MiniProject4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mini Project 4: Developing an Information Retrieval System with Document Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries as below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "    <li><b>pandas</b>: For reading xml and txt files.</li><br>\n",
    "    <li><b>numpy</b>: For working with vectors.</li><br>\n",
    "    <li><b>math</b>: For doing arithmatics.</li><br>\n",
    "    <li><b>nltk/string</b>: For working with NLP</li><br>\n",
    "    <li><b>matplotlib</b>: For visualizing.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import math \n",
    "from numpy import linalg as LA\n",
    "\n",
    "from nltk.tokenize import regexp_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "import string\n",
    "\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reading main data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define file paths for reading data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Documnets xml file path\n",
    "xml_doc_file_path = 'data/cranfield-xml/cran.all.1400.xml'\n",
    "\n",
    "# Queries xml file path\n",
    "xml_query_file_path = 'data/cranfield-xml/cran.qry.xml'\n",
    "\n",
    "# Evaluation txt file path\n",
    "txt_evaluation_apth = 'data/cranfield-xml/cranqrel.trec.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, it's time to read data from files!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read all data about all documents and drop \n",
    "# the fisrt column which is the indices of documents\n",
    "df = pd.read_xml(xml_doc_file_path)\n",
    "df.drop(df.columns[0], axis=1, inplace=True)\n",
    "\n",
    "# read all data about all queries and drop \n",
    "# the fisrt column which is the indices of queries.\n",
    "# Also, replace \"\\n\" with \" \" for cleaning the queries\n",
    "df_q = pd.read_xml(xml_query_file_path)\n",
    "df_q.drop(df_q.columns[0], axis=1, inplace=True)\n",
    "df_q['title'] = df_q['title'].str.replace('\\n', ' ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show data frames here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>bib</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>experimental investigation of the aerodynamics...</td>\n",
       "      <td>brenckman,m.</td>\n",
       "      <td>j. ae. scs. 25, 1958, 324.</td>\n",
       "      <td>experimental investigation of the aerodynamics...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>simple shear flow past a flat plate in an inco...</td>\n",
       "      <td>ting-yili</td>\n",
       "      <td>department of aeronautical engineering, rensse...</td>\n",
       "      <td>simple shear flow past a flat plate in an inco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>the boundary layer in simple shear flow past a...</td>\n",
       "      <td>m. b. glauert</td>\n",
       "      <td>department of mathematics, university of manch...</td>\n",
       "      <td>the boundary layer in simple shear flow past a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>approximate solutions of the incompressible la...</td>\n",
       "      <td>yen,k.t.</td>\n",
       "      <td>j. ae. scs. 22, 1955, 728.</td>\n",
       "      <td>approximate solutions of the incompressible la...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one-dimensional transient heat conduction into...</td>\n",
       "      <td>wasserman,b.</td>\n",
       "      <td>j. ae. scs. 24, 1957, 924.</td>\n",
       "      <td>one-dimensional transient heat conduction into...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title         author  \\\n",
       "0  experimental investigation of the aerodynamics...   brenckman,m.   \n",
       "1  simple shear flow past a flat plate in an inco...      ting-yili   \n",
       "2  the boundary layer in simple shear flow past a...  m. b. glauert   \n",
       "3  approximate solutions of the incompressible la...       yen,k.t.   \n",
       "4  one-dimensional transient heat conduction into...   wasserman,b.   \n",
       "\n",
       "                                                 bib  \\\n",
       "0                         j. ae. scs. 25, 1958, 324.   \n",
       "1  department of aeronautical engineering, rensse...   \n",
       "2  department of mathematics, university of manch...   \n",
       "3                         j. ae. scs. 22, 1955, 728.   \n",
       "4                         j. ae. scs. 24, 1957, 924.   \n",
       "\n",
       "                                                text  \n",
       "0  experimental investigation of the aerodynamics...  \n",
       "1  simple shear flow past a flat plate in an inco...  \n",
       "2  the boundary layer in simple shear flow past a...  \n",
       "3  approximate solutions of the incompressible la...  \n",
       "4  one-dimensional transient heat conduction into...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>what similarity laws must be obeyed when cons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>what are the structural and aeroelastic probl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>what problems of heat conduction in composite...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>can a criterion be developed to show empirica...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>what chemical kinetic system is applicable to...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title\n",
       "0   what similarity laws must be obeyed when cons...\n",
       "1   what are the structural and aeroelastic probl...\n",
       "2   what problems of heat conduction in composite...\n",
       "3   can a criterion be developed to show empirica...\n",
       "4   what chemical kinetic system is applicable to..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "extract data from dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = df['title']\n",
    "authors = df['author']\n",
    "bibs = df['bib']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reading evaluation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read data from the evaluation file and make a dictionary named ```evaluation_dict``` to store query ids as key and relevant document ids as values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_dict = {}\n",
    "\n",
    "# Open file and read all lines togeather\n",
    "with open(txt_evaluation_apth, 'r') as file:\n",
    "    lines = file.readlines()\n",
    "    \n",
    "    # Split the data in each line and extract query id, doc id, and \n",
    "    # a 0 of 1(respectively, for non-relevant and relevant documents)\n",
    "    for i in range(len(lines)):\n",
    "        query_id = int(lines[i].split()[0])\n",
    "        relevant = int(lines[i].split()[3])\n",
    "        doc_id = int(lines[i].split()[2])\n",
    "\n",
    "        # fill the evaluation dictionary\n",
    "        if query_id not in evaluation_dict and relevant == 1:\n",
    "            evaluation_dict[query_id] = [doc_id]\n",
    "        elif query_id in evaluation_dict and relevant == 1:\n",
    "            evaluation_dict[query_id].append(doc_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define 2 functions which are calculationg precision and recall respectivly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "P = \\frac{TP}{TP + FP}, \\;\\;\n",
    "R = \\frac{TP}{TP + FN}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute precision\n",
    "def compute_precision(results, computed):\n",
    "    common_elements = list(set(results) & set(computed))\n",
    "    return len(common_elements) / len(computed)\n",
    "\n",
    "# compute recall\n",
    "def compute_recall(results, computed):\n",
    "    common_elements = list(set(results) & set(computed))\n",
    "    return len(common_elements) / len(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to do the data preprocessing which does the following tasks:\n",
    "<ul>\n",
    "    <li>Tokenization</li><br>\n",
    "    <li>Delete stop words</li><br>\n",
    "    <li>Lowercase words</li><br>\n",
    "    <li>Delete punctuations</li><br>\n",
    "    <li>Stemming</li><br>\n",
    "\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(document):\n",
    "    '''\n",
    "    Define a regular expression pattern for tokenization \n",
    "    (matching words, 3-digit numbers, numbers with more than 3 digits and \n",
    "    have thousands seperators in which n > 3, and floating-point numbers)\n",
    "    '''\n",
    "    if document is not None:\n",
    "        pattern = r'\\d{1,3}(?:,\\d{3})*(?:\\.\\d+)?|\\w+'\n",
    "\n",
    "        # Tokenize the text using the regular expression pattern    \n",
    "        tokens = regexp_tokenize(document, pattern)\n",
    "\n",
    "        # Lowercase the tokens\n",
    "        # tokens = [word.lower() for word in tokens]\n",
    "        tokens = [word.lower() for word in tokens if word.isalpha()]\n",
    "\n",
    "\n",
    "        # Get the set of English stopwords\n",
    "        stop_words = set(stopwords.words('english'))\n",
    "\n",
    "        # Remove stopwords\n",
    "        tokens = [word for word in tokens if word not in stop_words]\n",
    "\n",
    "        # Remove punctuation\n",
    "        tokens = [word for word in tokens if word not in string.punctuation]\n",
    "\n",
    "        # Stemming\n",
    "        # document = stemming(tokens)\n",
    "        stemmer = PorterStemmer()\n",
    "        tokens = [stemmer.stem(word) for word in tokens]\n",
    "\n",
    "        # return the processed document\n",
    "        return ' '.join(tokens)\n",
    "    else:\n",
    "        return ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Document preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>bib</th>\n",
       "      <th>text</th>\n",
       "      <th>preprocessed_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>experimental investigation of the aerodynamics...</td>\n",
       "      <td>brenckman,m.</td>\n",
       "      <td>j. ae. scs. 25, 1958, 324.</td>\n",
       "      <td>experimental investigation of the aerodynamics...</td>\n",
       "      <td>experiment investig aerodynam wing slipstream ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>simple shear flow past a flat plate in an inco...</td>\n",
       "      <td>ting-yili</td>\n",
       "      <td>department of aeronautical engineering, rensse...</td>\n",
       "      <td>simple shear flow past a flat plate in an inco...</td>\n",
       "      <td>simpl shear flow past flat plate incompress fl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>the boundary layer in simple shear flow past a...</td>\n",
       "      <td>m. b. glauert</td>\n",
       "      <td>department of mathematics, university of manch...</td>\n",
       "      <td>the boundary layer in simple shear flow past a...</td>\n",
       "      <td>boundari layer simpl shear flow past flat plat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>approximate solutions of the incompressible la...</td>\n",
       "      <td>yen,k.t.</td>\n",
       "      <td>j. ae. scs. 22, 1955, 728.</td>\n",
       "      <td>approximate solutions of the incompressible la...</td>\n",
       "      <td>approxim solut incompress laminar boundari lay...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one-dimensional transient heat conduction into...</td>\n",
       "      <td>wasserman,b.</td>\n",
       "      <td>j. ae. scs. 24, 1957, 924.</td>\n",
       "      <td>one-dimensional transient heat conduction into...</td>\n",
       "      <td>one dimension transient heat conduct doubl lay...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title         author  \\\n",
       "0  experimental investigation of the aerodynamics...   brenckman,m.   \n",
       "1  simple shear flow past a flat plate in an inco...      ting-yili   \n",
       "2  the boundary layer in simple shear flow past a...  m. b. glauert   \n",
       "3  approximate solutions of the incompressible la...       yen,k.t.   \n",
       "4  one-dimensional transient heat conduction into...   wasserman,b.   \n",
       "\n",
       "                                                 bib  \\\n",
       "0                         j. ae. scs. 25, 1958, 324.   \n",
       "1  department of aeronautical engineering, rensse...   \n",
       "2  department of mathematics, university of manch...   \n",
       "3                         j. ae. scs. 22, 1955, 728.   \n",
       "4                         j. ae. scs. 24, 1957, 924.   \n",
       "\n",
       "                                                text  \\\n",
       "0  experimental investigation of the aerodynamics...   \n",
       "1  simple shear flow past a flat plate in an inco...   \n",
       "2  the boundary layer in simple shear flow past a...   \n",
       "3  approximate solutions of the incompressible la...   \n",
       "4  one-dimensional transient heat conduction into...   \n",
       "\n",
       "                                   preprocessed_text  \n",
       "0  experiment investig aerodynam wing slipstream ...  \n",
       "1  simpl shear flow past flat plate incompress fl...  \n",
       "2  boundari layer simpl shear flow past flat plat...  \n",
       "3  approxim solut incompress laminar boundari lay...  \n",
       "4  one dimension transient heat conduct doubl lay...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the preprocessing function to the 'text' column\n",
    "# and make a new column named preprocessed_text to store the \n",
    "# text which is preprocessed\n",
    "df['preprocessed_text'] = df['text'].apply(preprocessing)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Query processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>preprocessed_query</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>what similarity laws must be obeyed when cons...</td>\n",
       "      <td>similar law must obey construct aeroelast mode...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>what are the structural and aeroelastic probl...</td>\n",
       "      <td>structur aeroelast problem associ flight high ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>what problems of heat conduction in composite...</td>\n",
       "      <td>problem heat conduct composit slab solv far</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>can a criterion be developed to show empirica...</td>\n",
       "      <td>criterion develop show empir valid flow solut ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>what chemical kinetic system is applicable to...</td>\n",
       "      <td>chemic kinet system applic hyperson aerodynam ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0   what similarity laws must be obeyed when cons...   \n",
       "1   what are the structural and aeroelastic probl...   \n",
       "2   what problems of heat conduction in composite...   \n",
       "3   can a criterion be developed to show empirica...   \n",
       "4   what chemical kinetic system is applicable to...   \n",
       "\n",
       "                                  preprocessed_query  \n",
       "0  similar law must obey construct aeroelast mode...  \n",
       "1  structur aeroelast problem associ flight high ...  \n",
