Project_Text_classification_PIYUSH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1> Project : Text Classification Using Naive Bayes </h1>\n",
    "    <h4> Dataset -<a href=\"http://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups\">http://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups</a> </h4></center>\n",
    "    \n",
    "<center><h3>By : <a href=\"www.github.com/piyushP7pravin\">Piyush Pravin</a></h3></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries/packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import operator\n",
    "from string import punctuation\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import precision_recall_fscore_support as score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stop words\n",
    "stop_words=[\"a\",\"the\",\"able\",\"about\",\"data\", \"about\", \"above\", \"above\", \"across\", \"after\", \"afterwards\", \"again\", \n",
    "            \"against\", \"all\", \"almost\", \"alone\", \"along\", \"already\", \"also\",\"although\",\"always\",\"am\",\"among\", \"amongst\",\n",
    "            \"amoungst\", \"amount\",  \"an\", \"and\", \"another\", \"any\",\"anyhow\",\"anyone\",\"anything\",\"anyway\", \"anywhere\", \n",
    "            \"are\", \"around\", \"as\",  \"at\", \"back\",\"be\",\"became\", \"because\",\"become\",\"becomes\", \"becoming\", \"been\", \n",
    "            \"before\", \"beforehand\", \"behind\", \"being\", \"below\", \"beside\", \"besides\", \"between\", \"beyond\", \"bill\", \n",
    "            \"both\", \"bottom\",\"but\", \"by\", \"call\", \"can\", \"cannot\", \"cant\", \"co\", \"con\", \"could\", \"couldnt\", \"cry\", \n",
    "            \"de\", \"describe\", \"detail\", \"do\", \"done\", \"down\", \"due\", \"during\", \"each\", \"eg\", \"eight\", \"either\", \n",
    "            \"eleven\",\"else\", \"elsewhere\", \"empty\", \"enough\", \"etc\", \"even\", \"ever\", \"every\", \"everyone\", \"everything\",\n",
    "            \"everywhere\", \"except\", \"few\", \"fifteen\", \"fify\", \"fill\", \"find\", \"fire\", \"first\", \"five\", \"for\", \"former\", \n",
    "            \"formerly\", \"forty\", \"found\", \"four\", \"from\", \"front\", \"full\", \"further\", \"get\", \"give\", \"go\", \"had\", \"has\", \n",
    "            \"hasnt\", \"have\", \"he\", \"hence\", \"her\", \"here\", \"hereafter\", \"hereby\", \"herein\", \"hereupon\", \"hers\", \n",
    "            \"herself\", \"him\", \"himself\", \"his\", \"how\", \"however\", \"hundred\", \"ie\", \"if\", \"in\", \"inc\", \"indeed\", \n",
    "            \"interest\", \"into\", \"is\", \"it\", \"its\", \"itself\", \"keep\", \"last\", \"latter\", \"latterly\", \"least\", \"less\", \n",
    "            \"ltd\", \"made\", \"many\", \"may\", \"me\", \"meanwhile\", \"might\", \"mill\", \"mine\", \"more\", \"moreover\", \"most\", \n",
    "            \"mostly\", \"move\", \"much\", \"must\", \"my\", \"myself\", \"name\", \"namely\", \"neither\", \"never\", \"nevertheless\", \n",
    "            \"next\", \"nine\", \"no\", \"nobody\", \"none\", \"noone\", \"nor\", \"not\", \"nothing\", \"now\", \"nowhere\", \"of\", \"off\", \n",
    "            \"often\", \"on\", \"once\", \"one\", \"only\", \"onto\", \"or\", \"other\", \"others\", \"otherwise\", \"our\", \"ours\", \"ourselves\",\n",
    "            \"out\", \"over\", \"own\",\"part\", \"per\", \"perhaps\", \"please\", \"put\", \"rather\", \"re\", \"same\", \"see\", \"seem\", \n",
    "            \"seemed\", \"seeming\", \"seems\", \"serious\", \"several\", \"she\", \"should\", \"show\", \"side\", \"since\", \"sincere\", \"six\",\n",
    "            \"sixty\", \"so\", \"some\", \"somehow\", \"someone\", \"something\", \"sometime\", \"sometimes\", \"somewhere\", \"still\", \n",
    "            \"such\", \"system\", \"take\", \"ten\", \"than\", \"that\", \"the\", \"their\", \"them\", \"themselves\", \"then\", \"thence\", \n",
    "            \"there\", \"thereafter\", \"thereby\", \"therefore\", \"therein\", \"thereupon\", \"these\", \"they\", \"thickv\", \"thin\", \n",
    "            \"third\", \"this\", \"those\", \"though\", \"three\", \"through\", \"throughout\", \"thru\", \"thus\", \"to\", \"together\", \"too\",\n",
    "            \"top\", \"toward\", \"towards\", \"twelve\", \"twenty\", \"two\", \"un\", \"under\", \"until\", \"up\", \"upon\", \"us\", \"very\", \n",
    "            \"via\", \"was\", \"we\", \"well\", \"were\", \"what\", \"whatever\", \"when\", \"whence\", \"whenever\", \"where\", \"whereafter\", \n",
    "            \"whereas\", \"whereby\", \"wherein\", \"whereupon\", \"wherever\", \"whether\", \"which\", \"while\", \"whither\", \"who\", \n",
    "            \"whoever\", \"whole\", \"whom\", \"whose\", \"why\", \"will\", \"with\", \"within\", \"without\", \"would\", \"yet\", \"you\", \"your\",\n",
    "            \"yours\", \"yourself\", \"yourselves\", \"the\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'6', '!', '0', '+', ']', \"'\", '`', '^', '*', '.', '$', ',', '5', ')', '8', '}', '{', '3', '=', '(', '&', '/', '<', '@', '?', '|', ';', '_', ':', '\"', '-', '9', '~', '%', '#', '[', '\\\\', '7', '1', '>', '4', '2'}\n"
     ]
    }
   ],
   "source": [
    "# storing special_characters and punctuation marks\n",
    "specialChar_and_digits = set(punctuation)\n",
    "for i in range(0,10):\n",
    "    specialChar_and_digits.add(str(i))\n",
    "print(specialChar_and_digits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Word Dictionary (Vocabulary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Class done\n",
      "2 Class done\n",
      "3 Class done\n",
      "4 Class done\n",
      "5 Class done\n",
      "6 Class done\n",
      "7 Class done\n",
      "8 Class done\n",
      "9 Class done\n",
      "10 Class done\n",
      "11 Class done\n",
      "12 Class done\n",
      "13 Class done\n",
      "14 Class done\n",
      "15 Class done\n",
      "16 Class done\n",
      "17 Class done\n",
      "18 Class done\n",
      "19 Class done\n",
      "20 Class done\n",
      "96187\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'xref': 6064,\n",
       " 'cantaloupe': 26048,\n",
       " 'atheism': 2563,\n",
       " 'moderated': 58,\n",
       " 'news': 34309,\n",
       " 'answers': 913,\n",
       " 'path': 20369,\n",
       " 'crabapple': 6235,\n",
       " 'andrew': 4458,\n",
       " 'ohio': 15887,\n",
       " 'state': 18842,\n",
       " 'magnus': 1340,\n",
       " 'usenet': 8308,\n",
       " 'cwru': 2732,\n",
       " 'agate': 1827,\n",
       " 'spool': 581,\n",
       " 'uunet': 5369,\n",
       " 'pipex': 759,\n",
       " 'ibmpcug': 109,\n",
       " 'mantis': 625,\n",
       " 'mathew': 426,\n",
       " 'newsgroups': 20404,\n",
       " 'subject': 21589,\n",
       " 'atheist': 406,\n",
       " 'resources': 475,\n",
       " 'summary': 983,\n",
       " 'books': 950,\n",
       " 'addresses': 242,\n",
       " 'music': 481,\n",
       " 'related': 712,\n",
       " 'keywords': 1554,\n",
       " 'fiction': 148,\n",
       " 'contacts': 100,\n",
       " 'message': 21954,\n",
       " 'date': 20787,\n",
       " 'expires': 161,\n",
       " 'followup': 1485,\n",
       " 'distribution': 4753,\n",
       " 'world': 5354,\n",
       " 'organization': 19840,\n",
       " 'consultants': 92,\n",
       " 'cambridge': 470,\n",
       " 'approved': 1322,\n",
       " 'request': 848,\n",
       " 'supersedes': 50,\n",
       " 'lines': 20894,\n",
       " 'archive': 510,\n",
       " 'modified': 236,\n",
       " 'december': 201,\n",
       " 'version': 2616,\n",
       " 'organizations': 281,\n",
       " 'freedom': 713,\n",
       " 'religion': 5351,\n",
       " 'foundation': 261,\n",
       " 'darwin': 2403,\n",
       " 'fish': 129,\n",
       " 'bumper': 54,\n",
       " 'stickers': 31,\n",
       " 'assorted': 29,\n",
       " 'paraphernalia': 5,\n",
       " 'available': 2622,\n",
       " 'write': 1078,\n",
       " 'ffrf': 2,\n",
       " 'madison': 144,\n",
       " 'telephone': 351,\n",
       " 'evolution': 349,\n",
       " 'designs': 103,\n",
       " 'sell': 806,\n",
       " 'symbol': 89,\n",
       " 'like': 10350,\n",
       " 'ones': 826,\n",
       " 'christians': 1131,\n",
       " 'stick': 368,\n",
       " 'cars': 748,\n",
       " 'feet': 304,\n",
       " 'word': 1567,\n",
       " 'written': 910,\n",
       " 'inside': 615,\n",
       " 'deluxe': 59,\n",
       " 'moulded': 4,\n",
       " 'plastic': 200,\n",
       " 'postpaid': 5,\n",
       " 'laurel': 31,\n",
       " 'canyon': 26,\n",
       " 'north': 637,\n",
       " 'hollywood': 44,\n",
       " 'people': 10364,\n",
       " 'francisco': 320,\n",
       " 'area': 1171,\n",
       " 'lynn': 92,\n",
       " 'gold': 183,\n",
       " 'mailing': 452,\n",
       " 'figmo': 2,\n",
       " 'netcom': 3502,\n",
       " 'directly': 596,\n",
       " 'price': 1458,\n",
       " 'american': 1765,\n",
       " 'press': 990,\n",
       " 'publish': 117,\n",
       " 'various': 764,\n",
       " 'critiques': 11,\n",
       " 'bible': 1326,\n",
       " 'lists': 275,\n",
       " 'biblical': 315,\n",
       " 'contradictions': 79,\n",
       " 'book': 1648,\n",
       " 'handbook': 66,\n",
       " 'ball': 359,\n",
       " 'foote': 8,\n",
       " 'isbn': 124,\n",
       " 'edition': 234,\n",
       " 'absurdities': 27,\n",
       " 'atrocities': 83,\n",
       " 'immoralities': 5,\n",
       " 'contains': 477,\n",
       " 'contradicts': 43,\n",
       " 'based': 1841,\n",
       " 'king': 775,\n",
       " 'james': 1039,\n",
       " 'austin': 1041,\n",
       " 'cameron': 30,\n",
       " 'road': 740,\n",
       " 'prometheus': 12,\n",
       " 'including': 1187,\n",
       " 'haught': 4,\n",
       " 'holy': 400,\n",
       " 'horrors': 34,\n",
       " 'east': 947,\n",
       " 'amherst': 180,\n",
       " 'street': 522,\n",
       " 'buffalo': 1191,\n",
       " 'york': 944,\n",
       " 'alternate': 58,\n",
       " 'address': 1349,\n",
       " 'newer': 170,\n",
       " 'older': 309,\n",
       " 'glenn': 114,\n",
       " 'drive': 2700,\n",
       " 'african': 221,\n",
       " 'americans': 538,\n",
       " 'humanism': 19,\n",
       " 'promoting': 56,\n",
       " 'black': 1192,\n",
       " 'secular': 137,\n",
       " 'uncovering': 8,\n",
       " 'history': 1487,\n",
       " 'freethought': 5,\n",
       " 'quarterly': 42,\n",
       " 'newsletter': 190,\n",
       " 'examiner': 38,\n",
       " 'norm': 65,\n",
       " 'allen': 362,\n",
       " 'united': 786,\n",
       " 'kingdom': 229,\n",
       " 'rationalist': 4,\n",
       " 'association': 292,\n",
       " 'national': 1599,\n",
       " 'society': 1055,\n",
       " 'islington': 2,\n",
       " 'high': 2126,\n",
       " 'holloway': 16,\n",
       " 'london': 273,\n",
       " 'british': 451,\n",
       " 'humanist': 29,\n",
       " 'south': 552,\n",
       " 'place': 1775,\n",
       " 'ethical': 82,\n",
       " 'lamb': 61,\n",
       " 'conduit': 25,\n",
       " 'passage': 182,\n",
       " 'conway': 14,\n",
       " 'hall': 394,\n",
       " 'lion': 45,\n",
       " 'square': 161,\n",
       " 'freethinker': 2,\n",
       " 'monthly': 119,\n",
       " 'magazine': 391,\n",
       " 'founded': 112,\n",
       " 'germany': 813,\n",
       " 'ibka': 6,\n",
       " 'internationaler': 4,\n",
       " 'bund': 6,\n",
       " 'konfessionslosen': 4,\n",
       " 'atheisten': 4,\n",
       " 'postfach': 14,\n",
       " 'berlin': 445,\n",
       " 'journal': 250,\n",
       " 'materialien': 2,\n",
       " 'informationen': 2,\n",
       " 'zeit': 6,\n",
       " 'politisches': 2,\n",
       " 'konfessionslosesn': 2,\n",
       " 'hrsg': 2,\n",
       " 'vertrieb': 2,\n",
       " 'ibdk': 2,\n",
       " 'ucherdienst': 2,\n",
       " 'hannover': 40,\n",
       " 'thomas': 819,\n",
       " 'disch': 2,\n",
       " 'santa': 247,\n",
       " 'claus': 19,\n",
       " 'compromise': 170,\n",
       " 'short': 851,\n",
       " 'story': 739,\n",
       " 'ultimate': 124,\n",
       " 'proof': 518,\n",
       " 'exists': 660,\n",
       " 'characters': 186,\n",
       " 'events': 584,\n",
       " 'fictitious': 6,\n",
       " 'similarity': 29,\n",
       " 'living': 640,\n",
       " 'dead': 970,\n",
       " 'gods': 267,\n",
       " 'walter': 372,\n",
       " 'miller': 337,\n",
       " 'canticle': 2,\n",
       " 'leibowitz': 4,\n",
       " 'post': 2114,\n",
       " 'atomic': 128,\n",
       " 'doomsday': 5,\n",
       " 'novel': 52,\n",
       " 'monks': 9,\n",
       " 'spent': 312,\n",
       " 'lives': 551,\n",
       " 'copying': 75,\n",
       " 'blueprints': 3,\n",
       " 'saint': 111,\n",
       " 'filling': 51,\n",
       " 'sheets': 77,\n",
       " 'paper': 599,\n",
       " 'leaving': 207,\n",
       " 'white': 1168,\n",
       " 'letters': 282,\n",
       " 'edgar': 58,\n",
       " 'pangborn': 2,\n",
       " 'davy': 12,\n",
       " 'clerical': 7,\n",
       " 'states': 1283,\n",
       " 'church': 1211,\n",
       " 'example': 1786,\n",
       " 'forbids': 16,\n",
       " 'produce': 355,\n",
       " 'substance': 131,\n",
       " 'containing': 223,\n",
       " 'atoms': 149,\n",
       " 'philip': 91,\n",
       " 'dick': 175,\n",
       " 'wrote': 1705,\n",
       " 'philosophical': 98,\n",
       " 'thought': 1706,\n",
       " 'provoking': 15,\n",
       " 'stories': 271,\n",
       " 'novels': 12,\n",
       " 'bizarre': 67,\n",
       " 'times': 1578,\n",
       " 'approachable': 2,\n",
       " 'mainly': 183,\n",
       " 'truth': 1188,\n",
       " 'technology': 1815,\n",
       " 'believed': 322,\n",
       " 'sort': 1028,\n",
       " 'remained': 81,\n",
       " 'sceptical': 26,\n",
       " 'following': 1736,\n",
       " 'relevance': 57,\n",
       " 'galactic': 38,\n",
       " 'healer': 11,\n",
       " 'fallible': 13,\n",
       " 'alien': 188,\n",
       " 'deity': 82,\n",
       " 'summons': 12,\n",
       " 'group': 2291,\n",
       " 'earth': 1266,\n",
       " 'craftsmen': 3,\n",
       " 'women': 980,\n",
       " 'remote': 425,\n",
       " 'planet': 402,\n",
       " 'raise': 247,\n",
       " 'giant': 80,\n",
       " 'cathedral': 7,\n",
       " 'beneath': 33,\n",
       " 'oceans': 7,\n",
       " 'begins': 132,\n",
       " 'demand': 261,\n",
       " 'faith': 1045,\n",
       " 'earthers': 13,\n",
       " 'fernwright': 2,\n",
       " 'unable': 207,\n",
       " 'comply': 29,\n",
       " 'polished': 12,\n",
       " 'ironic': 47,\n",
       " 'amusing': 77,\n",
       " 'maze': 12,\n",
       " 'death': 1102,\n",
       " 'noteworthy': 18,\n",
       " 'description': 347,\n",
       " 'valis': 4,\n",
       " 'schizophrenic': 4,\n",
       " 'hero': 45,\n",
       " 'searches': 69,\n",
       " 'hidden': 162,\n",
       " 'mysteries': 40,\n",
       " 'gnostic': 8,\n",
       " 'christianity': 740,\n",
       " 'reality': 553,\n",
       " 'fired': 235,\n",
       " 'brain': 370,\n",
       " 'pink': 100,\n",
       " 'laser': 312,\n",
       " 'beam': 112,\n",
       " 'unknown': 234,\n",
       " 'possibly': 454,\n",
       " 'divine': 197,\n",
       " 'origin': 234,\n",
       " 'accompanied': 35,\n",
       " 'dogmatic': 21,\n",
       " 'dismissively': 2,\n",
       " 'friend': 746,\n",
       " 'invasion': 125,\n",
       " 'invades': 8,\n",
       " 'making': 1148,\n",
       " 'young': 822,\n",
       " 'woman': 412,\n",
       " 'pregnant': 66,\n",
       " 'returns': 121,\n",
       " 'star': 386,\n",
       " 'unfortunately': 543,\n",
       " 'terminally': 7,\n",
       " 'assisted': 42,\n",
       " 'wired': 52,\n",
       " 'hour': 268,\n",
       " 'easy': 863,\n",
       " 'listening': 169,\n",
       " 'margaret': 14,\n",
       " 'atwood': 7,\n",
       " 'handmaid': 2,\n",
       " 'tale': 81,\n",
       " 'premise': 119,\n",
       " 'congress': 550,\n",
       " 'mysteriously': 6,\n",
       " 'assassinated': 7,\n",
       " 'fundamentalists': 46,\n",
       " 'quickly': 314,\n",
       " 'charge': 407,\n",
       " 'nation': 377,\n",
       " 'right': 4943,\n",
       " 'diary': 18,\n",
       " 'life': 2260,\n",
       " 'tries': 123,\n",
       " 'live': 1266,\n",
       " 'christian': 5129,\n",
       " 'theocracy': 4,\n",
       " 'property': 363,\n",
       " 'revoked': 13,\n",
       " 'bank': 350,\n",
       " 'accounts': 188,\n",
       " 'closed': 442,\n",
       " 'sinful': 80,\n",
       " 'luxuries': 6,\n",
       " 'outlawed': 25,\n",
       " 'radio': 992,\n",
       " 'used': 4261,\n",
       " 'readings': 39,\n",
       " 'crimes': 222,\n",
       " 'punished': 86,\n",
       " 'retroactively': 3,\n",
       " 'doctors': 257,\n",
       " 'performed': 182,\n",
       " 'legal': 1287,\n",
       " 'abortions': 113,\n",
       " 'hunted': 7,\n",
       " 'hanged': 32,\n",
       " 'writing': 474,\n",
       " 'style': 328,\n",
       " 'difficult': 554,\n",
       " 'grows': 44,\n",
       " 'chilling': 19,\n",
       " 'goes': 911,\n",
       " 'authors': 180,\n",
       " 'somewhat': 297,\n",
       " 'dull': 48,\n",
       " 'rambling': 15,\n",
       " 'work': 3958,\n",
       " 'criticized': 20,\n",
       " 'probably': 2314,\n",
       " 'worth': 774,\n",
       " 'reading': 919,\n",
       " 'know': 9173,\n",
       " 'fuss': 19,\n",
       " 'different': 2369,\n",
       " 'versions': 364,\n",
       " 'make': 5191,\n",
       " 'sure': 2931,\n",
       " 'true': 2356,\n",
       " 'peter': 966,\n",
       " 'rosa': 42,\n",
       " 'vicars': 2,\n",
       " 'christ': 1123,\n",
       " 'bantam': 9,\n",
       " 'catholic': 365,\n",
       " 'enlighting': 2,\n",
       " 'papal': 26,\n",
       " 'adulteries': 2,\n",
       " 'fallacies': 19,\n",
       " 'german': 377,\n",
       " 'translation': 266,\n",
       " 'gottes': 2,\n",
       " 'erste': 4,\n",
       " 'diener': 2,\n",
       " 'dunkle': 2,\n",
       " 'seite': 2,\n",
       " 'papsttums': 2,\n",
       " 'droemer': 2,\n",
       " 'knaur': 2,\n",
       " 'michael': 1663,\n",
       " 'martin': 339,\n",
       " 'justification': 160,\n",
       " 'temple': 415,\n",
       " 'university': 9958,\n",
       " 'philadelphia': 244,\n",
       " 'detailed': 177,\n",
       " 'scholarly': 55,\n",
       " 'outstanding': 39,\n",
       " 'appendix': 12,\n",
       " 'defining': 68,\n",
       " 'terminology': 54,\n",
       " 'usage': 231,\n",
       " 'necessarily': 614,\n",
       " 'tendentious': 7,\n",
       " 'argues': 33,\n",
       " 'negative': 182,\n",
       " 'belief': 641,\n",
       " 'existence': 646,\n",
       " 'positive': 325,\n",
       " 'includes': 733,\n",
       " 'great': 2192,\n",
       " 'refutations': 11,\n",
       " 'challenging': 28,\n",
       " 'arguments': 443,\n",
       " 'particular': 868,\n",
       " 'attention': 341,\n",
       " 'paid': 421,\n",
       " 'refuting': 11,\n",
       " 'contempory': 2,\n",
       " 'theists': 126,\n",
       " 'platinga': 2,\n",
       " 'swinburne': 14,\n",
       " 'pages': 425,\n",
       " 'hardcover': 22,\n",
       " 'paperback': 41,\n",
       " 'case': 2817,\n",
       " 'comprehensive': 142,\n",
       " 'critique': 29,\n",
       " 'considers': 70,\n",
       " 'best': 2386,\n",
       " 'contemporary': 54,\n",
       " 'defences': 4,\n",
       " 'ultimately': 98,\n",
       " 'demonstrates': 61,\n",
       " 'unsupportable': 2,\n",
       " 'incoherent': 16,\n",
       " 'turner': 104,\n",
       " 'creed': 67,\n",
       " 'johns': 96,\n",
       " 'hopkins': 166,\n",
       " 'baltimore': 236,\n",
       " 'subtitled': 2,\n",
       " 'origins': 626,\n",
       " 'unbelief': 6,\n",
       " 'america': 663,\n",
       " 'examines': 5,\n",
       " 'agnostic': 57,\n",
       " 'atheistic': 22,\n",
       " 'mainstream': 89,\n",
       " 'alternative': 369,\n",
       " 'view': 1070,\n",
       " 'focusses': 3,\n",
       " 'period': 868,\n",
       " 'considering': 414,\n",
       " 'france': 238,\n",
       " 'britain': 130,\n",
       " 'emphasis': 121,\n",
       " 'particularly': 437,\n",
       " 'england': 265,\n",
       " 'developments': 37,\n",
       " 'religious': 1004,\n",
       " 'secularization': 3,\n",
       " 'intellectual': 121,\n",
       " 'fate': 97,\n",
       " 'single': 1084,\n",
       " 'idea': 1597,\n",
       " 'george': 720,\n",
       " 'seldes': 3,\n",
       " 'editor': 317,\n",
       " 'thoughts': 311,\n",
       " 'ballantine': 3,\n",
       " 'dictionary': 212,\n",
       " 'quotations': 41,\n",
       " 'kind': 1564,\n",
       " 'concentrating': 13,\n",
       " 'statements': 426,\n",
       " 'writings': 132,\n",
       " 'explicitly': 138,\n",
       " 'implicitly': 33,\n",
       " 'present': 739,\n",
       " 'person': 1853,\n",
       " 'philosophy': 267,\n",
       " 'obscure': 70,\n",
       " 'suppressed': 36,\n",
       " 'opinions': 1623,\n",
       " 'popular': 395,\n",
       " 'observations': 250,\n",
       " 'traces': 45,\n",
       " 'expressed': 452,\n",
       " 'twisted': 55,\n",
       " 'centuries': 137,\n",
       " 'quite': 1747,\n",
       " 'number': 2859,\n",
       " 'derived': 131,\n",
       " 'cardiff': 9,\n",
       " 'think': 8315,\n",
       " 'noyes': 35,\n",
       " 'views': 601,\n",
       " 'richard': 748,\n",
       " 'revised': 93,\n",
       " 'clarendon': 4,\n",
       " 'paperbacks': 4,\n",
       " 'oxford': 118,\n",
       " 'second': 2032,\n",
       " 'volume': 510,\n",
       " 'trilogy': 4,\n",
       " 'began': 297,\n",
       " 'coherence': 11,\n",
       " 'theism': 172,\n",
       " 'concluded': 81,\n",
       " 'reason': 1934,\n",
       " 'attempts': 229,\n",
       " 'construct': 54,\n",
       " 'series': 843,\n",
       " 'inductive': 30,\n",
       " 'rely': 145,\n",
       " 'imputation': 4,\n",
       " 'late': 524,\n",
       " 'century': 373,\n",
       " 'western': 913,\n",
       " 'values': 771,\n",
       " 'aesthetics': 13,\n",
       " 'supposedly': 209,\n",
       " 'simple': 975,\n",
       " 'conceived': 41,\n",
       " 'decisively': 5,\n",
       " 'rejected': 126,\n",
       " 'mackie': 37,\n",
       " 'miracle': 118,\n",
       " 'makes': 1492,\n",
       " 'attempt': 522,\n",
       " 'rebut': 10,\n",
       " 'posthumous': 2,\n",
       " 'review': 388,\n",
       " 'principal': 64,\n",
       " 'ranges': 51,\n",
       " 'classical': 59,\n",
       " 'positions': 206,\n",
       " 'descartes': 87,\n",
       " 'anselm': 6,\n",
       " 'berkeley': 1571,\n",
       " 'hume': 4,\n",
       " 'moral': 942,\n",
       " 'newman': 58,\n",
       " 'kant': 13,\n",
       " 'sidgwick': 2,\n",
       " 'recent': 533,\n",
       " 'restatements': 2,\n",
       " 'theses': 3,\n",
       " 'plantinga': 2,\n",
       " 'push': 230,\n",
       " 'concept': 382,\n",
       " 'realm': 54,\n",
       " 'rational': 214,\n",
       " 'kierkegaard': 6,\n",
       " 'kung': 6,\n",
       " 'philips': 216,\n",
       " 'replacements': 36,\n",
       " 'lelie': 2,\n",
       " 'axiarchism': 2,\n",
       " 'delight': 14,\n",
       " 'read': 3071,\n",
       " 'formalistic': 3,\n",
       " 'better': 3216,\n",
       " 'works': 1304,\n",
       " 'refreshingly': 2,\n",
       " 'direct': 426,\n",
       " 'compared': 372,\n",
       " 'hand': 1237,\n",
       " 'waving': 46,\n",
       " 'illustrated': 28,\n",
       " 'murder': 517,\n",
       " 'madness': 36,\n",
       " 'looks': 717,\n",
       " 'persecution': 107,\n",
       " 'ancient': 274,\n",
       " 'library': 584,\n",
       " 'catalog': 137,\n",
       " 'card': 1977,\n",
       " 'anthology': 7,\n",
       " 'listing': 124,\n",
       " 'gordon': 451,\n",
       " 'stein': 166,\n",
       " 'rationalism': 13,\n",
       " 'covering': 95,\n",
       " 'wide': 452,\n",
       " 'range': 495,\n",
       " 'subjects': 114,\n",
       " 'devil': 140,\n",
       " 'evil': 531,\n",
       " 'morality': 1002,\n",
       " 'bibliography': 59,\n",
       " 'edmund': 13,\n",
       " 'cohen': 72,\n",
       " 'mind': 1231,\n",
       " 'believer': 85,\n",
       " 'study': 861,\n",
       " 'effect': 846,\n",
       " 'small': 1392,\n",
       " 'mail': 4511,\n",
       " 'server': 1445,\n",
       " 'carries': 89,\n",
       " 'archives': 192,\n",
       " 'articles': 443,\n",
       " 'files': 1801,\n",
       " 'information': 3345,\n",
       " 'send': 1733,\n",
       " 'saying': 1472,\n",
       " 'help': 3325,\n",
       " 'index': 200,\n",
       " 'reply': 4307,\n",
       " 'europa': 5448,\n",
       " 'gtefsd': 5429,\n",
       " 'howland': 10981,\n",
       " 'reston': 10946,\n",
       " 'netsys': 37,\n",
       " 'introduction': 183,\n",
       " 'file': 3217,\n",
       " 'posting': 9793,\n",
       " 'april': 1195,\n",
       " 'begin': 408,\n",
       " 'signed': 211,\n",
       " 'article': 13435,\n",
       " 'provide': 940,\n",
       " 'general': 1889,\n",
       " 'whilst': 21,\n",
       " 'tried': 1038,\n",
       " 'neutral': 215,\n",
       " 'possible': 2015,\n",
       " 'regarding': 515,\n",
       " 'contentious': 17,\n",
       " 'issues': 709,\n",
       " 'remember': 1508,\n",
       " 'document': 355,\n",
       " 'represents': 196,\n",
       " 'viewpoint': 90,\n",
       " 'encourage': 124,\n",
       " 'widely': 186,\n",
       " 'draw': 414,\n",
       " 'conclusions': 164,\n",
       " 'relevant': 281,\n",
       " 'listed': 259,\n",
       " 'companion': 38,\n",
       " 'sense': 1023,\n",
       " 'cohesion': 2,\n",
       " 'progression': 31,\n",
       " 'presented': 271,\n",
       " 'imaginary': 34,\n",
       " 'conversation': 184,\n",
       " 'theist': 87,\n",
       " 'questions': 1562,\n",
       " 'asked': 952,\n",
       " 'cropped': 7,\n",
       " 'repeatedly': 120,\n",
       " 'newsgroup': 628,\n",
       " 'created': 627,\n",
       " 'frequently': 275,\n",
       " 'answered': 190,\n",
       " 'note': 1532,\n",
       " 'arguably': 32,\n",
       " 'slanted': 8,\n",
       " 'answering': 93,\n",
       " 'posed': 43,\n",
       " 'reflect': 182,\n",
       " 'actually': 2224,\n",
       " 'predominantly': 20,\n",
       " 'proselytize': 6,\n",
       " 'talk': 12308,\n",
       " 'talking': 995,\n",
       " 'primarily': 199,\n",
       " 'religions': 288,\n",
       " 'judaism': 103,\n",
       " 'islam': 475,\n",
       " 'involve': 161,\n",
       " 'superhuman': 12,\n",
       " 'discussion': 902,\n",
       " 'apply': 499,\n",
       " 'characterized': 39,\n",
       " 'absence': 96,\n",
       " 'atheists': 594,\n",
       " 'believe': 3699,\n",
       " 'does': 7267,\n",
       " 'exist': 828,\n",
       " 'referred': 193,\n",
       " 'weak': 264,\n",
       " 'position': 923,\n",
       " 'strong': 737,\n",
       " 'important': 1222,\n",
       " 'difference': 1056,\n",
       " 'scepticism': 13,\n",
       " 'disbelief': 31,\n",
       " 'fall': 496,\n",
       " 'trap': 81,\n",
       " 'assuming': 411,\n",
       " 'limit': 308,\n",
       " 'specific': 805,\n",
       " 'flat': 208,\n",
       " 'denials': 13,\n",
       " 'disbelieving': 3,\n",
       " 'thing': 2735,\n",
       " 'believing': 198,\n",
       " 'doesn': 2823,\n",
       " 'definitely': 306,\n",
       " 'proposition': 63,\n",
       " 'means': 1666,\n",
       " 'equivalent': 226,\n",
       " 'false': 489,\n",
       " 'simply': 1212,\n",
       " 'brings': 124,\n",
       " 'agnosticism': 22,\n",
       " 'term': 731,\n",
       " 'coined': 24,\n",
       " 'professor': 298,\n",
       " 'huxley': 7,\n",
       " 'meeting': 281,\n",
       " 'metaphysical': 26,\n",
       " 'defined': 416,\n",
       " 'disclaimed': 6,\n",
       " 'things': 3122,\n",
       " 'cause': 1152,\n",
       " 'unknowable': 11,\n",
       " 'believes': 206,\n",
       " 'words': 1068,\n",
       " 'slippery': 21,\n",
       " 'language': 617,\n",
       " 'inexact': 4,\n",
       " 'beware': 61,\n",
       " 'point': 3299,\n",
       " 'fact': 2675,\n",
       " 'calls': 456,\n",
       " 'mean': 2056,\n",
       " 'referring': 201,\n",
       " 'shades': 24,\n",
       " 'meaning': 619,\n",
       " 'generalize': 13,\n",
       " 'certainly': 1173,\n",
       " 'science': 2575,\n",
       " 'universe': 611,\n",
       " 'basis': 665,\n",
       " 'justifications': 26,\n",
       " 'chooses': 60,\n",
       " 'feel': 1260,\n",
       " 'major': 1030,\n",
       " 'essentially': 253,\n",
       " 'self': 967,\n",
       " 'contradictory': 99,\n",
       " 'logically': 59,\n",
       " 'impossible': 380,\n",
       " 'evidence': 1675,\n",
       " 'prove': 568,\n",
       " 'counter': 158,\n",
       " 'examples': 317,\n",
       " 'statement': 919,\n",
       " 'prime': 218,\n",
       " 'larger': 341,\n",
       " 'numbers': 845,\n",
       " 'course': 2218,\n",
       " 'deals': 124,\n",
       " 'objects': 377,\n",
       " 'obeying': 21,\n",
       " 'rules': 636,\n",
       " 'universes': 36,\n",
       " 'similarly': 173,\n",
       " 'matter': 1339,\n",
       " 'debate': 316,\n",
       " 'moment': 406,\n",
       " 'provably': 6,\n",
       " 'subtle': 47,\n",
       " 'reasons': 657,\n",
       " 'assume': 761,\n",
       " 'assumption': 252,\n",
       " 'invalid': 72,\n",
       " 'finding': 265,\n",
       " 'question': 3185,\n",
       " 'showing': 279,\n",
       " 'require': 502,\n",
       " 'exhaustive': 21,\n",
       " 'search': 489,\n",
       " 'places': 548,\n",
       " 'impractical': 20,\n",
       " 'problem': 3764,\n",
       " 'largest': 109,\n",
       " 'primes': 6,\n",
       " 'generally': 524,\n",
       " 'accepted': 364,\n",
       " 'unless': 1068,\n",
       " 'follow': 678,\n",
       " 'rule': 596,\n",
       " 'time': 7073,\n",
       " 'unicorns': 20,\n",
       " 'conclusively': 6,\n",
       " 'tested': 233,\n",
       " 'assumes': 107,\n",
       " 'default': 302,\n",
       " 'test': 923,\n",
       " 'profess': 18,\n",
       " 'usually': 905,\n",
       " 'claim': 1274,\n",
       " 'instead': 1143,\n",
       " 'restrict': 90,\n",
       " 'claims': 679,\n",
       " 'cover': 654,\n",
       " 'varieties': 17,\n",
       " 'described': 429,\n",
       " 'followers': 251,\n",
       " 'practice': 455,\n",
       " 'close': 737,\n",
       " 'sufficiently': 62,\n",
       " 'impossibility': 5,\n",
       " 'disproving': 10,\n",
       " 'really': 3926,\n",
       " 'applicable': 74,\n",
       " 'detectable': 25,\n",
       " 'interacts': 6,\n",
       " 'effects': 417,\n",
       " 'interaction': 53,\n",
       " 'measurable': 19,\n",
       " 'interact': 40,\n",
       " 'argue': 351,\n",
       " 'importance': 202,\n",
       " 'easily': 555,\n",
       " 'israelites': 67,\n",
       " 'surely': 269,\n",
       " 'today': 1596,\n",
       " 'demanding': 53,\n",
       " 'scientifically': 14,\n",
       " 'verifiable': 21,\n",
       " 'physical': 528,\n",
       " 'perceive': 39,\n",
       " 'caused': 464,\n",
       " 'presence': 211,\n",
       " 'distinguish': 80,\n",
       " 'common': 933,\n",
       " 'pastimes': 1,\n",
       " 'redefinition': 4,\n",
       " 'game': 2272,\n",
       " 'cynical': 49,\n",
       " 'follows': 319,\n",
       " 'points': 823,\n",
       " 'gradually': 44,\n",
       " 'defines': 90,\n",
       " 'arrives': 20,\n",
       " 'prepared': 218,\n",
       " 'accept': 812,\n",
       " 'records': 196,\n",
       " 'agreed': 207,\n",
       " 'continues': 171,\n",
       " 'eventually': 319,\n",
       " 'uses': 835,\n",
       " 'original': 1325,\n",
       " 'definitions': 138,\n",
       " 'redefinitions': 3,\n",
       " 'originally': 241,\n",
       " 'needed': 860,\n",
       " 'agree': 1324,\n",
       " 'seen': 1792,\n",
       " 'apparently': 519,\n",
       " 'inconsistent': 49,\n",
       " 'tend': 342,\n",
       " 'play': 1303,\n",
       " 'digression': 6,\n",
       " 'answer': 1370,\n",
       " 'depends': 296,\n",
       " 'crucially': 2,\n",
       " 'meant': 395,\n",
       " 'controlling': 52,\n",
       " 'power': 2529,\n",
       " 'especially': 994,\n",
       " 'worship': 255,\n",
       " 'pointing': 140,\n",
       " 'passing': 171,\n",
       " 'buddhism': 16,\n",
       " 'according': 861,\n",
       " 'definition': 558,\n",
       " 'categorized': 13,\n",
       " 'meaningful': 72,\n",
       " 'widening': 4,\n",
       " 'encompass': 17,\n",
       " 'tends': 122,\n",
       " 'result': 758,\n",
       " 'aspects': 199,\n",
       " 'human': 1803,\n",
       " 'behaviour': 140,\n",
       " 'suddenly': 166,\n",
       " 'classed': 2,\n",
       " 'politics': 7869,\n",
       " 'watching': 316,\n",
       " 'just': 9978,\n",
       " 'firstly': 44,\n",
       " 'entirely': 379,\n",
       " 'clear': 1024,\n",
       " 'secondly': 100,\n",
       " 'necessary': 723,\n",
       " 'adopt': 55,\n",
       " 'core': 261,\n",
       " 'beliefs': 448,\n",
       " 'assumptions': 119,\n",
       " 'sensory': 18,\n",
       " 'experience': 896,\n",
       " 'questioning': 57,\n",
       " 'throws': 48,\n",
       " 'doubt': 744,\n",
       " 'assumed': 200,\n",
       " 'laws': 895,\n",
       " 'physics': 847,\n",
       " 'observers': 58,\n",
       " 'basic': 623,\n",
       " 'ideas': 688,\n",
       " 'called': 2044,\n",
       " 'acts': 432,\n",
       " 'said': 4321,\n",
       " 'loses': 36,\n",
       " 'refer': 285,\n",
       " 'complete': 667,\n",
       " 'certain': 971,\n",
       " 'individual': 631,\n",
       " 'scientists': 225,\n",
       " 'claiming': 249,\n",
       " 'tendency': 60,\n",
       " 'reluctant': 31,\n",
       " 'certainty': 60,\n",
       " 'supporting': 182,\n",
       " 'closer': 209,\n",
       " 'match': 226,\n",
       " 'experimental': 146,\n",
       " 'lack': 492,\n",
       " 'asserting': 46,\n",
       " 'anti': 837,\n",
       " 'unfortunate': 84,\n",
       " 'label': 107,\n",
       " 'enemy': 134,\n",
       " 'runs': 785,\n",
       " 'believers': 117,\n",
       " 'speak': 766,\n",
       " 'antipathy': 2,\n",
       " 'hatred': 146,\n",
       " 'categorization': 2,\n",
       " 'hostile': 82,\n",
       " 'unfair': 67,\n",
       " 'attitudes': 41,\n",
       " 'broad': 119,\n",
       " 'spectrum': 163,\n",
       " 'attitude': 311,\n",
       " 'questioned': 58,\n",
       " 'mention': 534,\n",
       " 'friends': 490,\n",
       " 'socially': 26,\n",
       " 'acceptable': 256,\n",
       " 'countries': 453,\n",
       " 'convert': 423,\n",
       " 'historically': 60,\n",
       " 'little': 2493,\n",
       " 'impact': 216,\n",
       " 'outside': 769,\n",
       " 'eastern': 212,\n",
       " 'bloc': 9,\n",
       " 'digress': 16,\n",
       " 'slightly': 289,\n",
       " 'soviet': 1143,\n",
       " 'union': 189,\n",
       " 'dedicated': 97,\n",
       " 'separation': 92,\n",
       " 'citizens': 539,\n",
       " 'legally': 153,\n",
       " 'free': 2146,\n",
       " 'wished': 28,\n",
       " 'institution': 66,\n",
       " 'came': 1311,\n",
       " 'stalin': 76,\n",
       " 'took': 950,\n",
       " 'control': 1735,\n",
       " 'destroy': 186,\n",
       " 'churches': 187,\n",
       " 'order': 1843,\n",
       " ...}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing the all the classe names in a list from the given folder\n",
    "all_classes = os.listdir(\"20_newsgroups\")\n",
    "\n",
    "# creating the word dictionary\n",
    "word_dictionary = {}\n",
    "\"\"\"\n",
    "DICTIONARY :\n",
    "    Keys   : words\n",
    "    Values : count of word\n",
    "\"\"\"\n",
    "\n",
    "# using count for checking progress during runtime\n",
    "count = 1\n",
    "\n",
    "# Iterating through all classes\n",
    "for class_ in all_classes:\n",
    "    # storing the all documents in a list for a particular class\n",
    "    all_docs = os.listdir(\"20_newsgroups\"+\"/\"+class_)\n",
    "    \n",
    "    # traversing through all documents\n",
    "    for docs in all_docs:\n",
    "        \n",
    "        # getting the base path which is common for all documents of a particular class\n",
    "        path = \"20_newsgroups\"+\"/\"+class_+\"/\"+docs\n",
    "        \n",
    "        # opening the particular document as file object\n",
    "        with open(path, encoding = \"latin-1\") as file_obj:\n",
    "            \n",
    "            # reading the document data\n",
    "            file_data = file_obj.read()\n",
    "            \n",
    "            # stroing each words in a list , used re to remove the whitespaces \n",
    "            tokens = re.compile('\\w+').findall(file_data)\n",
    "            \n",
    "            # traversing through all words and updating the value in dictionary if already exist otherwise will add to it\n",
    "            for word in tokens:\n",
    "                \n",
    "                # checking if the word in a stop_word or not, is aphbetic and the lenth should be > 3 to avoid words like i, me etc\n",
    "                if (word.lower() not in stop_words) and (word.lower().isalpha()) and (len(word.lower())>3):\n",
    "                    \n",
    "                    # getting the value of word from dictionary and increasing its count by 1 if word does not exists in dictionart then adding it with count 1\n",
    "                    word_dictionary[word.lower()] = word_dictionary.get(word.lower(),0) + 1 \n",
    "    # using print statement to check the progress during runtime\n",
    "    print(count, \"Class done\")\n",
    "    count+=1\n",
    "'''for key in word_dictionary.copy():\n",
    "    for char in specialChar_and_digits:\n",
    "        if char in key:\n",
    "            word_dictionary.pop(key)\n",
    "            break '''     \n",
    "print(len(word_dictionary))\n",
    "word_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'news': 34309,\n",
       " 'cantaloupe': 26048,\n",
       " 'message': 21954,\n",
       " 'subject': 21589,\n",
       " 'lines': 20894,\n",
       " 'date': 20787,\n",
       " 'newsgroups': 20404,\n",
       " 'path': 20369,\n",
       " 'organization': 19840,\n",
       " 'state': 18842,\n",
       " 'ohio': 15887,\n",
       " 'writes': 14686,\n",
       " 'article': 13435,\n",
       " 'comp': 13101,\n",
       " 'references': 12981,\n",
       " 'talk': 12308,\n",
       " 'misc': 11892,\n",
       " 'sender': 11040,\n",
       " 'howland': 10981,\n",
       " 'reston': 10946,\n",
       " 'people': 10364,\n",
       " 'like': 10350,\n",
       " 'just': 9978,\n",
       " 'university': 9958,\n",
       " 'posting': 9793,\n",
       " 'nntp': 9377,\n",
       " 'know': 9173,\n",
       " 'host': 8934,\n",
       " 'think': 8315,\n",
       " 'usenet': 8308,\n",
       " 'zaphod': 8302,\n",
       " 'politics': 7869,\n",
       " 'windows': 7584,\n",
       " 'does': 7267,\n",
       " 'time': 7073,\n",
       " 'rutgers': 7014,\n",
       " 'harvard': 6923,\n",
       " 'near': 6868,\n",
       " 'good': 6308,\n",
       " 'crabapple': 6235,\n",
       " 'xref': 6064,\n",
       " 'europa': 5448,\n",
       " 'gtefsd': 5429,\n",
       " 'uunet': 5369,\n",
       " 'world': 5354,\n",
       " 'religion': 5351,\n",
       " 'make': 5191,\n",
       " 'christian': 5129,\n",
       " 'right': 4943,\n",
       " 'rochester': 4851,\n",
       " 'distribution': 4753,\n",
       " 'club': 4617,\n",
       " 'space': 4544,\n",
       " 'mail': 4511,\n",
       " 'andrew': 4458,\n",
       " 'want': 4442,\n",
       " 'gatech': 4413,\n",
       " 'magnesium': 4363,\n",
       " 'said': 4321,\n",
       " 'udel': 4310,\n",
       " 'reply': 4307,\n",
       " 'utexas': 4283,\n",
       " 'used': 4261,\n",
       " 'culture': 4241,\n",
       " 'need': 4142,\n",
       " 'uiuc': 3978,\n",
       " 'work': 3958,\n",
       " 'nasa': 3931,\n",
       " 'really': 3926,\n",
       " 'problem': 3764,\n",
       " 'computer': 3758,\n",
       " 'sura': 3709,\n",
       " 'believe': 3699,\n",
       " 'graphics': 3620,\n",
       " 'hardware': 3595,\n",
       " 'netcom': 3502,\n",
       " 'years': 3405,\n",
       " 'going': 3366,\n",
       " 'government': 3357,\n",
       " 'information': 3345,\n",
       " 'help': 3325,\n",
       " 'point': 3299,\n",
       " 'file': 3217,\n",
       " 'better': 3216,\n",
       " 'year': 3200,\n",
       " 'using': 3194,\n",
       " 'question': 3185,\n",
       " 'things': 3122,\n",
       " 'read': 3071,\n",
       " 'thanks': 3041,\n",
       " 'software': 3041,\n",
       " 'david': 2995,\n",
       " 'uucp': 2938,\n",
       " 'sure': 2931,\n",
       " 'number': 2859,\n",
       " 'access': 2850,\n",
       " 'doesn': 2823,\n",
       " 'case': 2817,\n",
       " 'forsale': 2805,\n",
       " 'look': 2778,\n",
       " 'thing': 2735,\n",
       " 'cwru': 2732,\n",
       " 'long': 2701,\n",
       " 'drive': 2700,\n",
       " 'program': 2690,\n",
       " 'fact': 2675,\n",
       " 'available': 2622,\n",
       " 'version': 2616,\n",
       " 'apple': 2606,\n",
       " 'guns': 2600,\n",
       " 'come': 2581,\n",
       " 'science': 2575,\n",
       " 'atheism': 2563,\n",
       " 'john': 2532,\n",
       " 'power': 2529,\n",
       " 'little': 2493,\n",
       " 'columbia': 2477,\n",
       " 'didn': 2454,\n",
       " 'darwin': 2403,\n",
       " 'sport': 2397,\n",
