assignment1/svm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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88sRF7YrWFppv6saWXp/rvSHDfndUHhVJPCPEfT1UV+vlPPY9v/TuFSyRHq4C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mDB/uN3WfDVPC4mur1WtYW6fAMEEMyQyf1ZUVjI/R/T5x4gwKhaOmnLSgWZaDfI4Ck3w+B9fN\ncn8jrPOY8j0FwbUQnhGgUKHgN4x9lJzUyHeUTg7ZdtIADnbJk+7TX/4Eh5vLAIDd1RUUCrRxdD0P\nJrMFJ2t1XH7iHF9/F1mmdrUaB1hZIXjJt2xM16mPh41D9JjFZuWy6DZpg8uZ24hTz7uoh4LNfoW2\nh+YB1VEsnHodGzu0WZTMCk5cGF0oECLSzyiOQwiGu1Uc63ErpdQeb6abRabMdOz9B4g42N/dkujv\npUG9RJ9p6bLkwkqVy1s+npyg1+tRBrttXocCQKUClyJ+yHA2PXFIKY94CBrIjCD7AADtZht+h+Z/\nabIGweMlFl0orlczTaWNzt/56AFOV2mT/85vVlCI6Vl5bQ8Zri2yhYfFBQrmsjBx7To9t91mCPb6\nRTmTyksAWwOFzU/p8OhLV7OUJ+ol1Gp0MEiiQxwe0nzq9Twk5uib8NeefQ7nZ4jVWqtPIVEEM928\ncg1PPEHwouPk8dGHBP9alqldBLKui7E61Y4mloUzZwgqqk9N4L279D1LNz6EC6qlyiY9tFkC4dyZ\nC9jbpeBib3sP/SaNhVazCcXB98LJk5iapvnR73eRZcFdIQRsPvhX+R78fe2ohMfRwNo0TRgcNCEx\n4AdM8Y8kbFatzuZcFPgwU6rUoPjQLQxHK/7DSDA5RtdRLbl44sxlAECh4KJep2tW4QDry7SOfvLh\nNRSrtCc5lzK4eZOEpP/mJ2/i5Em659PTcxg8hpK9SpQ+tFhCIlXQUJ4/rL9UCQasHB/6ofbXM4QB\nySbMhhCIWPzaMoSuaws8H0UOylQSQzH8LVQEj+tNIwGoNDgSAgkfROMwBHhPFVLC55ITz/cRP4Ys\n0jHMd9yO23E7bsftuB234/ZrtC8X5sPQEgSJOpKYkkdYe4kW55TDmnBY0tFu15bjoFTlSD7pY9Bn\nOMcQEGk2yjShIopg56oVnJiiE0Qlm0OLnb7vbO9rGOn01BhefYpO26fqJa2z8cGte/iABfBGaYHn\nIc/Kl94ggsXQSLcdwDLoO13HgslQnUoEisw8SAwDlTJF1+3DQ522lDAgONOkECJOvYaERMJFdIkR\naQG2WIZQHMmbsQ+DBer8wEI/ZIuBJEKe05/1ahmQow2Fo2w+HE2PRsP0I2dCbwAAIABJREFUKKRA\nevYfDDxsb1JW5f333sXde0t8b3xEHPWb5tBXKRFSF+YKCCjOCjqOjRzDZOVCBWdPUwo4CjxMsqZO\npVxJr1CfOvhGjdS3tLmWi4jJC1vrA2yv0jXknJrOFvn+ti6E9D0ffSvt+pAlIqRAxL573W4Xn31G\nheyl8jguPkXXnMnktQ1Cxs3pDJftmLDZZ6i5f4g+M9rcbKLFBI2cRHGMYS9losAu9yO1h9DrBM0W\nQW+t/S1IPgGfPXkCfa4P3Ws0SLAQwPTMGAYdup5iJtHPzvM9NJv09/VmA5UsQSPZqoVcjtIa3YHE\ng7uUmcqigoT1vLptA7UaQYe26+HKTYJkGo0dPPviGwAA066hyAWqo7Q7d2+iyAW5pXIZmSzdn0wm\nS4WsD90MQBgW5k4QAWS2vorDPSK5dA58eJyVszI+RJ7m94uvv4rVG5RprW0v4dJJKmre+rwFi605\nDGFoTzUhpWZKxgJaKPeLbVRvvl4vwj7DZ0axhN0W22oNQn1iF46FMtvMTFbHsdGi9eXuqsDzE3zC\ndxUCm7MAbgZWj/o9PxVrr7cHawqdbipYK5DlKTU2EFhjqLZvO2jwlrK128TeNmV8Wgdt+Km+nWlo\nAc9RWi2fR8+k66mV8yjyGK+N1fDMMySkWCjWsLZKUN3yyhqyXOw+OTGBHGf5en6IsxfJ+/EXNzbx\n5o/+PwCA6q3D4Pds7e1A8X3LZXNY3aI5MTYxFH5cW13Fp9dIA82wXLz6myRiefb8SUSphUmSwOVn\nOGpmKiUmGIah9cekHK4l2UwW6Kb7XKI9CsuVHGz2IrQcV7PSLMvU03t2poIyi1y7VgivT/AowWf0\nYLY2NhGl1mcqQARCDf7sz/4j7twhHSvbsjE7uwgA8LwQP3/7bQDA6199/pF99H0P26yhtzg5AxGn\nhJcEkvetIPRgmEPNKRWlqEQCydlMYRrY2CSY0nYkxsfq+u+zC5TBNGwLPsOvnaiDHu/lgQCcPD1r\npdTQvw8CSqSCwUrDml4QIIxHR6W+1GCq5iqU8qwwazlIGMDcbXnwmL5YzdtYrFPKebpaRsxp8Van\nD5+FzaTpowA2fUQP2SwFHZmcQrZHXer1FMAKvOfrNt54kha6UzMz2DmkVOL/+86H+HyDUt2N/QbW\n12iRf3q6hDqLk0XBBBqHo7OklIq1CODA68NNFzXT0hOmG3hIs42JSpBRqaFmRj/gQjkHyYFS4EdQ\n6c4tDEQq9ZVKCKMBs9NTDTIpEKeGjpHU7CAjNICAJnkc2dqI1DJtFEujSZ9TOvrvsvlMeygkt7O5\nhdu3aQLevfM5tpnF0R30U8FumFLC4aBpemYcDtd7bW7uaIaH5djIcS1SqZDDgGs8bCePfp/gjdmZ\nGZRLpeG9f4yF+r/URJKg26DrWb7bQNDnPhYN+AyZGoah1XoPDwNYNgXEsQICHneWbWjRPdd10OYa\nq/fe+4UWj3vq4tOQBkOTgQCXEiAILS3t0O0fwGUq8exCHrsNGu+B30XA9XCmTNA6HN1cFcBD0HqB\noXKlJFossLgwMYkBwxu5Qh4xQzjVyQoOV2muOJZENUMLmuXmYTODaOD14WZSqNmEzwvj6voeLE7B\nN2o2+gGNgfevb6DPfogvXLqESomCss9vXsX5Sy8DAEq1MuIUuRjhMb/zN3+NQpHGxsTkJOo1us6x\n8XEUy7TJZTI5ZPPUd9NxMTVD7KBvvxDg0kliK+03d7DdWQYAhOEGnvsqwUUfbixhTtFG/+KFMYSV\nRQDA75yehWLWWRxHaDElP+NYetz2+gPt2RdHQ/gPQmB2YrSAUcWJppvv7B1gl8UzTWkik6pWZy3t\n2Tc7PY5dDojeudrB/HP0nkrRh1tn9e5yCcUiq7THW5id4s3ZsLC1wxtUP9FMYDmIMVUn6HBtkMMn\n6zQnwkhg0OXDYCL1/JZCYBCMzpC6sbqKH//kxwCA117+OmRE31OtlNHwqK6nUMljbIbu2crqGhZZ\n7DaTz2NjhzbwYnUctzfo+j+4+hFaawQLZh0TPTYOH/S6ODE+yTdX4WCXvn9rew/3mb6vMECzx3vG\ngYcMq+E//fxZZGqp4j8eyydTGsOaqTiOIFQq6ROjxlD5xEQd167RmiqEA4eFhC985Rzm5ukQ4lgC\n0+MULExOTOL2LYIyq1kXpxaq/D1lrG/SQcXvBwgG9Lubm4dosgxKPl/A0s11vp8rmJqi79/eOcCJ\nMxSQ/uTNv8Vzzz89ch8TQMvTTE1PY3t9i19PaTgyUqGGOzudLuKQ7qfrDCVQLVPC5YLWSPkYMCTX\nHfTg8bp72Gwgz2xKKSQES2tEoQ+vS5BfHEWaFT3o9TXjFoaBHK+Fzzz3LCq10Q9vxzDfcTtux+24\nHbfjdtyO26/RvtTM1O8/exLPniNJ/6xt4sEapVH/8hfX0WKPuYvjJfzms1Tktjgzhs19es/Vuw+w\ncUin5NAQiNhJPEr6MASdhmw3RIaLasNQwOdMxt6BxM4epbdPTU9gskqp4vlSBqtL9J1rG+uIm3QS\nOTtVw5l5ymTNTU3iyZPNkfuYQCHLkfPO1gEs1gCBbcLkjJVlSO1bhTiGQCpK2IdTpkjYzmRhsvCb\nAamzMp3eQPvGhaEBPxUNFFK71kshodKC+4TEAgHOJwk6FfqDCA3OvgymfJRLo7GkVJJoOC9JSDsK\nADrNJraZTbS2uoFl1idaWVtHn4vLZ6bGMD9FGYFG4xAdzkDNTFRQKdAzmamX0O3S84xiBclYcBzF\nGHj0/vm5eSywZlOxWEAcp/0+ahly5GT4eIRMrK1sYGeVzhnthgeTIVZv0IPyaRzVywW0kDK/OpCc\nieh7ffgBnchJayglDkgUCikrsIsP3qMUebmYR+FJKggNghC2k9Gf7XLR9v7BIcoVOqHa+Vnkymwn\n1Bug2aDT5P7eHowRoVqAiR18v6JYoVKjwtup2QWsL9HJ9eqnt+CrIWEkTcEnUmF8hk6rvn+IFk1d\nhLEFh4twHdPQLL9iycFnt2ls3L77Ob56+SkAwMZOH5/do3Fy9f4yJidpbPRUDoGik/f29h7u3CBY\npVBbhMGis6MoMXU6Pezu0JxeundPF6hOTk5iYpKyKZVKFaVKha+zjJAtMkq2ifmzJAh4bTXCxh4J\nAgozwekqXdvetSUssg6TkxiYPk/9yhTHh559KkbEujVkb8PZ5jBAzASJOIoRx0PRwFEh9/FKCX22\nUXHdBKU8fa7faaHMdhoqVOj26HcerG7i9grBN+WshROMCj93YoAsCPqpL2RQr1AGr+sdQHipjU4I\nadI1OiUHSfp3KbHHrLflnTZWuIg5l88jmzIvLYVOkFpjhY8Fuu8HH+GJV2hMtczrCDjj/uTXM1A5\nzrAkNhafonX2YL+Ok7PEcNzY38Eyi7/KnT3YJ74OACgWSygy3JMzbQSc3Ziq1FDN0zXv7e6g1aF7\n0vc9rKxRf0s1gel5ev6R8rG/Q+P6oHcDcorLKbwAklPwMzj5yD5KeVRnLNYi0YZpIJej66mPVRGx\njVQhn8HCAo1fz+siDGkCTk5N4/U3CHLLZspwWJR1djYHkSI5cYLmIfu51mpweL3J54sYMJR59dMr\nGkWZnp7GzRukF/bCiy+jXGOIe/w5nD136pF9SxuVL/Be0e2gzWUCtaCOAhOwMrA0JCehYDO07vsD\nZBmiHwx6yOSYmBMbeOfdX9IPCIXDFu3Td37yY5w8SftD1spi0Kf7dtBuYpdFf7utFjyOOWzDxMkz\nBO9feu45fPgxMcWzhTy+9XvfG7mPX2owdXaihnmuCTpoNLHEi/bO9q6uw8kIAxWehEYSI/Do5ioV\nIeHUaSioMh8AktiESNikMIwQ9FmAre9jwDTn9S0fK3VaqJ+5oFDM0IdPjJfwUUgPdfXuVWyzOupk\nWaL8298EACxMT2Nxoj5yH6UBzRyLokC/9n0fXU7rZxxL+w5JCGyxnMPa1gYuXCYj14mFE1BsbJlz\nHeQLXFeTlWhzzVe5UkE/5HqVIIKtxcaSIwt4okucVBgj5o0+SAR6vCORaviI2HAyrHwzpKmZSj//\n2Vu4cpVrCRwXGX6GtVoF82wAOlbOw+T7Ucq5GGe16bxrodOkiSCkQJbrzIIwRMQbTj+MUBujDfw3\nv/kNDUtFQXRE8O5IzHSUwXcEmhyl7W51wUQemLKALCt/iyRGq0GbUaFQQF7j75FW2e10uzq48wN/\nqLZ/hLFjSVKOBoDVlWXMzi3SfShWINJ6MV/g4IAW88CXmJ6kjdqPFhGskDhdp7MMAYbNsyVMTo2P\n3McEUqunS5EgVmndQogS92tlcwd3HxDL7+Cwjbk5gkAO9g5Rq9Czaw4MLN9h0VEri7UtCl6C3gAx\ni152/AStDt2TZjPE8grT2/uH6HL93+UXX8D3//B/BgBUilUsfPoJAODuZ1fR7zCLKejD4Jo1jKBM\n/Pq3/gEOdwlO2NrcQJPH2MraJpaXqV+lYhFl9jMrl4sYMG3cWtvG5RMU5C6v3USvQ9ecy2fRukPw\nycm4B8GBZOQW0BuwIneni0jLKgwN14FEB1OxUrp+SqlEG7Z6vq8h0Ue1+ckSNhsMY+UNZPIpmzeB\nxyzizY6PDjMmpUowMU19/fz+Ad66xVDznA1s0jq72thAcJbGuy1dCDZVD5SHPgcdAxkj4kC834+w\nvU+/tbsrUeWA1Q8DeFyWEYYRBqnJrTBgm6MZOQPAbzz/NfRSgePQQ6tF1wkTMLnEwfXGdR3W2XMK\nB+v0HA4aB4iTVIW/iV6LyjVCq6g35HHXRqtD97vZPsDNO/Rb3SBGl5llKg4R+vSgXbuES08To3Rm\nvIsVPiRsL++jXKL1KYwkZLq1jkCwHQx6Q0cECMSKgiA/iLSSvZuRcFigd35uHBtrVMd77uxvIPD4\nsK/6mJ+jeVksVGEaFFC4rsJP3/w59XFsDhVmrPZ6XXgejfe1tTXcZaa1YRioMwvy6tWr2gT4O9/9\nNian6PlOTc1idZUC1fO8fv19zTINNDnYuXL1Ey0S3O50Mb9AwW9jfwuzM6mkhILHh2eqdaRn1Gju\no8dBZahCyFT2QIVo92lffPvdX+KDT4hhihgIfL6fUYiAa6lkkqDEB7/LT17Eq6+/BgC4t7yMv/rR\nDwEAiSnxje/8Nt2TzKPrGI9hvuN23I7bcTtux+24Hbdfo32pmanxvAuDoZG9xiHe/5RO2CtLqygz\nAw7hHJzUP0cKGHzCcwzAYTuDOIkBlsePZPRQSJimSOPI10WgB+0ONnZY06jVR5FF3WbHq6hxajwZ\n7GOP05/vva/wyiU6fUyO1VF8LGsAoNNJU+82alzA1uz3cMg2Ke1OHz6zE1zHRYc9pswkxO4asd1K\n5bJO/cZeFwXO6C3MjUMKgiC313cg2a/J6/XQOaBTqoj6OjMVRbFOY/ueB59ZYbGRQ/6Z1wAASgXw\nwt5I/UsSBZEKoCqlPbEuXr6EmROLAIBKrQaX2VL37t7C1gZlIIMowIBP3e12H1U+WTaDAG2G9qQQ\nsJnNEsYRAs5cWG4eL7xIzJlqrapd1qV4WDhUF6AfzVI9DsYHIPAcpBaCmUwR1SpBbOWii26LskW7\nu7vIpCKcrquLy1MROQCwLAs51o3KZrM6Y9U4PESRM2ulUgkDj60wTIEg5FO4F2CNCRH5UgF5n75H\nKRf9buoZ5mlR21I5B9cdjQU2bMymFAJNhmqW7i+hw3YczXYHzVaHv7+MvX0qtv78boTdEs2nft/X\nPnSxbGOH2WVzUwvwI3q+N+6u4d4qnUqDUGGZi9cLeQdTZ4hB+/Xv/gGeePplfWUTM3RanT95BjZn\npEmocHTdl/rEBKplenanTp0dFrd2u2ge0In/YH8Xuywoeu/eHSBgzbJ+gk8+J3jj3urnkFx87w88\n3L5BhcA5MwE4a5nr9lBI7XaKRcBKi4ilHn6WZWsPyoetmMj7DgAGvq+FWB/VTCNEbYz65+ZshF1a\nEwuFMuaqtJ7GOw0EOSbTeIdYPE22RCvbPdzZoue8381gsUi/ubkfYN+gNWtqqgyHs4KHrQCCCQX9\nDtDhEoEgjHD6FGV5PJHD1XWaB0rEUDx3/TiCwbpIEgLmY8DRWf88JEOEeQjkDBqPR4VNG90BChGN\n/bG8xLVthtArJS0M7Lca2FmntbWweFETYXY6uwg5c9Fs7KFQJQQjjKEJAgbIGxYATsyfw0uXfwsA\n0D7Zwg2bxki8mYOYJvaqkQwL9EdA+dAfdGDxemmaJiweI0EQYYdhxGzWxAn2bV1dvoWLTxM5Iusm\nCDgD7PU6aPE65Pc7uHOL4fFiEWMsDByGgWYYb29vYYcL9D/99FOcPHmSfzfAxwx1SSnxj/7bfwQA\nOH/+nM5Ubqxv4//6V/8WAPD1f/3CI/tomZb2ZW0028gyvHjQbMJjb8LlOzehnqFscH1sDIKzz4NB\nFz1GnNY2VvD2h+/TZxsHek3t9TtYWSaSQG8wgJdqcsVquCdIAYPnpZEANiMg0zNTGJ+kPfWHP/kx\nBlyWcuv2LXz0Af3WV1/9jUf28UsNpnIZR0sRNJr7WF4iMbDD7W04LGiYqB4sK2V+mBo3TSwTXswy\nAz4QpvUGSBAytXLQj+B5TN9VEhHDGFHoYe+QsNKNjQ1MTVDuNVuqYGKKBmi1No69Zdr0l7ab+PEV\nWjBvHkZoMT/8n/3B7CP7GMWRNmvc3tlGqUqDOFcua9+hxuEBfE4h9wcDrYx99tQ87t4jCKHXOIDF\ndQ/9XheZHPssiRgXztEGVM1kcOcBpa6tehnvfEip3IOtu5AGD6YjirqEg9F9y9dOwuGnv7axAtud\nfGTf6CuGhlEiSTQTY3p6GrNzs3yNw/dn3KcxNU3pb2/Qx4DZTM1WG0UONJaXPsfuPi0ahjC0OKft\nOJhdWAQAXHz6GZw+Q0yrMAh1fdRDpVHii4rsDLE9ZjDV7wUIWU5ikPSwu8eeWKKMrDtcxA8PKbDK\n5XK6/iUMj6SSpdTMTtM0NZPLtm2c4IUrl89qgTk/6CGM6bkN+j66A3q2u61beLDF32PZsJm9Wp8w\noGIW3YsNPe5GaRKAkZoSxyG2VmjsHzZauHmL0v1uPo9nv0oL5UGjjcYhwXm7Bx2k2rimYetarYmZ\nBZx9noKgB59dx81brHoedrG0SYHYWG0MtTLDSNkcXnj12wCAJy6/hIAPAEIBYPHBqflTQCqaa9hD\nyYsRiqaiwEMSp2KYtt6Aa/VJOOfpsBSrCH1m+Ozv7WBzmeQrtj+9ibai9Wbu3AUcbtKmGfQCHLJo\np5t3wGVEkEmI5599ia/5jBbcVVAw+HAQhTEE3yulFALerKM4RuinMF+gGYuPamESYp5rPfpeD7ce\n0DPc7nVxapJ+f/bcaWxxkHXr+gMMDFa/FsBOm57/0paHJxYpKNvZk/irP6dxd+5CFgsGbc7zVaA7\n4MPpbgiXD7zTc1nYRbrHra7AldWUzi41XD9Q0XD9ikNYxugwX7U+pkUyhRBI1KR+nTZh7mD+JK09\nTi6D//SffwQAMOplVLkWsNttIezTAUAkCcp8QGps7cPKUL/eeOYNHPLh4cqn17WXoxIKNgeSTzx1\nAWd47ARhBBXT87z+wYeolgkaq0yOIUpGp9RblqEDDSFNKN4jiZ1Mg2F8YgyfsSRDOZ9Fnv0/oTyM\nj9G+8tF7H2N2jvazjO2gwixnx62CUU14Xhef36MxfufWPWxt8rM+d07XK33y8cdaOPY73/kOvvvd\n79L3OA48Dl5+8c4HuH9vdJa7YUidlIgHgZZwEKbUvorVWhlbrEC/sraMuQWCKYMw1PWyP/3bn+I/\nvfk31JfA1zI6YeTpdV6axnDdNSgUBoBEJDBk6oJgYJy9W6dnphEw6351bQ0OJ1u2drbxL/7lvwQA\n/NkIwdQxzHfcjttxO27H7bgdt+P2a7QvNTNlOgYCZst4Xg+Rz/AGAm1DYIkERmqzkAiYBkWJibLh\nczq20zU0hGDCRhhQRN3p9NFo0Pf3+33tz1PIFeBxoe6bV+/hdodiyL12B3fXWWNk8isol4lFaLpZ\nvLnKbLvNJSg+Kv6zP3h0H5WUGJ+lU9LG9jru3qFC2snJeUxNk/5JpVzDYYP6rvwOJGdT/BA6M6Gi\nAXJ5yugIz0Dfo0d15+4yGgfU33o+B4cj8EKpgOdfeg0A8N4vAvQ7DPkJoS01LMuCadFpZerEV7QW\n1ebmNvL50SCi5AvWP2kmKFbxkRNkon8zny8gzyw2QwAes4CUAjLMVEmkQJ89pXw/wt4eQTDzEzN4\n9fVvAADG6mNHUl7DovoER+GSo3Afhtf5GFYyANn3+HwSsnMe+n26ttXVLvLMKnFcBzGnnvcOD3VB\nPAwTKkyzVAod1mnqNLo6czQxXtfCjN1OB/XxtHBcossQ29raErb3lgEAbglwGCZp9gfo7dMJe3y8\njFoqYpkIdJmdMlJLFFLDq9s3ruHaB8RWC8IY5y89AwB49bXX8MtfvEvv+fweTp+lzKDt2phhi4+5\n2UUUinTKP33hPLJcmP5//PP/Ffdv3Kb7KQOcPH8RAPDNb/wWBgPqY6k2josvvMLfWUjdJiCQaKgx\nDHzk+FidSPkrvSD/i12MAsRx6pGntG1PGMWQPKfdTB4lZq/NnziDCRYB/Mn9VRSq1JesG6OzSxCR\nlRXYEpQNLFoxTJ/GQBwF8Fr0XKxc9ciQSzSzT5qJzmAmSmm/zSQIqXQB7Bs4YlajPj+Jr1w8DwD4\nsz/9EVREa6VyE9zhDMtMycPyAzrtN5p9HLD9jCGzKJpptlOiUKd1YfXdA7x5jTIOny5l8EffoTla\n8Hzsb9GcrpWBKme+ClYWu6xL5okOqhUWtfWU7quZACF7Ni5MT0A+RgH6g/A/68y6kbiwZfpZU9+m\nXuDCT8sBpncwd46ueeXuGgT7OiZJjBwzL5XXxeQC/X18MouzT9G6/9xXX8Jf/Nt3AADhhyEm59ie\nJzbQaTHBJFnFevAz/k4Je5qup+k18NF7lDE59dIUDNacOo9HbxqGkIh4LqowQqVGv3v61AnMzdM8\nsywD1SIx9UrFPBbYK9C0DCwtUSZ5ZeMuLj1LRJVKeRztdqrnJbC+ScXitz67gc9uESLkGDa++hwR\nnu4treIzzkhXqhN4lTMx3/uHv6etrxQUdlgc9c0f/xS2HL2swDANnfEplIow2dpp4Pu65OGp08/j\n8zuUNXv/o48xu0DM/3ypgN1Dyrq+9cu30WD7MMM0IDV7P4TFWmYxFOp1gu2qpfIQCvQGWggbocLs\nbIpKVXHrNv3u1s4OrAxlsA+bzceyk/lSg6m3bm7ojt1a76Ar6SHFloGOog339paHN6/SjTNNA4ec\nmm35EUKZmhWHSAWMx4s1NA+ow41+gF6DhbgGPSSshh4JYMCT8H5HYbDGgQwkBQcARG4KVp43ZQCB\nNjqG9hQapQnDxeIpWuAKuRwO9iio2d85wA7XZtTq0yhWaPHqNHyA639sN6sx3f39HcCiRTtSRYQh\nPWDDBXYO2DNst4GYpRFMq4cSs90uvfwNtBv0W0kCrahrmiZsgxYa6eQQpD59QsFngb1Htl8RSA07\nn5q1SjzEntOMNmiDXyGlfsv58+e1ivm7v/wQDtehvPzKK6hyDQOJoaaJ1C947aVsqb/7k/znx9NG\n6PU9vQmn6XcA8LwIIbM/ZUfqmqmcbSPgZ6gSkjgAiIZsclBZq1SxuLjA3xkhw6y0uflFraq+urqO\njS0a+16wj55PMGKmWtXm1tlCHgHXGHRafYRca2PZEoH/GKKdQmCfKYs//MEPYPM1f+Wpi/jaNyiA\nvXXzJq4wtDAxOYn/5o//GAAwe+oMcllaAEnwkgUqVYSQr+3MqbNordLifNjfx3NfpaDpjd/9fQgj\n9VgMtTCpTBLNmPIDHz0WuiwWS7B4/MZJrKU4RmmtZgsuq2FLaSAMWPFYymHNTRwj4r4LIWC5rJJu\nm4gZy2we7Gm4xcraiLjcQCaRNpMNEmD7HtWZXHj5tzREJKQ8YhigoPR4GtZSJYYBjRcmBkRyZCD/\nPS1fr+kxf3jgwwZde9Nros2Mp/XNA+RYZmKymIewGBbxEtRzdO2nzpZhFOnvrdCAAK3LM1MJzsyw\n31lT4ux5VpMfi6DAxt59ICe55mhgwmXRVsQ+PO5guZCHYAi3YNnwHmM9PdjZ1aUERmLDEOlYELAF\n/VbYKcLrUhlHZdrQtZu3PrkKKWmtfP6Fy6hO0PzbbGzj2Vfo9eTkKRi8yXf9AwgnFdwFvvYGBRqF\nYh5/8ad0qPjgvZuYOT9kf/p8yD315Bw6OzSf1u93UAYfkEqP7mPo93XN1IsvPIOxMfpQkkRQEa3L\nFy8/jzBKEwUdnDp9mt+ToM6+e4kMUOUa3Xfefhf9Hj2XXr+Bd1lCYHt7B2e5XKJWKuPjj+iwv7G9\nh6kZuicvv/IyvvGbrwEApqdn8Kf/9/8DAGg0Gvj8czI6PtzvwbbKj+4ct3a7hTcZnrt78zampoi1\nNzk9q+G/fmMbOxsU9HV7XS2emS8WcXDlKl3//h4sW9tN6CVfCKHlHEzLwjSXlqggwPo2lxvw/QOA\n2A/xYIVqrKBiLG3QAWIQeEMG+WCAOpcfjdKOYb7jdtyO23E7bsftuB23X6N9qZmpf/4XPwOMtEA8\nQjRHonjuJNBnkcmfbg/w1g+JjSFggJE6TNfLKGU4q+EP4LXpFHDnpzex8YBS8Pt7e/C4Et+wTBQn\nKUr3J6qQBYLGYrMOhyX6EwkMi6mVTsdDJBBCYw5I1GOk+sw8jCz1cXLGQYUL0OvjO1hbY4G35i4q\nNUpDZnIO/A79brFY1PpMg34PfRYRjdUAcUSnoWKmhEqNvtNxTJ22bDXbOGjS6Uw4FgpjFPlLMRSE\nU4mCxbnxWPUR9LnoMYlhxiNqaalEJ4K+mJkSaQbqCx8RR+A5XQyoFCRnKHK5Ajodej6XLl1GhaGr\nifH6kW8bFpcL8XDmCUe//le2x9OZKhYK6LMSpUqUzoxEQYSI2Yjp94cvAAAgAElEQVRBMITUarUa\nGqwbFYXRMEMnhH6epmUi4RN5tVrR7uv5bBkPmIVy4+ZVTM7RqXT8RA1+SBkFJWL0WW/ND4bwkAoM\n3LxNmaxOuzeyJRB9OMKAWadhv4tpZiL9zn/1e9g7pGLxt99+C1XWZ/uD//r7uPD0Jfqs7cJIMzJx\nrOdHIhQchi/L1bIWyZSuQoWFMWNIhOwBdnDQRIUF+1QUImII2A8CZDg7aRgmvAHfa5lo3aCMW3xk\nF69eeR8T4zQPqtUayjwX3YyLATOgYJk6ayLjECYXVlerNfgdup6GDXCCCwO3ikrAWaqwrwU2pUqw\ns07PAnEAgzOwkUygUpsnZcJIMzdKQTLLFtLQhemGEUOFo51xl5Z20DpgiM0L0ONMWpyESHkSWWmi\nwpZGlbKLcRbNbR/0kDU585kLMWDrqr1OBOFQ/06OKxRDyhDmp7MozTLTzS/odSdy93CdtZkO21VM\nTtJJPkpCbfWhlEKirXN8eIPRM6iZ9hN6z4ijCDItLAZ08XG376PXYZuswxJufbDP/TJRrdFany/m\noUIWLLU9FAQRK2Q3AzHgzL2UmCizmGT9NiZKxJg7eXYBWy/R/Hv7nWv48K9p3rzx2nmoHsO8agCL\nBWXVoQPBJRqjsPmiuIdTzGqdmR3D53eJ/HTjxg2d/d7d3cOTT1Hh+9TUhNaE8jwPc1x0/rvf/X1s\nsZ9gq9XG+++R1tL6+gqmp+k9L774CtZZUPmzG+/B56xsuVbDS7/xIgDge7/7DzDDFjJ/+Zd/iX//\n7/4dAMrUGAzPVSuTMI3R9NAA4O7dO/jZz34KANhY3dAEjUyuAIeJCui1IThze/r8Bdjsgbl/2MBt\nzoj5YYgcMwEhoNdUYds6c+c6rmZ7B90eYu5jNuNqezolJPq8rtxfWsKDbbpvTr6AIC1FEdDMvlHa\nlxpMBVEIxUwYCMDlyWAYEn2OV8JEafNFIRKEHNQ0ul1ELC3g7azh6ls/AQD43UMUcqmBqYnQo8+q\n7gAH9+j93YNDmOzLZdRijJdoYCWmrWE+iBQO4geUBlNIkMSjDxrLLkKxYraQDgyLIBzLdeGw2evG\nxgbaXfbRSpRO8Tquq2GMOI7RZqkDy6qhUGImROBjf5cZfDKC5OsMe10E/ODjOEaJldRdx4Xnp+wy\nASaloNMdoNeiRcEyLJgjJinjROn7dBSO+CKT7lfVMSVHAlPDMDSbaWNzE3lO6c4tLBwpdRrqG6iH\nkL2Hf+fRdTSP69cntDyHUgoGp5Xr9RIGA1pUez3o5+Z5nvbjU4nSEFKilGYvev2B7q9pnhx62A0i\nlIoUaPzOd76NvQ6J8TXC+8iVGNKIDChefISQ8DmdHfgRXIOCr8NBhNgZvRYljiIw4oNBex+KFeVz\n41PYZXE9xzIwP0c1Gxef/yqETYuYiiNdE2dIqRm6cRhBMWSZqZUwAPU3jELk8rTRWIaEz/NYRAFc\nZu4GQaDFLaNIQfK9TYSAwRu9SqIjMNmjW7u1j1aD+mLbLqp8CDl1+gym56m/Tiaj5VRUODxQRUkM\nx6bnWM8WEDOsuRzbmK+w4fpBC1EqeyBNBAxN9gcDODZLWQQR4vR5hV2k0g5R4CPmeqsgCHW926Df\nx4ClUs5f+vt34qXPtvFpmzaZfhAiz8+nXiijxLVLrjSgBgx/2CSPAgDFQhauaPHFG9hr0vhaXvFg\nWwx1yRxyRVpHzLxCe0D3o78L1CZoflxbS/CTd1lWxSljkr00m4NoyEqMEsh04YkUzMcwjw3KtyCS\nIbyvXyIBeGOXUQYZLlls7wLji/T3F393AWGPgrt+x8ell1kMWmZgmRT4hpalvUuFBBaeovtW/WgC\nXYsOOaqa4IXv0thpJ+O4doWg77MvGBirsKOH7MPJcY3SngdkV0fuo5ACe+wDuL6+hjLLebz++usY\ncNB6eHCIG6xE/ilLCgF0AL9yhf7tez7u3KEgq9Ppoci+lC+88CIClgr46MOPNQvZtjIYZyeAV994\nDa8ytDc+OYYf/YgYkX/yJ3+i19dCoagFPw1pP1QC8ahWKVdQ4tKW1eXVoU+t7+nD6ve++Qbu8fV/\n9MkVfHT9OgDACyO95hmOPVz+EwXFe7OUgMVrpCUlKnwPY8MaIuimgb0DOih6QQTPoHGedTNaNDUI\nA82+tQ0DX3/t1ZH7eAzzHbfjdtyO23E7bsftuP0a7UvNTEXh0BXaNgWqLrM9wg7iAZ3Y+pEJwcXi\n0jS0doSvBvBblL7dv3sDhysE7U2dWMTlF54DAJQqJWxwCnPjwQr2NojFErbbUHyksTN1BKmmhzoi\n6JUMUx9JkujXAgp4DJjPtvMI0+JTw0ZiOdwXC2WT0pZutowNLrTbXL4PxWm5RqOBMEyLwgV6HT5V\nWwJln05YpVpee/Ntrixh0KPU9d7WOrwevd+yHFx8moonDVVAzOyHvu9jhzMrQWLi5HmCbXw/QTKi\nkF6SJEg02zI5ks1L/s77gIf1YOIEMBke8nwfq6t0eqvWahgbp8xhFIW6oNyUkjzk6NegIUKIISSb\nJEcST+IhTak0e0KXMnrhcqGQR5s97xKl0GePPJGYiPj5IFGI+XUEDDVdMi58hjcO9g9h8GmpVqth\nkdkpzz37jLbAGfQHOHmWtILMnIc7H/4CAHDY34Vosp1QJodyieEZ5UGxF1du3ESZsy0XLp3AAZ84\nR2nEMaMbZzsOxjmtryAwy/ouTz/7PD54/wPqYxzrZyekpa8/jmPEUer9KJEWo9cnJtFNoSA/0BZE\ncZzo6VQoFPUzMi0LHR6nvWYDk3OkV+XaEn6XsilBECIQoy9ZtmFB2qmuU4g+Z3xuf3Ydu1x8v3ji\nJOaYfVuv1eCF9Kxv3v4MpSLdk/HExc6Ash0t20HNolNyYpkIOdsohQnB0O/9m5/ALtF8FYGP2Es1\n5ZpIEoaFghAJn7YHA09nC3Z3txErmqPf+f4//nv71znoIs+lD5Nz4+gwuzSTdVHMc2YvjpDwe6Sw\nqNgdwPrmLiYrDJkWprC9Q/Oj28mgyLpLm02J2032RFvpQUW01pyaDdGLKBX08R0TzZCyOaY6wGGT\ntfFUjA7DJbYwkGGGVLZURLc/ItkFQCh3kKjUJscellwkCcB6Va49BRnT86lPR3j9NGVbcsUQrW3K\nUOxthjBLNKelMRz7MA2olO1su7Da9P6xyRyqswzx5HeQz9D9+dpvL8Brc8bK9JAp0BqQNxK4k/Q9\ncsyEaYzeR5G4aDXpt372s/eQZ2JAsZjXQpqO6+Ctt94CAOzv7+p11bFt5HL0LGZmFjA/T++3bQsd\n9m28ffsuNtk3NYoiTLG24tmzX8G5C0SW+trrr+D0efrsyv1l/OAHP+DbnGgWcjabh2Wl7HrovWqU\nNr8whxe/SjBi0Pext0d7eaPbhcOZfMt24LK+VRCF+neFaUFxfy3b0uQOKaQeD1JAW3qFQagz0rLv\nobFH2ahYAhHDgo7jwOCsOI6iG0Ig5HlZqpRx+fLlkfv4pQZTsRoGJkaSICtZBiBsIOFxqwIHGS5Q\nqBXzGvtE1MHAowcQx11k8vQAnrr4Fbz0Cikn58sVXL9GqcE7n91D4B1h6TDO6mQLSJguC5U8XIdz\ntPhGHDEnDUdPS0vDgkwHXGwALPxnShfSoE7aThGOTYGVCAJNNx30+5rtmEBoDBiJh8Y+QXtb2yt4\n5ZU3AAD1cgVLe/TZw51NJKxibkiBK+/SRBJSaIpyFMXa98ss1FGfpbodO1dBMILXGd2PRBsnJ0JA\ncbAjk4eTnGmdFkkzpMKJBvo92nB2d3Y0U6JaHSqaCyE0a08lwyciEoVE/KpE6hGpc4GHEL2j1/A4\nbWqmhq0NSrtHodSakRkHqJYJkuv1uhrCC48owRfyWUwWKMAxDYkMs8Pm5hcwP0vB1JlzX8EBb+ZK\nRbAZTtrvbKDZo0212w61ybMKBuh1KPguFAvI5Oi3PM+DzWJ8TtZG+Ygq9KNarIbj3XJcZBmGi5TQ\nzgEvvPIGbn9OcMj1T6/jFRYlDMPhPTWk1JIlCYA4oTFbqY2jzuzSlaW7CNhMszuI0WYRyVw2g1aL\n2FaDflcHFONTMxggZVOaiFgepdHtIJsfnUFkGhZSAqjtuDAZrrVsC8GA5sfq/Vs4ZMZtvT6OPAcS\n+VIef/0xMZ1O2wLXDmnu1hbryAgKKkLHhs/Bl2HaAM/Xxu13kOH7SX5qQyg+4cFkmjZMDgbCXhdZ\nDpCzYRPtzmgb8eUnKnjqDNVePmhLLG+ywOZ4HfUKlz64BrJ5eu0FCs0D2mQSfx9jZQpAjGgcSyt0\n74VlIqNNuxXev05r7kzNwckFGl8tR+LDz/iAYU1idor6vbGxjY0NUrc/c+4cZlnyIwoifZ8213cQ\nx6NvwtnSJPrMUjWkBclMSiORkDYHWa6DkJ0H2octjLOvXLaEIbS+Bzgm1/DJI6UKQiC2aJ4FgyIO\nt+m3XvqdMcwu0vcoBQQsrDs54+Lr3yaZD9fOw3Z4c44N2OBanoyAYY4O+iQJYHNZgddpYYv9Lbe2\nFA4YlhJCaFHNudl5zMxQQDQxMcHedUDoJ7h/n0Sfl5buo8WwMxJTQ36zc7N45RVi1s4sLOKpp58E\nACwszqLTp8PGzPQ0nrpIEgvr6xs6gIqiREu3xFGshTFHababwVMX6b45loONLZpzV6/fwIMlSoy8\n/+GH6LCCuwJ0XWakEg3FSiE1tCcwLJ6lWla655efeQaTEzwvbtyC5O+JZYJ6lZ7pP/6jP9LlO//h\n3/8HROleEcfDWt58XpefjNKOYb7jdtyO23E7bsftuB23X6N9qZkpFSVI3Z+ViBCzR5AQMaSgyNw2\nTeTZ52SmmEGGCzktuwJ1gjIZrcUp3GMJ/fGxGkQqhBfGyDqU8ck4RbQSlrt3XVhVZs/VphCz/YwQ\nw4JTgaNwUTKEl5IYyYgu7gAo3yhTB3BT22IImYFhpBosHiQXqZ899xQMTmN3ux2kF2FIA2aaTbMy\nUJxSXV++j7e5zvGJy8/CydF9iGHDMNJsWohWh04ZUTTUZ5JSQrLQWhzGWF6hbMf82RKcEU/8yUNF\n4UrDPUopbfECMbwH9E92CG+1dPahWq3pQssoirQeZ5IMtZ2ouHz4lek/Hj4PPRq+exyhRwCYmsnj\n/Ffo5Pfg7g567bTIcQhBO26WfNf4+tPXvb6HQom9/EpV7O4ys2jvAK+/9k0AwImTF9Bh8cTQGyDD\np9Kw6SEO6VqdTAkOw7n9QQsZm3qtjBBZLoYeDBSarKuW8UOY1ujTOYiVZqKpREAwHOmHEfqcOcrn\nazh5hhhEH33wIc5foNNqpxdpa5xqtaozn47jQAm6ZtPOosJ+YCsPHmDg03tWN/bQbPAYKBXQbtD9\n2dvdgsFZPLM6jYg1p6QEei06qcvQR6n8GP6DUmprImmaGh4wjQRp4kCoGANmzT7YWULeoMzRqfnT\neOsOZeV+cOUaHIapnkUXVjC0rWjzfXPdHJKQnkXU2YbPHn+hZeviVsu2hy73caRHrmUIBCxkaggF\nc8QD/8svnIMpKIv1yf1lLEydAkD31WARZCkSdFhMtO8BJuuVPfvsGZydoT71ukXcvEnQVaO5j9oY\nZXDGJ8o4fZYgs/GyjUTQGrS25yFgTavd/Q3EirIJsQS6fcrstFstnD1FcLFIYly/TQy1IIjwOJB7\nXmRgpGsAgEhQFsyUEr5KMw5NWBWGxJUF7zAVmewi4mdimhJRQP113SOZKUNA+TSn95YTWHW6/okT\nU4iZLGUIAclsc2UkmF3g+XfgwM7SOM2bFqIj2a5kVE8gAELGGHiURSoULUxMEMRtGqb29iyXy6iy\niGwYhlrY+Nq1z3TxerfT01C8lBIZRj+K1RrOnSdm4re+/S1cvkTlHdlqBoUivScceHjvLdKi+usf\n/TWuXyNhz2w2q5mziYLOCsVxAKjH2Bch8do3af179sWX4A1ojfmPf/7n+Bf/+/9G77DHYTAbOE4S\nLWbdH/T13uIYBlI+WJxAZ8ciBZ15/tqrr+MP//iPAAB/8q/+T/ybf/Ov6f70+3jmCVIQ+OP/4X/S\na9gPf/I36N4hIkc5m9XesGNjYxh/DJ2pLzeY6oeQvLjFiQKzWVE3BVxOeSuYMNP0rXQwziJkQZJg\nZ5foi8urh7h+jar+r31wA2fP0YM/cfokIsZNN7dWEDMjKF+bhlOmzVGYWSQqFe86UieFYWCVJOrI\nBpzo2oaRmhAweOIJ/R8AR8T7IA0IDqZsw8DUCfr+61c/RKNJqdxyIYdKnWCSbKaMmMUZpybnUOD6\nnLYXwmaGoJstI+oOmXJMdIEjpN40TcuGzQrohcoEKgzb5NwsHGu0TSo+EjQJNbSdlUIg0Wnfoyrp\nAs0mLba9bhc1TsHnCwXN6BBiWMMgHgJbv7ggpb823G2Sh2QPHpZAeFwOX9oMQ2DxBG0i3WYfoUfX\n7/keen3e9ExD10NBGBCsrh1FSDU3cObMeVgWbcj93gDLD6hG7OKlF1Cs0ALl94ReME/MnsMn16hm\nqukdoMC1EMowYTJr0+t1EXDdm2W5CFhmwIALU43O5uv2BvDDlOVlw2Sosdv10OMaMX+gcIqDqY8/\n/CVu3fwMAODkJtDr0SbVafeR5TqH2bk5eDxOiY7MUB0MxFwH5+RKGOegCZGPSWYTzU1PQ9opJjdk\nLxoyQdFi8T5nTFPFR2mmZUNy8KASaEja7+7CUhzMBj529mkj8xOBSoH68vQzZ/HNlwkOaTbaUF2C\nWxZzJkyuiUuUgMHrVpzEsI/WHfIa0OsNtIirNI0haygINXNXCKGFKZVSmv7/qNb1JdZ2eBMulVGr\ncjBqSrSZkdls9jWEbjsFTLCha3WygB0OIq+8ewP3lmhttU0D01O05jqOia1t+p7QL6DPYy1IbL0W\nJ/BQm6LflYUx7G9R4La9u4vVNQrQXFtge49gbTdfwICZzKO0eTUFZOlehlGImNduO1tCnw/mftDT\nYp7GeBaHu/T3O1f20Nijutm5E08i69O9Lzg2oBgSV8DaEo2FkmmiWKHfig6AAs8JFwJBwgeeJIHJ\nDG2VuHBDGo8xIlgx/b1gOdqfc5Rm2y4CDtBdN4cLFyjwiaNYS/3s7Ozg+rVrAIBGszn0W+XPA4Dl\nOlC8/7m5LKamaW5dfOE5vPraawCAs2fPolyhPcO2pGayRglw+zNSAf/5z3+uyxNsO6PXYdt29FiK\nY6HFLUdpSSJQqlDdZ/p/APhf/sk/wfLKMgDgxscfIebfsmxbw3y/8cxl7al3+9ZtpLkNPwi0p57t\nOtrcuFCuoDZO4/y//6f/FC++Tn3f2trCs89SLbHpuMjznvfGt76Fd7g2tNEcjs3vf//7+sA/SjuG\n+Y7bcTtux+24Hbfjdtx+jfblZqYCH0hS+4UEHcWCYUVbV9D3PQWPvc067RgPtihd3vUidNm/LQjK\nqD35Gn1p0EKDi14PlzcQ8WcL82dgmCyY6OZ08bLXOtDeUNKydXGaMOQXxCjTfyiox8lMJeS1BJDW\nXMowSERCQiagKN1y+HQTCRRZYPPcU5dhmnQyah7uwOPCy6DbgMVFccVaHo6balG1YEh6PT5ZwaDF\nIbuKoThONmwHkk/PlpOB7dIp38kWUCjR6aNYyMCUo6Wl1RHITCQK6ccSyKE1iBwWBjYaDS3SV6/V\ntRu5iuOHtKiOnsWTX/HqoX8nYvh4jvxXHMmIiaPMvsdzk4Eh81hZJlii1WrD4oxJHKohk0QKxCp1\nehfo9SlblM1UceYMFfZPTEzD4VPj8oNVXQSazTnIsBdip+Vrt6L56bP45te/BwD42Tt/Ba/JAnaL\nT0LxGG+2ttFhiyVlGMgZ7B8nc5isTo7cx0ajgZjJAEEQocuZjCAcoM+nPV/FyBYIBjh15hRu3SAR\nwJe/9g3MnCZLikiZaHfoehqtJjIstBd5XbS7lL2yHRcWdzKKA0hOo9erdVipFpXXgs/MVBkb+P/Z\ne9MgO64rTey7N7e3v1d7FapQ2EGAICmSokQto2mNPBMtqWd6plvtnqVb47Yd7vB/O8I/bEeM//m3\nI8YTY49jHNGOjm47JqLtXqcXtVoUF3GHwAUkdqAKtdert+Z67/WPc/LmA0URD4aHv/JjBPHwkC8z\nb+Zdzj3fOd8Jcr2qIEDAwqdCuACPg2ngOh7cXPBRK2Ts1UgPN+Ar8jRJrVBjD1F/AAzzenVBBRfW\nTgIA/uHXv4bLL/0RAKAlNFwrmSQstRpniQ03GB714NXYizPhXdDG2ODWTCnOfqSMSOR0RZZNTUsn\nlSUknIxQb2rS4AElsvQ4kBcq4yB4IGhm2D6i0IdhFOKjd8mjnxzsQbg8FzRqONyn93b9w32scKmS\nuYUQqHAyTVDDkIPkn3zmOMBt3Xt3H0csHCpVgj6Lwo6RIeY5oFlvY57Zhmng+4UAY6AdZBkHJY9D\n1Nij2wxmYJged+BhZonux806eGWfrjU+dDB/iul3HSDiBKNbN/YQblF7Lz13Ch57lbWbwlPsLU8y\nZDktDIPhiDWSdu5jjb37c80FqJjpXy0x5XTKkFbEstvt4y/+nMquCAFLoWtd6BHmfwI0d6bsBQuq\nPp5/nupqPnnpEi5coEy9E+dP4+RpolzTJMGYKeXRILWClu9d/imuvEv6We1my66XQRDYjLkkTe3a\nVqlUMBg+QvkqFIlQ+X0DQKvVxHe++x0AwJ/8339gqcPBcIivfvWrAIB//W/+Vzi8Ln7/n/0GrvyU\nPGhSSlS5FJCRAk0WCZ702s3MzuCb7JUjr2/uGU5smbXvf/+fo8ZB/D/84Q/teP3e9773CO373I2p\nCIZ5bpNKDGLqrBuJRMSDIc0caMXZSonBIafRGiGhwVSU60Mw12vEMjTyl2Tg8irraoF0RIZYkowh\n2AUo0xhZnhUmpK0TJlzPGlnCcSdq6upHovm0VshJXWci+4zkFgpOPX+pwgtgOP5gbmkdHRbbHA26\nONinjIf7m3dwdEgu+TAeoj+mhcAcblvXoiOBWodVoY20z9nxfPisJl1vtlDlzJ5aq4X2DMW0NNpt\nkoCYAkZrm6ZKi0FusGg7IaRZhi7HRhljLLUXBIEdUJNZfmJC0txATLB2ZiKWakKGQRgrGDpZ6Phn\n6gaKCWPqEbC7O8D+LvUX3+tYii2JYltY03EcOyHU63WssLTA+SfO4PQpSrWvVpvIl36VKpxap1iI\nWuBjaY6Of/f1y5hpXefvL+Gpk+SG3ru1hwFnKD156pJ9Do5TFPtNkwQhG0QV17dc/zRIwggVmVcj\nkLh5ne5h8fzT6PG41JGCx/FrzZlj6HWJ2nn3jR/j+Fk24tcuYL/L8U29PawsU5/SaR8Rx0RWqgFc\nLkausgj9iM7fbNTgcFxYGGkINgYcz7OxEEobpDo3YF24ZnpnepoWAsAkxMsFqKMEITjTF66t81l3\nYgR5TKcGqrzReub8Exh/RAVwEccQQR4TKSzl7foSfa6ZORclkD7PGRrwmPKLVWI3IxLCinlqrRHn\nwqdKF9nGD8FRmCFJ6PqbW1vo1Kkh1YqHoErzYzwOkXIG4cLqMrqsr3ns2JOo1EmQ9fKP/h/4PA/q\nEFb42MgmNllh/TAMrQEiXRenliiWZPP+EY44y3p7a2jV3tszM9jbJ2Oq6gAiYumb8AhOZ/qs081s\nH0bn84SB4XeV6ghJn67rOBX4+U44y5ATLrGnIZi27WcbeO82V4hQGhkXlL/64SZWVmkTcqCaSHp5\nGz07n3iAjb1LkxQbt2hcSiGQZLxh1zUknJKuU9iwicoUbdTK2Ht2pDshBhzaLLZKpWJjfOg4Or5a\nq+H8E2QoXXzmGbzwAhVDvnDxoq0F6lc8W2D+4+vXrOzBxp27iPicw6Medu+ToV1tNCDysButEDDl\np5SxBghgHjDqpkG+5hlj7Ocsy/DlL78IAHj2+efxyss0zoJqFd/5+78EADh28hQMZ8ouHTuGK1fI\nmPI8z25o/aCC/+S3fgsAiMrjd6e0suEknudZcWXHcazRNTs7i9/+7d8GAPzmb/6mlXxoNBqfKvHz\nc9v3SE+jRIkSJUqUKFGixAP4fHWm0ggy90wJD5oz+PYiBzL3i4oiA864EtpSOwoSHLCMiVImWsLk\nga7SWK8JpIRkwUydRtYjYqAt3SaFAXQeLK7t7l9rZXdDMJp3O1O2UWmkTEd6rmuDlCdDo6WUhVdG\n+vBkIYZY4R1lrTWLec7qWD1+Hr0j8gqEoyNkCYshRon1dilTaIt6rg9vQiw0L+VRqTfgVnKBtCo8\nDqQU0oEnp/NMCTPhCYKwApuQ0lr6+/v79pj5hTn4nGVhHvjtz5zZ/iE+8VUOe6nJXz1wvonP4gHV\nMJiHbywstjd2MB7QLjNwK5bmdSRQ4R2n73nwWddpeXkZZ85QJtXc3AyOuE7f5r37tpbV6rEldDoc\neK1SrHAg8Pd+9VdwlQO7X3v5ZZvpZpDgSRbUE8ogSYukiTHrzURRhBoHZI+TCP1H6KeOFIBD93/6\nqedw+f2rAIDun/4NTp4h7ZnRILaCnEniwARE7X186yZu7VMV92eec6BYrHJuroXDHj03oVPUmQIZ\n9AfY2KFA4PjGHQxT6g+DgwSz83mdyUUsLtD9BL6CyEuOqAQ69y5JY7W3psF4HObsGVzXheS0sFQF\niLgOTMWXUKwhVPcAL2ONJy2sp3pmZh7znRrfzhBZWnQmn7OPpOfhUOW6YClGQ/JSGTjWYyghbIKM\nMYY9Etw/J3bAeWjAw3CwfRtpn64jhkOEhr0kjgOXtfr6/SMrIrs8DNBcoOed+g6CBRaabc1D8pzS\nkBoNpnY/2ugiUTTa+lGIWOehAynai+RBN4nE/jZlBc80O6g32asyCDFmDxdcF+mAPb3GoDdR1/Jh\n2Iw2bPa163hQKqdtE6TcR9LMwMkTDVRmE0A8+Jg9l5dwcjDmeoxxkliP5eqLi2i26N3eM4cQubPF\nOPY9SEfA5yxe4wP6OAfBVzxsV7m+ZXho32EQBNYpPjNFGxjpRogAACAASURBVCuVOvIs90wlOLZC\n7+j5Lz6HLRbb/Ou//ms89TSNy6XFJQRMb50/fx7Pv0Ci1SfOnIPL3iLf9/Haa68BAH769hvY5+y/\n/qCPV1+hrL1xGEHyLCkh4XMSjVYa2uTi0Z4tRaNVwbZQIs4jhL9MwBhj12MpJVZWyDP43/+Lf4E/\n5TI2CwsL+LVf+zW6bpZB8r1duPQU/vzPmAadqNd6bOUYfvP73wcArK+vW2+UdKQdo1mWWU+fnhTs\nRrGO1Ot1+1lr/Uieqc/VmKqOxnDZ5e1AWFelcQw0N9JIx7r4HQkomWcEFfEwxggbbwA9ubgK8FhD\n5jhITW5o+DB5gV8VQXKKr/EkHJYKkMYBkjybL7WZPzAK4hEyM7RWCLmAYua4NvtITeSmORPGlIGw\nBpeUfqH+Dh/gCWJmroNWm2gho2MYpgRMmlnDUBsgyfJJWBWLjnRtppbrV2zMiTAeHORZkylWj9Wn\nal+mMghdGKOC79FkBns7XCwy8LG4RBMCBKxquBSysJmEfID2nLTPrKEsPmFP5cauJfw+cZDQxUGs\n8Z1/epSgqXCQWokNYYDcs10JXFTYMPQ932axSaOwtUELyt7WfShtA8lQ4UXNcRy8wxNUkozRaFDs\nWqfVRqtB57n24QcYcZzJc889jRpPmGEUWeFYrbUVIry/uYkNVvlPksSm7//CL/+Dh7Zx7fgq9lmW\n4NRTz0JVyfBRmYHDdEWrFWDEC6KsViDbdMxzq6fheXTMcHDP1nhcWf4CKhx7EI5SPPU8ue+brQY2\nmLIe4DKW18go291JsLVHVHx7Zgm7LOC+utJAnTN6K1LAzzckWQatmTrkmKrPQpqm8L2JGCvuV1pW\nkLEho5LQLspZkuW6m5COC4+LFVcrFXSYEj/c7MMmcaIQZ9TCwON7hhGFCK1jKNYLtKCrOE8t10Vs\nh5Q2ozMIAptN+TAkYYiIqySEwwhjjn8JR2MszBMN1+0NMOZr3rq5j7UqGUEy2cdMnb7vHJvBnatE\nXRmlEczQs5lba2CPxWsD6UEn1L5avVCWJ4Obvm83WhizUPLeYRcVjs9Mkgwhf1/zJNJ4+ooSiUmR\ncexSajQkz49wFBw2jr2KzKdBJJmBw3PAbKWBRNP8Thr8uRq+svSQ1rqozfdAnKWGw3Oo67kUvgGa\nXVxWQ1cihuL5IA5jW1sy1QqYMmwCAPxKGz73HeFo/NPf+FUAwLe/8/fQ69H4+OJXvoJvcVbaTKeN\n/mhSMJja+Dd/+Sp29ylrcnamjbfefgMA8OMf/RVGIzK6syyz81at2igMeiNh2JJ0PAnBhpLvBahx\nmEiSZDZ0wvEE0kcQs56ElEWowmQc1ZdffNEKe/q+XzgKtLFG37e/+138u9/7vwAAve6RFYv+zne/\na4VMuUHFx4nr5uuulNJe23XdB2vIThhQjyL4XNJ8JUqUKFGiRIkSjwHxqIKGJUqUKFGiRIkSJQqU\nnqkSJUqUKFGiRInHQGlMlShRokSJEiVKPAZKY6pEiRIlSpQoUeIxUBpTJUqUKFGiRIkSj4HSmCpR\nokSJEiVKlHgMlMZUiRIlSpQoUaLEY6A0pkqUKFGiRIkSJR4DpTFVokSJEiVKlCjxGCiNqRIlSpQo\nUaJEicdAaUyVKFGiRIkSJUo8BkpjqkSJEiVKlChR4jFQGlMlSpQoUaJEiRKPgdKYKlGiRIkSJUqU\neAyUxlSJEiVKlChRosRjoDSmSpQoUaJEiRIlHgOlMVWiRIkSJUqUKPEYcD/Pi/3Jv/+hkZLsNykk\npKDPQgCGjzEAhBCf+vv8GAENlw/RENAQE8eYiV/w90bCGPreaA0Yzf9qoDV9r7SGkOKTv4QxBhJ0\n/Le//a1Pv7FP3KZS6jMPEEJgMBgAAK5fv47eoA8AePfdd3Dv3j0AwOLiAnZ2t+n7969ACQcAcHz1\nOJ48fx4AcO3q+7hx82N6DlrbNkohUfMDAMBMu4N7mxu2jZ7v8fECaZYBADKT2Z7w7qvvfWYbPc+b\nfMAPvKv8+j/v/f2HwjTXy+8tSZKHHjzTrhmtuL8IwJOOPYd9xlIComjv5Pd5HwcMJPcpAcBwr/Jd\nCd+hc9ZcAY/vqDtOMEhTvocm/of/6j8FALz4/FOoVnwAgDYGWlN/1Fojv880zZDx+7z4rf/8oW28\n+MyTBi69dC8I4Dou37FBFlP/jccx0oTuJ45SDPtDum6aoRghEtx0CB+o1qoAgGqrgdryCQDAysVv\nwau2AQBZlj4wFh2+BwmNeNzndikIQ21ROobR9FmqCEbR/bzy+//qoW10hG80zwc+JBqC+n4gBYw9\nv4Lm8Q0AFb8CAGg32qjxZ6MUhtGYnoMwOAjpOYRpAp+fW9WvwOFnopRGzO8oUgquw3NeluH5c5cA\nAP/s138VSyvzdK1WC65H97a1tYXr168DAP67f/k/fWYbf/tfPm+a1RW67yBEd4/mi53NIc489ywA\nQMQ+Nt6+CQBYra1huUZzh3aqUAm952PH5lGdof740cHHEAGd54PLb+OgR+3uJ10EbWrr+plTiLoj\nAEBvp4fV49SO4UGE4b19AMDMsSrWFp6h52c8bGQfUVtXfTRbiwCA//H7rzz0Hd7sjo0dW460fV8Y\nDyrj560jCEnfe24NRtPzNlkIR+RzvYbL41XCINP023GiMY4SAMBRf2A/awAa9Hy00sj9DlJ6dswl\nOgJPy5COC8HHaA3w8Mb3vv70Q9v4/ofX8yZCCYFE03WTTCEc9gAAvb09tNozAIBqowmXp5jh4QEW\nl5YBAL7vIzP0jgZaw/AacO/Oh3jn7R8BAL7/3V/HXKcFAMiUemCuytsFACMaHrg2djDIxyv/BwA1\nAVT52G8/c2qqddG+u4n5chKT3wsh7LyulCIjATT/5XOq0UAc0/tKVPbAbx1+AZ4j4fHnoq3F9aaF\nmOLg0jNVokSJEiVKlCjxGPhcPVMAW5kAIA2kzHeEhU1nYDBptOYGoRDC7oUFDMC7SUe6yH1W2ggI\n/KwBaaDtMRAawtA9GG2Ko4UudtgTRqgE4ObbjCnx84zY/Pssy3Dnzh0AwPb2Dj76mLxLW9tb6HSa\nAIB7G7dw6+YtAEDV91Cp0z7g13/1H2CpvQAAuPr2OxC8g/AdD5mivwgpoccxAEDpPs7N0c6lWW+g\n06RdiYDE0Yg8AfvjPkLnZ3cKPw+5hS/EhMfP6E+0WzzwBx/0wN9/1g+YHzb9vdA1C5/lJ8/FZ5z6\nfPl57O7HGCjrySx2S8YYyInPD1zN/KzHSkPAdXPPlIBRdM5KUEXA3f8wyuBI8lBEcWZ34dVqBZJ3\n1dCwfhSlFJQqdo0pe7WmQVCrQuSeKc+3OzmtNaTkHTkMIPPrKsQeHaP4aQCAES40Px/pAj57Ph0p\n0Zw7BgBwaz7AfdMRtLukUxggo37q+j50QJ6gKBrCtlKY3AEIAwEhp9//uVIi5WdoAGR8TtfIiS5T\neBK11khSus9RHKHCO/soCuGwF92BhuK2GBjr1YrTGCL3chuNlI/XKLyZDoCVRRq7M+02fJ+8jdJx\n7DFpmtj3/jAszc5gfu04Xf+gC82ePXnGRaa7AIDTs8/i+IULAIC12dMYHdLz7g3GaDRrAICW56Oh\n6H6fnf8C4oy8V+tPXMDNbZqb3rr/Kg5H5CUZbiu0m7MAAK8pIH3yDgySGJGiPlWvatwNr+cPGaIT\ncfsArZKp2gcAUmsonc/XEpqfPQAc7FN7h+Mu1tbIQ5fqBND5fK2BfI0xQMZ9Nk1SHPSIGTjojxGx\n9zVVBobXIgOJfEGgMZazDcq+q0ykyMD3YyQ0e7u0MhCY7h0CgBFFP3KkA3aaIlKA71EfcXwf7SXy\n6PmOC0QhHdRqIKhQP9XGwOX7nHWBCg1FDKQCJK8NjrBzRpSoiXahYIoADFL2rIr8G/LoGZ5ek0+s\n09Ng0jP0aWukMcZ+H0Uh4pja2Gh2sLl1HwBQq9Yw1yEvt1IJfJ9NGAXLtAhRXEdDQNm5qrA08rdM\nxxfffNo9TYvP1Zgy2kDxIgKp7YsRQlg33uRnYyaNHepoACAk7ABzhLCdwAATL3jiZRkDYwpqBPlk\nZQwbWnRvk1RNfhqJicn//yekaYbd3V0AwAcfvI8jHthCCHz08VUAwGDYRRDQ61mcXQRcGvB3r/0U\nVzYP6DyDI7TqZHz5vo9ut2vbcur0OQDAxbPnUOcFuil9zNYaAIBo4z5GDn3+KHVhFuamunfHcbC0\nTBPX4vIyFHfgMBwj4gGeJgmiiCfPJIXi560yZWlVflv2vJ9myH62EfSzg/Fnx+dEP3oE5pFuN6dM\nJ0w+YR6w2/LeqQG72AoxMY0aDccO0oLLzhQs1dwdRagFPn+vbbPSTCFkF7brSGuwUP9lAy0ziHkh\nMNogSaY3ppygYjcJnufZiU4pZd+BlAJxTo0YoJJaqrRY8IWDfH/kehKOx1OKMNAJUURSOxCSNgMG\nMQ1gAEZldlwmSQrHpftxpIDmpggtIIrh+kiu+UatjsMhLbiQ0l43NZPBAAKew9Q3MmTcrjQeQ/L9\ntGs1VCpk6G0f7BWLu4DdwEAa3uRRKwW3yxEC2h4vsXSMFsRmuwk/oOu6rmvnnjCMUKvVp2rfrdfe\nx+Y8bbiOnzgOVMg4qnkaC4qMnVWcg6nR2J6fbcH3iIYLWhVUeF4IhEYS0niFKyBiautc9RxqJ9YA\nAK3ZRbzxztsAgEX3FE6s0ve72QaOYgojePIZjfciohS7SR+Ou0dtrVWRjqkvZz1g5hHGojISmcrN\nd4OMDQHfNdi5fxsA8NHHVzDX/hZ979UgBRkXcOz/YIREktC7OugOcHBEhuE4yaBNPo6lHbsGxhrH\nWhto3oBLAbtmZFrZtcGYgmZSWtnfToPCNUAbtnwugUowHhGdGqUpMu77VQFIhzfmjoTnFJR7Pn9o\nreHIfBxLCDb0hCkMBGfC6MDkGmwUNM8HrhD5I3zA+WAAZP8Bojny8R3HEX7yxmsAgFOnT2PrgPqt\nyjS++DTRx4cH+zh2jDYTFd+395Zl2s4lgCj6jxHwcsodwoZpfPoG/MH7mQYlzVeiRIkSJUqUKPEY\n+Fw9UzrTMLm1b5hGYBS7YfkAlVIE+cK6L4wRE0G4aUFRQOQbfpiJoHPgE0G7+aZaaEufCDzohrSu\nXK3Ik/Ao+BSD16BwYY5GQ9y+fRsAsLe3j4g9EBubd6A1ueGXl5YQDmlXosMYs/O0s7/x3mWYMW3b\nF2Y7qPDGuN/vo8E72hOnTuJX/uNfo3/IDG69R8GfjgwwDum39+5v4gMOXj9qNvCtX/6lqZrmBxWs\nn1gHADz51EV4krwq4/EYcRxzYxW6R4e23flTjRMDzXRCliZI05i/T5Ak9DmKQuzs7PLn8UN3BpPe\nCnplkxzx/0ePoig8ZRTwKO331mMlpfWyGeCB5IVJ17ClCIVAlgfGQsPNaUQFgINeISeohUxjOCJP\nn3Qc6CzfJWsOiCW6eDymYOgkSZGlj0CfCGHb5TiO/Tw5/lzXLY5xfQiP+mAcJ8gy9oipFIoHiON4\nCJgaE1IjPdoBAKS9HdRmT9E9w4EjcupTW6+AMbAB9I9C5X0Wqn5QhAZIiXxAJlpbyg8QiNVEMDq/\nL6M0kgF5ej3fR8A73UhnUCiSE3jDDwUN1+7mHfj8D6nRSHM6wXfQWiQvUaVRQ+7/DoIA4zF58fb2\n9lCt5qG9n420B4wHTL0Nhlg4TnR+GrmY0UwnXlzG9R55jnZ6mzh5igPWTYJ2QO9KhSlG/M6HwxAw\n1I4w1ZaSWw7O4ItniF6p1VqQI1o6ziyvYRhTH6xXgdalGwCA7f4mPtj9MR1fF5AcqB8mEY6S7lTt\nA4BRlBVsBopxqZIhhod36ZijDdz48E0AwMLCCmZmqI2i0kLMY2scJegN6Rn3BkPrUdTQUDk9Bwc6\n99YYAJZezJC7R5XJrGdKGBfIuL+YBNrQteJ4APUIlLtEQU0JAcg85EIaVHhOnxGAxwuX40gYUaxt\nrs3G0vY8Rgh4Ml87i6QrA1O4UISG5ndNTnf2PBuJML8F18Dn56+RQfF7FObnhVQ8HJN0Xv73/M88\nDKjZamJ2jgLu/+Qv/xAJt33Yj7B9n/rzhTPncWL9NLcd8CXdm1sROGSWRkqBCntshQY89sbigfn6\n01gNPi1f95PB65+Gzz1mKl+MlNJEm4Bjb/IJSn1iAcxphgcaLyx1hKzInNDCQKDIvMpfjDFFphu5\nZelcjoDtlJ804myWX/bpmQfTQKCgCDNoKKZh3nrnLRx1ydhYmJ/FR9euAQDmZ2exvk7u85lOBwlz\nxq1qFcNDnoAygyihtv/o9dcQsUFi4hgep3g8deEs5gS1ff+Dj9Dh7Kxzv/hNfPiTKwCAvUoDT/6T\nXwcArH/lBaw/+eRUbWq12viFv/0NAMA3fuFFABzjkySoVGly9t0ARz2iIh3XwOX7ypQDB0xppaml\nAsMwxHhEE92wP8Tv/u7vAgAuX37XxpVoXcRkiQlDoNOZgcs0TRiFSNmgMAY2tkXrR+P3HQDKxjoZ\nuwFwhLCDTkx+nshymYzZm4z/E4A9j9Ia+VRb8xz43JYo0yhYUIO9A1ootSo2IVJKGDlpTHGWWRwj\nieKp2+h7AQQPHNcLHqAz8yZIx4WQOW3nQ7rUjzzftVk0QMXS5irL4HPWoeMALugYFXeh5Ul+bm5B\ngRhl70ErbcecmIh9fDA7F7CxAVMgSzNy54P6T2QXFCCbPI+aIFlE3l5lJ/Dt7iEiNh5jlRULLgQc\npn+WZ9t45hLFJlUcF91dorjubN1HP+QxagwO9zfpkmoMmVOirsGIx/p7H3xgjbKH4WAvBGpMjdZi\nuEPahDi6jkOOh7y1sYkb94gKXFlZgy9pjDquQMrvsHvUsxlkMEDCmyIhJQY9mndqvsD6AmXtJUlq\nF2QjNULud0e3Miy0KFtxZfUSjmKKC03kfQR1WtAiB/Ba3lTtA4A7W3sTWcpFllbW34KKaQ5t14C7\nN2leG3a3MVwgSqg6dxKjhH7bH8VIeDND7z7fdafWoFdQE5nhBpKDl9I0Rr9P1zrq7aFSpT6+1FmD\nw9lzB/sb6Pbu8/PcwnBwRKf5z37roW10xERYiRDw8xjgOEHic2iL66DKFJUrKWYJ4LjMfFOnjTX6\njDFI+ftsYvJLlUCqmCLUYmLeKoyaEAIpr4uelNAcvmHSCB7HyrnABCk6HfL1WEpp1+/JiFcAuMeG\n0gcfvYuQ14fDbg9vX34dANBothCGFBbT6bTx3kc/pWfouEiShI/fR7VK/W1lec3S5q7jFPHPxlj6\nXYjJmOgHHTwlzVeiRIkSJUqUKPE54XP1TDlOYelR5sNE0DljMpOFPBATO/2cqoOwOiFaa0hrYWpI\nmbsMC00SA213uo6UReCwzqDZWnZdFx5rvaRpOuHVko/k1aAb4fud+MpogyuXaff0/pX34XIwfeAH\n+AIH1K0dX0Nnllybyhhs3qedzsa9TfRGZHWfOnYcHfYGeeJNuAG18cTCEi6tEv12Xtdw+y/JxX54\naxM7bI1Xvv5FnPjGlwAAx154GtUFutZ+r4f9m7QjwPMzn9k03w9w5vQJAMDTz5zFQW/E/+IiCGg3\nUA0aEJwlonRmPQ7NRh2SPYfjMLI7gziJkUaseZVk+MEP/goAcPkyHvAW5t4oz/MQMEXx3PPP4cQp\ncvUe7u/jYJ88An61Ap3m/Utaz9c0MMbAY68BebWKsFTrRp/ITBQoPGWTXszJbD6ltWUdHUdar1bV\nFVhosvdtGCNkCldC4KBLu9swDOG79M6FFDbI2wjqw3Rd/Ui0pu9XAd7pekHFekN0mqHeoKSGWr0N\nw30t1QYx71AHR12b7BBFEc6fp2SHg737GHHArOs4hSdRJ3DyIFmVwrDek5HS9gcBA2HyZ1iM0UkX\nvMkbPSW0MXbcawNLz5HvMb9uMU4lCo+YMNpmZMWZxkGfdsPKKIC9EZ4UWG3Trvebz13Ec09dpLNL\njeGA6Lzu6AT6Y+rn3cMeWpoosYONq2hzIkfQrqI3pvPfvHMHHd5VPwxCVBBtkQeqLitIquyJ9cYY\ngDwp/cF9XDi5BABoNetw2cMGLYqAfGVQrRC1WKkUlONRr2/1r4xJ0a5TPw1mmhCctdkbjtHtkgdh\nY+s+ql3ypq6fW8DaMWrfzeE+Ftfp2czWjsFPp/egbh6F9rOLDDUWZbtx5afob1G24MJcDVB0zkF3\nD3pMv+kMuvDZkyIzH8pQYH0qa5b2glLWG6xNBuGwppnU0MwAbN65gY+ukQek199Gs0XneeL0U0BK\n/ejmzY/R75NHbDQ8Qhg9wnyDwqvhSGEzEGtBoWUXphEEU67Sq9hkLEcKm7GqJ9Yqo41NaowNbGKF\n0hNJQELY8aRM4RX3hEaT72cMIOb7MdEI9SZRvS1XoPqILF8+R4ZhaHXtHBd4+6dE0W7t7uDeJnkz\nr167Ytd+16lie4vWp9rQQ4W93//m//jX8DjDeGFxzgbrH3X38Qvf+LsAgCfOP4NKg/q2Y1wkTPu2\nmk20Gnkr9QPztuEJZ//wwLInx4+tPrR9n6sxJSRshoFj3IJKM9qS4ZRJl/eIycVJ2HgGpQFlcjqP\ns6AAOI5GkeFfUCxSCgjufFIUsS4aQKNRzy+Fzc1NPt7B4iJl3SRxas8/LfJYMDmRdq3CGLdvkLv9\n9o3bOHmCDJKv/u2vY2WVUsi39nbx+uV3AADvX/sInKUN4dYRzNP9HHkSH79OWTVNIXDm7FkAwAsn\nzsPr0wC+/spb+OAuxS7sDQfo1cjwmNvfQX2JJtaXf/ImJLu9nTBFo9mhiz3/9Ge2zWiD/UOaNG5v\nbtp4gyTWNsW4FTStcJ5WCllGHdhxJHIGI05Tm5mjlIbi9C1POhgOh/Z6k7E8+WcyfKnrzsy2cf4c\nGVPR+jEM2L1eqdfQbNGCpo2yRtY0cFyJfO03AtbQEGJClkAXtPukbAf9qPiYx1I5kJN5fYXdo4EK\nXyxwPRh+DloIHHRpoRyFMSoznA2XxtbQEEJaoUCVqQdSgh8Gz/csxeb7Pjye3JZW57F6/Cy3UaLb\np3s4GAzgVij7s9KcQ22GFv+tjduYX6aJ5sSZs3jzVRIHlNA21iUL95Ed3OAHF8NpEpXt+HWbASeM\ntFaTFBI6FzvV0oYDSMhiEZwCYRRbYw2YsMNM8dzkJ55ZTncK49rrwii7kZMQqDGFfnJhBl//AhmS\n54/PQTHNNrs0j3Nn6BmunTwLAxp/d67dxNZtmgOi7h6O+Nlub+9hd4+epweJp564OFX7nvzKedx5\n50MAwFE/hHtEfWQ8BM6dpIXiwsl1LMxR/NRgHE3EggqMeaHITEEjp0mMCmcRtxrVCbo1wMY2jXvf\ndzEIyWA57A9s+yp+G3NzNI+YRODsAlF+C+uLcGY5Y1IBWzfuTdU+AAjTIsYnEAYu06FpliFiukq4\nATodmsfjwQi5tkA62ELFo8+dxjIypluz1Fg6zxGFxImQGqmi99A73MPBfTJI33jjNRwc0mKuMUY8\ny2tGNELvkOaq4WBox3QURXYRngaONBB5hp0UyLh/ZSqD4Hnu3sbH2N6hGLGnL70A389jGaW9f6UN\n8igZKYTt+f6EU0IUSa1QqS7+IoSNH3bsVhgwUsDle4vGIRZ4vFalhHyEsbixfR/jwZDbcg87BxQG\n0mh7+Osf/Skds3nbZk0aCCQJvYs4ypCk9Dy9VODajfcAAHvbh7Y/N5p1+9tqzcef/eAPAABXrr6N\nt69QVuB4oDA8ons4e+oM/s43vgkA2N0/wCjJJTcMlnjtv3LlCrZYkuG//a//m4e2saT5SpQoUaJE\niRIlHgOfbwC6gd1VOxDIt/9aGMhcBwNywu1aBKUaU2Ry0J9FllThDpTW86VNsRMVsIwGHGMQc5Zc\ntepjaZbclsNojFMnaYd96tRJNFv0/bs//RD7+4dTN1EBSDiTo3t0hJDppTRK0W4RhfbL/+gf4fSZ\nMwCAg8Mu/uCPyTL/4No1aM7MOHPxAjLOfIsSgcRJuC0KJ87Sb08vzcF3effvSnT5c3hiEavnyNt1\nvFbF7Cq169zp85j1iUL4h3/rF6DZGn/lpVchK9N1hd5RF3/2538JALi2cR31gLwVcZLZDKm6X0Vu\np7uetEKOSmlkmtrhBw6imMXy0hQVzhRr12ZwdEQU0ifLC+TIsgxxTH+/9tF1hJz1VqnVbYBqo93B\nsEfnH4z6GDGNMg1c10XGbkE9Ic4phSw8KRPJLJPBmw+4i42xAdBCigd2LjkFHaZAmJenyJSlf1Nt\ncMj6Y0laaDCpTHAmDQBtrGdFikJjZro2TnqmAjQ5QPi7v/Rt7B1Qn7185Qp2drYAAN1uDxEHJqeZ\nhs/aWMsrK9g54GQKZwGNJu3a0yiEYEVZkcaIdinJQpkIjYCEY/1qM08cA6Ch1cSzyulUEvYpnun0\nTUSWFQSKA/GABl3uJ3SE88A58wBVx3gTWbkahsUZpQBmPOpXL55ZxZefOAkA6B9tIuFM3NW1Czh+\nlij3E2fPARmdsyk0sj3ycFy/vYUjjuG/fvclsMYunrt4CU9dnC4ZZGm9DlkjL9/VG3eQ8By6Pnca\nxzwa83tbB/CYqq135pEJem9qPILHOlfS9axmUBJFNng6qFTQ7dPnKM2Q6yPWdMVmBddbHSzwTj7w\ngHab5oNsbCAc8oLvi7u4vUUBxLpRRa05naYdAAhVZBcaZNjbI/YgGg2Qpkw7jwTAc2W73oTrUBtT\nkyHkOaZZy7A2S/1unMCKdg5Hoc3uVirG1jbpZO3ubUDH9A8zNR+tgJ7nOBpYDbGDzS6OelxiadK7\nlyaI40Jc9GGQYjKzXcFwQLSsBVAhrVVXP7iC/UPyZK4fP4slTgYwWtmF0ZWwVLk2hefcdxw4TvG9\nti7aguSWopg+hAEq7KUKYJDkYrfSwJfFeqwfYcL5n//tv0LA7VJG4+o1yjD3K5nNGFUqxf4+eT+j\nOIZfyZkLiSTljOEosnNPtVZDb59LOzkR/ArT1kmKMSHc5gAAIABJREFUUZ/6xt34Nu7dI4+e61ag\nQ+rz9+7cweXLb1FbhAvHzyk/YG6e1unBYIAe65FNg89XtHNi1tKycItVlWONrMyZELzTgHak/a0x\nP5vNJeSE6vmkcJ4xlvuURsDhwR8e9pAw7dRpLdkU/lqzhiCgBf3unVtwXHJdDwaDR6JP/uTf/yka\ndZpQ3n77bZw/R2rCT118Ck8/S7FRG9tb+KO/+HMAwK2797C0QhPipS++gIiz0RqtJroHNJHJJEKN\neetmxcOpZ2iyXZ3rQHDMTKvZQsYLQS9JcNCjTrC6vo7lY2RYHd3fRovTrvtqgB++/jIA4OqND7A1\npMnlv/yNX/vM9iVJbOmbZqUBpBx3oz0bvzOMxzZbwxEOqmxwaW0QJtTJtUqt0RlFERp1mujCqsF4\nRC5dIcQDnSanf7WZoJAyhfubVEssTRN4PNDW1s+i79BEJKRAkk3vkk4zZe9fmyJOSjyQQ1vIbTgT\nmaBCCCtdYLS2BouYEIqczBKJtMH+mJ5bkmaW8jPGUJo6KEsu5UwVIR1kTKcqVdTjI8mP6dsoHAcu\nqyt7XoAGxw/MzDaxsUMu+Lub99Dj8ZElCTJ+X2mWQSWFqJ/foAnq4OAQjQ4tlKNeF+AadgIppIm4\nXRJqzAJ8ngeHFz5oYzPppJDQD4j4FmndkxIUD4PreBMbsMmpX9qagK7jYLJmmM/SDo7xC2rPIQkW\nugeF2RYdc3JlEYJjdYb9LhZWSZqg1W5DstFitLASJ2kcYW6WaLCXXnoLxqdxcXyug3fep/ifcWcF\n2ZSCj7s729iL6P20jrVQUWTUnG88ATGmfnc06GP7Fi0mK+unsXSCKPFwNLRza5TG8Hj81au+jRUz\njsDaOhkRShtEnLVnoDEzS++51qhZSnYcjsFhW3CFgitormnF85ARx4F1KogGG1O1DwCODu5Ccrau\npzMcbBBNevfGVczM0DO+e+8W0oj62otffBEd7oNQGg7HfHnSYKVNsU6eV8EBZxTe3drF/j6n0esM\n8006ZqlzCkvz9D7X144j4JiyJE5tOMhrr7+JV37yEwDA/sEBEiuxYaD19GuG0sZm3GUpoLNc5FXh\n4IjCE46fOIbFFa4o4DoYc0xWmqUTCvHCZqcbyEKo2kxUEdBFdrKEmZAGmuxzAh5y2tGx8i7VVt2K\nXhrxKKYU8MGVt22cc63iW0que3Ror50kCUY892ut7DNMs9SykUmkkAyoH/o1CY83mSYGRJXjkGsu\nQqaDTQw0ZmmcGakgPTpRnIyxcZ/W12arjWo1n1MdDEe0FmZqeoMYKGm+EiVKlChRokSJx8Ln6pnK\nsszuyB0hi0wbKZAxvSWULkJGJwJ7yagtMvImPQE5NAqdH08ImIi1YQZjjJii8AMPFc4E29y5jy7X\nm0rjCF3Onjo4PMLxE6cAAOcuPGmpo2nw0o9ewvwCBXwuLS/h5Gk6z+7hAXoDsnj/99/5HQi2qL/3\nj/8xTp8nfZrbt+/g/Z9ScF0aJlAsZLlQr2JtgXadZ06fRJOz8KrNChpc5sIxwlY8b4YRZjjLbpzG\nuHWLgn+7m/dtFtxudw/dhNzGSydX8De/+zdTtU9IgYCpwtn2sm2HmBBSTZVClNEOQwK27oDn+piZ\noeehhUbE3hajFTwOehWZsKKFAkUAt9BFNp/reaiyF/GJi09ghaump2mMjL1ja+un0G7O8HVdxMn0\nGURJmk54I8VEAoKBYz2iRSaa0fpT6cjJvwsUmi7GFMkR2gj02DPVCFzU+LIyKSqiG1MERitDO1mA\nPFb591o/mJHyMHi+B489U0EQwGdhu7cvf4yrHxMlN45CKyibxVGRMKIMwDvLMByjwhk+jlPD8ePU\nl9N4iJ1NokzGw0ObYOALDyYmz1R6OAQc6r+J8eA0OGNGeAWFKmD5CjGhizMNBBw4lqItygxCCEhb\nl9Ar9G+EKLKkAEjOFHIc0rEBgEo1QKdB5wyNwZ1tEibVxsXKKnl9ev0Mt+8SjTEe+djdo/aG/S5W\nOkTPdBYWMRjQ7vnY8jzGTLm/+/E1pCyE+188pH0H4RBb2+SR2d9LcLZDmWuXvvUUagH1/Wt3NlDh\naV74Akk85nZUrffP98a29I+WDmpM+ccKCDjpIE0TpFxyRjgSPfZ8b+3twvVZZDTVMEMaZ516gJg9\nw92DPiLWixv395Gq/kNaVuCDKy9B8vOoCIGU9esO9+/BaKKUw7ALrah/be5uYW6Rsgjr9SYCWzLJ\nBcdso13z0OC5YXVtCUlcCDpnip5DFA2QJtQvKkEV7TY92yCo4fzpJwAAT5y9iE6LPI1/9Cd/jM1d\n8pAbKazG3TQYa221zpSK0e9TG6Owi70ueabmlttIU3ovqYkxPmLvrgIU67k5qCMI6Jl4gffAfGNL\nRwmJnNhOYaAm2B7rnRKw3vUkDDEO6ZkEngOj84QE8yiJtVBxCJVnDGdAzAxMnMVIUjr/uB8jjfMA\ndG0zm7NUwWOOeZykOMuM3LmFGirHOKt0ZHCgaP5YcA/gubSW13yBmD2b13suBHueXQlU2DOcRjEc\nQeOoGrQx5mxT3/Ns1uE0+NxFO+2iYwDlcCdWCZwhPcTGzCxGnCmi5ESMxANxE7qIG5lwNjoQVhAw\nHYUIecB7gYOgQw8xgcIBK2wf7O/jkMUzB0ddSyOunz6H02coS8d1XehPCol+Bn7lu38fW0f0Igfh\nAL/3734PALC/f4g619ETDhDw5NI77OInP3kVADA66qPCLtu2kFjnTL0Tp9ewMEeu6yDw4bMBBUdA\nsfGQZArjiCavQb+PXpc6x+HeNoZDGuRSSGRVmkSEcCD49Y+PBmgE0wnp+b6PmQ5NIJ35GduDlNbw\nmRPPlMYwpgkzGQ6xcZ9c82Gc4VhIHb7WbEJyLFWWJgij3KCow3HzAQvkzlOtNWQez+IGljfvzLZx\nbG01fxxIeVJttjvWmHKEfCRjqrg2xz1Zi76gGoV8sE/YxBahkRcPNROGlRDG1tRTE/F/kJR6DwCd\niouA3dCjRFvBV0c6UDlNKQVSjueKk9jSkVJKq0o+DfwggO9V+LMPw4bhwWGMmBd233Ph5rFacSG6\nl6WFIKrrekhyAU81xiHHrzXrLaxwOjyyCHssVjkK+3bESpPBcMZOliqo3JCpLdsXYICJmDXxQNWE\nh0FKBy4bxYHrwbXhAMIKZgopbX1Jx3HsZoNCDejZep5EwOnYp0+dwOI8TcIHgx3oEY2zp544C7hE\nlf345XcRsZTJrds93NuiuLN2q4JLT1AW74mzT+DjDz4AAAwGPXRmaG7ohdfw8ltvTtU+EQlEu/RO\nkh2gWqdx2d2K0DxDxnGiHataLQMfES+Mm/e3scL0/5lT61Ac+mDSFN1Dmr/GYYRbTBEGroNjKxQD\n5VccIM2z6mBj4xrtiv283e3amCzhGQzHNDf1D3ewXJ8+Zqp3eAOBz7UKXR/ZmCg530kQcOyd1h72\nWdT44xvXsbRC8Wpnjp+2CuJ+JUDKBkKaqaIfSYEqF9g2Rth6mKNMY2+XjJokSTHToWfSbs9YEciK\nJ/HCs5T9/O67b+DuFqX1a1PMy9MgAuzm5HBvA90DynbMdIQxZyA6CGwtiThNbMZzFI6xe0D9a6Z1\nHKtrTb6HQovWTGbFC21jjLNUo8/hHb1BHwOOIwuHQ4z5+35/iGadxuX5cycBl/p+tV63NTOnQaIz\nKI5r2xv2kLAxJRxjM2vHwwQjHjee70JxnFQSxUg4GqDmZvjmMl14ua6wM+RzJh6Uoo3Nzd4QSws0\njs86B0BGa7xTWcU2xxIK4dgMZm0U6hwzCiPR9tggdV1E4+mzMkuar0SJEiVKlChR4jHw+Yp2ugVd\nZgRssFwgRjj4kAL53NWvonWaArL7yRjWP6EltCi8WlZcTwi7c037IQY98ohI10Ftjqz0xkwNKe8+\nP7x6FTdZ66XXPbLBoZ508fyXXgQAnD5/0epSTZYxmQYb12/hpXffBQDc2b4LSN61NVrIOLDw2PIK\nrl0j6u13f+d3cOHSUwCAE2vHcfE8BaxfPHMOC/PkWs5jdAEwDcEBe1GMQw4QPjg4sEKK3W7XCpgl\n4x4Odyngc3X1JJptcoFvH+wj5FpVT5x9AlJPJzIXBBUsMa22tLiEfPeuQCVKAMqqvPzOSwCA1//m\nx9jfIZf00WBoA7iDSg3NNu2ktSrqb7WbLSQx3Xu73SyCvxHAiry6rq311Wq2bKAlZZnRPWijrVCk\nIyWQTv8OpZCWWtJaw80FM82Ea3siFp36Rx7IKeCIIujc6lUZ2B9oowqNKgjrER0lKTLOxkmUwUn2\nRs502kU1e6UQsuc2yVIbgD5JHU4DxwvgM51aqTXg2oBsjYC9No1qBa7hxABXUBkRAIPBCPnUUa0F\nNjsoS0NsbVAg9b7joME04tLSCuaWyctaHR0h4+DZOImQsn6MyMZQQy5B5HhwXNopKmFgBdeM+bk1\ntD4NnXbL7j6FKbS96G3lgboGlTrTyo6wnikjBAx7+gJHoFqhPjC/NIMvvkCJJNeuvI3+Fo3pJ596\nDjPz9L7euPI+Vs9Rxu2HV+/gzbeIuj918hg6rFH0xPoaKnfpWr1hiP0ReSB64wiDeLotf6u6hKpP\nv6uYgc2SvHl/H+M0D/atQtbpOnuHh5hlPTkhHduP9vd3MMNiwZAuXBYNbXoBxux5W5zt4PQ6zR1p\nNMJCk47ZPurj+ibNL1vb9yD4eY+SBC98gea1vtpANKZ3K5IEqTO9S6Pih2g06FqeSVBf5M8rbdSZ\nertxa2Q9LN3eCIvLRFMfX15HwPS16/sYxrmGW1TMqWlqVZW0AmJOkElSDc1ahnEaY++Q5tnecGDD\nENq1GhpV6hezM02wox2pNI/kCdfG2HHcP9rCcETXSpWBy+WchAAyDlUJR0eIOcA6ikLrQZPHW1jL\n615qbUWRjVFI2fO4ub2J2+PbAICNjfvY3tnm6x7i+lVakw4PjzAK8zJVGf7WN57n7w/h+nTMF198\nDstMp06DmaYPw89zhATaz+dmgUzRgxu6Qxj2DEaRsvOlyoAhZ8WvzUtcO6Bn8s6eQJc1o6VvMBNw\nybXaLPZdsiGO5A0c1+S5O6kBcIZp2jqFEQea+27VJoykWYo60/txFBV1EqfA50zzFTcmnRRBlTrB\nrRtbyAxRctffeBWrDqcGxy7yUllVv2oFzFzpWLExz/Ns9kO834PHk14/G+PHL70CAPjFv/ctDDle\n6f7GJnbu08P1gwAXLpGwnCt8HFuneB4ImXtd+TrTP9C7127i7g2KFfHaFbg8wga9PtKAU8W1RKdN\ncSbLi0t4lmOmvvTlL+H4GnWCoBLYwrVKZTZeKE5iqzK9tbOF+9ucun54aAsNJ0mCNKVjVDKA5qW4\nWmtgiV37r771Bi5fvgwACMcRHDldG4NKA09coGf2ta98DRnHwkjHIVVtANsfvYc/fp9iRu7d20KD\nJx8pHcuVZ4O+VSX33UJItXewbz/PtGs2hiU3kgBAoyham6aRVeZ2HbcQahUCggeC4zrwsunrgTlS\n2IHsSNcK6gnpWOHYyVWdVBKKmCmHHb7CLWJ8tNE2lZgoVv4elv1DPzao8XvIYPDsJaKafb+QC0li\nZbN3skwVKv+moEGngef78JneCCoVBGz4ROHIygZUgxoCNpB937MG+qDfR8qGhoxdBHVa1HwnsIZt\nPOijz1l749Ehah1W+65WMbdMNEwaK/QGRLkn6j4ML3bZeB8uU0GuEFaE0Rj9SDSf77rWOIqiEJnJ\nlaUrEHkmpisRVJnmEdpm+RkhELOBk0Yh5njhXppv4ezp4wCAig7xg5s01n2vYpXjf/GXvoMKS6u8\n/PoVjMfU5/u9AY6xknJQd5ExLzDWAa5cJ2qnN05QrRdp2p+FlaU5bO8TtdEOJFaXKR5LJ9LGYM3U\nfVRqNP627m6iwoVeZ+fmMY7JkB1EKRzuC/2jPsYcG9VsNFFjY+H0qSV4TL9Hoxh375EBdXdnD302\npl/76WXs7tGc/rWv/l1IHvf3dm8iHNL8LpFht7c5VfsAYDzsIuaF3RMOxjntrDRwQIKKg0EPMRvl\nw0GETVbRTmJVLJKJQsQGSKy0nQ8c6Vg5HWNgJQ20dhBwrdFEJXbM9ccDHHC9wrBeg8tZ1qvHOzh3\ngRbqRGgcPkJKvdEGY17Y93Z2sN+ldyrdALMs3ROGGiGn+4ejEXw2kOMwQczyCcPeAXbukSE5ChPs\nsMjqfn8PQ46h/cFf/BA3rtKG59aNm2hxptulZ84ii+getre27TjzgwA+x9B9dP0Wqmywnzp7Am1e\nw6bBVy6egpfX4dTShkkMIoU0o3c06I+QccyUrtTtnN8/6uHWHepv7egO9kJ6thtjF4e88fBkDBPS\nu6tUh2iGFGv2qudhN6C55FQtxVp8ldoYVRH7c/z8U4yZhtbaoF7J5+kUKnuEsIKpjyxRokSJEiVK\nlCjxM/jcPVOWitA+oLhqvbOI6glyN2LuGn7w51QuZWe7jtlFDtoWHiL2ZBhtUOFdTxRF1uv0tRe/\nhK98lai6/q0N3L5JLsm/+iuJlLPL7m9tYMwKeV9/+jkssJbID/7qB4jYw/XlF79a3PGjqAQCyIxC\nvUHWfj8cIuX6TtEoRKNCLv6ZdhtPP/0FAMCF8+dxgr1RjWbT0khxElsNECmkDf5VShXZhQbWWxCG\noXUVp2lqKdRGq4P6PLm6j62dwK3btwEAr7z6KnY4E+ne3bsI/CkD7UxRJT6KI+u0E9IgOqQd5yv/\n9n/BzXepDmGt4sPJM/t0ZkUsjSiEVGGK+ndKFVlpUgjrAFJpZnczUgpIdn+/+ZNX7Dt0HN+WY2m3\n6phdoJ3H2fNPo8WZTtPAd9wHPSCTQrB5ELkuyrd4UtqOoo2x9SEDR8JlOqHqBzjqsyZRomByj5sU\nYIcrosyApZxQ9V08e4moIiFcqCwvhZEh5lIVAkVWq5SO1e2aBkEQIOAgft/3UeegWmhj6WghXJvf\nIT3faq8lUYQeiznG6RgJZ4J5TmqDW6UJSXARQL+7YyvYC9dDxH221VpGUCN6qQWBsE87zjjsI2GP\nFZwGXJnr6Cgk8fT0iVIp4iQPIndRYy9xLaha+tVxRVE3ENqWkEmTCDrLS7ykWGrTszq7No9ji9Sv\nRHYaK1wWqtJsos/tCio1zHHW3qnjx3G0wzpGKsURCwAnkY+rt8mz8vGtfWyw+KBXaVrduYfBM9sY\nK3pOXq1mQx9codBoF0kq23z93tEIDmfzGaNgOMup1mohY09Wu9VGhb1UQkgIptje/+h9q/fkArj8\nEXme+1GGxXXyNC4szGNlie59eX4GV27/NQBgF7dsjcf7u7tYbE0fgB7HITwuCaOkxCGXzPJ8Fz4H\nuBvXYGGJxvfMjEK1ylR5mtrEDWlSxOyJG6sEQcaaU9KF41Cfch3XJnEYFGECQqLI6hIVjJiG2x2N\n4FD4OOoNB515z57fazambuPNj15DxPUEd7Z37dxdD6SlTdM4xYAFQv0gQcJzahwp5Le2u30b3V1K\nGBjFGm/+hObgVGRYPU2e4bv37iBjkcyFtRbOnCBP6cWLJyyLsnM4ghfQ/NRqN9Bo0vczx2bwhSe/\nAgA4cfa0pQ6nwagf2nld6wzVKq3rz3/1F1Gv0xzgSg8BU8zSC2A4mWh/ewMHXPOxd/M1vP/y79Mx\nvkYWkR0w6GZgpxa0UjA5VepXIRrE/BwND3CpQp67dvo6jgKi64dOx9b3dR0HR5y4JoQDISZibB6C\nzz2bryhaapCGJCGwuGIwO0svZn3tJk7x4v/SH/p48oVvAgDu6xiX3yDF0kanhY0Ncot/8MEHtkDx\n3e1liDfphXUP9tFs0Xlu3r5hC09ub23hYJ8WgnqthYjlBA73DnDpmaIunc3meiTNZeCjjTtYPcnZ\nJLUAdXaRtjodrPAkOT+/iJMsntdut6xIKYyGkytau45Nw3edgnaK4xhdzlzxfR/1Oi2Cw+HQGlzV\nahWLTKVUgwAJZ0i89OPX8Po7VPtvNB5ZocbxaICFxekmuCgc4K03KftQOgk0Z1woaCScTTgepnju\n+S8CAA6jLjY3aIAf9RU8npAhhE3XTbOiGPInY9TyWCrKvymoNMGK1KO7G7h7d5Mfn8Yi1wa7+MR5\ndPf5ntcjVB8hVXkyg4/kNmDvLS8YCg1rNAnPoMbGnXQk2nWmUloBlniS7zTr2NqhAb5z1MfhIU2G\nnVoVi1x372gcocdyHudOr+P0yRV+BkDGadpJkti6X1E4trGAAngk0U7fryHgRbNWr6PTIvd9u1m1\nAqHVShUxnx+OgwoLFyqd2kK3o8HQpjIm0cBSchKpnTAVPKjc0EszHI6Jmh6PRqjw5OkGFQQ8qQrp\nIDygzVWcDlCt56KvGsOj6asRzMwWNISU0s4TjpDWqPc9CSevhZYm1hhvBcDyMj2T5ZkOzp0jo2lt\nvo4WUyzzzz6HGlOEi3Mz2N8jw2bj3j00uS0Xzp/H1l0ymvZ2NiA5bqE1u4zQ0P3c3t5DpU7G14mT\np7CwMJ3hPxr2MOIiyq4UuHqT6K2V5ioEx2pWa3NwOAW802hhdETPtVILIJxcWNTggEMfZjodO/78\nWg2NFv32zt3rCDkOKBqPMWa1+pnFNaiY5qmKquLCBYr57IY3cO0+1WnMGgmSiOcyX8CvTr9Aza+0\nrRp+GIWIeI5zAoG8EGuaJZAB3U81cJFwMenDwTbChDZaVde1VN14HCJjWtAXEtIWnfetFEuSZOCp\nDSrRE1q9GobHxHAwRJZxbb5kjJhp6nEa4RE0O/HGq7+PxTmi9NvtFhyvKP6dsjwApEG3yxUR4gyu\nzxtq4cDnisPD3giKZWiOHV/FqTMU0jGIQxv7s7hcR5sp9+ZMHR3OlBx2h3Z+PX1mFX6N+mazVgXX\nlka900Bzjvp1b3hkwx+mwWtvXoPDMijS0XjiSQrlCWMPjkvnqdQcvPZjCs0Zj8d48cUvAQBu3b2D\nH7/2BgDgVEuh6eaxVAEOD1mcMy1ka7TSNkPQcQ1cn43xxkn0A1ofTgb7EC5nUHotuJzZXK9V4bKR\nfufOHYTR9BvUkuYrUaJEiRIlSpR4DHzunqkcShjO0AJUGqF/xLvIU0186e9QEOOMlrh/nVxuz33p\ny1jm2nbBTBM3r5O7rtlsWNfse5evYIel/iVggxJHUWwDC1UmMWbtiJs3r2N2jq77hWefxtlz5+z9\n2UwtiEcSQ/zWt/8jHF8nr5PRGWbbtPMOKjU0OZNGOh48N3ffRta96krHBlxLISaKJQkMOavm5Zdf\nxh/94R8BAJ559hmsnyAPVKVSwfwc7W5nZ2YwYL2fn777Ht76CenWHHQP4HFA6cn1ddxhyg9I4PnT\ntVE6Lk4cPwkAePGFL1vvhu97NiPM+ac+9g5pp/tn/+f/hv2tDdtuhz1TQhRiS0opK0QJwOorUdGs\nPBhQoihJZyZcxoWw6/KxY/jqV74GAPjyl74Gv8K0VJrhESoC4cRy3e7ODYABByLv9BLY/YcwNsPO\nc1wsz9J7rlckOi16Ju12w2ZJ1ao11FjLZLHfxajLQnWDMeo1em4rq+vw2NPx5MULWGLxVwgDwRmC\n43BkdZFgtN1Vx1FoqdJpUK81UOcstlaziZkOJ0QstLG7yyVklAGYfsi0hpS0ixUiQKXKNbHmlK24\nniZH0GOu5dYbweUyQsLzkXLiQZoJm2GVJiE005ci9GA42N35f9l7s1jJsutKbJ1z7hhzxJvz5cs5\nqypZA6kaKE4lkTQldltyy+r21LBhQLAhG3B/95dtNAzDgP3nBtofhgx3A253W+iG2oJlyt0iRbUo\nDiLFKtY85Jwv880v5rjTGfxx9j0RSVYxI5VAyYDv+qiKjHcj4p5zz7DPXnuvDSAmj1Uh+05ossh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gMAnr521fWr8AMolLSXj0aZ5ecFTtPn9PQIH7z3AQCgWYtgqK2zLJ8LRQJOD8gPAlfPkXE2\npy67XRdAWK83nQdHa4MpeZEMOBS1VRa5E5NcBu12ywUqMqOc1/L8hXP4zPM22P3e7hG6bXvy3t7a\nwP6e9VycnJw4ujOKQ/fcfN9DessmCGz3apjQ6ace1GHKungecHpkvVdvvv4GnqOgy0aj6QLQA1/A\nJw+IYAwpeeI8z0P2GCfFKIoQ0VypxTFiCppPZ5kTxA18HzGV1Gi0YvDEDpJer412hzTcbl7HCQl6\nTaYTpOmQ2hsgoUQMWSTIhrZ/0kKDz+yYSXOF2toFuqEV5500nMO44mkGXJd1EtlD3uRHwfc8eDSW\nAt93pWIa9Qg7OzaI9dq1p7G5ZcdzEHruuUtmsH3Zeoxf+fzn8MFbFFh/OsIZlJla2p1GDeZZqMYY\n7L9r6YHp3X30LllPT2AYBI3bsN7A9ralTwaDMSLqfyk1OFsuE6yx6uH4lvU6b9a6uPAFOz9un17H\nhciGDoSNBkLywszMXdw+sgzAg+NTzOh5Mo8BFAaRyQIXnqNx125Aktfx7vEt/It/9Xv2fvtT1MjT\nO1YzbJO3rWYMptpm0gkhMB3asdltN3HpovX+Pbizj3awvGfKGOG8J1rN61gKeECpvZal4OVwERq8\n1DRmQFLYNfFocIT1lr2o3mxgaqxnzQhA1IlShnKeZO77KCtQTfUAYYuSh4ImCvrdaZIhpz7kPIMh\ntkEIjkgvn823vR3BJzGnMAREQCWZ8gRhVNLCxmWaXrx61nlrZVFgdbWgdoUIiL5c30hx/qLdh6ZZ\ngX0q8zMbz9Agym/zTM+NuyJXqJE4Z8xCR4Nyv4M4onJLeubmkOJAkizvJd5c7bkEmcxwrGzafT2K\nQty8e9u+9kOcPWvnxHQ6xYQ8aA92b2NMFO31H38bI0pcSxCi1qL6roC7XnCOKdXcjcLQZcJbFoUS\n3SZT+BTmMJslUJQYxTiDyux+PJydoN78/6hopwEDygwAzmHEnPNm6ZcBAGtbv4i1Tcq64SE8nwpw\nCuEUsJkJoCiuoBRiA2wG30cZPjs7O7h40S6MeZ4/OiPIPPzycYwppTSmU7u5ZFnkNjhbqLGkqRpY\nIWG+lbVVNBotej9CXgrjpSlGYxoQUQ3djt2A1tfPYnXF0pR37jxwm+x4MsE3vmFjPA7391GjzMS1\ntTXUanbj29297wzM9fV1jCcL2XRLts8XProdu3g2O23ElBEkuIdZTgrQ3IOmDLVxMnG01DPPvoA+\nFQx98y/+HCdEe41GY0wpDbkodMn2QBnmJBNCz3f1GPvDMSRlYN2//8DVJOTCQ0I1rsAYUoolODne\nd+rH/8lv/dYj2+iJOa0mOAMxFOCM4+o5uyk8d+UCwMuYJoMWZars7u5iMBhQW3IXb3V62seMaN4w\n8LFN6uzT8QTddbvoXbp8EV/+0pcA2Iy/WoOoF1M4A1MpuGxBwxhYWTycMQTB8hPf9wMEQUnzhQhp\nwYzCDBvra/R+4DLvPMERkZhdoxGhRzFHu14NeVlTT2okY2tMNTs9J4bZ6XUhys1CahREkwiToUhI\nHTzoonSUGw1I2jSNNjAka5Lnxi2Ay4CDwSsztYLQxQu1my30uhQLVI/he5QWPT/rQXMOQfIMT7/w\nGdy7Y43BwWkfqixiqzXmBa4xpxGKHIfXbRbZ3pvv40s7dr726jFMmann+46ebjQa7hAIU7jizI/C\nzkaINWGNws65No5SO7fu3XmAyysUIxO3cO++Neze37+HPrOxbhOdQuQkyWI4JIVfwBcIm7afoLQr\ndPynP/gT7J6SxMmMwdBYYw3PZX4FYFBEBXu+wHhcxp7McG2bRBGDdQS6u1T7AIApz2VaaWioUg0f\nBj6FUBRFjoLi0ownIEjUEVHk4l8m2RhNRXFhjRjjPhm+hQGFW0Ex6WhQVShA2ecQ8AI+xRkVxTzr\nNE0VaMlDq+6joD1Jwrjrl8H2Vg3DCcVejQZOMNOLGHxj358ZH+11a/Q32xG4m/cGvVUKDfGZW9/9\nwEOzaZ0Pw/EMexTq0VxtYm3Fvu9HgVtTNVPorhMtywSYKA82YiELX7pMYg1W2t9Lwx2MJYNP42dv\nbw/f/dM/AwDkSuI3/8bfAACsr6+5OMXAEygO7fxLzAS36Mz42tG3UBCNmxcaKVXm0EYjpT1BCOHo\nSCkllJofvMt9XUrpavEGoY+rJHa6eaaFkIRGl0FF81WoUKFChQoVKjwBPuEAdAUnwggDpstMFCCI\nFl2/ZuH/petcw8jSQs7x0ImQoH/q3yWklC4YfbFUySLYwmfNz7qmHtGuObJcYzAc0T1LMDr2KCNd\nZlcyO8WYgu+FyJ2UvdEC6+SlaLc7uHCeRMg6Hdy8YXWyJuMEBwf2lDGdJvjFV6ywmSeE876srq2h\nQ0JlUkncu0flXAZ9rFDgdpLMMKaahsIX8JYcCWk2ww3SnvnOt77vvE6yKDCaklaVMsiJbhtPx8iJ\n6sqLAhkJxhXMR9Sy9zieZTCGas8ZjZzcwXleOOokS+eV26XU8Cng9Pvf+747eXheCEWZX2EUY0Qn\n49OTfTC2fDafENxRTpxxaPpd7nmYlfUhTejoMM8XaAf2tNeoP4UhaZ2laerG3elJHxkFpdYbMeqk\nW7O6soFnnrXZjteeedq5wq1Hi2hT33c0KAxcpkoYhkipnxnnYI8R9OoHsRNcFVxA8NLr1EQUlVlv\nPiLyWHHDIehZRL6PNtU67KytIz0gj4WcuTIOjfUdxHTCZiZ31FU9FkjpdaoFJFFmjAWWc4E9CJeB\n6VAaILe+CBsAWz5LijOGmNoYR5Hz+ASecLX5ODPwXXLCfIwwLmBoeVzbuYhnX3yZ2uK5jFG9WKJo\nQUh2NB7CbxBtyiWOx9Zj1EIBr6Ros8KJUcZxDXFcBoAv/wzXQo53hKWrTsYDqAGNHW7w4W1b3/TF\nT/0K+qcUdD7eQ7hp+3VNRZgdkkcRAKM6aEHQdPcSeSFOx5Ymu3fvni1rBYBphkKUfcBRTEgwMw4B\nSZlipymiNnkC2y1kJNRZX+1h7/adpduIXLtdSisDXa43Okfgz8dvGXzMGMCI8UizBIbWCY4Zphl5\njNUMKQm15hkHp7VBC0CSXzxXxmVVSpYiIJqpGA0AKp8zmU6QkleuJ0I3RzOtnVbeMmi1as4zOOpL\nHO1NXVuiDq2FmY9au0v3X8BTZfkqBl1muzLfjSmt53VEpZ7T/0EUoBz8udQu+cUwwI/KUl8cirx+\nasGDQ8p/9pXBXNl6CSRJioD0ywbDKf7ZN/4FAOD69eu4/oH14nbXNpBQ+MNqp4WEymbdu7+PQ6Ip\n1z0fR4l9vkeTB5gQncc5nycqaA1N62gYRi6pQGnl9kjGAF6WpVHMChQDWIl9RGUSgoyQl/p4S+Cv\nQAG9fDAPPwjzkMjZ/G8PKZE/goz6uEf70wbUxxlUH/u9j3H9cJYi12XtoBSTcem+VW4wNWohIlKT\nng0nuHnDuuFf+/E7CKmO1pUrV7FNAoI8YHjjTVu0MvDrePppOyCef+5ZXLpgOeZvfOMbTvUVgMsc\nG49HTryNC4PJlDb6bAbPLw0GtvS8GAz6+M53vgMAeLD3wGVLGaWd0KUQnqvbNJ4MXaFgjwtEpQpu\nowGvpEVyoCxmlc5yJKVbVpsFSjZzfH0YhYgi25fbZ8+4By9l4TbP3uoazp61MXnHJz1EteXdtYJz\nBKLMrtLg5JIWHChVEgVjMGQUCCHmshC+h80N6473PM8J7f34R6/j6ralZGo1H1cpa+XZ5z6Ddodk\nfJVyMTuAni8CSsEr0+4L6cTvJtMh6kRFjaazxxqnQRDAD0o1+jk9EMdzqtzzPIRlFWZj3DWMMSfV\nsL6xillijejJ6ABlXVDjRRDctvfw/rsQZASFgQ9FFIgAwDtlVYDApdQqzV36tuYK2rebMmc+9GOI\nIdbjmitQKzh3C2yRZXhA9SKvt2qIAlsnrNGsgVF8jme4MwyDRhvPftYWUD89PMIpieZ2wnBuBBkD\nTRuZYQYRxWF1Lp3H7WOSLBiPsNW0B4g0m6IoSgMmcOPHD3yXifQo3M+PkTfsM9m7nuLKWRubJZs5\n9m/Ye0zzA6ztWMM3ufEsXnvt+wCAK880sXtgNyKpNSIay8lpAd+QoGUtxpTa5NUigLQ2pc8QUVJi\nc6jQJZmX3NfgARkaA4mcVM9fvLqFJLTrzsibofBmS7UPALhmCInK4ZzBL7P5pEHOS2FdBvCyEDF3\n1JUyEklOAspc4OiU+lUylPULBoMR/AYdzEMNRWuVMh6MLkNJMghTUpYSU4oFPDg6gCCejyEChYBC\nKuNiX5dBGIVoK7sfpN02rn9o40r7gxmav2DnR5ZMMB5QdvKClIzvCxcPqo126642FAsHgHvGre+5\nLFCQYWig5pUeFipuMLaw0zIGLBhQZsHl8PE77s8iVxqKJF3eff99fOvb3wYAzCYTZwQNh3382Z/Z\n2nzTychlb3PB0VujYvb+DLMBKetHdWxuWKHU6WzqssClVO7ACWNg9HztKauu5LJAj2qNroUxhqUc\nj6/RJFHq9ZVVTB6jeHxF81WoUKFChQoVKjwBPtkA9IXT7ccFdf/06Xp+2eMJQS3+1s/Dx99PeQp/\nzAB0Y1wZGGM0wohOATpwIpUnJzNEJOwotYfIWFdiEEQYDu0J7s6dGzgmHahmu4sXf+ElAMDx8Qmy\njMRO8wT/6B/+QwDA22+/7Sz8yWTiqDXOhaOsgAVPD9Mus09pA6aWbKMxWKVaXy+8+CJaVE6jWW+4\nwOJ6o4k6ZZ6MkhF8Curk6TEMBfvm+4fY/aF17549E+L+of3sfR460cDTRGFWzAMGOXn2mPDQ7dnT\n9osvv+gCFaMwdKeTZrONNQryPnd+x3nQlmoiDDQrXcYKNX8unhlS9qIfzAO4tdYPUcElhWRk7sbO\nv/lrX8cpeQtb7Ra2KJNrOpmBk1YU9zwURFcI4buxyTlzgZNhEECrMjAzwKhvqQtm6IS+JGxW3FzA\nrnSXe8JzWX5ciIVZt3AmNQYtEpm8FJ3HcGi9r/f3DqCYHcvMr0GQ4GfObqO1YgP3tejCq5OX7fgd\nGE4JDEyAodT2WtS/CcAosyjg3NESyyAOQ0ezeoy7oFcYjQPKvkymIycw+/QzT82pW8NcFqdiAgFp\nQtWhcHJsg+ZPjw7RpPEfxzXn6euu9NCkpI8Gi3GDaK1skENQsPBsNs8wbTTqjgaVReEyyh4F3fUx\nu2mf28mdMT51xf5mLaxBJnZcGAzwznXr+T7bfhY1ba85uJ2gu27bOjyYQhWlaC5AuSMoeARDY8Gv\neUgeUJZqx0OzYa//1LlVRDt2LbvZHyBYsfNvJVzFygq1r63x/pt2XDS222h01pdqH2BLyJQeQhgD\nTcK0mmsUkkR/uUEYleEUcw9hUmRgRJ9pbmloAGiGHdQb1hOUyAkkZXIpU8Cj7NXADyEp0Fwrg4LW\nlcGwj4S8+2HNg8zKIGYFo0vdIsDky3umGAN88vR22nV87vO/AAAYzzSGAxtSMewnGGe2jc1uA+Xc\nFR6HR95jzhm4E33mLvO1KKSbNnmWI/Xm60Tp7Qfn7rNgC3sYxJz8ZnDPQmsJ+Ri+mEmSo6D+3Ds6\ndOuZ7wd4+ukrAICTfh+HJwO6MQbjti0DUd6PVtC0Rnqhjzqtx4VSkLMykxvO69ts1lA2K01yl8hT\nDzz8NRLvXmcaf5HaeZTUIygaY2+8/S5kvLzO1CduTH0UHjZ65u7DxzFiHuf3lrt2gRt+nN/U2imu\ngguU66IfaHByRecpR0a1mKanI/Bxmcmm5rQZJFpUL6vbWcELz9kCqX/0zX+JN9+w4nzX33/biRgq\npTChjBBjjPstxgQE7e5C2MkHAJ5n4PnlZg0IsVxDG+0OXn75FQDAr//6150Y3yRJkBOd2Iya8Enc\nbVQM0OU2ZsTPA6csfjS+h0tft3EoJzduYWfHGmhybRWTsZ1Qo2mGKcUkFFIhJcMqSzV8on5O9j50\nSue83kFSZnQkE0QUCxE32jDe8plugnMYVsobRDD0/X7AncquLAwYKw0fgXK8BGGArBR9Y8wtdK1W\nC82mzdqMo8BlSYV+gIIK+Wql5+lkRi/QRgw5ZaeEQeQWyTiOEZHgncxTZOnyytJ37txFjajPRrOG\nmOjXRqOOTsf2c7vN4dEiFnrC0SeddgOrVMB5OJliSHW2RNTA3p5Vxz+dJCgUCW+O1hG3LeWKeA2a\njOtstA+/XL85RyEpKymOwWg8cjaX1jBGP1bMVBB4TiU7CDzX3ijkWF+3dNvlyxdxhupbGm2Ql32o\nbLgWACRFCk7zJozq2Niw33O4d4SbH9oDT7fbddl5fhBAUlab8GuIqODz7HAItWG/Pxkn4LQZ1Ws1\nFyslhACWrD84HBQ4uG0pRJ0EMHRYq+cx2sJSr4OTe0gKa/wxL8fOeTvPPti7i1qHshgzg8ke0XlB\ngIIojyBuuqw0k6eISbzRrwMXKePz4pkt7AU2e7m5wuAT7dxGF22qiyilRv8OxeExjnOf31yqfQAJ\ndZa1OpV0xrTn+3OJGzDIsran1C7NPSkUDMWCaQ9o0FwUvgCT5TzW4FG5a3NklDlaFAnKIg6e50Nm\ndL2WCMp+CAKMKO40z3OolA4njANy+YMNYGlIAIiiBpp1ez/DaYHvfcvOp0lS4PA1m4nZbETYvmAp\n9N5qxxmS3DPOEPc9f07tFQYexSZOhhNIKvhsGHNZvL7vWaoPtuBzWbnD8LlciJUWIHmDLIcioddl\nsP9gH2PKtGZa44VnrCHTH02seCgAsyC6LPzA1dRTWQZDa6FocDxzhUSu4zaOSEommc6cv0UAaDbK\nOrjCjZMo5ijo+V49t4neUxfs78YRnivX+zDCdGj3n71DhlavvXQbK5qvQoUKFSpUqFDhCfCJB6CX\nHiAb9PyzGXlsgVjQWsGQvTenqh7+Hvu3n38K+Hl/n9d++2gK0r5c3j3FhXClYoTg0LzMelkILua8\njLcGcuPczIyxBapuCo9OGfd37+B/+gd/336PVu7EEbe77nS7KO0vxNwa55w5b5TvC3jk4vVDOA0W\nMBcj/kgwxpGQO3Vv/wHSzLpHB4MRNJ1afCbgwXqCjJA427AnkvV2AEaaJV4cw2/Zk7xXP8H6OXvS\nins9zCgrMM8zJEmp6VI4za5C5gCdnmTVkUgAACAASURBVIU+QOhb1/zkdM9lgIw1cLRLJxsjIEgv\n5G//7f/gkW20Ip9Ee4A7TRetNAL6LQP+0Nip1awXMS8Kp/ElZTGv/aiko4HyPIcoBSoBd5rUYHMt\nFmbAS56Jc6eXxBjmulqcu+dmtHF05zJIkgSmDNRlHOQow3CYQWl7GmOcO72wOKo7DyfjQBzZ11Hg\n4drVcwCAaxfPOff66XCMMdEhP/puDi0s5Ro0Ok5TKT0bY3XTfpbHPdy5Z0/hxycDjFO6ISMACjov\ncukCh5dBs1VzGU1RHOHKFSsUePHCNrY2SedtpefmfZHnyIhyTZBClm0Z9B09HseRWw6m0xlyGp8n\nxRHGA6J/wgha2r6djWfQNKfTJMHdWzepnwcuE9bjBoaV/RwvHby8/8MTrFIywizOMSaKxB9qcKLw\n7929DxZT9tPxgSsbxJjB5MTOp85qEy99ynq+P3XuWaxtWUr27r1bePf9dwEAg+kIgkrXtDseZid2\n3v/g5Dq8Z+z7eaYws04qzPr34Z23wcFah1jtWK/slt/BhdrqUu0DgEQq+OS+tKWlKDSBMccaKKWh\nqN6LEHClQdJinpEHmUFQWZ12be6BklKBEa0jVYGc+o3xAgVl9nnclsUCgEar7sZC1i8Q0JyWUiOZ\n2rGQCR/e8g5UCCHcXDRhhBqV9qo1NJod6wHsT47Qp3I+Bw8y3LxhM2jrtRgr67Y/18900elZT3Wt\nGcMvs9W4QBLb14MkQ5xSPUxmwEZE73s+4hpRukEBjzTlPC9AENXd9Ypo8zTLERP1vQxGw1On/VTz\nPVy7ZMfYzbv3cf22bdfJYAxJ1xgloalerFbK7S1HvkBMnu1AT5wGYD3kbh9ttBvzjFvuOYHnJM2g\n6ZorVy9im9atWr1uQwhg11QD+/7R0eZjlcz5K6P5jDEwSv/M34wx4KzMTliIe+LzFE2t9UPfxd1G\nw5aKk/o4aFd09S9PLwouHGWmlHRmmOcBzJSDWMMjI4uHHHxG9w9AlrE6imNCop2NeoC7d+wqJUTg\n6v0l03sLqZ5z0UbLl9MPM4MyyzwImNsQOZcAuTY9z1863kYw7oQTReBjs2czDi9ffBqG2YEXxz6a\nsd2shM8wOSZphv0b841i5ayjrlhUR1gnI0UVCIjk9uMYHBQv40lo2ujyInPUni/mohZxEEGqGvWl\nxpSMvmmSu3iuZZBnKVpEyRnOnBZjlhUuI9P3hUs19bx5Np+SGpKoKCULdxgIwxBFYZ9V6IeYEW3g\ncwYF4b6nrCE4SxLU6HnmhXSGeJ4XrsaV0dpN4JlUTtF+GYzHYwyInosadZwhVXvf9xxN3e9PFqiU\neWwG2DyVOA44WoHth1E2QpGUcSkcrdAaZd4rX0BYtwtvs9txBunt6zF2Lth4iZWVdcwobuvwZOQK\ny56cjDEYWWN8Op26PlwGURS5ttTrNays2DG5vb2NKLT3fHx84iRCfD9wvH6R527jyLLMGYla64ey\nGrnLZp0fiopMQqt5vEpp8SqtcHR06L6zlJ3I89zdpy20utxcDCchtjbtOM0upTih8Z4dz1ydwPF4\ngnPn7eZw960jcJIgWY/aOLxvjb+nds7gF79wDQDQ4ytoUzbT9d07OCWDuL25hZmwxm7MPJyeUkxW\n2yBO7WbLJj7UiW13S/iISHF8sF+gTUKgUa2GfLr8OFXGOIrHZ/OYOc9opyWppHTyJUHoOTmVIofL\niM0LjSmNnaE3xnRGapvCuCoFRmioch+CdFlghTYwZB15PELhrlEoxfALbVDkJGMADfeHZdpY5C4+\nTzBgRmECs9kI156/CgA4c24Hk5k9ZO4+eOCKf/eHKfqn9hndfF84yq/Ta2PjjDVm65sNzGhNyhCg\nR9mXi44LzjkmkzJbuoa4YdfRqF4AZJQFLMJ4YseY4hF8v750G7u9lgtbSGcJZglVI6hF2NmijLxh\n3x0UU2VcZYvcGGRE/82SHBlRtEWRumctfOEMrlmWuXCPnfMXENNB97333nOxWrfu3IVPY6nX67qa\nnIHvu9q3Ua2O48Fo6TZWNF+FChUqVKhQocIT4BP3TJUnMMUeztSbv+YLlBNDWVmBm4c9U+5qzh/y\nRv1lvUpm4fuXzQT8KOSZdG1hZu6NAqwnAbBCayFZ+3lRAEHpmfIhSPROSebEH5Wel9DIsxwM5TXK\n0UKex51Xw2Z1kMUuGDyv9Nxp541SWjlXqDECy2qGBH6AzS0bQHrlygVEJIoXR02nYeX5Bn5Jk2mg\n1rXBxxoRphRcLr0pMqLzilXj6Lko5BCkKyNgnBuXaQ0elHpDApyXWkg2GBKwQcYRiU/6giOgANio\nJiEfw+3uhwFMOQi1dHlsQRA4DwW0RqmvyHQARZQAZxwZlY3xPeHGVJKmjm6Val6dXhY5ONFAeZ4j\nJM8XlHJ19wppnJ5RVswQhvQ6TxGQVopIUqfLsgwOjk7RJI0nOdQYkIdFoI4BlYQxLMLZMyQi2/ER\n1coaaRrJ1GZnHe4PnJjjYDJDQeN3bX0NG1S78MzmOmY0Jwz3oCkJYWvrDGqkFxbwHE0qgbK9uYVP\nY4v6RCGVZbkaBVMsf+Jf1G8yxmBvf59uQro6Z1JKV4aCc+7EH1VeuHnseZ7z0GmtXZKI73sIylok\nEI5SLArlvFpKza9Xaq7rwzl3mUW2zMW8NqW35HPMI4OWtu24crmL6x9SJlTHQ1sRVZsZZOSFGR71\nEVJ/nHl6B9GK9VCMk3387jf/sW3FkOPLL30NALC5cQUXtqnszsqLGCub+DLYu4dj6xBDb6cOObN0\nvckVOpQN98Knn53XUBvtgWs7v+PtDKPTe0u1DwB8Ntd70lI7j7vWymUjG2OcYKbv+W5uCSh4KMds\nAb+sQ6dTjFIKyg8YPEob8wLPPUOlFYpSY6vw0GhQMH0+wyShWm/gANHdGePg5I0MPPNYtUClLFzZ\nLGjjMpK1EQhojJzZquMrX30VAPC9H/4Qd+9ab7/O5ntqnkuMJ3YtHAzGODiw4+EXfvXTWD1n6cLb\nr9/Dez98DwDQbDdRI0YgDMKF5CSBZtc+YMaZK911fNhHneowfuYrr2JcLE+BrXQ6jjZHuwlj1qjt\nCpw8SkwXkNQPmZxrXSkDJKldb6ZJ5p6LUcrtG8kscfVop1mK4cTO6dNRYoPTYRN/Vojeb7VqSKW9\nZjAZu/6P4wge7RtxFOHS+fNLt/ETNaYW6Tm9oKDKGPup13ParoTR8/pxi3QeY+yRBtSyBtaiMfWX\n+TwARGELaVlYccGRWkC79E7BGQpa1ASTQECxYwxuEnI+rzvY7+curVsI7eLHPM9bWJzhYrU4Ny6L\nkHHjjCxj5pmGjM+LxhpjltZfk0pi72APAHD//i7CUgQtVxiTmF2aTp1bVuoMPhlB9agFRRNQawVd\n0pWih1qPZAbyAt/9zp8AAGqRj1pU0nweGi3rfg187toh/MhlfZgFkU8DjoB+17ACOVs+VVkr5hKq\ndCEhqF+9KELJLeR5hojcwUmWokUFN2WRgZdiqJ6AT1mVyWyKiOjRZJbAK2trKQNGz5kZg9FkPnZK\nI5F7/lytXgjMZqVoXeBS+RuFdJTvMrj34BZqQ2vs+H6EO/fs+FpZWcfGhl3oajWGN9+3af3v370P\nTRtinqao1exmXW803HgcjTKkpA7dOD3ElXMUy9Gpo1OnNPla5MpzrncuIyNqLE1yDE7sgqkxQVAv\nC/9KdyDxfB/1aPmsTMbmY9zzPOTUn8fHx0hn9vsZZ/PsW8ApSHucz9X9pXTjii3E6gghnPRCURQu\n60zwuegogIcOgWUMxuJntdYujszzPHfoehSMKFxs1mZ7C7stazRdfm4Nd//ExtQ04xgnpzQvpzla\nK/Z3OusCpmPvcVqk2KcxNUk4/vzWjwAA3eMPcXpoDzwb3fPorNkNdtBQ4GSwzGSCMLS0YJErnH/K\nZlptXG1hfNfeg7+VI5Z0AMAMRan+uVQjzTyMAwalMmauCmdMiYV10Bi4e4si7kRbWeAhispDaAqI\nMuPagywPuXqe+s85oEojy4tc5YaT01MoyuJthDVXENjw8j823qd4jNObAZtXXPAYOMVleAGDkWVs\nV44zdLB55ZWXXP3B48NjJDN7b36RQdJ4KDIJxinsIojgk9zMrMjx4PYefee9eSwyYw+FeqzQGpAl\nicsS9uo1fPbf/Q0AwNR4QLb8mhrweQ1Ez/foAE/Fy+kZaQOXzRdq5WwCwbkLbZBqnvGuFg1qNTcq\nizxBSuvKeJq6Oae1QkgxbkEQzuv6GgOPl0XQa4jCMpTDW6iO8GhUNF+FChUqVKhQocIT4K9MtJMx\n/lBtn/lLbQP77LswZfV4+re9ln00JWfMQ96rj/IosQV5/PI3yu/56QB5evVYWlNRFCOZzvWeyu9h\njDs6xDDMKSLmIy8zTjSDknMqkJf36XN4rvQHnCsa4K5/GDOkA2Ide2V5HpvZV3aumPcV+6nsxSU9\nU1meoX9KooWHfXQoI09K5cQeG/W6c8cnxciVkOm219y9CO7Ns2JUgZ2zVuvnrdfexLf+9f8IACiK\nzAZ6wwZ8l0KdvXYLZ85Yd/OnnrkKmSf0nczpynBlXPA6GIfwlj9FAXMqmXuBq+vH2JxSDuOaKxUj\nPB8J1XAyWs09kL6HnNzTURjCo1NgtCAICMbcKZYLDxzzbJnSo5hlmfvdPJ8Lgc6SxNUKnCXpvH7f\nEqjVapDl2IEHz7OUz+loiqNTGyQdeh40Sg2bEE161vVWGxPy2LNZimMSI/WFjxbVW+wfpNg7sV6N\n9U4bz1+0z+7qxQhRSLWvmEadxHxCP0CzWVaAl5jlxrVdUmAvyzTkY+j3GGOQpqWwI3drjCwkxmPb\ngDCM3LPWen7qZUI4Gs4YA12OKy6cd9Jol1QKtSDcqpVx49/3/YWMTuVo4jRN3UnaE+Ihz1SxZAZR\nb6UBxFSWapyi3bWvn/n0ZQzfshTPvfsjMCqNBC5Qa5UCjxOE9MyPDziyw1LfDhgy+9njkyNI6r+m\nbmPat/Rv5gtw0hjSmYKIbP/1NjiiVXv9rZM3cON9+z0bT7WwtWPHznMXP4X7t99Zqn2A7fuStjNQ\nzksViOChfUKpUjxzHoweNyL4pX4e15DKzsUiz10ijhf4zusBBvfMC1m4kklCe0iJNgJnTuiyMBIe\nKzWnQqQUFJ7KzNV9W66RC4wNhFvTtdZOSFPAgySvzdbaKl550Wr9vf3uW9i9bz1N2QiQnAQtPbhN\n5uj+fQwoSH12MHCsiDGAIabA/j7pZCmJeGrX7DgOXdD2K1/4LL74GVtHFMKbD/5l4IXwKcObLYT4\nhGHgkjiSNHNzIgp9BGEZ3sIcPc3MXGhUKe08ycrM70UrBU0hLFEYOo9RnmXIVcmMMaQJ1fVjDH6Z\n8R4ErrajMeZxNII/WWNqkZ7jfO4UW3iOYAtZEIuGktYP89DOJliMbWJsbiexRUXoh34AH2U5sMX/\nskXK7yMv/1gMBn1IEpe02rFl5gcDL/l7Yx4qyuhRgVoOD8wI9365KWujXLs45y6DwS7qpbtUzV3F\nHNC6TLH/aPkHxua1DrXWTmLhURCeh7PbNgbqU9eencdjwYOi1ONmswWPaI60GLvNp91acy5dxgBJ\nWTSTyRhrPetWFpw7fnw0Hrt6atpoPDiyRmqr1cTx0F4jdQ13btt0852dbTz7rM1KUkY5SkDrAjJf\nvh6YENxlhhhwcF7W6cP8mWiNglzJfiggizKTMXYGlEmzea8yg7R0VWu47Dyt4GIklMytfDKsiF+Z\nWSSlfMjQLzNeGMdDVBFnyw/Uv/6rfw0ZuekVGO7ft5TMoH+CRsMaPs1GAzFl4cXNVfR61oDl0Ni9\nbw2ovcEEHslOgHHMSDjSGIBRPNcklXjnnl3MTyYp2mQ0xYGPXtt+NgyARlTGxDE0KJ2/HjSgaN4U\nUmFJ1QB7D5hvjowx12+yYDCUGVqT2m2aeVaALocvgIxiuzwxT6/WhXLyIsbMwxNgGHK6nnHmaHa5\nQPcsHq601k5BXy5k/aZZBrmkMSVqBtEZirfzD1AjAyGbjeF1bb9uRj18WBZJz3P0aa3RA4VzXTvn\nuIjQprqe+8kBuE/xiJlAyOp0vxMgpKKvUiOgjXGltoa0IAHalkJMgq+jbAIWWGPKUwZXn7ICvZsX\nz2Oany7VPvu7xtGeSiswt+n58/qNWj90Pi6LhWdpAkPxLzqX4GVYklQAGZgM3O0TqlCAP18fy6Gm\njIGggRFGETKiuzUHcsr4g/bma5uUUMXyEh72oF0KYwoXYqCUdhUR7Bpg3w99gdUVa5xevfoppxR+\nfHyInOI10yRFqXyjhEbUsM+3027itEGHBGWgZKnarlydb6HmhaO3NrbRWLXz/uJnX8SEaLW60WiW\nk2UJRFENLlCHwa3Ni4LHvuBoUOiE53suTtSeU8r+nNN5nueDUcwaX6DjtJ4XZAbgDidR7MEv6476\nPtiCIOfczlDzuG6p3N61DCqar0KFChUqVKhQ4QnAnrRkS4UKFSpUqFChwv+fUXmmKlSoUKFChQoV\nngCVMVWhQoUKFSpUqPAEqIypChUqVKhQoUKFJ0BlTFWoUKFChQoVKjwBKmOqQoUKFSpUqFDhCVAZ\nUxUqVKhQoUKFCk+AypiqUKFChQoVKlR4AlTGVIUKFSpUqFChwhOgMqYqVKhQoUKFChWeAJUxVaFC\nhQoVKlSo8ASojKkKFSpUqFChQoUnQGVMVahQoUKFChUqPAEqY6pChQoVKlSoUOEJUBlTFSpUqFCh\nQoUKT4DKmKpQoUKFChUqVHgCeJ/kj/39f/BPDWMMAFD+v8RHvb/4Wj90scH8TwzMvZrDPPT1DLrI\nAAB/9q1/itn4CADwlV//z+AHjfITWLQtjTE/c///xW//LfYzb/4UfuXf+JJpnVkHAPQaLaT9YwDA\nf/vf/NfodDoAAKUUGnEIAJB5huE4AQD88Mc/xDOfugwAOHf2LAw9HuZFAPPpLhXA7L1xLmBA92k8\n10dKF/A825Y8z+DT61H/CP/X7/0uAODuzRs46Y/s++kMk/EYAPB7v//6z23jr/3W3zLtlm1Hs9FC\nLaoDAGphHZ6wbTLaIPTt/XbbdbSa9ppA+DBaAQC01iikBAAMpxM8ODwAAOydHGJczAAAnu8hYAIA\nEHo+Ij+w/QHm2q2NgTS25YwpcBoYXAj4wt6DL3z4nn393/3dv/fIZ/jbX/2yUeeu2e8JQtT6uwCA\nrb0P0a23AAC7IsbRhecAAHEU47nNMwCAL33+JQxze/+//0ffQVyz1weNOt6/+wEA4K3Xv4fnr7wE\nALhw7iK+//YfAwBu7b+PSxv2O7/4wq9gb3YDADASu6glZ+1vyRpe+sLL9povfw7/9+/9IQDg/Xdv\n4jf+5l8HAPzyq688so3//f/62yZJCwDAJEnRbHcB2D7XsM9FCIEit9dorSE8+ywk05DKXsMNg6A+\nN0ojk/R8wSGEcN/j5hMDWPk+98Ho+TIuAZbb10ogT+z3w6Ac4VBSgh41/oe/8788so2vXFwzWtsP\nSCldWzw/hOL2d4skA6f3oTWET/cW+hD+/CeUpjnHPBhaXLQ2UPT9uSpATcHaxiqabfvcDdeQzLZF\nGQPDaI3hHjyaI1x4UNTIPC+glO3D7//Jmz+3jV/87FXje/aDrXqAqFaz96UMuCqo3QyJsl/jceXW\ngtls5tbXTncVs5ldg3Q2w/aaHQtbm+voT+y6eTIcIy00tcmDNvZZ5UUKldP7EphO7dhPihSG+qbb\naCAO7dpQiz3Iwn72n3/z3Uc+w1/6t75uzq+fBwCMZjlmE7tOvXDtGQjPro9pmmBjfcve5+kEber7\n4+NDHB0dAgDWe1u4u3vX3s9aE7v7dwAAcVzHdDy1P8YYDvbtOrS508PaOdufXiDAA9s/YZxDZikA\nIKgpxG3b/9NsCpPFAABfMEhj7/N//rtvPLKN//k/+XOz3x/YfpuNUGva/g+Ro9NbAwBIxVBOCaXt\nWLK3zBHSvMylhNaS3mfzndEY96wNGODWTu0mlzHmoT2vvGlj8NDcVQ9tsvb9//M//eIj2/jt/+0f\nG91qAwC87hZCWsu5ZzCZ2L7df3CIew/2AQAPdndx762/sPd5ch+XN1cBAP/2b/4GOis9AEA6nYGX\nY9LzgJZ97klzA/3YXm9EA8KI8obLW0aeT3Gw9y4AYDy6izS3Y6DV20Yr3AAAzDKgtXEBAPAff+2p\nR7bxEzWmHsbCg2Psp/79s9eA/awBVl7L6dEv/lUz96whuMBPfvKvAQB37/wEjag0aiQCWtvstXOT\nrTSrDMxPW2Y/F1mi0IutgVYzdnMCgNODfbRqdrJppWAC+51CGGf4RHGE1VVriHEWwEDQvTFIMjw4\nBzQZJPf2dnF4YCd/KhUM3XQUethYt5Nw0D/ENg1EnUtMT60BNe5PkdKikGQpCtocHwWjFRRdq6UE\nM242uvvShUJOu15aCASFcNeUxo4xGrm0C36hCmij3PcLWoQ9zPtG+Bywew8YY+6ZC20g6LFJpebf\nowy0sn8oTA7+GE5YZgCOcvMUSLX97O2jE7tjACjqq+C0A3KjUdx6EwDwoTrELLLPX+YpULcLiMym\nuP7GawCAezdu4BoZYowZCDKO0zR1Rq0xEj4ng7vo42RgN6Cd9mdwcGo3iLuHD8CE7QcpJQrqz2WQ\nhhIstmMzbNQgaVNWrICm52tMhkxn9NpAp7Y/lQQU9W0YhYiiCACghQCE/R6fAwUdYEbj6XwxN0AQ\n2v6J4hBkVyHPUrdohzoGcprTnEGTcVGkBcxHrAEfh5UQzkgpJGBiu4BvbG0gpb7iBvDK53vrAKdD\nu6h6iiH0qS0eR0BrAGcSHllNSkkYNh+36cx+Z5IdwDTt97CQo9ay/VxvhgBtfInKkNIeLsIYwrP3\nFvoMPI6Wah83KTpk3F88v42E5vP+/j48mouhHyOk9UhpCUPzslarQ1IfZHnu+t4PAoQ1ej6NFrLB\nHgA7t+KIDkuMI6N5pj0f0HZshp6P1VYTANCf9ZGl9n1ohTSzG+Z0JiHE8nNxa3ULf++/+i8BANdv\n38Hdm7cAAF//8pdxdGwPxacnJ/j8F18FAPzhv/xjXLpsja92p4Z33nkbANCqd/H663aO/vv/4b+H\nf/x//BMAwIVzO2jV7T3fuXMHv/M7v2P7tigQ0ZohswRBYMdyI1JgDdrARYassO0qCgnB7TXM4zCZ\nWrqN/tEt/OR3/3cAgE4nULRnBBz46n/0dwAAK9vnMKLvzJSB1vYaJSUwuw8AaDY3kRe0pRsAzN6n\n5h6MLtd3CUGHXqY9SJkv3AlzHy23PLNggBgDt1YZsMXd+ZGIRQSva/chVavBo0MFYwzNur0fvS6Q\nK/u+VhxqYA3Mu4cPsHfHGr83X/8JPvfql+w9KAWV0fqkONSM1kKvCRnazSJnPjxTrj0LBiOPUOvY\nvfZ0dB+5sf1WGB9DOlgo1BHI5U2kiuarUKFChQoVKlR4AnyinqmHPUtm4d8Pv37Ix8Q+7rPl+471\n+ikYMLJ+Z+MT3Hn3BwCAwAsQt1cAAFHUBqfPasY+0s3JDIN+DBtcSoWQly7tAN2avYcf/+A72Fi1\nHrF2s+lOZ1PJoIQ9iUbNNZyO7AkimSWQpStXeCjodO4L4U6Xh0cDjMkNLwWQkydg2M/x4XuWUsrT\nEdJrljq8+d51HOydAACKzEDSKaCQgBfUlmyhgSHPhVLSnW4LnsMjr4RSBpweitLSUUKSMQjqWc/z\nHOUg8wwFnapNUcAnz5RQBXhANJDvgw5jYNyA02lDKLiHpbV29J8xynnzjPWLL9k+oD8do0737Kkc\nHp05okYLWWLdCcX6JdSbpQeqQH/X9vdoegvHHTu+psUKooZ9/ad//K9w893XAQC10EOaEq2iJJi2\nDWjU6s4LkyRTcGEbfHQ0wezQeqw26xKT8cT9LiNval6kSOhEtQyUZuCcaGQBZEXpiZtTtEpKR90y\nDqjC3lsrDCCJGmO+5+4zLfL5KVZzcPK2NJq+o/zABFRJk4kCGva5K64QEeXOlYAiagyMwSNKjkUh\nDF/eMxVBQZeUuNBQtGjk6RT9YzsPapHAuTOWIlr5zCX8+C17At7dHyD17PWBJxAHti0B1xB0DxwG\nnBYQTzBERBFmskBEc2F1fQWC236LIoGoZed6pgzefMt6WQzz0Wja+cc8AR4FS7Vva2sVAbdjc9Dv\noz+yJ/mVlS6unNsBANzb3cfRwFJvShtwuvcgCFB64qMwhF8n75XM0R/Z8TWc3MQosZ/NsgxZap9V\nr9eB4fZ+Q1FHHNm2XjjTwdkz1iP+3R9NUAR2XGjDkNHYOTw+wXS6/DhlmkFm1nsioNFq29/loYCE\n/U4vDjHL7PwIwxxa2jkahz0YSd5Oj+PctqVv8iTB+TOWNl9td/Glz38OAPD7/VNwWs9qqoOXz33e\n3kMrw3tH37H9E06g09L7XcB4dpz6gYFP4xRSIJku76dIpn3MHlhKP5+OkEz6AIBm9wxOdh8AALZ3\nziGs22c3zoCMPCZHu3+Mm9/7R7Yt51/FmRf/HXvPtQ5A4QY6PwGPiPEQMfau/7l9rTKsX3zRtot5\nYLxcO83COvrwvij1fON9DMIGtXYXaNj9LxcevJI55MzNp0ajjs0Ne59aA2pmn2N2sof+h28AAN6/\neQOXP229+r2VFRjy9Jq8mDMaSsHQfSqhAWafqWFwdJWRBoW01/hBgFyXzzHCeGA9//XmWXB/+X3j\nE6f5PspL/3FxUg/FQz1kY3E8bPb8bIM5OBgN7utvfxez2dC+7wmc3bkKAAh8H0qV7v7575qF/4I9\nnvvOMIOUNprRZAam7cIRd1bw/j37kDbWGeSRpdvevX4H4zHRGJrjneuWtvMBBESfBHFg+T0ATCsX\ni8AYQ0ixCH7gI/Ts67QYIRlRrMNJH8dHdvIc7x3AI0rM8yP4tJluddeXbh/ncHFPShYoF2QhmHP8\nWirQvpZ5jozcrwoMTJXXaMxmRH+k/AAAIABJREFUdrKPx2Pw3A7mhuc7GrFQEobc0EpzqJKGZZ6j\n+TiDMygYWxwx2t2bktJt/stAgEE76lCiSZvbpZ0LMMkpACCpRTC0IoTwcBTYvlfZELKg9po6Hjyw\ncRoffPAOuk1LyUiZIs9zei3B6NkGAcBp483yDCyy4zfy1nGaWVd+kSfwmX1u/y97b9ZkWXadh317\n7zPcc8e8eXPOysqauqq7unoE0GiAmEnCIkGGYYVlMmyLNERbDkfI4bD94B/gF/8Ah+WwQgxaliIk\nURJpERINggCJsQf0WN3VVdXVNWZVzpl3PvPZ2w9rnX2zwG71LSKi+ZLrAX1x69yTZ++zh7XXt75v\nOcZBmtJ71kWG3Z39qdtYwCDmZzBGweEDgCM1ioK+z/LC9oPOATAclmsDwTl8RaaR8TtyPRcWKsiF\nzbFSjoLiuag8B4WiNuYmhuZ8hqpSUJLzKIxA4U1ytSxEmGmbbzWNmUJD8YauYCDZ2cmKAmDn0Q8k\nBEOZrZkGnnuGDh7D5Dq2u+RUyFzD4372lYBX5uVJA4fHQADA57avnFjA7CmCNMZJinGXxnmWauic\n823cKkQ/5b6NkY9LzE9AT+lMKc9FwuP6sDdAymtZWyoMI1p34iyH4b53lOA5Cz4E0fPG4RgioLxG\nrQ0y3nA8x0FQJwgsThN7WEuiMdp1ev+ri20I3kbmOgHaLRpHy/NtZDmNl539HiKG/GrNBmqt1lTt\nA4C5mRnMztIzGDEP3ymdx314FXo/fsVAMsS2dnoRviLHcDw4xPICbeDVRgV+QE5zPfCwfuo0AKA/\n3sFgSGtxs13D0jmao7/6ua/i9/7Lv0/XxIf4J39K6/KNg5fs1uA6Bq7L6y8EMl7bxmGBPJ1+nH5w\n/U0YnnNpEqI8L3jNBsozbs334Ul6dxXPxdYurSv33vlDDHYoz2h49w/hV2nPOPHi76B3+zUAwN23\n/gQnP/ufAwByHePuq38MANi//wEufvG3AACPvfhNSFmmlUzgMEpz4RwrYeCUTkqRWIhtGnNaLWQe\npbl4UsIv4XFpoEtEVPiQ5YHczGLEc6Jz6gIOH9Ah52B/gM0tgncb7TkUPPaKIocOaW7Bj+HyM0eF\nscEQAQFdHvZywGQcFMhiyl0AHVDDIR1KVpYvQZa5JVPYMcx3bMd2bMd2bMd2bMf2C9gnHJkyH/EZ\nHwr5PRzF+nkosPz4EWE4IREOKIrw1s9+hMMxefULbRcnTl7gn+b2XkJMWAvC/s+jm3LVJKTdHaDK\nDL67Y4X9n1IyZKN+F2UOpqMcCEkeu1CehUOqrgI4AT2JcxQlY00bG7nxXBcZRwgck0JwUqgjfXTm\n6RTW7fdxf5O8+jRK4IpJaDPmELiTAr3D7lTtc6SA4YiP1jk0Q2l5lljYSBcFCn72NDQQHKZHXkCn\nk8TfEtLytIHv0YkqAKA5k36chYhKDFcUzETh92QzJAWkmUBUhqM8Rksb5TEokOXTn6K6cYJ57ksF\nByOOJmzvbqKR04l/qF1cPyRooe4GkHy86tSXEUh6J3lu8O67BO0FFRdr65QYe+XaFeQcRdBGo8jL\n01iGQpdRoRQqo/tksUAScYQuT9Fn5s8//kf/CDubFO08ubyGy29f5hb81se2MRpqm0QsIeBoaqNS\ngHDKwS+hLX1HIePoYS5TCxcJISAZTvegJiuKMjZiCAnkujwFppYBl+WZndOF0MhzijS4yrGnvLyY\nMDSDILCQ4jTmKA0OZCDPNVweY3EBhJxM33A18pCebez1sTRHEaWvPH8Gf/g96s8UuszlBSAx5s8x\njI2QzkuNF58maK2z0sH2iNoSjkNUGTY1poA0FHUK+zFcnjsNz4NACUkbIJyODLJz0EWF2wTHRzWg\nMEZvGKLL89n1qgDDrY4UqNVorSnyAlnGEH2WW2atNrARS+F6qDDreP3kSSQh3XO2EWB9iaJFC+06\nNrdoHH3w3gZe/hGnF5gISwz5CaTwfFoPtHZgxPTv0EiFP/vBD/m3Gi2OWI4PQoQ5RWQqtRZGh5zE\nnHgYhD8BALRne9i8R9d7C09A8di5df8eco6abm0f4PJL1wEAQWeMpVMEy+/tH+LeBkV/Tq6vwyt4\n7AxjwGUykFsgG1N/jkcFIk5GTyIBv5iZuo1Xf/h9RD3aq4TOLVs0GdzHvTe/DQB47OzjqM4Si+3+\nnZ/hcIOYbuMHW4jHHGEcDnD71X9Dn8ND7NygawY799GsUeQxC/cwvEPQYffuPdwNvgsAOP/C1+H5\nHDEs8klkSki7xwrlYOO9HwEA0niIsD/kFvzKx7ZRNAOUTCEHgMsTKhcahkk0LgwE53J0ak2MGfJL\nxmvYv0lzS27eQMwReHE6K4PlKBwg5TXSFAaKVxA3d5FzdEkLA0/QPqBUilaHIpjZ2EfBc8HkMTye\nU0K6SJidOo19wjlTwFHn5UNZe0f//1F6p/h558t+mvzKTHjUUgJvv0aU88BzsR4wTb4+i2Znlf+K\nhrCxuSNyC+avPtG0ppRrJ60nHYQJvaTt7gAVlkBYyIA6D+5a07d/VxcJKsykEZ5CyGH1LE0thi2h\noNhhcH2JmK+ROSxFXZoCDsNOzdk2io0P6D4wiJhKmmcRRuz0DXf2MTg4nKp9QhgLsuoiQ5pSm5QQ\ncJTH30+A2DzKLKRl0gybd2iBatXqmO8wy7Ao4DOkWXE9JOxQwHMsI6UAUJSbv+E8KAAw8gimP4GE\nKBlukj9V6Ok2KABQjmOZca7nIWSI5+XuAdZr9Jx5HGK7yw6oBKoBwQOZU0O+Q/k4Ozvv4869OwCA\nuc4M+uUGm+YoKftGawQc/k4SjTyja7I8Q8b0863tD5Ab+r4wGklGi9jO3g10u9Qnq4srSJLpc1Gy\noUDGzo6UBVz27osUMLz4CGFsfp5SAlKXjL8CHsPLUgho7v8cGQznXcRZZCn+jnFse3VioEQJJxw5\nwByVQXH1Q9+Xn8NBAkdOv2R5rrDsI6k1Kkyl92s17AmCCpTRiMfkSC6sL+Gxx04BAL70wiLW12kB\n7/d7UMziTNMM+316Fw8OQxyO6PunTs3j8ceWAAC3NvfQ3SGHtyYLVBrUtkxIVFu0Id7duosD5rp3\nfM8yBAUmG8HHWZ4CutygpIDi8e7AQagZhk1yNKrsyJgMKR8qKp6POkNCaQ5o0NzNMg2f10q/UiDg\nnKCqBC6cps32/Kk5DAc0BsNxgjt3KIXi7dfvWjj9yafn8dmnSS4kzIG3rtEG+GAvtOvBNPaXL72K\nH79KTkFrNsCzpyX3WYKUpTRqM8tYm+d5UG/jg3df5u8PkBXU9t0bV5BxjqjJI/gV+u37NxL09ulA\ncvr5AHOz5wEAa8EKGP2FyYGqzzlijmMZqyZ10Nule/Z7OSpNXv9iB1fe2Zq6jdnoABAlg5bGPwB4\n9Qx7998GAPSGGygCGkfv/PSfIzqk+8dxgXCf3kUc5uhxKslw948hEhrvaydPYY2fv77wJEyfDzD9\nETTnJckiRa1k5RaF3QBzPZHq8IIqRlu0fqdxiLSYnrEofA+OKeVUJErXQ0MiK/sTGqZGfVipVLDK\neXBhliAcsVPcC1G7ept/W4HbpP0ykikks+VVaxWSUwmkl8NwgMLJNFb4+7pvoEsnazyPSoXW77t7\ne4hZumOcDaHTeOo2HsN8x3Zsx3Zsx3Zsx3Zsv4B9wjCfRhlJEkIc0Z758KjT0ejVQ0nqR9KMy7va\n+zD8EPW7uPIuMfh8V1phzM8+9wW4qmSZ5B+qXWUsoDT5i9OalPKhqEObT3k1XyLhE5nn+/A5JJ+m\nufX8K5XARrXSLLPX51k+YUMBNjIFCBuqV0qiJFpkSYwq62w0WjOoNyjk3N/aRsxQzThJIQIOUUcx\nxuGUMJgpLMxndI6CvfhcOvZF6MLAMFPCUQIBRwTyPIfmpEXlVSC4fVkYQXAEz6kHCKrMMoM3SUCH\nBAfVUBQ53FL4EUBRJsQf0QnTGhM9LF1YWGoaW2pUEdoEdA2PO3bGb2GFxQG1q3CPQ8ar6yfQYkG6\nl155GVubJPIZRzEiZu3tmwL7+xT982ouwAmYeZaThhYo+FqwlkyaJKi26KQ4M+/gsGTAHQmbriwt\nQOXM1MpzBPXa1G306hKeR9cLGAScfJpnGjnHzg0KiBLfEtpGr+BVHmLylEFjIydsStdx4fBYPjon\ntNQoBdGkPHKWMyXDDICUSPi9Z9lkfhRFAc+Z/j36rmdZPRKTaKaERrtB82N5fgZLy8TyevbzL+KZ\n54nd1PZ8PP8UEVWG3R2YdMjPE2HEDLfDUQIVEJTVnJ3D918hHSOnlmNtjq5vOylCHntucxYh9+37\nDw7BHBF0DxPMMGvVkYA3pQ7TeDiC79DJvN6swmR0is7SFGXwRwmJCms/9Xtd7HVpDH76uSewME+/\nHYwz7BxQm4TQCOr03mY7AeZ5DNZVAwuztF5ILTDu09+6/N4dDBlmmp+fhdDU7hc/ewlLC8SY+94P\n38D9+xQJ1E5A2khT2vhwDMWwWmcusDpK41yi06Fnc/NDVDSlUKytnoNnngUA3N68DKUoIrZY7WJr\nm6KFczMNLM/QOF39lIeU4a39IkXVkGDmfL2Orfu3AABvvfE6DjkSl42VJZ5kicHGTYrs5FmBJ1Yp\nujHUBcLB9GkFsppa1imEgj/DrFOTIWLW6caNlzE/pnSN7buXLTs572eoZDyfXBfZkMd7z4HHumDD\nwyFu3SECy5kL59Ds0H7QnGvYZ0h37qHPZKkiD1ENqB8q1RYkQ6JxtI003uPPERozi9O3UbtQZWTb\nMTBMNqkqF1WOwGfJAMN71OcHb72Du6+TLt/u9Wuo3qXn70gDJTYAAPdu3kKV40Gp48LhyJSz8Caw\n8hIAwJtrw2nTe5l74nGcungSAKBVgYhTLVbmOxA1Whs2ekN4TIoZDvvwVCnq/fH2ycJ8R6A0KTBR\n8j4q2inEkWsmDpd4+EbWyzJa27QpKQDJG/fPLv8YrsNSAYMCs23aOFZPPWGhI/q7PwcT4mFogf9l\n6jZqoy00YoxBg9lELd9BVArz+RVkvLAncWw3mlarjTwvFZsL5KWysJk4jFIIFHxNlhV2oxFCwmUH\nKs+yCf3cKLRnadBv7/aRM21YOQEM5/Zoo2ye0se2TxdM7aJnKmEDowEBZpZpYyGhzAFy3iRbQR3n\nL5yja+LMMouqFQ9NXvD9oIJU8qaQpBCs0i21hseTGq60DI0kS62ApFHCsgh1oZExQ0abHEpNvwln\n8diKMRZFjibnvJyfb8HncPlACqws0yL8e9/6PbRmaNJdvvyWzf+SwrFOXJZnSBOm3wYOyjGVFxoZ\ns0o85SPl8ZhmMSo5h7zdJrQZ8PcjmIKx/rCCnCG/JIlQ5cVkGjOVFLCQmSrJbZAVxzpNjuPBlLk8\nJoeQ5Zgiph9As0eqoywgzm+SFfiMoSfQyHiMSy3gsfpxPagjiWnMhEWMvMyj0JkVXJVS2jVAOgri\nEYLpptCUgwTAdzzr9Pmug/XTBEGtzXXw5S9/CQAwe/YEHGZQZqaA4feoWm0UER8OCs/2c9AREzFY\np7D3vLV1iMVFcrIWAuBej2AwI13cfJ8giihOEHNfxalGynPaVQLq4XoPH2mL8x2EI3KOnLpbpkYh\nyXM0mTnqygDxmOZBOIzRqNI6ONMKEI4JEqo4PpZ4A3edipVpaLermK3ThpzFCnfvUa5NHEncuEKb\nan8YwWEx1OaMgy98/pcAAOcunMG3v/0zAMDP3noAr0V9ZkyGLJseHjJJjM48PcPTz7XgKlobhjsC\nvfvkKK126ogYyhz05xCCnjNyDtBn6RhZF4gDuqZXOFDsdFTmDBLOiRzstbG6RLT7YTfDD1+ifKX9\nnQHULPVh1wBei0Vbc4M4ZgkVx1gWbKtVxYUnTkzdxhQAShFUocG3QXpYWMX/t777bQSztA719rpA\nwnOiC2g+lOgqkEXct0mChO6Mne0D3N6mPrl+5T2kzBCMkggNQ+/l7T/5f1CwxEV3sA23Ss7UV77x\nOzgxT7lL3/n27+Puu28AAPJUY23t7NRtlMKF4jYGyqCiyXkP72xh503KK735k5/gwev0Obp7H2OQ\nc3fqM09hOEdtjHohQnCFjCyEy2uqKgBnm6G961chNaX41FQVQ5fH3re+CXmJpCMiGBS8t3jCQ8Bz\nvcgKeC51UBSHqDSmP6Aew3zHdmzHdmzHdmzHdmy/gH3iCeg/p73J/3DkGhyBDbR+GPM7+jt55CP/\nmxYSIQtuvf/eqwhYpKM6D9Q58bMzu2SvByZRMAMDrT88evFhdfo+0swEkmy1WliYp6RUx3PKHEO4\nrgvNjdba2PpkrutZock0zW2UynGUZcpRPTO6T5Fry4zSaQaHI0DK8WxZFQ2JWp1OGY3WHKpNOjE3\n2m1sHtLpElpBmemGAmlJTbSicoYZCzM5RZkCNto2FBl2uU0rnXmsdliUTcVIWbwvqNdQ4dOwNhrh\nuNTlSQA+PegosaU43GoFKcM9mc6RlX9YKugy/J0Xtk6VMYUVcJ3G7oxCLJWlYlwg1SxQqXNsblNk\noRfMYvYSif0trp7CTJNOz+fOncV9FtoDjE2sR2KQxOV9BMrOyrIMLocUKn4dKZ+80yxFGtGpevte\nDzFHcKIwgZjlyJFy7FhLs9iK7k1jBg5cZlxKAxhuY24mdeiSbALFSSmPTEzYyJEQEh7DeUYAgiPD\nnjZQDN0aT8EHnQ5X0cTpedL4Od1Zw5Chz+v7N/EBl8UYuLmd70pMIMIsz22kbBpLxiEcrs+olIuU\ntY7WF+axsk4klKfOnsPFi8Tu7ZsQOSe9etU6El4/hKeQ67KkjYTH4oO+H6DXJRhm98EDuLx+XHr8\nFFKu91ZFgbU5jljd2kQ0oLHtQdoIVC4EEo6caqU+QoT4r5owGhWOIs4EHtbWaa0Zxjm29ujvF5mB\nw9HxpdU1lMPxcDiEUjSmTnZmUWcShJDCzpXD7QPc2qJk+3EYo9KkPnji4nncula+nyqGrMtz8clV\nLC5Sv/7BH/y/uHqDIkdh4WGpQ1FcCkpOPxe1CNFaJqJKFsQwAf0t6czi4Dbd8/bVDPd6tJaM6nvo\nxncAAINshIzZf2E8hseQpTQCQyZpZVsReswEnHPOohPQu71y+218+8+JuVbzXHiz1J9jV+PkBSbO\nmBztObpntebi3m2KQLbqVYyiRxAmTQzMqIz2S6iSyJPnNqElGgwQVKi9xUAiEBwdrfvoxvSOojBF\nmZ/gOg7KvI9kkNvUlrzIbSQ5O8zBZEQoR2LI+8H2vbtI4xt0f6eBA4643nzth0BB+yhyg8HWxtRt\nzERmWcKbP/sZ3v6TfwUA2Lt8GTFDeGLYt9crAFXWTez3Qgw4utrMCjRZ2FrlCob3TuVWABZxzVSK\nnMkJkacQcbpHVAwxGBJpSDsexqUulVO3JWdqjoDicmBGFxiHg6nb+Ik6Ux+VG/VXrjtSa89SNI/+\nVsAOFLoPby6uxOtv0AQQOgYYl83zGCssh+B5vq09djRfyjwkvfDRz/xxJuQEIlRKITe0mCepsKyC\nMBxjOKLZPB7FFuLq94Y2zycvMjhOKUimkXIhRhQCgldbz3dt7TrpOZAMR/mOBGyfVFDl+nCrq6cw\nv0g5P6MkRpcVjZNaBj07HbSg1ESxFmYi4Jnn+WSD1RJp2Q4nh88ihPe7exZ/X52dh8PfK9dFaGjR\n6x0eQnOc21MSFaZRm0IjGtLzjochqktEYQ4qAaKE66AdEZgjdiY7REpYlfFpbJQVls2nUEGXN+F3\nNh9gjqnlhyZGJ6e2vHv1LhY5/ySKI/S5kHY1qCFmuq7ABLIOKhV47kRsrhzvh4MBNMNnWZFalpzn\nCKCgdxvHqa2nVejEQspFkSMqhR+nsDTJIUrdAK1RMNSY68w6j0G1+lCu3tG5UObqHf1OSgnBjnPi\nKgx9nrtZgdkD+n7JC7CgKXRejwo0RQkRrcJ9QIvplTTCsC74eQqbe1U54sRNZQoYcY6eX3WRF/T5\n2juXbQ7luRMnkaW0QYejHbhloeNG1c4tYQQCzmvzTAUVZm4Krwqf8dFxdANdZoV15k9hK6X3Nc5r\nuHeXnOtXXn0Ph30+KBTaniGlOCJDbKavt6AkcOY0MQ4vnl1Ff0D91+8ObK6Y5zrweR7UAgkYzosZ\nD/D4E5Q/0qjXwamPSNIQSVmDMVNwHXIWfC9DtcLCt1lq3aHOXBtzzIQScPAHv/9HAEi2odGhg1sa\nZjbPbLbSQLnpTWPNhRru3CPmWnNpGbMnWLBWxmgtcAWCOMNIUx/f7KZW6NJ4dSvyWYQKCa+hQS2G\nz+9TjmtoOTQem34dOauGz8214FWpD/0ZheUn6EB6/e5d3HyPronHGeYX6Pt21YNOaCwfRgWicPp9\nw/FrmF0jWHBve8c6UEal0Hygnu8s49OfouLorwx/inqV9oy11UXc+IDyjLwcWLlEKR17aYKqT++u\nP+jbteTJJy/BZ+j2pZ+8AQR0H1HroM7F4JfTBGlIh4rTywt4jMfY2602ipwcySCooj07fRtTTERi\n7/7sVWx/7y8BAMOdbSvpoeY7yPiwVIxCDFgyZmN33yqmn9AGFYY4c+MBJ+hw7l24iHBIg7iS5nAj\nZgKaFBmvi17QgIk5DURlGDK7OkkjBCxcvdBqIFX0PLs7m3Dl9KkTxzDfsR3bsR3bsR3bsR3bL2B/\nA+VkPj76U14SRbFl+JQRjfKCh+Q7+eTV3dnG1WukSaIcAV0mmqsK1tYvAgC0SW3UhoTmy8jX0ef5\n6Gf7WJOwzIxBf4RCUpQiaDQQ8eksTmKkfLKX0kVeiuQZg0aDTgpaF4hZ7LIoCpuom+eFjUZUfB8u\nh/DrzQbGDM8M8hzzHQrHVqtVOCU7a8FFzBGI+1vbGAwohDke9SHMdOwTRyk4ZT2kQqCwkUNtowbq\nCKEgdwCP2XlaGOwMCBZpthpocCJvmhf2t6MshuSoFnIFh0/+VenCYRgrHPZsOZDKTBWKo1qFzmw0\nSgjY8gjKmR46Ka8vRVJNnsPhhGknqCHhhP/NUQ+3fvYdAMC5c6fgOhT53Nnbx+IyndTjGOh2KZrm\nuA4kQzJ5XiBjaiLBKiVMnUL5fCotchv1kzDwKqw5lofIOIyQFmOrvZVmqYVcp2qj8mypGAlpNcJc\n34OvSlFWbWHnSsWH45SQ2UTr7OeZIaWIa4wc5VmtORSos7bNPkaIDLGSopV5+ExaSLOorOiAWqFQ\nonmx7yBnCMxIAe9RCoI5EmNOyM21RoWjgVkc48ZlYn99X+U4ufy36P4mQW9EzzyM99BZ4ihIESEb\n0ynWEQq6jJZKH3DoFDtODA4PuvxbiVfeoUjJ/c0x+lsU+RqFEcY8fnIIGB6UrpQIVKkFpZFPqd/j\nKYWMo8t3bt7G/U0qeRIZH402RW6LPEazTfPshYuriEa0Hm1t9+HGNP/3ezHASbd5kcLzaA3qzLcx\n26H7R+MAUUSRr/t3Q7z3HtWSO/fkOTQ8is688sqbGB3SNfVOBzMLtAbVXYHYCpHKCWtzCnv+i6dx\n/Rr9rSzJ4DEcmZshZEBjZ+VsHZ2A4MULy59BEtH4vf7eNUhJn2eaM9hNiWU7zDQUMxBNz4WrOR2k\n6sNw9HK20cDXv/hVAMDbG2+gtUDXf3rpNPbuU6Tj/p0eKpx8r/wMs1yVK01jNGem31qr7WUsrz1B\n92k/QBHSOwrjIRbnKdLUnGnjTRbllZ6HwwNaRwf9XayeIG2sZOcAkmGvxdVTuHiaol33tx7Y6Pel\nZy6hxTVFk1zh8hXSIDRpimadIokzjwWY58TrX/n6b2Bvn/rtU89+Ab0DGg95lkB500cYYZTdj5HF\ntnzOzJkVu07rokDO+oj1uSa2OHp8s97APCf6m60+9nmh2FEGMqUxPLPzAH2uVyiki7ygvbYOBcFR\nPLU9hHz1KgCgsjaLjMfGIE0BJiec6Czi8m26BoWGW5mesfiJFzqeMOaAj5Ic0LyJbO9soVlnWuP8\nnM0zischXJ8GsVASilXAX3v5B9AMUUihbLHdVruNmTmCt4hxNBF/tIWQiGRP18Dgkao4HrHcJMj5\nGdJ8UuT33OlTmGE1dK21lUDw/Ynqueu6qHBSg+tOagI5jmNzVwp9xGlxFDJ2xJRStnhymiaosOxB\nxRFWLK0fxXjrylXuH6AkuJk8wdzsdBRQVzp2Iz1aWFhJA6HKd6vhlgrZUsEwTBYEAYqyllg0RHOG\n4YHIQPG4aFYChEMK0RpprDKzkIWFpZozM4hYyiHb6UPx96rqI2earRST2nAo9MMe8sdYvd2aCFQW\nOYJy0/MDO14eO3MJuzxO52ZbeOICMVtWltfRZUG9w4Nde09jjH0/WVYgPVIrsMpw59pyCztcszFO\nEuTs+KahQLXJ778Y2+8LHcLwmM1TPJLKu1QKdWYmLi0sWYXvnYNtREUJKecPbXyl2KJylF0AHeXa\na4zRyLh+mBaAz3kglc0Mo0O6/lbcR22Dci2S7iKaMzTOu/1DbO2TM+I0JByGx9GpIBalqB/ldE1r\ncVRAs1BjFGYQFZpzrvThcr7QeDzCbp+e+bXLN/DyW5QTBxXg679MMglPnl3GLlO2daFx/klWwzYp\nXn7tPQDAg7tdmIQ2gmSvhxvXSNzwsBvBd1hM0HHgloK3WlsHXwphaxfqAnZOfZwlSYpdnisHSsDx\naQ57RqDOQ79VrcLljetw6x72DmhsjrMC+2PKV/Ml0OYN1g2acLnwul+tApKTrKSHwxHd59V376HL\nipahHmHrFgtFxiFUncaU166jKNM1jIMqC4TGUfxzwjb/YSucLp79LDkFRscYdLk+YFaFzzl/JxdO\n4fHTlL/49Kkv46W/JBbh7t0IgUd9ufXaAxwackBU3YUfkrPgpBJzHXpvn7m0hKBC4y4dx3hihaQx\nNgd3EYf0Ph0/x/war9e1WUAzM7UtUeF9ZW97gCx5hDYufAmbHnli/qmL8EJyXhaXWvB9GjuDB/cQ\n+rx/SA9pQs9z/rnz+Mq7GE3ZAAAgAElEQVQlWnv+4l/+C7z+Pv32S50F1PjdVZwWhhE599deewWV\n818GAAwTCbcg59cF0KzRegzjw+Wi43s7h3iwQfcsUmBmjhxknbsYp9ND7qaArSk7HoXIuZ8d38Hh\nfRo/KkxtBQuvWgHYUY2kQFzu03kFWx123nUfWw/uAABO7e5glw+oW0VOKtYAWsJFUMowfPA2Kn9O\nn+eeOoUnf536wWtXgQFDhE6MKq9hs9UAWTH9AfUY5ju2Yzu2Yzu2Yzu2Y/sF7G8A5pvmIvpPlmV4\n/wbVTZpfWkLGAojb2w9w5izpFTmuj+0d8rrv3HkXFat5Myk/srz2uGW65YW2LBqtJDSLJAqZ2YR1\nAdiEvUe1OOlja4dgjHqwiMcfo1PDuVMnceLEGrdrUk7haIKv5/kWQhNH9LaEkDaqIeSkVhIxrMrw\nkrYMPmOMTcqXOofL4cw5qVCiMz966SUUDB1CU9LqNOZIZeuRCQEYDmkIB7bGkjG6rLiAqnCgUZZx\n0BCc4NuPR+hzkuOcW0UZMfbcig0fa2EQlfXDFFBwFEa6PnRE3496fQhOtKyebIKj3NA6h8t9qUVu\nSQfTmOu5VgjU0YVNYj6MIpxp0elt/eRpnF97BgBw8eLTOHuaQuS/+7u/g3/2z2haDfrfR5ZOIDNd\nsg4h0GAdICGF1XU6f3YBKSeXR2FoxVFnGgH2xnTNKAttXT8jMkjWw8qyAkU2fWTKqwgEHC2oeAHA\nyfRB0ECUlHX6JDwmQaAwSHWpA1XYGloCPrKUfhtFfQjB4qvKQTHiMPo4R8Fh+iQrELg0Tna7hxA+\nJbQe9iMcckmjauTBbRJMpZWAw7CXazxk+fSn4aIwSBKO4nrS6rO5Stg6lq5fQSYpSvHnP72On/yM\nWUxBFdGY2GjJ115Av0ufleMg8Sh69cOX38EPXqb1aTwYod2gCEenWUd/VBIJJNcABaQU8BiWl0UO\nh8kGMAVchg6VklMn2Y/DCGOuD+nXAkDS+5+pu7jANQbn6hVs3KIoxrZwMOISNjv7XTRnCf4I6kei\n2okDw9Bru1NDUGWdLggMaxRNaDYdtEsxz4pBg0tjKZmjLGXW6rSQcnRXQ0Hb/Hpj2ZnT2IN7Azxx\niTPKHWB4h57tzvUU6yeJFdpYOIGQq7dsJPexxhpff+ebv4YP3ic4d9Tr4f5NYo31wz00meH81KXn\n8FXWGTt95jT2dylKkucRiowitK7KbGmvQjl2ruRZhIBLv2x9ILG7S+9Cejnq1ebUbTy9kkAYejbf\nZEgMs8zCEAWTAv1shFaLiETxOIJ3lqJ1iydXUfDYXGg08eW1x/jZhnjznWsAgLpSaK/weGit441b\nNM827t5FwMnoo3CIeINQi4tPXMJMk/qn1arj+jsMKR7soH2KdLhytwm9sDp1G8NBCMVr8DgDYu7D\n9H4XTpl2M9eEZDZi2g9hONI0X8nR4DUpMgW6SyxCnUh0o7Jsk8aNmAbfPV1WhgWayqDgmomdSg2f\nff4pAMCt8RDhWxRtPnFmGSsnaAx31ubR5ghzNBhjoT09JP03UOi43Ig/2qsq/+3U+ilsb1OI8frV\nN/Hyy1RzKY5CPLNHzIZOZxlX33uVfqdDGFXipo4Nlp9YvwBZymc7rnUo9vc38Z1/+/sAgKee+iIu\nPU+T6ucVeh8lb0qqAjnDLc888zSeeZpe3mgwwohzlFqtpoX5hBCoBCVjZvLiDCbinwKCnChQztJD\navDW45rAl0e/lzAwRSlfYPDYGWLwhOEAt2/TpjAsgDCabiMmSKLUpTBQZV0lM2HSGf43AHCFsKHb\n3FUQ7HAVxqDPEMXCQgsVwRBJrDE7x2yKgz0MQy5+6zkQJRSSZFCKrm/4FYw5tywPI3gtLniqjH1O\n4bnIHgEfElJax0fnGk6NFYAXPNzYool5ftjDSYc36qQPw4zCX/rcZxByfk2ejnHlPV7MRyEGfQqp\nLy4u4dKTtCgN+mNE7NA3K1WsLNKCeXBn4nyZRGC4x7lX9dQ641I54FQnhP0QaTK9M+WKwDINH8Qb\nVh1cKwPfZQG7PEfB88aVCh73eTQaA+Dwt5dgyDUBC50g8NmJN9JKQUgIC105UsLh4rnDJEW6Tfkh\nSZpiwFIZYR5jTnLODwwqnKvl5A4eoTQfanUf/YR3d1HY9SDX2qL7fsVHZ4HyIp557im89S4tsAtz\nDZw9fQYAkGkH2qPNsT7TwsGY+vnB3gF2OE8qSTKMGbpNjINeQn8tTQtUmIrkK2GFZwNHIuBpJIWB\nw2OpEBJiSodRuR4Ei3CmUsHljX1+qYPegMbgvevXMc/5O9tdAa3LYuQuoh6NWVNpYMjU8zjtYzSm\neTmOxlheJFjHFQVMTJvw0owDJyUY6MyJNrb36HpHAO12ndudIimFXR0P2pQHLWMLnE9ji0t1RFw/\nLi8ypBE77mGIMKa94WCvgdk5koXQWY4z60Tlv3v7fcsEffyJxzHm/LJxEuGXv07FeT/3+S/aVJIi\nyRAE9PzjQR97ITkRoRljcZ2ckYN+3xb8bnYacPgw8+YbNyEyehdPPnXaVjuYxvzRVfg8LrxaBW1O\n9Rjt7Nq0laDdsakbuhnC4Tk6eHAF9wZ0zebGFpb44LwnNHY4X7MSx/j02jcAAC/8yjcg/s0/AQC0\n1loYG3KosyhGzE7H4UEPaVJ6vxGSjMb4wsoSLj1D+5mWDuqt9tRtPNzvQpQ1PGdmgEuUY6pv34Zg\nSQad5lApS95IgSqPn7P7IeplpQcH2GeV/VH3EC47aFIoCF4n0hzw+W9VCtjc0GbQwtMvfBYAcPP+\nJsYDmoB7uxn8GrX9wjkfrqR9en7Wh+88QurE1Fce27Ed27Ed27Ed27Ed21+xTxzmm8bKuItfqWBp\nkbzfK6//Gfp7dFLQMsBPf/JDAECeA4HLGf0Vx4pYGh1jZpZOKLOLa0g5gpJvX0ei6ATx0o/+Pfp7\nFF51lGPFCh+lfMzPm1/xcXKVoL3Pfe4LWJgjaG93d9fWPEtzAbDXrRwHYChCU00WegIzSXyTUtpo\nWja5BBBiIkAqJmKaAuKIwKKxmlwkTMre/pmz+NQzBFO9lqSW2fdxJo6U91BKHYEZj6TNGmOTTB05\niUwZJSCOlB4J+aQ4imO4nCwpPA+NGXrnsS5wn+vc7e7v2zIejnLgcZIs4Njk9XQ4hM/MwcBzJ7Cn\nEHDEBE79OKu1mgiHZb2/AjKg08/MvMRBl078cRwiYy2T1//sX8JXvwEAWH/yGXz+c58GAAz6O3A4\nKrG7e4Bej/r4+U89h8+8QJHVP/3338eYowLFgUG9RifjvptbAdJapYbWMgtsygxZGYnzDEJF0a5x\nHqGdTK+JogofgiuoFyZDwXUGpXSgDEWOKp6PQpTlfAC3QhHDeGCQpnR9tS4nJ2ZUrH6PI11Uc2Ze\n7fWtdpXr+5AuQwv9LjZ3iYFWr1dRMNRVOBoOi7j6vgu3DCor3yZqT2O1wMV8m8ZJmmur5WTyHLpg\nduFeFz0u9/K1r33OwnDLC0uYZRhs7+AA927TOFz1anj8CYpYfeM/noVTpfqfr73xNnwmxeRGYljW\nzBQSeVn6Kte2DI8vBQKGvGu+B9cpITEBR07IJ/8h83wfCwxXVSoChmGprfu7iLh23vqCj26fxu9m\ntwvDCfmtoIpxj8ZvNBzbRaU1WwO/Hty48SZu36S18vEzJ/DUU7SeXnjcR5JR3/zRv/1L7B/QuG60\nmpYoM4pjQJVCwxKCgRejNFKe99PY/LLC/i7DbU4V1SY96OqZAguL1E/VisBMgyK67fkZKE5unpmb\nxxKnMmhs4Qu/RMjDU889izPnKE0kyVKUGKTQQJ0hy30p8WCPIia9YQy1Q3142B2jWmNtJlfBbVAb\nTz+5jME2XXOwf4BBfzR1G4WpoJbSfb4oFyzkqqo1OFx7UfgBgibNicQUuM2syZuDbRiOui8uruIB\nIwzOfA3FHkXHbt7bg3qNysD89m//XZxeI3TCq3u4vEkpKW6tiioL67quC8H7xOb9e0g5Eh64wPUr\nxJaH1qiUU/HvfPNj2zgajpDyBlHMzaLzHO09B5s7SDlC6gkHVU3vLhMadY7Q1vIIFSY8OMpFzaV3\ntDDj4uqY+kE4Duos8oleH9Uy6dx1bWqJki62723T48cxctac6vZSNGLq2wcH9zE3Q1HxnQe3kT1C\nLdBPnM1nheqO1J8yoA2v/H/WpTEFZlhMa2PnADMtLhpcq2NsBWZTeJoX8yK1DlFRFGhVmbZ8eIig\nTWHa4eY7eOU6OVAPNm+hM0PY8+lzT9r6SDBmUvvvUXOnjLSq575fR5rTxOgsnLBCkEle2MXL1aos\ndYcM+qGCvOVnAQFZsgsLY3uI4L8j/WZrAmLyGbBq2wLafoY2uHCW8PUb195HNCXM99C7OvI+YR4W\nPS2dPCkMdMmil8K2WwmFnBeB/nCEao2GYrvRhFOh97m4vIp9pgD3+n3LgHM8iYiVaaWRqHOR4d5o\njIydqcZcBxnnc2W6sMWEpzLXQZHTANPIbA2n1AgUa1wc1mQwmibyvX4P+qc/BgDkrsTJU6cAAOfO\nnkSefYaerTdGid586Utfw5lTtJh//y9ewjs/prybrBihNc95Kf4sNOdPzS4G6B8Q3LK1ESOvMiTj\nxNjLqH+McWAeoYCsp2BFZDOjkfE4VbmALJXjRQGHnReTF7bOnYgVfFYWbzUC6BKikgqlCLs0Arl/\nZGPlqaWUA4fpz8MwxnBc9rOB5twJtx4g4YU6TVJkccnQzBHq6SEitzBoMtMz1hqG609mwrF5fwdb\nB7h1neDup178LL76K1+h53Q9bG5R/sz3fvpdvP0O5ZOsru2i0ua6ewsd/NKXXwAADMYHVjriYH+I\nzU3aiAsNRHzAcwB47NQbDZTkSy0MXG6XUgKeO53DKIVEwTdxdYZFVuMOvSqKDm84cw10DymvZFa6\n2N+hTf6wF6HC70cLiR4riHf7kc2HGo4zhAyTPv3EeVy6RGN29+AAP32DNuqrt3v2YFgLHMw06e8O\nRiN47GQLx0OclFIwAo47nbMIAGmaoWDKu85zq8I+Iz3kDDU2KyfQYPq7X3ORl7mGnTm4DIedPX0B\niqUxgkbd5t7lhUbCkjUKwrKoPdeHJ+n+Oqzj5T97m9qiq2g0uOanjHHqcc616bTQZifuyls34Kjp\nDzbJ4BA9TXNiZz/FZxnqWq+78Kpc+6+SASU8Kqp4Mqf14EAJ1LlW4J8Uu3iXBWL74xG0pP3vxPOf\nxsoMFzU+vI1ajeUc9oGVRco7e+3N13C4R45GEATW6ZdG2LqmhXuAciL7rg/1CAfUNCsw5j1mXOS2\nTl890zafLlXGysE4xlhYEEjsQSjXDgxDe54jIflwUIVGp0G5VIstH1U+vFVbbQRz5ByZNMI7N2iu\n11t1eDxf4QJdFva8sXEXWGORYOGj7kyfM3UM8x3bsR3bsR3bsR3bsf0C9snDfB8WNROAhdaOlngx\nBrOzlBz6a9/4HfzkR/8OALC9tYFFZqtkeYLBiMPGRluYQbkSgx6dDjde/3fYTOgEUa2m2NthZo40\nWL/4LADAqzZR5Ec0Jf6abL4iKXCLvd/vOt/FzByXbGi2UeMQcrPZRLVahmxzwipBEE4Z0RHGWKaf\nkMKGz/9K4v7P9Vf53/LrPM9twnKWJVaXKo1D7G1QiDeMEuT5dCd+bQwER7eklDYx/ujfPMpE1EZP\nYpDGoBQ00kbA5d9GcYKhoJNxu95GwH2jHAcXniAxu3A8xN4OUXY8x8NwQCcJZQREyhChzpAMmFHT\nasHzSzafhlbTh2uHo0PojE/DUgEZRzFGBe5tMqtk7i5On6J3a5TBvZskqFerKwwHe3yfAdqspfX4\nhfNYP03ieotLp6xI5udefB7/33f+FADQO+ije2j4+jnUm/Tb1A1x4gk6WeZCWoKD6wRYPEnXHPSj\nh1iiH2v+pH6i0QIV1pwSQkz0x0DsK2qjRI8Tkw+H+1acc36lDVPqZyGzIpJV49swelgYe7JsKwEd\nlqWUBuiNKcK419+30dR2o4MFPjQGykHhTGpUelNCYAAh3VbLCUDGJ3tdFBAMKSZJgitXSCvqmc9/\nzq5PGsCtDYpMvfz6NSguq3L5vTu4+BxFA/16Gz9+iTSNHD/Al778RQDAtfdu4srVO9QneWH7U0ig\nKOe3EJa1p7OCQligiLKrposwZtpAMvyx2Kxixqf1q73ooslQxcbmDgqXa/Cdmbelna5duYdamzX8\nVppYWqVT/d5OiI07FMnyK1XMrdC4OPPYefQZ+n736h38iz+m8X4wztHwqG9ubxxYxicCfyIKqw3y\nrOz7Sb25aawmZxFrGiN9M7IkDjeuY6VJ6RSLwQnMWfZZG+GI6yI6Fcw0qV1QAmOOPqRxDN9nMkUY\nIxpStM53Pbvut9tzeO5ZijpuhV3c2SSWp9EFuod0vREaW7c5wnUzsULJC/MzqM9Wp25jLx4jzamN\nG5HG93bo83nPxac5MnI+8NHhOVoNauhwovwJ5aA2ove+f3ho5+h7aYbeCWLbicVliDpFynajELPM\nKh9sdHG6Tf3mnxtgl8uazbUa2OKUmhu7u0hLhnmeQ3L8JdMFonz6+oNJWiDiNTuNEtRQEpEkMlAb\nMymPhHf8h/c2/q8LiaJHEbRiNMIpSeNq0WicmKO2nHKb2L5FxB/RP4DMaU0yRQq5SXuIadThcs1H\nXzSQMRpy2KzjnQ9IJLYlKrg72J+6jZ+8NMIjXGuMsXXITp0+g9On/gcAwPe++69x7R3KVXBcB/d5\n8C20G3jmScL14zBGd0yDbG/4AElBjkw00mA9MgyHS+hw0dWjTLgPe45pTScat67Ty9jZC7HA+VNC\neqiWFGKlsMAMoqeffXZSm0ipidNkJrIJR6E/cVQO4eGnPJKzBGxt0aDZ29u1VPo0SWxItXewi4MH\npbJwbP/ux5k5CsMCD79Q+5wTOG+SycU5YWYipCkYdhEgOAcA9vf2oDj82pqZwcmTpwAAo34fPabO\nR2GEQY8psY6LfllsueHCMJvFpBlcFn0rhEEipoeHpI6QZjzBlQSY2t5oS+j75AhE4wwp5zk0W7No\nLhHUWKk5GA5osnt+gJkZWmDbTR9tpjYfzYm5ePFxnF4np6x7uGO/f7DxAI+dfRwA8Bvf+E9xR/4U\nADAYvw79AT1P4ACPXaR7isMUye3pBebgFBPYwOQTKQ2hLPNKCGFF67TWqHIe0/knz+H2NTowPLiz\ng4V1GsuZmyNnptP71+8h2qXPc+4ccnaWHRgMmMp9cLiLAS90XlCB4n6pz7Usw9UYwK1MmK/yQ8f+\nhxvV26TPynFQ6PLQYiZnJWGsyjuERp