       "2        problem heat conduct composit slab solv far  \n",
       "3  criterion develop show empir valid flow solut ...  \n",
       "4  chemic kinet system applic hyperson aerodynam ...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the preprocessing function to the 'title' column\n",
    "# and make a new column named preprocessed_query to store the \n",
    "# title which is preprocessed\n",
    "df_q['preprocessed_query'] = df_q['title'].apply(preprocessing)\n",
    "\n",
    "df_q.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we define the following function which we will use it in the future for preprocessing the queries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_preprocessing(query):\n",
    "    query = preprocessing(query)\n",
    "    query_tokens = query.split()\n",
    "    \n",
    "    return query, query_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract neccessary information from the processed data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we want to extract following useful information from the processed data:\n",
    "<ul>\n",
    "    <li>TF(Term Frequency): # occurances of term t in document d</li><br>\n",
    "    <li>DF(Document Frequency): # occurances of documents containing term t</li><br>\n",
    "    <li>IDF(Inversed Documnet Frequency): Some kind of inverse of DF</li><br>\n",
    "    <li>CF(Collection Frequency): # occurances of term t in all the collection</li><br>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute TF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>bib</th>\n",
       "      <th>text</th>\n",
       "      <th>preprocessed_text</th>\n",
       "      <th>tf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>experimental investigation of the aerodynamics...</td>\n",
       "      <td>brenckman,m.</td>\n",
       "      <td>j. ae. scs. 25, 1958, 324.</td>\n",
       "      <td>experimental investigation of the aerodynamics...</td>\n",
       "      <td>experiment investig aerodynam wing slipstream ...</td>\n",
       "      <td>{'experiment': 2, 'investig': 1, 'aerodynam': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>simple shear flow past a flat plate in an inco...</td>\n",
       "      <td>ting-yili</td>\n",
       "      <td>department of aeronautical engineering, rensse...</td>\n",
       "      <td>simple shear flow past a flat plate in an inco...</td>\n",
       "      <td>simpl shear flow past flat plate incompress fl...</td>\n",
       "      <td>{'simpl': 2, 'shear': 2, 'flow': 6, 'past': 4,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>the boundary layer in simple shear flow past a...</td>\n",
       "      <td>m. b. glauert</td>\n",
       "      <td>department of mathematics, university of manch...</td>\n",
       "      <td>the boundary layer in simple shear flow past a...</td>\n",
       "      <td>boundari layer simpl shear flow past flat plat...</td>\n",
       "      <td>{'boundari': 2, 'layer': 2, 'simpl': 1, 'shear...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>approximate solutions of the incompressible la...</td>\n",
       "      <td>yen,k.t.</td>\n",
       "      <td>j. ae. scs. 22, 1955, 728.</td>\n",
       "      <td>approximate solutions of the incompressible la...</td>\n",
       "      <td>approxim solut incompress laminar boundari lay...</td>\n",
       "      <td>{'approxim': 1, 'solut': 2, 'incompress': 2, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one-dimensional transient heat conduction into...</td>\n",
       "      <td>wasserman,b.</td>\n",
       "      <td>j. ae. scs. 24, 1957, 924.</td>\n",
       "      <td>one-dimensional transient heat conduction into...</td>\n",
       "      <td>one dimension transient heat conduct doubl lay...</td>\n",
       "      <td>{'one': 2, 'dimension': 1, 'transient': 2, 'he...</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title         author  \\\n",
       "0  experimental investigation of the aerodynamics...   brenckman,m.   \n",
       "1  simple shear flow past a flat plate in an inco...      ting-yili   \n",
       "2  the boundary layer in simple shear flow past a...  m. b. glauert   \n",
       "3  approximate solutions of the incompressible la...       yen,k.t.   \n",
       "4  one-dimensional transient heat conduction into...   wasserman,b.   \n",
       "\n",
       "                                                 bib  \\\n",
       "0                         j. ae. scs. 25, 1958, 324.   \n",
       "1  department of aeronautical engineering, rensse...   \n",
       "2  department of mathematics, university of manch...   \n",
       "3                         j. ae. scs. 22, 1955, 728.   \n",
       "4                         j. ae. scs. 24, 1957, 924.   \n",
       "\n",
       "                                                text  \\\n",
       "0  experimental investigation of the aerodynamics...   \n",
       "1  simple shear flow past a flat plate in an inco...   \n",
       "2  the boundary layer in simple shear flow past a...   \n",
       "3  approximate solutions of the incompressible la...   \n",
       "4  one-dimensional transient heat conduction into...   \n",
       "\n",
       "                                   preprocessed_text  \\\n",
       "0  experiment investig aerodynam wing slipstream ...   \n",
       "1  simpl shear flow past flat plate incompress fl...   \n",
       "2  boundari layer simpl shear flow past flat plat...   \n",
       "3  approxim solut incompress laminar boundari lay...   \n",
       "4  one dimension transient heat conduct doubl lay...   \n",
       "\n",
       "                                                  tf  \n",
       "0  {'experiment': 2, 'investig': 1, 'aerodynam': ...  \n",
       "1  {'simpl': 2, 'shear': 2, 'flow': 6, 'past': 4,...  \n",
       "2  {'boundari': 2, 'layer': 2, 'simpl': 1, 'shear...  \n",
       "3  {'approxim': 1, 'solut': 2, 'incompress': 2, '...  \n",
       "4  {'one': 2, 'dimension': 1, 'transient': 2, 'he...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert a new column to the df to store tf for eacg document\n",
    "def count_term_frequency(document):\n",
    "    tokens = document.split()\n",
    "    tf = {}\n",
    "\n",
    "    for term in tokens:\n",
    "        if term not in tf:\n",
    "            tf[term] = 1\n",
    "        else:\n",
    "            tf[term] += 1\n",
    "    \n",
    "    return tf\n",
    "\n",
    "df['tf'] = df['preprocessed_text'].apply(count_term_frequency)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unique terms in dataset:  4291\n",
      "\n",
      "Document Frequency dictiobnary: \n",
      " {'senior': 1, 'schwarz': 1, 'similitud': 9, 'stiffen': 36, 'half': 50, 'rubesin': 7, 'thoroughli': 2, 'aileron': 9, 'text': 5, 'absorpt': 6, 'instig': 1, 'proportion': 2, 'basin': 1, 'doubli': 1, 'film': 6, 'sometim': 9, 'pyramid': 1, 'suffix': 1, 'met': 2, 'lectur': 1, 'switch': 1, 'practic': 100, 'crush': 1, 'complic': 12, 'upper': 41, 'slipstream': 15, 'straight': 31, 'parasol': 1, 'armenaka': 2, 'volum': 24, 'isotop': 1, 'liouvil': 1, 'sugar': 1, 'papreg': 1, 'unspecifi': 1, 'adam': 2, 'driven': 6, 'halfway': 1, 'board': 1, 'heat': 306, 'gasdynam': 10, 'hovercraft': 2, 'perman': 2, 'abundantli': 1, 'tube': 71, 'pass': 20, 'greater': 68, 'suitabl': 45, 'enthalp': 1, 'among': 7, 'physcal': 1, 'preval': 1, 'wissler': 1, 'retard': 7, 'gage': 5, 'math': 5, 'bodi': 293, 'intergranular': 1, 'arrang': 12, 'perfor': 3, 'accept': 17, 'confid': 1, 'judg': 2, 'empir': 32, 'x': 84, 'chang': 122, 'igc': 1, 'mw': 1, 'national': 1, 'hash': 1, 'planar': 5, 'bright': 1, 'prefer': 6, 'store': 5, 'antielast': 1, 'nomenclatur': 1, 'slice': 2, 'solv': 82, 'qm': 2, 'endur': 1, 'memorandum': 3, 'disadvantag': 2, 'adjust': 11, 'fuller': 5, 'correlogram': 1, 'post': 5, 'stress': 154, 'cauchi': 2, 'criteria': 13, 'howorth': 1, 'inorgan': 2, 'rm': 7, 'similar': 149, 'ludwieg': 1, 'balsa': 1, 'shank': 1, 'compar': 247, 'law': 53, 'wherein': 7, 'search': 2, 'tetrachlorid': 1, 'powerpl': 1, 'good': 136, 'author': 88, 'manifest': 5, 'murray': 1, 'chien': 1, 'townsend': 1, 'ibm': 8, 'get': 3, 'rememb': 1, 'control': 61, 'figur': 14, 'aerothermodynam': 1, 'demonstr': 41, 'programm': 7, 'micromanomet': 1, 'spread': 16, 'nonconserv': 3, 'dust': 1, 'scheme': 9, 'angl': 212, 'gordon': 1, 'magnu': 2, 'thinner': 5, 'diagon': 1, 'regim': 30, 'throat': 13, 'rep': 2, 'app': 2, 'krypton': 1, 'attaindd': 1, 'faster': 5, 'encount': 29, 'methanol': 1, 'respons': 23, 'ture': 3, 'phosphoresc': 1, 'inclus': 8, 'nonsteadi': 6, 'annex': 1, 'detach': 27, 'even': 48, 'swallow': 1, 'prohibit': 2, 'ascrib': 1, 'nitzyn': 1, 'full': 31, 'lagrangian': 7, 'exploit': 2, 'hundr': 1, 'thermal': 90, 'gridwork': 1, 'sequenc': 5, 'volumetr': 1, 'molten': 1, 'mere': 3, 'hyperlipt': 1, 'fire': 4, 'seem': 21, 'modern': 7, 'paid': 3, 'symposium': 1, 'ob': 1, 'ministri': 2, 'cycl': 14, 'visibl': 3, 'thermoelast': 5, 'fractur': 2, 'subaudio': 1, 'letter': 1, 'advoc': 2, 'mistak': 1, 'emit': 6, 'invari': 6, 'unmodul': 2, 'unitari': 1, 'ideal': 37, 'preponder': 1, 'recognis': 1, 'map': 11, 'list': 7, 'unbalanc': 1, 'incident': 1, 'sweptback': 15, 'wire': 20, 'noninsul': 1, 'sec': 22, 'annular': 11, 'silenc': 1, 'perpendicular': 13, 'attenu': 6, 'teeth': 1, 'atlarg': 1, 'dissip': 19, 'dashn': 1, 'reactor': 1, 'servo': 1, 'mask': 2, 'american': 2, 'revers': 23, 'aeroballist': 1, 'claus': 1, 'bc': 1, 'boltzmann': 2, 'unexpectedli': 1, 'tungsten': 2, 'dashth': 1, 'old': 1, 'cover': 55, 'quadratur': 7, 'highspe': 2, 'apropri': 1, 'fraction': 19, 'sibulkin': 1, 'construct': 41, 'interact': 90, 'womersley': 2, 'life': 14, 'mid': 2, 'absent': 1, 'paramount': 3, 'coast': 1, 'vii': 1, 'techniqu': 99, 'match': 15, 'strang': 1, 'refer': 58, 'sign': 1, 'obscur': 1, 'donnel': 19, 'item': 1, 'lunar': 1, 'unbuckl': 2, 'invers': 24, 'peopl': 1, 'ar': 1, 'make': 50, 'japan': 1, 'studi': 240, 'yeild': 1, 'backup': 1, 'grashof': 1, 'flang': 6, 'carri': 75, 'vessel': 8, 'arbitrari': 87, 'nagamatsu': 2, 'brooklyn': 1, 'word': 2, 'argon': 9, 'convect': 45, 'infit': 1, 'obvious': 1, 'vanishingli': 2, 'fralich': 1, 'stepdown': 1, 'ignit': 4, 'exterior': 7, 'spindl': 1, 'extinct': 1, 'nonalign': 1, 'ampli': 1, 'plant': 4, 'encourag': 4, 'encompass': 4, 'unblunt': 1, 'overal': 22, 'nonconcurr': 1, 'member': 8, 'hydrodynam': 17, 'hugoniot': 4, 'warhead': 1, 'cure': 1, 'kt': 1, 'storag': 5, 'relat': 151, 'wide': 51, 'oscillatori': 21, 'excit': 23, 'agreement': 181, 'bakanov': 1, 'dead': 7, 'speci': 7, 'insofar': 3, 'spite': 1, 'park': 1, 'classificaiton': 1, 'inerti': 1, 'moder': 39, 'transmit': 4, 'outward': 7, 'persh': 1, 'deffer': 1, 'orlfic': 1, 'properli': 9, 'dryden': 2, 'dimension': 237, 'expect': 40, 'spirit': 1, 'winni': 1, 'delimit': 1, 'statu': 4, 'crank': 1, 'membran': 10, 'kopal': 2, 'aerodynam': 179, 'submerg': 3, 'forward': 47, 'circumscrib': 2, 'lennard': 1, 'nomograph': 1, 'charter': 1, 'nonneg': 1, 'recombin': 12, 'nontox': 1, 'interanl': 1, 'solubl': 2, 'instead': 9, 'preston': 4, 'play': 12, 'everi': 9, 'salmon': 1, 'began': 3, 'speric': 1, 'variou': 160, 'substanti': 41, 'exot': 1, 'placement': 1, 'kemp': 1, 'reservoir': 6, 'near': 110, 'spectrum': 14, 'bergdolt': 1, 'transfn': 1, 'oc': 1, 'cosmonaut': 1, 'delay': 9, 'ring': 20, 'cathod': 1, 'accur': 75, 'neighbour': 1, 'pore': 2, 'insuffici': 2, 'von': 29, 'vital': 1, 'interest': 65, 'illustri': 1, 'fourth': 18, 'tsien': 8, 'gyroscop': 1, 'btu': 6, 'inertia': 5, 'sensit': 16, 'viz': 3, 'commun': 1, 'smallest': 2, 'newton': 3, 'shercliff': 1, 'sophist': 1, 'involv': 101, 'repli': 1, 'addit': 96, 'tendenc': 5, 'deep': 2, 'fill': 7, 'dens': 2, 'feb': 1, 'imperfect': 24, 'weissing': 4, 'vertic': 43, 'frictionless': 1, 'astronaut': 1, 'wanlass': 1, 'question': 21, 'conjunct': 19, 'rapid': 33, 'attent': 36, 'answer': 4, 'skan': 8, 'verif': 9, 'prandtl': 63, 'net': 9, 'actur': 1, 'obviou': 6, 'utia': 1, 'chemisorpt': 1, 'bluish': 1, 'smoke': 3, 'well': 167, 'telegraph': 2, 'modifi': 76, 'truli': 2, 'supercrit': 3, 'ft': 34, 'sweat': 2, 'de': 4, 'magneto': 5, 'humid': 3, 'knudsen': 4, 'upflow': 1, 'bullet': 1, 'demarc': 1, 'psycholog': 1, 'elsewher': 6, 'oversw': 1, 'accompani': 13, 'eigenvector': 1, 'web': 6, 'afresh': 1, 'length': 102, 'divers': 1, 'dimensionless': 18, 'exagger': 1, 'gaseou': 7, 'mutual': 2, 'toru': 2, 'found': 322, 'suffici': 47, 'abil': 3, 'interfer': 46, 'planet': 5, 'helmholtz': 3, 'cx': 1, 'impuls': 8, 'rsult': 1, 'call': 18, 'tenth': 1, 'behav': 10, 'imping': 9, 'naca': 28, 'sound': 37, 'amongst': 1, 'layout': 2, 'wherebi': 8, 'spinner': 1, 'spacecraft': 3, 'abruptli': 3, 'origin': 48, 'magnetoacoust': 1, 'vlasov': 1, 'specialis': 1, 'j': 13, 'summat': 1, 'elong': 1, 'two': 432, 'shock': 240, 'varieti': 22, 'rapidli': 37, 'undershoot': 4, 'follow': 77, 'uncool': 5, 'recompress': 3, 'thn': 1, 'stimul': 3, 'unrestrain': 1, 'ink': 1, 'monoplan': 2, 'roger': 1, 'unfamiliar': 1, 'indirect': 2, 'mech': 2, 'dissert': 1, 'potenti': 58, 'dash': 13, 'corrobor': 2, 'dilat': 1, 'snap': 2, 'offset': 1, 'formul': 52, 'weight': 39, 'therebi': 9, 'easiest': 1, 'indium': 1, 'done': 14, 'hurwitz': 1, 'dodecagon': 1, 'amr': 5, 'lip': 1, 'synthes': 1, 'vortex': 33, 'acquisit': 1, 'cheski': 1, 'sole': 2, 'council': 1, 'delft': 1, 'descript': 34, 'beat': 1, 'thrust': 33, 'unduli': 1, 'electrostat': 1, 'compressor': 20, 'hirshfeld': 1, 'midspan': 1, 'ineffici': 1, 'withstand': 2, 'supposedli': 2, 'labor': 4, 'explain': 23, 'unswept': 13, 'stepwis': 2, 'endeavour': 1, 'bank': 3, 'ablat': 14, 'simmon': 1, 'probe': 5, 'licens': 1, 'steadi': 106, 'weaken': 2, 'chu': 1, 'poiseuil': 2, 'resourc': 1, 'rel': 85, 'serious': 2, 'shroud': 11, 'flow': 730, 'blend': 1, 'shockless': 1, 'viscos': 63, 'tangenti': 7, 'lucit': 2, 'avov': 1, 'pneumat': 1, 'undissoci': 4, 'mach': 388, 'flexibl': 22, 'explicit': 19, 'view': 29, 'airload': 1, 'roy': 1, 'kay': 1, 'fast': 8, 'circumlunar': 1, 'appreci': 39, 'definit': 16, 'stretch': 3, 'unidirect': 1, 'suit': 5, 'bagley': 1, 'belotserkovskii': 3, 'camera': 1, 'inconveni': 2, 'conpress': 1, 'blockag': 8, 'claim': 2, 'secur': 2, 'throw': 1, 'remain': 50, 'implement': 1, 'mol': 1, 'semicircl': 1, 'mark': 11, 'cylind': 178, 'less': 82, 'fig': 11, 'zuk': 1, 'prism': 1, 'polyatom': 3, 'routh': 1, 'agenc': 1, 'obliqu': 17, 'vector': 12, 'travel': 15, 'pinkerton': 1, 'decreas': 89, 'activ': 12, 'realiz': 9, 'dynam': 79, 'consist': 77, 'part': 106, 'stabl': 18, 'adequaci': 2, 'noncirculatori': 1, 'radial': 37, 'exactli': 11, 'analys': 34, 'slighlti': 1, 'adapt': 9, 'duplic': 2, 'guarante': 2, 'dampomet': 1, 'tri': 1, 'realis': 1, 'pierci': 1, 'nitrogen': 16, 'retain': 9, 'unpressur': 2, 'connexion': 1, 'hirschfeld': 1, 'g': 33, 'ampl': 1, 'probabl': 23, 'influenti': 1, 'deryagin': 1, 'inher': 5, 'take': 65, 'broaden': 2, 'unsepar': 1, 'written': 14, 'galerkin': 8, 'bound': 16, 'uniform': 125, 'compon': 62, 'friedman': 1, 'seriou': 8, 'neutral': 15, 'seek': 3, 'eddi': 11, 'crossflow': 8, 'request': 1, 'berthelot': 1, 'whose': 42, 'front': 33, 'riddel': 3, 'correctli': 6, 'hyperbol': 11, 'thesi': 1, 'skew': 2, 'tailwind': 1, 'sphere': 39, 'spiral': 4, 'filament': 3, 'onsag': 2, 'raleigh': 1, 'naval': 2, 'turbulen': 3, 'sinusoid': 16, 'abl': 10, 'ultracentrifug': 1, 'deg': 