       " 'best': 2386,\n",
       " 'public': 2374,\n",
       " 'different': 2369,\n",
       " 'true': 2356,\n",
       " 'probably': 2314,\n",
       " 'group': 2291,\n",
       " 'game': 2272,\n",
       " 'life': 2260,\n",
       " 'research': 2249,\n",
       " 'line': 2227,\n",
       " 'actually': 2224,\n",
       " 'course': 2218,\n",
       " 'tell': 2215,\n",
       " 'support': 2196,\n",
       " 'great': 2192,\n",
       " 'real': 2183,\n",
       " 'free': 2146,\n",
       " 'washington': 2130,\n",
       " 'high': 2126,\n",
       " 'post': 2114,\n",
       " 'systems': 2112,\n",
       " 'jesus': 2109,\n",
       " 'list': 2100,\n",
       " 'says': 2097,\n",
       " 'hard': 2057,\n",
       " 'mean': 2056,\n",
       " 'called': 2044,\n",
       " 'second': 2032,\n",
       " 'hockey': 2017,\n",
       " 'possible': 2015,\n",
       " 'image': 2008,\n",
       " 'internet': 1996,\n",
       " 'card': 1977,\n",
       " 'wrong': 1961,\n",
       " 'pitt': 1957,\n",
       " 'stanford': 1953,\n",
       " 'turkish': 1935,\n",
       " 'reason': 1934,\n",
       " 'network': 1930,\n",
       " 'athos': 1914,\n",
       " 'autos': 1913,\n",
       " 'general': 1889,\n",
       " 'mark': 1888,\n",
       " 'person': 1853,\n",
       " 'baseball': 1851,\n",
       " 'jewish': 1848,\n",
       " 'order': 1843,\n",
       " 'based': 1841,\n",
       " 'looking': 1836,\n",
       " 'problems': 1833,\n",
       " 'unix': 1832,\n",
       " 'agate': 1827,\n",
       " 'email': 1818,\n",
       " 'maybe': 1817,\n",
       " 'technology': 1815,\n",
       " 'security': 1807,\n",
       " 'human': 1803,\n",
       " 'files': 1801,\n",
       " 'team': 1794,\n",
       " 'seen': 1792,\n",
       " 'example': 1786,\n",
       " 'having': 1786,\n",
       " 'center': 1776,\n",
       " 'place': 1775,\n",
       " 'american': 1765,\n",
       " 'israel': 1751,\n",
       " 'quite': 1747,\n",
       " 'info': 1744,\n",
       " 'purdue': 1742,\n",
       " 'following': 1736,\n",
       " 'control': 1735,\n",
       " 'send': 1733,\n",
       " 'mideast': 1718,\n",
       " 'thought': 1706,\n",
       " 'wrote': 1705,\n",
       " 'heard': 1704,\n",
       " 'doing': 1680,\n",
       " 'evidence': 1675,\n",
       " 'chip': 1669,\n",
       " 'means': 1666,\n",
       " 'michael': 1663,\n",
       " 'electronics': 1663,\n",
       " 'crypt': 1661,\n",
       " 'virginia': 1655,\n",
       " 'trying': 1654,\n",
       " 'book': 1648,\n",
       " 'jews': 1646,\n",
       " 'phone': 1634,\n",
       " 'colorado': 1627,\n",
       " 'children': 1624,\n",
       " 'opinions': 1623,\n",
       " 'left': 1623,\n",
       " 'haven': 1610,\n",
       " 'away': 1609,\n",
       " 'given': 1603,\n",
       " 'start': 1600,\n",
       " 'national': 1599,\n",
       " 'idea': 1597,\n",
       " 'window': 1597,\n",
       " 'today': 1596,\n",
       " 'user': 1586,\n",
       " 'home': 1585,\n",
       " 'rights': 1584,\n",
       " 'times': 1578,\n",
       " 'berkeley': 1571,\n",
       " 'geneva': 1568,\n",
       " 'word': 1567,\n",
       " 'kind': 1564,\n",
       " 'questions': 1562,\n",
       " 'getting': 1562,\n",
       " 'keywords': 1554,\n",
       " 'president': 1544,\n",
       " 'paul': 1540,\n",
       " 'note': 1532,\n",
       " 'remember': 1508,\n",
       " 'service': 1501,\n",
       " 'games': 1499,\n",
       " 'steve': 1497,\n",
       " 'change': 1496,\n",
       " 'makes': 1492,\n",
       " 'scsi': 1489,\n",
       " 'standard': 1488,\n",
       " 'history': 1487,\n",
       " 'followup': 1485,\n",
       " 'saying': 1472,\n",
       " 'clipper': 1472,\n",
       " 'mike': 1470,\n",
       " 'toronto': 1463,\n",
       " 'local': 1462,\n",
       " 'code': 1460,\n",
       " 'price': 1458,\n",
       " 'source': 1458,\n",
       " 'wupost': 1458,\n",
       " 'wanted': 1458,\n",
       " 'department': 1449,\n",
       " 'server': 1445,\n",
       " 'open': 1437,\n",
       " 'caltech': 1433,\n",
       " 'abortion': 1429,\n",
       " 'frank': 1425,\n",
       " 'large': 1404,\n",
       " 'bogus': 1403,\n",
       " 'institute': 1401,\n",
       " 'money': 1400,\n",
       " 'stuff': 1393,\n",
       " 'small': 1392,\n",
       " 'disk': 1386,\n",
       " 'digex': 1383,\n",
       " 'motorcycles': 1380,\n",
       " 'answer': 1370,\n",
       " 'indiana': 1359,\n",
       " 'running': 1354,\n",
       " 'address': 1349,\n",
       " 'clinton': 1342,\n",
       " 'magnus': 1340,\n",
       " 'matter': 1339,\n",
       " 'speed': 1339,\n",
       " 'issue': 1338,\n",
       " 'interested': 1333,\n",
       " 'days': 1332,\n",
       " 'bible': 1326,\n",
       " 'original': 1325,\n",
       " 'agree': 1324,\n",
       " 'approved': 1322,\n",
       " 'came': 1311,\n",
       " 'sale': 1310,\n",
       " 'works': 1304,\n",
       " 'pretty': 1304,\n",
       " 'play': 1303,\n",
       " 'video': 1301,\n",
       " 'told': 1298,\n",
       " 'type': 1288,\n",
       " 'legal': 1287,\n",
       " 'current': 1286,\n",
       " 'states': 1283,\n",
       " 'care': 1283,\n",
       " 'hope': 1280,\n",
       " 'canada': 1278,\n",
       " 'claim': 1274,\n",
       " 'color': 1273,\n",
       " 'machine': 1273,\n",
       " 'stratus': 1269,\n",
       " 'earth': 1266,\n",
       " 'live': 1266,\n",
       " 'feel': 1260,\n",
       " 'robert': 1259,\n",
       " 'armenian': 1252,\n",
       " 'love': 1241,\n",
       " 'hand': 1237,\n",
       " 'understand': 1236,\n",
       " 'house': 1233,\n",
       " 'mind': 1231,\n",
       " 'college': 1229,\n",
       " 'important': 1222,\n",
       " 'math': 1213,\n",
       " 'simply': 1212,\n",
       " 'church': 1211,\n",
       " 'programs': 1205,\n",
       " 'memory': 1204,\n",
       " 'netnews': 1198,\n",
       " 'april': 1195,\n",
       " 'black': 1192,\n",
       " 'buffalo': 1191,\n",
       " 'engineering': 1191,\n",
       " 'went': 1190,\n",
       " 'truth': 1188,\n",
       " 'including': 1187,\n",
       " 'include': 1177,\n",
       " 'certainly': 1173,\n",
       " 'area': 1171,\n",
       " 'started': 1170,\n",
       " 'encryption': 1169,\n",
       " 'white': 1168,\n",
       " 'wouldn': 1168,\n",
       " 'guess': 1159,\n",
       " 'caen': 1156,\n",
       " 'cause': 1152,\n",
       " 'cornell': 1150,\n",
       " 'dept': 1149,\n",
       " 'health': 1149,\n",
       " 'making': 1148,\n",
       " 'instead': 1143,\n",
       " 'soviet': 1143,\n",
       " 'value': 1140,\n",
       " 'comes': 1137,\n",
       " 'christians': 1131,\n",
       " 'display': 1131,\n",
       " 'christ': 1123,\n",
       " 'later': 1122,\n",
       " 'sandvik': 1117,\n",
       " 'working': 1117,\n",
       " 'school': 1111,\n",
       " 'igor': 1105,\n",
       " 'death': 1102,\n",
       " 'newsreader': 1099,\n",
       " 'company': 1095,\n",
       " 'country': 1093,\n",
       " 'cost': 1092,\n",
       " 'private': 1085,\n",
       " 'single': 1084,\n",
       " 'feed': 1081,\n",
       " 'copy': 1080,\n",
       " 'write': 1078,\n",
       " 'text': 1077,\n",
       " 'view': 1070,\n",
       " 'words': 1068,\n",
       " 'unless': 1068,\n",
       " 'consider': 1068,\n",
       " 'light': 1065,\n",
       " 'hell': 1065,\n",
       " 'opinion': 1058,\n",
       " 'difference': 1056,\n",
       " 'society': 1055,\n",
       " 'pacific': 1053,\n",
       " 'california': 1050,\n",
       " 'police': 1046,\n",
       " 'armenians': 1046,\n",
       " 'faith': 1045,\n",
       " 'austin': 1041,\n",
       " 'known': 1040,\n",
       " 'james': 1039,\n",
       " 'tried': 1038,\n",
       " 'computers': 1037,\n",
       " 'major': 1030,\n",
       " 'freenet': 1029,\n",
       " 'sort': 1028,\n",
       " 'brian': 1027,\n",
       " 'check': 1026,\n",
       " 'clear': 1024,\n",
       " 'sense': 1023,\n",
       " 'deal': 1019,\n",
       " 'anybody': 1019,\n",
       " 'wisc': 1018,\n",
       " 'bitnet': 1017,\n",
       " 'ucsd': 1016,\n",
       " 'contact': 1015,\n",
       " 'political': 1014,\n",
       " 'cleveland': 1008,\n",
       " 'objective': 1007,\n",
       " 'services': 1007,\n",
       " 'religious': 1004,\n",
       " 'hedrick': 1003,\n",
       " 'morality': 1002,\n",
       " 'likely': 999,\n",
       " 'aramis': 999,\n",
       " 'iastate': 998,\n",
       " 'city': 998,\n",
       " 'talking': 995,\n",
       " 'especially': 994,\n",
       " 'similar': 994,\n",
       " 'radio': 992,\n",
       " 'night': 991,\n",
       " 'press': 990,\n",
       " 'argument': 990,\n",
       " 'nice': 985,\n",
       " 'package': 984,\n",
       " 'summary': 983,\n",
       " 'correct': 982,\n",
       " 'koresh': 982,\n",
       " 'women': 980,\n",
       " 'body': 980,\n",
       " 'couple': 979,\n",
       " 'simple': 975,\n",
       " 'blue': 972,\n",
       " 'certain': 971,\n",
       " 'force': 971,\n",
       " 'happened': 971,\n",
       " 'dead': 970,\n",
       " 'stop': 970,\n",
       " 'sound': 969,\n",
       " 'board': 968,\n",
       " 'self': 967,\n",
       " 'peter': 966,\n",
       " 'dave': 966,\n",
       " 'privacy': 964,\n",
       " 'exactly': 959,\n",
       " 'asked': 952,\n",
       " 'emory': 951,\n",
       " 'books': 950,\n",
       " 'took': 950,\n",
       " 'east': 947,\n",
       " 'killed': 947,\n",
       " 'york': 944,\n",
       " 'israeli': 944,\n",
       " 'screen': 944,\n",
       " 'fine': 943,\n",
       " 'driver': 943,\n",
       " 'moral': 942,\n",
       " 'theory': 941,\n",
       " 'provide': 940,\n",
       " 'fast': 940,\n",
       " 'sorry': 937,\n",
       " 'situation': 935,\n",
       " 'head': 935,\n",
       " 'monitor': 935,\n",
       " 'level': 934,\n",
       " 'common': 933,\n",
       " 'business': 928,\n",
       " 'taken': 926,\n",
       " 'format': 925,\n",
       " 'position': 923,\n",
       " 'test': 923,\n",
       " 'groups': 923,\n",
       " 'size': 920,\n",
       " 'reading': 919,\n",
       " 'statement': 919,\n",
       " 'images': 917,\n",
       " 'answers': 913,\n",
       " 'western': 913,\n",
       " 'goes': 911,\n",
       " 'written': 910,\n",
       " 'early': 907,\n",
       " 'form': 906,\n",
       " 'motif': 906,\n",
       " 'usually': 905,\n",
       " 'players': 904,\n",
       " 'discussion': 902,\n",
       " 'mchp': 902,\n",
       " 'application': 901,\n",
       " 'peace': 900,\n",
       " 'personal': 898,\n",
       " 'experience': 896,\n",
       " 'laws': 895,\n",
       " 'scott': 895,\n",
       " 'rest': 894,\n",
       " 'elroy': 893,\n",
       " 'bike': 893,\n",
       " 'manager': 892,\n",
       " 'interesting': 891,\n",
       " 'peachnet': 889,\n",
       " 'swrinde': 887,\n",
       " 'past': 881,\n",
       " 'hear': 879,\n",
       " 'division': 876,\n",
       " 'transfer': 875,\n",
       " 'keys': 872,\n",
       " 'disclaimer': 869,\n",
       " 'particular': 868,\n",
       " 'period': 868,\n",
       " 'microsoft': 866,\n",
       " 'corp': 865,\n",
       " 'advance': 864,\n",
       " 'users': 864,\n",
       " 'arizona': 864,\n",
       " 'easy': 863,\n",
       " 'account': 863,\n",
       " 'turn': 863,\n",
       " 'study': 861,\n",
       " 'according': 861,\n",
       " 'needed': 860,\n",
       " 'posted': 857,\n",
       " 'ames': 856,\n",
       " 'office': 855,\n",
       " 'astro': 855,\n",
       " 'happen': 853,\n",
       " 'needs': 852,\n",
       " 'short': 851,\n",
       " 'jpeg': 849,\n",
       " 'request': 848,\n",
       " 'physics': 847,\n",
       " 'effect': 846,\n",
       " 'special': 846,\n",
       " 'numbers': 845,\n",
       " 'series': 843,\n",
       " 'mouse': 842,\n",
       " 'members': 841,\n",
       " 'corporation': 841,\n",
       " 'future': 838,\n",
       " 'week': 838,\n",
       " 'season': 838,\n",
       " 'anti': 837,\n",
       " 'yale': 837,\n",
       " 'texas': 837,\n",
       " 'uses': 835,\n",
       " 'upenn': 835,\n",
       " 'kill': 834,\n",
       " 'lost': 834,\n",
       " 'months': 834,\n",
       " 'kent': 830,\n",
       " 'ogicse': 829,\n",
       " 'smith': 829,\n",
       " 'exist': 828,\n",
       " 'media': 828,\n",
       " 'output': 827,\n",
       " 'ones': 826,\n",
       " 'points': 823,\n",
       " 'young': 822,\n",
       " 'keith': 822,\n",
       " 'mode': 822,\n",
       " 'greek': 821,\n",
       " 'model': 821,\n",
       " 'error': 820,\n",
       " 'thomas': 819,\n",
       " 'drivers': 819,\n",
       " 'duke': 818,\n",
       " 'medical': 815,\n",
       " 'germany': 813,\n",
       " 'accept': 812,\n",
       " 'expect': 811,\n",
       " 'longer': 809,\n",
       " 'sell': 806,\n",
       " 'specific': 805,\n",
       " 'eric': 805,\n",
       " 'face': 804,\n",
       " 'plus': 804,\n",
       " 'considered': 803,\n",
       " 'convex': 803,\n",
       " 'recently': 802,\n",
       " 'wasn': 801,\n",
       " 'gets': 800,\n",
       " 'food': 796,\n",
       " 'communications': 796,\n",
       " 'project': 796,\n",
       " 'return': 793,\n",
       " 'united': 786,\n",
       " 'runs': 785,\n",
       " 'sources': 785,\n",
       " 'performance': 785,\n",
       " 'coming': 783,\n",
       " 'aren': 781,\n",
       " 'taking': 780,\n",
       " 'insurance': 780,\n",
       " 'land': 778,\n",
       " 'policy': 777,\n",
       " 'king': 775,\n",
       " 'shall': 775,\n",
       " 'worth': 774,\n",
       " 'international': 773,\n",
       " 'process': 772,\n",
       " 'values': 771,\n",
       " 'reference': 771,\n",
       " 'federal': 771,\n",
       " 'offer': 770,\n",
       " 'outside': 769,\n",
       " 'action': 767,\n",
       " 'speak': 766,\n",
       " 'thinking': 765,\n",
       " 'ground': 765,\n",
       " 'army': 765,\n",
       " 'various': 764,\n",
       " 'drives': 764,\n",
       " 'pittsburgh': 764,\n",
       " 'built': 764,\n",
       " 'optilink': 763,\n",
       " 'assume': 761,\n",
       " 'military': 760,\n",
       " 'pipex': 759,\n",
       " 'result': 758,\n",
       " 'weapons': 755,\n",
       " 'chris': 752,\n",
       " 'results': 752,\n",
       " 'quality': 750,\n",
       " 'response': 750,\n",
       " 'cars': 748,\n",
       " 'richard': 748,\n",
       " 'friend': 746,\n",
       " 'water': 746,\n",
       " 'million': 745,\n",
       " 'doubt': 744,\n",
       " 'total': 742,\n",
       " 'horus': 742,\n",
       " 'environment': 741,\n",
       " 'navy': 741,\n",
       " 'road': 740,\n",
       " 'christianity': 740,\n",
       " 'involved': 740,\n",
       " 'story': 739,\n",
       " 'present': 739,\n",
       " 'design': 739,\n",
       " 'build': 739,\n",
       " 'uchicago': 738,\n",
       " 'strong': 737,\n",
       " 'close': 737,\n",
       " 'voice': 737,\n",
       " 'higher': 735,\n",
       " 'allow': 734,\n",
       " 'includes': 733,\n",
       " 'save': 732,\n",
       " 'explain': 732,\n",
       " 'child': 732,\n",
       " 'term': 731,\n",
       " 'computing': 731,\n",
       " 'rate': 729,\n",
       " 'batf': 729,\n",
       " 'league': 728,\n",
       " 'newsserver': 726,\n",
       " 'field': 724,\n",
       " 'necessary': 723,\n",
       " 'cases': 723,\n",
       " 'report': 723,\n",
       " 'hold': 722,\n",
       " 'previous': 722,\n",
       " 'george': 720,\n",
       " 'building': 720,\n",
       " 'uknet': 720,\n",
       " 'programmer': 720,\n",
       " 'couldn': 719,\n",
       " 'comments': 718,\n",
       " 'looks': 717,\n",
       " 'asking': 717,\n",
       " 'player': 716,\n",
       " 'west': 714,\n",
       " 'freedom': 713,\n",
       " 'knowledge': 713,\n",
       " 'leave': 713,\n",
       " 'tape': 713,\n",
       " 'related': 712,\n",
       " 'bear': 712,\n",
       " 'parts': 712,\n",
       " 'deleted': 711,\n",
       " 'issues': 709,\n",
       " 'wants': 709,\n",
       " 'demon': 708,\n",
       " 'population': 707,\n",
       " 'soon': 706,\n",
       " 'party': 705,\n",
       " 'lord': 705,\n",
       " 'armenia': 705,\n",
       " 'athena': 704,\n",
       " 'choice': 703,\n",
       " 'site': 702,\n",
       " 'knows': 701,\n",
       " 'univ': 701,\n",
       " 'willing': 698,\n",
       " 'tech': 698,\n",
       " 'boston': 696,\n",
       " 'crime': 694,\n",
       " 'watson': 693,\n",
       " 'technical': 690,\n",
       " 'applications': 690,\n",
       " 'development': 689,\n",
       " 'ideas': 688,\n",
       " 'portal': 688,\n",
       " 'market': 687,\n",
       " 'muslim': 686,\n",
       " 'appreciated': 685,\n",
       " 'decwrl': 684,\n",
       " 'clearly': 682,\n",
       " 'anonymous': 681,\n",
       " 'claims': 679,\n",
       " 'follow': 678,\n",
       " 'modem': 678,\n",
       " 'mentioned': 677,\n",
       " 'section': 677,\n",
       " 'half': 674,\n",
       " 'sent': 674,\n",
       " 'mcgill': 674,\n",
       " 'sounds': 669,\n",
       " 'currently': 669,\n",
       " 'complete': 667,\n",
       " 'ways': 667,\n",
       " 'basis': 665,\n",
       " 'arms': 664,\n",
       " 'america': 663,\n",
       " 'henry': 663,\n",
       " 'exists': 660,\n",
       " 'nature': 660,\n",
       " 'administration': 660,\n",
       " 'useful': 659,\n",
       " 'takes': 659,\n",
       " 'drug': 659,\n",
       " 'arab': 659,\n",
       " 'reasons': 657,\n",
       " 'defense': 657,\n",
       " 'drugs': 655,\n",
       " 'cover': 654,\n",
       " 'included': 654,\n",
       " 'chicago': 654,\n",
       " 'main': 653,\n",
       " 'machines': 651,\n",
       " 'europe': 650,\n",
       " 'purpose': 649,\n",
       " 'completely': 649,\n",
       " 'directory': 649,\n",
       " 'command': 648,\n",
       " 'apps': 647,\n",
       " 'controller': 647,\n",
       " 'existence': 646,\n",
       " 'court': 646,\n",
       " 'scientific': 645,\n",
       " 'average': 645,\n",
       " 'page': 642,\n",
       " 'belief': 641,\n",
       " 'genocide': 641,\n",
       " 'living': 640,\n",
       " 'north': 637,\n",
       " 'poster': 637,\n",
       " 'rules': 636,\n",
       " 'gives': 636,\n",
       " 'doug': 634,\n",
       " 'cards': 634,\n",
       " 'green': 634,\n",
       " 'individual': 631,\n",
       " 'waco': 630,\n",
       " 'illinois': 629,\n",
       " 'father': 629,\n",
       " 'bought': 629,\n",
       " 'newsgroup': 628,\n",
       " 'family': 628,\n",
       " 'engine': 628,\n",
       " 'created': 627,\n",
       " 'origins': 626,\n",
       " 'conspiracy': 626,\n",
       " 'unit': 626,\n",
       " 'mantis': 625,\n",
       " 'required': 625,\n",
       " 'basic': 623,\n",
       " 'serial': 623,\n",
       " 'create': 620,\n",
       " 'weeks': 620,\n",
       " 'community': 620,\n",
       " 'shot': 620,\n",
       " 'meaning': 619,\n",
       " 'digital': 619,\n",
       " 'algorithm': 619,\n",
       " 'language': 617,\n",
       " 'mass': 617,\n",
       " 'base': 617,\n",
       " 'chance': 617,\n",
       " 'inside': 615,\n",
       " 'alaska': 615,\n",
       " 'necessarily': 614,\n",
       " 'brown': 614,\n",
       " 'universe': 611,\n",
       " 'manual': 611,\n",
       " 'noise': 611,\n",
       " 'looked': 610,\n",
       " 'lead': 610,\n",
       " 'gateway': 610,\n",
       " 'normal': 610,\n",
       " 'appears': 608,\n",
       " 'faster': 608,\n",
       " 'cheap': 607,\n",
       " 'turkey': 607,\n",
       " 'supposed': 606,\n",
       " 'device': 606,\n",
       " 'interface': 606,\n",
       " 'torn': 606,\n",
       " 'student': 603,\n",
       " 'suggest': 603,\n",
       " 'obviously': 602,\n",
       " 'energy': 602,\n",
       " 'record': 602,\n",
       " 'views': 601,\n",
       " 'thank': 601,\n",
       " 'paper': 599,\n",
       " 'details': 599,\n",
       " 'object': 598,\n",
       " 'muslims': 598,\n",
       " 'class': 598,\n",
       " 'directly': 596,\n",
       " 'rule': 596,\n",
       " 'gave': 596,\n",
       " 'finally': 595,\n",
       " 'atheists': 594,\n",
       " 'author': 594,\n",
       " 'gary': 594,\n",
       " 'mcsun': 592,\n",
       " 'owner': 592,\n",
       " 'shuttle': 590,\n",
       " 'reasonable': 589,\n",
       " 'russian': 589,\n",
       " 'zuma': 589,\n",
       " 'figure': 588,\n",
       " 'commercial': 588,\n",
       " 'port': 586,\n",
       " 'learn': 585,\n",
       " 'lots': 585,\n",
       " 'events': 584,\n",
       " 'library': 584,\n",
       " 'princeton': 584,\n",
       " 'fido': 584,\n",
       " 'final': 584,\n",
       " 'advice': 583,\n",
       " 'civil': 582,\n",
       " 'rush': 582,\n",
       " 'heart': 582,\n",
       " 'spool': 581,\n",
       " 'happy': 581,\n",
       " 'acsu': 581,\n",
       " 'wait': 580,\n",
       " 'mitre': 579,\n",
       " 'event': 578,\n",
       " 'entire': 578,\n",
       " 'hours': 578,\n",
       " 'umich': 578,\n",
       " 'andy': 577,\n",
       " 'folks': 577,\n",
       " 'products': 576,\n",
       " 'joseph': 576,\n",
       " 'release': 576,\n",
       " 'colostate': 576,\n",
       " 'wish': 575,\n",
       " 'condition': 575,\n",
       " 'secret': 575,\n",
       " 'carleton': 575,\n",
       " 'murdoch': 575,\n",
       " 'terms': 574,\n",
       " 'function': 574,\n",
       " 'received': 574,\n",
       " 'goal': 573,\n",
       " 'acns': 573,\n",
       " 'stated': 572,\n",
       " 'oracle': 571,\n",
       " 'processing': 571,\n",
       " 'attack': 570,\n",
       " 'stephen': 569,\n",
       " 'room': 569,\n",
       " 'ncsu': 569,\n",
       " 'prove': 568,\n",
       " 'entry': 567,\n",
       " 'break': 567,\n",
       " 'product': 567,\n",
       " 'plan': 566,\n",
       " 'clock': 566,\n",
       " 'enforcement': 566,\n",
       " 'miles': 563,\n",
       " 'matthew': 562,\n",
       " 'actions': 561,\n",
       " 'letter': 561,\n",
       " 'turks': 561,\n",
       " 'illegal': 560,\n",
       " 'adobe': 560,\n",
       " 'msdos': 560,\n",
       " 'responsible': 559,\n",
       " 'wonder': 559,\n",
       " 'definition': 558,\n",
       " 'extra': 558,\n",
       " 'bits': 558,\n",
       " 'cramer': 556,\n",
       " 'easily': 555,\n",
       " 'difficult': 554,\n",
       " 'reality': 553,\n",
       " 'constitution': 553,\n",
       " 'bring': 553,\n",
       " 'station': 553,\n",
       " 'south': 552,\n",
       " 'nott': 552,\n",
       " 'lives': 551,\n",
       " 'devices': 551,\n",
       " 'rose': 551,\n",
       " 'congress': 550,\n",
       " 'designed': 550,\n",
       " 'conference': 550,\n",
       " 'door': 550,\n",
       " 'printer': 550,\n",
       " 'places': 548,\n",
       " 'tools': 548,\n",
       " 'primate': 548,\n",
       " 'batcomputer': 547,\n",
       " 'limited': 547,\n",
       " 'happens': 546,\n",
       " 'wondering': 545,\n",
       " 'equipment': 544,\n",
       " 'unfortunately': 543,\n",
       " 'carry': 543,\n",
       " 'context': 543,\n",
       " 'changed': 543,\n",
       " 'cable': 543,\n",
       " 'jeff': 542,\n",
       " 'reports': 541,\n",
       " 'saimiri': 540,\n",
       " 'citizens': 539,\n",
       " 'allowed': 539,\n",
       " 'americans': 538,\n",
       " 'suppose': 537,\n",
       " 'imagine': 536,\n",
       " 'laboratory': 536,\n",
       " 'mention': 534,\n",
       " 'analysis': 534,\n",
       " 'recent': 533,\n",
       " 'stand': 532,\n",
       " 'central': 532,\n",
       " 'evil': 531,\n",
       " 'jvnc': 531,\n",
       " 'amiga': 531,\n",
       " 'solution': 530,\n",
       " 'method': 529,\n",
       " 'engr': 529,\n",
       " 'fans': 529,\n",
       " 'physical': 528,\n",
       " 'decided': 528,\n",
       " 'originator': 528,\n",
       " 'hate': 527,\n",
       " 'launch': 527,\n",
       " 'companies': 526,\n",
       " 'avoid': 525,\n",
       " 'jack': 525,\n",
       " 'late': 524,\n",
       " 'generally': 524,\n",
       " 'decision': 524,\n",
       " 'facts': 524,\n",
       " 'trouble': 524,\n",
       " 'wife': 524,\n",
       " 'ucsu': 524,\n",
       " 'knew': 523,\n",
       " 'studies': 523,\n",
       " 'street': 522,\n",
       " 'attempt': 522,\n",
       " 'member': 522,\n",
       " 'online': 522,\n",
       " 'mnemosyne': 521,\n",
       " 'pick': 520,\n",
       " 'stupid': 520,\n",
       " 'apparently': 519,\n",
       " 'continue': 519,\n",
       " 'pictures': 519,\n",
       " 'proof': 518,\n",
       " 'died': 518,\n",
       " 'guys': 518,\n",
       " 'compound': 518,\n",
       " 'murder': 517,\n",
       " 'uwaterloo': 516,\n",
       " 'widget': 516,\n",
       " 'regarding': 515,\n",
       " 'double': 515,\n",
       " 'psinntp': 515,\n",
       " 'watch': 514,\n",
       " 'ctron': 514,\n",
       " 'escrow': 514,\n",
       " 'moon': 513,\n",
       " 'roger': 513,\n",
       " 'chips': 513,\n",
       " 'input': 512,\n",
       " 'define': 511,\n",
       " 'month': 511,\n",
       " 'utnut': 511,\n",
       " 'safety': 511,\n",
       " 'archive': 510,\n",
       " 'volume': 510,\n",
       " 'held': 510,\n",
       " 'livesey': 510,\n",
       " 'reported': 510,\n",
       " 'brought': 509,\n",
       " 'bell': 508,\n",
       " 'forget': 508,\n",
       " 'intercon': 508,\n",
       " 'coverage': 508,\n",
       " 'floppy': 506,\n",
       " 'switch': 505,\n",
       " ...}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting the dictionary in decreasing order of count, so that we can pick certain top words\n",
    "word_dictionary = dict(sorted(word_dictionary.items(), key=operator.itemgetter(1),reverse=True))\n",
    "word_dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the word frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJAAAAFZCAYAAADHD9XyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzde5xddX3v/9dn77nkMiEXEkK4CAgRwSDIcLfohHqJ9gK2eqptkVo9tB49l9Zj1fOoP1vvntPWarWtVahY20ZqtVCEUowJiApCuN8T7iEJCeQ6mWRu+/v7Y6092TOzk0kyCXvtmdfzcfZj7/1da33WZ+2Jjwfn3e/6rkgpIUmSJEmSJO1JqdENSJIkSZIkqdgMkCRJkiRJkrRXBkiSJEmSJEnaKwMkSZIkSZIk7ZUBkiRJkiRJkvbKAEmSJEmSJEl7ZYAkSZKaVkR8JSJWNLoPSZKkic4ASZIk7beI+P2I2BERbTVjbRHRExH3j9h3YUSkiLjope/04IiIj0bE9w/w2Kfy6699bTnYPUqSJB1KBkiSJOlA/AiYBpxTM3YusBV4RUTMqxnvAnqBnx7IiSKiFBHlA+zzYLkYuGYcx38SWFDzesWedqwN5SRJkorCAEmSJO23lNJjwFpgcc3wYuCHwJ1koVHt+M9SSrsAImJ2RFwVEZsjYmdE/DAiXlXdOSJ+JyK6I+KtEfEA0AecEhHliPiz/LjNEfGXwLBgKSJeFxG35cdvjYjbI2JRvWuIiM9FxMo64z+NiC/VfD8SOAu4Lv/+axFxX977poi4OSLmj/GTbU8pra95bchrteQzkn4/Iq6JiB1kYRMRsSgiboiI7RGxISL+sfY8+bFfjIgteR9/HhF/FxE/rNnn1vx3qr2+b0fEv9V8L0XExyLiifya7o+Id9VsPynv8W0RsSyfZfbgyBllEXFqRPx7RGzLf/+f5mMXRUTfiFCRiPhCRNw1xu8mSZIKwgBJkiQdqOWMDpBW5K/a8a5836pvks1WuphsBlMP8B8RMbVmnynAHwO/B5wKPA18CPiv+dj5ZOHRb1UPiIgWsllCtwKn5+f4EjC4h/7/ATgzIl5ZU+OEvPa3a/b7VbIA7IU8TFoKXAWcArwurzNef5r3fhrwtxFxNHAzcDdwNvBGYBbw/YiI/Jg/At4DvA+4AJgKvPMAzv054N3A+8l+6y8AV0TEkhH7fRb4C7Lf9m7gOxExDSAijiX73fuBXwTOBP4GaCH72z8NXFotlM8ouxS44gD6lSRJDRAppUb3IEmSmlBEvBf4ClmwEcBmYBFwIvCllNIpeTjzMHBhSunWiFgIPAa8PqV0S15nJvAM8KGU0jci4neAvwfOSimtrDnfWuCrKaXP5N9LwCPA2pRSV0TMAV4EulJKN+/jNdwNXJdS+nj+/Y+BS1NKJ9fs8wNgWUrpLyLiTGAlcHxK6el9PMdTZLet9dcMfzal9Nk89OoH/jKl9Ac1x3wW6EwpvblmbC6wMR+/KyI2AH+eUvpCze+xCngypfSGfOxW4M6U0v+qqfNtoCOldElEzMhrLk4p/axmn68AL0sp/WpEnJTXfV9K6Yp8+3HAU8D5KaXbIuILwDuAk1NKtddZrfdR4LdTSovy778CXA0clVLavC+/oyRJaixnIEmSpAO1nGym0Pn564WU0uPAT4AT89k6i8lmGN2eH3MKUAGGwoqU0lbgfrLZL1UDwD3VL3nItGDEcZWauqSUNpHNbroxIn4QEX+Yz4zZm28Dv1nz/beomX0UER1kM2qq6x/dS3ab3gMR8a8R8f6Rt2btwV8AZ9S8/nbE9jtHfO8EFue3gnVHRDdZYAPZb3s4MI/Rv8fP96GXWouAduCmEef6r2RBYK37aj6vzd+PyN9fA/y4XniU+yZwckRU18z6XeB7hkeSJDWPlkY3IEmSmlNK6YmIeJrsFrUgu3WNlNKOfG2hrvx1a02wEKMK1ZSs+dybUtrTrWd76+k9+Zo/S8huPftMRFySUrpxD4f8E/B/I+J8soW+Xwn8Y832JcDqPBgjpTQYEW8CzgPeBLwX+FxEvD6ldO9eWnsxpbR6L9t3jPheAv4d+EidfdeTBXf7osLo37x1xHkAfgl4bsR+fSO+14ZD1b9V9fi9/V1JKa2PiOuA381nZP0S2W8rSZKahDOQJEnSeFTXQaquf1S1AriILED6Uc34Q2T//XF+dSAiDiNb++ehPZ0kn6W0jiy4qR4XDH8KXHXfe1NKX0gpdeV9XLaXuuvy/n4rf/00pfREzS4XA/824piUUvpZSulPydYnWgv8xp7OcYDuAl4FPJVSWj3i1Z1SeoHs1rPa36OU91NrI9nMreo+QbaGUVV1kfKX1TnPM/vZ74UR0bqXfb5OtkbT5cAahq+LJUmSCs4ASZIkjcdyshDjXIYHSDeThQVHUBMUpJRWkd0O9rWIuDAiTiO7ZWwb2WygvfkS8EcR8faIOBn4S4aHIydExOcj4oKIOC4iFgOvZi/BVO7bZAHQOxl++1oL2UyZa2rGzouIP46IsyPiZWSznI7dh3Psr78C5gL/HBHnRMTLI+KNEfGNmsXGvwR8NLKnwr0S+DK7bymr+hHwSxHxy/V+szyY+yLwxciefndiRLwmIv5bRLxvP/r9CjCbbGHts/Int/1mRLy6Zp//IPs7fxz4++RCnJIkNRUDJEmSNB7LgTZgQ/U2r9ytZE8F20a26HSt95Ct1XNt/j4NWJJS2jnGuf6cbHHtb5CtfVRi+O1mPcArgH8hW6j7qnz7F8ao+695D/PIFnaueh2wk+HrE20FXgtcR7aw9J8Dn0op1T61bdxSSmvy85SBG4EHyUKaHnbfSvYFsifAXQncRrZu1HdGlPo68C2y3+JWYBPZrXG1PgZ8mux2uYeB/wQuAZ7cj36fJfu9ppEFiXcDH8h7qu5TIVsLqSV/lyRJTcSnsEmSJNUREV8CWlNK/63RveyriPhb4KTqU9iKJiK+DhyTUnpLo3uRJEn7x0W0JUmS6nuQmqec6cDlT9E7m2ydqbc1uB1JknQADJAkSZLqSCn9XaN7mEB+AJwJfG0vT8STJEkF5i1skiRJkiRJ2isX0ZYkSZIkSdJeGSBJkiRJkiRpr5p2DaS5c+em448/vtFtjNuOHTuYPn26NawxoWsUoQdrWKMZahShB2tYoxlqFKEHa1ijGWoUoQdrWONQ1yhCDxPNypUrX0gpzRu1IaXUlK/Ozs40ESxfvtwa1pjwNYrQgzWs0Qw1itCDNazRDDWK0IM1rNEMNYrQgzWscahrFKGHiQa4M9XJYbyFTZIkSZIkSXtlgCRJkiRJkqS9GjNAiogpEfHziLg3Ih6MiD/Nx78ZEU9GxD3564x8PCLiyxGxOiLui4gza2pdFhGr8tdlNeOdEXF/fsyXIyIOxcVKkiRJkiRp/+3LItq9wEUppe6IaAVujYgb8m0fTil9d8T+bwEW5q9zgb8Bzo2IOcAngLOABKyMiGtTSpvzfS4HbgOuB5YANyBJkiRJkqSGG3MGUr6GUnf+tTV/pb0ccjHwrfy424BZEbEAeDNwU0ppUx4a3QQsybcdllL6Wb5Y07eAS8ZxTZIkSZIkSTqIIstsxtgpogysBE4CvppS+khEfBM4n2yG0jLgoyml3oi4Dvh8SunW/NhlwEeALmBKSunT+fjHgZ3Ainz/N+TjFwIfSSn9cp0+LiebqcT8+fM7ly5deuBXXhDd3d10dHRYwxoTukYRerCGNZqhRhF6sIY1mqFGEXqwhjWaoUYRerCGNQ51jSL0MNEsXrx4ZUrprFEb6j2abU8vYBawHFgELAACaAeuAv6/fJ8fAL9Qc8wyoBP4MPDHNeMfBz4EnA38sGb8QuDfx+qls7PzoD2irpGK8shBa1jjUNYoQg/WsEYz1ChCD9awRjPUKEIP1rBGM9QoQg/WsMahrlGEHiYa4M5UJ4fZr6ewpZS2kM0YWpJSWpfX7gX+Hjgn320NcGzNYccAa8cYP6bOuCRJkiRJkgpgX57CNi8iZuWfpwJvAB7J1y4if2LaJcAD+SHXAu/On8Z2HrA1pbQOuBF4U0TMjojZwJuAG/Nt2yPivLzWu4FrDu5lSpIkSZIk6UDty1PYFgBX5esglYCrU0rXRcSPImIe2W1s9wC/n+9/PfBWYDXQA7wHIKW0KSI+BdyR7/fJlNKm/PP7gW8CU8mevjYpnsD2o0ee5w+X9/DdU7s56Qjvt5QkSZIkScU0ZoCUUroPeE2d8Yv2sH8CPrCHbVcCV9YZv5NsXaVJpbe/wpbeRP9gpdGtSJIkSZIk7dF+rYGkgyu7Yw/24UF4kiRJkiRJDWOA1EB5fkTFBEmSJEmSJBWYAVIDlaoJkiRJkiRJUoEZIDVQNT5yBpIkSZIkSSoyA6QGKuW/vvmRJEmSJEkqMgOkBop8DpIzkCRJkiRJUpEZIDVSfg+b8ZEkSZIkSSoyA6QGqi6i7QQkSZIkSZJUZAZIDVRdRDuZIEmSJEmSpAIzQGqgoRlIDe5DkiRJkiRpbwyQGijPj6hUjJAkSZIkSVJxGSA1ULiItiRJkiRJagIGSA0U+SpIFddAkiRJkiRJBWaA1ECloVW0G9qGJEmSJEnSXhkgNVBEdQZSgxuRJEmSJEnaCwOkBioNrYFkgiRJkiRJkorLAKmBhp7CZn4kSZIkSZIKzACpobIEKbmItiRJkiRJKjADpAbafQubJEmSJElScRkgNVB1EW1nIEmSJEmSpCIzQGqgoRlI5keSJEmSJKnADJAaKPI1kFxEW5IkSZIkFZkBUgPF0AwkEyRJkiRJklRcBkgNVA2QnIEkSZIkSZKKzACpgUrVBMnnsEmSJEmSpAIzQGogZyBJkiRJkqRmYIDUQNVFtF0CSZIkSZIkFdmYAVJETImIn0fEvRHxYET8aT5+QkTcHhGrIuI7EdGWj7fn31fn24+vqfWxfPzRiHhzzfiSfGx1RHz04F9mMZWGZiCZIEmSJEmSpOLalxlIvcBFKaXTgTOAJRFxHvAF4IsppYXAZuC9+f7vBTanlE4CvpjvR0ScCrwTeBWwBPjriChHRBn4KvAW4FTgXfm+E97QU9ga24YkSZIkSdJejRkgpUx3/rU1fyXgIuC7+fhVwCX554vz7+TbfzEiIh9fmlLqTSk9CawGzslfq1NKT6SU+oCl+b4TXkT1FjYjJEmSJEmSVFyxL+FFPktoJXAS2Wyh/wfcls8yIiKOBW5IKS2KiAeAJSmlNfm2x4FzgT/Jj/l2Pn4FcEN+iiUppffl45cC56aUPlinj8uBywHmz5/fuXTp0gO97kJYv6PCR3+8k8tf3c4FR7UccJ3u7m46OjrG1Ys1rHEoaxShB2tYoxlqFKEHa1ijGWoUoQdrWKMZahShB2tY41DXKEIPE83ixYtXppTOGrUhpbTPL2AWsBy4kGzWUHX8WOD+/PODwDE12x4HDicLnn67ZvwK4NeBdwDfqBm/FPirsXrp7OxMze7Jjd3puI9cl75317PjqrN8+fJx92INaxzKGkXowRrWaIYaRejBGtZohhpF6MEa1miGGkXowRrWONQ1itDDRAPcmerkMPv1FLaU0hZgBXAeMCsiqtNmjgHW5p/X5IES+faZwKba8RHH7Gl8wquugVSpNLYPSZIkSZKkvdmXp7DNi4hZ+eepwBuAh8lmIr093+0y4Jr887X5d/LtP8oTrGuBd+ZPaTsBWAj8HLgDWJg/1a2NbKHtaw/GxRVdqboGUoP7kCRJkiRJ2pt9WXhnAXBVvg5SCbg6pXRdRDwELI2ITwN3k92SRv7+DxGxmmzm0TsBUkoPRsTVwEPAAPCBlNIgQER8ELgRKANXppQePGhX2AQqLqItSZIkSZIKbMwAKaV0H/CaOuNPkD1BbeT4LrJ1jerV+gzwmTrj1wPX70O/E0qplN/DZn4kSZIkSZIKbL/WQNLBlcdHzkCSJEmSJEmFZoDUQOEEJEmSJEmS1AQMkBqouoi2M5AkSZIkSVKRGSA1UPUWNvMjSZIkSZJUZAZIDRT5DCTzI0mSJEmSVGQGSA00tAaSU5AkSZIkSVKBGSA1UHUNJPMjSZIkSZJUZAZIDVRdA8lFtCVJkiRJUpEZIDWQM5AkSZIkSVIzMEBqpHwKkjOQJEmSJElSkRkgNVApxt5HkiRJkiSp0QyQGijyW9icgSRJkiRJkorMAKmBqhOQzI8kSZIkSVKRGSA10NAi2g3uQ5IkSZIkaW8MkBooXERbkiRJkiQ1AQOkBqoGSOZHkiRJkiSpyAyQGijyVZCSCZIkSZIkSSowA6QGKjkDSZIkSZIkNQEDpAaK/B62igGSJEmSJEkqMAOkBhqageRz2CRJkiRJUoEZIDWQM5AkSZIkSVIzMEBqsAAXQZIkSZIkSYVmgFQAzkCSJEmSJElFZoDUYKWAijOQJEmSJElSgRkgNVgEDBogSZIkSZKkAjNAarBSQMV72CRJkiRJUoEZIDVYCRisNLoLSZIkSZKkPRszQIqIYyNieUQ8HBEPRsT/zMf/JCKei4h78tdba475WESsjohHI+LNNeNL8rHVEfHRmvETIuL2iFgVEd+JiLaDfaFF5RpIkiRJkiSp6PZlBtIA8KGU0inAecAHIuLUfNsXU0pn5K/rAfJt7wReBSwB/joiyhFRBr4KvAU4FXhXTZ0v5LUWApuB9x6k6yu8UsCgt7BJkiRJkqQCGzNASimtSyndlX/eDjwMHL2XQy4GlqaUelNKTwKrgXPy1+qU0hMppT5gKXBxRARwEfDd/PirgEsO9IKaTclFtCVJkiRJUsFF2o/wIiKOB24BFgF/CPwOsA24k2yW0uaI+ApwW0rp2/kxVwA35CWWpJTel49fCpwL/Em+/0n5+LHADSmlRXXOfzlwOcD8+fM7ly5dun9XW0D/40fdnHFEK7+7qP2Aa3R3d9PR0TGuPqxhjUNZowg9WMMazVCjCD1YwxrNUKMIPVjDGs1Qowg9WMMah7pGEXqYaBYvXrwypXTWqA0ppX16AR3ASuDX8u/zgTLZLKbPAFfm418FfrvmuCuAXwfeAXyjZvxS4K+AeWQzk6rjxwL3j9VPZ2dnmghe84kfpP999T3jqrF8+fJx92ENaxzKGkXowRrWaIYaRejBGtZohhpF6MEa1miGGkXowRrWONQ1itDDRAPcmerkMC37kj5FRCvwr8A/ppS+lwdPz9ds/zpwXf51TR4CVR0DrM0/1xt/AZgVES0ppYER+094gbewSZIkSZKkYtuXp7AF2Syih1NKf1EzvqBmt7cBD+SfrwXeGRHtEXECsBD4OXAHsDB/4lob2ULb1+bp1nLg7fnxlwHXjO+ymkcpoOIi2pIkSZIkqcD2ZQbSa8luN7s/Iu7Jx/4P2VPUzgAS8BTwewAppQcj4mrgIbInuH0gpTQIEBEfBG4ku/XtypTSg3m9jwBLI+LTwN1kgdWkUAowP5IkSZIkSUU2ZoCUUrqV7E6rka7fyzGfIVsXaeT49fWOSyk9QfaUtknHp7BJkiRJkqSiG/MWNh1a3sImSZIkSZKKzgCpwUoRDBogSZIkSZKkAjNAarBsDSQDJEmSJEmSVFwGSA0W4AwkSZIkSZJUaAZIDZYtot3oLiRJkiRJkvbMAKnBXERbkiRJkiQVnQFSg5XCW9gkSZIkSVKxGSA1WACDLqItSZIkSZIKzACpwbyFTZIkSZIkFZ0BUoOVAirOQJIkSZIkSQVmgNRgpQifwiZJkiRJkgrNAKnBvIVNkiRJkiQVnQFSg/kUNkmSJEmSVHQGSA3mGkiSJEmSJKnoDJAaLHAGkiRJkiRJKjYDpAYrBQw6A0mSJEmSJBWYAVKDtZWDnt7BRrchSZIkSZK0RwZIDXb4lOD57bvoHTBEkiRJkiRJxWSA1GDzpgUpwdotuxrdiiRJkiRJUl0GSA02qz0AeLG7t8GdSJIkSZIk1WeA1GDTWrMAaevO/gZ3IkmSJEmSVJ8BUoNNb8kCpC09BkiSJEmSJKmYDJAabLozkCRJkiRJUsEZIDXYtNbsfXNPX2MbkSRJkiRJ2gMDpAYrRfDqY2byo0c2NLoVSZIkSZKkugyQCuDcE+awekM3KaVGtyJJkiRJkjSKAVIBHD1rKr0DFTZ29za6FUmSJEmSpFHGDJAi4tiIWB4RD0fEgxHxP/PxORFxU0Ssyt9n5+MREV+OiNURcV9EnFlT67J8/1URcVnNeGdE3J8f8+WIiENxsUW1cP4MAK65e22DO5EkSZIkSRptX2YgDQAfSimdApwHfCAiTgU+CixLKS0EluXfAd4CLMxflwN/A1ngBHwCOBc4B/hENXTK97m85rgl47+05nHBiYczY0oLqzZsb3QrkiRJkiRJo4wZIKWU1qWU7so/bwceBo4GLgauyne7Crgk/3wx8K2UuQ2YFRELgDcDN6WUNqWUNgM3AUvybYellH6WskWAvlVTa1KICF42ZxovdPskNkmSJEmSVDyxPws3R8TxwC3AIuCZlNKsmm2bU0qzI+I64PMppVvz8WXAR4AuYEpK6dP5+MeBncCKfP835OMXAh9JKf1ynfNfTjZTifnz53cuXbp0Py+3eLq7u+no6ODP7tzFjr7EJy6YesA1DkYf1rDGoahRhB6sYY1mqFGEHqxhjWaoUYQerGGNZqhRhB6sYY1DXaMIPUw0ixcvXplSOmvUhpTSPr2ADmAl8Gv59y0jtm/O338A/ELN+DKgE/gw8Mc14x8HPgScDfywZvxC4N/H6qezszNNBMuXL08ppfQH37k7XfC5ZeOqcTD6sIY1DkWNIvRgDWs0Q40i9GANazRDjSL0YA1rNEONIvRgDWsc6hpF6GGiAe5MdXKYfXoKW0S0Av8K/GNK6Xv58PP57Wfk7xvy8TXAsTWHHwOsHWP8mDrjk8qM9hZ29A00ug1JkiRJkqRR9uUpbAFcATycUvqLmk3XAtUnqV0GXFMz/u78aWznAVtTSuuAG4E3RcTsfPHsNwE35tu2R8R5+bneXVNr0pjW3kJP72Cj25AkSZIkSRqlZR/2eS1wKXB/RNyTj/0f4PPA1RHxXuAZ4B35tuuBtwKrgR7gPQAppU0R8Sngjny/T6aUNuWf3w98E5gK3JC/JpXpbWX6Biv0DVRoa9mniWGSJEmSJEkviTEDpJQthh172PyLdfZPwAf2UOtK4Mo643eSLcw9aU1ry/4UO/sGDZAkSZIkSVKhmFQUxPT2MoDrIEmSJEmSpMIxQCqI6gykHgMkSZIkSZJUMAZIBTGtLZuB1O1C2pIkSZIkqWAMkAqiuu7RwGClwZ1IkiRJkiQNZ4BUEOVStk75QCU1uBNJkiRJkqThDJAKoqWU/SkGDZAkSZIkSVLBGCAVhDOQJEmSJElSURkgFURLNUByDSRJkiRJklQwBkgF4QwkSZIkSZJUVAZIBdFSzgIk10CSJEmSJElFY4BUEC3OQJIkSZIkSQVlgFQQ5aGnsLkGkiRJkiRJKhYDpILYvYi2M5AkSZIkSVKxGCAVRHURbddAkiRJkiRJRWOAVBCugSRJkiRJkorKAKkgnIEkSZIkSZKKygCpIFryRbSdgSRJkiRJkorGAKkgWsrVGUg+hU2SJEmSJBWLAVJBlF0DSZIkSZIkFZQBUkFUF9EeHDRAkiRJkiRJxWKAVBDOQJIkSZIkSUVlgFQQEUG5FD6FTZIkSZIkFY4BUoGUS+EMJEmSJEmSVDgGSAXSUgqfwiZJkiRJkgrHAKlAWkpB34ABkiRJkiRJKhYDpAKZ2lZmV78BkiRJkiRJKhYDpAKZ2lpm18Bgo9uQJEmSJEkaZswAKSKujIgNEfFAzdifRMRzEXFP/nprzbaPRcTqiHg0It5cM74kH1sdER+tGT8hIm6PiFUR8Z2IaDuYF9hMprSW2dlngCRJkiRJkoplX2YgfRNYUmf8iymlM/LX9QARcSrwTuBV+TF/HRHliCgDXwXeApwKvCvfF+ALea2FwGbgveO5oGY2pbXMzn4DJEmSJEmSVCxjBkgppVuATftY72JgaUqpN6X0JLAaOCd/rU4pPZFS6gOWAhdHRAAXAd/Nj78KuGQ/r2HCmNpaZpcBkiRJkiRJKpjxrIH0wYi4L7/FbXY+djTwbM0+a/KxPY0fDmxJKQ2MGJ+UprY5A0mSJEmSJBVPpJTG3inieOC6lNKi/Pt84AUgAZ8CFqSUfjcivgr8LKX07Xy/K4DryYKqN6eU3pePX0o2K+mT+f4n5ePHAtenlE7bQx+XA5cDzJ8/v3Pp0qUHeNnF0d3dTUdHBwBfvWcXz22v8NkLpx1wjYPRhzWscbBrFKEHa1ijGWoUoQdrWKMZahShB2tYoxlqFKEHa1jjUNcoQg8TzeLFi1emlM4atSGlNOYLOB54YKxtwMeAj9VsuxE4P3/dWDP+sfwVZEFUSz4+bL+9vTo7O9NEsHz58qHPf/ide9IFn1s2rhoHow9rWONg1yhCD9awRjPUKEIP1rBGM9QoQg/WsEYz1ChCD9awxqGuUYQeJhrgzlQnhzmgW9giYkHN17cB1Se0XQu8MyLaI+IEYCHwc+AOYGH+xLU2soW2r80bWw68PT/+MuCaA+lpIpjSWnINJEmSJEmSVDgtY+0QEf8MdAFzI2IN8AmgKyLOILuF7Sng9wBSSg9GxNXAQ8AA8IGU0mBe54NkM5LKwJUppQfzU3wEWBoRnwbuBq44aFfXZKb6FDZJkiRJklRAYwZIKaV31RneY8iTUvoM8Jk649eTrYc0cvwJsvWQJr2pbdlT2FJKZA+okyRJkiRJarzxPIVNB9mU1jKVBH2DlUa3IkmSJEmSNMQAqUCmtpYB2NVngCRJkiRJkorDAKlApuQBkusgSZIkSZKkIjFAKpCpbdmfwyexSZIkSZKkIjFAKpCpzkCSJEmSJEkFZIBUINVb2Hr6DJAkSZIkSVJxGCAVyNyOdgA2bt/V4E4kSZIkSZJ2M0AqkJcdPg2Ap17saXAnkiRJkiRJuxkgFchhU1pZMHMKdz61udGtSJIkSZIkDTFAKpiuk4/g50++2Og2JEmSJEmShhggFcy8Ge1s7x1gsJIa3YokSZIkSRJggFQ4s6a2khJs39Xf6FYkSZIkSZIAA6TCmTWtFYAtPQZIkiRJkiSpGAyQCmYoQGorheUAACAASURBVNppgCRJkiRJkorBAKlg5kxvB2Dj9t4GdyJJkiRJkpQxQCqYo2ZOAWD91p0N7kSSJEmSJCljgFQwczvaaS0H67buanQrkiRJkiRJgAFS4ZRKweHT23mh21vYJEmSJElSMRggFVDHlBZ29A42ug1JkiRJkiTAAKmQOtpb2N470Og2JEmSJEmSAAOkQpoxpYXuXf2NbkOSJEmSJAkwQCqkjvYWup2BJEmSJEmSCsIAqYA62lvYvssASZIkSZIkFYMBUgGddEQH67bu4l/ufLbRrUiSJEmSJBkgFdH7Lnw5R82cwi2rXmh0K5IkSZIkSQZIRVQuBfNnTmHzjr5GtyJJkiRJkmSAVFSHT2/jRQMkSZIkSZJUAGMGSBFxZURsiIgHasbmRMRNEbEqf5+dj0dEfDkiVkfEfRFxZs0xl+X7r4qIy2rGOyPi/vyYL0dEHOyLbEazp7Xx8Lpt7OwbbHQrkiRJkiRpktuXGUjfBJaMGPsosCyltBBYln8HeAuwMH9dDvwNZIET8AngXOAc4BPV0Cnf5/Ka40aea1I66YgOAG5+bGODO5EkSZIkSZPdmAFSSukWYNOI4YuBq/LPVwGX1Ix/K2VuA2ZFxALgzcBNKaVNKaXNwE3AknzbYSmln6WUEvCtmlqT2m+cfSwAazb3NLgTSZIkSZI02R3oGkjzU0rrAPL3I/Lxo4HaZ8+vycf2Nr6mzvikN3NqKx3tLdz00PONbkWSJEmSJE1ykU38GWOniOOB61JKi/LvW1JKs2q2b04pzY6IHwCfSyndmo8vA/4IuAhoTyl9Oh//ONAD3JLv/4Z8/ELgj1JKv7KHPi4nu92N+fPndy5duvSALrpIuru76ejoqLvtygd6uWXNAF/smsrsKXvO+vZW42D0YQ1rjLdGEXqwhjWaoUYRerCGNZqhRhF6sIY1mqFGEXqwhjUOdY0i9DDRLF68eGVK6axRG1JKY76A44EHar4/CizIPy8AHs0/fw1418j9gHcBX6sZ/1o+tgB4pGZ82H57e3V2dqaJYPny5Xvcdu+zm9NxH7kuXX/f2gOucTD6sIY1xlujCD1YwxrNUKMIPVjDGs1Qowg9WMMazVCjCD1YwxqHukYRephogDtTnRzmQG9huxaoPkntMuCamvF3509jOw/YmrJb3G4E3hQRs/PFs98E3Jhv2x4R5+VPX3t3Ta1J79jZ0wBYu3VXgzuRJEmSJEmTWctYO0TEPwNdwNyIWEP2NLXPA1dHxHuBZ4B35LtfD7wVWE12i9p7AFJKmyLiU8Ad+X6fTClVF+Z+P9mT3qYCN+QvAbOmtTKltcT6rTsb3YokSZIkSZrExgyQUkrv2sOmX6yzbwI+sIc6VwJX1hm/E1g0Vh+TUURw7OxpPPnCjka3IkmSJEmSJrEDvYVNL5HTjpnJPc9ura4RJUmSJEmS9JIzQCq404+ZxQvdvaxzHSRJkiRJktQgBkgFt+jomQA8uHZbgzuRJEmSJEmTlQFSwc0/rB2ATTt6G9yJJEmSJEmarAyQCm7O9DYANu3ob3AnkiRJkiRpsjJAKriprWXaWkps6elrdCuSJEmSJGmSMkAquIhgzrQ2vnbLEzy/zYW0JUmSJEnSS88AqQksWXQkALc8trHBnUiSJEmSpMnIAKkJfPQtrwRwBpIkSZIkSWoIA6QmMKW1zJzpbazbaoAkSZIkSZJeegZITeLoWVN5ZlNPo9uQJEmSJEmTkAFSk3jF/Bk8sn57o9uQJEmSJEmTkAFSk3jlkTPYuL2XTTv6Gt2KJEmSJEmaZAyQmsQrF8wA4JH12xrciSRJkiRJmmwMkJrEyUdmAdKj3sYmSZIkSZJeYgZITWJeRztzprdx1zNbGt2KJEmSJEmaZAyQmkRE8NbTjuS6+9bywHNbG92OJEmSJEmaRAyQmsiH3ngyHe0tfOVHqxvdiiRJkiRJmkQMkJrI7OltnHHsLNZv29XoViRJkiRJ0iRigNRkDp/exos7ehvdhiRJkiRJmkQMkJrMnOntbOrua3QbkiRJkiRpEjFAajILZk5hR98gj6zf1uhWJEmSJEnSJGGA1GR+vfMYyqXg2nvWNroVSZIkSZI0SRggNZk509s4dcFh3P/c1ka3IkmSJEmSJgkDpCY0b0Y7m3a4DpIkSZIkSXppGCA1oVnTWtnS09/oNiRJkiRJ0iRhgNSE5kxrcwaSJEmSJEl6yRggNaE5HW3s7B/kazc/zq7+wUa3I0mSJEmSJrhxBUgR8VRE3B8R90TEnfnYnIi4KSJW5e+z8/GIiC9HxOqIuC8izqypc1m+/6qIuGx8lzTx/fqZx/Cqow7jczc8wrdve7rR7UiSJEmSpAnuYMxAWpxSOiOldFb+/aPAspTSQmBZ/h3gLcDC/HU58DeQBU7AJ4BzgXOAT1RDJ9U3/7ApXPOB13LsnKksveNZBiup0S1JkiRJkqQJ7FDcwnYxcFX++Srgkprxb6XMbcCsiFgAvBm4KaW0KaW0GbgJWHII+ppQWsol/sdFC1m9oZuntlUa3Y4kSZIkSZrAIqUDn70SEU8Cm4EEfC2l9HcRsSWlNKtmn80ppdkRcR3w+ZTSrfn4MuAjQBcwJaX06Xz848DOlNKf1Tnf5WSzl5g/f37n0qVLD7j3ouju7qajo+OAjt3Wm/hfK3p4/YLEZa8+sBoHow9rWKMZerCGNZqhRhF6sIY1mqFGEXqwhjWaoUYRerCGNQ51jSL0MNEsXrx4Zc1dZrullA74BRyVvx8B3Au8DtgyYp/N+fsPgF+oGV8GdAIfBv64ZvzjwIfGOndnZ2eaCJYvXz6u4//gO3enhR+7Lm3Z0dfQPqxhjaL3YA1rNEONIvRgDWs0Q40i9GANazRDjSL0YA1rHOoaRehhogHuTHVymHHdwpZSWpu/bwC+T7aG0fP5rWnk7xvy3dcAx9Ycfgywdi/j2ge/c8Hx9FXgn+94ptGtSJIkSZKkCeqAA6SImB4RM6qfgTcBDwDXAtUnqV0GXJN/vhZ4d/40tvOArSmldcCNwJsiYna+ePab8jHtg9OOnslpc8t8/oZH+Oz1D9M34HpIkiRJkiTp4GoZx7Hzge9HRLXOP6WU/iMi7gCujoj3As8A78j3vx54K7Aa6AHeA5BS2hQRnwLuyPf7ZEpp0zj6mlQigv/+mnZufHEOf3fLE9y66gXe9pqjufT845jSWm50e5IkSZIkaQI44AAppfQEcHqd8ReBX6wznoAP7KHWlcCVB9rLZNdWDv78v5zOa086nCt/8iSfuf5hnt3cwycvXtTo1iRJkiRJ0gQwrjWQVCy/duYxXPffL+RXTj+K79/9HFt39je6JUmSJEmSNAEYIE1Ab+88hu27Bnjd/13O2i07G92OJEmSJElqcgZIE9DrXzGPf3rfuWzd2c/7//EuZyJJkiRJkqRxMUCaoC44aS6Xnncc9z67hXdfcTs9fQONbkmSJEmSJDUpA6QJ7FOXLOILv34a967Zyjd/+lSj25EkSZIkSU3KAGmC+42zX0bXyfP44k2P8f5vr+SJjd2NbkmSJEmSJDUZA6RJ4P+9/XT+y1nH8uNVL/Arf3Ur//tf7mXD9l2NbkuSJEmSJDUJA6RJYN6Mdj7zttO46nfP5uwT5vC9u9bwm1+/ndufeLHRrUmSJEmSpCZggDSJdB43h2++5xy+cdlZrN+6i3d9/Tb+9ubHqVRSo1uTJEmSJEkFZoA0CV30yvnc/OEuTjt6Jp+/4RHO+ewPufbxPjbt6Gt0a5IkSZIkqYBaGt2AGuPwjna++/4LuO6+tfz7vev43iMb+N6nbqLr5Hn86ulH8dqT5jL/sCmNblOSJEmSJBWAAdIk1lou8bbXHMPbXnMMV/zbMta2LOAfbnuaFY9uJALesuhI/vCNr+CkI2Y0ulVJkiRJktRABkgC4MRZZd7bdSofe8sreez5bq6+81m+c8ez/McD6zn92Fm8ddECfue1x9Na9q5HSZIkSZImGwMkDdNSLnHqUYfxJ7/6Ki5/3cv5+o+f4O5ntvCZ6x/mr1es5twTDuf4udN5wylH0HncbCKi0S1LkiRJkqRDzABJe3TUrKl84ldeRUqJmx/byPfueo4H125l2SPP87c3P87cjnaOnjWFrpOP4LUnzaVv0Ke5SZIkSZI0ERkgaUwRQdfJR9B18hEAbNvVzzX3rOXeZ7fw0NptfGnZKr60bBXlgNMe+QkLj+jgjJfN4oIT53LC3OkN7l6SJEmSJI2XAZL222FTWrn0vOO49LzjSCmxsbuXe5/dyvd/fC8vUGL5oxv4l5VrAJgxpYXZ09qYPa2VuR3tHDlzCnM72pk3o50T53Uwb0YbC2ZOZXq7/xQlSZIkSSoq/3/tGpeI4IgZU3jjqVNo3dBGV9f5pJRYvaGbmx5+ng3betnS08emnn7Wbt3FXc9sZnNP/7AaLaXgyJlTOGXBYfRu6+W2nY9w9OypHHnYFGZPa2X29DbmTGtj5tRWSiXXXJIkSZIk6aVmgKSDLiJYOH8GC+fPqLt9YLDChu29PL6xm007+njs+e08sm47z27qYf3mQW5b9yR9g5VRx5UCZk1rY9a0VuZMa+Pl86az6OiZzJjSwoz21ux9Sisbeyps29XPjPYWF/mWJEmSJOkgMEDSS66lXOKoWVM5atbUUdtWrFjB61//ep7f1svG7b1s6ulj844+Nu3oY3NP9r6lp58Xd/TyHw+s5+o719Q9x4dv+U+mtpZZOL+Do2ZOZebUVo6dM5UjZ05l1tRWZk5rZXpbC7OmtXLEjHZayqVDfdmSJEmSJDUtAyQVTkR2S9uRM6fsdb+BwQqbe/rZvquf7bsG8lc/d9z7AEe+7OWs39rLA2u38sQL3Wzu6Wfj9t491morl5gzvY3p7WWmt7fQ17OTf3jqDuZ2tHPEYe0cPr2NOR3tzMlnQFVnO82Y0kKr4ZMkSZIkaYIzQFLTaimXmDcjW5C71tQXH6XrdSeO2n9H7wAvdPeydWc/W3f2s6N3kBd39PLC9j56+gfY1N1HT98gPX0DrN0B67bu4v7ntvJCdy+VtOc+prSW6GhvZXp7mamtZTraW5jb0U7vtl5u3/UIHe0tdLS3MD1/72hvoWNKCx3tZaa1tTCltcyU1hJTWsqu8SRJkiRJKiQDJE0a0/MQZ1+sWLGCrq4LARispGG3z23p6Rua7bR91wDbe7PZTz19A/T0DdK9a4DVG7t5fvMAP37uCQb2lj6N0FYu0d5aGgqVBnt3MeveH9PWUqKtpUR7S4n2ljLtrTWfW0r59/xzS4nWcomWctBaLvH42gF23r9uqEZbuTSsXlu5vHtbvr21HK4fJUmSJEkaYoAkjaFcCuZ2tDO3o33snWtU13PqHaiwo3eAHb2DbO/NZj7t6M2Cp57eAXb1D7JroJK992fvvQPZ52fXrmfWrCn0DlToHaiwfdcALw700TswODTW27/78x7dd9d+X/NhU1qY1tZCazno693JzHt/TFs5aMkDptZyiZbS7u8tpTy0Ku0Or8qloKUcrH22j3sGHtvzMfnn4WPD931m2yCrnt9OSz4+FJLl+7fkx5adxSVJkiRJB50BknQIRUQ+m6jM4R37f3w2E+rsfdo3pUTfYBYk9Q9UGKgk+gYq3PrT2zij8yz6Bir0DVay9zxwqv3eNzA49L2nb5Btu/rZ1V+hf7DCc+t6mTVrKgOV7Hv/YKK7d4CBwUT/YHaugXx8oFIZMZ59Tk+s2v8fYKSf3jLmLhHsDpVqgqaWUomBvl3MuOvmPQRQJVrz8daWLLQams1VU2/Ns33cN7iKcikLvsp58FUu7a5VLu0O0Ib2KUUeqJV4Yusg89ZuHVa3ev7qPi3551Jk2729UZIkSVIjGSBJE0RE5LexlYeNL+goccqCw8ZVOwuyzhrX8Re+7vVD4dLAYKJ/VNC0O4DqH8y+D1Ty7YOJe+67n5NPObVm+5737a9UGBxMo8bWrlvPnLkdo47pG6iwo28w7yGr2TeQfe4bzGr1VyoMVhL9gwkef2xcvycAP7t1vw8p56FSOYJUGaT95v8cCpnKJShHUC5n20v5fkPH5PuVgvw92Lp1J1977DbKpSCGxrP3yGu21IRi5fy4UinbVopg3dpeftz9UHZczTlLI869uycol0v5flmNx57rZ/Pdayjlt01W+ytF9u+69j3rbfg+pVLw6KZBZjy9Ket7qE+G+qjuO/R5qKd8nwh29Ce27+ofOmbkebytU5IkSZOZAZKkl0QWJJTZx2WoRmnZ8DBdpx81rh6yIKxzXDWWL1/O617fNRR8DQ+XdodMtd+HgrNK9rr7nnt55amLRmzbPWOrGqgNpkQlP6ZSSQymxGAFBisVnn7mWY46+mgGKhUGKwxtr+5f/TxYSVRSXiNlM9UqKVHJ73gcqFToGyQbq9lerVkN6QYriZR291D93Ns3QHn9swzWnjMl0r4v/ZW5/95x/V0A+PnPxl9j2X/ucVM1UCrHiMCtJqAaHOin/Sc/zMKqPIiLEcFX5LVqQzHI3nfs2Mlh999KKd+pVHNMtcaoepEFc9XPm17cxXfWrBwRtjHUSzA8jNsd0u0+19q1vdyy/aF8bHftUd+p7WV3yFaK4Mkn+nis9DjB7vNk+9f0MvLaaj6XS8EjawfYes9zo4K86r61vY88T/V6HnpxkLbHX6j57XcfA8P/LsN/192/3druCqs3dA8779Dfkd1/l7rj2WnY2pt4obu37vEMXUPtb1FTN/9cyf83Vj2XJEnSS6kwAVJELAG+BJSBb6SUPt/gliRplBiaWVMee+c9SGtb6Fp05Lj6WLFiA11drxpnjRV0dV1wEGp0jRpPKeUBWTWQykKrwZSFUtXPlUriZ7fdxjnnnDsUYkH2Xg26KnkgVUmJRPV7HnLlgdfdd9/Laa9+dR5e1WyrBluJYYFaZcTxg5XEY6tWc+KJJ+b7jD5PNTTbvY2hetXPa557jiMXzKdSve6UIPt/o64j1XzPwjvYOLCDOR1t+f7D90k1v8kgld2/0YhgcFtPhe6N3TW/YRp2/jS0//BeKjXjff0DtKx/dnd90qh+9un5AI89Mq5/XwDcd8/4a9xx+/hr3Hrz+Gss/+H4a9x4/dDHsUKneoHb4EA/bT++aSjA2h2iDQ+9qgHVyBArgJ07dzL9zhUwMsjL94ERIdmoQC3Yvm0nX3roJ6P6311zZL3h3zdv3sWVT/y8Zt/h58yPqtk2ut7Gjbv4l7V3DTuWOvUY9dvkvQLr1vdy46b7yK+s7j4jf1NGbH9uTS+3dj806rejbo2hK9v9ewJPP93H3f2P1f2thh1X83cdWefxJ3eHvtQ7Zthxu/9tVb8Twapn+llz29N1r3/YdQ8bG/53e2TtAFvufm5YDyP7GPkbjrzWB9YPsOuB9XXOX6dGzW9R3RjAfRsHiMc21rn20dc1+t9I9nnV5kE6nto07N/kyH731Ff1XE9tHeSB57ZSq24vdcYAnuuusOr57ePqYUNPhWc39dTtYfhYzb/xEftt3lVh/dZdo85b+6Xe37O2bndfYvOOvrq/wdBbnfq117ZrINHTN1D3fPWur15Pg/n/oW/kbzZsP0N+TVCFCJAiogx8FXgjsAa4IyKuTSk91NjOJEkHIiJf2HwfcrbHp5U4fu70cZ2v79kyr3vFvHHVWNH/NF0Xvnx8NVa8QFfXaeM4fgVdXeeMs4cVdHW9/iDU6Bpzv9rgqTakArj55lv4hQsvrAmpRgRitWOMCNjy95/ddjtnn3POsMArmw1XPf+IcIvd56jWv+vuuzn99DNI1ARoezimNkQbeifxwIMPccoppww75+5ateccfu7a74899hgLFy4cds21x4++ntrzZO9PPPkkxx1/fN3z7umYyoixNc89x4KjjhzWJwz/7Wv7p/Zc+fjzz/cy74jDyA8dqj/yeIb9HsOvebAn6Ghv2d1/ZcTvV4FEpeZ3GV6jpz9R3tkPw+qO/Lcx+ret3bZjR4UXB7cNXSOj+tz9dx65Lb86ensHeXjrhqG6tddc95j8O0M1YGBggNK6Z0b/+xr6H9qIa6utU1Obxw/CWoOPHoTQ96EHxl/jYATH96wcf42VPx9/jdsPwuzYA7jtfZhbx15Dcky3LB9/jRXLxl/jRzeNv8YPbxx/jf+8Yb8PGZYpJYgbf1A3dKvdN2pHR4RjlUqF8rIbRu03VohWW29gYIDWFTeO3m/0afdYp7+/j7Zbbxo2Wr+H4ddUG8L29vYy5bYfjdq33nH1+tu5cydT71he//cc41qq4zt6eph+181jnq/WyN/jzONm8elLDvy/A5tBIQIk4BxgdUrpCYCIWApcDBggSZJUUNXb50b8JykA7S3B9AO9ZzX35PQSJ847gCcQ1Oh5usz5Jx4+rhodmx6j64yjx1Vjxa4n6Tr/+PHVWPEcXV2vGGeN8YWcWY0VdHWdeRBqnDvO4197EHromlA1agNUGB001QZRtWMAt9zyY37hwguHHTe0b50Abiicq9nvJz/9CRecf8Eez1kbfNbrISW47fbbOffcc+vWZ0QP1GyrPc8dd9zBWWedPep697R/vd/rrpV38Zozz4S6+42+/uGBa/b93nvv49WvfvWoc9R+p6afYf3l7/c/cD+LFp026rja36Ref9WaDz34EKecemrdHur9JvV6ePiRh3nlK08Z3fuwIrUfR9QBHn30UV7xipPrbh92+B6uM6XEqtWrWXjSSXWus/511bu21Y8/zokvP7HO71i/r9q61fEnn3qS448/YZ+OH3ld1bGnnn6a41523Khj611XvesAeOaZZ3nZsceO2G/PPdTrdc2aNRx99DF1r3dfekgJ1q5dy4Kjjhy1H/WuaQ//+1u3bj1HHnn4qPrU/S1G9/f8870cccSsUeN7upZ6/143bNjJvPy/Oer9VqPHR4/Nmb5/T+1uRlHvf2gveRMRbweWpJTel3+/FDg3pfTBEftdDlwOMH/+/M6lS5e+5L0ebN3d3XR0jO8/jq1hjaLXKEIP1rBGM9QoQg/WsEYz1ChCD9awRjPUKEIP1rDGoa5RhB4mmsWLF69MKY1+ilL2fwVo7At4B9m6R9XvlwJ/tbdjOjs700SwfPlya1hjwtcoQg/WsEYz1ChCD9awRjPUKEIP1rBGM9QoQg/WsMahrlGEHiYa4M5UJ4cpvZQp1l6sAY6t+X4MsLZBvUiSJEmSJKlGUQKkO/j/2zv3cLmq8oz/VhJSMNxDhNwIqBEDVFFouYnGoqhB66VeEClaKm3FtgJqpQKCoPBULIJURZBbiojYKreCoIjUBngQimK4S6IEgiFRCQESQs75+se3Ts8+kzl775m9Z2adM+/vedZz9uy193veWTN77bW+WXstmBtC2DmEMBk4BLi6x56EEEIIIYQQQgghBIlMom1mG0IIfw/cAEwELjSze3tsSwghhBBCCCGEEEKQSAAJwMyuA67rtQ8hhBBCCCGEEEIIMZJUHmETQgghhBBCCCGEEImiAJIQQgghhBBCCCGEyEUBJCGEEEIIIYQQQgiRiwJIQgghhBBCCCGEECIXBZCEEEIIIYQQQgghRC7BzHrtoS1CCCuB3/TaRw1sB6yShjTGuUYKHqQhjbGgkYIHaUhjLGik4EEa0hgLGil4kIY0Oq2Rgofxxhwzm7bRXjNT6mEC7pSGNMa7RgoepCGNsaCRggdpSGMsaKTgQRrSGAsaKXiQhjQ6rZGCh35JeoRNCCGEEEIIIYQQQuSiAJIQQgghhBBCCCGEyEUBpN5znjSk0QcaKXiQhjTGgkYKHqQhjbGgkYIHaUhjLGik4EEa0ui0Rgoe+oIxO4m2EEIIIYQQQgghhOgOGoEkhBBCCCGEEEIIIXJRAEkIIYQQQgghhBBC5KIAkhBCCCGEEEIIIYTIRQEkIYQQQgghhBBCCJHLpF4b6CdCCLOAjwL7ATsABqwAFgHfMLNlPbTXE0IIc9m4PG41s4d7aqwHqCxGovIYSSrlkYoPIYQQ1QkhzCFTn5vZb1o8f3NgT0beE+4ys2e6rFH53jRefKTgQT465qOSRgghAG9sorEIuMlKrq6VQnmk4KFf0SpsXSKE8FrgeuAJ4Eb8CxqAFwNvAqYDbzWzRV301LOLJoSwFbAQeDvwLPAkXh7TgBcB1wCHm9nTBTp1VYQqi5FaKo9ESKU8UvHRoKWOj3wk6yMFD4n5UMcnIR8hhGOAY4EZeF1O1FgO/KuZnVVw/iTgX4EjgU2BgZg1EViHL4f9KTN7ocMale9N48VHCh7ko2M+6tCYCVwLvBK4n5F90XnAz4E/N7PHUy6PFDz0PWam1IUE3Al8JSf/bOBnJXSm4BfMRXhA6rq4/RFgSkkvWwFXAYPAGuARYEncHgCuBLYsoRPw4NdJwNeBr8XtNxKDkznnLgQWA/s3ydsPuAe4pEBjJnB39LwYuAn4cdweAO4CZqosypWFyqPe91HXNZtKeaTiI+ocAyyL5wzGNBD3HV3i/El4nftcPPeFmAbjvrOATbqgUfl6k4/0fKTgITEfdWjUUYelUh499wGcCDwNHA/sBcwGdozbnwFWAycUeDgbeBz4ILBdZv92wKHAY8BZXdCo4940Lnyk4EE+OuajDo2rgJuBWU3yZuF16pWpl0cKHvo99dxAvyRgLbBLTv4rgLUFGrvGC+ZpPIJ8AXBh3F4dL5hdS3jpeUcQeArYOyd/X+CpAg91VIQqC5VHR95H1Kh8zSZUHqn4UMdHPpL2kYKHxHyo45OYDzzY/q6c/HcDjxd4WAkcmJP/RmBlFzTquDeNCx8peJCPjvmoQ+MZYI+c/FcDz6ReHil46PfUcwP9kvBfho7Iyf9rYEmBxs3Ad4BNm+RtClwO3FzCS887giU87N2lilBlofLoyPuIx1W+ZhMqj1R8qOMjH0n7SMFDYj7U8UnMBz7SaV5O/m7AcyU+k1fl5O9R4jOpQ6Oue9OY95GCB/nomI86NFYCHGarZQAAGXVJREFUf5aTf2CJuqfn5ZGCh35PWoWte3wJODeEcG4I4S9CCK8NIewft88Fvgp8sUBjb+BzZrauMSPu+3w8piqDJY45EDjGzB5r4uUx4BN4I2g0rgG+GULYpzEj7jsPuLrAw1pg25z8beMxVVBZjKSfyqPq+4B6rtlUyiMVH1OBB3LyHwS2KdDYDFiVk78qHtNpjSLKXG/ykZ6PFDyk5KMOjTrqjlTKIwUfdwCfDSFMbsyI+46Px+RxM/DlEMKMJhoz8Hbvj7ugUce9abz4SMGDfHTGRx0alwMLQwiHhBCmZs6fGkI4BLgYuKxAI4XySMFDf9PrCFY/JeD9wO0MP+s+9Oz77cD7Spz/OPDOnPx3UfDLezzu34FfAvs0ydsH+AWwsECjUhQb2BqfD2YQf+TkV8DDcXsAnydm6wIP5+BDvQ8Bpmb2T437lgFnqyzKlYXKo973EY+pfM0mVB6p+PgJ8G1gcpO8yXjj5ycFGtfgjYsZTfJmAD8Cru6CRh3Xm3wk5iMFD4n5qEOjjrojlfLouQ9gd3xRl6ein28C58ftp/CJtHcr8DA7etgQ//4I+GFm3y9oMoK3Axp13JvGhY8UPMhHx3zUoTEZn8/z+XjO+pgG4r6v0aRtlVp5pOCh35NWYesBIYRN8GfdAVZZzizxDeedjE8eezp+oazAV8zYAZ/o9zjgTDM7pUBna7wD9mZ8wsWVUefFwObADcChZvZUjsY5eOf3k8APzex3cf/U6OUM4Htm9vECL/Pwodbbx12/BW4zs7wRBkPnTsbnEjgCn5QyOwv/Bny+maPNbH2OhspipI7Ko8b3Udc1G7V6/v1IwUcIYXe8LDcDfsrIMj0AfzTjTWZ2b47GbLxxMI/hlUiGNOYB9wIHW5PRZzVr1HG9yUdiPlLwkJiPOjTqqDtSKY9UfGwBHIYHnHaIu38L3AZcZiVWHwohTIgemmncaGaFI7Lq0Ig6r8DvTSM0ytybxpuPFDzIR/0+okbbbbCMxpYMrwA5pHFXmWs+nt/z8kjBQz+jANIYI4TwaeDjDC/ZCr5S1G/xSReLHoPLaiXRIa1K1YowaqgsRur0fXnUGJSr7ZqtSl3fj177UMdHPlL3kYKHlHxEDXV8EvMhhBBCtIoCSGOUEMLOZBoMZra0Rz6qdNCn4CuO7Mdw53oFsAj4tpk92xHTHUJlMZLxUh7xfezFyE5PO0G5tq/ZVMojFR9CCCGqE0KYy8b1+a1m9nCbetsALwOeyBtB1XBOwOcTbHZfucna6KiEEPYA5uKP6S0qozFefaTgQT5q81FrGyz4EzEHZ3x8v4xGCuWRgod+RgGkMUy8UX8I/7Ivx593X1by3J52BEMIu+KPoWwB/Hf83wEffn0APiT7IDO7rwXNditClcXIc1UeCZFKeaTiI3OuOj7ykayPFDwk5kMdn4R8hBC2AhYCbweeBZ7E6/NpwIvwuZAOz/uhJIRwGvB5M3sufh5fxVcUDvH9XIU/RrcuR2MmcC3wSoYf5xu6r8wDfg78uZk9nqNxGfC3ZrYmhLA58J/4I+ID+Ijhu/DHmvMe5xsXPlLwIB8d81G5DRZCuBVYYGZPhRCm4XOx7YLPLzcT/4F0v4L30vPySMFD32MJTMSkVC7hQaKpcXvn+HoFXgEsxyc+fEUJnV3xyX2fxi/AC4AL4/ZqvCLZtUVvmwDvBD6FP1oypeD4m6m+vPmtxAnO8EbPL/HJ4Jbgk8H9BpipsihXFiqPpjqzgC9ET/cD98XtLwCzWymHqLcNcDTe0D6hjEYq5ZGQj63wjskg3mB6JJ6/Br/xXwlsWaBxGvCizPfzvHjuYPz7vWbvs0FjJnB3PH4xcBNeFy+O++4q8V4uA7aI20PzlgwtrjAI/IziSTHlIzEfKXhIzEfl+wr11B2plEfPfeDBo8XA/k3y9gPuAS4p8DAAvDhufwafh+m9wI7AO+LnemKBxlX4vWWjCW/x+++PgStb8HFG/E78Cd6hfBV+3z6zH3yk4EE+OuajjjbYYMbHeXigZXp8vR1ez16Qenmk4KHfU88NKLXwYY288L8dL54p8fWmeGPsuyV0et4RxCe6HbXBiK8Q8lwL5dFuRaiyUHmMpvFaPCjxEPBvwInAZ+P2g3hnaKPGd4NGY9D3CVoM+iZUHqn4UMdHPpL2kYKHxHyo45OYD/zes3dO/r7AUy18JncDRzTkvw+4r0DjGWCPnPxXA8+04GMxDasaAwuAh/rBRwoe5KNjPupugz2IT9afzZ8PLE29PFLw0O+p5waUWviwRn7Zl9CwxDiwN7CshE7PO4LUs7x5HRWhykLlMZrGncBXcvLPBn7Wgo+2gr4JlUcqPtTxkY+kfaTgITEf6vgk5oPienRvytWj0+L2KmD3hvydgGcLNFbS0JZtyD8QWNmCj5XAbg35c4C1/eAjBQ/y0TEfdbXBhnysGMXHutTLIwUP/Z4mIMYaFv9Oxp9Zz7ICH/FRxB+Al+fkz43HlOX1wPFm9gSAma3Cf9X/s5xzzgcuCSEcF0LYM4QwK4QwM24fB1wEfKPE/x4qj62BXzfkLQWmF5yvshiJymOY3fBHzUbj63jHpyx7A6danKfDfF6IU/FVePJIpTxS8VFEqdWLMj5mA3c05N2BNx7yWAtsm5O/bTymrI/t8eW7s9wb/cnH2PKRgoeUfNR1X6lad6RSHin4uAb4Zghho/tP3HcecHUJDx8NIRyLjzKe2pC3FT7qOI/LgYUhhENCCP9/fghhagjhEOBi/HG9Ik4PIXwFL5MZDXnb4UG7fvGRggf5qN9HXW2wS0MIV+OP7+/ckDcdDy7nkUJ5pOChr5nUawOiZW4JIWzAG1CvwH95GmJH/FegIoYqodPxCdlW4BfPDvgEYscBZ5bQabsxZ2YnhxDW4subn5bRGlre/DQrt7z5pSGE5xmuCLONqDIVocpiJCqPYZ4A9sd/7W7G/vGYIioFfVMpj1F8hJjdzc9lqONzpJndns2IHZ9vUL7j8wzVOz6fBH5oZr+LHqbi18oZlG/APMdwAyZbHq00buUjHR8peEjJR133lap1RyrlkYKPf8BHxd4aQliD/wJv+CS0Q3Mq/WPB/38U+Ku4/Tw+cuqWTP4bGP3+OcQn8L7IJcCkEMJA3D8R2IDPl/WpAo3/Bl4at+9l4+D/AjYOsI1XHyl4kI8O+KipLXhJZvsqfML8LH+BPzWQRwrlkYKHvkarsI0hQggnNey63cxuyOSfgT9T/4ESWp/GK6Gh1T9guBI6q6gSCiEM4g3B5/G5Yg43s2sz+fvgz/DvMIpEVqut5c1DCBc17LrezK7I5J8B/LGZvaVAJ9Wy+J2ZPVR0TjyvlrKIx6ZaHl39boQQjgLOwid7bdbp+TBwtJmdm6MxCDyA39BeAnzYzP4jk/864DIzm1XyffX0WknBRwhha7zj82Z8jqpmHZ9DLX81k18z/N0GONvMzsrkHw2838z2zdGYjD/GeATekGnWgDnazNbnaPykwce3zOybmfwTgQPNbL58jB0fKXhIyUc8rup9pY66I4nySMVHPG4e/tjv9nHXb4HbzOyBvPPKEO/1z5vZ3SWO3RLYk8x9BbjLclaBa8HHS4D1VmJ1zfHuIwUP8lHNRzy+rTZYCd0pwIDlrJyYObbn5RE97MXI+qsnn0m/oQBSn5NKh7QTtFIRxuOTKosQwnrgVWZ2fyvnNWhsYmYvtFoW8dykyqNOypZHCOH9wDH4TXJi3D20Ss6Z2fc1yvknM7KR33bQt5O0UB7TgY/igcHpeFksxVc+u9jMBnJOr81HPFYdH/lI2kcKHlLyEY9Xx2ekD3V+hBBCjCkUQBIbEUKYDXzOzI6ooFHYmIsjCfbH5z64zTJfxnj+J8zslHY9tOB1d7wjusjM7gsh7IoHDf4IuNTMbqyon1sWwZ+/bcbH8JEWvwcws6Lh5M20KwWhQgjbAB/C56hYDiw0s2XtaGU0i8rjAOBJM3swvj4KL4sd8RXc/i1v1E8nCCFsgj8SALDKzF7o4v+eAPwzPofSf5nZN0IIfxX3TcCXnD8h79fqmnzsBfwI+BU+P8e++GMWk/HRQPcDbzazNQU6HQ1CCSGEKCbeiw/FV68cGhm2AlgEfNvifH2d1ijQ3x742yptwbIa8V47D/iDmS1vyNsUn6h8YRc0pgF7AD83s5XR/0fwNukVZra4k+fn6D6Kj2p7uJ3zq2rEdtjBeHv0CeD7rX6/2tEIIeyCT2r/eHy9gI3bpD/ogsY5wHfM7H8K32gHNaLOB/E26fVmdn0I4SDgk8Q2qZl9rdMaIYTNgA/QpC1pZjeVfB+VNfoWS2Amb6W0Er4E7EBFjdnAhTn5u+GV9wZ80ts7gTmZ/O3LeMDn2DkYb7yEhrwpwGcLzl+Az4HyO2Ad8Fb8kZgfAjdFf28s4WN34EjiSjPArvi8DwuBgwrOHcRXgrq5IQ3ik/neDPy4QOMro6QB4NKh1yXeR+Oy88tpcdn5JprbAEfjE1KfAMwuOH4x8Ia4/TH8MaUvAIfhk04/BXysQOMAYJfM66PwZ5nXRP2/K+H7mvg/N1p6uoX3XofGqXgQ8WJ8zolT4vf1eDyI9CQ+OXeexsR4/NV4Qxp87oqH8IDQF4HJBRr/A5yUeX0YPqJq6DO+G38ULE9jr/j53Qn8NF5fC/E5Qf6Ar9C3RYkymRKvt4uA64Hr4vZHiKvcdUOjQH97CuqfujTwBtduwIwmeZvij5F2Q2Ma/njn0Mom28fv3Sk0rNDUSY1RdB8F5lb8PNrSwOfseSc+J8Nh7Xy/2tEAdgFmZl4vAP4L+CW+AuRbuqRxDvDaimVfWSPqfBC/H741vj4IuBEPjh/VRY3N8EfYLoz1z7XxPR7YwntpWwNvozwOPB3PuyDqXAusBh4jZ+W8ujRK+KyjPVqogbdZF+PtrgHg+8A2mfzCNmlNGvvGshvE74l74R3aoXv1WmCvTp0fNY4dJW3A2wrHAsd2QeNWYOu4PQ2vc9bjq1A/jwdeZraocU8bGncS6zl8hdYXgG/h7dmFUed9XdAY+l7dj//IPbWNa6EOjaPx/tJP4nfsb/C29fnAufiqm8d0UgN4GT6/6gr8vjyI1zu3x+/YFcCkAg+VNfo59dyAUg8+dDi8IJ1Ah2/YeEf2e3gnbnq8UB8jNs4pd6OtHISKN5fPx+1D8I76FzL5pwM3FmhUCkLhq5ItAeY37H+Bko0vaghCZXSqLjvfGIR6ghaCUHgDZ07cvhv4UEP+e4EHCjzUEYQautH+Hp+v4o/buA7q0FgCvC1u7xr1PpjJfxfwqwKNOoJQzwEvybyeEL/328fXb6J4Cdk6glDq+Iw8Zlx0fGrU6HnnB3V8GjXU8Rmp0fPOD35v/w5NftzA7/WXAzcXeKhD43UF6a8prnvq0PgWPqnuPHxl1EX4dZsNZHdD40fxu7QFPhpjGXB+Jv9CfNRMR87PXGvL8Lo3mwbxe+NSYEmXNIbao+fhkztPj6+3w+vIC7qg8SywU9y+g4brG/8h6udd0BjEfzA/H2+7rMPb6aMuZ98hjQeAw+L2fvg94KhM/oeB+zqpgf/gdy7DT1J9Grgubs+N36+TCzxU1ujn1HMDSj340L0CeQZv9DRLz1J8k6sUhMI7rLs17DsT79S9nHI32jqCUKuBl8XtCXjj+DWZ/N3xeRryNOoIQu2Dd5BOAybGfa0EkCoHoTLfjaGb7ZLGmwo+3HRZCxotB6Hid2PPuL0CfwQvm/9S4LkCD3UEoQbxYeDHA4/gnZfb8V98y45yqUNjLbBj5vX67LWDrxzxbIFGHUGoXwMHZF7PiO9vs/h6J2BtgUYdQSh1fEZqjIuOT40aPe/8oI5Ps/eijs/wMT3v/OB18ahtA7ztU3SfrUNjKDA4mJOK6p46NJaTCU7jI/6uAO7DF2goUwfWofF7YF7m/AHgTzP5rwEe69T58ZjzgP8lM4o77m+lTVqHRrYOfBA4uCF/PrC0Cxp/ILZD8fbpKxvyX0pxG6wOjex72RwPXt8R9z+Cr2a5Qxc0nmPjNunumdc7lXgvlTTw+9LczOvJUWPox+t3lPhcK2v0c+q5AaUefOjeAH53Tv4elLthtx2EwgM3G91EgC/jI1bml/BQRxBqNSM7tGsaXs+huFNcOQgVj9sK79T+LPpvNfhTKQiV+VyHOo2P0fDISKzU15XQaDsIhY+SuShuX04mGBf3fYbiDksdQaj/fx/x9UHAd/EOw2q88b5nFzSWAAvi9i54g/C9mfwFFN8o6whCnYU/Bvg2PNBzC5kgC/AWuheEUsdnWGNcdHxq1Oh55wd1fPLeS193fOIxPe/84O2kd+bkv4viYH4dGk/ijwROHSXNp1xbsKrGMzQ8noo/+v1dfNRbmXZxXRo7ZV43tklnk3OPrHp+5rj34EH0v8nsa7U9WUmDke3RFWzc3p9DufZoVY3/BL4Yt68HPt6QfyTwYBc0RrQnM/v3AL6Gj6xf3wWNZcDr4vasqLkgk/96in9orqSB1z17Zl5vEzW2iK93LvG5Vtbo59RzA0o9+NDhKuKImVHyXwUMFmhUCkLhDb+mc2rgj/qszjs/HldHEOoX+MS/Q693JzPsG59YbWkJH5WCUA16R+INooFm76/g3KpBqEG803gP3hB5T0P+6yjuwFUKQuETcT6Cz5HzJbzB/lO8U3cLHnx5a4GHi6kehBrtRjsN/7X3oRLfrzo0TgVW4fPz/Br4PP74yt/jczs9iq8Il6dRRxBq8/jdeiG+r0XAzpn8g7Kao2jUEYRSx2ekxrjo+NSlEY/raecHdXyavRd1fIaP6XnnBzgZb7sch69INwuYGbePi+VZNIdkHRo/AE7MyS/THq1D4xc0tHfi/onxGlhGufZkVY37iY/fx9cHE39gia//lPwRSJXOb9Cag9/nr8TvRy3VoVU14vf5Bvxpg98TR1Fn8veh+AmBOjRejt+r/x1/5H9N3P4McAk+GjJ3jsCaNAZoUgdm8jcDPtwFjXPwH6tPwh9xviB+796GtyXvoXh0bCUNvI3/U7zPNhdvq/xvJn8+8GiBh8oa/Zx6bkCpBx+6TzA8agccfyTs9QUalYJQsQK9Pif/q3nnx2PqCEIdBbw9J/90YiAi55jKQagmmi/HVz/bss3PuK0gVKzMs+nNDfln4Cur5GnUEYTaCh9JdS8+euZ5PHjyLQrmQInn1xGEatphaThmfhc0JuCNjGuAT8d9h+CBo6HAUu7jcNQQhMpobQps3ub3so4g1Mmo45M9Zlx0fOrSyBzbs84P6vg0aqjjM1LjYhLo/OA/YixneCTk0GjI5cA/lbxGKmngwfrDcvK3oeER9A5p/Atwwyh5k/A6oKgOrEPjFODQnPxT8RWqOnJ+k+Mn4vNHPobPrdXy3IDtauDtlWx6X0P+GcAPOq0Rj9sJb38+zfDo4PX4/eEdJd9PJQ1KtCe7pDEFb0v/Evg6Plr4E3i7ehCfIqCo3VtJAx8ZfVum3lkKvDqT/x7gHwo8VNbo59RzA0pjM1FDEKoGD5WDUDX5qByE6pCvSkGoCv+3chCqJh9Vg1BLaWOi1ro1aiqLykGomv20HYSK56vjM3xMmU5LURCqDo3KHZc6NBqO70nnB3V8Gs9Xx2ekRlKdH3y00r4x7VzmnE5o9DLFem7UtlKsB+Z0WqOEzy3IBNW7dT4+su4kYLsK/7uyRoPeFCqscNuOBhDwR7qnA5u0+T8ra6SY8HZd4Sq6dWrgwfMRP9q38T8ra/RjGpp8TwghhBhThBB2xkebgY/GWNoLjV4SQpgEvMjMnh4lfyIwy8x+00mNEj63ADaY2dpua4QQXo+PyPiqma1q839X1shoTcFHIqzrlkYIIeBBhwnAKjN7oY3/WVkjRUIIm+IduTXd1AghzAX+CF/QYUOb/7eyhhBCCNEKE3ptQAghhGgHM1tqZrfFtBQghDA7hHBhNzWa0S0NM9swWuAnMgP/1bejGiXYGh8V2nUNM7vFzD5nZqva/Vzq0MiwLT5vTxVa0jBnhZk9MRT4aeNaqazRjF5rmNk6M1vTbQ0ze9jMFjcGfrqlEULYOoRwcAhhvxgczOZNCSF8tuj/SyM9jRQ8SEMandZIwUNf0+shUEpKSkpKSnUlfN6g3HkmpCGNXmqk4EEa/a0B7IYvNrIBfwzuTjKPV1Fu5UVpJKaRggdpSKNfvuf9nCYhhBBCjBFCCIcXHLKjNKTRS40UPEhDGgX5p+NzKP0lsCW+8MiiEMIbzOzhov8vjWQ1UvAgDWl0WiMFD32N5kASQggxZgghDOKr6o1285qAT4o5URrS6IVGCh6kIY08jRDCk/iKh/dm9p0JvB94A75K5fICD9JITCMFD9KQRqc1UvDQ9/R6CJSSkpKSklLZhK+G9e6c/D0oHrosDWl0TCMFD9KQRsF3dDVNVhQEvow/1jG/hAdpJKaRggdpSKNfvuf9nDSJthBCiLHEXcBrcvINCDn50pBGpzVS8CANaeRpPAjstdFJZscAVwBXFfx/aaSpkYIHaUij0xopeOhrFEASQggxlvgSsCgn/1f48GNpSKNXGil4kIY08jS+D3ygWYaZfRy4lOIgljTS00jBgzSk0S/f875FcyAJIYQQQgghhBBCiFw0AkkIIYQQQgghhBBC5KIAkhBCCCGEEEIIIYTIRQEkIYQQQgghhBBCCJGLAkhCCCGEEEIIIYQQIhcFkIQQQgghhBBCCCFELv8HFBstuXYzcI8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# storing the words ( keys of dictionary) and its frequencies ( values of dictionary) in a array inorder to plot the graph\n",
    "words = [key for key,val in word_dictionary.items()]\n",
    "frequencies = [val for key,val in word_dictionary.items()]\n",
    "\n",
    "# setting the limit to avoid the least frequencies words\n",
    "lower_limit=0   \n",
    "upper_limit=3000    \n",
    "\n",
    "# defining the figure size for better visualization\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "# plotting the line graph\n",
    "plt.plot(np.arange(len(words))[lower_limit:upper_limit],frequencies[lower_limit:upper_limit])\n",
    "plt.title(\"Words v/s Frequency \",size=14)\n",
    "plt.xticks(np.arange(lower_limit, upper_limit, 50), rotation=90,size=14)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph we can visualize that the frequencies of words after the index 3000 is almost close to 0. So, we would pick the top 3000 words for building the vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# picking the top 3000 words for vocabulary\n",
    "words = words[0:3000]\n",
    "frequencies = frequencies[0:3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000\n"
     ]
    }
   ],
   "source": [
    "print(len(words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Building dataset from the given documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Class done\n",
      "2 Class done\n",
      "3 Class done\n",
      "4 Class done\n",
      "5 Class done\n",
      "6 Class done\n",
      "7 Class done\n",
      "8 Class done\n",
      "9 Class done\n",
      "10 Class done\n",
      "11 Class done\n",
      "12 Class done\n",
      "13 Class done\n",
      "14 Class done\n",
      "15 Class done\n",
      "16 Class done\n",
      "17 Class done\n",
      "18 Class done\n",
      "19 Class done\n",
      "20 Class done\n",
      "19997\n"
     ]
    }
   ],
   "source": [
    "# dataset is a array to store the datapoints of all documents\n",
    "dataset = []\n",
    "\n",
    "# getting all the class names in a list\n",
    "all_classes = os.listdir(\"20_newsgroups\")\n",
    "\n",
    "# using count for checking the progress during runtime\n",
    "class_count = 1\n",
    "\n",
    "# traversing through each class\n",
    "for class_ in all_classes:\n",
    "    # storing the all documents in a list for a particular class\n",
    "    all_docs = os.listdir(\"20_newsgroups\"+\"/\"+class_)\n",
    "    \n",
    "    # traversing through each documents of a particular class\n",
    "    for docs in all_docs:\n",
    "        \n",
    "        # initializing all the features of a datapoint with 0\n",
    "        datapoint = [0]*len(words)\n",
    "        \n",
    "        # getting the base path which is common for all documents of a particular class\n",
    "        path = \"20_newsgroups\"+\"/\"+class_+\"/\"+docs\n",
    "        \n",
    "        # opening the particular document as file object   \n",
    "        with open(path, encoding = \"latin-1\") as file_obj:\n",
    "            \n",
    "            # reading the document data\n",
    "            file_data = file_obj.read()\n",
    "            \n",
    "            # stroing each words in a list , used re to remove the whitespaces \n",
    "            tokens = re.compile('\\w+').findall(file_data)\n",
    "            \n",
    "            # traversing through each word in token and checking if it is in vocabulary or not\n",
    "            for word in tokens:\n",
    "                word = word.lower()\n",
    "                \n",
    "                # if the word is in vocabulary then increasing its count by 1 otherwise continue\n",
    "                if word in words:\n",
    "                    datapoint[words.index(word)] = datapoint[words.index(word)] + 1\n",
    "                    \n",
    "        # appending the class name to target column of the datapoint to which class it belongs\n",
    "        datapoint.append(class_)\n",
    "        \n",
    "        # adding the datapoint to the dataset\n",
    "        dataset.append(datapoint)\n",
    "        \n",
    "    # using print to check the progress during the runtime\n",
    "    print(class_count, \"Class done\")\n",
    "    class_count+=1\n",
    "print(len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding a column to words which will be the target for the particular datapoint\n",
    "words.append(\"CLASS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a pandas DataFrame for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news</th>\n",
       "      <th>cantaloupe</th>\n",
       "      <th>message</th>\n",
       "      <th>subject</th>\n",
       "      <th>lines</th>\n",
       "      <th>date</th>\n",
       "      <th>newsgroups</th>\n",
       "      <th>path</th>\n",
       "      <th>organization</th>\n",
       "      <th>state</th>\n",
       "      <th>...</th>\n",
       "      <th>albany</th>\n",
       "      <th>northeastern</th>\n",
       "      <th>vram</th>\n",
       "      <th>cacs</th>\n",
       "      <th>clarku</th>\n",
       "      <th>listening</th>\n",
       "      <th>campaign</th>\n",
       "      <th>wearing</th>\n",
       "      <th>ccwf</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>alt.atheism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   news  cantaloupe  message  subject  lines  date  newsgroups  path  \\\n",
       "0     4           2        1        1      2     1           1     1   \n",
       "1     3           2        2        2      1     1           1     1   \n",
       "2     2           1        1        1      1     2           1     1   \n",
       "3     1           2        1        1      1     1           1     1   \n",
       "4     0           2        1        1      1     1           1     1   \n",
       "\n",
       "   organization  state  ...  albany  northeastern  vram  cacs  clarku  \\\n",
       "0             2      2  ...       0             0     0     0       0   \n",
       "1             2      9  ...       0             0     0     0       0   \n",
       "2             1      0  ...       0             0     0     0       0   \n",
       "3             1      3  ...       0             0     0     0       0   \n",
       "4             1      0  ...       0             0     0     0       0   \n",
       "\n",
       "   listening  campaign  wearing  ccwf        CLASS  \n",
       "0          1         0        0     0  alt.atheism  \n",
       "1          0         2        0     0  alt.atheism  \n",
       "2          0         0        0     0  alt.atheism  \n",
       "3          0         0        0     0  alt.atheism  \n",
       "4          0         0        0     0  alt.atheism  \n",
       "\n",
       "[5 rows x 3001 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting dataset array to the pandas DataFrame\n",
    "data_df = pd.DataFrame(dataset,columns = words, index = None)\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>news</th>\n",
       "      <th>cantaloupe</th>\n",
       "      <th>message</th>\n",
       "      <th>subject</th>\n",
       "      <th>lines</th>\n",
       "      <th>date</th>\n",
       "      <th>newsgroups</th>\n",
       "      <th>path</th>\n",
       "      <th>organization</th>\n",
       "      <th>state</th>\n",
       "      <th>...</th>\n",
       "      <th>albany</th>\n",
       "      <th>northeastern</th>\n",
       "      <th>vram</th>\n",
       "      <th>cacs</th>\n",
       "      <th>clarku</th>\n",
       "      <th>listening</th>\n",
       "      <th>campaign</th>\n",
       "      <th>wearing</th>\n",
       "      <th>ccwf</th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997.000000</td>\n",
       "      <td>19997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>comp.sys.ibm.pc.hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.715707</td>\n",
       "      <td>1.302595</td>\n",
       "      <td>1.097865</td>\n",
       "      <td>1.079612</td>\n",
       "      <td>1.044857</td>\n",
       "      <td>1.039506</td>\n",
       "      <td>1.020353</td>\n",
       "      <td>1.018603</td>\n",
       "      <td>0.992149</td>\n",
       "      <td>0.942241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008501</td>\n",
       "      <td>0.008501</td>\n",
       "      <td>0.008501</td>\n",
       "      <td>0.008501</td>\n",
       "      <td>0.008501</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.480837</td>\n",
       "      <td>0.459393</td>\n",
       "      <td>0.554378</td>\n",
       "      <td>0.779541</td>\n",
       "      <td>0.362166</td>\n",
       "      <td>0.316006</td>\n",
       "      <td>0.230535</td>\n",
       "      <td>0.280838</td>\n",
       "      <td>0.309528</td>\n",
       "      <td>1.302672</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202820</td>\n",
       "      <td>0.170099</td>\n",
       "      <td>0.184485</td>\n",
       "      <td>0.173302</td>\n",
       "      <td>0.200589</td>\n",
       "      <td>0.099903</td>\n",
       "      <td>0.126023</td>\n",
       "      <td>0.124827</td>\n",
       "      <td>0.151272</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                news    cantaloupe       message       subject         lines  \\\n",
       "count   19997.000000  19997.000000  19997.000000  19997.000000  19997.000000   \n",
       "unique           NaN           NaN           NaN           NaN           NaN   \n",
       "top              NaN           NaN           NaN           NaN           NaN   \n",
       "freq             NaN           NaN           NaN           NaN           NaN   \n",
       "mean        1.715707      1.302595      1.097865      1.079612      1.044857   \n",
       "std         1.480837      0.459393      0.554378      0.779541      0.362166   \n",
       "min         0.000000      1.000000      1.000000      1.000000      0.000000   \n",
       "25%         1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "50%         1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "75%         2.000000      2.000000      1.000000      1.000000      1.000000   \n",
       "max        30.000000      2.000000     24.000000     50.000000     15.000000   \n",
       "\n",
       "                date    newsgroups          path  organization         state  \\\n",
       "count   19997.000000  19997.000000  19997.000000  19997.000000  19997.000000   \n",
       "unique           NaN           NaN           NaN           NaN           NaN   \n",
       "top              NaN           NaN           NaN           NaN           NaN   \n",
       "freq             NaN           NaN           NaN           NaN           NaN   \n",
       "mean        1.039506      1.020353      1.018603      0.992149      0.942241   \n",
       "std         0.316006      0.230535      0.280838      0.309528      1.302672   \n",
       "min         1.000000      1.000000      1.000000      0.000000      0.000000   \n",
       "25%         1.000000      1.000000      1.000000      1.000000      0.000000   \n",
       "50%         1.000000      1.000000      1.000000      1.000000      0.000000   \n",
       "75%         1.000000      1.000000      1.000000      1.000000      2.000000   \n",
       "max        14.000000     15.000000     22.000000      8.000000     21.000000   \n",
       "\n",
       "        ...        albany  northeastern          vram          cacs  \\\n",
       "count   ...  19997.000000  19997.000000  19997.000000  19997.000000   \n",
       "unique  ...           NaN           NaN           NaN           NaN   \n",
       "top     ...           NaN           NaN           NaN           NaN   \n",
       "freq    ...           NaN           NaN           NaN           NaN   \n",
       "mean    ...      0.008501      0.008501      0.008501      0.008501   \n",
       "std     ...      0.202820      0.170099      0.184485      0.173302   \n",
       "min     ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%     ...      0.000000      0.000000      0.000000      0.000000   \n",
       "50%     ...      0.000000      0.000000      0.000000      0.000000   \n",
       "75%     ...      0.000000      0.000000      0.000000      0.000000   \n",
       "max     ...      9.000000      9.000000     15.000000      7.000000   \n",
       "\n",
       "              clarku     listening      campaign       wearing          ccwf  \\\n",
       "count   19997.000000  19997.000000  19997.000000  19997.000000  19997.000000   \n",
       "unique           NaN           NaN           NaN           NaN           NaN   \n",
       "top              NaN           NaN           NaN           NaN           NaN   \n",
       "freq             NaN           NaN           NaN           NaN           NaN   \n",
       "mean        0.008501      0.008451      0.008451      0.008451      0.008451   \n",
       "std         0.200589      0.099903      0.126023      0.124827      0.151272   \n",
       "min         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max         9.000000      4.000000      7.000000      8.000000      6.000000   \n",
       "\n",
       "                           CLASS  \n",
       "count                      19997  \n",
       "unique                        20  \n",
       "top     comp.sys.ibm.pc.hardware  \n",
       "freq                        1000  \n",
       "mean                         NaN  \n",
       "std                          NaN  \n",
       "min                          NaN  \n",
       "25%                          NaN  \n",
       "50%                          NaN  \n",
       "75%                          NaN  \n",
       "max                          NaN  \n",
       "\n",
       "[11 rows x 3001 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the Dataset to a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.to_csv(\"text_classification_dataset.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y is the target class\n",
    "Y = data_df[\"CLASS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X is features\n",
    "X = data_df[data_df.columns[:-1]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Sklearn multinomialNB Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into train - test, using random state to be 0 \n",
    "X_train,X_test,Y_train,Y_test = model_selection.train_test_split(X,Y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          precision    recall  f1-score   support\n",
      "\n",
      "             alt.atheism       0.82      0.82      0.82       233\n",
      "           comp.graphics       0.83      0.78      0.81       253\n",
      " comp.os.ms-windows.misc       0.77      0.88      0.82       249\n",
      "comp.sys.ibm.pc.hardware       0.65      0.78      0.71       240\n",
      "   comp.sys.mac.hardware       0.75      0.76      0.75       236\n",
      "          comp.windows.x       0.91      0.73      0.81       240\n",
      "            misc.forsale       0.85      0.92      0.88       261\n",
      "               rec.autos       0.88      0.90      0.89       269\n",
      "         rec.motorcycles       0.91      0.95      0.93       284\n",
      "      rec.sport.baseball       0.97      0.93      0.95       248\n",
      "        rec.sport.hockey       0.92      0.99      0.96       231\n",
      "               sci.crypt       0.98      0.95      0.97       233\n",
      "         sci.electronics       0.88      0.85      0.86       244\n",
      "                 sci.med       0.95      0.78      0.86       256\n",
      "               sci.space       0.94      0.94      0.94       246\n",
      "  soc.religion.christian       0.96      1.00      0.98       252\n",
      "      talk.politics.guns       0.83      0.92      0.87       249\n",
      "   talk.politics.mideast       0.94      0.91      0.92       281\n",
      "      talk.politics.misc       0.78      0.73      0.75       259\n",
      "      talk.religion.misc       0.71      0.65      0.68       236\n",
      "\n",
      "                accuracy                           0.86      5000\n",
      "               macro avg       0.86      0.86      0.86      5000\n",
      "            weighted avg       0.86      0.86      0.86      5000\n",
      "\n",
      "[[190   0   0   0   0   0   0   2   1   0   0   0   1   0   0   3   0   0\n",
      "    1  35]\n",
      " [  0 198  21  16   4   5   1   1   0   1   0   0   3   2   1   0   0   0\n",
      "    0   0]\n",
      " [  0   4 220  14   1   8   1   0   0   0   0   0   1   0   0   0   0   0\n",
      "    0   0]\n",
      " [  0   3   7 186  31   1   7   0   1   0   1   0   3   0   0   0   0   0\n",
      "    0   0]\n",
      " [  0   1   4  42 179   0   7   0   0   0   0   0   3   0   0   0   0   0\n",
      "    0   0]\n",
      " [  0  21  31   4   4 175   1   0   0   0   0   1   0   1   2   0   0   0\n",
      "    0   0]\n",
      " [  0   0   0   5   2   0 240   5   2   0   1   1   3   0   1   0   1   0\n",
      "    0   0]\n",
      " [  0   0   0   1   2   0   8 243   3   0   1   0   7   0   3   1   0   0\n",
      "    0   0]\n",
      " [  0   1   0   0   1   0   2   7 270   0   1   0   1   0   0   0   0   0\n",
      "    1   0]\n",
      " [  0   0   1   1   0   0   4   1   1 231   8   0   0   0   1   0   0   0\n",
      "    0   0]\n",
      " [  0   0   0   0   0   0   0   0   1   1 229   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [  0   3   0   1   0   4   0   0   0   0   0 222   0   1   0   0   0   0\n",
      "    2   0]\n",
      " [  0   1   1  11   6   0   4  10   1   0   2   0 207   1   0   0   0   0\n",
      "    0   0]\n",
      " [  0   5   1   2   9   0   2   6  13   5   1   0   7 199   4   0   0   1\n",
      "    0   1]\n",
      " [  0   1   1   0   1   0   1   1   2   0   1   1   0   2 232   0   0   0\n",
      "    3   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 252   0   0\n",
      "    0   0]\n",
      " [  0   0   0   1   0   0   0   0   1   0   1   1   0   0   0   0 228   0\n",
      "   11   6]\n",
      " [  1   0   0   0   0   0   3   0   1   0   1   0   0   0   0   0   6 255\n",
      "   13   1]\n",
      " [  0   0   0   1   0   0   0   0   0   1   0   1   0   2   2   0  30  14\n",
      "  188  20]\n",
      " [ 40   0   0   0   0   0   1   0   0   0   1   0   0   1   0   7  10   2\n",
      "   21 153]]\n"
     ]
    }
   ],
   "source": [
    "# creating a classifier objecy\n",
    "clf = MultinomialNB()\n",
    "\n",
    "# fitting the classifier object to training data\n",
    "clf.fit(X_train, Y_train)\n",
    "\n",
    "# testing the classifier and storing the predicted Y_pred classes\n",
    "Y_pred = clf.predict(X_test)\n",
    "\n",
    "# Generating the classification report and printing the confusion matrix\n",
    "print(classification_report(Y_test,Y_pred))\n",
    "print(confusion_matrix(Y_test,Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the classification report to comparing it with own multinomial Naive Bayes classifier\n",
    "report_list_sklearn = classification_report(Y_test,Y_pred).split(\"\\n\")\n",
    "for i in report_list_sklearn:\n",
    "    if i==\"\":\n",
    "        report_list_sklearn.remove(i)\n",
    "sklearn_classifier_score = []\n",
    "class_names = []\n",
    "for i in report_list_sklearn[1:-3]:\n",
    "    sklearn_classifier_score.append(float(i.split()[3]))\n",
    "    class_names.append(i.split()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing my own Multinomial Naive Bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting training data\n",
    "def fit(X_train,Y_train):\n",
    "    \"\"\"\n",
    "    FUNCTION :\n",
    "            fit : \n",
    "                Arguments : Training Data (X_train and Y_train)\n",
    "        returns   : \n",
    "                A DICTIONARY :\n",
    "                    Keys   : class_names and total count of all features\n",
    "                    Values : DICTIONARY\n",
    "                         Keys   : Words and total count of word of current_class\n",
    "                         Values : number of times word appears in a sample of current_class \n",
    "                                    \n",
    "    \"\"\"\n",
    "    # initializing the result dictionary\n",
    "    result = {}\n",
    "    \n",
    "    # initializing the total count of words to 0\n",
    "    result[\"total_data\"] = 0\n",
    "    \n",
    "    # storing the names of all classes by using set()\n",
    "    class_values = set(Y_train)\n",
    "    \n",
    "    # traversing through each class as current_class\n",
    "    for current_class in class_values:\n",
    "        \n",
    "        # initializing the current_class dictionary\n",
    "        result[current_class] = {}\n",
    "        \n",
    "        # getting the data points which belongs to current class by using a boolean array\n",
    "        current_class_rows = (Y_train == current_class)\n",
    "        \n",
    "        # storing the features of current_class data points\n",
    "        X_train_current = X_train[current_class_rows]\n",
    "        \n",
    "        # storing the target class of data points of current_class\n",
    "        Y_train_current = Y_train[current_class_rows]\n",
    "        \n",
    "        # finding the total number of features\n",
    "        num_features = X_train.shape[1]\n",
    "        \n",
    "        # initializing the total count for the current_class to 0\n",
    "        result[current_class][\"total_count\"] = 0\n",
    "        \n",
    "        # traversing through all features\n",
    "        for j in range(num_features):\n",
    "            \n",
    "            # finding the number of time the jth word appeared in current_class features\n",
    "            result[current_class][j] = X_train_current[:,j].sum()\n",
    "            \n",
    "            # updating the total_count value of that word\n",
    "            result[current_class][\"total_count\"] += result[current_class][j]\n",
    "        # updating the total number of words in all documents\n",
    "        result[\"total_data\"] += result[current_class][\"total_count\"]\n",
    "        \n",
    "        # using print to check the progress during runtime\n",
    "        print(\"#\",end=\"\")\n",
    "        \n",
    "    # returning the required dictionary\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding probablity for a specific class\n",
    "def probability(dictionary, x, current_class):\n",
    "    \"\"\" FUNCTION\n",
    "            probability :\n",
    "                Arguments : dictionary (returned from fit function), a data point from X_test, and the class to find the \n",
    "                            probablity\n",
    "                Returns   : probablity of the particular current_class\n",
    "    \"\"\"\n",
    "    # calculating the log probablities to avoid the final probablities to approaching zero\n",
    "    # using the multinomial Naive Bayes formula to calucate the probality for a particular class\n",
    "    output = np.log(dictionary[current_class][\"total_count\"]) - np.log(dictionary[\"total_data\"])\n",
    "    \n",
    "    # finding the total number of features\n",
    "    num_features = len(x)\n",
    "    \n",
    "    # traversing through each feature\n",
    "    for j in range(num_features):\n",
    "        xj = x[j]\n",
    "        # ignoring the features whose value is zero t0 avoid unnecessary calcualtions\n",
    "        if x[j]==0:\n",
    "            continue\n",
    "        # using the multinomial Naive Bayes formula to calucate the probality\n",
    "        # also implementing the Laplace Correction\n",
    "        current_class_with_xj = dictionary[current_class][j] + 1\n",
    "        current_class_total_count = dictionary[current_class][\"total_count\"] + num_features\n",
    "        \n",
    "        # finding the log probablities\n",
    "        current_xj_prob = np.log(current_class_with_xj) - np.log(current_class_total_count)\n",
    "        # adding to the intial output value to get the final value\n",
    "        output += current_xj_prob\n",
    "    \n",
    "    # returning the probablity\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction for a single data point\n",
    "def predictSinglePoint(dictionary,x):\n",
    "    \"\"\" FUNCTION\n",
    "            predictSinglePoint :\n",
    "                Arguments : dictionary (returned from fit function), a data point from X_test\n",
    "                Returns   : the predicted classes (Y_pred) for particular data point\n",
    "    \"\"\"\n",
    "    # storing the class names from the dictionary keys\n",
    "    classes = dictionary.keys()\n",
    "    \n",
    "    # assuming the initial probablity to be a big negative number\n",
    "    best_p = -1000\n",
    "    \n",
    "    # initialising the variable for best predicted class\n",
    "    best_class = -1\n",
    "    \n",
    "    # using a variable so that we can find the class for the first time run in order to get the best class\n",
    "    first_run = True\n",
    "    \n",
    "    # traversing through each classes\n",
    "    for current_class in classes:\n",
    "        \n",
    "        # as one of the keys in dictionary is the total_count to checking if it is then continue\n",
    "        if (current_class == \"total_data\"):\n",
    "            continue\n",
    "            \n",
    "        # calling the probablity function to get the probality for the current_class\n",
    "        p_current_class = probability(dictionary, x, current_class)\n",
    "        \n",
    "        # checking if the probablity is best from the previous one, if yes then update it otherwise continue\n",
    "        if (first_run or p_current_class > best_p):\n",
    "            \n",
    "            # updating best_class and probablity values\n",
    "            best_p = p_current_class\n",
    "            best_class = current_class\n",
    "        # changing the value of First run to False\n",
    "        first_run = False\n",
    "        \n",
    "    # returning the best predicted class\n",
