Sentiment Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2bf8a5f9",
   "metadata": {},
   "source": [
    "# Sentiment Classification with NumPy\n",
    "\n",
    "### Outline:\n",
    "\n",
    "- [Explore Dataset](#header_1)\n",
    "\n",
    "- [Develop and Validate a Predictive Theory](#header_2)\n",
    "\n",
    "- [Transforming Text Into Numbers](#header_3)\n",
    "\n",
    "- [Building a Neural Network](#header_4)\n",
    "\n",
    "- [Weight Analysis](#header_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd822fbb",
   "metadata": {},
   "source": [
    "# Explore Dataset <a id= \"header_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a7e03d",
   "metadata": {},
   "source": [
    "We have movie reviews from IMDB. Reveiws that had less than 3 stars were considered negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1f86944",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get our data \n",
    "f = open(\"reviews.txt\", \"r\")\n",
    "reviews = [review[:-1] for review in f.readlines()] \n",
    "f.close()\n",
    "\n",
    "# Get our labels\n",
    "f = open(\"labels.txt\", \"r\")\n",
    "labels = [label[:-1].upper() for label in f.readlines()]\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c3dd67f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(reviews) # 25 thousand movie reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e15af14a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life  such as  teachers  . my   years in the teaching profession lead me to believe that bromwell high  s satire is much closer to reality than is  teachers  . the scramble to survive financially  the insightful students who can see right through their pathetic teachers  pomp  the pettiness of the whole situation  all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school  i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i  m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn  t   ',\n",
       " 'POSITIVE')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[0], labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe6f1da",
   "metadata": {},
   "source": [
    "# Develop and Validate a Predictive Theory <a id=\"header_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3231742a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pretty_print_review_and_label(*indices):\n",
    "    print(\"labels.txt \\t : \\t reviews.txt \\n\")\n",
    "    for i in indices:\n",
    "        print(f\"{labels[i]} \\t : \\t {reviews[i][:80]}...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1335feb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels.txt \t : \t reviews.txt \n",
      "\n",
      "NEGATIVE \t : \t this movie is terrible but it has some good effects .  ...\n",
      "POSITIVE \t : \t adrian pasdar is excellent is this film . he makes a fascinating woman .  ...\n",
      "NEGATIVE \t : \t comment this movie is impossible . is terrible  very improbable  bad interpretat...\n",
      "POSITIVE \t : \t excellent episode movie ala pulp fiction .  days   suicides . it doesnt get more...\n",
      "NEGATIVE \t : \t if you haven  t seen this  it  s terrible . it is pure trash . i saw this about ...\n",
      "POSITIVE \t : \t this schiffer guy is a real genius  the movie is of excellent quality and both e...\n"
     ]
    }
   ],
   "source": [
    "pretty_print_review_and_label(2137, \n",
    "                              12816, \n",
    "                              6267, \n",
    "                              21934, \n",
    "                              5297, \n",
    "                              4998)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a9f047",
   "metadata": {},
   "source": [
    "It seems there are some correlations between the number of occurrences of some words and the labels. Let's do a quick validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd8dc296",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7860b9",
   "metadata": {},
   "source": [
    "We'll create three `Counter` objects, one for words from postive reviews, one for words from negative reviews, and one for all the words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0b14b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_counts = Counter()\n",
    "negative_counts = Counter()\n",
    "total_counts = Counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83fc28e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(reviews)):\n",
    "    words = reviews[i].split(\" \")\n",
    "    for word in words:\n",
    "        total_counts[word] += 1 \n",
    "        if labels[i] == \"POSITIVE\":\n",
    "            positive_counts[word] += 1\n",
    "        else:\n",
    "            negative_counts[word] += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73728bda",
   "metadata": {},
   "source": [
    "Examine the counts of most common words in negative and positive reviews (sorted from most to least common):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "181db8df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('', 550468),\n",
       " ('the', 173324),\n",
       " ('.', 159654),\n",
       " ('and', 89722),\n",
       " ('a', 83688),\n",
       " ('of', 76855),\n",
       " ('to', 66746),\n",
       " ('is', 57245),\n",
       " ('in', 50215),\n",
       " ('br', 49235),\n",
       " ('it', 48025),\n",
       " ('i', 40743),\n",
       " ('that', 35630),\n",
       " ('this', 35080),\n",
       " ('s', 33815),\n",
       " ('as', 26308),\n",
       " ('with', 23247),\n",
       " ('for', 22416),\n",
       " ('was', 21917),\n",
       " ('film', 20937),\n",
       " ('but', 20822),\n",
       " ('movie', 19074),\n",
       " ('his', 17227),\n",
       " ('on', 17008),\n",
       " ('you', 16681),\n",
       " ('he', 16282),\n",
       " ('are', 14807),\n",
       " ('not', 14272),\n",
       " ('t', 13720),\n",
       " ('one', 13655),\n",
       " ('have', 12587),\n",
       " ('be', 12416),\n",
       " ('by', 11997),\n",
       " ('all', 11942),\n",
       " ('who', 11464),\n",
       " ('an', 11294),\n",
       " ('at', 11234),\n",
       " ('from', 10767),\n",
       " ('her', 10474),\n",
       " ('they', 9895),\n",
       " ('has', 9186),\n",
       " ('so', 9154),\n",
       " ('like', 9038),\n",
       " ('about', 8313),\n",
       " ('very', 8305),\n",
       " ('out', 8134),\n",
       " ('there', 8057),\n",
       " ('she', 7779),\n",
       " ('what', 7737),\n",
       " ('or', 7732),\n",
       " ('good', 7720),\n",
       " ('more', 7521),\n",
       " ('when', 7456),\n",
       " ('some', 7441),\n",
       " ('if', 7285),\n",
       " ('just', 7152),\n",
       " ('can', 7001),\n",
       " ('story', 6780),\n",
       " ('time', 6515),\n",
       " ('my', 6488),\n",
       " ('great', 6419),\n",
       " ('well', 6405),\n",
       " ('up', 6321),\n",
       " ('which', 6267),\n",
       " ('their', 6107),\n",
       " ('see', 6026),\n",
       " ('also', 5550),\n",
       " ('we', 5531),\n",
       " ('really', 5476),\n",
       " ('would', 5400),\n",
       " ('will', 5218),\n",
       " ('me', 5167),\n",
       " ('had', 5148),\n",
       " ('only', 5137),\n",
       " ('him', 5018),\n",
       " ('even', 4964),\n",
       " ('most', 4864),\n",
       " ('other', 4858),\n",
       " ('were', 4782),\n",
       " ('first', 4755),\n",
       " ('than', 4736),\n",
       " ('much', 4685),\n",
       " ('its', 4622),\n",
       " ('no', 4574),\n",
       " ('into', 4544),\n",
       " ('people', 4479),\n",
       " ('best', 4319),\n",
       " ('love', 4301),\n",
       " ('get', 4272),\n",
       " ('how', 4213),\n",
       " ('life', 4199),\n",
       " ('been', 4189),\n",
       " ('because', 4079),\n",
       " ('way', 4036),\n",
       " ('do', 3941),\n",
       " ('made', 3823),\n",
       " ('films', 3813),\n",
       " ('them', 3805),\n",
       " ('after', 3800),\n",
       " ('many', 3766),\n",
       " ('two', 3733),\n",
       " ('too', 3659),\n",
       " ('think', 3655),\n",
       " ('movies', 3586),\n",
       " ('characters', 3560),\n",
       " ('character', 3514),\n",
       " ('don', 3468),\n",
       " ('man', 3460),\n",
       " ('show', 3432),\n",
       " ('watch', 3424),\n",
       " ('seen', 3414),\n",
       " ('then', 3358),\n",
       " ('little', 3341),\n",
       " ('still', 3340),\n",
       " ('make', 3303),\n",
       " ('could', 3237),\n",
       " ('never', 3226),\n",
       " ('being', 3217),\n",
       " ('where', 3173),\n",
       " ('does', 3069),\n",
       " ('over', 3017),\n",
       " ('any', 3002),\n",
       " ('while', 2899),\n",
       " ('know', 2833),\n",
       " ('did', 2790),\n",
       " ('years', 2758),\n",
       " ('here', 2740),\n",
       " ('ever', 2734),\n",
       " ('end', 2696),\n",
       " ('these', 2694),\n",
       " ('such', 2590),\n",
       " ('real', 2568),\n",
       " ('scene', 2567),\n",
       " ('back', 2547),\n",
       " ('those', 2485),\n",
       " ('though', 2475),\n",
       " ('off', 2463),\n",
       " ('new', 2458),\n",
       " ('your', 2453),\n",
       " ('go', 2440),\n",
       " ('acting', 2437),\n",
       " ('plot', 2432),\n",
       " ('world', 2429),\n",
       " ('scenes', 2427),\n",
       " ('say', 2414),\n",
       " ('through', 2409),\n",
       " ('makes', 2390),\n",
       " ('better', 2381),\n",
       " ('now', 2368),\n",
       " ('work', 2346),\n",
       " ('young', 2343),\n",
       " ('old', 2311),\n",
       " ('ve', 2307),\n",
       " ('find', 2272),\n",
       " ('both', 2248),\n",
       " ('before', 2177),\n",
       " ('us', 2162),\n",
       " ('again', 2158),\n",
       " ('series', 2153),\n",
       " ('quite', 2143),\n",
       " ('something', 2135),\n",
       " ('cast', 2133),\n",
       " ('should', 2121),\n",
       " ('part', 2098),\n",
       " ('always', 2088),\n",
       " ('lot', 2087),\n",
       " ('another', 2075),\n",
       " ('actors', 2047),\n",
       " ('director', 2040),\n",
       " ('family', 2032),\n",
       " ('between', 2016),\n",
       " ('own', 2016),\n",
       " ('m', 1998),\n",
       " ('may', 1997),\n",
       " ('same', 1972),\n",
       " ('role', 1967),\n",
       " ('watching', 1966),\n",
       " ('every', 1954),\n",
       " ('funny', 1953),\n",
       " ('doesn', 1935),\n",
       " ('performance', 1928),\n",
       " ('few', 1918),\n",
       " ('bad', 1907),\n",
       " ('look', 1900),\n",
       " ('re', 1884),\n",
       " ('why', 1855),\n",
       " ('things', 1849),\n",
       " ('times', 1832),\n",
       " ('big', 1815),\n",
       " ('however', 1795),\n",
       " ('actually', 1790),\n",
       " ('action', 1789),\n",
       " ('going', 1783),\n",
       " ('bit', 1757),\n",
       " ('comedy', 1742),\n",
       " ('down', 1740),\n",
       " ('music', 1738),\n",
       " ('must', 1728),\n",
       " ('take', 1709),\n",
       " ('saw', 1692),\n",
       " ('long', 1690),\n",
       " ('right', 1688),\n",
       " ('fun', 1686),\n",
       " ('fact', 1684),\n",
       " ('excellent', 1683),\n",
       " ('around', 1674),\n",
       " ('didn', 1672),\n",
       " ('without', 1671),\n",
       " ('thing', 1662),\n",
       " ('thought', 1639),\n",
       " ('got', 1635),\n",
       " ('each', 1630),\n",
       " ('day', 1614),\n",
       " ('feel', 1597),\n",
       " ('seems', 1596),\n",
       " ('come', 1594),\n",
       " ('done', 1586),\n",
       " ('beautiful', 1580),\n",
       " ('especially', 1572),\n",
       " ('played', 1571),\n",
       " ('almost', 1566),\n",
       " ('want', 1562),\n",
       " ('yet', 1556),\n",
       " ('give', 1553),\n",
       " ('pretty', 1549),\n",
       " ('last', 1543),\n",
       " ('since', 1519),\n",
       " ('different', 1504),\n",
       " ('although', 1501),\n",
       " ('gets', 1490),\n",
       " ('true', 1487),\n",
       " ('interesting', 1481),\n",
       " ('job', 1470),\n",
       " ('enough', 1455),\n",
       " ('our', 1454),\n",
       " ('shows', 1447),\n",
       " ('horror', 1441),\n",
       " ('woman', 1439),\n",
       " ('tv', 1400),\n",
       " ('probably', 1398),\n",
       " ('father', 1395),\n",
       " ('original', 1393),\n",
       " ('girl', 1390),\n",
       " ('point', 1379),\n",
       " ('plays', 1378),\n",
       " ('wonderful', 1372),\n",
       " ('far', 1358),\n",
       " ('course', 1358),\n",
       " ('john', 1350),\n",
       " ('rather', 1340),\n",
       " ('isn', 1328),\n",
       " ('ll', 1326),\n",
       " ('later', 1324),\n",
       " ('dvd', 1324),\n",
       " ('whole', 1310),\n",
       " ('war', 1310),\n",
       " ('d', 1307),\n",
       " ('found', 1306),\n",
       " ('away', 1306),\n",
       " ('screen', 1305),\n",
       " ('nothing', 1300),\n",
       " ('year', 1297),\n",
       " ('once', 1296),\n",
       " ('hard', 1294),\n",
       " ('together', 1280),\n",
       " ('set', 1277),\n",
       " ('am', 1277),\n",
       " ('having', 1266),\n",
       " ('making', 1265),\n",
       " ('place', 1263),\n",
       " ('might', 1260),\n",
       " ('comes', 1260),\n",
       " ('sure', 1253),\n",
       " ('american', 1248),\n",
       " ('play', 1245),\n",
       " ('kind', 1244),\n",
       " ('perfect', 1242),\n",
       " ('takes', 1242),\n",
       " ('performances', 1237),\n",
       " ('himself', 1230),\n",
       " ('worth', 1221),\n",
       " ('everyone', 1221),\n",
       " ('anyone', 1214),\n",
       " ('actor', 1203),\n",
       " ('three', 1201),\n",
       " ('wife', 1196),\n",
       " ('classic', 1192),\n",
       " ('goes', 1186),\n",
       " ('ending', 1178),\n",
       " ('version', 1168),\n",
       " ('star', 1149),\n",
       " ('enjoy', 1146),\n",
       " ('book', 1142),\n",
       " ('nice', 1132),\n",
       " ('everything', 1128),\n",
       " ('during', 1124),\n",
       " ('put', 1118),\n",
       " ('seeing', 1111),\n",
       " ('least', 1102),\n",
       " ('house', 1100),\n",
       " ('high', 1095),\n",
       " ('watched', 1094),\n",
       " ('loved', 1087),\n",
       " ('men', 1087),\n",
       " ('night', 1082),\n",
       " ('anything', 1075),\n",
       " ('believe', 1071),\n",