7, 'interfac': 9, 'hour': 3, 'pli': 1, 'durat': 9, 'beyween': 1, 'unload': 2, 'merg': 4, 'radiant': 2, 'excel': 15, 'octahedr': 1, 'incipi': 3, 'kallmann': 1, 'pibal': 2, 'hamper': 1, 'boom': 12, 'loitsianskii': 1, 'import': 110, 'atmospher': 77, 'top': 8, 'inasmuch': 1, 'gerard': 1, 'meteorit': 1, 'mth': 1, 'rung': 1, 'world': 1, 'nautic': 1, 'whitham': 5, 'outstand': 2, 'proof': 5, 'prone': 2, 'mitig': 1, 'septemb': 1, 'harmon': 15, 'defici': 5, 'product': 25, 'mahoni': 1, 'aerofoil': 30, 'us': 6, 'danger': 2, 'dalton': 1, 'still': 23, 'element': 28, 'toler': 9, 'lundquist': 3, 'permit': 39, 'tr': 1, 'fulfil': 4, 'infer': 3, 'degrad': 1, 'keep': 9, 'peclet': 1, 'pai': 2, 'marin': 1, 'unpublish': 1, 'reliabl': 12, 'complement': 3, 'forecast': 2, 'fifth': 4, 'repeat': 4, 'aerothermoelast': 1, 'eventu': 3, 'higher': 106, 'emphasis': 1, 'anomal': 2, 'octagon': 1, 'afford': 4, 'propound': 1, 'rod': 1, 'densiti': 88, 'held': 13, 'boattail': 6, 'oge': 3, 'standard': 21, 'lighthil': 14, 'harri': 1, 'castigliano': 1, 'reader': 6, 'hemispheroid': 1, 'maintain': 22, 'ame': 2, 'land': 8, 'orific': 8, 'pod': 1, 'massbal': 1, 'bleich': 1, 'crosscorrel': 1, 'notion': 1, 'osoen': 1, 'avoid': 25, 'align': 1, 'reveal': 13, 'assert': 1, 'line': 72, 'lin': 1, 'univac': 4, 'hollow': 2, 'tubul': 1, 'ao': 1, 'wrinkl': 3, 'speak': 3, 'intensifi': 1, 'almen': 1, 'cm': 2, 'amer': 1, 'coat': 1, 'curvilinear': 3, 'dx': 1, 'crack': 2, 'para': 1, 'simpl': 184, 'simpli': 39, 'charg': 13, 'effort': 9, 'fan': 5, 'internat': 1, 'foredrag': 1, 'launch': 7, 'octav': 1, 'ascertain': 4, 'blast': 15, 'tension': 8, 'avro': 1, 'merger': 1, 'england': 1, 'corner': 17, 'redefinit': 1, 'estim': 106, 'maxwellian': 3, 'prolat': 1, 'go': 3, 'motionless': 1, 'limit': 136, 'lobe': 2, 'former': 12, 'vapor': 6, 'nondissip': 2, 'zurich': 2, 'snow': 1, 'inaccuraci': 3, 'heisenberg': 2, 'recover': 1, 'fay': 3, 'scan': 1, 'monocoqu': 2, 'rather': 34, 'notch': 3, 'noninclin': 1, 'effici': 26, 'add': 4, 'spheric': 37, 'preliminari': 33, 'entrant': 1, 'corridor': 5, 'intend': 12, 'trochoid': 1, 'alfven': 2, 'persist': 3, 'seal': 2, 'neg': 28, 'scatter': 7, 'reiss': 1, 'unsolv': 2, 'reynold': 199, 'zbrozek': 1, 'approach': 96, 'instabl': 38, 'discern': 2, 'disagr': 7, 'strictli': 5, 'caution': 1, 'bolshakov': 1, 'outboard': 7, 'vent': 1, 'resler': 2, 'univers': 12, 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'precis': 5, 'diatom': 6, 'path': 31, 'atom': 16, 'sea': 3, 'gaussian': 3, 'hexagon': 2, 'design': 144, 'comment': 11, 'calib': 1, 'calcul': 328, 'km': 10, 'eigenvalu': 6, 'theta': 1, 'restrain': 6, 'martian': 2, 'tediou': 5, 'adjoin': 1, 'categori': 2, 'plume': 2, 'bornel': 1, 'payload': 2, 'happen': 2, 'say': 11, 'core': 25, 'tangenc': 3, 'axial': 147, 'rae': 8, 'report': 103, 'unresolv': 1, 'past': 84, 'trapezoid': 2, 'polynomi': 14, 'hoff': 4, 'overexpand': 5, 'stimulu': 1, 'magnitud': 73, 'adsorpt': 1, 'meridian': 5, 'fredholm': 1, 'hopelessli': 1, 'rotari': 5, 'nonisentrop': 1, 'enc': 1, 'nonexist': 1, 'minim': 20, 'someth': 2, 'fuselag': 21, 'ascend': 5, 'satisfactorili': 11, 'bore': 1, 'difficulti': 21, 'build': 2, 'unabl': 1, 'pimpl': 1, 'cite': 4, 'california': 6, 'neighborhood': 7, 'measur': 252, 'open': 19, 'numer': 200, 'satisfi': 34, 'ellipsoid': 7, 'quantit': 22, 'lumin': 5, 'diurnal': 2, 'forst': 1, 'yield': 74, 'depend': 125, 'neglect': 45, 'aft': 7, 'synthesi': 1, 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'postpon': 1, 'leggett': 1, 'hypothet': 4, 'exist': 117, 'onic': 1, 'instant': 1, 'unpow': 1, 'tool': 10, 'hardwar': 1, 'predigest': 1, 'jet': 96, 'minu': 2, 'smell': 1, 'apoge': 1, 'variabl': 85, 'amen': 5, 'lyon': 1, 'sizabl': 4, 'abstract': 2, 'togeth': 40, 'intim': 4, 'ratio': 296, 'literatur': 12, 'barrel': 1, 'divid': 21, 'unflang': 1, 'z': 8, 'properti': 111, 'garrick': 1, 'ethylen': 3, 'accident': 1, 'would': 64, 'owe': 12, 'affin': 2, 'fail': 16, 'sourc': 35, 'introduc': 64, 'franc': 1, 'alminar': 1, 'nonconduct': 7, 'utilz': 1, 'sizeabl': 1, 'inadvert': 1, 'exhaust': 29, 'devreas': 1, 'lay': 2, 'outermost': 1, 'geckel': 1, 'dihedr': 3, 'lack': 10, 'hantzsch': 1, 'mean': 150, 'intermedi': 23, 'modal': 2, 'kilometr': 1, 'pitchingmo': 1, 'interrel': 2, 'rippl': 2, 'failur': 34, 'daytim': 1, 'nonabl': 1, 'emphas': 15, 'cooler': 1, 'sakurai': 1, 'residu': 2, 'stratford': 3, 'destabil': 8, 'taxi': 1, 'regain': 2, 'reshotko': 1, 'taken': 59, 'unnecessari': 2, 'soon': 2, 'rib': 1, 'spragu': 1, 'rev': 6, 'cross': 70, 'stone': 2, 'remark': 13, 'planform': 23, 'semivertex': 8, 'talbot': 1, 'uniti': 35, 'squir': 2, 'polycrystallin': 1, 'compact': 3, 'horsesho': 2, 'dioxid': 4, 'hankel': 2, 'configur': 81, 'nonparallel': 2, 'lattic': 3, 'size': 41, 'accord': 37, 'walkden': 2, 'diversifi': 1, 'specular': 2, 'employ': 70, 'serv': 11, 'way': 36, 'said': 3, 'propel': 35, 'statement': 6, 'diverg': 38, 'capac': 12, 'ray': 3, 'interplanetari': 2, 'siren': 2, 'satisfactori': 49, 'quantum': 1, 'without': 76, 'bump': 1, 'clockwis': 1, 'hammitt': 2, 'maslen': 1, 'unretard': 1, 'solidif': 1, 'attain': 10, 'tunnel': 180, 'halv': 2, 'oscil': 58, 'triangl': 2, 'toroid': 5, 'turbanc': 1, 'injector': 1, 'insensit': 5, 'undamp': 1, 'k': 33, 'posit': 83, 'poudr': 1, 'twist': 12, 'easi': 5, 'analyt': 106, 'prevent': 14, 'foil': 3, 'typifi': 3, 'scienc': 4, 'drag': 138, 'split': 4, 'aircraft': 71, 'nitric': 1, 'disequilibrium': 1, 'veociti': 1, 'cellulos': 1, 'encircl': 1, 'bulg': 1, 'commonli': 7, 'librat': 1, 'electro': 1, 'loudspeak': 1, 'block': 4, 'non': 63, 'theoret': 235, 'planeto': 1, 'kirchhoff': 4, 'inplan': 1, 'exchang': 5, 'thermopropuls': 1, 'freedom': 22, 'air': 184, 'thermochem': 3, 'rotor': 11, 'inafinit': 1, 'superscript': 1, 'jmin': 1, 'best': 16, 'situat': 22, 'chord': 42, 'gardless': 1, 'expound': 1, 'imposs': 5, 'condens': 6, 'minor': 15, 'flare': 7, 'horizont': 15, 'deby': 2, 'mission': 3, 'sectori': 2, 'inform': 44, 'profound': 1, 'scar': 1, 'scientist': 2, 'cheng': 3, 'amount': 43, 'extension': 4, 'survey': 40, 'futur': 15, 'forti': 1, 'adjoint': 1, 'drum': 1, 'unaffect': 7, 'slab': 14, 'grid': 2, 'zamm': 1, 'torispher': 3, 'chap': 1, 'sedov': 1, 'miss': 2, 'give': 144, 'rough': 28, 'ship': 2, 'inspir': 1, 'protrud': 2, 'notic': 5, 'quench': 2, 'negat': 1, 'face': 27, 'reduc': 131, 'orthogon': 5, 'reentri': 9, 'coupon': 1, 'centripet': 1, 'coeffici': 224, 'compens': 3, 'algebra': 11, 'manner': 59, 'galcit': 5, 'attach': 33, 'surpris': 1, 'virtual': 8, 'polish': 2, 'johnson': 1, 'duct': 13, 'crew': 1, 'also': 308, 'composit': 23, 'consolid': 1, 'lewi': 6, 'rang': 298, 'jefferi': 1, 'outer': 42, 'previou': 51, 'demand': 3, 'intersect': 11, 'matric': 7, 'frequent': 8, 'valued': 1, 'slider': 1, 'deuc': 2, 'nonpor': 2, 'first': 171, 'schemat': 1, 'burst': 2, 'directli': 34, 'colour': 1, 'system': 84, 'dri': 2, 'munk': 1, 'entrain': 2, 'nonzero': 2, 'sheath': 1, 'four': 45, 'gradient': 111, 'riolat': 1, 'room': 4, 'certain': 118, 'complementari': 3, 'th': 6, 'admit': 5, 'expand': 20, 'hypass': 1, 'mathemat': 40, 'like': 20, 'margin': 5, 'relativist': 1, 'hole': 8, 'distinct': 9, 'endors': 1, 'geometr': 26, 'meksyn': 1, 'graphit': 3, 'disk': 14, 'premix': 1, 'ration': 7, 'ruptur': 3, 'hing': 8, 'releas': 6, 'movement': 14, 'qualiti': 9, 'doctor': 1, 'flown': 4, 'steel': 8, 'count': 1, 'secondari': 16, 'band': 11, 'decrement': 2, 'william': 2, 'version': 7, 'objection': 1, 'stanton': 4, 'junctur': 4, 'chernyi': 1, 'instanc': 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'versatil': 1, 'skill': 1, 'isofoam': 1, 'hardli': 2, 'reuss': 1, 'variat': 141, 'wakelik': 1, 'explor': 4, 'excresc': 3, 'consequ': 22, 'multipl': 15, 'cascad': 9, 'microphon': 2, 'overlook': 1, 'linearli': 21, 'cant': 1, 'curtain': 1, 'missil': 32, 'frequenc': 56, 'york': 4, 'electron': 19, 'exponenti': 16, 'fundament': 31, 'unaccompani': 1, 'hoop': 3, 'reimping': 1, 'isochron': 1, 'exact': 106, 'inaccur': 2, 'told': 1, 'auxiliari': 2, 'tap': 2, 'choleski': 1, 'particl': 15, 'euler': 10, 'inconsist': 2, 'contract': 8, 'sideslip': 9, 'centigrad': 1, 'cubic': 4, 'essenc': 2, 'appendic': 2, 'airscrew': 1, 'quick': 2, 'lotz': 1, 'stator': 2, 'pose': 1, 'pump': 3, 'monatom': 2, 'alway': 20, 'supplement': 5, 'steam': 2, 'impli': 5, 'along': 137, 'affect': 45, 'nonisotherm': 1, 'nodal': 3, 'whatev': 2, 'academ': 1, 'semidiamet': 1, 'specimen': 10, 'seed': 1, 'declassifi': 1, 'bimetal': 1, 'contour': 17, 'diamet': 80, 'radiat': 28, 'jacobian': 1, 'eckert': 3, 'feldman': 1, 'multistag': 4, 'revis': 3, 'splitter': 1, 'rigor': 10, 'satisfact': 2, 'symmetri': 26, 'stall': 17, 'van': 11, 'fore': 3, 'manoeuvr': 3, 'bear': 12, 'beta': 1, 'sandpap': 1, 'amplitud': 22, 'kernel': 6, 'berger': 1, 'hinder': 1, 'industri': 1, 'potter': 1, 'wendt': 1, 'kempf': 1, 'physiolog': 1, 'necessari': 59, 'examin': 63, 'muffler': 1, 'tne': 1, 'lengthen': 1, 'dyke': 8, 'shape': 193, 'elabor': 3, 'massachusett': 1, 'slight': 15, 'polyaxi': 1, 'partial': 40, 'uni': 1, 'unten': 1, 'handley': 1, 'overexpans': 1, 'whenev': 5, 'optic': 10, 'concentr': 31, 'asymptot': 42, 'wilson': 1, 'overlap': 3, 'gun': 4, 'tent': 10, 'today': 1, 'spectrograph': 1, 'basic': 41, 'inaccess': 1, 'ramp': 1, 'ion': 8, 'nusselt': 9, 'transit': 93, 'clamshel': 1, 'ld': 1, 'tensor': 2, 'book': 2, 'technolog': 8, 'except': 63, 'degreer': 1, 'reciproc': 9, 'bellevil': 1, 'hg': 2, 'littl': 26, 'jupit': 2, 'irregular': 8, 'mechan': 46, 'proxim': 8, 'run': 15, 'fring': 4, 'turbo': 1, 'obey': 5, 'puzzl': 1, 'krasilshchikova': 1, 'uncamb': 3, 'viscoelast': 4, 'beskin': 1, 'zeroth': 1, 'improp': 1, 'gyrat': 1, 'illingworth': 1, 'convers': 9, 'interv': 12, 'environ': 9, 'end': 39, 'quadrupol': 2, 'ser': 2, 'magnetoaerodynam': 2, 'axi': 50, 'electrod': 1, 'fiber': 4, 'apart': 3, 'iowa': 1, 'comprehens': 4, 'bell': 2, 'millimet': 1, 'insect': 1, 'approx': 2, 'field': 168, 'inboard': 1, 'deflexion': 2, 'rankin': 5, 'per': 82, 'calor': 5, 'equip': 16, 'intak': 3, 'rose': 1, 'choos': 5, 'enjoy': 1, 'wash': 2, 'reaffirm': 1, 'teller': 1, 'consier': 1, 'wet': 2, 'untrim': 1, 'obliter': 1, 'incomplet': 2, 'inflect': 3, 'ludweig': 1, 'weitbrecht': 1, 'bluff': 10, 'root': 22, 'seri': 85, 'organ': 1, 'creep': 35, 'obtain': 464, 'throughout': 28, 'ramet': 1, 'terminolog': 1, 'gather': 1, 'hydrocarbon': 1, 'hopeless': 2, 'zone': 13, 'log': 3, 'magnet': 39, 'convex': 7, 'improv': 44, 'mixtur': 22, 'trefitz': 1, 'diffus': 40, 'poor': 7, 'delin': 1, 'inviscid': 79, 'hold': 13, 'oblat': 3, 'effect': 541, 'farther': 6, 'love': 4, 'bay': 3, 'r': 49, 'phase': 20, 'five': 22, 'tailplan': 3, 'output': 6, 'sr': 1, 'other': 11, 'decad': 1, 'pursu': 1, 'kingdom': 1, 'bluntnos': 1, 'equilibria': 1, 'freestream': 10, 'dismiss': 1, 'pyrex': 1, 'creas': 1, 'brief': 30, 'distort': 20, 'mound': 1, 'graphic': 18, 'oversimplifi': 1, 'levi': 2, 'subarc': 1, 'numger': 1, 'oper': 67, 'excus': 1, 'reson': 6, 'associ': 68, 'orient': 9, 'hog': 1, 'screen': 4, 'kept': 5, 'radii': 12, 'jan': 1, 'upstream': 38, 'standpoint': 4, 'booster': 2, 'incorrect': 4, 'tabular': 5, 'phenomena': 32, 'maximum': 115, 'acrothermochemistri': 1, 'program': 23, 'centr': 13, 'closest': 1, 'increasingli': 6, 'laplac': 4, 'concret': 1, 'nonhomogen': 1, 'stem': 5, 'slosh': 1, 'prager': 1, 'radon': 1, 'salient': 3, 'momentari': 1, 'perturb': 24, 'slender': 96, 'continu': 56, 'explicitli': 10, 'unyaw': 8, 'deduct': 3, 'succeed': 2, 'earth': 21, 'overcom': 3, 'psia': 3, 'side': 34, 'aris': 40, 'practition': 1, 'sharpedg': 1, 'picard': 1, 'benefici': 4, 'engess': 1, 'painlev': 1, 'complex': 25, 'power': 92, 'capabl': 12, 'choke': 11, 'super': 4, 'enquiri': 1, 'rabotnov': 1, 'crop': 3, 'ensur': 3, 'cold': 14, 'ribbon': 1, 'thick': 185, 'formula': 102, 'main': 55, 'nd': 1, 'societi': 1, 'decompos': 1, 'low': 154, 'contain': 41, 'db': 3, 'reinforc': 6, 'wb': 1, 'np': 1, 'correspond': 110, 'extens': 78, 'median': 5, 'wake': 46, 'isovel': 4, 'plain': 2, 'tip': 26, 'laid': 1, 'relay': 1, 'climin': 1, 'problem': 323, 'circumst': 6, 'divis': 4, 'latent': 1, 'passeng': 1, 'class': 28, 'deposit': 3, 'nation': 10, 'safeti': 2, 'stochast': 1, 'foreign': 9, 'sponsor': 3, 'popular': 1, 'remaind': 2, 'ident': 18, 'dome': 1, 'steadili': 4, 'jmjn': 1, 'rogh': 1, 'cowl': 2, 'dirac': 1, 'confluent': 2, 'windward': 8, 'defect': 4, 'caplan': 1, 'randon': 1, 'inhibit': 2, 'row': 12, 'differ': 192, 'pronounc': 3, 'forebodi': 8, 'present': 507, 'plu': 6, 'union': 2, 'total': 87, 'deceler': 18, 'havelock': 1, 'schultz': 1, 'airplan': 30, 'shortcom': 3, 'basi': 68, 'dashin': 1, 'hick': 1, 'name': 18, 'markedli': 5, 'firm': 1, 'russian': 1, 'theori': 456, 'ellipt': 37, 'convolut': 1, 'fale': 1, 'ejector': 1, 'assumpt': 108, 'extrem': 25, 'mc': 1, 'mph': 1, 'absorb': 9, 'nonuniform': 18, 'actual': 41, 'unbound': 2, 'hoshizaki': 1, 'nonrigor': 1, 'made': 352, 'roar': 1, 'shanley': 3, 'coundari': 1, 'discov': 5, 'meh': 1, 'child': 1, 'coars': 1, 'drive': 4, 'occur': 124, 'weaker': 1, 'infinitesim': 11, 'lose': 1, 'moment': 96, 'wherev': 1, 'pound': 8, 'poincar': 1, 'correct': 67, 'classic': 41, 'led': 9, 'yarymovych': 1, 'hysol': 1, 'middl': 9, 'woven': 1, 'around': 55, 'wavi': 4, 'woodgat': 1, 'cosin': 1, 'braunschweig': 1, 'ardc': 1, 'plane': 96, 'closer': 4, 'twenti': 3, 'decay': 17, 'adequ': 20, 'anisotrop': 2, 'initi': 109, 'drosophila': 1, 'semispan': 6, 'briefli': 39, 'nylon': 3, 'descent': 6, 'anisotropi': 1, 'unheat': 5, 'look': 1, 'superson': 270, 'disclos': 2, 'legendr': 2, 'billow': 1, 'trace': 11, 'droop': 2, 'afterbodi': 29, 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'favour': 2, 'grow': 6, 'janzen': 1, 'melt': 6, 'anhedr': 1, 'mercuri': 2, 'corros': 1, 'eye': 1, 'opposit': 16, 'repercuss': 1, 'conic': 67, 'show': 202, 'close': 96, 'minimum': 43, 'valid': 70, 'diminish': 14, 'atm': 2, 'unintent': 1, 'poetic': 1, 'reissner': 5, 'phenomenolog': 2, 'neglig': 36, 'oval': 5, 'aluminum': 16, 'foundat': 1, 'night': 3, 'iron': 1, 'append': 2, 'rigid': 33, 'speed': 292, 'self': 15, 'exclud': 6, 'frame': 8, 'paragraph': 2, 'oxygen': 12, 'parallel': 67, 'univer': 1, 'sweep': 20, 'machin': 20, 'generalis': 9, 'nonvanish': 2, 'drop': 12, 'enforc': 2, 'unreal': 1, 'unlik': 6, 'noninject': 1, 'experiment': 339, 'acceler': 29, 'spar': 3, 'prevail': 6, 'schubauer': 3, 'trick': 1, 'lower': 76, 'cabin': 1, 'con': 1, 'discrep': 16, 'thought': 6, 'edg': 189, 'becam': 3, 'continuum': 11, 'salt': 2, 'role': 13, 'aim': 8, 'gershuni': 1, 'shorter': 1, 'analysi': 278, 'avenu': 2, 'floor': 2, 'secant': 1, 'suffer': 2, 'pole': 1, 'escap': 4, 'semicircular': 1, 'uniqu': 10, 'idea': 17, 'predict': 193, 'equilibr': 1, 'meridion': 5, 'nondecompos': 1, 'see': 10, 'gibb': 1, 'nearest': 2, 'mangler': 5, 'object': 25, 'superimpos': 2, 'retrocket': 1, 'wise': 4, 'tailor': 3, 'undetermin': 1, 'fog': 1, 'castolit': 1, 'u': 25, 'reason': 72, 'accentu': 1, 'protein': 1, 'prewhirl': 1, 'schlicht': 4, 'dictat': 1, 'mind': 1, 'interconnect': 3, 'opportun': 2, 'monoxid': 1, 'anemomet': 8, 'oswatitsch': 2, 'molybdenum': 1, 'interpret': 35, 'sequel': 1, 'cruis': 7, 'reliev': 1, 'multiweb': 3, 'firmer': 1, 'nonrot': 3, 'hysteresi': 3, 'feel': 2, 'never': 3, 'attack': 114, 'rand': 1, 'appraoch': 1, 'strength': 64, 'judici': 1, 'proposit': 1, 'director': 1, 'truth': 2, 'intent': 2, 'elliott': 1, 'interstellar': 1, 'perform': 81, 'curv': 123, 'acrothermoelast': 