    "    return best_class       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the classes\n",
    "def predict(dictionary, X_test):\n",
    "    \"\"\" FUNCTION\n",
    "            predict :\n",
    "                Arguments : dictionary (returned from fit function), testing data (X_test)\n",
    "                Returns   : the predicted classes (Y_pred)\n",
    "    \"\"\"\n",
    "    # initializing the array to store the predicted classes\n",
    "    Y_pred = []\n",
    "    \n",
    "    # using count to check the progress during runtime\n",
    "    count=1\n",
    "    \n",
    "    # traversing through each data point in testing data\n",
    "    for x in X_test:\n",
    "        \n",
    "        # calling the predictSinglePoint function to get the prediction for a sigle data point\n",
    "        x_class = predictSinglePoint(dictionary,x)\n",
    "        \n",
    "        # adding the predicted class to the Y_pred array\n",
    "        Y_pred.append(x_class)\n",
    "        \n",
    "        # calulations for checking the progress during runtime\n",
    "        per = (count/len(X_test))*100\n",
    "        count+=1\n",
    "        print(\"Progress \",\"%.2f\" % per,\"%\")\n",
    "    \n",
    "    # returning the predicted classes\n",
    "    return Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y_ target data\n",
    "Y_ = data_df[\"CLASS\"]\n",
    "\n",
    "# X_ features\n",
    "X_ = data_df[data_df.columns[:-1]].values\n",
    "\n",
    "# splitting the data into train-test \n",
    "X_train_,X_test_,Y_train_,Y_test_ = model_selection.train_test_split(X,Y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting own classifier with training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####################"
     ]
    }
   ],
   "source": [
    "dictionary = fit(X_train_,Y_train_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  0.02 %\n",
      "Progress  0.04 %\n",
      "Progress  0.06 %\n",
      "Progress  0.08 %\n",
      "Progress  0.10 %\n",
      "Progress  0.12 %\n",
      "Progress  0.14 %\n",
      "Progress  0.16 %\n",
      "Progress  0.18 %\n",
      "Progress  0.20 %\n",
      "Progress  0.22 %\n",
      "Progress  0.24 %\n",
      "Progress  0.26 %\n",
      "Progress  0.28 %\n",
      "Progress  0.30 %\n",
      "Progress  0.32 %\n",
      "Progress  0.34 %\n",
      "Progress  0.36 %\n",
      "Progress  0.38 %\n",
      "Progress  0.40 %\n",
      "Progress  0.42 %\n",
      "Progress  0.44 %\n",
      "Progress  0.46 %\n",
      "Progress  0.48 %\n",
      "Progress  0.50 %\n",
      "Progress  0.52 %\n",
      "Progress  0.54 %\n",
      "Progress  0.56 %\n",
      "Progress  0.58 %\n",
      "Progress  0.60 %\n",
      "Progress  0.62 %\n",
      "Progress  0.64 %\n",
      "Progress  0.66 %\n",
      "Progress  0.68 %\n",
      "Progress  0.70 %\n",
      "Progress  0.72 %\n",
      "Progress  0.74 %\n",
      "Progress  0.76 %\n",
      "Progress  0.78 %\n",
      "Progress  0.80 %\n",
      "Progress  0.82 %\n",
      "Progress  0.84 %\n",
      "Progress  0.86 %\n",
      "Progress  0.88 %\n",
      "Progress  0.90 %\n",
      "Progress  0.92 %\n",
      "Progress  0.94 %\n",
      "Progress  0.96 %\n",
      "Progress  0.98 %\n",
      "Progress  1.00 %\n",
      "Progress  1.02 %\n",
      "Progress  1.04 %\n",
      "Progress  1.06 %\n",
      "Progress  1.08 %\n",
      "Progress  1.10 %\n",
      "Progress  1.12 %\n",
      "Progress  1.14 %\n",
      "Progress  1.16 %\n",
      "Progress  1.18 %\n",
      "Progress  1.20 %\n",
      "Progress  1.22 %\n",
      "Progress  1.24 %\n",
      "Progress  1.26 %\n",
      "Progress  1.28 %\n",
      "Progress  1.30 %\n",
      "Progress  1.32 %\n",
      "Progress  1.34 %\n",
      "Progress  1.36 %\n",
      "Progress  1.38 %\n",
      "Progress  1.40 %\n",
      "Progress  1.42 %\n",
      "Progress  1.44 %\n",
      "Progress  1.46 %\n",
      "Progress  1.48 %\n",
      "Progress  1.50 %\n",
      "Progress  1.52 %\n",
      "Progress  1.54 %\n",
      "Progress  1.56 %\n",
      "Progress  1.58 %\n",
      "Progress  1.60 %\n",
      "Progress  1.62 %\n",
      "Progress  1.64 %\n",
      "Progress  1.66 %\n",
      "Progress  1.68 %\n",
      "Progress  1.70 %\n",
      "Progress  1.72 %\n",
      "Progress  1.74 %\n",
      "Progress  1.76 %\n",
      "Progress  1.78 %\n",
      "Progress  1.80 %\n",
      "Progress  1.82 %\n",
      "Progress  1.84 %\n",
      "Progress  1.86 %\n",
      "Progress  1.88 %\n",
      "Progress  1.90 %\n",
      "Progress  1.92 %\n",
      "Progress  1.94 %\n",
      "Progress  1.96 %\n",
      "Progress  1.98 %\n",
      "Progress  2.00 %\n",
      "Progress  2.02 %\n",
      "Progress  2.04 %\n",
      "Progress  2.06 %\n",
      "Progress  2.08 %\n",
      "Progress  2.10 %\n",
      "Progress  2.12 %\n",
      "Progress  2.14 %\n",
      "Progress  2.16 %\n",
      "Progress  2.18 %\n",
      "Progress  2.20 %\n",
      "Progress  2.22 %\n",
      "Progress  2.24 %\n",
      "Progress  2.26 %\n",
      "Progress  2.28 %\n",
      "Progress  2.30 %\n",
      "Progress  2.32 %\n",
      "Progress  2.34 %\n",
      "Progress  2.36 %\n",
      "Progress  2.38 %\n",
      "Progress  2.40 %\n",
      "Progress  2.42 %\n",
      "Progress  2.44 %\n",
      "Progress  2.46 %\n",
      "Progress  2.48 %\n",
      "Progress  2.50 %\n",
      "Progress  2.52 %\n",
      "Progress  2.54 %\n",
      "Progress  2.56 %\n",
      "Progress  2.58 %\n",
      "Progress  2.60 %\n",
      "Progress  2.62 %\n",
      "Progress  2.64 %\n",
      "Progress  2.66 %\n",
      "Progress  2.68 %\n",
      "Progress  2.70 %\n",
      "Progress  2.72 %\n",
      "Progress  2.74 %\n",
      "Progress  2.76 %\n",
      "Progress  2.78 %\n",
      "Progress  2.80 %\n",
      "Progress  2.82 %\n",
      "Progress  2.84 %\n",
      "Progress  2.86 %\n",
      "Progress  2.88 %\n",
      "Progress  2.90 %\n",
      "Progress  2.92 %\n",
      "Progress  2.94 %\n",
      "Progress  2.96 %\n",
      "Progress  2.98 %\n",
      "Progress  3.00 %\n",
      "Progress  3.02 %\n",
      "Progress  3.04 %\n",
      "Progress  3.06 %\n",
      "Progress  3.08 %\n",
      "Progress  3.10 %\n",
      "Progress  3.12 %\n",
      "Progress  3.14 %\n",
      "Progress  3.16 %\n",
      "Progress  3.18 %\n",
      "Progress  3.20 %\n",
      "Progress  3.22 %\n",
      "Progress  3.24 %\n",
      "Progress  3.26 %\n",
      "Progress  3.28 %\n",
      "Progress  3.30 %\n",
      "Progress  3.32 %\n",
      "Progress  3.34 %\n",
      "Progress  3.36 %\n",
      "Progress  3.38 %\n",
      "Progress  3.40 %\n",
      "Progress  3.42 %\n",
      "Progress  3.44 %\n",
      "Progress  3.46 %\n",
      "Progress  3.48 %\n",
      "Progress  3.50 %\n",
      "Progress  3.52 %\n",
      "Progress  3.54 %\n",
      "Progress  3.56 %\n",
      "Progress  3.58 %\n",
      "Progress  3.60 %\n",
      "Progress  3.62 %\n",
      "Progress  3.64 %\n",
      "Progress  3.66 %\n",
      "Progress  3.68 %\n",
      "Progress  3.70 %\n",
      "Progress  3.72 %\n",
      "Progress  3.74 %\n",
      "Progress  3.76 %\n",
      "Progress  3.78 %\n",
      "Progress  3.80 %\n",
      "Progress  3.82 %\n",
      "Progress  3.84 %\n",
      "Progress  3.86 %\n",
      "Progress  3.88 %\n",
      "Progress  3.90 %\n",
      "Progress  3.92 %\n",
      "Progress  3.94 %\n",
      "Progress  3.96 %\n",
      "Progress  3.98 %\n",
      "Progress  4.00 %\n",
      "Progress  4.02 %\n",
      "Progress  4.04 %\n",
      "Progress  4.06 %\n",
      "Progress  4.08 %\n",
      "Progress  4.10 %\n",
      "Progress  4.12 %\n",
      "Progress  4.14 %\n",
      "Progress  4.16 %\n",
      "Progress  4.18 %\n",
      "Progress  4.20 %\n",
      "Progress  4.22 %\n",
      "Progress  4.24 %\n",
      "Progress  4.26 %\n",
      "Progress  4.28 %\n",
      "Progress  4.30 %\n",
      "Progress  4.32 %\n",
      "Progress  4.34 %\n",
      "Progress  4.36 %\n",
      "Progress  4.38 %\n",
      "Progress  4.40 %\n",
      "Progress  4.42 %\n",
      "Progress  4.44 %\n",
      "Progress  4.46 %\n",
      "Progress  4.48 %\n",
      "Progress  4.50 %\n",
      "Progress  4.52 %\n",
      "Progress  4.54 %\n",
      "Progress  4.56 %\n",
      "Progress  4.58 %\n",
      "Progress  4.60 %\n",
      "Progress  4.62 %\n",
      "Progress  4.64 %\n",
      "Progress  4.66 %\n",
      "Progress  4.68 %\n",
      "Progress  4.70 %\n",
      "Progress  4.72 %\n",
      "Progress  4.74 %\n",
      "Progress  4.76 %\n",
      "Progress  4.78 %\n",
      "Progress  4.80 %\n",
      "Progress  4.82 %\n",
      "Progress  4.84 %\n",
      "Progress  4.86 %\n",
      "Progress  4.88 %\n",
      "Progress  4.90 %\n",
      "Progress  4.92 %\n",
      "Progress  4.94 %\n",
      "Progress  4.96 %\n",
      "Progress  4.98 %\n",
      "Progress  5.00 %\n",
      "Progress  5.02 %\n",
      "Progress  5.04 %\n",
      "Progress  5.06 %\n",
      "Progress  5.08 %\n",
      "Progress  5.10 %\n",
      "Progress  5.12 %\n",
      "Progress  5.14 %\n",
      "Progress  5.16 %\n",
      "Progress  5.18 %\n",
      "Progress  5.20 %\n",
      "Progress  5.22 %\n",
      "Progress  5.24 %\n",
      "Progress  5.26 %\n",
      "Progress  5.28 %\n",
      "Progress  5.30 %\n",
      "Progress  5.32 %\n",
      "Progress  5.34 %\n",
      "Progress  5.36 %\n",
      "Progress  5.38 %\n",
      "Progress  5.40 %\n",
      "Progress  5.42 %\n",
      "Progress  5.44 %\n",
      "Progress  5.46 %\n",
      "Progress  5.48 %\n",
      "Progress  5.50 %\n",
      "Progress  5.52 %\n",
      "Progress  5.54 %\n",
      "Progress  5.56 %\n",
      "Progress  5.58 %\n",
      "Progress  5.60 %\n",
      "Progress  5.62 %\n",
      "Progress  5.64 %\n",
      "Progress  5.66 %\n",
      "Progress  5.68 %\n",
      "Progress  5.70 %\n",
      "Progress  5.72 %\n",
      "Progress  5.74 %\n",
      "Progress  5.76 %\n",
      "Progress  5.78 %\n",
      "Progress  5.80 %\n",
      "Progress  5.82 %\n",
      "Progress  5.84 %\n",
      "Progress  5.86 %\n",
      "Progress  5.88 %\n",
      "Progress  5.90 %\n",
      "Progress  5.92 %\n",
      "Progress  5.94 %\n",
      "Progress  5.96 %\n",
      "Progress  5.98 %\n",
      "Progress  6.00 %\n",
      "Progress  6.02 %\n",
      "Progress  6.04 %\n",
      "Progress  6.06 %\n",
      "Progress  6.08 %\n",
      "Progress  6.10 %\n",
      "Progress  6.12 %\n",
      "Progress  6.14 %\n",
      "Progress  6.16 %\n",
      "Progress  6.18 %\n",
      "Progress  6.20 %\n",
      "Progress  6.22 %\n",
      "Progress  6.24 %\n",
      "Progress  6.26 %\n",
      "Progress  6.28 %\n",
      "Progress  6.30 %\n",
      "Progress  6.32 %\n",
      "Progress  6.34 %\n",
      "Progress  6.36 %\n",
      "Progress  6.38 %\n",
      "Progress  6.40 %\n",
      "Progress  6.42 %\n",
      "Progress  6.44 %\n",
      "Progress  6.46 %\n",
      "Progress  6.48 %\n",
      "Progress  6.50 %\n",
      "Progress  6.52 %\n",
      "Progress  6.54 %\n",
      "Progress  6.56 %\n",
      "Progress  6.58 %\n",
      "Progress  6.60 %\n",
      "Progress  6.62 %\n",
      "Progress  6.64 %\n",
      "Progress  6.66 %\n",
      "Progress  6.68 %\n",
      "Progress  6.70 %\n",
      "Progress  6.72 %\n",
      "Progress  6.74 %\n",
      "Progress  6.76 %\n",
      "Progress  6.78 %\n",
      "Progress  6.80 %\n",
      "Progress  6.82 %\n",
      "Progress  6.84 %\n",
      "Progress  6.86 %\n",
      "Progress  6.88 %\n",
      "Progress  6.90 %\n",
      "Progress  6.92 %\n",
      "Progress  6.94 %\n",
      "Progress  6.96 %\n",
      "Progress  6.98 %\n",
      "Progress  7.00 %\n",
      "Progress  7.02 %\n",
      "Progress  7.04 %\n",
      "Progress  7.06 %\n",
      "Progress  7.08 %\n",
      "Progress  7.10 %\n",
      "Progress  7.12 %\n",
      "Progress  7.14 %\n",
      "Progress  7.16 %\n",
      "Progress  7.18 %\n",
      "Progress  7.20 %\n",
      "Progress  7.22 %\n",
      "Progress  7.24 %\n",
      "Progress  7.26 %\n",
      "Progress  7.28 %\n",
      "Progress  7.30 %\n",
      "Progress  7.32 %\n",
      "Progress  7.34 %\n",
      "Progress  7.36 %\n",
      "Progress  7.38 %\n",
      "Progress  7.40 %\n",
      "Progress  7.42 %\n",
      "Progress  7.44 %\n",
      "Progress  7.46 %\n",
      "Progress  7.48 %\n",
      "Progress  7.50 %\n",
      "Progress  7.52 %\n",
      "Progress  7.54 %\n",
      "Progress  7.56 %\n",
      "Progress  7.58 %\n",
      "Progress  7.60 %\n",
      "Progress  7.62 %\n",
      "Progress  7.64 %\n",
      "Progress  7.66 %\n",
      "Progress  7.68 %\n",
      "Progress  7.70 %\n",
      "Progress  7.72 %\n",
      "Progress  7.74 %\n",
      "Progress  7.76 %\n",
      "Progress  7.78 %\n",
      "Progress  7.80 %\n",
      "Progress  7.82 %\n",
      "Progress  7.84 %\n",
      "Progress  7.86 %\n",
      "Progress  7.88 %\n",
      "Progress  7.90 %\n",
      "Progress  7.92 %\n",
      "Progress  7.94 %\n",
      "Progress  7.96 %\n",
      "Progress  7.98 %\n",
      "Progress  8.00 %\n",
      "Progress  8.02 %\n",
      "Progress  8.04 %\n",
      "Progress  8.06 %\n",
      "Progress  8.08 %\n",
      "Progress  8.10 %\n",
      "Progress  8.12 %\n",
      "Progress  8.14 %\n",
      "Progress  8.16 %\n",
      "Progress  8.18 %\n",
      "Progress  8.20 %\n",
      "Progress  8.22 %\n",
      "Progress  8.24 %\n",
      "Progress  8.26 %\n",
      "Progress  8.28 %\n",
      "Progress  8.30 %\n",
      "Progress  8.32 %\n",
      "Progress  8.34 %\n",
      "Progress  8.36 %\n",
      "Progress  8.38 %\n",
      "Progress  8.40 %\n",
      "Progress  8.42 %\n",
      "Progress  8.44 %\n",
      "Progress  8.46 %\n",
      "Progress  8.48 %\n",
      "Progress  8.50 %\n",
      "Progress  8.52 %\n",
      "Progress  8.54 %\n",
      "Progress  8.56 %\n",
      "Progress  8.58 %\n",
      "Progress  8.60 %\n",
      "Progress  8.62 %\n",
      "Progress  8.64 %\n",
      "Progress  8.66 %\n",
      "Progress  8.68 %\n",
      "Progress  8.70 %\n",
      "Progress  8.72 %\n",
      "Progress  8.74 %\n",
      "Progress  8.76 %\n",
      "Progress  8.78 %\n",
      "Progress  8.80 %\n",
      "Progress  8.82 %\n",
      "Progress  8.84 %\n",
      "Progress  8.86 %\n",
      "Progress  8.88 %\n",
      "Progress  8.90 %\n",
      "Progress  8.92 %\n",
      "Progress  8.94 %\n",
      "Progress  8.96 %\n",
      "Progress  8.98 %\n",
      "Progress  9.00 %\n",
      "Progress  9.02 %\n",
      "Progress  9.04 %\n",
      "Progress  9.06 %\n",
      "Progress  9.08 %\n",
      "Progress  9.10 %\n",
      "Progress  9.12 %\n",
      "Progress  9.14 %\n",
      "Progress  9.16 %\n",
      "Progress  9.18 %\n",
      "Progress  9.20 %\n",
      "Progress  9.22 %\n",
      "Progress  9.24 %\n",
      "Progress  9.26 %\n",
      "Progress  9.28 %\n",
      "Progress  9.30 %\n",
      "Progress  9.32 %\n",
      "Progress  9.34 %\n",
      "Progress  9.36 %\n",
      "Progress  9.38 %\n",
      "Progress  9.40 %\n",
      "Progress  9.42 %\n",
      "Progress  9.44 %\n",
      "Progress  9.46 %\n",
      "Progress  9.48 %\n",
      "Progress  9.50 %\n",
      "Progress  9.52 %\n",
      "Progress  9.54 %\n",
      "Progress  9.56 %\n",
      "Progress  9.58 %\n",
      "Progress  9.60 %\n",
      "Progress  9.62 %\n",
      "Progress  9.64 %\n",
      "Progress  9.66 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  9.68 %\n",
      "Progress  9.70 %\n",
      "Progress  9.72 %\n",
      "Progress  9.74 %\n",
      "Progress  9.76 %\n",
      "Progress  9.78 %\n",
      "Progress  9.80 %\n",
      "Progress  9.82 %\n",
      "Progress  9.84 %\n",
      "Progress  9.86 %\n",
      "Progress  9.88 %\n",
      "Progress  9.90 %\n",
      "Progress  9.92 %\n",
      "Progress  9.94 %\n",
      "Progress  9.96 %\n",
      "Progress  9.98 %\n",
      "Progress  10.00 %\n",
      "Progress  10.02 %\n",
      "Progress  10.04 %\n",
      "Progress  10.06 %\n",
      "Progress  10.08 %\n",
      "Progress  10.10 %\n",
      "Progress  10.12 %\n",
      "Progress  10.14 %\n",
      "Progress  10.16 %\n",
      "Progress  10.18 %\n",
      "Progress  10.20 %\n",
      "Progress  10.22 %\n",
      "Progress  10.24 %\n",
      "Progress  10.26 %\n",
      "Progress  10.28 %\n",
      "Progress  10.30 %\n",
      "Progress  10.32 %\n",
      "Progress  10.34 %\n",
      "Progress  10.36 %\n",
      "Progress  10.38 %\n",
      "Progress  10.40 %\n",
      "Progress  10.42 %\n",
      "Progress  10.44 %\n",
      "Progress  10.46 %\n",
      "Progress  10.48 %\n",
      "Progress  10.50 %\n",
      "Progress  10.52 %\n",
      "Progress  10.54 %\n",
      "Progress  10.56 %\n",
      "Progress  10.58 %\n",
      "Progress  10.60 %\n",
      "Progress  10.62 %\n",
      "Progress  10.64 %\n",
      "Progress  10.66 %\n",
      "Progress  10.68 %\n",
      "Progress  10.70 %\n",
      "Progress  10.72 %\n",
      "Progress  10.74 %\n",
      "Progress  10.76 %\n",
      "Progress  10.78 %\n",
      "Progress  10.80 %\n",
      "Progress  10.82 %\n",
      "Progress  10.84 %\n",
      "Progress  10.86 %\n",
      "Progress  10.88 %\n",
      "Progress  10.90 %\n",
      "Progress  10.92 %\n",
      "Progress  10.94 %\n",
      "Progress  10.96 %\n",
      "Progress  10.98 %\n",
      "Progress  11.00 %\n",
      "Progress  11.02 %\n",
      "Progress  11.04 %\n",
      "Progress  11.06 %\n",
      "Progress  11.08 %\n",
      "Progress  11.10 %\n",
      "Progress  11.12 %\n",
      "Progress  11.14 %\n",
      "Progress  11.16 %\n",
      "Progress  11.18 %\n",
      "Progress  11.20 %\n",
      "Progress  11.22 %\n",
      "Progress  11.24 %\n",
      "Progress  11.26 %\n",
      "Progress  11.28 %\n",
      "Progress  11.30 %\n",
      "Progress  11.32 %\n",
      "Progress  11.34 %\n",
      "Progress  11.36 %\n",
      "Progress  11.38 %\n",
      "Progress  11.40 %\n",
      "Progress  11.42 %\n",
      "Progress  11.44 %\n",
      "Progress  11.46 %\n",
      "Progress  11.48 %\n",
      "Progress  11.50 %\n",
      "Progress  11.52 %\n",
      "Progress  11.54 %\n",
      "Progress  11.56 %\n",
      "Progress  11.58 %\n",
      "Progress  11.60 %\n",
      "Progress  11.62 %\n",
      "Progress  11.64 %\n",
      "Progress  11.66 %\n",
      "Progress  11.68 %\n",
      "Progress  11.70 %\n",
      "Progress  11.72 %\n",
      "Progress  11.74 %\n",
      "Progress  11.76 %\n",
      "Progress  11.78 %\n",
      "Progress  11.80 %\n",
      "Progress  11.82 %\n",
      "Progress  11.84 %\n",
      "Progress  11.86 %\n",
      "Progress  11.88 %\n",
      "Progress  11.90 %\n",
      "Progress  11.92 %\n",
      "Progress  11.94 %\n",
      "Progress  11.96 %\n",
      "Progress  11.98 %\n",
      "Progress  12.00 %\n",
      "Progress  12.02 %\n",
      "Progress  12.04 %\n",
      "Progress  12.06 %\n",
      "Progress  12.08 %\n",
      "Progress  12.10 %\n",
      "Progress  12.12 %\n",
      "Progress  12.14 %\n",
      "Progress  12.16 %\n",
      "Progress  12.18 %\n",
      "Progress  12.20 %\n",
      "Progress  12.22 %\n",
      "Progress  12.24 %\n",
      "Progress  12.26 %\n",
      "Progress  12.28 %\n",
      "Progress  12.30 %\n",
      "Progress  12.32 %\n",
      "Progress  12.34 %\n",
      "Progress  12.36 %\n",
      "Progress  12.38 %\n",
      "Progress  12.40 %\n",
      "Progress  12.42 %\n",
      "Progress  12.44 %\n",
      "Progress  12.46 %\n",
      "Progress  12.48 %\n",
      "Progress  12.50 %\n",
      "Progress  12.52 %\n",
      "Progress  12.54 %\n",
      "Progress  12.56 %\n",
      "Progress  12.58 %\n",
      "Progress  12.60 %\n",
      "Progress  12.62 %\n",
      "Progress  12.64 %\n",
      "Progress  12.66 %\n",
      "Progress  12.68 %\n",
      "Progress  12.70 %\n",
      "Progress  12.72 %\n",
      "Progress  12.74 %\n",
      "Progress  12.76 %\n",
      "Progress  12.78 %\n",
      "Progress  12.80 %\n",
      "Progress  12.82 %\n",
      "Progress  12.84 %\n",
      "Progress  12.86 %\n",
      "Progress  12.88 %\n",
      "Progress  12.90 %\n",
      "Progress  12.92 %\n",
      "Progress  12.94 %\n",
      "Progress  12.96 %\n",
      "Progress  12.98 %\n",
      "Progress  13.00 %\n",
      "Progress  13.02 %\n",
      "Progress  13.04 %\n",
      "Progress  13.06 %\n",
      "Progress  13.08 %\n",
      "Progress  13.10 %\n",
      "Progress  13.12 %\n",
      "Progress  13.14 %\n",
      "Progress  13.16 %\n",
      "Progress  13.18 %\n",
      "Progress  13.20 %\n",
      "Progress  13.22 %\n",
      "Progress  13.24 %\n",
      "Progress  13.26 %\n",
      "Progress  13.28 %\n",
      "Progress  13.30 %\n",
      "Progress  13.32 %\n",
      "Progress  13.34 %\n",
      "Progress  13.36 %\n",
      "Progress  13.38 %\n",
      "Progress  13.40 %\n",
      "Progress  13.42 %\n",
      "Progress  13.44 %\n",
      "Progress  13.46 %\n",
      "Progress  13.48 %\n",
      "Progress  13.50 %\n",
      "Progress  13.52 %\n",
      "Progress  13.54 %\n",
      "Progress  13.56 %\n",
      "Progress  13.58 %\n",
      "Progress  13.60 %\n",
      "Progress  13.62 %\n",
      "Progress  13.64 %\n",
      "Progress  13.66 %\n",
      "Progress  13.68 %\n",
      "Progress  13.70 %\n",
      "Progress  13.72 %\n",
      "Progress  13.74 %\n",
      "Progress  13.76 %\n",
      "Progress  13.78 %\n",
      "Progress  13.80 %\n",
      "Progress  13.82 %\n",
      "Progress  13.84 %\n",
      "Progress  13.86 %\n",
      "Progress  13.88 %\n",
      "Progress  13.90 %\n",
      "Progress  13.92 %\n",
      "Progress  13.94 %\n",
      "Progress  13.96 %\n",
      "Progress  13.98 %\n",
      "Progress  14.00 %\n",
      "Progress  14.02 %\n",
      "Progress  14.04 %\n",
      "Progress  14.06 %\n",
      "Progress  14.08 %\n",
      "Progress  14.10 %\n",
      "Progress  14.12 %\n",
      "Progress  14.14 %\n",
      "Progress  14.16 %\n",
      "Progress  14.18 %\n",
      "Progress  14.20 %\n",
      "Progress  14.22 %\n",
      "Progress  14.24 %\n",
      "Progress  14.26 %\n",
      "Progress  14.28 %\n",
      "Progress  14.30 %\n",
      "Progress  14.32 %\n",
      "Progress  14.34 %\n",
      "Progress  14.36 %\n",
      "Progress  14.38 %\n",
      "Progress  14.40 %\n",
      "Progress  14.42 %\n",
      "Progress  14.44 %\n",
      "Progress  14.46 %\n",
      "Progress  14.48 %\n",
      "Progress  14.50 %\n",
      "Progress  14.52 %\n",
      "Progress  14.54 %\n",
      "Progress  14.56 %\n",
      "Progress  14.58 %\n",
      "Progress  14.60 %\n",
      "Progress  14.62 %\n",
      "Progress  14.64 %\n",
      "Progress  14.66 %\n",
      "Progress  14.68 %\n",
      "Progress  14.70 %\n",
      "Progress  14.72 %\n",
      "Progress  14.74 %\n",
      "Progress  14.76 %\n",
      "Progress  14.78 %\n",
      "Progress  14.80 %\n",
      "Progress  14.82 %\n",
      "Progress  14.84 %\n",
      "Progress  14.86 %\n",
      "Progress  14.88 %\n",
      "Progress  14.90 %\n",
      "Progress  14.92 %\n",
      "Progress  14.94 %\n",
      "Progress  14.96 %\n",
      "Progress  14.98 %\n",
      "Progress  15.00 %\n",
      "Progress  15.02 %\n",
      "Progress  15.04 %\n",
      "Progress  15.06 %\n",
      "Progress  15.08 %\n",
      "Progress  15.10 %\n",
      "Progress  15.12 %\n",
      "Progress  15.14 %\n",
      "Progress  15.16 %\n",
      "Progress  15.18 %\n",
      "Progress  15.20 %\n",
      "Progress  15.22 %\n",
      "Progress  15.24 %\n",
      "Progress  15.26 %\n",
      "Progress  15.28 %\n",
      "Progress  15.30 %\n",
      "Progress  15.32 %\n",
      "Progress  15.34 %\n",
      "Progress  15.36 %\n",
      "Progress  15.38 %\n",
      "Progress  15.40 %\n",
      "Progress  15.42 %\n",
      "Progress  15.44 %\n",
      "Progress  15.46 %\n",
      "Progress  15.48 %\n",
      "Progress  15.50 %\n",
      "Progress  15.52 %\n",
      "Progress  15.54 %\n",
      "Progress  15.56 %\n",
      "Progress  15.58 %\n",
      "Progress  15.60 %\n",
      "Progress  15.62 %\n",
      "Progress  15.64 %\n",
      "Progress  15.66 %\n",
      "Progress  15.68 %\n",
      "Progress  15.70 %\n",
      "Progress  15.72 %\n",
      "Progress  15.74 %\n",
      "Progress  15.76 %\n",
      "Progress  15.78 %\n",
      "Progress  15.80 %\n",
      "Progress  15.82 %\n",
      "Progress  15.84 %\n",
      "Progress  15.86 %\n",
      "Progress  15.88 %\n",
      "Progress  15.90 %\n",
      "Progress  15.92 %\n",
      "Progress  15.94 %\n",
      "Progress  15.96 %\n",
      "Progress  15.98 %\n",
      "Progress  16.00 %\n",
      "Progress  16.02 %\n",
      "Progress  16.04 %\n",
      "Progress  16.06 %\n",
      "Progress  16.08 %\n",
      "Progress  16.10 %\n",
      "Progress  16.12 %\n",
      "Progress  16.14 %\n",
      "Progress  16.16 %\n",
      "Progress  16.18 %\n",
      "Progress  16.20 %\n",
      "Progress  16.22 %\n",
      "Progress  16.24 %\n",
      "Progress  16.26 %\n",
      "Progress  16.28 %\n",
      "Progress  16.30 %\n",
      "Progress  16.32 %\n",
      "Progress  16.34 %\n",
      "Progress  16.36 %\n",
      "Progress  16.38 %\n",
      "Progress  16.40 %\n",
      "Progress  16.42 %\n",
      "Progress  16.44 %\n",
      "Progress  16.46 %\n",
      "Progress  16.48 %\n",
      "Progress  16.50 %\n",
      "Progress  16.52 %\n",
      "Progress  16.54 %\n",
      "Progress  16.56 %\n",
      "Progress  16.58 %\n",
      "Progress  16.60 %\n",
      "Progress  16.62 %\n",
      "Progress  16.64 %\n",
      "Progress  16.66 %\n",
      "Progress  16.68 %\n",
      "Progress  16.70 %\n",
      "Progress  16.72 %\n",
      "Progress  16.74 %\n",
      "Progress  16.76 %\n",
      "Progress  16.78 %\n",
      "Progress  16.80 %\n",
      "Progress  16.82 %\n",
      "Progress  16.84 %\n",
      "Progress  16.86 %\n",
      "Progress  16.88 %\n",
      "Progress  16.90 %\n",
      "Progress  16.92 %\n",
      "Progress  16.94 %\n",
      "Progress  16.96 %\n",
      "Progress  16.98 %\n",
      "Progress  17.00 %\n",
      "Progress  17.02 %\n",
      "Progress  17.04 %\n",
      "Progress  17.06 %\n",
      "Progress  17.08 %\n",
      "Progress  17.10 %\n",
      "Progress  17.12 %\n",
      "Progress  17.14 %\n",
      "Progress  17.16 %\n",
      "Progress  17.18 %\n",
      "Progress  17.20 %\n",
      "Progress  17.22 %\n",
      "Progress  17.24 %\n",
      "Progress  17.26 %\n",
      "Progress  17.28 %\n",
      "Progress  17.30 %\n",
      "Progress  17.32 %\n",
      "Progress  17.34 %\n",
      "Progress  17.36 %\n",
      "Progress  17.38 %\n",
      "Progress  17.40 %\n",
      "Progress  17.42 %\n",
      "Progress  17.44 %\n",
      "Progress  17.46 %\n",
      "Progress  17.48 %\n",
      "Progress  17.50 %\n",
      "Progress  17.52 %\n",
      "Progress  17.54 %\n",
      "Progress  17.56 %\n",
      "Progress  17.58 %\n",
      "Progress  17.60 %\n",
      "Progress  17.62 %\n",
      "Progress  17.64 %\n",
      "Progress  17.66 %\n",
      "Progress  17.68 %\n",
      "Progress  17.70 %\n",
      "Progress  17.72 %\n",
      "Progress  17.74 %\n",
      "Progress  17.76 %\n",
      "Progress  17.78 %\n",
      "Progress  17.80 %\n",
      "Progress  17.82 %\n",
      "Progress  17.84 %\n",
      "Progress  17.86 %\n",
      "Progress  17.88 %\n",
      "Progress  17.90 %\n",
      "Progress  17.92 %\n",
      "Progress  17.94 %\n",
      "Progress  17.96 %\n",
      "Progress  17.98 %\n",
      "Progress  18.00 %\n",
      "Progress  18.02 %\n",
      "Progress  18.04 %\n",
      "Progress  18.06 %\n",
      "Progress  18.08 %\n",
      "Progress  18.10 %\n",
      "Progress  18.12 %\n",
      "Progress  18.14 %\n",
      "Progress  18.16 %\n",
      "Progress  18.18 %\n",
      "Progress  18.20 %\n",
      "Progress  18.22 %\n",
      "Progress  18.24 %\n",
      "Progress  18.26 %\n",
      "Progress  18.28 %\n",
      "Progress  18.30 %\n",
      "Progress  18.32 %\n",
      "Progress  18.34 %\n",
      "Progress  18.36 %\n",
      "Progress  18.38 %\n",
      "Progress  18.40 %\n",
      "Progress  18.42 %\n",
      "Progress  18.44 %\n",
      "Progress  18.46 %\n",
      "Progress  18.48 %\n",
      "Progress  18.50 %\n",
      "Progress  18.52 %\n",
      "Progress  18.54 %\n",
      "Progress  18.56 %\n",
      "Progress  18.58 %\n",
      "Progress  18.60 %\n",
      "Progress  18.62 %\n",
      "Progress  18.64 %\n",
      "Progress  18.66 %\n",
      "Progress  18.68 %\n",
      "Progress  18.70 %\n",
      "Progress  18.72 %\n",
      "Progress  18.74 %\n",
      "Progress  18.76 %\n",
      "Progress  18.78 %\n",
      "Progress  18.80 %\n",
      "Progress  18.82 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  18.84 %\n",
      "Progress  18.86 %\n",
      "Progress  18.88 %\n",
      "Progress  18.90 %\n",
      "Progress  18.92 %\n",
      "Progress  18.94 %\n",
      "Progress  18.96 %\n",
      "Progress  18.98 %\n",
      "Progress  19.00 %\n",
      "Progress  19.02 %\n",
      "Progress  19.04 %\n",
      "Progress  19.06 %\n",
      "Progress  19.08 %\n",
      "Progress  19.10 %\n",
      "Progress  19.12 %\n",
      "Progress  19.14 %\n",
      "Progress  19.16 %\n",
      "Progress  19.18 %\n",
      "Progress  19.20 %\n",
      "Progress  19.22 %\n",
      "Progress  19.24 %\n",
      "Progress  19.26 %\n",
      "Progress  19.28 %\n",
      "Progress  19.30 %\n",
      "Progress  19.32 %\n",
      "Progress  19.34 %\n",
      "Progress  19.36 %\n",
      "Progress  19.38 %\n",
      "Progress  19.40 %\n",
      "Progress  19.42 %\n",
      "Progress  19.44 %\n",
      "Progress  19.46 %\n",
      "Progress  19.48 %\n",
      "Progress  19.50 %\n",
      "Progress  19.52 %\n",
      "Progress  19.54 %\n",
      "Progress  19.56 %\n",
      "Progress  19.58 %\n",
      "Progress  19.60 %\n",
      "Progress  19.62 %\n",
      "Progress  19.64 %\n",
      "Progress  19.66 %\n",
      "Progress  19.68 %\n",
      "Progress  19.70 %\n",
      "Progress  19.72 %\n",
      "Progress  19.74 %\n",
      "Progress  19.76 %\n",
      "Progress  19.78 %\n",
      "Progress  19.80 %\n",
      "Progress  19.82 %\n",
      "Progress  19.84 %\n",
      "Progress  19.86 %\n",
      "Progress  19.88 %\n",
      "Progress  19.90 %\n",
      "Progress  19.92 %\n",
      "Progress  19.94 %\n",
      "Progress  19.96 %\n",
      "Progress  19.98 %\n",
      "Progress  20.00 %\n",
      "Progress  20.02 %\n",
      "Progress  20.04 %\n",
      "Progress  20.06 %\n",
      "Progress  20.08 %\n",
      "Progress  20.10 %\n",
      "Progress  20.12 %\n",
      "Progress  20.14 %\n",
      "Progress  20.16 %\n",
      "Progress  20.18 %\n",
      "Progress  20.20 %\n",
      "Progress  20.22 %\n",
      "Progress  20.24 %\n",
      "Progress  20.26 %\n",
      "Progress  20.28 %\n",
      "Progress  20.30 %\n",
      "Progress  20.32 %\n",
      "Progress  20.34 %\n",
      "Progress  20.36 %\n",
      "Progress  20.38 %\n",
      "Progress  20.40 %\n",
      "Progress  20.42 %\n",
      "Progress  20.44 %\n",
      "Progress  20.46 %\n",
      "Progress  20.48 %\n",
      "Progress  20.50 %\n",
      "Progress  20.52 %\n",
      "Progress  20.54 %\n",
      "Progress  20.56 %\n",
      "Progress  20.58 %\n",
      "Progress  20.60 %\n",
      "Progress  20.62 %\n",
      "Progress  20.64 %\n",
      "Progress  20.66 %\n",
      "Progress  20.68 %\n",
      "Progress  20.70 %\n",
      "Progress  20.72 %\n",
      "Progress  20.74 %\n",
      "Progress  20.76 %\n",
      "Progress  20.78 %\n",
      "Progress  20.80 %\n",
      "Progress  20.82 %\n",
      "Progress  20.84 %\n",
      "Progress  20.86 %\n",
      "Progress  20.88 %\n",
      "Progress  20.90 %\n",
      "Progress  20.92 %\n",
      "Progress  20.94 %\n",
      "Progress  20.96 %\n",
      "Progress  20.98 %\n",
      "Progress  21.00 %\n",
      "Progress  21.02 %\n",
      "Progress  21.04 %\n",
      "Progress  21.06 %\n",
      "Progress  21.08 %\n",
      "Progress  21.10 %\n",
      "Progress  21.12 %\n",
      "Progress  21.14 %\n",
      "Progress  21.16 %\n",
      "Progress  21.18 %\n",
      "Progress  21.20 %\n",
      "Progress  21.22 %\n",
      "Progress  21.24 %\n",
      "Progress  21.26 %\n",
      "Progress  21.28 %\n",
      "Progress  21.30 %\n",
      "Progress  21.32 %\n",
      "Progress  21.34 %\n",
      "Progress  21.36 %\n",
      "Progress  21.38 %\n",
      "Progress  21.40 %\n",
      "Progress  21.42 %\n",
      "Progress  21.44 %\n",
      "Progress  21.46 %\n",
      "Progress  21.48 %\n",
      "Progress  21.50 %\n",
      "Progress  21.52 %\n",
      "Progress  21.54 %\n",
      "Progress  21.56 %\n",
      "Progress  21.58 %\n",
      "Progress  21.60 %\n",
      "Progress  21.62 %\n",
      "Progress  21.64 %\n",
      "Progress  21.66 %\n",
      "Progress  21.68 %\n",
      "Progress  21.70 %\n",
      "Progress  21.72 %\n",
      "Progress  21.74 %\n",
      "Progress  21.76 %\n",
      "Progress  21.78 %\n",
      "Progress  21.80 %\n",
      "Progress  21.82 %\n",
      "Progress  21.84 %\n",
      "Progress  21.86 %\n",
      "Progress  21.88 %\n",
      "Progress  21.90 %\n",
      "Progress  21.92 %\n",
      "Progress  21.94 %\n",
      "Progress  21.96 %\n",
      "Progress  21.98 %\n",
      "Progress  22.00 %\n",
      "Progress  22.02 %\n",
      "Progress  22.04 %\n",
      "Progress  22.06 %\n",
      "Progress  22.08 %\n",
      "Progress  22.10 %\n",
      "Progress  22.12 %\n",
      "Progress  22.14 %\n",
      "Progress  22.16 %\n",
      "Progress  22.18 %\n",
      "Progress  22.20 %\n",
      "Progress  22.22 %\n",
      "Progress  22.24 %\n",
      "Progress  22.26 %\n",
      "Progress  22.28 %\n",
      "Progress  22.30 %\n",
      "Progress  22.32 %\n",
      "Progress  22.34 %\n",
      "Progress  22.36 %\n",
      "Progress  22.38 %\n",
      "Progress  22.40 %\n",
      "Progress  22.42 %\n",
      "Progress  22.44 %\n",
      "Progress  22.46 %\n",
      "Progress  22.48 %\n",
      "Progress  22.50 %\n",
      "Progress  22.52 %\n",
      "Progress  22.54 %\n",
      "Progress  22.56 %\n",
      "Progress  22.58 %\n",
      "Progress  22.60 %\n",
      "Progress  22.62 %\n",
      "Progress  22.64 %\n",
      "Progress  22.66 %\n",
      "Progress  22.68 %\n",
      "Progress  22.70 %\n",
      "Progress  22.72 %\n",
      "Progress  22.74 %\n",
      "Progress  22.76 %\n",
      "Progress  22.78 %\n",
      "Progress  22.80 %\n",
      "Progress  22.82 %\n",
      "Progress  22.84 %\n",
      "Progress  22.86 %\n",
      "Progress  22.88 %\n",
      "Progress  22.90 %\n",
      "Progress  22.92 %\n",
      "Progress  22.94 %\n",
      "Progress  22.96 %\n",
      "Progress  22.98 %\n",
      "Progress  23.00 %\n",
      "Progress  23.02 %\n",
      "Progress  23.04 %\n",
      "Progress  23.06 %\n",
      "Progress  23.08 %\n",
      "Progress  23.10 %\n",
      "Progress  23.12 %\n",
      "Progress  23.14 %\n",
      "Progress  23.16 %\n",
      "Progress  23.18 %\n",
      "Progress  23.20 %\n",
      "Progress  23.22 %\n",
      "Progress  23.24 %\n",
      "Progress  23.26 %\n",
      "Progress  23.28 %\n",
      "Progress  23.30 %\n",
      "Progress  23.32 %\n",
      "Progress  23.34 %\n",
      "Progress  23.36 %\n",
      "Progress  23.38 %\n",
      "Progress  23.40 %\n",
      "Progress  23.42 %\n",
      "Progress  23.44 %\n",
      "Progress  23.46 %\n",
      "Progress  23.48 %\n",
      "Progress  23.50 %\n",
      "Progress  23.52 %\n",
      "Progress  23.54 %\n",
      "Progress  23.56 %\n",
      "Progress  23.58 %\n",
      "Progress  23.60 %\n",
      "Progress  23.62 %\n",
      "Progress  23.64 %\n",
      "Progress  23.66 %\n",
      "Progress  23.68 %\n",
      "Progress  23.70 %\n",
      "Progress  23.72 %\n",
      "Progress  23.74 %\n",
      "Progress  23.76 %\n",
      "Progress  23.78 %\n",
      "Progress  23.80 %\n",
      "Progress  23.82 %\n",
      "Progress  23.84 %\n",
      "Progress  23.86 %\n",
      "Progress  23.88 %\n",
      "Progress  23.90 %\n",
      "Progress  23.92 %\n",
      "Progress  23.94 %\n",
      "Progress  23.96 %\n",
      "Progress  23.98 %\n",
      "Progress  24.00 %\n",
      "Progress  24.02 %\n",
      "Progress  24.04 %\n",
      "Progress  24.06 %\n",
      "Progress  24.08 %\n",
      "Progress  24.10 %\n",
      "Progress  24.12 %\n",
      "Progress  24.14 %\n",
      "Progress  24.16 %\n",
      "Progress  24.18 %\n",
      "Progress  24.20 %\n",
      "Progress  24.22 %\n",
      "Progress  24.24 %\n",
      "Progress  24.26 %\n",
      "Progress  24.28 %\n",
      "Progress  24.30 %\n",
      "Progress  24.32 %\n",
      "Progress  24.34 %\n",
      "Progress  24.36 %\n",
      "Progress  24.38 %\n",
      "Progress  24.40 %\n",
      "Progress  24.42 %\n",
      "Progress  24.44 %\n",
      "Progress  24.46 %\n",
      "Progress  24.48 %\n",
      "Progress  24.50 %\n",
      "Progress  24.52 %\n",
      "Progress  24.54 %\n",
      "Progress  24.56 %\n",
      "Progress  24.58 %\n",
      "Progress  24.60 %\n",
      "Progress  24.62 %\n",
      "Progress  24.64 %\n",
      "Progress  24.66 %\n",
      "Progress  24.68 %\n",
      "Progress  24.70 %\n",
      "Progress  24.72 %\n",
      "Progress  24.74 %\n",
      "Progress  24.76 %\n",
      "Progress  24.78 %\n",
      "Progress  24.80 %\n",
      "Progress  24.82 %\n",
      "Progress  24.84 %\n",
      "Progress  24.86 %\n",
      "Progress  24.88 %\n",
      "Progress  24.90 %\n",
      "Progress  24.92 %\n",
      "Progress  24.94 %\n",
      "Progress  24.96 %\n",
      "Progress  24.98 %\n",
      "Progress  25.00 %\n",
      "Progress  25.02 %\n",
      "Progress  25.04 %\n",
      "Progress  25.06 %\n",
      "Progress  25.08 %\n",
      "Progress  25.10 %\n",
      "Progress  25.12 %\n",
      "Progress  25.14 %\n",
      "Progress  25.16 %\n",
      "Progress  25.18 %\n",
      "Progress  25.20 %\n",
      "Progress  25.22 %\n",
      "Progress  25.24 %\n",
      "Progress  25.26 %\n",
      "Progress  25.28 %\n",
      "Progress  25.30 %\n",
      "Progress  25.32 %\n",
      "Progress  25.34 %\n",
      "Progress  25.36 %\n",
      "Progress  25.38 %\n",
      "Progress  25.40 %\n",
      "Progress  25.42 %\n",
      "Progress  25.44 %\n",
      "Progress  25.46 %\n",
      "Progress  25.48 %\n",
      "Progress  25.50 %\n",
      "Progress  25.52 %\n",
      "Progress  25.54 %\n",
      "Progress  25.56 %\n",
      "Progress  25.58 %\n",
      "Progress  25.60 %\n",
      "Progress  25.62 %\n",
      "Progress  25.64 %\n",
      "Progress  25.66 %\n",
      "Progress  25.68 %\n",
      "Progress  25.70 %\n",
      "Progress  25.72 %\n",
      "Progress  25.74 %\n",
      "Progress  25.76 %\n",
      "Progress  25.78 %\n",
      "Progress  25.80 %\n",
      "Progress  25.82 %\n",
      "Progress  25.84 %\n",
      "Progress  25.86 %\n",
      "Progress  25.88 %\n",
      "Progress  25.90 %\n",
      "Progress  25.92 %\n",
      "Progress  25.94 %\n",
      "Progress  25.96 %\n",
      "Progress  25.98 %\n",
      "Progress  26.00 %\n",
      "Progress  26.02 %\n",
      "Progress  26.04 %\n",
      "Progress  26.06 %\n",
      "Progress  26.08 %\n",
      "Progress  26.10 %\n",
      "Progress  26.12 %\n",
      "Progress  26.14 %\n",
      "Progress  26.16 %\n",
      "Progress  26.18 %\n",
      "Progress  26.20 %\n",
      "Progress  26.22 %\n",
      "Progress  26.24 %\n",
      "Progress  26.26 %\n",
      "Progress  26.28 %\n",
      "Progress  26.30 %\n",
      "Progress  26.32 %\n",
      "Progress  26.34 %\n",
      "Progress  26.36 %\n",
      "Progress  26.38 %\n",
      "Progress  26.40 %\n",
      "Progress  26.42 %\n",
      "Progress  26.44 %\n",
      "Progress  26.46 %\n",
      "Progress  26.48 %\n",
      "Progress  26.50 %\n",
      "Progress  26.52 %\n",
      "Progress  26.54 %\n",
      "Progress  26.56 %\n",
      "Progress  26.58 %\n",
      "Progress  26.60 %\n",
      "Progress  26.62 %\n",
      "Progress  26.64 %\n",
      "Progress  26.66 %\n",
      "Progress  26.68 %\n",
      "Progress  26.70 %\n",
      "Progress  26.72 %\n",
      "Progress  26.74 %\n",
      "Progress  26.76 %\n",
      "Progress  26.78 %\n",
      "Progress  26.80 %\n",
      "Progress  26.82 %\n",
      "Progress  26.84 %\n",
      "Progress  26.86 %\n",
      "Progress  26.88 %\n",
      "Progress  26.90 %\n",
      "Progress  26.92 %\n",
      "Progress  26.94 %\n",
      "Progress  26.96 %\n",
      "Progress  26.98 %\n",
      "Progress  27.00 %\n",
      "Progress  27.02 %\n",
      "Progress  27.04 %\n",
      "Progress  27.06 %\n",
      "Progress  27.08 %\n",
      "Progress  27.10 %\n",
      "Progress  27.12 %\n",
      "Progress  27.14 %\n",
      "Progress  27.16 %\n",
      "Progress  27.18 %\n",
      "Progress  27.20 %\n",
      "Progress  27.22 %\n",
      "Progress  27.24 %\n",
      "Progress  27.26 %\n",
      "Progress  27.28 %\n",
      "Progress  27.30 %\n",
      "Progress  27.32 %\n",
      "Progress  27.34 %\n",
      "Progress  27.36 %\n",
      "Progress  27.38 %\n",
      "Progress  27.40 %\n",
      "Progress  27.42 %\n",
      "Progress  27.44 %\n",
      "Progress  27.46 %\n",