       " ('guy', 1071),\n",
       " ('top', 1063),\n",
       " ('amazing', 1058),\n",
       " ('hollywood', 1056),\n",
       " ('looking', 1053),\n",
       " ('main', 1044),\n",
       " ('definitely', 1043),\n",
       " ('gives', 1031),\n",
       " ('home', 1029),\n",
       " ('seem', 1028),\n",
       " ('episode', 1023),\n",
       " ('audience', 1020),\n",
       " ('sense', 1020),\n",
       " ('truly', 1017),\n",
       " ('special', 1011),\n",
       " ('second', 1009),\n",
       " ('short', 1009),\n",
       " ('fan', 1009),\n",
       " ('mind', 1005),\n",
       " ('human', 1001),\n",
       " ('recommend', 999),\n",
       " ('full', 996),\n",
       " ('black', 995),\n",
       " ('help', 991),\n",
       " ('along', 989),\n",
       " ('trying', 987),\n",
       " ('small', 986),\n",
       " ('death', 985),\n",
       " ('friends', 981),\n",
       " ('remember', 974),\n",
       " ('often', 970),\n",
       " ('said', 966),\n",
       " ('favorite', 962),\n",
       " ('heart', 959),\n",
       " ('early', 957),\n",
       " ('left', 956),\n",
       " ('until', 955),\n",
       " ('script', 954),\n",
       " ('let', 954),\n",
       " ('maybe', 937),\n",
       " ('today', 936),\n",
       " ('live', 934),\n",
       " ('less', 934),\n",
       " ('moments', 933),\n",
       " ('others', 929),\n",
       " ('brilliant', 926),\n",
       " ('shot', 925),\n",
       " ('liked', 923),\n",
       " ('become', 916),\n",
       " ('won', 915),\n",
       " ('used', 910),\n",
       " ('style', 907),\n",
       " ('mother', 895),\n",
       " ('lives', 894),\n",
       " ('came', 893),\n",
       " ('stars', 890),\n",
       " ('cinema', 889),\n",
       " ('looks', 885),\n",
       " ('perhaps', 884),\n",
       " ('read', 882),\n",
       " ('enjoyed', 879),\n",
       " ('boy', 875),\n",
       " ('drama', 873),\n",
       " ('highly', 871),\n",
       " ('given', 870),\n",
       " ('playing', 867),\n",
       " ('use', 864),\n",
       " ('next', 859),\n",
       " ('women', 858),\n",
       " ('fine', 857),\n",
       " ('effects', 856),\n",
       " ('kids', 854),\n",
       " ('entertaining', 853),\n",
       " ('need', 852),\n",
       " ('line', 850),\n",
       " ('works', 848),\n",
       " ('someone', 847),\n",
       " ('mr', 836),\n",
       " ('simply', 835),\n",
       " ('picture', 833),\n",
       " ('children', 833),\n",
       " ('face', 831),\n",
       " ('keep', 831),\n",
       " ('friend', 831),\n",
       " ('dark', 830),\n",
       " ('overall', 828),\n",
       " ('certainly', 828),\n",
       " ('minutes', 827),\n",
       " ('wasn', 824),\n",
       " ('history', 822),\n",
       " ('finally', 820),\n",
       " ('couple', 816),\n",
       " ('against', 815),\n",
       " ('son', 809),\n",
       " ('understand', 808),\n",
       " ('lost', 807),\n",
       " ('michael', 805),\n",
       " ('else', 801),\n",
       " ('throughout', 798),\n",
       " ('fans', 797),\n",
       " ('city', 792),\n",
       " ('reason', 789),\n",
       " ('written', 787),\n",
       " ('production', 787),\n",
       " ('several', 784),\n",
       " ('school', 783),\n",
       " ('based', 781),\n",
       " ('rest', 781),\n",
       " ('try', 780),\n",
       " ('dead', 776),\n",
       " ('hope', 775),\n",
       " ('strong', 768),\n",
       " ('white', 765),\n",
       " ('tell', 759),\n",
       " ('itself', 758),\n",
       " ('half', 753),\n",
       " ('person', 749),\n",
       " ('sometimes', 746),\n",
       " ('past', 744),\n",
       " ('start', 744),\n",
       " ('genre', 743),\n",
       " ('beginning', 739),\n",
       " ('final', 739),\n",
       " ('town', 738),\n",
       " ('art', 734),\n",
       " ('humor', 732),\n",
       " ('game', 732),\n",
       " ('yes', 731),\n",
       " ('idea', 731),\n",
       " ('late', 730),\n",
       " ('becomes', 729),\n",
       " ('despite', 729),\n",
       " ('able', 726),\n",
       " ('case', 726),\n",
       " ('money', 723),\n",
       " ('child', 721),\n",
       " ('completely', 721),\n",
       " ('side', 719),\n",
       " ('camera', 716),\n",
       " ('getting', 714),\n",
       " ('instead', 712),\n",
       " ('soon', 702),\n",
       " ('under', 700),\n",
       " ('viewer', 699),\n",
       " ('age', 697),\n",
       " ('days', 696),\n",
       " ('stories', 696),\n",
       " ('felt', 694),\n",
       " ('simple', 694),\n",
       " ('roles', 693),\n",
       " ('video', 688),\n",
       " ('name', 683),\n",
       " ('either', 683),\n",
       " ('doing', 677),\n",
       " ('turns', 674),\n",
       " ('wants', 671),\n",
       " ('close', 671),\n",
       " ('title', 669),\n",
       " ('wrong', 668),\n",
       " ('went', 666),\n",
       " ('james', 665),\n",
       " ('evil', 659),\n",
       " ('budget', 657),\n",
       " ('episodes', 657),\n",
       " ('relationship', 655),\n",
       " ('fantastic', 653),\n",
       " ('piece', 653),\n",
       " ('david', 651),\n",
       " ('turn', 648),\n",
       " ('murder', 646),\n",
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       " ('earth', 437),\n",
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       " ('typical', 435),\n",
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       " ('brings', 426),\n",
       " ('fantasy', 426),\n",
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       " ('america', 423),\n",
       " ('unfortunately', 422),\n",
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       " ('surprise', 405),\n",
       " ('era', 404),\n",
       " ('body', 404),\n",
       " ('hour', 403),\n",
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       " ('five', 403),\n",
       " ('note', 399),\n",
       " ('learn', 398),\n",
       " ('truth', 398),\n",
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       " ('buy', 392),\n",
       " ('william', 391),\n",
       " ('stewart', 391),\n",
       " ('fall', 390),\n",
       " ('joe', 390),\n",
       " ('meet', 390),\n",
       " ('unlike', 389),\n",
       " ('talking', 389),\n",
       " ('shots', 389),\n",
       " ('rating', 389),\n",
       " ('difficult', 389),\n",
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       " ('means', 388),\n",
       " ('situation', 386),\n",
       " ('wonder', 386),\n",
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       " ('appears', 386),\n",
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       " ('gone', 379),\n",
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       " ('talk', 375),\n",
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       " ('beyond', 375),\n",
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       " ('non', 369),\n",
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       " ('using', 368),\n",
       " ('return', 368),\n",
       " ('doubt', 367),\n",
       " ('add', 367),\n",
       " ('hear', 366),\n",
       " ('solid', 366),\n",
       " ('success', 366),\n",
       " ('jokes', 365),\n",
       " ('oh', 365),\n",
       " ('touching', 365),\n",
       " ('political', 365),\n",
       " ('hell', 364),\n",
       " ('awesome', 364),\n",
       " ('boys', 364),\n",
       " ('sexual', 362),\n",
       " ('recently', 362),\n",
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       " ('setting', 360),\n",
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       " ('married', 359),\n",
       " ('mark', 359),\n",
       " ('social', 357),\n",
       " ('interested', 356),\n",
       " ('adventure', 356),\n",
       " ('actual', 355),\n",
       " ('terrific', 355),\n",
       " ('sees', 355),\n",
       " ('brothers', 355),\n",
       " ('move', 354),\n",
       " ('call', 354),\n",
       " ('various', 353),\n",
       " ('theater', 353),\n",
       " ('dr', 353),\n",
       " ('animated', 352),\n",
       " ('western', 351),\n",
       " ('baby', 350),\n",
       " ('space', 350),\n",
       " ('leading', 348),\n",
       " ('disappointed', 348),\n",
       " ('portrayed', 346),\n",
       " ('aren', 346),\n",
       " ('screenplay', 345),\n",
       " ('smith', 345),\n",
       " ('towards', 344),\n",
       " ('hate', 344),\n",
       " ('noir', 343),\n",
       " ('outstanding', 342),\n",
       " ('decent', 342),\n",
       " ('kelly', 342),\n",
       " ('directors', 341),\n",
       " ('journey', 341),\n",
       " ('none', 340),\n",
       " ('looked', 340),\n",
       " ('effective', 340),\n",
       " ('storyline', 339),\n",
       " ('caught', 339),\n",
       " ('sci', 339),\n",
       " ('fi', 339),\n",
       " ('cold', 339),\n",
       " ('mary', 339),\n",
       " ('rich', 338),\n",
       " ('charming', 338),\n",
       " ('popular', 337),\n",
       " ('rare', 337),\n",
       " ('manages', 337),\n",
       " ('harry', 337),\n",
       " ('spirit', 336),\n",
       " ('appreciate', 335),\n",
       " ('open', 335),\n",
       " ('moves', 334),\n",
       " ('basically', 334),\n",
       " ('acted', 334),\n",
       " ('inside', 333),\n",
       " ('boring', 333),\n",
       " ('century', 333),\n",
       " ('mention', 333),\n",
       " ('deserves', 333),\n",
       " ('subtle', 333),\n",
       " ('pace', 333),\n",
       " ('familiar', 332),\n",
       " ('background', 332),\n",
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       " ('supposed', 330),\n",
       " ('secret', 329),\n",
       " ('die', 328),\n",
       " ('jim', 328),\n",
       " ('question', 327),\n",
       " ('effect', 327),\n",
       " ('natural', 327),\n",
       " ('impressive', 326),\n",
       " ('rate', 326),\n",
       " ('language', 326),\n",
       " ('saying', 325),\n",
       " ('intelligent', 325),\n",
       " ('telling', 324),\n",
       " ('realize', 324),\n",
       " ('material', 324),\n",
       " ('scott', 324),\n",
       " ('singing', 323),\n",
       " ('dancing', 322),\n",
       " ('visual', 321),\n",
       " ('adult', 321),\n",
       " ('imagine', 321),\n",
       " ('kept', 320),\n",
       " ('office', 320),\n",
       " ('uses', 319),\n",
       " ('pure', 318),\n",
       " ('wait', 318),\n",
       " ('stunning', 318),\n",
       " ('review', 317),\n",
       " ('previous', 317),\n",
       " ('copy', 317),\n",
       " ('seriously', 317),\n",
       " ('reading', 316),\n",
       " ('create', 316),\n",
       " ('hot', 316),\n",
       " ('created', 316),\n",
       " ('magic', 316),\n",
       " ('somehow', 316),\n",
       " ('stay', 315),\n",
       " ('attempt', 315),\n",
       " ('escape', 315),\n",
       " ('crazy', 315),\n",
       " ('air', 315),\n",
       " ('frank', 315),\n",
       " ('hands', 314),\n",
       " ('filled', 313),\n",
       " ('expected', 312),\n",
       " ('average', 312),\n",
       " ('surprisingly', 312),\n",
       " ('complex', 311),\n",
       " ('quickly', 310),\n",
       " ('successful', 310),\n",
       " ('studio', 310),\n",
       " ('plus', 309),\n",
       " ('male', 309),\n",
       " ('co', 307),\n",
       " ('images', 306),\n",
       " ('casting', 306),\n",
       " ('following', 306),\n",
       " ('minute', 306),\n",
       " ('exciting', 306),\n",
       " ('members', 305),\n",
       " ('follows', 305),\n",
       " ('themes', 305),\n",
       " ('german', 305),\n",
       " ('reasons', 305),\n",
       " ('e', 305),\n",
       " ('touch', 304),\n",
       " ('edge', 304),\n",
       " ('free', 304),\n",
       " ('cute', 304),\n",
       " ('genius', 304),\n",
       " ('outside', 303),\n",
       " ('reviews', 302),\n",
       " ('admit', 302),\n",
       " ('ok', 302),\n",
       " ('younger', 302),\n",
       " ('fighting', 301),\n",
       " ('odd', 301),\n",
       " ('master', 301),\n",
       " ('recent', 300),\n",
       " ('thanks', 300),\n",
       " ('break', 300),\n",
       " ('comment', 300),\n",
       " ('apart', 299),\n",
       " ('emotions', 298),\n",
       " ('lovely', 298),\n",
       " ('begin', 298),\n",
       " ('doctor', 297),\n",
       " ('party', 297),\n",
       " ('italian', 297),\n",
       " ('la', 296),\n",
       " ('missed', 296),\n",
       " ...]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_counts.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f638743",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ('excuse', 343),\n",
       " ('crazy', 342),\n",
       " ('ideas', 342),\n",
       " ('baby', 342),\n",
       " ('loved', 341),\n",
       " ('meant', 341),\n",
       " ('worked', 340),\n",
       " ('fire', 340),\n",
       " ('unbelievable', 339),\n",
       " ('follow', 339),\n",
       " ('theme', 337),\n",
       " ('barely', 336),\n",
       " ('producers', 336),\n",
       " ('twist', 336),\n",
       " ('plus', 336),\n",
       " ('appear', 336),\n",
       " ('directors', 335),\n",
       " ('team', 335),\n",
       " ('viewers', 333),\n",
       " ('leads', 332),\n",
       " ('tom', 332),\n",
       " ('slasher', 332),\n",
       " ('wrote', 331),\n",
       " ('villain', 331),\n",
       " ('gun', 331),\n",
       " ('working', 331),\n",
       " ('island', 330),\n",
       " ('strong', 330),\n",
       " ('open', 330),\n",
       " ('realize', 330),\n",
       " ('positive', 329),\n",
       " ('disappointing', 329),\n",
       " ('yeah', 329),\n",
       " ('quickly', 329),\n",
       " ('weren', 328),\n",
       " ('release', 328),\n",
       " ('simple', 328),\n",
       " ('honestly', 328),\n",
       " ('eventually', 327),\n",
       " ('period', 327),\n",
       " ('tells', 327),\n",
       " ('kills', 327),\n",
       " ('doctor', 327),\n",
       " ('nowhere', 326),\n",
       " ('list', 326),\n",
       " ('acted', 326),\n",
       " ('herself', 326),\n",
       " ('dog', 326),\n",
       " ('walk', 325),\n",
       " ('air', 324),\n",
       " ('apart', 324),\n",
       " ('makers', 323),\n",
       " ('subject', 323),\n",
       " ('learn', 322),\n",
       " ('fi', 322),\n",
       " ('sci', 319),\n",
       " ('bother', 319),\n",
       " ('admit', 319),\n",
       " ('jack', 318),\n",
       " ('disappointment', 318),\n",
       " ('hands', 318),\n",
       " ('note', 318),\n",
       " ('certain', 317),\n",
       " ('e', 317),\n",