1, 'hydraul': 2, 'summari': 15, 'favor': 24, 'breadth': 2, 'asimplifi': 1, 'cellular': 2, 'sheet': 17, 'homann': 1, 'deriv': 213, 'curl': 1, 'rocketon': 1, 'embrac': 4, 'constantli': 1, 'static': 95, 'motion': 120, 'mathieu': 2, 'airstream': 9, 'ting': 1, 'devot': 9, 'magnesium': 1, 'calculu': 7, 'schlieren': 24, 'shown': 285, 'unconstrain': 1, 'scope': 14, 'subject': 98, 'extrapol': 12, 'digit': 20, 'bond': 6, 'airfoil': 66, 'plasma': 8, 'gothic': 1, 'unanticip': 1, 'topic': 9, 'pertin': 10, 'eucken': 1, 'lingitudin': 1, 'heaslet': 1, 'prikl': 1, 'temp': 1, 'thwait': 3, 'fuse': 1, 'worst': 2, 'compos': 12, 'sure': 2, 'photothermoelast': 3, 'sector': 3, 'unlimit': 5, 'globul': 1, 'begin': 14, 'reflex': 1, 'gener': 331, 'read': 9, 'whisker': 1, 'publish': 33, 'downwash': 20, 'clay': 1, 'oncom': 4, 'edelfelt': 1, 'brake': 3, 'coefstant': 1, 'biconvex': 3, 'precess': 1, 'embryon': 1, 'rudimentari': 1, 'react': 6, 'windstream': 1, 'nuclear': 3, 'transient': 35, 'bimolecular': 1, 'unequ': 2, 'coulomb': 1, 'scarc': 2, 'surround': 12, 'defin': 48, 'avail': 81, 'adopt': 7, 'handl': 9, 'rewrit': 2, 'averag': 41, 'shadow': 2, 'third': 22, 'scale': 51, 'arriv': 8, 'tni': 1, 'write': 4, 'kempner': 1, 'mccune': 1, 'local': 125, 'sgn': 1, 'terrestri': 1, 'draw': 4, 'tollmien': 7, 'strongli': 7, 'cast': 1, 'prototyp': 2, 'twin': 2, 'verifi': 16, 'new': 78, 'rf': 1, 'efflux': 1, 'grad': 1, 'tical': 1, 'inlet': 14, 'assembl': 3, 'fibrou': 1, 'precipit': 3, 'bean': 2, 'refractori': 3, 'conceptu': 2, 'satelit': 1, 'slug': 1, 'permeabl': 2, 'ninefold': 1, 'aeroelastician': 1, 'crocco': 16, 'recur': 1, 'investigaion': 1, 'widespread': 1, 'ultim': 10, 'inert': 3, 'heater': 2, 'tumbl': 5, 'cent': 41, 'threaten': 1, 'breguet': 1, 'subsidiari': 1, 'nonflamm': 1, 'mainstream': 5, 'bonu': 1, 'noncommun': 1, 'shield': 4, 'alter': 23, 'subroutin': 1, 'photograph': 21, 'expenditur': 2, 'meant': 1, 'undertaken': 4, 'decis': 2, 'dispar': 1, 'hise': 1, 'edit': 1, 'underexpand': 1, 'safe': 2, 'simplic': 12, 'stream': 241, 'torda': 1, 'meet': 6, 'easier': 1, 'matrix': 10, 'tomotika': 1, 'mise': 7, 'structur': 107, 'exercis': 2, 'catalyt': 2, 'clearanc': 1, 'erod': 2, 'outsid': 31, 'instrument': 12, 'unimport': 1, 'difficult': 16, 'buri': 1, 'hypothes': 4, 'foreknowledg': 1, 'sharp': 53, 'disregard': 2, 'acr': 1, 'realli': 2, 'taylor': 16, 'ammonium': 2, 'viscou': 122, 'form': 239, 'prescrib': 25, 'refin': 15, 'necessit': 4, 'microwav': 1, 'shear': 99, 'sting': 6, 'rocket': 34, 'modest': 2, 'compil': 5, 'interpos': 1, 'gain': 7, 'blow': 8, 'flux': 17, 'phugoid': 1, 'rate': 145, 'yeh': 1, 'mckinnon': 1, 'equat': 401, 'burn': 6, 'appendix': 18, 'uncorrect': 1, 'orthotrop': 12, 'magnetogasdynam': 1, 'undroop': 1, 'newtonian': 45, 'invalid': 5, 'readili': 14, 'hosokawa': 1, 'interferogram': 1, 'counteract': 1, 'order': 197, 'distinguish': 4, 'multicellular': 1, 'clearli': 12, 'protrus': 1, 'ban': 1, 'fabric': 7, 'wedg': 42, 'preclud': 1, 'burger': 2, 'wing': 226, 'bleed': 5, 'planetari': 7, 'exposit': 2, 'adjac': 24, 'neither': 5, 'quotat': 1, 'intuit': 1, 'seidel': 2, 'metal': 13, 'deal': 34, 'monograph': 1, 'irrevers': 4, 'solut': 407, 'necess': 7, 'michael': 1, 'redistribut': 2, 'pears': 2, 'concur': 2, 'lock': 3, 'flap': 18, 'parabol': 21, 'mix': 54, 'li': 6, 'reexamin': 2, 'newli': 1, 'heterogen': 2, 'geophys': 1, 'sweptw': 1, 'titan': 1, 'influx': 2, 'c': 32, 'branchlin': 1, 'evvard': 1, 'argu': 3, 'stack': 4, 'explos': 4, 'quarterchord': 1, 'transcend': 1, 'furnac': 1, 'aeronaut': 16, 'foot': 25, 'incorpor': 11, 'interpol': 6, 'eros': 5, 'occasion': 1, 'max': 2, 'coaxial': 4, 'curvatur': 48, 'ostrach': 1, 'schmidt': 2, 'strengthen': 1, 'langley': 25, 'amplifi': 4, 'undoubtedli': 1, 'highest': 12, 'head': 20, 'wider': 11, 'resolv': 5, 'background': 6, 'vapour': 1, 'vs': 1, 'redund': 2, 'lengthwis': 3, 'satellit': 32, 'lindsey': 1, 'cruciform': 8, 'unbank': 1, 'possess': 5, 'presenc': 68, 'sidewash': 3, 'extent': 26, 'subsequ': 22, 'sq': 3, 'journal': 3, 'nontrivi': 1, 'et': 3, 'flight': 135, 'small': 240, 'turbul': 145, 'conclus': 53, 'bang': 1, 'hroma': 1, 'act': 23, 'cuss': 1, 'pari': 1, 'consumpt': 1, 'contradict': 1, 'plk': 1, 'sodium': 4, 'suppli': 10, 'anomali': 2, 'haveg': 1, 'lockhe': 1, 'asymmetri': 2, 'terrain': 3, 'similarli': 4, 'equilater': 1, 'outlet': 5, 'press': 2, 'permiss': 4, 'graze': 1, 'constant': 183, 'trip': 5, 'rear': 19, 'troublesom': 2, 'maxwel': 1, 'aerotherm': 1, 'group': 10, 'blasiu': 16, 'oppos': 4, 'quadrat': 2, 'i': 1, 'stine': 1, 'hodograph': 3, 'spring': 6, 'forego': 4, 'collis': 5, 'counterpart': 5, 'airlin': 1, 'deepest': 1, 'trigonometr': 6, 'chemic': 39, 'degreek': 5, 'assort': 1, 'section': 150, 'plastic': 30, 'egger': 1, 'sought': 3, 'lap': 1, 'aspect': 76, 'includ': 225, 'reflect': 35, 'centrelin': 1, 'fair': 18, 'polystyren': 1, 'usabl': 2, 'parabola': 1, 'blowout': 1, 'muller': 1, 'zoom': 1, 'quartic': 1, 'unifi': 10, 'across': 45, 'fix': 42, 'goal': 1, 'labori': 4, 'canard': 1, 'provok': 1, 'methan': 2, 'swept': 33, 'unsymmetr': 1, 'floccul': 1, 'disloc': 1, 'l': 20, 'edgewis': 2, 'sigular': 1, 'index': 2, 'wiley': 1, 'warren': 1, 'bed': 1, 'immers': 2, 'magnetofluidmechan': 1, 'dirichlet': 1, 'onset': 12, 'circl': 7, 'overshadow': 1, 'gravit': 2, 'simplifi': 59, 'gust': 12, 'einbind': 1, 'ii': 11, 'rayleigh': 17, 'concis': 2, 'supplementari': 2, 'linear': 147, 'porou': 29, 'contrari': 5, 'scoop': 2, 'stratiform': 2, 'pseudo': 1, 'unfir': 2, 'clamp': 31, 'guidanc': 5, 'area': 59, 'enter': 30, 'dorodnitsyn': 1, 'beam': 17, 'suppress': 2, 'jastrow': 2, 'conjectur': 4, 'frontal': 1, 'lb': 7, 'fluid': 160, 'be': 1, 'twice': 5, 'quart': 2, 'troubl': 2, 'gw': 1, 'subtend': 1, 'manufactur': 2, 'complimentari': 1, 'exceed': 22, 'fighter': 4, 'nonspher': 2, 'lifetim': 5, 'stratagem': 1, 'display': 4, 'rott': 1, 'novel': 4, 'unexplain': 1, 'emiss': 9, 'unavoid': 1, 'whilst': 5, 'poind': 1, 'dvl': 1, 'boundari': 470, 'thirteen': 3, 'coverag': 2, 'geoffrey': 1, 'greatli': 25, 'unchang': 5, 'diam': 1, 'experienc': 12, 'fp': 3, 'valv': 1, 'perpendicularli': 2, 'suck': 1, 'repres': 97, 'condit': 335, 'principl': 33, 'deduc': 20, 'du': 1, 'martin': 1, 'conclud': 46, 'branch': 6, 'constrain': 3, 'vehicl': 65, 'violat': 1, 'turbin': 14, 'diaphragm': 2, 'elast': 82, 'multilay': 2, 'typographi': 1, 'artific': 2, 'nonun': 1, 'va': 1, 'layer': 414, 'laguerr': 1, 'caus': 70, 'slot': 13, 'protect': 7, 'diamond': 3, 'proton': 1, 'slightli': 39, 'subson': 113, 'dive': 3, 'circuitri': 1, 'eleg': 1, 'inquir': 2, 'enclos': 5, 'histori': 23, 'aerfoil': 1, 'perhap': 7, 'come': 4, 'graviti': 14, 'ordinari': 39, 'heimel': 1, 'vi': 2, 'inop': 1, 'torqu': 5, 'set': 67, 'breakdown': 5, 'capsul': 1, 'grunow': 1, 'erickson': 1, 'dashthat': 1, 'daley': 1, 'tensil': 4, 'transduc': 1, 'seventh': 3, 'hitherto': 1, 'downward': 4, 'inner': 17, 'mag': 1, 'target': 2, 'impress': 1, 'protuber': 1, 'characterist': 184, 'analogu': 6, 'belief': 1, 'start': 26, 'mile': 6, 'roe': 1, 'colloc': 1, 'nonmagnet': 2, 'jetlik': 1, 'vtol': 13, 'find': 33, 'accuraci': 75, 'disturb': 59, 'convict': 1, 'feed': 2, 'requir': 146, 'promot': 5, 'solar': 6, 'although': 62, 'natur': 55, 'ohm': 1, 'thereof': 2, 'fort': 1, 'carrier': 1, 'ahead': 21, 'region': 173, 'warn': 1, 'nonsimilar': 3, 'pattern': 51, 'yaw': 37, 'weinstein': 1, 'plot': 24, 'save': 5, 'blown': 1, 'day': 7, 'stein': 3, 'interim': 1, 'crabtre': 1, 'certainti': 1, 'zero': 137, 'quickli': 1, 'veloc': 317, 'proceed': 5, 'jointli': 1, 'implic': 8, 'equal': 56, 'sand': 3, 'hub': 7, 'unsteadi': 46, 'ionospher': 6, 'navi': 3, 'prismat': 1, 'jeffrey': 1, 'hencki': 1, 'interior': 13, 'character': 20, 'tribut': 1, 'subtract': 2, 'phi': 3, 'drape': 1, 'typic': 38, 'nozzl': 80, 'mager': 3, 'langer': 1, 'feet': 10, 'toward': 21, 'thing': 1, 'thermodynam': 31, 'corrug': 6, 'asm': 2, 'goldstein': 3, 'farnborough': 3, 'current': 32, 'microinch': 2, 'glassi': 1, 'equatori': 1, 'unstiffen': 5, 'nineteen': 1, 'otherwis': 11, 'tractabl': 1, 'boat': 3, 'strong': 39, 'uneven': 1, 'suddenli': 7, 'abbrevi': 1, 'arc': 16, 'circuit': 1, 'slew': 1, 'handbook': 1, 'undesir': 1, 'depress': 1, 'upon': 74, 'kirk': 1, 'stabil': 120, 'qualit': 37, 'nonreact': 2, 'fin': 11, 'prebuckl': 1, 'paradox': 3, 'six': 13, 'intract': 1, 'degre': 78, 'analyz': 62, 'treat': 79, 'vibrat': 57, 'remot': 2, 'light': 27, 'expans': 76, 'allmov': 1, 'distinctli': 2, 'america': 1, 'incur': 4, 'retrorocket': 1, 'ab': 1, 'significantli': 18, 'acoust': 15, 'spoken': 1, 'whirl': 3, 'clear': 8, 'fifti': 2, 'outcom': 2, 'lead': 174, 'key': 3, 'none': 4, 'shorten': 5, 'dipol': 1, 'disc': 7, 'oversimplif': 1, 'crook': 1, 'flowmet': 2, 'octob': 2, 'harden': 3, 'paraplex': 2, 'wood': 4, 'alreadi': 6, 'vibratori': 4, 'calcualt': 2, 'skin': 95, 'ordin': 14, 'matter': 3, 'absolut': 11, 'discuss': 271, 'bulk': 6, 'upward': 1, 'hazard': 1, 'channel': 17, 'attend': 5, 'backward': 1, 'drawn': 15, 'reserv': 1, 'belong': 3, 'consider': 159, 'simul': 35, 'loos': 5, 'hemi': 1, 'hypothesi': 8, 'note': 118, 'singl': 61, 'termwis': 1, 'anoth': 24, 'caret': 1, 'disappear': 6, 'boost': 1, 'distanc': 116, 'apprais': 3, 'million': 7, 'hope': 5, 'skirt': 2, 'groov': 1, 'sidewal': 2, 'nash': 2, 'pr': 2, 'believ': 20, 'tend': 28, 'enskog': 1, 'fulli': 30, 'multi': 7, 'coupl': 21, 'mainten': 1, 'midchord': 1, 'landahl': 4, 'doubl': 24, 'spectra': 7, 'nasa': 8, 'reproduc': 5, 'common': 17, 'devic': 9, 'normal': 129, 'affirm': 1, 'q': 6, 'preced': 7, 'centerlin': 2, 'choic': 17, 'spectral': 3, 'cusp': 3, 'rearrang': 1, 'prove': 16, 'wassermann': 1, 'given': 387, 'voyag': 2, 'deplet': 1, 'shell': 106, 'irbm': 1, 'degener': 2, 'gross': 8, 'introduct': 21, 'recogn': 5, 'tangent': 19, 'irrit': 1, 'movabl': 4, 'fl': 1, 'erfc': 1, 'prepar': 8, 'alloy': 18, 'time': 153, 'deioniz': 1, 'eq': 2, 'drift': 1, 'age': 1, 'allow': 39, 'toxic': 1, 'preserv': 4, 'identifi': 10, 'merit': 10, 'unstabl': 8, 'overrid': 1, 'lesser': 2, 'tietjen': 2, 'determinant': 1, 'assur': 1, 'seban': 4, 'shyperson': 1, 'owen': 1, 'account': 82, 'proce': 5, 'modif': 35, 'previous': 57, 'stipul': 1, 'facil': 14, 'stringer': 7, 'dent': 1, 'mat': 2, 'hybrid': 1, 'joukowski': 6, 'whether': 12, 'adiabat': 21, 'join': 8, 'period': 29, 'chemistri': 2, 'fed': 1, 'forth': 5, 'especi': 20, 'conductor': 1, 'corpor': 2, 'davi': 2, 'simplif': 17, 'extern': 74, 'projectil': 4, 'winglik': 2, 'aeroplan': 4, 'occurr': 6, 'isotrop': 11, 'spatial': 5, 'paraboloid': 5, 'benser': 1, 'hydrogen': 9, 'eight': 6, 'acrodynam': 1, 'laval': 4, 'dy': 2, 'probstein': 5, 'ms': 1, 'either': 71, 'obtainec': 1, 'nine': 2, 'eccentr': 10, 'peng': 1, 'proc': 2, 'revert': 2, 'intens': 28, 'hover': 10, 'abrupt': 3, 'mesh': 1, 'vision': 1, 'periment': 1, 'trajectori': 15, 'cut': 3, 'percentag': 7, 'upwash': 5, 'vertex': 3, 'freon': 7, 'rectilinear': 1, 'network': 2, 'arrheniu': 3, 'conflict': 4, 'phenol': 2, 'knowledg': 30, 'understand': 17, 'referenc': 3, 'date': 5, 'spanwis': 25, 'spray': 1, 'test': 274, 'warmer': 1, 'kinet': 25, 'resili': 1, 'isentrop': 13, 'arbitrarili': 10, 'longest': 1, 'denc': 1, 'slide': 1, 'concurr': 3, 'contempl': 1, 'render': 4, 'compani': 1, 'peak': 21, 'arisen': 2, 'understood': 5, 'orgin': 1, 'corpuscular': 1, 'hyper': 1, 'immobil': 1, 'blowdown': 5, 'integro': 1, 'unfavor': 2, 'necessarili': 13, 'imprimeri': 1, 'heliocentr': 1, 'lightweight': 2, 'goodier': 1, 'decidedli': 1, 'quarter': 5, 'busemann': 6, 'ofr': 1, 'wall': 191, 'refract': 4, 'behavior': 62, 'palt': 1, 'flutter': 56, 'describ': 154, 'blunt': 140, 'airforc': 1, 'redirect': 1, 'specif': 63, 'eratur': 1, 'ignor': 7, 'mayer': 3, 'plast': 1, 'justif': 4, 'fasten': 1, 'determin': 299, 'domain': 7, 'price': 1, 'propos': 46, 'w': 16, 'wagner': 2, 'filter': 2, 'becom': 52, 'sweepback': 10, 'trim': 8, 'plung': 1, 'longitudin': 50, 'capacit': 2, 'centroid': 1, 'cushion': 3, 'immov': 1, 'unfold': 1, 'inst': 1, 'pipe': 8, 'sanger': 1, 'joul': 1, 'seren': 1, 'inch': 31, 'effus': 1, 'disagre': 2, 'restraint': 8, 'fluidiz': 1, 'surfac': 330, 'inflow': 2, 'debat': 1, 'compat': 6, 'centrifug': 10, 'born': 2, 'attitud': 5, 'spacewis': 1, 'soft': 5, 'sin': 3, 'imag': 5, 'surg': 4, 'content': 4, 'unsupport': 1, 'altitud': 45, 'venu': 2, 'belt': 1, 'grimming': 1, 'howev': 110, 'nocturn': 1, 'helium': 39, 'versu': 6, 'forefront': 1, 'airspe': 2, 'blade': 33, 'induc': 73, 'nonstationari': 2, 'delta': 35, 'incid': 59, 'took': 1, 'maccol': 4, 'occupi': 1, 'event': 3, 'desir': 27, 'combust': 15, 'malfunct': 1, 'reattach': 14, 'feasibl': 12, 'agre': 59, 'punctur': 2, 'warp': 1, 'bomber': 3, 'work': 113, 'hotter': 1, 'psi': 1, 'rotat': 52, 'neater': 1, 'progress': 17, 'collect': 10, 'nickel': 2, 'transport': 23, 'migrat': 1, 'term': 158, 'ask': 1, 'legitim': 1, 'simplest': 7, 'review': 52, 'pohlhausen': 23, 'last': 5, 'mm': 1, 'dissimilar': 1, 'cylinder': 1, 'underneath': 1, 'one': 268, 'recommend': 7, 'comput': 124, 'venturi': 1, 'retroveloc': 1, 'constitut': 6, 'propag': 17, 'increas': 206, 'art': 3, 'bubbl': 3, 'facet': 1, 'page': 3, 'silica': 1, 'quasi': 20, 'sturm': 1, 'unit': 23, 'hpyerson': 1, 'philo': 1, 'soak': 1, 'unorthodox': 1, 'sensibl': 1, 'highli': 27, 'discoveri': 3, 'appear': 95, 'tripl': 3, 'nonlift': 7, 'advent': 1, 'economi': 1, 'nonsingular': 1, 'justifi': 11, 'thenc': 2, 'late': 1, 'brass': 3, 'kutta': 6, 'depict': 1, 'photo': 5, 'downstream': 63, 'ogiv': 14, 'assess': 14, 'millisecond': 5, 'utilis': 2, 'lever': 1, 'rl': 1, 'compris': 7, 'sutherland': 3, 'ferri': 7, 'batdorf': 1, 'reichardt': 1, 'peculiar': 4, 'resin': 3, 'aero': 3, 'tab': 4, 'perige': 10, 'outlin': 22, 'popularli': 1, 'enhanc': 2, 'uniformli': 29, 'verfi': 1, 'laplacian': 1, 'might': 15, 'regist': 1, 'serbin': 1, 'variant': 1, 'meter': 2, 'known': 89, 'xiii': 1, 'stoker': 1, 'ellips': 3, 'richmond': 1, 'lastli': 2, 'uhf': 1, 'accordingli': 8, 'nearli': 31, 'truncat': 8, 'far': 40, 'strut': 3, 'icbm': 2, 'unsatisfactori': 2, 'vari': 144, 'sideforc': 1, 'restor': 2, 'exhibit': 19}\n"
     ]
    }
   ],
   "source": [
    "# Create a dictionary(inverted index) to store in a dictionart\n",
    "document_frequency = {}\n",
    "\n",
    "# Obtain unique terms in each document\n",
    "unique_terms = set([term for doc in df['preprocessed_text'] for term in doc.split()])\n",
    "print(\"Total unique terms in dataset: \", len(unique_terms))\n",
    "\n",
    "# Fill document_frequency dictionary\n",
    "for term in unique_terms:\n",
    "    doc_freq = sum([1 for doc in df['preprocessed_text'] if term in doc.split()])\n",
    "    document_frequency[term] = doc_freq\n",
    "\n",
    "print(\"\\nDocument Frequency dictiobnary: \\n\", document_frequency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N:  1400\n",
      "\n",
      "IDF: \n",
