhUDBw7Erl27eCl7YxB/U1NV3aSgQ4cOmDJlip5NJSR6/PgxiAh2dnZwdnZG37599aTYxADcgoIC/Pzzz4r9BcBbDanIz89HcHAwNBqNycTDQ4cOcf0Gs59t6cPEZDLdpDIxd4OIFMlRVVRU8Mre1iBRTk4OIiIiIAgC5s+fj/z8fNk2RJI0a9YMBw8exO3bt3H79m2EhIQYkMhaCWu6iIiIsEr1a/E33b17t2IbS5YsQVhYGC+4ppREYk6auc1aICKTldSNYcOGDWjQoIHFl83SpUvh4OBgtkC12W+xevVqCIKgJ0skbkOHDpXssDGkpaUpEicxh3nz5vFxgRK8fPkSOTk58PDwQOPGjeHk5ATGGCIjI7F+/XoMGDCAj590uyjmsHv3bi6JtX79eoMu27Fjx9CkSRPFD9SdO3c4ucVxplpZ3RcvXoAxBjs7O3h7e0uSUTOFIUOGGCVMcHCwVUk0cuRIEJFqgRZdfPDBBwgPDwcRmX3BWfwWN2/exLp161BQUICePXvygaeLiwuuXLmi2ME3QaKysjJOoo0bNyq2s3//fpw/fx7bt2/HunXruD5aRkYGHyNFR0dLsnXp0iXUq1dP7wV05MgRHDlyBAkJCfy3XLFihSJfZ82aZSCAqRbPnj3T81eNRO+TJ0+MviTmzJkDIrKKEipQPY6tX78+6tWrp9jG4cOH8dlnn6FTp05ca5CIMHr0aLPXyXoVvH79GpcvXwZRdZHZ58+fK3b46NGjVicR8Itoh7VzlERkZGSgQYMG0Gg0knN2RM0KMa1Zd4uOjsbRo0cV+/PkyRO939HR0RFz5sxBamqqYptA9XS+OBuoVud6+fLlBtPQRUVFmDp1Knx8fPDdd9+psi9i9uzZqlo3MWVd3Hx8fPDJJ59Y1NLgn+jk5MQV+8WtV69e8PPzQ926dfWMr1692ippuP7+/vzmK5mgMAZdDbY3hejoaD5GUgpxSlWNrJUxREdH64011SjEFhcXo0ePHggPD1etVpqVlWUwG1dZWQk7Ozs0aNBAdq/m/v37Bt/t9evX6N27t2IBnJcvX4KI4OTkhD59+ki+jpPoo48+MjsAfO+99/Dee+9h+fLlihw0Bt2FQ6WVAmr++KK+wJsk0Y8//qiaROXl5dixYwccHBwkj6+k4MWLF5g8eTI8PDzAGMPnn3+uyt6FCxcUTyzUxPnz5w32hYWFgYhkTcnfu3cP4eHhRvUZBgwYoKpy+PTp0/kzn5qainbt2iEwMBArVqxAamqqce1A8Y/y8nI8fPgQycnJSE5Oxvvvv4/k5GTs2LFDkoCdEqgh0dOnTzFs2DADnTGxOzdmzBhruqqHnJwcRd3QixcvGujLOTg4ID4+3uIakSmkp6cjODgYKSkpeovAW7duBWNMsoqnOdB/pKikSoTJQWRkJIgIvXv3lnzN4sWLQUSIjIxEZGQk+vXrh9jYWCxatAjNmzfHkiVLFPtTUFCASZMmYebMmUYbE19fX4NralVjobCwkD+MchX909PT9VRTdaMUAgICzK6lWIKDgwM8PDyg0WgQHR2NQYMG8SnvjRs3wsPDA05OTiaVUk3h8OHD8PHxwe7du/mL6eDBg9BqtZLCm4whOTmZ/4YzZsxASkoKUlJSMHHiRL5fzZsZqH7zBwYGStJgk4tXr16he/fucHFxUWUnIyMDe/fuxYoVK1SRqCZevXqFwsJCFBYWYtq0abCzszM45zdDIiXIyclBQkKCHon8/f0xY8YMVX4tXboUx44d412imutEGo0Gy5YtU2T70qVL8PDwgI+PD4KCguDi4gIiUlx9rqysDEFBQXBwcEDv3r0NYun69OmjON5PF3PmzIFWq9WLZLAWlixZYtXp7jeJkpISg321mh5eWFhIXl5eRNW/oCIbpaWldOLECbpy5QoxxqhHjx705z//2Sr+HT58mLp3765XRbyqqooiIiLo9OnTpNFoFNm9f/8+TZs2jUpKSsjFxYXc3d1p9uzZVLduXUX2cnNzKSMjg4YMGaJXWDg6Opp27NhBHh4eiuzWxLvvvkv+/v60YsUKq9gTERcXR5mZmZSbm2tVu78WbBoLNtigErakPBtsUAkbiWywQSVsJDKC06dPkyAI5O7uXtuu2PA7gI1EZvDs2bPaduEPhb/97W8kCH+8R07SNzpy5AgJgkD16tWjEydOvGmfJOHgwYO0adMmevvttyksLIw2bdpEmzZtolOnTqm23bp1a3r58iX17duXBEGgJUuWqLa5fft2EgSBGGPEGON/L168WJG99PR0bkt3U4OqqirKysqikJAQcnV1JS8vL/rrX/9KU6dOpalTp1L9+vXpo48+Uiz5fPr0aVX+mUNhYSENHDiQ7O3t6fz581RYWKjIzq5du2jBggW0YMECmj9/Pv/bLKTOj8fFxUEQBD2NgNrC69eveRHhmpuvry969uxplYoDWVlZYIwhLi5OcUSBiMzMTAwaNIiv3+imL2g0GtllYDp37sy/c1JSklXyc7755hsQVVc1NxYrV1xcDCJSnBohZhC/CQwdOhR2dnZ8++abb2Rd/9NPP2HSpEm8UnpNqQE3Nze4ubkZrXsl+VcXV8B/CyTaunWrxWQvHx8ffPnll6pC7V++fMlTwpVWDa+Jbdu28U30VUoNnJrQJRAATiTx/0ogCAK6devGk/GMoWvXrhAEQXaECQBs375dVWawOegSKDo6WlbtrB07diAkJMQg+qVFixYICQmBs7Mz3xcaGmpwvVkSffXVV7z1OXLkiKqW6M6dO5g1a5bZcularVZSnk5MTAzc3NxMVunTrcqt9u0sRlszxlS3RsYgvpyISK+8pyXUJJG4TymJFixYgMzMTEnnitoTclFRUYG2bdvC398fd+7ckX29OYgEkit8k5ycDEEQLBLv6dOn2LlzJ3x8fBAcHKwXpyh5lNe0aVOppxrFli1bKCkpiaqqqqhnz5588/T05Oe8evWKjh07Rg8fPrRo78WLF3TlyhWjxzw9PSk4OJj/v6SkRLHf//rXv8jBwYGIyOTnScGOHTto586d9MMPP+jtHzBgAPn6+hJjTNaYoXPnzop9qYmKigravn07tW7dWtL5Q4YMoS5dusj+HI1GQ3v27KHc3Fxav3697OtNYc+ePURE1L59e3rrrbdkXZufn09ERJMmTdJ7ZmrC1dWV+vfvTxs3bqQbN27Qli1bfjlojqV37tzhXTjdv+Xiu+++g6OjI4jIIDR/x44dICJ07doVc+fORdOmTS1meW7cuBFEZLLKNwAsXLiQv63V5j7NnTsXRIR+/foptkH/ac0aNWpkcGzgwIEgIlldOmPjoM6dOytqicSMXTkYO3as7M8RwRiDn5+f6lKbIlq1aqVoHCT6MmjQIFkp9YIgIDw8nP/f4i8nSk4lJSUp7s55enqa7VqJRBo+fDiOHj0KIpKVFCVi9OjRCAoKwqRJk3gGrjVIBAAHDhzgugNyJbPu3LnDSST6VJNQ4n45SXq6RBInGpSSyNPTU9Y1e/bskS15lZaWhrS0NLRv315VDpkuRo8erXgyAYAioZya+WoWSVRcXIz+/fvzmYo3QSLgl4dI3KSoo9aEg4MDiKqrSi9btsyqJAJ+UdJRkuhmbEau5t++vr6yZ+mOHTum97spJZGS3/vkyZOSzsvLy8O0adP493VxcQFjDB07dpT9mTWhlkREhGHDhkk+/8GDB2CMoUePHnyfxTFR3bp1afr06YRqwlk63SjatWtn8Zy0tDSyt7dXZF+EGFX99OlTGj9+vCIby5cvp9jYWKPHxCjrb775RrZdAFRVVUUAaMKECfxv3f1RUVEUFRUly27NsdHx48dp5syZsv2TiyNHjkiOYk9ISKAFCxbQuHHjKD8/n2JiYoiIKCMj4026KAnR0dGUmppK169ft3hufn4+de3alRhj+r+7VAYyxuDq6gpXV1dERERg165dktkLAH369JHUXSkqKlLVckyfPh329vb8zSynuPCGDRv42zIgIMAgV3/Xrl2K859IpzunC3G6W+rMmCnbpjYpLZOSMZGcwsei7kNNhISEqBIpWblyJW+FxGRHuSguLoYgCHB2dsa6detMnpefn4+ZM2dCEAQDDXpZJFq+fDl27tyJoKAguLq6ol27dpKd3bt3L4gILVu2lHyNUuhOKiglEWOMTzmLpeOzs7MRGBioiETbtm3j3TZroyZhjh07pjdeOnbsmNnrjZFIV6yzJk6dOiVLmktcZK6piTBv3jxZaeE1oUuihIQExXZ8fHwgCILZ7FpdAZyaQo6SSJSWlob69eujtLQU169fR1FREb766it89dVXkh3Ny8tD69atUbduXeTl5alK366Jmus3Dx8+5OtRckh069Yt1K9fn5PI2dkZw4YNQ2RkJBhjCA8Ph5eXlyIiiJUw5L7xpYD+M7FgbL/U1qhjx4746aefAFSTysPDw2SPYNKkSYiMjJTs36lTp1C3bl3MmjXL4JgxWWepsBaJ1qxZg7p16+pNFly7do3/vXz5cri4uEAQBKMqq5LuqPggFhcX603tKYGSFsIcOnbsyG0eP34cmZmZvMqC0s85deoUiAiurq56+7/++mskJycbCJ9bgqnZuZp/K4UuWcRNbpfu0qVLBl3Bdu3aGSj0+Pn5ISwsTJGElr29PRhjmDZtGr755huEhoZy2StjSkCWoEsitYu39+7dQ7t27UxWGOndu7dJwR7JJFq7di2aN2+uWooqLy/PKpEEIubNm2d2TGAtsqpF27ZtLc7OKYXaMZGI5s2bIzg4GMOHD8f8+fPh4eEBrVaLjRs34uDBgxg9ejS0Wq1i+axHjx5h6tSpXBFV9xlQEg2iSyI1irci8vPzDUjUoEEDi0Kgkp7kjIwMdOrUCUeOHFHN+NLSUq7DrEaGWERFRQWf2tbdnJ2dERMTw7sotQ1Rwkq39YmKiuJ/b926VbHtmtPc1npJ5eTkIDExUc+mtcUm1UCXRNZCmzZtOIneeecdlJWVWbym1jQW/t//+380b948OnToEHXt2rU2XLDBBqugVoVKmjVrRgcPHqS33367tlywwQbVqNU0w+fPn1NpaWltumCDDaphk8yywQaVMNkSnT9/nmJiYqiqqurX9McGG353MEmihg0b0oEDBygrK+vX9McGG353MEkiLy8vcnZ2po4dO1JmZqbVPvD27dtkZ2dHdnZ2VrP5e8G9e/do9uzZJAgCderUicaOHavYVnh4OAmCwLevvvqKvv76a1X+2dnZ6dkUt71796qyq4t//OMf1Lx5c1WJkr85mJv/jouL4yH61kJOTg4Xh1cSum4K5eXlvPSij4+P7OuXLFmCyMhICIKAJUuWKCqebA4LFy7kcXfiepEYj2hMJN0Url27hhkzZhgI14v2dNOW5WDHjh0GNsXN3t7eqECHEqSkpIAxhvXr11vF3vXr1zFq1CgkJCRg48aNqgT38/LyMHz4cL7dvHlT0u9plkT79u2zejlIXRJZEx9//DHPVSEi2Wo/NUUqoqKirFYG8e7du3oLrHv27MGePXsQGxsLxhi6d+8u6eZnZ2fz6oIajQZubm5wdXWFm5sbtFotGGMICwtDXl6ebB8TEhL4vW7cuDHCwsL0iGRMoEMJMjIywBjDxIkTrWJv1apVqvPQSktLkZiYCG9vb4MXiIuLCzZv3mz2eklJef7+/rIzH01Bt0CWnGQoc2jdujUYYzzEpby8HIwxxMbGSq43qtVq0ahRI5SUlCAsLAxarRaCICA4OBiXL19GSUkJDwWR+5CGhoYiPDzcqC+lpaUIDw+XFBHfq1cv9OrVC8uXLzdqKz8/n/+2cgJEAeDbb781Gnpz69Yt3mJaC6KPc+bMUW0rPT1dLzpj8+bN8PLyklxOZunSpWCMwcPDA99++63esezsbDRq1AiMMYPIbV1Iig15+PAhGGP48MMPJTlmDrotkTXSgwHwbFbdKnR+fn4gIsnZl2KUtag2tGnTJkRERPAWRMyJISJZJNqzZw8YY2a7Q2JFdmtAfECNaTnIRUVFBYYOHWrxIZIDXaI3aNBAsZ29e/dixowZGDp0qMF9JiI0adLE4gt0zJgx8Pb2xtixY01Gk4stp7lUeMl3Tmwu5aQ/GMPLly8RHx8PjUaDgQMHWqWEIRFh2rRpevtOnDgBBwcHLFiwQJKNtm3b8q6cOEYRxVl0u3lyK9qJJDIHa5KoR48eVuuCp6engzGGZcuWGa2PKgXl5eWorKxEeXk5oqKi4OTkxP2rW7euIpubN282iJf08/PjEeHiPkuVDInIYjnOsrIyxMTEYMiQIabtyHHe2dkZjDGrPPgxMTFgjKFNmzaqbRGR0erjcXFxGDFihGQ7r169wrRp03hXrmY4vCAIigbuXl5eWLx4sdFjDx8+RKNGjaxCohs3bvAHVMnkii7Gjh3Lx2tKcerUKe6PIAjo0KEDn1AIDQ0FYwzPnj2TbbdLly7893rw4AGmTZvGiTN69GiMHTtWEomk4MaNGyAixMTEmDxH1p3LzMyEm5sb4uLiUFpaqso5MfI4ICBAlR2gmkRz58412D916lRZJBKRlpbG07aHDBmCbdu2ITg4GIIgYNSoUbLL2g8bNgxNmzY1muQmZvyqJVF6ejo8PDz03sxqwBhD/fr1kZ2drdjG5cuXwRhDr169DLqzixcvBmPM4qDdGHRJBFTLSovJlPSfCH4iMpoEaAmlpaWYO3cuPv30U71UFXPZC7Lv3Jw5c6wyRXns2DHuoJIvqwsi4+qhU6ZMUf1GFjFv3jx+k4y1euYgEkU3W1JEu3btwBhTpWkH/KLhwBiDv7+/Ki3yS5cugTFmFelkxhh69+5tMGmxZ88eCIKgiESzZ882+tLJzMxEu3btJHfnjMHNzU2PPCEhIdi0aZPZa0yS6NmzZyYHk+Ige/To0bKdFGFtEjVt2tRg/9SpU606Pa/bvZMLcfJDNyP00aNHYIzh5MmTePz4saJsURHXr1/n62TipnQywMvLi+tOJyYm4vbt27LEDUXcv38fjFXryxmTA5g+fToaNmwou4tcVVUFT09PVa2kKfj4+MgeU5ok0dixYxETE2NU3Hz//v1gjMHNzU2Zp/hFLF6j0aiePvfz84O3t7fB/qlTp8Lf31+VbV2I2mmCIMiuyC22FLoZmE+ePEFgYCDOnTuHMWPGKM4Y1UXnzp35Q2BuMGwOoo6E7ta+fXucOXNGlp2qqiowxvDxxx8bPf706VMwxnDgwAHZPkZFRSExMVH2dZYQGxsLBwcHMMbQs2dPDBkyBBkZGWbHbiZJdP78ed41CAoKwqpVq7B27VqsW7dO742nBrpdEDWYOHEiNBoN9u/fr7d/9OjRsmfTLMHNzQ2CIMjO8Jw4cSKIyOCFUV5eDgAYPny4VUj09OlTDBkyBPXq1UPdunXNykAZw61bt1C3bl2jkQtOTk6YM2cO9u3bh1evXkmy17JlS5PT+yUlJWCM4ZNPPpHlIwDk5ubCxcXljbRGKSkpsLOz0/vuERER6Nixo1H5ZLNjooqKChQXF2PhwoVITExE27ZtQVStSZ2YmIjVq1erclZsiawRvRAcHGzQ6nh5eUleJzKG4uJiLFmyRC8sZ/HixYpIBFSLYYSFhcHd3d1gYa9169aKSOTm5obExEQkJiZi5cqVfP/t27fBGEPz5s1l27x48SImTZqENWvWYM2aNfD39+ciI7qb2m64SCIlLREANGnSBA4ODmZnztQgJSUF8fHxiI+Px4wZM/gwpuZL3/r6TTJgTRKJrWNhYSGfBRs+fLjkiAVjENeJGjVqxCcu1JAI+GWx0dPTE7GxsUhNTUW/fv1ARDh16pRse+JAmIhgb2+PTZs24dNPP4WrqysYYxg+fLgiP2vi1KlTmDFjBmbNmsW7O/Xq1cOPP/6o2GZJSQnc3NwUKf0AQGpqKp9EUDJVLhcvXrxAfn6+QVzlb4JE1oijevXqFV/b8PT0xPbt22WrtBqz6e/vz9eI2rdvz/+vRrDj008/1ZsFEkmgBLp6djVbCjc3N9n1eqTgzJkzSEhIABFZVMIxh5KSEqM6blJRWVmJ9PR01K9fH927d8fatWsldzOtiVol0ZvEzJkz+XjDGti0aRN/WNXceBveDDZv3oxmzZqhSZMmFkvzWBu29HAbbFCJP149dBts+JVhI5ENNqiEjURG8MMPP5AgCOTo6Fjbrvwm8O677/K6qDYYwkYiMygrK6Py8vLadqPW8f3339Nnn32m2o6fnx8Jwh/vkZP0jXbv3k2bN2+mNm3aGIhYbN68mTZv3ky3b99+w67Kh4eHBzHGaMWKFbKu+/Of/0xDhgwhIqKZM2eqItLz589pxYoVxBjjW2JiIq1evZqKiooU2/3HP/5BHh4eevfio48+UmzPEvr376/aRkREBBERPXv2TLWtmli/fj29/fbb/LdgjNG4ceMkX19SUkJLly6lOnXq8E0QBP730qVLTV9cc7pOTEHW3cT8fXOb3HTkXwPu7u6KK0Pk5ubyBcuLFy8q9qF///5mqzYoEQBZvny5yfswfPhwo9HiasAYw/Tp01XbETU71NfL3mUAACAASURBVCZ2GkNISAiio6MxZ84cnpEsNZxo+/bt6Nmzp4HORs1cMl9fX3z//fcG1xuQyBJZam7h4eGy4t9++OEHvexGa8TOmYIaEgHVldMZYwgMDMStW7cU2ZgyZQpatWqF7du3802XRKGhobKj4T09PcEYQ3BwMCZMmMA3X19fMMasXrnBWiR6+PAhGjduDH9/f6skdupCN8o8ODgY7u7uku+ZLlHatm2LuLg4ZGZmIjMzEzExMTzrmTEGb29vg+BjAxJNnjxZ7+Hu2bOnLEknY8jLy0NERIRFQrZs2RIPHjyQZPP169d4/Pixnq5CTaglEQAe4mJNovfu3RtEhE6dOqGyslJW7s+CBQvAGDOaGCjGdv1WSQSAPwfPnz+3ir2aELMDpFa5/+KLL8AYMxpYWhNnzpzBsGHD4OrqivT0dL7fYEw0f/58WrJkCYWGhhIR0b59+6hVq1aUk5MjuX9ZE4WFhXThwgUiIvrLX/5CQ4YMoffee4+IiLRaLR9/nD17lrp16ybJ5vfff09vvfUWvfXWW7Rjxw76+eefFftnDoMGDaI//elPREQ0a9Ysq9iMjIwkoupK33Xq1KFmzZpJvvbPf/4z2dnZ0ePHj63ii1Q8ePDAKnZGjx5NREQXL160ij1dVFRU0IcffkgNGjSgpKQkSddMnjyZGGPUsWNHi+f+5S9/oXfffZdKSkro/PnzvxwwxbqnT59i4sSJ/C0cHBysOFuypKQE/fv3B2MMn3/+OYBf3hharRarV69GXFwc1/2S0hrVq1dPr1vUqVMng7qd1miJAGDRokU8ENUaqKys5AW+lCAqKgqxsbEGAatvqiUiIoSEhFjF1rZt28CYdbJmdVFaWoqBAweCMSYrni8iIkJWLGRpaSkEQYCHhwffJ+ku3rt3Ty8Uftu2bZKdFCEGhw4dOpTHtIkTFo6Ojvy8oKAgrFmzxqI9MjNYF1PCRRIRkayS8cZw/PhxHjBrjYe0T58+ICLF8X1t27bl92Pw4MG8m6c2buznn3/GuHHj+HcVRTHVijfevn2bj9nEzZguhhzcvXsXQ4YMgb29PerWrYvk5GTZY9fr169DEAT07dtX0vnbtm2DIAh6cm+SX4UvXrzg+gr29vayk6FEEjH2i8JLSEgI37dnzx48e/YMjRs3lqRhoEuadevWYd26dXqt07Bhw/RkleSQKCcnBxMmTMCJEyf09ou+WpJZEpGSkoK1a9caFSi5f/8+GjVqhEWLFkn2SxcFBQVISUlBs2bN9B5MY7NHUrF8+XI4OjqCsWq1IB8fH/6iCwoKUlRXFajOS+vTpw+PsBdr1BqreC4VRUVFPJXbx8dH1YwkY8xgnGMKQ4YMAWMMS5Ys4fsU9SfatGkje7D99ddf8x+vV69evBLz7du3+Q9ck2TmoEsiXYwaNQpOTk4GrZMcEnXq1Mno91u2bBnfL6WCeFlZGc9oXb9+vcFDeOLECRCRqvLxwC/yU7qbk5OTrOnzsrIyMFatu15T2Ua0qbTGriiAqAsxwVNqC6CL77//HoIgqMpl0oWoKWFJO8Pd3R2CIKBnz556s4GKSJSbm4umTZuCsWrNAKnCfiNHjuQ35NChQ3rH/v3vf/NNCkyRCKguiKuGRGPGjOF+7ty5k293797l+6X6WVZWhvHjx4OI8P777xv9HoIgYPfu3ZL9qwnRp+joaHz99dcICgoCY9UpzVKzj4uKisAYM1ptXC2JRAH+mhg0aBB8fX3x6NEj2Tbfe+89REREIDU1VbGwpIi4uDizJLp06RLat2/P140sTnFLxWeffcZ/XGPdFWMQJxeMkUguHB0dTZKosrISjx49gpubmyISZWVlGZ2Cb9Gihd4DK1UBp6qqinctY2Ji9FokOzs71TlKok9Hjx4FUK3UtHLlSt5NkWOnpvTYixcvVJNIFJW8cOGC3v7ExETFEyEVFRVwd3fn90UNzpw5A2dnZwiCgJ9//pnvLykpwXfffQdvb2++TtS+fXuD61Ul5YlEkkoi4BehD8YYCgoKFH/24cOHOUHmz59v9JwVK1aAiPjDJRUiiQRBwNq1a5Geno68vDysXbsWq1atgrOzMzw8PHD27FlZdisrK3kVg4YNG2Lu3LlYtmwZ/x7vvPOOonIz4hpbhw4dDPbLIZFYSocxBnd3d559K67tqHlYv/zySzDG0KNHDz6GdXd3R9++ffVeRjWJZgkvX76EnZ2dYr9EpKSk8NaocePGSEpK4s+AJfloAxJlZWUhKyvL6Fv22rVr/Hh0dDR/E8ghEfCLLkCzZs0URwKUlpbyh2/gwIFGz6mqqkJpaalRzTNzyMrKAhHBxcXF6PGTJ08alRKTihEjRsDV1ZWH/eh2O5W00MHBwWCsWrG0sLAQX375JS+VIodE5eXlOHXqFC5dusT9adiwISorK5GWlqZaWadv3756LTsRGSjpmpuxq6ioQHZ2NvLy8vDixQts27aNd8XUoqKiQo9I4ovez88PU6ZMMXutybCfAQMGYOzYsXpbkyZNDLo4KSkpsvPaN23axAkYGhqqSN+6vLwcAwcONEui3zL+/e9/o2XLlvxhrVevHk6dOqUoHObgwYN80qbmpqRez5tCRUUFli5dyr+zsZnEsrIyk9c/f/4c9vb20Gq1eqFjShSNTEGXRHFxcbh+/brFawxItHHjRmzcuBFNmzblX3bMmDFYv349P2YNPH78WO9m/1/F/fv3kZCQYFR8Ui5E9RsvLy/Mnj1bttiiDdUQJaOlTHkDtayx0KpVKzp79iwREb1+/bq23LDBBlWo1Qyp6dOnEwDq2bNnbbphgw2qYFP7scEGlfjj5eraYMOvDBuJaglJSUkkCMIbSZW24deFRRJduHCB+vXrRz4+PvT3v/+drl279mv4pRht27YlQRBo5MiRte2KWWzYsIGIiOdV/Zbw5MkTGj9+PI0ePZoEQaDx48fTvn37qKysrLZd+23C3NTd+vXrDfQVnJyc1M4gYvLkydBoNIpKaliCqE2tZjH014Cfn5/iSObbt2/rFTim/+hwKymtaQyjR48GY9Vp8b169eKf065dO9W2f/jhBxARtmzZYgVPrYuKigqUlZXxTSrMksjOzg6xsbF4/vw532bMmIFvv/1WVXqveNPfxPqQSKI3DY1GoyriIiUlBYxVi84fPHhQ0jXZ2dlo3rw5AgICDFb+xb/T0tIUV8gDqtdI7OzscPLkSW7n8uXLPG1FbS6VSKLBgwersiPi3LlzmDVrFoKDgzFixAjZYUNAdeECUcubdKJHrl27Jmnx2ySJZsyYYTISoaSkBDExMYqUaoDfJomWLl3Ky7ww9kvJF91Ql5ycHB6EKh7fsGGDIj/Ly8vx3nvvgTH9pERz0CVOYmIiv8G7d+820MZQ8huItVpNVaA7ffo03N3dJaWBmIMYGGyqorpU+Pv7G32ZSG1FXr9+jfv37/NI+tGjRyMlJQUpKSmYOnUq7O3tTQY568Lk0a5du5q9cOnSpWjdurUkZ2ti27ZtVqtLVBNKSRQdHW2URD4+Pvjwww9x/PhxNGjQQO94fHy8KhGXUaNGcVtSEhGTkpLQq1cvTJs2zeDYy5cvkZuby5PqlPwGkyZNMksioJrIkydPlm1bFyKJ1NZOEknTqFEjLFiwAL1790ZgYCCaNGkiqUV6+PAhiAi+vr5GCzAvWrSIE8lcQqJiEpWWlqJx48YWHTUGMc/+t9QSiVmSISEhBrV+jP1/3bp1qlWQxBwexhjGjRunypaIpUuXciLJhZgzY4lEcpII58+fbxBUO3bsWBARWrVqJdtHEbdv3+athG5uV3FxMZcIsBT3JtZYMpcVm5SUBCJCbm6uyXMUkwiAqsBPUVSiUaNGyMrKUmynJpSSKDY2Fh4eHvD09ER0dDS6dOmCxo0bG7RM1iy2Kxb+ZYwpfiHVRF5eHiIjI8EYk53OLU4oODs7mzzH399fsqwZYF4Lw1I3yRzE7padnZ1BpPqrV6/QokULi/ZTUlKMVp3Xxc6dO0FEZtP4Va0TnTx5UvG1bdq0oTp16lBeXh7du3dPjRtWwebNm2ncuHH09OlTSk9PpxMnTtCdO3cMzgsODrbaZ7q6uvK/1UiSERHl5+fT3bt3qUePHpSdnU1ERBqNRpGtqqoqo/uPHz9OjRs3Jm9vb8m2JkyYwP+uV68eTZ8+nR48eEADBw4kIlK8ZHLo0CFKTEyk//qv/6Li4mK9Yw4ODtS7d2+LNv7+97/T/v37TR4vKiqimzdvEtEvMmdGYYpdUlqiBg0aWDzHHMSukTWmTkWomZ0rKirC+++/Dx8fH0RHRyM6Oho5OTn4/PPPVU8kmILuFLJSJCQkIDQ0VC/y3sytNYnDhw+b9WXNmjXo0qWLLJtHjx7l4wpdnD9/Hk5OTopLgnp5eWHNmjU4cOAAXFxckJycrJfXtnjxYtm/wdWrV9GtWzfMnj0b3bp143LERGS29q/JT3n48CEyMjJMXrh+/XrUq1dPlpM1oTveUILnz58jLi5Or/r0m5rifvToESeSud9FLkQpLsaY4mrsKSkpcHZ25nZ8fHwU+zh48GCejKYrGPPs2TMEBgbi+PHjsm0uWbIERMTFaUSMHj1aceXvqVOn6kmriZMVumPXfv36SbZXUFCABg0acNJ4eHhg+/btklSEzFK1R48eJvvVMTExqvvxuuMNJXj06BEaN24MrVbLcz/e5DqRSCIpY8G9e/fi6NGjFhVpVq9erZpEQPWEgijAr6ZlKyoqQr169cAY03vojSn2SMXz589BRAYL4KtXr0a9evVw9+5d2TZnzJihVzD70KFDGDZsGH+ZtGnTBqdPn5Zl84MPPuD5c1KS8USYJRFjzOibp6CgAN7e3pgxY4YsJ43ZV9MSAdUp6+LbNyUl5VchkSX7lZWVaNWqFV9MHTp0qMlNt4r4jRs3VPknqsqKm5KHEwAGDBgAxhifSi8pKUHz5s1VkUhXMVTE6tWrQUSKlHWvXr0KT09P5OXl6e2/ceMG9uzZY1aj3RSys7NBRHB0dMSwYcMkX2eWRKdOneJhP7pwdnaWtK5hCdYgEVCtYqm7im9pxkUK5v1/9s48Kqor2//nXBmKx1TQTD4BCzsIPBXQxoFmUIwRfA6IrQzt7HJczrQTPwccWk2iONFqjJqotCbimPBi1BgnOmKM0UiWQTQGUUQFnBAUEfj+/qDvSRU1cCfEpOuz1l1iVd1dp+69+55zz9n7u1eu1FG5BH5d3BMy1l64cCFCQ0PRtWtXtrVq1cpgCrdKpdK7GIRQUVGBGTNmoF+/frh8+TKA+htcly5dZKVNe3p6srZlZGTAy8sLlFLZN82GyHEioH7m76233jI5/SyWqqoqREVFgRCCJUuWYPbs2Vi5ciXS0tLwzTffGG5HY0YXLlyoM11aVFQEd3d35OXlyW5wQkICG87t379fli1enZVSikGDBsluG7/4qh3W4+PjI6gnMsaFCxeY2k+7du0QERGBDz/8UJBssiF2797NnNrb2xtLliwB8OtkhVQn6tOnDzuWvEMFBweLmtoWwrZt29CqVSvcvXtX0v782p4UhSRTPHz4EDk5ORg/frzORI2trS1atmypp/XdqBO9fPkSAwcORFhYGEaOHAmO40Sr+xiDFxpRIiartraWLYzJDScBgMGDB7P4uKioKBw/fhy2traynEhptJ2I0np5p/nz5zPddKkRAfx6Eb8FBgZKvtBNsW3bNkkKqDzHjh1j60RSNc1N8fLlS2RnZ8PZ2dnk+pYs3TklUGI411Q8fvyYDcH456F27drJFsdXkoMHD2Ljxo3YtGkT09lzcnKSLWz/Oti2bZtgAUxj1NbWMjH/uLg43LhxQ6HWGadhTGmzO9GbTnZ2NsaOHcvuyjt27GjuJpkxwEcffYS2bduiffv2r/27zRoLZszIxJwebsaMTMxOZMaMTMxOZMaMTF67E7Vo0UIv6vZN4NtvvyWrVq0iq1atIiqViqhUKtORu79Tdu3aRWbPnk0mTJhAWrRoQYYNG0aKiopISUlJczftzUXoDMT+/fsRFhYma1H0zJkz4DgOY8aMEVQNzxR1dXU4ePAggoODMX/+fGzfvl2SGDyPvb29wXwXBwcHzJgxw2DmY3MSGRmpaB5SWVkZEhIS0Lp1a1YRQbs6gpubGxYuXCjK5sWLF/H++++zTWkOHjzIIj6U4PTp01i7di18fHzY7168eHGjoiqCnUiJEJ2srCx2YqQUT9amvLxcL3ymbdu2qKmpkWRP23EmTpyITp06YeLEiew1JWrgKAm/6CumdIopli9fruM48fHxbNN+XUxgZmJios75cXR0RHBwsKQqIIbg4xMppXqV1MVQVlaGbdu2sdpZ2jcPjuNgY2NjMjj4tToR8GvpiqioKFl2gPoobhcXFzx9+hTFxcVYsmQJS/EWy4gRIzB06FCDcVzXrl1DTEwMrKysWGiNFMrLy9GnTx8dh5VafW7r1q1sETgzM1NWwbT4+HhwHAd/f3+TTsIf34kTJ5oUA4mLiwOlFCNGjEBRURHKy8tRVFSEoqIipivRsCCZFIqKipgTDRgwQFJh5mfPnuk4ztatW5GZmYmsrCxkZmZiwYIF8PDwYE5lKH