       " ('value', 317),\n",
       " ('casting', 317),\n",
       " ('grade', 316),\n",
       " ('peter', 316),\n",
       " ('suddenly', 315),\n",
       " ('missing', 315),\n",
       " ('form', 313),\n",
       " ('stick', 313),\n",
       " ('previous', 313),\n",
       " ('break', 313),\n",
       " ('soundtrack', 312),\n",
       " ('surprised', 311),\n",
       " ('front', 311),\n",
       " ('expecting', 311),\n",
       " ('parents', 310),\n",
       " ('surprise', 310),\n",
       " ('relationship', 310),\n",
       " ('shoot', 309),\n",
       " ('today', 309),\n",
       " ('painful', 308),\n",
       " ('ways', 308),\n",
       " ('leaves', 308),\n",
       " ('ended', 308),\n",
       " ('creepy', 308),\n",
       " ('concept', 308),\n",
       " ('somewhere', 308),\n",
       " ('vampire', 308),\n",
       " ('spend', 307),\n",
       " ('th', 307),\n",
       " ('future', 306),\n",
       " ('difficult', 306),\n",
       " ('effect', 306),\n",
       " ('fighting', 306),\n",
       " ('street', 306),\n",
       " ('c', 305),\n",
       " ('america', 305),\n",
       " ('accent', 304),\n",
       " ('truth', 302),\n",
       " ('project', 302),\n",
       " ('joe', 301),\n",
       " ('f', 301),\n",
       " ('deal', 301),\n",
       " ('indeed', 301),\n",
       " ('biggest', 300),\n",
       " ('rate', 300),\n",
       " ('paul', 299),\n",
       " ('japanese', 299),\n",
       " ('utterly', 298),\n",
       " ('begins', 298),\n",
       " ('redeeming', 298),\n",
       " ('college', 298),\n",
       " ('york', 297),\n",
       " ('fairly', 297),\n",
       " ('disney', 297),\n",
       " ('crew', 296),\n",
       " ('create', 296),\n",
       " ('cartoon', 296),\n",
       " ('revenge', 296),\n",
       " ('co', 295),\n",
       " ('outside', 295),\n",
       " ('computer', 295),\n",
       " ('interested', 295),\n",
       " ('stage', 295),\n",
       " ('considering', 294),\n",
       " ('speak', 294),\n",
       " ('among', 294),\n",
       " ('towards', 293),\n",
       " ('channel', 293),\n",
       " ('sick', 293),\n",
       " ('talented', 292),\n",
       " ('cause', 292),\n",
       " ('particular', 292),\n",
       " ('van', 292),\n",
       " ('hair', 292),\n",
       " ('bottom', 291),\n",
       " ('reasons', 291),\n",
       " ('mediocre', 290),\n",
       " ('cat', 290),\n",
       " ('telling', 290),\n",
       " ('supporting', 289),\n",
       " ('store', 289),\n",
       " ('hoping', 288),\n",
       " ('waiting', 288),\n",
       " ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negative_counts.most_common()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b3de51f",
   "metadata": {},
   "source": [
    "Most common words are the words like `the`, `a`, etc. That's not what we want. We want the words found in positive reviews more often than in negative reviews, and vice versa. To accomplish this, we'll calculate the `ratios` of word usage between positive and negative reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96c6a8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_neg_ratios = Counter()\n",
    "\n",
    "for word, count in total_counts.most_common():\n",
    "    if count< 100: # Don't consider very uncommon words\n",
    "        break\n",
    "    pos_neg_ratios[word] = positive_counts[word]/ float(negative_counts[word] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a4f8a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pos-to-neg ratio for 'the' = 1.0607993145235326\n",
      "Pos-to-neg ratio for 'amazing' = 4.022813688212928\n",
      "Pos-to-neg ratio for 'terrible' = 0.17744252873563218\n"
     ]
    }
   ],
   "source": [
    "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n",
    "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n",
    "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230cce65",
   "metadata": {},
   "source": [
    "- Positive Words : have a ratio greater than 1\n",
    "- Negative Words : have a ratio less than zero (closer to 0 is more negative)\n",
    "- A perfectly neutral word : would be almost exactly 1\n",
    "\n",
    "Values we've calculated are a bit difficult to work with. A very positive word like \"amazing\" has a value above 4, whereas a very negative word like \"terrible\" has a value around 0.18. Those values aren't easy to compare for a couple of reasons:\n",
    "\n",
    "* Right now, 1 is considered neutral, but the absolute value of the postive-to-negative rations of very postive words is larger than the absolute value of the ratios for the very negative words. So there is no way to directly compare two numbers and see if one word conveys the same magnitude of positive sentiment as another word conveys negative sentiment. So we should center all the values around netural so the absolute value from neutral of the postive-to-negative ratio for a word would indicate how much sentiment (positive or negative) that word conveys.\n",
    "* When comparing absolute values it's easier to do that around zero than one.\n",
    "\n",
    "\n",
    "To fix these issues, we'll convert all of our ratios to new values using logarithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28d4c995",
   "metadata": {},
   "outputs": [],
   "source": [
    "for word, count in pos_neg_ratios.items():\n",
    "    pos_neg_ratios[word] = np.log(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cdd149eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pos-to-neg ratio for 'the' = 0.05902269426102881\n",
      "Pos-to-neg ratio for 'amazing' = 1.3919815802404802\n",
      "Pos-to-neg ratio for 'terrible' = -1.7291085042663878\n"
     ]
    }
   ],
   "source": [
    "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n",
    "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n",
    "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43adc1ef",
   "metadata": {},
   "source": [
    "Neutral words will be close to `0`, words will get more positive as their ratios approach and go above `1`, and words will get more negative as their ratios approach and go below `-1`. That's why we decided to use the logs instead of the raw ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0622c317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lumet', 1.7462970951512977),\n",
       " ('elvira', 1.739703107272002),\n",
       " ('underrated', 1.7197859696029656),\n",
       " ('gripping', 1.7165360479904674),\n",
       " ('superb', 1.7091514458966952),\n",
       " ('delight', 1.6714733033535532),\n",
       " ('welles', 1.667706820558076),\n",
       " ('sadness', 1.663505133704376),\n",
       " ('sinatra', 1.6389967146756448),\n",
       " ('touching', 1.637217476541176),\n",
       " ('timeless', 1.62924053973028),\n",
       " ('macy', 1.6211339521972916),\n",
       " ('unforgettable', 1.6177367152487956),\n",
       " ('favorites', 1.6158688027643908),\n",
       " ('stewart', 1.611998733295774),\n",
       " ('sullivan', 1.6094379124341003),\n",
       " ('extraordinary', 1.6094379124341003),\n",
       " ('hartley', 1.6094379124341003),\n",
       " ('brilliantly', 1.5950491749820008),\n",
       " ('friendship', 1.5677652160335325),\n",
       " ('wonderful', 1.5645425925262093),\n",
       " ('palma', 1.5553706911638245),\n",
       " ('magnificent', 1.54663701119507),\n",
       " ('finest', 1.546259010812569),\n",
       " ('jackie', 1.5439233053234738),\n",
       " ('ritter', 1.540445040947149),\n",
       " ('tremendous', 1.5184661342283736),\n",
       " ('freedom', 1.5091151908062312),\n",
       " ('fantastic', 1.5048433868558566),\n",
       " ('terrific', 1.5026699370083942),\n",
       " ('noir', 1.493925025312256),\n",
       " ('sidney', 1.493925025312256),\n",
       " ('outstanding', 1.4910053152089213),\n",
       " ('pleasantly', 1.4894785973551214),\n",
       " ('mann', 1.4894785973551214),\n",
       " ('nancy', 1.488077055429833),\n",
       " ('marie', 1.4825711915553104),\n",
       " ('marvelous', 1.4739999415389962),\n",
       " ('excellent', 1.4647538505723599),\n",
       " ('ruth', 1.4596256342054401)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# words most frequently seen in a review with a \"POSITIVE\" label\n",
    "pos_neg_ratios.most_common()[30:70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71aede22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('boll', -4.969813299576001),\n",
       " ('uwe', -4.624972813284271),\n",
       " ('seagal', -3.644143560272545),\n",
       " ('unwatchable', -3.258096538021482),\n",
       " ('stinker', -3.2088254890146994),\n",
       " ('mst', -2.9502698994772336),\n",
       " ('incoherent', -2.9368917735310576),\n",
       " ('unfunny', -2.6922395950755678),\n",
       " ('waste', -2.6193845640165536),\n",
       " ('blah', -2.5704288232261625),\n",
       " ('horrid', -2.4849066497880004),\n",
       " ('pointless', -2.4553061800117097),\n",
       " ('atrocious', -2.4259083090260445),\n",
       " ('redeeming', -2.3682390632154826),\n",
       " ('prom', -2.3608540011180215),\n",
       " ('drivel', -2.3470368555648795),\n",
       " ('lousy', -2.307572634505085),\n",
       " ('worst', -2.286987896180378),\n",
       " ('laughable', -2.264363880173848),\n",
       " ('awful', -2.227194247027435),\n",
       " ('poorly', -2.2207550747464135),\n",
       " ('wasting', -2.204604684633842),\n",
       " ('remotely', -2.1972245773362196),\n",
       " ('existent', -2.0794415416798357),\n",
       " ('boredom', -1.995100393246085),\n",
       " ('miserably', -1.9924301646902063),\n",
       " ('sucks', -1.987068221548821),\n",
       " ('uninspired', -1.9832976811269336),\n",
       " ('lame', -1.981767458946166),\n",
       " ('insult', -1.978345424808467)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# words most frequently seen in a review with a \"NEGATIVE\" label\n",
    "pos_neg_ratios.most_common()[-1: -31:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a0ac91",
   "metadata": {},
   "source": [
    "# Transforming Text Into Numbers <a id = \"header_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a2eecd1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d72879",
   "metadata": {},
   "source": [
    "Following image will show the layers of the neural network we'll be building throughout this notebook. `layer_0` is the input layer, `layer_1` is a hidden layer, and `layer_2` is the output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "75a03622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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COmEmB9YBnB5xhCzTWlFBcTVLRUARUAQUgT5C4P19VNbaFBUioWSiNtWhiigCioAioAjkiIBaLHIEU5NSBJqMQK8N5ppcNtVdEVAEykNAiUV5WGtOikCtEWCdlMMPP7zWOqpyioAiUH8ElFjUv45UQ0VAEVAEFAFFoDEIKLFoTFWpooqAIqAIKAKKQP0RUGJR/zrqquGvf/1r88gjj3QNow8VAUVAEVAEFIGyEFBiURbSBeXzkY98xMyePdv893//d0E5aLL9gsBrr71m96Hpl/JqORUBRaAYBJRYFINrqamy46nuKloq5K3M7L333jO6iVwrq1YLpQiUioASi1LhLiYz1sS45ZZbiklcU1UEFAFFQBFQBBIgoMQiAVh1Dfq3f/u3Zvjw4eaZZ56pq4qqlyKgCCgCikCfIKDEoiUVPXfuXMOGZiqKQFoE3n333bRRNZ4ioAgoAh0EhgUbTg10rvSksQjgvPmxj33MsHoiDp0qikBSBNi/Rj8HSVHT8IqAIuAjoBYLH5GGXv/1X/+1mTlzpnn44YcbWgJVWxFQBBQBRaANCCixaEMt/rEMzA5ZvHhxi0qkRVEEFAFFQBFoGgJKLJpWY130Peqoo8z27dvNm2++2SWUPlIE/oQAQ2g33HBDZx0UXXDtT9jomSKgCKRDQH0s0uFW21hXXHGF2bFjh7n22mtrq6MqVi8E8M1xF1ibNm2aeeihh+qlpGqjCCgCjUFALRaNqap4ik6fPl1nh8SDSkP9EQGG0Fw55ZRT3Es9VwQUAUUgEQJKLBLBVf/ArGnBT/cPqX9d1UXDSZMmdVQZN26cOeGEEzrXeqIIKAKKQFIElFgkRawB4efMmWOWLVvWAE1VxToggG+OyFe+8hU51aMioAgoAqkQUB+LVLDVOxIOeDvttJP5r//6L8M0VBVFoBcCZ555pt1vRtdB6YWUPlcEFIFeCLzv3wLpFUifNwuBD3zgA+Y3v/mNJRYHH3xws5RXbStB4Pe//73dgOz444+vJH/NVBFQBNqDgFos2lOXg0rCviEXXHCB2bBhw6D7eqEIhCEgs0LUwhWGjt5TBBSBJAioj0UStBoUVsbNf/jDHzZIa1W1KgQgFEoqqkJf81UE2oWAEot21eeg0uCI98ADDwy6pxeKgCKgCCgCikCRCOhQSJHoVpy2bkxWcQVo9oqAIqAI9CECSixaXuknnniiYcEjXZug5RUds3gMjW3evNm8/vrrZsuWLeatt94yGzduHBKb1Td32WUXs99++5mxY8eaT3/607pr7hCU9IYioAiEIaDEIgyVFt1joawbb7zRrF27tkWl0qIkQQBH3scff9yuyDp8+HAzceJEs++++5oxY8bYacksqOYKli6mKr/zzjtm06ZN5tlnn7U/yAazRr7whS8oyXAB03NFQBEYhIASi0FwtO+CNS0+8YlPGHqq6pzXvvqNKhH1ftddd5k777zTBpk7d645+uijU78DpLdmzRrz2GOP2fUumHH01a9+NXV6UXrrfUVAEWg+Auq82fw67FqCj3zkI4a9IJYuXdo1nD5sDwLsVgqZfO6558ztt99upxyffvrpmUgA7xHDaXfccYe1ZoAWm5ddeOGFgzYwaw+KWhJFQBFIi4ASi7TINSjerFmzzMqVKxuksaqaBgGsUvvvv7959NFH7Y8dSg866KA0SXWNg+WL3XMZLkHI45577ukaRx8qAopA/yCgxKIP6lrG0BlrV2knAlgppk6dahjywJ+mCELhIycEAyKzePFig6MwQyYqioAi0N8IKLHok/qnwVm+fHmflLZ/iklDzj4fNO4QCoY8yhZIzLp168yIESPsEMybb75ZtgqanyKgCNQIAXXerFFlFKmKrmlRJLrVpA2pmDJlitl1112toyZ+EFULQyKXXHKJJTliKataJ81fEVAEykVALRbl4l1ZbpitZ86caR5++OHKdNCM80NASMUBBxxg8KWoA6mgdFhMbrrpJnPkkUcatVzkV9+akiLQJASUWDSptjLqyuwQdbLLCGINorukAifKugmzR5Rc1K1WVB9FoDwEdCikPKxrkRNTBBmLVzN1LaojlRJM8Vy/fr2tx1QJlBQJEotTJ/4XdbGolFR0zUYR6GsE1GLRZ9V/9tln27UN+qzYrSkujTVOuKtWrap9mRgW+dSnPmXOOOOM2uuqCioCikB+CKjFIj8sG5ES496Mf8saBDh16oqcjag6uxAVMzCYAVLGdNI8UGHY5rDDDrPTYKuYsZJHGTQNRUARSIaAWiyS4dX40AyB8GMPkSuuuMI6/jW+UH1SgPPOO89gcWoKqaBaGAJh9U9mikAy+lXY+G3YsGF9by381a9+Za655hqz5557WjzAhB/3eKbSDgSUWLSjHmOVAusEqzPus88+du2Dr3/967HiaaDqEaDeXnzxRfPP//zP1SuTUAOIEI7Dl112WcKYGrxNCLCpHd+e+++/367cOjAwYPg98cQTdh8ankHAVJqPwPubXwQtQVwE6DGyOiMEQ6VZCCxatMgsXLiwsU6QbFiG47BuXNas9y5PbZnujuAftPvuu3eSPvbYY82ECRPMEUccYU499VTz6quvdp7pSTMRUItFM+stldYMgej26amgqzSSWCua7KOAHw87on7jG9+oFEvNvBoEXnrpJfPUU09Z4uCSCtFmp512ssN8zHYirEqzEVBi0ez6S6w95EIXyUoMW6URbr31VjNv3rxKdcgjc6wVzGjpZ1+LPHBsYhpbt261ao8bN66n+q+//nrPMBqg3ggosah3/RSiHQsY3X333YWkrYnmiwCNMFves8FX0wWrBcR2zZo1TS9Krvrje4DzojgyyvHcc88d4nOAIyzPn3zyyUgd6PEThrCuiOOkpM+RYQg/HHFIX/JZsWKFOfDAA0PTdNPvdo7/xNVXX20OOeSQbsHss5133rlnGA1QbwSUWNS7fgrTDrM6pmmVeiNAI8zYdFumBPPePfbYY/UGvUTtIBU0ttQzTozi0MgRGTt27KChgZNOOsnsscceXdcxWb16tQ1DWBGcImncGWrYtm1bJx8I6y233GKdayWse8QfAv8eLGboNGvWLPdx7PMxY8aYiy66aJBvhR+ZpemROOTDj6vXNUMgeFlU+hiBn/zkJ31c+voXPSAVA4F1qf6KxtQwWD+FFjNm6PYEe+ONN2y5b7vttkGFCqbhDgREYWDHjh2D7stFsBfMwMknnyyX9njOOed0jUN6hBEhbdIhrzAJiIZNL7AodB4HJMfqS1o8L1rQl/fC1bvoPDX94hBQi0XNiF7Z6mCaVqkvAo8//rgZP358fRVMqBmWF7z/cUhVMdZKceihhxqcF8Nk9OjR5t133x30iB1tsTo88MADg+5zwbAFzwgjQrjt27dH+ungTMlsjBtvvFGidI7cD3O27ATIeIIlhanIS5YsMcccc4y56qqrMqao0euAgBKLOtSC6qAIhCAg04LbRv7YkZU1OVSMnVqJQ2uY4Pvw/PPPD3mEX4Tsaus/ZOE7nhFGhKGObuSFcDhVQkj8GRlxnC0ln6THSy+