03nXFcIGWVfcetoMs5MPVGE1UudLy5tY+rV0lB+td//Vfx\n9NNP02/HCWoNXpwPRpb6XRhjFfRTM5k6MjeQPOalFIinhGtHUQo3o7WvSBWMP6kl2N2n+bGyNI+9\nHs2be9shJKhfF090EHENvms3eqjxwcMIB26rap/xyecJ2huFEf7oj98EAFy5tYMHuzwPcomC8zuF\n0MgZUuzMzkAzPO5VanbN0oWxorPTmBgnWGZnraU0ejwvZ+fXcHae1lavXofi/q7XG9jnwzI8jTqz\nHTOTWQXuLE/hls+TZahWyLHyPR/dHjHX8iy3BwPlJ3jsWUo3CVoGQ2bPuY6Bx2ue1A309hiKMh56\ng0nR3o+zTLgwHuf1+BXssdO6f+8BXuU1csnxcHaR0kdOLdewygy4E2mME3wIOef5+Pvz1N68CHEg\nybG6tnkdh2367eH1O3jA72Xj8mUcjOg5K60GLp4nht3apSew2KRN8u777+EH3/tzAMD1UY6UD7q+\nFJBieihTa42E2cZ5liHm3MT43Gmks7w3o4BfsBiwMyni7lUqUBGzmS+/B2+X3u8ZXVgpnNUCcO6T\nBIg0FRiuZK1NgWFEc8SBxhynBIlBjrRLa1I86qDX5Nzm8Ri3+7QOH2zvwmTTu0jHMN+xHduxHdux\nHduxHdsvYJ94AvqjMuX0kbIeFZbWf+HzfwtXr9ApScoCiqs8GwF8cIu8ytF4CM3Rlm1oVDxmftSb\nWGItlNOLM7h5lcLVJ06uw+Gk2kcRlft580UFCZ/UmkEDDuMMUTSA5FIneV4gYcHKBxv3Jh6458Nl\nkURHTUp2KKUQ8Ek9M9qWqFFywqaTQtjab0ejTGmaImaxU10U8FnD5PBwH70uncKEgK0J+HGmpLKR\nuqIoHoIfpZh8LiEbHHkWUxibSC804HPk7eTSMjpcvfzW1fexw1G18xcuoHaOTp/r66ewt00Rn/ev\nXIHLJ/lGvYE0Yn2ibgyX/244GKPNpytHOUgfYdzlByPkhstN5ApK06loZbaC6CmKNGXDeVSqFFY2\nIoPmxMzBaB85l5hot2ehwAmt/gykxXsmf2tpcRGnT68DAF559acWao6jCG+8QdHX/+a//a9w8QTV\nloxuNvHyzbfomrFGknDSdlUhEdOfjdJwDM8royFAUQqBag1hJlESV9M7KgogZxgxlwKLrOuz/WAX\nmolLlXoVLpc0qZ5s4Fb3DgBg1BsiYKg0HI/Q5bEP30W9yXo54xHazMhaXF6Fw8ycLMvguaXgbm7L\nC01jUkquEwkS7BSTcGnJaq03anjxRWJWtlp1GE5SzrtD1Fmkcv3kEuR9GntLK8vIU4Irrl65hjpH\nzZ48fxotJlF8+tkLOPzmLwMAXn3lXVy9RnC6MQa51WIzk2GgAXC0xlESasr4vSMMZrlEFaSPJC1r\nZsYIStbb6gnce0CRqdFwhGeepOhZxVvF9VvEUNzZ6mP7oM/XxJhtcwTywknUGP54+61XEbGW2t0H\nPaSGYdIit1pz1YoLxy0FXEdAqQsnDGQpiGYebX0NuwIznJzdUS2scdmrmfYcfI6yIc1t0n6zWUPE\n7+eNt1/Di5+lMjP1as2OoyxOLHs5yWMojgrFeWx3RI0C0YjWR7+aoMn9KSsaEZfn8V0HzSo9mzIV\nbD+gaJFyHXQ608N82p9F3Kdoi4MMYAZ7M0nQ2ia4LU1GeIsTxK/Vmjj14i8BAII0xEKXks6XM42Z\nLr3rS8MQn/cpSvjrvofuLn2//a8/QI/f3YuZgccEjfeGu7i7ScSsf3jlCv77f/APAAC/9du/iy/P\nEuHipW//K/zFmO75fqaQP0JytjHGCiFrrZGVZcjWl1FfI7gtikZImYUH17flZ/yZGRhGIsJr11AM\n6bczGvC5iKcHAZ9L49SLPizpV7pYYFFnXzkQI/67woGKWVh3Y4CooHfX6QHpCY5Urq1gZ296vbBP\n1JkyxtiSdwIPrW0PXXPUyrwC1/Xtb19/9fvwXepE35EIfPqsdYFCUR7AF3/tb6Ma0OKcZxnu3iFI\n64c/+g4eO0mde/7CHC6deYb/sHhokj8yi4+tXp/FcMhwVJQg3aNJEicxatVJzlQZZn73nXdxwIw1\n13XhlIrTYlIA13EdzM/TgB6Mhhb7PxoKVcqByxtBrVbH8889R9d4CuNhn58nstBhHI4s5dVRCsWU\nQnpZkT+UG2VT3XDksxHQZQ2yghSz6XldFMzqmanU8fg6OUrLzQ40CyGeWlqy7KOdu3dtP3UW5nHm\nDEEO3Z197CYlNb+GjD+LzCBhdtj+7j7aqyzkp81HQKMfbsNuBG+Gw+U6tQXhRjdGmJnl2okr59Dk\nPA2djwEuDpwkGVx2UvIjG3glaB7JL5vk7DSaDZw/T6zKRqOJfu+Af5vh3j2CkL7/3e/gf/yf/mcA\nwO9+87+DPvgDAMDlq29jn2utRVlhobpprEilVfzXWiMpC4mmGSq8wEpXQZVFfVONMfetcTVUzm0c\nFQj3qP8bpoGNA1rY15bWcHLlFADg2sY7qPFBaDTsozeijSwTBuceJ2jh3WvvIWB1+Vq9YWsvuq5L\n/QjAGIFHIPORMC4714We1AZLsxwZMxOb1QBzC3N8vYRi4dyFuVl87jMkUOhK4K23Ka8qjMa48d5r\n9Hk0wFc+TzIY6ytzmGU16Zrbwde+QA7apccv4p/+c8qJe+31t63wrMFkLZQCkOUBJS8w7WsUeQSf\niwzPzs9h4ybXPByH+AwLjm7c34YEHVTmZ2L092m8hL6DKCUoZLs3hM/Mu5rjWhHcwWCEG9fuUPuW\nPSyu0jXX7qfob5CjEdQqqAcsRVBzIbkmWq1WBcrizQLQvOkZLSew6hSWCx/3WHqh6I3QnqFnnu2M\n4UpaZ8+c9JDxmnjvzi38H//wfwcAXLl+FbucN/v551+0zrrQxhYFH0ZD7HJtuDiKkJXf90J0R7R2\ni0oKA841jQo4rOCvhcRhj97b5u0d7D+gPnnu02cwtzD9XMydNlJmBkfJGIb3gNr6E1hfoXlw4v4d\nVLq0NkS3r0AxI8//2i+jyhI3ozjFqKzVeOcDvM7rzczefSzynrTs5rjA81vUPZLpAXAmBg45F/Op\nqoOn2afpvvka5pj1/Zt/9+/hhW1yuH68s42fXL8+dRt937eHbQOgxWLAu9duIuX8LNWZgbtGaTqu\n0Xa+epmB4vVJBHUsf4YcydnGAmREJ7nt628hOKBASjvKkfHhJIJBhftTu1Xk52lPqK6uIuJ95jAK\n4fq03i+bJno8R9OVFg760ztTxzDfsR3bsR3bsR3bsR3bL2CfeGTKqiIJA2MTXT/6esedJJ8esIja\nzRuX4fPpXwBWmE+5Pn7zP/mvAQBzS+u2Np+SElevEOumojI89cJXAADnLjxjoUOj9V87GnXUxklh\nT72HwwHcUkRNKAxTOjUkYQLl0ClGQaPFldallCTiCWLwlZEGIQWSiKNREmjw9eJImQ6K9JXRoBS3\nb3/AT2SQcF2joihsG7MkhmAGX2tKiA8A0jyzZVoerg14NDHdoDx2F1qUMjRwhYDHCehrC0tYnaWI\ngJcWEPxrvxrAL5k/SmH3AUU6Nu7ew9IqsVNOnTmD2x+8DwDYPTxAiyveDw4SZHyC2d3exzzXG6zO\nzzwSOzPJc6iS+XInbh8AACAASURBVCVhAZnhSGN5lWCSE0+8AM/lRNSgBY+TZF0nR62sYdbuoMra\nJ9oAWcZJlG4BiTLR0sVpjiKsrZ1EzCetLEsQc1t+8uMf4lvf+hb93dUT+Nbfo8+vvv4yXrn7EwDA\nGD00OzNTt9FTdZQCYBXXmdTacwFPTcZgGbGUSqLBkaNKtQLH0NHVUT52t+gEn5sMh/dojsabIcDa\nVSYrkHK9t/G4jyH3QyaB2Xlinc3szlhRQi/wYHwe10JOxriZ6ABNY0IBHp8XdV5AlJ+Fi5gH5SDS\nEJz47ioHDi+J9aCKxkmaF51GHevL9JzXr1/HcEifz184j7Pn6NTeajZRYU0jaRSWmGCydqKKmBN+\n79y6gf0uzWOBCVx+lNRBxObp9HuMLjDq0+k6GgVwWWBwszdCl5NrR2GKWo2eZX7WQTyi7xPkOOjR\nWOsOUwvzNmseqgyLvH11E+eZXKB0gMaIxuPjj53AldvUDt8TmKuVQqdtjDm6kZsCMUe4KvUGFI/3\nMEkeqkf6cZYHIZJS10kA1ziZfvfGIR4/TVHNlccu4pXLlPbxg5d/jJ+9Q2v97sEB/vE//b8BEAJw\n6XEidHz66c9itcOkie42fvyTHwAA7u8cTNAAR8EEzI48l8IkDFMPUrRXKYpRqfjY3aB+CMME9Rql\nAOztxDgcTC9MmsoZZB5FTJSnIVmQeLtI8FOP7vPU+dN45pDWy5XtLair7wAAfFeg8qu/DgBofuoz\nyDh1Izv/GGKOyg67Q3SHNE6ubNxEwHUGF3pdzPk0DyqzHlox7U9f6G5D/p//FwCgeO5pFJcoQouF\nNSw2CSH55nmDc2Zu6jbmRWa1/iQ0BJejur9zH3vXiCm5dukSGuvUD0JqW5u0kNrqUgZuBV/4L36b\n+uHZz2HI7M7/7X/9X3DnLt3nP1MtrCzTXjF7YgX927SHDPd3sPLVzwMA7q4vY+uQ+mrzYA/nlkhv\nciVoYtbQPXeTAo+dPzd1Gz9xZ2pC8zriQQl8aGhbCGGdKVdKvP36SwCAJO4DjJE7UkKy0/TFL/4m\nZrj4YhQPrTOSAWhw4dRf/frfxpNPf5qfRyEvGT4/l6fw13WsDrp99Jn6bbIcnqKFwBWOXURCRHb5\nNDCTGmxa2JwZjYkIphQCiZw4VqUDI4SwitBCKSt4VuSZdaBocZ7AlzYHLU/BhAoMxxFq1emYGXkx\nqXGmhLBOisARqv2R9yzgWSHHPCnQYYXvtfklCBZjNFGKSqlyXRTwuACv8iuIOB9qZ2cX+zz4K0GA\nsxcoh+jdt97AeExtnZubxzClz1vDLu7cIXbHxfkOHgUfqlddsEA2Kq4HwRvN8twqLlyiXBhnpgOT\nUQ5Do9lEtUrvx/cBfnycWF1HjdsbJzFiFgesBDkgJhBbqZz92LnHcHhAzshg0LfFtqMwRMJ5b1E4\nwtISy4X8R7+GZ3cIzh33e7h59f2p26gcCcctx5SG49HfkkohtSw/QDHLS7quzVfKXNj8B3+uihqL\nJw72h+gopthv7EKwiOHCbBt7m7SgRa7BwlliL27v7cBhB6BWq6BW5/fuCBSyHFfGhvulkpO6kFOY\n1rll/xmtkXO74jhDFNE9O8ojSArk2JYHPJ0buya1Gg08xzU2L5w7a9cG3/cBZ1JYvXTWXMfD/8/e\nm8Valp33fb+19nTme865Y1XdGrqqups9sNnNJlviIJGSLcWincQ2YgeJDRgCYsPOQ4woCPKQp7wG\nMBIkdgYEeZGTQIFtWLaEwLRES7JlirOa7Lm75uHeuvM989nTWnlY397nNkV2nUI7bSDY/wfy9Kl9\n91nz+r7v/w2roijk1rC+4cbnxRee4kdvuCjI48FkoVhKT/nQ/z8eyvPL4taffvYp8lxqBj7a5/Yd\noSeUIoocjfXcxX6ZBDKrK5QUeJ7NpngS7Xp8OmdSK4rfWm7vOIVkeDDk6afcOt282OF8z81t3dNc\n3XBCttGWIxEicuUxlH05nasyzUea5k+k2CQmZ/fACQLtpoeSwstBM6Kx4to/jWO+820X7frdG3/E\nq7/g6l5+59tvlNTn0fER3/rhN2XcWlz8qoveMjZhbN1YfbD3AfncnYO1Ws6lF9znVFkOdtzezSZ+\nGeG6ds6j1XKH6OpmyLooM6PTMfdvT5fuo1OL3dhqzwcJ9yc+5GTiKLA/9CJuSGLSF5+6xKeEGru0\nt0fnhqPbmtuXMV3JmL5xDiuRiSd33qfzWZc82L72WWIRbE9v3GR3x61HjKUnlQA27t9iXTKjr+w/\ngBsuYa33lV9Cf/4LAAz/8PvYN+8v3cNhPCaXigvWZCD+vfdMjrriIur9jQ0QQwS+VyqxxmqsFOWc\nTYb81v/yPwDQWfmNUgl//4d/zDtyz11N5/yFl5yg/dzf/k8Y35ICyP/z3ytTicTtDjOp4jA78TkR\nH9zV1T7nErf+d+JHeO0qmq9ChQoVKlSoUOETwSeeZ+onWnzUIjngjyelLKLFTk6O+OBdV18o8v0z\nSSlTtq846uXqc6+SSuXrDznjKsXP/SlXO83TmqRIzmny0iJ1JkvT49v7EdDaQ4u2aqCs+p2qHENB\nWXqUGqii1NTyPOfiJacxXb92payufrYeHsqUjt5a++zvOUvGe7dvo/3FdBZWDWvMTx5bq5gUZVLS\nAbVajaWgfooF70xpGWtt+b1WCiQ6LE9iAqEE8mnMWJLlrdfqGHE47jXq9FedVj83MBg5M3o9DJlL\nnS2rNA0pIdPur3L7A2fePT0ZsXb+HACtZoexUBrHx0OizvI5Ua5efZpYcqjU6lGZpHTr8itEG5ek\nvwZPOw2mXtesSpRRsxmw1nda+ObGubLEyMHhbkl7ZGmK5IBkOh1z/8EdADY2NnjlZWdp2t19wEyo\n3dV+l/09ZwW7d/8mn/nMZ6Vtba7Ieolna3RX+kv3MU1TctECw8gvoy+NURRrU3uaWLS36XSKEUf8\n+tDDE9Pd+OSYwaGzXuzt7FOTOpODkxN0ze3BSZLQFo35qZefQTfEeT0wzrsbGI0mIDl1bK5IZW16\nnlc6rmZZ9kS1+YwxZYkaYwyp5MDKMkNNIrhefeUF+n23lpI0wRdzbZylFKUzrLWlRTcIgtJJeR7H\nC7ozCMqzSmtd0qNJnKIkv9FnX32+rFf3zT/6UVnSxBrIS0u1lH1aAkkGkVD77777Pq2m+/y5z17m\nB6+79RLUmsymzvKyqw1R3e250A+YTArqNSOQAIocOJaklyioS66+dquOyZwF7/7tHT59zVlhal6T\no12h3gYTphIRPUt1ST6MRqPSKhgGAfX68ntxNpszkzZ02x3WpUZlf0WTyVr75ne+w8P9OwBcvbrB\nnlhBx6eTkn5PEsXhSVGr7n2m8ZcB+O6t10n7bv1euB5Qm7vI2k6zTVx3rhJZmjEaiiXWNNi/5Sw4\n2ni0Vl1/e9srJLGjyVbWI5r9i0v30bMxJnZtS0yKnjurk1/XhEK5GmBfrFT/SsF7HbfuruPz4huO\n4jz/ztusPO0sMq2r12g+72jNjQsXaGw4GsuYnFhyVOnhEZs//4uuj/GcRPbE/s1b3H3orPrZv/wD\nzv3oXfee999h/Q9/H4DRKOJoZ3nLVBhGZT3PYZKSSd62wTTjc59xVrPVZpO5JAL1QlOeuz6mvCOD\n9QvodUetD/cOGElkc3NwQkcS0t7LUl7/5rcAODmesH7RjYONZxzflX6tblCXwKXWcIZuuvZMwyMO\n7t0C4LR2yjBbPirzk/eZ+gkOUs5E/ifN3MZ5FgDw9hvfJsuduS70oThSg1aXn/tFV2jRaoMyXvme\nhTuPJReKIoPSNFj8G3x0woYnEahWuit0+lILCL2gwZQqi31q7S+SJOqFwJgkCV/8ogvl/dW/9h+T\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H+f7NGzc4kXPoxs0bRL/zm+6ZbMaF7WtL9E5eufST/wZg7Yfpt1JQsVBmdlSqLPD75hvf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdaf2d2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837  130.05569388  135.34804082\n",
      "  131.75402041  130.96055102  136.14328571  132.47636735  131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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3X9KngWeBTzXP/2FE3C7pfOBh4LeAF4AbIuLX44/X2k8phjW3lwfolJQG7xQbdmy9xDGg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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdaf2b3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.567014\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 28.029802 analytic: 28.029802, relative error: 2.894665e-12\n",
      "numerical: -46.627974 analytic: -46.591411, relative error: 3.922333e-04\n",
      "numerical: -10.849471 analytic: -10.849471, relative error: 2.656217e-11\n",
      "numerical: 12.386984 analytic: 12.386984, relative error: 3.241716e-11\n",
      "numerical: 17.736382 analytic: 17.736382, relative error: 1.976124e-12\n",
      "numerical: -5.674749 analytic: -5.674749, relative error: 4.137513e-11\n",
      "numerical: 5.675387 analytic: 5.651191, relative error: 2.136273e-03\n",
      "numerical: -40.198702 analytic: -40.198702, relative error: 5.089206e-12\n",
      "numerical: 9.908739 analytic: 9.908739, relative error: 1.987989e-11\n",
      "numerical: 15.548199 analytic: 15.548199, relative error: 1.052508e-11\n",
      "numerical: 14.652581 analytic: 14.652581, relative error: 4.147578e-12\n",
      "numerical: 8.106300 analytic: 8.050256, relative error: 3.468815e-03\n",
      "numerical: -45.958910 analytic: -45.931548, relative error: 2.977622e-04\n",
      "numerical: 33.063784 analytic: 33.009543, relative error: 8.209243e-04\n",
      "numerical: -28.671489 analytic: -28.671489, relative error: 1.973159e-12\n",
      "numerical: 22.037724 analytic: 22.007761, relative error: 6.802614e-04\n",
      "numerical: 12.199104 analytic: 12.199104, relative error: 4.222579e-12\n",
      "numerical: 0.074840 analytic: 0.074840, relative error: 4.545702e-09\n",
      "numerical: -8.487935 analytic: -8.487935, relative error: 2.873051e-11\n",
      "numerical: -18.359947 analytic: -18.359947, relative error: 3.286376e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** \n",
    "It's possible on the edge where $max$ defines contribution:\n",
    "$$\n",
    "w^T_jx_i = w^T_{y_i}x_i + \\Delta\n",
    "$$\n",
    "In one dimesion it will be:\n",
    "$$\n",
    "w_j x - w_{y_i} x = \\Delta\n",
    "$$\n",
    "and for $x \\neq 0$:\n",
    "$$\n",
    "w_j - w_{y_i} = \\frac{\\Delta}{x}\n",
    "$$\n",
    "In our analytical impelentation we return $\\Delta = 0$. So we could stuck in one point where equation above works for all dimentions. But it's very unlikely case if it isn't attractor or saddle. \n",
    "\n",
    "**Is it attractor or saddle?**\n",
    "\n",
    "It is not. Because for attractor or saddle derivation should change its sign when cross this point but we trim all negitive values there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.567014e+00 computed in 0.131727s\n",
      "Vectorized loss: 8.567014e+00 computed in 0.016416s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.138757s\n",
      "Vectorized loss and gradient: computed in 0.004362s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 782.853321\n",
      "iteration 100 / 1500: loss 284.673944\n",
      "iteration 200 / 1500: loss 106.860644\n",
      "iteration 300 / 1500: loss 45.877533\n",
      "iteration 400 / 1500: loss 19.100031\n",
      "iteration 500 / 1500: loss 10.864671\n",
      "iteration 600 / 1500: loss 6.151106\n",
      "iteration 700 / 1500: loss 5.493499\n",
      "iteration 800 / 1500: loss 5.672912\n",
      "iteration 900 / 1500: loss 4.586462\n",
      "iteration 1000 / 1500: loss 6.071684\n",
      "iteration 1100 / 1500: loss 9.005899\n",
      "iteration 1200 / 1500: loss 5.147316\n",
      "iteration 1300 / 1500: loss 6.301812\n",
      "iteration 1400 / 1500: loss 3.827235\n",
      "That took 1.031156s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S9o3lLANOPKgasJxJkqR9ZznLgBEl3Qug13lZU5Ik7SPLWQaMKS8kJyuwbOP2pKNIkqRB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\nFfJ/j0gjGlKUoeEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdaf2b3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.319490\n",
      "validation accuracy: 0.326000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 1000.0\n",
      "#\n",
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      "iteration 1400 / 1500: loss 45.732707\n",
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 101.997140\n",
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      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 165.600852\n",
      "iteration 100 / 1500: loss 165.498300\n",
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      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 328.599253\n",
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      "iteration 1400 / 1500: loss 313.962573\n",
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 690.946933\n",
      "iteration 100 / 1500: loss 674.536410\n",
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      "iteration 1400 / 1500: loss 602.758107\n",
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1461.650923\n",
      "iteration 100 / 1500: loss 1417.758982\n",
      "iteration 200 / 1500: loss 1389.794598\n",
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      "iteration 1300 / 1500: loss 1132.981443\n",
      "iteration 1400 / 1500: loss 1114.505769\n",
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3125.513725\n",
      "iteration 100 / 1500: loss 2994.099572\n",
      "iteration 200 / 1500: loss 2871.659897\n",
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      "iteration 1300 / 1500: loss 1855.437585\n",
      "iteration 1400 / 1500: loss 1773.661015\n",
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6525.905991\n",
      "iteration 100 / 1500: loss 5984.468409\n",
      "iteration 200 / 1500: loss 5493.800283\n",
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      "iteration 500 / 1500: loss 4243.769760\n",
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      "iteration 700 / 1500: loss 3571.138662\n",
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      "iteration 1300 / 1500: loss 2131.786045\n",
      "iteration 1400 / 1500: loss 1955.165058\n",
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14331.201148\n",
      "iteration 100 / 1500: loss 11898.137539\n",
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      "iteration 300 / 1500: loss 8202.817818\n",
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      "iteration 1300 / 1500: loss 1284.813807\n",
      "iteration 1400 / 1500: loss 1065.198106\n",
      "\n",
      "#\n",
      "learning_rate 1e-09\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30063.925422\n",
      "iteration 100 / 1500: loss 20152.481061\n",
      "iteration 200 / 1500: loss 13502.436258\n",
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      "iteration 1300 / 1500: loss 173.073215\n",
      "iteration 1400 / 1500: loss 117.379711\n",
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 51.294575\n",
      "iteration 100 / 1500: loss 50.526223\n",
      "iteration 200 / 1500: loss 53.515553\n",
      "iteration 300 / 1500: loss 41.244695\n",
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      "iteration 700 / 1500: loss 53.440243\n",
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      "iteration 1400 / 1500: loss 43.870353\n",
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 87.361172\n",
      "iteration 100 / 1500: loss 91.598381\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 160.578487\n",
      "iteration 100 / 1500: loss 170.761032\n",
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      "iteration 1400 / 1500: loss 142.262165\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 327.070547\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 694.457863\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1427.082189\n",
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      "iteration 1400 / 1500: loss 813.338109\n",
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3081.314510\n",
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      "iteration 1400 / 1500: loss 925.022339\n",
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6651.935803\n",
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      "iteration 1300 / 1500: loss 599.174605\n",
      "iteration 1400 / 1500: loss 497.319853\n",
