nlpMXnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b1e9745-249a-43e2-9c1f-ecbed289b04c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mxnet\n",
      "  Using cached mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)\n",
      "Requirement already satisfied: requests<3,>=2.20.0 in /opt/conda/lib/python3.8/site-packages (from mxnet) (2.25.1)\n",
      "Collecting graphviz<0.9.0,>=0.8.1\n",
      "  Using cached graphviz-0.8.4-py2.py3-none-any.whl (16 kB)\n",
      "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /opt/conda/lib/python3.8/site-packages (from mxnet) (1.21.5)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.20.0->mxnet) (1.26.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.20.0->mxnet) (2021.10.8)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.20.0->mxnet) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.20.0->mxnet) (4.0.0)\n",
      "Installing collected packages: graphviz, mxnet\n",
      "  Attempting uninstall: graphviz\n",
      "    Found existing installation: graphviz 0.20.1\n",
      "    Uninstalling graphviz-0.20.1:\n",
      "      Successfully uninstalled graphviz-0.20.1\n",
      "Successfully installed graphviz-0.8.4 mxnet-1.9.1\n"
     ]
    }
   ],
   "source": [
    "!pip install mxnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d448b091-aba4-4088-ad9c-ce757a0ffe8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "from mxnet import nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "def011df-e0d7-4c51-8b33-3f947a6b714f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11.]\n",
       "<NDArray 12 @cpu(0)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = nd.arange(12)\n",
    "x\n",
    "y = nd.arange(12)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5290a326-80b4-4c0f-a492-1b38c2ae2c53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
       "<NDArray 12 @cpu(0)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x==y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b135735-34ab-45dc-8b84-bf3990fc796e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.494442"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum()\n",
    "x.norm().asscalar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5d63579-da5c-42e0-a0e4-158792a25905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       " [[0.]\n",
       "  [1.]\n",
       "  [2.]]\n",
       " <NDArray 3x1 @cpu(0)>,\n",
       " \n",
       " [[0. 1.]]\n",
       " <NDArray 1x2 @cpu(0)>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = nd.arange(3).reshape((3 , 1))\n",
    "b = nd.arange(2).reshape((1 , 2)) \n",
    "a , b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f727c40-bf42-4e3f-85aa-17902af0a11f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[[0. 1.]\n",
       " [1. 2.]\n",
       " [2. 3.]]\n",
       "<NDArray 3x2 @cpu(0)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "358f528a-125f-4499-8e52-f022c070096f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[1. 2.]\n",
       "<NDArray 2 @cpu(0)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f626093-898d-451b-8bf3-27f012c57d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[ 0.  9.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11.]\n",
       "<NDArray 12 @cpu(0)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:2] = 9 \n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83172c50-0704-40a5-9521-0b4125e7e25e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[12. 12.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11.]\n",
       "<NDArray 12 @cpu(0)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0:2 , ] = 12\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d18abb1d-4896-4b86-9e4a-7847f2f055b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140621596114368\n",
      "140617592654768\n",
      "140621596114368\n",
      "140617592654768\n"
     ]
    }
   ],
   "source": [
    "before = id(y)\n",
    "y = y + x \n",
    "id(y) == before\n",
    "print(id(y))\n",
    "print(before)\n",
    "print(id(y))\n",
    "print(before)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8dfde34-932a-42ac-a420-dba0a7d39ec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id(z) : 140617567068224\n",
      "id(z): 140617567068224\n"
     ]
    }
   ],
   "source": [
    "z = y.zeros_like()\n",
    "print('id(z) :' , id(z))\n",
    "z[:] = x + y\n",
    "print('id(z):' , id(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd60fff2-94fe-4850-8156-95d00ed1c20f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before = id(z)\n",
    "nd.elemwise_add(x , y , out = z)\n",
    "id(z) == before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e93dab6-e354-4ede-a3b3-f4cfb02b31e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before = id(x)\n",
    "x += y \n",
    "id(x) == before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "233acf0d-433e-4860-a604-bde2a891e3c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'mxnet.ndarray.ndarray.NDArray'>\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = x.asnumpy()\n",
    "print(type(a))\n",
    "b = nd.array(a)\n",
    "print(type(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06ff9693-bafd-470e-8335-11dc3a34ef72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "from mxnet import autograd, nd, gluon, init\n",
    "import d2l\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1baa7b85-631e-4b4f-b902-cb0062bf7c65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# tokens: 1 ['.in.the.beginning.god.created.the.heaven.and.the.earth.']\n",
      "# tokens: 1 ['.']\n",
      "# tokens: 1 ['.and.the.earth.was.without.form.and.void.and.darkness.was.upon.']\n",
      "# tokens: 1 ['the.face.of.the.deep.and.the.spirit.of.god.moved.upon.the.face.of.the.']\n",
      "# tokens: 1 ['waters.']\n",
      "# tokens: 1 ['.']\n",
      "# tokens: 1 ['.and.god.said.let.there.be.light.and.there.was.light.']\n",
      "# tokens: 1 ['.']\n",
      "# tokens: 1 ['.and.god.saw.the.light.that.it.was.good.and.god.divided.the.light.']\n",
      "# tokens: 1 ['from.the.darkness.']\n"
     ]
    }
   ],
   "source": [
    "import sys \n",
    "sys.path.insert(0, '..')\n",
    "\n",
    "import collections\n",
    "import re\n",
    "with open('bible.txt','r') as f:\n",
    "    lines = f.readlines()\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+','.',st).lower().split() for st in lines]\n",
    "\n",
    "    \n",
    "for st in raw_dataset[0:10]:\n",
    "    print('# tokens:' ,len(st), st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "17a4e9fa-224c-4a96-ad41-5412bf2fd4a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.in.the.beginning.god.created.the.heaven.and.the.earth.']\n",
      "['.']\n",
      "['.and.the.earth.was.without.form.and.void.and.darkness.was.upon.']\n",
      "['the.face.of.the.deep.and.the.spirit.of.god.moved.upon.the.face.of.the.']\n",
      "['waters.']\n",
      "['.']\n",
      "['.and.god.said.let.there.be.light.and.there.was.light.']\n",
      "['.']\n",
      "['.and.god.saw.the.light.that.it.was.good.and.god.divided.the.light.']\n",
      "['from.the.darkness.']\n",
      "['.']\n",
      "['.and.god.called.the.light.day.and.the.darkness.he.called.night.']\n",
      "['and.the.evening.and.the.morning.were.the.first.day.']\n",
      "['.']\n",
      "['.and.god.said.let.there.be.a.firmament.in.the.midst.of.the.waters.']\n",
      "['and.let.it.divide.the.waters.from.the.waters.']\n",
      "['.']\n",
      "['.and.god.made.the.firmament.and.divided.the.waters.which.were.']\n",
      "['under.the.firmament.from.the.waters.which.were.above.the.firmament.']\n",
      "['and.it.was.so.']\n",
      "['.']\n",
      "['.and.god.called.the.firmament.heaven.and.the.evening.and.the.']\n",
      "['morning.were.the.second.day.']\n",
      "['.']\n",
      "['.and.god.said.let.the.waters.under.the.heaven.be.gathered.together.']\n",
      "['unto.one.place.and.let.the.dry.land.appear.and.it.was.so.']\n",
      "['.']\n",
      "['.and.god.called.the.dry.land.earth.and.the.gathering.together.of.']\n",
      "['the.waters.called.he.seas.and.god.saw.that.it.was.good.']\n",
      "['.']\n",
      "['.and.god.said.let.the.earth.bring.forth.grass.the.herb.yielding.']\n",
      "['seed.and.the.fruit.tree.yielding.fruit.after.his.kind.whose.seed.is.']\n",
      "['in.itself.upon.the.earth.and.it.was.so.']\n",
      "['.']\n",
      "['.and.the.earth.brought.forth.grass.and.herb.yielding.seed.after.']\n",
      "['his.kind.and.the.tree.yielding.fruit.whose.seed.was.in.itself.after.']\n",
      "['his.kind.and.god.saw.that.it.was.good.']\n",
      "['.']\n",
      "['.and.the.evening.and.the.morning.were.the.third.day.']\n",
      "['.']\n",
      "['.and.god.said.let.there.be.lights.in.the.firmament.of.the.heaven.']\n",
      "['to.divide.the.day.from.the.night.and.let.them.be.for.signs.and.for.']\n",
      "['seasons.and.for.days.and.years.and.let.them.be.for.lights.in.']\n",
      "['the.firmament.of.the.heaven.to.give.light.upon.the.earth.and.it.was.']\n",
      "['so.']\n",
      "['.']\n",
      "['.and.god.made.two.great.lights.the.greater.light.to.rule.the.day.']\n",
      "['and.the.lesser.light.to.rule.the.night.he.made.the.stars.also.']\n",
      "['.']\n",
      "['.and.god.set.them.in.the.firmament.of.the.heaven.to.give.light.']\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Optimized\"\"\"\n",
    "import sys\n",
    "import re\n",
    "import pprint\n",
    "\n",
    "sys.path.insert(0, \"..\")\n",
    "\n",
    "# Open the file and read the lines\n",