      " {'senior': 3.146128035678238, 'schwarz': 3.146128035678238, 'similitud': 2.191885526238913, 'stiffen': 1.5898255349109507, 'half': 1.4471580313422192, 'rubesin': 2.3010299956639813, 'thoroughli': 2.845098040014257, 'aileron': 2.191885526238913, 'text': 2.4471580313422194, 'absorpt': 2.3679767852945943, 'instig': 3.146128035678238, 'proportion': 2.845098040014257, 'basin': 3.146128035678238, 'doubli': 3.146128035678238, 'film': 2.3679767852945943, 'sometim': 2.191885526238913, 'pyramid': 3.146128035678238, 'suffix': 3.146128035678238, 'met': 2.845098040014257, 'lectur': 3.146128035678238, 'switch': 3.146128035678238, 'practic': 1.146128035678238, 'crush': 3.146128035678238, 'complic': 2.066946789630613, 'upper': 1.5333441789585025, 'slipstream': 1.9700367766225568, 'straight': 1.6547663418439653, 'parasol': 3.146128035678238, 'armenaka': 2.845098040014257, 'volum': 1.765916793966632, 'isotop': 3.146128035678238, 'liouvil': 3.146128035678238, 'sugar': 3.146128035678238, 'papreg': 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3.146128035678238, 'subtract': 2.845098040014257, 'phi': 2.6690067809585756, 'drape': 3.146128035678238, 'typic': 1.5663444390614278, 'nozzl': 1.2430380486862944, 'mager': 2.6690067809585756, 'langer': 3.146128035678238, 'feet': 2.146128035678238, 'toward': 1.8239087409443189, 'thing': 3.146128035678238, 'thermodynam': 1.6547663418439653, 'corrug': 2.3679767852945943, 'asm': 2.845098040014257, 'goldstein': 2.6690067809585756, 'farnborough': 2.6690067809585756, 'current': 1.6409780573583321, 'microinch': 2.845098040014257, 'glassi': 3.146128035678238, 'equatori': 3.146128035678238, 'unstiffen': 2.4471580313422194, 'nineteen': 3.146128035678238, 'otherwis': 2.104735350520013, 'tractabl': 3.146128035678238, 'boat': 2.6690067809585756, 'strong': 1.5550634286517389, 'uneven': 3.146128035678238, 'suddenli': 2.3010299956639813, 'abbrevi': 3.146128035678238, 'arc': 1.9420080530223132, 'circuit': 3.146128035678238, 'slew': 3.146128035678238, 'handbook': 3.146128035678238, 'undesir': 3.146128035678238, 'depress': 3.146128035678238, 'upon': 1.276896315947262, 'kirk': 3.146128035678238, 'stabil': 1.066946789630613, 'qualit': 1.577926311611243, 'nonreact': 2.845098040014257, 'fin': 2.104735350520013, 'prebuckl': 3.146128035678238, 'paradox': 2.6690067809585756, 'six': 2.0321846833714012, 'intract': 3.146128035678238, 'degre': 1.2540334329877576, 'analyz': 1.3537363461799843, 'treat': 1.2485009443877966, 'vibrat': 1.3902531800057467, 'remot': 2.845098040014257, 'light': 1.7147642715192508, 'expans': 1.2653144433974468, 'allmov': 3.146128035678238, 'distinctli': 2.845098040014257, 'america': 3.146128035678238, 'incur': 2.5440680443502757, 'retrorocket': 3.146128035678238, 'ab': 3.146128035678238, 'significantli': 1.890855530574932, 'acoust': 1.9700367766225568, 'spoken': 3.146128035678238, 'whirl': 2.6690067809585756, 'clear': 2.2430380486862944, 'fifti': 2.845098040014257, 'outcom': 2.845098040014257, 'lead': 0.9055787873956384, 'key': 2.6690067809585756, 'none': 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1.360798200667471, 'termwis': 3.146128035678238, 'anoth': 1.765916793966632, 'caret': 3.146128035678238, 'disappear': 2.3679767852945943, 'boost': 3.146128035678238, 'distanc': 1.0816700464513196, 'apprais': 2.6690067809585756, 'million': 2.3010299956639813, 'hope': 2.4471580313422194, 'skirt': 2.845098040014257, 'groov': 3.146128035678238, 'sidewal': 2.845098040014257, 'nash': 2.845098040014257, 'pr': 2.845098040014257, 'believ': 1.845098040014257, 'tend': 1.6989700043360187, 'enskog': 3.146128035678238, 'fulli': 1.6690067809585756, 'multi': 2.3010299956639813, 'coupl': 1.8239087409443189, 'mainten': 3.146128035678238, 'midchord': 3.146128035678238, 'landahl': 2.5440680443502757, 'doubl': 1.765916793966632, 'spectra': 2.3010299956639813, 'nasa': 2.2430380486862944, 'reproduc': 2.4471580313422194, 'common': 1.915679114299964, 'devic': 2.191885526238913, 'normal': 1.0355383253789892, 'affirm': 3.146128035678238, 'q': 2.3679767852945943, 'preced': 2.3010299956639813, 'centerlin': 2.845098040014257, 'choic': 1.915679114299964, 'spectral': 2.6690067809585756, 'cusp': 2.6690067809585756, 'rearrang': 3.146128035678238, 'prove': 1.9420080530223132, 'wassermann': 3.146128035678238, 'given': 0.5584170706593267, 'voyag': 2.845098040014257, 'deplet': 3.146128035678238, 'shell': 1.1208221704134678, 'irbm': 3.146128035678238, 'degener': 2.845098040014257, 'gross': 2.2430380486862944, 'introduct': 1.8239087409443189, 'recogn': 2.4471580313422194, 'tangent': 1.867374434725409, 'irrit': 3.146128035678238, 'movabl': 2.5440680443502757, 'fl': 3.146128035678238, 'erfc': 3.146128035678238, 'prepar': 2.2430380486862944, 'alloy': 1.890855530574932, 'time': 0.9614366048606392, 'deioniz': 3.146128035678238, 'eq': 2.845098040014257, 'drift': 3.146128035678238, 'age': 3.146128035678238, 'allow': 1.5550634286517389, 'toxic': 3.146128035678238, 'preserv': 2.5440680443502757, 'identifi': 2.146128035678238, 'merit': 2.146128035678238, 'unstabl': 2.2430380486862944, 'overrid': 3.146128035678238, 'lesser': 2.845098040014257, 'tietjen': 2.845098040014257, 'determinant': 3.146128035678238, 'assur': 3.146128035678238, 'seban': 2.5440680443502757, 'shyperson': 3.146128035678238, 'owen': 3.146128035678238, 'account': 1.2323141832945215, 'proce': 2.4471580313422194, 'modif': 1.6020599913279623, 'previous': 1.3902531800057467, 'stipul': 3.146128035678238, 'facil': 2.0, 'stringer': 2.3010299956639813, 'dent': 3.146128035678238, 'mat': 2.845098040014257, 'hybrid': 3.146128035678238, 'joukowski': 2.3679767852945943, 'whether': 2.066946789630613, 'adiabat': 1.8239087409443189, 'join': 2.2430380486862944, 'period': 1.6837300377792819, 'chemistri': 2.845098040014257, 'fed': 3.146128035678238, 'forth': 2.4471580313422194, 'especi': 1.845098040014257, 'conductor': 3.146128035678238, 'corpor': 2.845098040014257, 'davi': 2.845098040014257, 'simplif': 1.915679114299964, 'extern': 1.276896315947262, 'projectil': 2.5440680443502757, 'winglik': 2.845098040014257, 'aeroplan': 2.5440680443502757, 'occurr': 2.3679767852945943, 'isotrop': 2.104735350520013, 'spatial': 2.4471580313422194, 'paraboloid': 2.4471580313422194, 'benser': 3.146128035678238, 'hydrogen': 2.191885526238913, 'eight': 2.3679767852945943, 'acrodynam': 3.146128035678238, 'laval': 2.5440680443502757, 'dy': 2.845098040014257, 'probstein': 2.4471580313422194, 'ms': 3.146128035678238, 'either': 1.2948696869591627, 'obtainec': 3.146128035678238, 'nine': 2.845098040014257, 'eccentr': 2.146128035678238, 'peng': 3.146128035678238, 'proc': 2.845098040014257, 'revert': 2.845098040014257, 'intens': 1.6989700043360187, 'hover': 2.146128035678238, 'abrupt': 2.6690067809585756, 'mesh': 3.146128035678238, 'vision': 3.146128035678238, 'periment': 3.146128035678238, 'trajectori': 1.9700367766225568, 'cut': 2.6690067809585756, 'percentag': 2.3010299956639813, 'upwash': 2.4471580313422194, 'vertex': 2.6690067809585756, 'freon': 2.3010299956639813, 'rectilinear': 3.146128035678238, 'network': 2.845098040014257, 'arrheniu': 2.6690067809585756, 'conflict': 2.5440680443502757, 'phenol': 2.845098040014257, 'knowledg': 1.6690067809585756, 'understand': 1.915679114299964, 'referenc': 2.6690067809585756, 'date': 2.4471580313422194, 'spanwis': 1.7481880270062005, 'spray': 3.146128035678238, 'test': 0.7083774728578501, 'warmer': 3.146128035678238, 'kinet': 1.7481880270062005, 'resili': 3.146128035678238, 'isentrop': 2.0321846833714012, 'arbitrarili': 2.146128035678238, 'longest': 3.146128035678238, 'denc': 3.146128035678238, 'slide': 3.146128035678238, 'concurr': 2.6690067809585756, 'contempl': 3.146128035678238, 'render': 2.5440680443502757, 'compani': 3.146128035678238, 'peak': 1.8239087409443189, 'arisen': 2.845098040014257, 'understood': 2.4471580313422194, 'orgin': 3.146128035678238, 'corpuscular': 3.146128035678238, 'hyper': 3.146128035678238, 'immobil': 3.146128035678238, 'blowdown': 2.4471580313422194, 'integro': 3.146128035678238, 'unfavor': 2.845098040014257, 'necessarili': 2.0321846833714012, 'imprimeri': 3.146128035678238, 'heliocentr': 3.146128035678238, 'lightweight': 2.845098040014257, 'goodier': 3.146128035678238, 'decidedli': 3.146128035678238, 'quarter': 2.4471580313422194, 'busemann': 2.3679767852945943, 'ofr': 3.146128035678238, 'wall': 0.8650946684305105, 'refract': 2.5440680443502757, 'behavior': 1.3537363461799843, 'palt': 3.146128035678238, 'flutter': 1.3979400086720377, 'describ': 0.958607314841775, 'blunt': 1.0, 'airforc': 3.146128035678238, 'redirect': 3.146128035678238, 'specif': 1.3467874862246563, 'eratur': 3.146128035678238, 'ignor': 2.3010299956639813, 'mayer': 2.6690067809585756, 'plast': 3.146128035678238, 'justif': 2.5440680443502757, 'fasten': 3.146128035678238, 'determin': 0.6704568473538084, 'domain': 2.3010299956639813, 'price': 3.146128035678238, 'propos': 1.4833702039966639, 'w': 1.9420080530223132, 'wagner': 2.845098040014257, 'filter': 2.845098040014257, 'becom': 1.430124692043439, 'sweepback': 2.146128035678238, 'trim': 2.2430380486862944, 'plung': 3.146128035678238, 'longitudin': 1.4471580313422192, 'capacit': 2.845098040014257, 'centroid': 3.146128035678238, 'cushion': 2.6690067809585756, 'immov': 3.146128035678238, 'unfold': 3.146128035678238, 'inst': 3.146128035678238, 'pipe': 2.2430380486862944, 'sanger': 3.146128035678238, 'joul': 3.146128035678238, 'seren': 3.146128035678238, 'inch': 1.6547663418439653, 'effus': 3.146128035678238, 'disagre': 2.845098040014257, 'restraint': 2.2430380486862944, 'fluidiz': 3.146128035678238, 'surfac': 0.6276140958003505, 'inflow': 2.845098040014257, 'debat': 3.146128035678238, 'compat': 2.3679767852945943, 'centrifug': 2.146128035678238, 'born': 2.845098040014257, 'attitud': 2.4471580313422194, 'spacewis': 3.146128035678238, 'soft': 2.4471580313422194, 'sin': 2.6690067809585756, 'imag': 2.4471580313422194, 'surg': 2.5440680443502757, 'content': 2.5440680443502757, 'unsupport': 3.146128035678238, 'altitud': 1.4929155219028944, 'venu': 2.845098040014257, 'belt': 3.146128035678238, 'grimming': 3.146128035678238, 'howev': 1.104735350520013, 'nocturn': 3.146128035678238, 'helium': 1.5550634286517389, 'versu': 2.3679767852945943, 'forefront': 3.146128035678238, 'airspe': 2.845098040014257, 'blade': 1.6276140958003504, 'induc': 1.282805175557782, 'nonstationari': 2.845098040014257, 'delta': 1.6020599913279623, 'incid': 1.375276024036094, 'took': 3.146128035678238, 'maccol': 2.5440680443502757, 'occupi': 3.146128035678238, 'event': 2.6690067809585756, 'desir': 1.7147642715192508, 'combust': 1.9700367766225568, 'malfunct': 3.146128035678238, 'reattach': 2.0, 'feasibl': 2.066946789630613, 'agre': 1.375276024036094, 'punctur': 2.845098040014257, 'warp': 3.146128035678238, 'bomber': 2.6690067809585756, 'work': 1.0930495921948182, 'hotter': 3.146128035678238, 'psi': 3.146128035678238, 'rotat': 1.430124692043439, 'neater': 3.146128035678238, 'progress': 1.915679114299964, 'collect': 2.146128035678238, 'nickel': 2.845098040014257, 'transport': 1.7844001996606451, 'migrat': 3.146128035678238, 'term': 0.9474709487238153, 'ask': 3.146128035678238, 'legitim': 3.146128035678238, 'simplest': 2.3010299956639813, 'review': 1.430124692043439, 'pohlhausen': 1.7844001996606451, 'last': 2.4471580313422194, 'mm': 3.146128035678238, 'dissimilar': 3.146128035678238, 'cylinder': 3.146128035678238, 'underneath': 3.146128035678238, 'one': 0.7179932416494492, 'recommend': 2.3010299956639813, 'comput': 1.052706350516003, 'venturi': 3.146128035678238, 'retroveloc': 3.146128035678238, 'constitut': 2.3679767852945943, 'propag': 1.915679114299964, 'increas': 0.8322608153090846, 'art': 2.6690067809585756, 'bubbl': 2.6690067809585756, 'facet': 3.146128035678238, 'page': 2.6690067809585756, 'silica': 3.146128035678238, 'quasi': 1.845098040014257, 'sturm': 3.146128035678238, 'unit': 1.7844001996606451, 'hpyerson': 3.146128035678238, 'philo': 3.146128035678238, 'soak': 3.146128035678238, 'unorthodox': 3.146128035678238, 'sensibl': 3.146128035678238, 'highli': 1.7147642715192508, 'discoveri': 2.6690067809585756, 'appear': 1.1684044303893903, 'tripl': 2.6690067809585756, 'nonlift': 2.3010299956639813, 'advent': 3.146128035678238, 'economi': 3.146128035678238, 'nonsingular': 3.146128035678238, 'justifi': 2.104735350520013, 'thenc': 2.845098040014257, 'late': 3.146128035678238, 'brass': 2.6690067809585756, 'kutta': 2.3679767852945943, 'depict': 3.146128035678238, 'photo': 2.4471580313422194, 'downstream': 1.3467874862246563, 'ogiv': 2.0, 'assess': 2.0, 'millisecond': 2.4471580313422194, 'utilis': 2.845098040014257, 'lever': 3.146128035678238, 'rl': 3.146128035678238, 'compris': 2.3010299956639813, 'sutherland': 2.6690067809585756, 'ferri': 2.3010299956639813, 'batdorf': 3.146128035678238, 'reichardt': 3.146128035678238, 'peculiar': 2.5440680443502757, 'resin': 2.6690067809585756, 'aero': 2.6690067809585756, 'tab': 2.5440680443502757, 'perige': 2.146128035678238, 'outlin': 1.8037053548560318, 'popularli': 3.146128035678238, 'enhanc': 2.845098040014257, 'uniformli': 1.6837300377792819, 'verfi': 3.146128035678238, 'laplacian': 3.146128035678238, 'might': 1.9700367766225568, 'regist': 3.146128035678238, 'serbin': 3.146128035678238, 'variant': 3.146128035678238, 'meter': 2.845098040014257, 'known': 1.1967380290333252, 'xiii': 3.146128035678238, 'stoker': 3.146128035678238, 'ellips': 2.6690067809585756, 'richmond': 3.146128035678238, 'lastli': 2.845098040014257, 'uhf': 3.146128035678238, 'accordingli': 2.2430380486862944, 'nearli': 1.6547663418439653, 'truncat': 2.2430380486862944, 'far': 1.5440680443502757, 'strut': 2.6690067809585756, 'icbm': 2.845098040014257, 'unsatisfactori': 2.845098040014257, 'vari': 0.9877655435829883, 'sideforc': 3.146128035678238, 'restor': 2.845098040014257, 'exhibit': 1.867374434725409}\n"
     ]
    }
   ],
   "source": [
    "# Define number of documents as N\n",
    "N = len(df)\n",
    "print(\"N: \", N)\n",
    "\n",
    "# Fill idf which has unique terms as its keys and their idfs ad values\n",
    "idf = {}\n",
    "for key in document_frequency:\n",
    "    idf[key] = math.log10(N/document_frequency[key])\n",
    "\n",
    "print(\"\\nIDF: \\n\", idf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute CF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "T (collection size):  128923\n",