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14034.712953\n",
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      "iteration 300 / 1500: loss 4228.771885\n",
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      "iteration 1300 / 1500: loss 81.399998\n",
      "iteration 1400 / 1500: loss 59.425844\n",
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-09\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30766.446891\n",
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      "iteration 1400 / 1500: loss 10.628734\n",
      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 38.315079\n",
      "iteration 100 / 1500: loss 50.879052\n",
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      "iteration 1300 / 1500: loss 41.496018\n",
      "iteration 1400 / 1500: loss 39.569706\n",
      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 77.963967\n",
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      "iteration 1300 / 1500: loss 74.324196\n",
      "iteration 1400 / 1500: loss 74.176798\n",
      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 174.698031\n",
      "iteration 100 / 1500: loss 156.186194\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 324.828184\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1438.046100\n",
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      "iteration 1100 / 1500: loss 548.635243\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 1200 / 1500: loss 503.508414\n",
      "iteration 1300 / 1500: loss 468.292150\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 100000.0\n",
      "#\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6587.727558\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14324.460863\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-09\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30840.785756\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 52.914176\n",
      "iteration 100 / 1500: loss 44.884003\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 82.991384\n",
      "iteration 100 / 1500: loss 76.078482\n",
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      "iteration 1400 / 1500: loss 70.081654\n",
      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 160.875488\n",
      "iteration 100 / 1500: loss 149.131222\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 321.546826\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 675.586226\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1471.055968\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3101.036280\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6600.098621\n",
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      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14366.436040\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 1200 / 1500: loss 6.582578\n",
      "iteration 1300 / 1500: loss 8.766583\n",
      "iteration 1400 / 1500: loss 4.743837\n",
      "\n",
      "#\n",
      "learning_rate 1e-08\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30614.905997\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 60.350453\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 85.291842\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 162.301824\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 327.441891\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 691.878735\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1468.454220\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3109.734012\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 215443.469003\n",
      "#\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14019.764532\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-08\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30852.622081\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 56.493295\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 84.108010\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 1200 / 1500: loss 46.783016\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 166.234036\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 324.206626\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 680.775521\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1451.757096\n",
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      "iteration 1300 / 1500: loss 3.784774\n",
      "iteration 1400 / 1500: loss 8.675564\n",
      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3038.109680\n",
      "iteration 100 / 1500: loss 474.638130\n",
      "iteration 200 / 1500: loss 78.848962\n",
      "iteration 300 / 1500: loss 18.745794\n",
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      "iteration 1300 / 1500: loss 3.680234\n",
      "iteration 1400 / 1500: loss 7.977337\n",
      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6627.404994\n",
      "iteration 100 / 1500: loss 122.524280\n",
      "iteration 200 / 1500: loss 8.940768\n",
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      "iteration 1400 / 1500: loss 5.878875\n",
      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14275.916705\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-08\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30844.268378\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 52.165430\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 85.939248\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 324.348401\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 684.996734\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1441.164969\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3074.422271\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6608.829690\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14352.987462\n",
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      "\n",
      "#\n",
      "learning_rate 1e-07\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 31432.824288\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 45.582449\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 79.508510\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 156.403735\n",
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      "iteration 1400 / 1500: loss 7.363004\n",
      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 340.098917\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 655.966978\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1428.092438\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3091.014654\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6645.026255\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14276.925554\n",
      "iteration 100 / 1500: loss 11.578237\n",
      "iteration 200 / 1500: loss 8.066323\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 300 / 1500: loss 9.259405\n",
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      "\n",
      "#\n",
      "learning_rate 2.15443469003e-07\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30590.423077\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 47.501156\n",
      "iteration 100 / 1500: loss 51.495184\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 87.667847\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 161.909388\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 322.007394\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 685.207025\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
      "iteration 0 / 1500: loss 1458.123242\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3041.161628\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 215443.469003\n",
      "#\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 464158.883361\n",
      "#\n",
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      "\n",
      "#\n",
      "learning_rate 4.64158883361e-07\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 30599.474793\n",
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      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 1000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 53.796936\n",
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      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 2154.43469003\n",
      "#\n",
      "iteration 0 / 1500: loss 90.180180\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 400 / 1500: loss 15.777940\n",
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      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 4641.58883361\n",
      "#\n",
      "iteration 0 / 1500: loss 158.861290\n",
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      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 10000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 338.226095\n",
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      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 21544.3469003\n",
      "#\n",
      "iteration 0 / 1500: loss 685.287837\n",
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      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 46415.8883361\n",
      "#\n",
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      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 100000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 3134.970151\n",
      "iteration 100 / 1500: loss 23.799800\n",
      "iteration 200 / 1500: loss 16.978257\n",
      "iteration 300 / 1500: loss 32.702847\n",
      "iteration 400 / 1500: loss 20.500929\n",
      "iteration 500 / 1500: loss 25.983867\n",
      "iteration 600 / 1500: loss 18.714529\n",
      "iteration 700 / 1500: loss 18.367983\n",
      "iteration 800 / 1500: loss 21.724753\n",
      "iteration 900 / 1500: loss 33.062122\n",
      "iteration 1000 / 1500: loss 18.467113\n",
      "iteration 1100 / 1500: loss 21.182907\n",
      "iteration 1200 / 1500: loss 16.462836\n",
      "iteration 1300 / 1500: loss 28.322266\n",
      "iteration 1400 / 1500: loss 12.559607\n",
      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 215443.469003\n",
      "#\n",
      "iteration 0 / 1500: loss 6685.044396\n",
      "iteration 100 / 1500: loss 33.312326\n",
      "iteration 200 / 1500: loss 28.138871\n",
      "iteration 300 / 1500: loss 19.760480\n",
      "iteration 400 / 1500: loss 35.318249\n",
      "iteration 500 / 1500: loss 34.613319\n",
      "iteration 600 / 1500: loss 27.781056\n",
      "iteration 700 / 1500: loss 27.575868\n",
      "iteration 800 / 1500: loss 35.265315\n",
      "iteration 900 / 1500: loss 24.979482\n",
      "iteration 1000 / 1500: loss 35.967940\n",
      "iteration 1100 / 1500: loss 17.304919\n",
      "iteration 1200 / 1500: loss 17.608535\n",
      "iteration 1300 / 1500: loss 25.821204\n",
      "iteration 1400 / 1500: loss 51.533758\n",
      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 464158.883361\n",
      "#\n",
      "iteration 0 / 1500: loss 14372.815464\n",
      "iteration 100 / 1500: loss 29.206605\n",
      "iteration 200 / 1500: loss 47.158498\n",
      "iteration 300 / 1500: loss 43.789219\n",
      "iteration 400 / 1500: loss 30.979355\n",
      "iteration 500 / 1500: loss 44.214681\n",
      "iteration 600 / 1500: loss 42.615914\n",
      "iteration 700 / 1500: loss 47.375092\n",
      "iteration 800 / 1500: loss 31.400861\n",
      "iteration 900 / 1500: loss 44.600137\n",
      "iteration 1000 / 1500: loss 40.861998\n",
      "iteration 1100 / 1500: loss 53.685558\n",
      "iteration 1200 / 1500: loss 20.812460\n",
      "iteration 1300 / 1500: loss 27.132198\n",
      "iteration 1400 / 1500: loss 29.137431\n",
      "\n",
      "#\n",
      "learning_rate 1e-06\n",
      "regularization_strength 1000000.0\n",
      "#\n",
      "iteration 0 / 1500: loss 31150.961835\n",
      "iteration 100 / 1500: loss 100832.919189\n",
      "iteration 200 / 1500: loss 313095.399000\n",
      "iteration 300 / 1500: loss 688380.818406\n",
      "iteration 400 / 1500: loss 1257243.132234\n",
      "iteration 500 / 1500: loss 1967957.477168\n",
      "iteration 600 / 1500: loss 2835106.815567\n",
      "iteration 700 / 1500: loss 3908406.280666\n",
      "iteration 800 / 1500: loss 5238738.379678\n",
      "iteration 900 / 1500: loss 6642244.564118\n",
      "iteration 1000 / 1500: loss 8192769.009785\n",
      "iteration 1100 / 1500: loss 9919190.739961\n",
      "iteration 1200 / 1500: loss 11815287.810823\n",
      "iteration 1300 / 1500: loss 13789218.369504\n",
      "iteration 1400 / 1500: loss 16036934.777559\n",
      "lr 1.000000e-06 reg 1.000000e+06 train accuracy: 0.062571 val accuracy: 0.059000\n",
      "lr 4.641589e-07 reg 1.000000e+06 train accuracy: 0.112306 val accuracy: 0.109000\n",
      "lr 1.000000e-06 reg 4.641589e+05 train accuracy: 0.117429 val accuracy: 0.126000\n",
      "lr 1.000000e-06 reg 2.154435e+05 train accuracy: 0.142776 val accuracy: 0.129000\n",
      "lr 4.641589e-07 reg 4.641589e+05 train accuracy: 0.135490 val accuracy: 0.130000\n",
      "lr 1.000000e-09 reg 4.641589e+03 train accuracy: 0.133918 val accuracy: 0.133000\n",
      "lr 1.000000e-09 reg 4.641589e+04 train accuracy: 0.136000 val accuracy: 0.135000\n",
      "lr 1.000000e-09 reg 1.000000e+04 train accuracy: 0.137020 val accuracy: 0.146000\n",
      "lr 1.000000e-09 reg 1.000000e+03 train accuracy: 0.149020 val accuracy: 0.147000\n",
      "lr 1.000000e-09 reg 2.154435e+04 train accuracy: 0.143633 val accuracy: 0.147000\n",
      "lr 2.154435e-09 reg 4.641589e+03 train accuracy: 0.142265 val accuracy: 0.151000\n",
      "lr 1.000000e-06 reg 4.641589e+04 train accuracy: 0.153796 val accuracy: 0.152000\n",
      "lr 2.154435e-09 reg 1.000000e+03 train accuracy: 0.156245 val accuracy: 0.154000\n",
      "lr 2.154435e-09 reg 2.154435e+03 train accuracy: 0.153653 val accuracy: 0.154000\n",
      "lr 2.154435e-09 reg 1.000000e+05 train accuracy: 0.164796 val accuracy: 0.154000\n",
      "lr 1.000000e-09 reg 1.000000e+05 train accuracy: 0.133592 val accuracy: 0.156000\n",
      "lr 2.154435e-09 reg 1.000000e+04 train accuracy: 0.157000 val accuracy: 0.157000\n",
      "lr 2.154435e-09 reg 2.154435e+04 train accuracy: 0.161694 val accuracy: 0.158000\n",
      "lr 1.000000e-06 reg 1.000000e+05 train accuracy: 0.166551 val accuracy: 0.164000\n",
      "lr 1.000000e-09 reg 4.641589e+05 train accuracy: 0.171163 val accuracy: 0.168000\n",
      "lr 1.000000e-09 reg 2.154435e+03 train accuracy: 0.150102 val accuracy: 0.169000\n",
      "lr 4.641589e-09 reg 4.641589e+03 train accuracy: 0.191122 val accuracy: 0.171000\n",
      "lr 2.154435e-07 reg 1.000000e+06 train accuracy: 0.179449 val accuracy: 0.173000\n",
      "lr 2.154435e-09 reg 4.641589e+04 train accuracy: 0.180939 val accuracy: 0.179000\n",
      "lr 1.000000e-07 reg 1.000000e+06 train accuracy: 0.191612 val accuracy: 0.182000\n",
      "lr 1.000000e-09 reg 2.154435e+05 train accuracy: 0.153653 val accuracy: 0.184000\n",
      "lr 4.641589e-09 reg 1.000000e+03 train accuracy: 0.192612 val accuracy: 0.188000\n",
      "lr 4.641589e-09 reg 2.154435e+03 train accuracy: 0.192204 val accuracy: 0.191000\n",
      "lr 4.641589e-09 reg 2.154435e+04 train accuracy: 0.197694 val accuracy: 0.199000\n",
      "lr 2.154435e-09 reg 2.154435e+05 train accuracy: 0.211959 val accuracy: 0.200000\n",
      "lr 4.641589e-09 reg 1.000000e+04 train accuracy: 0.204041 val accuracy: 0.205000\n",
      "lr 4.641589e-07 reg 1.000000e+05 train accuracy: 0.197327 val accuracy: 0.205000\n",
      "lr 2.154435e-07 reg 4.641589e+05 train accuracy: 0.203061 val accuracy: 0.210000\n",
      "lr 4.641589e-09 reg 4.641589e+04 train accuracy: 0.217633 val accuracy: 0.211000\n",
      "lr 1.000000e-07 reg 2.154435e+05 train accuracy: 0.210796 val accuracy: 0.212000\n",
      "lr 4.641589e-07 reg 4.641589e+04 train accuracy: 0.211592 val accuracy: 0.212000\n",
      "lr 2.154435e-07 reg 1.000000e+05 train accuracy: 0.239755 val accuracy: 0.216000\n",
      "lr 4.641589e-07 reg 2.154435e+05 train accuracy: 0.207755 val accuracy: 0.217000\n",
      "lr 1.000000e-07 reg 4.641589e+05 train accuracy: 0.220224 val accuracy: 0.228000\n",
      "lr 1.000000e-06 reg 2.154435e+04 train accuracy: 0.221286 val accuracy: 0.228000\n",
      "lr 1.000000e-08 reg 1.000000e+03 train accuracy: 0.224694 val accuracy: 0.231000\n",
      "lr 1.000000e-08 reg 4.641589e+03 train accuracy: 0.231612 val accuracy: 0.237000\n",
      "lr 2.154435e-07 reg 2.154435e+05 train accuracy: 0.224204 val accuracy: 0.237000\n",
      "lr 4.641589e-09 reg 1.000000e+05 train accuracy: 0.247347 val accuracy: 0.240000\n",