    "with open(\"bible.txt\", \"r\") as f:\n",
    "    lines = f.readlines()\n",
    "\n",
    "# Preprocess the lines and create the dataset\n",
    "raw_dataset = []\n",
    "for line in lines:\n",
    "    # Replace non-alphabetic characters with '.' and lowercase the line\n",
    "    line = re.sub(\"[^A-Za-z]+\", \".\", line).lower()\n",
    "    # Split the line into tokens\n",
    "    tokens = line.split()\n",
    "    # Add the tokens to the dataset\n",
    "    raw_dataset.append(tokens)\n",
    "\n",
    "# Print the first 10 elements of the dataset\n",
    "for st in raw_dataset[:50]:\n",
    "    pprint.pprint((st))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88eb87a8-c137-4581-ac5e-aa6343bce3a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('.', 25243), ('lord.', 75), ('them.', 67), ('him.', 58), ('god.', 52), ('thee.', 51), ('me.', 46), ('israel.', 37), ('it.', 31), ('people.', 30)]\n",
      "[('.', 25243), ('lord.', 75), ('them.', 67), ('him.', 58), ('god.', 52), ('thee.', 51), ('me.', 46), ('israel.', 37), ('it.', 31), ('people.', 30)]\n"
     ]
    }
   ],
   "source": [
    "counter = collections.Counter([tk for st in raw_dataset for tk in st])\n",
    "#print(\"frequency of 'lord':\", counter['lord'])\n",
    "print(counter.most_common(10))\n",
    "cnt=counter.most_common(10)\n",
    "print(cnt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "71cdba71-56d9-42bb-8b59-2c773654e05d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('.', 25243),\n",
      " ('lord.', 75),\n",
      " ('them.', 67),\n",
      " ('him.', 58),\n",
      " ('god.', 52),\n",
      " ('thee.', 51),\n",
      " ('me.', 46),\n",
      " ('israel.', 37),\n",
      " ('it.', 31),\n",
      " ('people.', 30)]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"In this version, we are reading the file line by line using the for st in f loop, \n",
    "which is more efficient than reading all the lines into a list first. \n",
    "We are also using a list comprehension to create the raw_dataset list, \n",
    "which is more concise and efficient than using a separate loop.\n",
    "Finally, we are using tuple unpacking to assign the most_common_10 \n",
    "variable to the first element of the tuple returned by counter.most_common(10). \n",
    "This avoids the need to create an extra variable cnt.\"\"\"\n",
    "\n",
    "import collections\n",
    "import re\n",
    "\n",
    "with open('bible.txt', 'r') as f:\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '.', st).lower().split() for st in f]\n",
    "\n",
    "counter = collections.Counter([tk for st in raw_dataset for tk in st])\n",
    "\n",
    "most_common_10 = counter.most_common(10)\n",
    "pprint.pprint(most_common_10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3fe7db73-dfc0-4c91-9861-8eb45bab884e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('shall', 9840), ('unto', 8997), ('lord', 7964), ('thou', 5474), ('thy', 4600)]\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "\n",
    "# Download the list of stop words\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words] for st in raw_dataset]\n",
    "\n",
    "# Recompute the counter with the filtered dataset\n",
    "counter = collections.Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "    \n",
    "# Print the most common 10 words\n",
    "most_common_10 = counter.most_common(5)\n",
    "pprint.pprint(most_common_10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6e9add11-c7aa-425f-a8e8-79c49281aa27",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shall: 9840\n",
      "unto: 8997\n",
      "lord: 7964\n",
      "thou: 5474\n",
      "thy: 4600\n",
      "god: 4472\n",
      "said: 3999\n",
      "ye: 3983\n",
      "thee: 3827\n",
      "upon: 2750\n",
      "man: 2735\n",
      "israel: 2575\n",
      "king: 2543\n",
      "son: 2392\n",
      "hath: 2264\n",
      "people: 2146\n",
      "came: 2093\n",
      "house: 2024\n",
      "come: 1972\n",
      "one: 1971\n",
      "children: 1821\n",
      "also: 1771\n",
      "day: 1743\n",
      "land: 1718\n",
      "men: 1677\n",
      "shalt: 1616\n",
      "let: 1511\n",
      "go: 1492\n",
      "hand: 1466\n",
      "us: 1452\n",
      "saying: 1445\n",
      "made: 1405\n",
      "went: 1400\n",
      "even: 1395\n",
      "behold: 1326\n",
      "saith: 1262\n",
      "therefore: 1237\n",
      "every: 1236\n",
      "things: 1164\n",
      "father: 1127\n",
      "sons: 1094\n",
      "hast: 1070\n",
      "david: 1064\n",
      "make: 1059\n",
      "say: 1056\n",
      "may: 1041\n",
      "earth: 987\n",
      "jesus: 983\n",
      "great: 962\n",
      "name: 959\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "import re\n",
    "import nltk\n",
    "\n",
    "# Download the list of stop words\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', ' ', st).lower().split() for st in f]\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words and word != '.'] for st in raw_dataset]\n",
    "\n",
    "# Recompute the counter with the filtered dataset\n",
    "counter = collections.Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "# Create a dictionary with the counter.most_common()\n",
    "counter_dict = dict((word, count) for word, count in counter.most_common() if word != '.')\n",
    "\n",
    "# Print the 5 most popular words in descending order\n",
    "for word, count in sorted(counter_dict.items(), key=lambda item: item[1], reverse=True)[:50]:\n",
    "    print(f'{word}: {count}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f267b892-2334-43a1-9b6e-2f39b5655a01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe0b7a57-bf35-4b44-a3b5-b8e2326ae052",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lord: 75\n",
      "god: 52\n",
      "thee: 51\n",
      "israel: 37\n",
      "people: 30\n",
      "jerusalem: 30\n",
      "thelord: 25\n",
      "ever: 23\n",
      "andthelordspakeuntomosessayingspeakuntothe: 18\n",
      "selah: 16\n",
      "day: 15\n",
      "house: 15\n",
      "christ: 15\n",
      "away: 14\n",
      "egypt: 14\n",
      "thereof: 14\n",
      "congregation: 14\n",
      "endurethforever: 14\n",
      "theearth: 13\n",
      "hand: 13\n",
      "righteousness: 12\n",
      "years: 11\n",
      "ofthelord: 11\n",
      "earth: 11\n",
      "place: 11\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from collections import Counter\n",
    "import re\n",
    "\n",
    "# Download the list of stop words\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words] for st in raw_dataset]\n",
    "\n",
    "# Compute the counter with the filtered dataset\n",
    "counter = Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "# Create a dictionary of the 25 most common phrases\n",
    "most_common_25 = dict(counter.most_common(25))\n",
    "\n",
    "# Print the dictionary in descending order\n",
    "for phrase, count in sorted(most_common_25.items(), key=lambda item: item[1], reverse=True):\n",
    "    print(f'{phrase}: {count}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "14ef7098-eb79-407f-89e4-534f32724d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nltk\n",
    "from collections import Counter\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Download the list of stop words\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words] for st in raw_dataset]\n",
    "\n",
    "# Compute the counter with the filtered dataset\n",
    "counter = Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "# Create a dictionary of the 25 most common phrases\n",
    "most_common_25 = dict(counter.most_common(25))\n",
    "\n",
    "# Get the list of phrases and their counts\n",
    "phrases = list(most_common_25.keys())\n",
    "counts = list(most_common_25.values())\n",
    "\n",
    "# Set the figure size and title\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"25 Most Common Phrases\")\n",
    "\n",
    "# Plot the bar chart\n",
    "plt.bar(phrases, counts, color='#1f77b4')\n",
    "\n",
    "# Rotate the x-tick labels\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e78fe4de-43c5-420a-a5d7-ba36f1c338f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nltk\n",
    "from collections import Counter\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Download the list of stop words\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words] for st in raw_dataset]\n",
    "\n",
    "# Compute the counter with the filtered dataset\n",
    "counter = Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "# Create a dictionary of the 25 most common phrases\n",
    "most_common_25 = dict(counter.most_common(25))\n",
    "\n",
    "# Get the list of phrases and their counts\n",
    "phrases = list(most_common_25.keys())\n",
    "counts = list(most_common_25.values())\n",
    "\n",
    "# Set the figure size and title\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"25 Most Common Phrases\")\n",
    "\n",
    "# Plot the bar chart with a grid and different colors for each bar\n",
    "plt.bar(phrases, counts, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "                                '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n",
    "                                '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n",
    "                                '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n",
    "                                '#e6550d', '#fd8d3c', '#31a354', '#756bb1', '#636363'])\n",
    "\n",