5QsBPJTVvg4RurVqtl2eFZtGgRBg0axIaHMTExgoU/xPD8+XMQQqBWqyWXLNm9e7fecLFfv36SAyj5m1pAQIDklfqysjI2hBMCf3c2VWOWb5chDYrnz5+jVatWUKlUOHLkiKQ282g7EaVU0nA+LS2t0fyju3fvmuxEBE8s1NbWEgBGU4eF0r17d0IIUWxyYd68eeTQoUPk8OHDhBBCvvzySzJu3Djy97//XRH7PDY2NoQQQp4/f07u3Lkj256LiwtZvXo1+b//+z8SERFBampqRNv4+OOP2UTN/fv3JbXjypUrkn7Pp59+avS96dOnk4iICGJlZaX3no2NDenQoQN5+fIluXDhgujvVRpKKaGUkuHDh5ODBw8a/Mx///d/k0OHDrHP8injDKEey3uiFLmke/fu6Qw31q9fD3t7e7Rq1UqRLNGcnBx4eHggJiYGkydPhqurK2JiYmTbBYBHjx5hx44dUKvVaN++vazEOb4nCgoKYj0RX7NVakW7jz/+mMX1SRlq3r59W1D5eamf12bkyJHo0qULy6Hy9/cXbaMhfC1YqT0RpYaLGRvD29sbHh4eKC4uZq8JcqKEhARYWFggISFBdCOB+oC98+fP67yWnp4OQghatWolOgPREDNnzmQHs3PnzrIUWnkWL16sk2cvNxWAd6KHDx+ySQuVSiVL+aa0tFQnulzsQ/vrdCK+tiy/2dra6qUViOWDDz5gv12KhNmoUaNEOxHHcVi7di17TdBwDv8exkFimJ21tTXp2rWrzmvdu3cnlFJy7949snXrVkl2tfnhhx8I6m8KpHfv3sTW1la2zStXrjDlHEIIsbS0lG2TEEJUKhX5+9//TqZOnUqqqqrY69p/C8XFxYW4urqy8/P48WNR+//Xf/0X8fT0FPx5vaGMCAIDA8mf//xnEhgYSAipHxrPnj2bvP322yQtLU2SzQkTJpC3336bEEKYgpAYOnToQAoKCsiVK1cEfX7AgAH6fiDE+/hpTqULDI8ePZrd1eSkRrdr1w6UUiQkJGDr1q2KPLRqM3z4cPj5+elMCHz88ceiU5Dv3LkDFxcXnXyn5cuXG1TDEcPjx49ZIt1bb70leqaOr5otJC1Be7pbLuXl5Rg2bBjrmaT29Nr6fZs3bxa9P8dxaNu2baPXYH5+PlxdXaHRaHReb/TM3blzB2FhYbKGc8bgn2U4jkO7du0ki8MnJSWBUoq+ffsCqE8qs7W1VVSVB6iXYbKzs2OOJGUBMSoqSi9p8Pz587LbeujQIcTFxcHCwgLJyck6GuKNUVZWhh49eiAiIgJlZWUmP8s7kVJVyZ8/f87EWgIDAyXbkOtEDYdohpgxYwY4jkNkZKTO64060d69e1kDm6LU/WeffcZ+RKdOnSTZKCkpYeNtPsORV3w5fPiwaHsPHjzA3LlzsWrVKr31kFOnTjFtgC5dumDPnj2ibKenpytWAqUhERERLBv3iy++ELUvL+0cFRXFFGnLysr0FF95JxJ6Qz18+DAuX75sNPM0NzeXjXScnJxEtZlHrhOpVCpwHIfFixcb/Qw/gcFxnJ4ATLM7EVCvdcY7klhxQG1Wr14NSimSkpIA/Kq7YGpNwxCjRo3SeeA3tVlbW4tup52dHRMWURp+pk7Kel5sbCzs7Oz0wn6AeocaP3486zG0Z6dModFo2IO/oc3e3h4uLi4YMWKE6PbyaDuRlGCAc+fOsd9aVFSETz75BGvXrmXbzJkz2TGZMGGC3v6NOlFTVnDgqayshL29veyx9qNHj5g6UWRkJBvmqdVq0T1SXl4eU7kx5UDGFGBMQQiRPStljNGjR7PzJUR4sCHnzp1jNx/+fDSMqROrh7F9+3asXbtWz4EsLCwkK/1oI9eJ8vPz2e/z8vJiPZP2MeA4Dunp6QbDyhrNbM3MzCSJiYmEEELq6uoEzWBIITQ0lC2+yVnQ/eWXX8jQoUPJt99+y15zd3cnz58/J//85z8FaTRr8/HHH5OioiKD77399tvkz3/+s+g2UkpJQEAA+emnn0Tv2xinT58mf/nLX0h5eTlJS0sj06ZNk2SH4zhCKSWE1M/O8n+vX7+eJCUlkT/84Q+ibb7//vs6/3d2diZjx46V1D5tamtriZ2dHSuHKeU6vXDhAvnLX/5CiouL2Wv873ZwcCBHjhwhoaGhhnduzEtv376N0NBQ2Wo8zU1ubi6beGhukpKSQAjBkCFDmuw7Ro0aheXLlzeZ/TeNCxcuwNLSUtYs5+nTp1lvNmrUKKaq2xhmjYVmIDk5maxdu5aEh4eT7Ozs5m6OGZmYnagZKCkpIfHx8WTNmjWkU6dOzd0cMzIxO5EZMzIxayyYaZSePXuSf/7zn83djDcWsxMZQKVSkT/96U/k0KFDzd0Uo6xbt45wHEeWLl3a5N915swZMnLkSPKPf/yjyb/rt4jZiQzw8uVLcunSJeLl5dXk38VPHYuFD7BdvHixpFwkMeDfgb1SWL16NVGr1YTjOFJYWKhwy94MzE5kgEGDBhFCCHnnnXea9HsWL16s868YBg0aRHr37k1UKhV59913m9SR+GQ0KcydO5c8e/aMEELIuHHjyMOHD5VsGiGEkEuXLpEWLVqwraKiQvHvMImQefDCwkK0bt2ardQPHz5clDbAs2fPMH78eFYaXq1Wo1u3bnql2aVw8eJFg+Ek7u7uWLlypWS7z549Q3x8PMLDw3XKWSoNIQQ9evSQvH9JSQn7zVJLtDRGaGgoW7UXW32BUork5GS8evUKLVu2VKx4wUcffaQjKBIaGorQ0FB4enqC4zhROUJyadSJdu/ereNA/GZlZcUqqTVGWFgYCCGIjY3VC5uRI6V0//599OzZE1ZWVujXrx/b3NzcmMyT1PIiQH2oPvl35bTBgwdLtmMK/ljIISkpCSqVCq1bt1amUQ04cuQIc1SxkfYqlQoTJ04EUC90YmVlZbKIsFC8vb3h6uqKqVOn6ihHXb58WZQThYeHw97eHm5ubliwYIGkZE69s+fh4QFnZ2c4Oztj9OjRLNeFEILw8HD4+/uLrlAdHh6uJwJx4cIFpp0mNQ2AL2bV0JmvXLkiK5ZKm1mzZjFHagqUcCKgPsVCqbt8Q44cOcLu+mKdiO/F+EJckyZNwoYNG2S3ycvLy2gUDaVUcEX6UaNG6Vwr7du3x7Rp0zB37lxMnDgRCxYswPLly03G+OmdvYY9Dr81LOxVXV2NoUOHYtq0aYIau2vXLp20gRcvXiA9PR2dOnVC27ZtBdloSEBAACil+OCDD/Teq6mpQWxsrEEnE0t2djZ8fX3h4uIiKk9Hmx49erBj2aNHD6SmpgL49Xjz/5fD8uXLYWFh0ajYoFjkOBFQ3wMRQlBcXIzTp08jNDRU0fbxVFZWYsCAAWjdurUoyYHY2FhWGC08PBy//PILiouLMXnyZNjZ2cHOzg6WlpbM0Tw8PHRSRPScSDuDc9KkSejSpQsIIQZL+h0/flyvrqkpSktLkZ6ejpSUFHTo0AEWFhYICQmRXJDKlBMBwNKlS0EplfXMAdRnjg4aNAitW7eWLJdl7OakpBO9fPmSlbJUkoKCApY9LDVx8vDhw+A4Dp07dxbcSwjh1q1byMnJwfnz5zF27FhwHCdpZHPhwgUMGjSIOUq/fv103v/666+xdetWtmn/Bj0nKi8vx4ULF5gnv3r1ChMnToRarcaVK1f0vtzT01NwQ58+fcouGldXV1kP/kB9NqcpJ3r16hUcHBzg6+uLgoICWd/1448/smHdRx99JGrfU6dONbkT8cmD/EUwZcoUWfYa0qdPH3Ach3fffVfS/pWVlXB0dATHcYo5UU1NDROD1E5ZuHfvnmSbgwcPZsewf//+uHnzZqP7CB6M+/j4gBCCgwcP6rwuVpqKz88RmxFqiJycHDg5OcHS0tJo8dvNmzeDUoqsrCyTtrQv6PPnz+PatWt6ZQU3btzIjoMYjQV+KNfQURo6lxQiIiKMJrwJpbq6Gu+++y44joODgwM+//xzvc/wTjR79mxJ7eThc7RsbW0bPSdCOHz4MHr06CE4xVsM/fv3Z8fy1KlTRj8n+MwlJiaCECL7+WLLli0ICgpS7EG9e/fuoJTC19fX4PRrSEgIgoKCUFJSYtKOm5ubQXXSgoICtgH1qSG8+L3QmUVjTqT9nhwncnFxQXBwMCZPnowOHTqIdqLr16/rJZ9pc/XqVSbyLmca/YcffoCjoyO8vb1Z4psSlJeXN4kTVVRUID09Hfb29nB2dsbq1asNfk7wmbtz5w6sra3h7u4uu3FnzpxBcHAw0tLSsH79euzfv1/ytCff01BKUVpaqvPewYMHYWVlJeiZ6MqVKxgzZgw8PDyMDrnmzp2LYOFYsQAAIABJREFU4uJidkPp1q2bIIUcvqq5ISfi35PjRNqCklVVVRgzZgwopYKzRrdv384uQn9/f9TV1em8f+vWLbRv317yxALP+vXrmeOMGzcOGo0GCQkJspSeeJTUeG9IamoqKKXo1q2bwetU0JkrKirC8uXLmRi70hQVFcHOzk5vOCJUIbNTp06glOoMQ6qqqpgdqRUDdu3ahYCAANjZ2cHGxsaocwmB/6yhYYEcJ/roo4+Y7sHu3bt13ouNjcXcuXNN7q898yZkk+NE3bp10+sh+RR+BwcHk/vu3bsXrVq1Mvo+f66l9EQ//vgjrK2tda49Ly8vJCUlISkpiSm2UiOyXoLO3MCBA2WP3Rvj9u3bWLVqFbp168amG4UOSTIyMnDw4EHU1dUhNTUVd+7cYTpslFLJ5Va0KS4uxrvvvovOnTuzDEpCCPr06SNof2PHT/u5SAoVFRU6J79jx44YPHgw2rZtC0ppo6o8BQUF2LBhA9sWLVpkNAKkR48eeksdYuDXjBqyZ88eeHt7m9Sdu3z5Mjw9PY2Kzssdzo0ePVrvNxNC2N9hYWFITU01+MjQ6JkrLy+Hu7t7kzuRNjdv3sT169dFd/PZ2dlQq9U6dWuaYgGyvLwcS5cuFZV+bez4yR3OAfUOnpKSonPz4bf33ntPsl2l4Z1o165dyMnJQU5ODpuoys/PR1BQkMnhMb+/t7c3tm3bhuvXr2Pfvn2YOnUqOI5D3759BQ2vDfHs2TPcv38fd+7cQUpKClJSUth6UEpKikmdb0FnLjo6GlFRUeA47rU4kRz44QGlFK6urvjuu++au0kA6nsc7UVWbVJTU2WvZf1WKC8vZ+coJCQElZWV7L2qqiq9GdGGDBs2DF5eXrCystKJnRO77KAkgjJbeU0AHgG7NBsrVqwgCxYsYH/PmzevmVtkpinYunUrOXfuHAkMDCQASHJycrO1RZATVVdXE2tra2JhYUFqa2tJTU0N4bg3M4vi+PHjJDo6mqSlpTXrgTXzn4NZY8GMGZm8md2JGTO/IcxOZMaMTF67E/3jH/8gubm5r/trRRMVFSU5Jfp18Le//Y24ubkRNze35m7KfzxN7kSFhYUkLi6OBAQEkICAADJ9+nQSHR1N/Pz8mvqrZZGamkoIqde2fhOprKwkpaWlpLS0tLmb0mxwHEdatmxJxowZQwICAgjHccTJyen1N6Sp59BTU1MNho9QSjF69GhZYSQ82rFdSlVxA34NDm0K+CoJUsnNzUXXrl1BaX3pGDmRBL9FsrOzYWVlhTNnzgCoT3sJCwsDpRS1tbWvtS0mrxA+NZoQgoCAADg5OSEwMBCxsbGCU3x79OiBo0ePGnxP26GkUldXB47j4OnpibZt2yI0NBT29vYsx6RhcpVYiImYN6kcP35csWiKK1eugBACFxcX2RH22pSVlSEhIUGvxEhycrJgG9u2bUP79u11ojViYmIwevRotkklOzsbM2fO1Hs9ODgYO3bsEG2vvLwc58+fR05Ojl5ht8YQ7EQPHjzAiRMnANSvLAvNHjx8+LDRbNBJkyYp4kSUUnz55Zc630m04p7kwIflKBlREBcXp2hIknbkOX+O5OLq6gqO4+Dk5ITt27fjo48+wurVq9GyZUtBAb1qtRrW1taNJiM6OTmhXbt2OHv2LPLz8wW3r7i42KAT9enTR7QTJSYm4p133mHXYmxsLBITE5GYmIiNGzeiqKgIRUVFRquTm3Si6upqZGdnw8PDA127dtUrsycXPo+FUqoT/iGGuro6g72NdqLW9OnTZbWTyIxta0hwcLCiTjR58mTWxrS0NNF30oY8e/aMHbsZM2bovKdWqxEZGdlo6sqoUaMadaCGW/v27QWnbxw6dAh2dnY6N43a2lpER0fj5MmTon6vsaDThv+Piooy6LiCrwxCCGxtbY0OzaQyffp0UEoVtwvUa7J16dIFS5culW2LKKSDAPzqRF5eXorY49EeOcg5nnwWqyEn2rFjBziOQ2Jiokkbhw4d0nGQxvJ8srOzMX/+fFGiNQ4ODuwCHzNmDPu/qWBRQ/AlNtu2bYvbt2/rbBkZGYiPj9epFMgfG2dnZwAinMjV1RWEECxcuFBUAw2xdOlStG3bFkePHsXAgQNBKZUsAGKKq1ev6pVLF8qpU6d0nEbJ3qipnOjKlStsaOfu7o6nT5+KtvH48WN06tQJ7dq1A8dxeupGvIM15kSPHj3CsGHDREf/i4ncLygoYDlKfI/RWF6SIbZv3870H4w9E+Xn5+PcuXM4d+4cE4rkVYsEXxUrVqwAIUSwRJYp+N5He2IhKytL0RmmH374AZMnT5Y0U2dqyKFEb9RUTgTUa/zxbZUy83np0iVwHIeMjAyDzhIUFASO47Bs2TJB9rSP3dmzZ0W3xxRt2rRhtvmbh1QRlO3bt7PE0EWLFjUqJ6CNqFtr//79FX02KCkpwfTp03WeXyiluHXrlmSblZWVyMnJAaX1lanT0tJE7c+nLDSEn+5WYoJBqhOlpaVh2bJlbDM2ubN582YQUq+o9P3334v6jqFDh7JntdWrVzM9CX7YlJiYKDrPa+rUqejQoQO74D/99FPZiZKzZ89Gp06ddHrKO3fuICgoCG5ubpKfsWfOnMl6JT7Jz9iEAk+zOhHP48eP4e7uzpyoY8eOkh2JF2zs3r07PvvsM9H7G3Mi7QxUudPdUp2IH7bwm0qlgoODAz744AOdSZ/Tp0+jd+/e4DgO48aNEyU4mZ6ertd7L1++HCtWrMDmzZtFtVcbXnKM3xISEnD8+HHJ9uLi4gyOCk6dOgVKqSw5tokTJ7LZSUMadA15bU5UWlqqJySizYULF3DkyBG4ubkZVJwxxa5du5Cens4SB52dnWUVajbmKEr1RlKdyNSUccuWLdGxY0ccOHAABw4cwOzZs9l7YvTO+eFcU9C7d284Ozuzdsl5NFCr1UYLE1NKTeoxNOT8+fN6vU1WVpbORIIpBHvEd999h5YtW0pyopKSEgQHB+tFLHAch4SEBKSnp7NN+1mpMbSlnlQqFVJSUiR349o0lPw19LccvL29QSmVrb2Xl5eHn3/+Gd7e3iaf49zc3ATbbEonAuqXJJycnFjbhD5bNaQxJ1Kr1YJtCZni9vb2Nrq/4KthypQpsi6gkpISg05k7G8hD/CPHz9mz1POzs4sBITn6tWrokrAaKPtMIY2OfAnWaqQf0OKioqwfft2bN++HS4uLjrb5MmTkZubK9hWfn4+XFxcFGmXMQ4cOAALCws29W1KoMQY48eP13HAZ8+e4datW7h165bonujy5ctITk6Gv7+/zrWofT2OGDHC6P6CrwZe3NCUR5qirq4OR44cwc6dO9G2bVs2bDPkRGFhYYKfifiwH37TfrbinwnkoO1MqampsmfnPv30U/bc96bSp08fRWsyFRYW4ubNm5g7dy5u3ryJmzdv6gxNIyMjUVZWJsrmypUrodFocO3aNQC6USBqtRqHDh0S3c6ysjIMGTIE8fHx7JnIz88P8fHxJkc4gpyI19D28PAQ3bDXQUlJic4CoaenJ8aMGfPGiJRoM3z4cDg5ORkdirwJnD9/nh3LxYsXy7bHSy8b2lQqFfbv3y/J7rp16xAVFQVCCIYMGYJ9+/YpMpwXiwURwKNHj4R8rNlwdXUle/bsIXFxcWTQoEFkwIABpHXr1s3dLIP86U9/IsnJySQ4OLi5m2KUrl27sr+fPHki297gwYNZykZtbS3JyMhg782fP5/85S9/kWR3+vTpZPr06bLbJxdBTvSHP/yB2NnZkeHDhzd1eySjVqvJqVOnmrsZjfImnHQhxMbGkoKCAvL222/LtvX++++zv2tra1lNXEIICQgIkG2/uTELlZgxIxOzxoIZMzIxO5EZMzIR7EQnT54kHMcRe3v7pmyPLIqLiwnHcUSj0TR3U343vHjxgnAcRziOI6Ghoc3dnDeS31VP1KJFC0IIeeNP9osXL4iHhwdZvXp1czelUe7du0cIIWTatGnkb3/7myQb/fr1IxzH/S4mEQzxu3Iid3f35m6CIIYOHUpKS0tJTExMczdFMEuWLCGDBw+WZaOsrIwcPHhQoRYph7OzM7GxsdHbtKfiTSJmUWnSpElQqVSSFqSKioowbdo0nbikadOmSS6FYQwhAYPNjRKKRNOnT4eVlZXOwqWjo6OkyHVT3Lx5U1b6PgBs2rQJlFJYWFigZ8+eku2YKiAtBVtbW3Ytjhw5EpMmTdLZ+PcsLS31CqhpI+rbFyxYIEkXYMKECVCpVAaLR6lUKtjY2MDGxgbOzs7IyMgQbV8b3onE6kE8ePAAixYtwqJFi7B+/Xp8+eWX2LJli6y2mGqjHH2FefPmGTyW/G/fuXOnYm0tKCgApdIq0GmzdOlS2b+bL0GjHWGfmprKnEtsSBb5d3DpjRs3DOY3FRUVISUlBZRStG7dGnl5eYbtiPlSXiutoqJCUmP5aFg/Pz9YWFgYvRB69OiBHj164Ny5c6K+B6iPqVKpVKJTIWJiYgze4RYvXqx4bym3J4qNjYWLiwsWLlyIu3fvsu306dMghMDBwUFWYmNDKKVYsWKFLBsXLlyAhYUFLCwsJEsB8A5jCClpKr/88gs6d+5sspd9+fIlcyRj+VSinCg/Px+EEMGl53m++uor9O7dm+XAOzk56RyM2tpaPH78mG2DBg1i3agUMjMz4ejoKCo+raKiAh4eHsjJydF5ffXq1SCEGC1zKAU+LYSvSC6W69evIyYmxuB7/HFTopgwDyFEVFVAY6hUKlhYWCAqKkqSFICpYRt/PTXsqZQgLy9PmhN9++23oJQiNDSUpVg/ffoUvr6+op0IqBfHGzVqFOttPDw8UFhYaLAbrampwbFjxyRHEmdmZoLjOEUu/Js3byIgIEBUfkpj8E60atUqyTYMRSkfOXIEhBAsWLBAsUDMsrIyRXoiAEhOTmYjkISEBNGOpN3LNMzz4jGWmSyEu3fv4ujRozh69CgePXrEXs/KyoK7u7vRlA2DTnTt2jW4ubmhT58+rHIy/4yRlpYm+Q4KABqNRmfolpyczMLZpdJQA62yslL2kCkvLw/p6em4c+cOhg8frmhafFBQECilspyI59SpU7C2toa1tTU4joOVlZVsGd2TJ0/iypUrAOpFEpVyouvXr+skuokVUmk4sWDMWYjEFP6MjAx2XQYFBbHUF1dXV0RERBjdz+CVkZWVpfcA6OXlBUopfHx84OTkJHvMfffuXaauQml9dWptFdPGGDt2rJ6+d3h4OIqKilBTU4P4+HhwHAd/f39J7Xv27Bm6deuGiIgI7Ny502Rqu1h27dolW/mVZ8eOHSCEQKPRSJLI0qa6uppJUPEl6fn6qko4EQBs2LCBzdTNmTNH9P5C6tvyTiYl92vNmjXo3LmzXvHs8PBwXL582fD3GXrxzJkzsLKywt69e9lr169fR0JCAjOqxLjz+PHjOjZtbGwEy+Bq/8A1a9YwGS5CCCZMmID4+Hj2f6nU1dWxXBUlUdKJgF+PRe/evWXZ4VWSVCoVvv76awQGBrJeQ66KLI+2E0m9wTUG32PJ1cLYuHEjAgMD2WhMpVIhISFB73NGr7A5c+aAUopevXrpzE7xMxWurq6SGvbq1Su91168eKHjFEKoqqpC//79wXEc7O3todFo8MUXXyAjIwNhYWE6mbJisya10da59vLyQnx8PObMmSN7yNSvXz9Fh4gjRoxgFzzfM61evRr37t0T1dapU6eyaV/g12ciCwsLSdmiDVm/fj1rp5+fn2x7xhCzflRYWIjHjx8bvDZ5jh49itatW4NSqqcLYfRbSktL2UXdunVrlJSUYNu2bfDw8AClVJKi/4kTJzBlyhSsXr0aq1evZq9XVFSIdiLgV1XK0NBQODo6sjtbWVmZjuTR0KFDRbeVp6CgALGxsUhKSoKDgwM7OeHh4ZgzZw4uXLggye7GjRtBKVVsFi0oKIgp+/Anm79Yo6OjcfjwYRw+fLhROz///DM0Gg1LvU5MTGTn5ZNPPpHdzsOHDzOntLCwkD1MbqhUyyOmLI5KpUJISAgSExOxatUqFBcXo7i4GFVVVTqf++WXX0ApRUhIiM7rJr9l06ZNaNWqFZtNs7KyAsdxOHLkiN4XCIG/y/Gbv78//P394evrq7OOJIUTJ04gMzOT/f/cuXPIzMwEpRRubm56U9dSKCsrw+7duxEXFwe1Wi1btIRSKkt7TZuioiL23FJeXo5169bB2dmZORMf0SAEfgKgdevWbD+lnAiAjhPJ1WA35ix8NQ8heHt7s3I82lv//v11Ihjat29v8Ebf5EW+GvLNN99g6tSpbOvXrx8opZg6dSouXbr0upvTrCj5wP5b4siRI8yJLCwsFLHZo0cPncVYMU6kzXvvvYdx48bB2toaVlZWzGn4GVBDyyav3Yka8uTJE1EKnb8nOnfurFhP9FviyJEj7OKUqjtnCO2oBbmjBKB+Bnnp0qWNVhUxp4eb+V1x+vRpEhUVxf7/Oi5vsxOZMSOT31U+kRkzzYHZicyYkYnZicw0KcXFxcTX15dw3O/3Uvvd/bL/+Z//YVoLZpqfhw8fkqKiIkIIITU1NYrabtOmDWnRokWzn+/fnRPl5eURAKSsrEyyDWdnZ3LmzBkFW2Wc7du3E0op6dev32v5vtdNhw4dSFxcHCGEkLNnzyrqSIWFhezv+/fvK2ZXNELmy8eMGYPo6GhYWFjAyclJ9Hy7oexVJycnTJkyBYGBgQgKCkJ2djauXr0q2nZDLl68CEqp6DKLDVGpVPjggw9kt6cxIiMjFVnTUBp+sVJMsTVTlJSUgFIquRB1Q9asWaMTwV9UVCTZ1rVr11j0uqGtsQBkQWeuf//++O6775CUlARCiOjgS2ON047vorS+ctywYcPw9OlTk8GAprh48SI4jpPtRJ06dWLVoZuS4OBgWU6knQNDKcWmTZsUadeBAwdYIKtSdO/eXVIekSHS0tJ0frccJ+JTf0xtphA1nNu2bRvx8/MjGzZskNzzRUdHG32vqKiI7N69m6jVajJt2jRy+fJlSd8BBZa+unfvTu7cuSPbTlMSHR1NpkyZovPa3LlzSWhoqM5QRwo9e/YkTk5O5NatW7LsaHPgwAESFhZGevXqRX7++WdZtv70pz8RSinbzp49K9nWxYsX2d99+/Zlm0qlYq/n5eUZNyDEU9PT05GVlQWgPhRCrGyWdnnzxsJcnj9/jqysLLaJRanhXFVVFXr16gVPT0/k5+dLtjNr1iwEBARg48aNuHz5sl5WsHYNUzGsXLlS5045e/ZsHDp0CMOGDWOvib3jZ2VlIS4ujuWR3bt3DzY2NopV9OMhhMDCwgKzZ8+WZadhUqbUaH3tnqghpaWliIuLw5QpU1BdXW1wf0Fnrq6uDt7e3ixzUor2HF8hzlStTSUoKSlBSEgI1q1bJ8tOWlqaToHiNWvWiLaRnZ0NlUqlk9Ls6uqKwYMH4/z58wAAV1dXRZyIl/d68eIFrl27Bn9/f9HV+Dp06MDaolarMXnyZLRr1w4ffvihKDuN0a1bNxBCZJe17Nq1q15msxRMOREPpRTz5883qAki+MwRQrBlyxa8evUKDg4OohtaV1fHHMnGxgZff/21aBvGKCkpwZYtW9iFtGXLFkycOFGSraqqKhb637AcZufOnZGcnIyTJ08KsvX555+brBA3YMAAndfEpJekp6fD0tKSnXxHR0cdgcEbN27A1tZWlHSYra0tCCGwtrZmNVXJvyW47t+/L9hOQ2pqanDlyhXMnz8fDx48wLNnz1iEtNRnX6A+3UX7RhIWFibJjrYTnT171uBn+PdfvHih955gJwoJCWEHlRexkEJ5eTmio6NBCEH37t0l2+HJyMgAIQSDBg3ChAkTdCYqpIgvnjt3DhzHISIiAufOncOBAweYE40ePRpDhgxBr169BKU2P3r0SCcztrHN2tpa1NCJT77T3hwcHKBWq1lK87vvvivY3u7du1ktVZ5ly5axUqOFhYWCbWnvr51OwOdh8b382LFj9YRmxFBWVgZ/f392wwsKCpJkZ+bMmTpJqNoMGTJEuhM9efIE3333HTZs2KBzMchNjQbqZ/z42Tk5M0oTJkzQ0WBbtmwZQkJCwHEcBg0aJFo6ilf89PLyYklonTp1AqUUxcXFAOp7K6EKqxqNBoQQ/PTTT3j06BE+/PBDhIeHw9XVVWcoRwhBWlqa0XG3IXgnio6OxsCBA+Hi4qLjUJ06dcLt27dF/f6BAwfqDS3feustEEIkKavyF2DHjh0xYMAAZGdnY+/evejTpw9rZ3Jysmi72iQnJzMnsrGxkWRD24ns7e2xdetWvd8g2okuXbqkd5L5ra6uTlJDtXn8+DFatmwJQgjs7e0lqwdNmDDBYI/DO6gUWWInJydQSuHp6YmamhpkZWXBzs4OnTt3FqWG+vDhQ3bzMTYFq32MxQ5tvLy8QAhB27Zt8fPPPzMFVH6TckxXrFgBQgi++eYb9lpcXBwIIRgwYIBoe/zF2a1bN53Xr169yqbn3d3dZT0nL1myROfZSAqPHz/GO++8o+NI/PngnWjnzp0Gr32jTpSVlQUPDw89DYAZM2bA19fX4AOWEDIzM/VStQMDA0EpRUBAgOhe6csvv4Stra2eTjLvRFKfjXJzc9GrVy9wHIfAwED89NNP4DgOGo1G8DMRUD8BQAjBwoULDb7PK+oQQkT1Qjz8c4GNjY2OOpMcZsyYoffQHxYWJnkt69SpU3B1dYWLiwsmTJig8x7fIxlS0RGDthNFRUXh2bNnom1UVVUZXHvineiLL74wuJ/Jo6JSqdCyZUu9OxohBMOHDxfdSKD+4dXJyUlnFfjs2bOs4VZWVqLuyPxKeMOLlOM4hISE4ODBg5LaCdQP7RwcHGBvb48ffviBKfRER0cLtsHLEM+bN8/g+9qLrVIesisqKtjkR9euXUXvb4hPP/1Uz4nWrFkDQgh+/PFHSTZPnz7NzrGvry+b3g4MDISXl5fo7OZ9+/Zh9erVTFRF24nkLL5qO5GzszOL1pk8eTJevnxpcB+TTsTLGwUHB+vc6ePj42FpaYkpU6aIbqSNjQ1r5O7du3H79m3cvn1bp/GLFi0SNVPFTyYsW7YMJSUliImJwYIFC0S3rTEuXrwIBwcHcBwnuCfmnciYAIvciAUATGOvsZV1oRhyopKSEgQEBMjS4EtNTUX37t3ZOlHLli1BKUXfvn1F2Vm/fr3O7GnDTY4T8eq0DbdZs2YZ3UfQmSsuLkZsbKykRjWktrYWT548Qc+ePfXCfrQ3sQt8Bw4cgL+/P1xdXfWGDEpz4MABcBzXaO49UF9VgJ/SNjS7NWDAALRt21aSBJk2mZmZcHBwgIuLiyL6cIaorKwEIURStQ6l4G+4hBDMmDEDa9aswZo1a/D999+zv+Xw8uVLDBgwQO96XL9+vdF9BN/+LCwsmKCfEjx48ACBgYHsbuTu7o7AwEAEBgbiq6++ki2J25RUVVUhNTUVFhYWGDhwoMnP1tXVYfny5dixY4fB9588eSKpQIAh+MJUSorva1NbWwtLS0t8+umnTWL/TeHJkyeYP38+c6DBgweb/LxgJ1Kr1UaLHMnhxx9/xKZNmxTRhftP5/nz52yYk5iY2CTfIXbo9VvGxcUFhJBGF6zNQiW/M+7du0diY2PJd99991qUbsyY1X7MmJHN7y6z1YyZ143ZiQyQmprapPZramrI8OHDSUpKinnI1QS0b9/+tX6feThngPDwcDJx4kTy17/+tUlUav71r3+RyMhI4uDgQEpLS4mlpaXi3/GfDMdxpK6u7vV9n5APVVdXk06dOjV1W94YMjIyyMKFC8mBAwea9HsGDRrU7Eo1v0copeTDDz98fV8oZKovNzdXsdXwpoAPSeE3Jabiq6qqEBkZiW+//VaBFury7NkzlgrwplBWVoawsDCEhobCx8eH/S2lasXkyZMNLqBbW1vD3d0dP/30UxP8gl/hQ75eF4Kc6MGDB6CU6kT2SkW7FhGl9aVFVq5cKcvm+fPndZxIqXIlmzZtQlJSkqRaTI3B12qSmlZy9+5dVrVQe5MacDt69Gi9CBL+74EDB+LixYuCbfG5THw0fMPNy8sLWVlZshLyTBEQECDJia5cuYL169dj/fr1ooqPCV5spZQiJSVFdMMM2dHe+BMlh+fPn8Pf3x+EEKxatUpW0mBDKKUYOXKkYvZ4+GxKqQ7K11dtuPExaWJqPWVkZOiFMV28eBFpaWlMmkqMg7Zs2RKjR4/GpUuXcPr0abbFxcWhQ4cOrK1yNRaMERISIsqJ+Nwu7UJfTk5O2Lx5s6AYPJNOVFlZibVr17KiwhqNBpWVlaIT3XgiIyMRFBSEmzdvAvg1XWHXrl2glOKdd95BSUmJJNulpaVwc3NDu3btFL3DzZgxQ1H9NW127NgBOzs7PHr0SPS+vPyUWq1GUVER3NzcQCnFZ599hi5dukCj0RhMIDMEH+VgimPHjsHb2xuenp6i29oQ7ZSDphguu7u7i8pq1q4Unp6ejhkzZmDYsGE6+UX8ZmlpiTZt2mDSpEksol3PiZ49e4ZPP/0U48aNM9pr2NjYYNy4cRg3bpyoC8Df318nOYu36eHhwQJStWu5iiU8PByEEMXqoAL1YiOEEIwZM0Yxmzx8MOXGjRtF7xsXFwdKKdO74HOynj17hpqaGkyaNAlz585l2bimEDoa4J+NpQ4ZeV69eoVevXpJdqItW7YY1UIA6q+rmTNnCrZXVlaGYcOGQaVSQa1WY8iQIXjy5AkqKiqQl5eHvLw8LF68GPHx8TpZrm5ubgAMOBGfCq298