      "Progress  27.48 %\n",
      "Progress  27.50 %\n",
      "Progress  27.52 %\n",
      "Progress  27.54 %\n",
      "Progress  27.56 %\n",
      "Progress  27.58 %\n",
      "Progress  27.60 %\n",
      "Progress  27.62 %\n",
      "Progress  27.64 %\n",
      "Progress  27.66 %\n",
      "Progress  27.68 %\n",
      "Progress  27.70 %\n",
      "Progress  27.72 %\n",
      "Progress  27.74 %\n",
      "Progress  27.76 %\n",
      "Progress  27.78 %\n",
      "Progress  27.80 %\n",
      "Progress  27.82 %\n",
      "Progress  27.84 %\n",
      "Progress  27.86 %\n",
      "Progress  27.88 %\n",
      "Progress  27.90 %\n",
      "Progress  27.92 %\n",
      "Progress  27.94 %\n",
      "Progress  27.96 %\n",
      "Progress  27.98 %\n",
      "Progress  28.00 %\n",
      "Progress  28.02 %\n",
      "Progress  28.04 %\n",
      "Progress  28.06 %\n",
      "Progress  28.08 %\n",
      "Progress  28.10 %\n",
      "Progress  28.12 %\n",
      "Progress  28.14 %\n",
      "Progress  28.16 %\n",
      "Progress  28.18 %\n",
      "Progress  28.20 %\n",
      "Progress  28.22 %\n",
      "Progress  28.24 %\n",
      "Progress  28.26 %\n",
      "Progress  28.28 %\n",
      "Progress  28.30 %\n",
      "Progress  28.32 %\n",
      "Progress  28.34 %\n",
      "Progress  28.36 %\n",
      "Progress  28.38 %\n",
      "Progress  28.40 %\n",
      "Progress  28.42 %\n",
      "Progress  28.44 %\n",
      "Progress  28.46 %\n",
      "Progress  28.48 %\n",
      "Progress  28.50 %\n",
      "Progress  28.52 %\n",
      "Progress  28.54 %\n",
      "Progress  28.56 %\n",
      "Progress  28.58 %\n",
      "Progress  28.60 %\n",
      "Progress  28.62 %\n",
      "Progress  28.64 %\n",
      "Progress  28.66 %\n",
      "Progress  28.68 %\n",
      "Progress  28.70 %\n",
      "Progress  28.72 %\n",
      "Progress  28.74 %\n",
      "Progress  28.76 %\n",
      "Progress  28.78 %\n",
      "Progress  28.80 %\n",
      "Progress  28.82 %\n",
      "Progress  28.84 %\n",
      "Progress  28.86 %\n",
      "Progress  28.88 %\n",
      "Progress  28.90 %\n",
      "Progress  28.92 %\n",
      "Progress  28.94 %\n",
      "Progress  28.96 %\n",
      "Progress  28.98 %\n",
      "Progress  29.00 %\n",
      "Progress  29.02 %\n",
      "Progress  29.04 %\n",
      "Progress  29.06 %\n",
      "Progress  29.08 %\n",
      "Progress  29.10 %\n",
      "Progress  29.12 %\n",
      "Progress  29.14 %\n",
      "Progress  29.16 %\n",
      "Progress  29.18 %\n",
      "Progress  29.20 %\n",
      "Progress  29.22 %\n",
      "Progress  29.24 %\n",
      "Progress  29.26 %\n",
      "Progress  29.28 %\n",
      "Progress  29.30 %\n",
      "Progress  29.32 %\n",
      "Progress  29.34 %\n",
      "Progress  29.36 %\n",
      "Progress  29.38 %\n",
      "Progress  29.40 %\n",
      "Progress  29.42 %\n",
      "Progress  29.44 %\n",
      "Progress  29.46 %\n",
      "Progress  29.48 %\n",
      "Progress  29.50 %\n",
      "Progress  29.52 %\n",
      "Progress  29.54 %\n",
      "Progress  29.56 %\n",
      "Progress  29.58 %\n",
      "Progress  29.60 %\n",
      "Progress  29.62 %\n",
      "Progress  29.64 %\n",
      "Progress  29.66 %\n",
      "Progress  29.68 %\n",
      "Progress  29.70 %\n",
      "Progress  29.72 %\n",
      "Progress  29.74 %\n",
      "Progress  29.76 %\n",
      "Progress  29.78 %\n",
      "Progress  29.80 %\n",
      "Progress  29.82 %\n",
      "Progress  29.84 %\n",
      "Progress  29.86 %\n",
      "Progress  29.88 %\n",
      "Progress  29.90 %\n",
      "Progress  29.92 %\n",
      "Progress  29.94 %\n",
      "Progress  29.96 %\n",
      "Progress  29.98 %\n",
      "Progress  30.00 %\n",
      "Progress  30.02 %\n",
      "Progress  30.04 %\n",
      "Progress  30.06 %\n",
      "Progress  30.08 %\n",
      "Progress  30.10 %\n",
      "Progress  30.12 %\n",
      "Progress  30.14 %\n",
      "Progress  30.16 %\n",
      "Progress  30.18 %\n",
      "Progress  30.20 %\n",
      "Progress  30.22 %\n",
      "Progress  30.24 %\n",
      "Progress  30.26 %\n",
      "Progress  30.28 %\n",
      "Progress  30.30 %\n",
      "Progress  30.32 %\n",
      "Progress  30.34 %\n",
      "Progress  30.36 %\n",
      "Progress  30.38 %\n",
      "Progress  30.40 %\n",
      "Progress  30.42 %\n",
      "Progress  30.44 %\n",
      "Progress  30.46 %\n",
      "Progress  30.48 %\n",
      "Progress  30.50 %\n",
      "Progress  30.52 %\n",
      "Progress  30.54 %\n",
      "Progress  30.56 %\n",
      "Progress  30.58 %\n",
      "Progress  30.60 %\n",
      "Progress  30.62 %\n",
      "Progress  30.64 %\n",
      "Progress  30.66 %\n",
      "Progress  30.68 %\n",
      "Progress  30.70 %\n",
      "Progress  30.72 %\n",
      "Progress  30.74 %\n",
      "Progress  30.76 %\n",
      "Progress  30.78 %\n",
      "Progress  30.80 %\n",
      "Progress  30.82 %\n",
      "Progress  30.84 %\n",
      "Progress  30.86 %\n",
      "Progress  30.88 %\n",
      "Progress  30.90 %\n",
      "Progress  30.92 %\n",
      "Progress  30.94 %\n",
      "Progress  30.96 %\n",
      "Progress  30.98 %\n",
      "Progress  31.00 %\n",
      "Progress  31.02 %\n",
      "Progress  31.04 %\n",
      "Progress  31.06 %\n",
      "Progress  31.08 %\n",
      "Progress  31.10 %\n",
      "Progress  31.12 %\n",
      "Progress  31.14 %\n",
      "Progress  31.16 %\n",
      "Progress  31.18 %\n",
      "Progress  31.20 %\n",
      "Progress  31.22 %\n",
      "Progress  31.24 %\n",
      "Progress  31.26 %\n",
      "Progress  31.28 %\n",
      "Progress  31.30 %\n",
      "Progress  31.32 %\n",
      "Progress  31.34 %\n",
      "Progress  31.36 %\n",
      "Progress  31.38 %\n",
      "Progress  31.40 %\n",
      "Progress  31.42 %\n",
      "Progress  31.44 %\n",
      "Progress  31.46 %\n",
      "Progress  31.48 %\n",
      "Progress  31.50 %\n",
      "Progress  31.52 %\n",
      "Progress  31.54 %\n",
      "Progress  31.56 %\n",
      "Progress  31.58 %\n",
      "Progress  31.60 %\n",
      "Progress  31.62 %\n",
      "Progress  31.64 %\n",
      "Progress  31.66 %\n",
      "Progress  31.68 %\n",
      "Progress  31.70 %\n",
      "Progress  31.72 %\n",
      "Progress  31.74 %\n",
      "Progress  31.76 %\n",
      "Progress  31.78 %\n",
      "Progress  31.80 %\n",
      "Progress  31.82 %\n",
      "Progress  31.84 %\n",
      "Progress  31.86 %\n",
      "Progress  31.88 %\n",
      "Progress  31.90 %\n",
      "Progress  31.92 %\n",
      "Progress  31.94 %\n",
      "Progress  31.96 %\n",
      "Progress  31.98 %\n",
      "Progress  32.00 %\n",
      "Progress  32.02 %\n",
      "Progress  32.04 %\n",
      "Progress  32.06 %\n",
      "Progress  32.08 %\n",
      "Progress  32.10 %\n",
      "Progress  32.12 %\n",
      "Progress  32.14 %\n",
      "Progress  32.16 %\n",
      "Progress  32.18 %\n",
      "Progress  32.20 %\n",
      "Progress  32.22 %\n",
      "Progress  32.24 %\n",
      "Progress  32.26 %\n",
      "Progress  32.28 %\n",
      "Progress  32.30 %\n",
      "Progress  32.32 %\n",
      "Progress  32.34 %\n",
      "Progress  32.36 %\n",
      "Progress  32.38 %\n",
      "Progress  32.40 %\n",
      "Progress  32.42 %\n",
      "Progress  32.44 %\n",
      "Progress  32.46 %\n",
      "Progress  32.48 %\n",
      "Progress  32.50 %\n",
      "Progress  32.52 %\n",
      "Progress  32.54 %\n",
      "Progress  32.56 %\n",
      "Progress  32.58 %\n",
      "Progress  32.60 %\n",
      "Progress  32.62 %\n",
      "Progress  32.64 %\n",
      "Progress  32.66 %\n",
      "Progress  32.68 %\n",
      "Progress  32.70 %\n",
      "Progress  32.72 %\n",
      "Progress  32.74 %\n",
      "Progress  32.76 %\n",
      "Progress  32.78 %\n",
      "Progress  32.80 %\n",
      "Progress  32.82 %\n",
      "Progress  32.84 %\n",
      "Progress  32.86 %\n",
      "Progress  32.88 %\n",
      "Progress  32.90 %\n",
      "Progress  32.92 %\n",
      "Progress  32.94 %\n",
      "Progress  32.96 %\n",
      "Progress  32.98 %\n",
      "Progress  33.00 %\n",
      "Progress  33.02 %\n",
      "Progress  33.04 %\n",
      "Progress  33.06 %\n",
      "Progress  33.08 %\n",
      "Progress  33.10 %\n",
      "Progress  33.12 %\n",
      "Progress  33.14 %\n",
      "Progress  33.16 %\n",
      "Progress  33.18 %\n",
      "Progress  33.20 %\n",
      "Progress  33.22 %\n",
      "Progress  33.24 %\n",
      "Progress  33.26 %\n",
      "Progress  33.28 %\n",
      "Progress  33.30 %\n",
      "Progress  33.32 %\n",
      "Progress  33.34 %\n",
      "Progress  33.36 %\n",
      "Progress  33.38 %\n",
      "Progress  33.40 %\n",
      "Progress  33.42 %\n",
      "Progress  33.44 %\n",
      "Progress  33.46 %\n",
      "Progress  33.48 %\n",
      "Progress  33.50 %\n",
      "Progress  33.52 %\n",
      "Progress  33.54 %\n",
      "Progress  33.56 %\n",
      "Progress  33.58 %\n",
      "Progress  33.60 %\n",
      "Progress  33.62 %\n",
      "Progress  33.64 %\n",
      "Progress  33.66 %\n",
      "Progress  33.68 %\n",
      "Progress  33.70 %\n",
      "Progress  33.72 %\n",
      "Progress  33.74 %\n",
      "Progress  33.76 %\n",
      "Progress  33.78 %\n",
      "Progress  33.80 %\n",
      "Progress  33.82 %\n",
      "Progress  33.84 %\n",
      "Progress  33.86 %\n",
      "Progress  33.88 %\n",
      "Progress  33.90 %\n",
      "Progress  33.92 %\n",
      "Progress  33.94 %\n",
      "Progress  33.96 %\n",
      "Progress  33.98 %\n",
      "Progress  34.00 %\n",
      "Progress  34.02 %\n",
      "Progress  34.04 %\n",
      "Progress  34.06 %\n",
      "Progress  34.08 %\n",
      "Progress  34.10 %\n",
      "Progress  34.12 %\n",
      "Progress  34.14 %\n",
      "Progress  34.16 %\n",
      "Progress  34.18 %\n",
      "Progress  34.20 %\n",
      "Progress  34.22 %\n",
      "Progress  34.24 %\n",
      "Progress  34.26 %\n",
      "Progress  34.28 %\n",
      "Progress  34.30 %\n",
      "Progress  34.32 %\n",
      "Progress  34.34 %\n",
      "Progress  34.36 %\n",
      "Progress  34.38 %\n",
      "Progress  34.40 %\n",
      "Progress  34.42 %\n",
      "Progress  34.44 %\n",
      "Progress  34.46 %\n",
      "Progress  34.48 %\n",
      "Progress  34.50 %\n",
      "Progress  34.52 %\n",
      "Progress  34.54 %\n",
      "Progress  34.56 %\n",
      "Progress  34.58 %\n",
      "Progress  34.60 %\n",
      "Progress  34.62 %\n",
      "Progress  34.64 %\n",
      "Progress  34.66 %\n",
      "Progress  34.68 %\n",
      "Progress  34.70 %\n",
      "Progress  34.72 %\n",
      "Progress  34.74 %\n",
      "Progress  34.76 %\n",
      "Progress  34.78 %\n",
      "Progress  34.80 %\n",
      "Progress  34.82 %\n",
      "Progress  34.84 %\n",
      "Progress  34.86 %\n",
      "Progress  34.88 %\n",
      "Progress  34.90 %\n",
      "Progress  34.92 %\n",
      "Progress  34.94 %\n",
      "Progress  34.96 %\n",
      "Progress  34.98 %\n",
      "Progress  35.00 %\n",
      "Progress  35.02 %\n",
      "Progress  35.04 %\n",
      "Progress  35.06 %\n",
      "Progress  35.08 %\n",
      "Progress  35.10 %\n",
      "Progress  35.12 %\n",
      "Progress  35.14 %\n",
      "Progress  35.16 %\n",
      "Progress  35.18 %\n",
      "Progress  35.20 %\n",
      "Progress  35.22 %\n",
      "Progress  35.24 %\n",
      "Progress  35.26 %\n",
      "Progress  35.28 %\n",
      "Progress  35.30 %\n",
      "Progress  35.32 %\n",
      "Progress  35.34 %\n",
      "Progress  35.36 %\n",
      "Progress  35.38 %\n",
      "Progress  35.40 %\n",
      "Progress  35.42 %\n",
      "Progress  35.44 %\n",
      "Progress  35.46 %\n",
      "Progress  35.48 %\n",
      "Progress  35.50 %\n",
      "Progress  35.52 %\n",
      "Progress  35.54 %\n",
      "Progress  35.56 %\n",
      "Progress  35.58 %\n",
      "Progress  35.60 %\n",
      "Progress  35.62 %\n",
      "Progress  35.64 %\n",
      "Progress  35.66 %\n",
      "Progress  35.68 %\n",
      "Progress  35.70 %\n",
      "Progress  35.72 %\n",
      "Progress  35.74 %\n",
      "Progress  35.76 %\n",
      "Progress  35.78 %\n",
      "Progress  35.80 %\n",
      "Progress  35.82 %\n",
      "Progress  35.84 %\n",
      "Progress  35.86 %\n",
      "Progress  35.88 %\n",
      "Progress  35.90 %\n",
      "Progress  35.92 %\n",
      "Progress  35.94 %\n",
      "Progress  35.96 %\n",
      "Progress  35.98 %\n",
      "Progress  36.00 %\n",
      "Progress  36.02 %\n",
      "Progress  36.04 %\n",
      "Progress  36.06 %\n",
      "Progress  36.08 %\n",
      "Progress  36.10 %\n",
      "Progress  36.12 %\n",
      "Progress  36.14 %\n",
      "Progress  36.16 %\n",
      "Progress  36.18 %\n",
      "Progress  36.20 %\n",
      "Progress  36.22 %\n",
      "Progress  36.24 %\n",
      "Progress  36.26 %\n",
      "Progress  36.28 %\n",
      "Progress  36.30 %\n",
      "Progress  36.32 %\n",
      "Progress  36.34 %\n",
      "Progress  36.36 %\n",
      "Progress  36.38 %\n",
      "Progress  36.40 %\n",
      "Progress  36.42 %\n",
      "Progress  36.44 %\n",
      "Progress  36.46 %\n",
      "Progress  36.48 %\n",
      "Progress  36.50 %\n",
      "Progress  36.52 %\n",
      "Progress  36.54 %\n",
      "Progress  36.56 %\n",
      "Progress  36.58 %\n",
      "Progress  36.60 %\n",
      "Progress  36.62 %\n",
      "Progress  36.64 %\n",
      "Progress  36.66 %\n",
      "Progress  36.68 %\n",
      "Progress  36.70 %\n",
      "Progress  36.72 %\n",
      "Progress  36.74 %\n",
      "Progress  36.76 %\n",
      "Progress  36.78 %\n",
      "Progress  36.80 %\n",
      "Progress  36.82 %\n",
      "Progress  36.84 %\n",
      "Progress  36.86 %\n",
      "Progress  36.88 %\n",
      "Progress  36.90 %\n",
      "Progress  36.92 %\n",
      "Progress  36.94 %\n",
      "Progress  36.96 %\n",
      "Progress  36.98 %\n",
      "Progress  37.00 %\n",
      "Progress  37.02 %\n",
      "Progress  37.04 %\n",
      "Progress  37.06 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  37.08 %\n",
      "Progress  37.10 %\n",
      "Progress  37.12 %\n",
      "Progress  37.14 %\n",
      "Progress  37.16 %\n",
      "Progress  37.18 %\n",
      "Progress  37.20 %\n",
      "Progress  37.22 %\n",
      "Progress  37.24 %\n",
      "Progress  37.26 %\n",
      "Progress  37.28 %\n",
      "Progress  37.30 %\n",
      "Progress  37.32 %\n",
      "Progress  37.34 %\n",
      "Progress  37.36 %\n",
      "Progress  37.38 %\n",
      "Progress  37.40 %\n",
      "Progress  37.42 %\n",
      "Progress  37.44 %\n",
      "Progress  37.46 %\n",
      "Progress  37.48 %\n",
      "Progress  37.50 %\n",
      "Progress  37.52 %\n",
      "Progress  37.54 %\n",
      "Progress  37.56 %\n",
      "Progress  37.58 %\n",
      "Progress  37.60 %\n",
      "Progress  37.62 %\n",
      "Progress  37.64 %\n",
      "Progress  37.66 %\n",
      "Progress  37.68 %\n",
      "Progress  37.70 %\n",
      "Progress  37.72 %\n",
      "Progress  37.74 %\n",
      "Progress  37.76 %\n",
      "Progress  37.78 %\n",
      "Progress  37.80 %\n",
      "Progress  37.82 %\n",
      "Progress  37.84 %\n",
      "Progress  37.86 %\n",
      "Progress  37.88 %\n",
      "Progress  37.90 %\n",
      "Progress  37.92 %\n",
      "Progress  37.94 %\n",
      "Progress  37.96 %\n",
      "Progress  37.98 %\n",
      "Progress  38.00 %\n",
      "Progress  38.02 %\n",
      "Progress  38.04 %\n",
      "Progress  38.06 %\n",
      "Progress  38.08 %\n",
      "Progress  38.10 %\n",
      "Progress  38.12 %\n",
      "Progress  38.14 %\n",
      "Progress  38.16 %\n",
      "Progress  38.18 %\n",
      "Progress  38.20 %\n",
      "Progress  38.22 %\n",
      "Progress  38.24 %\n",
      "Progress  38.26 %\n",
      "Progress  38.28 %\n",
      "Progress  38.30 %\n",
      "Progress  38.32 %\n",
      "Progress  38.34 %\n",
      "Progress  38.36 %\n",
      "Progress  38.38 %\n",
      "Progress  38.40 %\n",
      "Progress  38.42 %\n",
      "Progress  38.44 %\n",
      "Progress  38.46 %\n",
      "Progress  38.48 %\n",
      "Progress  38.50 %\n",
      "Progress  38.52 %\n",
      "Progress  38.54 %\n",
      "Progress  38.56 %\n",
      "Progress  38.58 %\n",
      "Progress  38.60 %\n",
      "Progress  38.62 %\n",
      "Progress  38.64 %\n",
      "Progress  38.66 %\n",
      "Progress  38.68 %\n",
      "Progress  38.70 %\n",
      "Progress  38.72 %\n",
      "Progress  38.74 %\n",
      "Progress  38.76 %\n",
      "Progress  38.78 %\n",
      "Progress  38.80 %\n",
      "Progress  38.82 %\n",
      "Progress  38.84 %\n",
      "Progress  38.86 %\n",
      "Progress  38.88 %\n",
      "Progress  38.90 %\n",
      "Progress  38.92 %\n",
      "Progress  38.94 %\n",
      "Progress  38.96 %\n",
      "Progress  38.98 %\n",
      "Progress  39.00 %\n",
      "Progress  39.02 %\n",
      "Progress  39.04 %\n",
      "Progress  39.06 %\n",
      "Progress  39.08 %\n",
      "Progress  39.10 %\n",
      "Progress  39.12 %\n",
      "Progress  39.14 %\n",
      "Progress  39.16 %\n",
      "Progress  39.18 %\n",
      "Progress  39.20 %\n",
      "Progress  39.22 %\n",
      "Progress  39.24 %\n",
      "Progress  39.26 %\n",
      "Progress  39.28 %\n",
      "Progress  39.30 %\n",
      "Progress  39.32 %\n",
      "Progress  39.34 %\n",
      "Progress  39.36 %\n",
      "Progress  39.38 %\n",
      "Progress  39.40 %\n",
      "Progress  39.42 %\n",
      "Progress  39.44 %\n",
      "Progress  39.46 %\n",
      "Progress  39.48 %\n",
      "Progress  39.50 %\n",
      "Progress  39.52 %\n",
      "Progress  39.54 %\n",
      "Progress  39.56 %\n",
      "Progress  39.58 %\n",
      "Progress  39.60 %\n",
      "Progress  39.62 %\n",
      "Progress  39.64 %\n",
      "Progress  39.66 %\n",
      "Progress  39.68 %\n",
      "Progress  39.70 %\n",
      "Progress  39.72 %\n",
      "Progress  39.74 %\n",
      "Progress  39.76 %\n",
      "Progress  39.78 %\n",
      "Progress  39.80 %\n",
      "Progress  39.82 %\n",
      "Progress  39.84 %\n",
      "Progress  39.86 %\n",
      "Progress  39.88 %\n",
      "Progress  39.90 %\n",
      "Progress  39.92 %\n",
      "Progress  39.94 %\n",
      "Progress  39.96 %\n",
      "Progress  39.98 %\n",
      "Progress  40.00 %\n",
      "Progress  40.02 %\n",
      "Progress  40.04 %\n",
      "Progress  40.06 %\n",
      "Progress  40.08 %\n",
      "Progress  40.10 %\n",
      "Progress  40.12 %\n",
      "Progress  40.14 %\n",
      "Progress  40.16 %\n",
      "Progress  40.18 %\n",
      "Progress  40.20 %\n",
      "Progress  40.22 %\n",
      "Progress  40.24 %\n",
      "Progress  40.26 %\n",
      "Progress  40.28 %\n",
      "Progress  40.30 %\n",
      "Progress  40.32 %\n",
      "Progress  40.34 %\n",
      "Progress  40.36 %\n",
      "Progress  40.38 %\n",
      "Progress  40.40 %\n",
      "Progress  40.42 %\n",
      "Progress  40.44 %\n",
      "Progress  40.46 %\n",
      "Progress  40.48 %\n",
      "Progress  40.50 %\n",
      "Progress  40.52 %\n",
      "Progress  40.54 %\n",
      "Progress  40.56 %\n",
      "Progress  40.58 %\n",
      "Progress  40.60 %\n",
      "Progress  40.62 %\n",
      "Progress  40.64 %\n",
      "Progress  40.66 %\n",
      "Progress  40.68 %\n",
      "Progress  40.70 %\n",
      "Progress  40.72 %\n",
      "Progress  40.74 %\n",
      "Progress  40.76 %\n",
      "Progress  40.78 %\n",
      "Progress  40.80 %\n",
      "Progress  40.82 %\n",
      "Progress  40.84 %\n",
      "Progress  40.86 %\n",
      "Progress  40.88 %\n",
      "Progress  40.90 %\n",
      "Progress  40.92 %\n",
      "Progress  40.94 %\n",
      "Progress  40.96 %\n",
      "Progress  40.98 %\n",
      "Progress  41.00 %\n",
      "Progress  41.02 %\n",
      "Progress  41.04 %\n",
      "Progress  41.06 %\n",
      "Progress  41.08 %\n",
      "Progress  41.10 %\n",
      "Progress  41.12 %\n",
      "Progress  41.14 %\n",
      "Progress  41.16 %\n",
      "Progress  41.18 %\n",
      "Progress  41.20 %\n",
      "Progress  41.22 %\n",
      "Progress  41.24 %\n",
      "Progress  41.26 %\n",
      "Progress  41.28 %\n",
      "Progress  41.30 %\n",
      "Progress  41.32 %\n",
      "Progress  41.34 %\n",
      "Progress  41.36 %\n",
      "Progress  41.38 %\n",
      "Progress  41.40 %\n",
      "Progress  41.42 %\n",
      "Progress  41.44 %\n",
      "Progress  41.46 %\n",
      "Progress  41.48 %\n",
      "Progress  41.50 %\n",
      "Progress  41.52 %\n",
      "Progress  41.54 %\n",
      "Progress  41.56 %\n",
      "Progress  41.58 %\n",
      "Progress  41.60 %\n",
      "Progress  41.62 %\n",
      "Progress  41.64 %\n",
      "Progress  41.66 %\n",
      "Progress  41.68 %\n",
      "Progress  41.70 %\n",
      "Progress  41.72 %\n",
      "Progress  41.74 %\n",
      "Progress  41.76 %\n",
      "Progress  41.78 %\n",
      "Progress  41.80 %\n",
      "Progress  41.82 %\n",
      "Progress  41.84 %\n",
      "Progress  41.86 %\n",
      "Progress  41.88 %\n",
      "Progress  41.90 %\n",
      "Progress  41.92 %\n",
      "Progress  41.94 %\n",
      "Progress  41.96 %\n",
      "Progress  41.98 %\n",
      "Progress  42.00 %\n",
      "Progress  42.02 %\n",
      "Progress  42.04 %\n",
      "Progress  42.06 %\n",
      "Progress  42.08 %\n",
      "Progress  42.10 %\n",
      "Progress  42.12 %\n",
      "Progress  42.14 %\n",
      "Progress  42.16 %\n",
      "Progress  42.18 %\n",
      "Progress  42.20 %\n",
      "Progress  42.22 %\n",
      "Progress  42.24 %\n",
      "Progress  42.26 %\n",
      "Progress  42.28 %\n",
      "Progress  42.30 %\n",
      "Progress  42.32 %\n",
      "Progress  42.34 %\n",
      "Progress  42.36 %\n",
      "Progress  42.38 %\n",
      "Progress  42.40 %\n",
      "Progress  42.42 %\n",
      "Progress  42.44 %\n",
      "Progress  42.46 %\n",
      "Progress  42.48 %\n",
      "Progress  42.50 %\n",
      "Progress  42.52 %\n",
      "Progress  42.54 %\n",
      "Progress  42.56 %\n",
      "Progress  42.58 %\n",
      "Progress  42.60 %\n",
      "Progress  42.62 %\n",
      "Progress  42.64 %\n",
      "Progress  42.66 %\n",
      "Progress  42.68 %\n",
      "Progress  42.70 %\n",
      "Progress  42.72 %\n",
      "Progress  42.74 %\n",
      "Progress  42.76 %\n",
      "Progress  42.78 %\n",
      "Progress  42.80 %\n",
      "Progress  42.82 %\n",
      "Progress  42.84 %\n",
      "Progress  42.86 %\n",
      "Progress  42.88 %\n",
      "Progress  42.90 %\n",
      "Progress  42.92 %\n",
      "Progress  42.94 %\n",
      "Progress  42.96 %\n",
      "Progress  42.98 %\n",
      "Progress  43.00 %\n",
      "Progress  43.02 %\n",
      "Progress  43.04 %\n",
      "Progress  43.06 %\n",
      "Progress  43.08 %\n",
      "Progress  43.10 %\n",
      "Progress  43.12 %\n",
      "Progress  43.14 %\n",
      "Progress  43.16 %\n",
      "Progress  43.18 %\n",
      "Progress  43.20 %\n",
      "Progress  43.22 %\n",
      "Progress  43.24 %\n",
      "Progress  43.26 %\n",
      "Progress  43.28 %\n",
      "Progress  43.30 %\n",
      "Progress  43.32 %\n",
      "Progress  43.34 %\n",
      "Progress  43.36 %\n",
      "Progress  43.38 %\n",
      "Progress  43.40 %\n",
      "Progress  43.42 %\n",
      "Progress  43.44 %\n",
      "Progress  43.46 %\n",
      "Progress  43.48 %\n",
      "Progress  43.50 %\n",
      "Progress  43.52 %\n",
      "Progress  43.54 %\n",
      "Progress  43.56 %\n",
      "Progress  43.58 %\n",
      "Progress  43.60 %\n",
      "Progress  43.62 %\n",
      "Progress  43.64 %\n",
      "Progress  43.66 %\n",
      "Progress  43.68 %\n",
      "Progress  43.70 %\n",
      "Progress  43.72 %\n",
      "Progress  43.74 %\n",
      "Progress  43.76 %\n",
      "Progress  43.78 %\n",
      "Progress  43.80 %\n",
      "Progress  43.82 %\n",
      "Progress  43.84 %\n",
      "Progress  43.86 %\n",
      "Progress  43.88 %\n",
      "Progress  43.90 %\n",
      "Progress  43.92 %\n",
      "Progress  43.94 %\n",
      "Progress  43.96 %\n",
      "Progress  43.98 %\n",
      "Progress  44.00 %\n",
      "Progress  44.02 %\n",
      "Progress  44.04 %\n",
      "Progress  44.06 %\n",
      "Progress  44.08 %\n",
      "Progress  44.10 %\n",
      "Progress  44.12 %\n",
      "Progress  44.14 %\n",
      "Progress  44.16 %\n",
      "Progress  44.18 %\n",
      "Progress  44.20 %\n",
      "Progress  44.22 %\n",
      "Progress  44.24 %\n",
      "Progress  44.26 %\n",
      "Progress  44.28 %\n",
      "Progress  44.30 %\n",
      "Progress  44.32 %\n",
      "Progress  44.34 %\n",
      "Progress  44.36 %\n",
      "Progress  44.38 %\n",
      "Progress  44.40 %\n",
      "Progress  44.42 %\n",
      "Progress  44.44 %\n",
      "Progress  44.46 %\n",
      "Progress  44.48 %\n",
      "Progress  44.50 %\n",
      "Progress  44.52 %\n",
      "Progress  44.54 %\n",
      "Progress  44.56 %\n",
      "Progress  44.58 %\n",
      "Progress  44.60 %\n",
      "Progress  44.62 %\n",
      "Progress  44.64 %\n",
      "Progress  44.66 %\n",
      "Progress  44.68 %\n",
      "Progress  44.70 %\n",
      "Progress  44.72 %\n",
      "Progress  44.74 %\n",
      "Progress  44.76 %\n",
      "Progress  44.78 %\n",
      "Progress  44.80 %\n",
      "Progress  44.82 %\n",
      "Progress  44.84 %\n",
      "Progress  44.86 %\n",
      "Progress  44.88 %\n",
      "Progress  44.90 %\n",
      "Progress  44.92 %\n",
      "Progress  44.94 %\n",
      "Progress  44.96 %\n",
      "Progress  44.98 %\n",
      "Progress  45.00 %\n",
      "Progress  45.02 %\n",
      "Progress  45.04 %\n",
      "Progress  45.06 %\n",
      "Progress  45.08 %\n",
      "Progress  45.10 %\n",
      "Progress  45.12 %\n",
      "Progress  45.14 %\n",
      "Progress  45.16 %\n",
      "Progress  45.18 %\n",
      "Progress  45.20 %\n",
      "Progress  45.22 %\n",
      "Progress  45.24 %\n",
      "Progress  45.26 %\n",
      "Progress  45.28 %\n",
      "Progress  45.30 %\n",
      "Progress  45.32 %\n",
      "Progress  45.34 %\n",
      "Progress  45.36 %\n",
      "Progress  45.38 %\n",
      "Progress  45.40 %\n",
      "Progress  45.42 %\n",
      "Progress  45.44 %\n",
      "Progress  45.46 %\n",
      "Progress  45.48 %\n",
      "Progress  45.50 %\n",
      "Progress  45.52 %\n",
      "Progress  45.54 %\n",
      "Progress  45.56 %\n",
      "Progress  45.58 %\n",
      "Progress  45.60 %\n",
      "Progress  45.62 %\n",
      "Progress  45.64 %\n",
      "Progress  45.66 %\n",
      "Progress  45.68 %\n",
      "Progress  45.70 %\n",
      "Progress  45.72 %\n",
      "Progress  45.74 %\n",
      "Progress  45.76 %\n",
      "Progress  45.78 %\n",
      "Progress  45.80 %\n",
      "Progress  45.82 %\n",
      "Progress  45.84 %\n",
      "Progress  45.86 %\n",
      "Progress  45.88 %\n",
      "Progress  45.90 %\n",
      "Progress  45.92 %\n",
      "Progress  45.94 %\n",
      "Progress  45.96 %\n",
      "Progress  45.98 %\n",
      "Progress  46.00 %\n",
      "Progress  46.02 %\n",
      "Progress  46.04 %\n",
      "Progress  46.06 %\n",
      "Progress  46.08 %\n",
      "Progress  46.10 %\n",
      "Progress  46.12 %\n",
      "Progress  46.14 %\n",
      "Progress  46.16 %\n",
      "Progress  46.18 %\n",
      "Progress  46.20 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  46.22 %\n",
      "Progress  46.24 %\n",
      "Progress  46.26 %\n",
      "Progress  46.28 %\n",
      "Progress  46.30 %\n",
      "Progress  46.32 %\n",
      "Progress  46.34 %\n",
      "Progress  46.36 %\n",
      "Progress  46.38 %\n",
      "Progress  46.40 %\n",
      "Progress  46.42 %\n",
      "Progress  46.44 %\n",
      "Progress  46.46 %\n",
      "Progress  46.48 %\n",
      "Progress  46.50 %\n",
      "Progress  46.52 %\n",
      "Progress  46.54 %\n",
      "Progress  46.56 %\n",
      "Progress  46.58 %\n",
      "Progress  46.60 %\n",
      "Progress  46.62 %\n",
      "Progress  46.64 %\n",
      "Progress  46.66 %\n",
      "Progress  46.68 %\n",
      "Progress  46.70 %\n",
      "Progress  46.72 %\n",
      "Progress  46.74 %\n",
      "Progress  46.76 %\n",
      "Progress  46.78 %\n",
      "Progress  46.80 %\n",
      "Progress  46.82 %\n",
      "Progress  46.84 %\n",
      "Progress  46.86 %\n",
      "Progress  46.88 %\n",
      "Progress  46.90 %\n",
      "Progress  46.92 %\n",
      "Progress  46.94 %\n",
      "Progress  46.96 %\n",
      "Progress  46.98 %\n",
      "Progress  47.00 %\n",
      "Progress  47.02 %\n",
      "Progress  47.04 %\n",
      "Progress  47.06 %\n",
      "Progress  47.08 %\n",
      "Progress  47.10 %\n",
      "Progress  47.12 %\n",
      "Progress  47.14 %\n",
      "Progress  47.16 %\n",
      "Progress  47.18 %\n",
      "Progress  47.20 %\n",
      "Progress  47.22 %\n",
      "Progress  47.24 %\n",
      "Progress  47.26 %\n",
      "Progress  47.28 %\n",
      "Progress  47.30 %\n",
      "Progress  47.32 %\n",
      "Progress  47.34 %\n",
      "Progress  47.36 %\n",
      "Progress  47.38 %\n",
      "Progress  47.40 %\n",
      "Progress  47.42 %\n",
      "Progress  47.44 %\n",
      "Progress  47.46 %\n",
      "Progress  47.48 %\n",
      "Progress  47.50 %\n",
      "Progress  47.52 %\n",
      "Progress  47.54 %\n",
      "Progress  47.56 %\n",
      "Progress  47.58 %\n",
      "Progress  47.60 %\n",
      "Progress  47.62 %\n",
      "Progress  47.64 %\n",
      "Progress  47.66 %\n",
      "Progress  47.68 %\n",
      "Progress  47.70 %\n",
      "Progress  47.72 %\n",
      "Progress  47.74 %\n",
      "Progress  47.76 %\n",
      "Progress  47.78 %\n",
      "Progress  47.80 %\n",
      "Progress  47.82 %\n",
      "Progress  47.84 %\n",
      "Progress  47.86 %\n",
      "Progress  47.88 %\n",
      "Progress  47.90 %\n",
      "Progress  47.92 %\n",
      "Progress  47.94 %\n",
      "Progress  47.96 %\n",
      "Progress  47.98 %\n",
      "Progress  48.00 %\n",
      "Progress  48.02 %\n",
      "Progress  48.04 %\n",
      "Progress  48.06 %\n",
      "Progress  48.08 %\n",
      "Progress  48.10 %\n",
      "Progress  48.12 %\n",
      "Progress  48.14 %\n",
      "Progress  48.16 %\n",
      "Progress  48.18 %\n",
      "Progress  48.20 %\n",
      "Progress  48.22 %\n",
      "Progress  48.24 %\n",
      "Progress  48.26 %\n",
      "Progress  48.28 %\n",
      "Progress  48.30 %\n",
      "Progress  48.32 %\n",
      "Progress  48.34 %\n",
      "Progress  48.36 %\n",
      "Progress  48.38 %\n",
      "Progress  48.40 %\n",
      "Progress  48.42 %\n",
      "Progress  48.44 %\n",
      "Progress  48.46 %\n",
      "Progress  48.48 %\n",
      "Progress  48.50 %\n",
      "Progress  48.52 %\n",
      "Progress  48.54 %\n",
      "Progress  48.56 %\n",
      "Progress  48.58 %\n",
      "Progress  48.60 %\n",
      "Progress  48.62 %\n",
      "Progress  48.64 %\n",
      "Progress  48.66 %\n",
      "Progress  48.68 %\n",
      "Progress  48.70 %\n",
      "Progress  48.72 %\n",
      "Progress  48.74 %\n",
      "Progress  48.76 %\n",
      "Progress  48.78 %\n",
      "Progress  48.80 %\n",
      "Progress  48.82 %\n",
      "Progress  48.84 %\n",
      "Progress  48.86 %\n",
      "Progress  48.88 %\n",
      "Progress  48.90 %\n",
      "Progress  48.92 %\n",
      "Progress  48.94 %\n",
      "Progress  48.96 %\n",
      "Progress  48.98 %\n",
      "Progress  49.00 %\n",
      "Progress  49.02 %\n",
      "Progress  49.04 %\n",
      "Progress  49.06 %\n",
      "Progress  49.08 %\n",
      "Progress  49.10 %\n",
      "Progress  49.12 %\n",
      "Progress  49.14 %\n",
      "Progress  49.16 %\n",
      "Progress  49.18 %\n",
      "Progress  49.20 %\n",
      "Progress  49.22 %\n",
      "Progress  49.24 %\n",
      "Progress  49.26 %\n",
      "Progress  49.28 %\n",
      "Progress  49.30 %\n",
      "Progress  49.32 %\n",
      "Progress  49.34 %\n",
      "Progress  49.36 %\n",
      "Progress  49.38 %\n",
      "Progress  49.40 %\n",
      "Progress  49.42 %\n",
      "Progress  49.44 %\n",
      "Progress  49.46 %\n",
      "Progress  49.48 %\n",
      "Progress  49.50 %\n",
      "Progress  49.52 %\n",
      "Progress  49.54 %\n",
      "Progress  49.56 %\n",
      "Progress  49.58 %\n",
      "Progress  49.60 %\n",
      "Progress  49.62 %\n",
      "Progress  49.64 %\n",
      "Progress  49.66 %\n",
      "Progress  49.68 %\n",
      "Progress  49.70 %\n",
      "Progress  49.72 %\n",
      "Progress  49.74 %\n",
      "Progress  49.76 %\n",
      "Progress  49.78 %\n",
      "Progress  49.80 %\n",
      "Progress  49.82 %\n",
      "Progress  49.84 %\n",
      "Progress  49.86 %\n",
      "Progress  49.88 %\n",
      "Progress  49.90 %\n",
      "Progress  49.92 %\n",
      "Progress  49.94 %\n",
      "Progress  49.96 %\n",
      "Progress  49.98 %\n",
      "Progress  50.00 %\n",
      "Progress  50.02 %\n",
      "Progress  50.04 %\n",
      "Progress  50.06 %\n",
      "Progress  50.08 %\n",
      "Progress  50.10 %\n",
      "Progress  50.12 %\n",
      "Progress  50.14 %\n",
      "Progress  50.16 %\n",
      "Progress  50.18 %\n",
      "Progress  50.20 %\n",
      "Progress  50.22 %\n",
      "Progress  50.24 %\n",
      "Progress  50.26 %\n",
      "Progress  50.28 %\n",
      "Progress  50.30 %\n",
      "Progress  50.32 %\n",
      "Progress  50.34 %\n",
      "Progress  50.36 %\n",
      "Progress  50.38 %\n",
      "Progress  50.40 %\n",
      "Progress  50.42 %\n",
      "Progress  50.44 %\n",
      "Progress  50.46 %\n",
      "Progress  50.48 %\n",
      "Progress  50.50 %\n",
      "Progress  50.52 %\n",
      "Progress  50.54 %\n",
      "Progress  50.56 %\n",
      "Progress  50.58 %\n",
      "Progress  50.60 %\n",
      "Progress  50.62 %\n",
      "Progress  50.64 %\n",
      "Progress  50.66 %\n",
      "Progress  50.68 %\n",
      "Progress  50.70 %\n",
      "Progress  50.72 %\n",
      "Progress  50.74 %\n",
      "Progress  50.76 %\n",
      "Progress  50.78 %\n",
      "Progress  50.80 %\n",
      "Progress  50.82 %\n",
      "Progress  50.84 %\n",
      "Progress  50.86 %\n",
      "Progress  50.88 %\n",
      "Progress  50.90 %\n",
      "Progress  50.92 %\n",
      "Progress  50.94 %\n",
      "Progress  50.96 %\n",
      "Progress  50.98 %\n",
      "Progress  51.00 %\n",
      "Progress  51.02 %\n",
      "Progress  51.04 %\n",
      "Progress  51.06 %\n",
      "Progress  51.08 %\n",
      "Progress  51.10 %\n",
      "Progress  51.12 %\n",
      "Progress  51.14 %\n",
      "Progress  51.16 %\n",
      "Progress  51.18 %\n",
      "Progress  51.20 %\n",
      "Progress  51.22 %\n",
      "Progress  51.24 %\n",
      "Progress  51.26 %\n",
      "Progress  51.28 %\n",
      "Progress  51.30 %\n",
      "Progress  51.32 %\n",
      "Progress  51.34 %\n",
      "Progress  51.36 %\n",
      "Progress  51.38 %\n",
      "Progress  51.40 %\n",
      "Progress  51.42 %\n",
      "Progress  51.44 %\n",
      "Progress  51.46 %\n",
      "Progress  51.48 %\n",
      "Progress  51.50 %\n",
      "Progress  51.52 %\n",
      "Progress  51.54 %\n",
      "Progress  51.56 %\n",
      "Progress  51.58 %\n",
      "Progress  51.60 %\n",
      "Progress  51.62 %\n",
      "Progress  51.64 %\n",
      "Progress  51.66 %\n",
      "Progress  51.68 %\n",
      "Progress  51.70 %\n",
      "Progress  51.72 %\n",
      "Progress  51.74 %\n",
      "Progress  51.76 %\n",
      "Progress  51.78 %\n",
      "Progress  51.80 %\n",
      "Progress  51.82 %\n",
      "Progress  51.84 %\n",
      "Progress  51.86 %\n",
      "Progress  51.88 %\n",
      "Progress  51.90 %\n",
      "Progress  51.92 %\n",
      "Progress  51.94 %\n",
      "Progress  51.96 %\n",
      "Progress  51.98 %\n",
      "Progress  52.00 %\n",
      "Progress  52.02 %\n",
      "Progress  52.04 %\n",
      "Progress  52.06 %\n",
      "Progress  52.08 %\n",
      "Progress  52.10 %\n",
      "Progress  52.12 %\n",
      "Progress  52.14 %\n",
      "Progress  52.16 %\n",
      "Progress  52.18 %\n",
      "Progress  52.20 %\n",
      "Progress  52.22 %\n",
      "Progress  52.24 %\n",
      "Progress  52.26 %\n",
      "Progress  52.28 %\n",
      "Progress  52.30 %\n",
      "Progress  52.32 %\n",
      "Progress  52.34 %\n",
      "Progress  52.36 %\n",
      "Progress  52.38 %\n",
      "Progress  52.40 %\n",
      "Progress  52.42 %\n",
      "Progress  52.44 %\n",
      "Progress  52.46 %\n",
      "Progress  52.48 %\n",
      "Progress  52.50 %\n",
      "Progress  52.52 %\n",
      "Progress  52.54 %\n",
      "Progress  52.56 %\n",
      "Progress  52.58 %\n",
      "Progress  52.60 %\n",
      "Progress  52.62 %\n",
      "Progress  52.64 %\n",
      "Progress  52.66 %\n",
      "Progress  52.68 %\n",
      "Progress  52.70 %\n",
      "Progress  52.72 %\n",
      "Progress  52.74 %\n",
      "Progress  52.76 %\n",
      "Progress  52.78 %\n",
      "Progress  52.80 %\n",
      "Progress  52.82 %\n",
      "Progress  52.84 %\n",
      "Progress  52.86 %\n",
      "Progress  52.88 %\n",
      "Progress  52.90 %\n",
      "Progress  52.92 %\n",
      "Progress  52.94 %\n",
      "Progress  52.96 %\n",
      "Progress  52.98 %\n",
      "Progress  53.00 %\n",
      "Progress  53.02 %\n",
      "Progress  53.04 %\n",
      "Progress  53.06 %\n",
      "Progress  53.08 %\n",
      "Progress  53.10 %\n",
      "Progress  53.12 %\n",
      "Progress  53.14 %\n",
      "Progress  53.16 %\n",
      "Progress  53.18 %\n",
      "Progress  53.20 %\n",
      "Progress  53.22 %\n",
      "Progress  53.24 %\n",
      "Progress  53.26 %\n",
      "Progress  53.28 %\n",
      "Progress  53.30 %\n",
      "Progress  53.32 %\n",
      "Progress  53.34 %\n",
      "Progress  53.36 %\n",
      "Progress  53.38 %\n",
      "Progress  53.40 %\n",
      "Progress  53.42 %\n",
      "Progress  53.44 %\n",
      "Progress  53.46 %\n",
      "Progress  53.48 %\n",
      "Progress  53.50 %\n",
      "Progress  53.52 %\n",
      "Progress  53.54 %\n",
      "Progress  53.56 %\n",
      "Progress  53.58 %\n",
      "Progress  53.60 %\n",
      "Progress  53.62 %\n",
      "Progress  53.64 %\n",
      "Progress  53.66 %\n",
      "Progress  53.68 %\n",
      "Progress  53.70 %\n",
      "Progress  53.72 %\n",
      "Progress  53.74 %\n",
      "Progress  53.76 %\n",
      "Progress  53.78 %\n",
      "Progress  53.80 %\n",
      "Progress  53.82 %\n",
      "Progress  53.84 %\n",
      "Progress  53.86 %\n",
      "Progress  53.88 %\n",
      "Progress  53.90 %\n",
      "Progress  53.92 %\n",
      "Progress  53.94 %\n",
      "Progress  53.96 %\n",
      "Progress  53.98 %\n",
      "Progress  54.00 %\n",
      "Progress  54.02 %\n",
      "Progress  54.04 %\n",
      "Progress  54.06 %\n",
      "Progress  54.08 %\n",
      "Progress  54.10 %\n",
      "Progress  54.12 %\n",
      "Progress  54.14 %\n",
      "Progress  54.16 %\n",
      "Progress  54.18 %\n",
      "Progress  54.20 %\n",
      "Progress  54.22 %\n",
      "Progress  54.24 %\n",
      "Progress  54.26 %\n",
      "Progress  54.28 %\n",
      "Progress  54.30 %\n",
      "Progress  54.32 %\n",
      "Progress  54.34 %\n",
      "Progress  54.36 %\n",
      "Progress  54.38 %\n",
      "Progress  54.40 %\n",
      "Progress  54.42 %\n",
      "Progress  54.44 %\n",
      "Progress  54.46 %\n",
      "Progress  54.48 %\n",
      "Progress  54.50 %\n",
      "Progress  54.52 %\n",
      "Progress  54.54 %\n",
      "Progress  54.56 %\n",
      "Progress  54.58 %\n",
      "Progress  54.60 %\n",
      "Progress  54.62 %\n",
      "Progress  54.64 %\n",
      "Progress  54.66 %\n",
      "Progress  54.68 %\n",
      "Progress  54.70 %\n",
      "Progress  54.72 %\n",
      "Progress  54.74 %\n",
      "Progress  54.76 %\n",
      "Progress  54.78 %\n",
      "Progress  54.80 %\n",
      "Progress  54.82 %\n",
      "Progress  54.84 %\n",
      "Progress  54.86 %\n",
      "Progress  54.88 %\n",
      "Progress  54.90 %\n",
      "Progress  54.92 %\n",
      "Progress  54.94 %\n",
      "Progress  54.96 %\n",
      "Progress  54.98 %\n",
      "Progress  55.00 %\n",
      "Progress  55.02 %\n",
      "Progress  55.04 %\n",
      "Progress  55.06 %\n",
      "Progress  55.08 %\n",
      "Progress  55.10 %\n",
      "Progress  55.12 %\n",
      "Progress  55.14 %\n",
      "Progress  55.16 %\n",
      "Progress  55.18 %\n",
      "Progress  55.20 %\n",
      "Progress  55.22 %\n",
      "Progress  55.24 %\n",
      "Progress  55.26 %\n",
      "Progress  55.28 %\n",
      "Progress  55.30 %\n",
      "Progress  55.32 %\n",
      "Progress  55.34 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  55.36 %\n",
      "Progress  55.38 %\n",
      "Progress  55.40 %\n",
      "Progress  55.42 %\n",
      "Progress  55.44 %\n",
      "Progress  55.46 %\n",
      "Progress  55.48 %\n",
      "Progress  55.50 %\n",
      "Progress  55.52 %\n",
      "Progress  55.54 %\n",
      "Progress  55.56 %\n",
      "Progress  55.58 %\n",
      "Progress  55.60 %\n",
      "Progress  55.62 %\n",
      "Progress  55.64 %\n",
      "Progress  55.66 %\n",
      "Progress  55.68 %\n",
      "Progress  55.70 %\n",
      "Progress  55.72 %\n",
      "Progress  55.74 %\n",
      "Progress  55.76 %\n",
      "Progress  55.78 %\n",
      "Progress  55.80 %\n",
      "Progress  55.82 %\n",