    "# Add a grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Rotate the x-tick labels\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "523b5e28-b0aa-49ac-a1e5-4b0ae967a935",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nltk\n",
    "from collections import Counter\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Download the list of stop words\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('666.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words] for st in raw_dataset]\n",
    "\n",
    "# Compute the counter with the filtered dataset\n",
    "counter = Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "# Create a dictionary of the 25 most common phrases\n",
    "most_common_25 = dict(counter.most_common(25))\n",
    "\n",
    "# Get the list of phrases and their counts\n",
    "phrases = list(most_common_25.keys())\n",
    "counts = list(most_common_25.values())\n",
    "\n",
    "# Set the figure size and title\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"25 Most Common Phrases\")\n",
    "\n",
    "# Plot the bar chart with a grid and different colors for each bar\n",
    "plt.bar(phrases, counts, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "                                '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n",
    "                                '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n",
    "                                '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n",
    "                                '#e6550d', '#fd8d3c', '#31a354', '#756bb1', '#636363'])\n",
    "\n",
    "# Add a grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Rotate the x-tick labels\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f907a147-088f-47a3-ae52-d669d05e567c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of instances of all words: 74939\n",
      "25 most common words and their counts: [('lord', 75), ('them', 67), ('him', 58), ('god', 52), ('thee', 51), ('me', 46), ('israel', 37), ('it', 31), ('people', 30), ('jerusalem', 30), ('thelord', 25), ('ever', 23), ('you', 19), ('andthelordspakeuntomosessayingspeakuntothe', 18), ('selah', 16), ('day', 15), ('house', 15), ('christ', 15), ('away', 14), ('egypt', 14), ('thereof', 14), ('not', 14), ('congregation', 14), ('endurethforever', 14), ('theearth', 13)]\n",
      "Total number of instances of the 25 most common words: 720\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from nltk.tokenize import word_tokenize\n",
    "import collections\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Flatten the dataset into a single list of words\n",
    "words = [word for st in raw_dataset for word in st]\n",
    "\n",
    "# Count the frequency of each word\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Calculate the total number of instances of all words\n",
    "total_instances = sum(count for _, count in counter.items())\n",
    "\n",
    "# Get the 25 most common words and their counts\n",
    "most_common_25 = counter.most_common(25)\n",
    "\n",
    "# Calculate the total number of instances of the 25 most common words\n",
    "total_instances_most_common_25 = sum(count for _, count in most_common_25)\n",
    "\n",
    "# Print the results\n",
    "print(\"Total number of instances of all words:\", total_instances)\n",
    "print(\"25 most common words and their counts:\", most_common_25)\n",
    "print(\"Total number of instances of the 25 most common words:\", total_instances_most_common_25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "110123f9-8b73-4c0d-bf11-a9362741516b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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glxExPyKOqPZNz8xbqp9vBaZ3PTtJkqQhFJm58pMitsrMmyNiC+BXwFHA6Zm5cdM5d2fmJmPc9gjgCIDp06fve8opp3Qr9zEtuLn1nEvT14fb7m8dY+ZWo+Z9uuXSlucvXndLpj741/FPmLH3iM0r77qyZbzN19ycOx6+Y9zju2+6e8vbj2Xx4sVMnTq19u36Fa8XMSdjjpPxPvcipjkOZ7xexBz2eL2IOVlzbMeBBx44v2no0ggTKqBG3CDieGAx8GZgdmbeEhEzgLmZuWur286aNSvnzZtX6/fVtf2xZ7Y8fszMZZywoPXY+Rs+/oKRO1YykebcXT/E7GuOG/+EURNpTmQQ+UmLTxr3eDuDyOfOncvs2bNr365f8XoRczLmOBnvcy9imuNwxutFzGGP14uYkzXHdkTEuAXUSrvwImJKRGzY+Bl4NvAH4HTgsOq0w4DTupOuJEnScJvINAbTgR9HROP872TmzyPiIuB7EXE4cCNwcO/SlCRJGh4rLaAy83pgrzH23wU8oxdJSZIkDTNnIpckSarJAkqSJKkmCyhJkqSaLKAkSZJqsoCSJEmqyQJKkiSpJgsoSZKkmiygJEmSarKAkiRJqskCSpIkqSYLKEmSpJosoCRJkmqygJIkSarJAkqSJKkmCyhJkqSaLKAkSZJqsoCSJEmqyQJKkiSpJgsoSZKkmiygJEmSarKAkiRJqskCSpIkqSYLKEmSpJosoCRJkmqygJIkSarJAkqSJKkmCyhJkqSaLKAkSZJqsoCSJEmqyQJKkiSpJgsoSZKkmiygJEmSarKAkiRJqmnCBVRErBkRl0TEGdX2DhFxYURcFxGnRsQ6vUtTkiRpeNRpgToauKpp+z+BT2fmzsDdwOHdTEySJGlYTaiAioitgRcAX6u2A3g68IPqlDnAS3uQnyRJ0tCZaAvUfwHvAR6ptjcF7snMZdX2TcBW3U1NkiRpOEVmtj4h4oXA8zPzbRExG/gX4PXABVX3HRGxDfCzzNxjjNsfARwBMH369H1POeWUbua/ggU3L2x5fPr6cNv9rWPM3GrayB23XNry/MXrbsnUB/86/gkz9h6xeeVdV7aMt/mam3PHw3eMe3z3TXdvefuxLF68mKlTp9a+Xb/i9SLmZMxxMt7nXsQ0x+GM14uYwx6vFzEna47tOPDAA+dn5qyxjk2kgPoP4HXAMmA9YCPgx8BzgEdn5rKIeBJwfGY+p1WsWbNm5bx589q4CxO3/bFntjx+zMxlnLBgrZbn3PDxF4zccfy0sU+szN31Q8y+5rjxTzh+ZFE3c87MlvGOnHokJy0+adzjCw5b0PL2Y5k7dy6zZ8+ufbt+xetFzMmY42S8z72IaY7DGa8XMYc9Xi9iTtYc2xER4xZQK+3Cy8z3ZubWmbk98Grg15l5KHAO8IrqtMOA07qUryRJ0lDrZB6ofwXeFRHXUcZEfb07KUmSJA231n1Zo2TmXGBu9fP1wBO6n5IkSdJwcyZySZKkmiygJEmSarKAkiRJqskCSpIkqSYLKEmSpJosoCRJkmqygJIkSaqp1jxQGl5XPXa3lscfOOodXPXWI8c9vtvVV3U7JUmSVlu2QEmSJNVkASVJklSTBZQkSVJNFlCSJEk1WUBJkiTVZAElSZJUkwWUJElSTRZQkiRJNVlASZIk1WQBJUmSVJMFlCRJUk0WUJIkSTVZQEmSJNVkASVJklSTBZQkSVJNFlCSJEk1WUBJkiTVZAElSZJUkwWUJElSTRZQkiRJNVlASZIk1WQBJUmSVJMFlCRJUk0WUJIkSTWttICKiPUi4vcRcVlEXBERH6r27xARF0bEdRFxakSs0/t0JUmSBm8iLVAPAk/PzL2AvYHnRsT+wH8Cn87MnYG7gcN7lqUkSdIQWWkBlcXianPt6iuBpwM/qPbPAV7aiwQlSZKGzYTGQEXEmhFxKXA78Cvgj8A9mbmsOuUmYKueZChJkjRkIjMnfnLExsCPgQ8A36i674iIbYCfZeYeY9zmCOAIgOnTp+97yimndCHt8S24eWHL49PXh9vubx1j5lbTRu645dKW5y9ed0umPvjX8U+YsfeIzSvvurJlvM3X3Jw7Hr5j3OO7b7r7CvseuOKKljGXbrEF69x++7jH13vc40Zs3/HnRS3jrTXlEZYtGb/+3nzbDUds33b9dS3jAawzbWOWLrxn3OPTd9x5pTGaLV68mKlTp9a6Tb9jDnu8XsQ0x+GM14uYkzHHyXifexGzFzm248ADD5yfmbPGOlargAKIiA8C9wP/Cjw6M5dFxJOA4zPzOa1uO2vWrJw3b16t31fX9see2fL4MTOXccKCtVqec8PHXzByx/HTxj6xMnfXDzH7muPGP+H4kUXdzDkzW8Y7cuqRnLT4pHGPLzhswQr7rnrsbi1j3njUO9juc58f9/huV181YvsLb/11y3hbHLCE28+fMu7xt3/p6SO2T3jVC1vGA9j6OS/lpl/8ZNzjx5x6xkpjNJs7dy6zZ8+udZt+xxz2eL2IaY7DGa8XMSdjjpPxPvciZi9ybEdEjFtATeQqvM2rliciYn3gWcBVwDnAK6rTDgNO60q2kiRJQ651U0wxA5gTEWtSCq7vZeYZEXElcEpEfBS4BPh6D/OUJEkaGistoDLzcuDxY+y/HnhCL5KSJEkaZs5ELkmSVJMFlCRJUk0WUJIkSTVZQEmSJNVkASVJklSTBZQkSVJNFlCSJEk1WUBJkiTVZAElSZJUkwWUJElSTRZQkiRJNVlASZIk1WQBJUmSVJMFlCRJUk0WUJIkSTVZQEmSJNW01qATkNp107HntTz+0MzFLc/Z+uNP7XZKkqRJwhYoSZKkmiygJEmSarKAkiRJqskCSpIkqSYLKEmSpJosoCRJkmqygJIkSarJeaCkyvHHH7/Sc3bdddeW500khiRp1WcLlCRJUk0WUJIkSTVZQEmSJNVkASVJklSTBZQkSVJNFlCSJEk1WUBJkiTVZAElSZJU00oLqIjYJiLOiYgrI+KKiDi62v+oiPhVRFxbfd+k9+lKkiQN3kRaoJYBx2Tm7sD+wNsjYnfgWODszNwFOLvaliRJWu2ttIDKzFsy8+Lq50XAVcBWwEuAOdVpc4CX9ihHSZKkoRKZOfGTI7YHzgX2AP6cmRtX+wO4u7E96jZHAEcATJ8+fd9TTjml46RbWXDzwpbHp68Pt93fOsbMraaN3HHLpS3PX7zulkx98K/jnzBj7xGbV951Zct4m6+5OXc8fMe4x3ffdPcV9j1wxRUtYy7dYgvWuf32cY+v97jHjdi+48+LWsZba8ojLFsyfv29+bYbjti+7frrWsYDWGfaxixdeM+4x6fvuPOI7YduXtwy3gPrP8x696857vG1t5o6YvuWW25ZaY7rrrsuDz744LjHZ8yYsdIYzRYvXszUqVNXfuKA4vUipjkOZ7xexJyMOU7G+9yLmL3IsR0HHnjg/MycNdaxCRdQETEV+A3wscz8UUTc01wwRcTdmdlyHNSsWbNy3rx5E8+8Ddsfe2bL48fMXMYJC1qvoXzDx18wcsfx08Y+sTJ31w8x+5rjxj/h+JFF3cw5M1vGO3LqkZy0+KRxjy84bMEK+6567G4tY9541DvY7nOfH/f4bldfNWL7C2/9dct4WxywhNvPnzLu8bd/6ekjtk941QtbxgPY+jkv5aZf/GTc48ecesaI7ZuOPa9lvKtmLmS3BeP/77b++FNHbE90MeFrrrlm3ON1FxOeO3cus2fPrnWbfsbrRUxzHM54vYg5GXOcjPe5FzF7kWM7ImLcAmpCV+FFxNrAD4FvZ+aPqt23RcSM6vgMYPzmDUmSpNXIRK7CC+DrwFWZeWLTodOBw6qfDwNO6356kiRJw6d1X1bxZOB1wIKIuLTa9z7g48D3IuJw4Ebg4J5kKEmSNGRWWkBl5m+BGOfwM7qbjiRJ0vBzJnJJkqSaLKAkSZJqsoCSJEmqaSKDyCW16exf79Ty+H1L/pmzf334uMef8fQ/djslSVIX2AIlSZJUkwWUJElSTRZQkiRJNVlASZIk1WQBJUmSVJMFlCRJUk0WUJIkSTU5D5S0Cnn0OZe2PP6+xffz6pWcc+uBe3ctH0marGyBkiRJqskCSpIkqSYLKEmSpJocAyVNcj++4LaWx2PJspbnvGz/6SN3nPXe1r/w/j1Wfs4z/2PE5pdv+GTL0zddum3Lc96y/btb/z5JqskWKEmSpJosoCRJkmqygJIkSarJMVCSJp8vn9T6+KabtT7nLUeusOv8065sGXIJD7Q854CX7D5i+3df/0LreNO2aHnOkw9/+4jtB362oGU8gHzo/pbnrfe8mSO2L7jggtY5LlnS8pz9999/pTlJw8oWKEmSpJosoCRJkmqygJIkSarJMVCSpKFw3XUnrvScBx/cquV5O+/8rhHbJ1/715bxNnrwoZbnvGGXLVeakyYnW6AkSZJqsoCSJEmqyQJKkiSpJsdASZI0QbccvFHL4w8990Pc8sUXtzxnxvfu7WZKGhBboCRJkmqygJIkSarJAkqSJKkmCyhJkqSaVjqIPCL+G3ghcHtm7lHtexRwKrA9cANwcGbe3bs0JUlaPT1y4lNbn7DVa3nkxA+Me3iNd503Ynuf/3lly3Bv3uB5vOt/Wi9WffFrvz9i+z3vOL3l+Xs9MVue84nPjxxY/6Y3vallvKc+9aktz/na177W8vb9MJEWqG8Azx2171jg7MzcBTi72pYkSZoUVlpAZea5wN9G7X4JMKf6eQ7w0u6mJUmSNLzaHQM1PTNvqX6+FZjepXwkSZKGXmTmyk+K2B44o2kM1D2ZuXHT8bszc5NxbnsEcATA9OnT9z3llFO6kPb4Fty8sOXx6evDbfe3jjFzq2kjd9xyacvzF6+7JVMfbLFg5Yy9R2xeedeVLeNtvubm3PHwHeMe333T3VfY98AVV7SMuXSLLVjn9tvHPb7e4x43YvuOPy9qGW+tKY+wbMn49ffm2244Yvu2669rGQ9gnWkbs3ThPeMen77jziO2H7p5cct4D6z/MOvdv+a4x9feauqI7VtuuWWcM5dbd911efDBB8c9PmPGjBHbixb9oWW8Rx6Zzhpr3Dbu8Q033GPE9uWLWj94H/3IQ9y6xtotz9lzw/VHbN+zZFnL81l2P6y1/riHN54yaijloptbhlv8yPpMXWMlT8INtxqxeefS8f9GAGs+sA4Pr7d03OObrTPqM96d4z+/ABavuRZTH27xd9ls8xV2LVn4QMuYy1jKWqwz7vEp09YbGW8lOS5bcy3WapHjlFE55r0r+ZsDi/Mhpsb4j5/YaOTjYMmSJa1zXLaMtdYaf6jtlClTRmw/+GDr/3M5Zx3WXXf8//W66478X9/14EMt46354AM8vO564x7fdN2Rf4+Hrr+kZbwHpm3FegtbPwfW3vHxI3fcdk3L8xevsylTl941/gnTdx2xedXfrm8Zb7M1pnHnI63fK3d71I4jtm/+S+vz158C97d4OGy1zcj31RtvvLFlvKlTp7J48fiv8dttt13L23fLgQceOD8zZ411rN2ZyG+LiBmZeUtEzADGfWfOzK8AXwGYNWtWzp49u81fOTGvP/bMlsePmbmMExa0vts3HDp75I7jX9Ly/Lm7fojZ1xw3/gmHjHzgHTXnqJbxjpx6JCctPmnc4wsOWrDCvqveemTLmDce9Q62+9znxz2+29VXjdj+wlt/3TLeFgcs4fbzp4x7/JX/OHvE9gknfaplPICtn/NSbvrFT8Y9/qpTzxixfdOx541zZnHVzIXstmDauMe3PnTkwM3jjz9+pTnuuuuuXHPN+C92hxxyyIjts399eMt49y35ZzaY8ulxj8+e/ccR268+59KW8d63+Bb+feqMlufcOnvvEds/vqD1m1bctYDcdOb4Oe4/qjg5670t4829fw9mr9+6sGT2oSM2v3zDJ1uevun/25a7HvPncY+/YvtXjdzx5fGfXwBzN92M2XfdOf4Jr1hxkO75p7X+YHQ3f2YTth33+AGzR34w+t3XWw/yvWfaFmy8cPwPRU8eleMDP1vxdWO0/3voJp609tbjHl9v9sjHwQUXXNAy3l133cWmm2467vH9999/xPZ115240hz/+Met2Gmn8QuUnXce+b8++doWH26Bja6/gnt3fNy4xw/aZcsR2yubZfwPz/0Qe/y8xfsBK85E3mqAOMC5W72Wp938P+MeX+NVI18LVzZA/M0bPI+v3vezludc/PL6g8gvuzDGPX7o62aP2J7IIPLzzhv/Nf6www5reft+aLcL73Sgkf1hwGndSUeSJGn4rbSAiojvAv8H7BoRN0XE4cDHgWdFxLXAM6ttSZKkSWGlXXiZecg4h57R5VwkSZJWCc5ELkmSVJMFlCRJUk0WUJIkSTVZQEmSJNVkASVJklSTBZQkSVJNFlCSJEk1WUBJkiTVZAElSZJUkwWUJElSTRZQkiRJNVlASZIk1WQBJUmSVJMFlCRJUk0WUJIkSTVZQEmSJNVkASVJklSTBZQkSVJNFlCSJEk1WUBJkiTVZAElSZJUkwWUJElSTRZQkiRJNVlASZIk1WQBJUmSVJMFlCRJUk0WUJIkSTVZQEmSJNVkASVJklSTBZQkSVJNFlCSJEk1WUBJkiTV1FEBFRHPjYhrIuK6iDi2W0lJkiQNs7YLqIhYE/gC8Dxgd+CQiNi9W4lJkiQNq05aoJ4AXJeZ12fmUuAU4CXdSUuSJGl4dVJAbQX8pWn7pmqfJEnSai0ys70bRrwCeG5mvqnafh3wxMx8x6jzjgCOqDZ3Ba5pP92u2Ay4c8hjmuNwxutFzGGP14uY5jic8XoRczLmOBnvcy9i9iLHdmyXmZuPdWCtDoLeDGzTtL11tW+EzPwK8JUOfk9XRcS8zJw1zDHNcTjj9SLmsMfrRUxzHM54vYg5GXOcjPe5FzF7kWO3ddKFdxGwS0TsEBHrAK8GTu9OWpIkScOr7RaozFwWEe8AfgGsCfx3Zl7RtcwkSZKGVCddeGTm/wL/26Vc+qUX3YndjmmOwxmvFzGHPV4vYprjcMbrRczJmONkvM+9iDk0Q3/G0/YgckmSpMnKpVwkSZJqsoCSJEmqyQJKkoZYROww6BwkrcgCSrVFxAZdirNDRJwYET+KiNMbX92I3Q0RsUZEHNDD+F35O3Zbtc5lt2NGRLw2Ij5YbW8bEU/oIN63JrJvkCLiyRPZNwE/qG57dsdJ9VivnjMRsWZEbFk9braNiG27/TtWVxGxT6uvQec3WkRsFxHPrH5ePyI2HHRO41mtB5FHxMtbHc/MH9WMtwAY9w+WmXvWidcU9zHAScD0zNwjIvYEXpyZH20z3rtaHc/ME9uMewDwNWBqZm4bEXsBb8nMt7UZ7zLg68AC4JGm/H5TM85Paf1/eXE7+VWxL8nMx7d7+3Fidvvv+GTgeGA7ypW1AWRm7thmvOuBHwInZ+aV7cQYI+ZJlP/x0zNzt4jYBPhlZu7XZryLM3Ofpu01gQWZ2faC5hExH/hv4DuZeXe7ccbLcbx9E4hzCfB94Ejg06OPt/t8rmK/HPhPYAvK46bx2Nmog5hdfc5ExFHAccBtLH+dyHZeb7v9ntAU92jgZGAR5bn9eODYzPxlm/E+AXwUuB/4ObAn8M+Z+T9txDqn+nE9YBZwGeX/vCcwLzOf1GaOjwHezfLXHQAy8+ntxKtivpmycsmjMnOniNgF+FJmPqPdmL3U0TQGq4AXVd+3AA4Afl1tHwicD9R9sryw+v726nvjE++h7SZY+SrlgfhlgMy8PCK+Q3kCtaNXFfungedQTZiamZdFxNM6iPdAZn62C3l9qvr+cuDRQONF5hDKi24nzo6Ig4AfZfc+bXT77/h14J+B+cDDnafHXpSJcb8WEWtQiopTMvPeDmI+MTP3qYoBMvPuagLeWiLivcD7gPUjopFPAEvp/LLnVwFvAC6KiHmUN8Rf1v2/R8STKK83m4/6MLMRZc68ul4NvJTyet3t5/YngBdl5lVdjNnt58zRwK6ZeVcXYnX7PaHhjZn5mYh4DrAJ8DrK+0NbBRTw7Mx8T0S8DLiB8tp2Lstf2yYsMw8EiIgfAftk5oJqew/KB692fR/4EuX9qxuvO1DeW58AXAiQmddGxBZdit19mbnaf1EexDOatmcAv+gg3iVj7Lu4g3gXjY4LXDrov9sYeV44Rp6XdRDvNZRPlk8C9ml8dRBv3kT21Yy5iPKp9yHg3mr73iH7O17Yw//5P1CWaFoCzAF2bjdHSvFwcbW9+VjPoxrx/qOH93kN4MXV/f4z8CHKJ+I6f7PjgFuq742vdwG7dJDX83pwX3/Xg5hdfc4A5wBrdTnHbr8nXF59/wzwsurnSzqId0X1/WuUNWc7eo1ojrmyfTXize/BY2fEayPlQ8Pl3f493fpa3VugGrbJzFuatm8DOulDj4h4cmb+rto4gM7Gk90ZETtRdUNFWaj5ltY3mVCSXe0aBP5S3deMiLUpnww7+eQ6k/JJ7ek0Nc1X2+2YEhE7Zub18PfBt1M6yI/M7EVrXlf+jk3jF86JiE9SPj0/2DiemRe3k1zVHfYCSmvM9sAJwLeBp1Imzn1MG2E/C/wY2CIiPga8Anh/O/lVzoiIKZm5JCJeSym+P5OZN3YQk+o58gbg+ZRuzG8DT6G0VOw9kRhZuqB/ExHfyMwbI2KjsjsXdZIbcH5EnAg0Wit/A3w4Mxd2EHNeRJwK/ISRj512W2K69pxpar27HpgbEWcyMse2uy7p/nvC/Ij4JbAD8N5q3M4jK7lNK6dHxNWULrwjI2Jz4IEO4gEsiIivsbwV61Dg8rpBIuJR1Y8/jYi3UZ7Xzf+Xv3WQ428iotHC/CzgbcBPO4jXU6v1GKiGiPg8sAvw3WrXq4DrMvOoNuPtS+nWmEbpPrib0oTb7hvWjpTuhwOqWH8CXpuZN7QTrynub6i6BrMakxARf8jMPdqMtxnlE9YzKff7l8DR2WbTekRcB+yemUvbuf0Y8Z5DaU6+vspvO+CIbHMcQhUzKC80O2TmRyJiG8on1993ELMrf8emsQ1jyWxzLEI1Buoc4OuZef6oY5/NzHe2GfexwDMo9/ns7KDbKCIup3Q17gl8g/JJ/eDM/IcOYs4H7qF0if4wMx9sOvajzGw5fmaMeLMo3YCNgmIh5XVifpv5/RD4A6UlEMqHj73q5jUq5slj7M7MfGMHMbvynImI41oczsz8cAc5dvs9YQ1KgX19Zt5TFRlbZ2Y7BcoawP7A1cDCzHw4IqYAG2bmre3kV8VdjzKOrlGAnwuclJm1CrOI+BPlg26McTizzbGXVew1gMOBZ1fxfwF8LYe0UJkUBRRA1Zf89wdOZv64CzGnAXT4CbA53hRgjS58Um3Euygz92se1BkRl2bm3t2I36mI+AmlwLm9C7HWoLRqnAY8ttp9dfObYJtxuzr4eVUQEVMzc3GXY+4E3JSZD0bEbErh883MvKfNeBdnGVP1QeDmzPx6OwO0R8X8e+tlN1RF3tsz87xq+ynAF7P9i01WeO4O0/O5odvPmYh4ZWZ+f2X72oj7ckqrKnT4nhDlQo5Lu9UiGt0fiL8mcFZW46G6FHO90cXXWPtqxpxCGRv7cLW9JrBuZt7XWba9sdp34VX/gCsy87GUpsZuxJxGGdPwtGq7o6b0iJgO/DuwZWY+LyJ2B56UmV/vMNWudA1GxOcaMcbSbosEsDFwdURcxMgm4NpXzWXmIxHxnsz8HuUqk27pyuDnZlVz/Jsp3WPNV6908qn/BcDjKFfaNOK1+wl9WUS8fYx4bedH6Q6bFRE7Uy6WOB34DqWrrB2Logwofy3wtKqAXruD/MjM67v8d3y4UTxVcX4bEcs6SPH+iHhKZv4W/v6mfX8H8YiIrYHPAY3pFc6jtIbe1EHYbj9n3ksZsLyyfbVU3ZRtd1WOchKwV5Qrao+htIh+kzIerh1dHYhftWI9EhHTuvWBnzLofvQHlrH21XE2pWW+8QFufUoLfc+mk+nEal9AVQ+cayJi28z8c5fC/jelKf3gavt1lKb6dpvSv1Hd/t+q7f8HnErpSujE2yldg4+NiJupugbbiDOvwzzG06qJvh1nRcS/UP52Sxo7O+yTf6gqwhtF6OZ0NrYBSivZecBZdOHqlYj4ErAB5Uqir1Fa4truYqRcPXQ15UrBD1O6Yzq9SuuRzFxWfer/fGZ+rvEG26ZXUS5CODwzb40yL9AnO0mwB3/H30TElyndRFnlPDeqsWttdPkfCcxptHxTuvtf30F+UF53vgO8stp+bbXvWR3E7MpzJiKeRymwt4qI5qt1NwI6KUR7MX3DsszMiHgJ5fH99Yg4vIMU30K56GBZRDzQhfygFCULIuJXjHx9rPUBOCIeDWxFGaf0eJZ35W1Eef50Yr3m1u/MXBxDOl8eTJIuvIg4lzIvx+8Z+cBpa36gbjel97qrrQddg90aFEtEbEe5Mums6omyZrtxq7750Trtkz+U8sa3D2XsySuA93fSfdDtbpeIuDwz92z6PhX4WWY+daU3HjveJZn5+KZ4awPnZeb+HeR4IfBflA8JL8rMP0UH4/F6oQd/x16NUduoCtDJtBKNWF3vFuzWc6ZqzdmbUsR/sOnQIuCc7GCurijjL7s2fUPVC/Fz4I2UbsHbKVfNzexG/G6IiMPG2p+Zc8bav5I4r6fMKXURywuoe4E52cEFCBHxO+CoxoeLKOONP59tzlXVa6t9C1TlA12O1+2m9CURsSnLP7HtTxlw2rHmLomI8jhvt0siRg6KjYi4h84Gxf590jRgJ8qnmi9RBhrXlpldX/IiM78dZXBxY/DzS7vwontGRDw/M/+38wyB5Y+9+yJiS+AuymXZ7Xqo+n5PlLlibqV8Uu/EG4C3Ah+riqcdWD6P2oRFxG8z8ykRsYiR3crd+ITe7b/jMxtjObqhR139d1VjdhqDqQ+h3O+2des5k2V+tD8Az6n7Jj8Bt3WreKo0WkTf2MUW0U0oA92bu5PPbTdeZs6pulIbV9Fek5kPtbpNizjfAg7JzG+3m884/gn4fkT8lfLYeTTlbzuccgjmUujHFzCdMhHmC4EtOoy1F2WczQ3V1yXAnh3E2wf4HaVo+h2lC6/teE1xv0Tph/8LpbtsAeXKqnbjXQ48tWn7KXQwRwdwKbAOI+dDWtBBvLWBd1KWvvgB8A5g7Q7/hp8FDujSY3ARy+fFeYTyht2NeXI+QBlPdhCl2LkF+EgH8d5EmQzwaZQrGm+nzJTe8d9gmL/G+Tt+uIN411PeRHfrUn4/owwbuKzaXquT50sVYzvKeLQ7qv/zT4BtO4zZtedMFe88YJ0u/68/Q+nqP4Qy9OLlwMu78Ld8ZvXzBpSr5tqN9abq9fpuyhWx9wO/7jC/2cCNlOkvzqUM6XhaB/E6mmOvRdy1gT2qr45ev3v9NVm68A6mvJDNpVS1TwXenZk/aDNeY36SqdX3xZTiZ35mXtpmzLWAXav82vpkMEbMnnTtjNrX9pVPEXFhZj6xqctoLcpEi+1epfQ1ypOv+TLvhzPzTe3Eq2IeRvkEtCvlIoRTMrNXY8I6FhHrUsYR1G7BjLGXAGo0z2d2tmRI49LnEbKN7tUYeWFIT3Tyd2yKsSFlFvE3UOaJ62hG91539XdLt58zEfFNYDdKodc8BKOTx2NXp2+ILi9BEmXZsP2ACzJz7yhTgPx7djZlxXzgNZl5TbX9GOC7mblvm/E+DtxJd8ecUrV6787IlrdvdhKzVyZLF96/Aftldbl8NajxLKpFOtswq/o6nfIG05iQ7K0R8f3M/EQbMZ/A8quy9omIbjxoGpeTdqtLotuDYn8T3Z00bb/M3Ktp+9dR1ttrW5augzlR5nU5CPjPKBck7NJuzIg4e/QL61j7JhBn3BfT6vFTdyxCY76iXSkv3o2FnV9EZ4OpoTxfGtajDFp+1DjntpS9uTCEiPgt5dP5eZQZujvqRs8ylu+rwFcj4h8og7U/HRE/oLQQXlczZNe6+qNcsfqJGOcK22z/ytpePGf+WH2tQZeWssnMN3QjTpNuL0HyQGY+EBFExLqZeXVE7Nphjms3iqcqx/9XjW9sV6Nr7e1N+xLoZMzpcZSWst0pk/Y+D/gtpSdl6EyWAmqNHDnX0F10NnP41pQlRxbD3//pZ1K6POZT1peasKo/eSdKl1ZjzETS+YPmpxGxMaX17eIq5lc7iNcoTkZfPfd42ptB/FjKpGkLKFed/C/l6qd2PRwRO2XmH6HM60P31mjamTK/1Ha0eUValInspgCbVeMbmq9e2aqNkC9qcSypeYl2Zn6oyvNcyuN7UbV9POXx3bZccZLQ/6o+EX9wrPMnYBPgiojoyoUhlddRWqcPAj4ZEQ9SBs//czvBovszur+LUtTuVA223ZwyQLsdjcdwL1tTO37OwPLHZTdVz8XD6d5UHQ9m5tLGONOqNb2T7p2bqtfunwC/ioi7Kd1vnZgXK85E3vb/P3sw5pTyeN6LMqzjDdW4v9rr//XLZCmgfh4Rv2DkrLOdDODdgqZ5iyiDbqdn5v3Vi25dsygzcnetPzXKvDhnZ5mo8IcRcQYddklkFydhq+I9QvUJvUsh301Z1qR5JvKOPmlGWRX9pZTxLKdQWg7uaTPcWyiDJLekFLQN9wKfrxusB5+iG6ZTFudtWFrta1ssX3YGyoeXWXT2+tPtC0PIMrj9Acr9XUqZzmC3DkJeSxm/8skcOaP7D6Lm4tFVMfYP1VfHXf2Z2WjpvS/HmKSynZhNt/8E8DJKq1Gnz5lGj8F7WLHYaXfJJ+j+VB1dbU3PzJdVPx4f5WrOaZSr/DpxJKW1qNG6eB7wxU4C9qC77f4sc/oti3K16e3ANp3k2EuTYgwUQJRJyf4+WVx2NuvsBygvEKdVu15E+WR4AvCVzDy0ZrzvA+/MkWszdWysMUsdxhsxgShtrsVV9e+3mpizrTFQVex1KW8wUN5gOp2J/G2UMW7bZ+aHq6trHp2dLeVyVGZ+rpO8RsXr6tVZEfFvlMHKjefIS4FTM/M/Osix+ZL+ZZSLLz7V3KUwaBHxR8qYju9Q3lwurYr8duN1dUb3iPh9Zj6hW/GqmCuMYexkXGN1+7dQlsK5s+MES7xfUsbZ/AvlSs7DgDsy8187iNkYd9mVqTqiB0uQRJm5fpfMPLkqIqdm5lhTtQzEeN1tmdluqygR8UXgfZSxg8dQXnsv7eGHxY5MmgKq26Jc0t8oyH7XziDJiPgppZDYkDLfye/pcEbuUfE/BfwfXZrNNrq0FleUuZ/GlTWXP2g1FqiK18m8JF1fyiXKpcRvZXkhOpeyXmFbrQkR8TOqiVgzc6+q++CS7GAOmqrFqHmZi04mvey66P5EiETE0ZQrS7ehtE78hnLf/9hmvM+OsXsh5eql08Y4trJ4n6ZcJDF60G7tNThj+SSVB1fxGjaitIa3XahVxcRrgB278aEjIuZn5r6NYqfad1GHz8HfZ+YTqu7qt1Guuvx9djBnXDdVxcksYNfMfEyUMazfz8wnr+SmY8XqyQfWKm6ju22vRndbZnYyCWtz/O2BjbKN9QT7ZbUuoGLFuWL+fojO54zpWDWwNChvBO9pPgT8Z2Y+scP4iyhjbpZRBpR3dL9jSNfiirGvqGnIDsY1/P3TeIy88umyHDlYvW7Mrl4tGKvA1Vndar1sitfViRBHxZ5K6fr9F8qCsGu2GecrlDFAjS6ygyiXjm9KWXT2n2rGG2tizmynKyt6O0llt9fCuyAz96+GYXwW+Cvwg8zcqYMc30RZXmgmZSWIqcAHMvPLbcZ7MnA8ZdjAWix/rW2rIIuISyljSy9uek7/vYCsGaurH1ib4jaK0PmU7u5FwFXZxtWxo7r4x8qx9oeEflitx0BlZleu2OiVzPwNQESs3fi5ISLW70L8bt//rk4gGuUqos9RxpmsA6wJLKlb4PW4ebcXS7l0+2rBnk3E2kXdXv6o2xMhEhEnUFrdplDW9PogpSuvXXsCT87lC6OeVMV7CuXCiVqyi2MQM/My4LKI+E67LZ8tdHstvI9WBfgxlNeLjYC2BvY3+RaloN2e5R9kOhnn9/Uqp/l058KVpZmZEdF4Tk9pN1BzgRQjV35Yn85qgHlRBrp/hXK/F1OeN+04oennFSbIpf4FSn2xWhdQwy4ijqQ0H+8YZeX2hg0pE2p2Gn+sFcL/K9u/9Lvba3F9ntLX/X1Kc/U/Uv/KpL/r9ligymcpY4G2iIiPUS1L0UE86P7Vgt28OqtXdsrMg5q2P1R9yq6lqbt2XkScSrlKqbnbu5PFYf8P+BSwLbButW9rygUE7diE0rLRKGanUOYJejjauNikR4/v7SPiP1hxIHAnXVld/dCRmWdUPy6ktHR0w2lVvPmMvCCoXQsz82ddiNPwvShTxmwcZY6pN9LhxTax4soPW9PByg+UiYpfQyk8n0V53jzQ8hbjaHw4qIq6t1E+ZCTlA8dJbebXcxZQg/UdyuzC/0G5pL9hUXY4GVllrBXCv0WbK4RnmSR0r+jiWlyZeV1ErFl9Sj+5+tT63jbDfYMuL8qcvVnKZayrBdvuZqS8GD6PMnbnIOCJDN9zu1utl42pGxK4jzJol6Z9nRRQm1BWft+aMqXI/pSiqt1Pv58ALomyTlpQui//vWpNOKuNeN+g+4uOn0zpWv00pThpTPrZia5+6Igy4eNJlCud94iIPYEXZ+ZHO8hx68x8bge3H+2ciPgk5fHXXNC32/W0lPIYuZdyUcwHM/NXHebY7bmqvsDyrtoPR8RCyvOn7bFplNbAeymPISgF2jdZ3nI9XHIIpkP3qzdflP5zKF0RhzfvazPedMqL9c+q7d0bcduMdy6l6+6blDebf6ZapqLNeBdV3y9p2nfpoP8PY+S5bvW1Z/W1LrBuB/Eur74/hXLZ/AuACwd9P0fluDfdXf5oDrBx0/YmwH93mOMCSivMpdX2YykXYLQbLyhdlRcDL6Z8Qn9CB/G6/vimrJ4ATUvCNPa1GW8N4IDqb/d2SitFR0vZUMbLPWHU/f5DhzG/AszsJMaoeOeM8dX20ivAR4HrgO8Bz6Uar9xhjhc2P34oH7I6WYrr4uZ41c9tv35Xt79yIvuG5WvYPqWquxZFxHuB1wJPq66O6WTm2W/Q3U/Ar6O84L6DUjw1WlDatSqMBQL4vyyXif+92zYiLqZ0sbaj0f33AuCrmXlmRHTy6bwXrqIUyTtR1ptbSJkeod0rbPbMprmFsoyz6XTKjm7P/vxFyif09TPz9Gow9Q9p/xN6Lx7fD1avC9dGxDuAm1m+RFVtWebw+UKWgc9Xd5hbwwaZ+fuIaN63rJ1ATVekrQW8oWoFfpDlg77buiItuz9H3vujTJfzbEqr4Ocj4nuUtUzbuiqU7q/80IvxoRdHxP6ZeUEV84n0drLXjlhArd4aK4Qfnt1ZIXyzzPxeVZSRmcsiou2xO7l8cOMDUS753ibrL2/RbKjHAkXEoykzjq9fvdk3z0S+QQehb67GSzyLsmzGunTeDdNtpwH3UFpjbu5CvDUiYpOsrhaLsmxIp69n3Z79uduDqXvx+D6a8th7J/ARSnflYR3GPDvKvHtdmT4FuDMidmL5G/UrKAs9t+OFXchnBVGmwDiZciXaVykfho7NzF+2GzMzMyJupUyxsIzSyvqDiPhVZr6n9a3H1O2VH3oxPnRf4PyIaIzT3Ra4plH4tlvg9spqPY2Buisi5lJaiH5VvTHsT5luoa0xVVW8F1Pe+OZTZp39XWaOtajtRGN2fVHmbomyyOrrKQPmmz9VLQK+kW0OgI6IDSjN/AuyjGuYQemeaPvFu9si4g+ZuUcX4/0jZcK9xhQBrwQ+lpnf6lL8f6Ca/Tkzl67s/HFiXEjpzrqoer5sTrmcv+2WsmF+fDfE8ulTHmb5oOLM9qdP2ZHS5XYA5cKVPwGvzcwbOs+2O6Ka2iQinkOZ4+39wLey/YXWj6ZcVHMnpcj5SWY+1GgtzA6mcOimKIscN8aHnp0djg+NHk250CsWUKuhiPhtZj4lVpwHq9N5oPahXEb8OOAKqk/A2eZEZ7F8NuA3UVqfjos25zppinkAyxdlBoZvJe+IOCgzfzjoPPopypxIn8vM2pfvt4i5O8sHeP86M6/sVuxuiIhDKa3A+1DGbL0CeH+OWjqlZsyuPr5j+WS+zRZSCvwvZ2ZbV1X1QjX4fo2s1mgcJrF8RvPPAHMz88fRwUoQEfEhypi+FQqGiNitnUIlujxXlSygVEOUBTjfQVk/ahHlCqXPtfsiWzXLPpvy5vJvmXlRJwVUjLMoc3awsnwvVF1sjTlomt8IPzyonHpl1JiTXShTAnQ85mRV0c1P6L14fFdv+Jszcp3Qeyn/s40y83Vtxn0xTTPt5/KpCNqJ1YvpG7oqymS+WwE7UGbnXpNyv/cdaGJNIuJqxpirKldc6FsTZAGlCasGMd5LWVEeyviqjTOzrcVHoyxa+gHK+klvq5rqP5kj5wuqE+8qurwocy9ExM9ZPgdN8wvZCePeaBW1qjXJD7NePL5jjCVRYvnM9ldk5uPaiPlxykD5xuvEIZTla9qaniR6sFRRt1Vda3tTZpi/pxrsv1W7rfO9EBEXZoerW2gkCyhNWERcmZm7r2zfoESPFmXutm6PB9Lk0IvHd1WUPSeryXWrC01+kWUJlra6oKJMCrx3VgsxV1dqXdJBy/KqsFTR08ban5nn9juX0WL5MikHU1rGujVX1aTnVXiqo6uXmFbN3itU8Flz7boYuSjzlRHR1UWZe+D8iJjZzfFAWn31+PF9DPDbiPgjpZtxB+Bt1XijOS1v2drGQGMy4GktzpuIVWF6knc3/bweZd6q+QzHEiSjW7ZnNf08tMukrApsgdKEVZ9WdwVGXGJKucS29niW6lLnhvWAlwF/rTumI3q8KHO3RcSVwM6Uq4kmzXggtafXj+9qTF5jAdhrOh04HhGHAB+nTCbZmIH92Mw8tc14jYtX9qCsp9jRxSv9EBHbUJbN6mReu66KiB0z8/qV7dPEWUBpwno9nqUaR/DbzDygzdtfPPqy4U6v6uuF8f6OjgdSK714fFdTYLwL2C4z3xwRuwC7tjPoOyKenJm/qwqyR7F8wtDfZ+at7eZYxR766RuaRUQAVwzL8AYY9/Ezf5gGuq9q7MLThPXhDX4XoPbaTNHjRZl7wE8tmrAeP75PpnQ1Panavpkyt1Y7V819ljIRYmOm/dM7zK3ZE1h+1eo+ETFU05NExOdY/rxuDCgfirFF1ZWgjwOmxfLFuKFM4Lve2LfSRFhAaWDGmKfqVuBf2wjV60WZu+1Myv0OygvYDpSu0NpXPGlS6OXje6fMfFXV7UZm3le1nrTjoWq+r62jrCwwQrvTLYw3fQNlDc1h0TwWdBnw3cwclg9vu1JmYN+Y5YtxQ5mK5s2DSGh1YQGlgahepB/XuPqnE5m5kDKo9JCOE+uD0ZdfV2M83jagdDTkevz4XhoR67N8gPZONA1Qr+mFwDMp88TN7056QBn0PNTTk2TmnCjL9Dym2nXNIPMZ5RWZ+bqIeF9m/vugk1mdWEBpIDIzI+JMYGjmchmUzLy4uqJR6rfjgJ8D20TEt4EnU5Ybqi0z7wROiYirMvOy7qXIH4BH0/76dz0XEbMpVy3eQGlZ3iYiDhuGaQyAfSNiS+BVEXESMKKFcUhb6VcJFlAapIsjYr/MvGjQifRTRDSv9bcGZamPvw4oHU1S1UUbmwAvB/anvLEeXRVCnfhrRLyPFWfaX52nJzkBeHZmXgMQEY+hzO4+DAO0vwScDezIyJbBoPx9XcqlTV6Fp4GplhbYmbLi/RImyeX8EXFc0+YyyqfWH3Z6+bhUV0TMy8xZKz+zVszzgfNYcab9Wus/rkrTk4x1NeSwXQFctT59ieVL7Jzb5ZbCSccWKA3ScwadwID8PjN/1rwjIt5KeXGT+umsiPgX4FTKhxig426dDTKznYtBRsjM3wBExNqNnxuqcVvDZF5EfA34n2r7UDqYZLhHrqbk9yNKEfqtiPhqZn5usGmtumyB0kBFxFOAXTLz5IjYHJiamX8adF69VH1Cf39m/rrafg9wYGY+b7CZabKJiLGea5mZbXfrRMRHgfMz83/bz2zk9A3AH5sObQj8LjNf20n8bqrmvno78JRq13nAFzOz3QH5XVdNgfGkzFxSbU+hTDkxNK1kqxoLKA1M1ZU1izJx32OqgY7fz8wnDzi1noqIzSjz7LwbeC5lFuhDMnPpQBOTOtA0LUkAUyjjlR5iedf8RjXjTaOM0VolpiepWsW2bYyDGjYRsQDYrzFUICLWAy4apkWZVzV24WmQXgY8nmrCucz8a0RsONiUei8z74yIFwNnUcaJvGKYL9HW6mvUxIoNC4EFmXl7nViZ2dXn7qo0PUn1fP4ksA6wQ0TsDXx4yAa6nwxcGBE/rrZfCnx9cOms+iygNEhLq+kMGnPQTBl0Qr00xsSh61C6J14REbU/oUtdcDhlFvJzqu3ZlKJ+h4j4cGZ+q27AiDg7M5+xsn2rmeMos6XPBcjMSyNih4FmNEpmnhgRc1nezfiGzLxkgCmt8iygNEjfi4gvAxtHxJspL+ZfG3BOPdPtT+hSF6wF7JaZtwFExHTKDN9PBM4FJlxAVV1CU4DNImITls83tBGwVTeTHkIPZebCUZO4D12rcmZezJAsMbM6sIDSwGTmpyLiWcC9lBl835+ZZw04rb6o3mB2oWktqiGZdE+TyzaN4qlye7XvbxFRd8HetwD/BGzJyDfpe4HPd5Tl8LsiIl4DrFktyPxO4PwB56QecxC5+i4ifpuZTxk16LThEeBvwCcz84sDSbDHIuJNwNHA1pT1vfanXA3z9EHmpcknIr4IbEtZQBjgIOAmygUOZ2TmgW3EPGqyXRofERsA/wY8u9r1C+Ajw3QVnrrPAkpDJyI2pVwGveugc+mFxtUwwAWZuXe1Wvq/Z+ZYA3qlnqnWpDyIsoQLwO8ok7q2/cZQjWX8Z8oVaUdULTK7ZuYZHSc8pCLilZn5/ZXt0+rFAkpDKSJmZObQrn3ViYi4KDP3i4hLgSdm5oMRcUVmPm7QuUmdiohTKQPR/zEz96haZ87PzL0Hm1nvRMTFmbnPyvZp9eIYKA2l1bV4qtwUERsDPwF+FRF3U5azkfqqmsbgP4EtKF3pbc3ZNMpOmfmqiDiEEuy+GDW6enUREc8Dng9sFRGfbTq0EWWZJq3GLKCkPsvMl1U/Hh8R5wDTgJ8PMCVNXp8AXpSZV3Ux5tJqUsnG9CQ70bQI8Grmr5QlW17MyIV6F1G6MbUaswtPGoDJuISNhk9E/K7bM/9XV9a+H9gd+CVlfNXrM3NuN3/PMKnW66t71aJWcRZQUp9N1iVsNHwi4jPAoyndyX9vJcrMH7UZbw3gFcDZlKtLg3KxxJ0dJzvEIuLJwPHAdpSenUZXaNtrCmr4WUBJfVYNHn88cHFmPr7ad7mLeqrfIuLkMXZnZr6xg5jzMnNWB2mtciLiakqX3Xzg4cb+zLxrYEmp5xwDJfXfpFrCRsMrM9/Qg7BnRcS/AKcCS5p+19AtANxFCzPzZ4NOQv1lC5TUZ9Wbyy7Asygrzb8R+M5km3xQgxcRWwOfY/k8UOcBR2fmTR3EHGss32rZnRURjWkKDgbWBH7EyK5Ql01ZjVlASQNQDbRtzFr8y8z81SDz0eQUEb8CvsPyNe9eCxyamc8aXFarjuoq2vGkqwus3iygpAGIiEdTVm9P4KLMvHXAKWkSiohLR09wOda+mjH/caz9mfnNdmNKw8gxUFKfVWvhfRD4NeVqnc9FxIcz878Hm5kmobsi4rXAd6vtQ4BOBz7v1/TzesAzKIsLr7YFVES8a4zdC4H5mXlpn9NRn9gCJfVZRFwDHNC4Qmd1X/tPwysitqOMgXoSpTX0fOCozPxLF3/HxsApmfncbsUcNhHxHcrUJD+tdr0QuBzYnjJFyScGlJp6yBYoqf/uosxU3LCIzj/1S+34MHBYZt4NEBGPAj5FubChW5YAO3Qx3jDaGtgnMxfD3+d6OxN4GmVqAwuo1ZAFlNR/1wEXRsRplE/9LwEub3QDZOaJg0xOk8qejeIJylQDEfH4TgJGxE+plnEB1qDMSP69TmKuArZg5HI1DwHTM/P+iFhdl7GZ9CygpP77Y/XVcFr1fcMB5KLJbY2I2GRUC1Sn7wufavp5GXBjJ9MirCK+zfIPRQAvAr5TzfF25eDSUi85BkoaoGrpi6mZee+gc9HkU10x9z7g+9WuVwIfy8xvjX8rjSUiZrF8Pq3fZea8Qeaj3rOAkvqsGnD6VsqSDxcBGwGfycxPDjQxTUoRsTvQmK/o15nZVotJRCxiedfdCjJzo3biDrOI2Cgz761a7lawms++PulZQEl91phnJyIOBfYBjqVc7uxaeFrlRcRHgFsok3MGcCgwIzM/ONDEeiAizqB01z0M3NB8iNV09nUtZwEl9VlEXAHsTZkB+vOZ+ZuIuCwz9xpsZlLnxnosr+6P74j4Q2buMeg81F9rDDoBaRL6MuXT6hTg3GouHsdAaXWxJCIOjYg1I2KNqqV1yUpvtWqbHxH7rfw0rU5sgZKGQESslZnLBp2H1KmI2B74DGVAdQK/A/4pM28YYFo9FRFXAzsDN1KKxUYXnt3yqzELKKnPXPZBWr1UrcgryMwb+52L+scCSuozl33Q6iwiNgfeTHk8/31Oqczs5uzm0sBZQEl9FhHnAs9vWvZhKmXZh+dSWqF2H2R+Uici4nzgPMoSJg839mfmDweWlNQDzkQu9Z/LPmh1tkFm/uugk5B6zQJK6j+XfdDq7IyIeH5m/u+gE5F6yS48aQCqS54PqDZd9kGrjWpG8g2ApZTW1cYVaavdTOSa3CygpAGIiDWB6YwcZPvnwWUkdUe1vuOhwA6Z+eGI2JYyE/mFA05N6ioLKKnPIuIo4DjgNsogW+eM0WojIk4CHgGenpm7RcQmwC8z04kmtVpxDJTUf0cDu2bmXYNOROqBJ2bmPhFxCUBm3h0R6ww6KanbXMpF6r+/UCbOlFZHD1Vd1Al/nxfqkcGmJHWfLVBS/10PzI2IM2maziAzTxxcSlLXfBb4MbBFRHwMeAXw/sGmJHWfBZTUf3+uvtapvqTVRmZ+OyLmA8+gjO97aWZeNeC0pK5zELkkSVJNtkBJfRIR/5WZ/xQRP6UaH9IsM188gLQkSW2wgJL651vV908NNAtJUscsoKQ+ycz51Y8bAWdmplcmSdIqymkMpP57FXBtRHwiIh476GQkSfU5iFwagIjYCDgEeANlPNTJwHczc9FAE5MkTYgtUNIAZOa9wA+AU4AZwMuAi6tlXiRJQ84WKKnPIuLFlJannYFvAnMy8/aI2AC4MjO3H2R+kqSVcxC51H8HAZ/OzHObd2bmfRFx+IBykiTVYAuUJElSTY6BkvosIvaPiIsiYnFELI2IhyPi3kHnJUmaOAsoqf8+T7kC71pgfeBNwBcGmpEkqRYLKGkAMvM6YM3MfDgzTwaeO+icJEkT5yByqf/ui4h1gEsj4hPALfhhRpJWKb5oS/33Ospz7x3AEmAbypV5kqRVhFfhSQMUEZsA22Tm5YPORZI0cbZASX0WEXMjYqOIeBRwMfDViDhx0HlJkibOAkrqv2nVUi4vB76ZmU8EnjngnCRJNVhASf23VkTMAA4Gzhh0MpKk+iygpP77MPAL4LrMvCgidqTMCSVJWkU4iFySJKkmW6CkPouIT1SDyNeOiLMj4o6IeO2g85IkTZwFlNR/z64Gkb8QuAHYGXj3QDOSJNViASX1X2MFgBcA38/MhYNMRpJUn0u5SP13RkRcDdwPHBkRmwMPDDgnSVINDiKXBqCaRHNhZj4cEVOADTPz1kHnJUmaGLvwpD6LiA2AtwEnVbu2BGYNLiNJUl0WUFL/nQwsBQ6otm8GPjq4dCRJdVlASf23U2Z+AngIIDPvA2KwKUmS6rCAkvpvaUSsDyRAROwEPDjYlCRJdXgVntR/xwE/B7aJiG8DTwZeP9CMJEm1eBWeNAARsSmwP6Xr7oLMvHPAKUmSarALT+qziHgy8EBmnglsDLwvIrYbbFaSpDosoKT+Owm4LyL2At4F/BH45mBTkiTVYQEl9d+yLH3nLwG+kJlfADYccE6SpBocRC7136KIeC/wOuCpEbEGsPaAc5Ik1WALlNR/r6JMW/DGavmWrYFPDjYlSVIdXoUnDUBETAf2qzZ/n5m3DzIfSVI9tkBJfRYRBwO/B14JHAxcGBGvGGxWkqQ6bIGS+iwiLgOe1Wh1iojNgbMyc6/BZiZJmihboKT+W2NUl91d+FyUpFWKV+FJ/ffziPgF8N1q+1XAzwaYjySpJrvwpAGIiIMoa+ABnJeZPx5kPpKkeiygpAGJiI1oagXOzL8NMB1JUg124Ul9FhFvAT4EPAA8QllQOIEdB5mXJGnibIGS+iwirgWelJl3DjoXSVJ7vPJH6r8/AvcNOglJUvtsgZL6LCIeD5wMXEhZ0gWAzHznwJKSJNXiGCip/74M/BpYQBkDJUlaxdgCJfVZRFySmY8fdB6SpPZZQEl9FhH/DtwA/JSRXXhOYyBJqwgLKKnPIuJPY+zOzHQaA0laRVhASZIk1eQgcqnPImJt4EjgadWuucCXM/OhgSUlSarFFiipzyLia8DawJxq1+uAhzPzTYPLSpJUhwWU1GcRcVlm7rWyfZKk4eVM5FL/PRwROzU2ImJH4OEB5iNJqskxUFL/vRs4JyKupywkvB3wxsGmJEmqwy48qc8iYt3qx12r79cAZOaDY99CkjRsLKCkPouIizNzn5XtkyQNL7vwpD6JiEcDWwHrVwsKR3VoI2CDgSUmSarNAkrqn+cArwe2Bk5s2r8IeN8gEpIktccuPKnPIuKgzPzhoPOQJLXPAkrqs2oQ+UHA9jS1AmfmhweVkySpHrvwpP47DVgIzAe88k6SVkG2QEl9FhF/yMw9Bp2HJKl9zkQu9d/5ETFz0ElIktpnC5TUZxFxJbAz8CdKF14AmZl7DjQxSdKEWUBJfRYR2421PzNv7HcukqT2OIhc6j8/tUjSKs4WKKnPImIBpYgKYD1gB+CazHzcQBOTJE2YLVBSn2XmiAHkEbEP8LYBpSNJaoMtUNIQiIgFowsrSdLwsgVK6rOIeFfT5hrAPsBfB5SOJKkNFlBS/23Y9PMy4EzAtfEkaRViF540IBGxQWbeN+g8JEn1ORO51GcR8aRqMs2rq+29IuKLA05LklSDBZTUf/8FPAe4CyAzLwOeNsiEJEn1WEBJA5CZfxm16+GBJCJJaouDyKX++0tEHABkRKwNHA1cNeCcJEk1OIhc6rOI2Az4DPBMymzkvwTemZl/G2hikqQJswVK6r/9MvPQ5h0R8VbgSwPKR5JUk2OgpP77QEQ8vbEREe8GXjLAfCRJNdmFJ/VZ1YV3BvBu4LnAY4FDMnPpQBOTJE2YBZQ0ABGxBXAWMB94Y/pElKRVigWU1CcRsQhofsKtQ1nKJYHMzI0GkpgkqTYLKEmSpJq8Ck8agIjYBNgFWK+xLzPPHVxGkqQ6LKCkPouIN1Emz9wauBTYH/g/4OktbiZJGiJOYyD139HAfsCNmXkg8HjgnoFmJEmqxQJK6r8HMvMBgIhYNzOvBnYdcE6SpBrswpP676aI2Bj4CfCriLgbuHGgGUmSavEqPGmAIuIfgGnAz51IU5JWHbZASQMQEfsAT6HMAfU7iydJWrU4Bkrqs4j4IDAH2BTYDDg5It4/2KwkSXXYhSf1WURcA+zVNJB8feDSzHQguSStImyBkvrvrzRNoAmsC9w8oFwkSW2wBUrqs4j4CWUeqF9RxkA9C/g9cBNAZr5zYMlJkibEAkrqs4g4rNXxzJzTr1wkSe2xgJIGqFoTb5vMvHzQuUiSJs4xUFKfRcTciNgoIh4FXAx8NSJOHHRekqSJs4CS+m9aZt4LvBz4ZmY+EXjmgHOSJNVgASX131oRMQM4GDhj0MlIkuqzgJL678PAL4DrMvOiiNgRuHbAOUmSanAQuSRJUk2uhSf1SUR8jjLv05ic/0mSVh124Un9Mw+YT5mFfB9Kt921wN7AOoNLS5JUl114Up9FxAXAUzJzWbW9NnBeZu4/2MwkSRNlC5TUf5sAGzVtT632SZJWEY6Bkvrv48AlEXEOEMDTgOMHmpEkqRa78KQ+iog1gP2B64EnVrsvzMxbB5eVJKkuCyipzyLiksx8/KDzkCS1zzFQUv+dHREHRUQMOhFJUntsgZL6LCIWAVOAh4EHqt2ZmRuNfytJ0jCxgJIkSarJq/CkAYiIF1OuvgOYm5kuKixJqxBboKQ+i4iPA/sB3652HQLMy8z3Di4rSVIdFlBSn0XE5cDemflItb0mcElm7jnYzCRJE+VVeNJgbNz087RBJSFJao9joKT++w9WnIn82MGmJEmqwy48aQAiYgZlHBTA752JXJJWLRZQUp9ExD6tjmfmxf3KRZLUGQsoqU+qLrvxZGY+vW/JSJI6YgElSZJUk1fhSX0WERtExPsj4ivV9i4R8cJB5yVJmjgLKKn/TgaWAgdU2zcDHx1cOpKkuiygpP7bKTM/ATwEkJn3UaYzkCStIiygpP5bGhHrAwkQETsBDw42JUlSHU6kKfXfccDPgW0i4tvAk4HXDzQjSVItXoUnDUBEbArsT+m6uyAz7xxwSpKkGiygpAGIiK2A7WhqBc7McweXkSSpDrvwpD6LiP8EXgVcATxS7U7AAkqSVhG2QEl9FhHXAHtmpgPHJWkV5VV4Uv9dD6w96CQkSe2zC0/qk4j4HKWr7j7g0og4m6bpCzLznYPKTZJUjwWU1D/zqu/zgdNHHbMvXZJWIRZQUp9k5hyAiDg6Mz/TfCwijh5MVpKkdjgGSuq/w8bY9/p+JyFJap8tUFKfRMQhwGuAHSKiuQtvQ+Bvg8lKktQOCyipf84HbgE2A05o2r8IuHwgGUmS2uI8UJIkSTXZAiX1SUQsosXVdpm5UR/TkSR1wAJK6pPM3BAgIj5C6cr7FmUx4UOBGQNMTZJUk114Up9FxGWZudfK9kmShpfTGEj9tyQiDo2INSNijYg4FFgy6KQkSRNnASX132uAg4Hbqq9XVvskSasIu/AkSZJqchC51GcRsTnwZmB7mp6DmfnGQeUkSarHAkrqv9OA84CzgIcHnIskqQ124Ul9FhGXZubeg85DktQ+B5FL/XdGRDx/0ElIktpnC5TUZ9WM5FOAB4GHKJNppjORS9KqwzFQUp9l5oYR8ShgF2C9QecjSarPAkrqs4h4E3A0sDVwKbA/cD7wjAGmJUmqwTFQUv8dDewH3JiZBwKPBxYONiVJUh0WUFL/PZCZDwBExLqZeTWw64BzkiTVYBee1H83RcTGwE+AX0XE3cCNA81IklSLV+FJAxQR/wBMA36emUsHnY8kaWIsoCRJkmpyDJQkSVJNFlCSJEk1WUBJkiTVZAElSZJUkwWUJElSTf8f2lNJy3VunMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "from collections import Counter\n",