Q/BxEDEdUBAAA4cOCCosQ2dqEuXLszm1q1bWXcqlX379oEQgsuXL0u2YYiIiIgmmwQQe8J5tIdzQL0+AqUU8fHxOraFlKEX0hPxeHt7g1KKjz/+WHSbeWpqaliZUbFOxOeemXJkjUYjKd/to49cZpetAAAgAElEQVQ+YsfUlIbGlStX2AYYcKKwsDBmSHtoxTvR559/rvP5r776CuvWrcOTJ08abWRhYSHc3NxYXU2g/u7m4+Oj45hyIITInpmpqKjQySsJDQ2FSqXCxo0bJaXG79u3Dx4eHvDw8MCwYcN03rOyspJU3r6goIBN0vDJYnwZR3t7ewD1s1RCnCgpKQkcxwkWDPHz8xOVhr1w4UKDz2+UUlRUVAi2AwDLly8HpaZ1BWNiYmRdAzt37kRUVBQorVdRakwyTM+JSkpK8Nlnn0GlUqFnz54s993GxsagE4mF9/KGqdAqlQoODg4ICAiQZZ8QgpYtW8qykZycDEopevXqhZUrV+roNItJW6itrcWqVavAcRzs7OxgZ2cHKysrtGnTBvn5+aiqqkJMTAwo1S/rLoSlS5eCUoqkpCRUVlbixYsXrFfx8vIS3BP99NNPoJQaLGVvCF4bQajexI0bNxASEoK33npLx4GkyDSHhITA39/f5HP5li1bFJni5qXTKKUoKysz+jmjEwsffvghKKVQqVS4du0afHx8QAiRnQvPT0M3VI7R7p3k4O7uLitLFPhVGJEXtK+ursaePXtAqbhCvTdu3NDrXbOzs+Hh4cGGwi9fvkT37t3Rvn170e28e/cucxb+Znfq1Cn2vEEpFdzDjR49GhzHCXaM+Ph40c9GxcXF2L17t84sWE5OjuAJEMDwGlBeXh6+//57TJw4EceOHcOWLVug0WhETVMbYvfu3fD39wel9WpFxkRvTF5tt27dYjNz2pMAcuDHtFJVWRojICBAthMBwJQpU/SGlrNnz0Z0dDQeP34syEZlZSWGDh3KZs0appQXFRWx6WNKqSS54urqarb/3LlzRe+vDS/1LERTLz4+XvY55CdH2rVrJ3gfjuMQEBDAnGnmzJk6kwwHDhxgv0OolDQ/IWOMhw8fsmOsLc/G0+jVVlNTg9mzZyu2pvPy5Uu2SNgUKOVEP//8Mzw9PfXEDz08PET1Rs+ePUNISAgorZeNajgJc+HCBSxbtgy9evXCtGnTJLXV2tqaLUHI4e7duzoLrMaeBZYsWQJbW1uMHz9elP07d+7o6PWdOXOGrW0J/e38dWhq2H/gwAHMnz9f8FIMpRRXr17F3r17mV56Q1ESfn3LUCci6Gp79eoVOI5TxImAX8fgUmalGoN3IilrLw05fPgw1Go1/Pz80KVLF/j7+8PS0hLTp08XZaeoqEjnWWDr1q3YunWrjtba7du3sXnzZknt1BZClKrGw1NeXo6ePXvqyCdzHAcHBwfMmjWLva5SqURp8AH1zzOtWrXSec3Z2RmU1ssDCIHjOMGzwUJp1aqVzoiAUopBgwZh7NixbONfN1SfS1QU9+zZs0laWhp5/vy5TtkJKeTk5JCwsDDSunVrcvz4ceLr6yvLHg+llBCiTHmV3xLZ2dlkwIAB5Pnz52T69Onk/fffl23z1q1b5N69e3qvh4aGSrIXGhpKvv32W9KiRQvyv//7v6SwsJDk5uYSQgixsLAg1dXVstqrBOvWrSOEEJKfn0/27NlDCCFkxIgR5I9//COJiYkh/v7++juJ8diysjL4+PiInpY0Bt81v/XWW0Y1vcRCZMpP/ZbZv38/6z2k6JA3NUVFRQgNDdWb5lar1YKew95UmvVqGzp0KBwcHEApxYIFC0xOIwqla9eu/7FOBPyq1unn59fcTTHKe++9x4oaTJ06VZHKec3JG5GUd/HiRdK5c2fy1ltvka+++opoNJrmbtJvmmPHjpGYmJj/uCFtc/FGOJEZM79lzBoLZszI5LU4Ecdx5OHDh6/jq8yYee28FieKiYkhnp6eZN++fa/j68yYea0YdaKcnBzSokULwnEcSU9Pl/UlR44cIQcPHiRjxowhN2/elGXLFOvXryerVq1qMvtKYWlpSZKTk8m//vUvkpSUROzs7Mj69eubu1k6Je0//PBDUllZ2dxNMsm6desIx3Fk06ZNsuxcuXKFODs7kxYtWrCN4zjSokULYm1tTaZPn27agKmpOz7v3tHRUZGpwP79+8PDw0OnQK+SPH361GBs05vE999/r5dQtnbtWtja2oqyc/XqVZw6dUqxwN1bt27pVUT09vbG8uXLFbEPAO+++66iMZOfffYZKKVwdXWVZYeP2g8JCcG5c+cwb948dOvWDY6Ojixi47vvvjO6f6MLKnySntgQD0OcOnWKxWU1FXJTKZqa3r17672Wl5cnam2rurqara9RWl80a8OGDZIq7fFol6rka7QShReuVSoVOI7Dvn37ZBU75nn16hUL85IjCXD58mVkZmbqtenEiRPo378/HB0dodFojDqS4COkUql0isFKZf/+/SxPoymwtraWtfp9/fp1g8ljbdq0Qd++fdG3b19MnDhRUjlDAHptq6mpwfDhw0W1mRCCu3fv4u7du3j48CG2b9/Oih7LifzgK4fzBb6Ki4sRFxeHGTNmSLYJ1JcqCQ4O1inCxXEcS6+Wy8KFC9GzZ88mUw86fPgwOI5DeHi4wfcFO1HPnj2hUqlE1Ss1xsaNG0EpFSXDJBRXV1f4+/tjw4YNKCsrw/r160XlqzQMFqWUonfv3ix5jt86deqEqVOnitZQa+gse/fuBaUU27ZtE2zDx8dH77Xz58/D1dUV+/fvF9UebT755BMWaDxixAj88MMPuHfvHtatWyfZJlBf/JgYkOLSzoMSyv79+7FixQqdXKGamhpQSvHVV1/JaqcpFHEivrsPDAyU3aCysjL06dMHgYGBrMy9Enz99ddQqVR6TtCxY0dR1d20o3YvXbrE8oAuXbqERYsWsdRhMdHHPA21CXx9fREfHy+qkLShquAPHjxAp06dMHnyZFHtaUhBQQErkdmxY0f069dPdnIb/8yhXZh4zpw58PT0xNq1a0XZatOmDTv2vFgOL6rj4uLClKSURhEnAuovUiUfDPlhndzCUXyJeJVKhezsbKSkpCAtLY29f/36dWzevFmUgElVVRWWLVsGSuUpEBnCyckJ69atQ1hYmKx4we3bt7NcIqXj5SZNmsSeieRKAvBOFB4eznLT3NzcEBsbC47jTJZyNEZtbS1u3ryJmzdv4vLly+jVqxdrr5TKg9q9Y1RUlE6azsyZM1kWrsF9xXzR7du3RQlaNKTh0Gfr1q2s4UKzRQ3BzyDy3XlWVpbRu4ZY+BMTFxeniD0ALCnR0LBMKAkJCazX9fPzw44dOyQ/pxkiJCQEzs7O7Oa0evVqPHjwQJKtJUuWMCeqrKzU6ZU4jkNycrIibeZLl0rh9OnTOrNxDdvo7+9vcAQAiHSiV69eoV+/fvjss89EN/LYsWOwsbFBQkICUlNTcefOHZSWljI9gz59+ghSDGrIiRMnDI6H7e3tZSeoAcDw4cPZOP7cuXOKPLz6+vrKTrXnbz5iEwSF0qtXL1y5cgWHDh1iNxI5wo2EEISFhQEAYmNjdYbbSiVnnjt3TtaE1d27dxEfH890FbSdyNQwUXQ+UevWrSVPLuTn58PKykrnALq5ubFce0op3n//fVE2Dx8+bPDA8UWAhY6RR48ejXbt2hk9CSNGjGDj7jNnzohqI1D/8Ltu3TrY2tqyIdzTp09hZWUl+jcD9b9vzpw5bCLE0dERCQkJiojF//TTTzo3i9raWqxevRrt27cHIQS9e/cWPVOblZUFjuMQFBTEXrt//76OTp5Y3nnnHZ1p/bq6OgwZMkSRfLf8/Hw2Jc9xHH755Rejn5X0TCRnHL9nzx5MmzZNR45Ie+OnV4Vy6dIlg2sZpaWlIISge/fuguxoj4kTEhLYpg0/jRwZGSmqjUC9sAelVE+8PzU1FSqVStZM5QcffIDevXvD2toaDg4O0Gg08PX1xY0bN3Dv3j3RovmmNCR4zXNKKb7++mtRdjmOk60DoQ2lVO/iTkpKkjzsbAg/SqKUKjec4/WflaCgoAAODg6wtbXVcySxUEoNTu2uWrXKYE68IebMmaOjL8dvffv2xcmTJ5GdnY2+ffuCUoqhQ4eKbiMhBNu3b9ebhXvx4gUiIiLg7e0t2mZDvvnmG8TFxaF169Y6vyE6OhqHDx8WPLFiaoKiuLgYW7ZsQfv27dGlSxdR7eM4jglLKgGlVE9IRqwTffLJJyZnCHfs2IGQkBB5s3NVVVXIyckBx3FQqVSSxTQa4+DBg+jYsaOkFXIXFxe4uLgYVPCXMoX+7NkzXLp0CZcuXcLo0aMxZMgQxMXFoUuXLtizZw+ePn0q2qa9vT2CgoIMrrk8fPgQBQUFijzDNeSHH37AyZMnMXToUCxZskTQPn5+fmjTpg2mTZum9wzYt29fyaVwQkNDFZ1IOHnyJOt9gfpQKDE34bKyMqb+yj8D+fv7Izw8HGFhYUhJScHRo0fh6ekpzYlycnKQkpLCvJDjuEblVJuLmTNnwsPDA/fu3Wuy76itrZXkPDw3b95kVQaCgoIwd+5czJ07F0lJSXB3dxe12NrULFq0SCfsZ/To0Zg/fz6GDx8OQojkOlVHjhwBx3FwdHRU7Fylp6eDEIJevXpBo9EIHnloM2zYMHh5ecHKysro7BzHcUzMsyFGnYgfD86cORMzZ85s8upmcjh79izc3d2b1ImU4NWrVzhz5gz69OnDLtDY2FjMmjVL0sxkU1FTUwNHR0e9gFRCCBYuXChqYVib8vJyjBkzBpRSdOnSBRMmTFCkvZcvX2aOdPfuXcl2eNXfhlEVlNZLLBursGFODzfz2uHTLf785z+Tf/3rX83dHNlYNHcDzPzn8Xu7b5s1FsyYkYnZicyYkYnZiYxQUFBAVCqVWWDFTKO8VidKSEggN27ceJ1fKRkfHx+SkJBAZs2a1dxNMfOmY2jK7tChQ7C2toatrS0sLCzQo0cPLF++XJFpbo7jEBcXp0hoRm5uLt5//30WzTx06FBJYfCmIISgU6dOitrU5sKFC1iwYAEsLCwajRRPT0/HiBEj9NYvVq5cqZiWOY+Tk5PO1LaVlZWkhL9Tp07p2FFKEwKoL9Uyb948+Pr6Yt68ebhz547stPOIiAh4enoKrm0EmFgnWrt2rd4agVqtlrxazcOf+Bs3bsiyw1NQUAALCwudnJr09HRFbAP1TtShQwdZ+gWmcHNzg4WFRaNOdPHiRVhaWuo5EL8wqFarERUVpVi77OzsEBUVhffffx/BwcEsJUIsDZ1ISPlLIdy6dctg7KXYUKSGLFy4EBzHGdTCMIbJGJunT59i9+7dLOmN3+bPny+5kX369GHZjUoxY8YMBAQEoH///qCUwtraWjHbrVq1AiFEUqS1EIQ60YoVK5jTeHp6YuTIkWzTXl2XU9Vbm+PHj7O/6+rqkJqaCo7jUFhYKNpWw5uxXDIzM3ViLtu2bavjSDt37pS8eP38+XMWniQU0b/o4cOH7K4kNj+e59GjR4iOjkZwcDDy8vIk2TDG4sWLQSnVuQjkkJWVBUqppLtwY+Tm5jIHsrCwaPTzt2/fNvre/fv32cnnOA6HDx9WsqkA6s/90qVLJe3bo0cPRYZ02ipH2tXteQoLC0EphYODA/bt2yfpO/Lz8+Hu7o7U1FRB+hyinYgvSahWq1FVVSWpkUB9qA7HcVixYoVkG4aIjIwEpRT9+/dXzCbfEyvJjRs30K1bN+ZAY8eOlW2zuLi4SZ3o/v37kp0oNTVVkd6IdyBTWh/8Z4YMGSLKdklJCUaOHInevXuzYXJkZGSjVdVF/ZqrV68iJCQEhBBkZmaKaqA23333HXbt2sXKGI4aNQrp6elsk0N2drbiTvTtt99Kzt03xpdffqnTCyk1DGtKJ8rIyJDsRIDusK6xC9MY/MVtbW2N8+fPG/yMFCfatm0b/P399Z41+b9jYmKMjrwEO5Grqys7AGLHxYWFhQYfiPlZNY7jMHLkSEyYMAFWVlbIysoSZV+bpnAioD5SvF+/forY2rRpEyitL/jr4eGBL7/8UrKtzMxMTJ06FdOmTYODgwM4jpN9DHNzc6HRaBAcHIyBAwdCo9Gwcy93gkWuE5ni4cOH6NixIyiliIiIEKxSVFBQwPQZ7t27xzby7yBUjUYDOzs7JjzZEEFONG7cOBBC0K5dO2RkZIj7ZQCmTZvGHKd9+/bYsGEDNmzYgAsXLjAnAuo136ToN2hz9uxZEEIktVObJ0+eYOzYsZg9ezZWrFiBK1euwNLSUhHdPbVazU6QXMXWKVOm6EQc+/r6YseOHbJsZmVl6fQa/IWkraAkFSV6I0NkZ2eja9euLAv122+/FbX/9OnTwXEcsrOz2WuzZ88Gx3Hw8fHBxo0b4ePjg6tXr+rtK8iJfHx8QAjBsWPHRDWMJycnh8kjtW7dGt988w27ow0fPhwcxyk2EeDp6QlKqcmHcCHwuTP85ubmBkIIoqOjJdusrq7GuXPn2JS8hYWF7CHi1KlTdYYdYqZmTeHi4gJCCOzs7CTnDxlC6Zk6Hu3ZOUMTDo2RlZUFa2truLi4sImPM2fOwNnZudGZOkG/oqKiAvb29orMrqjVanbC+byUqqoqcByH4OBgWYlvQP3BtLOzk2UDqM+n0j54/ISKjY2NZF1yf39/9gzUrl072b2uNs+ePcPnn3/Ojm27du1k27x16xYcHR1lDQ210b5+lOiNHjx4ADc3N+Y8W7dulS0bxh8/Xuvjhx9+AMdxJq95wbeCwsJCzJgxA5aWlrCyssKlS5ckNfLgwYPMkebOncteb9myJTiOw+DBgyXZ5eGFRpQgODgYSUlJeP78Oerq6lii2t69e0XbGj16NKytrZkTJSUlKdLGhvDpzhzHKXLxf/jhh4pV9tbuffjZOjlOFBYWxnr0gwcPoq6uTnYb+cLRfJp5dXU1evTogTlz5hjdR7ATPX36FI8ePWKr13KeDQ4dOsQegnkWLVrETr4cKKXYs2ePLBs8/Kycs7MzEhMT2UUg1omePHmiMxM3YMAARdpniO+//x5du3ZlEww7d+4UvO+hQ4f0XisoKICVlZUibWvoNHJ7I/5inzZtmiLt41m1apWOJuCpU6fAcZz42bnk5GS98auFhQWCg4MlL7I2pEuXLjrPWcuWLQPHcQgNDUVlZaUoW7m5ueygKlm6paSkBPPmzQMhBBs3bhQ9lCsvL0enTp2YA9nY2CjSrp9++klnKtbX1xeDBg3C3Llzce3aNbbW0VANxxT8CCEuLg47duzA3bt3sXfvXkWdyNgmBV6skd80Gk2T3aA4joOzs7PBFHGjrR8wYABznNjYWOzatUtxnYWGi7XPnz9n079i9RK0ncja2lq0EzYVAwcO1HEgpRaXnzx5gp07d2Lw4MEGRTX4TYwTnThxAr6+vrC0tNS5wJWSUG4YRyc3lq66uhpHjhxBjx49dJypa9euonpgIfCL+J07d9Z7z6gT/fjjj8jIyHhjLsbGqKiowOTJk0EphZWVlaxoCiU5cOAAHBwcYGFh0SRrIwCY/YYOpFarJYlCXrhwAW3btmXLGkpy6tQpFgKk1PF4/vw55s2bpxNPZ2tri9zcXEXsA/XXV+fOnQ1KcpmFSsyYkYk5s9WMGZmYnciMGZmYnUiLnJwc0qJFi+ZuhpnfGGYnMmNGJmYn0qJ9+/akZ8+e5P/9v/9Hqqurm7s5Zn4jmHQijuPYRikl9vb25O9//7ukLyotLSV5eXmkRYsWxMrKim1+fn4kLy+PlJeXS7KrJPb29uTgwYNk5cqVJCgoiBQWFir+HX/+858VUTz65z//yeR4KaXkr3/9K8nPz1eghcoTGxtLHB0dm8T22bNnCcdxpEWLFuQf//hHk3xHo5iaG9dewCL/DolXq9WS5tkXL17MhDYsLS3Zxv9/zpw5ssrHK4n24qXSZGVlKVJekV8Q5c8Nv0kVm29IeXk5xo8frxMVMWDAAEn2P/74Y8WTGnmmT5/OzpfchM4vvvgCH3zwgU4ImpDSPCadSKPRGFTJv379uqjGXb16FT179jTpRPwmtIbOe++9B1tbW8ybN0/n9fHjx+s4/6BBg0S1FajXMuAFVaTm6ZtCCeecOXMmCCFo27Yt1qxZgy5dusDS0lJW5qk2/Ap9wyzPxYsXi7ZVU1ODlJQUhIeHK1bFjkfbiTp06CDZzpkzZ3QyDDiOg5+fnyBVqkaDlnx9fQ2WmtDeGotIbt26NWvYW2+9hR9//BE//vgjiyp48uQJYmJiwHEcCCGCHKl3795G2xMXF4cZM2awArZSSrwD9UWg+AtISS0IJQpdVVZWIiIiQi/ujBAiy5GysrLg4eEBSqlOFPirV6+QmpoKSqnk5Dy+t3z48KEiOnmPHz9GVFSUIj2RRqNh16iHhwdSUlLY/3v27Gly30adaM+ePVCpVCadyFAohDbaPY0pBgwYwHqm9evXmwzdef78OZKTk1mBY34rLS1lJ6i8vBw3b94UXVtUGz5ERal0AEAZJwLqMy8JITqhPYQQeHt7G6waKAR+CDdr1iyd11+9eoXPPvtMdLG3zMxMDB48GNHR0cyJPD090a1bN4wYMUJSG3mKiorg4+Mj24mOHj0KR0dH5jTz589HYWEhxo8fDy8vL/Tp08fk/oLCZ/fu3WvSiYKDg03uL9SJACAmJoZ9VmjAK19UmFIKT09PRVU2gV+fkZQa2inlREVFRVCr1Rg+fDh7jb9QpZau5H+rv78/xowZg5ycHGzcuBH9+/cHx3Fo1aqVYFsNC4X5+/vr6DUQQnDixAlJ7eRR6pmoW7duTEeBTxCtrq7GN998A41GY7TAFyBBMuvFixfsAKxatUrQPrxTzJ49u9HPPnnyRLQT8Rw/flxQzyiFEydOgOM4tGnTxmgVaaFQShWZXADqM1oJIejSpQu6desGtVotORkvLS2N3Sz5Hpg/nvb29pg5c6ZeDVdTvHr1il0rffv2Za93794dhBD4+fmJrmyuzfXr13We1+Q4UXV1NXuk0H4munDhAoD6NBFj8giSEjn4Ays0g5RvlNDZN/7zbdu2ldy2DRs2iN5XaLvkDu2Sk5NBKUViYqIi7eL1EAghmDRpkmQ7a9as0RGOAeqfkdasWSMpGhyonwDhn3WPHj2Kuro6bNiwgek3yEFJJwLqJcG0ncjJyYnpATaZEwnVPRYznAOADh06iPp8Q/jnJDGKP/n5+fDx8cHo0aONfubx48cICQkBpRTvvfeepLYB9dmilFK4ublJtqGNtvj848ePZdniL0glZ9FevXqFY8eOgRACd3d31NXV4dGjR0hNTYWdnZ2snn3atGk6PaYcNm7caDQvy5RMnI4TFRcXNzqEqqqqYg2eOnWqoMaJdSJ+TUmqEz19+hSUUvTq1UvwPpmZmeyAeXt7M4mohs9Bjx8/ZrJMd+7ckdQ+oP5ijY2Nlbw/z61bt0AIgZOTEyIiImQrqY4bNw6U0ibJferduzcsLCx0ZKkIIRg2bBguX74s2W6PHj0UWdd7+PAhrK2tma2DBw8iMjISkZGRJjOadZyoTZs2sLOzw7Jly7Bz505kZ2cjOzsbL1++RHZ2Nt577z188sknoJTC2dlZ8HpRcnIyc4rExESsW7fO5OflOhEAlpglVObrxIkTCA0Nhaurq16maFhYGMLDw9mmxEQDf/eUS35+PtRqNY4ePYqKigp07NgRjx49kmzv9u3b4DgOEydOlN02Qzg6OuoUB+Bnfjdv3izZ5oYNGxRbHPfw8GC2KioqBO2j40SmZuAavid2dik9PR1OTk46i6tffPEFfvrpJ7a1bdsWN2/eVMyJPDw88Msvv0jaPz8/H2vWrMGaNWuQlpYGtVqtcyyErGSbYs2aNYqc9NWrV8Pa2hopKSnIyspCRkaGjnjl7t27RWf5pqWlgeM4uLq6yupthZKeng5/f3+MGjVKksKqkk4E1Iu98OtG8+bNw7Vr10x+XseJNm7cKMiJoqOjkZ+fL7px/MkxFbHQoUMHxZyoTZs2kvdvaviTJJf79++zaWNra2v2fKRSqbBp0yY4ODhICrfhz7kp4XglSU9PByFEUu9++vRp5kTTp09XpD25ubk6s3Sm0HGi2tpabN26FdOmTYNGo0FcXBxznJCQEKSkpGDatGmy9AvEOJHQZy5DSOktXyd8iIkSrFixwqiKzgcffCBpGnngwIFs2Dpr1iyT6yRKwE+He3h4SOrllXYibZuNnSdl64WI4MGDB2zqk2+oHLHBixcvYuPGjZg5cyY0Go1Jsb3/FBwcHCQJTWpTXl6uM43s6empUOuUhb/ZKyXnxjNv3jwmLGpsRrbZnEhpgoOD2YHUaDTN3ZzfFYcPH0b37t3h5+cnu1DAb5Hvv/8elFKj8mO/G7Wf/Px88vbbbxMLCwvy9ddfkz/+8Y/N3SQz/yH8bpzIjJnmwpwebsaMTMxOZMYks2fPbu4mvPFIcqLFixcTjuNIQECAyc9VV1eT4uJi0r9/fxIQEKCj2cBxHOnfvz/bzEhj69atZM+ePU36Hdu2bWtS+795pMxWuLu7g1KKlStXmvxcRkaGyUQ+7U1KxQVDyF174TMa5cRyGeL+/ftYtWoVZsyYwTaxov2GuHv3Ljp37txk6zizZs2SVR2wKeFrPiUlJWHWrFk4d+6cJDsrVqzQux4bS8TTRpITkX+vjhcUFJj8XGhoqE7DvLy8oNFooNFocObMGWg0GtjY2Oikmct1JKWcyNbWVnTdT2Pk5eUZjAymlGL27NmyS2Pa2dlh1KhRTRKiM2vWLPj5+Ukq4cjz888/Izc3F7m5uYpFh584ccJg6Z/IyEjRtozd2BuL8eSR7ERCym08efIEV69eFST2IDTVvDE4jkNKSork/U+ePCl4pboxrl+/Dh8fHyaiERkZiY8//hgLFy5Ez549ERERwb6HL28olYZF05SitLQUlFLMmzdPVELehAkTdG6QhjaVSgUbGxsEBQWJKsy2YMECgzWNHj16hEWLFsHb21tQL19aWorExESd9vTr1w/9+vXTWXds7JoU7UQVFRWglGLLli1idzXJO++8I9uJ+P8E4dMAACAASURBVCgIOU4E1Iuz8Bf3rl27JNtJTExkdozFr2mH8U+cOFFyKZuvv/4aHMehrKxMcnuNwZ+X+/fvC96H/H/2vjusqitd/1vbQxs6Q72AAiMKV1H0WnDEghPbFWN5rNeKY4KMBiU/69WAytg1tsTEFjXcxK5RJ/Yo6mjsGBl7AbEjKgJWwPf3B7NXzuG03SyTOe/z7EfZZ+9vr33Ofvda61vf9356sZZVq1ZFzZo14eLiYpZQOp0OCQkJKCwstGq7Zs2aBpXsKsPBwQHdu3fHs2fPLNrZu3evQRtSU1P5Z9nZ2Rg3bhz/bOTIkWbThGSTqLCwEIwx/Pjjj3JPtYh+/frxPB2lEEkUGhqqqi3Z2dk84jwwMBDr1q1TZEcMlbEUQXHnzh1e1U7Mznz+/Lnsa7148QJJSUkmi1Cphfgg6acwWINYfIwxhoMHDwKoyNmaNWsW36+vjSF1LnLjxg24u7tblMcSS6Ja09oQSdS0aVMcPXrUiHTPnz/HhAkTeNvWrl1rsi6sSRJduHDB4Mb0Cz0VFhbCw8NDdjbihQsXsGnTJjg5OWHEiBH8/+LGGMOIESNk2TQFca6hFk+fPjXokRYtWiTbhkgiKeUaX758ya8VFxdn9fibN28iIyMDI0eOxMiRI3Hz5k3+EnFzczNIfJOKH3/80aAHady4MTIzMxUPtdevX29w7p49e8weW1ZWhr59+1qd14kFky09K9euXYOLi4ukOL/x48eDMYZmzZpZVEiy9B2YJNGUKVPAWEXFOTs7O4PAvsLCQlmKLyLS09OteuikzJ2sQSsSARVp3PrzI7nqn/oP5O7du60eL+q9WWu/GPovklT8v6+vr8H/5cLJyckob6x+/fpwc3NTPNQWnx/GKtRz1QaIispTlqQJbt68CU9PTzg6Olq1N336dN4+S6pV+kO7yjBJourVq/ODCwsLMX78eJ6gdOfOHUUk2rp1q0Fim6mtRo0aquvCMsYUF9I1BX0SWRtjV8auXbvg6OgIQRDQu3dvq5nAmzZt4mUjLWHjxo2cLE2bNuX/9/HxMSCX3PaK312rVq3Qpk0b1KxZE0TEdSWUkOjevXs4c+YMPvzwQzDG4ObmhtmzZ8u2I+LAgQMgIqtqp0lJSZKfA/He3NzczB6zY8cOBAcHgzHjesImr/Ltt98avT23bNli8MArnRPpp9wePXoUI0eONCpcK8dTUxla9kSiPXHT13eTAsYY2rRpg+7du3MblryaixcvluwV7Nu3Lxir0NmrPBnftm0bXFxcZLu8RVfxmDFjDPaPHDlSVZVvEXPnzjV6aSqBWNkeAFJTU02Ky6Snp0tu7759++Dp6cnbtXfvXj4qKiwsRHFxMQAgPj4ejDHs27fP4HyTVykoKICjoyNiYmJ4vv7r168xc+ZM3j0rJZGpt+O9e/cwbtw4ODg4gDGmyjGgNYnGjh3LH2xLHiFTqFu3rsn1oaysLJOLuV5eXrJc68OGDYOPjw/69OljsNhaVFSE6Oho2W/80NBQPpy7evWqwWejR4+GTqeTZa8ySktLjYikxCN58+ZN+Pj4oKioCM2bNzdLIjnFF9atW8f1NcTt9u3b6NOnD9ebkEUioGLIIA4X9N2m7dq1A2NMdgbh/v37kZqaalEbTF9AXym0JtGrV68wdOhQRSQqLS3FsGHDzMowde7cGUCF0yU5OVnR+pSYcq+vN1dUVIQePXrI9tRlZ2fzB6lXr17o378/li1bBqBCc0LuWta4ceMwd+5co/WlpUuXwtHR0cB9LBeiNHG9evX491j5c7kJhBs2bDAgkajzLT5PskkEwEDnet68eahfvz7/W8piK1CxAGZpHuTq6soVdJR6gfQRFBSkKYlEiDrhTZo0UXT+li1b0Lx5c6OIBf2/09LScPr0adliHaaUeW7fvg1BEBSJ2z979gz9+vUz6WiQg8aNG4OxikXMiIgIo00ceTg7OyuaJ9WoUcPkMHPNmjXw9PRULKWs70QwtckiUWlpKTZt2sQnVIwxBAYG4tSpU5J1FsrLy8EYQ3h4OFxdXa166BhjWLp0qaKb1/8CtIZIopCQEGzZskWRjZKSEmzdutWARB4eHkhISMDOnTs1bnFFr+zh4aFIVAaomFu1aNGCu9HXrFkj6/zFixdL+r2tTerN4fr165g3bx4iIiJw+vRpvr9mzZqqyqw8e/YMKSkpZtv64MEDg+Mlzbzy8/M548WFMznIyMjAmTNnsHXrVowbN86sqIa4WYvJswQxmFCrYlciRBIJgoA+ffqosrV161YkJyfzSeybwsKFC+Ho6Ihu3bq9sWtYw4gRI/DJJ5+gXr16Zh/KoKAgrnmtBBkZGfD29kZWVhZycnKg0+lUp7GXlpYahf6Y641/MxoL+jh8+PAbsSsKDVqrx2TD20efPn1ARPD19ZUsuigFixYt4tUr9u7dixMnThgdY0sPt8EGlbBlttpgg0rYSGSDDSphI5ENNqjEOyHR69ev6dWrV/T69et3cXlJqFevHgmC7R1jg3XIfkomTJhAdevWpfLycsUXnT17Njk4ONCsWbMU23jT8PT0JCJ6r4luw/sB2ST6y1/+QtnZ2aoerlatWpGnpye9fPmSSktLFdsxBcYYrVq1SrWdsWPHkoODwxtV0lm5ciVNnjxZ8flXr16l8PBw+n//7//Rs2fPNGyZOmRkZFBUVBRVqVKFvvjiC749efLkjV3z/v37b8y2VSjxnTPGFNWR0YcYZat1pW8iwsqVKzWxNX/+fAwYMEC1kIg5REZGYtKkSYrP119VV5sSXxnLli1DbGwsiAiJiYmSC7oBFeE4pmIFw8LCEBERgRo1amgSoXHr1i3MnDkT0dHRCAgIQGRkJCIjI9GpUyfMnDlT1aK9HMgi0Z07d1C9enVER0drEhHg5OSEiIgIzRZHN2zYADs7Ozx58kQTewDg7OwMR0dHTRIGRTx+/Bjt27fXJDxJXFWvVq2a+oYBWL58ORwdHeHv748JEyYgPj6eR5LY29tL1nD44YcfjIpPL1iwgBc9ZozB09NTURvz8/ORkJAAxioUpPr164cVK1bgwYMHePDgAWbMmIFq1aqBMaY4tV8OrJKovLwcs2bNQnp6OurUqQPGtCsf36lTJ03yVESsWLFCUiq2HIwaNQpEZJAirxb79+8HY9okD4okcnd3V12978CBA7C3twcRGSSetW3blv9Offv2VdtkZGZmcg2LAwcOyDo3NzcX1atXBxFh0qRJZgVgnjx5gqSkJDg7O+Ps2bOS7aenp2PIkCEYMmQIhg8fLqmQtNVfsWvXrkaxQ1rVgDl06BCPxFVTOEzEunXrNCdRcXExGjRooDqXRh/79++HTqfTpCfSV6yxVPlcCtavX2/ypVZaWsrTABhjqupIiejUqZOiolzNmzcHY0xyWgpjTFIx6EOHDiE1NdUojjMyMhJxcXEWyW6RRPn5+fD399f8wayMQYMGoV69eqqFG48fPw5BEBSHwJtDcXExXFxcMGzYME3spaSkcIkotfjmm2/4w61GKQmoqO8qPjwnT57kQ/bCwkKsWrUKjo6OiqQBgAqCVi5u3L59eyQnJ0u2cf78eTDGZEWTu7u7W31ZDRs2jA8x9QOCly1bhl69eiE6OhoeHh5GBBPzlSyS6Pvvv+f5IEpQXl4uKQM2OzubF+t9+fKlomsBb2Y4J6KkpAR+fn4mAxDlIjQ0VDMSlZWVGaSYqJmrtmrVyuAh6dChA3r37s2HnkSEX375RZHt4cOHGyU2tm/fXlZPNHfuXNSuXVuWdoROp0Pjxo0tHrN//34+5zOHCxcu4NChQ9ixYwd69uzJ9SeAN0yinj17goiwefNmvpmDmACmVE8ZqEgxsLOzeyMChgDg6uoKf39/WZ4qUxAzeAcOHKhJu4YMGcJJZEn2yRq+/vprEBEvpKy/6XQ6xMbGKra9aNEi7qWrUaMGIiIi4ObmJotEzZo1w5IlS2Rdl4isDudKS0v5vG/IkCG4ffu2VbtFRUVcElkSiZRW8S4tLUVycjL69evHJ4P0z8rWrVu3xk8//cSPnTx5Mk81UFKoV4QaF/ezZ89w6NAhs949UUfNz89PcfuAirejlnOs1NRUniWqZkgnzonc3d01a1tlPH782CBbVs5wjohkJUQ+e/YMjDFs375d0vHl5eVo0aIFiAjOzs6SRTQtLrbu2bOHiIjKysrkLj8REZFOp6P58+fTt99+S+PHj6fu3bsTEdGLFy9oz549FB8fT+3bt6fly5dTfn4+vXr1iqiij1R0PbWYMWMGNW/enFq0aEEtWrSg5ORkg3sXS1jm5+e/k/aZw6RJk+gvf/mLajuurq6k0+ne6KKoh4cHJScnk4eHBzHGiDEm+dxq1arJutYXX3xBf/zjH6lx48aSjhcEgXbv3k3Tp0+n8vJymjJlirQLWWKYfmafFigtLcWpU6eQlJSEzp07m81stSRmYg2koif68ssvDVLhGWNISkpCSkoK1q9fj6dPn3I9CFFGSS5u3rwJnU6HOXPmKDrfHPLy8rirW016/aBBg0BEshVu5UL0znl6ekpeeB00aJDkjNWDBw/C29tbkWZ8WVkZOnTogMjISEnHW3Vx62sRiwtkauYtlVFaWoqioiKMHj0arVu3Nql7JgdqSKSPgwcPIjk5GcnJyQZzDnFTWmokJycHgYGBimR+rWHlypWauLu/+uorEBE++eQTzdq2YcMGo32XL1/mYi1SiHTv3j0wVlGORh/Vq1cHUHH/M2bMgL+/P4gITZs2ldQ2X19fODs7IzY2FjVr1oSzszOISPLc1yqJRPem/gPk4eGB8PBw1K9fX9JFpKKsrAwvXryQVcKjMrQikT5ev36NoqIi/paWK+Koj5ycHLRq1UrD1v0KsTdSO3p4/vw5HxWIclajR4+WtPBoCpcvX+blZRYuXMi3tLQ0WSQCKlL0HR0d0alTJyxevBgzZ86Ei4sLWrRowYvPOTk5ITExUbKnd//+/YiLi+NzcnE7efKkpPMlL5k/fPgQv/zyC8aMGcOZqkSh5U3jTZBIS7xJEgG/SoapHYKLnlVPT0+Ul5ejefPmVisjmsOtW7d4nSZTGuJxcXGSdRHS09MNNOsYq9BG7NSpEzp37ozOnTsrrnJ4/vx5JCYmIjExEWvXrpV83m9OqGT8+PHvugkWkZOT80bnGxcvXuRv0k8//VSVrfLycvTo0YPbk1I7yBp++OEHA++cmuiXo0ePqm6PFrAJldhgEbm5uVS/fn367rvvqE2bNlSlSpV33aT3DjYS2WCDStjyn22wQSXeCokaNWpEDx8+fBuXssGGt463QqLMzEwaOnQoBQQEvI3L2aABGGMkCAL5+Pi866a893grJPrd735HS5cupbi4OIqLi6ObN2++8WvGxsbSRx999MavIxVubm4kCAJVqVKFVq5cSSUlJe+6SZKwZMkSVedPnDiR3/eSJUvo7t27mrSruLiYqlSpwre///3vquzl5uYqV3d6m67AwsJCCIKA+vXry85olAsfHx9JyVhvC5XXR9RULQAq1nEqR1E4ODjg3r17mrS3Xr16ICL8/PPPquxUvm9nZ2fVeWMAcPfuXQP9Bm9vb6SlpSnWw7hx4wbs7e0NqktIhUUSPXv2DPPnz0fHjh3BWEUhZLVZrUVFRejZsycEQVCt3G8JDg4OiklkrnqBGu0GZ2dneHh44Pr167wwV506dRTbs1aqROmCowh9ERQ1YIxBp9PxbNivvvoK0dHRqon06tUrXLt2jf89ZswY2QXSzLW3atWquH79uuRzzJLo1atXCAwMNPhh6J+LZGrzdZ4+fYquXbuiTp06b4xIrq6uqkmUnJxsUIJw0qRJeP36tSKbouBHeHg4r4mr5gcX2+Tl5YXDhw+jRo0aBpUGpRZhM4djx45pQqKqVauCMcYDQYuKiiAIgqLAUEt49eoVRo0aBcaYqjItERER8Pf3l1WA2yyJfvrpJwMChYeHIzQ0FIwxeHt7Y9CgQaqS04qKisBYRdlzpcGcliA1t97cuV26dEFZWRnatGnDvwNHR0e0bdsWN27ckG3z+PHjaN++PU9fF+O8lEJsk340+KtXrzBixAhZGgTmUFpaig4dOqgWUxFz0sQXhkgiuaUwpWD//v1wcHDArFmzFNuYPHkyGKsoWC0VJr8hMbpWfxM1vF68eIHt27ejYcOGqt9SU6dO1aQLzsjIMCqLrmZOxFhF5banT5/y+xejrsVatkqIBFS8nPTjxpRCbFdcXJzRZ8ePH0dQUBCmTp2q2D4AnD17Fm5ubpqkbUybNo2TXouQH3OIjo5WRSIA2L17N8LCwpCeni7peJMkEqNrGWOIjY3F5MmT0bt3b+zevRtABcm8vLxUk+jFixfYtWsX3NzcMG7cONkl44GKtGMxsrxDhw64ePEiLl68CE9PT3Tv3l1Ru8R7Lykp4f8Xf5jXr18jKSkJu3btUmQb+LVKuBYkMmejb9++qtK5RQwYMACNGjVSbUesISv2Sh07dlRtszLu3LmDkJAQ1SR69OgRkpOT4e/vb1RF3RRMkkgUxgsICOA9kJ2dHRcxPH36tKbJeqKIhZLit02bNkXdunV5LlLlTQnEe9u6dSucnJzAGEO7du0AVARlLlq0SBWJAgIC+BxTKayR6Ny5c5qQKCsrC4IgaCKT1bZtW04iJS9Ma9i1axcYY5qo6rZu3Rre3t6Spiwmf8WHDx8iKioKjBkWIS4oKMC2bdsMfkApuT9+fn6IiYnBt99+i4ULF+Knn34y+hLFIU5ISIhVe/p4/Pgxxo4da7R/zJgx8PT0lJwnr4+RI0eCMYbIyEgUFRXxKtjt2rXjb1NRpEIOCgsLee3X+Ph4DB48GEFBQZg+fbrsHCpRhYcxhl27dhk4PLZs2QJPT09NSARUpJf4+PioFmjRd04xpl7iy5RUsVqbIp48ecLn/9Y8dWZfhd27dwcRoUePHkafFRUVYenSpZL1n03dbGRkJI4fP843/c/kwhyJGGNYvny5bHui3p74lt+1axcfvirtgXNychAXFwdBENCmTRte0dvHxweCIODYsWOy7G3duhUeHh68Pfril7NmzeJDcS1Qo0YNMMYwYsQIxTZycnIgCAKCg4Nx6dIlREZGgjGGiRMnKpb5+vjjj+Hu7g5BEODu7o4lS5ZosgYlQvxurSn+miXRwoUL+dtYrUs7NTUVkZGRcHZ2NiCL/iKc1iRKT09XTCIAXCtbnFzu3r2br5UpSZsWH2xBEAwEAjMyMoz02KRCfBAZYxg7diyeP3+OsrIyPqeVS6L9+/djzpw5Ro6ElJQUEBGio6Nlt1HEhAkTEBgYyF8eAFC/fn0IgoCJEycqtnvp0iUsW7YMzs7Oin9rc6hbty4YY1YLXVsclNeoUQNubm5wcHBQJWOlj0ePHmHhwoUG42O1JPL19TXat3XrVlUkAn4l4qhRo1QL+IeFhYExho4dOxoN3bKzs+Hu7o5Tp07Jsvn06VP079/fwAV/5MgR3ovKJdG8efMQExMDNzc3g2GXvoSwEjx+/Bjx8fFGzoTevXvz+aEc50VJSQnS0tIM9okvaDUiN5UxduxYSfdtkUQJCQno3Lkzqlat+kYWRadOnYqpU6fC3d0d4eHh/G+5SEpKMtp34cIF1SQqKyvjX+KOHTsU2wEqRDR8fX0hCAI6depkEE7z6NEj+Pj4KJq8iz2kuLm5uaF169aqhnNnz57F1KlT0apVK6N5jFIMGTKEe+Vu3ryJmzdvgjHGe2I5tsVeXd+B0L9/fwiCgAEDBihuY2VkZ2fD19cXjFkWxbTqHlq6dClq1KiB8PBw1a7DNwVTwzktSARYXo+Ri9OnT6Nt27a8x23bti2WLl2KunXrahK9UHlTs3IPVCxB7N27l8fRqV14NaWvsGPHDu7Ekop169YhJCQEvr6+SElJwe7duxEUFKQ5iYCKGEJr64KSv5U+ffrwN5KWXeabRNOmTbFs2TJVNp49e2ZQiUCNEpE+SkpKuBdw4MCBsoQxTNnq1KkTH8YxxjQtBaMVioqKkJeXZ+Cdffr0KeLj4+Hq6irb3sKFC3lP0aFDB9kSw1Kwbt06EJHFcC9Zr5Yvv/ySj71btWqlWcTw+47jx4/zB/R9foHs2bNHEwFMG+RBFolevHiB5ORko1CYfwecOnUKRCQ5FMSGfx/YhEpssEElbEIlNtigEjYS2WCDSthI9A4gCAIxxug///M/33VTLKKsrIxKS0uptLSUTp06RWlpaVReXi7Lxt///neuL/G+43/+539k3x/RWyZRUVERrVy5kgRBIEEQaMyYMXT06NG32YT3Aowx8vX1pZ07dyq2ERoayr/HPXv2UFFRkYYtJHr+/DnFxcWRg4MDOTg4UMOGDSk9PZ3++Mc/0tatWyXXkIqNjaUZM2YQEdHf/vY3Tdv4JvDXv/5V/knWPA+FhYU4duwYhg0bhri4OMycOVPRWsmaNWvQpUsXowXB9u3by3eH/IsjMjISRIQJEyYoOv/+/fs8OqF69epgrKLkzaRJkzRrY/Xq1REdHY25c+cabOIi6atXryTbev78Obp166ZZFvOlS5d4ZnDlTU3ukyiWf/HiRVnnWSSR/ury5s2b0aZNGwQGBiIoKEjWA3Dt2jWeCWtuk4v9+/fD0dHRZCCr+LeXl5dBSUupoEo5SS1btpRtwxIWL17MF66VgjFmEEVfUlKCoUOHGsWUaY25c+eCMSar1qoIT09P1TloW7Zs4c+MvhjL7du3kZaWBnt7ezg7O2PVqlWKBPhFwZOPP/5Y8jlmSXTt2jUwViGEkZqayvffuHGDRx2fO3dO0kUOHDjAb/zKlSt8GzJkCBfXmDhxoqzcn3Xr1hmQxtHREfb29rC3tzcglbOzM06ePCkr9q0yiUQiaZHsJUKMo1OKRo0aITY2Fk+fPjXYr0VFcksQSdS5c2fZ5y5btkx1KFa3bt0svngPHTpkkGclF8+fP0eHDh1k/TZmSfTtt9/C2dkZBw8eNPpMDLUIDQ2VdBF9ElXG5cuX0aBBA04kqdAnUWpqKo4cOYKdO3di586d+OSTTwx6Jn9/f8mpBmI59rS0NL5VJpQWZEpMTARjDA8ePFB0flxcnFFvBFSoHE2ePFl1+8xBJJH+i1UqCgsLkZSUhFq1ailKagSskwj4Nf9JaR2ou3fvIjQ0VHJFSLMkGjdunEUhiVmzZsHHx0dywz799FOD/JzKYKxCUUgOIiIiLA7nxK1BgwZ49uyZJJsiaSr/LRJHn2RqkZ6ebjKNQypEJR19SEkiUwOxSrmcOdGdO3c4+cQhHWMM8+bNk3198dw+ffqYPebx48c8F+jRo0cW7WVnZ2Pr1q0mN8YqMltFiQRzsNgTWYranjVrFjw9PS0a14c41gwKCjL5AzDG4OLiIiuIUD+M3hKJpGbgitAnjUiiyvMiCT4Zqzh//jwEQcCmTZsUnX/t2jUMHz6cD1tEdSL9pD8tIWrR9erVS3J+2bFjx3h6feVNSaVF8VxrgcVZWVlgjFkVGhk+fLjZebo4bw0ODrZoQzGJEhISZGdjDhw4EIwxDBs2zOgzseGVi9paQnl5OTIyMtCiRQsjEvn5+cHPz0+Raqs+acSepzJptBrWMcZQrVo1xcM6AIiJicHp06exZs0afPDBBwap4lrhq6++gqOjI3r06GGgPGoNYgq7t7c3xo4di2XLlsHd3V2xQ0k878yZMxaPk0qi1NRUCIIAe3t7hIWFISIiAvPmzUOLFi0QGhoKFxcXCIJgMmdNhNWkPBHr1q3Dtm3b+Fi2Y8eOCAsLs9hAUxC/BHd3d5P77e3tZdu0hFq1askmkdj7iEM2U72RVkO6vn37qnJ3AxVDEvH7Cw8P10zP7eDBg5g5cya3LSoeyYGbmxu6d++OO3fuoLi4GNu2bTOY/M+fP1+WPfG8jRs3WjxOVEOVInnVpUsXiyI5derU4eIypqYFFkl0584dTJ8+HQMGDDAYJg0YMADOzs6KkvTMubVFTQO1LlB9lJSUKH6oKvc++g6GyiRTg/Pnz/PvVSkmTJhg8L26uLhInhSbwvDhwxEVFQUXFxfeU6anp0uuxq0PcR0rLCwM165dQ0ZGBk6cOMElqt3c3LBx40bJAiNS5kQlJSWIiYkBY0xSm1+9eoVq1aqZHfqLQjpOTk4m874skigkJMQgC3P8+PFo0qQJ3xcTE2O1gZUhek7EiaUow6T/IGiFZcuWQRAERSRq2bKlySGc/qYV1K4Z6XQ6/nITX3ZOTk6SnSmVkZqayn+L4OBg/PLLL4rbdv78eT6k0/ca5ufncw8jYwwzZ86UZE88vkGDBmaPEVVq5Xynz58/R9euXeHg4IDk5GSj4gWCIKBdu3YYPXq00bkWnwQigp+fn4FCC1AxRGJMmd5zZY1vNzc31KlTB/fu3UN4eLiihykzMxOPHz822CeKDqp5w1f2zMnF+fPnsXjxYr6ZG4KcOHFCFYnEahNAhbNBfNGZm39Kwc8//8x7C7VYtWoVHB0d+W8eFBSEWbNm4eeff+b7pHp6v/rqK35OeXk5r14h4vbt22CsosyMlJdn5XU2US/d0dHRYP++ffsgCAI8PT2NngdJEQs//PCDwf7s7GyEhYXhyy+/tNrIyigsLMQHH3xg5AkBgNq1ayv60YgIISEhBu2pVq2aJj2b0oiFgwcPmtQU0Nfdq7xfKby8vIx068RFZ2trJQEBAZg3bx4X6SwoKMC8efOQkpLCbWiB7Oxs2NnZmfSC2dnZoX///pLsFBQU8Jf46tWrMXXqVDDGkJaWhoyMDDg5OcHBwUGxfvjXX38NQRDwwQcfGOy/f/8+qlevDkEQjBaaJZGo8oRSaSElEbdv3zboyhljaNmyJVxdXRV7bMQH8tChQzh37pzBQqwaKA39yc/PB2MMiYmJJjdxCNe1a1ckJiZi586ditvIWEVNHf2ezt7eHh988AHc3d0tRpaI378giIBLmAAAIABJREFUCHBzc+PzIHGrXr264nZVxsWLFzF58mSDIXH37t1l11IS9dcHDBiAq1evYtCgQQZt/vzzzxW3saysDGvXrjU5hM3IyICjoyP8/PwM5pwWSVRYWMjZFxUVhejoaERERGD69OmKG6mP2NhYTfSzTa0TVa1aVTNXb+UF2PcNW7duRe3atQ2+Q3GCfOXKFc1FDd8HiKV5Km/vQvdDUhS36C4U50hyVqvfBhISEoxIdPLkSc3saxmlYIN22LZtGxo2bAgiQt++fXH58mXNREbl4DehsfDixQvavXs3bdmyhZ4/f07/+7//S7Vr19b0GpmZmZSZmUkTJ07U1K4N//r4TZDIBhveJd7/nF0bbHjP8VZINGTIEAoICKB//OMfb+NyNtjwVvHWhnNZWVn0X//1X/T8+XNycHB4G5e0wQZJCAsLoxs3btCWLVsoPj5e9vlvbTgXEhJCEydOpGnTpr2tS75XKC4upqioKKpSpQrFxMTQmDFjKDMzU5XNv/3tb9SxY0dKSkqi1atX07p167RprMaIj48nQRDo2rVr77opJpGQkEBERB999JGi89+qY+HRo0dUu3Zt+uGHH6hRo0aa258/fz796U9/0twzpwVWrFhBgwcP5n8DIMYYlZSUkJOTk2x7HTt2pIMHD1JxcTHfp9PpqKCggNzc3FS3Nycnh7Kzs432x8XFkaurqyxb8fHxtH37drpy5Qr94Q9/UN22vLw8Wrp0KXl7e9Pw4cNV2wNAVapUISKi169fKzJgFpMmTTKZpKSmcp5Ya9TPz0+xDXOoV6+eJqnRor6EuIWEhGDFihWqbI4aNQpeXl7YsmUL7t+/jyNHjqB69erw9vaWrWku1hMNDg5GSEgIDh8+jMOHD6N79+48Nu3w4cOK2llSUoKPPvqIL9r6+PjwyIVmzZqhQYMGICK4ublJTsoUIwykpCVIRUpKCl8T7NatmyY2Bw0axMN6KsfUWYJFEt28eRPR0dFGJPL29sauXbsURQkfP34czZo10zRaW0S9evXQpk0b1XbE5MHK29WrVxUv5q1ZswYfffSRwb4rV67w1AU5EEuBXrhwwWD/q1evuPCLkuBgoKLAFxGhadOmmDVrFrZt24a5c+fy0JzHjx9j1qxZfJMKIrKYviAXYliV+ExqBZGYcpJDrUYs5OXl8bIiYviP+FB9+umnihr69OlT+Pn5SQ46lIrRo0cjKCjIKKJbLhhj0Ol0SEhIMCLSjBkzUFxcrFGLfy0KLUczzhyJAODMmTMICgoCY0xRb9S0adM3Uv1C/E611H/QD96VW6rTHMQUGDlV7CUFhC1fvtyA7SkpKfD19VVUAFgf9vb2cHV1NSi9qBZEpEgAQx+MMdSsWRPffvst/P39cf78eQDApk2beLFhLXDt2jVFuVklJSVgjBlFGuvj8OHDiIqKslgm0RS2bt0KV1dXzJgxQ9Z51pCZmQmdTgedTqeZTf2ERkEQTL5UlOCHH36Aq6ur5N5cEYkA4OrVq6rfyPv27QNjDB4eHqrs6IOI0Lx5c1U2GKtIX9+5cycYY/joo4/4w3j9+nVERkZq0VScOXOGPwA//vij7Db6+PiY1VY7d+4cGGOK0lXS09PRokULzSvPaU0iABg/frykRD25WLRoEQRBkKR9IYlEBw8efCNzmJKSEoSGhoIxJmsiZwlEpKh4sj7ENI21a9fCzc0NjDGDudaqVat4Rq4aLF26FIwxDB06VPa5Yk6Ng4ODyc/VkOjq1asgIgiCoPq71IdWSX76OH/+PBcTYUyZDJc5MMZQt25dq0SSHN+vZpz87NkzfPrpp+jQoQMOHTqEnJwcg9z3zMxMMMbQt29f1V0yEaF+/fqqbNy5c4cLVAK/OhqaN2+OMWPGYMmSJbIf/AMHDsDLywuCIGD//v2oXbs2BEFAs2bNFKdxi0PLQYMGoayszOAzsWCvEhIBFfMu0UOn1fzo8uXL0Ol0YEzbsp0bN25Uldzo5eWFiIgIZGRkIDc3l+8XnReCICAjI8Ps+ZJJxBjDlClTZDcQqCDR4MGDebdbrVo1xMTEYPfu3Qb2GWNo0aKFomuI0IJEAPhQ7uHDh3j06BHi4+MNHAxKeo/PPvvMKJt169atitv4888/84S0uLg4Xhxg7dq1cHd3R1RUFC8wLBelpaX49NNPQURo0KCB6kRMAHj58iXXb5g4caKmKTVqPHWenp78d4mKikKHDh24d06Ue46KijLbI1kk0ZUrV/DixQu+LqGURPrYsmULd3ETEezs7FCjRg0EBwdrls6tBYmAX3Wd9+zZw/c9ePBAtSPE399fU9esKATCGOPDY8YYYmNjVdveuXMn75FMSUrLRU5ODr/3mjVrqrYnQp9ESjX8Xr9+bTGl39xal9X08BEjRmDt2rWakQiocHGPGzeO33TlTQ20JBFQ8R20bt1albgiUDH/E3H8+HH+45w9e1ZtE43So+WSaMKECRZ170QSqSlbog/R3a2lk0EULFHr7r58+TLat2+PRo0amSTRtm3bjM6xSiLGKvQPGGMWVSCVoKCgAOPGjeOEIiJERESoskkaZ6CKk2E1C4WHDh1CgwYNMGDAAAwYMICv82hFovz8fAM1HXGrW7cu7t69a/V8X19fEBGioqJw+PBhPHz40OBze3v7955E+uVqtFgzys/PR40aNRAeHo6goCBOooEDBxoda5FE27dvNxgquLq6qm7cm0ZwcLCRZphatG7dGoypC3fSf6OJP4iXl5dmbdy1axdfFBe3xMREyToTFy9e5Lp14guNiDjBnJycNNX4Hj58uKZE0ppEcmDVsXDjxg0eSuLo6MgXHt9H3LlzB3Xr1tXcbkFBAde7Voo1a9ZwL5+LiwtatmypyRxDHwcOHOBV+AIDAxWt440aNQoDBw5ETEwMJ1JgYKCiYmmWcO7cOU1d3osXL+YvKrVDb7mQFMW9d+9eatOmDdnZ2dHu3bupRYsW8iNd3wLu3LlDZ8+epXbt2mlue/To0XT37l3KyMjQ3Pb7iOvXr9PZs2eJiMjX15f++Mc/vuMWWcbBgwepRYsWFBkZSefPn3+r17ZpLEhAaWkppaWlkY+PD6WkpLzr5tjwnsFGIhtsUAmbUIkNNqiEjUQ22KASb5VEBQUF1Ldv37d5SRtseON4qyRydnaWnZ//NnHlyhWys7MjQRDIzs6O7OzsaOrUqbRz507Ky8vT/Hpbt26lv/71r5rbfR8RGhr6rpsgCT169CB7e3uqUqUKTZw4kbKysigrK8vySWr841JWwyvD0dFR88Wwc+fOGawqM8YwduxYWTa8vLz44l94eDhq1KjBNzHdWuv1h7i4OPTu3VvRuS9fvsSVK1cMwoneZ/Tu3RtRUVGaCs7rr2WZ2uRk0U6dOhXdunWDnZ0d7OzsIAiCwf/F0jOmoIpEH374oewoYSJCv3791FzWCAEBAUYhLzqdDps3b5ZsQyRQvXr1jD7r1KkTdDodWrVqpWkI/9KlS9GsWTPZ0cy5ublc+yIiIgILFy5UnRIvQr/QM2kYQnX06FEQEU6cOKGJvZSUFF6apXIJyLVr1yI4OBhEJCn6PD09HS4uLjyHSoza0P//GyNRTEyMbBWcLl26wMPDQ9OUcJE4PXv2xLFjx9ClSxcwVlEnVGrynGhjxIgRRp/l5+ejU6dOigNkxdq3t2/fNghJevbsGYgId+7ckWWve/fuPM4wMDDQoJSKWph7q2tRKZ2IsHjxYlU28vLyOEGs4ciRIwgODpZkt2HDhhAEAaGhoTynLT09ne+3s7MzG7Fi1JKioiLMmDEDSUlJSEpKQkZGBg4cOICioiK+iVBCotevX2PixImaBh8yxhATE2OgUyCmVkitRi72RDqdDufOnUNKSgpSUlIMwpwWLFjAk8pM1e40BXHI4ebmZvBQhoSE8HydHj16yOqNKg9V27ZtC8aYarkw/fbpQ7/osxb29WEp2c2cje7du0s+Pjg42Kp+xZAhQ/iwzRRkk8hUSH3lLTU1FampqQgKClI0pj979iw8PDwsVnCTA1NpFPq1YaVAf04UFBQEnU4HR0dHgznH48ePcfnyZQQGBsLR0RGjR4+2mtYukujp06f4/vvvsWLFCjg5ORm96ZUqJwHAnj17wBhDQkKCYhuAeRIBhsM8tfb18dVXX0k+X+yF5EAc9llCbm4ujzk0NQXIyMiAIAioVasW+vbta/S5gXVxGCRnc3Nzk+woyMvLw8qVKzFx4kQEBgbC398fcXFxaNWqFQYMGKCYVPrt6dKlCx48eIBffvlFFonWr1/PiSTOqcxpj23fvp0TzlqZSJFEr1+/5vuuXLmChQsX4pNPPuHJZFFRUdJvuBL27t3L50dqYIlEwK89ktJ5kqurK4gIq1ev5vvkzKnnzJljUOZRCtauXWuVRDdu3ODp+vrDORHfffcddzJYJRER4cMPP7TasNOnT+P06dNcg05qhuKGDRtAROjYsSOmTZsGZ2dnEBFOnz6NrKwsfPfdd5LsVIZIlubNmxuRPDAwULKdW7du8eLLOp2OT1h37NhhQBaRRCJhraFfv37w8PDAjRs3TH6ekJCg6A1/6dIlrjEXFBSENWvWyLZRGZV7yLS0NL6JmmxKSfT555+DiODh4YHjx48raptcSCER8KtOg52dHXr06GHw2fjx4zmJTAm3GFgPCAiAr6+vWRmmyoiJiYGHhwdycnIkHQ9UjC+9vb0xffp0bNmyBURkVI1ZLkTC+Pr6YunSpWjatCmaNm2Kzp07y35znTlzhpPI29sbX3/9Nf9bhJyeCKgopktECA0NNUp4A4Bp06aBiGQXAD58+DC/91mzZqG0tFTW+aagP//R2slw7949hIeHg4gUFXpWQqI5c+ZI1vQTh22V50YnT57k+03Jchm0asaMGWCMoX79+lYVaJ49e4Z69erJ1tQePnw4iAje3t4AgKCgINUT1vr162uSWi5CP/NSlMzS6XQYPXo0rl69iuXLl0On08HLy0tyTpD4AJpKbHv+/DkaN26Mtm3bGjhurKGsrAzjxo3juV41a9bE7t27Vbu79XucyptaL52o5f22SCTHEREZGcl7nFq1avH9+mtGJq9ReYcoYC4+5OYQFhYGxpgiofesrCy4uLhwH37Hjh0xcOBAFBYWyrYFVIifODg4gDGG4cOHK7Khj8mTJxsMCamSFgQRISgoSNYcrqCggAuUmMK9e/dApFy9NTMzE5s3bzboMdq3b883ueszWrq29SE6KLy8vHDq1ClZ625HjhyR5ZmT0wsBQFJSktFia48ePQwWYTdu3Gh0ntEvqq8/bQlqSARUZCKK84ni4mIQEQYOHKj4LTplyhQ4ODjA399fcUUEfeinL+v3TOLfSibxp0+fhru7u3npJSK0bNlSbdNx/PhxjBkzxugF0KpVK8k2xN5I7SjBFES7GRkZsjSvxXPNESkvL48P36V45UzZNrfYKgiC2Tm70VVKSkrQqFEjMMYwbNgws1UQRBLJGX5YuwEiwtdff63YRtOmTXmIDgA8evQI9+/fV2QrMzMT/v7+Zkn0+eefK7JbrVo1zJ071+Rnvr6+0Ol0yMzMVGS7Mk6ePIn58+djwIABsoe7+i5trXsjIoJOp8PEiRMVkYiIMGfOHKPPUlJS+EK03LAfoEJN1VzYT1pamtnlDLNU3blzJyIjI00uAq5evRoODg5GXgy1mD17tuSaN+bg7OwMxhgnVFhYmCp7ixYt4mE/4rZo0SLF9saPHw8iQsOGDY0+i4+PBxFh1apVapqsKcQeSYse0hTkDM/0oR/2k5eXx71wanvOAwcOIDQ01IhEAwYMUCbeaAnr169Hfn6+4sa+KeTm5qJmzZr8zRseHq7apn7YjxaSup6eniZdpUVFRcjKyjLpwXuX0CKObs+ePVzfbsmSJdizZ4/saIXK0A9AFUOBKsfRKYHo/BCHcxERERaXMrQf8L4HyMvLA1FFlTctdN3+3fEm50dqkZeXx4dwWtY+kgObxoINkhEXF0f79+9/181472AjkQ02qIRNY8EGG1TiN0UiQRCoSpUq1KtXL1V2Fi5cqFGLTCMhIYGGDBlCubm5b/Q67wuGDBlCtWvXftfNeHN4JzOxNwR9vWs1lfd8fX2xb98+DVtmCNFzaKkSg1QbYmrK2bNnFVc2f9P4+OOPNQvJeh/xVkh04cIFA2F8/fCZ/v37yw4SNQd9Eh09elSxnQkTJqBHjx6KF2qtgbGKmkdqAkZNpaVo6Z26fPkyvvrqK7Ro0YLbF4uIycXGjRvh4uLyRoXmd+3apWn8pByYJdHZs2dNRvBaqlhtDqY0ECqHpGilXWAqClcJhg4dqmkhXX2IAaNqwpMYqxDGF6sNfvPNN3B0dMSVK1cU23zx4oVRTpmTkxM8PDzg4eEBR0dHVK1aFU+ePDHIj5IKa2Ub1UC/zUoWhm/evMmrarRt21ZWZRGTJCouLkZwcDBCQkKwevVq7NmzBwkJCTypSq5ii3hzHh4eOH36NB48eIAHDx7gp59+QkREBNzc3ODk5ARfX1/VISZaDOcAIDs7G8HBwXj+/LkqO6bg7e0NxtQV6XV3dwdjzCDTs3fv3hgzZoxim+3btzd4GPv162eghbF//37+mZLyNeKL801Av6yMEhJlZ2fz3yU2NlZWIIHJOxLjpi5dumSw/9q1a2jVqhXq1KkjSz5KvLmPPvrI5Ofz58/nx7i7u0u2awpihTM1wzkRc+bMURXLZw7ffvstGGNo1qyZYhsHDhzgQ2QxfjE/P9+gyrkcPHz40IBAEydONFnbyM/PD4wxdOvWTfY1zp8/D8aY5iVlgIqAYTUkAiriGhljJrNXLUHRa8HHxwceHh6SjtVP0zZHIgCYNWsWr/amtJq2iOHDh0MQBIwfP16VHaAi5GXGjBmq7VSGGNundt41YcIEnhZ/6dIlxSSqU6cOj043VZdUxI0bN1TPPRhjkhM/pWLLli2qSQT8+sKXUxlREYmWLl0qmUT6DbNEIuDX2qOzZs1S0iyO/Px8CIKA4OBgg5LqSuDg4GCxzKLSOcjmzZvh6empakgHVNS/Fb9fT09PxWIlIok6duxo8bjy8nJeH1UpiEh1lfjK0KInAn59VuUkmypaJ/L29qbS0lIqKCjQwsuuOXx8fMje3p5u375NHTt2VGVr3LhxdPz4cRo2bJjJz69du6bIbufOnWn+/Pn05Zdf0sOHDxW373e/+x3169ePiIgKCwvJwcFBsS0isrrGJggCOTo6yrZ78eJFSkpKov/7v/+jxMREOnToEPXv31+2nS+++IKqVq1qsKWnp9OKFSv4MV5eXrLtihgxYgQREeXn50s/SQlb4+PjZfVE+sKH5oZqy5YtM9ALUItz587xkHa5GbNlZWUYOHCgkWdyyJAhGDJkCCIiIkBEJitJm8KDBw9Qr149gzdl586dubBKaGio4mTEZ8+e8Wxk/R7pxx9/lGVHak+UkZEhezgnvtkXL15sUBZSbm924MABk17eytuxY8dk2a2MxMREEBEiIyNx+/Ztq8dbJNGJEyfQpUsXpKam4vHjx3yzt7eXJRSoT6KJEyeaPEYpiXJzcxEbG2ty2Pbpp59CEATZQ5xt27Zx4qxbtw6jRo0y8iqtW7dOknLpw4cPERMTg+rVqyMiIoJvTk5OBj+80jWYnTt38jnRqVOnDIgkB507d+aOnbi4OLPHxcfHyyaR+AISMX78eMWeOv3vzN/fH0FBQUaV0/fs2SPbrj6OHTuGgIAAEJGk3DGLd5GZmYmQkBCjN3KdOnVkNero0aPcJevr62vymNzcXF6lWwqJXr16henTpyMqKgrp6ekmSXT58mUIggB/f3+cPHlScntfvnyJXr16gYhw+PBh3L17F66urop6CzFLuHIPvHPnTkyZMsXgx5eLzMxMeHl5oXPnztzlrG9PTtbxkSNHDM598uSJycVgkUQODg6SbTdv3txo7U6cV8ktEqDfRrGw86ZNmxAXF6fJnEjEgQMHQERwdXXF9u3bLR4r+VUQFxcHOzs7xe7JO3fuoFmzZgZfwjfffIMePXqAMSa7J9IvZ79w4UKjzc/Pz+AYJeoyL1++BBGhSZMm2L9/Pw4dOmR0zMqVKy3a6NatGxhjKCgoMFvNOyMjA46OjvD29pa1LpWUlATGGK5du8b37d692+A7/uGHHyTbqzwscnBwwLhx4/Dw4UNs2LABnp6eitaJ8vPz0aBBA9SqVYtHLZw/fx7jx49H165dZa3pmXvpvHjxQlMSAb/KMzs5OVk8TjKJSkpKEBERIVsiSx/6RBG7c/H/akgkbuJYu/I2cuRIxSVI4uLiULVqVQBAmzZtjN7u1kh04sQJ9OzZE4wxREZGIicnB6dPn8aCBQtQVlbGjxMFYtq1a4eXL19KatutW7cQGRmJiIgILFq0CLm5uXjx4gWPVWOMwdnZ2eqbVISp0CzGfk21FzcPDw+zLwRzyM/P5795SkoKDh48iNzcXBCRrBjCt0mibdu28RGUpbmRrEHp0aNHodPpFIV8ABVa1l5eXiZJpB8aJIVEq1atQo0aNSySKCgoSHW1hHv37vE3PREZiYysW7dOkh07OzuDB0AQBANBksePH/PPZs6cKbl9t27d4ue5u7tj3rx5SExMNLiW1EXdI0eOmCWS/jZ9+nTJ7auM9PR0A8eCi4sLNm3aJPn8t0ki4FeHi6XnyCyJ2rZti08++QQTJ07Erl27cPbsWVy5ckW13jNQMVeZMWOGkZ6bVt6Vf0fk5eXhs88+Q9WqVfnc9cWLF5g1axZcXFwk924PHz5E7969DX6POnXq8O190H8YP348zpw5Y7DvxYsXcHFx0ZxEpaWl6N69OxgzL5NmlkRXr16Fh4cHiIh7PxwdHTWNfVq0aJGR00KO69yGN4dFixYhLCzsvdRVMIfJkyeDiPD9999ravfkyZNgjJmV97KYHl5SUkL/+Mc/aP369XT9+nW6e/cuDRkyhAYOHCh9IcoGG/7F8fLlS3J0dKRq1aqZTKS0aSzYYINK/KbSw22w4V3ARiIbbFCJ3xSJ/vd//5f27t37rpvxVrF+/XqKiYmhp0+fvuum/NviN0Wi3//+97RhwwbVdk6dOkVVqlShli1bqm+UCdtJSUm0e/duzWyeOHGCPvvsM83s2SATUlx8KSkpaNiwoepUadFVKAgCGjduLKvCnlS4u7vj1q1bqu1s3LhRUWyXVPTt21dRvJwpREdHQxAEuLm5qbaVl5cHX19fzJ49G7Nnz8bYsWPh7OyMzz77TLYt/Rqvb0IQ/9WrV8jOzkZMTAxfzLZWV8sUxAVmQRBgb2+PkJAQVK1aVbLik2QSMcZMxo7JgbiIpx9VkJSUhEePHqmyqw9SULbRHLp06fJG9Z27dOkiOxXZFHJyclCzZk04OzurtpWenm4UXCoG4y5ZskSWLTJTrlIriJHxgiDgypUr6Nevn2z7RUVFJqNdxPCmDh06WJUaeKvDuQsXLpCHhwft2LGDduzYQfb29vT111/Tn//8Z02vc/PmTU3sLF68mObNm0fz58/XxJ4p+9999x2lpqaqshMSEkIDBgygZ8+eqW5TeXk5vXz50mDf6NGjSRAEunv3rixbaWlpJvfHxcUpbp8+Xr9+TR4eHvTVV19R9erVKSAgQLaNy5cv8/8vWrSIOnToQK6urkREVFRURDt27KA//vGPlo1IYevdu3fBWIX6ixp8++23ICKDkpBiJLJWaNmyJaKjozWz5+vrC0EQZMV3yUFiYqJm9uvWrau4cqGI7777DkRkECa0dOlSVaUw9SGK4LyJ4d3o0aM1j7BYuHAhBEFAr169zB4ji0SdOnVS3Six2xRD9EeNGgVBECQluEmB1iTasWMHGGNvTINOS/sbN25EWFiYKu25Bw8ewMPDg9fTBSoKp8XGxmo2P/xXIlFBQQEiIiIshhK9de+cj48PEZGRZoGSvH1TCAgIoLNnz2pii4ioWrVq5OzsTKdOndLMJlGF5sCuXbto6dKlxBjTxGbr1q0pJyeHFi1apNiGt7c3LVy4kJYsWUJERMXFxfT73/+e0tPTydvbW3UbMzMzDf5931FeXk4lJSX0yy+/mD9IChu17ImACi9dYGAg/P394efnBy8vL03sAhVKlnKyLqWCMYbFixertpOYmIiQkBCuN3D+/HlFdirnTDVu3Bj3799HTEwM/P39FdsV0bVrV80cAaKXTtzUVNwzhw0bNsDBwUFT2/rpOZbypyR9Q48ePeLuQ7USVCI2bdrEc4q0HH4BFS5f/YQ3Nejbty8aNGiAiIgITUj0+eefo1q1aqoFDAVBQHR0NL755htER0fDx8cHLi4uSEhIgCAISE5OVmXf3d1dMxKJdlq2bPlGCARUqLcSkWo9u7lz52Lu3Lno0qULBEGAh4eH1ULUkr8hkZFq33AAUFhYiNGjR3NXoljtWytER0crVs/RR9euXQ0SyLSaF4k21cwxBEFAYGAgX2tbtmwZ/P39ERYWBkEQFLW1rKwM2dnZiI2NRdu2bXH37l2kpqZaFHOUAq3IWBmnTp3CqVOncOfOHU5SU4W65UDfxT148GD1aj/6EEmkVBxQhEgeQRCQlZWF06dPIzAwEEFBQbLXd9atW4fOnTtj8+bNBmVFsrKyMG3aNFXtbNCggZHXkDF1+tn6EDM8laJPnz4QBMEoXT8+Pl6xqL+dnR0iIiKM9BOGDh0KOzs7LFu2THF7xbqvWmHp0qWIi4vD2rVrERUVheDgYNnSaKawZMkSnhIufo/169e3OLJ56yQSG6evhrpnzx4IgmBV86wy9u3bx9WIPv74Yz5EysrKQmpqqqp2pqenG/zoovqLvvSTWhARNm7cqOjc4uJi1K9fH4IgIDExEdnZ2QAq7r1q1aoQBEF2b6zT6dC7d2+j/dnZ2YiLi0OtWrUUtVWEOJRTW7QAABYsWIDRo0cDACZNmoRBgwaptikiMzMTc+bMMchsybIyAAAgAElEQVTuTUxMxPz5800e/9ZJRP9MCa9Xrx6Aii/0008/ha+vr2L7vXr1goODA4gIn3zyCZKSktClSxdV7QQq1ojE9Zv8/Hw0b94c3bp108zVe/LkSVVrbzdv3kTv3r0hCAK8vb2RkZGBJ0+eICUlhUeDyEHfvn2h0+lQu3Zt3Lt3D1euXMG0adPg6OiI5cuXIzw8XHFb9VF5jqSEVJVJNHXqVE3atmzZMmRmZmLu3LkYOHCggfMmLCwMly9fNjpHMokaNmxoUiBCCQ4dOoRWrVoZhFucPXsWUVFR8PHxUWX79u3bmDdvHvz9/XHjxg3VbRUnmPoCI+3atZN8vjgs1J9bif8PCQnRZI5ZUlKCNm3a8BeUv78/xo8fr9gJ9OjRIyxZsgQjRoyAIAjYu3cvrl69ylWPtEBlj13lzRoWLFgAT09P5ObmIikpSfa93r5920h3Yu/evVaVo0wNkyWTqFWrVmCMoWbNmrIaaw4FBQVcu0EQBFy/fh2zZs3SNHpBC+Tm5qJ58+b8x42MjJRVu6Zv374YP348Tp48abTJsfMucOHCBXTt2hWtWrWCn5+f6hecKZgik5SeKSsri+t/aFHUTUTVqlWN5kSiE6dq1aomA3Elk6hjx46SZVWlIjs7G2fOnOHKLe8jif5dUV5ejsTERDRo0AApKSk4fPiwpoHCWqBOnTogInTt2lVTu6bmRJYyA2waCzbYoBK/qaQ8G2x4F7CRyAYbVMJGIhtsUAkbif6JsrIyGjhwIPXu3ftdN8WGfzHYHAt6yMvLow8++IDatWtHs2fPJnt7+3fdJBv+BfBWeqKAgAASBMFgc3FxeRuXloWqVavS5cuXafXq1eTo6EhjxozRzPbixYtJEASys7Pj278Dbty4QTExMSQIAq1cuZJ27dr1Rq4zb948Yoypqn+rj/j4eHr9+rW0g6X6zl++fImHDx9iypQpSElJQUpKCtasWSPpXNHX7unpiZCQEP63nEJRbxOnTp1CcHCwJuo5Itzc3MAYg06n41vNmjVVRx2/SRQVFWHatGlo164dmjZtqiioNy4uDjqdzuDev/76a83bOnfuXBARCgoKVNu6fv06qlevLjldRTKJKte8ETcpeTtiyYv09HRcunQJ48aNUxTbZQlz5szBhx9+CCKCt7c3D8hUgrKyMmRkZEAQBHz55ZeatK/ygyT+LTfNYO3atYiJiQERoXv37m9MjSgtLY0Tn/RK4Ny/f1+WHf17Ff/v6uqKiIgIqyo6ciBGlaglUUpKCsLDw8EYQ8+ePfHll19aLScjKwC1bdu2OHDgAICKNIQ+ffpIijDIyMiATqdDo0aN+JtXlM9SW6QWAGrWrAkiwjfffAOg4g0q1ulRE5h4584dzfKI9B8kAPjss8/43126dJEc1EpEiImJMdonkkotbt++bVQZT59ELi4uWL58uWR7+ja2b9+OpKQkA3ty8eOPPyIjI8NIAzE6Olo1iQYPHgzGKuoK+/v7G3wHlYu76UMyierXr28U9nH48GHJX0RkZCQYY4iPj0dmZibmzZunGYk8PDwwbNgwvH79GoMHD8aOHTtQWlrKS9xLLXBlCnZ2dhAEAUOHDlXVxsokAiqUjsS3s9SasmvXrjW5f86cOQgODjYQGFGCQ4cO8dgx/aJrYsU4xpgRiS1BvL9GjRrxfdu3b5d93yLCw8NBRBg7dizu37+PgoICFBQU8GrfSkn0+PFj1KhRA46Ojti9ezd+/vlnMMaQlpaG8ePHo3r16iYjuAEZJIqNjcWRI0cM9s2dOxcBAQGSzs/KykJQUBAYY7Czs0OXLl00IxERYceOHQAAR0dHDBw4EEBF9bRGjRph9+7dim2vWrWKByOqiR0zRaKSkhL+MKmVugIqiKTF8E6seM4YQ3JyMo4fP466devyfXLS5PWHc2Jvm5OTgwULFiA1NRVdunSRNUfy9fW1GP29efNm2fcL/DrqiIuLA1ChesQYw5gxYwBUPFfVq1c3maNlkUTnzp3DwYMH+bZhwwZ06tQJ8+fP5xHYpsq0W8K4ceMM3nJaBLQSEbZt28b/3rBhA3Q6HQYPHozExETUrl1blf3z58+jZs2aiIuLU/ymEzUAKvfc4vhbydBmzpw5RmkDRKSqNxK1L8wN5+TmUumTqDIuX77M85d++eUXRe09f/48Tp8+zXsiUU5ZbHfz5s0lZSM/ePAAfn5+BnloLVq0AGMV9YTFl+ndu3eNzrVIIrG3MLf16NFD9k2XlpZi9+7dqFatGhirKNarxI4+iAizZ8822Ddy5EgMHjwYrq6uqsQxnjx5gsLCQixevBiMMQwcOFCxR83UHKhGjRr8QVu4cKEse0eOHEFMTAzmzJnDifOmSOTq6qpIK04U5yQiI23BkpIS/pnaF53onatWrRqAisrfGzZs4JsUDB48GFFRUfzvfv368e9AvA/ZJBo1apTRF9qiRQt06NABaWlpqgTpL168aGD3m2++USwuEhQUhGbNmpn0FKpJzNOvzK2/KVX9MTUHmj17tmISiRCdDXPmzEH37t1VkejixYsICgriw6NOnTphypQpiueVCxYsMBjK6g83xeFs5WGuEqxcuVK1IEp6ejpcXFy4a/vRo0eIj483+O1lk0jE9evXuREt0q5FmOvh5K4jjBs3TvNcJwBo3LgxPv/8c5SXl2Pbtm1o1qyZ4qEXYDih1n9oxAdNKYnWrl1r0munFMnJyfw+lbZJH7du3cL3338PnU4Hb29v9OnTB4WFhXj58qVmJCosLNREVYgxhurVqxuQ5fHjx0hLSzObPcuvWKNGDYOurDL69++v6gEy12AfHx8cOHDAYDKr0+nw3XffSbbz6NEj+Pn5Ydy4cZq1DQA++OADzJw5Ey9evAAAjBkzBq6urlZdnuawfft2g5eFmCU5f/581Q9sSkoKgoODjZw/SpCdnc3bOHjwYNX2ROjPj3r27ImFCxfyv8UJvSU8ffrU7GeiN1YLEjHGEBsbK/kcfkVx8hgSEoKQkBD0798fy5Yt49uIESPeCIlEwZJHjx5xGSnGmOw1j/bt20v2FEpFRkYGGGMYMGAAvv/+e/j5+fH2TZ48WbFd/YdpxYoVGDp0qKqeSET37t35epE5V7hU6L/QtIIpR4X4t5R779y5M5o0aWJya9y4sWY9kbjdu3dP2jmAYQDqw4cP6W9/+xudOXPGZJhQcnIyhYaGSospsgJBqAjdq1evHgUFBRER0b59+6i4uFi2rQMHDtAHH3xApaWlmrRNH9nZ2XTkyBEaMmQIERG1bduWdu7cqdhezZo16d69e1RcXExVqlTh++fOnWukUa4E69ato6NHj9LcuXP5vu7du9O6desk20hOTqYvvviCiIj+9Kc/0aJFiyg8PFx12w4ePEgtW7Y0uO8NGzZQp06drJ774sULevDgAR0+fJgA0JUrV/hndnZ21LdvX/qP//gPA9tycfnyZWrfvj3l5OTQnDlzKCUlxfpJqmirEqbmQ2p0uRMSElSLNr4tdOjQgb/pxU2MBtEKeXl5SElJQUxMjOz50tWrV3kFOSJCv379ZC9nmMLjx48RGBjI73nKlCmqbWoN0Svt4OAgaZ79Tkn00UcfoUOHDiAizJgxAzNmzFAl/3vs2DHUr1+fz2HeZxw/ftxgWJOenv6um2SEH3/8Ea6urrydUiR1pUDfwaKlo0orXL9+nbu34+PjrR7/m8snmjhxIs2YMYPGjBlDXbt2pTp16rzrJv1LY9euXXTq1Clq0KABtWnT5l03573Eb45EREQTJkygjIwMOnfu3HuZt2TDbwu/SRLZYMPbhE1jwQYbVMJGIhtsUInfBIn8/f1V1Sm1wTRGjx5NsbGx77oZ7z0Uk+j777+nevXq0dOnT2WdV7t2bbKzs6OoqCj6+9//rvTyBkhLS6Nhw4ZRdna2JvYcHR1JEASaMWOGJvbeBEpKSmj16tU0bdo0EgSBkpKSaPXq1XTs2DHNrvHf//3fdOTIEdnnZWVl0Z///GdycnIyEqgRBIESEhLoz3/+MxUWFqpqX5MmTSg4ONjkNf7whz9QQEAAL7QtBXl5ecoaosSPfvz4cfj5+UEQBKv1LEXcunULqampBouLAQEB6Nmzp+oKZ8+fP0d0dDRCQkJU2RHh7e0NxhgmTZqk2lZOTg7q1KnDU9e1glilo/JWrVo1HDlyBIWFhSgsLDSoICgXZ86cURRGs3nzZospNOLm6emJCxcuKGqb1Gv89NNPkuydOXMGzs7OitqiiEQBAQG85ERJSYnV4zMzM+Hl5WVAIP3o3VatWilphgFOnDgBOzs71XYA8FwnLeIEP//8cx7TFR8fr0mVuBcvXkh6gMRFXDViK0rEZMw94BEREUb7EhISsHXrVtnXePXqlZGtrl27ol+/fujXrx/2799vpMNgCZmZmSAiHD58WHZbZJPo9OnTFgsemYL+jVZGcXExD7OoXr06Ll68KLdJHPv37zdZP0Yutm3bxtt76NAh1fZevnyJlJQUODs7g4jg4OAALy8vTi5fX194eHhg1KhRVm1lZWUZZMPqP+RpaWmYMGGCgSyZ2pfB3bt3NY+Ov3z5skHxMyn3bQr69zd69GjVxa7z8vJARPDz85OcyAeYINGTJ0+QkJCAFi1a4OzZs0Yn6JNIauU0sedZtWqV2WNq1KgBxtRVEn/9+jXs7e1x/PhxxTYA4MqVK/ytKZY01AKXL1/GsWPHcPbsWdy+fRvr16/H+vXrkZ+fj9zcXGzZskWSnfPnz/OHp3Xr1kaf5+bm4ty5c6hXrx7flLxhgYpeLyoqCs+ePVN0vjnk5OTwEqNJSUmKkv569OjBBXAYY2jSpAl++OEHVe0Sw5zkDGONjtQniY+PD+rXr4/vv//e5OdSc01EEn3++edmj7lw4QJ8fHyg0+kkh6CLWLVqFZo3b45evXohODhYUm6KNeTl5aFWrVqws7PTVB9PH02aNJE8p6wM/bdwUVGRxi0zBBHJ/k2s4cCBAzzAVU2PP3PmTIPht4eHh6q2btu2DTqdDkSE69evSzrHIomsbVJJFBoaapTNaQrFxcVo0qSJ7PqbRMR7zWrVqoGINCnHLtqmf5aZ1BpNmjQxW5FaCvTzr1q0aKFZZm9ZWRm6dOmC6Ohorue2detW1VHca9aswZo1a+Dg4GDwElA6nNPHsmXLEBMTA8YYHB0dVVeP37p1K4gIYWFh2L59u8VjjUgkCjJK2ZYtWyapQVlZWfDx8ZE0Nm/ZsqUiEolR0E+ePEGTJk2QlJSkiUxxQEAATwfQEgUFBfD391cVxfzq1SuDoZ2dnR2cnJyMRFvk4NGjR+jSpQuICD169ED//v35i8TSSMIaHj58aPIZ8vf3x+nTpxXb1cf9+/fRvn177rBasmSJKnsuLi4gIqvTFqMnw5zTYMGCBViwYAGaNWvGP79y5YrkBq1YsQLu7u5wcXFBq1atcO7cOaxYsQL169fHggULACjviVavXg3GmMHQSJSr1WIYkpKSAiJC69atZXl8gF/ltogIHh4e+Pjjj1FaWoqcnBwQEdatW6e6fQDwzTffwM7OzuABFWvhykFISIiRUCURITo6WpJktDmIyj6VNw8PD2zcuFGx3cooLy/nstVyHSqRkZGIiYnBtm3b+DM4c+ZMODk5ITQ01EitSIQBiQoLCzlBoqOjTZ6gP9yTQyKgwvUpDutatWqFFStWGAzzxL/lkqi0tBQRERGoVasW3xcbGwsiwsiRI2XZMgf6Z06NXE9VcXExDh06hCFDhsDT0xNEhAULFuDq1auakggA9u3bZ/CAVhbcsIbbt2/Dw8PDyJni5OQEZ2dnXLp0SVX7rl+/joYNG6JmzZoG7XRzczOYdytFUVER7t69y3Xn5JIoIiLCQAhS1B1fvnw5iAiJiYkmz+MkKisrw+TJkzlBli5davKE27dvKyYRAL5e5OLiAl9fX06ijRs3KiYRUDFc8PHx4eP23NxchIWF/f/2zjwmquvt4+degQ5htyJjWESqFBUUCAGtRhzSIhRwK1SpS8UISpRiCS6JIi4ptVW7SOvWKAqpgkqRUo3WarGmiFZAISIiCrigBKZQlLJI/b5/0Hs6w8wwcxdL/b3zSW6id7hnnpk5z73nPOc830dUtE8VT09PeucUo6h64MABEELg6+sruRMBvXp7qhNtvvMkuVyuMUdZsGABCCG8BRazsrK0nq+pqaHzF+4QutAJALt27cL27dthZ2dnUGhfV6SxsbERKSkpVPDE19cXmzdvhr+/PwghkMlkWq+jTrRu3TqwLAtbW1v89ttvOg1+8uQJdSKhqb15eXlqY1cu/C3GiYDe9RiZTEafFiUlJZIsmHJIse4CAAkJCfRuJ9SJuLW1YcOG4cmTJ2qvcepB3MFnsfW7776DlZWV2rmioiKYmZnxnqwnJyerDdu0LbaqHv1V8uAW5w09zMzMEBoaystebfT09KC6uhrV1dU6M6Y1nMiQOLuYJxFHS0sLXF1dYWFhQaVkR48eLcqJgN5Hr7W1NQ4cOIBNmzZJ6kSffPIJ/ZFu3LghuJ1nz55h3rx5opxItcO4urrCzc2NHubm5oKdCIDWYVtoaCjvbVXOzs4Gd3p9taD27dtncFtOTk6i1wr5oOFEhiCFE3FkZGRoFIIS40RA71DO1NQUhBBER0eLtpGjra2NltwwNDKpi/LychBCMGvWLEH729LT0xEREaG3Q23atEnrojlf6uvreUuSVVVVISYmBjExMZgzZ45OG728vPTOt/Ly8qjmn7bD2dkZ3t7eCAgIELywLBRBma1BQUHk22+/JcOGDeN7qU5YliUASGFhIQkMDJSs3f8yb7zxBmlubiY3btwQVX4yNzeXdHR0kJKSEnL9+nWyePFiMmbMGOLr6yuhtUZ0YUwPH0Dee+89kpiYSAICAgbaFCMiMDqRESMi+Z/IbDViZCAxOpERIyIxOtFLyrlz58igQYNIfn7+QJvy0nPlyhXi5OREUlNTBV3/0jtRV1cXiYuLI9u3bydTpkwhcXFxJC4ujpSVlQ20aTqpq6sjEydOJHv27BHchoODA3n77bfJ7Nmzya1btyS0Tlo+/fRTYmVlRRQKxUCbohN/f39y+/ZtolQqyZw5c/g3oCv23dnZiU8//RTR0dF082RJScm/Enc3lLt371L7+h6mpqZYtWoViouLB9pMDZYsWQJCiOi8p+joaLAsKyr9+0XDreMQQrBhwwbJk/ukpLa2Fubm5rw3xGp1oqSkJOzatUujY4qp2PAi2LdvH7XNx8cHCoUCPj4+ajb33cLyX8DPzw+EEERERIhq5+bNm1iyZAk8PDxEL1ADvRWyzczMYGZmhsjISFqRXQwM01vblysiLERPQRfp6em0ALeNjQ1SUlIEl8XkMDc3h6WlJa8MWa35RIQQeHh40B0JV69epclZhBDe6QAAsG3bNkRHR2Po0KGwt7dHWFiYqK31QG/+/7hx47Qm4K1evZraO27cOIPbvHr1KliWBcMwamkhNjY2omxVhbOLSwERQ1tbG7VRTIIfIUQt6a6jowPh4eEghIgSVyGE0ILJ6enpsLe3l+TJ2dDQoLG5tru7G4sXL+Zd4VyV1tZWuLi4GLx7B9DiRM3NzZg+fbpGuceGhgbY2NiAEII7d+7wMiwtLQ3m5ua087i7u4MQIsnwsKGhQWtVuJ6eHgQFBdGhHVfMVh9FRUVgWRZFRUXYvXs37aAMw+DkyZOi7QX+caKamhpJ2tu7dy+1UahYByFEIznu2bNnWL16NRwcHAT/VlwKeEFBAWprazFy5EhJpMh++uknrWnx3d3dWLFihai2KysrER4ejmPHjhn097zSNVetWgVCCLZt28bLKLlcDnNzc7WSguvWrUNwcLAk2ae64JLp+Ni8du1amm1aVlYGlmURExMDZ2dn+Pv748GDBxq7pvnC2XT//n1R7XB0dXUhJCQEDNNb91XIXrzg4GDIZDKEh4cjISEBFy9epPaRv9OkhXzuzMxMOidqa2tDZmYmYmJieLfTl7Nnz+rUlpBi1JCXlweFQoHa2lq9f8vLiZ4/f47U1FSMHDlS0Bicm+QrlUqkpqbC2toalZWVvNsxBNUanuTvImKGsHXrVrVHOfckKiwspE8pPo96bXA2CRUp0QU3BN27d6+g6//880/U1dUhNzcXfn5+8PPzw9ixY9W+R66m78aNGw0W8gCAyMhIGmQQOxcEep9EXN/p60xS7NwvLy8Hy7IGJSIa5ESnTp3C4cOHkZmZCRsbG0RGRooykKsULia5TRv19fU4deoUjX5xx9SpUw2+O586dQosy9IhR0REBJ0TdXZ2oqKiAh4eHhop1HyQck6kyvfffw+WZfHKK69IFrHr7OykmZ2EEMTExMDb2xsuLi6wtrbG+++/b1A7PT09aiKWYqmoqEBeXh4A4NKlS2qvTZkyRVTbly5dwrFjx8CyrEGfT68Tffnll5DJZGqdUqwTce1Ixa+//orQ0FDY29trRBRZlqVpvobATdQdHR2RkpKCW7duITo6GgzDYN68eSgtLUVhYaGoiJhYJzp+/DgUCoXW1G/uScknmKKPqqoqjd+sqakJW7Zs4SW2qRruDg8PF51uzs1Zurq6cOzYMbS1tSE7O1tUVHH06NEwNzen36OVlZXe3LF+e/LNmzfh4eFBc3O4g29goS/t7e3YsGED8vPzceLECUHRPlUuX75MtcLI3wqj3d3dgtubOnUq1VTg2LFjh5qy6IMHDwS3L9aJzpw5Q39kbYly3NPzww8/FGxjX7jorFA46WNHR0e171EMJiYmatOBn3/+ud/sWENQKBRgGAY5OTmoqKhAcnIyFApFvzlZBn0rWVlZ9A5CCMHnn38uylCOxMREMAyDDz74QHRb586dQ0xMDAghMDMzE+WYSUlJdH6hyhdffIGkpCQkJSWJslWK4Zxq+D09PV0tkzM+Ph4sy8LNzU1UuJeju7ubzo2EUlxcDIZhsHDhQjx8+JAmFIqR4Zo8eTKmTZsm+HptBAUFgWVZ7Nmzh56rrq7GsGHDdN44Df5Wxo0bJ9lwjqO5uRmxsbEghMDPzw/Hjx8XpcXNaSxoC9fy4cSJE9SJpIqgqcJ9j6o/FF9cXV01xDSrqqpQVVWF8+fPY/jw4WBZFiEhIQa32dHRgfb2do3z2oZzfElOToarqyuVnbp9+zaioqJgYWEheP62efNmWFpaSrqTZuLEiWBZVk2fobq6GpaWljolyAz6Vry9veHl5SXZcE4bZ8+eRXx8vJq0rLbHfU1NDU6ePImamhqtOmBtbW1UBTUuLk6UTevXr0d4eLiooZs2CCFYsGCBZO0VFhYiPT0dUVFRak5lamoKDw8Pg9v58ccfwTAM5s6dq3Y+JSWFRubEwP2mMpkMCxcuhKOjIx3h6FIG0kdycrJOFR6hFBUVYcKECWrf5aJFi3T+vV4n2r17NywsLGjHJIRIOtbuS11dHTZs2AB7e3vY29trvO7m5kbtGDJkCBQKBT3i4+PVtv0UFBSIsuXWrVv0hxcrlK4KIUSwUpI+0tLSsGDBAjAMI0j62NfXF4MHD4ZSqaTnrKys1CKWQomNjUVwcLDanGjmzJkghAgur9PR0YHAwEDJ1xuVSiWcnJwMmrv160RKpRIODg4aEa8zZ85IajAfuAXf/g5OzovPOoYuVHcsSEF7ezsIITp1/QaapqYmzJgxA3Z2dggLC0NYWBjdBiYVDx8+xLVr13Dt2jUcPHhQdPvV1dWYNm2aqIJmYtCbHl5ZWUl++OEHcu3aNeLt7U0I6a3l+f+FxsZGMmvWLFJcXEzGjRtHzp07R1599dWBNstIH4qKikhISAgZMmQIKSkpIXZ2dv/emw+I676ErFixAizLYsaMGQNtihEd3LlzB++++67gEpZCMQqV8GDEiBHk+fPnpL6+fqBNMfIfwuhERoyI5KVPDzdiZKAxOtEA0tbWRgYNGkQqKysH2hQjIlBzorVr1xKWZQnLssTT05OsX7+etLa2DpRtojh48CBhGIYsXbqU97WBgYGEZVny2muvvQDLejl48CCxs7MjFRUVZMyYMS/sfQaSrq4uYm9vTwYNGkTeeecd8vjx4xf2XuPHjx+4uapqlKG4uFhDyd/CwkKSEuwNDQ1ITU2l22le5IIt0FtZmgjctcCJ67u5ub0Ay3oZNWqU6Lykf4O6ujq6TsYnE7e0tBQxMTFqq/7u7u7w9vaWRGC/LzKZTJKoHMuy4oVKlEolIiMjMXLkSDVnysvLE6zUcv/+fbqNQlW7QIqNp7r4LzsRVxFCyAIulx2alpaGx48fixbm0AeXX8XXicaPH6+zYLZcLkdcXJzo3fuqEEIkKVtpaA6R2nv39+KjR4+wY8cOjBgxQs2h8vPzeb3J0aNHwbIszMzMUFZWhnv37uHo0aNwcXGh9XX6JlaJhXOiQ4cO8b520aJFtJNLoaLTl9OnT2vdJW4IcrkcLMvCx8cHr7/+Ou2YW7ZsES38oo1Ro0bR/W18nOiXX35BSEgI7t27p3ZeqVTC1taW2n3+/HlJ7LS2tpbEKdPS0qijG4pBi61VVVW0ijbD9Fap5lOFLCcnBwzDaK3CvGPHDtja2kq6IRP4x4mKiooEXc/VSpKilqiu9oU40a5du8CyLI4cOQKlUolTp07BwsJC5962w4cPIywsDNXV1bzfa+fOnbSzJyYm8r6+u7tb6+hl//79dD+elOIv8fHxottpbW3F8uXLeY0SDN6xcP36daSkpFBBDG9vb4P3KnFPIl3DN04QREw5+r6IdSJ7e3uYmJhg0qRJooVJ+iLmSdTT04M1a9bA2toaX3/9NX7//XdcuXIFAQEBanf2P/74A/Pnz4e1tbXO5D19cHlJQp2oP3JyciRzohMnTkAmkwnOlO37tLxw4QJsbW2Rk5ODGzduiMts7UtLSwtVb3nzzTcNvo5zov4ekZ2dnVi7di0YhtHbabdu3Yrp0xShEV0AAAUCSURBVKfTY//+/RrDwb1791KVGqEsW7aM1pQVk+fEJaS5u7tj1apVePToEQIDA0UFFvLz82kHv3jxIoDeoZLq3CM7O1tnnVFD4Ia0UVFRgtsAeoMTW7ZsQUVFBT1WrlwpmRN99tlnohR+TExMIJPJIJfLUVZWBoVCoTZ/NzEx6Xcoa7ATlZeX46233gLDMIiPj+eVY1NQUACZTGZQp2EYpl8RkPj4ePok5A7yt7acjY0NVq5ciaysLHR3d2Po0KEwNTU12M6+1NbW0iFsf/kk/dHS0kJLVHKd29nZGU5OTqKcqLu7G5cvX4aTkxMsLS3h6Oiolk8kl8tFBx04m1WlzvhSXl6ulvrQ95DCiSIiIkQ5UVZWlloEkfs39xvpmx8Z7EQ2Njb0g2tLhtMHV3JdH4QQTJo0Sefr3KRfldLSUmzbtg2JiYmwtraGpaUlwsPD6VBGDDY2NjAxMRGsINPY2AiWZVFfX4/GxkZs3boV1tbWtIOKldVtb29HeHi4Rud0dHQU1S4grRPpitRJQUREhKg59bNnz5CRkUG/u4CAAHz00UdoaGiAv7+/3huxXid6+vQpFAqF6Byizs5Oesc4ffo0mpubtf4dwzCYPHmyzna0OZEu5s+fD0KIYFVQoPdp5OPjQ4sIC2HTpk2QyWSYMWMGnWh3dHQgPz8fDMPwLm2vD259hhvmCYUbzolxIm2MHTuWjkxYloWvr68oofuIiAisXLlSEtscHBwgk8nUNAHj4uLg7Oys8xq9vTE5ORkMw0iS8MRlig4dOhTjx4/X+jcMw/Rb8fvjjz822IkeP34MuVyuMXHkS1NTExiGwZAhQ3Du3DlBbXB39YSEBLXzXEcSkjn78OFDpKamakiCffPNN5I4kRRPIl0cOXKEzj1YlhWcmdrS0gJbW1vJnGj9+vUwMTHB2LFj6bmnT5+CZVlMnDhRq4pUv73x+fPniI2NlSyrE4DaGsGTJ09QUFCAtLQ0+jrDMP0KT1y/fh3m5uYGv9+ECRMkWYNasmQJGIYRXA4lIyNDa4fhzmkL/+sjNDSUqvrMnTsX9fX1AP4RcRTjRJs3b36hTgT8E3ASE/1ramoCIURSG729vTWGmlZWVmAYRuuSh1YnUiqVGjsWJkyYgNDQUElEIebPn6+Wds2yLDIyMqjmlz74RNwePHgg2RrU4MGDYWJiAk9PT8ELe9nZ2fRzq96JhdyoWltbsXTpUrU5Rl1dHaqrq0U70dSpUwWH4fvCrTfJ5XKsW7eOHqqfX+j7cE5UVlYm2k6OCxcuIDg4WO3c4cOHMXjwYLi7u2v8vVYnKi0t1VDdUT0yMzORm5uL3NxcQaHf9vZ2XLx4Uc2JONlWQzoTn7nJX3/9BS8vL0n2VW3cuJGGvC9cuCC4neXLl8PLy4vqrmVnZ4uSJV66dCldcB0+fDg8PT3Bsqyoz8xpAkrhRJyYpL5DCC/CiXSha1lC53Cuvr4ehw4dQmxsLKKiotScyMXFBbNnz6ZBAKH1azg1FZZlMXToULAsa9DiKN8OHBYWJmqdhyMjIwMWFhainehFcPfuXSxbtkyyyNfMmTMlcyJOSdTZ2VmnAwmtFME5kTZJZalZs2YNZDKZRimfAc9sLSkpIV999RUhhJCEhATi6+s7kOYY+ZvExESyc+dO4unpSSoqKgbanP8UQUFB5Pz58/T/A+5ERoy87BgzW40YEYnRiYwYEcn/ARsGEgzC9ZkNAAAAAElFTkSuQmCC\" y=\"-6.6\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p188b2867a9\">\n   <rect height=\"626.4\" width=\"208.8\" x=\"7.2\" y=\"7.2\"/>\n  </clipPath>\n </defs>\n</svg>\n",