91A71sK5FYJUxDz74YCTBSpq2hq8WASUW1eKvuSsCkQiw7Drz+9sm++67r5FZAm0rW5ry0GvHeZOeu+tYydTwvffeOzRJVmBl+qa7oBT+GjTSPHMFawX33bT9c/HHCIZN3KimCEdKdIb4sC4L/iJYVCBXUVabQQrpRSMQUGLRiGpSJfsRAXr1I0aMaF3RceTbsmVL68qVpkAQChw0f/aznxlmawWj3p0fje3HP/7x0GQnTZpk77uOsAybIfLMXvzxT+C70EnXzcM/dy0dbvy8zhmqobyQIpZ5h2BOnz49r+Q1nZogoMSiJhWhaigCYQiwp4JKOxFg2OHiiy+20zBvvvnmRA0sfg8MH2CJEGFWBfd8n4jhw4ebl19+WYJVdoRUYBlhqCZwZjVXXnllZbpoxsUioMSiWHw1dUVAEfAQ+MQnPmFWrlzp3e2/S1kIit1fo6QbIcDCIStVMryAFYB7vhx33HE2HEMlUYK/A8Mj7tBKVNg098n7wgsvtA6azz77rMFqpdJeBJRYtLdutWSKQC0RYMdTFdPxX4jyN8F5E+IQJQwh4KPAuhUMiXAeNqxwyimn2CRYRyJMaPTvu+8+w3BJUQ0+sz5wDl28eLH6UoRVQsvuKbFoWYVqcRQBRaAZCEACmGLJAlTuSppYDVh1k9kcDG384he/iCwQ00EhBQyJzJkzJzQcZCFYP8OGwUHUtVxAXliQiuGSIqd6vvLKK3YIpCjiElpwvVkZAkosKoNeM1YE+hOBN998szUriWatQQgF5IHhCpmpAVnYa6+97FTUL3/5y9ZqwTOXfEi+WCOwBGDZmDx5stwecmTFTPwaRo4caS0bktcVV1xhCQnDE0XOyvjxj3/cKYfkHXUMK+eQAumNWiMwjLW3aq2hKqcI9CEC7BHyzW9+05rLzzvvvFYhwOJY9NJlCedWFa6CwuDgy7RUbZArAF+zDEXg/aF39aYi0AUBGgbMtTifMW3wrbfeMhs3bhwUgxUWWYNhl112Mfvtt589P+iggwaF0YvBCEAmgiXW7U16j+zt4E4nHBy6uVdshqWSDwIMa2CxmD9/fj4JaiqKQA4IKLHIAcR+SOKZZ56xi9gwV57x2IkTJxoWOmL6GI2gvzokq0aywBMfvk2bNtnlhDG3sqEWJlviqxOfMYIT7xA4uuSLBvjtt99u3euFSf7www9vXbmqKBBrXTB9M+3mYEl15p1MumgWi24VOcyStAwavngEdCikeIwbmwM96LvuusuarSETc+fONUcffXTq8XHSYxfHZcuW2SWdcST76le/mjq9pgLrkomPfexjXcvPODQf8zaRMHbUxDcgbGpkU+u0Cr2xGuKbgbWiLGJRRTk1z+YhoM6bzauzwjWGANxwww2G9Qaee+458+ijj5oNGzYYtrzOsn03jSONCWPrsgkVDSvz28mzzYLDImWWcmOZ4NcLTzbs+sEPftAqaFhR9OCDD25VmcosDDM5IJysYKmkokzkNa+4CKjFIi5SfRKOho/ez6hRo+zwhWuaLwICeu9f//rXDUMs7B0AeWmLCImgPJC0NFYHvPaRtoyhy/sFUVVpPgL8//7d3/2d7Xg0vzRagrwQUGKRF5ItSIdGjLnzVTTw0uB86lOfsovopGmE61AFLpnIg5Rh6cCPpS0NMe8Y6zJcf/31dagu1SEjAtQnPhTXXnttxpQ0epsQUGLRptpMWRaGIaZMmWJjf/vb3+5pnk+ZTc9o6IEfBw6LrNTnO4T2TKCCAOgsMznIPg8y4Rdj//33N9ddd5056qij/EeNu2boi5UiP/zhDzeifhsHcMkK824yNFPEe19yUTS7HBFQH4scwWxiUkIq8CxftWpVZaQC7LBS3HHHHQa/giOPPNLQW6+jYP7FMsGPcz6q8itC36985St2Rk4RaZeZ5iOPPGLJBO8aviWudadMPTSvfBCQ+lNSkQ+ebUpFLRZtqs2EZXFJRd1MmTRCs2fPNmvXrq1FzxYCwfRZpNdMjoTV0DM49cR0PfLv5ezZM7EKA9C7XbBgwaDZIDRO2jBVWCkZssbpmqmnbfH/yQCFRvUQUGLhAdIvl3UmFVIHVZMLLCaymFPZZEIw4AipYaXK3/72t9ai4z5ryjl1edlll4X6iii5aEot/klPvh84JFN3TSa7fyqRnuWJgBKLPNFsUFpnnnmmee+992q/rDLOYSysxTBNGQ6dYt6lKtPO5MjzNYDcUG5+6MPU36b18GmE2Brct1a4OIF7HfB2ddLzaAQgijfeeKO1KEaH0if9ioASiz6sedaouPPOO826detKaayzQsyCSiwNjv9FEeKSiTo12n5je88999gZM02pN6kryCFLvvfaGwQSRe+3DAIpuukxHQJ0TFhBVxc5S4df22MpsWh7DXvl4+NNz5CZDE2YdYH6Yna99957c5kZQXpFz+TwYE98CakIIzmQrHHjxjVmXJtyTJ06NbbJXMlF4lel9AgMzTE02LYVYUsHssUZKrFoceWGFY2GiX0amrZjJnuVnHbaaZYQpOnRumQibG+TMKyquCd6hpEK9JFZKDfddFPte4tpdVVyUcWbFz9PLGdYzYqyIMbXREPWFQElFnWtmQL0Ege6ppnSBYqkpIiGTWZy1JlMSPnQF2LRy5IkJKsuM2ZEf/dIOVgbhamlaWYcQS4gkOoY6KJaj3Omgl966aW5WA/rUSLVIm8ElFjkjWiN0wub7ldjdYeoRmPDR41hjCirBWHqMJNjiPI9bkAqkDgNKWG/973vmYsuuqg203Hd4gmpGDlyZKZebRJM3Pz1vDgE5H9QCHtxOWnKTUZAt01vcu0l0B1rBdJkZyt68uyIyo6r7lCOSybwH+nV408AWylB0T9J75yP+j/8wz+YD37wg5Zo1clyIaQC4BYvXpwJP0gW5IJfHMKVKTONHAuBFStW2P/BWIE1UP8iMFCQPPDAAwMBqvZ38sknF5RLccm6+lMOV6RcHIOdGt1HPc8D03AHl9tuu61n+LwCTJs2beDhhx/OK7nK0gl2+hwIGpkBjvILLBSV6ZM1Y8qQRH/Cu0Kd8h7WoW4DS9JAsGrqwAUXXOCqmPk8IFIDpK1SPQL872ldVF8PdddAl/QOvsptF3p8K1euNBMnTmxFUdlnguEOHBz5RQ2L1L2wMvMjrv7UI974rmCBCsiGXaW0yu3nsYgxTMUMkDQ+FW6Z/HOsFfyw7KhUhwC+PcOHD2+cRbA6xPo3ZyUWfVD3Tz/9tJk5c2ZjG2C3iiASOI6xzXpTheECIRVJysAQCA2sL2BCelu2bLELUXFelkB2WNOA5ddZvMsdospTBxkqUnKRJ6rJ0uJ/jk0CVRSBXggosYhAaPr06Yx/dH4RwRpxm90kWcymLTJ+/PjGbspFQ8wPMpBEehERCAcLUPHhx2rADJoiCQbkiIXWKAeLl+FQm7RMScpPWCUXSRHLLzz1zQ67Rx99dH6JakqtRaBUYsH2usOGDev86Hm+8847keC+9NJLtnfqxtlzzz3tNr3i+e9HdsMSn9+xxx5r8yR/JE4YnJTccH4+7vWTTz7ZyYM45Me9NBJWZhwW0SetMAxy8MEHp42eOZ7gSDnyEHHObFrvFUKBiP5xsSCePwQSFff000+3jTxrlQjBwISdl4A5Qy44yT733HN2VgpDH3GHc7LqIeSiSNKUVcc2xl+zZo0J/LRCLWZtLK+WKSMCRTmBuM6POG/yC1Qd8ttjjz0Gtm3bNkSNq6++ekhYNz7x3njjjSHx3DB+GuIsGSeMqz/hXXHjX3LJJZF6Sn5u3G7Om4R30/bPySup4GgVrNSYNFqu4aUcxxxzTG7pBkM7tXBYjFsg6gEnxDTiO2zGTQOn0LvvvtvWf2DRsE6VQQORWA/yv/zyywelk7YscXWPEy4tLnHS1jCDEWiL8/fgUulVUQiUMt30/vvvD9qWcAlIhZ2Pv3z58k4ALAsXX3xx5zrshHjHHXec2bRpk91SOixMrzSIEydMWNpyb+HChXI65HjWWWeZfffd12C67yVYOAjfTchrxIgRZtasWd2CDXq2efNmM2rUqEH3qrp46qmncst6v/32M7wDTRB612k32Oo1BNKt/PTusWDww9LAO7ZkyRLryMuy4LwXvE9YAX3BGvHuu+/aDeCCmR6GH6bwo446yg9a2bX4lhQ9BFNZAWuSMRYzrJ5M81ZRBOIgUNpQSNBbNYGFwfos7Nixw3At4hIPhjjYtEiElfuCKZ0dX4eg1y6PbMNy6623dq7DTggfsDL7i2qQ44QJS1vuBZaGTh6BpUNu2yP7W8QRt1wuVjSegbWnkwTYRA0DdQI5J8THLF4XYagnDxk7dqw1xeeRVpFpCDFIM1SQZAikVxkYfsGxEj8M/h94T+fNmxdKKkjrnHPOsVu1E5Z1MubPn18rUiHlFXKBD4BKMQjwzgRTiEsb7iqmFJpqqQgUZQrxhxKCBm5QVqz/EBS08wvIhn3uxwtbJ8IdVmFIxBU3TcKFSZwwvh5uOm78gBC4j+y5P6QhZeNh2FAIQzpumj5WxKecEgbd4sr1118/wK9KEb05Mhzi4pFWL8zgmGfrKgxDZDXVZ41fV2yK0IuhJjBXyR8BhlIZQlNRBOIiUIrFAqvD7rvvHrQrfxL/WnrhbK8sEjSmocMIX/7ylyWItVpg7g8T5tX3kjhhuqVx/PHHD3l86KGHDrrXzUGVgAzniGCt8LFhn4tTTz1Vgpif//znnfNeJ5i06d1nFRwvxQkz6dHNm+EQzOoMd/XCxY3XpHMsDfyymOjF0tGkclepKxYZMFfLRb61IA7SdRoCy7eEmloRCJRCLEaPHh1b97fffrsT1m+g5cHOO+8sp/YopGTQzeDCD+c/5zpOmLB4cs8nAdyHCLgSpZ+ECXrwcmpoeMMabteX47333uuEj3Pi6xMnTpFh1q9fb/1JmrwWRRQ+NG5I0pkfbnqkEXcWiBuv38+VXOT/BtABYDaIiiKQBIFSiEUShTSsItBUBKR3F7aIVZIyRS2ElSSNfg0r5EIIXr/ikFe5capnTR8VRSAJArUjFuyIKPL888/L6aCj28PnQZU9chxSffEtFGFWDTeOazXBUTMYx+r6u/LKK93oXc/x+o8aKuoaUR8mQoChCwhFVlKhQyCJYA8NLNYiJReh8MS+yfonYCl4xo6oAfsegVKmmyZBmWlwIsxoYBaBP10zmJsvQQx+GGPGjOlcl32CDwMLYrniEiL060Us9tlnn0504kJM8iJLTCX0iVgnswQnzAo4//zzE8ToHbQXLr1TqEeIvMhAGUMg5PHyyy9b3yTeXUSmlXIOMZowYQKnhv9F3h/+/5rWuFAOysovK9mzYPThH6wVTFVWUQSSIlA7YsHaFDTGkArkn/7pn8y3vvWtDrlgtU53eqrr1Ji08HmE99eWYIVMdz2KOPpBjHBwxfeAcn/xi1+0W04LYSJNVjsUTIJZIYnMk3kQC9ElD8zySAMrDNaYKgVHwTyXsmYIJIvDZxQWDNHwDrEWwfbt2y1xYAryKaec0iG9ki8NMXogTPNet26dfReJN2nSJLs0PBufNUGEXFD+phGjqvHl3V66dKkJFkarWhXNv4kIxJ0+kjScO10zbNpn0Eh2pk8GuA1afdOfrsnzsF9AQIZMXXTDRU3LjBPG1Z/wrrjxe51TTlfCppvy3M8vKl3iJ5G6T8tMUhY3LCtBVjmNlpUnmeKYlxQxtZSt1JkqGDSwduXMLHlQXlbxZFt00gN77jVBAgtgrnXVhDJn1ZG6ZnVbFUUgDQK187EIGlS7sqS7YBT3fMGq8cQTT+Q2ZOCnH/c6WDY8MigLZ8U19+MgRfhuglVj1apV3YIMecbsAnqqbROmJYvJvuyy0atH8uoFk16es0DYwnz//fc3l112mVmwYIG1QDCUJVaJNHjR+8cszmJZ7GK6detWqzMbkdV9iqfsLyLOtWnK329x7rnnnlZtXNhv9Vd1eWtJLAAFB0WIg08wIBQ0wKz9UAfzPOs7BNYGO5QhlclaFOgetdKnhPOPhMcZ1CcrEArK/Oqrr8YmKpI2DQJj5W36qNKQQZZYJrtsERzBNS9h6CGP9NCNXU2FUGzYsMEUMWwBQWHjMfTGT4N6yHOjs7xwddNRcuGi0f0cosu7VMS70z1nfdoWBIZh5mhLYbQc4Qjgn4ETHks6t0FoxCCe9J7LFJw00+75EaVnXo6f9DAh4SzTfcYZZ5S6/DL1cdppp1kfjMWLF5eadxSuUffz9ouJyqfJ99k2AL8syKOKIpAGgdpaLNIURuOEI4DTHebrNgiN2LJly0r3VhcCkGbPjyjc8xoCgThCKqhjyGOeOkbp7t5nVUacWFm4bcqUKbW2joENFhfqUyUcAayBJ510UvhDvasIxEBAiUUMkJoehA8/pk0x4ze5PJ/+9KftMNPw4cNt40ADUWQjQQ9XSEXeuGUdAkE3lqRnNlGes1PSlJMGm82qGLZDp7q/a0ouwmtZ/pey+OOEp6x3+wkBHQrpk9pui3kTk//q1attI+ZWnXwQ5V4eQxZYFGi883LSFN04ZiUr6IV1AMGht2wrhc044g/Oo7Nnz7ZDVUVgF5FtqttZ6yFVpjWOhPWLBftw9lVRBNIioMQiLXINiydm96y95KqLzWyH6667rucW3vSY3RVQmXWRxEESvJAkceJik0faOGmysFXdSIVg0DRykQcRlbI39QhZBQfIVhHvfVNxUb2TI6DEIjlmjY1BbwRpqlMW1gqcA5ntkFRozCFVIqxsGtWbhpRgASjq45q1l4z16dlnn60tqRCMm6In+lLn1HedLD+CY1lHyOCNN95YulN0WeXTfMpDQIlFeVhXnpNYLRiPj2pUK1cyQgHpTeGgmMf4L+mBgysy7l5k7zUrqZAZGE3pVWJZ2WWXXcwdd9zhQl3L834nF2eeeWajVlat5UukSlkElFj02YvAgkY0zmVP1cwKMx89pMgGCouIuzZKHgTGLXfWIRAhV/fee2/PoSA33yrPm6Zz0daqKuuiW97S6WD4sJ+tNt0w0mfxEVBiER+r1oTEa3/q1KmNWdei6F66WC98IoFVwJWsloys1ooyyJVb3rzOpf6wEDWh0cpKAPPCrcx0INXsC1MkcS+zPJpXtQgosagW/0pyp1cGuWhCz7doXWlEIBZxhobQJa1DaFZSQXzIYFMaZ//FZkiEFWCbMtug38gF3wM2eGRquooikBUBJRZZEWxo/CZ47dPgM6Uy2PiqkAYpa+NB/DgOoVnz4RWjYWZH0qaungoGzMxp0qykPOqtCZ8HIe/uu9wEvVXH+iKgxKK+dVO4Zpg/WbERf4s4PfbCFXIygFQcffTR5jOf+Uwhs1j4mOY980OGVJxidFZ59IdZ3DC9zpturZDyNXGNBMhFXIuWlLNpx7ascdM03NusrxKLNtdujLLxsV++fHmtyIVYKvbZZx9z6qmn5jILxIWChjqrv4SbXrdz3yE0Tb7UURv2emlqz5j3EYJRN/Ld7b1L8gxLUh07F0nKoGHrhYASi3rVRyXayLBIHXwuaHzYp2DixIkdS0WeRIC0slgPklRQmCmd8iXx06BRY82NJg0hdMOIsfw5c+Y0bufMtpILHGsvuOCCVGvDdKtnfdbfCLzv3wLpbwi09HvvvbcZOXKk3eb9d7/7nfnsZz9bCSj07tkl82tf+5q5+OKLOzrQo9qyZYv5v//7v9SzCmgYXnvttdJIBcrjaImFwpXddtvN+hpQJn7oRThICL/f/OY3hjAi3/3ud83//M//2CWy5V7Tj2vWrDF///d/36hifOADHzD8eIeot7bIzTffbP2YWNFWRRHICwG1WOSFZAvSoTd9zjnn2JIsWrSotEaYBhWnxLfeesvcfvvtkfkSjoY4qUk6bbwsVZrWMiJEQ/K+/vrrra/J6aefLrcafaQusBg12VEwbd3WreJ419pkDasbvv2sj+5u2s+175WdBpuxVqY18mNsn4agKOHDhuMYPUCmIjKPvtswBUsu8+PDHldE/6RkJG76YeHIM22vFodSMJDfpk2bzIc//OHa7xYahkPYPepPdqYNe96Ee9RNknewbmXi/w7BcjRt2rTClq6vW7lVn/IQUGJRHtaNyQnrAeZ5hAaShZkYi81LsIxAWugt7dixw/ZeWd8gzuJJ0vDyYZcPZJRe5IPQmJUpeflDQFA2btxop5qWSYyKxgr/mc2bNxedTaHpN5lczJ071/4/L1u2zJxyyimF4qSJ9ycCSiz6s957lpoGnM3KcDTcb7/9rIMX47AQAkhGr0bdzwAigHWCNHDgY2vm5557zixYsCBVw8+HnYZXLBJh+YmFw39W5DXlRLc8BIJCj7JtwgyX119/vfHFEnKR9H+h6oK/99571jl65cqVdviRZf6j/o+q1lXzbyYC72+m2qp1WQhAMLBg8MMCsGLFCrNkyRL7YWL4YtSoUXYYA6LgC8SBrb3ZiZNFrvj5W55naYjpxfNBRC+3R58lTb8MSa7RJe0QSFg+9OpHjBgR9qjR9yZMmGBJZaML8UflIRe8f5DYOBa3upUZJ2lmhZRt1asbDqpPvggoscgXz1anRuPtLsnMBxWLxosvvhhabhxBGe7o1oOXXl+3MKGJ//EmH0R6jJAJZmAwhJM2rW75xHmGhSHPvBkmonevUm8E+L9oKrmA7GOZVFEE8kRAiUWeaPZZWmIlyNqY0sunt5+210RPkTSefPJJc+yxx1ZSC1VZSSopbMZMIYCY4dskQi54F9O+x2XjAalYtWpV2dlqfn2AgPpY9EEl172IYnVIO1Yt48Psp8F52nTS4pT3EEhaPZoSr4lDBnGwFaIt72OcOFWF4X+OIc221kVVuGq+f0BAiYW+CbVAgI+yzERJohAmaER6iaTDh73Mj3tes0CSlFvD1hMBeQ/LfP/SIPHoo48O8ktKk4bGUQSiEFBiEYWM3i8dAUzkQhTiZM7wAx9y+ZhLHOk5JklL4iY96hBIUsRMpmGv5LmVH0Pex7qSiyuvvDJXX6DyEdYc646AEou611Af6YdZll+cD7I06FGmXCEchCtK0DPPWSBF6Vm3dLHwMDOkzSLkogxymxRHId5J42l4RSAuAuq8GRcpDddBgAb15ZdfNtu2betMG5RppQSSaahyzsyG8ePHxzK98kEWS0QnQ+cE/4m4Mz8gHTiWkh7WkCgS4iSf6DTvWSB+5nvssYd57LHH/NuNv3Y3YWt8YboUgHeZ9xVykUdjzv/dj3/8Y/PGG2/YPUtYj8L9vyM/IWz8D/J/N2bMGLVOdKkjfVQMArpXSDG4ti5VPo6sYYE3//bt2+0H7PDDDzdjx461U0opsMwOISyNBz/5CL7yyis23owZM8ykSZPMUUcd1RUjsUi4gfiw8qFO85FGJ4iF9CTddNOch+mXJp1ucchj3rx5dpn1buGa9owFmRDWRukH4Z3l3U373sr/HauwsmAa/3eQzt13393C5//fcZMp4Fu3brXLdvP/yv/c5MmT7fozeRPsfqhDLWMyBJRYJMOrr0LzQWQ/gcsuu8yWm4/a9OnTU30gSYDG/aWXXjKLFy+2JING84wzzgi1JPgfYz7MSBZikIWY2Mz/+CcPXdz0os7BgHVAIGhpG6aotKu8zxLxNIyzZs3KVJ9VliFp3tRlXEsbaT/yyCPmxhtvtP8zccl4lE68O08//bRh92D+B88++2wzc+bMvsE+Che9XyACAyqKQAgCDz/88EDQiA8EZGKA87zlBz/4gU2bPIIdPAeCxnNIFsGH2N7nGAw7DHme5gb5kHcWyRo/Tt7kwe/AAw8cePDBB+NEaUwY6lzqVMpZBqZ1AKhXOQMiPxAMY9hfEf934B6stDkQNCn2GPZ/VwecVIdmI2Carb5qnzcCfHiChXPsh63XRzCPvMkD8sLHlI+qL3fffXco6fDDJb0m3zQf1aIwIV33J+W5/PLLB/i1RXi/qOswccufF5EMy6fqe2HvEOXl/wDSVQSh8MtMfoHVwubH/5iKIpAnAkos8kSz4WnxgaEngwWhbBELCY2oNPh8gDmnMSpCSDdJA0bYJOG76UzebkMaFVZ6sFHPm3af+qXH3EvAOQ4+vdKp63OXXIS9+2XpjR4QPUiG/N+Vlbfm014E1MeiwGGmpiTN+C9+FPhTPPDAA6l9KLKWl7HgL33pS+b3v/+9CRog89nPftYmWaRPA2lT/jiOdVkcNsknaCw7ECWZpcKUVhY0Eie9TiINPGF329tvvz1xWcBeBDyCnr1cNvZIme677z7rEF1l/fL+s5U6DtZV/v83tiJV8SEIKLEYAkl/3eCjMmXKFFto9g2o2mOcBvhrX/ua3fdj7dq1nQY/S6Peq0bBILAgdG3skuYvaUreWRpDtptnQ7KmbxaFQyIEdsOGDQJLqqNL0nBujUMKU2VUcKQLL7zQfP/73zf33nuvGT16dMG59U6e2TqLFi2ys5CaimnvUmqIMhBQYlEGyjXNQ0jFAQccUJtGC50gN3x0ly9fPugjl7RxTwo76YdZEmjIkF69ZOKL5NngkT/EBItHLx0k/zoe2cvllFNOMSeccEJu6vkELqz+csssx4R4v9evX283AaMMdalXyN/s2bMH/d/lWGxNqk8QUGLRJxXtF7OOpMLXUcgF1gTIBjrTyBbZmwpb7yKK0LhEAt2LHKoAC6SpVguwmjp1qrUMFWkVo/4CXwGLVZ7kziaY0x+XVBSJRVp1lVykRU7jCQJKLASJPjvW/eMm1SF6MiyC0HDQuyvygwx5gcRAYFxS4TZa6FIkkSB9V9CJ/NzhIfd53c/xrViwYEGu1opeZaYOIaUidbBmMNxw5513mnXr1hX6DkuZ0x5Z84L1ZuquZ9ryabxiEVBiUSy+tUydj8Yll1xSeO8xr8IfeeSRJpgCa+bPn2+TdBv7vPLw06FRwqHuL/7iL8zw4cPt46obJholdBKS5etc1+u66O0SwyqsGWK1aQo5ZCEzlg1/6KGH6vpqqV41RUCJRU0rpii1pOdbpRd60rKF6VwEufB7uCyFDKmomlC4eEGyGFJoynLYNObgh+WgyCEsF6M4535dF13H5EceCxcuNKeffnocFSsPg86HHXaYnTHSFJ0rB00VsAgoseizFwEHumDeeqf335Ti+6ZZyAaS1ekNgiLi9mJd4lLG8Ivo0OuILpALzNQsr15naVLD5FozsszgiaoPZvaw10fTev9iZeGY9X8tChu93z4ElFi0r04jSyQfCXGGjAxY0weQIjZgkt662/jHVdltQIgT5icRRlqIh19HHT6uN910k/l//+//mf/4j/+olRXArQNIBdOY6zTjyNWv2zn17645EvaOdIvvPyO9Js/qabrjsF8fel0CAu1d+0tL5iPACntlLBfs55vXNasEBg37oBUC3RUMw/IJSNSgFRzjrC4YlSarQZJelYJulIFVUsGian2isGB1TZaGj4N3VBp1uQ/m8uMdSCpgUcVqtkn1jApPmYOmKPOqs8GOqzYd0rrtttuisuur+8GCZB1MwKVsoR7Il1+wE3Vu2ZdfktxUzyehoEfVAdZ/2bs9yyf38lJpy9LQ7KfgfqRpuNzGlY+gNALSCCdBmTjdhPx6hekWP+2zsHxpsOpGLtCT5aHbQir8+vLfL/+5fy2NMrg0WXjX+GUR+Z5y7CeRhpsjRMKVqokFuki9HHPMMa5qmc6VWDSEWHR7OeO8AXzs27DZEI26u4kVH2x2//ze975nG3w+5GmFuHHjpyEtWfRyyZObjlguwjZwc8OVcU5dQCjaSirCMOQ9kF/Yu5NHgxyWb9n3KBvfoLQEye0Z+41r2WUpO79u3+46EAtXh7zqRolFHxCLrB+Fsv8Re+X3N3/zNwPf+ta37AddGlw+7lklaRrkHdaYZNXDjR8nD9nEyrXkuGmUcQ52WE+y9mrL0LWoPHgXhGTIe1k3i1KWsmMtTDOUGixHP7DHHntYYsKx36QbsagDFn79cJ1V/iwotErLEXj66adNYLGo9YI8casAh01mQ7z99tvW8VKmMOJgx7O0ksYRVPLGsbMIQaegYerpMMoS2ayNwBRiZowUpU9YGXHSZMYDU2BxKm3qyqBhZUt6j7riPeTH+c0332zcmUZJ06tbeJZjX7ZsWWK1gl6w2bZtm403Z86cQfFXrFhhhg0bZn+XXnqpfXbNNdd07vHs3HPPNZs3bx4Uz71gU7sDDzxwUBzuvfPOO26wQeekR7qS95577mnQhThyj6OfBte+foTjnq/jjBkzbFpuxieddJK9R16IW37SQQKr0CAd0NMXP8yTTz45KMhLL71kwNMtC/pIvm5g3tFTTz3V3qKennjiCfdxuvM0zASzlozLBLlaJsq9QKlByQWLMHX8F2Cq/nM3jbBxN5iTa0LrlpebcVz9iOPqQDxXuj0jHM5IbhnR7+STTx4yjiZpuumBBfEZ15JygZGvA+nJc/8Y12zFMEianoboXbcjvUF3OET0o8eYxoKQNp7ki3k4qbVD4oYds6SH1YJeMpaDNFiE6RN2Dx0lL96vIvMKy78J99ginl9bhDrnG8QxibjfPb55rrhmeL6l7vfQ/975cfmGdgvP9zTMIZF0/LTl+uqrrx70zG2zSEvCRR3d73ecb7dbftIUOeecczp5hVl5yEd04LlrZXCfSRj3CM6+BGSik14evhZ/KomfU8h10ookPIWWQrkF8gH1Xxoq0Y0rabhHP05S/Sii+9K7L0WvZ2kqz8/LLYt7zgspEufllLBRR9LO+8MP1tQhdYqOYXXFPZ4RhrDEyUtoPMPKBOlI+uHLixSQTtK8fTwok5jR/Wdxr0kDYkG9c8yanpsvaQuhwDSeF3ZuHm05x9ckb3yq/r+jTEn8efzG2K9bvx1wv4P+ud/gdSMVEpdvkNvoch72rZLw/tH9Zrnfbz+cey35xfl2++UXfPz7PkFyiQfnIi5BcHXyz/22Dp3dMH5+kn7cYyJikaYi/QZYKsqtJBcYFI9b+aThShr9XD18sKOepa08Nz23EsPOyQOJ83K6GPjnUb17P1zca+rPfanDdO92j7jyDsTNMyxct/HeJB/zJGHD9PDvgXcY4fHDhV1niRuWHnrQY4aEYeHhPE150QsnUSwT1C3HNOmE6djme2CVl9Tl/453KIkvj/v994kB2PgNKGGkUaMd8L9/0mj78dxvd7dnrj7UjxvPt1bwXL5VvpXDjec/k2+31D3pyA/dXPF1lWd+Q+/mRxiXHLn5uW2MiyXlcLH0CRdpunH9/HieRGK/+T4AbsbdnqGMq7D0XgVoCiiVJ4qTtjzn6OblV76A2k2Hbs9c3dx8fL3dZ26cJJXnxvPLRTncMlNOV9xnlCeu0LugEc5D0NF9oV2dkpyThtRbWr34uEV94LAa0Bj2EhretCSgW9qkGSd/Nw3CZ7V2uOn557wHNAoQDOqKnicEQXD0j4TlvYGU8CMsw2lF6ujr3ORriBcY5yF1+r/jHeBdiCtuJ8TvQJKG/2322wLf4iHP3XRdS7jo5bYhfHdF+F7LtyosHvfkOUfJz02P75cv7rfd/z676fnP/PK76bpldLFzMUEXIVvufVd3SZNw7vfb18UlHmHYSDpxjrGdN4PpfIGuf5AgUzNr1iy5tM50AfCd61tuuaVzzkngwd+5Zq38RYsWda7ZWGr33XfvXHPiLnvr53XRRRfZ1fwkwmuvvWZPs+gnacU54qCzfv36TtClS5eaMWPG2GvKceutt5qg8ux18FJGOsL45Tr22GNN8PJ00mXznzwkeNnsapVZ08JpiTqnTFmFNEjLd4xKki4YP/fcc6FRZOdTHAu7SUAAejpGdosf9SxoiG26cZ1JxUlT9I5KN8v9o446yi7jvmHDBuscxv8g+0BEyc4772zmzZtnHWLB6Y477rA7kxapY5QuTbwfEDCz6667Zla9bv93fOOSfJtefvnlDgZ77bVX5zzsJGich7QF8m31w7vpshqvL5MmTerc4ntNffB76qmnOvfD4oXdIwLfq6BBtb+tW7faNHCQxEkU50q3TehkkPHkc5/7XCeF73znO53z559/vnP++c9/3joIc2PTpk2d+wGBGoKl66RJwJ///Oed8JyMHDmyc/3KK690ztOcvD9upDQVSUGQ8ePH2+WZDG8AACv4SURBVN00IRWIVAIvkktQ7MPgj1v5QQ9LbneOr776audcTrLoJ2nEOcatPCmrX3mSR1i5PvnJT8rj2h2XL18+hFRQfwHLNbvssovZd999Q3V+/fXX7Yco6DEPqlfIBWlCFNOIT0b9NPwtz/3naWaB+Gl0u6YBlpkqURtcQXwCS4UN1y2tvJ+JbuinUgwCeRH6uv3fsTT5ypUrY4PGRn4ifCe6yejRo7s9HvRM2hBuHnfccYOehV1AKnwZO3asf6vTKRzyILhBGkEv35x11llhj3O/55aL7yWdWojWj370o05eLJsvElgk5NR+a2WWSeemd9KNIP7iF7/wQie7jE0s0lSkEAtUojd+3333DWqcXEuGqO33YpkGFEey6hcnD8LkVXlxyxVXr6hw9OqZJpZVIAau8A8WZxMsSCUCgYDhT5gwoZMMaaYlFp1EupxIw+43oLJ3Q5eouT0i77B9RtABYuHrllvGmlArEKjb/x3WuCSSh4UzSX5FhYVUBENbnU6x5IPlecSIEQYrvdsGyfMsR9pPOm7333+/TQZLBR2qJUuW2Gustu73NEteecf9s7wTjEqPivFfMnqzKsUj0Kt3H0cD14rEP1McUuGnK5Yrue+mKffyPtLDohF3paghEDcP99xf70LWmZD7blg9VwRcBNz/kSb930kZsGoWIW66gfNkZ5hChiv8Y9g3EKuSL34bJc/pSAlxIG/CkceVV14ZanWXeFmPrEsjgqWCsoq4wyDcY/hSBELiY+Bfo3tREptYZK1IFtHxhXu+hcK1chBexrP8uP51Vv389KKu61R5UToWfR+GnlayxE2TJz0sLAPib1H0EEiUjuJ3wfbvch4VVu8rAmEIZPnfyRI3TJe493bbbbdO0G6m906gmCcHH3xwJ2TcDirkQvzfiBzmoxV2j7AsQCdy9tlnD/JfoNMspEPC5HVkQS0RLBWufu4wCGH22WcfCWqwbqBXWkkyLBWWR2xikaYiJUNW+xJzDhWLYwkC6xOzjoSFWLiVH+ajwDCCrCiG8wySRT/JO84xz8qLk1/WMIxr+ivCZU3T/SdLmlaWuEnzkvBYBvBlKHMIRPKWo/hTnH766VYXITryXI+KQC8EsvzvZInbS69uzz/+8Y93Hv/sZz/rnGc9cR0b8XmQdoB06ay6q2qyKqeIrDDJNX5wbjzOxTdOwocdmVwgjTbDu1/84hfDgoXec4fSQwN4N2U4RG6LfmHDIPhfSAebthW93G8/7bDbdvqrcLKasYikI9eJj4F5JJYwNSVIvPNzp18GhRi0tkGgVCdNf4oL8fx5v1y7Epj8OvmQpzs10Z9uyhQbJK1+6CrlcstEmlHP3PvudFPRI6j0TproJeLG88tMGPIXXcDAFbnP0dfTDeefyzRC/37Sa3cqEjpQD9RtXCGsWz7SIM20wnS+JNNogw+B3awsbX5Z4oVNP2V6KffLFnAAO94LmVIKjv6PhbUIE4zxV6Jn2bjknZ/gmzXduv3f8d4GFrfYxXL/5/lW+uJ+t6O+B+63j7ZGxP2eumH8c/cbTHz/ebdryc9vd7rFcfND17CwEsYtP+HCxMVQ0nKnn7px/PQkvH8EO1/cdsttc/1wca7DSxIRM01FuiQBxaUxcv9h/ELGrXwf3DT6uXH8BjvqWdrKc9PLQizkJZGXM6K67O28PnBhLzd68LGgLnke9uMZYURn9+jj3a0c/jMWbKLxiys0pjTkZTfm3QhEWfqQD3ixrgL4cxTiAC5hP8Lz7kA4iJNlga24ddSmcGCahPhGlb1u/3dJy+V/y+X7L+V1v6VJiQVpR31b5DsT9o1x85RwcvQJhBALjm7DK+E50sbRFsk90nAlTEf5dvu6uPHkHMwkbTl2a/ij3hmJSzsk5YrKw68nCRf3mIhYJK1IrAlSGI5uJXd7hvI+4G46nFNZfuGT6kc+bmPv6tfrWZrKc/NKSiy6vZzoGiVJPwRR6YB1mA5+vcS9Dqu/qLzD7idZAdBtwMGjLCEvLATdBN0gH0UI1gYhBhAJrnvpE6UHZZEFtiAZEI+0aUXl0ab71Ck4ZZW6/d8lJfSU3/1u+A2i+51PSiwEW77Fbh58g2jsw76xEodn5CffK77N6MJ9ucfRbWNos1wCIXFIM4qQ8MyPR7rkhbjl536UuOVzO+hR4cnT1wl9/TZO4lMvUu6oepCwcY7RJekSO25FumBQKF98a4bPogDHDUPBu4Ej6cfVj/CkJ4D6oHd7RtykleemF/bSk7/oQrld6fZyuuH8cxquJKZLP757jQ5unYquSY+kQVpZhAaThjKO+GTCv46TRpIwMtwQN07S8L3SpXw0akURACEsvFdYNVTCEeD/Ig/yVaf/Owgq5CKJuI2n/11Lkk4ZYd1vMA14v4hLmIT0ZCl7KmKRJUONWz4CNDB59or553NJUlxiQRyfvKVFI+5HO4xEuBaMtPlHxctigUDXLA0RebPcMg1+0o9/VHm63UdfCB7vVxjO3eL2w7Mk5DcOHnX4v0tT1/T6ZRghTm87DhZpw7gdI75HbmeWzp7oyfeFsP0g1I98w8EkDxlGIkGiKi1G4MILL7QrYzIjIU/B+/qFF16wi4Yxx/qXv/zloOQ/+tGPGlYTZYruIYccMmiK1qCACS+eeeYZO3+8l6e7rF8RNLRDcmAtCe7nuUR12EJYQzLucSNtGmBy2mmnmRkzZpgFCxbkWq4eKhum0AY9UcM0PJboV/kDAjfccIOdLn/ttdfmCklV/3f8P7GgW0BgE5eHGReyYmVAkApd+6Gbcq4e3cLxLOi5p1qvp1e6dXvuYpJbmfNgJ5pGvRFg46C8NkSquqSY4efMmWPH+3vp0qsX3et5r/Td51iEslgb3LSSWj3wfcBKEXdoyM0rr3N05h3jlxcOeelWRTpg8OCDDw4MHz68NXik8a9wsRdrQV69YjftJOeub0XQyHd66+553YdskpS3W1jXmpSnhUaHQrqh3pJnfORoePj4N10oy1/+5V/ajzbEIIocRN13y09aeQwRkRdp5SmkF6cMjHnTmOdRjjz0R5+8h97y0KuMNKgD6oyf1AdYVEn48ix31rLgKyKNd15DomnLhx+B61cgeuHwGOb/ljafusejHig7Q0DusFBWvXUoJEC1H4ThECRvs2zZ2D3yyCPmxhtvHLQSnruLKAvKyPBG2BCIr2+34RI/rH8ti14Vud8HZYvaxIw6ZcW/VatWdcrs61jFNXqxeRZDVW1etpx3xx0WCKsnholWr149aMfmKuoka568h1OnTh1U3qxpavz2IqDEor11O6hkMj4a9KRq1QgNUjLGxf777299CE444YTQ0DT2wdBPZyt79grpRTDSLPMNnuRVRsMZ5ndRV1IhlSLkounvm5RHji6JjfNu8Y5AOIjX6z2UPOp4PPHEEw1bip933nl1VE91qhkCSixqViFFqsPHniVdm/pxwFpx2WWXmQ0bNkTC5JOEOL1KEvPjRWYQPAhr6LuFz+OZS2RwCrzzzjvNunXrak0S605+4tQLdR0MS3WCprFOUV/s8cBS0E0U/jewVrSNJDaxLpqisxKLptRUDnrSONHLwnzbtN4TPb/DDjvMXHfddeaoo44KRYPyId3KFtVQkD7xe1kg+MiGmbxDFcr5JjpijfnXf/1X8+yzz/bUNefsUyXH7oyBD0hjZouAMQ2oSB51TZqk8+ijj9pZFZJ2U45qrWhKTdVHTyUW9amLUjRhR9mNGzc2rvcUR+8kVgcBmzgi//u//2sOPfTQUCuANDhpeqySftajNFA33XSTiRoKyppH3vEha2B27733RhLCvPNMmp77DuCj04tcJk2f8PhaLF68uPZWJr9sonc3K6EfR68VASUWffYO0DjR8587d67Je12LoqDkw48plmOUNYJnWRt9sAnzz6Bx5FkRDU4SzBhaYOvpO+64I0m0ysPKEFZdhm6oT9fpMut7Exdgev7BzIrGWG+w7mFxaqqlJW69aLj8EVBikT+mtU+RDwamWUy+VTeWvcCK00unoUCiSEevPNzn5Ed64MKRBcA+9KEPmWA9gsqGQNBPPvLdyJVbjrqdV21OBzeROE6XEjbPI+8TJKYJFif+D6ZMmWIJfVN9svKsO00rGQJKLJLh1ZrQ9CJnz55d6ymB8nELFtTpOk02D2uFW7FCVOjVumPsUf4ZbtyizqtumLOWizoq0wGwyrrqhpWskMoUVN7rusqZZ55prWNNdTitK679opcSi36p6ZBy1tlrX0jFrrvu2tUfJG9SAUzkzZBIr6Eitxdc1Ng8+oi1oule+UWSI+osb6dLsC9C/v3f/90ORTJTpI4Wwzp/F4qoD00zfwSUWOSPaaNSlI/IkiVLavORE1IBkN0WfxLLQh5DIFJppEn+fPCTkBa/YcvT3E4dsd9K0/fhEKuF698guKc5lkXs0ugWFUfer82bN9fSYijfg27/d1Fl0/uKgCCgxEKQ6OMjH5O6rJTIh/dLX/qSGTlypPWil1U0w6onScMfFt+/h2WA/ISoQBbQJ02vknhuA+oOqfj5drtGB+JSVtGrW/i6P2OBs25Thrvp72NaltNlN52SPEN/ROpRhiPr4HPBe4ZPBaKkwsKgfzIg8L5/CyRDfI3aAgSCzWdsQ85skb322svw8a9CVqxYYcfhp0+fbhufD3zgA5FqFEEq+ODvtttunTzJnymoHLvp0ongnEBQsFrIb8uWLeZnP/uZJSq/+c1vBuXjRBty+v3vf99I73bIwwbe+OAHP2ief/55wzvXS2jsXnvtNYsZjTLDTZAswbRX/Do990kFuu299972f23WrFnmd7/7nfnsZz9bicr8LzF9manWt9xyS+h060oU00wbi4BaLBpbdfkrTo/9pJNOMqNGjTKsFig9q/xzGpwiDQjTXx9//HFrpWBKXjcrQdhHenCKya74sHazKORNYiiv6w/QbdgEa1KTV0v1a4K6w9LgWnPcMK7TZZF+K26eRZ/3el95jpUOWbRoUeZp03HLw3v4zW9+05KJhQsX9vQpipuuhlMEdHfTrNu4tSw+u2pef/31dsc7dqoMGoDCSih5BQRmYObMmZ0dQrkfNLyR+cbZ9TMysvOAfOKmFTeck3zsUzAmffmhlwg7SnbDQsI16eiWSeogrOxNKlOUrtRt3P8h/u/4Xyj6/w5dA+dkm9e0adNi6xdVRr2vCPgIKLHwEdFriwAfQz5wAfe2xzwbN9KWjygftrBGWxocvzrCwvph4lyjQ5IyEZ5fGYJelPPVV1+1+JeRZ5l5nHbaaQMXX3yxLWOSOihTxzzySvPO8N67/3d5ve+Uh7T5v4PY8WvLdu551JWmkS8Cf6ZGG0UgDAGGQdhiPfg42seswMePIRKGBpIKJm6WByYNTP9bt261K/oxTz7MCQ8fBe6TFyZbBJMxcbMKuiDdhlv8PMADPUQX/3me1+hF2X//+9+bgHjlmXQt0jrwwAPNn//5n9syJqmDWigfU4lewx9RyfDey/8dQ2D4X+DzxJL2af7v0AMnUdalYGgJn5Xbb7/dbuQXtedOlG56XxGIi4D6WMRFSsMZFvfBDyLo6dj9Rmj0RowYYX0AgIcpkXy8tm3bZtHasWOHDffiiy/a60mTJpnJkyebiRMnJnIQgwjwwYXkhJEQm3jMP3ycu/lT9EqG+Fl16JWHPIeIvf76610XB5OwTTqCIb4EbV18KS2piKpD/u9YAZaN5/ixqRuzpvbbb79OlLFjx5o33njDXkf93x188MGl+U11FNOTvkRAiUVfVnv2QtNzD8zYdsYCHzIEKwR7WbgfvAkTJlgrAz3+LPLd737XfPzjH09kZXDzE32zkgLSoeEoo6eNdQhp25LKbSYWeZMK9x3mXN5jZgp1+7+DaOy+++6lvKe+jnqtCCix0Heg9gjIxxqrBWQmKTkgPh/kvMgAFhSIEvoUKW0lFtQFlq1gVLdI+EpPW97TrCS6dMU1Q0UgZwTUxyJnQDW5/BFgCEQackgFPV4apziSxp+iV7oQFAiOSjoEiiZk6bTKFkveMyUV2XDU2O1AQIlFO+qxtaUI82mAXNA7lB5iVOGJy4e+iI+9EJyovPV+/yAgFqwi3rP+QVFL2iYElFi0qTZbVhaIQ9QsEBnWkJ6iW3SsGUJIiuwdo1svcuPqped/QADM2tIIC6ko8j3T90YRaBoCSiyaVmN9pK8MgUQVWawRkAgRGi1+Sf0wJH6SI/lDYuIOyyRJu81hqVecepsuSiqaXoOqf1EIvL+ohDXd9iJAQ4qPAdNIZWpbWGllKioe6uyLkKSXKhaHsHTde/QUZVjife97n/mrv/qr3Jw03XyizrGcxNU1Ko2o+0zfXbduXdTjxt4PFmpqrO6iuJIKQUKPisBQBJRYDMVE74QggBXg6aeftotcyVz6Aw44wK5hMW/evJAYxk5FZQoqW7KvXLnSBKv92QWf2GRMhjLCIpJX1BBIWHjuMcvgt7/9bdTjQu+zLgYNTbcypVFgzJgxFu80cesch/UWeBeaKkoqmlpzqndZCOh007KQbmg+rNq3bNkya52YMWOGYZGrT3/606mmWspCP+ygOHz4cLNgwYLQxbKSWgAIL4teQUqwqOTdyPeqPvJFklhleqVJOdo4LZNVIFkojR01myZKKppWY6pvFQgosagC9QbkCQkI9iywmkYRgCzFcAnLTTfd1GlkkpAKGZLx/Smi7mfRN07cJLrHSY8wLOfMEsx+GePGr2M4rFFr164tnfxlxUJJRVYENX6/IKDEol9qOmY56Xmz0iP+E26DHzN64mA0xuyHsOuuuxq2CKchjdPrj2OZKKKh71XAvPMEE3wt5s+f3yvrRjyncWa/GBw4myRKKppUW6pr1QjorJCqa6BG+WNFoGfM+DfOmWWYqslvw4YNZurUqdY8Hmf/CD7ySK/hDtKmoceCUZbkOQUV8sSeEPfdd58tR1llKDKfFStWGIbUmiS8Q5BdnVLapFpTXatEQC0WVaJfo7zpGS9fvtzuOFqV2R3CcM4551jrxV133RX6IecjL/4UceEjXRqFOJaQuGl2C5e2d0s8d8YEJAWdmzp0EIYRFqnrrrvONGVnzbwtUGGY6D1FoG0IKLFoW40mLA+9+TPOOMO8++675tvf/nZpjW+Umugzd+5c8/bbb5tVq1Z1yEVWv4k4QydROqW5H6dB8olEFGFiy2ym9bKddpNF/HawUDVB4tRhE8qhOioCZSOgxKJsxGuUH431lClTrEZuI14HFbGgrF+/3pIL9OTXa+ijl95ZyUmv9N3n5AWZcXWmoXIliki4YTgnHawWvRYM8+PV7frEE080hx9+eCN2a1VSUbe3R/VpEgJKLJpUWznryrQ/3zKQcxaZkoNcfP/73zf33nuvGT16dKa03Mg0GnEbdTde0nO2ekeYWotkGWICC6SpVgswx48G3526+yooqbCvmv5RBFIjoMQiNXTNjoh5nYWu6mapcFHFdA6pYOGrOE6dbtxe53n7XYg1xM1XnEezEApJr+lWC2aCQCyYcVRnUVJR59pR3ZqCgBKLptRUjnrSYJ922ml2pkFZDo1p1afBZrimiEYpi9+FTyRYyMod9nDLm1djdcMNN5jnnnsud5Ll6lrE+T333GMWL15sZ/8UkX5eaeZVT3npo+koAk1FQIlFU2supd40iAwDYAloimc+1gV6vI8++mim4YQwyIQg9LIqSDhJoxuRkDByJK7vbyHPkhxJ57DDDrPOraeffnqSqJWFLbLu8iyUkoo80dS0+h0BJRZ99gbgV4HccccdjSp50b1eGhbX7wIi4C7ilIRIhAFLA5vHWgikg574KkRZSMLyr+IeRKgoa1Oe5VFSkSeampYiYIwSiz56C/iANsWBLqxasFrQUy+itw6ReOGFF8yHPvQh62wpa0iE6ZH2Xl4NGAuZzZ49u/bLYkNi33vvvVoP3eRVJ2nfCY2nCLQRASUWbazViDI1abpfWBHyJEb0/MMWo8ridxGms3svryER0nSn49ZxlkXd9aMusPr0GgJz60/PFQFFIB4CSizi4dT4UHk2ylWCATk6/vjjE1stfCLhDnv45Smy0YG4IHk4zUrjXYeFzVwMRa+6zjjKk+C55dZzRUAR+AMCuldIjzfhwAMPNMOGDbM/dpl0Re5zfOmll9xHXc/ZL8GN2zVwTg9vvfVWM2/evNqvIdCruCz5zQyDXgKRcn805PRO5detl88zwhGfRihPQQ/XdyNL2qxpccABB1hdIU5VC1hB/GRhs24YV6WrkoqqkNd8+wmByokFjbU0sjTiKvkjwMd06dKl9qOff+rlpigzWWj0XXFJBOdCIOSYppEjLhYGsTK4+WU5F9KSJQ2JC7lgF1osMDi4ViUQG2as7LLLLrVdG0VJRVVvh+bbbwi8v98K3I/lXbNmjZk5c2Yu5vc64Pf5z3/eEiVXFxrrIoSZF0Iu8hi+EB0hAjTGeczsYBfaH/zgB3b7+dWrVxvWu8hTV9E57EhjzYZx559/vrn77rsTD1GFpVnEPSUVRaCqaSoC4QhUbrEIV6s+d1999VUzMDBgf7NmzaqPYgk0eeyxx2xvMkGU2gZlca/PfOYzduhJrBFFkQoBQRrpPIcbxIJCg5eHgMG6devslvfMaIFc5JV2lH7MTsFKwaJdOEIWMVsnKu8k95VUJEFLwyoCOSAQNJqVyIsvvjgQqB/6C8aNh+h02223DXDfjcO9bdu2hYaVcCeffLJ9fvXVV3fiSgQJwxF9+B1zzDE2HGkjbp5yLyr+E0880YlPmqTFPV8eeOCBji6EC5Mk5Q2L794LGsaBYFzfvdX48yrKFMwiGQgsA7lil3d6KBc08gPTpk0bAKPLL78817oHg4cffnhg3Lhx9sd5nQV9wUNFEVAEykMgvFUrIf+4xALiII29SwTkfI899hh44403BmlMoyzPIRZ+fAksYTi6xINrIRFxicUll1zSydNN101L8u1GLNKUV9INO/JRpYFpm9BwBkM8lRQLMkCDlZcUQS7Qjbq/4IIL7Ht5xBFHDARDFakaWSETpMW7BPZ1JxSUX0kFKKgoAuUjUHsfC3wDnnrqqaB9DpegITbHHXec2bRpk2F1RF/uv/9+/1bo9cUXXxx6P+7NhQsXRgY966yzzL777mvGjx8fGUYeZC2vpCPHd955x0yYMEEuKznOmDHDSD0Er3guOrD9dkAoK1mWnGEHhkUYzgga2szlYegCP4480nKVwX8D584FCxYY/GwYEgsIsA3COwGGyNixYwf972zevNns2LHDvPbaa+aVV14xGzduNAGZsNN8v/rVr+aup1Ui5z86/JEzoJqcIpAAgcp8LGhkaWQCy0BH3cA6YO/h14AwLdMlFYQlDr+g19+JB7lwrzsP/nhCuoGFpBPXfy7XfHQl/TT+FFH6kT57c/SSvMrr5kPjKw2Ie7+q86AXmUvWgaXKNny5JJYiEXG6zMPvAkKR1xTUsKJAgHDwZBl38lm7dq1h2i4CgViyZIlZtGhR5/f666/bZ5MnTzbM2uJ/gt1l8aHIm/zYjHL+I862Ukc5J6/JKQKKQC8Ego9GpcKQQ6Cj/THs4Erw8es8Y6jBl6i47n3SZtglTCRfjuKL4YdLMhTix/X1CD7iNkjUUEja8vr5utfXX3/9AL8qBWwF66i6SKofwweY5KsWzO15DWXklU7VmFSZP75EbfMnqhJPzVsRSINAZRaLoKHpKS+//HInTFive9KkSZ3nLMoT1RuOMwTBPhRZhNUgfTn00EMH3WJYopvkVV43D8zZ9O7rItIbros+WfXAGsDQSB6LackU1Kw69Wt8WW+kCVaVfq0jLXd/IFBrYgFZEMGPQhbSkqPfYIYRC4ZB4sjOO+8cJ1hkmN13333IM9/nI0w/N1Ie5XXT45xNoMJ088OVdX3FFVcYxvDbJpALMcGnLVveU1DT6tHEeEoqmlhrqnNbEag1sWgr6E0sl7tCqhC7uEdx3KTc4mx7zTXXtI5gyJh+Fr8L0ghmczTxFalMZyUVlUGvGSsCoQjUmli41gbX+TIY8+k4WbrnVfbMcZL0xbdQ9NKviPKyxHKvIRhf76KvIRfMwsF60TbBDM/PX3I8STllaCVJnH4Nq6SiX2tey11nBGo93fTggw+2GxoBIGPzcXwlqgKb1QePPfbYQdk///zznWuGbXoRiyLKu99++1krQUcRPSkcAdfvotsuqt0UKWoKKnliUWE4CoK3detWs2XLlkGqQEZ5bxgeHDNmjPUhGRSgJhdKKmpSEaqGIuAhUCuLxfbt2wep97nPfa5zzVoQ7u6i9MLPPffcjt9F1RuYsY6Fqx9TR9FZ5NRTT5XTyGNR5WVKYduEhpEGsM6Sxe8CqwdrMeSxLDdpsPz2mWeeaSAsJ510klm2bJmFbs8997S73rLzrfyYZopA5rnHkBfOzSwTLo25DVDhH9FDHTUrrATNWhGIQKBWFgt6UHzEGBJgLYvp06fbufXi1EhD7TbWbpn4AFYt3fSTdQO66VhEeVn8iHUKsgozcBiOylN859YkaUOW6FXXXfCZoBHESiA+GHF1Jrzs1Bo3jhuOuKzvct1113UWuAqW+O65FgWEyBWISTAV1jz++OPWeoFec+bMsWtjuOHKOldSURbSmo8ikBKBNHNU84zDXhqB6oN+AbHoZBGQjSFLcvvhWS/CFXf9CDctNwznbjqsLREmxJdwfj5yn6O/JLj7zI8XtY4F+acpb5jeco85/UGvTi5bc6xySe80IAb+NqmX006614Ws8UG9s4ZJnus6UI4q9wrRdSrSvH0aRxEoFwGcICsXGt7AB6HTgIeRAcL4e36waFbYgkuElYY9LC0psIThmJVYQAhIwyUiWTchi1teKU/UkQYmaeMUlVZd7lNneTaYZZSLRjnNIlhxG1PSZ9MxIRRcFylCMNiHpIz3izyaVudF4q9pKwJ1RWAYigUfaZUWI8DYOttb13Vb66TQs3V6sCGW2bBhQ9KotQjPEEVSp85eQyI8Zxn6UaNGWV+IgFyUVlZ8L84//3wTWEfMeeedV0i+DCVRJlnro5BMNFFFQBHIBYFaOW/mUiJNZAgCOOPdc889Q+439cYLL7xgfQaaqn8ap85uU1Cp26lTp5q5c+faPT3KJBXUAWQisCaYRx991DqI5uFw6tatkgoXDT1XBOqPgFos6l9HmTXkQ4+jZGBG7um4lzmzEhJgZgMbaSV1hixBtURZ4IRI3cQtR5jT4oUXXmiWL19eCzwoC+Tm7bffNqtWrcrFuqCkItErpYEVgVogoBaLWlRDsUpgPmboYOnSpcVmVELqTJscPnx47Ma4BJVSZ4FlgR/DGHGEsDTe/BBIBTOmsBbEJSdx8kkbhveMHVQDPyMzZcqUjp5p01NSkRY5jacIVIuAWiyqxb+03Gm8MJfTCDV5nPqTn/ykmT17tpk5c2Zp2JWREfUT1++CsIGjsCUVeVkG8i6jkJ60+impyLtGND1FoDwE1GJRHtaV5sQY/YQJE8xdd91VqR5ZMsdaga8xa2rQuLq/LOnWIW4SvwsWB2P449vf/nZtSeK1115rRo4cac4444zE8CqpSAyZRlAEaoWAWixqVR3FKkNDjNWCY9kOfnmUbP/99zcLFiwIXZiJMrmCT0kdhgdcneKc9/K7oNHFslGX4Y9uZWLIhiGRYDqqmT9/fregnWdKKjpQ6Iki0FgElFg0turSKY6Jmq3UGQtvkjClkVkHOG3GERo1Gl9X4g41uHGqOBfdsWK4wn2mDeMg2ZSpwxAFlgOn7vzyuGXjXEmFj4heKwLNRECJRTPrLbXWNE40sDfddFNozz91wgVGzKuXTjrBolGDNO3V2A0KXPIFVhiXDLEb7MaNG+2U0pJVyZQd02EXL17cdd0Rv6yZMtTIioAiUCkCSiwqhb+azPmIMyTShCmbRffS/SEUprLWaZgIMiTOtujW1CnDWC1458IW0KIO6kzwqvkv1VwVgeYioMSiuXWXSXOGFu68806zbt26TsOVKcGCItMgMX0RZ8AyBB8HGm9XXKuBe7+sc3T62te+Zvbaa6/Yvgpl6RY3HyGz/qwkJRVxEdRwikBzEFBi0Zy6yl3TrFMCc1fIS7Au+vlDKGU7hkIssFagx+jRoz2UmnN54oknmsMPP7xjtVBS0Zy6U00VgSQIKLFIglYLw9al8XahZfjjsssuq+06DejnO4YWOYRCHSFlWW3cusjzHCLBfibs8aKkIk9kNS1FoF4IKLGoV31Uog0N15o1a+yiS1VP0aTRZooiknZxpSpADBtCyctvANLSBH+YOLgzZfiss84y5557bpzgGkYRUAQaiMD7G6izqpwzAvSE8bnAn6HK2SL0YnHwmzFjhl2vQpwWcy5uIcnh8Ok7fVIeV9IMobCTK2SvasLnliPL+bRp0+xeIlnS0LiKgCJQbwTUYlHv+ilVO2nYWdny8ssvH9JQFqUMVopvfvOb5pZbbjELFy5szBoNSfEIG0Lp5RiKNWnnnXdurNOmjxF+IhBY30HWD6fXioAi0FwElFg0t+4K0ZzGD/8GloyeN2+eXZK5SMsBy3ST36hRo6zVxO/1F1LIGiXqO4aimjuEwtDB7bffPuhejdRPpUqbhnZSAaCRFIGWI6DEouUVnLZ4WC8WLVpkXnzxRUsw8OjPq9GHvDz88MN20ST0u+6668xRRx2VVtXWxZMhlN/97nfmc5/7XGPXroiqmDPPPNOuINqU1UOjyqH3FQFFIBwB3YQsHJe+v0uv+aGHHrJLMW/dutVOd6RBYBVFHBWTCmQC6wRp4GuwevVqSyiYIaCkYjCaYM/vQx/6kMEnAcGy0RbZb7/9DO+UiiKgCLQTAbVYtLNecy8VxICZI4899ph5/PHHzfDhw+3wBesSIOyc6go7cO7YscO89tpr5pVXXrFLUdNIHn/88eboo4/Ozfrh5tm2c0gcC5g1bV+XXvWAQ+qSJUsatzR5r3Lpc0VAEfgDAjorRN+EWAjgZ3HCCSd09hehBw152LZtmyUQDJu4MmLECLPnnnuayZMnm3/8x39slY+AW84izyFm9O7bJlisVBQBRaC9CCixaG/dFlqyNk2BLBQoTXwIAjhv4rujoggoAu1EQH0s2lmvWipFoLYI4AScxk+ntgVSxRQBRWAQAkosBsGhF4qAIqAIKAKKgCKQBQElFlnQ07iKgCKQGAG1ViSGTCMoAo1CQIlFo6pLlVUEmo8Aq27KNNrml0ZLoAgoAj4CSix8RPRaEagJAizlres91KQyVA1FQBGIjYASi9hQaUBFoFwExowZY7Zs2VJupiXkxoyQcePGlZCTZqEIKAJVIKDEogrUNU9FIAYCbFC2cuXKGCGbFYRF01jjREURUATaiYASi3bWq5aqBQiwKBk9+zYt5021sBLr+PHjW1BDWgRFQBEIQ0CJRRgqek8RqAkCEydONE8++WRNtMmuBiRp+/bthgXWVBQBRaCdCCixaGe9aqlagsCkSZPsRnAtKY556aWXzIwZM9pSHC2HIqAIhCCgxCIEFL2lCNQFAXZ+pZfflrUfFi9ebCBLKoqAItBeBJRYtLdutWQtQYAe/je+8Y3Gl+aHP/yhHQaBLKkoAopAexHQbdPbW7daspYggLXioIMOMj/5yU8MDp1NlRNPPNEcfvjh5rzzzmtqEVRvRUARiIGAWixigKRBFIEqEWDTrgkTJpi77rqrSjUy5Y21gvUrzjjjjEzpaGRFQBGoPwJqsah/HamGioD1s2BdC5bDhmg0TdRa0bQaU30VgfQIqMUiPXYaUxEoDQGmZ15++eWNHEa45557zFtvvaXWitLeFs1IEagWAbVYVIu/5q4IxEbg17/+tcFqsXDhQnP66afHjldlQPEPefTRR62fSJW6aN6KgCJQDgJKLMrBWXNRBHJBAF+FqVOnmrVr1zZikakjjzzSHHHEEWb+/Pm5lF8TUQQUgfojoEMh9a8j1VAR6CDA7JB58+aZk046yWDBqLNceOGFVj0lFXWuJdVNEcgfAbVY5I+ppqgIFI4Ajfb69evNqlWrajkFFf3WrFlj1q1bV0v9Cq8gzUAR6GME1GLRx5WvRW8uAtdee6054IADzJQpU2pnuYBULF++3DzwwANKKpr7iqnmikBqBJRYpIZOIyoC1SLgkos67IDK0IxYUpriA1JtDWruikA7EVBi0c561VL1CQKQC5wjcZJ85plnKis1sz+wnsjwjO5eWllVaMaKQOUIKLGovApUAUUgGwI4R957773mtNNOM2eeeWbpQyOsU4FTKQQHS0WTlx3PVhMaWxFQBEBAiYW+B4pACxBgYy/2EkFY6+KGG24ovFRMfcVSwo6lrFOhsz8Kh1wzUAQagYDOCmlENamSikB8BGjwFy1aZFe7/MpXvmJXvMzTivDII4+YZcuW2b0/mrRYV3wENaQioAhkQUCJRRb0NK4iUGMEIBi33nqrWbp0qZk5c6aZPHmymThxYqqhCtJ64oknzC233GKGDx9u5s6da77whS+kSqvGkKlqioAikAMCSixyAFGTUATqjACOlU8//bRZvXq1WblypfWFYKrqnnvuacaOHWt22mmnIeqzE+mOHTvMxo0bbZxx48aZadOmmenTpzdixc8hBdIbioAiUBoCSixKg1ozUgTqgQDWh82bN1vi8Nxzz4UqNWLEiA7x2HvvvRu5o2powfSmIqAIFI6AEovCIdYMFAFFQBFQBBSB/kFAZ4X0T11rSRUBRUARUAQUgcIRUGJROMSagSKgCCgCioAi0D8IKLHon7rWkioCioAioAgoAoUjoMSicIg1A0VAEVAEFAFFoH8QUGLRP3WtJVUEFAFFQBFQBApHQIlF4RBrBoqAIqAIKAKKQP8goMSif+paS6oIKAKKgCKgCBSOgBKLwiHWDBQBRUARUAQUgf5BQIlF/9S1llQRUAQUAUVAESgcASUWhUOsGSgCioAioAgoAv2DgBKL/qlrLakioAgoAoqAIlA4AkosCodYM1AEFAFFQBFQBPoHASUW/VPXWlJFQBFQBBQBRaBwBJRYFA6xZqAIKAKKgCKgCPQPAv8fPYIkU9UI+YoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"sentiment_network_2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d172fd7e",