      "lr 4.641589e-07 reg 2.154435e+04 train accuracy: 0.227776 val accuracy: 0.240000\n",
      "lr 1.000000e-08 reg 1.000000e+04 train accuracy: 0.235286 val accuracy: 0.242000\n",
      "lr 1.000000e-06 reg 1.000000e+04 train accuracy: 0.247327 val accuracy: 0.243000\n",
      "lr 2.154435e-08 reg 1.000000e+03 train accuracy: 0.247082 val accuracy: 0.245000\n",
      "lr 4.641589e-07 reg 4.641589e+03 train accuracy: 0.256469 val accuracy: 0.245000\n",
      "lr 1.000000e-08 reg 2.154435e+03 train accuracy: 0.226429 val accuracy: 0.247000\n",
      "lr 4.641589e-08 reg 1.000000e+06 train accuracy: 0.248102 val accuracy: 0.251000\n",
      "lr 1.000000e-08 reg 2.154435e+04 train accuracy: 0.251184 val accuracy: 0.252000\n",
      "lr 2.154435e-08 reg 2.154435e+03 train accuracy: 0.256918 val accuracy: 0.252000\n",
      "lr 2.154435e-07 reg 4.641589e+04 train accuracy: 0.259041 val accuracy: 0.254000\n",
      "lr 4.641589e-08 reg 4.641589e+05 train accuracy: 0.242776 val accuracy: 0.256000\n",
      "lr 2.154435e-08 reg 1.000000e+06 train accuracy: 0.242388 val accuracy: 0.259000\n",
      "lr 1.000000e-06 reg 4.641589e+03 train accuracy: 0.249061 val accuracy: 0.259000\n",
      "lr 2.154435e-08 reg 4.641589e+03 train accuracy: 0.262327 val accuracy: 0.262000\n",
      "lr 4.641589e-08 reg 2.154435e+05 train accuracy: 0.270592 val accuracy: 0.263000\n",
      "lr 4.641589e-07 reg 1.000000e+04 train accuracy: 0.267327 val accuracy: 0.265000\n",
      "lr 1.000000e-09 reg 1.000000e+06 train accuracy: 0.246265 val accuracy: 0.269000\n",
      "lr 1.000000e-07 reg 1.000000e+05 train accuracy: 0.267510 val accuracy: 0.271000\n",
      "lr 2.154435e-08 reg 1.000000e+04 train accuracy: 0.282612 val accuracy: 0.279000\n",
      "lr 4.641589e-08 reg 1.000000e+03 train accuracy: 0.283776 val accuracy: 0.281000\n",
      "lr 1.000000e-08 reg 4.641589e+04 train accuracy: 0.288082 val accuracy: 0.282000\n",
      "lr 2.154435e-08 reg 4.641589e+05 train accuracy: 0.274510 val accuracy: 0.282000\n",
      "lr 2.154435e-07 reg 2.154435e+04 train accuracy: 0.280163 val accuracy: 0.282000\n",
      "lr 2.154435e-09 reg 1.000000e+06 train accuracy: 0.266735 val accuracy: 0.287000\n",
      "lr 4.641589e-09 reg 1.000000e+06 train accuracy: 0.269816 val accuracy: 0.290000\n",
      "lr 1.000000e-06 reg 2.154435e+03 train accuracy: 0.252388 val accuracy: 0.291000\n",
      "lr 4.641589e-08 reg 2.154435e+03 train accuracy: 0.281816 val accuracy: 0.293000\n",
      "lr 1.000000e-07 reg 4.641589e+04 train accuracy: 0.288000 val accuracy: 0.294000\n",
      "lr 1.000000e-07 reg 2.154435e+03 train accuracy: 0.304816 val accuracy: 0.296000\n",
      "lr 1.000000e-06 reg 1.000000e+03 train accuracy: 0.290224 val accuracy: 0.298000\n",
      "lr 2.154435e-09 reg 4.641589e+05 train accuracy: 0.275980 val accuracy: 0.300000\n",
      "lr 4.641589e-07 reg 2.154435e+03 train accuracy: 0.313918 val accuracy: 0.300000\n",
      "lr 1.000000e-08 reg 4.641589e+05 train accuracy: 0.293327 val accuracy: 0.302000\n",
      "lr 1.000000e-08 reg 1.000000e+06 train accuracy: 0.289429 val accuracy: 0.302000\n",
      "lr 2.154435e-07 reg 1.000000e+04 train accuracy: 0.288388 val accuracy: 0.304000\n",
      "lr 4.641589e-08 reg 1.000000e+04 train accuracy: 0.319898 val accuracy: 0.307000\n",
      "lr 4.641589e-08 reg 1.000000e+05 train accuracy: 0.301816 val accuracy: 0.308000\n",
      "lr 2.154435e-07 reg 4.641589e+03 train accuracy: 0.302388 val accuracy: 0.309000\n",
      "lr 1.000000e-07 reg 1.000000e+03 train accuracy: 0.298776 val accuracy: 0.311000\n",
      "lr 4.641589e-07 reg 1.000000e+03 train accuracy: 0.301347 val accuracy: 0.311000\n",
      "lr 2.154435e-07 reg 1.000000e+03 train accuracy: 0.319918 val accuracy: 0.312000\n",
      "lr 1.000000e-07 reg 4.641589e+03 train accuracy: 0.317755 val accuracy: 0.316000\n",
      "lr 2.154435e-08 reg 2.154435e+05 train accuracy: 0.305449 val accuracy: 0.319000\n",
      "lr 4.641589e-09 reg 4.641589e+05 train accuracy: 0.305041 val accuracy: 0.320000\n",
      "lr 4.641589e-08 reg 4.641589e+03 train accuracy: 0.310673 val accuracy: 0.321000\n",
      "lr 4.641589e-08 reg 4.641589e+04 train accuracy: 0.299551 val accuracy: 0.323000\n",
      "lr 2.154435e-08 reg 2.154435e+04 train accuracy: 0.313429 val accuracy: 0.328000\n",
      "lr 4.641589e-09 reg 2.154435e+05 train accuracy: 0.312490 val accuracy: 0.334000\n",
      "lr 1.000000e-08 reg 2.154435e+05 train accuracy: 0.322714 val accuracy: 0.334000\n",
      "lr 2.154435e-08 reg 4.641589e+04 train accuracy: 0.327653 val accuracy: 0.336000\n",
      "lr 4.641589e-08 reg 2.154435e+04 train accuracy: 0.333347 val accuracy: 0.336000\n",
      "lr 1.000000e-07 reg 2.154435e+04 train accuracy: 0.314694 val accuracy: 0.337000\n",
      "lr 1.000000e-07 reg 1.000000e+04 train accuracy: 0.323531 val accuracy: 0.339000\n",
      "lr 2.154435e-07 reg 2.154435e+03 train accuracy: 0.324980 val accuracy: 0.341000\n",
      "lr 1.000000e-08 reg 1.000000e+05 train accuracy: 0.333469 val accuracy: 0.348000\n",
      "lr 2.154435e-08 reg 1.000000e+05 train accuracy: 0.321714 val accuracy: 0.348000\n",
      "best validation accuracy achieved during cross-validation: 0.348000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "#learning_rates = [1e-7, 5e-5]\n",
    "learning_rates = np.logspace(-9, -6, 10)\n",
    "#regularization_strengths = [2.5e4, 5e4]\n",
    "regularization_strengths = np.logspace(3, 6, 10)\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "for learning_rate in learning_rates:\n",
    "    for regularization_strength in regularization_strengths:\n",
    "        print('\\n#')\n",
    "        print('learning_rate', learning_rate)\n",
    "        print('regularization_strength', regularization_strength)\n",
    "        print('#')\n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train, y_train, \n",
    "                  learning_rate=learning_rate, \n",
    "                  reg=regularization_strength,\n",
    "                  num_iters=1500, \n",
    "                  verbose=True,\n",
    "                 )\n",
    "        trained_accuracy = np.mean(y_train == svm.predict(X_train))\n",
    "        val_accuracy = np.mean(y_val == svm.predict(X_val))        \n",
    "        results[(\n",
    "            learning_rate, regularization_strength,\n",
    "        )] = (\n",
    "            trained_accuracy, \n",
    "            val_accuracy,\n",
    "        )\n",
    "        if best_val < val_accuracy:\n",
    "            best_val = val_accuracy\n",
    "            best_svm = svm\n",
    "\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "#for lr, reg in sorted(results, key=lambda (lr, reg): results[(lr, reg)][1]):\n",
    "for lr, reg in sorted(results, key=lambda el: results[(el[0], el[1])][1]):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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paalQrRyjoWnakKUP8vLyhMpIh8PMCAaD8Pv9YGa43W7L41WyBTPj1KlTienZ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdaf2b39b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.343000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jjPFoI4yV6adw+QbaMvZH/Y+p2MWf8eRRyLUWBv7ZdNX3yGTJfTN9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ZPA9cw62u2t+ZyPmprAPNX1K6Jgg5L53vownpvd5YzrmYd9c+I0nnU1Vch/F5fddd79RER/37N7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\nkJcpQRAEQRAEB/hvZP8fCSbSJMQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdaef492b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** \n",
    "*Weights look noisy and the reason of it because we haven't done a lot of iterations. So lets try to test this hypothesis:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 15000: loss 3112.457272\n",
      "iteration 100 / 15000: loss 1309.997003\n",
      "iteration 200 / 15000: loss 553.857579\n",
      "iteration 300 / 15000: loss 238.248660\n",
      "iteration 400 / 15000: loss 102.525016\n",
      "iteration 500 / 15000: loss 44.952571\n",
      "iteration 600 / 15000: loss 22.556887\n",
      "iteration 700 / 15000: loss 12.347826\n",
      "iteration 800 / 15000: loss 8.137193\n",
      "iteration 900 / 15000: loss 9.904574\n",
      "iteration 1000 / 15000: loss 7.420480\n",
      "iteration 1100 / 15000: loss 6.052271\n",
      "iteration 1200 / 15000: loss 7.344525\n",
      "iteration 1300 / 15000: loss 3.740679\n",
      "iteration 1400 / 15000: loss 4.788720\n",
      "iteration 1500 / 15000: loss 8.349595\n",
      "iteration 1600 / 15000: loss 6.769541\n",
      "iteration 1700 / 15000: loss 5.049936\n",
      "iteration 1800 / 15000: loss 5.759028\n",
      "iteration 1900 / 15000: loss 8.040454\n",
      "iteration 2000 / 15000: loss 8.027323\n",
      "iteration 2100 / 15000: loss 7.004320\n",
      "iteration 2200 / 15000: loss 6.886071\n",
      "iteration 2300 / 15000: loss 7.800039\n",
      "iteration 2400 / 15000: loss 5.383136\n",
      "iteration 2500 / 15000: loss 7.491668\n",
      "iteration 2600 / 15000: loss 4.800762\n",
      "iteration 2700 / 15000: loss 4.034625\n",
      "iteration 2800 / 15000: loss 5.160973\n",
      "iteration 2900 / 15000: loss 4.930535\n",
      "iteration 3000 / 15000: loss 8.069037\n",
      "iteration 3100 / 15000: loss 5.956415\n",
      "iteration 3200 / 15000: loss 6.917191\n",
      "iteration 3300 / 15000: loss 5.667769\n",
      "iteration 3400 / 15000: loss 9.972453\n",
      "iteration 3500 / 15000: loss 5.316794\n",
      "iteration 3600 / 15000: loss 7.384875\n",
      "iteration 3700 / 15000: loss 4.098832\n",
      "iteration 3800 / 15000: loss 6.734984\n",
      "iteration 3900 / 15000: loss 5.267333\n",
      "iteration 4000 / 15000: loss 6.265026\n",
      "iteration 4100 / 15000: loss 6.568615\n",
      "iteration 4200 / 15000: loss 6.632837\n",
      "iteration 4300 / 15000: loss 4.612926\n",
      "iteration 4400 / 15000: loss 7.446621\n",
      "iteration 4500 / 15000: loss 5.289736\n",
      "iteration 4600 / 15000: loss 9.948152\n",
      "iteration 4700 / 15000: loss 7.093529\n",
      "iteration 4800 / 15000: loss 6.333165\n",
      "iteration 4900 / 15000: loss 7.353537\n",
      "iteration 5000 / 15000: loss 4.620264\n",
      "iteration 5100 / 15000: loss 6.279953\n",
      "iteration 5200 / 15000: loss 6.568740\n",
      "iteration 5300 / 15000: loss 8.944568\n",
      "iteration 5400 / 15000: loss 5.862440\n",
      "iteration 5500 / 15000: loss 6.724123\n",
      "iteration 5600 / 15000: loss 7.754630\n",
      "iteration 5700 / 15000: loss 4.853232\n",
      "iteration 5800 / 15000: loss 5.662351\n",
      "iteration 5900 / 15000: loss 5.568016\n",
      "iteration 6000 / 15000: loss 3.644218\n",
      "iteration 6100 / 15000: loss 7.479018\n",
      "iteration 6200 / 15000: loss 6.973860\n",
      "iteration 6300 / 15000: loss 6.996599\n",
      "iteration 6400 / 15000: loss 4.574312\n",
      "iteration 6500 / 15000: loss 5.109472\n",
      "iteration 6600 / 15000: loss 7.176702\n",
      "iteration 6700 / 15000: loss 6.340819\n",
      "iteration 6800 / 15000: loss 6.967720\n",
      "iteration 6900 / 15000: loss 6.269733\n",
      "iteration 7000 / 15000: loss 4.853524\n",
      "iteration 7100 / 15000: loss 4.501611\n",
      "iteration 7200 / 15000: loss 5.052532\n",
      "iteration 7300 / 15000: loss 5.437039\n",
      "iteration 7400 / 15000: loss 4.606404\n",
      "iteration 7500 / 15000: loss 9.314152\n",
      "iteration 7600 / 15000: loss 6.967692\n",
      "iteration 7700 / 15000: loss 5.580282\n",
      "iteration 7800 / 15000: loss 5.537572\n",
      "iteration 7900 / 15000: loss 5.473415\n",
      "iteration 8000 / 15000: loss 7.620415\n",
      "iteration 8100 / 15000: loss 4.580274\n",
      "iteration 8200 / 15000: loss 5.759601\n",
      "iteration 8300 / 15000: loss 8.118575\n",
      "iteration 8400 / 15000: loss 5.883164\n",
      "iteration 8500 / 15000: loss 6.831067\n",
      "iteration 8600 / 15000: loss 7.323770\n",
      "iteration 8700 / 15000: loss 6.789753\n",
      "iteration 8800 / 15000: loss 8.046162\n",
      "iteration 8900 / 15000: loss 5.203697\n",
      "iteration 9000 / 15000: loss 5.462715\n",
      "iteration 9100 / 15000: loss 5.774289\n",
      "iteration 9200 / 15000: loss 7.191022\n",
      "iteration 9300 / 15000: loss 5.647774\n",
      "iteration 9400 / 15000: loss 5.651208\n",
      "iteration 9500 / 15000: loss 4.750714\n",
      "iteration 9600 / 15000: loss 5.849810\n",
      "iteration 9700 / 15000: loss 8.518714\n",
      "iteration 9800 / 15000: loss 7.968097\n",
      "iteration 9900 / 15000: loss 8.156675\n",
      "iteration 10000 / 15000: loss 5.045534\n",
      "iteration 10100 / 15000: loss 7.643521\n",
      "iteration 10200 / 15000: loss 6.510809\n",
      "iteration 10300 / 15000: loss 5.098661\n",
      "iteration 10400 / 15000: loss 6.009384\n",
      "iteration 10500 / 15000: loss 6.702095\n",
      "iteration 10600 / 15000: loss 6.813568\n",
      "iteration 10700 / 15000: loss 7.740579\n",
      "iteration 10800 / 15000: loss 5.231221\n",
      "iteration 10900 / 15000: loss 6.530749\n",
      "iteration 11000 / 15000: loss 7.904968\n",
      "iteration 11100 / 15000: loss 8.395862\n",
      "iteration 11200 / 15000: loss 8.032698\n",
      "iteration 11300 / 15000: loss 5.754393\n",
      "iteration 11400 / 15000: loss 6.840425\n",
      "iteration 11500 / 15000: loss 6.482064\n",
      "iteration 11600 / 15000: loss 6.390676\n",
      "iteration 11700 / 15000: loss 5.642865\n",
      "iteration 11800 / 15000: loss 4.217695\n",
      "iteration 11900 / 15000: loss 7.011360\n",
      "iteration 12000 / 15000: loss 4.766137\n",
      "iteration 12100 / 15000: loss 7.167121\n",
      "iteration 12200 / 15000: loss 5.681320\n",
      "iteration 12300 / 15000: loss 4.057793\n",
      "iteration 12400 / 15000: loss 6.621809\n",
      "iteration 12500 / 15000: loss 9.216116\n",
      "iteration 12600 / 15000: loss 6.599740\n",
      "iteration 12700 / 15000: loss 8.547731\n",
      "iteration 12800 / 15000: loss 6.431402\n",
      "iteration 12900 / 15000: loss 4.425795\n",
      "iteration 13000 / 15000: loss 6.567280\n",
      "iteration 13100 / 15000: loss 4.741138\n",
      "iteration 13200 / 15000: loss 6.735519\n",
      "iteration 13300 / 15000: loss 8.136366\n",
      "iteration 13400 / 15000: loss 9.338039\n",
      "iteration 13500 / 15000: loss 5.800607\n",
      "iteration 13600 / 15000: loss 5.653421\n",
      "iteration 13700 / 15000: loss 6.831175\n",
      "iteration 13800 / 15000: loss 5.279510\n",
      "iteration 13900 / 15000: loss 8.347783\n",
      "iteration 14000 / 15000: loss 5.717025\n",
      "iteration 14100 / 15000: loss 5.310025\n",
      "iteration 14200 / 15000: loss 6.559866\n",
      "iteration 14300 / 15000: loss 7.132780\n",
      "iteration 14400 / 15000: loss 8.381873\n",
      "iteration 14500 / 15000: loss 4.418518\n",
      "iteration 14600 / 15000: loss 6.214542\n",
      "iteration 14700 / 15000: loss 5.936460\n",
      "iteration 14800 / 15000: loss 4.538550\n",
      "iteration 14900 / 15000: loss 9.358751\n"
     ]
    }
   ],
   "source": [
    "best_options = sorted(results, key=lambda el: results[(el[0], el[1])][1])[-1]\n",
    "learning_rate, regularization_strength = best_options\n",
    "svm = LinearSVM()\n",
    "svm.train(X_train, y_train, \n",
    "          learning_rate=learning_rate, \n",
    "          reg=regularization_strength,\n",
    "          num_iters=15000, \n",
    "          verbose=True,\n",
    "         )\n",
    "trained_accuracy = np.mean(y_train == svm.predict(X_train))\n",
    "val_accuracy = np.mean(y_val == svm.predict(X_val))   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.328000\n"
     ]
    },
    {
     "data": {
      "image/png": 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D/z0+8qSLk8RlmgkSNpqZnZy4g+144bLVmo7f0s/VEm+PBHJRxsSguC+UnSPmXKpiRQaH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\nZII2eeIJ36vaLL1tj5/2NAb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LcC0SntLtu6YRMhnGbA7QNzok/9078LGzu+PjeQZJukN9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdaef17128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])\n",
    "    \n",
    "y_test_pred = svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we could see after 15000 iteration we have much smoother looking weights. But what looks interesting on test set we got less accuracy. So it seems somehow more iteration overfit our model."
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}