      "Progress  55.84 %\n",
      "Progress  55.86 %\n",
      "Progress  55.88 %\n",
      "Progress  55.90 %\n",
      "Progress  55.92 %\n",
      "Progress  55.94 %\n",
      "Progress  55.96 %\n",
      "Progress  55.98 %\n",
      "Progress  56.00 %\n",
      "Progress  56.02 %\n",
      "Progress  56.04 %\n",
      "Progress  56.06 %\n",
      "Progress  56.08 %\n",
      "Progress  56.10 %\n",
      "Progress  56.12 %\n",
      "Progress  56.14 %\n",
      "Progress  56.16 %\n",
      "Progress  56.18 %\n",
      "Progress  56.20 %\n",
      "Progress  56.22 %\n",
      "Progress  56.24 %\n",
      "Progress  56.26 %\n",
      "Progress  56.28 %\n",
      "Progress  56.30 %\n",
      "Progress  56.32 %\n",
      "Progress  56.34 %\n",
      "Progress  56.36 %\n",
      "Progress  56.38 %\n",
      "Progress  56.40 %\n",
      "Progress  56.42 %\n",
      "Progress  56.44 %\n",
      "Progress  56.46 %\n",
      "Progress  56.48 %\n",
      "Progress  56.50 %\n",
      "Progress  56.52 %\n",
      "Progress  56.54 %\n",
      "Progress  56.56 %\n",
      "Progress  56.58 %\n",
      "Progress  56.60 %\n",
      "Progress  56.62 %\n",
      "Progress  56.64 %\n",
      "Progress  56.66 %\n",
      "Progress  56.68 %\n",
      "Progress  56.70 %\n",
      "Progress  56.72 %\n",
      "Progress  56.74 %\n",
      "Progress  56.76 %\n",
      "Progress  56.78 %\n",
      "Progress  56.80 %\n",
      "Progress  56.82 %\n",
      "Progress  56.84 %\n",
      "Progress  56.86 %\n",
      "Progress  56.88 %\n",
      "Progress  56.90 %\n",
      "Progress  56.92 %\n",
      "Progress  56.94 %\n",
      "Progress  56.96 %\n",
      "Progress  56.98 %\n",
      "Progress  57.00 %\n",
      "Progress  57.02 %\n",
      "Progress  57.04 %\n",
      "Progress  57.06 %\n",
      "Progress  57.08 %\n",
      "Progress  57.10 %\n",
      "Progress  57.12 %\n",
      "Progress  57.14 %\n",
      "Progress  57.16 %\n",
      "Progress  57.18 %\n",
      "Progress  57.20 %\n",
      "Progress  57.22 %\n",
      "Progress  57.24 %\n",
      "Progress  57.26 %\n",
      "Progress  57.28 %\n",
      "Progress  57.30 %\n",
      "Progress  57.32 %\n",
      "Progress  57.34 %\n",
      "Progress  57.36 %\n",
      "Progress  57.38 %\n",
      "Progress  57.40 %\n",
      "Progress  57.42 %\n",
      "Progress  57.44 %\n",
      "Progress  57.46 %\n",
      "Progress  57.48 %\n",
      "Progress  57.50 %\n",
      "Progress  57.52 %\n",
      "Progress  57.54 %\n",
      "Progress  57.56 %\n",
      "Progress  57.58 %\n",
      "Progress  57.60 %\n",
      "Progress  57.62 %\n",
      "Progress  57.64 %\n",
      "Progress  57.66 %\n",
      "Progress  57.68 %\n",
      "Progress  57.70 %\n",
      "Progress  57.72 %\n",
      "Progress  57.74 %\n",
      "Progress  57.76 %\n",
      "Progress  57.78 %\n",
      "Progress  57.80 %\n",
      "Progress  57.82 %\n",
      "Progress  57.84 %\n",
      "Progress  57.86 %\n",
      "Progress  57.88 %\n",
      "Progress  57.90 %\n",
      "Progress  57.92 %\n",
      "Progress  57.94 %\n",
      "Progress  57.96 %\n",
      "Progress  57.98 %\n",
      "Progress  58.00 %\n",
      "Progress  58.02 %\n",
      "Progress  58.04 %\n",
      "Progress  58.06 %\n",
      "Progress  58.08 %\n",
      "Progress  58.10 %\n",
      "Progress  58.12 %\n",
      "Progress  58.14 %\n",
      "Progress  58.16 %\n",
      "Progress  58.18 %\n",
      "Progress  58.20 %\n",
      "Progress  58.22 %\n",
      "Progress  58.24 %\n",
      "Progress  58.26 %\n",
      "Progress  58.28 %\n",
      "Progress  58.30 %\n",
      "Progress  58.32 %\n",
      "Progress  58.34 %\n",
      "Progress  58.36 %\n",
      "Progress  58.38 %\n",
      "Progress  58.40 %\n",
      "Progress  58.42 %\n",
      "Progress  58.44 %\n",
      "Progress  58.46 %\n",
      "Progress  58.48 %\n",
      "Progress  58.50 %\n",
      "Progress  58.52 %\n",
      "Progress  58.54 %\n",
      "Progress  58.56 %\n",
      "Progress  58.58 %\n",
      "Progress  58.60 %\n",
      "Progress  58.62 %\n",
      "Progress  58.64 %\n",
      "Progress  58.66 %\n",
      "Progress  58.68 %\n",
      "Progress  58.70 %\n",
      "Progress  58.72 %\n",
      "Progress  58.74 %\n",
      "Progress  58.76 %\n",
      "Progress  58.78 %\n",
      "Progress  58.80 %\n",
      "Progress  58.82 %\n",
      "Progress  58.84 %\n",
      "Progress  58.86 %\n",
      "Progress  58.88 %\n",
      "Progress  58.90 %\n",
      "Progress  58.92 %\n",
      "Progress  58.94 %\n",
      "Progress  58.96 %\n",
      "Progress  58.98 %\n",
      "Progress  59.00 %\n",
      "Progress  59.02 %\n",
      "Progress  59.04 %\n",
      "Progress  59.06 %\n",
      "Progress  59.08 %\n",
      "Progress  59.10 %\n",
      "Progress  59.12 %\n",
      "Progress  59.14 %\n",
      "Progress  59.16 %\n",
      "Progress  59.18 %\n",
      "Progress  59.20 %\n",
      "Progress  59.22 %\n",
      "Progress  59.24 %\n",
      "Progress  59.26 %\n",
      "Progress  59.28 %\n",
      "Progress  59.30 %\n",
      "Progress  59.32 %\n",
      "Progress  59.34 %\n",
      "Progress  59.36 %\n",
      "Progress  59.38 %\n",
      "Progress  59.40 %\n",
      "Progress  59.42 %\n",
      "Progress  59.44 %\n",
      "Progress  59.46 %\n",
      "Progress  59.48 %\n",
      "Progress  59.50 %\n",
      "Progress  59.52 %\n",
      "Progress  59.54 %\n",
      "Progress  59.56 %\n",
      "Progress  59.58 %\n",
      "Progress  59.60 %\n",
      "Progress  59.62 %\n",
      "Progress  59.64 %\n",
      "Progress  59.66 %\n",
      "Progress  59.68 %\n",
      "Progress  59.70 %\n",
      "Progress  59.72 %\n",
      "Progress  59.74 %\n",
      "Progress  59.76 %\n",
      "Progress  59.78 %\n",
      "Progress  59.80 %\n",
      "Progress  59.82 %\n",
      "Progress  59.84 %\n",
      "Progress  59.86 %\n",
      "Progress  59.88 %\n",
      "Progress  59.90 %\n",
      "Progress  59.92 %\n",
      "Progress  59.94 %\n",
      "Progress  59.96 %\n",
      "Progress  59.98 %\n",
      "Progress  60.00 %\n",
      "Progress  60.02 %\n",
      "Progress  60.04 %\n",
      "Progress  60.06 %\n",
      "Progress  60.08 %\n",
      "Progress  60.10 %\n",
      "Progress  60.12 %\n",
      "Progress  60.14 %\n",
      "Progress  60.16 %\n",
      "Progress  60.18 %\n",
      "Progress  60.20 %\n",
      "Progress  60.22 %\n",
      "Progress  60.24 %\n",
      "Progress  60.26 %\n",
      "Progress  60.28 %\n",
      "Progress  60.30 %\n",
      "Progress  60.32 %\n",
      "Progress  60.34 %\n",
      "Progress  60.36 %\n",
      "Progress  60.38 %\n",
      "Progress  60.40 %\n",
      "Progress  60.42 %\n",
      "Progress  60.44 %\n",
      "Progress  60.46 %\n",
      "Progress  60.48 %\n",
      "Progress  60.50 %\n",
      "Progress  60.52 %\n",
      "Progress  60.54 %\n",
      "Progress  60.56 %\n",
      "Progress  60.58 %\n",
      "Progress  60.60 %\n",
      "Progress  60.62 %\n",
      "Progress  60.64 %\n",
      "Progress  60.66 %\n",
      "Progress  60.68 %\n",
      "Progress  60.70 %\n",
      "Progress  60.72 %\n",
      "Progress  60.74 %\n",
      "Progress  60.76 %\n",
      "Progress  60.78 %\n",
      "Progress  60.80 %\n",
      "Progress  60.82 %\n",
      "Progress  60.84 %\n",
      "Progress  60.86 %\n",
      "Progress  60.88 %\n",
      "Progress  60.90 %\n",
      "Progress  60.92 %\n",
      "Progress  60.94 %\n",
      "Progress  60.96 %\n",
      "Progress  60.98 %\n",
      "Progress  61.00 %\n",
      "Progress  61.02 %\n",
      "Progress  61.04 %\n",
      "Progress  61.06 %\n",
      "Progress  61.08 %\n",
      "Progress  61.10 %\n",
      "Progress  61.12 %\n",
      "Progress  61.14 %\n",
      "Progress  61.16 %\n",
      "Progress  61.18 %\n",
      "Progress  61.20 %\n",
      "Progress  61.22 %\n",
      "Progress  61.24 %\n",
      "Progress  61.26 %\n",
      "Progress  61.28 %\n",
      "Progress  61.30 %\n",
      "Progress  61.32 %\n",
      "Progress  61.34 %\n",
      "Progress  61.36 %\n",
      "Progress  61.38 %\n",
      "Progress  61.40 %\n",
      "Progress  61.42 %\n",
      "Progress  61.44 %\n",
      "Progress  61.46 %\n",
      "Progress  61.48 %\n",
      "Progress  61.50 %\n",
      "Progress  61.52 %\n",
      "Progress  61.54 %\n",
      "Progress  61.56 %\n",
      "Progress  61.58 %\n",
      "Progress  61.60 %\n",
      "Progress  61.62 %\n",
      "Progress  61.64 %\n",
      "Progress  61.66 %\n",
      "Progress  61.68 %\n",
      "Progress  61.70 %\n",
      "Progress  61.72 %\n",
      "Progress  61.74 %\n",
      "Progress  61.76 %\n",
      "Progress  61.78 %\n",
      "Progress  61.80 %\n",
      "Progress  61.82 %\n",
      "Progress  61.84 %\n",
      "Progress  61.86 %\n",
      "Progress  61.88 %\n",
      "Progress  61.90 %\n",
      "Progress  61.92 %\n",
      "Progress  61.94 %\n",
      "Progress  61.96 %\n",
      "Progress  61.98 %\n",
      "Progress  62.00 %\n",
      "Progress  62.02 %\n",
      "Progress  62.04 %\n",
      "Progress  62.06 %\n",
      "Progress  62.08 %\n",
      "Progress  62.10 %\n",
      "Progress  62.12 %\n",
      "Progress  62.14 %\n",
      "Progress  62.16 %\n",
      "Progress  62.18 %\n",
      "Progress  62.20 %\n",
      "Progress  62.22 %\n",
      "Progress  62.24 %\n",
      "Progress  62.26 %\n",
      "Progress  62.28 %\n",
      "Progress  62.30 %\n",
      "Progress  62.32 %\n",
      "Progress  62.34 %\n",
      "Progress  62.36 %\n",
      "Progress  62.38 %\n",
      "Progress  62.40 %\n",
      "Progress  62.42 %\n",
      "Progress  62.44 %\n",
      "Progress  62.46 %\n",
      "Progress  62.48 %\n",
      "Progress  62.50 %\n",
      "Progress  62.52 %\n",
      "Progress  62.54 %\n",
      "Progress  62.56 %\n",
      "Progress  62.58 %\n",
      "Progress  62.60 %\n",
      "Progress  62.62 %\n",
      "Progress  62.64 %\n",
      "Progress  62.66 %\n",
      "Progress  62.68 %\n",
      "Progress  62.70 %\n",
      "Progress  62.72 %\n",
      "Progress  62.74 %\n",
      "Progress  62.76 %\n",
      "Progress  62.78 %\n",
      "Progress  62.80 %\n",
      "Progress  62.82 %\n",
      "Progress  62.84 %\n",
      "Progress  62.86 %\n",
      "Progress  62.88 %\n",
      "Progress  62.90 %\n",
      "Progress  62.92 %\n",
      "Progress  62.94 %\n",
      "Progress  62.96 %\n",
      "Progress  62.98 %\n",
      "Progress  63.00 %\n",
      "Progress  63.02 %\n",
      "Progress  63.04 %\n",
      "Progress  63.06 %\n",
      "Progress  63.08 %\n",
      "Progress  63.10 %\n",
      "Progress  63.12 %\n",
      "Progress  63.14 %\n",
      "Progress  63.16 %\n",
      "Progress  63.18 %\n",
      "Progress  63.20 %\n",
      "Progress  63.22 %\n",
      "Progress  63.24 %\n",
      "Progress  63.26 %\n",
      "Progress  63.28 %\n",
      "Progress  63.30 %\n",
      "Progress  63.32 %\n",
      "Progress  63.34 %\n",
      "Progress  63.36 %\n",
      "Progress  63.38 %\n",
      "Progress  63.40 %\n",
      "Progress  63.42 %\n",
      "Progress  63.44 %\n",
      "Progress  63.46 %\n",
      "Progress  63.48 %\n",
      "Progress  63.50 %\n",
      "Progress  63.52 %\n",
      "Progress  63.54 %\n",
      "Progress  63.56 %\n",
      "Progress  63.58 %\n",
      "Progress  63.60 %\n",
      "Progress  63.62 %\n",
      "Progress  63.64 %\n",
      "Progress  63.66 %\n",
      "Progress  63.68 %\n",
      "Progress  63.70 %\n",
      "Progress  63.72 %\n",
      "Progress  63.74 %\n",
      "Progress  63.76 %\n",
      "Progress  63.78 %\n",
      "Progress  63.80 %\n",
      "Progress  63.82 %\n",
      "Progress  63.84 %\n",
      "Progress  63.86 %\n",
      "Progress  63.88 %\n",
      "Progress  63.90 %\n",
      "Progress  63.92 %\n",
      "Progress  63.94 %\n",
      "Progress  63.96 %\n",
      "Progress  63.98 %\n",
      "Progress  64.00 %\n",
      "Progress  64.02 %\n",
      "Progress  64.04 %\n",
      "Progress  64.06 %\n",
      "Progress  64.08 %\n",
      "Progress  64.10 %\n",
      "Progress  64.12 %\n",
      "Progress  64.14 %\n",
      "Progress  64.16 %\n",
      "Progress  64.18 %\n",
      "Progress  64.20 %\n",
      "Progress  64.22 %\n",
      "Progress  64.24 %\n",
      "Progress  64.26 %\n",
      "Progress  64.28 %\n",
      "Progress  64.30 %\n",
      "Progress  64.32 %\n",
      "Progress  64.34 %\n",
      "Progress  64.36 %\n",
      "Progress  64.38 %\n",
      "Progress  64.40 %\n",
      "Progress  64.42 %\n",
      "Progress  64.44 %\n",
      "Progress  64.46 %\n",
      "Progress  64.48 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  64.50 %\n",
      "Progress  64.52 %\n",
      "Progress  64.54 %\n",
      "Progress  64.56 %\n",
      "Progress  64.58 %\n",
      "Progress  64.60 %\n",
      "Progress  64.62 %\n",
      "Progress  64.64 %\n",
      "Progress  64.66 %\n",
      "Progress  64.68 %\n",
      "Progress  64.70 %\n",
      "Progress  64.72 %\n",
      "Progress  64.74 %\n",
      "Progress  64.76 %\n",
      "Progress  64.78 %\n",
      "Progress  64.80 %\n",
      "Progress  64.82 %\n",
      "Progress  64.84 %\n",
      "Progress  64.86 %\n",
      "Progress  64.88 %\n",
      "Progress  64.90 %\n",
      "Progress  64.92 %\n",
      "Progress  64.94 %\n",
      "Progress  64.96 %\n",
      "Progress  64.98 %\n",
      "Progress  65.00 %\n",
      "Progress  65.02 %\n",
      "Progress  65.04 %\n",
      "Progress  65.06 %\n",
      "Progress  65.08 %\n",
      "Progress  65.10 %\n",
      "Progress  65.12 %\n",
      "Progress  65.14 %\n",
      "Progress  65.16 %\n",
      "Progress  65.18 %\n",
      "Progress  65.20 %\n",
      "Progress  65.22 %\n",
      "Progress  65.24 %\n",
      "Progress  65.26 %\n",
      "Progress  65.28 %\n",
      "Progress  65.30 %\n",
      "Progress  65.32 %\n",
      "Progress  65.34 %\n",
      "Progress  65.36 %\n",
      "Progress  65.38 %\n",
      "Progress  65.40 %\n",
      "Progress  65.42 %\n",
      "Progress  65.44 %\n",
      "Progress  65.46 %\n",
      "Progress  65.48 %\n",
      "Progress  65.50 %\n",
      "Progress  65.52 %\n",
      "Progress  65.54 %\n",
      "Progress  65.56 %\n",
      "Progress  65.58 %\n",
      "Progress  65.60 %\n",
      "Progress  65.62 %\n",
      "Progress  65.64 %\n",
      "Progress  65.66 %\n",
      "Progress  65.68 %\n",
      "Progress  65.70 %\n",
      "Progress  65.72 %\n",
      "Progress  65.74 %\n",
      "Progress  65.76 %\n",
      "Progress  65.78 %\n",
      "Progress  65.80 %\n",
      "Progress  65.82 %\n",
      "Progress  65.84 %\n",
      "Progress  65.86 %\n",
      "Progress  65.88 %\n",
      "Progress  65.90 %\n",
      "Progress  65.92 %\n",
      "Progress  65.94 %\n",
      "Progress  65.96 %\n",
      "Progress  65.98 %\n",
      "Progress  66.00 %\n",
      "Progress  66.02 %\n",
      "Progress  66.04 %\n",
      "Progress  66.06 %\n",
      "Progress  66.08 %\n",
      "Progress  66.10 %\n",
      "Progress  66.12 %\n",
      "Progress  66.14 %\n",
      "Progress  66.16 %\n",
      "Progress  66.18 %\n",
      "Progress  66.20 %\n",
      "Progress  66.22 %\n",
      "Progress  66.24 %\n",
      "Progress  66.26 %\n",
      "Progress  66.28 %\n",
      "Progress  66.30 %\n",
      "Progress  66.32 %\n",
      "Progress  66.34 %\n",
      "Progress  66.36 %\n",
      "Progress  66.38 %\n",
      "Progress  66.40 %\n",
      "Progress  66.42 %\n",
      "Progress  66.44 %\n",
      "Progress  66.46 %\n",
      "Progress  66.48 %\n",
      "Progress  66.50 %\n",
      "Progress  66.52 %\n",
      "Progress  66.54 %\n",
      "Progress  66.56 %\n",
      "Progress  66.58 %\n",
      "Progress  66.60 %\n",
      "Progress  66.62 %\n",
      "Progress  66.64 %\n",
      "Progress  66.66 %\n",
      "Progress  66.68 %\n",
      "Progress  66.70 %\n",
      "Progress  66.72 %\n",
      "Progress  66.74 %\n",
      "Progress  66.76 %\n",
      "Progress  66.78 %\n",
      "Progress  66.80 %\n",
      "Progress  66.82 %\n",
      "Progress  66.84 %\n",
      "Progress  66.86 %\n",
      "Progress  66.88 %\n",
      "Progress  66.90 %\n",
      "Progress  66.92 %\n",
      "Progress  66.94 %\n",
      "Progress  66.96 %\n",
      "Progress  66.98 %\n",
      "Progress  67.00 %\n",
      "Progress  67.02 %\n",
      "Progress  67.04 %\n",
      "Progress  67.06 %\n",
      "Progress  67.08 %\n",
      "Progress  67.10 %\n",
      "Progress  67.12 %\n",
      "Progress  67.14 %\n",
      "Progress  67.16 %\n",
      "Progress  67.18 %\n",
      "Progress  67.20 %\n",
      "Progress  67.22 %\n",
      "Progress  67.24 %\n",
      "Progress  67.26 %\n",
      "Progress  67.28 %\n",
      "Progress  67.30 %\n",
      "Progress  67.32 %\n",
      "Progress  67.34 %\n",
      "Progress  67.36 %\n",
      "Progress  67.38 %\n",
      "Progress  67.40 %\n",
      "Progress  67.42 %\n",
      "Progress  67.44 %\n",
      "Progress  67.46 %\n",
      "Progress  67.48 %\n",
      "Progress  67.50 %\n",
      "Progress  67.52 %\n",
      "Progress  67.54 %\n",
      "Progress  67.56 %\n",
      "Progress  67.58 %\n",
      "Progress  67.60 %\n",
      "Progress  67.62 %\n",
      "Progress  67.64 %\n",
      "Progress  67.66 %\n",
      "Progress  67.68 %\n",
      "Progress  67.70 %\n",
      "Progress  67.72 %\n",
      "Progress  67.74 %\n",
      "Progress  67.76 %\n",
      "Progress  67.78 %\n",
      "Progress  67.80 %\n",
      "Progress  67.82 %\n",
      "Progress  67.84 %\n",
      "Progress  67.86 %\n",
      "Progress  67.88 %\n",
      "Progress  67.90 %\n",
      "Progress  67.92 %\n",
      "Progress  67.94 %\n",
      "Progress  67.96 %\n",
      "Progress  67.98 %\n",
      "Progress  68.00 %\n",
      "Progress  68.02 %\n",
      "Progress  68.04 %\n",
      "Progress  68.06 %\n",
      "Progress  68.08 %\n",
      "Progress  68.10 %\n",
      "Progress  68.12 %\n",
      "Progress  68.14 %\n",
      "Progress  68.16 %\n",
      "Progress  68.18 %\n",
      "Progress  68.20 %\n",
      "Progress  68.22 %\n",
      "Progress  68.24 %\n",
      "Progress  68.26 %\n",
      "Progress  68.28 %\n",
      "Progress  68.30 %\n",
      "Progress  68.32 %\n",
      "Progress  68.34 %\n",
      "Progress  68.36 %\n",
      "Progress  68.38 %\n",
      "Progress  68.40 %\n",
      "Progress  68.42 %\n",
      "Progress  68.44 %\n",
      "Progress  68.46 %\n",
      "Progress  68.48 %\n",
      "Progress  68.50 %\n",
      "Progress  68.52 %\n",
      "Progress  68.54 %\n",
      "Progress  68.56 %\n",
      "Progress  68.58 %\n",
      "Progress  68.60 %\n",
      "Progress  68.62 %\n",
      "Progress  68.64 %\n",
      "Progress  68.66 %\n",
      "Progress  68.68 %\n",
      "Progress  68.70 %\n",
      "Progress  68.72 %\n",
      "Progress  68.74 %\n",
      "Progress  68.76 %\n",
      "Progress  68.78 %\n",
      "Progress  68.80 %\n",
      "Progress  68.82 %\n",
      "Progress  68.84 %\n",
      "Progress  68.86 %\n",
      "Progress  68.88 %\n",
      "Progress  68.90 %\n",
      "Progress  68.92 %\n",
      "Progress  68.94 %\n",
      "Progress  68.96 %\n",
      "Progress  68.98 %\n",
      "Progress  69.00 %\n",
      "Progress  69.02 %\n",
      "Progress  69.04 %\n",
      "Progress  69.06 %\n",
      "Progress  69.08 %\n",
      "Progress  69.10 %\n",
      "Progress  69.12 %\n",
      "Progress  69.14 %\n",
      "Progress  69.16 %\n",
      "Progress  69.18 %\n",
      "Progress  69.20 %\n",
      "Progress  69.22 %\n",
      "Progress  69.24 %\n",
      "Progress  69.26 %\n",
      "Progress  69.28 %\n",
      "Progress  69.30 %\n",
      "Progress  69.32 %\n",
      "Progress  69.34 %\n",
      "Progress  69.36 %\n",
      "Progress  69.38 %\n",
      "Progress  69.40 %\n",
      "Progress  69.42 %\n",
      "Progress  69.44 %\n",
      "Progress  69.46 %\n",
      "Progress  69.48 %\n",
      "Progress  69.50 %\n",
      "Progress  69.52 %\n",
      "Progress  69.54 %\n",
      "Progress  69.56 %\n",
      "Progress  69.58 %\n",
      "Progress  69.60 %\n",
      "Progress  69.62 %\n",
      "Progress  69.64 %\n",
      "Progress  69.66 %\n",
      "Progress  69.68 %\n",
      "Progress  69.70 %\n",
      "Progress  69.72 %\n",
      "Progress  69.74 %\n",
      "Progress  69.76 %\n",
      "Progress  69.78 %\n",
      "Progress  69.80 %\n",
      "Progress  69.82 %\n",
      "Progress  69.84 %\n",
      "Progress  69.86 %\n",
      "Progress  69.88 %\n",
      "Progress  69.90 %\n",
      "Progress  69.92 %\n",
      "Progress  69.94 %\n",
      "Progress  69.96 %\n",
      "Progress  69.98 %\n",
      "Progress  70.00 %\n",
      "Progress  70.02 %\n",
      "Progress  70.04 %\n",
      "Progress  70.06 %\n",
      "Progress  70.08 %\n",
      "Progress  70.10 %\n",
      "Progress  70.12 %\n",
      "Progress  70.14 %\n",
      "Progress  70.16 %\n",
      "Progress  70.18 %\n",
      "Progress  70.20 %\n",
      "Progress  70.22 %\n",
      "Progress  70.24 %\n",
      "Progress  70.26 %\n",
      "Progress  70.28 %\n",
      "Progress  70.30 %\n",
      "Progress  70.32 %\n",
      "Progress  70.34 %\n",
      "Progress  70.36 %\n",
      "Progress  70.38 %\n",
      "Progress  70.40 %\n",
      "Progress  70.42 %\n",
      "Progress  70.44 %\n",
      "Progress  70.46 %\n",
      "Progress  70.48 %\n",
      "Progress  70.50 %\n",
      "Progress  70.52 %\n",
      "Progress  70.54 %\n",
      "Progress  70.56 %\n",
      "Progress  70.58 %\n",
      "Progress  70.60 %\n",
      "Progress  70.62 %\n",
      "Progress  70.64 %\n",
      "Progress  70.66 %\n",
      "Progress  70.68 %\n",
      "Progress  70.70 %\n",
      "Progress  70.72 %\n",
      "Progress  70.74 %\n",
      "Progress  70.76 %\n",
      "Progress  70.78 %\n",
      "Progress  70.80 %\n",
      "Progress  70.82 %\n",
      "Progress  70.84 %\n",
      "Progress  70.86 %\n",
      "Progress  70.88 %\n",
      "Progress  70.90 %\n",
      "Progress  70.92 %\n",
      "Progress  70.94 %\n",
      "Progress  70.96 %\n",
      "Progress  70.98 %\n",
      "Progress  71.00 %\n",
      "Progress  71.02 %\n",
      "Progress  71.04 %\n",
      "Progress  71.06 %\n",
      "Progress  71.08 %\n",
      "Progress  71.10 %\n",
      "Progress  71.12 %\n",
      "Progress  71.14 %\n",
      "Progress  71.16 %\n",
      "Progress  71.18 %\n",
      "Progress  71.20 %\n",
      "Progress  71.22 %\n",
      "Progress  71.24 %\n",
      "Progress  71.26 %\n",
      "Progress  71.28 %\n",
      "Progress  71.30 %\n",
      "Progress  71.32 %\n",
      "Progress  71.34 %\n",
      "Progress  71.36 %\n",
      "Progress  71.38 %\n",
      "Progress  71.40 %\n",
      "Progress  71.42 %\n",
      "Progress  71.44 %\n",
      "Progress  71.46 %\n",
      "Progress  71.48 %\n",
      "Progress  71.50 %\n",
      "Progress  71.52 %\n",
      "Progress  71.54 %\n",
      "Progress  71.56 %\n",
      "Progress  71.58 %\n",
      "Progress  71.60 %\n",
      "Progress  71.62 %\n",
      "Progress  71.64 %\n",
      "Progress  71.66 %\n",
      "Progress  71.68 %\n",
      "Progress  71.70 %\n",
      "Progress  71.72 %\n",
      "Progress  71.74 %\n",
      "Progress  71.76 %\n",
      "Progress  71.78 %\n",
      "Progress  71.80 %\n",
      "Progress  71.82 %\n",
      "Progress  71.84 %\n",
      "Progress  71.86 %\n",
      "Progress  71.88 %\n",
      "Progress  71.90 %\n",
      "Progress  71.92 %\n",
      "Progress  71.94 %\n",
      "Progress  71.96 %\n",
      "Progress  71.98 %\n",
      "Progress  72.00 %\n",
      "Progress  72.02 %\n",
      "Progress  72.04 %\n",
      "Progress  72.06 %\n",
      "Progress  72.08 %\n",
      "Progress  72.10 %\n",
      "Progress  72.12 %\n",
      "Progress  72.14 %\n",
      "Progress  72.16 %\n",
      "Progress  72.18 %\n",
      "Progress  72.20 %\n",
      "Progress  72.22 %\n",
      "Progress  72.24 %\n",
      "Progress  72.26 %\n",
      "Progress  72.28 %\n",
      "Progress  72.30 %\n",
      "Progress  72.32 %\n",
      "Progress  72.34 %\n",
      "Progress  72.36 %\n",
      "Progress  72.38 %\n",
      "Progress  72.40 %\n",
      "Progress  72.42 %\n",
      "Progress  72.44 %\n",
      "Progress  72.46 %\n",
      "Progress  72.48 %\n",
      "Progress  72.50 %\n",
      "Progress  72.52 %\n",
      "Progress  72.54 %\n",
      "Progress  72.56 %\n",
      "Progress  72.58 %\n",
      "Progress  72.60 %\n",
      "Progress  72.62 %\n",
      "Progress  72.64 %\n",
      "Progress  72.66 %\n",
      "Progress  72.68 %\n",
      "Progress  72.70 %\n",
      "Progress  72.72 %\n",
      "Progress  72.74 %\n",
      "Progress  72.76 %\n",
      "Progress  72.78 %\n",
      "Progress  72.80 %\n",
      "Progress  72.82 %\n",
      "Progress  72.84 %\n",
      "Progress  72.86 %\n",
      "Progress  72.88 %\n",
      "Progress  72.90 %\n",
      "Progress  72.92 %\n",
      "Progress  72.94 %\n",
      "Progress  72.96 %\n",
      "Progress  72.98 %\n",
      "Progress  73.00 %\n",
      "Progress  73.02 %\n",
      "Progress  73.04 %\n",
      "Progress  73.06 %\n",
      "Progress  73.08 %\n",
      "Progress  73.10 %\n",
      "Progress  73.12 %\n",
      "Progress  73.14 %\n",
      "Progress  73.16 %\n",
      "Progress  73.18 %\n",
      "Progress  73.20 %\n",
      "Progress  73.22 %\n",
      "Progress  73.24 %\n",
      "Progress  73.26 %\n",
      "Progress  73.28 %\n",
      "Progress  73.30 %\n",
      "Progress  73.32 %\n",
      "Progress  73.34 %\n",
      "Progress  73.36 %\n",
      "Progress  73.38 %\n",
      "Progress  73.40 %\n",
      "Progress  73.42 %\n",
      "Progress  73.44 %\n",
      "Progress  73.46 %\n",
      "Progress  73.48 %\n",
      "Progress  73.50 %\n",
      "Progress  73.52 %\n",
      "Progress  73.54 %\n",
      "Progress  73.56 %\n",
      "Progress  73.58 %\n",
      "Progress  73.60 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  73.62 %\n",
      "Progress  73.64 %\n",
      "Progress  73.66 %\n",
      "Progress  73.68 %\n",
      "Progress  73.70 %\n",
      "Progress  73.72 %\n",
      "Progress  73.74 %\n",
      "Progress  73.76 %\n",
      "Progress  73.78 %\n",
      "Progress  73.80 %\n",
      "Progress  73.82 %\n",
      "Progress  73.84 %\n",
      "Progress  73.86 %\n",
      "Progress  73.88 %\n",
      "Progress  73.90 %\n",
      "Progress  73.92 %\n",
      "Progress  73.94 %\n",
      "Progress  73.96 %\n",
      "Progress  73.98 %\n",
      "Progress  74.00 %\n",
      "Progress  74.02 %\n",
      "Progress  74.04 %\n",
      "Progress  74.06 %\n",
      "Progress  74.08 %\n",
      "Progress  74.10 %\n",
      "Progress  74.12 %\n",
      "Progress  74.14 %\n",
      "Progress  74.16 %\n",
      "Progress  74.18 %\n",
      "Progress  74.20 %\n",
      "Progress  74.22 %\n",
      "Progress  74.24 %\n",
      "Progress  74.26 %\n",
      "Progress  74.28 %\n",
      "Progress  74.30 %\n",
      "Progress  74.32 %\n",
      "Progress  74.34 %\n",
      "Progress  74.36 %\n",
      "Progress  74.38 %\n",
      "Progress  74.40 %\n",
      "Progress  74.42 %\n",
      "Progress  74.44 %\n",
      "Progress  74.46 %\n",
      "Progress  74.48 %\n",
      "Progress  74.50 %\n",
      "Progress  74.52 %\n",
      "Progress  74.54 %\n",
      "Progress  74.56 %\n",
      "Progress  74.58 %\n",
      "Progress  74.60 %\n",
      "Progress  74.62 %\n",
      "Progress  74.64 %\n",
      "Progress  74.66 %\n",
      "Progress  74.68 %\n",
      "Progress  74.70 %\n",
      "Progress  74.72 %\n",
      "Progress  74.74 %\n",
      "Progress  74.76 %\n",
      "Progress  74.78 %\n",
      "Progress  74.80 %\n",
      "Progress  74.82 %\n",
      "Progress  74.84 %\n",
      "Progress  74.86 %\n",
      "Progress  74.88 %\n",
      "Progress  74.90 %\n",
      "Progress  74.92 %\n",
      "Progress  74.94 %\n",
      "Progress  74.96 %\n",
      "Progress  74.98 %\n",
      "Progress  75.00 %\n",
      "Progress  75.02 %\n",
      "Progress  75.04 %\n",
      "Progress  75.06 %\n",
      "Progress  75.08 %\n",
      "Progress  75.10 %\n",
      "Progress  75.12 %\n",
      "Progress  75.14 %\n",
      "Progress  75.16 %\n",
      "Progress  75.18 %\n",
      "Progress  75.20 %\n",
      "Progress  75.22 %\n",
      "Progress  75.24 %\n",
      "Progress  75.26 %\n",
      "Progress  75.28 %\n",
      "Progress  75.30 %\n",
      "Progress  75.32 %\n",
      "Progress  75.34 %\n",
      "Progress  75.36 %\n",
      "Progress  75.38 %\n",
      "Progress  75.40 %\n",
      "Progress  75.42 %\n",
      "Progress  75.44 %\n",
      "Progress  75.46 %\n",
      "Progress  75.48 %\n",
      "Progress  75.50 %\n",
      "Progress  75.52 %\n",
      "Progress  75.54 %\n",
      "Progress  75.56 %\n",
      "Progress  75.58 %\n",
      "Progress  75.60 %\n",
      "Progress  75.62 %\n",
      "Progress  75.64 %\n",
      "Progress  75.66 %\n",
      "Progress  75.68 %\n",
      "Progress  75.70 %\n",
      "Progress  75.72 %\n",
      "Progress  75.74 %\n",
      "Progress  75.76 %\n",
      "Progress  75.78 %\n",
      "Progress  75.80 %\n",
      "Progress  75.82 %\n",
      "Progress  75.84 %\n",
      "Progress  75.86 %\n",
      "Progress  75.88 %\n",
      "Progress  75.90 %\n",
      "Progress  75.92 %\n",
      "Progress  75.94 %\n",
      "Progress  75.96 %\n",
      "Progress  75.98 %\n",
      "Progress  76.00 %\n",
      "Progress  76.02 %\n",
      "Progress  76.04 %\n",
      "Progress  76.06 %\n",
      "Progress  76.08 %\n",
      "Progress  76.10 %\n",
      "Progress  76.12 %\n",
      "Progress  76.14 %\n",
      "Progress  76.16 %\n",
      "Progress  76.18 %\n",
      "Progress  76.20 %\n",
      "Progress  76.22 %\n",
      "Progress  76.24 %\n",
      "Progress  76.26 %\n",
      "Progress  76.28 %\n",
      "Progress  76.30 %\n",
      "Progress  76.32 %\n",
      "Progress  76.34 %\n",
      "Progress  76.36 %\n",
      "Progress  76.38 %\n",
      "Progress  76.40 %\n",
      "Progress  76.42 %\n",
      "Progress  76.44 %\n",
      "Progress  76.46 %\n",
      "Progress  76.48 %\n",
      "Progress  76.50 %\n",
      "Progress  76.52 %\n",
      "Progress  76.54 %\n",
      "Progress  76.56 %\n",
      "Progress  76.58 %\n",
      "Progress  76.60 %\n",
      "Progress  76.62 %\n",
      "Progress  76.64 %\n",
      "Progress  76.66 %\n",
      "Progress  76.68 %\n",
      "Progress  76.70 %\n",
      "Progress  76.72 %\n",
      "Progress  76.74 %\n",
      "Progress  76.76 %\n",
      "Progress  76.78 %\n",
      "Progress  76.80 %\n",
      "Progress  76.82 %\n",
      "Progress  76.84 %\n",
      "Progress  76.86 %\n",
      "Progress  76.88 %\n",
      "Progress  76.90 %\n",
      "Progress  76.92 %\n",
      "Progress  76.94 %\n",
      "Progress  76.96 %\n",
      "Progress  76.98 %\n",
      "Progress  77.00 %\n",
      "Progress  77.02 %\n",
      "Progress  77.04 %\n",
      "Progress  77.06 %\n",
      "Progress  77.08 %\n",
      "Progress  77.10 %\n",
      "Progress  77.12 %\n",
      "Progress  77.14 %\n",
      "Progress  77.16 %\n",
      "Progress  77.18 %\n",
      "Progress  77.20 %\n",
      "Progress  77.22 %\n",
      "Progress  77.24 %\n",
      "Progress  77.26 %\n",
      "Progress  77.28 %\n",
      "Progress  77.30 %\n",
      "Progress  77.32 %\n",
      "Progress  77.34 %\n",
      "Progress  77.36 %\n",
      "Progress  77.38 %\n",
      "Progress  77.40 %\n",
      "Progress  77.42 %\n",
      "Progress  77.44 %\n",
      "Progress  77.46 %\n",
      "Progress  77.48 %\n",
      "Progress  77.50 %\n",
      "Progress  77.52 %\n",
      "Progress  77.54 %\n",
      "Progress  77.56 %\n",
      "Progress  77.58 %\n",
      "Progress  77.60 %\n",
      "Progress  77.62 %\n",
      "Progress  77.64 %\n",
      "Progress  77.66 %\n",
      "Progress  77.68 %\n",
      "Progress  77.70 %\n",
      "Progress  77.72 %\n",
      "Progress  77.74 %\n",
      "Progress  77.76 %\n",
      "Progress  77.78 %\n",
      "Progress  77.80 %\n",
      "Progress  77.82 %\n",
      "Progress  77.84 %\n",
      "Progress  77.86 %\n",
      "Progress  77.88 %\n",
      "Progress  77.90 %\n",
      "Progress  77.92 %\n",
      "Progress  77.94 %\n",
      "Progress  77.96 %\n",
      "Progress  77.98 %\n",
      "Progress  78.00 %\n",
      "Progress  78.02 %\n",
      "Progress  78.04 %\n",
      "Progress  78.06 %\n",
      "Progress  78.08 %\n",
      "Progress  78.10 %\n",
      "Progress  78.12 %\n",
      "Progress  78.14 %\n",
      "Progress  78.16 %\n",
      "Progress  78.18 %\n",
      "Progress  78.20 %\n",
      "Progress  78.22 %\n",
      "Progress  78.24 %\n",
      "Progress  78.26 %\n",
      "Progress  78.28 %\n",
      "Progress  78.30 %\n",
      "Progress  78.32 %\n",
      "Progress  78.34 %\n",
      "Progress  78.36 %\n",
      "Progress  78.38 %\n",
      "Progress  78.40 %\n",
      "Progress  78.42 %\n",
      "Progress  78.44 %\n",
      "Progress  78.46 %\n",
      "Progress  78.48 %\n",
      "Progress  78.50 %\n",
      "Progress  78.52 %\n",
      "Progress  78.54 %\n",
      "Progress  78.56 %\n",
      "Progress  78.58 %\n",
      "Progress  78.60 %\n",
      "Progress  78.62 %\n",
      "Progress  78.64 %\n",
      "Progress  78.66 %\n",
      "Progress  78.68 %\n",
      "Progress  78.70 %\n",
      "Progress  78.72 %\n",
      "Progress  78.74 %\n",
      "Progress  78.76 %\n",
      "Progress  78.78 %\n",
      "Progress  78.80 %\n",
      "Progress  78.82 %\n",
      "Progress  78.84 %\n",
      "Progress  78.86 %\n",
      "Progress  78.88 %\n",
      "Progress  78.90 %\n",
      "Progress  78.92 %\n",
      "Progress  78.94 %\n",
      "Progress  78.96 %\n",
      "Progress  78.98 %\n",
      "Progress  79.00 %\n",
      "Progress  79.02 %\n",
      "Progress  79.04 %\n",
      "Progress  79.06 %\n",
      "Progress  79.08 %\n",
      "Progress  79.10 %\n",
      "Progress  79.12 %\n",
      "Progress  79.14 %\n",
      "Progress  79.16 %\n",
      "Progress  79.18 %\n",
      "Progress  79.20 %\n",
      "Progress  79.22 %\n",
      "Progress  79.24 %\n",
      "Progress  79.26 %\n",
      "Progress  79.28 %\n",
      "Progress  79.30 %\n",
      "Progress  79.32 %\n",
      "Progress  79.34 %\n",
      "Progress  79.36 %\n",
      "Progress  79.38 %\n",
      "Progress  79.40 %\n",
      "Progress  79.42 %\n",
      "Progress  79.44 %\n",
      "Progress  79.46 %\n",
      "Progress  79.48 %\n",
      "Progress  79.50 %\n",
      "Progress  79.52 %\n",
      "Progress  79.54 %\n",
      "Progress  79.56 %\n",
      "Progress  79.58 %\n",
      "Progress  79.60 %\n",
      "Progress  79.62 %\n",
      "Progress  79.64 %\n",
      "Progress  79.66 %\n",
      "Progress  79.68 %\n",
      "Progress  79.70 %\n",
      "Progress  79.72 %\n",
      "Progress  79.74 %\n",
      "Progress  79.76 %\n",
      "Progress  79.78 %\n",
      "Progress  79.80 %\n",
      "Progress  79.82 %\n",
      "Progress  79.84 %\n",
      "Progress  79.86 %\n",
      "Progress  79.88 %\n",
      "Progress  79.90 %\n",
      "Progress  79.92 %\n",
      "Progress  79.94 %\n",
      "Progress  79.96 %\n",
      "Progress  79.98 %\n",
      "Progress  80.00 %\n",
      "Progress  80.02 %\n",
      "Progress  80.04 %\n",
      "Progress  80.06 %\n",
      "Progress  80.08 %\n",
      "Progress  80.10 %\n",
      "Progress  80.12 %\n",
      "Progress  80.14 %\n",
      "Progress  80.16 %\n",
      "Progress  80.18 %\n",
      "Progress  80.20 %\n",
      "Progress  80.22 %\n",
      "Progress  80.24 %\n",
      "Progress  80.26 %\n",
      "Progress  80.28 %\n",
      "Progress  80.30 %\n",
      "Progress  80.32 %\n",
      "Progress  80.34 %\n",
      "Progress  80.36 %\n",
      "Progress  80.38 %\n",
      "Progress  80.40 %\n",
      "Progress  80.42 %\n",
      "Progress  80.44 %\n",
      "Progress  80.46 %\n",
      "Progress  80.48 %\n",
      "Progress  80.50 %\n",
      "Progress  80.52 %\n",
      "Progress  80.54 %\n",
      "Progress  80.56 %\n",
      "Progress  80.58 %\n",
      "Progress  80.60 %\n",
      "Progress  80.62 %\n",
      "Progress  80.64 %\n",
      "Progress  80.66 %\n",
      "Progress  80.68 %\n",
      "Progress  80.70 %\n",
      "Progress  80.72 %\n",
      "Progress  80.74 %\n",
      "Progress  80.76 %\n",
      "Progress  80.78 %\n",
      "Progress  80.80 %\n",
      "Progress  80.82 %\n",
      "Progress  80.84 %\n",
      "Progress  80.86 %\n",
      "Progress  80.88 %\n",
      "Progress  80.90 %\n",
      "Progress  80.92 %\n",
      "Progress  80.94 %\n",
      "Progress  80.96 %\n",
      "Progress  80.98 %\n",
      "Progress  81.00 %\n",
      "Progress  81.02 %\n",
      "Progress  81.04 %\n",
      "Progress  81.06 %\n",
      "Progress  81.08 %\n",
      "Progress  81.10 %\n",
      "Progress  81.12 %\n",
      "Progress  81.14 %\n",
      "Progress  81.16 %\n",
      "Progress  81.18 %\n",
      "Progress  81.20 %\n",
      "Progress  81.22 %\n",
      "Progress  81.24 %\n",
      "Progress  81.26 %\n",
      "Progress  81.28 %\n",
      "Progress  81.30 %\n",
      "Progress  81.32 %\n",
      "Progress  81.34 %\n",
      "Progress  81.36 %\n",
      "Progress  81.38 %\n",
      "Progress  81.40 %\n",
      "Progress  81.42 %\n",
      "Progress  81.44 %\n",
      "Progress  81.46 %\n",
      "Progress  81.48 %\n",
      "Progress  81.50 %\n",
      "Progress  81.52 %\n",
      "Progress  81.54 %\n",
      "Progress  81.56 %\n",
      "Progress  81.58 %\n",
      "Progress  81.60 %\n",
      "Progress  81.62 %\n",
      "Progress  81.64 %\n",
      "Progress  81.66 %\n",
      "Progress  81.68 %\n",
      "Progress  81.70 %\n",
      "Progress  81.72 %\n",
      "Progress  81.74 %\n",
      "Progress  81.76 %\n",
      "Progress  81.78 %\n",
      "Progress  81.80 %\n",
      "Progress  81.82 %\n",
      "Progress  81.84 %\n",
      "Progress  81.86 %\n",
      "Progress  81.88 %\n",
      "Progress  81.90 %\n",
      "Progress  81.92 %\n",
      "Progress  81.94 %\n",
      "Progress  81.96 %\n",
      "Progress  81.98 %\n",
      "Progress  82.00 %\n",
      "Progress  82.02 %\n",
      "Progress  82.04 %\n",
      "Progress  82.06 %\n",
      "Progress  82.08 %\n",
      "Progress  82.10 %\n",
      "Progress  82.12 %\n",
      "Progress  82.14 %\n",
      "Progress  82.16 %\n",
      "Progress  82.18 %\n",
      "Progress  82.20 %\n",
      "Progress  82.22 %\n",
      "Progress  82.24 %\n",
      "Progress  82.26 %\n",
      "Progress  82.28 %\n",
      "Progress  82.30 %\n",
      "Progress  82.32 %\n",
      "Progress  82.34 %\n",
      "Progress  82.36 %\n",
      "Progress  82.38 %\n",
      "Progress  82.40 %\n",
      "Progress  82.42 %\n",
      "Progress  82.44 %\n",
      "Progress  82.46 %\n",
      "Progress  82.48 %\n",
      "Progress  82.50 %\n",
      "Progress  82.52 %\n",
      "Progress  82.54 %\n",
      "Progress  82.56 %\n",
      "Progress  82.58 %\n",
      "Progress  82.60 %\n",
      "Progress  82.62 %\n",
      "Progress  82.64 %\n",
      "Progress  82.66 %\n",
      "Progress  82.68 %\n",
      "Progress  82.70 %\n",
      "Progress  82.72 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  82.74 %\n",
      "Progress  82.76 %\n",
      "Progress  82.78 %\n",
      "Progress  82.80 %\n",
      "Progress  82.82 %\n",
      "Progress  82.84 %\n",
      "Progress  82.86 %\n",
      "Progress  82.88 %\n",
      "Progress  82.90 %\n",
      "Progress  82.92 %\n",
      "Progress  82.94 %\n",
      "Progress  82.96 %\n",
      "Progress  82.98 %\n",
      "Progress  83.00 %\n",
      "Progress  83.02 %\n",
      "Progress  83.04 %\n",
      "Progress  83.06 %\n",
      "Progress  83.08 %\n",
      "Progress  83.10 %\n",
      "Progress  83.12 %\n",
      "Progress  83.14 %\n",
      "Progress  83.16 %\n",
      "Progress  83.18 %\n",
      "Progress  83.20 %\n",
      "Progress  83.22 %\n",
      "Progress  83.24 %\n",
      "Progress  83.26 %\n",
      "Progress  83.28 %\n",
      "Progress  83.30 %\n",
      "Progress  83.32 %\n",
      "Progress  83.34 %\n",
      "Progress  83.36 %\n",
      "Progress  83.38 %\n",
      "Progress  83.40 %\n",
      "Progress  83.42 %\n",
      "Progress  83.44 %\n",
      "Progress  83.46 %\n",
      "Progress  83.48 %\n",
      "Progress  83.50 %\n",
      "Progress  83.52 %\n",
      "Progress  83.54 %\n",
      "Progress  83.56 %\n",
      "Progress  83.58 %\n",
      "Progress  83.60 %\n",
      "Progress  83.62 %\n",
      "Progress  83.64 %\n",
      "Progress  83.66 %\n",
      "Progress  83.68 %\n",
      "Progress  83.70 %\n",
      "Progress  83.72 %\n",
      "Progress  83.74 %\n",
      "Progress  83.76 %\n",
      "Progress  83.78 %\n",
      "Progress  83.80 %\n",
      "Progress  83.82 %\n",
      "Progress  83.84 %\n",
      "Progress  83.86 %\n",
      "Progress  83.88 %\n",
      "Progress  83.90 %\n",
      "Progress  83.92 %\n",
      "Progress  83.94 %\n",
      "Progress  83.96 %\n",
      "Progress  83.98 %\n",
      "Progress  84.00 %\n",
      "Progress  84.02 %\n",
      "Progress  84.04 %\n",
      "Progress  84.06 %\n",
      "Progress  84.08 %\n",
      "Progress  84.10 %\n",
      "Progress  84.12 %\n",
      "Progress  84.14 %\n",
      "Progress  84.16 %\n",
      "Progress  84.18 %\n",
      "Progress  84.20 %\n",