   "metadata": {},
   "source": [
    "In `layer_0` we need to have the count of each word in the vocabulary for the intended review. \n",
    "So we need to build two function: \n",
    "1. `update_input_layer` which transforms a review into word counts\n",
    "2. `get_target_for_label` which transforms review label into 0(negative) or 1(positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a52406db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'': 0,\n",
       " 'countenance': 1,\n",
       " 'munnera': 2,\n",
       " 'actally': 3,\n",
       " 'multinationals': 4,\n",
       " 'intro': 5,\n",
       " 'bondage': 6,\n",
       " 'thata': 7,\n",
       " 'misfortunes': 8,\n",
       " 'innovates': 9,\n",
       " 'bajillion': 10,\n",
       " 'rebuffs': 11,\n",
       " 'specifically': 12,\n",
       " 'titlesyou': 13,\n",
       " 'soever': 14,\n",
       " 'theat': 15,\n",
       " 'kingdom': 16,\n",
       " 'bigalow': 17,\n",
       " 'chiselled': 18,\n",
       " 'finleyson': 19,\n",
       " 'foreshadowed': 20,\n",
       " 'textbook': 21,\n",
       " 'cedrac': 22,\n",
       " 'herapheri': 23,\n",
       " 'nechayev': 24,\n",
       " 'nippy': 25,\n",
       " 'autorenfilm': 26,\n",
       " 'committed': 27,\n",
       " 'miki': 28,\n",
       " 'merton': 29,\n",
       " 'gandhis': 30,\n",
       " 'foremost': 31,\n",
       " 'compacted': 32,\n",
       " 'rations': 33,\n",
       " 'fastforwarding': 34,\n",
       " 'sue': 35,\n",
       " 'yee': 36,\n",
       " 'tebaldi': 37,\n",
       " 'wuxia': 38,\n",
       " 'customs': 39,\n",
       " 'popeyelikethe': 40,\n",
       " 'glut': 41,\n",
       " 'borneo': 42,\n",
       " 'squadron': 43,\n",
       " 'nothings': 44,\n",
       " 'felix': 45,\n",
       " 'subconsciously': 46,\n",
       " 'vendetta': 47,\n",
       " 'coixet': 48,\n",
       " 'enters': 49,\n",
       " 'devils': 50,\n",
       " 'spadafore': 51,\n",
       " 'rottweiler': 52,\n",
       " 'flawed': 53,\n",
       " 'sathoor': 54,\n",
       " 'witty': 55,\n",
       " 'perky': 56,\n",
       " 'photons': 57,\n",
       " 'housesitting': 58,\n",
       " 'behavior': 59,\n",
       " 'girlish': 60,\n",
       " 'uproariously': 61,\n",
       " 'bolden': 62,\n",
       " 'reciprocate': 63,\n",
       " 'actings': 64,\n",
       " 'rockfalls': 65,\n",
       " 'handlebar': 66,\n",
       " 'ppl': 67,\n",
       " 'disregards': 68,\n",
       " 'boles': 69,\n",
       " 'satire': 70,\n",
       " 'magnificently': 71,\n",
       " 'doli': 72,\n",
       " 'phoenician': 73,\n",
       " 'chatham': 74,\n",
       " 'bucketloads': 75,\n",
       " 'arduno': 76,\n",
       " 'polically': 77,\n",
       " 'staden': 78,\n",
       " 'swoons': 79,\n",
       " 'best': 80,\n",
       " 'turds': 81,\n",
       " 'bombay': 82,\n",
       " 'kneeling': 83,\n",
       " 'evolutionists': 84,\n",
       " 'villainizing': 85,\n",
       " 'famarialy': 86,\n",
       " 'blitzstein': 87,\n",
       " 'detainee': 88,\n",
       " 'merkley': 89,\n",
       " 'dalloway': 90,\n",
       " 'fallow': 91,\n",
       " 'deasy': 92,\n",
       " 'venality': 93,\n",
       " 'thebg': 94,\n",
       " 'feux': 95,\n",
       " 'phedon': 96,\n",
       " 'renown': 97,\n",
       " 'humor': 98,\n",
       " 'reviving': 99,\n",
       " 'existant': 100,\n",
       " 'pillows': 101,\n",
       " 'deteriorating': 102,\n",
       " 'humpp': 103,\n",
       " 'loophole': 104,\n",
       " 'fouled': 105,\n",
       " 'patni': 106,\n",
       " 'spot': 107,\n",
       " 'macmurray': 108,\n",
       " 'ajnabi': 109,\n",
       " 'flatop': 110,\n",
       " 'bitto': 111,\n",
       " 'faucet': 112,\n",
       " 'bruijning': 113,\n",
       " 'hopefuls': 114,\n",
       " 'toole': 115,\n",
       " 'underexplained': 116,\n",
       " 'jukeboxes': 117,\n",
       " 'awaking': 118,\n",
       " 'moviegoer': 119,\n",
       " 'shahin': 120,\n",
       " 'brenden': 121,\n",
       " 'palpable': 122,\n",
       " 'manjayegi': 123,\n",
       " 'lullaby': 124,\n",
       " 'disneyland': 125,\n",
       " 'colm': 126,\n",
       " 'ancillary': 127,\n",
       " 'dermott': 128,\n",
       " 'redemptions': 129,\n",
       " 'ofcourse': 130,\n",
       " 'inheritances': 131,\n",
       " 'watchable': 132,\n",
       " 'surrenders': 133,\n",
       " 'schlongs': 134,\n",
       " 'phds': 135,\n",
       " 'centerpiece': 136,\n",
       " 'entertains': 137,\n",
       " 'unjaded': 138,\n",
       " 'animaster': 139,\n",
       " 'underpins': 140,\n",
       " 'haq': 141,\n",
       " 'comon': 142,\n",
       " 'intuition': 143,\n",
       " 'villas': 144,\n",
       " 'czarist': 145,\n",
       " 'orin': 146,\n",
       " 'teapot': 147,\n",
       " 'resolutions': 148,\n",
       " 'hoosegow': 149,\n",
       " 'nelkin': 150,\n",
       " 'argument': 151,\n",
       " 'nymph': 152,\n",
       " 'scummiest': 153,\n",
       " 'eject': 154,\n",
       " 'yachting': 155,\n",
       " 'sadek': 156,\n",
       " 'inartistic': 157,\n",
       " 'promoter': 158,\n",
       " 'infos': 159,\n",
       " 'continual': 160,\n",
       " 'smacks': 161,\n",
       " 'sugden': 162,\n",
       " 'god': 163,\n",
       " 'esteban': 164,\n",
       " 'intimidates': 165,\n",
       " 'aapke': 166,\n",
       " 'hobbyhorse': 167,\n",
       " 'flapjack': 168,\n",
       " 'lovejoy': 169,\n",
       " 'paisley': 170,\n",
       " 'guiding': 171,\n",
       " 'danielsen': 172,\n",
       " 'heartbeat': 173,\n",
       " 'covell': 174,\n",
       " 'shrew': 175,\n",
       " 'manticore': 176,\n",
       " 'cleaning': 177,\n",
       " 'adrian': 178,\n",
       " 'smarter': 179,\n",
       " 'formatted': 180,\n",
       " 'baddiel': 181,\n",
       " 'beautify': 182,\n",
       " 'gimore': 183,\n",
       " 'courtyard': 184,\n",
       " 'kampala': 185,\n",
       " 'jorney': 186,\n",
       " 'denunciation': 187,\n",
       " 'serena': 188,\n",
       " 'tappin': 189,\n",
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       " 'stepsisters': 257,\n",
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       " 'vacano': 260,\n",
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       " 'documentarian': 275,\n",
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       " 'doqui': 825,\n",
       " 'fry': 826,\n",
       " 'resultthey': 827,\n",
       " 'oru': 828,\n",
       " 'ghunguroo': 829,\n",
       " 'afterworld': 830,\n",
       " 'mangling': 831,\n",
       " 'ozone': 832,\n",
       " 'methinks': 833,\n",
       " 'sulk': 834,\n",
       " 'waldo': 835,\n",
       " 'doc': 836,\n",
       " 'parceled': 837,\n",
       " 'slipping': 838,\n",
       " 'loni': 839,\n",
       " 'berel': 840,\n",
       " 'phonograph': 841,\n",
       " 'depp': 842,\n",
       " 'massacring': 843,\n",
       " 'pseudonyms': 844,\n",
       " 'streneously': 845,\n",
       " 'enormously': 846,\n",
       " 'actualization': 847,\n",
       " 'playthings': 848,\n",
       " 'deservingly': 849,\n",
       " 'unconfirmed': 850,\n",
       " 'helpfuls': 851,\n",
       " 'desmond': 852,\n",
       " 'crescendo': 853,\n",
       " 'partin': 854,\n",
       " 'marvelled': 855,\n",
       " 'ville': 856,\n",
       " 'vaporize': 857,\n",
       " 'taji': 858,\n",
       " 'hormonally': 859,\n",
       " 'distinguishable': 860,\n",
       " 'millionth': 861,\n",
       " 'intercuts': 862,\n",
       " 'dhoom': 863,\n",
       " 'adamson': 864,\n",
       " 'gut': 865,\n",
       " 'overacted': 866,\n",
       " 'procession': 867,\n",
       " 'axed': 868,\n",
       " 'terorism': 869,\n",
       " 'arisen': 870,\n",
       " 'divergence': 871,\n",
       " 'artists': 872,\n",
       " 'mikl': 873,\n",
       " 'ixpe': 874,\n",
       " 'penpals': 875,\n",
       " 'revenge': 876,\n",
       " 'malfatti': 877,\n",
       " 'jutra': 878,\n",
       " 'infuriating': 879,\n",
       " 'quanxiu': 880,\n",
       " 'teheran': 881,\n",
       " 'lacked': 882,\n",
       " 'rubric': 883,\n",
       " 'tennis': 884,\n",
       " 'hoaky': 885,\n",
       " 'irina': 886,\n",
       " 'mushrooming': 887,\n",
       " 'submissive': 888,\n",
       " 'vfc': 889,\n",
       " 'ethic': 890,\n",
       " 'bacri': 891,\n",
       " 'librarian': 892,\n",
       " 'posterous': 893,\n",
       " 'filmher': 894,\n",
       " 'rodrguez': 895,\n",
       " 'noirs': 896,\n",
       " 'descending': 897,\n",
       " 'division': 898,\n",
       " 'magda': 899,\n",
       " 'snorefest': 900,\n",
       " 'supper': 901,\n",
       " 'hog': 902,\n",
       " 'chester': 903,\n",
       " 'quaaludes': 904,\n",
       " 'macrae': 905,\n",
       " 'switchblade': 906,\n",
       " 'sentimentalization': 907,\n",
       " 'intrudes': 908,\n",
       " 'furiously': 909,\n",
       " 'swigging': 910,\n",
       " 'archambault': 911,\n",
       " 'restrooms': 912,\n",
       " 'lutte': 913,\n",
       " 'orc': 914,\n",
       " 'rambo': 915,\n",
       " 'scrumptious': 916,\n",
       " 'conculsioni': 917,\n",
       " 'propitious': 918,\n",
       " 'alloyed': 919,\n",
       " 'tidy': 920,\n",
       " 'barrack': 921,\n",
       " 'whereupon': 922,\n",
       " 'fondles': 923,\n",
       " 'darling': 924,\n",
       " 'critised': 925,\n",
       " 'yeats': 926,\n",
       " 'conductors': 927,\n",
       " 'compliance': 928,\n",
       " 'acknowledgment': 929,\n",
       " 'avenet': 930,\n",
       " 'delilah': 931,\n",
       " 'kinked': 932,\n",
       " 'bushman': 933,\n",
       " 'stopwatch': 934,\n",
       " 'ifyou': 935,\n",
       " 'thenceforth': 936,\n",
       " 'buttock': 937,\n",
       " 'situated': 938,\n",
       " 'pharaoh': 939,\n",
       " 'mildest': 940,\n",
       " 'wilson': 941,\n",
       " 'shiztz': 942,\n",
       " 'serum': 943,\n",
       " 'dodged': 944,\n",
       " 'amorous': 945,\n",
       " 'occhipinti': 946,\n",
       " 'doones': 947,\n",
       " 'ghastliness': 948,\n",
       " 'sinden': 949,\n",
       " 'ozjeppe': 950,\n",
       " 'sufferngs': 951,\n",
       " 'anansie': 952,\n",
       " 'miiki': 953,\n",
       " 'melendez': 954,\n",
       " 'contended': 955,\n",
       " 'couplea': 956,\n",
       " 'highen': 957,\n",
       " 'raines': 958,\n",
       " 'entrepreneur': 959,\n",
       " 'guire': 960,\n",
       " 'lemons': 961,\n",
       " 'kilometers': 962,\n",
       " 'squarepants': 963,\n",
       " 'suds': 964,\n",
       " 'bhandarkar': 965,\n",
       " 'padruig': 966,\n",
       " 'hoopers': 967,\n",
       " 'olan': 968,\n",
       " 'respect': 969,\n",
       " 'exceptions': 970,\n",
       " 'unforeseen': 971,\n",
       " 'eithier': 972,\n",
       " 'crystina': 973,\n",
       " 'papa': 974,\n",
       " 'sunspot': 975,\n",
       " 'rajnikant': 976,\n",
       " 'alongs': 977,\n",
       " 'hatted': 978,\n",
       " 'brutti': 979,\n",
       " 'enlivened': 980,\n",
       " 'bubby': 981,\n",
       " 'verhopven': 982,\n",
       " 'inherent': 983,\n",
       " 'fuji': 984,\n",
       " 'gjs': 985,\n",
       " 'overdid': 986,\n",
       " 'thtdb': 987,\n",
       " 'signifying': 988,\n",
       " 'starsky': 989,\n",
       " 'consultant': 990,\n",
       " 'shanti': 991,\n",
       " 'supersentimentality': 992,\n",
       " 'location': 993,\n",
       " 'spurred': 994,\n",
       " 'pooh': 995,\n",
       " 'flee': 996,\n",
       " 'kingmaker': 997,\n",
       " 'andrei': 998,\n",
       " 'chomiak': 999,\n",
       " ...}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = set(total_counts.keys())\n",
    "\n",
    "word2index = {}\n",
    "\n",
    "for i, word in enumerate(vocab):\n",
    "    word2index[word] = i\n",
    "    \n",
    "word2index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c10a81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create layer_0\n",
    "layer_0 = np.zeros((1, len(vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57766125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 74074)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4fa2b56f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_input_layer(review):\n",
    "    \"\"\" \n",
    "    Modify the global layer_0 to represent the vector form of review.\n",
    "    The element at a given index of layer_0 should represent\n",
    "    how many times the given word occurs in the review.\n",
    "    Args:\n",
    "        review(string) - the string of the review\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    global layer_0\n",
    "    \n",
    "    layer_0 *= 0 #Reset layer\n",
    "    \n",
    "    for word in review.split(\" \"):\n",
    "        layer_0[0, word2index[word]]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a8665734",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target_for_label(label):\n",
    "    \"\"\"\n",
    "    Convert a label to `0` or `1`.\n",
    "    Args:\n",
    "        label(string) - Either \"POSITIVE\" or \"NEGATIVE\".\n",
    "    Returns:\n",
    "        `1` or `0`.\n",
    "    \"\"\"\n",
    "    \n",
    "    return int(label==\"POSITIVE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5bbe511",
   "metadata": {},
   "source": [