      "CF:  {'experiment': 470, 'investig': 524, 'aerodynam': 309, 'wing': 838, 'slipstream': 45, 'studi': 321, 'propel': 102, 'made': 449, 'order': 288, 'determin': 399, 'spanwis': 42, 'distribut': 650, 'lift': 406, 'increas': 380, 'due': 171, 'differ': 240, 'angl': 473, 'attack': 231, 'free': 377, 'stream': 407, 'veloc': 580, 'ratio': 614, 'result': 1088, 'intend': 12, 'part': 164, 'evalu': 86, 'basi': 76, 'theoret': 311, 'treatment': 44, 'problem': 591, 'compar': 283, 'span': 62, 'load': 414, 'curv': 184, 'togeth': 42, 'support': 103, 'evid': 34, 'show': 242, 'substanti': 44, 'increment': 29, 'produc': 94, 'destal': 5, 'boundari': 1216, 'layer': 1164, 'control': 117, 'effect': 998, 'integr': 226, 'remain': 52, 'subtract': 2, 'found': 424, 'agre': 63, 'well': 188, 'potenti': 87, 'flow': 2083, 'theori': 883, 'empir': 38, 'specif': 86, 'configur': 129, 'experi': 201, 'simpl': 222, 'shear': 209, 'past': 122, 'flat': 291, 'plate': 526, 'incompress': 175, 'fluid': 261, 'small': 315, 'viscos': 89, 'high': 336, 'speed': 513, 'viscou': 197, 'two': 639, 'dimension': 366, 'bodi': 854, 'usual': 63, 'necessari': 62, 'consid': 307, 'shock': 746, 'wave': 485, 'emit': 8, 'nose': 252, 'lead': 312, 'edg': 440, 'consequ': 22, 'exist': 132, 'inviscid': 118, 'rotat': 80, 'region': 300, 'situat': 25, 'aris': 45, 'instanc': 12, 'hyperson': 351, 'somewhat': 23, 'prandtl': 80, 'classic': 53, 'origin': 56, 'outsid': 36, 'irrot': 6, 'must': 59, 'possibl': 189, 'vortic': 102, 'recent': 104, 'discuss': 328, 'ferri': 7, 'libbi': 1, 'present': 698, 'paper': 247, 'shown': 347, 'treat': 93, 'approxim': 555, 'novel': 4, 'featur': 51, 'constant': 265, 'restrict': 47, 'steadi': 151, 'equat': 778, 'pressur': 1391, 'gradient': 185, 'solut': 849, 'laminar': 428, 'thick': 297, 'skin': 204, 'friction': 169, 'obtain': 643, 'karman': 42, 'pohlhausen': 31, 'techniqu': 120, 'comparison': 159, 'uniform': 166, 'also': 362, 'one': 325, 'transient': 53, 'heat': 847, 'conduct': 217, 'doubl': 30, 'slab': 27, 'subject': 119, 'linear': 212, 'input': 12, 'time': 246, 'intern': 65, 'analyt': 130, 'composit': 32, 'expos': 14, 'surfac': 691, 'triangular': 27, 'rate': 273, 'type': 257, 'may': 311, 'occur': 155, 'exampl': 140, 'multilay': 2, 'contribut': 31, 'reader': 6, 'forum': 2, 'wassermann': 2, 'gave': 18, 'temperatur': 629, 'face': 45, 'insul': 35, 'thermal': 179, 'resist': 44, 'interfac': 12, 'three': 196, 'particular': 124, 'case': 547, 'propos': 53, 'give': 165, 'gener': 453, 'indic': 253, 'briefli': 40, 'use': 734, 'method': 888, 'refer': 74, 'point': 368, 'given': 521, 'incomplet': 2, 'longer': 9, 'durat': 12, 'rough': 114, 'transit': 276, 'superson': 470, 'perform': 111, 'wind': 182, 'tunnel': 312, 'jet': 462, 'propuls': 34, 'laboratori': 29, 'california': 6, 'institut': 8, 'technolog': 8, 'element': 52, 'sphere': 79, 'tain': 3, 'local': 183, 'mach': 823, 'number': 1348, 'vari': 182, 'trip': 11, 'size': 52, 'posit': 108, 'space': 42, 'reynold': 383, 'per': 103, 'inch': 37, 'turbul': 368, 'induc': 98, 'begin': 16, 'row': 16, 'spiral': 5, 'trail': 89, 'contamin': 6, 'break': 9, 'surround': 13, 'field': 313, 'appear': 107, 'rather': 36, 'suddenli': 7, 'becom': 56, 'violent': 2, 'height': 75, 'rel': 98, 'breakdown': 7, 'strength': 92, 'still': 27, 'persist': 3, 'sublay': 14, 'ensu': 1, 'later': 63, 'littl': 27, 'upon': 96, 'initi': 174, 'u': 36, 'v': 32, 'kinemat': 10, 'outer': 59, 'k': 51, 'fourth': 20, 'power': 134, 'viz': 3, 'x': 138, 'measur': 429, 'h': 51, 'l': 25, 'dryden': 2, 'avail': 92, 'data': 325, 'wire': 21, 'could': 66, 'repres': 110, 'reason': 81, 'term': 201, 'function': 330, 'relat': 204, 'base': 375, 'distanc': 175, 'displac': 126, 'suggest': 84, 'addit': 112, 'primarili': 17, 'extend': 108, 'rang': 432, 'higher': 120, 'valu': 497, 'cours': 14, 'nation': 11, 'bureau': 4, 'standard': 24, 'desir': 31, 'see': 10, 'whether': 13, 'would': 82, 'behav': 11, 'way': 37, 'galcit': 6, 'nomin': 19, 'phosphoresc': 1, 'lacquer': 1, 'detect': 9, 'good': 146, 'agreement': 221, 'total': 117, 'head': 38, 'rake': 1, 'along': 173, 'pitot': 42, 'survey': 48, 'least': 31, 'transvers': 100, 'caus': 79, 'sidewal': 2, 'far': 48, 'downstream': 97, 'spread': 22, 'degre': 137, 'low': 216, 'disturb': 93, 'air': 335, 'inject': 153, 'mean': 198, 'hasten': 1, 'extens': 86, 'although': 66, 'onset': 13, 'fulli': 38, 'develop': 326, 'much': 72, 'regardless': 2, 'amount': 50, 'qualit': 37, 'emphasi': 13, 'greater': 77, 'stabil': 227, 'lower': 89, 'direct': 155, 'float': 6, 'verifi': 17, 'complet': 99, 'earlier': 23, 'coeffici': 390, 'r': 72, 'impact': 22, 'tube': 173, 'analysi': 387, 'densiti': 145, 'extrem': 37, 'appreci': 42, 'correspond': 138, 'continuum': 13, 'similar': 232, 'compress': 338, 'mix': 118, 'mani': 45, 'practic': 114, 'interest': 74, 'wherein': 8, 'stagnat': 252, 'anoth': 24, 'major': 41, 'interact': 163, 'take': 75, 'place': 34, 'presenc': 81, 'axial': 228, 'characterist': 269, 'influenc': 137, 'significantli': 19, 'devic': 9, 'phenomena': 38, 'cite': 4, 'program': 31, 'research': 73, 'carri': 78, 'polytechn': 1, 'brooklyn': 1, 'structur': 195, 'aerelast': 1, 'consider': 174, 'flight': 221, 'domin': 12, 'factor': 108, 'design': 240, 'aircraft': 131, 'aeroelast': 26, 'matter': 3, 'concern': 74, 'larg': 281, 'interrel': 2, 'summari': 17, 'tool': 11, 'aeronaut': 19, 'engin': 60, 'meet': 6, 'demand': 3, 'state': 85, 'art': 3, 'respect': 99, 'transfer': 492, 'mode': 108, 'failur': 41, 'combin': 171, 'acrothermoelast': 1, 'allevi': 7, 'summar': 16, 'final': 66, 'avenu': 2, 'fundament': 37, 'law': 93, 'stress': 350, 'differenti': 145, 'proper': 9, 'modif': 41, 'isotherm': 17, 'fact': 33, 'calcul': 552, 'strain': 68, 'unheat': 6, 'seri': 121, 'call': 20, 'applic': 276, 'analog': 61, 'solid': 63, 'detail': 120, 'howev': 124, 'complic': 12, 'involv': 115, 'concept': 64, 'feedback': 2, 'forc': 330, 'box': 3, 'solv': 99, 'piston': 29, 'new': 104, 'aeroelastician': 1, 'describ': 171, 'illustr': 77, 'extent': 30, 'simplif': 18, 'unsteadi': 79, 'achiev': 38, 'certain': 131, 'known': 104, 'collect': 11, 'physic': 78, 'model': 336, 'hay': 7, 'lighthil': 19, 'airfoil': 179, 'finit': 112, 'systemat': 20, 'landahl': 4, 'util': 40, 'expans': 128, 'invers': 29, 'neglig': 40, 'predict': 277, 'relationship': 33, 'normal': 181, 'compon': 73, 'motion': 186, 'comput': 175, 'flutter': 238, 'alway': 23, 'reduc': 169, 'elementari': 10, 'assum': 190, 'essenti': 64, 'close': 116, 'form': 306, 'bend': 131, 'torsion': 46, 'properti': 154, 'typic': 39, 'section': 252, 'exact': 128, 'wherev': 1, 'fairli': 20, 'moreov': 9, 'demonstr': 43, 'increasingli': 6, 'import': 127, 'profil': 184, 'shape': 320, 'grow': 6, 'larger': 42, 'discoveri': 3, 'almost': 22, 'imposs': 5, 'complex': 29, 'analys': 35, 'e': 99, 'g': 36, 'panel': 204, 'iter': 40, 'procedur': 117, 'outlin': 24, 'improv': 53, 'eigenvalu': 10, 'success': 47, 'introduct': 21, 'review': 62, 'includ': 267, 'gust': 28, 'respons': 41, 'rapid': 42, 'maneuv': 10, 'elast': 120, 'slight': 18, 'ackeret': 4, 'formula': 156, 'regard': 35, 'futur': 16, 'area': 101, 'labor': 4, 'minimum': 54, 'loss': 32, 'precis': 5, 'zone': 20, 'nonlinear': 65, 'neglect': 56, 'logic': 4, 'estim': 136, 'analyz': 64, 'thermoelast': 6, 'buckl': 346, 'deviat': 37, 'static': 178, 'across': 47, 'rais': 5, 'critic': 129, 'galerkin': 14, 'unbuckl': 3, 'question': 21, 'sever': 165, 'author': 125, 'condit': 516, 'formul': 55, 'numer': 266, 'matrix': 23, 'thu': 83, 'avoid': 27, 'constraint': 16, 'rigid': 54, 'confirm': 16, 'limit': 170, 'amplitud': 28, 'transform': 100, 'equival': 55, 'first': 227, 'stewartson': 8, 'except': 68, 'explicit': 20, 'requir': 187, 'key': 3, 'fluctuat': 38, 'postul': 6, 'appar': 44, 'associ': 85, 'mass': 111, 'invari': 6, 'rise': 57, 'separ': 344, 'independ': 60, 'report': 117, 'self': 17, 'preserv': 4, 'remark': 13, 'eddi': 19, 'connect': 33, 'wake': 110, 'behind': 108, 'missil': 61, 'vehicl': 127, 'divis': 6, 'electr': 65, 'compani': 1, 'axisymmetr': 73, 'lack': 10, 'make': 52, 'ration': 8, 'appli': 235, 'infinitesim': 11, 'mager': 3, 'partial': 46, 'valid': 86, 'assumpt': 130, 'establish': 55, 'diffus': 65, 'radial': 55, 'compressor': 76, 'note': 138, 'impel': 31, 'wheel': 2, 'guid': 17, 'vane': 13, 'arrang': 19, 'circular': 208, 'radiu': 70, 'fig': 15, 'start': 32, 'circl': 9, 'vector': 17, 'prescrib': 34, 'ga': 268, 'zero': 184, 'infin': 17, 'fulfil': 4, 'conic': 126, 'work': 140, 'without': 84, 'symmetri': 30, 'yet': 17, 'deriv': 328, 'ellipt': 57, 'cone': 293, 'approach': 121, 'generalis': 12, 'newtonian': 80, 'lee': 19, 'amr': 5, 'rev': 6, 'blunt': 280, 'express': 180, 'sin': 3, 'inclin': 25, 'subscript': 3, 'max': 3, 'maximum': 184, 'revert': 2, 'superior': 7, 'correl': 130, 'figur': 15, 'variat': 227, 'paramet': 235, 'believ': 23, 'engag': 2, 'hardwar': 1, 'slip': 34, 'perturb': 36, 'dash': 17, 'impuls': 10, 'infinit': 110, 'energi': 135, 'account': 83, 'jump': 11, 'move': 74, 'rarefi': 12, 'path': 38, 'appropri': 46, 'maslen': 1, 'j': 13, 'aero': 3, 'sci': 1, 'june': 3, 'behavior': 75, 'knudsen': 9, 'cylind': 449, 'exterior': 7, 'parallel': 81, 'axi': 74, 'blasiu': 28, 'curvatur': 83, 'plane': 159, 'magnitud': 82, 'signific': 87, 'recoveri': 39, 'quantiti': 45, 'insensit': 5, 'dissoci': 91, 'extern': 108, 'atom': 22, 'recombin': 19, 'exactli': 12, 'set': 82, 'ordinari': 45, 'even': 52, 'chemic': 58, 'reaction': 61, 'proceed': 6, 'slowli': 13, 'thermochem': 3, 'equilibrium': 156, 'nonequilibrium': 35, 'enter': 39, 'depend': 156, 'catalyt': 3, 'noncatalyt': 3, 'wall': 378, 'frozen': 24, 'slow': 10, 'fast': 8, 'introduc': 66, 'interpret': 40, 'scale': 88, 'conclud': 47, 'accur': 86, 'unaffect': 7, 'provid': 136, 'lewi': 6, 'near': 148, 'uniti': 44, 'slender': 185, 'drag': 319, 'afterbodi': 64, 'contain': 42, 'right': 12, 'nearli': 35, 'dimens': 29, 'exhibit': 21, 'directli': 35, 'intens': 45, 'blast': 24, 'taylor': 21, 'c': 46, 'lin': 1, 'sakurai': 1, 'hammitt': 2, 'va': 1, 'bogdonoff': 2, 'helium': 68, 'less': 96, 'satisfactori': 52, 'hemispher': 63, 'junction': 13, 'class': 35, 'revolut': 126, 'entir': 45, 'geometri': 54, 'find': 46, 'growth': 35, 'diamet': 124, 'replac': 29, 'forward': 80, 'portion': 72, 'taken': 64, 'current': 38, 'led': 9, 'realiz': 12, 'purpos': 77, 'aid': 22, 'aerodynamieist': 1, 'frame': 10, 'faster': 5, 'examin': 69, 'view': 32, 'dynam': 114, 'basic': 53, 'name': 20, 'fall': 13, 'whose': 48, 'explos': 11, 'sedov': 2, 'probstein': 6, 'inquir': 2, 'einbind': 1, 'variou': 185, 'mention': 18, 'converg': 69, 'nonuniform': 23, 'difficult': 16, 'non': 87, 'expect': 43, 'simul': 47, 'whole': 21, 'built': 3, 'varieti': 24, 'convect': 89, 'side': 61, 'restraint': 12, 'environ': 13, 'linearli': 23, 'period': 39, 'acceler': 43, 'inform': 54, 'second': 152, 'wide': 58, 'flang': 10, 'beam': 38, 'length': 167, 'photo': 6, 'multiweb': 4, 'photothermoelast': 5, 'long': 73, 'literatur': 13, 'dimensionless': 20, 'biot': 10, 'reveal': 13, 'observ': 130, 'center': 68, 'web': 12, 'sourc': 54, 'discrep': 20, 'trace': 11, 'employ': 82, 'multicellular': 1, 'criterion': 32, 'cover': 57, 'descend': 6, 'atmospher': 137, 'rapidli': 41, 'ballist': 32, 'trajectori': 21, 'oscillatori': 34, 'graviti': 20, 'allow': 45, 'written': 15, 'manner': 62, 'undamp': 1, 'oscil': 109, 'spring': 16, 'system': 127, 'equal': 59, 'variabl': 102, 'envelop': 8, 'damp': 77, 'prospect': 1, 'magneto': 12, 'magnet': 103, 'probabl': 25, 'pure': 40, 'seed': 2, 'hydrodynam': 18, 'gasdynam': 16, 'poiscuil': 1, 'liquid': 37, 'quasi': 36, 'last': 5, 'attract': 4, 'deceler': 37, 'subson': 165, 'channel': 38, 'regim': 50, 'chart': 50, 'construct': 45, 'advantag': 27, 'accru': 1, 'suffici': 52, 'justifi': 11, 'among': 7, 'unsolv': 2, 'reduct': 77, 'electrod': 1, 'contact': 21, 'provis': 1, 'continu': 75, 'plasma': 12, 'cross': 107, 'hand': 21, 'duct': 26, 'serv': 12, 'nontrivi': 1, 'sort': 3, 'resler': 3, 'sear': 3, 'sought': 3, 'possess': 6, 'algebra': 12, 'simplic': 12, 'sinc': 53, 'cool': 148, 'like': 21, 'gaseou': 8, 'follow': 83, 'paragraph': 2, 'devis': 10, 'yield': 88, 'symmetr': 125, 'slightli': 39, 'henc': 27, 'detach': 49, 'herein': 19, 'better': 26, 'conclus': 58, 'refin': 18, 'around': 64, 'familiar': 7, 'nonvisc': 11, 'perhap': 7, 'open': 22, 'seem': 25, 'therefor': 37, 'inevit': 3, 'proce': 6, 'need': 38, 'either': 80, 'happen': 2, 'unfamiliar': 1, 'extrapol': 14, 'test': 526, 'forebodi': 21, 'sonic': 91, 'line': 107, 'furthermor': 17, 'materi': 111, 'conjunct': 19, 'convent': 40, 'suppli': 13, 'semiempir': 15, 'scheme': 11, 'transon': 120, 'link': 4, 'mechan': 66, 'pattern': 69, 'domain': 8, 'reflect': 57, 'chang': 175, 'slope': 50, 'quantit': 25, 'uncamb': 4, 'superimpos': 2, 'smooth': 29, 'level': 64, 'cent': 46, 'singl': 73, 'fix': 58, 'locat': 120, 'optic': 13, 'magnifi': 2, 'schlieren': 26, 'definit': 16, 'delay': 10, 'promot': 6, 'advers': 29, 'decreas': 129, 'moder': 45, 'hot': 37, 'anemomet': 8, 'princip': 26, 'natur': 62, 'light': 29, 'overal': 27, 'fruit': 3, 'drawn': 16, 'gyroscop': 7, 'vibrat': 109, 'flexibl': 40, 'mount': 30, 'ask': 1, 'vibratori': 5, 'whirl': 4, 'thought': 7, 'horizont': 24, 'cantilev': 27, 'vertic': 73, 'moment': 167, 'pitch': 104, 'yaw': 93, 'stiff': 37, 'tical': 1, 'play': 12, 'role': 14, 'undergo': 6, 'tip': 56, 'disc': 10, 'divid': 21, 'four': 52, 'deal': 37, 'photograph': 26, 'end': 59, 'coupl': 34, 'airplan': 58, 'consist': 92, 'affect': 52, 'frequenc': 133, 'subsequ': 23, 'ambient': 17, 'argu': 3, 'explain': 29, 'revers': 33, 'phenomenon': 31, 'eter': 1, 'undisturb': 16, 'station': 29, 'submit': 2, 'opposit': 18, 'characteristi': 1, 'standpoint': 5, 'contempl': 1, 'mainli': 24, 'actual': 43, 'benefici': 4, 'receiv': 9, 'necessarili': 13, 'tangent': 29, 'let': 4, 'us': 7, 'say': 11, 'quit': 33, 'blunter': 2, 'spheric': 55, 'conceiv': 4, 'porou': 51, 'suction': 38, 'gupta': 2, 'unit': 32, 'act': 28, 'neg': 38, 'semi': 44, 'obviou': 6, 'sens': 12, 'magnetohydrodynam': 32, 'electromagnet': 6, 'freedom': 27, 'excess': 14, 'charg': 21, 'ii': 11, 'caviti': 11, 'notch': 7, 'visual': 9, 'spark': 2, 'pictur': 14, 'depth': 22, 'oncom': 4, 'momentum': 68, 'comment': 11, 'matric': 11, 'publish': 36, 'journal': 3, 'statement': 6, 'bring': 8, 'attent': 39, 'shed': 7, 'hopeless': 2, 'intim': 4, 'aspect': 133, 'arbitrari': 114, 'orient': 9, 'member': 12, 'permit': 40, 'ideal': 54, 'network': 3, 'flexur': 24, 'interconnect': 3, 'deflect': 158, 'compat': 7, 'product': 41, 'column': 89, 'assembl': 3, 'step': 61, 'fashion': 5, 'accomplish': 12, 'multipl': 15, 'squar': 67, 'compos': 12, 'oper': 101, 'unknown': 28, 'favor': 28, 'tabl': 49, 'entropi': 21, 'windward': 10, 'meridian': 5, 'tend': 30, 'enabl': 18, 'veiociti': 1, 'special': 81, 'crocco': 21, 'infer': 3, 'within': 119, 'effort': 9, 'readili': 15, 'mathemat': 43, 'polynomi': 20, 'anomali': 2, 'attribut': 21, 'customari': 4, 'inappropri': 1, 'net': 9, 'incorrect': 4, 'brief': 30, 'facilit': 6, 'entitl': 1, 'thin': 210, 'simultan': 41, 'held': 13, 'sutherland': 3, 'f': 51, 'main': 68, 'abil': 3, 'withstand': 2, 'correctli': 6, 'angular': 7, 'flux': 25, 'deform': 71, 'camber': 33, 'chord': 71, 'store': 7, 'previous': 59, 'delta': 58, 'untap': 4, 'sweptback': 18, 'cylindr': 167, 'ogiv': 24, 'shadowgraph': 8, 'scope': 14, 'encompass': 5, 'adiabat': 25, 'great': 18, 'attach': 46, 'upstream': 48, 'reattach': 24, 'otherwis': 11, 'reson': 9, 'accompani': 13, 'sixth': 2, 'accuraci': 91, 'short': 48, 'seventh': 3, 'adequ': 21, 'proport': 40, 'throughout': 30, 'tsien': 9, 'fine': 37, 'wider': 11, 'might': 19, 'similarli': 4, 'defin': 54, 'rule': 21, 'technic': 13, 'error': 48, 'ignor': 7, 'percent': 104, 'sharp': 74, 'half': 71, 'modifi': 93, 'attain': 11, 'immedi': 19, 'orific': 15, 'true': 10, 'width': 37, 'tenth': 1, 'falkner': 10, 'skan': 10, 'picard': 1, 'euler': 10, 'nondimension': 23, 'imperm': 6, 'five': 26, 'uncool': 5, 'ture': 3, 'weight': 57, 'tabul': 35, 'nusselt': 9, 'roll': 60, 'tail': 75, 'conform': 12, 'often': 20, 'interfer': 94, 'quot': 4, 'admit': 6, 'absolut': 13, 'twice': 6, 'uniqu': 12, 'famili': 17, 'identifi': 10, 'viscid': 1, 'remaind': 2, 'exceed': 23, 'semivertex': 11, 'talbot': 1, 'ramet': 1, 'maccol': 7, 'chapman': 14, 'rubesin': 7, 'obliqu': 24, 'imping': 13, 'weak': 27, 'sound': 79, 'refract': 5, 'isentrop': 19, 'attenu': 10, 'incid': 100, 'stationari': 33, 'spatial': 5, 'sinusoid': 23, 'fourier': 18, 'passag': 11, 'acoust': 42, 'nois': 80, 'joint': 12, 'stiffen': 87, 'winglik': 2, 'electron': 25, 'interior': 17, 'averag': 52, 'peak': 38, 'greatli': 27, 'travers': 12, 'ascend': 6, 'skip': 6, 'recur': 1, 'distinguish': 4, 'bessel': 3, 'trigonometr': 6, 'medium': 29, 'imperfect': 59, 'compil': 6, 'gase': 50, 'suitabl': 48, 'chosen': 25, 'volum': 39, 'entri': 85, 'capsul': 1, 'capabl': 12, 'real': 44, 'supercrit': 5, 'diverg': 58, 'crest': 2, 'chordwis': 29, 'satisfactorili': 