      "Progress  84.22 %\n",
      "Progress  84.24 %\n",
      "Progress  84.26 %\n",
      "Progress  84.28 %\n",
      "Progress  84.30 %\n",
      "Progress  84.32 %\n",
      "Progress  84.34 %\n",
      "Progress  84.36 %\n",
      "Progress  84.38 %\n",
      "Progress  84.40 %\n",
      "Progress  84.42 %\n",
      "Progress  84.44 %\n",
      "Progress  84.46 %\n",
      "Progress  84.48 %\n",
      "Progress  84.50 %\n",
      "Progress  84.52 %\n",
      "Progress  84.54 %\n",
      "Progress  84.56 %\n",
      "Progress  84.58 %\n",
      "Progress  84.60 %\n",
      "Progress  84.62 %\n",
      "Progress  84.64 %\n",
      "Progress  84.66 %\n",
      "Progress  84.68 %\n",
      "Progress  84.70 %\n",
      "Progress  84.72 %\n",
      "Progress  84.74 %\n",
      "Progress  84.76 %\n",
      "Progress  84.78 %\n",
      "Progress  84.80 %\n",
      "Progress  84.82 %\n",
      "Progress  84.84 %\n",
      "Progress  84.86 %\n",
      "Progress  84.88 %\n",
      "Progress  84.90 %\n",
      "Progress  84.92 %\n",
      "Progress  84.94 %\n",
      "Progress  84.96 %\n",
      "Progress  84.98 %\n",
      "Progress  85.00 %\n",
      "Progress  85.02 %\n",
      "Progress  85.04 %\n",
      "Progress  85.06 %\n",
      "Progress  85.08 %\n",
      "Progress  85.10 %\n",
      "Progress  85.12 %\n",
      "Progress  85.14 %\n",
      "Progress  85.16 %\n",
      "Progress  85.18 %\n",
      "Progress  85.20 %\n",
      "Progress  85.22 %\n",
      "Progress  85.24 %\n",
      "Progress  85.26 %\n",
      "Progress  85.28 %\n",
      "Progress  85.30 %\n",
      "Progress  85.32 %\n",
      "Progress  85.34 %\n",
      "Progress  85.36 %\n",
      "Progress  85.38 %\n",
      "Progress  85.40 %\n",
      "Progress  85.42 %\n",
      "Progress  85.44 %\n",
      "Progress  85.46 %\n",
      "Progress  85.48 %\n",
      "Progress  85.50 %\n",
      "Progress  85.52 %\n",
      "Progress  85.54 %\n",
      "Progress  85.56 %\n",
      "Progress  85.58 %\n",
      "Progress  85.60 %\n",
      "Progress  85.62 %\n",
      "Progress  85.64 %\n",
      "Progress  85.66 %\n",
      "Progress  85.68 %\n",
      "Progress  85.70 %\n",
      "Progress  85.72 %\n",
      "Progress  85.74 %\n",
      "Progress  85.76 %\n",
      "Progress  85.78 %\n",
      "Progress  85.80 %\n",
      "Progress  85.82 %\n",
      "Progress  85.84 %\n",
      "Progress  85.86 %\n",
      "Progress  85.88 %\n",
      "Progress  85.90 %\n",
      "Progress  85.92 %\n",
      "Progress  85.94 %\n",
      "Progress  85.96 %\n",
      "Progress  85.98 %\n",
      "Progress  86.00 %\n",
      "Progress  86.02 %\n",
      "Progress  86.04 %\n",
      "Progress  86.06 %\n",
      "Progress  86.08 %\n",
      "Progress  86.10 %\n",
      "Progress  86.12 %\n",
      "Progress  86.14 %\n",
      "Progress  86.16 %\n",
      "Progress  86.18 %\n",
      "Progress  86.20 %\n",
      "Progress  86.22 %\n",
      "Progress  86.24 %\n",
      "Progress  86.26 %\n",
      "Progress  86.28 %\n",
      "Progress  86.30 %\n",
      "Progress  86.32 %\n",
      "Progress  86.34 %\n",
      "Progress  86.36 %\n",
      "Progress  86.38 %\n",
      "Progress  86.40 %\n",
      "Progress  86.42 %\n",
      "Progress  86.44 %\n",
      "Progress  86.46 %\n",
      "Progress  86.48 %\n",
      "Progress  86.50 %\n",
      "Progress  86.52 %\n",
      "Progress  86.54 %\n",
      "Progress  86.56 %\n",
      "Progress  86.58 %\n",
      "Progress  86.60 %\n",
      "Progress  86.62 %\n",
      "Progress  86.64 %\n",
      "Progress  86.66 %\n",
      "Progress  86.68 %\n",
      "Progress  86.70 %\n",
      "Progress  86.72 %\n",
      "Progress  86.74 %\n",
      "Progress  86.76 %\n",
      "Progress  86.78 %\n",
      "Progress  86.80 %\n",
      "Progress  86.82 %\n",
      "Progress  86.84 %\n",
      "Progress  86.86 %\n",
      "Progress  86.88 %\n",
      "Progress  86.90 %\n",
      "Progress  86.92 %\n",
      "Progress  86.94 %\n",
      "Progress  86.96 %\n",
      "Progress  86.98 %\n",
      "Progress  87.00 %\n",
      "Progress  87.02 %\n",
      "Progress  87.04 %\n",
      "Progress  87.06 %\n",
      "Progress  87.08 %\n",
      "Progress  87.10 %\n",
      "Progress  87.12 %\n",
      "Progress  87.14 %\n",
      "Progress  87.16 %\n",
      "Progress  87.18 %\n",
      "Progress  87.20 %\n",
      "Progress  87.22 %\n",
      "Progress  87.24 %\n",
      "Progress  87.26 %\n",
      "Progress  87.28 %\n",
      "Progress  87.30 %\n",
      "Progress  87.32 %\n",
      "Progress  87.34 %\n",
      "Progress  87.36 %\n",
      "Progress  87.38 %\n",
      "Progress  87.40 %\n",
      "Progress  87.42 %\n",
      "Progress  87.44 %\n",
      "Progress  87.46 %\n",
      "Progress  87.48 %\n",
      "Progress  87.50 %\n",
      "Progress  87.52 %\n",
      "Progress  87.54 %\n",
      "Progress  87.56 %\n",
      "Progress  87.58 %\n",
      "Progress  87.60 %\n",
      "Progress  87.62 %\n",
      "Progress  87.64 %\n",
      "Progress  87.66 %\n",
      "Progress  87.68 %\n",
      "Progress  87.70 %\n",
      "Progress  87.72 %\n",
      "Progress  87.74 %\n",
      "Progress  87.76 %\n",
      "Progress  87.78 %\n",
      "Progress  87.80 %\n",
      "Progress  87.82 %\n",
      "Progress  87.84 %\n",
      "Progress  87.86 %\n",
      "Progress  87.88 %\n",
      "Progress  87.90 %\n",
      "Progress  87.92 %\n",
      "Progress  87.94 %\n",
      "Progress  87.96 %\n",
      "Progress  87.98 %\n",
      "Progress  88.00 %\n",
      "Progress  88.02 %\n",
      "Progress  88.04 %\n",
      "Progress  88.06 %\n",
      "Progress  88.08 %\n",
      "Progress  88.10 %\n",
      "Progress  88.12 %\n",
      "Progress  88.14 %\n",
      "Progress  88.16 %\n",
      "Progress  88.18 %\n",
      "Progress  88.20 %\n",
      "Progress  88.22 %\n",
      "Progress  88.24 %\n",
      "Progress  88.26 %\n",
      "Progress  88.28 %\n",
      "Progress  88.30 %\n",
      "Progress  88.32 %\n",
      "Progress  88.34 %\n",
      "Progress  88.36 %\n",
      "Progress  88.38 %\n",
      "Progress  88.40 %\n",
      "Progress  88.42 %\n",
      "Progress  88.44 %\n",
      "Progress  88.46 %\n",
      "Progress  88.48 %\n",
      "Progress  88.50 %\n",
      "Progress  88.52 %\n",
      "Progress  88.54 %\n",
      "Progress  88.56 %\n",
      "Progress  88.58 %\n",
      "Progress  88.60 %\n",
      "Progress  88.62 %\n",
      "Progress  88.64 %\n",
      "Progress  88.66 %\n",
      "Progress  88.68 %\n",
      "Progress  88.70 %\n",
      "Progress  88.72 %\n",
      "Progress  88.74 %\n",
      "Progress  88.76 %\n",
      "Progress  88.78 %\n",
      "Progress  88.80 %\n",
      "Progress  88.82 %\n",
      "Progress  88.84 %\n",
      "Progress  88.86 %\n",
      "Progress  88.88 %\n",
      "Progress  88.90 %\n",
      "Progress  88.92 %\n",
      "Progress  88.94 %\n",
      "Progress  88.96 %\n",
      "Progress  88.98 %\n",
      "Progress  89.00 %\n",
      "Progress  89.02 %\n",
      "Progress  89.04 %\n",
      "Progress  89.06 %\n",
      "Progress  89.08 %\n",
      "Progress  89.10 %\n",
      "Progress  89.12 %\n",
      "Progress  89.14 %\n",
      "Progress  89.16 %\n",
      "Progress  89.18 %\n",
      "Progress  89.20 %\n",
      "Progress  89.22 %\n",
      "Progress  89.24 %\n",
      "Progress  89.26 %\n",
      "Progress  89.28 %\n",
      "Progress  89.30 %\n",
      "Progress  89.32 %\n",
      "Progress  89.34 %\n",
      "Progress  89.36 %\n",
      "Progress  89.38 %\n",
      "Progress  89.40 %\n",
      "Progress  89.42 %\n",
      "Progress  89.44 %\n",
      "Progress  89.46 %\n",
      "Progress  89.48 %\n",
      "Progress  89.50 %\n",
      "Progress  89.52 %\n",
      "Progress  89.54 %\n",
      "Progress  89.56 %\n",
      "Progress  89.58 %\n",
      "Progress  89.60 %\n",
      "Progress  89.62 %\n",
      "Progress  89.64 %\n",
      "Progress  89.66 %\n",
      "Progress  89.68 %\n",
      "Progress  89.70 %\n",
      "Progress  89.72 %\n",
      "Progress  89.74 %\n",
      "Progress  89.76 %\n",
      "Progress  89.78 %\n",
      "Progress  89.80 %\n",
      "Progress  89.82 %\n",
      "Progress  89.84 %\n",
      "Progress  89.86 %\n",
      "Progress  89.88 %\n",
      "Progress  89.90 %\n",
      "Progress  89.92 %\n",
      "Progress  89.94 %\n",
      "Progress  89.96 %\n",
      "Progress  89.98 %\n",
      "Progress  90.00 %\n",
      "Progress  90.02 %\n",
      "Progress  90.04 %\n",
      "Progress  90.06 %\n",
      "Progress  90.08 %\n",
      "Progress  90.10 %\n",
      "Progress  90.12 %\n",
      "Progress  90.14 %\n",
      "Progress  90.16 %\n",
      "Progress  90.18 %\n",
      "Progress  90.20 %\n",
      "Progress  90.22 %\n",
      "Progress  90.24 %\n",
      "Progress  90.26 %\n",
      "Progress  90.28 %\n",
      "Progress  90.30 %\n",
      "Progress  90.32 %\n",
      "Progress  90.34 %\n",
      "Progress  90.36 %\n",
      "Progress  90.38 %\n",
      "Progress  90.40 %\n",
      "Progress  90.42 %\n",
      "Progress  90.44 %\n",
      "Progress  90.46 %\n",
      "Progress  90.48 %\n",
      "Progress  90.50 %\n",
      "Progress  90.52 %\n",
      "Progress  90.54 %\n",
      "Progress  90.56 %\n",
      "Progress  90.58 %\n",
      "Progress  90.60 %\n",
      "Progress  90.62 %\n",
      "Progress  90.64 %\n",
      "Progress  90.66 %\n",
      "Progress  90.68 %\n",
      "Progress  90.70 %\n",
      "Progress  90.72 %\n",
      "Progress  90.74 %\n",
      "Progress  90.76 %\n",
      "Progress  90.78 %\n",
      "Progress  90.80 %\n",
      "Progress  90.82 %\n",
      "Progress  90.84 %\n",
      "Progress  90.86 %\n",
      "Progress  90.88 %\n",
      "Progress  90.90 %\n",
      "Progress  90.92 %\n",
      "Progress  90.94 %\n",
      "Progress  90.96 %\n",
      "Progress  90.98 %\n",
      "Progress  91.00 %\n",
      "Progress  91.02 %\n",
      "Progress  91.04 %\n",
      "Progress  91.06 %\n",
      "Progress  91.08 %\n",
      "Progress  91.10 %\n",
      "Progress  91.12 %\n",
      "Progress  91.14 %\n",
      "Progress  91.16 %\n",
      "Progress  91.18 %\n",
      "Progress  91.20 %\n",
      "Progress  91.22 %\n",
      "Progress  91.24 %\n",
      "Progress  91.26 %\n",
      "Progress  91.28 %\n",
      "Progress  91.30 %\n",
      "Progress  91.32 %\n",
      "Progress  91.34 %\n",
      "Progress  91.36 %\n",
      "Progress  91.38 %\n",
      "Progress  91.40 %\n",
      "Progress  91.42 %\n",
      "Progress  91.44 %\n",
      "Progress  91.46 %\n",
      "Progress  91.48 %\n",
      "Progress  91.50 %\n",
      "Progress  91.52 %\n",
      "Progress  91.54 %\n",
      "Progress  91.56 %\n",
      "Progress  91.58 %\n",
      "Progress  91.60 %\n",
      "Progress  91.62 %\n",
      "Progress  91.64 %\n",
      "Progress  91.66 %\n",
      "Progress  91.68 %\n",
      "Progress  91.70 %\n",
      "Progress  91.72 %\n",
      "Progress  91.74 %\n",
      "Progress  91.76 %\n",
      "Progress  91.78 %\n",
      "Progress  91.80 %\n",
      "Progress  91.82 %\n",
      "Progress  91.84 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress  91.86 %\n",
      "Progress  91.88 %\n",
      "Progress  91.90 %\n",
      "Progress  91.92 %\n",
      "Progress  91.94 %\n",
      "Progress  91.96 %\n",
      "Progress  91.98 %\n",
      "Progress  92.00 %\n",
      "Progress  92.02 %\n",
      "Progress  92.04 %\n",
      "Progress  92.06 %\n",
      "Progress  92.08 %\n",
      "Progress  92.10 %\n",
      "Progress  92.12 %\n",
      "Progress  92.14 %\n",
      "Progress  92.16 %\n",
      "Progress  92.18 %\n",
      "Progress  92.20 %\n",
      "Progress  92.22 %\n",
      "Progress  92.24 %\n",
      "Progress  92.26 %\n",
      "Progress  92.28 %\n",
      "Progress  92.30 %\n",
      "Progress  92.32 %\n",
      "Progress  92.34 %\n",
      "Progress  92.36 %\n",
      "Progress  92.38 %\n",
      "Progress  92.40 %\n",
      "Progress  92.42 %\n",
      "Progress  92.44 %\n",
      "Progress  92.46 %\n",
      "Progress  92.48 %\n",
      "Progress  92.50 %\n",
      "Progress  92.52 %\n",
      "Progress  92.54 %\n",
      "Progress  92.56 %\n",
      "Progress  92.58 %\n",
      "Progress  92.60 %\n",
      "Progress  92.62 %\n",
      "Progress  92.64 %\n",
      "Progress  92.66 %\n",
      "Progress  92.68 %\n",
      "Progress  92.70 %\n",
      "Progress  92.72 %\n",
      "Progress  92.74 %\n",
      "Progress  92.76 %\n",
      "Progress  92.78 %\n",
      "Progress  92.80 %\n",
      "Progress  92.82 %\n",
      "Progress  92.84 %\n",
      "Progress  92.86 %\n",
      "Progress  92.88 %\n",
      "Progress  92.90 %\n",
      "Progress  92.92 %\n",
      "Progress  92.94 %\n",
      "Progress  92.96 %\n",
      "Progress  92.98 %\n",
      "Progress  93.00 %\n",
      "Progress  93.02 %\n",
      "Progress  93.04 %\n",
      "Progress  93.06 %\n",
      "Progress  93.08 %\n",
      "Progress  93.10 %\n",
      "Progress  93.12 %\n",
      "Progress  93.14 %\n",
      "Progress  93.16 %\n",
      "Progress  93.18 %\n",
      "Progress  93.20 %\n",
      "Progress  93.22 %\n",
      "Progress  93.24 %\n",
      "Progress  93.26 %\n",
      "Progress  93.28 %\n",
      "Progress  93.30 %\n",
      "Progress  93.32 %\n",
      "Progress  93.34 %\n",
      "Progress  93.36 %\n",
      "Progress  93.38 %\n",
      "Progress  93.40 %\n",
      "Progress  93.42 %\n",
      "Progress  93.44 %\n",
      "Progress  93.46 %\n",
      "Progress  93.48 %\n",
      "Progress  93.50 %\n",
      "Progress  93.52 %\n",
      "Progress  93.54 %\n",
      "Progress  93.56 %\n",
      "Progress  93.58 %\n",
      "Progress  93.60 %\n",
      "Progress  93.62 %\n",
      "Progress  93.64 %\n",
      "Progress  93.66 %\n",
      "Progress  93.68 %\n",
      "Progress  93.70 %\n",
      "Progress  93.72 %\n",
      "Progress  93.74 %\n",
      "Progress  93.76 %\n",
      "Progress  93.78 %\n",
      "Progress  93.80 %\n",
      "Progress  93.82 %\n",
      "Progress  93.84 %\n",
      "Progress  93.86 %\n",
      "Progress  93.88 %\n",
      "Progress  93.90 %\n",
      "Progress  93.92 %\n",
      "Progress  93.94 %\n",
      "Progress  93.96 %\n",
      "Progress  93.98 %\n",
      "Progress  94.00 %\n",
      "Progress  94.02 %\n",
      "Progress  94.04 %\n",
      "Progress  94.06 %\n",
      "Progress  94.08 %\n",
      "Progress  94.10 %\n",
      "Progress  94.12 %\n",
      "Progress  94.14 %\n",
      "Progress  94.16 %\n",
      "Progress  94.18 %\n",
      "Progress  94.20 %\n",
      "Progress  94.22 %\n",
      "Progress  94.24 %\n",
      "Progress  94.26 %\n",
      "Progress  94.28 %\n",
      "Progress  94.30 %\n",
      "Progress  94.32 %\n",
      "Progress  94.34 %\n",
      "Progress  94.36 %\n",
      "Progress  94.38 %\n",
      "Progress  94.40 %\n",
      "Progress  94.42 %\n",
      "Progress  94.44 %\n",
      "Progress  94.46 %\n",
      "Progress  94.48 %\n",
      "Progress  94.50 %\n",
      "Progress  94.52 %\n",
      "Progress  94.54 %\n",
      "Progress  94.56 %\n",
      "Progress  94.58 %\n",
      "Progress  94.60 %\n",
      "Progress  94.62 %\n",
      "Progress  94.64 %\n",
      "Progress  94.66 %\n",
      "Progress  94.68 %\n",
      "Progress  94.70 %\n",
      "Progress  94.72 %\n",
      "Progress  94.74 %\n",
      "Progress  94.76 %\n",
      "Progress  94.78 %\n",
      "Progress  94.80 %\n",
      "Progress  94.82 %\n",
      "Progress  94.84 %\n",
      "Progress  94.86 %\n",
      "Progress  94.88 %\n",
      "Progress  94.90 %\n",
      "Progress  94.92 %\n",
      "Progress  94.94 %\n",
      "Progress  94.96 %\n",
      "Progress  94.98 %\n",
      "Progress  95.00 %\n",
      "Progress  95.02 %\n",
      "Progress  95.04 %\n",
      "Progress  95.06 %\n",
      "Progress  95.08 %\n",
      "Progress  95.10 %\n",
      "Progress  95.12 %\n",
      "Progress  95.14 %\n",
      "Progress  95.16 %\n",
      "Progress  95.18 %\n",
      "Progress  95.20 %\n",
      "Progress  95.22 %\n",
      "Progress  95.24 %\n",
      "Progress  95.26 %\n",
      "Progress  95.28 %\n",
      "Progress  95.30 %\n",
      "Progress  95.32 %\n",
      "Progress  95.34 %\n",
      "Progress  95.36 %\n",
      "Progress  95.38 %\n",
      "Progress  95.40 %\n",
      "Progress  95.42 %\n",
      "Progress  95.44 %\n",
      "Progress  95.46 %\n",
      "Progress  95.48 %\n",
      "Progress  95.50 %\n",
      "Progress  95.52 %\n",
      "Progress  95.54 %\n",
      "Progress  95.56 %\n",
      "Progress  95.58 %\n",
      "Progress  95.60 %\n",
      "Progress  95.62 %\n",
      "Progress  95.64 %\n",
      "Progress  95.66 %\n",
      "Progress  95.68 %\n",
      "Progress  95.70 %\n",
      "Progress  95.72 %\n",
      "Progress  95.74 %\n",
      "Progress  95.76 %\n",
      "Progress  95.78 %\n",
      "Progress  95.80 %\n",
      "Progress  95.82 %\n",
      "Progress  95.84 %\n",
      "Progress  95.86 %\n",
      "Progress  95.88 %\n",
      "Progress  95.90 %\n",
      "Progress  95.92 %\n",
      "Progress  95.94 %\n",
      "Progress  95.96 %\n",
      "Progress  95.98 %\n",
      "Progress  96.00 %\n",
      "Progress  96.02 %\n",
      "Progress  96.04 %\n",
      "Progress  96.06 %\n",
      "Progress  96.08 %\n",
      "Progress  96.10 %\n",
      "Progress  96.12 %\n",
      "Progress  96.14 %\n",
      "Progress  96.16 %\n",
      "Progress  96.18 %\n",
      "Progress  96.20 %\n",
      "Progress  96.22 %\n",
      "Progress  96.24 %\n",
      "Progress  96.26 %\n",
      "Progress  96.28 %\n",
      "Progress  96.30 %\n",
      "Progress  96.32 %\n",
      "Progress  96.34 %\n",
      "Progress  96.36 %\n",
      "Progress  96.38 %\n",
      "Progress  96.40 %\n",
      "Progress  96.42 %\n",
      "Progress  96.44 %\n",
      "Progress  96.46 %\n",
      "Progress  96.48 %\n",
      "Progress  96.50 %\n",
      "Progress  96.52 %\n",
      "Progress  96.54 %\n",
      "Progress  96.56 %\n",
      "Progress  96.58 %\n",
      "Progress  96.60 %\n",
      "Progress  96.62 %\n",
      "Progress  96.64 %\n",
      "Progress  96.66 %\n",
      "Progress  96.68 %\n",
      "Progress  96.70 %\n",
      "Progress  96.72 %\n",
      "Progress  96.74 %\n",
      "Progress  96.76 %\n",
      "Progress  96.78 %\n",
      "Progress  96.80 %\n",
      "Progress  96.82 %\n",
      "Progress  96.84 %\n",
      "Progress  96.86 %\n",
      "Progress  96.88 %\n",
      "Progress  96.90 %\n",
      "Progress  96.92 %\n",
      "Progress  96.94 %\n",
      "Progress  96.96 %\n",
      "Progress  96.98 %\n",
      "Progress  97.00 %\n",
      "Progress  97.02 %\n",
      "Progress  97.04 %\n",
      "Progress  97.06 %\n",
      "Progress  97.08 %\n",
      "Progress  97.10 %\n",
      "Progress  97.12 %\n",
      "Progress  97.14 %\n",
      "Progress  97.16 %\n",
      "Progress  97.18 %\n",
      "Progress  97.20 %\n",
      "Progress  97.22 %\n",
      "Progress  97.24 %\n",
      "Progress  97.26 %\n",
      "Progress  97.28 %\n",
      "Progress  97.30 %\n",
      "Progress  97.32 %\n",
      "Progress  97.34 %\n",
      "Progress  97.36 %\n",
      "Progress  97.38 %\n",
      "Progress  97.40 %\n",
      "Progress  97.42 %\n",
      "Progress  97.44 %\n",
      "Progress  97.46 %\n",
      "Progress  97.48 %\n",
      "Progress  97.50 %\n",
      "Progress  97.52 %\n",
      "Progress  97.54 %\n",
      "Progress  97.56 %\n",
      "Progress  97.58 %\n",
      "Progress  97.60 %\n",
      "Progress  97.62 %\n",
      "Progress  97.64 %\n",
      "Progress  97.66 %\n",
      "Progress  97.68 %\n",
      "Progress  97.70 %\n",
      "Progress  97.72 %\n",
      "Progress  97.74 %\n",
      "Progress  97.76 %\n",
      "Progress  97.78 %\n",
      "Progress  97.80 %\n",
      "Progress  97.82 %\n",
      "Progress  97.84 %\n",
      "Progress  97.86 %\n",
      "Progress  97.88 %\n",
      "Progress  97.90 %\n",
      "Progress  97.92 %\n",
      "Progress  97.94 %\n",
      "Progress  97.96 %\n",
      "Progress  97.98 %\n",
      "Progress  98.00 %\n",
      "Progress  98.02 %\n",
      "Progress  98.04 %\n",
      "Progress  98.06 %\n",
      "Progress  98.08 %\n",
      "Progress  98.10 %\n",
      "Progress  98.12 %\n",
      "Progress  98.14 %\n",
      "Progress  98.16 %\n",
      "Progress  98.18 %\n",
      "Progress  98.20 %\n",
      "Progress  98.22 %\n",
      "Progress  98.24 %\n",
      "Progress  98.26 %\n",
      "Progress  98.28 %\n",
      "Progress  98.30 %\n",
      "Progress  98.32 %\n",
      "Progress  98.34 %\n",
      "Progress  98.36 %\n",
      "Progress  98.38 %\n",
      "Progress  98.40 %\n",
      "Progress  98.42 %\n",
      "Progress  98.44 %\n",
      "Progress  98.46 %\n",
      "Progress  98.48 %\n",
      "Progress  98.50 %\n",
      "Progress  98.52 %\n",
      "Progress  98.54 %\n",
      "Progress  98.56 %\n",
      "Progress  98.58 %\n",
      "Progress  98.60 %\n",
      "Progress  98.62 %\n",
      "Progress  98.64 %\n",
      "Progress  98.66 %\n",
      "Progress  98.68 %\n",
      "Progress  98.70 %\n",
      "Progress  98.72 %\n",
      "Progress  98.74 %\n",
      "Progress  98.76 %\n",
      "Progress  98.78 %\n",
      "Progress  98.80 %\n",
      "Progress  98.82 %\n",
      "Progress  98.84 %\n",
      "Progress  98.86 %\n",
      "Progress  98.88 %\n",
      "Progress  98.90 %\n",
      "Progress  98.92 %\n",
      "Progress  98.94 %\n",
      "Progress  98.96 %\n",
      "Progress  98.98 %\n",
      "Progress  99.00 %\n",
      "Progress  99.02 %\n",
      "Progress  99.04 %\n",
      "Progress  99.06 %\n",
      "Progress  99.08 %\n",
      "Progress  99.10 %\n",
      "Progress  99.12 %\n",
      "Progress  99.14 %\n",
      "Progress  99.16 %\n",
      "Progress  99.18 %\n",
      "Progress  99.20 %\n",
      "Progress  99.22 %\n",
      "Progress  99.24 %\n",
      "Progress  99.26 %\n",
      "Progress  99.28 %\n",
      "Progress  99.30 %\n",
      "Progress  99.32 %\n",
      "Progress  99.34 %\n",
      "Progress  99.36 %\n",
      "Progress  99.38 %\n",
      "Progress  99.40 %\n",
      "Progress  99.42 %\n",
      "Progress  99.44 %\n",
      "Progress  99.46 %\n",
      "Progress  99.48 %\n",
      "Progress  99.50 %\n",
      "Progress  99.52 %\n",
      "Progress  99.54 %\n",
      "Progress  99.56 %\n",
      "Progress  99.58 %\n",
      "Progress  99.60 %\n",
      "Progress  99.62 %\n",
      "Progress  99.64 %\n",
      "Progress  99.66 %\n",
      "Progress  99.68 %\n",
      "Progress  99.70 %\n",
      "Progress  99.72 %\n",
      "Progress  99.74 %\n",
      "Progress  99.76 %\n",
      "Progress  99.78 %\n",
      "Progress  99.80 %\n",
      "Progress  99.82 %\n",
      "Progress  99.84 %\n",
      "Progress  99.86 %\n",
      "Progress  99.88 %\n",
      "Progress  99.90 %\n",
      "Progress  99.92 %\n",
      "Progress  99.94 %\n",
      "Progress  99.96 %\n",
      "Progress  99.98 %\n",
      "Progress  100.00 %\n"
     ]
    }
   ],
   "source": [
    "Y_pred_ = predict(dictionary,X_test_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          precision    recall  f1-score   support\n",
      "\n",
      "             alt.atheism       0.77      0.87      0.82       233\n",
      "           comp.graphics       0.78      0.81      0.80       253\n",
      " comp.os.ms-windows.misc       0.77      0.86      0.81       249\n",
      "comp.sys.ibm.pc.hardware       0.71      0.76      0.73       240\n",
      "   comp.sys.mac.hardware       0.73      0.82      0.78       236\n",
      "          comp.windows.x       0.93      0.71      0.81       240\n",
      "            misc.forsale       0.89      0.91      0.90       261\n",
      "               rec.autos       0.93      0.93      0.93       269\n",
      "         rec.motorcycles       0.93      0.98      0.95       284\n",
      "      rec.sport.baseball       0.97      0.98      0.97       248\n",
      "        rec.sport.hockey       0.97      0.99      0.98       231\n",
      "               sci.crypt       0.97      0.94      0.96       233\n",
      "         sci.electronics       0.88      0.89      0.89       244\n",
      "                 sci.med       0.95      0.82      0.88       256\n",
      "               sci.space       0.95      0.93      0.94       246\n",
      "  soc.religion.christian       0.97      1.00      0.99       252\n",
      "      talk.politics.guns       0.77      0.92      0.84       249\n",
      "   talk.politics.mideast       0.94      0.91      0.92       281\n",
      "      talk.politics.misc       0.75      0.70      0.72       259\n",
      "      talk.religion.misc       0.74      0.53      0.62       236\n",
      "\n",
      "                accuracy                           0.86      5000\n",
      "               macro avg       0.87      0.86      0.86      5000\n",
      "            weighted avg       0.87      0.86      0.86      5000\n",
      "\n",
      "[[203   1   0   0   0   0   0   0   2   2   0   0   0   1   0   0   0   0\n",
      "    2  22]\n",
      " [  0 206  20  15   7   1   0   0   0   0   0   1   0   2   1   0   0   0\n",
      "    0   0]\n",
      " [  0  11 214  10   4   8   0   0   0   0   0   0   1   0   1   0   0   0\n",
      "    0   0]\n",
      " [  0   5   5 183  36   0   5   0   0   0   0   0   6   0   0   0   0   0\n",
      "    0   0]\n",
      " [  0   1   2  30 194   0   6   0   0   0   0   0   3   0   0   0   0   0\n",
      "    0   0]\n",
      " [  0  21  35   1   7 171   1   1   0   0   0   1   0   1   1   0   0   0\n",
      "    0   0]\n",
      " [  0   0   0   8   1   0 237   4   0   0   0   1   7   1   0   0   1   0\n",
      "    1   0]\n",
      " [  0   0   0   0   2   1   7 249   2   0   0   0   5   1   1   0   1   0\n",
      "    0   0]\n",
      " [  0   0   0   0   0   0   1   3 277   0   1   0   0   1   0   0   1   0\n",
      "    0   0]\n",
      " [  0   0   0   0   0   0   1   1   1 242   3   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [  0   0   0   0   0   0   0   1   0   0 229   0   0   0   1   0   0   0\n",
      "    0   0]\n",
      " [  0   4   0   2   0   3   0   0   0   0   0 220   0   1   0   0   2   0\n",
      "    1   0]\n",
      " [  0   1   2   7   3   0   4   6   1   0   0   0 218   1   0   0   1   0\n",
      "    0   0]\n",
      " [  1   8   0   1   9   0   1   3  10   4   1   0   5 209   3   0   0   1\n",
      "    0   0]\n",
      " [  0   4   0   0   1   0   2   0   3   0   0   0   2   1 229   0   1   0\n",
      "    3   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 252   0   0\n",
      "    0   0]\n",
      " [  0   0   0   0   0   0   0   0   1   0   0   2   0   0   0   0 230   0\n",
      "   12   4]\n",
      " [  0   0   0   0   0   0   0   0   1   0   1   0   0   0   0   0   6 255\n",
      "   16   2]\n",
      " [  1   0   0   1   0   0   0   0   0   1   0   1   0   2   3   0  39  14\n",
      "  181  16]\n",
      " [ 59   1   0   0   0   0   0   0   0   0   0   0   0   0   0   7  16   2\n",
      "   26 125]]\n"
     ]
    }
   ],
   "source": [
    "# generating the classification report and printing the confusion matrix\n",
    "print(classification_report(Y_test_,Y_pred_))\n",
    "print(confusion_matrix(Y_test_,Y_pred_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the classification report values in variable for comparing it to Sklearn multinomialNB Classifier\n",
    "report_list = classification_report(Y_test_,Y_pred_).split(\"\\n\")\n",
    "for i in report_list:\n",
    "    if i==\"\":\n",
    "        report_list.remove(i)\n",
    "own_classifier_score = []\n",
    "for i in report_list[1:-3]:\n",
    "    own_classifier_score.append(float(i.split()[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparision : sklearn multinomialNB classifier V/S my own multinomial NB Classifier\n",
    "### Using different graphs for visualizing the score comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABCMAAAIFCAYAAADoajfZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzde9xlc934/9d7xmhiUAmVMSjE5GycEhHlVKTSl6jklLqlkop0QLe77pzdySGn7iJJkV/kkFN0wEypHDPkMJQwheEep96/Pz7rMtt2HfY15lp77Wtez8djP2bvz1p77fdee1+z13qvz+f9icxEkiRJkiSpLmO6HYAkSZIkSZq/mIyQJEmSJEm1MhkhSZIkSZJqZTJCkiRJkiTVymSEJEmSJEmqlckISZIkSZJUK5MRkqSuiogxEXFyRDwaERkRm3Y7pqabF/ssIg6JiJtHILxaRMTVEfHtbsfRTXV+hhGxXPVdm9LStlFE/Ckinqk+j5esI0nSQBbodgCSpPneNsDHgE2Bu4GZEbEJcACwDvAG4GOZeWa3Amygl+yzrkaj+cH9wOuBR1rajgP+CGwLPAk81s86kiT1y54RkqRuWwH4W2b+JjP/npnPABOAm4FPA//X1egGEBELRER06eX722fSiMnM56vv2nMtzSsAV2bm/Zk5c4B1hiUiFnz50UqSeoHJCElS10TEmcAxwKSqe/c9AJl5cWZ+KTPPA/49jO19PCL+EhGzI+LhiLg0IhZoWf7RiPhzRDwdEQ9Vr9+3bFJEnB8RT1S3n0bExJblh0TEzRGxW0TcBTwNLBzFFyLiroj4v2r7u7bF9dWIuLd63b9HxP8O8T42iYjrq/fxUEQc03eSNtA+G2A7K0fEhRHxWETMiojfRsRqA6y7bkRcFhGPRMTjEXFdRGzY6f6NiNUi4orquU9ExB8jYrOW506OiIuqZf+IiB9GxOtalg/6/AEsEBHHRcQ/q9sRETGmZZ+/ZAhDRPw6Io4fZJ+tFhG/rD7LmRFxZkQsVi1bpdrnr6seL1QNUfhFy/P3iog7q/t9wxbeHxGXR8RTEXFrRLxziPfVGk8jPsOW9zKl7z6wGHB61b5b9D+UY6jP/cyI+HlEfDEiZgAzOt03kqTeZjJCktRNnwYOo5yAvB5Yd243VJ0AnQAcCrwZ2AK4pGX5x4GTgTOA1SlDHW6plgVwAbAU8A5gM8rwkAuqZX2WBz4E7AisAcwG/hPYA/gPYDLwDeDkiNi22vb7KUNOPgmsCLwbuGGQ97E08AvgD8Ba1bZ3rrYLHe6ziHgDcB2QwDuBtav9M3aAl14E+D6wMbAecBNwcUS8ttreoPsXOBv4W/XctYBDqv1DRLwe+BWlt8t61XMnABf2JQ8Ge/4gdqEcy2wIfBzYG/hMtex0YOWIWK9ln7wZeCtwWn8bi4iFqvc0q4pjh2r90wEy8zbgIcrwGICNKEMT3hZzkl6bAle3bfpw4HjKd+ZG4JyImDDEe2vUZ9imb8jGU5T9/XrgR/3E38nnDvB2yt/kVsDmA7w3SdJok5nevHnz5s1b126UE/V7Blk+C9itg+28j3JiuMgAy2cA3xxg2TuB54HlWtreSOmVsUX1+BDgWWCplnUWpgwj2bhte8cCF1f39wfuAMZ1uD8OB6YDY1radqP0xFiok33Wsp17gQUHWH4IcPMgzw/KiemuHe7fx4GPDrDsMOCKtrZXU06y1xvq+QNs82rgL0C0tH0ZmNHy+OfASS2P/xuYOsg292p/j5TkQgIrVI9/BJzcso9PBO4BNmz5nu1S3V+ueu7HW7a3dNX2tg6/C035DPvey5SWthf9bbav0+HnfibwMPCKTj97b968efM2Om72jJAk9ZyI+FLVZb3vNgm4nHLi9teIOCvKkIxFqvWXpJwEXjHAJlcBHszMe/oaMvNu4EFKb4c+MzLzoZbHk4HxwCWt8QCfAN5UrfPjap2/RsRpEbFjRLxikLe3CvDbzGwdnnIdsCBljH6n1gKuyw7rSUTEklFm6PhLRDwGPAEsCUyqVhlw/1aOBk6NiCsj4uCIWLll2TrAJm376P5q2Zs6eP5AfpeZ2fL4t8DSEbFo9fi7wE4R8cqIGAt8mAF6RVRWAf6UmU+0tP2GkpTq+x5czZyeEZsCVwHXAJtGxIqU79nVbdv9U8v9B6t/lxzsjVWa9BnOjU4+dygJladf5mtJknqMyQhJUi86CViz5fZgdQK5NvBB4D7gIOD2qqv7UIUmg3K1tj+t7U+2Lev7HX1PWzxvAd4FkJn3U7rEf5xy5fkoYFpELPwyYxnKcItrfo8y5OOzlKEJa1Ku8i8IMMT+JTMPoZywX1A9/08RsXu17THARbx4H61JGbby8w6eP7cuogwleD9lWM6rgB8Osn4n+/5qYKUq8TClenw1ZWjPpsD0zHyg7bnPvrCROcmTTo7BmvQZzo0hP/dK+9+VJGk+YDJCktRzslTun95ye65qfy4zr8zMgyhj0BcG3l31ZniAgcej30q5or5cX0NEvJFSN+LWQUK5lTJ8Ytm2eKZn5r0t8c7OzIsy87OUk8W3UOoNDLTNDdvG1L8NeAa4a5BY2v2eUsug09kJ3gb8TxXnLZSr6q9vXWGg/duy/M7MPD4zt6X0QNizJZa3APf2s5+e6OD5A1m/rabHBpTE1ON98VKGAexe3X6amf8aZHu3Amu09RZ4K+V46bZqm311Iw6mJB7+QekdsRFluM/VQ8Q8HE36DOc2/iE/d0nS/MlkhCSpcSJiQkSsGRFrUn6rJlWPJw3ynHdHxKcjYq2IWJZSaHIRqpNIyvj7z0TEZyNipWp7n6uW/RL4I3BWRKxTFfo7i3IydeVAr1mdUB0JHBkRu0fECtV294mIvau4douIPaPMVLA88DHKlfI7B9jsdyhJkO9Emb1hW+CbwLcz86khd96LtzMBODfKLAsrRMTO1T7tz1+AXavZD9YFzqEkQKjex4D7txoGcUJEbBplRoX1KSfGfYmcEygzL/woItaPiDdGxBYRcUpELNLB8wfyBuDYiHhzRHwA+DxlppFWp1IKJL6bwYdoQPnMnwT+t/q8NqEUPf1pZk5vWe8aYFdKEoJqeM/DlJoMVw/xGsPRpM9wbgz6ub+M7UqSRgGTEZKkJppCmU3iD8ArKdX//0ApiDeQfwHvpSQWbqcUedwzM68FyMwTKTNe7EWp7n8J5aptX9f591JOKK+mnGT+HXhvW02C/nyFUkjwAMrsHJdThgX8tSWuPYBrq9d9P/C+zPzrS7ZUYnkA2JpSL+AmykwOPwS+NEQc/W1nE0oX/aso++9TwHMDPGV3yonvNMpJ7OmUwox9Btu/z1MKE36PUqzzfEr9hv2rWB6k9Bz4N2W/30I5UX26ug36/EGcRZlZ4npKfYjTaEtGVLU/rqEMS7h6sI1VyZ4tgUUpM578rIqjfajCVdXrtm7v6n7aXpYmfYZzGf9Qn7skaT4WQx9jSZIk9a6IuBU4KzMP73YskiSpWGDoVSRJknpPlFlUdqZMOXlyd6ORJEmtahumERGnR8Q/IuLmAZZHRBwfEdMj4k8RsXZdsUmSpFHpIeDLwMcz85FuB9MuIk6KF09R23o7qdvxSZI0kmobplEVgZoF/G9mrtrP8m0o4yC3AdYHjsvM9WsJTpIkqWZVz41FB1j8eDVThyRJo1JtwzQy81etU6b1Y3tKoiKB30XEqyLi9Zn5t1oClCRJqlGVbDDhIEmaLzVpNo2lgftbHs+o2iRJkiRJ0ijSpAKW0U9bv2NIqrnb9wZYeOGF11l55ZVHMi5JkiRJkjRM06ZNeyQzl+hvWZOSETOAZVoeTwQe7G/FzDwFOAVgypQpOXXq1JGPTpIkSZIkdSwi7h1oWZOSERcC+0bEOZQClo9ZL0KSJEnqcWf31wF6HvpQPQX5Jc1btSUjIuKHwKbAayNiBvA1YBxAZp4EXEyZSWM68BTwsbpikyRJkiRJ9alzNo2dh1iewH/UFI4kSZIkSeqSJg3TkCRJGp6R7P5t129JaoRnn32WGTNmMHv27G6HogGMHz+eiRMnMm7cuI6fYzJCkiRJktRYM2bMYJFFFmG55ZYjYoRrkGjYMpNHH32UGTNmsPzyy3f8vDEjGJMkSZIkSS/L7NmzWXzxxU1ENFREsPjiiw+754o9IyRJmt9Z6V6S1HAmIpptbj4fe0ZIkiRJkqRa2TNCkiRJktQ75nWPvob34JswYQKzZs2aJ9s66aSTWGihhfjIRz7C7bffzk477UREcN555/HhD3+Y3/zmN/PkdTphMkKSJKkbnAlEklSzffbZ54X7F1xwAdtvvz2HHnoowLASEZlJZjJmzNwPtnCYhiRJkiRJQzj66KNZddVVWXXVVTn22GP51re+xfHHHw/AZz/7Wd7xjncAcMUVV7DrrrsCpVfDwQcfzBprrMEGG2zAQw89NOD2H3roIXbYYQfWWGMN1lhjjZckB2bNmsXmm2/O2muvzWqrrcbPfvYzAJ588km23XZb1lhjDVZddVV+9KMfAXDggQcyefJkVl99dQ444AAADjnkEI488kguvvhijj32WE499VQ222yzF2Ltc8QRR7Duuuuy+uqr87WvfQ2Ae+65h1VWWYVPfvKTrL322tx///0va3/aM0KSpHnBIpCSJI1a06ZN44wzzuD6668nM1l//fU59dRTOfroo9lvv/2YOnUqTz/9NM8++yzXXXcdG2+8MVASBRtssAGHH344X/jCF/jud7/Ll7/85X5fY7/99uPtb387559/Ps8///xLhmaMHz+e888/n0UXXZRHHnmEDTbYgO22245LLrmEN7zhDVx00UUAPPbYY8ycOZPzzz+f22+/nYjgX//614u2tc0227DPPvswYcKEFxIVfS677DLuvPNObrjhBjKT7bbbjl/96ldMmjSJO+64gzPOOIPvfOc7L3uf2jNCkiRJkqRBXHfddeywww4svPDCTJgwgfe9733ccMMNTJs2jSeeeIJXvOIVbLjhhkydOpVrr732hWTEggsuyLvf/W4A1llnHe65554BX+PKK6/kE5/4BABjx45lscUWe9HyzORLX/oSq6++OltssQUPPPAADz30EKutthq//OUv+eIXv8i1117LYostxqKLLsr48ePZc889+elPf8pCCy3U8Xu97LLLuOyyy1hrrbVYe+21uf3227nzzjsBWHbZZdlggw2Gs+sGZDJCkiRJkqRBZL60h2JEsNxyy3HGGWfw1re+lY033pirrrqKu+66i1VWWQWAcePGvTDt5dixY3nuuefmOoazzjqLhx9+mGnTpnHTTTex1FJLMXv2bFZaaSWmTZvGaqutxkEHHcRhhx3GAgsswA033MD73/9+LrjgArbaaqthvdeDDjqIm266iZtuuonp06ezxx57ALDwwgvPdfztHKYhSZIkNZ1DwaSu2mSTTdhtt9048MADyUzOP/98vv/97zNz5kyOPPJITj/9dFZbbTX2339/1llnnRcSEMOx+eabc+KJJ/KZz3yG559/nieffJJFF130heWPPfYYSy65JOPGjeOqq67i3nvvBeDBBx/kNa95DbvuuisTJkzgzDPPZNasWTz11FNss802bLDBBqywwgodx7Hlllvyla98hV122YUJEybwwAMPMG7cuGG/n6GYjJAkSZIk9Y4uJM/WXnttdtttN9Zbbz0A9txzT9Zaay1mzpzJ4YcfzoYbbsjCCy/M+PHjXxiiMVzHHXcce++9N6eddhpjx47lxBNPZMMNN3xh+S677MJ73vMepkyZwpprrsnKK68MwJ///Gc+//nPM2bMGMaNG8eJJ57IE088wfbbb8/s2bPJTI455piO43jXu97Fbbfd9sJrT5gwgR/84AeMHTt2rt7XQKK/7ia9ZMqUKTl16tRuhyFJmt/18lVLY+/fSB/s9nLsqp9/pwPz+z7q3XbbbS8Me1Bz9fc5RcS0zJzS3/r2jJAkNYsnaJIkSaOeyQhJkiRJkmpy+OGH8+Mf//hFbTvuuCMHH3xwlyLqDpMRkiRJkjTa2NOwsQ4++OD5LvHQH6f2lCRJkiQ1Wq/XOhzt5ubzMRkhSZIkSWqs8ePH8+ijj5qQaKjM5NFHH2X8+PHDep7DNCRJkiRJjTVx4kRmzJjBww8/3O1QNIDx48czceLEYT3HZIQkSZIkqbHGjRvH8ssv3+0wNI85TEOSJEmSJNXKnhGSNBpZQVuSJEkNZs8ISZIkSZJUK5MRkiRJkiSpVg7TkCRJ0vA4FEyS9DLZM0KSJEmSJNXKnhGSJEmaP4xkjw6wV4ckDYM9IyRJkiRJUq1MRkiSJEmSpFqZjJAkSZIkSbUyGSFJkiRJkmplMkKSJEmSJNXKZIQkSZIkSaqVyQhJkiRJklQrkxGSJEmSJKlWJiMkSZIkSVKtTEZIkiRJkqRaLdDtACRJkiSpkc6Okdv2h3Lkti31AHtGSJIkSZKkWpmMkCRJkiRJtTIZIUmSJEmSamUyQpIkSZIk1cpkhCRJkiRJqpWzaXSLlXklSZIkSfMpe0ZIkiRJkqRa1dozIiK2Ao4DxgKnZuY325ZPAr4HvKpa58DMvLjOGDWKjWRvFLBHymhkDyZJkiRpRNTWMyIixgInAFsDk4GdI2Jy22pfBs7NzLWAnYDv1BWfJEmSJEmqR53DNNYDpmfm3Zn5DHAOsH3bOgksWt1fDHiwxvgkSZIkSVIN6hymsTRwf8vjGcD6bescAlwWEZ8CFga26G9DEbE3sDfApEmT5nmgGoJd1yVJkiRJL0OdPSP6O4NtP/PcGTgzMycC2wDfj4iXxJiZp2TmlMycssQSS4xAqJIkSZIkaaTUmYyYASzT8ngiLx2GsQdwLkBm/hYYD7y2lugkSZIkSVIt6kxG3AisGBHLR8SClAKVF7atcx+wOUBErEJJRjxcY4ySJEmSJGmE1ZaMyMzngH2BS4HbKLNm3BIRh0XEdtVqnwP2iog/Aj8EdstMiwhIkiRJkjSK1FnAksy8GLi4re2rLfdvBTaqMyZJkiRJklSvWpMRkuZDzr4iSZIkqU2dNSMkSZIkSZJMRkiSJEmSpHqZjJAkSZIkSbUyGSFJkiRJkmplMkKSJEmSJNXKZIQkSZIkSaqVyQhJkiRJklQrkxGSJEmSJKlWJiMkSZIkSVKtTEZIkiRJkqRamYyQJEmSJEm1MhkhSZIkSZJqtUC3A5DUgbNjZLf/oRzZ7UuSJElSC3tGSJIkSZKkWpmMkCRJkiRJtXKYhiRJkiSpOUZyiLLDkxvDnhGSJEmSJKlWJiMkSZIkSVKtTEZIkiRJkqRamYyQJEmSJEm1MhkhSZIkSZJqZTJCkiRJkiTVymSEJEmSJEmqlckISZIkSZJUK5MRkiRJkiSpViYjJEmSJElSrUxGSJIkSZKkWpmMkCRJkiRJtTIZIUmSJEmSamUyQpIkSZIk1cpkhCRJkiRJqpXJCEmSJEmSVCuTEZIkSZIkqVYmIyRJkiRJUq1MRkiSJEmSpFqZjJAkSZIkSbUyGSFJkiRJkmplMkKSJEmSJNXKZIQkSZIkSaqVyQhJkiRJklQrkxGSJEmSJKlWJiMkSZIkSVKtFuh2AJIkSZIk9byzY2S3/6Ec2e3XzJ4RkiRJkiSpVrUmIyJiq4i4IyKmR8SBA6zzwYi4NSJuiYiz64xPkiRJkiSNvNqGaUTEWOAE4J3ADODGiLgwM29tWWdF4CBgo8z8Z0QsWVd8kiRJkiSpHnX2jFgPmJ6Zd2fmM8A5wPZt6+wFnJCZ/wTIzH/UGJ8kSZIkSapBncmIpYH7Wx7PqNparQSsFBG/jojfRcRW/W0oIvaOiKkRMfXhhx8eoXAlSZIkSdJIqDMZ0V9p0fZyoAsAKwKbAjsDp0bEq17ypMxTMnNKZk5ZYokl5nmgkiRJkiRp5AwrGRERW0fEz6sCk8tUbXtGxOYdPH0GsEzL44nAg/2s87PMfDYz/wrcQUlOSJIkSZKkUaLjZERE7AKcC9wJLA+MqxaNBb7QwSZuBFaMiOUjYkFgJ+DCtnUuADarXu+1lGEbd3caoyRJkiRJar7h9Iz4ArBXZn4WeK6l/XfAmkM9OTOfA/YFLgVuA87NzFsi4rCI2K5a7VLg0Yi4FbgK+HxmPjqMGCVJkiRJUsMNZ2rPFYHf9tM+C1i0kw1k5sXAxW1tX225n8D+1U2SJEmSJI1Cw+kZ8SBl2ES7TYC75k04kiRJkiRptBtOMuIU4PiI2Kh6vExEfBT4FnDiPI9MkiRJkiSNSh0P08jMb0XEYsDlwHhKTYengSMz84QRik+SJEmSJI0yHScjImIh4KvA4cBkSq+KWzNz1gjFJkmSJEmSRqGOkhERMRZ4DFgjM28Fpo5oVJIkSZIkadTqqGZEZj4P3AssOLLhSJIkSZKk0W44BSy/DnwzIl47UsFIkiRJkqTRr+OaEcABwPLAAxExA3iydWFmrj4vA5MkSZIkSaPTcJIR541YFJIkSZIkab4xnKk9Dx3JQCRJkiRJ0vxhOD0jAIiId1Cm9kzglsy8el4HJUmSJEmSRq+OkxERsTRwPrAO8GDV/IaImArskJkPDvhkSZIkSZKkynBm0zgeeB5YITOXycxlgBWrtuNHIjhJkiRJkjT6DGeYxjuBTTPzr30NmXl3ROwHXDHPI5MkSZIkSaPScHpGDOTf82AbkiRJkiRpPjGcZMQVwPERsUxfQ0RMAo7DnhGSJEmSJKlDw0lG7AcsBNwdEfdGxD3AXVXbfiMQmyRJkiRJGoU6rhmRmfcDa0fEO4GVgQBuzcxfjlRwkiRJkiRp9BlOAUsAMvNy4PIRiEWSJEmSJM0HOh6mERGnR8Tn+mnfPyJOnbdhSZIkSZKk0Wo4NSO2Aa7sp/3KapkkSZIkSdKQhpOMeBUwq5/2J4HXzJtwJEmSJEnSaDecZMRf6L8HxLbA9HkTjiRJkiRJGu2GU8DyKOCkiFiSOcM1Ngc+A/zHvA5MkiRJkiSNTsOZ2vN7ETEe+DJwUNX8ALB/Zp4xEsFJkiRJkqTRZ1hTe2bmycDJEbEEEJn5j5EJS5IkSZIkjVbDmdpzTESMAcjMh4ExEbFnRLx1xKKTJEmSJEmjznAKWF4EfAogIiYAU4EjgGsi4iMjEJskSZIkSRqFhpOMWIc5hSvfBzwOLAnsBRwwj+OSJEmSJEmj1HCSEYsA/6ruvws4PzOfpSQo3jSvA5MkSZIkSaPTcJIR9wEbRcTCwJbA5VX7a4Cn5nVgkiRJkiRpdBrObBpHA98HZlESE7+q2jcB/jyP45IkSZIkSaNUx8mIzDw5IqYCk4DLM/Pf1aK7gK+MRHCSJEmSJGn0GU7PCDJzGjAtIiZGxFOZ+e/MvGiEYpMkSZIkSaPQcGpGtLoVWG4exiFJkiRJkuYTc5uMiHkahSRJkiRJmm/MbTJCkiRJkiRprsxtMuK/gJnzMhBJkiRJkjR/GFYByz6Z+Y15HYgkSZIkSZo/vOxhGhGxTEScPi+CkSRJkiRJo9+8qBnxGuCj82A7kiRJkiRpPjDkMI2I+MgQq0yaR7FIkiRJkqT5QCc1I84EngJygOXOyCFJkiRJkjrWSSLhQeAjmblIfzdgoxGOUZIkSZIkjSKdJCOmAWsPsjyBmDfhSJIkSZKk0W7QZEREbAIcCfx6kNWmA5t18mIRsVVE3BER0yPiwEHW+0BEZERM6WS7kiRJkiSpdwzVM+Iq4I7M/EVE3B0Ri7evkJlPZuY1Q71QRIwFTgC2BiYDO0fE5H7WWwTYD7i+kzcgSZIkSZJ6y1DJiH8Cy1f3l+tg/cGsB0zPzLsz8xngHGD7ftb7OvAtYPbLeC1JkiRJktRQQ82m8RPgmoj4G6U2xNSIeL6/FTPzjUNsa2ng/pbHM4D1W1eIiLWAZTLz5xFxwBDbkyRJkiRJPWioZMQ+wIXAisDRwBnAE3P5Wv0VuXxhutCIGAMcA+w25IYi9gb2Bpg0adJchiNJkiRJkrph0GREZiZwEUBErAEclZlzm4yYASzT8ngiZdrQPosAqwJXRwTA64ALI2K7zJzaFtcpwCkAU6ZMSSRJkiRJUs8YqmfECzLzYy/ztW4EVoyI5YEHgJ2AD7Vs/zHgtX2PI+Jq4ID2RIQkSZIkSeptL6cg5bBk5nPAvsClwG3AuZl5S0QcFhHb1RWHJEmSJEnqro57RswLmXkxcHFb21cHWHfTOmKSJEmSJEn1qq1nhCRJkiRJEpiMkCRJkiRJNTMZIUmSJEmSamUyQpIkSZIk1cpkhCRJkiRJqpXJCEmSJEmSVCuTEZIkSZIkqVYmIyRJkiRJUq1MRkiSJEmSpFqZjJAkSZIkSbUyGSFJkiRJkmplMkKSJEmSJNXKZIQkSZIkSaqVyQhJkiRJklQrkxGSJEmSJKlWJiMkSZIkSVKtTEZIkiRJkqRamYyQJEmSJEm1MhkhSZIkSZJqZTJCkiRJkiTVymSEJEmSJEmqlckISZIkSZJUK5MRkiRJkiSpViYjJEmSJElSrUxGSJIkSZKkWpmMkCRJkiRJtTIZIUmSJEmSamUyQpIkSZIk1cpkhCRJkiRJqpXJCEmSJEmSVCuTEZIkSZIkqVYmIyRJkiRJUq1MRkiSJEmSpFqZjJAkSZIkSbUyGSFJkiRJkmplMkKSJEmSJNXKZIQkSZIkSaqVyQhJkiRJklQrkxGSJEmSJKlWJiMkSZIkSVKtTEZIkiRJkqRamYyQJEmSJEm1MhkhSZIkSZJqZTJCkiRJkiTVymSEJEmSJEmqlckISZIkSZJUq1qTERGxVUTcERHTI+LAfpbvHxG3RsSfIuKKiFi2zvgkSZIkSdLIqy0ZERFjgROArYHJwM4RMblttT8AUzJzdeA84Ft1xSdJkiRJkupRZ8+I9YDpmXl3Zj4DnANs37pCZl6VmU9VD38HTKwxPkmSJEmSVIM6kxFLA/e3PJ5RtQ1kD+AX/S2IiL0jYmpETH344YfnYYiSJEmSJGmk1ZmMiH7ast8VI3YFpgBH9Lc8M0/JzCmZOWWJJZaYhyFKkiRJkqSRtkCNrzUDWKbl8UTgwfaVImIL4GDg7Zn5dE2xSZIkSZKkmtTZM+JGYMWIWD4iFgR2Ai5sXSEi1gJOBrbLzH/UGJskSZIkSapJbcmIzHwO2M0KcCUAACAASURBVBe4FLgNODczb4mIwyJiu2q1I4AJwI8j4qaIuHCAzUmSJEmSpB5V5zANMvNi4OK2tq+23N+izngkSZIkSVL96hymIUmSJEmSZDJCkiRJkiTVy2SEJEmSJEmqlckISZIkSZJUK5MRkiRJkiSpViYjJEmSJElSrUxGSJIkSZKkWpmMkCRJkiRJtTIZIUmSJEmSamUyQpIkSZIk1cpkhCRJkiRJqpXJCEmSJEmSVCuTEZIkSZIkqVYmIyRJkiRJUq1MRkiSJEmSpFqZjJAkSZIkSbUyGSFJkiRJkmplMkKSJEmSJNXKZIQkSZIkSaqVyQhJkiRJklQrkxGSJEmSJKlWJiMkSZIkSVKtTEZIkiRJkqRamYyQJEmSJEm1MhkhSZIkSZJqZTJCkiRJkiTVymSEJEmSJEmqlckISZIkSZJUK5MRkiRJkiSpViYjJEmSJElSrUxGSJIkSZKkWpmMkCRJkiRJtTIZIUmSJEmSamUyQpIkSZIk1cpkhCRJkiRJqpXJCEmSJEmSVCuTEZIkSZIkqVYmIyRJkiRJUq1MRkiSJEmSpFqZjJAkSZIkSbUyGSFJkiRJkmplMkKSJEmSJNXKZIQkSZIkSaqVyQhJkiRJklQrkxGSJEmSJKlWtSYjImKriLgjIqZHxIH9LH9FRPyoWn59RCxXZ3ySJEmSJGnk1ZaMiIixwAnA1sBkYOeImNy22h7APzNzBeAY4L/rik+SJEmSJNWjzp4R6wHTM/PuzHwGOAfYvm2d7YHvVffPAzaPiKgxRkmSJEmSNMLqTEYsDdzf8nhG1dbvOpn5HPAYsHgt0UmSJEmSpFpEZtbzQhE7Altm5p7V4w8D62Xmp1rWuaVaZ0b1+K5qnUfbtrU3sHf18M3AHTW8hW56LfBIt4OYS8beHcbeHb0ae6/GDcbeLcbeHb0ae6/GDcbeLcZev16NG4y96ZbNzCX6W7BAjUHMAJZpeTwReHCAdWZExALAYsDM9g1l5inAKSMUZ+NExNTMnNLtOOaGsXeHsXdHr8beq3GDsXeLsXdHr8beq3GDsXeLsdevV+MGY+9ldQ7TuBFYMSKWj4gFgZ2AC9vWuRD4aHX/A8CVWVfXDUmSJEmSVIvaekZk5nMRsS9wKTAWOD0zb4mIw4CpmXkhcBrw/YiYTukRsVNd8UmSJEmSpHrUOUyDzLwYuLit7ast92cDO9YZU4/o5SEpxt4dxt4dvRp7r8YNxt4txt4dvRp7r8YNxt4txl6/Xo0bjL1n1VbAUpIkSZIkCeqtGSFJkiRJkmQyQpIkSZIk1ctkhCRJUsNExKSIiH7aIyImdSMmSZLmJZMRDRcR4yNiodZbt2MaTESMiYgxLY9fFxF7RsRG3YyrExHxlohYvZ/21SNicjdiGo7qu/KBiPhiRLyqantTRLym27GNRhFxfES8op/2JSPiom7EJGlU+SuwRD/tr6mW9YyIeGVEbBERy3Y7FmleioglImKJlserRcR/RsTO3YyrExHxkQGOYxaMiI90I6bRLiL2jYhd+2nfNSI+2Y2Yus0Clg1U/VgfD2wGLNy+PDPH1h5UhyLiF8AlmXlcREwAbqe8hwnAHpn5v10NcBAR8WvghMw8u619J2DfzHxbdyIbWkSsAFwOLAK8ClgpM++OiCOBV2Xmnl0NcBARsTXwH8AbgS0z8/6I2BP4a2Ze0d3oBhYRdwKzgZ0z8+aq7T2UKYpvzsx3dDO+TkXEa4E3ATdl5tPdjmcoEfF2YHZmXl893g3YE7gF+FxmzupieMNW/e3OqGaT0giIiM8A/5uZM7sdy3BExL+BpTLz4bb2ZYFbM/MlxwdNERFnAjdk5nciYkFgGvAW4Blgh8z8RTfjazecE68mH8dA7/6mAkTE+sDmwJK0XTDNzP26EtQQIuIq4PuZeXr1e3on8CAwETgsM4/qaoCDiIjngddn5j/a2hcH/tHw843TgVva929E7A9Mbupxb0RMp5wPXdPW/jbgjMxcsTuRdU+tU3uqYz8AxgOfAh4CeiljtA7wher++4DHgeWBXYADgCb/iK8O3NBP+43AajXHMlzHUpIRnwD+1dJ+IXBGVyLqQETsApwEnEo5ABlXLRpL+R41+cBpTeB/gBsj4mBgRWB34BDgm12MqyMRsQglcfIByv8xKwJ3R8RJwN8z85AuhjeYYyn7mIh4M3Ay5X28DTiC8jfQSBHxX8Admfm9qvv9ZZTv/WMRsVVfgqUpIuLCTtfNzO1GMpaX6bPAN6v3