    "from nltk.corpus import stopwords\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset and split it into individual words\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words] for st in raw_dataset]\n",
    "\n",
    "# Compute the counter with the filtered dataset\n",
    "counter = Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "# Get the 25 most common phrases\n",
    "most_common_25 = counter.most_common(25)\n",
    "\n",
    "# Get the list of phrases and their counts\n",
    "phrases, counts = zip(*most_common_25)\n",
    "\n",
    "# Set the figure size and title\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"25 Most Common Phrases\")\n",
    "\n",
    "# Plot the bar chart with a grid and different colors for each bar\n",
    "plt.bar(phrases, counts, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "                                '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n",
    "                                '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n",
    "                                '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n",
    "                                '#e6550d', '#fd8d3c', '#31a354', '#756bb1', '#636363'])\n",
    "\n",
    "# Add a grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Rotate the x-tick labels\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "12b8c238-7006-490d-a870-fc6105a7b83b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "from collections import Counter\n",
    "from nltk.corpus import stopwords\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('kin.txt', 'r') as file:\n",
    "    # Only keep letters in the dataset and split it into individual words\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in file]\n",
    "\n",
    "# Get the list of stop words\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# Remove stop words from the dataset\n",
    "filtered_dataset = [[word for word in st if word not in stop_words] for st in raw_dataset]\n",
    "\n",
    "# Compute the counter with the filtered dataset\n",
    "counter = Counter([tk for st in filtered_dataset for tk in st])\n",
    "\n",
    "# Get the 25 most common phrases\n",
    "most_common_25 = counter.most_common(25)\n",
    "\n",
    "# Get the list of phrases and their counts\n",
    "phrases, counts = zip(*most_common_25)\n",
    "\n",
    "# Set the figure size and title\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"25 Most Common Phrases\")\n",
    "\n",
    "# Plot the bar chart with a grid and different colors for each bar\n",
    "plt.barh(phrases, counts, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "                                 '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',\n",
    "                                 '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',\n",
    "                                 '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5',\n",
    "                                 '#e6550d', '#fd8d3c', '#31a354', '#756bb1', '#636363'])\n",
    "\n",
    "# Add a grid\n",
    "plt.grid(True)\n",
    "\n",
    "# Adjust the word labels to make them easier to read\n",
    "plt.yticks(rotation=0, horizontalalignment='right', fontsize='large')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "61c3087c-0bd7-4fa2-905e-2efcbf0f3d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pprint\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Flatten the dataset into a single list of words\n",
    "words = [word for st in raw_dataset for word in st]\n",
    "\n",
    "# Count the frequency of each word\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Calculate the total number of instances of all words\n",
    "total_instances = sum(count for _, count in counter.items())\n",
    "\n",
    "# Get the 25 most common words and their counts\n",
    "most_common_25 = counter.most_common(25)\n",
    "\n",
    "# Calculate the total number of instances of the 25 most common words\n",
    "total_instances_most_common_25 = sum(count for _, count in most_common_25)\n",
    "\n",
    "# Generate a string representation of the results using pprint\n",
    "results_string = pprint.pformat({\"Total number of instances of all words\": total_instances,\n",
    "                                 \"25 most common words and their counts\": most_common_25,\n",
    "                                 \"Total number of instances of the 25 most common words\": total_instances_most_common_25})\n",
    "\n",
    "# Write the results to a file\n",
    "with open('output.txt', 'w') as f:\n",
    "    f.write(results_string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8f0fb4c5-749f-420e-9e68-6d50f802e310",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(\"{'25 most common words and their counts': [('lord', 75),\\n\"\n",
      " \"                                           ('them', 67),\\n\"\n",
      " \"                                           ('him', 58),\\n\"\n",
      " \"                                           ('god', 52),\\n\"\n",
      " \"                                           ('thee', 51),\\n\"\n",
      " \"                                           ('me', 46),\\n\"\n",
      " \"                                           ('israel', 37),\\n\"\n",
      " \"                                           ('it', 31),\\n\"\n",
      " \"                                           ('people', 30),\\n\"\n",
      " \"                                           ('jerusalem', 30),\\n\"\n",
      " \"                                           ('thelord', 25),\\n\"\n",
      " \"                                           ('ever', 23),\\n\"\n",
      " \"                                           ('you', 19),\\n\"\n",
      " '                                           '\n",
      " \"('andthelordspakeuntomosessayingspeakuntothe',\\n\"\n",
      " '                                            18),\\n'\n",
      " \"                                           ('selah', 16),\\n\"\n",
      " \"                                           ('day', 15),\\n\"\n",
      " \"                                           ('house', 15),\\n\"\n",
      " \"                                           ('christ', 15),\\n\"\n",
      " \"                                           ('away', 14),\\n\"\n",
      " \"                                           ('egypt', 14),\\n\"\n",
      " \"                                           ('thereof', 14),\\n\"\n",
      " \"                                           ('not', 14),\\n\"\n",
      " \"                                           ('congregation', 14),\\n\"\n",
      " \"                                           ('endurethforever', 14),\\n\"\n",
      " \"                                           ('theearth', 13)],\\n\"\n",
      " \" 'Total number of instances of all words': 74939,\\n\"\n",
      " \" 'Total number of instances of the 25 most common words': 720}\")\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint(results_string) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9cab974d-14e9-4670-82c5-984af73a2ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'25 most common words and their counts': [('lord', 75),\n",
      "                                           ('them', 67),\n",
      "                                           ('him', 58),\n",
      "                                           ('god', 52),\n",
      "                                           ('thee', 51),\n",
      "                                           ('me', 46),\n",
      "                                           ('israel', 37),\n",
      "                                           ('it', 31),\n",
      "                                           ('people', 30),\n",
      "                                           ('jerusalem', 30),\n",
      "                                           ('thelord', 25),\n",
      "                                           ('ever', 23),\n",
      "                                           ('you', 19),\n",
      "                                           ('andthelordspakeuntomosessayingspeakuntothe',\n",
      "                                            18),\n",
      "                                           ('selah', 16),\n",
      "                                           ('day', 15),\n",
      "                                           ('house', 15),\n",
      "                                           ('christ', 15),\n",
      "                                           ('away', 14),\n",
      "                                           ('egypt', 14),\n",
      "                                           ('thereof', 14),\n",
      "                                           ('not', 14),\n",
      "                                           ('congregation', 14),\n",
      "                                           ('endurethforever', 14),\n",
      "                                           ('theearth', 13)],\n",
      " 'Total number of instances of all words': 74939,\n",