11, 'glauert': 9, 'wherebi': 8, 'divers': 1, 'advanc': 22, 'bound': 25, 'ident': 20, 'progress': 19, 'strong': 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'stochast': 1, 'exagger': 1, 'hash': 1, 'burst': 3, 'sea': 3, 'firm': 1, 'dramat': 1, 'pose': 1, 'spectacular': 1, 'malfunct': 1, 'confid': 1, 'caravel': 1, 'bare': 1, 'tungsten': 2, 'fifti': 2, 'indirectli': 1, 'spectral': 4, 'negat': 1, 'defi': 1, 'tractabl': 1, 'preclud': 2, 'premis': 1, 'beg': 1, 'ban': 1, 'forcibl': 1, 'pin': 1, 'conidt': 1, 'adam': 2, 'heav': 1, 'easiest': 1, 'broke': 1, 'recognis': 1, 'ko': 2, 'munk': 1, 'breadth': 2, 'nielsen': 1, 'katzen': 1, 'tang': 1, 'trapezoid': 2, 'unbank': 1, 'speak': 3, 'cylinder': 1, 'mild': 1, 'duralumin': 1, 'proposel': 1, 'hundr': 1, 'unsymmetr': 1, 'rewrit': 2, 'vlasov': 1, 'prikl': 1, 'meh': 1, 'placement': 1, 'insect': 2, 'mainten': 1, 'rivet': 1, 'lap': 1, 'roughen': 2, 'drosophila': 1, 'housefli': 1, 'mistak': 1, 'circumfer': 2, 'synthesi': 1, 'temp': 1, 'magnetoga': 1, 'asid': 1, 'tan': 1, 'pre': 2, 'kirk': 1, 'transduc': 1, 'decompos': 1, 'pump': 6, 'hub': 12, 'block': 5, 'sharpli': 2, 'vertex': 6, 'hyper': 1, 'shanley': 3, 'gerard': 2, 'rabotnov': 1, 'shesterikov': 1, 'herrmann': 1, 'armenaka': 2, 'toru': 3, 'mismatch': 1, 'nonconcurr': 1, 'closur': 1, 'sand': 4, 'wich': 1, 'snap': 2, 'bellevil': 1, 'bifurc': 2, 'scoop': 2, 'furnish': 2, 'bornel': 1, 'kernal': 1, 'mitig': 1, 'stoni': 1, 'meteorit': 1, 'solidif': 1, 'venturi': 1, 'tional': 1, 'asm': 3, 'untest': 1, 'magnetogasdynam': 1, 'magnetofluidmechan': 1, 'sail': 1, 'sizabl': 5, 'usabl': 2, 'obvious': 1, 'bonu': 1, 'perpendicularli': 4, 'arrow': 1, 'berthelot': 1, 'planck': 1, 'coalesc': 1, 'swallow': 1, 'spite': 1, 'mc': 2, 'uhf': 1, 'requenc': 1, 'inafinit': 1, 'ordnanc': 1, 'diversifi': 1, 'modest': 2, 'kay': 1, 'yeh': 1, 'copper': 1, 'graphit': 4, 'molybdenum': 1, 'deffer': 1, 'meridion': 10, 'streamtub': 2, 'viii': 1, 'redesign': 1, 'skew': 4, 'turbo': 1, 'turbomachin': 1, 'somewher': 2, 'prewhirl': 1, 'splitter': 7, 'impart': 1, 'prolat': 2, 'unexpectedli': 1, 'gardless': 1, 'benefit': 5, 'hous': 2, 'halv': 2, 'retrocket': 1, 'retrorocket': 6, 'plateau': 1, 'revis': 3, 'princeton': 3, 'protrud': 2, 'poind': 1, 'marin': 2, 'unimport': 1, 'palt': 1, 'concret': 1, 'librov': 1, 'isochron': 1, 'forti': 1, 'oven': 1, 'elaps': 2, 'crook': 1, 'phenomenolog': 2, 'polycrystallin': 1, 'equicohes': 1, 'iron': 1, 'trefftz': 1, 'levi': 2, 'nongyroscop': 1, 'lobe': 2, 'uncontrol': 1, 'crinolin': 1, 'drape': 1, 'nearer': 1, 'marguerr': 2, 'internat': 1, 'mech': 2, 'cambridg': 1, 'wiley': 1, 'stepdown': 3, 'creat': 6, 'judici': 1, 'meissner': 1, 'argument': 2, 'geckel': 1, 'cabin': 2, 'undeform': 2, 'nth': 2, 'mth': 2, 'russian': 1, 'inaccess': 1, 'grossman': 3, 'lyskov': 1, 'specialis': 1, 'satisfact': 2, 'attaindd': 1, 'indefinit': 1, 'labour': 1, 'fasten': 1, 'evidenc': 2, 'symposium': 2, 'delft': 1, 'holland': 1, 'atten': 1, 'scientist': 2, 'bimetal': 1, 'tradit': 1, 'keller': 1, 'reiss': 1, 'immov': 1, 'versatil': 1, 'corpor': 2, 'almen': 1, 'laszlo': 1, 'violat': 1, 'arrheniu': 3, 'crank': 1, 'nicolson': 1, 'bean': 2, 'tilt': 28, 'sensor': 4, 'administr': 1, 'hammerhead': 2, 'reflex': 1, 'packard': 1, 'torispher': 9, 'toroid': 7, 'prebuckl': 1, 'ignit': 14, 'quench': 3, 'decomposit': 1, 'enorm': 1, 'ardc': 1, 'goe': 1, 'orlfic': 1, 'hemispheroid': 1, 'mesh': 2, 'old': 1, 'skill': 1, 'practition': 1, 'superscript': 1, 'newli': 1, 'la': 1, 'wb': 2, 'dirichlet': 1, 'twin': 3, 'profound': 1, 'tractor': 1, 'buri': 4, 'redirect': 2, 'regain': 2, 'mph': 1, 'upward': 3, 'auxiliari': 4, 'plain': 2, 'lucit': 2, 'polystyren': 1, 'inorgan': 2, 'salt': 2, 'ammonium': 2, 'chlorid': 2, 'sodium': 6, 'phenol': 2, 'melamin': 1, 'fiber': 4, 'lamin': 1, 'unman': 1, 'haveg': 1, 'rocketon': 1, 'unabl': 1, 'nonabl': 1, 'inconel': 1, 'polym': 1, 'boost': 1, 'payload': 3, 'nautic': 2, 'creas': 1, 'access': 1, 'index': 3, 'unblunt': 1, 'decrement': 3, 'moor': 2, 'audio': 1, 'subaudio': 1, 'cathod': 1, 'dampomet': 1, 'windtunnel': 1, 'centaur': 1, 'midchord': 1, 'whisker': 1, 'tailor': 4, 'secant': 2, 'octahedr': 1, 'perplex': 1, 'basin': 1, 'repair': 1, 'sequel': 1, 'conceptu': 2, 'foundat': 1, 'groov': 1, 'papreg': 1, 'cellular': 2, 'cellulos': 1, 'acet': 1, 'bulg': 1, 'admiss': 2, 'handbook': 1, 'kendrick': 2, 'nash': 2, 'embryon': 1, 'threshold': 1, 'weaken': 3, 'caution': 1, 'edit': 1, 'unfir': 2, 'psi': 1, 'undergon': 1, 'safe': 2, 'interim': 1, 'prism': 1, 'knuckl': 4, 'rigidli': 1, 'unflang': 1, 'toricon': 2, 'np': 1, 'nonhomogen': 1, 'langer': 1, 'amer': 1, 'wissler': 1, 'dissert': 1, 'jointli': 1, 'beskin': 1, 'randomli': 1, 'satelit': 1, 'standoff': 3, 'apropri': 1, 'millisecond': 7, 'driver': 7, 'tuft': 2, 'gravel': 8, 'stronger': 1, 'deeper': 1, 'intensif': 1, 'intensifi': 1, 'erod': 2, 'thrown': 2, 'struck': 1, 'errat': 2, 'recircul': 3, 'soft': 6, 'nocturn': 2, 'daytim': 1, 'lost': 1, 'capillari': 2, 'claus': 3, 'reinvestig': 1, 'tempratur': 1, 'singli': 1, 'morn': 1, 'millimet': 1, 'devreas': 1, 'deton': 11, 'jouguet': 2, 'periment': 1, 'halfway': 1, 'helicopt': 3, 'upflow': 1, 'slower': 1, 'dirt': 3, 'soak': 1, 'spray': 1, 'snow': 1, 'obliter': 1, 'vision': 1, 'dent': 1, 'punctur': 2, 'superfici': 1, 'multipropel': 1, 'inadvert': 1, 'taxi': 1, 'macadam': 1, 'crush': 2, 'stone': 3, 'chase': 1, 'hangl': 1, 'accentu': 1, 'rudimentari': 1, 'undesir': 1, 'airfram': 3, 'vz': 1, 'strengthen': 1, 'unfil': 1, 'expound': 1, 'bleich': 1, 'salmon': 1, 'goodier': 1, 'append': 2, 'readi': 1, 'yuan': 1, 'rectangl': 2, 'garabedian': 1, 'wet': 7, 'dorodnitzin': 1, 'heterogen': 2, 'nonreact': 2, 'reactiv': 3, 'deflagr': 1, 'vent': 1, 'boiloff': 1, 'nonalign': 1, 'superfast': 1, 'unexpect': 1, 'millikan': 1, 'heimenz': 1, 'wakelik': 2, 'jetlik': 2, 'enskog': 1, 'bakanov': 1, 'deryagin': 1, 'curvilinear': 5, 'synthes': 1, 'locu': 1, 'unperturb': 2, 'hyperlipt': 1, 'grad': 1, 'mccune': 1, 'underexpand': 1, 'calib': 1, 'hroma': 1, 'iodin': 1, 'gibb': 1, 'dalton': 1, 'dilut': 1, 'lennard': 1, 'centripet': 1, 'nonneg': 1, 'erdmann': 1, 'weierstrass': 1, 'switch': 1, 'subarc': 1, 'pyramid': 1, 'weaker': 1, 'nonisentrop': 1, 'paint': 1, 'hemi': 1, 'dewpoint': 1, 'utilz': 1, 'dust': 1, 'cusp': 4, 'mhd': 1, 'acrodynam': 2, 'shockless': 1, 'sesir': 1, 'consumpt': 1, 'motionless': 1, 'coher': 1, 'subsidiari': 1, 'veociti': 1, 'aerothermodynam': 1, 'reshotko': 1, 'elong': 1, 'hydroballist': 1, 'glycerin': 1, 'oversimplifi': 1, 'unrealist': 1, 'unconstrain': 1, 'semiballist': 1, 'balsa': 1, 'isofoam': 1, 'modul': 19, 'kelley': 1, 'ribbon': 1, 'dipol': 1, 'maximuin': 1, 'plk': 1, 'portiou': 1, 'spirit': 1, 'poincar': 1, 'kuo': 1, 'annul': 1, 'termperatur': 1, 'hypersoul': 1, 'summat': 1, 'avov': 1, 'cosin': 1, 'curtain': 2, 'undersid': 1, 'steamlin': 1, 'sturm': 1, 'liouvil': 1, 'bounari': 1, 'murphi': 3, 'nonmagnet': 3, 'ponderomot': 1, 'implement': 1, 'cancel': 7, 'scar': 1, 'haack': 1, 'integro': 1, 'integrand': 1, 'rung': 1, 'momentari': 2, 'instant': 1, 'overshadow': 1, 'fale': 1, 'reserv': 1, 'suppress': 2, 'schemat': 1, 'silenc': 1, 'teeth': 1, 'nonplanar': 1, 'termwis': 1, 'mahoni': 1, 'conductor': 2, 'coulomb': 1, 'loitsianskii': 1, 'bolshakov': 1, 'merger': 1, 'outermost': 1, 'dilat': 1, 'ifer': 1, 'acrothermochemistri': 1, 'maxwel': 1, 'photomultipli': 3, 'mirel': 1, 'martin': 1, 'inhomogen': 1, 'unresolv': 1, 'depict': 1, 'cast': 1, 'pseudo': 1, 'semidiamet': 1, 'nonslend': 1, 'holder': 1, 'calcualt': 2, 'sugar': 1, 'hypass': 1, 'lightli': 1, 'helic': 1, 'evvard': 1, 'krasilshchikova': 1, 'nonorthogon': 1, 'noncommun': 1, 'complimentari': 1, 'unsteadili': 1, 'recov': 1, 'aerotherm': 1, 'age': 1, 'cumbersom': 1, 'sigular': 1, 'withdraw': 1, 'tubul': 1, 'shyperson': 1, 'constantli': 1, 'inconclus': 1, 'sobson': 1, 'unmodul': 3, 'plume': 6, 'user': 1, 'nitric': 4, 'shuffl': 2, 'deplet': 1, 'rf': 1, 'deioniz': 1, 'sheath': 1, 'decad': 1, 'hantzsch': 1, 'wendt': 1, 'bullet': 1, 'spindl': 1, 'fort': 1, 'halstead': 1, 'climin': 1, 'origint': 1, 'diaphragm': 4, 'sight': 1, 'oscillograph': 2, 'spectrograph': 1, 'mirror': 1, 'interpos': 1, 'bright': 1, 'degreec': 1, 'disequilibrium': 1, 'indium': 1, 'blue': 1, 'flash': 1, 'thirdli': 1, 'coat': 1, 'orr': 1, 'sommerfeld': 1, 'nondissip': 2, 'sketchi': 1, 'resili': 1, 'altogeth': 1, 'unfavour': 1, 'china': 1, 'clay': 1, 'pimpl': 2, 'protuber': 1, 'drill': 3, 'throttl': 2, 'handley': 1, 'ltd': 1, 'suck': 2, 'ejector': 1, 'gascou': 1, 'injector': 2, 'hick': 1, 'chu': 1, 'kussner': 2, 'sweptw': 1, 'nineteen': 1, 'stipul': 1, 'untwist': 2, 'rectilinear': 1, 'price': 1, 'deepest': 2, 'billow': 4, 'unitari': 1, 'inop': 2, 'ramp': 2, 'thing': 1, 'supplort': 1, 'dispar': 1, 'rand': 1, 'warp': 1, 'yeild': 1, 'interdepend': 1, 'agenc': 1, 'provok': 1, 'osoen': 1, 'globul': 2, 'lengthen': 1, 'superaerodynam': 1, 'warhead': 1, 'carrier': 4, 'ofr': 1, 'debat': 1, 'breguet': 1, 'grand': 1, 'exot': 1, 'unrestrict': 1, 'thermopropuls': 1, 'strang': 1, 'unorthodox': 1, 'outweight': 1, 'shelter': 2, 'shortli': 1, 'financi': 1, 'midway': 1, 'regist': 1, 'cauchi': 4, 'reformul': 1, 'delin': 1, 'aeolotrop': 1, 'determinant': 2, 'intuit': 1, 'hoshizaki': 1, 'recover': 1, 'fralich': 1, 'ing': 1, 'ob': 1}\n"
     ]
    }
   ],
   "source": [
    "# all_collection_unique_words = set()\n",
    "cf = {}\n",
    "\n",
    "# define collection size as T\n",
    "T = 0\n",
    "\n",
    "for document in df['preprocessed_text']:\n",
    "    for term in document.split():\n",
    "        if term not in cf:\n",
    "            cf[term] = 1\n",
    "        else:\n",
    "            cf[term] += 1\n",
    "    T += len(document.split())\n",
    "\n",
    "print(\"\\nT (collection size): \", T)\n",
    "print(\"CF: \", cf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to know, we have read the data, proccesed the data, and extracted informative thins of the data. Now, it's time to use difference approaches to retrive ranked documents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector Space Model(VSM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using SKLEARN library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the below cell, i create tf-idf matrix using sklearn package. Also relevancy measures by cosine distnance which is define in sklearn too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sklean library to use its vectorizor and cosine similarity\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def rank_document_vsm_sklean(query, k):\n",
    "    # Preprocess the query\n",
    "    query = preprocessing(query)\n",
    "    \n",
    "    # Combine query and document text for tf-idf vectorization\n",
    "    combined_text = pd.concat([df['preprocessed_text'], pd.Series(query)], ignore_index=True)\n",
    "    \n",
    "    # Create tfidf matrix\n",
    "    tfidf_vectorizer = TfidfVectorizer()\n",
    "    tfidf_matrix = tfidf_vectorizer.fit_transform(combined_text)\n",
    "    \n",
    "    # Vectorize the query\n",
    "    query_vector = tfidf_matrix[-1]\n",
    "    \n",
    "    # Vectorize all documents\n",
    "    document_vectors = tfidf_matrix[:-1]\n",
    "    \n",
    "    # Compute cosine similarity between the query vector and all document vectors\n",
    "    similarity_scores = cosine_similarity(query_vector, document_vectors)\n",
    "    \n",
    "    # Get the indices of top documents\n",
    "    top_document_indices = similarity_scores.argsort()[0, ::-1][:k]\n",
    "\n",
    "    return top_document_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of implementing VSM using sklearn library: \n",
      "\n",
      "[ 50 183  11 358 745 878 485 877  12 664]\n",
      "[  11   50  745 1168  883  183   99  252  140   13]\n",
      "[484   4  89 143  90 398 581 180   5 578]\n",
      "[ 165  487 1060  409  166 1274 1373  184 1188  574]\n",
      "[ 102  366 1031  409  551  367  942  400 1373 1378]\n",
      "[ 490  385  384  256  150  797 1272  882   98 1281]\n",
      "[ 491  433  972   56 1230 1039  353  232 1306   55]\n",
      "[ 491  121 1230  568  442  353  233 1306  906 1082]\n",
      "[  20   21  549  305  570  101  325   44 1214  397]\n",
      "[ 492  301 1142  948 1198  523 1285  615  690 1008]\n"
     ]
    }
   ],
   "source": [
    "sklearn_vsm_results = []\n",
    "\n",
    "print(\"Results of implementing VSM using sklearn library: \\n\")\n",
    "for i in range(10):\n",
    "    # k = len(evaluation_dict[i+1])\n",
    "    top_k_documents = rank_document_vsm_sklean(df_q['preprocessed_query'][i], 10)\n",
    "    sklearn_vsm_results.append(list(top_k_documents))\n",
    "    print(top_k_documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manually"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section i want to create tf-idf matrix manually"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compute TF-IDF matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "tf_idf_matrix = []\n",
    "\n",
    "# Fill the matrix\n",
    "for term in document_frequency:\n",
    "    tmp = []\n",
    "    for index, row in df.iterrows():\n",
    "        if term in row['tf']:\n",
    "            tmp.append(row['tf'][term] * idf[term])\n",
    "        else:\n",
    "            tmp.append(0.0)\n",
    "    tf_idf_matrix.append(tmp)\n",
    "\n",
    "print(tf_idf_matrix[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1st document corresponding vector in tf-idf matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[9.850183883112784,\n",
       " 1.6409780573583321,\n",
       " 0.7534310824185723,\n",
       " 1.360798200667471,\n",
       " 0.8197921747494866,\n",
       " 0.7659167939666319,\n",
       " 0.8932750046983449,\n",
       " 1.5333441789585025,\n",
       " 0.9234115645306548,\n",
       " 0.638272163982407,\n",
       " 1.3827000421153008,\n",
       " 0.2828051755577821,\n",
       " 1.4471580313422192,\n",
       " 2.2416443408269355,\n",
       " 2.066946789630613,\n",
       " 0.5886208337725801,\n",
       " 1.5440680443502757,\n",
       " 0.6748363246192994,\n",
       " 1.2376430167995882,\n",
       " 1.6020599913279623,\n",
       " 0.7750601734065017,\n",
       " 0.8258615411433371,\n",
       " 0.6369255133471352,\n",
       " 2.588480420924065,\n",
       " 1.3136191229720018,\n",
       " 0.48716319301380306,\n",
       " 1.199170744400214,\n",
       " 0.8076715420736331,\n",
       " 8.53529412004277,\n",
       " 1.614649118635983,\n",
       " 0.8450980400142568,\n",
       " 3.778923645431146,\n",
       " 1.3010299956639813,\n",
       " 0.5886208337725801,\n",
       " 1.5333441789585025,\n",
       " 1.2540334329877576,\n",
       " 0.9642844477334656,\n",
       " 0.978810700930062,\n",
       " 0.30602194122148024,\n",
       " 3.831358228599928,\n",
       " 2.6020599913279625,\n",
       " 1.9815839964263524,\n",
       " 1.5228787452803376,\n",
       " 0.8407766662316143,\n",
       " 1.2318566749503117,\n",
       " 1.0892231843417655,\n",
       " 1.0562229242388401,\n",
       " 0.7641109931033696,\n",
       " 0.851661809516645,\n",
       " 2.3760587895925114,\n",
       " 0.47403017774252054,\n",
       " 0.5291276945573391,\n",
       " 0.6450687734604865,\n",
       " 2.845098040014257,\n",
       " 1.7481880270062005,\n",