cypweTb4SkxEHF/dTeAbEfFUy+KxwHrATbUHNjxbUi6qAGxHSZS/jjn/RzYqGQGc0PZ4Qcrv0b+rx2OAZ4GnafBxTC//pkbEAcC3gOmUk/nWv9HG/r1Sjh1/V93/ADA9M9eNiO0pv0mNTUYAQf/7dhLwWM2xDNc2lGOwdldSzjeaaiJwbz/tM6pl8x2TEc20FrBuZt7W7UDmwiLMORl+F3B+Zj4bEVfy0h/7pnkeWKyf9ldT/sNusrcCG2Tm821DjO8D3tCdkDryBWCvzDynunLT53fAYV2KqSOZ+SSwe0TMAI4EngPe2Z7tbrD/BpYG1gaua2n/OXA41Ql/A70J+HN1//2UE8tPVlfUfkKDkxGUpOz/q+5vTUlobVC1f5PSG65JHu12APPIcpTfo48BPwMerq7cn5GZTRzu0Jf8DmAVSm+CPs8Av6f8n9Nkrwb6rrZuBfwkM/8REecAB3cvrP5l5iJ99yNiW8r/f58B+hKE6wNHA1+vPbjh6dnfVODTwH6Z+e1uBzJMrwT6euRtQbkIBOXvdJmuRDSEiPgzJQmRwDUR8VzL4rHAssDF3YhtGF7FnP3e6knKULam+jvlt/+etva1gUdqj6YBTEY00x8p40R7MRlxH7BRRPx/lCsjO1btrwGeGvBZzXANcHBE7JiZzwNExAKUA6dfdTWyzozrp63p2e0Vgd/20z4LWLTmWIYtIj4HfB44E1gXOCUidsnMqV0NrDPbUbpL3xQRrVdGbqN0722qpBwsQbnyd351/+/A4l2JqHNLUa5+QLmqc25m3hARM4HGfWcy82PdjmFeqHpBXApcWtXQ2YWSmPhS1cX6NOC8zHxukM3UJjM3A4iIM4BPZ+bjXQ5pbvwdWDUi/kY5Fti7ap9A6WHQZEcCu2dm62/Tr6vhPmdSErZN1cu/qYvS/BPg/twJvC8ifkJJeh5RtS/Fi3uqNsl51b+rAhfx4pP6Zygnyj+pOabh+gvld/S4tvZtKb1rmups4PiIeBK4umrbjNLr86xuBdVNJiOaaW/KF/V44Gbafrgz876uRNWZo4HvU/5ju5c5J/GbMOdqZlN9gXKFeHpE9F0pfhvl4GmTrkXVmcuA/YE9qscZEYsCh1J+aJrqQWAlXtplbRPgrvrD6VxEXA6sAeyUmT+rikAdCVwXEV/PzMO7G+GQXk3/V74XofQSaqobga9U+39j5pzkLEc5AWqyRylXnGZQDloPqtoXoPm9r0aFzJwZEdMoV6YmU4YRngAcERG7NWlMfV8yKCJeSekRBHBXZv5f96Lq2OnAjyj/xz/PnOEB61NqSTXZcpSrq+2eoiT4m6xnf1OBH1J60Xyn24EM06GU2I8CrmgZbrcl8IeuRTWIzDwUICLuAc7phXpR/TgKOCkilqQMzYBygeIzlJopTfU1yu/Opcw51hoLnAt8pVtBdZPJiGYaQynecz4vHsvVN7arsQVlMvPk6kBvGUr36b7xlnfR8D+yzLyjmk1jX8qBalCylN/JzAe7GtzQ9geuiog7KPVGfgSsQKk58sFuBjaEUyiJt77upMtExMaUcaOHdC2qziSwRmb+DaD6Mf9UNZPGGZShDk12I6V3xLHV477/az4O/KYrEXXmM5QrC9sDh2dm3wH2jjQ7bihXms6OiL9QeotdUrWvSQOv5IyimhFExFLARyk9IpYFfgpslZlXR8R4Shf206tljVAlOP+b8je5IOU36emIOAX4YpOLnmbmYRFxC+Xk/ceZ2TfU5DnKe2qy6ym/S7tk5gMAEbE0cAxzagM0VS//pt4PHBpl9rU/8dILcUd3JaohZOZPo0y1+wZKz+Y+v6T5vQsupvRIeRjKTCCUoYS3ZOYPuxnYUKraS+OBLzMnsf8AsH9mNrZWWmY+C+wcEV+hDMsP4PeZ2bhjgLo4m0YDRcTvKV27jqKfApaZOa0bcanZqqtnO1PGnY2hjFc8q+lX0SLicEqBufFV09PAkZnZ6OTVYCLitZnZ6LF/EfFWSmb+HGBXSsGzt1CK422Smb/vYnjDVh2UPF/90DdSNezr05QTtDMz8w9V+2eBJzLz1G7G164aJtCRJg/paBk2eAfwXUrl+3+2rfN64IHMbMyU51W1+HcBX2RO1/sNgW8Av8zM3bsV22gWEW8CLgBWppzcQKmvcwfw3qafNPTqb2pEDFa/JTOzycMHe1IvzwTSKsrUqtE+K0ivmN9n1TIZ0UBV5ew1M/Mv3Y5luHp1qh3o3SkDI2IcZQaWL7VcJe4JEbEQ5UDpFZQu02MoU9Y1cl+PNtVVkAMos+D0JbD+OzObPqSKiHgj5TuTwG2ZeXeXQ1JDRcRpwHczc8Cr2tXsJpMys78q510REU8A78vMy9va30kpCNnYGgDVCfH9mXlSW/s+wNI9cGIcwDspCYkAbqUkgHrioLn6bfU3tSYR8f8YeErSxvYai4hHgY0z89bqb3OP1plAMnOlLoc4oIgYA9DXAzsiXge8m/J9b2wvycFm1aL01mvUrFp1MBnRQBFxNfCNzLy027EMV0T8Hdi674pfS/uawMWZ2diZHSLiD8Ah1fj/N1O6CfZNGfjrzGxslf6I+CewTi+dkEXEWGA2ZajDrd2OZ16JiO8BEzNz827HMhpVtVBOo8yk0TcMLCjdYffIzCe6FVsnqgTQxyk1AHbPzL9FxHuBe9v/39T8rfo9fUf7/48RMRm4KjOX6k5kQ4uI+4Ad2w+sI2JdSrHQxgyHGU2qE7IFMnNGW/tE4NnMfKg7kY1eEXEEZfjgVbx0StKm9xp7Clg5M++LiPOAP2bm1yNiGeAvmfnKLoc4oIj4BXBJZh4XERMotWgWptR52yMzGzkFb0TcC/y/zPxdRGwDfI9SdHMXYPW+AsbzE2tGNNOJwLERcRSl6GP7uLkmd5/u1al2oLenDPwp8D6aP93bC7JMQ3ovZSz0aBK0XRlpimomgY5k5syRjOVlOI4yr/tmzKkRsRFwEqX+xR4DPK/rIuJdlGnffgG8gzIlHJT/e3YD3tudyDoTEZtRhoJNou3vNjPf0ZWgOhQRn6QUNVseWDUz746IA4G7M/Pc7kY3oP8BvlYV1vw/eGE43leqZU22JNU49DaPUmYZaLQe/b5AKSB+LmU4UqstKbUA3lV7RMMQESsBH6D//2OaOizpI8DOmXnekGs2Ty/OBNJnHUrheSjHv49T/l53ofT4bGQygh6bVasOjTxgFj8E3kwpRPRbypez73ZjF+PqRN9UO+2aPtUOvHTKwL7icr0wZeB9wJcj4mcR8ZWI2L/11u3gBvF14JvVWMVRITM/0uDM9iOUE4TBbn3rNNV2wJ6ZeU1mPlvdrqbMqtHok3nK933/zNyBMn1an6sptToaqxq29gvKbCubUr4jr6bUqGl0z6YoUzJ+mfKb2jpryQOUgsVNtQGwNfBARFxd9ZqcQfmNXS8iLuy7dTPIAdxHme2m3SbMORBvpB7+vkCZYrq/qcivBabUHMuwRMS2lB6p7wF2pxwHbwPsADT5GGEMcFO3g5hLh1IKyt4D/K4XZgJpsQhzEibvAs6vakZdyZzZh5qob1YtKHH3zQQy386qZc+IZlq+2wG8DL061Q709pSBuwH/pFwxXr1tWVKmXG2iAyjf9wciYgZt06llZvt7abSIGNfkAoqU3gS97pX0PyXpTOYUbGuqt1Cql7ebSfN7jh0A7JuZp1a1DA6qrhZ/m/57wzXJPsBemXlRRPxnS/vvKZ9JUz3CS6vxD1bkr0lOBo6JiAV58bHAN2j+bBq9+n2Bclz/in7axw/Q3iSHAYdm5jeq/2M+TBn28H3mFHBtolMoRaAP6XIcw9bjM4HcB2zUUqB4x6r9NZRpeJuqp2bVqoPJiAZqUgGt4erVqXYqPTtlYGb2agKrF7s1AhAR+1Gq7/+kenwa8NGIuAvYLjPv6GqA/cjMa7odwzzwa+DrEfHhzHwKICIWplzhafTfKSVhuDTlKlSrtWn41WLgjZQDVChFZydU979N6dlxYBdi6tSywM39tD/LnKEyjdPkseZDycyjqh5vxzOnu/0zwHGZ+a3uRdaRnvy+VK6nDCltH1b6HzS/Z+2bKdOSQ9nXC2Xm7Ig4DLiI5l5UeRXwoaqwbH9Tku7Xlag6VNUReaitrReKKB5NSVTNAu5lTo+gTZgz5LqJ9qfEOwn4Qmb2XYR7PWWY/nzHZEQDRcQHgX9l5mXV469SrtLfAuyWmX/rZnxDycyTgZN7baqdzLyZl/YqgHJF8Pmaw5kvZOah3Y7hZdiP0pWUiNgE+CDwIUq9kaMoVZ17QlX0rH187n1dCmco+1OGCzwQEX+i9PxZg9KrZstuBtaBs4Ejqv/jE1igmsXnSKDpydpHKd1ioSSYV6UceC9O80/Q7qYkfNoT/dvQ4CEmEXEscGr129RzMvOgqmfBZKoZKXpkVoee/L5UDgaujIg1gCuqtncAawFbdC2qzjzBnN5tfwNWoCSFFqAMCWuqycwZprFy27LGzxLQo3U6yMyTI2IasAylxltfQeu7KHV1Gikzn6McI7a3H9OFcBrBZEQzHUK5Sk9ErA18CfgqsBXlC/yhrkU2DJnZ5HHnHeuVeX979Qelh7Ve4X4P8OPMPDci/kwZn9toEbEY5arlB+m/iOjYftq6LjP/HBErUrrF9k279wPgrL4ifw32ZeBMyklO33SBQUlSHN69sDpyLWV8658pBfKOr64Ebg5cPtgTG+BI4NvVdIcBbBgRH6YUP2vy/43rAp+qDrhPBc7JzMe7HNOwVFf9mn5Fvl2vfl+oKvRvCHyeUtQvKMNLPpmZfxz0yd13PWX2slspPSGOqpIqO9DgYRoNrhE1pKpOx08o9SHWofytvokypKfxxzGZ2VdPr7Xtoi6FM6DqXO6mzPx3dX9ADZ+kYEQ4tWcDRcSTwOTMvDcivg6smJk7VdNjXtq06byqq5Nvz8x/VidiA36pmlYDoJdjbzXUD0pT57muxhMfzJwK/eNal2dmI0+IASLiIWCbzJwWETdR5uQ+KyJWoPzoTBhiE10VEd+lnOx8kTIby+6UBMungc81tTJ41QvlN9XVhdb2BYC3ZmZ/xdsaJSLeRLlSOQb4Q2be2eWQhlTNxDI+Mx+MMr/75ymzmPwF+M/MbHTl9YjYi5IMWqZqeoAylfNp3YtqaNU007tTkm+LUf5WT2vikKuqkOaumfn4UEU1m/qb1KdXvy+9LCLeCEzIzD9ViaCjmPN/zP4N7q3Xs6pE53ktdTrWoKVOR2Y2amhMVZD9O9XwnUGLszcp9oj4N/C6zPxHdT/pv1hlNvm4d6TYM6KZZjOnO+zmwOnV/cda2pvkJ5QxxNB7NQB6OfZWvVr46euU6ca+ARxDOcFZDtiJBnezq1wGfDci/kDpTvqLqv0t9EaRua0p05FdGxHPA9My80cR8Tfg4zT37+EqytjK9uFfi1XLGv9DXtWjuWvIFRukdarXqjts04sQvkhmfpfy9/paYEwPDR+8A/hiRBxEGSawO3BZRNwHnAac0qBpeB9lTkK/vyKzPaNXvy+temz4HZl5d8v9p2j2dOov6PHEW6/V6fgU8D3KedKnBlmvaYXbl2fOLGW9WuNtxNgzooEi4gLKGNzrKCdky1VXo7YEjs/MN3c1QDVORMwCVq+q288ENsnMmyNiNeCizJzU5RD7FRF/BT6RmZdUSZQ1M/OuiPgEsHlmfqDLIQ4oIhaldK2fBJyYmZdU7YcCT2fmf3UzvqFU35nJmXlfRNwPfCAzr4+I5YBbMnPhrgY4gOqqwlLtw8CqYUpTM3PR7kTWv4g4vtN1m17orCpO/CHKGGko3al/2PThMRGxV3Vy2d+ykzJzn7pjGq6IeAWlHs0ewNuBa4DXUYot7p2ZZ3cxPDXEUMPv5serriMtItrr/Yyj9DBYBvhpk4fJVhcfNs/MWyPiFuDgzLwgItYCfpWZTbwAqlHGnhHNtC+louoHgH0y88GqfWvg0q5FpSbr1cJPSzGnINgsSlVqKFMdNfrKazV2+yWZ+cz8WhfCmRt3UWZIuA+4DdgpIm6gjDNuypXWF7RcfUrgBxHxdMvisZSCik2cTWO1Dtdr9JWBapzrzymJ8r5K5bsDh0fEtg0f5/qtiJjZN/NNn4g4hYYXPY2IKZT9vBNlurrvAXtm5l+r5Z+m9CozGTGPRMSrKbW7NgOWpAynekFmLtmFsDp1JOVE+L30M/yui3H1KyIeB96YmY9UFyQGGyrbqERzn4FmvYn/n73zjpKsqt728w6gIggKnwRRkiQByTkOIEHwh0iUIJIVQXIQEEmCKEkQEUSygIJIEFDiDCBRQAQGJOcMEiQH3++PfWq65k51d/Uw0+fcnvusVWuqT1XPeqen+t6z99n73dJRxN6sZGrp01F3Uovy/HS+vnQa/z2kaZIRBWL7acIQr7q+SwY5AyL1FR9KtJd0+iUr8mYCtd+A1PWG8iQx3/pJYr7y6sAdwNJA6aetJxFtASNtP59bzzhwOjE9ZiRwOBFo7kh87nfOpqp3WmXfIkZktn8+3icqyTqefuekzuZmFX5L/Iy3bI0iSyNVT02vLZZRW3+sD/xZ0mu2r4HRiYg1gOE5hfVF8jGamziE2IKocqtOdjqHSEYURaqi2Zne9wLFejABZxLtdmcQIw+LThRWqFv73Q/pCdh/SL1+1v1xEnHNPDCzjr7YjZ4xzQcSreDrkXw6MmnqmlTB0duefa8sovohGT+fRWiuYmrQajq+ado0GsYrki4kjNl+S3gWjPEBs31GDl3dIOkv9LEBcYwsLZK6Gj9J+hnwpu1DJa0PnAs8TZzkHGF7v6wC+0DS2cQ86y8QiZSRrYcLH7/bCUkzEwHlQ7aLndEt6QDgSPfM5q4N6TRkWHVCTwrc/mf7/TzK+kfSO8Citu+rrM9HtMcUPd5T0nrERIo1gG2IySArtfepl4ak/YFTbT+TW8tAkXQqkQw/n857gWLHOqcT+hULr/bpSF3b74Yikv6PMJst+SCrtkjaizhIeYKx9+y2vUwWYf0g6UHgesIzrVOs8V6n7xvKNMmIQhgKpWow+t+xqu1bc2sZKHXegAwVJC1JSqLYvjS3nm5QjJlckThhXYFIpDxkuzpvvHgkTWb7g9w6hiqSLgauq7p8S9oFGG57nTzK+icZte5p++rK+teAo2wvmEdZ96QJCccTrWzDbT+eV1HvSJoMeIro5x6VW89ASd5FG1Y/L3UgTUfaxjE2sFZI+hews+2Rkq4ERhEn3LsCu9r+Up9/QUYkPQosbvuVyvpngTttz55HWd908AUSYbL8dSKZ2JfRYnZSMvwbxAS2k2y/liY+vVqQOe5YpGqfA0s+KOxEa2pJyYnwwaZp0yiH9lK1HXMK+Zi8SPT+15FHqJR51YXktD4C+EeHMt7akJJYdUtkPQJMA3yeKLubkRipWjSSdgKeafXRSzoF+K6kR4C1k4t/cajGI3iJRFunap+rgH0HWUu/pLa7Fj8Gjksu67ektaXS+o8GW1t/9GEc+gLhebGbFJPVSjQOtf2BpA+ob9n620QypY7sDPxM0h7AvTW7p55Ovdrv2pmVziXqnwS+OLhSBkTVF+h/xOSEXemZhlckilHkVxOtGp8lKpleIyaZfJaoIiuVYcA1uUWMA5cCywBNMiLRVEY0jFckbUS4OH/Xdq2SEpJWJDbWtduASPo7sDjRO38TPS0Dt5X870jlaiOoYXuDpD2JXsXlgJeJsruRwAjbT2SU1hWSHga2sn29pBUIr5GtiX7RKWx/I6vAXkhtGu1MBixEBPq/tv3jwVfVHZLeBhax/e/K+leIk7+iWh3UMw999FL609WvS3PplzSiy7fa9soTVMw4ksqQv0r4dHyYW89ASMnO+YhpSf/LrWcgSJqJGHe4dKfXS/us94WkWYBFKbj9TtK66emfiHvQ620vT0L4jqzkZpLceEfSpUQb1fZEEmJBx1S2FYDTbH85q8A+kHQgMFnJ7bydSBNvzgYeIozmx6hGtX1mDl05aZIRhVKnsqkOJ5WzETeQJxj7l6zYU8u6b0AkTU4Exq2WgcWIn/+NttfIKK1XUtn0iulRK++FFKi9RLiXn+7KqMnSSR4Ac6fe4iOAaW1vlQLjG2z/v8wSB0RKDs1iu9jKMkm3AFdUJ65IOgRYw/bieZR1JiVou8L2dRNSy8RI8jFakTBrvRcYwyfF9to5dHVD0r48EVjex9h7gZK1X09MoTqRzj3dF3T6voZxI91LIX7Oqrz8AfA4sHtdWjfrRGqnWsr2g+3tA8lj5P7SEuTtKErbLieqUe9h7GtMkSNVJW1IeNN9kqggq3pdFNuKP6Fo2jQKpIZlU6W5M48r5wJTAztRPwdtbL8DXJWSQ6OAtYCNCB+DIrF9MmkCQvrcDwdWJS7Uwyj7GrUaofebwMGp0mAkUelxXbXvtUDeIFpLniR+5kek9Q/oGRNbJ/4M3E7ZbW6HABelz/q1aW0VYAPC7K8ohkqCQdIMwKSOSVXt618EPrD9Qh5l/fIyUNfA92XgwtwixpHFgCVs35tbyLggaR3CJ2LetHQ/cLTtIv8/bA8DkPQY4RnxcmZJAyJVYXXaLxp4lzhkOaNgP7LJOqzNzJgVKiVyKLEPu5NIHtZlz34k4V10YB2NuCcETWVEgdS5bKrOpBLqWm5AJG1AtAysRNxEbgOuI4Ljm0t255U0jGgxGQ6sTJTbv0RURnSctWLcJgAAIABJREFU310aqSplWWDT9JDtTjf4YpB0FlFG/U8iaTWz7f9I+ibwU9vVPtiikbQloXum3Fr6QtIaRDvYwmnpn8Chtv+aT1V3SPoqMR7wy0SLz3Mp8HnC9j/zqusdSVcB56XkZ/v61sBGtlfLo6yhRCTdDuxk+6bcWgaKpN2Bw4jxpK2x3ksDmwH72z4yl7ZxoQ6mypJOADYhjHH/kZYXB2YALiI8PBYgqt+K8jiQ9AfgLdtbp8qIBYgx2hcDj9reOqvAPpD0GvA923/MrWUgJKP/hW0/kltLKZR86jgxswxRNvVRy2Ar8SRRyl48klamJyt/n+1r+3p/IdwH1LU86o9EAH8UcLzttzPr6QpJlxGtJa8QyZNzge3q4LkAIGl6IokynEgEzUVU1YzMJqp7dgB+CsxCjH9rtX8tQvw/FImkS6pLRJnmwkCx4wJb2P4b8LfcOgaKpNWAS4C/EknDVvnul4EtgGIngRCBQaeKmRvoqQgqDsXY1Els311ZXwD40JUxqyWiGDs9L3FqeX9NHOR/DBwt6cd0Lv8uqlW2wh7AjpXE26mSbgMOJk5li6SDqfKpwOalmyoT1Q+n296lfVHSUUTZ/aKSjiXut0UlI4gKmhGSHiAqIv8IzEHsYzbMKawL3iGS+XXjAuBrhPl5A01lRJGkHq7lbY+q9HCtQJzuzJBZYq9Imo0ol/4qUd0BkUC5B1iv5I1IOrE8kNiI1GoDUvFe+AyxyR5JtAz804X+okt6n6j+OZ9kZFmXEk1J9wFzExNkWlUoI6vmhCUiaVJgO+Ai28/29/6SkHRaZanl3XGt7SszSOoaSRcBvwMur6Gp361EqfEJlfvSosBfbBebKJf0JrBML0H9zbanyKOsbyTdSJiynlNZ/zYRcC6XR1n/SJoKOIUwxG191kVsxLe2/d/evjc3bR4GMLaBa3Fmre2k382FbT9cWZ+D2At8Jo+y/qmxqfIrxAHiQ5X1uYjry7QpsXiT7amziOyDVNm5MXEQMYxoezg7tf4WSzL4nRXYodQ9bick7U9MtrkCuJuxY42jO33fUKZJRhRIzcumriXMK79j+8m0NjPhAVCsaznUewPSTsV74VvAm7an6fObMtHW3jCcHtPNh4jExIhSe1wBJH2fmiQfOiHpLWDeulShDAUknU1UELxOjOA7rbqBLZUU0M9v+/FKMmI24sS7WJ8RSdcAD9revrJ+EmHiOjyLsH7oI7D8MjF9pbjApkVKGi5DJD1b7Q7LEqaQNxa+j+nTuLVkL5XUfjfK9uGV9b2BBWxvmkdZ/9TVVDkdIG5l+6LK+jrAqbanSYmJW21/LovIIUgyyV2BONCqjUlu8kbpDduefdDEFELTplEmdS6bWprIED/ZWkg3ll3p6V8slZVyC/g49OK9AFBqaWPLdPPq9GglUvYj/FJ2oPPM8SKwfWLruaQpY6lWZkS3ECPfmmTEIGF703RivCmwJfAjxVje3wHnF34S9SowE+Fs384iwNNjvbss9gOulbQgPWXSKxOtPV/Lpqp/PiJMlat8jrGnDpTG2sA6tm9oWxspaTvC2LLYZETJyYYueJi4rqxEz55rqfQ4WtJurTcWeAJbV1PlM4BTJM1JeEYYWALYm0g6Q1StFuFHJmnzbt/rssdMvkxUYtcK27Pl1lAaTWVEodS4bOoB4Lu2b6msLwWcaXuuPMqGNpIuJ5IPkxOflZHpcUPJAbKk6ejxWxhOeC602h5G2D4pm7gukLQDseFomSY+Dfzc9gn5VHVHKvU+DDgOuIOxxwaW6vzdMqvcmDBr/UT7a3U6VUilu9sA3wfeB/4A/NL2/VmFdUDSz4lRjRsSp1CLEV4dpxMVHgfnU9c/KRGxF7AQEcjfCRxh+19ZhfWBpIuJhMQGtj9Ka5MSbW2TlVq2DqMNoRer+lpImp84IS6yNabu9HPq2k5xJ7B1NVWWNAmwJzGJrdVG/TxwLHBk8n+bGfifKxN9cpAqrtr5BDFRo1UdPIxIAL3niXDMZMPg0yQjGsYrkr4B/IS4KLe7Cv+ScIz/Sy5tQxlJh1OD5EOV1BrzPHA9NfJcAJC0L7APYQj297S8PFHZdFi1TLY0Km1JVYptS5K0J/FzPwnYFTiBqBxbgdj4/TSjvK6R9AXC+HFLYgN7HhHcrwrs48Jc7yVNRiQevk0E8/9Lf54DbNEKlhvGH5LmJq4tb9JzjVmOGPu9QolJqxaKCSZvEC2bb6e1KYgpD1PZXjWnvnFB0v3AnLabquIJQKoaO5RIMv/GYfaLpIOIwPiwnPq6If0bsP1Gbi3dIGktwittF+DWtLwkcDRwiO1LM0mb6EhJt6kLr0aZIDTJiEKR9CUisJmOyFKOpsDSutGkjOsnifL69izrR4Tj8GjqknFtNiATDknz1CX5UEXSk8Dets+trG9KJCNmyaOsOyT1qa9ULwlJDwL72v5Txbtgf+IkbdvMEnslBfTfBLYikg7/BE4GzrX9ZnrPhsBvbX82m9A+UExHaFXs/bNGnhefJNpjWpMdRhE/92LHHgNImpGYBNJe0XGCCzeeVYyB/SswBWHSZmBBogJrddujMsobJyTtSPgYFD+1p53U/vi07Xf7fXPDREXa325l++bK+tLEhJC58ygbdyQdCsxQsi9NJ9L/xVylHgRNSJpkRIGkYOZU4EPCJb79P6m40rp2JH232/faPmNCahlflLoBkfSTbt9begl1HZH0LmHoVzWXmxO4p2RDvzqTyr/nSV40LwKr2b4rbbhvK9WsFUDSy/RUE5zsynSH9J7PEeaExfeV1iXIkTQvMU51KmJSEsTEp9eBNUquMKgzqd10M2Ae4nN/HzVoN60zkg4DHrB9hiQBVxH+KK8DX6+20OZG0jROk8ok9XntdsETzTpRh6A4mYYuWb0Xpba2W2xP3vk7yyUZFs9WcqzUiVQtOVmpB0ETkiYZUSCKmcp/BPZvSl8bekPSPZWlWYBPM+ZI1beBx20vMJja+iL1tHZ14Sn5ZiLpbuBP1USPpAOAdW0vmEdZ9yhGG+5Bz2nxfUSrQ/WzVQySHgXWt32npH8QbuW/UYzmPdv2tJkl9oqk7xBGlUUH753oEORcCaxCT0B/a59/QUZSy8DbRMvAG2ltKuD3wCdtr55T31AjVQD9nqhgeiS3nokJSU8AG9m+RdKahLniWkRV0AK2izLqlvQRMKPtF1PrYKe9Qa0mmrWoQ1AsaWR6uqntZ9LaTMBZRIxY1OelYWjSlJ2XyfTA7+qeiJA0A2Obyz3Zy9uLQz1jJx8qMVPZbuaUDP02J8xD20eqngacnUdhrxzf9nxKwmPhNnqcv5cmnKiPGmRdA+VA4DzFPPQbiU3UcoRr9gYZdXWFpLUJJ+obiHJqCP13Slq3YH+Xawmn/juBU4BjUmvDIoTvQslcBfw/KtMnJH0R+MD2C1lUdcemhKkcwNeJtoGl0vrhlD2NaFlg8fY+bttvSNqPmCpTK0pvHbT9gaTVCG+XWiDpEmCz9Lm4pK/3utCRgYnp6bm+rAmcZ/s2xfjJ2/PJ6pWVgVbFQ8nXkAFje5XcGrpga+Ai4HFJz6S1mYgpbOtkUzURkCohOrXiF2sePqEo8kbWwOWEgcyjuYUMFElTE+78G1JJRCSKzWxLOp0o8z5B0ieIAHk+4H1J37L91z7/grz8hBijVh2pujtwMdH2UwS2RycZ0s/851VjKkn7ED/7YrH9Z0lLEiaK36CnDHkJ2//MKq47fkqYyh7Qvijp4PRaqcmI7Ug3b9snSnqVCDYvIEwtS+YsImFycmV9dSLQX23QFXVP3YKcdt4FOnlwTE3Fy6gm/BootgIo8WdgXcLgtw68Qs+p/Cs5hXxMXiGqJJ8mriethNCkFDgO1mmMapoSMx9wUel+KEMJ24+kCslVGbOd6moXXjqfDiFes31l+vonxP5gFGGq/FxOfb0haWGicqz1827HFBwnTSiaNo1CkLRu25fTAvsTrtP3ECN2RmO72Lm6kk4mpmfsTWxGtiKyrDsDu9v+U0Z5fSLpOWCtVP69PrGJWoL4N3zL9pJZBfZB6qNfudoPqhipeo0LHaMm6Q1gkQ6+C3MQffO1MDmtI43nxeAj6TWiP/eByvpcRH9uyX4XzwAb2r4xmYjuY/sCSfMQoxqnziyxVySdQdyXtqWnEmJpInl1m+0tc2kbqqR2tV2JMc23M/bo4GKNuOuMpOMIk9wHgYWBWWy/pRjlvKftRbMK7ANJbwHzlliJ2hd1DYrrjqT7gF1sXylpEeAm4mBuDeB525tkFdgLqb30FeBgoq16jEC8bp//8UFTGVEOnYL0fTuslZ41+zqwse0bUi/gHbb/mAL979H531kKnwNeTM/XAC5IfYx/APbLJ6srrgJOlrQtY45UPSm9VipvAcOBhyvrw4ke76JIrS9dUYOWpBeBRRn7Z78oUHK7QJ2ZlJg2VOVTvayXxAXAOSkRMQ1hCAnRrlH9DJXGzkTv/A3EZCeI6ppLiJF2DeOfLYBXgQXSox0TowOLJLWYTmq7ju1UuwFPEOMx93LPqO8Zgd9kU9UdtxD3n7oFYweSriMpKN6XnqD4KKCooFjSbsREnnfT814pPGk4C9FOAvAtoqrmF5KuBK7IJ6tf5gUWtv1gbiGl0CQjCsH2sP7fVQs+S8+N5HWiyuNhwg/gd7lEdcnzwPwpcbI6kdmG8DX4oNfvKoNtiM32TYy52b6COA0slWOAX0tajJ4Ty6WA7xI3+NJ4nC7NNyk7aQjRKnBSqkK5iR7Piz2AI3IKGxdK76NP3Apsnx7t7EBPErFUahvk2H4N+Gaq+hldilytCiqBoeJdUIdpMH1Q23Yq2x/SwW/J9jEZ5AyUk4EjU9L/Dsaupim1l75uQfEPif3iu+l5bxSdNCT0fyY9X4WeduTX29ZL5B5gBqJ6qYEmGVEkkjYH/ujK/PPkY/Bt22fmUdYVjwCzA08C9wPflnQb0Tta+limU4kpJs8SAf01aX1J4N+5RHWD7ZeANdNm+yvEZvv+0jOv6Yb9OHFyuWFavp8w4izRjHDxtudzAb8ATmRM883vEW1KpfNT4E1gd+CQtPYscADh+1I36tBHvx9wbRqb1rq+rEyUU38tm6ouqHmQA4Dth4CHcuvohyHhXZBK1Y+0/XZlfXKiXaDkcdOLAzt2WL+BGiZqa8Q56c9OAXDJVcG1CorbE4U1TxreABwl6e/AYsD6aX0u4KlsqvpnX+AXkn5M51b80mOl8U7jGVEg7aOOKuvTAi+WPN5I0q7AR7aPk7QycCkwGXFKv7Pt4/v8CzIjaT3i5O/8VommpO8S/YAXZxXXC2mM2lPAKrZH5dbTLcmwajWi37x2m25J1wG/qvqgJL+RnW0vn0dZ7/SR6PwMgO3/ZhE2EZHMwvYiEhAipoIcYftfWYV1SXIAn5mxJyVdn0dRd0jaiAgUOrmXF1thUFdqvo95E1jG9t2V9QWAm0v1YOqLOlSOSZqlr9dL7aWXdBEwOfB3wu9tVtvPSlodOM723FkFDlFS29RviPvRsbZPTeu/BIbZ3imnvt5II2xbtAfhtRxhOz5okhEFkj6o06fT7vb1hQkzwmJNzqqkcrvFiPGY9+TWM1SR9BSwuu37cmsZCMlEcR7bj+fWMlAkvQMsWK0+SWaEd9n+dB5lvZMChBlsv9RbsFA6de3nTsm37aipW3xKQpwLLE9soETbRqrkDZSkI4ie7hF0Ngwr0sCyrp916HMf8zXgXNufz6OsfyRdAzxoe/vK+knA3LaHZxH2MZC0IzCt7YNyaxlq1DUobpFii5XonKjdK4uoIYykFft6vTVhZmKi2AzpxIike4hNkoHrJH3Y9vIkRF/a5Tm0dUM6of87sHnLLT6Z+JVu5AdAMmYbAYwERtbMAflXwD6Stkzl1HXhX8AchBdD3Xgc+AFjG+D9gHINuF4iWkkuoRJM1oha9nPb/jAFxZfl1jKO/BL4kDDf+gdhzjY94Qi+a0Zd3bA5YaxcsoFyJ2r3WZf0X3r2MY9Kar/GTEKYtZ6YQ9sA6K2dahGiuqZ2lF6V2kLSl4iEZ6fAuEj/gpQs/L8O68Wb40raCzic2LO8wJh7gqL3B62gvhq8p3WXWq03MSYb+qNJRpRFa6M0P7FhfbPttfeJ4OeCQdbUNbY/kDQbhV/A+uAIYEXCB+ALkh4mJSYoPzmxPKH9GUn3MrbxU6llyAcSPX8H0NmwquTeuV2BCyWtQY/55pLArIRHSomcCFyUAgQDz0udR88XfNJd537uurrFQ1xf1rL97/T5eSmN+XyP8BwpeWrPMOCu3CLGgTp+1nckEp2nEkH9622vvQ88bvvmTt9YCrZvSWOx9yKu5SLuTz+oSztVHZG0KfG5+ZBInFcD4yKTEXUNihO7AtvbPim3kHHgGCIZXmUqYm9Z8hjb6Qnj6nmJz/Yo4DclV7tNSJo2jQJJHgV/tP1ubi0DJZ38YXvP3Fo+DmnCwHBgVcIdeVjhvZan9fV6wWXIte6dS+WZP6DNoR840Xax5kmS5gPmBP5MTFp5rdP7bBeZ+KxzP7ekbwOHEQahdXKLR9IbwAK2H0+ms5vZ/ntKQI8qsS2phaRDibaGA3NrGQg1/6yvCNxYs0o9ACTNS3hfPZC+Xo2orhkF/ML2R319/2AzVCawSHqEMBHfv7SfcV9IuhM42PZFlfX/Aw60XXJQ/AKwrAucLNQfkt4C5rf9WGV9VuBe21Pm0NUfkpYlRmO/wJjm59MR7dZFJ2snBMUGVxMzts/IreFjMAWwqaRV6bzZLr13bhhxGjWcKMtcFniGqI4ollKTDV2wUm4BH4dUnrlvbh0DIZmcjpJ0ENG7/XZ/31MYdR6PWVe3eIiJQvMQFXp3Ad9PXjU7ENfIopDUPhFmGD33pbsZ27281PtSnT/r0wFrAWMYP0v6JjBZ4S0zpwDHAg+khPOFwHXEz30qYJ+M2joxJCawEG1fv6tTIiIxN9FyWuWe9FrJ/AbYkqhiqhvvAF8AHqusf5GowiqVIwn/pe/b/h+Mjj1OJCZWLZNRWxaayogCSSM89wM2JgxxJmt/veTTYkkj+njZtlceNDEDRNJlwHLEzfy69BhRqoNzQ34kfRpYiM79rX/OImqIk8qnryUC4rHGY9q+KZe2/qirWzyMLqGezPbpkhYhTnamBd4jRvGen1VghX7uRe0Ue1/q47O+CDE9qeTP+ihgN9tXVNa/BvzS9vx5lPWPpNeAJWw/mCaErW17JUkrAafZnjWvwqGJpPOAC22fm1vLQJD0MvBN2zdW1pcDLinZdF7Rp3k5MCOdx0xulUNXN0g6m4iR1rb9alqbBrgIeMb2xjn19UYyP1+oVXnVtj4P8E/bk+dRlo+mMqJMDiHMqX5G9ETtSfShf5sYG1Qstut80r0qUbL+V5KRpe2X80rqHklb0pPAqo7dmz2LqC5RDccFtlzhiYCsSumn3L2SStpnsL11bi2dSP3cSzNmP/ed1KCfu+RkQ3/YPrvt+Z2pFHYe4MkSr5M1vxcBtfcumB14oMP6w+m1kpmEnpPVVegxDn+EOL0vlrpNYJHU7q90FfDz1ErYKTAuNcF/BXC4pGpQfFh6rWQOJYxw7wQ+R7083/YArgcel9RqZVsAeJGIl0rldWA2xr4+zkYvbbNDnaYyokAkPUYYyvwtOVMvZPsRSdsTpyHrZ5Y4JJE0OdGWMTw9FgMeIhITI2xfmE1cP0jakygdPYkwJDqBmFKxAnCk7Z9mlNcrKQlxDqGzbuMCRxGl0vu6hqMaeyONtZut9ARWXUn9/nvQY1x1H/E7WpvRx5KmBLD9Zn/vbRh36uZd0I6kZ4mKmasq66sBZ9kuNqiXdDMR5FwKXElUSdyTkqDn2f5SVoF9IOkqQuPJlfWtgY1sFzWBpeIb1RfFekhJmpH4vExHtIFBT1C8Ysn7g1QF9D3bf8ytZVxI1ambEhWqrYOJc0puP1WMfN2ASDLfROwDliOmmpxne7eM8rLQJCMKRNLbwDy2n5T0HPAN23cko7B/2Z4qs8ReSaWxnT5UBt4lTkXOKNmorUUysdwP2IwwsCzyRgijx5Lua/tPKYG1oO1HJe0PzGx728wSO5LKMqelpwd6jHGB1Y1sSSTzpAVsP5Jby8RGcqL+DnHC+hPbLydTqGerZlYlIWltwjj0BmIMMsQmZDlgXdt/yaWtGyTtAuwGzJSWniX8L37pGm4mJP0AmNb2Ibm1dCIFxcfa/kM62X6AaB9cgAjoS/MuGI2k3xBTnta1/WBam5uYCHaj7e/l1NcXklYgSr2nJvYrW6X1nwFz2V4vp76+SMHlkh1KwOcCbim5ZaDO1DEoBkgxxoqt39GGCU9qxT8C+D49HQofEP4de9su2e9igtAkIwpE0r+BLVKJ5g3AX20fJmkT4JjCTxROADYBnqPHYGtxYAbi5r5Aeqxh+5qOf0kmJE1HVESslP6ci8hst7wjih19VElgvQisZvuulFC5rdQNSHJyXsv27cmtf7HUp7sW4ai9VGaJvSLpSiIIu7zfNzeMNyQtSvTPPwbMR3zuH5V0IBEobJJTX1+kUtILbR9QWT+Y6DleMI+y/pH0C2A7YhPV7gC+B3Cy7b1yaRtX0r12zlITzXX2LpD0GcJXZEliPwDRl34bcf9/I5e2bpA0CTBVq+w+rc0KvG37xVy6+kM1nsDSMPhI2otoA9+hDgnl1NbzF9sfVFp8xqLgth5gdALry0Ty6uHSE1cTksYzokwuJPoUbyEcnc+VtC1xGlXqbPEW7wKn296lfVHSUUSZ3aKSjgV+So8hVyk8nx7XEz/3kbb/nVdS1zwP/D/gSeAJIki4i2jVKPkGMznQ6jf/D1Hm+CBRur5ALlFdciJwZGo16dTfWnT1T/KGeMr2iZX17wMz2S7Vn+ZI4rT4gFQF1OIKwhW8ZOYCzuqwfhZRslky2wDbeMwpCNdKeoBoDytd/1jYnie3hn6orXeB7f8CyyommLSfFl9Th6AntcC8Wll7PI+aAVHbCSySTiXGBB9VWd8NmNf2NnmUjc0QCoqXJ9pk15J0H2PvY0obBfsn4nDzxfS8N4r37UrJh9q0Z05ImmREgbSXXqay+6cIL4MHbV+aT1lXfBfodJp9EnGatgfwW2CLQdTULfPWKPlQ5VpgbWKzdwpwjKQNCdf183IK64dajQus0LoR/rbDa8XfCIk2hw06rN9B+I+UmoxYFOhkrvkchQdoxAZqUaJdrZ1FiZnjpXN3L2vDOqwXg6SZicTbWEGwpJltP5lBVjfcC2wv6VIiGdHaG8xETxK3aFKrXbHtdkOQ/Ygk4YJ0mMCSTVV3rAn8qsP6tcTesSSGSlD8MtE6WAtsD+v0vHQkXQJsZvuN9LxXCkwATXCaZEQNsH0rke2uAyJKpx+qrM+bXoPIvHZrWjRotBIRkmanx1zuftuPZhXWHduRAgLbJ0p6lUhgXUAkgkrlWOKGDuET8TdiIsh7RGKrZGbLLeBjMh3wUof1Vyg7qH+HcP2uMg+xMSyZk4GTUvtUu3HVHpRf9XYmkSTcubK+PZ2rPUriMaJFYIzPh6Rp02ulBgt7E+2NexDeBa1TtLWJdoeiSZ4cOxDXyvlTO9WPgEdtl5wkry2u9wSWzwKdTHHfAopqNa1rUFzFdunVhL2SvF1usv1hZX0SYFmXNY3tFXqqlF/JKaREmmREjUiOvZMVfIoDcAZwiqQ5iZJAA0sQm6rT03tWJE58ikLSVERVwXr0JEsk6QJg61R2WiS2/0dbgic5IxfvjuyajQtsxzUe05h4kijRrCbbVgCeHvvtxXAxcICkVlWH0+fm50TyrWR+Smy2dydGOEOYQB4AHJdLVG9Iatc0KbCZpNWJFkIIP4AvAGdXv7cwxpjS08aURGthkdi+XtLnqXgXEAnmovuLk9npXsTv5eFtLz0D7EjZFXu1JU1gec/2Zunr1gSWNSXd64InsBAtmmsShxTtrMXY1WTFULOgeCgxgg5JZiKpNYKCksztSZ86J4AmFI2BZY2QdD9h0FbML1iVdPHdE9iJnhPv54mby5G2P0ols/9zZQ52biSdBixDVBnclJaXJbwBbrTdqTS8GJIZzkLEifcYmfrCexZrwxDqE0XS7kRJ795EGSxEGe/PgJ/b/kUubX2RkoaXAQsCUxDXl+mBG4E1bb+VUV7XJIM/Sk5ypulI3WDbK09QMeNAWzJlB+A0xgzgJyES5e/bXnawtQ11kjno7rYvq0x4mg+43va0mSUOSWo+geW7xH7raMa8J+1CGCyelktbX0j6CJixamyaKq9eLHnP3hvJU2qGkve9irGw09t+qbI+F3C7C5482DAmTTKiRkhaB5ja9hm5tXRDChoo3TW7haRXgHVs31BZX4FwwC928yTpa8C5xJjMKi75ZihpI2LD0SmJUlTvXLr5zWD7RfU9H73on3mLNKpuF+ATael9YiP7o3yqekfSZMRIzM2JvvlFiM/MnbavzqmtGyRdS4w6fK2yPhVwUYkBfZ1pS6asSHgWtY9Me5/wqjnSdrWtsOFjIukdYtLNE5VkxFzAXbY/nVnikKTOE1gAJH0P+DE944OfAQ6tGi2XxFAMiiVdA8xme/bcWqq0eS6sBVxNtPW2mASYn2ixXmOwtXVDui91Cr5NVOo9TLTlFW2CPj5p2jRqhO2LcmsYCHVJQrQxOZ17uf4DfGqQtQyUY4nT4n1tP5tbTLdIOoIIhkcQ5epFZ0eHSp9oC9v7SPopPZ4u99nu1LNbBKkiZbZ46mvpOT2rC8PpSfy08ymiZaZhPJKCMBEtAVuVXIUyBHmUSBZW29nWJKYlNUwYajuBBcAxQv2k1J6karVBSbQFxQZ+L6lTUHzTWN9YA2yXbHba2qeLmHjzTttr7xMHFicPtqgBcD+wCWG63ZpwszhRTX4R4SP1A0lr2C5t6uAEoUlGFMhQPD2rQ8kXUeZ9iKTvpJE7SJoCOIjybyizEicgtUmFral4AAAgAElEQVREJDYHNvaY4wIbBpHU1vAPSZMTo/geKtwP4wxgW6IdrBZIWqTtywUk/aft60mA1Sl/ekxHkknhtLYP6ffNeRgGrEP4cjRB8OBxJHB8ah8UsLSk7xA+EltlVTa0qf0EFoBqlUGh1D0oriUtzwVJjxOVbbVozWzjXeB027u0L0o6ijhoWVTSsYTH1ESRjGjaNAqkvRS8sj4d8IztyfIoG3dKLvlqIWl+YprDFMS4OhN96W8Bq9selVFen0i6Evil7cv7fXNBSHoJWNp2seZUvSHpQaKiYyQw0vZzeRUNDEmnA7fZPkHSJ4DbiZOc94Fv2f5rTn29IekEYFNiCsIdxO/naGzvlENXX6Rreutmqw5veQf4oe1TB0/V+CF5A8xZcluSpIeB9W3flVvLxISkbYmS+y+lpWeAA22fkk/V0Ca1lV4ETE2Uem+V1n9GeI6tl1PfuFD6YZakA6hnUEwaAf+a7SvT1z8hfNNGAVvUbV9TB1JL+FLV9sDU1nOz7WmTt85NtqfOInKQaZIRBdF2enY7sBrRHtCidXq2Tek9f3UmnQ5vRkx0EHGSdrbtd/r8xgxUTltnJbKoRwP3EONTR1Nq71naZHxg+8DcWgZK2mivmB5fIPr8RlKT5ISk54C10hST9YGjiFLBrYhkxJJZBfZCP6aKpRopzkJcTx4lTBPbT/3eJ0zOSna5rzXJGG9jYs57bU6HhwqS/h8wrOSS+6FEMhIfYwJLmjj0dh3/D+pwmFVXJN0H7GL7yrSnvAn4CbAG8LztTbIKrCDpbmBF269Kuoc+WnttLzB4yronVUZuVW29T76Ap9qeJiUmbrXdaYz5kKNJRhTEUD49axj/tH1eOn1W2inKTFFjjgscRpxy30dUo1STKMWdcndC0hyEH8CqwLeIjXfRbXCS3gXmsP20pN8Br9vePW1a77H9mawCG4oiTUF6yh02DZJmdsEjp9OmdTZgMmJsbbWapshNa0NDQ5kMhaAYQNJbwLzJaPYQosrt25IWAq6wXZTPSKpCOcL22+l5r9g+aJBkDQhJxxAtyocTnhEmDin2Bs60vVs67Nrc9kThJVX0ZnkiZDaGwOlZyq7uQpjiQZi1HFPi6bykzbt9r+0zJ6SWcWC23ALGka9Wvm6VTs9TWS8+UyppGFFNMBxYmRgF+wxRHVE6zwPzpwqJ1YnSTIApqSSFGsYfkhYA9iCujyYScUfaviersP55jA4z3dP4uscoaKZ7BxpPmkGgv6CsnZIDtIaGLriAnikOdb6+vAu0Dh5WAVqHna+3rRdDe4Kh1GRDF+wBvADsSphWQuzHjiC8dgCuAIpslZ0QNJURDeMVSZsCZxIu9zen5aWIQG0L27/Ppa0TaeRYO58gTs9aYxuHEYHZe3Ucz9Qw4ZB0GeF6/Aoxx/06YETh5o+jSb2huxNTTCYn+onfl7Q1sLXtZbIKHIJIWhv4M3ADYW4G8RlajjAt/ksubf3Rx/i6WYgpLFPkUdZQCv2dVLZT40CiYQIi6UvEZKFOo76PziJqCCPpIuL+/3dgf2BW289KWh04zvbcWQUOcdJggjpOHxyvNMmIQpC0brfvtf3nCanl45DcbX9r+7DK+j7A90r2u5C0FnAgUdVxa1pekvBhOMT2pZmkjTOSZgQmK7mEuq5Ieh94DTifZGRZt350SesBMwPn2346rX2XMLS6OKu4IUgq7b3Q9gGV9YOBb9peMI+y3mlrq9oBOA14u+3lSYgqvvdtLzvY2gaKpJXpqUgZZXtkXkUNDQ0t0mHWqcCHRGVwe4DixjNi/CPpi8BviH3Asa02cEm/JNpNi2qVbaqvhiZNMqIQ0qlTNxTV/18l9Z8tWJ2OkHrq77b96TzK+kfS/YSpzM2V9aWJMTy1yxCnf9NcJX9mOlGDcYEts9NliRaN4cBiwENEYmKE7QuziWsokuTTMX+H6+OchE/Hp/Io6502w9AViWq399tefh94nGgzeYhCkTQTcCGwKFEJBGE6ezth1lq3kci1QdJiwJeBS22/lcZlv2f7w8zSGgpD0iPAH4H9S29JboLiPNS1+mqoeIxMKBrPiEKwPaz/d9WCEURgVh3VOJwoYy+ZWakYmyXeJrLGdWQfYsRX3dgJmBMoNhmRJqxcnR6thNt+wPbEKXJxCaBUgfUX2x/0V41VcgVWjXmRCIir18dFiR7S4rC9kiQB5xHJ2mprWx04DviIMGx9DEDS7MDv02vrZ9Q2JJE0PXAJ4alj4nr+KFFp+C6wcz51DYUyPfC70hMRiTr7RIxG0ooAtq/rsG7b12cR1gslJRgGyFDxGJkgNJURhSJpUqL8dWbCx6CFbZ+VR1X/SNqBaHW4ALglLS8FrJvWn2+9t7RgR9LI9HRT28+ktZmAs4jflZVyaWsoD0nTEUm2ldKfcxHBZss74qRs4nohVWDNYPvFfqqxiq7AqiuS9id8Oo4gRqiZ8IvYg3AIPzSjvF5JowLfJare7sutZ6BIegMYXjVRTqf213gimeU+mEg6B5gC2AJ4kvjsPCrpa8CvbH8lp76G8pB0HtHGdm5uLRMLku4EDu4wZvL/gANtL5pHWfekxHKr/e5+249mltQwQJpkRIFImgf4Cz3TNT4iqliKN1Ksc7uJpC8DFxFTHZ5JyzMBDwDrVEurS0LSfMAktu+urC8AfFjHAKJ00mf9eeB6YnrGSNv/ziqqoWhShcEuRELiC2n5WSI5cVynsZmlIOlhYH3bd/X75sLoIxmxCJE4bJIR4xlJLwCr2L43GUW3khGzAfc2hqcNVdI4w/0JE/R7GHvUd1EHWFXqGBSn1ur5WxVjbeuzEr+nU+bQ1Q3J/PEUYD16TOdFHIZuXdMqvomSJhlRIJL+RhjjbU0EOwsRpfa/AX5s+6qM8oY0KVhYlUhIiBi7d3XJQQKApBuBX9s+p7L+bWBH28vlUdY3knYkzBJ/X1nfDJjK9gl5lPWPpHnqnnyQNAOwDGM7l9v2b/KomjiQ9BmAumyYkrHpxsBmNTRqvRD4PLCx7afS2szA2cBLtrs2kG7ojpQAWsz2g5VkxBLAX21Pm1liQ2HUtVqvzkGxpJcJ8+QbK+vLAZfYniaPsv6RdBqxf9mOqDSE8PE6EbjR9ta5tFVpPEb6pklGFIikVwijk3slvQ4sYfuB1MP1q4nxg9rQN2mzt3AHY7wvA3eWevKXTlu37tCvuBxwmu058ygb+qSEz++ITdOrjO1c/oWO39gwUZI2U7MRo4+fpuKvU/J9KY0LvBj4KlGJYqLq7W5iI/50RnlDEkmXEqbV+6b70wJEu8Z5wEe2N8wqsKFhPFGnoLiKpLOJdvC1bb+a1qYhqoSfsb1xTn19kWKldWzfUFlfgWj3KSbhWVfjzcGiMbAsE9EzPu0leloFngbmyCWqWyQtTPTRd5oTvVcWUV0iaUlgFTprL2rEUYWP6GxU+Tni81QqXwSe6LD+dHqtdkg6lPBlKHYDkjgU+AXRL9o42w8Ckj5HeOf0dn2cLoOsbqmt6VaqhlhE0hhVb7avzqtsSLMXcJ2kxYFPAkcB8xH3qeLHwDY0DIC1GTsoHilpO2KKT8l7gT2IVtPH08QHiMThi8C3s6nqjsmBVzqs/wcoajLVxJhgGAhNMqJM7gUWJJynbwP2lvQRsC1ju7AXhaS9gMOJAPMFKqetWUR1iaQ9iODsYXpOz1oUrZ0wTdxP0gYtJ+pkgrofcaMplVYb0uOV9UWAWpWCt7EUcYJcOlMRI2ubRMTgcSYRkJ3B2NfHohkKm6nU4ti0OQ4Ctu+T9FViutB7RHBwPtFO+FxWcQ3FImktYG96vBfuA35u+/KswvqmNkFxFdvPSVoQ2JTYi4m4P51j++0+vzk/NwKHSPpOS2saHXwQPRUqxdKMPe6hadMoEEmrA1PY/nMyxLmUOM15GdjQ9sic+vpC0nOEA29xkwT6Q9JTxE3v+NxaBoqkuYG/A2+mPyFc+qcEVrB9fy5tfSHpMGAz4uRgZFpeiWgfOMf2jzJJG/JIOh54wPavcmuZWEjl6itWjRTrhKSV6QkURpV8P2pH0jrAboR2gPuBo21fmE9VQ0NDC0nbACcQXi6tfczyhFfN9rZPzaWtLyRdBbwBVIPiMwnvq1Vz6huqSJof+Bsxtedu4p60INFCuLrtURnl9UqnscfJT+ck4F3bE93Y4yYZURNSD9erNTBSfAFYtuTJE72R/DkWroMDcickzQjsSE92+07gBNvPZhXWB5ImI27YGxGtJhCl6+cTN/YPevveho+HpE8QfaHv09m5/OAcuoYyku4CtrF9e24tAyWNOb4QWJSoHIOYCHI78K3CrzO7A4cR15qb0/LSRCJ0f9tH5tLW0NAQSHoIOLZ6ICTph8APbc+VR1nf1C0olrQu8BfbH6TnvVKDCSaTE9fxdtP5s22/k1VYHzRjj8emSUY0jFckHQhMZnu/3FoGiqQTCcOtYic4DFUkzUlbEqUuyaw0GnAXxjxtPaYOJ99pg3csUXH1ImMbWBZrSFhXkgnxj4k+3XtbLVV1QNIFRPJhk9YYuFS593vgWdvr59TXF6li7ye2T66sb0t4psyYR9nEh6T7iZPApk24YQwkvQfM18GIew6iCuuTeZT1T52C4jS1ZAbbL9Z1gkmdacYej01zM2gY3xwEXJ5OADudtm6VRVV3PAUcJGlZIrtd1X50FlVdkiojtqcnML4POLHkE8sWth8CHsqtYyBI2pQ4ab0WaPWzLgXcJmmL6rjSAtkf2N32MbmFTEQ8TPQX3wkQk4R7KHzjtyow3G3z6NMGaifgmnyyumJKYESH9RHptYbB49dAMS73DUXxJHGdqR5GrEZno+tiSEmHk/t9YwHYHtbpeR2p6YHQ5ERFapXPA+8OspYiaJIRDeObQ4kbx53EJIc6ld5sQ3guLJMe7RgoNhmRXOIvJhIqt6blDYE9JK1j+8ps4vpB0lzA+sR4qU+0v1Z48upQosT7sPZFSfsAPyVOjEtmEqJvsWHwOJeYJrATNTOw7IO+TtZK4SLiGnN4ZX09mt+BQaWOnkwNg8aRwK9SgHkTcX1cDvgO8MOcwvqjpkFxawzmTVXTREmTEC3XxRqg1/hA6HqiRWPf9LXTz3tvyk/sTxCaNo2G8Yqk14Dv2f5jbi0TE6n09Spg53ZfEUnHAquV2oOWnLMvAP5J9KL/g3AX/iRwg+21M8rrE0lvEeV1nUpK77b96TzKukPSkcAbjTfE4CHpbWAJ2/fm1jJQJF1InNxsnEZlImlmwmzuJdt99h7nJM1435VI1LY8I5ZKj6OJJDRQfgVcQ8NQRtK3gN2B1p7lfuAI2xfnU9U3laC4/fqyMlByUEya1Dej7Rcr69MCL5ZcrSfpceC3vRwIfc/2rDl09YekeYkJeHcBKxJDCkaPPbb9SEZ5WWiSEQ3jldSbu6LtB3NrmZiQ9A4RGD9YWZ8LuKvUwFjSHcCfbP+s1TtHmOOdBdxccmAg6VLgItu/q6xvA6xn++t5lHWHpBOATYBRdG5L2imHrqGMpNuBnWwXP3asiqQvEdVXX6Vn9PFMxGfnm7afziivTyQ91v+7gOiRnn2CihnCSLoE2Mz2G+l5r5ScaG5oGAh1DYphtH/E9LZfqqzPBdxue6o8yvqnzgdCkmYg2qoXJUzb72QiHnvctGk0jG+OAXaRtEPpkz8AJB0H7JNm/B7X13sLD85uJ4KEahLoq0TVQanMDbSqaD4APm37XUkHA5dRcGsM8FfgZ2lW9C1pbSlgXeDAdpfqQh2pv0LPZ2OeymvF/+7WlB8DR0v6MZ09df6TRVUXpGqIRVJL2GiTNttX51XWP7Zny61hIuEVeq4dr+QU0tAwiHweOK/D+vmEN1NxtCULDfw+mYe2mASYn2iVKZkRwHDG9hgZTlQeFEeaIHcokXg4ILeeUmgqIxrGK5L+AqwAvEYYKFY320WdhkgaQYyley097w3bXnmwdA0USRsDPydmdLcHxtsDPwIeaL23pB7GVEmziu37JI0C9rN9kaSFgettfyazxF7px4W6ncaRugEY6zPTfvMVzedkUEmnZ0/bnigNwxoaSkDSG8Dstl9O1ZG9BiWlntLXsUpS0mnp6XeJREr71I/3gceBk22/PMjSukbSDsCBRKvvWAdCwPOt95Z0ICTpTWB+24/n1lIKTTKiYbzSdoHriO0tB0vLxERdA2NJFwGX2/6tpF8QhnJnAt8i+hVXyyqwoWE8kkZ79ortIk9zWkhaB9iNMU3ajrZ9YT5V/SPpMOAB22coRphcRfRzvw583fYtff4FDQMmlSFPWm3fkfRF4APbL+RR1lASkr4L/MH2e+l5r9g+Y5BkDYi6BsUw2k/nSNtv5dYyUGq8770AuMz2qbm1lEKTjGhoSEiasa79WpJm6fa9tosZkSVpdmBK23dL+jRwFLAs0W6ym+0nswpsaGgAQNLuwGFEsrBl0rY0sBkxVebIXNr6Q9ITwEa2b5G0JnAGsBawKbCA7ZWyChyCSLoKOM/2yZX1rYn/iybR3DAkqGtQ3JAHST8AfgL8AbgDGCMRVFrCajBokhENE4QUZM5LlNzdb/vRzJL6Jd1QHgJGth51TU40DA6pnWQlYDrChGg0tvfKIqqhYQKQWqp+0iG43BY42PaMeZT1j6R3gTlsPy3peGLvs0Nq1bjd9mczSxxypMlaS9p+oLI+F3CL7WnyKGtomLiRdDdhNP+qpHvouzVmgcFTNnHQT/JqokxYNQaWDeMVSVMBpxDl9v/rWdYFwNa2/5tNXP/MRRjfDAd+AcwkqZWcGGH7D9mUjSOSZgQmq0OFgaRPMXZA/3YmOf0iaS/gcOAJ4AXGvKE3Wd6Grkmjeee0XfI9eUrCMKzKiPRaybwCzAI8DawG7JPWJyX8OhrGP5MSI5qrfKqX9YaJnBSk9XbvfJcwKjzFdp9m4w39cgHQMqz8U04hE4JkKv5p29fn1tIJ28P6f9fERVMZ0TBeSZ4RywDb0ePEuyxwInCj7a1zaRsokr4C7EWUIQ+rY7YyBTlzlao9tZccR1QXTFF9vVTdMPqk+EDbJ+XW0lBvJO0ITGv7oNxaekPSWcAo24dX1vcmWh02zaOsf9KkpG8S7V8LA7OkCUrfBva0vWhWgUMQSdcAD9revrJ+EjC37eFZhDUUSypfPxC4ELg1LS8JrEMYdH8J+B6wt+1f5dA4EEoPiocqpe97G8amSUY0jFckvQKsY/uGyvoKwIW2p82jrH8kDQMWIwLj4UQS5RViRNCIUs2T+iIZzk1dqnZJNxAnZcczdnUBtq/IoasbJL0ALFudcd3QMBRJRme7EkFCyzNiqfQ4Gniz9V7bRY3klTQpsDMwM3C67X+m9V2B/1Zd8Bs+PpKWAq4F7gKuScsrA4sQE5RKHxvYMMhIuhi4xPYplfWtgbVtf1PS94Ef2p4vi8gB0ATFeZC0OJEEKtoQusrEnLxqkhEN4xVJbwOL2b6vsj4/cKvtsU6/SyGNmHoXuIwez4hizB6HImnE0eK278+tZaBIOpBogdkvt5aGelDnCQOSHuvyrbY9+wQV01ALJC1AVBcuTLTD3EE49/8rq7CGIkn7gYWqCf7k7fIv21NI+jJwj+1PZxE5AEoNivvziWinrp4Rkiaz/UFuHQNhYk5eldyf2lBPbgQOkfSdVr+/pCmAg+hp2yiVe4BFgSUId9s3Jb1V8pzlIcC/gM8TIwLrxkHA5ZLuIj47Y9z4bG+VRVVDyZxFzHQ/ubK+OrAR4WdQJLZny62hoT5Imhd4z/Zm6evVgM2BNSXda/ujrAIbSuQVoiWjOplnHaC1D5uSGMlbPLb/IWmy3Do6MCR8IiQdYnv/DuufIP6Naw++qo/F5kDxSbYJQVMZ0TBekfRV4G/EL9TdRPZ1QeBtYDXbozLK6xdJkxPtGcPTY1FiwsYI2zvnU9Y3kj5H9Fr2Ntlhugyy+kXSfIRnxHHAvYwd0BdrvCnpMGBv4E46t5j8Xw5dDeUy1CYMpBPLp22/m1vLuFAT49BaIulm4Fjbf0iVPw8QLY8LAGfZ3qfPv6BhokPSVkSi9grgNuKeugSRpN3W9umS9iCqb7+dT+mY9BcU265bUFwLJD0LHN5uaJqSPxcCX7S9UDZx40gdKzrGB80NuGG8YvuetEHdDJiHKM38PXC27XeyiuuCpPFqSfcCo4hZ9BsB8xE9x6VyJqHxDDoExgUzjEieXMiYmpW+Lrlc7QfAJrb/mFtIQ22o7YSBlHx7wPYZkgRcRXgAvC7p67ZvyatwnPg1UKyPUc35CpGoBdgAuM32mpJWAk6jZ6JJQwMAtk9NCcIfEqfaIqoml29dX2xXqyZKYGtJL3UIiv8MfDGfrO6RNDswL7Hvut/2o5kldcPXgWslvWL77JT8uZD4ma+cV1rvDMGKjo9Nk4xoGK9IOhR4yvaJlfXvS5qp0y9gKUjagKgsWIkY8/kCcD1xY+w00q4khhNzo+/s742FcQbwEvB/1CuJAvAO8M/cIhpqxa3A9unRzg7APwZfzoDYlEjMQmwCFyTMKzcFfkZcN2uF7eNzaxjCTAK8n56vAlyenj8CTJ9FUUPx2L6ZHoPculDLoBhA0lTAKcB6wP96lnUBsLXt/2YT1w+2/5VM2i+V9C6wFTATsLLtV/Kq65PaJ6/GN02bRsN4RdKTwAa2b62sLwGcb3uWPMr6J41qvI4e88p/51XUPcm3YBvbt+fWMhCS4elCth/MrWWgSNoLmBXYwc2FtKEL+pgwsDDwtZInDKTN3hy2n5Z0PLF/2CFVwt1u+7OZJfZKnY1D60pq07geuBS4ElgiVU4uDZxn+0tZBTYUgaRpbP+n9byv97beVyKSlic+61vRExSvUnhQjKTTgGWA7ejxdVsWOBG40fbWubR1i6S1iOTPKOJnXuznBEDSgsQ+YKdOyavSPzMTgiYZ0TBeSRvWeaslXqkE7D7bn8qjbGgjaUXgx8AeQG3MwSSNBH5W8gjP3pD0F2AF4DXgPsb2u5joSu0a+idtRPYCFiLKkO8Ejih9woCkZ4ANbd8o6UFgH9sXSJqHmJQ0dWaJvSLpKiIAPrmyvjWwke1ijUPrShrnfREwNXBGy9BX0s8Ix/j1cuprKANJHwEz2n5R0v/oXB0pYkpPyW2btQuKASS9Aqxj+4bK+grAhbaLamOTdEkvLy0GPAqM/pmXvAera/JqQtG0aTSMb54ElicuCu2sADw99tvLRNJlRKXBc7m1dMnDwOSkHt1o6e6h4Jv4b4BfSjqKzhMpSm47eZkoq2to6JqUdNg0t45x4ALgnJSImIYwKoZIqjzc63eVweLAjh3WbwCOGGQtEwW2r5f0eWAq26+2vXQSYWjd0ABRGdYKIGvT6tVHUPwyMY3t9NY+rOSgmNg3dgqA/0N4GZVGb8F6rQ60bN8gaRN6klcr1yF5NaFokhEN45uTgGNS2dG1aW0Voqf459lUDZwViIt0XTiXOIHaiXp5L5yb/vxth9eKNrC0vWVuDQ31Q9L0wHeA2YGf2H5Z0rLAs7Yfy6uuT3YDngBmBvay/VZan5FIKpZMbY1D60yq0Hu1svZ4HjUNJWL7uk7Pa8CQCIqBG4FDJH3H9tsAkqYgRpcX1zZY133XEEpeTRCaNo2G8U4qw9wF+ERaep8Y8fWjfKoGhqT/AgvWxFG45b2whO17c2sZCJL69BCx/cRgaRlXaupC3ZABSYsSXhGPEdNv5rH9qKQDidL1TXLqG6pIugZ40Pb2lfWTgLltD88irKFhIqc/n4h2JuaT4wmFpPmJKrcpgLuJfcyCRIC8uu1RGeUNGZI3R1fUNeHycWiSEQ0ThJRZnZfo9bvP9puZJQ2INNrz67afyq2lGyTdTpjhFJfJHqr05kJNlLMX7ULdkAdJI4DrbR/QnvBMpn5/KNngt87U2Ti0oWEo04dPxBhvowaeEXVF0uTAZsA8pD07cHYadV8Uku6hy8pf2wtMYDkN44kmGdHQMASQtAZwIGFi2cl7oVYnCpJmBCaz/WRuLb0xFFyoGwYXSW8Q02MerSQjZgX+XUeDX0n3A3PaLrrts67GoQ0NQ5lkvt0VJbVxNEFxHiQd0O17bR80IbU0jD+aZERDQyLdFN9tjSWVtAWwDWEus3vJ1R3pdKFF+y91LU8UUoAzV8m66+ZC3ZAfSS8Aa9q+o5KMWAP4re2ZM0scMJJ2BKZtNn4NDQ0TC0MpKJa0CNFaPW9auh84pnAD8VrRJK/6puiTjIaGQeaXRHUBkuYmzDhPAZYjHNe37/U781MbF+ou2Ycw5CyZurlQN+TnYuAASRukr52qIn5OtPfUDtvH59bQDTU2Dm1omGho+z39MrB/qb+npScYukXSpsCZRBvb5Wl5KeA2SVvY/n02cUOLP+UWUDJNZURDQ6JSQr0vsIztb0haErjA9hczS2woCElXAW8AVRfqM4lxdqvm1NdQHsln5DLCIGwK4HlgesLRfM22CRXFIWkGYFLbT1fWvwh8YPuFPMr6pzEObWgon+b3dPCR9DhRlXdYZX0f4Hu2Z82hq1skbQlsTEx5+kT7a7ZnzyKqYcAMyy2goaEg2kdJrkI4DEMEDMWX3EuaUdLBkv6UHodI+kJuXX0haZikYW1fzyBpG0nL5NTVJbsRJwjPSLpO0kjg6bS2S05hDeUhaTLgKqL1ax1gb+BYYA3bK5aciEicBXy9w/rq6bWSOZKY6LQw8F7b+hWEz0tDQ0N+avt7KmlLSVdK+rekR9sfubX1w+eB8zqsnw9MN8haBoSkPYGjgDuAWYGLgHuBaYBT8ylrGChNMqKhoYd/APtL+g6wPPDXtD4rkZAoFkmrAo8AGwFvp8cGwMOSVsuprR8uA34IIGlK4HaiJeY6SZvnFNYftu8B5iBM8W4nDPH2BOZoxmE1VLH9ATBbPPW1to+0/QvbV+fW1iWLA9d3WL8BWGyQtQyURYEzOqw/R1SmNDQ05KeWv6c1D4pHAGRCq7cAABm+SURBVMM7rA8HijEM7YVtge1s70OYth9ve23i/6LoyVQ1Tl5NEBrPiIaGHnYBzgG+CRxq+5G0vgE90xJK5Tjgd8DObuu9knQscfr6lVzC+mFRIpgHWJdoe5gN2BTYg2h5KBJJhwJP2T6xsv59STPZ3j+TtIZyOYPYQO2ZW8g4MOn/b+/OwyQvy3OPf28QwiYEQZaDsgmCyCYiomwCJ0QDEgJBkrDIpmw5CIJERMIokgGDIoQgEJaooCLIEmXxIGM8yiLggCCL7IwQYRiQsDsE7vPH+2u7pqanu2aYrreq+v5cV19Wv78quPG6urvq+T3v8wJ/MsL6InNY7yUvA0uPsL42ML3LWSJiZP36czr0ofiSZqDv6c32kmPp8Q/FlJtukyVtDNzUrG1KeT82SdLOQ0+0fWmFfKN5G3Bz8/hlYMnm8Xea9U/UCDWWpnh1NGUu3ZbAGZQbW1tSuoMmnMyMiBiDpEWA15o7mz1J0suUyfz3ta2/E7jd9mJ1ko2uyf1O27+VdAHwqO1jJK0M3GN78coR50jSNGDXodNXWtY3AS623etvQqLLJJ1BKbQ9TLmLNsvWDNuH1sjVCUnXAffZPqht/SxgLdsfqhKsA5LOBlagFJZnAOtTtuVdAUyxfXjFeBFB//6cSnqJMt9imqTpwHa2b5e0BnCz7bdUjjhHbSexjabnTmVrugj+2vZUSbcA59n+enM61YW9eqKZpPuAzzXFq9ZTtY4FVrbdk0WU8ZTOiIgx2H6ldoYO3AqsB9zXtr4ecFv343RsGrCZpB9Q9p4PnTLwFspWk162HPDUCOsz6OGW0qjqXZTtPFBOdWjV63cGjgGmSNqAMmQOYBvgPcD/rpaqM0dStoQ9BSwG/JzhwaGfr5grIoYdSTnRod9+Tp8AlqW8n3kU+ABwO+Vud0//Xrfdz9v1pwA7Uv6mngucIuljwEaMPAejV/RlR8d4SjEioiFpacrRnltTPmjO8kvadk8N82nOhh5yBuUX8ZrM2mp3EPDZbmebC1+lDL97gfJHfGhP+pbAnbVCdWgaZbZI+x6/LSmDLCNmYbtvj+C1fZOkD1C2Ve0MiPIm8GDbv6oabhQtg0P3AlaivFFdAJjaR/M6Igae7eeAzSVtQ3/9nPbrh+J+dwLwOIDtMyX9njLo9DKGjyntRX1bvBov2aYR0Wjuzr+bsq/7Sdp+Kdg+q0auOWna60z5UDCanmuva9XsVXw7cK3tF5q17YFnbV9fNdwoJB1BuVv8D5Q3I1BOYZkMnGT7y7WyRcSwpnV68/ZtbBHRG5qi4c+BvWz/pnaeuSFpNeBx2zOb73ejfCh+ALjK9gM1882L5n3ZYrZHGlrcEyS9Bqxoe3rb+jLA9F593yvpHOAx25MkHQicQrmJuBHwvYm4TSPFiIhGs3drK9tTx3xyD5DU8UwC24+OZ5Z5JWlD27fXzjGvJE2mDD4dOt96JuVosl7uRomYJ5KWB/akbDH5R9szJG0G/Jfth+ummzNJ/wxgux8Hh0ZMCP1aNOzXD8WjkXQPZZ5Xz2Zvbsgtb/uptvVVgLt7debYIBav3qgUIyIakm4H9rd9a+0sE0Xzx+Q2ykkg37b935UjzTVJiwPrUDpU7h7q7ogYJJLeS5kV8TClg2ztZujWJMqb1r+rmW80/Tw4NGKi6NeiYb9+KB6NpPdROiN67nhPSac1Dw8BzmfW+WILApsAM21v1u1snRjE4tUblZkREcM+RTni6Ejg17Zfqx1oNM2RSz+w/Wrr8Usj6cEjmYasBexL2e7wFUmXAufa/kndWJ2z/SJwS+0cEePsZErXz3FNF9mQHwH7VMrUqX4eHBoxUSwO7C7pz+iDomHLh2JT3juO9KG4Lzs/bd/SbJ3pRes1/yvK7/aZLddmUn7X9/IRmWLkvztLAP0wMH++S2dEREPSSsBFlGEys+m1amVTjV/B9vQxjmfq6ZkRAJIWAD5C+VDzUcoAyPOAb9jOMMiIyiQ9B2zYdEO0Hke2KnCv7UWqBoyIviZptJsQtr1N18J0oCXvVsCNzP6h+BHgZNv3dzlaxyQdb/vYEdYXBi6xvWOFWB2RdD7wqWbwac/r946O8ZTOiIhh3wGWAg5lhAGWvab1SKY+P54J268DV0q6jnICyGTgeOC4plviCNuP18wYMcG9DCw9wvrawPQR1iMiOtZvpw0N5e23D8Vt9pP0lO2hD8pDw0QvpRxB2bNs93pHXrt+7+gYN+mMiGg0LXab2P517SwTjaRNKNs1dgOeo1SNzwNWpBQllrb9vnoJIyY2SWcDKwC7AjOA9SkF2yuAKbYPrxgvIiLmkqQNKKeBHWr7wqYj4jJKIWIb209XDTiA+rx4NS5SjIhoSLqV8gv5htpZ5hdJKwIL2Z5WO8tIJH2aUoRYE7iSMsjymqZTYug5a1DawNPJFVGJpCUpP6MbUPZ2PwEsD1wP/EUzOyUiYr6SdDCwjO3ja2cZRJK2AH5IeS+2L7ASsG0KEdEtKUZENCR9GJgEfB64E3i19brtZyrEekN6/XgmSfcD5wLn235yDs9ZGPhb29/oariIAP7YtvtzYC/KG9WNgAWAqbZ/XDNbRAw2SfcCa/bq+5hBIGl7SkfEXZRCRN+9343+lWJERKNtCGTrD4bogyGQI5G0E7BUPshHxBshaTqwue37ameJiIh5I+k/5nBpY+Ah4I+FiF4eYBmDI23PEcP6anhSJ2xfXjvDaCRtBbxi+xfN93sD+1Oq80fYfqFivIgY9g3gE8BnageJiIh5NqftFz/qaoqIRjojIgZAczTm0KkUSFoB2AG4u5dnYEi6DZhk+wpJawF3ULZtbA5cb/ugqgEjAgBJZwC7Aw8DvwRmmRFh+9AauSJiMEj6e+BZ2xe0re8BLGn7jDrJImI8pRgRMQAkXU0Z/HiqpCWAeylD5pYA9rP9zaoB50DSc8CGth+S9Dngg7Z3kPR+4Pu2e/poqYiJQtJPRrls29t0LUxEDBxJD1Der/y0bX1zylypNeski4jxlG0aEWNohkCu2eOnObwXOKp5vDPleMzVKHcyjwR6shhBmc0xNItjW8oAJSiT+pepkigiZmN74LaxRURPeRvw6AjrjzXXYj6QdCezzkWbI9vrj3OciBQjIjrwr/T+B+M3A882j7cDLrP9qqQplPy96hbgWEnXAlsAn2zWV6UUJCIiImLwPQFsCDzStr4RMKPraQbXJbUDRLRKMSJiDLZPr52hA9OAzST9APhzYNdm/S3AS9VSje0w4NvAXwIn2H6wWd8V6NlZFxERETFffRs4TdKLwH82a1sDXwMurBVq0Nj+Qu0MEa0yMyJiDiQtCmwG3G97pNbBniHpAOB04AVKm+NGtl+XdCiwU7/t55a0CPCa7VdrZ4mIiIjxJWkhypbS3YDXmuUFgIuBPfN+IGIwpRgR0ZD078DNts+QtDBlYvy7gZnAX9m+uma+sUjaGHg7cO3QkZiStqdMp76+ariIiIiIMUhaE3hP8+1U2w/UzDPoJO0D/C2wMrBw6zXbq1cJFRPKArUDRPSQPwduah7vSJnDsAIwqfnqWZI2tH2r7cuGChEAtq/sx0KEpBMknVs7R0RERHSP7fuBq4ArU4gYX5I+A3yFcvNtVeBy4NeULb7n1UsWE0mKERHDlgamN48/TDlacjrwXWCdaqk6M1XSLyUdJGmp2mHmg00pe0UjIiJiApB0iKRpwH8Dz0l6VNLBtXMNsE8An7R9NPAqcLrtHSkFilWqJosJI8WIiGFPAOtKWpDSJfHjZn0Jyi/pXrYW8H+BY4DfSbpAUt9+mLe9bdoDIyIiJgZJnwNOBM6lnAq2HXA+cKKkz9bMNsDeBtzcPH4ZWLJ5/B1glyqJYsJJMSJi2HnARZQWtdeA65r19wP31grVCdv3N5XtlSknUSwCXCPpQUnHSMoZ3REREdGrDqTcpf+C7euar0nAQc1XzH9PAMs2jx8FPtA8XgPIUMHoihQjIhq2vwjsC5wNbG57ZnPpf4CTqgWbC7Zft30lsAfwWWAl4HjgIUnflbRS1YAjkLSRpG9KurX5+pakjWrnioiIiK5ZDrhlhPWbgeW7nGWimEKZkQalI+Wrkn5CuTF3abVUMaHkNI2IASJpE0pBZTfgOUqL43nAipSixNK231cv4awk7U45ymsKcGOzvCmwDbC37QtqZYuIiIjukHQHcElzY6h1/ThgZ9sb1Ek2uCStBjw+dPNN0m6UI+0fAK7KANHohhQjIlpIWh84kjKw0sDdwMm276wabAySPk0pQqwJXAmcA1xj+/WW56wB3Gv7TXVSzk7SI8DZtv+pbf1o4ADbq9bIFREREd0jaWfge8B/AtdT3oNtDnwI+Gvbl1cLN6AkvQas2Axrb11fBphue8E6yWIiyTaNiIakHYGpwNuBq4FrKDMYpkr6aM1sHTgIuABY2fbOtq9qLUQ0pgH7dT/aqN5KefPR7mJKy2ZEREQMONuXUmZ0PQHsQNk+8DvgfSlEjBsx8myIJYBXupwlJqh0RkQ0mhbBy2wf17b+ReAv0yI4/0n6IXC57XPa1vcHdrH9kTrJIiIiolskrQO8Zvs3zffbAXsBdwFftv1azXyDRNJpzcNDKNt5X2q5vCCwCTDT9mbdzhYTT4oREQ1JrwDrtu+Rk7QmcKftReokG5ukrYBXbP+i+X5vYH/KH/EjbL9QMd4cSToEmAR8H7ipWd4U2LlZf2Louc1dk4iIiBgwkm4ETrX93eYEsN8APwXWB77VnBgW80EzpBJgK8q8rpktl2cCj1C2KN/f5WgxAaUYEdGQNA34jO2L2tb/BjjJ9ip1ko1N0m3AJNtXSFoLuIMyGXlz4HrbPXkslqT2rSRz4uxdjIiIGEySngU2sX2fpMOBHW1vLWlr4PzMkJr/JJ0PfMr2c7WzxMTVM4PsInrAvwFnNYMeb2B4eNKRwD/XDNaBdwBDQzZ3Aa61fbCk91O6DnqyGGE7c2siIiJiQYbv0G8LXNU8fpAc7TkubO9TO0NEihERw74EvAAcQTkGE+C/gOOA0+b0oh5hyh9yKH/EL2sePwEsUyVRRERERGd+DRzUzJLaFhjalrESMKNaqogYV9mmETECSW8GsP187SydkPRjSuHkWsr2jHfZfrCZJfHvtlerGnAUkt4DbE05PWOWTgnbR1UJFREREV0jaUvgcmAp4Bu2923WJwPvtL1LzXwRMT5SjIgYAJLWBb4NrAJ81fYXmvXTgaVt714z35xIOgo4EXgUeJJZj5iy7Q9WCRYRERFdJWlBYEnbv29ZWxV4yfb0WrkiYvykGBExBkknACvY3q92lrklaRHKUVmv1s4yEkm/owzePKt2loiIiIiI6J7MjIgY26ZAz25zGI3tV2pnGMMCwHW1Q0RERERERHelMyJigPV6V4ekScBCto+pnSUiIiIiIronxYiIASbpOmA126vXzjISSaIc37Ui5WjSWbaTDA2wioiIiIiIwZJtGhEtJG0EHAas0yzdA5xie2q9VPPO9ra1M4zhBGA7YCqwNLMOsIyIiIiIiAGVzoiIhqTdgW8CU4Abm+VNgW2AvW1fUCvboJL0LHCA7YtqZ4mIiIiIiO5JMSKiIekR4Gzb/9S2fjTlA/OqNXJ1qh+7OprTNLayfV/tLBERERER0T0L1A4Q0UPeCnxvhPWLgeW6nGWuNF0dt1BmL1zVfC0P3Cxpj5rZxnAKcFgzOyIiIiIiIiaIzIyIGPYT4EPAA23rHwJ+2u0wc+kE4Ng5dHV8CejVLSZbAFsC20u6m9kHWO5YJVVERERERIyrbNOIaEg6BJgEfB+4qVneFNi5WX9i6Lm2L+1yvFFJehHYwPYDbetrAHfYXqxOstFJOn+067b36VaWiIiIiIjonhQjIhqSXu/wqba94LiGmUuSfghcbvuctvX9gV1sf6ROsoiIiIiIiNllm0ZEw3Y/z1C5GpgsaWNG6OqQtPPQE3utqwNA0uqUwZsG7rH9UOVIERERERExjtIZETEA+rWrQ9KSwLnALsDQf4MoW2X2s/18rWwRERERETF+UoyIaCHpPcDWlNMzZumUsH1UlVADrJkZ8UHgk8ANzfJmwJnA9bb3q5UtIiIiIiLGT4oREQ1JRwEnAo8CT1K2DAyx7Q9WCTbAJD0N7GT7Z23rWwKX2V6mTrKIiIiIiBhPmRkRMexw4CDbZ9UOMi/6tKtjUeDpEdafARbpcpaIiIiIiOiSFCMihi0AXFc7xLwYq6ujSqjOXA8cL2lP2y8BSFoc+ALD2zYiIiIiImLAZJtGREPSJGAh28fUzjK3JP0OmNRvXR2S1gOuARYD7qAUTjYAXgK2s31XxXgRERERETFOUoyIaEgScBWwInAn8Grrddv71sjVCUlPApvZfqB2lrklaVFgD2BtykkadwMX2n65arCIiIiIiBg32aYRMewEYDtgKrA0vb29od3XgX2AvurqkHQC8FvbZ7atHyhpJdvHVooWERERERHjKJ0REQ1JzwIH2L6odpa51a9dHZKmAbva/kXb+ibAxbZXqZMsIiIiIiLGUzojIoa9DNxWO8Q86teujuWAp0ZYnwEs3+UsERERERHRJSlGRAw7BThM0iHuv5ahg4G/68OujmnAFsBDbetbAo91P05ERERERHRDihERw7agfAjeXtLdzL7VYccqqTrTr10dZwGnSFoYmNKsbQtMBk6qlioiIiIiIsZVihERw2YAl9YOMY/6sqvD9lckLQucBizcLM8ETrX95XrJIiIiIiJiPGWAZcQAkPQDSlfHs5SjMfupqwNJiwPr0BztafuFypEiIiIiImIcpTMioo2k1SkfjA3cY7t9nkEv6ueuDmy/CNxSO0dERERERHRHOiMiGpKWBM4FdgFeH1oGvg/sZ/v5WtkiIiIiIiIGyQK1A0T0kFOB9YGtgUWbr22bta9VzNUxSatL2kHS9k2HR0RERERERM9JZ0REQ9LTwE62f9a2viVwme1l6iQbW7o6IiIiIiKin6QzImLYosDTI6w/AyzS5Sxzq++7OiIiIiIiYuJIZ0REQ9K1wHPAnrZfatYWB74JLGn7z2rmG00/d3VERERERMTEk9M0IoZ9GrgGeFzSHZTTNDYAXgK2qxmsA/3c1RERERERERNMOiMiWkhaFNgDWJsyc+Fu4ELbL1cNNoZ+7uqIiIiIiIiJJ8WIiIakE4Df2j6zbf1AYCXbx9ZJNjZJ61G6OhYDZuvqsH1XxXgRERERERGzSDEioiFpGrCr7V+0rW8CXGx7lTrJOtOvXR0RERERETHxZGZExLDlgKdGWJ8BLN/lLHNltK4OST3d1RERERERERNPjvaMGDYN2GKE9S2Bx7qcZW7tCdw2wvpUYK8uZ4mIiIiIiBhVOiMihp0FnCJpYWBKs7YtMBk4qVqqzvRtV0dEREREREw8KUZENGx/RdKywGnAws3yTOBU21+ul6wjQ10dD7Wt90NXR0RERERETDApRkS0sH20pC8B69AMgbT9QuVYnejnro6IiIiIiJhgcppGxICQNBk4jNm7Oj5bL1VERERERMTsUoyIGCCSFqf/ujoiIiIiImKCSTEiIiIiIiIiIroqR3tGRERERERERFelGBERERERERERXZViRERERMw3kh6RdGTtHBEREdHbUoyIiIiIjklaXtKpkh6U9AdJj0u6WtJf1M4WERER/eNNtQNEREREf5C0KnA98DxwNPAryo2NbYEzgZVrZYuIiIj+ks6IiIiI6NQZlKODN7b9Pdu/sX2P7dOBDUZ6gaRPS7pD0otNF8U5kv605fpSkr4labqkVyQ9JOmwlusHSLqvufaUpB9JelPL9X0k3d1cv0/S4ZIW6PT1ERERUUf+GEdERMSYJL0F+DDwedsvtF+3/fs5vPR14DDgIWAV4F+arz2b618C1gN2AKYDqwJvbf6dGwP/Cnwc+Dnwp8A2LZk+AXwR+D/AL4F1gX8DXgVOH+v1ERERUU+KEREREdGJNShdEffMzYtsf63l20ckHQVcIenjtl+nFChus33z0HNanr8y8CLwH7afBx6lbA0ZcixwlO1Lmu8flnQicDBwegevj4iIiEqyTSMiIiI6oXl6kbSNpGslPSbpeeBSYGFgheYpXwc+JulXkk6WtFXLy6+lFBAelnShpI9LenPzz30r8HbgLEkvDH0BJwLvGOv1ERERUVeKEREREdGJ+wED7+r0BZJWAa6kdFPsCrwX2Le5vDCA7asp3REnA8sCV0o6v7n2PLAR8DFgGmVo5r2S/hfD72EOBDZs+VoXeHcHr4+IiIiKUoyIiIiIMdl+BvgR8PeSlmi/3jqUssXGlKLD4bZvtH0fMFshwPYM29+yvTewH/BxSX/SXPsf21NsHw2sDywO7GD7SeBx4B22H2j/avlnj/j6N/R/RkRERLxhmRkRERERnToYuAG4VdKxwB2U7RtbU7oO2o/2vJ9y4+MwSZcCm1KGWf6RpC8CU4G7KO9LdgYesv0HSTtQtlz8P+CZ5t/zZobnVkwC/kXSs8BVwEKUToiVbE/u4PURERFRSYoRERER0RHbD0vaCPgccBKwEvA0ZSjkASM8/w5JnwL+gXJqxg3AkcBFLU/7A3ACsBrwCnAT8NHm2rPATsA/AosBDwL72/5Z888/R9KLwGeAycDLlKLG6Z28PiIiIuqR7doZIiIiIiIiImICycyIiIiIiIiIiOiqFCMiIiIiIiIioqtSjIiIiIiIiIiIrkoxIiIiIiIiIiK6KsWIiIiIiIiIiOiqFCMiIiIiIiIioqtSjIiIiIiIiIiIrkoxIiIiIiIiIiK6KsWIiIiIiIiIiOiq/w8UxBzaLVF9sgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting graphs\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.5\n",
    "\n",
    "# Make the plot\n",
    "plt.subplots(figsize=(18,5))\n",
    "plt.bar(class_names, sklearn_classifier_score, color=\"teal\", width=barWidth, label='sklearn_classifier')\n",
    "plt.xlabel(\"Classes\",size=14)\n",
    "plt.xticks(rotation=90,size=14)\n",
    "plt.axis((None,None,0,1))\n",
    "plt.ylabel(\"f1-score\",size=14)\n",
    "plt.title(\"f1-scores of classes by sklearn_classifier\",size=14)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make the plot\n",
    "plt.subplots(figsize=(18,5))\n",
    "plt.bar(class_names, own_classifier_score, color='orange', width=barWidth,label='own_classifier')\n",
    "plt.xlabel(\"Classes\",size=14)\n",
    "plt.xticks(rotation=90,size=14)\n",
    "plt.axis((None,None,0,1))\n",
    "plt.ylabel(\"f1-score\",size=14)\n",
    "plt.title(\"f1-scores of classes by own_classifier \",size=14)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Acurracy seems to be almost same\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Overall Acurracy seems to be almost same\")\n",
    "\n",
    "# Comparing through line graphs\n",
    "plt.subplots(figsize=(18,5))\n",
    "plt.plot(class_names, sklearn_classifier_score, color=\"teal\",label='sklearn_classifier')\n",
    "plt.plot(class_names, own_classifier_score, color='orange',label='own_classifier')\n",
    "plt.xlabel(\"Classes\",size=14)\n",
    "plt.xticks(rotation=90,size=14)\n",
    "plt.axis((None,None,0,1))\n",
    "plt.ylabel(\"f1-score\",size=14)\n",
    "plt.title(\"Comaprision of f1-scores of classes by sklearn_classifier and own_classifier \",size=14)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall accuracy seems to be almost same."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}