      " 'Total number of instances of the 25 most common words': 720}\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import pprint\n",
    "\n",
    "# Open the text file and preprocess the dataset\n",
    "with open('bible.txt', 'r') as f:\n",
    "    # Only keep letters in the dataset\n",
    "    raw_dataset = [re.sub('[^A-Za-z]+', '', st).lower().split() for st in f]\n",
    "\n",
    "# Flatten the dataset into a single list of words\n",
    "words = [word for st in raw_dataset for word in st]\n",
    "\n",
    "# Count the frequency of each word\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Calculate the total number of instances of all words\n",
    "total_instances = sum(count for _, count in counter.items())\n",
    "\n",
    "# Get the 25 most common words and their counts\n",
    "most_common_25 = counter.most_common(25)\n",
    "\n",
    "# Calculate the total number of instances of the 25 most common words\n",
    "total_instances_most_common_25 = sum(count for _, count in most_common_25)\n",
    "\n",
    "# Print the results using pprint\n",
    "pprint.pprint({\"Total number of instances of all words\": total_instances,\n",
    "               \"25 most common words and their counts\": most_common_25,\n",
    "               \"Total number of instances of the 25 most common words\": total_instances_most_common_25})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08f04d9a-c72f-46eb-a044-9bc653418115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the text file and split it into words\n",
    "with open('bible.txt', 'r') as file:\n",
    "    text = file.read()\n",
    "words = text.split()\n",
    "\n",
    "# Create a Counter object with the words\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Create a Counter object with the bigrams (i.e. pairs of consecutive words)\n",
    "bigrams = [pair for pair in zip(words[:-1], words[1:])]\n",
    "counter_bigrams = collections.Counter(bigrams)\n",
    "\n",
    "# Create a Counter object with the trigrams (i.e. triples of consecutive words)\n",
    "trigrams = [triple for triple in zip(words[:-2], words[1:-1], words[2:])]\n",
    "counter_trigrams = collections.Counter(trigrams)\n",
    "\n",
    "# Generate a list of word counts for the most common 10000 words in the counter object\n",
    "wordcounts = [count for _, count in counter.most_common(10000000000000)]\n",
    "\n",
    "# Generate a list of bigram counts for the most common bigrams in the counter_bigrams object\n",
    "bigramcounts = [count for _, count in counter_bigrams.most_common()]\n",
    "\n",
    "# Generate a list of trigram counts for the most common trigrams in the counter_trigrams object\n",
    "trigramcounts = [count for _, count in counter_trigrams.most_common()]\n",
    "\n",
    "# Plot the word counts, bigram counts, and trigram counts on a log-log scale\n",
    "plt.loglog(wordcounts, label='word counts')\n",
    "plt.loglog(bigramcounts, label='bigram counts')\n",
    "plt.loglog(trigramcounts, label='trigram counts')\n",
    "\n",
    "# Add a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3b8db93-cdb7-4621-861b-92c548d52eaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_950/810931597.py:4: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  display.set_matplotlib_formats('svg')\n"
     ]
    },
    {
     "data": {
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2023-02-04T06:31:30.485954</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
       "      <dc:title>Matplotlib v3.5.1, https://matplotlib.org/</dc:title>\n",
       "     </cc:Agent>\n",
       "    </dc:creator>\n",
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       " </metadata>\n",
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       " <g id=\"figure_1\">\n",
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       "   <g id=\"matplotlib.axis_1\">\n",
       "    <g id=\"xtick_1\">\n",
       "     <g id=\"line2d_1\">\n",
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   ],
   "source": [
    "%matplotlib inline \n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "display.set_matplotlib_formats('svg')\n",
    "\n",
    "wordcounts = [count for _,count in counter.most_common()]\n",
    "plt.loglog(wordcounts);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c90754b8-1842-4c2a-b1b2-f657cad2c41b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "\u001b[1;36m  Input \u001b[1;32mIn [8]\u001b[1;36m in \u001b[1;35m<cell line: 1>\u001b[1;36m\u001b[0m\n",
      "\u001b[1;33m    wseq = [tk for st in raw_dataset for tk in st]\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m\u001b[1;31m:\u001b[0m name 'raw_dataset' is not defined\n",
      "\n",
      "Use %tb to get the full traceback.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Custom TB Handler failed, unregistering\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)\n",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\n",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m----> 1\u001b[0m wseq \u001b[38;5;241m=\u001b[39m [tk \u001b[38;5;28;01mfor\u001b[39;00m st \u001b[38;5;129;01min\u001b[39;00m \u001b[43mraw_dataset\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m tk \u001b[38;5;129;01min\u001b[39;00m st]\n",
      "\u001b[1;32m      2\u001b[0m word_pairs \u001b[38;5;241m=\u001b[39m [pair \u001b[38;5;28;01mfor\u001b[39;00m pair \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(wseq[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], wseq[\u001b[38;5;241m1\u001b[39m:])]\n",
      "\n",
      "\u001b[0;31mNameError\u001b[0m: name 'raw_dataset' is not defined\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)\n",
      "File \u001b[0;32m~/.ipython/profile_default/startup/00_start.py:83\u001b[0m, in \u001b[0;36mcustom_exc\u001b[0;34m(shell, etype, evalue, tb, tb_offset)\u001b[0m\n",
      "\u001b[1;32m     81\u001b[0m html \u001b[38;5;241m=\u001b[39m HTML(html_str)\n",
      "\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m IN_JUPYTER:\n",
      "\u001b[0;32m---> 83\u001b[0m   \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhtml\u001b[49m\u001b[43m)\u001b[49m\n",
      "\n",
      "\u001b[0;31mTypeError\u001b[0m: 'module' object is not callable\n",
      "The original exception:\n"
     ]
    }
   ],
   "source": [
    "wseq = [tk for st in raw_dataset for tk in st]\n",
    "word_pairs = [pair for pair in zip(wseq[:-1], wseq[1:])]\n",
    "print('Beginneing of the book\\n', word_pairs[:10])\n",
    "counter_pairs = collections.Counter(word_pairs)\n",
    "print('Most common word pairs\\n', counter_pairs.most_common(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f980c9d-5fdd-42b6-85bb-49caec832998",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "\u001b[1;36m  Input \u001b[1;32mIn [6]\u001b[1;36m in \u001b[1;35m<cell line: 1>\u001b[1;36m\u001b[0m\n",
      "\u001b[1;33m    word_triples = [triple for triple in zip(wseq[:-2], wseq[1:-1], wseq[2:])]\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m\u001b[1;31m:\u001b[0m name 'wseq' is not defined\n",
      "\n",
      "Use %tb to get the full traceback.\n"
     ]
    },
    {
     "data": {
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    "word_triples = [triple for triple in zip(wseq[:-2], wseq[1:-1], wseq[2:])]\n",
    "counter_triples = collections.Counter(word_triples)\n",
    "\n",
    "\n",
    "wordcounts = [count for _,count in counter.most_common(10000)]\n",
    "bigramcounts = [count for _,count in counter_pairs.most_common()]\n",
    "triplecounts = [count for _,count in counter_triples.most_common()]\n",
    "plt.loglog(wordcounts, label='word counts');\n",
    "plt.loglog(bigramcounts, label='bigram counts');\n",
    "plt.loglog(triplecounts, label='triple counts');\n",
    "plt.legend();"
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  {
   "cell_type": "code",
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   "id": "5832d84a-e04f-477c-9240-e54d19136a97",
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   "source": [
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_frequency_distribution(filename, words, counter, counter_bigrams, counter_trigrams):\n",
    "    # Generate a list of word counts for the most common words in the counter object\n",
    "    wordcounts = [count for _, count in counter.most_common()]\n",
    "    \n",
    "    # Generate a list of bigram counts for the most common bigrams in the counter_bigrams object\n",
    "    bigramcounts = [count for _, count in counter_bigrams.most_common()]\n",
    "    \n",
    "    # Generate a list of trigram counts for the most common trigrams in the counter_trigrams object\n",
    "    trigramcounts = [count for _, count in counter_trigrams.most_common()]\n",
    "    \n",
    "    # Plot the word counts, bigram counts, and trigram counts on a log-log scale\n",
    "    plt.loglog(wordcounts, label='word counts')\n",
    "    plt.loglog(bigramcounts, label='bigram counts')\n",
    "    plt.loglog(trigramcounts, label='trigram counts')\n",
    "    \n",
    "    # Add a title to the plot\n",
    "    plt.title(f\"Frequency distribution of words, bigrams, and trigrams in {filename}\")\n",
    "    \n",
    "    # Add a legend to the plot\n",
    "    plt.legend()\n",
    "    \n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Read the text file and split it into words\n",
    "with open('time/timemachine.txt', 'r') as file:\n",
    "    text = file.read()\n",
    "words = text.split()\n",
    "\n",