       " 1.3467874862246563,\n",
       " 0.6704568473538084,\n",
       " 1.375276024036094,\n",
       " 0.8322608153090846]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_tf_idf = np.array(tf_idf_matrix)\n",
    "print(\"Example: 1st document corresponding vector in tf-idf matrix:\\n\")\n",
    "list(filter(lambda x: x != 0, np_tf_idf.T[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### TF-IDF Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section I eant to normalize the elements of the matrix vertically(based on each document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "    V_n = \\frac{V}{|| V ||_2}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize each vector corresponding to the documnets\n",
    "for j in range(N):\n",
    "    norm2 = 0.\n",
    "    for i in range(len(document_frequency)):\n",
    "        element = tf_idf_matrix[i][j]\n",
    "        norm2  += element ** 2\n",
    "    norm2 = math.sqrt(norm2)\n",
    "\n",
    "    if norm2 != 0.:\n",
    "        for i in range(len(document_frequency)):\n",
    "            tf_idf_matrix[i][j] = tf_idf_matrix[i][j] / norm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example: 1st document's corresponding normalized vector:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5731025781678482,\n",
       " 0.09547524863989983,\n",
       " 0.04383606447653686,\n",
       " 0.07917384755686868,\n",
       " 0.047697080022661174,\n",
       " 0.04456250710576743,\n",
       " 0.0519724519136326,\n",
       " 0.08921290329273321,\n",
       " 0.053725854727423375,\n",
       " 0.037135897876811305,\n",
       " 0.08044813867156492,\n",
       " 0.016454147166657523,\n",
       " 0.08419842803142086,\n",
       " 0.13042316430922343,\n",
       " 0.1202589259378091,\n",
       " 0.03424708831849444,\n",
       " 0.08983683833566035,\n",
       " 0.039263270825195734,\n",
       " 0.07200844290788312,\n",
       " 0.09321092096399221,\n",
       " 0.04509448644669526,\n",
       " 0.04805020739261052,\n",
       " 0.037057547161725796,\n",
       " 0.15060275223002076,\n",
       " 0.07642887838840376,\n",
       " 0.028344088315279504,\n",
       " 0.06977005235987951,\n",
       " 0.04699187838196226,\n",
       " 0.496599771503106,\n",
       " 0.09394338052035431,\n",
       " 0.04916942376751977,\n",
       " 0.2198650207544796,\n",
       " 0.07569641883204167,\n",
       " 0.03424708831849444,\n",
       " 0.08921290329273321,\n",
       " 0.07296206873722301,\n",
       " 0.05610391741322186,\n",
       " 0.05694908266667067,\n",
       " 0.017804943092548344,\n",
       " 0.2229157653046097,\n",
       " 0.15139283766408335,\n",
       " 0.1152923550143117,\n",
       " 0.08860400430223989,\n",
       " 0.0489179979580681,\n",
       " 0.07167178244841026,\n",
       " 0.0633730925791806,\n",
       " 0.061453074194790853,\n",
       " 0.04445744215036415,\n",
       " 0.04955131645794632,\n",
       " 0.13824365457059085,\n",
       " 0.027579984314744718,\n",
       " 0.03078566344844636,\n",
       " 0.037531337643307974,\n",
       " 0.165533257167702,\n",
       " 0.1017129301633714,\n",
       " 0.07835867733624807,\n",
       " 0.03900846444374499,\n",
       " 0.08001619507010053,\n",
       " 0.04842252942906308]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_np_tf_idf = np.array(tf_idf_matrix)\n",
    "print(\"Example: 1st document's corresponding normalized vector:\")\n",
    "list(filter(lambda x: x != 0, n_np_tf_idf.T[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cosine similarity: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It measures the similarity between two vectors of an inner product space. So we can find the similarity between the vectors correcsponding to documents easily!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "    cos(\\theta) = \\frac{u.v}{||u||_2.||v||_2}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_similarity1(v1, v2):\n",
    "    v1 = np.array(v1)\n",
    "    v2 = np.array(v2)\n",
    "    \n",
    "    return (np.dot(v1, v2) / LA.norm(v1) * LA.norm(v2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Query Vectorization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the query to a vector to find the similarity between query and documnets in the collecton using cosine similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_vectorization(query):\n",
    "    \n",
    "    # Preprocess the query\n",
    "    query, query_tokens = query_preprocessing(query)\n",
    "\n",
    "    # Compute query's term frequency\n",
    "    query_freq = {}\n",
    "    for term in query_tokens:\n",
    "        if term not in query_freq:\n",
    "            query_freq[term] = 1\n",
    "        else:\n",
    "            query_freq[term] += 1\n",
    "        \n",
    "    # Vectorize the query\n",
    "    query_vector = []\n",
    "    for term in document_frequency:\n",
    "        if term in set(query_tokens):\n",
    "            tf_idf = query_freq[term] * idf[term]\n",
    "            query_vector.append(tf_idf)\n",
    "        else:\n",
    "            query_vector.append(0.0)\n",
    "    return query_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, it's time to define the base function for VSM algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def VSM_ranking(query, k, tfidf_matrix):\n",
    "    \n",
    "    query_vector = query_vectorization(query)\n",
    "    tfidf_matrix = np.array(tfidf_matrix)\n",
    "    docs = tfidf_matrix.T.tolist()\n",
    "    scores = []\n",
    "    top_k_documents = []\n",
    "    for document_vector in docs:\n",
    "        scores.append(cosine_similarity1(query_vector, document_vector))\n",
    "    \n",
    "    # Sort documents based on their similarity and return best k\n",
    "    cnt = k\n",
    "    while cnt > 0:\n",
    "        max_score = max(scores)\n",
    "        index = scores.index(max_score)\n",
    "        top_k_documents.append((index+1, scores[index]))\n",
    "        scores[index] = -1\n",
    "        cnt -= 1\n",
    "    return top_k_documents\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BM25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BM25_ranking(query, k, tfidf_matrix, b=0.75, k1=1.5):\n",
    "    \n",
    "    preprocessed, query_tokens = query_preprocessing(query)\n",
    "    tfidf_matrix = np.array(tfidf_matrix)\n",
    "    \n",
    "    mean_doc_length = np.mean([len(df['preprocessed_text'][i]) for i in range(len(df)) if df['preprocessed_text'][i]])\n",
    "\n",
    "    scores = []\n",
    "\n",
    "    for i, doc in enumerate(df['preprocessed_text']):\n",
    "        for token in query_tokens:\n",
    "            if token in df['tf'][i] and doc:\n",
    "                term_freq = df['tf'][i][token]\n",
    "                score = (idf[token] * term_freq * (k1 + 1))/ (term_freq + k1 * (1 - b + (b*(len(df['preprocessed_text'][i])) / (mean_doc_length))))\n",
    "            else:\n",
    "                score = 0\n",
    "                # term_freq = 0\n",
    "                # score = (idf[token] * term_freq * (k1 + 1))/ (term_freq + k1 * (1 - b + (b*(len(df['preprocessed_text'][i])) / (mean_doc_length))))\n",
    "                \n",
    "        scores.append((i+1, score))\n",
    "\n",
    "    top_documents = sorted(scores, key=lambda x: x[1], reverse=True)[:]\n",
    "    return top_documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Language Model(LM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ML_ranking(query, k, l):\n",
    "    query, query_tokens = query_preprocessing(query)\n",
    "\n",
    "    P_q_c = []\n",
    "    for index, row in df.iterrows():\n",
    "        document = row['preprocessed_text']\n",
    "        if document != \"\":\n",
    "            for term in query_tokens:\n",
    "                tf_d, tf_c = 0, 0\n",
    "                P_q_d = []\n",
    "                if term in cf:\n",
    "                    tf_c = cf[term]\n",
    "                if term in document.split():\n",
    "                    tf_d = row['tf'][term]    \n",
    "                P_t_d = l * (tf_d/len(document.split())) + (1.-l) * (tf_c/T)\n",
    "                P_q_d.append(P_t_d)\n",
    "    \n",
    "            tmp = 1\n",
    "            for i in P_q_d:\n",
    "                tmp *= i\n",
    "            P_q_c.append((index+1, tmp))\n",
    "\n",
    "    top_docs = sorted(P_q_c, key=lambda x: x[1], reverse=True)[:]\n",
    "    return top_docs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11-point interpolation average precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we will plot the 11-point mAP for first two queries using different approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_average_precision(scores, results):\n",
    "    TP = 0\n",
    "    recall_levels = np.arange(0.0, 1.1, 0.1)\n",
    "    precisions, recalls = [], []\n",
    "\n",
    "    for i in range(len(scores)):\n",
    "        if scores[i][0] in results:\n",
    "            TP += 1\n",
    "            new_recall = TP/len(results)\n",
    "            recalls.append((TP, i+1, new_recall))\n",
    "\n",
    "    y, tmp, level = [1.], [], recall_levels[1]\n",
    "    mean_value = [1.]\n",
    "    for recall in recalls:\n",
    "        if recall[2] <= level:\n",
    "            tmp.append(recall[0]/recall[1])\n",
    "        else:\n",
    "            max_precision = max(tmp)\n",
    "            mean_value.append(np.mean(np.array(tmp)))\n",
    "            y.append(max_precision)\n",
    "            if (level + 0.1 <= recall_levels[-1]):\n",
    "                level += 0.1\n",
    "                tmp = []\n",
    "    print(len(y))       \n",
    "    return y, mean_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpolated_average_precision_plotter(query_id, N, tf_idf_matrix):\n",
    "    query = df_q['preprocessed_query'][query_id]\n",
    "    query_results = evaluation_dict[query_id]\n",
    "    \n",
    "    query_VSM_scores  = VSM_ranking(query, N, tf_idf_matrix)\n",
    "    query_BM25_scores = BM25_ranking(query, N, tf_idf_matrix)\n",
    "    query_ML_scores   = ML_ranking(query, N, 0.1)\n",
    "    \n",
    "    x = np.arange(0.0, 1.1, 0.1)\n",
    "    y1, x1 = calculate_average_precision(query_VSM_scores, query_results)\n",
    "    y2, x2 = calculate_average_precision(query_BM25_scores, query_results)\n",
    "    y3, x3 = calculate_average_precision(query_ML_scores, query_results)\n",
    "\n",
    "    # Create subplots\n",
    "    fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "    # Plot on the first subplot\n",
    "    axs[0].plot(x, y1)\n",
    "    axs[0].set_title('VSM')\n",
    "    axs[0].set_xlabel(\"Recall\")\n",
    "    axs[0].set_ylabel(\"Precision\")\n",
    "\n",
    "    # Plot on the second subplot\n",
    "    axs[1].plot(x, y2)\n",
    "    axs[1].set_title('BM25')\n",
    "    axs[1].set_xlabel(\"Recall\")\n",
    "    axs[1].set_ylabel(\"Precision\")\n",
    "\n",
    "\n",
    "    # Plot on the third subplot\n",
    "    axs[2].plot(x, y3)\n",
    "    axs[2].set_title('ML')\n",
    "    axs[2].set_xlabel(\"Recall\")\n",
    "    axs[2].set_ylabel(\"Precision\")\n",
    "\n",
    "\n",
    "    # Adjust layout for better spacing\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for different approaches of the 1st query:\n",
      "11\n",
      "11\n",
      "11\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Results for different approaches of the 1st query:\")\n",
    "interpolated_average_precision_plotter(1, N, tf_idf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it it obviuos from the above picture, in 11-point interpolation average precision, recall and precision start from 0 and 1 respectively. As we go furthur, the recall always increases, but precision could be go up and down. But in general, it decreases. The sudden fall in the above pictures means that the most relevant documents have been returned and the rest of documents are not that much relevant. I mean during the time, the probsbility of the revalcy of a document to the query decreases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for different approaches of the 2nd query:\n",
      "11\n",
      "11\n",
      "11\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Results for different approaches of the 2nd query:\")\n",
    "interpolated_average_precision_plotter(2, N, tf_idf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate above approaches among queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the below, the precision of each approach is showed for fisrt 30 queries. The distribution is not uniform and slight and that;s because of the evaluation data which is provided in the dataset. It means that the labels that are given to the queries in the data set, not good enoght."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 10\n",
    "precision1, x1 = [], []\n",
    "precision2, x2 = [], []\n",
    "precision3, x3 = [], []\n",
    "\n",
    "# VSM\n",
    "for i in range(30):\n",
    "    top_k_documents = VSM_ranking(df_q['preprocessed_query'][i], k, tf_idf_matrix)\n",
    "    result = []\n",
    "    for j in range(k):\n",
    "        result.append(top_k_documents[j][0]+1)\n",
    "    precision1.append(compute_precision(evaluation_dict[i+1], result))\n",
    "    x1.append(i+1)\n",
    "\n",
    "# BM25\n",
    "for i in range(30):\n",
    "    top_k_documents = BM25_ranking(df_q['preprocessed_query'][i], k, tf_idf_matrix)\n",
    "    result = []\n",
    "    for j in range(k):\n",
    "        result.append(top_k_documents[j][0]+1)\n",
    "    precision2.append(compute_precision(evaluation_dict[i+1], result))\n",
    "    x2.append(i+1)\n",
    "\n",
    "# ML\n",
    "for i in range(30):\n",
    "    top_k_documents = ML_ranking(df_q['preprocessed_query'][i], k, .2)\n",
    "    result = []\n",
    "    for j in range(k):\n",
    "        result.append(top_k_documents[j][0]+1)\n",
    "    precision3.append(compute_precision(evaluation_dict[i+1], result))\n",
    "    x3.append(i+1)\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "axs[0].plot(x1, precision1)\n",
    "axs[0].set_title('VSM')\n",
    "axs[0].set_xlabel(\"DocID\")\n",
    "axs[0].set_ylabel(\"Precision\")\n",
    "\n",
    "axs[1].plot(x2, precision2)\n",
    "axs[1].set_title('BM25')\n",
    "axs[1].set_xlabel(\"DocID\")\n",
    "axs[1].set_ylabel(\"Precision\")\n",
    "\n",
    "axs[2].plot(x3, precision3)\n",
    "axs[2].set_title('ML')\n",
    "axs[2].set_xlabel(\"DocID\")\n",
    "axs[2].set_ylabel(\"Precision\")\n",
    "\n",
    "# Adjust layout for better spacing\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compere Document Ranking Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it is obvious from tha last plot, the maen precision for fisrt 30 queries in VSM is more that BM25 and ML. It means that in Cranfield dataset, the VSM approuchs works better based of the labels which are given to the queries. Also, the plots of BM25 and ML in Fig.2 are mostly the same which means that these two apploaches work similarly on the Cranfield dataset."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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