    "See if functions work as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b75aaae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "update_input_layer(reviews[0])\n",
    "layer_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f082544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('POSITIVE', 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0], get_target_for_label(labels[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74b719a",
   "metadata": {},
   "source": [
    "# Building a Neural Network <a id=\"header_4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff43f8d",
   "metadata": {},
   "source": [
    "###### Encapsulate our neural network in a class:\n",
    "\n",
    "**Note:** Instead of word count, we consider just the appearance of a word. For example, if the word `terrible` occurs `7` times in a review, we will pass `1` to the corresponding position of this word in the input. This method will keep all the words the same and reduces noise. If we don't do this, common words which don't have enough predictive power (e.g. `.`, `' '`, `it`, etc.) will get more weight and make our data noisy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "8cdcd6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "from IPython.display import  clear_output\n",
    "\n",
    "class SentimentNetwork():\n",
    "    def __init__(self, reviews, labels, min_count = 10, cutoff = 0.1 , hidden_nodes= 10, learning_rate = 0.1):\n",
    "        \n",
    "        \"\"\"\n",
    "        Create a SentimenNetwork with the given settings\n",
    "        Args:\n",
    "            reviews(list) - List of reviews used for training\n",
    "            labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n",
    "            min_count(int) - Words should only be added to the vocabulary \n",
    "                             if they occur more than this many times\n",
    "            cutoff(float) - The absolute value of a word's positive-to-negative\n",
    "                                     ratio must be at least this big to be considered.\n",
    "            hidden_nodes(int) - Number of nodes to create in the hidden layer\n",
    "            learning_rate(float) - Learning rate to use while training\n",
    "        \n",
    "        \"\"\"\n",
    "        \n",
    "        np.random.seed(1) # For the results to be reproducible \n",
    "        \n",
    "        self.preprocess_data(reviews, labels, min_count, cutoff)\n",
    "        \n",
    "        self.init_network(self.review_vocab_size, hidden_nodes, 1, learning_rate)\n",
    "        \n",
    "    def preprocess_data(self, reviews, labels, min_count, cutoff):\n",
    "        \n",
    "        \"\"\"\n",
    "        Preprocess reviews to create word2index \n",
    "        based on our min_count and cutoff\n",
    "        \"\"\"\n",
    "        \n",
    "        total_counts = Counter()\n",
    "        positive_counts = Counter()\n",
    "        negative_counts = Counter()\n",
    "        \n",
    "        # Calculate word counts for negative and positive reviews as well as for total word counts\n",
    "        for i in range(len(reviews)):\n",
    "            for word in reviews[i].split(\" \"):\n",
    "                total_counts[word] += 1\n",
    "                if labels[i] == \"POSITIVE\":\n",
    "                    positive_counts[word] += 1\n",
    "                else:\n",
    "                    negative_counts[word] += 1\n",
    "        \n",
    "        # Calculate positive to negative ratios for words that aren't too rare \n",
    "        # Because rare words can get very large ratios and that's not what we \n",
    "        \n",
    "        pos_neg_ratios = Counter()\n",
    "        \n",
    "        total_counts_sorted = total_counts.most_common()\n",
    "        \n",
    "        for word, count in total_counts_sorted:\n",
    "            if count < 70:\n",
    "                break\n",
    "            pos_neg_ratios[word] = np.log(positive_counts[word]/float(1 + negative_counts[word]))\n",
    "        \n",
    "        # Create our vocab list\n",
    "        \n",
    "        self.review_vocab = list()\n",
    "        \n",
    "        for word, count in total_counts_sorted:\n",
    "            if count <= min_count:\n",
    "                break\n",
    "            if word in pos_neg_ratios.keys():\n",
    "                if np.abs(pos_neg_ratios[word])>=cutoff:\n",
    "                    self.review_vocab.append(word)\n",
    "            else :\n",
    "                self.review_vocab.append(word)\n",
    "        \n",
    "        # Create word to index vector to have constant positions for each \n",
    "        # word in out input layer of the network\n",
    "        \n",
    "        self.word2index = {}\n",
    "        \n",
    "        for i, word in enumerate(self.review_vocab):\n",
    "            self.word2index[word] = i\n",
    "        \n",
    "        # Keep size of our input layer\n",
    "        self.review_vocab_size = len(self.review_vocab)\n",
    "    \n",
    "    def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        \n",
    "        \"\"\"\n",
    "        Build the network to have the number of hidden nodes and the learning rate that\n",
    "        were passed into this initializer. Make the same number of input nodes as\n",
    "        there are vocabulary words and create a single output node.\n",
    "        \"\"\"\n",
    "          \n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "        \n",
    "        self.learning_rate = learning_rate\n",
    "        \n",
    "        # Initialize layer 1 weights with zeros\n",
    "        self.weights_0_1 = np.zeros((self.input_nodes, self.hidden_nodes))\n",
    "        \n",
    "        # Initialize layer 2 wieghts with random values \n",
    "        self.weights_1_2 = np.random.normal(0.0, \n",
    "                                            self.hidden_nodes**-0.5, \n",
    "                                            (self.hidden_nodes, self.output_nodes))\n",
    "        \n",
    "        # Initialize layer 1 to prevent allocating memory every time do a forward pass\n",
    "        self.layer_1 = np.zeros((1, self.hidden_nodes))\n",
    "        \n",
    "    def get_target_for_label(self, label):\n",
    "        return int(label==\"POSITIVE\")\n",
    "    \n",
    "    def sigmoid(self, x):\n",
    "        return 1/(1 + np.exp(-x))\n",
    "    \n",
    "    def sigmoid_prime(self, sigmoid_output):\n",
    "        return sigmoid_output * (1 - sigmoid_output)\n",
    "    \n",
    "    def tanh(self, x):\n",
    "        return (np.exp(x) - np.exp(-x))/(np.exp(x) + np.exp(-x))\n",
    "    \n",
    "    def tanh_prime(self, tanh_output):\n",
    "        return 1- (tanh_output**2)\n",
    "    \n",
    "    def train(self, training_reviews_raw, training_labels):\n",
    "        \n",
    "        # In order to make training faster, we only consider inputs wich are one\n",
    "        \n",
    "        \n",
    "        training_reviews = []\n",
    "        \n",
    "        for review in training_reviews_raw:\n",
    "            indices = set()\n",
    "            for word in review.split(\" \"):\n",
    "                if word in self.word2index.keys():\n",
    "                    indices.add(self.word2index[word])\n",
    "            training_reviews.append(indices)\n",
    "            \n",
    "        # Make sure the number of reviews matches the number of lables      \n",
    "        assert(len(training_reviews) == len(training_labels))\n",
    "        \n",
    "        # Keep start time of training for printing time statistics\n",
    "        start = time.time()\n",
    "        \n",
    "        # Keep track of correct predictions to display accuracy during training \n",
    "        correct_so_far = 0\n",
    "        \n",
    "        for i in range(len(training_reviews)):\n",
    "            \n",
    "            review_indices = training_reviews[i]\n",
    "            label = get_target_for_label(training_labels[i])\n",
    "            \n",
    "            # The forward pass (feedforward)\n",
    "            \n",
    "            self.layer_1 *= 0 # Reset layer 1 outputs to zero\n",
    "            \n",
    "            for index in review_indices:\n",
    "                self.layer_1 += self.weights_0_1[index] # Calculate output of layer 1 with shape (1, hidden_nodes)\n",
    "            \n",
    "            self.layer_1 = self.tanh(self.layer_1)\n",
    "\n",
    "            output = self.sigmoid(self.layer_1.dot(self.weights_1_2)) # Calculate output with shape (1, 1)\n",
    "            \n",
    "            # The backward pass (backpropagation)\n",
    "            \n",
    "            output_error = label - output\n",
    "            output_del = output_error * self.sigmoid_prime(output)\n",
    "            \n",
    "            layer_1_error = output_del.dot(self.weights_1_2.T)\n",
    "#             layer_1_del = layer_1_error\n",
    "            layer_1_del = layer_1_error * self.tanh_prime(self.layer_1)\n",
    "            \n",
    "            self.weights_1_2 += self.learning_rate * self.layer_1.T.dot(output_del) # Update layer 2 weights\n",
    "            \n",
    "            for index in review_indices:\n",
    "                self.weights_0_1[index] += self.learning_rate * layer_1_del[0] # Update layer 1 weights used in feedforward\n",
    "            \n",
    "            \n",
    "            correct_so_far += int(np.abs(output_error[0, 0])<0.5)\n",
    "            \n",
    "            elapsed_time = float(time.time() - start)\n",
    "            reviews_per_second = elapsed_time and (i+1)/elapsed_time\n",
    "            \n",
    "\n",
    "            if (i+1) % 100 ==0:\n",
    "                # clear_output(wait=True)\n",
    "                print(f\"Progress: {100*(i+1)/len(training_reviews):.2f}% Speed: {reviews_per_second:.2f} \"+\\\n",
    "                      f\"#Correct: {correct_so_far} #Trained: {i+1} \"+ \\\n",
    "                      f\"Training Accuracy: {100*correct_so_far/(i+1):.2f}%\",end=\"\\r\")\n",
    "    \n",
    "        \n",
    "\n",
    "            \n",
    "    \n",
    "    def test(self, test_reviews, test_labels):\n",
    "        \"\"\"\n",
    "        Attempts to predict the labels for the given testing_reviews,\n",
    "        and uses the test_labels to calculate the accuracy of those predictions.\n",
    "        \"\"\"\n",
    "        correct_so_far = 0\n",
    "        \n",
    "        start = time.time()\n",
    "        \n",
    "        for i in range(len(test_reviews)):\n",
    "            prediction = self.run(test_reviews[i])\n",
    "\n",
    "            correct_so_far += int(prediction == test_labels[i])\n",
    "            \n",
    "            elapsed_time = float(time.time() - start)\n",
    "            reviews_per_second = elapsed_time and (i+1)/elapsed_time\n",
    "            \n",
    "            \n",
    "            print(f\"Progress: {100*(i+1)/len(test_reviews):.2f}% Speed: {reviews_per_second:.2f} \"+\\\n",
    "                  f\"#Correct: {correct_so_far} #Trained: {i+1} \"+ \\\n",
    "                  f\"Test Accuracy: {100*correct_so_far/(i+1):.2f}%\",end=\"\\r\")\n",
    "        \n",
    "    def run(self, review):\n",
    "        \"\"\"\n",
    "        Returns a POSITIVE or NEGATIVE prediction for the given review.\n",
    "        \"\"\"\n",
    "        \n",
    "        review = review.lower() # Make sure characters are lowercase\n",
    "        \n",
    "        review_indices = set()\n",
    "        \n",
    "        for word in review.split(\" \"):\n",
    "            if word in self.word2index.keys():\n",
    "                review_indices.add(self.word2index[word])\n",
    "        \n",
    "        \n",
    "        \n",
    "        self.layer_1 *= 0 # Reset layer 1 outputs to zero\n",
    "            \n",
    "        for index in review_indices:\n",
    "            self.layer_1 += self.weights_0_1[index] # Calculate output of layer 1 with shape (1, hidden_nodes)\n",
    "        \n",
    "        self.layer_1 = self.tanh(self.layer_1)\n",
    "\n",
    "        output = self.sigmoid(self.layer_1.dot(self.weights_1_2)) # Calculate output with shape (1, 1)\n",
    "        \n",
    "        return \"POSITIVE\" if output[0,0] >= 0.5 else \"NEGATIVE\"\n",
    "             \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "3a2cf304",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=30 ,cutoff= 0.2 ,learning_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "734d983e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.00% Speed: 4897.21 #Correct: 42111 #Trained: 48000 Training Accuracy: 87.73%\r"
     ]
    }
   ],
   "source": [
    "mlp.train(reviews[:-1000] *2   ,labels[:-1000] *2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "ba2bbc87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 0.10% Speed: 6326.25 #Correct: 1 #Trained: 1 Test Accuracy: 100.00%\r",
      "Progress: 0.20% Speed: 6428.05 #Correct: 1 #Trained: 2 Test Accuracy: 50.00%\r",
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      "Progress: 98.40% Speed: 6122.25 #Correct: 840 #Trained: 984 Test Accuracy: 85.37%\r",
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      "Progress: 99.40% Speed: 6142.18 #Correct: 850 #Trained: 994 Test Accuracy: 85.51%\r",
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      "Progress: 99.60% Speed: 6138.52 #Correct: 851 #Trained: 996 Test Accuracy: 85.44%\r",
      "Progress: 99.70% Speed: 6137.77 #Correct: 852 #Trained: 997 Test Accuracy: 85.46%\r",
      "Progress: 99.80% Speed: 6138.18 #Correct: 853 #Trained: 998 Test Accuracy: 85.47%\r",
      "Progress: 99.90% Speed: 6139.45 #Correct: 853 #Trained: 999 Test Accuracy: 85.39%\r",
      "Progress: 100.00% Speed: 6141.60 #Correct: 854 #Trained: 1000 Test Accuracy: 85.40%\r"
     ]
    }
   ],
   "source": [
    "mlp.test(reviews[-1000:], labels[-1000:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1155e5c4",