      "text/plain": "<Figure size 648x648 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to plot digits\n",
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    image_size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(image_size, image_size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((image_size, image_size * n_empty)))\n",
    "    \n",
    "    for row in range(n_rows):\n",
    "        r_images = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(r_images, axis=1))\n",
    "    \n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap=mpl.cm.binary, **options)\n",
    "    plt.axis('off')\n",
    "\n",
    "# plot digits \n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "# get the first N rows (digits) from X\n",
    "n_rows = 300\n",
    "example_images = X[:n_rows]\n",
    "\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "save_fig('the_first_' + str(n_rows) + '_digits')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "X_train shape is: \t: (60000, 784)\ny_train shape is: \t: (60000,)\nX_test shape is: \t: (10000, 784)\ny_test shape is: \t: (10000,)\n"
    }
   ],
   "source": [
    "# get the training and test data set\n",
    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n",
    "print('X_train shape is:', '\\t:', X_train.shape)\n",
    "print('y_train shape is:', '\\t:', y_train.shape)\n",
    "print('X_test shape is:',  '\\t:', X_test.shape)\n",
    "print('y_test shape is:',  '\\t:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "the last digit in X_train is:\n\n[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.  38.  48.  48.  22.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  62.\n   97. 198. 243. 254. 254. 212.  27.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  67. 172.\n  254. 254. 225. 218. 218. 237. 248.  40.   0.  21. 164. 187.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  89. 219. 254.\n   97.  67.  14.   0.   0.  92. 231. 122.  23. 203. 236.  59.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  25. 217. 242.  92.\n    4.   0.   0.   0.   0.   4. 147. 253. 240. 232.  92.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 101. 255.  92.   0.\n    0.   0.   0.   0.   0. 105. 254. 254. 177.  11.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 167. 244.  41.   0.\n    0.   0.   7.  76. 199. 238. 239.  94.  10.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 192. 121.   0.   0.\n    2.  63. 180. 254. 233. 126.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 190. 196.  14.   2.\n   97. 254. 252. 146.  52.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 130. 225.  71. 180.\n  232. 181.  60.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 130. 254. 254. 230.\n   46.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   6.  77. 244. 254. 162.   4.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0. 110. 254. 218. 254. 116.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0. 131. 254. 154.  28. 213.  86.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.  66. 209. 153.  19.  19. 233.  60.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0. 142. 254. 165.   0.  14. 216. 167.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.  90. 254. 175.   0.  18. 229.  92.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.  26. 229. 249. 176. 222. 244.  44.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.  73. 193. 197. 134.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]]\n"
    },
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print and plot the last digit in X_train\n",
    "the_last_digit_in_X_train = X_train[-1]\n",
    "the_last_digit_in_X_train_image = the_last_digit_in_X_train.reshape(28, 28)\n",
    "print('the last digit in X_train is:\\n')\n",
    "print(the_last_digit_in_X_train_image)\n",
    "plt.imshow(the_last_digit_in_X_train_image, cmap='binary')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "the last digit in y_train is \t: 8\n"
    }
   ],
   "source": [
    "# print the last value in y_train\n",
    "the_last_value_in_y_train = y_train[-1]\n",
    "print('the last digit in y_train is', '\\t:', the_last_value_in_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# training a binary classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "item  0 in y_train   is: 5\nitem  0 in y_train_5 is: True \n\nitem  1 in y_train   is: 0\nitem  1 in y_train_5 is: False \n\nitem  2 in y_train   is: 4\nitem  2 in y_train_5 is: False \n\nitem  3 in y_train   is: 1\nitem  3 in y_train_5 is: False \n\nitem  4 in y_train   is: 9\nitem  4 in y_train_5 is: False \n\nitem  5 in y_train   is: 2\nitem  5 in y_train_5 is: False \n\nitem  6 in y_train   is: 1\nitem  6 in y_train_5 is: False \n\nitem  7 in y_train   is: 3\nitem  7 in y_train_5 is: False \n\nitem  8 in y_train   is: 1\nitem  8 in y_train_5 is: False \n\nitem  9 in y_train   is: 4\nitem  9 in y_train_5 is: False \n\nitem  0 in y_test   is: 7\nitem  0 in y_test_5 is: False \n\nitem  1 in y_test   is: 2\nitem  1 in y_test_5 is: False \n\nitem  2 in y_test   is: 1\nitem  2 in y_test_5 is: False \n\nitem  3 in y_test   is: 0\nitem  3 in y_test_5 is: False \n\nitem  4 in y_test   is: 4\nitem  4 in y_test_5 is: False \n\nitem  5 in y_test   is: 1\nitem  5 in y_test_5 is: False \n\nitem  6 in y_test   is: 4\nitem  6 in y_test_5 is: False \n\nitem  7 in y_test   is: 9\nitem  7 in y_test_5 is: False \n\nitem  8 in y_test   is: 5\nitem  8 in y_test_5 is: True \n\nitem  9 in y_test   is: 9\nitem  9 in y_test_5 is: False \n\n"
    }
   ],
   "source": [
    "# create target vectors\n",
    "y_train_5 = (y_train == 5) # true for all 5s, False for all other digits\n",
    "y_test_5  = (y_test  == 5) \n",
    "\n",
    "num = 10\n",
    "for i in range(num):\n",
    "    print('item ', str(i), 'in y_train   is:', y_train[i])\n",
    "    print('item ', str(i), 'in y_train_5 is:', y_train_5[i], '\\n')\n",
    "\n",
    "for i in range(num):\n",
    "    print('item ', str(i), 'in y_test   is:', y_test[i])\n",
    "    print('item ', str(i), 'in y_test_5 is:', y_test_5[i], '\\n')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n              power_t=0.5, random_state=42, shuffle=True, tol=0.001,\n              validation_fraction=0.1, verbose=0, warm_start=False)"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply Stochastic Gradient Descent (SGD) classifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train, y_train_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "the 0 item in the data set is:\t 5\n...predition on if it is 5 is:\t [ True]\n"
    }
   ],
   "source": [
    "item_index = 0\n",
    "some_digit = X[item_index]\n",
    "\n",
    "print('the', str(item_index), 'item in the data set is:\\t', y[item_index])\n",
    "print('...predition on if it is 5 is:\\t', sgd_clf.predict([some_digit]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# performance measure\n",
    "# measuring accuracy using cross-validation\n",
    "# implementing cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.95035, 0.96035, 0.9604 ])"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n  FutureWarning\n0.95035\n0.96035\n0.9604\n"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "skfolds = StratifiedKFold(n_splits=3, random_state=42)\n",
    "\n",
    "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
    "    clone_clf = clone(sgd_clf)\n",
    "    \n",
    "    X_train_folds = X_train[train_index]\n",
    "    y_train_folds = y_train_5[train_index]\n",
    "    \n",
    "    X_test_fold = X_train[test_index]\n",
    "    y_test_fold = y_train_5[test_index]\n",
    "\n",
    "    clone_clf.fit(X_train_folds, y_train_folds)\n",
    "\n",
    "    y_pred = clone_clf.predict(X_test_fold)\n",
    "\n",
    "    n_correct = sum(y_pred == y_test_fold)\n",
    "\n",
    "    print(n_correct/len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a base estimator guessing \"not-5\"\n",
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class Never5Classifier(BaseEstimator):\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "    def predict(self, X):\n",
    "        return np.zeros((len(X), 1), dtype=bool) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.91125, 0.90855, 0.90915])"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "never_5_clf = Never5Classifier()\n",
    "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "True <class 'numpy.bool_'>\n"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)\n",
    "\n",
    "print(y_train_pred[0], type(y_train_pred[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[53892,   687],\n       [ 1891,  3530]])"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[54579,     0],\n       [    0,  5421]])"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test perfect prediction\n",
    "y_train_perfect_pred = y_train_5\n",
    "confusion_matrix(y_train_5, y_train_perfect_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# precision and recall\n",
    "\n",
    "precision = TP / (TP + FP)\n",
    "\n",
    "recall = TP / (TP + FN)\n",
    "\n",
    "F1 = TP / (TP + (FP + FN)/2) _# harmonic mean of precision and recall_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "precision score:\t 0.8370879772350012\nrecall score:   \t 0.6511713705958311\n"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "print('precision score:\\t', precision_score(y_train_5, y_train_pred))\n",
    "print('recall score:   \\t', recall_score(y_train_5, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "f1 score:\t\t 0.7325171197343846\nf1 score (manual):\t 0.7325171197343847\n"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "print('f1 score:\\t\\t', f1_score(y_train_5, y_train_pred))\n",
    "f1_score_manual = 3530 / (3530 + (687 + 1891)/2)\n",
    "print('f1 score (manual):\\t', f1_score_manual)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# precision/recall trade-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "y_scores is:\t 2164.2203023926586\n"
    }
   ],
   "source": [
    "item_index = 0\n",
    "some_digit = X[item_index]\n",
    "y_scores = sgd_clf.decision_function([some_digit])\n",
    "print('y_scores is:\\t', y_scores[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "is y_scores 2164.2203023926586 > threshold 0 ?:\t [ True]\n"
    }
   ],
   "source": [
    "threshold = 0\n",
    "is_y_scores_larger_than_threshold = (y_scores > threshold)\n",
    "print('is y_scores', y_scores[0], '> threshold', threshold, \n",
    "      '?:\\t', is_y_scores_larger_than_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "is y_scores 2164.2203023926586 > threshold 8000 ?:\t [False]\n"
    }
   ],
   "source": [
    "threshold = 8000\n",
    "is_y_scores_larger_than_threshold = (y_scores > threshold)\n",
    "print('is y_scores', y_scores[0], '> threshold', threshold, \n",
    "      '?:\\t', is_y_scores_larger_than_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "### how to decide which threshold to use? \n",
    "- first, use **_cross_val_predict()_** to get the scores of all instances in the training set, \n",
    "- but specify the method as **_decision_function_** to return **_decision scores_** rather than predictions.\n",
    "- with all the decision scores, use **_precision_recall_curve()_** to compute precision and recall for all possible thresholds.\n",
    "- Finally, use **_Matplotlib_** to plot precision and recall as functions of the threshold value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, \n",
    "                             cv=3, method='decision_function')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "saving figure \t: precision_recall_vs_threshold_plot\n"
    },
    {
     "data": {
      "image/png": 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300.599497 45.158555 \nL 300.876499 44.812382 \nL 301.04168 44.757783 \nL 301.221634 44.56563 \nL 301.222077 44.574048 \nL 301.316809 44.518754 \nL 301.396123 44.405985 \nL 301.444133 44.43965 \nL 301.455467 44.456509 \nL 301.473695 44.358598 \nL 301.531144 44.302599 \nL 301.577764 44.336288 \nL 301.695802 44.395412 \nL 301.706994 44.346199 \nL 302.00762 44.002713 \nL 302.019407 44.011123 \nL 302.028359 44.019537 \nL 302.044285 43.969918 \nL 302.086817 43.928671 \nL 302.150967 43.920987 \nL 302.154717 43.871226 \nL 302.251948 43.688641 \nL 302.373589 43.638926 \nL 302.524491 43.446816 \nL 302.965143 43.471436 \nL 303.419639 42.960562 \nL 303.652726 42.957339 \nL 303.654868 42.905844 \nL 303.778877 42.75809 \nL 303.794472 42.783231 \nL 303.896361 42.781814 \nL 303.900303 42.729965 \nL 304.010146 42.607705 \nL 304.017259 42.624462 \nL 304.128157 42.553839 \nL 304.29133 42.169106 \nL 304.343988 42.243987 \nL 304.452552 42.213947 \nL 304.647201 41.818101 \nL 304.647244 41.826411 \nL 304.677438 41.728293 \nL 304.730519 41.753192 \nL 304.840481 41.597729 \nL 304.850334 41.60602 \nL 304.928853 41.556946 \nL 304.94099 41.449586 \nL 305.020017 41.515934 \nL 305.207168 41.51637 \nL 305.394656 41.266217 \nL 305.407025 41.299502 \nL 305.567533 41.303438 \nL 305.571298 41.248913 \nL 305.857568 40.973962 \nL 305.897355 41.007226 \nL 305.899339 40.8972 \nL 305.921733 40.913807 \nL 306.010477 40.726329 \nL 306.035378 40.751172 \nL 306.142683 40.451723 \nL 306.186437 40.484665 \nL 306.250697 40.453775 \nL 306.254834 40.398097 \nL 306.380719 40.391781 \nL 306.388297 40.335957 \nL 306.696788 39.647829 \nL 306.699461 39.664029 \nL 306.969481 39.542834 \nL 306.970085 39.550946 \nL 307.017731 39.453421 \nL 307.028944 39.477728 \nL 307.149444 39.420429 \nL 307.15506 39.363288 \nL 307.251746 39.420103 \nL 307.348663 39.403481 \nL 307.361365 39.288682 \nL 307.443233 39.321175 \nL 307.712678 39.303714 \nL 307.787398 39.228896 \nL 307.933434 39.195448 \nL 307.985171 38.905237 \nL 308.050088 38.953813 \nL 308.180374 38.893991 \nL 308.207422 38.777136 \nL 308.306516 38.817618 \nL 308.376216 38.833845 \nL 308.386505 38.775259 \nL 308.720621 38.358398 \nL 308.756419 38.398627 \nL 308.826614 38.35549 \nL 308.865963 38.379669 \nL 308.956623 38.138625 \nL 308.985968 38.14665 \nL 309.096638 38.083407 \nL 309.260211 37.779522 \nL 309.276111 37.787494 \nL 309.405469 37.867483 \nL 309.446165 37.762755 \nL 309.497862 37.710294 \nL 309.555684 37.734286 \nL 309.857808 37.664391 \nL 309.952606 37.52955 \nL 309.974182 37.537558 \nL 310.001383 37.561613 \nL 310.011486 37.439237 \nL 310.073103 37.279237 \nL 310.126452 37.287197 \nL 310.15207 37.311107 \nL 310.175995 37.188116 \nL 310.395456 37.182176 \nL 310.492414 37.144324 \nL 310.694026 37.068336 \nL 310.882759 37.15616 \nL 310.886991 37.093995 \nL 310.980693 37.118001 \nL 311.109869 37.025392 \nL 311.130257 37.049401 \nL 311.460508 37.131657 \nL 311.461526 37.068849 \nL 311.483172 36.943114 \nL 311.529501 36.975287 \nL 311.603985 36.991405 \nL 311.606843 36.928372 \nL 311.668486 36.755028 \nL 311.760154 36.803143 \nL 311.786884 36.827271 \nL 311.791463 36.763881 \nL 311.987799 36.549784 \nL 312.001083 36.565768 \nL 312.15878 36.534006 \nL 312.381099 36.285822 \nL 312.419872 36.341637 \nL 312.512099 36.389681 \nL 312.516214 36.32524 \nL 312.528912 36.333248 \nL 312.763421 36.405548 \nL 312.817608 36.276171 \nL 312.862164 36.292221 \nL 312.894457 36.300254 \nL 312.902685 36.235446 \nL 312.908514 36.170596 \nL 313.010814 36.210654 \nL 313.115571 36.258898 \nL 313.121986 36.20184 \nL 313.20499 35.988841 \nL 313.245651 36.012863 \nL 313.36809 35.913764 \nL 313.381725 35.929772 \nL 313.422138 35.806293 \nL 313.425116 35.814272 \nL 313.475133 35.838239 \nL 313.478187 35.772352 \nL 313.653217 35.563797 \nL 313.683461 35.579683 \nL 313.777523 35.627468 \nL 313.783542 35.569032 \nL 313.897278 35.432938 \nL 313.911477 35.44089 \nL 313.923905 35.374116 \nL 313.975294 35.240434 \nL 314.036884 35.264145 \nL 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      "text/plain": "<Figure size 576x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], 'b--', label='Precision')\n",