    "# Create a Counter object with the words\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Create a Counter object with the bigrams (i.e. pairs of consecutive words)\n",
    "bigrams = [pair for pair in zip(words[:-1], words[1:])]\n",
    "counter_bigrams = collections.Counter(bigrams)\n",
    "\n",
    "# Create a Counter object with the trigrams (i.e. triples of consecutive words)\n",
    "trigrams = [triple for triple in zip(words[:-2], words[1:-1], words[2:])]\n",
    "counter_trigrams = collections.Counter(trigrams)\n",
    "\n",
    "# Plot the frequency distribution of words, bigrams, and trigrams\n",
    "plot_frequency_distribution('The Time Machine', words, counter, counter_bigrams, counter_trigrams)\n",
    "\n"
   ]
  },
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   "execution_count": 79,
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   "source": [
    "wordcounts = [count for _,count in counter.most_common()]\n",
    "bigramcounts = [count for _,count in counter_pairs.most_common()]\n",
    "triplecounts = [count for _,count in counter_triples.most_common()]\n",
    "\n",
    "#plt.loglog(wordcounts, label='word counts');\n",
    "plt.loglog(bigramcounts, label='bigram counts');\n",
    "plt.loglog(triplecounts, label='triple counts');\n"
   ]
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   "execution_count": null,
   "id": "ce73c743-5a72-4c55-8688-e089f20482df",
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   "source": []
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   "execution_count": 80,
   "id": "eaf3deb3-d8a1-4e35-ae0e-b75b65c5d237",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of characters:  4324026\n",
      "1:1 in the beginning god created the heaven and the earth. 1:2 and the\n"
     ]
    }
   ],
   "source": [
    "import sys \n",
    "sys.path.insert(0, '..')\n",
    "\n",
    "from mxnet import nd\n",
    "import random\n",
    "\n",
    "import re\n",
    "with open('time/bible.txt','r') as f:\n",
    "    lines = f.readlines()\n",
    "    raw_dataset = ' '.join(' '.join(lines).lower().split())\n",
    "\n",
    "print('number of characters: ', len(raw_dataset))\n",
    "print(raw_dataset[0:70])\n"
   ]
  },
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   "execution_count": 81,
   "id": "b75ace22-cace-47e2-9d4f-fe00d4755907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{')': 0, '5': 1, '9': 2, '?': 3, 's': 4, '0': 5, 'r': 6, '8': 7, '/': 8, 'l': 9, 'e': 10, '4': 11, '%': 12, \"'\": 13, '6': 14, 'd': 15, '@': 16, 'v': 17, 'n': 18, '.': 19, 'q': 20, 'o': 21, 'z': 22, '(': 23, 'g': 24, 'w': 25, 'u': 26, ',': 27, 'p': 28, ':': 29, 'h': 30, 'a': 31, 'f': 32, 't': 33, 'y': 34, 'i': 35, '2': 36, 'c': 37, ' ': 38, '$': 39, ';': 40, '!': 41, '\"': 42, '1': 43, '-': 44, '3': 45, 'k': 46, 'j': 47, '7': 48, 'm': 49, '*': 50, 'b': 51, 'x': 52}\n"
     ]
    }
   ],
   "source": [
    "idx_to_char = list(set(raw_dataset))\n",
    "char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)])\n",
    "vocab_size = len(char_to_idx)\n",
    "print(char_to_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c52ccc45-e496-43c1-9365-572c486a99b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "char: 1:1 in the\n",
      "indices: [43, 29, 43, 38, 35, 18, 38, 33, 30, 10]\n"
     ]
    }
   ],
   "source": [
    "corpus_indices = [char_to_idx[char] for char in raw_dataset]\n",
    "sample = corpus_indices[:10]\n",
    "print('char:', ''.join([idx_to_char[idx] for idx in sample]))\n",
    "print('indices:', sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: \n",
      "[[ 7.  8.  9. 10. 11. 12. 13.]\n",
      " [14. 15. 16. 17. 18. 19. 20.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.]]\n",
      "<NDArray 3x7 @cpu(0)> \n",
      "y: \n",
      "[[ 8.  9. 10. 11. 12. 13. 14.]\n",
      " [15. 16. 17. 18. 19. 20. 21.]\n",
      " [ 1.  2.  3.  4.  5.  6.  7.]]\n",
      "<NDArray 3x7 @cpu(0)>\n"
     ]
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   ],
   "source": [
    "def data_iter_random(corpus_indices, batch_size, num_steps, ctx=None):\n",
    "    offset = int(random.uniform(0,num_steps))\n",
    "    corpus_indices =corpus_indices[offset:]\n",
    "    num_examples = ((len(corpus_indices) -1) // num_steps) -1\n",
    "    num_batches = num_examples // batch_size\n",
    "    example_indices = list(range(0, num_examples + num_steps, num_steps))\n",
    "    random.shuffle(example_indices)\n",
    "    \n",
    "    def _data(pos):\n",
    "        return corpus_indices[pos: pos + num_steps]\n",
    "    \n",
    "    for i in range(0, batch_size * num_batches, batch_size):\n",
    "        batch_indices = example_indices[i:(i+batch_size)]\n",
    "        x = [_data(j) for j in batch_indices]\n",
    "        y = [_data(j + 1) for j in batch_indices]\n",
    "        \n",
    "        yield nd.array(x, ctx), nd.array(y, ctx)      \n",
    "        \n",
    "my_seq = list(range(70))\n",
    "for x, y in data_iter_random(my_seq, batch_size=5, num_steps=7):\n",
    "    print('x:', x, '\\ny:', y)"
   ]
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   ],
   "source": [
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the text file and split it into characters\n",
    "with open('bible.txt', 'r') as file:\n",
    "    text = file.read()\n",
    "characters = list(text)\n",
    "\n",
    "# Create a Counter object with the characters\n",
    "counter = collections.Counter(characters)\n",
    "\n",
    "# Generate a list of character counts for the most common characters in the counter object\n",
    "charactercounts = [count for _, count in counter.most_common()]\n",
    "\n",
    "# Plot the character counts on a log-log scale\n",
    "plt.loglog(charactercounts, label='character counts')\n",
    "\n",
    "# Add a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d456d85-6fab-4ee4-b259-49ab732de744",
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     "output_type": "display_data"
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   ],
   "source": [
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the text file and split it into words\n",
    "with open('time/timemachine.txt', 'r') as file:\n",
    "    text = file.read()\n",
    "words = text.split()\n",
    "\n",
    "# Create a Counter object with the words\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Generate a list of word counts for the most common words in the counter object\n",
    "wordcounts = [count for _, count in counter.most_common()]\n",
    "\n",
    "# Plot the word counts on a log-log scale\n",
    "plt.loglog(wordcounts, label='word counts')\n",
    "\n",
    "# Add a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c0c3e63e-3b56-4458-bb99-f6ca15376344",
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   ],
   "source": [
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the text file and split it into words\n",
    "with open('bible.txt', 'r') as file:\n",
    "    text = file.read()\n",
    "words = text.split()\n",
    "\n",
    "# Create a Counter object with the words\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Generate a list of word counts for the most common words in the counter object\n",
    "wordcounts = [count for _, count in counter.most_common()]\n",
    "\n",
    "# Plot the word counts on a log-log scale\n",
    "plt.loglog(wordcounts, label='word counts')\n",
    "\n",
    "# Add a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2c805861-4c55-43e8-99a3-eea3f403d3e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: \n",
      "[[ 7.  8.  9. 10. 11. 12. 13.]\n",
      " [14. 15. 16. 17. 18. 19. 20.]\n",
      " [ 0.  1.  2.  3.  4.  5.  6.]]\n",
      "<NDArray 3x7 @cpu(0)>\n",
      "y: \n",
      "[[ 8.  9. 10. 11. 12. 13. 14.]\n",
      " [15. 16. 17. 18. 19. 20. 21.]\n",
      " [ 1.  2.  3.  4.  5.  6.  7.]]\n",
      "<NDArray 3x7 @cpu(0)>\n"
     ]
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   ],
   "source": [
    "print('x:', x)\n",
    "print('y:', y)"
   ]
  },
  {
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   "execution_count": 15,
   "id": "d3a9db9b-2002-432a-a570-daa07b69fa2d",
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   "source": [
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the first text file and split it into words\n",
    "with open('time/timemachine.txt', 'r') as file:\n",
    "    text = file.read()\n",
    "words = text.split()\n",
    "\n",
    "# Create a Counter object with the words\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Generate a list of word counts for the most common words in the counter object\n",
    "wordcounts = [count for _, count in counter.most_common()]\n",
    "\n",
    "# Plot the word counts on a log-log scale for the first text file\n",
    "plt.loglog(wordcounts, label='word counts - The Time Machine')\n",
    "\n",
    "# Read the second text file and split it into words\n",
    "with open('bible.txt', 'r') as file:\n",
    "    text = file.read()\n",
    "words = text.split()\n",
    "\n",
    "# Create a Counter object with the words\n",
    "counter = collections.Counter(words)\n",
    "\n",
    "# Generate a list of word counts for the most common words in the counter object\n",
    "wordcounts = [count for _, count in counter.most_common()]\n",
    "\n",
    "# Plot the word counts on a log-log scale for the second text file\n",
    "plt.loglog(wordcounts, label='word counts - The Bible')\n",
    "\n",
    "# Add a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
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