   "metadata": {},
   "source": [
    "# Weight Analysis <a id = \"header_5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "be669b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_full_vocab = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=0,cutoff=0,learning_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "cf30e8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.00% Speed: 2493.60 #Correct: 20367 #Trained: 24000 Training Accuracy: 84.86%\r"
     ]
    }
   ],
   "source": [
    "mlp_full_vocab.train(reviews[:-1000]   ,labels[:-1000] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "5724c97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_most_similar_words(current_word):\n",
    "    \n",
    "    words_similarity = Counter()\n",
    "    \n",
    "    for word, index in mlp_full_vocab.word2index.items():\n",
    "        words_similarity[word] = np.dot(mlp_full_vocab.weights_0_1[index], \n",
    "                                        mlp_full_vocab.weights_0_1[mlp_full_vocab.word2index[current_word]])\n",
    "    return words_similarity.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "dbde683f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " ('awful', 0.12610891607340127),\n",
       " ('waste', 0.12011640848811592),\n",
       " ('poor', 0.10232923807610074),\n",
       " ('terrible', 0.09765930889536606),\n",
       " ('boring', 0.09707950402168647),\n",
       " ('bad', 0.08178646317669996),\n",
       " ('dull', 0.08145097500906476),\n",
       " ('worse', 0.07489964945344904),\n",
       " ('poorly', 0.0745454901856284),\n",
       " ('stupid', 0.07451858526982089),\n",
       " ('annoying', 0.07428136201998306),\n",
       " ('unfortunately', 0.07327422276883178),\n",
       " ('disappointing', 0.07187791200578525),\n",
       " ('nothing', 0.07168657768101701),\n",
       " ('fails', 0.07005079969104978),\n",
       " ('horrible', 0.06732550843797941),\n",
       " ('mess', 0.0660469260857896),\n",
       " ('disappointment', 0.06551449922023724),\n",
       " ('supposed', 0.06254548247400042),\n",
       " ('save', 0.06105156272883919),\n",
       " ('badly', 0.06102835442631666),\n",
       " ('wasted', 0.06013700431577114),\n",
       " ('ridiculous', 0.059810577731371446),\n",
       " ('minutes', 0.058725467264885506),\n",
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       " ('laughable', 0.05574127136866952),\n",
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       " ('effort', 0.05117269114348531),\n",
       " ('pointless', 0.05075983097817636),\n",
       " ('predictable', 0.04892541805637509),\n",
       " ('instead', 0.04860830465885852),\n",
       " ('seems', 0.047664512713581624),\n",
       " ('basically', 0.04713871746354731),\n",
       " ('lacks', 0.04655748323915443),\n",
       " ('silly', 0.04628478932095949),\n",
       " ('looks', 0.04611800349442409),\n",
       " ('guess', 0.04579620493026838),\n",
       " ('money', 0.04507369093206681),\n",
       " ('pathetic', 0.044724473821564276),\n",
       " ('attempt', 0.044546388659858105),\n",
       " ('wonder', 0.04399655778136685),\n",
       " ('redeeming', 0.04386330163091461),\n",
       " ('wooden', 0.042517966563029974),\n",
       " ('none', 0.0422534561298895),\n",
       " ('weak', 0.04142079104102917),\n",
       " ('might', 0.04139064086634807),\n",
       " ('reason', 0.04062625608661079),\n",
       " ('idea', 0.04044812449851627),\n",
       " ('someone', 0.04029631773102907),\n",
       " ('flat', 0.03983612090322541),\n",
       " ('sorry', 0.03975994461278036),\n",
       " ('mediocre', 0.0393259671175577),\n",
       " ('failed', 0.03918293614579722),\n",
       " ('couldn', 0.03900273929582353),\n",
       " ('garbage', 0.03800156564678888),\n",
       " ('bored', 0.03779816324332724),\n",
       " ('hoping', 0.037222348753171736),\n",
       " ('painful', 0.036970788624951664),\n",
       " ('either', 0.03655254762154893),\n",
       " ('slow', 0.036501630529842816),\n",
       " ('unfunny', 0.03602979372055164),\n",
       " ('trying', 0.03584414390835835),\n",
       " ('decent', 0.035765445943483146),\n",
       " ('problem', 0.03563223003295028),\n",
       " ('no', 0.0353763855898947),\n",
       " ('forgettable', 0.03518190050668625),\n",
       " ('neither', 0.034845070941984226),\n",
       " ('horror', 0.034642916692544),\n",
       " ('half', 0.03357523764798413),\n",
       " ('making', 0.03251242341954244),\n",
       " ('even', 0.03241635777859761),\n",
       " ('interest', 0.03229259176046974),\n",
       " ('wasn', 0.032163864914678354),\n",
       " ('sadly', 0.03210869051633048),\n",
       " ('lack', 0.031798421218309406),\n",
       " ('didn', 0.03172595003630904),\n",
       " ('bland', 0.031541925196025195),\n",
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       " ('entire', 0.030596398804292872),\n",
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       " ('total', 0.029920440118038405),\n",
       " ('was', 0.029795533212818),\n",
       " ('something', 0.029521827462559852),\n",
       " ('fake', 0.029393559773318874),\n",
       " ('apparently', 0.0293178680048626),\n",
       " ('sex', 0.0292272666211386),\n",
       " ('nowhere', 0.029216504785400853),\n",
       " ('uninteresting', 0.029192755981979103),\n",
       " ('dumb', 0.029119577708222676),\n",
       " ('honestly', 0.029093863679651014),\n",
       " ('then', 0.029067175476499313),\n",
       " ('ok', 0.02895556801153229),\n",
       " ('tries', 0.028773638618145688),\n",
       " ('insult', 0.028764207580375165),\n",
       " ('bunch', 0.028746324320069287),\n",
       " ('barely', 0.028721920933216884),\n",
       " ('writing', 0.02867785417183875),\n",
       " ('were', 0.02852041400344131),\n",
       " ('tedious', 0.028337213616003235),\n",
       " ('wrong', 0.028318648465717057),\n",
       " ('clich', 0.02819381684946258),\n",
       " ('least', 0.028158656285520035),\n",
       " ('anything', 0.028145432156670647),\n",
       " ('plot', 0.028089441158676613),\n",
       " ('absurd', 0.027972245843161367),\n",
       " ('t', 0.02797198094230228),\n",
       " ('gore', 0.027846777033684926),\n",
       " ('group', 0.027744126764012743),\n",
       " ('result', 0.02769486646079017),\n",
       " ('lousy', 0.027385748380016513),\n",
       " ('believe', 0.027372743205614898),\n",
       " ('laughs', 0.027282463971149373),\n",
       " ('dreadful', 0.02715825185929838),\n",
       " ('material', 0.02706333620924878),\n",
       " ('alright', 0.027011719684932902),\n",
       " ('mst', 0.026817476337707147),\n",
       " ('scenes', 0.026518187111624143),\n",
       " ('daughter', 0.026482208702914786),\n",
       " ('embarrassing', 0.026427237403574746),\n",
       " ('mildly', 0.02636140072542402),\n",
       " ('looked', 0.026300038343146835),\n",
       " ('re', 0.026242468042041223),\n",
       " ('ludicrous', 0.026149529929448086),\n",
       " ('been', 0.025942915930951334),\n",
       " ('k', 0.025864731119706004),\n",
       " ('excuse', 0.025754765269407683),\n",
       " ('incoherent', 0.025754484935830546),\n",
       " ('to', 0.025705061167826816),\n",
       " ('any', 0.025665854462730155),\n",
       " ('director', 0.025630429022826712),\n",
       " ('left', 0.025514999230397455),\n",
       " ('pretentious', 0.025495989787198074),\n",
       " ('complete', 0.02541983704200828),\n",
       " ('endless', 0.025304085505189245),\n",
       " ('original', 0.025058017695205787),\n",
       " ('unconvincing', 0.025045847502088513),\n",
       " ('so', 0.024939087414027495),\n",
       " ('falls', 0.024905551773143988),\n",
       " ('turkey', 0.02480220975538428),\n",
       " ('enough', 0.02478782555114606),\n",
       " ('paid', 0.02451324467501752),\n",
       " ('rent', 0.024383307961507835),\n",
       " ('watching', 0.024275989760366365),\n",
       " ('m', 0.024267556096046465),\n",
       " ('dimensional', 0.024253264518908826),\n",
       " ('bizarre', 0.024226714317443863),\n",
       " ('appears', 0.024182391640034995),\n",
       " ('nudity', 0.02417524478496134),\n",
       " ('tried', 0.024118209952851286),\n",
       " ('unbelievable', 0.02399005628976455),\n",
       " ('obvious', 0.023919440715607723),\n",
       " ('miscast', 0.023749479806502653),\n",
       " ('sequel', 0.02371200498769667),\n",
       " ('obnoxious', 0.023666029798683128),\n",
       " ('confusing', 0.02365644524192237),\n",
       " ('body', 0.0235363908526102),\n",
       " ('bother', 0.023536121465102616),\n",
       " ('sucks', 0.023490759254525685),\n",
       " ('stay', 0.023334596338773723),\n",
       " ('crappy', 0.02327047733951829),\n",
       " ('ugly', 0.023269985961054635),\n",
       " ('okay', 0.023258503552483274),\n",
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       " ('accent', 0.022947606629243607),\n",
       " ('however', 0.022857290168163413),\n",
       " ('f', 0.022761328311985973),\n",
       " ('seem', 0.02270988972025069),\n",
       " ('positive', 0.02267577082397858),\n",
       " ('camera', 0.02265159315737985),\n",
       " ('amateurish', 0.02259696545193402),\n",
       " ('seemed', 0.022573825682761894),\n",
       " ('why', 0.022416161373454334),\n",
       " ('except', 0.022395257337506098),\n",
       " ('joke', 0.0223540185127055),\n",
       " ('below', 0.022288200865758043),\n",
       " ('stupidity', 0.022211130674056206),\n",
       " ('potential', 0.022073555596924622),\n",
       " ('just', 0.021943064743609204),\n",
       " ('actor', 0.021853528454967682),\n",
       " ('major', 0.021768294704271336),\n",
       " ('christmas', 0.021753986101347086),\n",
       " ('wouldn', 0.021751089624639404),\n",
       " ('start', 0.02169262497428401),\n",
       " ('otherwise', 0.021668012388225095),\n",
       " ('seconds', 0.02150828347549595),\n",
       " ('attempts', 0.02150167330541501),\n",
       " ('through', 0.021433123798093583),\n",
       " ('soap', 0.021429852451452965),\n",
       " ('storyline', 0.021416233271247174),\n",
       " ('care', 0.021391755503286355),\n",
       " ('dialog', 0.021383386542830632),\n",
       " ('maybe', 0.021346632744224027),\n",
       " ('production', 0.021234605196264723),\n",
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       " ('aliens', 0.00791286617224611),\n",
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       " ('fall', 0.007825901434480299),\n",
       " ('bits', 0.0078069334695041685),\n",
       " ('drive', 0.00780687185289361),\n",
       " ('holiday', 0.0078029434092789),\n",
       " ('mindless', 0.007799359054722241),\n",
       " ('attempting', 0.007797883728985799),\n",
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       " ('forwarding', 0.007783445444414861),\n",
       " ('feed', 0.007764960914077198),\n",
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       " ('insomnia', 0.007569575127935411),\n",
       " ('screw', 0.007564320940535367),\n",
       " ('simpson', 0.0075638948059368214),\n",
       " ('appalled', 0.007555555891682343),\n",
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       " ('unlikable', 0.0075497045168074015),\n",
       " ('ad', 0.007534465856773414),\n",
       " ('plan', 0.007523627168665576),\n",
       " ('apes', 0.007519553179744549),\n",
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       " ('expects', 0.007489203416714969),\n",
       " ('inexplicable', 0.0074806832301667035),\n",
       " ('dying', 0.007478584476801992),\n",
       " ('preposterous', 0.007477439102884191),\n",
       " ('boyfriend', 0.007476818124980935),\n",
       " ('weekend', 0.007471890975926763),\n",
       " ('hurts', 0.007466510472136877),\n",
       " ('phrase', 0.007460310105626833),\n",
       " ('intent', 0.007454924403729167),\n",
       " ('saif', 0.007447793802475261),\n",
       " ('relies', 0.007441710888703318),\n",
       " ('subplot', 0.007426575206079054),\n",
       " ('van', 0.007422827913484846),\n",
       " ('susan', 0.007421379862171248),\n",
       " ('conversations', 0.00741144729225479),\n",
       " ('agenda', 0.007409597847201606),\n",
       " ('letdown', 0.007406701226493392),\n",
       " ('designed', 0.007400440534930131),\n",
       " ('hopelessly', 0.007392826321354833),\n",
       " ('store', 0.007387931787459089),\n",
       " ('common', 0.007385725000026336),\n",
       " ('sitcom', 0.007365815455134775),\n",
       " ('player', 0.007364971821497037),\n",
       " ('bible', 0.007362783957417845),\n",
       " ('offer', 0.00736252537796896),\n",
       " ('fools', 0.007360233241221497),\n",
       " ('spelling', 0.00735797277366859),\n",
       " ('murdered', 0.007354514042329817),\n",
       " ('proceedings', 0.007350796633754136),\n",
       " ('according', 0.007350522377682009),\n",
       " ('clothes', 0.007345734830941147),\n",
       " ('believer', 0.007345238765902895),\n",
       " ('mcdowell', 0.007340880477658189),\n",
       " ('vacuous', 0.007338692800815106),\n",
       " ('dumber', 0.007334701860117758),\n",
       " ('entitled', 0.007327182974510125),\n",
       " ...]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_most_similar_words(\"terrible\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "06723052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " ('hot', 0.009959067154050262),\n",
       " ('realizes', 0.009955464575812466),\n",
       " ('admit', 0.009954679397040521),\n",
       " ('desperation', 0.009942062980904401),\n",
       " ('nevertheless', 0.009941471950853117),\n",
       " ('poignant', 0.009933422515344601),\n",
       " ('kitty', 0.009928800177120736),\n",
       " ('marvel', 0.00992734210761194),\n",
       " ('unsettling', 0.009924278947691073),\n",
       " ('iran', 0.009922260058015126),\n",
       " ('scoop', 0.00991591590836768),\n",
       " ('quintessential', 0.009907906610526228),\n",
       " ...]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_most_similar_words(\"excellent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57681fb1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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