    "    plt.plot(thresholds, recalls[:-1], 'g-', label='Recall')\n",
    "    plt.legend(loc='center right', fontsize=16)\n",
    "    plt.xlabel('threshold', fontsize=16)\n",
    "    plt.grid(True)\n",
    "    plt.axis([-50000, 50000, 0, 1])\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.plot([3370, 3370], [0.0, 0.9], 'r:')\n",
    "plt.plot([-50000, 3370], [0.9, 0.9], 'r:')\n",
    "plt.plot([-50000, 3370], [0.48, 0.48], 'r:')\n",
    "plt.plot([3370], [0.9], 'ro')\n",
    "plt.plot([3370], [0.48], 'ro')\n",
    "save_fig('precision_recall_vs_threshold_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train_pred == (y_scores > 0)).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "saving figure \t: precision_vs_recall_plot\n"
    },
    {
     "data": {
      "image/png": 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264.077498 79.516009 \nL 263.972735 79.455319 \nL 263.867971 79.090737 \nL 263.763207 79.029547 \nL 263.710825 78.475406 \nL 263.658443 78.488221 \nL 263.606061 78.501041 \nL 263.606061 78.413531 \nL 263.501297 77.912541 \nL 263.448915 77.925292 \nL 263.448915 77.837273 \nL 263.344151 77.377311 \nL 263.239387 77.40269 \nL 263.239387 77.31418 \nL 263.187005 77.105257 \nL 263.134623 77.117916 \nL 262.977478 77.155922 \nL 262.977478 77.06711 \nL 262.977478 76.933762 \nL 262.872714 76.959062 \nL 262.610804 76.933329 \nL 262.50604 76.780273 \nL 262.191749 76.766897 \nL 262.191749 76.45352 \nL 262.086985 76.478781 \nL 261.982221 76.414318 \nL 261.929839 76.202255 \nL 261.877457 76.214864 \nL 261.825075 76.227477 \nL 261.825075 76.137449 \nL 261.720311 75.982347 \nL 261.563165 75.92983 \nL 261.563165 75.839455 \nL 261.458401 75.864619 \nL 261.406019 75.877209 \nL 261.406019 75.786725 \nL 261.353637 75.527388 \nL 261.301256 75.539936 \nL 261.196492 75.519645 \nL 261.196492 75.474226 \nL 261.14411 75.168293 \nL 261.091728 75.180802 \nL 260.8822 75.185287 \nL 260.8822 75.139667 \nL 260.829818 74.923785 \nL 260.777436 74.936278 \nL 260.672672 74.915524 \nL 260.672672 74.869751 \nL 260.567908 74.573736 \nL 260.463144 74.552713 \nL 260.463144 74.506751 \nL 260.410763 74.289084 \nL 260.358381 74.301511 \nL 260.305999 74.313942 \nL 260.305999 74.221739 \nL 260.253617 74.141872 \nL 260.201235 74.154286 \nL 259.886943 74.182658 \nL 259.886943 74.136419 \nL 259.886943 74.090163 \nL 259.782179 74.115026 \nL 259.625033 74.059721 \nL 259.52027 73.899059 \nL 259.415506 73.877462 \nL 259.415506 73.830997 \nL 259.310742 73.669737 \nL 259.153596 73.660365 \nL 259.153596 73.613754 \nL 259.153596 73.567123 \nL 259.048832 73.591921 \nL 258.629777 73.597856 \nL 258.577395 73.516746 \nL 258.525013 73.529169 \nL 258.367867 73.56647 \nL 258.367867 73.472796 \nL 258.263103 72.792483 \nL 258.053575 72.841853 \nL 258.053575 72.74745 \nL 257.948811 72.488384 \nL 257.791666 72.525324 \nL 257.791666 72.430543 \nL 257.739284 72.300516 \nL 257.686902 72.312809 \nL 257.477374 72.31451 \nL 257.477374 72.266971 \nL 257.37261 72.053525 \nL 256.848791 72.081022 \nL 256.744027 71.770676 \nL 256.639263 71.747279 \nL 256.639263 71.699312 \nL 256.534499 71.627832 \nL 256.272589 71.593025 \nL 256.167825 71.521296 \nL 255.853534 71.546771 \nL 255.853534 71.498536 \nL 255.74877 71.136401 \nL 255.696388 71.148636 \nL 255.696388 71.051744 \nL 255.644006 70.91846 \nL 255.591624 70.930668 \nL 255.434478 70.918749 \nL 255.434478 70.870155 \nL 255.329714 70.699952 \nL 255.015423 70.675657 \nL 254.910659 70.211067 \nL 254.701131 70.210622 \nL 254.701131 70.161558 \nL 254.648749 69.878846 \nL 254.596367 69.890952 \nL 254.177312 69.88939 \nL 254.072548 69.716178 \nL 253.86302 69.715213 \nL 253.86302 69.665738 \nL 253.810638 69.330892 \nL 253.758256 69.342962 \nL 253.496346 69.303971 \nL 253.391583 68.929523 \nL 253.182055 68.877748 \nL 253.077291 68.601434 \nL 252.920145 68.637441 \nL 252.920145 68.537094 \nL 252.815381 68.410363 \nL 252.658235 68.396015 \nL 252.658235 68.345684 \nL 252.553471 68.168049 \nL 252.291562 68.126882 \nL 252.186798 68.049693 \nL 251.71536 68.056166 \nL 251.610597 67.775445 \nL 251.453451 67.709522 \nL 251.453451 67.454659 \nL 251.348687 67.478481 \nL 251.086777 67.43591 \nL 250.982013 67.306232 \nL 250.667722 67.275214 \nL 250.61534 66.978852 \nL 250.562958 66.990732 \nL 250.35343 66.986803 \nL 250.35343 66.93528 \nL 250.35343 66.728963 \nL 250.248666 66.752678 \nL 249.462937 66.827605 \nL 249.358173 66.695864 \nL 249.201027 66.627711 \nL 249.201027 66.523725 \nL 249.096263 66.54751 \nL 248.781972 66.566892 \nL 248.781972 66.514773 \nL 248.72959 66.370166 \nL 248.677208 66.382059 \nL 248.258152 66.425089 \nL 248.258152 66.372768 \nL 248.20577 66.175143 \nL 248.153388 66.187035 \nL 247.943861 66.182165 \nL 247.943861 66.129653 \nL 247.943861 66.077119 \nL 247.839097 66.100906 \nL 247.524805 66.067113 \nL 247.524805 65.96174 \nL 247.420041 65.985539 \nL 247.262895 65.968511 \nL 247.262895 65.915722 \nL 247.158132 65.516243 \nL 247.000986 65.445678 \nL 247.000986 65.339473 \nL 246.896222 65.363123 \nL 246.84384 65.374957 \nL 246.84384 65.268603 \nL 246.84384 65.215391 \nL 246.739076 65.239018 \nL 246.58193 65.167912 \nL 246.529548 64.966239 \nL 246.477166 64.978026 \nL 246.267639 64.918246 \nL 246.162875 64.834729 \nL 245.639055 64.845438 \nL 245.639055 64.791706 \nL 245.534291 64.815333 \nL 245.377146 64.797011 \nL 245.377146 64.743184 \nL 245.377146 64.689332 \nL 245.272382 64.712957 \nL 245.115236 64.748433 \nL 245.115236 64.640564 \nL 245.010472 64.394014 \nL 244.905708 64.417581 \nL 244.905708 64.309298 \nL 244.800944 63.898591 \nL 244.539035 63.902735 \nL 244.539035 63.848271 \nL 244.539035 63.793783 \nL 244.434271 63.817195 \nL 244.119979 63.778355 \nL 244.015215 63.692471 \nL 243.124722 63.782624 \nL 243.07234 63.684522 \nL 243.019958 63.696296 \nL 242.653285 63.668719 \nL 242.653285 63.392911 \nL 242.548521 63.416413 \nL 242.338993 63.352864 \nL 242.338993 63.131325 \nL 242.234229 63.154772 \nL 241.972319 63.102422 \nL 241.867556 62.959062 \nL 241.71041 62.93854 \nL 241.71041 62.88282 \nL 241.605646 62.683036 \nL 241.343736 62.629651 \nL 241.238972 62.316705 \nL 241.134208 62.283826 \nL 241.134208 62.227634 \nL 241.081826 62.070502 \nL 241.029445 62.082107 \nL 240.924681 62.048998 \nL 240.924681 61.992634 \nL 240.819917 61.790045 \nL 240.558007 61.734792 \nL 240.505625 61.519693 \nL 240.453243 61.531233 \nL 240.191333 61.532216 \nL 240.191333 61.475392 \nL 240.08657 61.270856 \nL 239.877042 61.259947 \nL 239.877042 61.145829 \nL 239.772278 61.168853 \nL 239.719896 61.180373 \nL 239.719896 61.123242 \nL 239.719896 60.951688 \nL 239.615132 60.974659 \nL 239.353222 60.974868 \nL 239.30084 60.87165 \nL 239.248459 60.883144 \nL 239.091313 60.917657 \nL 239.091313 60.86021 \nL 239.091313 60.802736 \nL 238.986549 60.825729 \nL 238.829403 60.802704 \nL 238.724639 60.652837 \nL 238.253202 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205.933544 47.264145 \nL 205.933544 47.108325 \nL 205.82878 47.129614 \nL 205.619253 47.094165 \nL 205.619253 47.016016 \nL 205.514489 47.0373 \nL 205.095433 47.044293 \nL 205.095433 46.965844 \nL 204.990669 46.987175 \nL 204.938287 46.99785 \nL 204.938287 46.919293 \nL 204.938287 46.840688 \nL 204.833524 46.861975 \nL 204.72876 46.883288 \nL 204.72876 46.804553 \nL 204.72876 46.72577 \nL 204.623996 46.747026 \nL 204.46685 46.700024 \nL 204.46685 46.621041 \nL 204.362086 46.642279 \nL 204.152558 46.605676 \nL 204.152558 46.447219 \nL 204.047794 46.468409 \nL 203.995413 46.479014 \nL 203.995413 46.399649 \nL 203.995413 46.320236 \nL 203.890649 46.341381 \nL 203.785885 46.283006 \nL 203.785885 46.203412 \nL 203.681121 46.224524 \nL 203.157301 46.250575 \nL 203.10492 46.101199 \nL 203.052538 46.111772 \nL 202.371572 46.169452 \nL 202.371572 46.089035 \nL 202.266808 46.110285 \nL 202.162045 46.131562 \nL 202.162045 46.051008 \nL 202.057281 45.910924 \nL 201.638225 45.914957 \nL 201.533461 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188.54274 41.014935 \nL 188.54274 40.922775 \nL 188.54274 40.830551 \nL 188.437976 40.850781 \nL 187.914157 40.859726 \nL 187.914157 40.767029 \nL 187.809393 40.787314 \nL 187.652247 40.724889 \nL 187.599865 40.548968 \nL 187.547483 40.559062 \nL 187.285573 40.516336 \nL 187.233192 40.339576 \nL 187.18081 40.349633 \nL 186.709372 40.346633 \nL 186.709372 40.15878 \nL 186.604608 40.178864 \nL 185.871261 40.225753 \nL 185.766497 40.056677 \nL 185.29506 40.052596 \nL 185.29506 39.95749 \nL 185.190296 39.97766 \nL 184.509331 40.013843 \nL 184.404567 39.746675 \nL 184.352185 39.756757 \nL 184.352185 39.660756 \nL 184.352185 39.564686 \nL 184.247421 39.584748 \nL 184.090275 39.5186 \nL 184.090275 39.422236 \nL 183.985511 39.442245 \nL 183.252164 39.48619 \nL 183.252164 39.389183 \nL 183.1474 39.409299 \nL 183.095018 39.419368 \nL 183.095018 39.322195 \nL 183.095018 39.224952 \nL 182.990254 39.244985 \nL 182.780727 39.187634 \nL 182.675963 39.012337 \nL 181.890234 39.064473 \nL 181.890234 38.966132 \nL 181.78547 38.986181 \nL 181.575942 38.927759 \nL 181.52356 38.740289 \nL 181.471178 38.750264 \nL 181.104504 38.721169 \nL 181.052123 38.532633 \nL 180.999741 38.542575 \nL 180.528303 38.532674 \nL 180.528303 38.432888 \nL 180.423539 38.452794 \nL 179.690192 38.492571 \nL 179.690192 38.291545 \nL 179.585428 38.311486 \nL 179.428282 38.240658 \nL 179.323518 38.058648 \nL 178.27588 38.156631 \nL 178.27588 38.054768 \nL 178.171116 38.074772 \nL 177.909206 38.02273 \nL 177.909206 37.920466 \nL 177.804442 37.940437 \nL 177.333005 37.927951 \nL 177.228241 37.742178 \nL 176.232984 37.933009 \nL 176.232984 37.829287 \nL 176.12822 37.641634 \nL 175.656783 37.627803 \nL 175.656783 37.523357 \nL 175.552019 37.543414 \nL 174.556762 37.630228 \nL 174.556762 37.52484 \nL 174.451998 37.54508 \nL 173.875797 37.551012 \nL 173.875797 37.444969 \nL 173.771033 37.465266 \nL 173.404359 37.430089 \nL 173.404359 37.323535 \nL 173.299595 37.343824 \nL 173.090068 37.277631 \nL 173.037686 37.073712 \nL 172.985304 37.083794 \nL 172.304338 37.107881 \nL 172.304338 36.892209 \nL 172.199575 36.912372 \nL 171.047172 37.027403 \nL 171.047172 36.918398 \nL 170.942408 36.938795 \nL 169.790005 37.055298 \nL 169.790005 36.834902 \nL 169.685241 36.855456 \nL 169.475714 36.786085 \nL 169.475714 36.453799 \nL 169.37095 36.474124 \nL 168.061401 36.618963 \nL 168.061401 36.506795 \nL 167.956637 36.527409 \nL 166.908999 36.622256 \nL 166.908999 36.508957 \nL 166.804235 36.529782 \nL 166.751853 36.540207 \nL 166.751853 36.426684 \nL 166.647089 36.447474 \nL 166.542325 36.468299 \nL 166.437561 36.261302 \nL 166.280415 36.292396 \nL 166.280415 36.178191 \nL 166.175651 36.198876 \nL 165.59945 36.313275 \nL 165.59945 36.198398 \nL 165.494686 36.219224 \nL 165.075631 36.302885 \nL 165.023249 36.08235 \nL 164.970867 36.092767 \nL 164.708957 36.144985 \nL 164.708957 36.02905 \nL 164.604193 36.049909 \nL 164.551811 36.060352 \nL 164.551811 35.944181 \nL 164.447047 35.965003 \nL 164.132756 36.027686 \nL 164.132756 35.911049 \nL 164.027992 35.931923 \nL 163.97561 35.942374 \nL 163.97561 35.825498 \nL 163.870846 35.846335 \nL 162.927971 36.035502 \nL 162.927971 35.917596 \nL 162.823207 35.938704 \nL 162.770825 35.949272 \nL 162.718443 35.604947 \nL 162.666061 35.615392 \nL 162.456534 35.657263 \nL 162.456534 35.538518 \nL 162.35177 35.559414 \nL 161.985096 35.632839 \nL 161.880332 35.415045 \nL 161.356513 35.519949 \nL 161.251749 35.3006 \nL 160.832693 35.384564 \nL 160.832693 35.26379 \nL 160.72793 35.284777 \nL 160.046964 35.422107 \nL 160.046964 35.300482 \nL 159.9422 35.321652 \nL 159.523145 35.406716 \nL 159.470763 34.804854 \nL 159.418381 34.815287 \nL 159.051707 34.888581 \nL 158.999326 34.652378 \nL 158.946944 34.662793 \nL 158.475506 34.756961 \nL 158.423124 34.519172 \nL 158.370742 34.529589 \nL 157.846923 34.634281 \nL 157.846923 34.509384 \nL 157.742159 34.530333 \nL 157.21834 34.635658 \nL 157.21834 34.510011 \nL 157.113576 34.531087 \nL 156.904048 34.573355 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150.513451 33.663824 \nL 150.513451 33.528591 \nL 150.408687 33.55022 \nL 150.356305 33.561051 \nL 150.356305 33.425497 \nL 150.251541 33.447066 \nL 150.094396 33.479501 \nL 150.094396 33.207366 \nL 149.989632 33.228793 \nL 149.67534 33.293331 \nL 149.67534 33.156556 \nL 149.570576 33.178029 \nL 149.465812 33.199545 \nL 149.465812 33.062379 \nL 149.361048 33.083811 \nL 148.941993 33.169972 \nL 148.889611 32.904637 \nL 148.837229 32.915338 \nL 148.31341 33.022949 \nL 148.208646 32.626783 \nL 146.42766 32.994973 \nL 146.322896 32.732957 \nL 146.218132 32.754794 \nL 146.218132 32.612396 \nL 146.113368 32.634141 \nL 146.060986 32.64503 \nL 145.956222 32.237724 \nL 145.589549 32.312973 \nL 145.589549 32.169147 \nL 145.484785 32.190608 \nL 145.327639 32.222886 \nL 145.327639 32.078557 \nL 145.222875 32.099991 \nL 144.646674 32.218691 \nL 144.646674 32.073288 \nL 144.54191 32.094875 \nL 144.070472 32.192592 \nL 144.070472 32.046242 \nL 143.965709 32.06794 \nL 143.127598 32.243205 \nL 143.075216 31.958306 \nL 143.022834 31.969225 \nL 142.760924 32.023995 \nL 142.760924 31.875331 \nL 142.65616 31.897173 \nL 142.603778 31.908112 \nL 142.603778 31.759062 \nL 142.499014 31.780825 \nL 142.237105 31.83544 \nL 142.237105 31.6857 \nL 142.132341 31.707478 \nL 141.765667 31.784076 \nL 141.765667 31.633486 \nL 141.660903 31.655326 \nL 141.503757 31.688176 \nL 141.503757 31.537036 \nL 141.398993 31.558842 \nL 140.560882 31.735036 \nL 140.560882 31.582323 \nL 140.456119 31.604411 \nL 140.089445 31.682105 \nL 139.984681 31.088355 \nL 139.722771 31.142743 \nL 139.722771 30.987891 \nL 139.618008 31.00957 \nL 139.460862 31.042179 \nL 139.460862 30.886746 \nL 139.356098 30.908384 \nL 138.779896 31.028271 \nL 138.779896 30.871591 \nL 138.675133 30.893384 \nL 138.308459 30.970053 \nL 138.308459 30.812442 \nL 138.203695 30.834292 \nL 136.579855 31.179494 \nL 136.579855 31.018876 \nL 136.475091 31.041402 \nL 135.63698 31.223525 \nL 135.532216 30.921193 \nL 135.479834 30.932538 \nL 135.479834 30.769489 \nL 135.37507 30.79204 \nL 133.960758 31.101799 \nL 133.960758 30.935902 \nL 133.855994 30.959062 \nL 132.913119 31.170052 \nL 132.913119 31.002068 \nL 132.808355 31.025607 \nL 132.12739 31.180024 \nL 132.022626 30.86413 \nL 131.86548 30.89962 \nL 131.813098 30.569981 \nL 131.760716 30.581657 \nL 131.132133 30.722915 \nL 131.027369 30.228322 \nL 130.136876 30.42742 \nL 130.136876 30.252464 \nL 130.032112 30.275962 \nL 129.613057 30.370557 \nL 129.613057 30.194334 \nL 129.508293 30.217924 \nL 129.246383 30.277163 \nL 129.246383 30.099975 \nL 129.141619 30.123564 \nL 127.25587 30.558799 \nL 127.25587 30.377244 \nL 127.151106 30.401807 \nL 127.098724 30.414112 \nL 126.99396 30.073821 \nL 126.889196 30.098095 \nL 126.889196 29.915031 \nL 126.784432 29.939142 \nL 126.417758 30.024039 \nL 126.365377 29.667084 \nL 126.312995 29.679062 \nL 126.051085 29.739197 \nL 126.051085 29.553555 \nL 125.946321 29.577489 \nL 125.684411 29.637605 \nL 125.684411 29.450892 \nL 125.579647 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109.969829 25.875889 \nL 109.969829 25.631314 \nL 109.865065 25.655566 \nL 109.707919 25.692104 \nL 109.707919 24.952736 \nL 109.603156 24.975964 \nL 109.341246 25.034394 \nL 109.288864 24.547715 \nL 109.236482 24.559062 \nL 107.769788 24.885177 \nL 107.769788 24.629008 \nL 107.665024 24.652493 \nL 107.56026 24.676064 \nL 107.455496 24.183283 \nL 107.403114 24.194692 \nL 107.403114 23.675898 \nL 107.29835 23.697864 \nL 106.512621 23.865231 \nL 106.512621 23.601158 \nL 106.407857 23.623355 \nL 105.4126 23.838452 \nL 105.4126 23.568805 \nL 105.307836 23.591407 \nL 104.260198 23.822282 \nL 104.260198 23.546562 \nL 104.155434 23.56963 \nL 103.736378 23.66281 \nL 103.736378 23.383939 \nL 103.631614 23.406936 \nL 103.212559 23.49984 \nL 103.107795 22.957461 \nL 101.483955 23.316087 \nL 101.431573 22.74186 \nL 101.379191 22.753253 \nL 101.169663 22.799062 \nL 101.169663 22.503588 \nL 101.064899 22.526043 \nL 100.069642 22.744137 \nL 100.069642 22.441667 \nL 99.964878 22.464517 \nL 97.869601 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      "text/plain": "<Figure size 360x360 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_vs_recall(precisions, recalls):\n",
    "    plt.plot(recalls, precisions, 'b-', linewidth=2)\n",
    "    plt.xlabel('Recall', fontsize=16)\n",
    "    plt.ylabel('Precision', fontsize=16)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plot_precision_vs_recall(precisions, recalls)\n",
    "plt.plot([0.48, 0.48], [0., 0.9], 'r:')\n",
    "plt.plot([0., 0.48], [0.9, 0.9], 'r:')\n",
    "plt.plot([0.48], [0.9], 'ro')\n",
    "save_fig('precision_vs_recall_plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "### get the lowest threshold value that provides a min precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "threshold providing at least 90.0% precision is: 3370.0194991439557\n"
    }
   ],
   "source": [
    "min_precision = 0.9\n",
    "threshold_for_min_precision = thresholds[np.argmax(precisions >= min_precision)]\n",
    "print('threshold providing at least', str(min_precision*100)+'% precision is:', threshold_for_min_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "[False False False ...  True False False]\n"
    }
   ],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_for_min_precision)\n",
    "print(y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.9000345901072293"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.4799852425751706"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC curve\n",
    "\n",
    "* **_ROC curve_** plots **_ture positive rate (recall)_** against **_false positive rate (FPR)_**\n",
    "\n",
    "* The **_false positive rate (FPR)_** (specificity) = 1 - **_true negative rate(TNR)_**\n",
    "\n",
    "    | Name 1                          | Name 2      | Calculation    |\n",
    "    | ------------------------------- | ----------- |--------------- |\n",
    "    | **_ture positive rate (TPR)_**  | recall      | TP / (TP + FN) |\n",
    "    | **_false positive rate (FPR)_** | specificity | FP / (TN + FP) |\n",
    "    | **_true negative rate (TNR)_**  |             | TN / (TN + FP) |\n",
    "\n",
    "* there is a trade-off btw recall (TPR) and specificity (FPR).\n",
    "\n",
    "* the ROC curve of a good classifier tends bend towards the top left corner.\n",
    "\n",
    "* area-under-curve (AUC) for ROC curve is an indicator to compare classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "saving figure \t: roc_curve_plot\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 576x360 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate (Recall)', fontsize=16)\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.plot([4.8e-3, 4.8e-3], [0, 0.48], 'r:')\n",
    "plt.plot([0.0, 4.8e-3], [0.48, 0.48], 'r:')\n",
    "plt.plot([4.8e-3], [0.48], 'ro')\n",
    "save_fig('roc_curve_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.9604938554008616"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "## train a RandomForestClassifier to compare with SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5,\n",
    "                                    cv=3, method='predict_proba')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "saving figure \t: roc_curve_comparison_plot\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 576x360 with 1 Axes>"
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "plt.figure(figsize=[8, 5])\n",
    "plt.plot(fpr, tpr, 'b:', linewidth=2, label='SGD')\n",
    "plot_roc_curve(fpr_forest, tpr_forest, 'Random Forest')\n",
    "plt.plot([4.8e-3, 4.8e-3], [0, 0.48], 'r:')\n",
    "plt.plot([0.0, 4.8e-3], [0.48, 0.48], 'r:')\n",
    "plt.plot([4.8e-3], [0.48], 'ro')\n",
    "plt.plot([4.8e-3, 4.8e-3], [0, 0.9], 'r:')\n",
    "plt.plot([4.8e-3], [0.9], 'ro')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='lower right', fontsize=16)\n",
    "save_fig('roc_curve_comparison_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "AUC of RandomForest classifer is: 0.9983436731328145\n"
    }
   ],
   "source": [
    "auc_RandomForest = roc_auc_score(y_train_5, y_scores_forest)\n",
    "print('AUC of RandomForest classifer is:', auc_RandomForest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "precision score:\t 0.9905083315756169\nrecall score:   \t 0.8662608374838591\n"
    }
   ],
   "source": [
    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n",
    "precision_score = precision_score(y_train_5, y_train_pred_forest)\n",
    "recall_score = recall_score(y_train_5, y_train_pred_forest)\n",
    "\n",
    "print('precision score:\\t', precision_score)\n",
    "print('recall score:   \\t', recall_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiclass Classification\n",
    "\n",
    "* SGD, Random Forest and naive Bayes classifiers can handle multiclass Classification\n",
    "\n",
    "* Logistic Regression and SVM are binary classifiers.\n",
    "\n",
    "* one-vesus-the-rest (OvR): one classifier for each class\n",
    "\n",
    "* one-vesus_one (OvO): one classifier for each pair fo classes [N*(N-1)/2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* apply a SVM classifer to the training set with all the digits, not just 5.\n",
    "\n",
    "* Scikit-Learn detects that a binary classifier (SVM) is applied for multiclass classification (because y_train contains more than one class). \n",
    "\n",
    "* So, OvO strategies is applied, i.e., it trains 45 binary classifiers, get the decision scores for the image, and select the class with the highest score.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([5], dtype=uint8)"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "item_index = 0\n",
    "some_digit = X[item_index]\n",
    "\n",
    "svm_clf = SVC(gamma='auto', random_state=42)\n",
    "svm_clf.fit(X_train[:1000], y_train[:1000])\n",
    "svm_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* use the **_decision_function()_** to get the decision scores for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 2.81585438,  7.09167958,  3.82972099,  0.79365551,  5.8885703 ,\n         9.29718395,  1.79862509,  8.10392157, -0.228207  ,  4.83753243]])"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
    "some_digit_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* use **_np.argmax()_** function to get the indice of the max value in the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "5"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(some_digit_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* use **_.classes\\_ _** method of the classifier to get the classes as identified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "5"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* force Scikit-Learn to use OvO or OvA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([5], dtype=uint8)"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "ovr_clf = OneVsRestClassifier(SVC(gamma='auto', random_state=42))\n",
    "ovr_clf.fit(X_train[:1000], y_train[:1000])\n",
    "ovr_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "10"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovr_clf.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([3], dtype=uint8)"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.fit(X_train, y_train)\n",
    "sgd_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[-31893.03095419, -34419.69069632,  -9530.63950739,\n          1823.73154031, -22320.14822878,  -1385.80478895,\n        -26188.91070951, -16147.51323997,  -4604.35491274,\n        -12050.767298  ]])"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_clf.decision_function([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.87365, 0.85835, 0.8689 ])"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* scaling the input to increase accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.8983, 0.891 , 0.9018])"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# error analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[5577,    0,   22,    5,    8,   43,   36,    6,  225,    1],\n       [   0, 6400,   37,   24,    4,   44,    4,    7,  212,   10],\n       [  27,   27, 5220,   92,   73,   27,   67,   36,  378,   11],\n       [  22,   17,  117, 5227,    2,  203,   27,   40,  403,   73],\n       [  12,   14,   41,    9, 5182,   12,   34,   27,  347,  164],\n       [  27,   15,   30,  168,   53, 4444,   75,   14,  535,   60],\n       [  30,   15,   42,    3,   44,   97, 5552,    3,  131,    1],\n       [  21,   10,   51,   30,   49,   12,    3, 5684,  195,  210],\n       [  17,   63,   48,   86,    3,  126,   25,   10, 5429,   44],\n       [  25,   18,   30,   64,  118,   36,    1,  179,  371, 5107]])"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
    "conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
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    }
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   "source": [
    "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* plot the error = each num in confusion matrix / sum of the num in the same row\n",
    "\n",
    "* i.e. the num of cases in which a given digit is correctely classified / the total num of cases for that digit\n",
    "\n",
    "* e.g., the top left num in confusion matrix is **_5576_**, which is the num of cases in which digit '1' was correctly classified, the sum of the values in that row is **_5923_** which is the total num of digit \"1\" in the dataset. So, the error of this classifier to identify digit \"1\" correctly is calculated as **_5576/5923 = 0.941414823569137 = 9.41414824e-01_**\n",
    "\n",
    "* remember that in confusion matrix, **_rows represent actural classes_**, while **_columns represent predicted classes_**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
    "norm_conf_mx = conf_mx / row_sums\n",
    "norm_conf_mx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
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      "text/plain": "<Figure size 288x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# paint the diagonal cells in dark(zero) to make the rest cells look clearer\n",
    "np.fill_diagonal(norm_conf_mx, 0) \n",
    "\n",
    "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "* The classifier seems to get confused on 3s and 5s. Let's plot out those cases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\" 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y=\"-244.08\"/>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"pc5fb71f97f\">\n   <rect height=\"197.672727\" width=\"197.672727\" x=\"7.2\" y=\"7.2\"/>\n  </clipPath>\n  <clipPath id=\"p0ae60d805e\">\n   <rect height=\"197.672727\" width=\"197.672727\" x=\"250.690909\" y=\"7.2\"/>\n  </clipPath>\n  <clipPath id=\"pd3081fdd95\">\n   <rect height=\"197.672727\" width=\"197.672727\" x=\"7.2\" y=\"244.407273\"/>\n  </clipPath>\n  <clipPath id=\"peacecf121f\">\n   <rect height=\"197.672727\" width=\"197.672727\" x=\"250.690909\" y=\"244.407273\"/>\n  </clipPath>\n </defs>\n</svg>\n",
      "text/plain": "<Figure size 576x576 with 4 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cl_a, cl_b = 3, 5\n",
    "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
    "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n",
    "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n",
    "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "outputs": [],
   "source": [
    "# multilabel classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n                     weights='uniform')"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7)\n",
    "y_train_odd = (y_train % 2 == 1)\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[False,  True]])"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_index = 0\n",
    "some_digit = X[item_index]\n",
    "\n",
    "knn_clf.predict([some_digit])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # !!! the following code takes a long time to finish\n",
    "# y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
    "# f1_score(y_multilabel, y_train_knn_pred, average='macro')"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# multioutput classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_X_train = np.random.randint(0, 100, (len(X_train), 784))\n",
    "X_train_mod = X_train + noise_X_train\n",
    "noise_X_test = np.random.randint(0, 100, (len(X_test), 784))\n",
    "X_test_mod = X_test + noise_X_test\n",
    "y_train_mod = X_train\n",
    "y_test_mod = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "saving figure \t: noise_digit_example_plot\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "some_index = 0\n",
    "plt.subplot(121);  plot_digit(X_test_mod[some_index])\n",
    "plt.subplot(122);  plot_digit(y_test_mod[some_index])\n",
    "save_fig('noise_digit_example_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf.fit(X_train_mod, y_train_mod)\n",
    "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
    "plot_digit(clean_digit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}